From e4fd630aecccd2435f39eabcf4359747a8a72182 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Wed, 3 Sep 2025 14:55:27 -0700 Subject: [PATCH 001/103] chore: PoC + ipynb --- .../annotation_import/audio_temporal.ipynb | 786 ++++++++++++++++++ .../data/annotation_types/__init__.py | 3 + .../labelbox/data/annotation_types/audio.py | 109 +++ .../labelbox/data/annotation_types/label.py | 24 + .../serialization/ndjson/classification.py | 5 +- .../data/serialization/ndjson/label.py | 41 + .../data/serialization/ndjson/objects.py | 42 + .../tests/data/annotation_import/conftest.py | 113 ++- .../test_generic_data_types.py | 96 +++ .../tests/data/annotation_types/test_audio.py | 403 +++++++++ 10 files changed, 1618 insertions(+), 4 deletions(-) create mode 100644 examples/annotation_import/audio_temporal.ipynb create mode 100644 libs/labelbox/src/labelbox/data/annotation_types/audio.py create mode 100644 libs/labelbox/tests/data/annotation_types/test_audio.py diff --git a/examples/annotation_import/audio_temporal.ipynb b/examples/annotation_import/audio_temporal.ipynb new file mode 100644 index 000000000..69a8eb4a0 --- /dev/null +++ b/examples/annotation_import/audio_temporal.ipynb @@ -0,0 +1,786 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Audio Temporal Annotation Import\n", + "\n", + "This notebook demonstrates how to create and upload **temporal audio annotations** - annotations that are tied to specific time ranges in audio files.\n", + "\n", + "## What are Temporal Audio Annotations?\n", + "\n", + "Temporal audio annotations allow you to:\n", + "- **Transcribe speech** with precise timestamps (\"Hello world\" from 2.5s to 4.1s)\n", + "- **Identify speakers** in specific segments (\"John speaking\" from 10s to 15s)\n", + "- **Detect sound events** with time ranges (\"Dog barking\" from 30s to 32s)\n", + "- **Classify audio quality** for segments (\"Clear audio\" from 0s to 10s)\n", + "\n", + "## Supported Temporal Annotations\n", + "\n", + "- **AudioClassificationAnnotation**: Radio, checklist, and text classifications for time ranges\n", + "- **AudioObjectAnnotation**: Text entities (transcriptions) for time ranges\n", + "\n", + "## Key Features\n", + "\n", + "- **Time-based API**: Use seconds for user-friendly input\n", + "- **Frame-based storage**: Internally uses milliseconds (1 frame = 1ms)\n", + "- **MAL compatible**: Works with existing Model-Assisted Labeling pipeline\n", + "- **UI compatible**: Uses existing video timeline components\n", + "\n", + "## Import Methods\n", + "\n", + "- **Model-Assisted Labeling (MAL)**: Upload pre-annotations for labeler review\n", + "- **Label Import**: Upload ground truth labels directly\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q \"labelbox[data]\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import labelbox as lb\n", + "import labelbox.types as lb_types\n", + "import uuid\n", + "from typing import List\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replace with your API key\n", + "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add your api key\n", + "API_KEY = \"\"\n", + "client = lb.Client(api_key=API_KEY)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Temporal Audio Annotations\n", + "\n", + "### Audio Classification Annotations\n", + "\n", + "Use `AudioClassificationAnnotation` for classifications tied to specific time ranges.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Speaker identification for a time range\n", + "speaker_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=2.5, # Start at 2.5 seconds\n", + " end_sec=4.1, # End at 4.1 seconds\n", + " name=\"speaker_id\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"john\"))\n", + ")\n", + "\n", + "print(f\"Speaker annotation frame: {speaker_annotation.frame}ms\")\n", + "print(f\"Speaker annotation start time: {speaker_annotation.start_time}s\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Audio quality assessment for a segment\n", + "quality_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=0.0,\n", + " end_sec=10.0,\n", + " name=\"audio_quality\",\n", + " value=lb_types.Checklist(answer=[\n", + " lb_types.ClassificationAnswer(name=\"clear_audio\"),\n", + " lb_types.ClassificationAnswer(name=\"no_background_noise\")\n", + " ])\n", + ")\n", + "\n", + "# Emotion detection for a segment\n", + "emotion_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=5.2,\n", + " end_sec=8.7,\n", + " name=\"emotion\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"happy\"))\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Audio Object Annotations\n", + "\n", + "Use `AudioObjectAnnotation` for text entities like transcriptions tied to specific time ranges.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Transcription with precise timestamps\n", + "transcription_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=2.5,\n", + " end_sec=4.1,\n", + " name=\"transcription\",\n", + " value=lb_types.TextEntity(text=\"Hello, how are you doing today?\")\n", + ")\n", + "\n", + "print(f\"Transcription frame: {transcription_annotation.frame}ms\")\n", + "print(f\"Transcription text: {transcription_annotation.value.text}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sound event detection\n", + "sound_event_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=10.0,\n", + " end_sec=12.5,\n", + " name=\"sound_event\",\n", + " value=lb_types.TextEntity(text=\"Dog barking in background\")\n", + ")\n", + "\n", + "# Multiple transcription segments\n", + "transcription_segments = [\n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=0.0, end_sec=2.3,\n", + " name=\"transcription\",\n", + " value=lb_types.TextEntity(text=\"Welcome to our podcast.\")\n", + " ),\n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=2.5, end_sec=5.8,\n", + " name=\"transcription\", \n", + " value=lb_types.TextEntity(text=\"Today we're discussing AI advancements.\")\n", + " ),\n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=6.0, end_sec=9.2,\n", + " name=\"transcription\",\n", + " value=lb_types.TextEntity(text=\"Let's start with machine learning basics.\")\n", + " )\n", + "]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Cases and Examples\n", + "\n", + "### Use Case 1: Podcast Transcription with Speaker Identification\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Complete podcast annotation with speakers and transcriptions\n", + "podcast_annotations = [\n", + " # Host introduction\n", + " lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=0.0, end_sec=5.0,\n", + " name=\"speaker_id\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"host\"))\n", + " ),\n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=0.0, end_sec=5.0,\n", + " name=\"transcription\",\n", + " value=lb_types.TextEntity(text=\"Welcome to Tech Talk, I'm your host Sarah.\")\n", + " ),\n", + " \n", + " # Guest response\n", + " lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=5.2, end_sec=8.5,\n", + " name=\"speaker_id\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"guest\"))\n", + " ),\n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=5.2, end_sec=8.5,\n", + " name=\"transcription\",\n", + " value=lb_types.TextEntity(text=\"Thanks for having me, Sarah!\")\n", + " ),\n", + " \n", + " # Audio quality assessment\n", + " lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=0.0, end_sec=10.0,\n", + " name=\"audio_quality\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"excellent\"))\n", + " )\n", + "]\n", + "\n", + "print(f\"Created {len(podcast_annotations)} podcast annotations\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use Case 2: Call Center Quality Analysis\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Call center analysis with sentiment and quality metrics\n", + "call_center_annotations = [\n", + " # Customer sentiment analysis\n", + " lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=0.0, end_sec=30.0,\n", + " name=\"customer_sentiment\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"frustrated\"))\n", + " ),\n", + " \n", + " # Agent performance\n", + " lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=30.0, end_sec=60.0,\n", + " name=\"agent_performance\",\n", + " value=lb_types.Checklist(answer=[\n", + " lb_types.ClassificationAnswer(name=\"professional_tone\"),\n", + " lb_types.ClassificationAnswer(name=\"resolved_issue\"),\n", + " lb_types.ClassificationAnswer(name=\"followed_script\")\n", + " ])\n", + " ),\n", + " \n", + " # Key phrases extraction\n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=15.0, end_sec=18.0,\n", + " name=\"key_phrase\",\n", + " value=lb_types.TextEntity(text=\"I want to speak to your manager\")\n", + " ),\n", + " \n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=45.0, end_sec=48.0,\n", + " name=\"key_phrase\",\n", + " value=lb_types.TextEntity(text=\"Thank you for your patience\")\n", + " )\n", + "]\n", + "\n", + "print(f\"Created {len(call_center_annotations)} call center annotations\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use Case 3: Music and Sound Event Detection\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Music analysis and sound event detection\n", + "music_annotations = [\n", + " # Musical instruments\n", + " lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=0.0, end_sec=30.0,\n", + " name=\"instruments\",\n", + " value=lb_types.Checklist(answer=[\n", + " lb_types.ClassificationAnswer(name=\"piano\"),\n", + " lb_types.ClassificationAnswer(name=\"violin\"),\n", + " lb_types.ClassificationAnswer(name=\"drums\")\n", + " ])\n", + " ),\n", + " \n", + " # Genre classification\n", + " lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=0.0, end_sec=60.0,\n", + " name=\"genre\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"classical\"))\n", + " ),\n", + " \n", + " # Sound events\n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=25.0, end_sec=27.0,\n", + " name=\"sound_event\",\n", + " value=lb_types.TextEntity(text=\"Applause from audience\")\n", + " ),\n", + " \n", + " lb_types.AudioObjectAnnotation.from_time_range(\n", + " start_sec=45.0, end_sec=46.5,\n", + " name=\"sound_event\",\n", + " value=lb_types.TextEntity(text=\"Door closing in background\")\n", + " )\n", + "]\n", + "\n", + "print(f\"Created {len(music_annotations)} music annotations\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uploading Audio Temporal Prelabels\n", + "\n", + "### Step 1: Import Audio Data into Catalog\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create dataset with audio file\n", + "global_key = \"sample-audio-temporal-\" + str(uuid.uuid4())\n", + "\n", + "asset = {\n", + " \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", + " \"global_key\": global_key,\n", + "}\n", + "\n", + "dataset = client.create_dataset(name=\"audio_temporal_demo_dataset\")\n", + "task = dataset.create_data_rows([asset])\n", + "task.wait_till_done()\n", + "print(\"Errors:\", task.errors)\n", + "print(\"Failed data rows:\", task.failed_data_rows)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Create Ontology with Temporal Audio Tools\n", + "\n", + "Your ontology must include the tools and classifications that match your annotation names.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ontology_builder = lb.OntologyBuilder(\n", + " tools=[\n", + " # Text entity tools for transcriptions and sound events\n", + " lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"transcription\"),\n", + " lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"sound_event\"),\n", + " lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"key_phrase\"),\n", + " ],\n", + " classifications=[\n", + " # Speaker identification\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"speaker_id\",\n", + " scope=lb.Classification.Scope.INDEX, # Frame-based classification\n", + " options=[\n", + " lb.Option(value=\"host\"),\n", + " lb.Option(value=\"guest\"),\n", + " lb.Option(value=\"john\"),\n", + " lb.Option(value=\"sarah\"),\n", + " ],\n", + " ),\n", + " \n", + " # Audio quality assessment\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"audio_quality\",\n", + " scope=lb.Classification.Scope.INDEX,\n", + " options=[\n", + " lb.Option(value=\"clear_audio\"),\n", + " lb.Option(value=\"no_background_noise\"),\n", + " lb.Option(value=\"good_volume\"),\n", + " lb.Option(value=\"excellent\"),\n", + " ],\n", + " ),\n", + " \n", + " # Emotion detection\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"emotion\",\n", + " scope=lb.Classification.Scope.INDEX,\n", + " options=[\n", + " lb.Option(value=\"happy\"),\n", + " lb.Option(value=\"sad\"),\n", + " lb.Option(value=\"angry\"),\n", + " lb.Option(value=\"neutral\"),\n", + " ],\n", + " ),\n", + " \n", + " # Customer sentiment (for call center example)\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"customer_sentiment\",\n", + " scope=lb.Classification.Scope.INDEX,\n", + " options=[\n", + " lb.Option(value=\"satisfied\"),\n", + " lb.Option(value=\"frustrated\"),\n", + " lb.Option(value=\"angry\"),\n", + " lb.Option(value=\"neutral\"),\n", + " ],\n", + " ),\n", + " \n", + " # Agent performance (for call center example)\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"agent_performance\",\n", + " scope=lb.Classification.Scope.INDEX,\n", + " options=[\n", + " lb.Option(value=\"professional_tone\"),\n", + " lb.Option(value=\"resolved_issue\"),\n", + " lb.Option(value=\"followed_script\"),\n", + " lb.Option(value=\"empathetic_response\"),\n", + " ],\n", + " ),\n", + " \n", + " # Music instruments (for music example)\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"instruments\",\n", + " scope=lb.Classification.Scope.INDEX,\n", + " options=[\n", + " lb.Option(value=\"piano\"),\n", + " lb.Option(value=\"violin\"),\n", + " lb.Option(value=\"drums\"),\n", + " lb.Option(value=\"guitar\"),\n", + " ],\n", + " ),\n", + " \n", + " # Music genre\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"genre\",\n", + " scope=lb.Classification.Scope.INDEX,\n", + " options=[\n", + " lb.Option(value=\"classical\"),\n", + " lb.Option(value=\"jazz\"),\n", + " lb.Option(value=\"rock\"),\n", + " lb.Option(value=\"pop\"),\n", + " ],\n", + " ),\n", + " ],\n", + ")\n", + "\n", + "ontology = client.create_ontology(\n", + " \"Audio Temporal Annotations Ontology\",\n", + " ontology_builder.asdict(),\n", + " media_type=lb.MediaType.Audio,\n", + ")\n", + "\n", + "print(f\"Created ontology: {ontology.name}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3: Create Project and Setup Editor\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create project\n", + "project = client.create_project(\n", + " name=\"Audio Temporal Annotations Demo\",\n", + " media_type=lb.MediaType.Audio\n", + ")\n", + "\n", + "# Connect ontology to project\n", + "project.setup_editor(ontology)\n", + "\n", + "print(f\"Created project: {project.name}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4: Create Batch and Add Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create batch\n", + "batch = project.create_batch(\n", + " \"audio-temporal-batch-\" + str(uuid.uuid4())[:8],\n", + " global_keys=[global_key],\n", + " priority=5,\n", + ")\n", + "\n", + "print(f\"Created batch: {batch.name}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5: Upload Temporal Audio Annotations via MAL\n", + "\n", + "Now we'll upload our temporal audio annotations using the Model-Assisted Labeling pipeline.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create label with temporal audio annotations\n", + "# Using the podcast example annotations\n", + "label = lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=podcast_annotations\n", + ")\n", + "\n", + "print(f\"Created label with {len(podcast_annotations)} temporal annotations\")\n", + "print(\"Annotation types:\")\n", + "for i, annotation in enumerate(podcast_annotations):\n", + " ann_type = type(annotation).__name__\n", + " if hasattr(annotation, 'frame'):\n", + " time_info = f\"at {annotation.start_time}s (frame {annotation.frame})\"\n", + " else:\n", + " time_info = \"global\"\n", + " print(f\" {i+1}. {ann_type} '{annotation.name}' {time_info}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload via MAL (Model-Assisted Labeling)\n", + "upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"audio_temporal_mal_{str(uuid.uuid4())[:8]}\",\n", + " predictions=[label],\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Upload completed!\")\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status:\", upload_job.statuses)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NDJSON Format Examples\n", + "\n", + "Temporal audio annotations serialize to NDJSON format similar to video annotations, with frame-based timing.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's examine how temporal audio annotations serialize to NDJSON\n", + "from labelbox.data.serialization.ndjson.label import NDLabel\n", + "import json\n", + "\n", + "# Serialize our label to NDJSON format\n", + "ndjson_generator = NDLabel.from_common([label])\n", + "ndjson_objects = list(ndjson_generator)\n", + "\n", + "print(f\"Generated {len(ndjson_objects)} NDJSON objects\")\n", + "print(\"\\nNDJSON Examples:\")\n", + "print(\"=\" * 50)\n", + "\n", + "for i, obj in enumerate(ndjson_objects[:3]): # Show first 3 examples\n", + " print(f\"\\nObject {i+1}:\")\n", + " # Convert to dict for pretty printing\n", + " obj_dict = obj.dict(exclude_none=True)\n", + " print(json.dumps(obj_dict, indent=2))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison with Video Annotations\n", + "\n", + "Audio temporal annotations use the same frame-based structure as video annotations:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Frame-based Structure Comparison:\")\n", + "print(\"=\" * 40)\n", + "\n", + "# Audio: 1 frame = 1 millisecond\n", + "audio_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", + " start_sec=2.5, end_sec=4.1,\n", + " name=\"test\", value=lb_types.Text(answer=\"test\")\n", + ")\n", + "\n", + "print(f\"Audio Annotation:\")\n", + "print(f\" Time: 2.5s → Frame: {audio_annotation.frame} (milliseconds)\")\n", + "print(f\" Frame rate: 1000 frames/second (1 frame = 1ms)\")\n", + "\n", + "print(f\"\\nVideo Annotation (for comparison):\")\n", + "print(f\" Time: 2.5s → Frame: depends on video frame rate\")\n", + "print(f\" Frame rate: varies (e.g., 30 fps = 30 frames/second)\")\n", + "\n", + "print(f\"\\nBoth use the same NDJSON structure with 'frame' field\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "### 1. Time Precision\n", + "- Audio temporal annotations use millisecond precision (1 frame = 1ms)\n", + "- Always use the `from_time_range()` method for user-friendly second-based input\n", + "- Frame values are automatically calculated: `frame = int(start_sec * 1000)`\n", + "\n", + "### 2. Ontology Alignment\n", + "- Ensure annotation `name` fields match your ontology tool/classification names\n", + "- Use `scope=lb.Classification.Scope.INDEX` for frame-based classifications\n", + "- Text entity tools work for transcriptions and sound event descriptions\n", + "\n", + "### 3. Segment Organization\n", + "- Use `segment_index` to group related annotations\n", + "- Segments help organize timeline view in the UI\n", + "- Each segment can contain multiple annotation types\n", + "\n", + "### 4. Performance Optimization\n", + "- Batch multiple labels in a single MAL import for better performance\n", + "- Use appropriate time ranges - avoid overly granular segments\n", + "- Consider audio file length when planning annotation density\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup (Optional)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to clean up resources\n", + "# project.delete()\n", + "# dataset.delete()\n", + "# ontology.delete()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook demonstrated:\n", + "\n", + "1. **Creating temporal audio annotations** using `AudioClassificationAnnotation` and `AudioObjectAnnotation`\n", + "2. **Time-based API** with `from_time_range()` for user-friendly input\n", + "3. **Multiple use cases**: podcasts, call centers, music analysis\n", + "4. **MAL import pipeline** for uploading temporal prelabels\n", + "5. **NDJSON serialization** compatible with existing video infrastructure\n", + "6. **Best practices** for ontology setup and performance optimization\n", + "\n", + "### Key Benefits:\n", + "- **No UI changes needed** - uses existing video timeline components\n", + "- **Frame-based precision** - 1ms accuracy for audio timing\n", + "- **Seamless integration** - works with existing MAL and Label Import pipelines\n", + "- **Flexible annotation types** - supports classifications and text entities with timestamps\n", + "\n", + "### Next Steps:\n", + "1. Upload your temporal audio annotations using this notebook as a template\n", + "2. Review annotations in the Labelbox editor (uses video timeline UI)\n", + "3. Export annotated data for model training or analysis\n", + "4. Integrate with your audio processing pipeline\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index fc75652cf..455535c09 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -19,6 +19,9 @@ from .video import MaskInstance from .video import VideoMaskAnnotation +from .audio import AudioClassificationAnnotation +from .audio import AudioObjectAnnotation + from .ner import ConversationEntity from .ner import DocumentEntity from .ner import DocumentTextSelection diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py new file mode 100644 index 000000000..35866f62a --- /dev/null +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -0,0 +1,109 @@ +from typing import Optional + +from labelbox.data.annotation_types.annotation import ClassificationAnnotation, ObjectAnnotation +from labelbox.data.mixins import ConfidenceNotSupportedMixin, CustomMetricsNotSupportedMixin + + +class AudioClassificationAnnotation(ClassificationAnnotation): + """Audio classification for specific time range + + Examples: + - Speaker identification from 2.5s to 4.1s + - Audio quality assessment for a segment + - Language detection for audio segments + + Args: + name (Optional[str]): Name of the classification + feature_schema_id (Optional[Cuid]): Feature schema identifier + value (Union[Text, Checklist, Radio]): Classification value + frame (int): The frame index in milliseconds (e.g., 2500 = 2.5 seconds) + segment_index (Optional[int]): Index of audio segment this annotation belongs to + extra (Dict[str, Any]): Additional metadata + """ + + frame: int + segment_index: Optional[int] = None + + @classmethod + def from_time_range(cls, start_sec: float, end_sec: float, **kwargs): + """Create from seconds (user-friendly) to frames (internal) + + Args: + start_sec (float): Start time in seconds + end_sec (float): End time in seconds + **kwargs: Additional arguments for the annotation + + Returns: + AudioClassificationAnnotation: Annotation with frame set to start_sec * 1000 + + Example: + >>> AudioClassificationAnnotation.from_time_range( + ... start_sec=2.5, end_sec=4.1, + ... name="speaker_id", + ... value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="john")) + ... ) + """ + return cls(frame=int(start_sec * 1000), **kwargs) + + @property + def start_time(self) -> float: + """Convert frame to seconds for user-facing APIs + + Returns: + float: Time in seconds (e.g., 2500 -> 2.5) + """ + return self.frame / 1000.0 + + +class AudioObjectAnnotation(ObjectAnnotation, ConfidenceNotSupportedMixin, CustomMetricsNotSupportedMixin): + """Audio object annotation for specific time range + + Examples: + - Transcription: "Hello world" from 2.5s to 4.1s + - Sound events: "Dog barking" from 10s to 12s + - Audio segments with metadata + + Args: + name (Optional[str]): Name of the annotation + feature_schema_id (Optional[Cuid]): Feature schema identifier + value (Union[TextEntity, Geometry]): Localization or text content + frame (int): The frame index in milliseconds (e.g., 10000 = 10.0 seconds) + keyframe (bool): Whether this is a keyframe annotation (default: True) + segment_index (Optional[int]): Index of audio segment this annotation belongs to + classifications (Optional[List[ClassificationAnnotation]]): Optional sub-classifications + extra (Dict[str, Any]): Additional metadata + """ + + frame: int + keyframe: bool = True + segment_index: Optional[int] = None + + @classmethod + def from_time_range(cls, start_sec: float, end_sec: float, **kwargs): + """Create from seconds (user-friendly) to frames (internal) + + Args: + start_sec (float): Start time in seconds + end_sec (float): End time in seconds + **kwargs: Additional arguments for the annotation + + Returns: + AudioObjectAnnotation: Annotation with frame set to start_sec * 1000 + + Example: + >>> AudioObjectAnnotation.from_time_range( + ... start_sec=10.0, end_sec=12.5, + ... name="transcription", + ... value=lb_types.TextEntity(text="Hello world") + ... ) + """ + return cls(frame=int(start_sec * 1000), **kwargs) + + @property + def start_time(self) -> float: + """Convert frame to seconds for user-facing APIs + + Returns: + float: Time in seconds (e.g., 10000 -> 10.0) + """ + return self.frame / 1000.0 diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index d13fb8f20..6f20b175e 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -13,6 +13,7 @@ from .metrics import ScalarMetric, ConfusionMatrixMetric from .video import VideoClassificationAnnotation from .video import VideoObjectAnnotation, VideoMaskAnnotation +from .audio import AudioClassificationAnnotation, AudioObjectAnnotation from .mmc import MessageEvaluationTaskAnnotation from pydantic import BaseModel, field_validator @@ -44,6 +45,8 @@ class Label(BaseModel): ClassificationAnnotation, ObjectAnnotation, VideoMaskAnnotation, + AudioClassificationAnnotation, + AudioObjectAnnotation, ScalarMetric, ConfusionMatrixMetric, RelationshipAnnotation, @@ -85,6 +88,27 @@ def frame_annotations( frame_dict[annotation.frame].append(annotation) return frame_dict + def audio_annotations_by_frame( + self, + ) -> Dict[int, List[Union[AudioObjectAnnotation, AudioClassificationAnnotation]]]: + """Get audio annotations organized by frame (millisecond) + + Returns: + Dict[int, List]: Dictionary mapping frame (milliseconds) to list of audio annotations + + Example: + >>> label.audio_annotations_by_frame() + {2500: [AudioClassificationAnnotation(...)], 10000: [AudioObjectAnnotation(...)]} + """ + frame_dict = defaultdict(list) + for annotation in self.annotations: + if isinstance( + annotation, + (AudioObjectAnnotation, AudioClassificationAnnotation), + ): + frame_dict[annotation.frame].append(annotation) + return dict(frame_dict) + def add_url_to_masks(self, signer) -> "Label": """ Creates signed urls for all masks in the Label. diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index fedf4d91b..302231b7a 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -12,6 +12,7 @@ from ...annotation_types.annotation import ClassificationAnnotation from ...annotation_types.video import VideoClassificationAnnotation +from ...annotation_types.audio import AudioClassificationAnnotation from ...annotation_types.llm_prompt_response.prompt import ( PromptClassificationAnnotation, PromptText, @@ -425,7 +426,7 @@ def to_common( def from_common( cls, annotation: Union[ - ClassificationAnnotation, VideoClassificationAnnotation + ClassificationAnnotation, VideoClassificationAnnotation, AudioClassificationAnnotation ], data: GenericDataRowData, ) -> Union[NDTextSubclass, NDChecklistSubclass, NDRadioSubclass]: @@ -448,7 +449,7 @@ def from_common( @staticmethod def lookup_classification( annotation: Union[ - ClassificationAnnotation, VideoClassificationAnnotation + ClassificationAnnotation, VideoClassificationAnnotation, AudioClassificationAnnotation ], ) -> Union[NDText, NDChecklist, NDRadio]: return {Text: NDText, Checklist: NDChecklist, Radio: NDRadio}.get( diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 2f4799d13..31a9d32b0 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -24,6 +24,10 @@ VideoMaskAnnotation, VideoObjectAnnotation, ) +from ...annotation_types.audio import ( + AudioClassificationAnnotation, + AudioObjectAnnotation, +) from labelbox.types import DocumentRectangle, DocumentEntity from .classification import ( NDChecklistSubclass, @@ -69,6 +73,7 @@ def from_common( yield from cls._create_relationship_annotations(label) yield from cls._create_non_video_annotations(label) yield from cls._create_video_annotations(label) + yield from cls._create_audio_annotations(label) @staticmethod def _get_consecutive_frames( @@ -159,6 +164,40 @@ def _create_video_annotations( segments.append(segment) yield NDObject.from_common(segments, label.data) + @classmethod + def _create_audio_annotations( + cls, label: Label + ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]: + """Create audio annotations + + Args: + label: Label containing audio annotations to be processed + + Yields: + NDClassification or NDObject: Audio annotations in NDJSON format + """ + audio_annotations = defaultdict(list) + for annot in label.annotations: + if isinstance( + annot, (AudioClassificationAnnotation, AudioObjectAnnotation) + ): + audio_annotations[annot.feature_schema_id or annot.name].append( + annot + ) + + for annotation_group in audio_annotations.values(): + # For audio, treat each annotation as a single frame (no segments needed) + if isinstance(annotation_group[0], AudioClassificationAnnotation): + annotation = annotation_group[0] + # Add frame information to extra (milliseconds) + annotation.extra.update({"frame": annotation.frame}) + yield NDClassification.from_common(annotation, label.data) + + elif isinstance(annotation_group[0], AudioObjectAnnotation): + # For audio objects, treat like single video frame + annotation = annotation_group[0] + yield NDObject.from_common(annotation, label.data) + @classmethod def _create_non_video_annotations(cls, label: Label): non_video_annotations = [ @@ -170,6 +209,8 @@ def _create_non_video_annotations(cls, label: Label): VideoClassificationAnnotation, VideoObjectAnnotation, VideoMaskAnnotation, + AudioClassificationAnnotation, + AudioObjectAnnotation, RelationshipAnnotation, ), ) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py index 55d6b5e62..3c9def746 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py @@ -14,6 +14,9 @@ from labelbox.data.annotation_types.video import ( VideoObjectAnnotation, ) +from labelbox.data.annotation_types.audio import ( + AudioObjectAnnotation, +) from labelbox.data.mixins import ( ConfidenceMixin, CustomMetric, @@ -715,6 +718,7 @@ def from_common( ObjectAnnotation, List[List[VideoObjectAnnotation]], VideoMaskAnnotation, + AudioObjectAnnotation, ], data: GenericDataRowData, ) -> Union[ @@ -742,6 +746,9 @@ def from_common( return obj.from_common(**args) elif obj == NDVideoMasks: return obj.from_common(annotation, data) + elif isinstance(annotation, AudioObjectAnnotation): + # Handle audio object annotation like single video frame + return cls._handle_single_audio_annotation(annotation, data) subclasses = [ NDSubclassification.from_common(annot) @@ -765,6 +772,41 @@ def from_common( **optional_kwargs, ) + @classmethod + def _handle_single_audio_annotation(cls, annotation: AudioObjectAnnotation, data: GenericDataRowData): + """Handle single audio annotation like video frame + + Args: + annotation: Audio object annotation to process + data: Data row data + + Returns: + NDObject: Serialized audio object annotation + """ + # Get the appropriate NDObject subclass based on the annotation value type + obj = cls.lookup_object(annotation) + + # Process sub-classifications if any + subclasses = [ + NDSubclassification.from_common(annot) + for annot in annotation.classifications + ] + + # Add frame information to extra (milliseconds) + extra = annotation.extra.copy() if annotation.extra else {} + extra.update({"frame": annotation.frame}) + + # Create the NDObject with frame information + return obj.from_common( + str(annotation._uuid), + annotation.value, + subclasses, + annotation.name, + annotation.feature_schema_id, + extra, + data, + ) + @staticmethod def lookup_object( annotation: Union[ObjectAnnotation, List], diff --git a/libs/labelbox/tests/data/annotation_import/conftest.py b/libs/labelbox/tests/data/annotation_import/conftest.py index e3c9c8b98..75a748459 100644 --- a/libs/labelbox/tests/data/annotation_import/conftest.py +++ b/libs/labelbox/tests/data/annotation_import/conftest.py @@ -1630,6 +1630,82 @@ def video_checklist_inference(prediction_id_mapping): return checklists +@pytest.fixture +def audio_checklist_inference(prediction_id_mapping): + """Audio temporal checklist inference with frame-based timing""" + checklists = [] + for feature in prediction_id_mapping: + if "checklist" not in feature: + continue + checklist = feature["checklist"].copy() + checklist.update( + { + "answers": [ + {"name": "first_checklist_answer"}, + {"name": "second_checklist_answer"}, + ], + "frame": 2500, # 2.5 seconds in milliseconds + } + ) + del checklist["tool"] + checklists.append(checklist) + return checklists + + +@pytest.fixture +def audio_text_inference(prediction_id_mapping): + """Audio temporal text inference with frame-based timing""" + texts = [] + for feature in prediction_id_mapping: + if "text" not in feature: + continue + text = feature["text"].copy() + text.update({ + "answer": "free form text...", + "frame": 5000, # 5.0 seconds in milliseconds + }) + del text["tool"] + texts.append(text) + return texts + + +@pytest.fixture +def audio_radio_inference(prediction_id_mapping): + """Audio temporal radio inference with frame-based timing""" + radios = [] + for feature in prediction_id_mapping: + if "radio" not in feature: + continue + radio = feature["radio"].copy() + radio.update({ + "answer": {"name": "first_radio_answer"}, + "frame": 7500, # 7.5 seconds in milliseconds + }) + del radio["tool"] + radios.append(radio) + return radios + + +@pytest.fixture +def audio_text_entity_inference(prediction_id_mapping): + """Audio temporal text entity inference with frame-based timing""" + entities = [] + for feature in prediction_id_mapping: + if "text" not in feature: + continue + entity = feature["text"].copy() + entity.update({ + "frame": 3000, # 3.0 seconds in milliseconds + "location": { + "start": 0, + "end": 11, + } + }) + del entity["tool"] + entities.append(entity) + return entities + + @pytest.fixture def message_single_selection_inference( prediction_id_mapping, mmc_example_data_row_message_ids @@ -1767,9 +1843,18 @@ def annotations_by_media_type( radio_inference, radio_inference_index_mmc, text_inference_index_mmc, + audio_checklist_inference, + audio_text_inference, + audio_radio_inference, + audio_text_entity_inference, ): return { - MediaType.Audio: [checklist_inference, text_inference], + MediaType.Audio: [ + audio_checklist_inference, + audio_text_inference, + audio_radio_inference, + audio_text_entity_inference + ], MediaType.Conversational: [ checklist_inference_index, text_inference_index, @@ -2009,7 +2094,7 @@ def _convert_to_plain_object(obj): @pytest.fixture def annotation_import_test_helpers() -> Type[AnnotationImportTestHelpers]: - return AnnotationImportTestHelpers() + return AnnotationImportTestHelpers @pytest.fixture() @@ -2091,6 +2176,7 @@ def expected_export_v2_audio(): { "name": "checklist", "value": "checklist", + "frame": 2500, "checklist_answers": [ { "name": "first_checklist_answer", @@ -2107,11 +2193,34 @@ def expected_export_v2_audio(): { "name": "text", "value": "text", + "frame": 5000, "text_answer": { "content": "free form text...", "classifications": [], }, }, + { + "name": "radio", + "value": "radio", + "frame": 7500, + "radio_answer": { + "name": "first_radio_answer", + "classifications": [], + }, + }, + ], + "objects": [ + { + "name": "text", + "value": "text", + "frame": 3000, + "annotation_kind": "TextEntity", + "classifications": [], + "location": { + "start": 0, + "end": 11, + }, + } ], "segments": {}, "timestamp": {}, diff --git a/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py b/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py index 805c24edf..4a86fd834 100644 --- a/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py +++ b/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py @@ -268,6 +268,102 @@ def test_import_mal_annotations( # MAL Labels cannot be exported and compared to input labels +def test_audio_temporal_annotations_fixtures(): + """Test that audio temporal annotation fixtures are properly structured""" + # This test verifies our fixtures work without requiring the full integration environment + + # Mock prediction_id_mapping structure that our fixtures expect + mock_prediction_id_mapping = [ + { + "checklist": { + "tool": "checklist_tool", + "name": "checklist", + "value": "checklist" + }, + "text": { + "tool": "text_tool", + "name": "text", + "value": "text" + }, + "radio": { + "tool": "radio_tool", + "name": "radio", + "value": "radio" + } + } + ] + + # Test that our fixtures can process the mock data + # Note: We can't actually call the fixtures directly in a unit test, + # but we can verify the structure is correct by checking the fixture definitions + + # Verify that our fixtures are properly defined and accessible + from .conftest import ( + audio_checklist_inference, + audio_text_inference, + audio_radio_inference, + audio_text_entity_inference + ) + + # Check that all required fixtures exist + assert audio_checklist_inference is not None + assert audio_text_inference is not None + assert audio_radio_inference is not None + assert audio_text_entity_inference is not None + + # Verify the fixtures are callable (they should be functions) + assert callable(audio_checklist_inference) + assert callable(audio_text_inference) + assert callable(audio_radio_inference) + assert callable(audio_text_entity_inference) + + +def test_audio_temporal_annotations_integration( + client: Client, + configured_project: Project, + annotations_by_media_type, + media_type=MediaType.Audio, +): + """Test that audio temporal annotations work correctly in the integration framework""" + # Filter to only audio annotations + audio_annotations = annotations_by_media_type[MediaType.Audio] + + # Verify we have the expected audio temporal annotations + assert len(audio_annotations) == 4 # checklist, text, radio, text_entity + + # Check that temporal annotations have frame information + for annotation in audio_annotations: + if "frame" in annotation: + assert isinstance(annotation["frame"], int) + assert annotation["frame"] >= 0 + # Verify frame values are in milliseconds (reasonable range for audio) + assert annotation["frame"] <= 600000 # 10 minutes max + + # Test import with audio temporal annotations + label_import = lb.LabelImport.create_from_objects( + client, + configured_project.uid, + f"test-import-audio-temporal-{uuid.uuid4()}", + audio_annotations, + ) + label_import.wait_until_done() + + # Verify import was successful + assert label_import.state == AnnotationImportState.FINISHED + assert len(label_import.errors) == 0 + + # Verify all annotations were imported successfully + all_annotations = sorted([a["uuid"] for a in audio_annotations]) + successful_annotations = sorted( + [ + status["uuid"] + for status in label_import.statuses + if status["status"] == "SUCCESS" + ] + ) + assert successful_annotations == all_annotations + + @pytest.mark.parametrize( "configured_project_by_global_key, media_type", [ diff --git a/libs/labelbox/tests/data/annotation_types/test_audio.py b/libs/labelbox/tests/data/annotation_types/test_audio.py new file mode 100644 index 000000000..3163f1079 --- /dev/null +++ b/libs/labelbox/tests/data/annotation_types/test_audio.py @@ -0,0 +1,403 @@ +import pytest +import labelbox.types as lb_types +from labelbox.data.annotation_types.audio import ( + AudioClassificationAnnotation, + AudioObjectAnnotation, +) +from labelbox.data.annotation_types.classification.classification import ( + ClassificationAnswer, + Radio, + Text, + Checklist, +) +from labelbox.data.annotation_types.ner import TextEntity + + +def test_audio_classification_creation(): + """Test creating audio classification with time range""" + annotation = AudioClassificationAnnotation.from_time_range( + start_sec=2.5, + end_sec=4.1, + name="speaker_id", + value=Radio(answer=ClassificationAnswer(name="john")) + ) + + assert annotation.frame == 2500 # 2.5 seconds * 1000 + assert annotation.start_time == 2.5 + assert annotation.segment_index is None + assert annotation.name == "speaker_id" + assert isinstance(annotation.value, Radio) + assert annotation.value.answer.name == "john" + + +def test_audio_classification_creation_with_segment(): + """Test creating audio classification with segment index""" + annotation = AudioClassificationAnnotation.from_time_range( + start_sec=10.0, + end_sec=15.0, + name="language", + value=Radio(answer=ClassificationAnswer(name="english")), + segment_index=1 + ) + + assert annotation.frame == 10000 + assert annotation.start_time == 10.0 + assert annotation.segment_index == 1 + + +def test_audio_classification_direct_creation(): + """Test creating audio classification directly with frame""" + annotation = AudioClassificationAnnotation( + frame=5000, # 5.0 seconds + name="quality", + value=Text(answer="excellent") + ) + + assert annotation.frame == 5000 + assert annotation.start_time == 5.0 + assert annotation.name == "quality" + assert isinstance(annotation.value, Text) + assert annotation.value.answer == "excellent" + + +def test_audio_object_creation(): + """Test creating audio object annotation""" + annotation = AudioObjectAnnotation.from_time_range( + start_sec=10.0, + end_sec=12.5, + name="transcription", + value=lb_types.TextEntity(start=0, end=11) # "Hello world" has 11 characters + ) + + assert annotation.frame == 10000 + assert annotation.start_time == 10.0 + assert annotation.keyframe is True + assert annotation.segment_index is None + assert annotation.name == "transcription" + assert isinstance(annotation.value, lb_types.TextEntity) + assert annotation.value.start == 0 + assert annotation.value.end == 11 + + +def test_audio_object_creation_with_classifications(): + """Test creating audio object with sub-classifications""" + sub_classification = AudioClassificationAnnotation( + frame=10000, + name="confidence", + value=Radio(answer=ClassificationAnswer(name="high")) + ) + + annotation = AudioObjectAnnotation.from_time_range( + start_sec=10.0, + end_sec=12.5, + name="transcription", + value=lb_types.TextEntity(start=0, end=11), # "Hello world" has 11 characters + classifications=[sub_classification] + ) + + assert len(annotation.classifications) == 1 + assert annotation.classifications[0].name == "confidence" + assert annotation.classifications[0].frame == 10000 + + +def test_audio_object_direct_creation(): + """Test creating audio object directly with frame""" + annotation = AudioObjectAnnotation( + frame=7500, # 7.5 seconds + name="sound_event", + value=lb_types.TextEntity(start=0, end=11), # "Dog barking" has 11 characters + keyframe=False, + segment_index=2 + ) + + assert annotation.frame == 7500 + assert annotation.start_time == 7.5 + assert annotation.keyframe is False + assert annotation.segment_index == 2 + + +def test_time_conversion_precision(): + """Test time conversion maintains precision""" + # Test various time values + test_cases = [ + (0.0, 0), + (0.001, 1), # 1 millisecond + (1.0, 1000), # 1 second + (1.5, 1500), # 1.5 seconds + (10.123, 10123), # 10.123 seconds + (60.0, 60000), # 1 minute + ] + + for seconds, expected_milliseconds in test_cases: + annotation = AudioClassificationAnnotation.from_time_range( + start_sec=seconds, + end_sec=seconds + 1.0, + name="test", + value=Text(answer="test") + ) + assert annotation.frame == expected_milliseconds + assert annotation.start_time == seconds + + +def test_audio_label_integration(): + """Test audio annotations in Label container""" + # Create audio annotations + speaker_annotation = AudioClassificationAnnotation.from_time_range( + start_sec=1.0, end_sec=2.0, + name="speaker", value=Radio(answer=ClassificationAnswer(name="john")) + ) + + transcription_annotation = AudioObjectAnnotation.from_time_range( + start_sec=1.0, end_sec=2.0, + name="transcription", value=lb_types.TextEntity(start=0, end=5) # "Hello" has 5 characters + ) + + # Create label with audio annotations + label = lb_types.Label( + data={"global_key": "audio_file.mp3"}, + annotations=[speaker_annotation, transcription_annotation] + ) + + # Test audio annotations by frame + audio_frames = label.audio_annotations_by_frame() + assert 1000 in audio_frames + assert len(audio_frames[1000]) == 2 + + # Verify both annotations are in the same frame + frame_annotations = audio_frames[1000] + assert any(isinstance(ann, AudioClassificationAnnotation) for ann in frame_annotations) + assert any(isinstance(ann, AudioObjectAnnotation) for ann in frame_annotations) + + +def test_audio_annotations_by_frame_empty(): + """Test audio_annotations_by_frame with no audio annotations""" + label = lb_types.Label( + data={"global_key": "image_file.jpg"}, + annotations=[ + lb_types.ObjectAnnotation( + name="bbox", + value=lb_types.Rectangle( + start=lb_types.Point(x=0, y=0), + end=lb_types.Point(x=100, y=100) + ) + ) + ] + ) + + audio_frames = label.audio_annotations_by_frame() + assert audio_frames == {} + + +def test_audio_annotations_by_frame_multiple_frames(): + """Test audio_annotations_by_frame with multiple time frames""" + # Create annotations at different times + annotation1 = AudioClassificationAnnotation( + frame=1000, # 1.0 seconds + name="speaker1", + value=Radio(answer=ClassificationAnswer(name="john")) + ) + + annotation2 = AudioClassificationAnnotation( + frame=5000, # 5.0 seconds + name="speaker2", + value=Radio(answer=ClassificationAnswer(name="jane")) + ) + + annotation3 = AudioObjectAnnotation( + frame=1000, # 1.0 seconds (same as annotation1) + name="transcription1", + value=lb_types.TextEntity(start=0, end=5) # "Hello" has 5 characters + ) + + label = lb_types.Label( + data={"global_key": "audio_file.mp3"}, + annotations=[annotation1, annotation2, annotation3] + ) + + audio_frames = label.audio_annotations_by_frame() + + # Should have 2 frames: 1000ms and 5000ms + assert len(audio_frames) == 2 + assert 1000 in audio_frames + assert 5000 in audio_frames + + # Frame 1000 should have 2 annotations + assert len(audio_frames[1000]) == 2 + assert any(ann.name == "speaker1" for ann in audio_frames[1000]) + assert any(ann.name == "transcription1" for ann in audio_frames[1000]) + + # Frame 5000 should have 1 annotation + assert len(audio_frames[5000]) == 1 + assert audio_frames[5000][0].name == "speaker2" + + +def test_audio_annotation_validation(): + """Test audio annotation field validation""" + # Test frame must be int + with pytest.raises(ValueError): + AudioClassificationAnnotation( + frame="invalid", # Should be int + name="test", + value=Text(answer="test") + ) + + # Test frame must be non-negative (Pydantic handles this automatically) + # Negative frames are allowed by Pydantic, so we test that they work + annotation = AudioClassificationAnnotation( + frame=-1000, # Negative frames are allowed + name="test", + value=Text(answer="test") + ) + assert annotation.frame == -1000 + + +def test_audio_annotation_extra_fields(): + """Test audio annotations can have extra metadata""" + extra_data = {"source": "automatic", "confidence_score": 0.95} + + annotation = AudioClassificationAnnotation( + frame=3000, + name="quality", + value=Text(answer="good"), + extra=extra_data + ) + + assert annotation.extra["source"] == "automatic" + assert annotation.extra["confidence_score"] == 0.95 + + +def test_audio_annotation_feature_schema(): + """Test audio annotations with feature schema IDs""" + annotation = AudioClassificationAnnotation( + frame=4000, + name="language", + value=Radio(answer=ClassificationAnswer(name="spanish")), + feature_schema_id="1234567890123456789012345" # Exactly 25 characters + ) + + assert annotation.feature_schema_id == "1234567890123456789012345" + + +def test_audio_annotation_mixed_types(): + """Test label with mixed audio, video, and image annotations""" + # Audio annotation + audio_annotation = AudioClassificationAnnotation( + frame=2000, + name="speaker", + value=Radio(answer=ClassificationAnswer(name="john")) + ) + + # Video annotation + video_annotation = lb_types.VideoClassificationAnnotation( + frame=10, + name="quality", + value=Text(answer="good") + ) + + # Image annotation + image_annotation = lb_types.ObjectAnnotation( + name="bbox", + value=lb_types.Rectangle( + start=lb_types.Point(x=0, y=0), + end=lb_types.Point(x=100, y=100) + ) + ) + + # Create label with mixed types + label = lb_types.Label( + data={"global_key": "mixed_media"}, + annotations=[audio_annotation, video_annotation, image_annotation] + ) + + # Test audio-specific method + audio_frames = label.audio_annotations_by_frame() + assert 2000 in audio_frames + assert len(audio_frames[2000]) == 1 + + # Test video-specific method (should still work) + video_frames = label.frame_annotations() + assert 10 in video_frames + assert len(video_frames[10]) == 1 + + # Test general object annotations (should still work) + object_annotations = label.object_annotations() + assert len(object_annotations) == 1 + assert object_annotations[0].name == "bbox" + + +def test_audio_annotation_serialization(): + """Test audio annotations can be serialized to dict""" + annotation = AudioClassificationAnnotation( + frame=6000, + name="emotion", + value=Radio(answer=ClassificationAnswer(name="happy")), + segment_index=3, + extra={"confidence": 0.9} + ) + + # Test model_dump + serialized = annotation.model_dump() + assert serialized["frame"] == 6000 + assert serialized["name"] == "emotion" + assert serialized["segment_index"] == 3 + assert serialized["extra"]["confidence"] == 0.9 + + # Test model_dump with exclusions + serialized_excluded = annotation.model_dump(exclude_none=True) + assert "frame" in serialized_excluded + assert "name" in serialized_excluded + assert "segment_index" in serialized_excluded + + +def test_audio_annotation_from_dict(): + """Test audio annotations can be created from dict""" + annotation_data = { + "frame": 7000, + "name": "topic", + "value": Text(answer="technology"), + "segment_index": 2, + "extra": {"source": "manual"} + } + + annotation = AudioClassificationAnnotation(**annotation_data) + + assert annotation.frame == 7000 + assert annotation.name == "topic" + assert annotation.segment_index == 2 + assert annotation.extra["source"] == "manual" + + +def test_audio_annotation_edge_cases(): + """Test audio annotation edge cases""" + # Test very long audio (many hours) + long_annotation = AudioClassificationAnnotation.from_time_range( + start_sec=3600.0, # 1 hour + end_sec=7200.0, # 2 hours + name="long_audio", + value=Text(answer="very long") + ) + + assert long_annotation.frame == 3600000 # 1 hour in milliseconds + assert long_annotation.start_time == 3600.0 + + # Test very short audio (milliseconds) + short_annotation = AudioClassificationAnnotation.from_time_range( + start_sec=0.001, # 1 millisecond + end_sec=0.002, # 2 milliseconds + name="short_audio", + value=Text(answer="very short") + ) + + assert short_annotation.frame == 1 # 1 millisecond + assert short_annotation.start_time == 0.001 + + # Test zero time + zero_annotation = AudioClassificationAnnotation.from_time_range( + start_sec=0.0, + end_sec=0.0, + name="zero_time", + value=Text(answer="zero") + ) + + assert zero_annotation.frame == 0 + assert zero_annotation.start_time == 0.0 From dbcc7bf45c17898810166cec1d396e5e0f905d53 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 8 Sep 2025 10:46:16 -0700 Subject: [PATCH 002/103] chore: use ms instead of s in sdk interface --- .../annotation_import/audio_temporal.ipynb | 67 ++++++++++--------- .../labelbox/data/annotation_types/audio.py | 34 +++++----- .../tests/data/annotation_types/test_audio.py | 58 ++++++++-------- 3 files changed, 80 insertions(+), 79 deletions(-) diff --git a/examples/annotation_import/audio_temporal.ipynb b/examples/annotation_import/audio_temporal.ipynb index 69a8eb4a0..73ac01004 100644 --- a/examples/annotation_import/audio_temporal.ipynb +++ b/examples/annotation_import/audio_temporal.ipynb @@ -111,7 +111,7 @@ "\n", "### Audio Classification Annotations\n", "\n", - "Use `AudioClassificationAnnotation` for classifications tied to specific time ranges.\n" + "Use `AudioClassificationAnnotation` for classifications tied to specific time ranges. The interface now accepts milliseconds directly for precise timing control.\n" ] }, { @@ -122,8 +122,8 @@ "source": [ "# Speaker identification for a time range\n", "speaker_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=2.5, # Start at 2.5 seconds\n", - " end_sec=4.1, # End at 4.1 seconds\n", + " start_ms=2500, # Start at 2500 milliseconds (2.5 seconds)\n", + " end_ms=4100, # End at 4100 milliseconds (4.1 seconds)\n", " name=\"speaker_id\",\n", " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"john\"))\n", ")\n", @@ -140,8 +140,8 @@ "source": [ "# Audio quality assessment for a segment\n", "quality_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=0.0,\n", - " end_sec=10.0,\n", + " start_ms=0,\n", + " end_ms=10000,\n", " name=\"audio_quality\",\n", " value=lb_types.Checklist(answer=[\n", " lb_types.ClassificationAnswer(name=\"clear_audio\"),\n", @@ -151,8 +151,8 @@ "\n", "# Emotion detection for a segment\n", "emotion_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=5.2,\n", - " end_sec=8.7,\n", + " start_ms=5200,\n", + " end_ms=8700,\n", " name=\"emotion\",\n", " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"happy\"))\n", ")\n" @@ -164,7 +164,7 @@ "source": [ "### Audio Object Annotations\n", "\n", - "Use `AudioObjectAnnotation` for text entities like transcriptions tied to specific time ranges.\n" + "Use `AudioObjectAnnotation` for text entities like transcriptions tied to specific time ranges. The interface now accepts milliseconds directly for precise timing control.\n" ] }, { @@ -175,8 +175,8 @@ "source": [ "# Transcription with precise timestamps\n", "transcription_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=2.5,\n", - " end_sec=4.1,\n", + " start_ms=2500,\n", + " end_ms=4100,\n", " name=\"transcription\",\n", " value=lb_types.TextEntity(text=\"Hello, how are you doing today?\")\n", ")\n", @@ -193,8 +193,8 @@ "source": [ "# Sound event detection\n", "sound_event_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=10.0,\n", - " end_sec=12.5,\n", + " start_ms=10000,\n", + " end_ms=12500,\n", " name=\"sound_event\",\n", " value=lb_types.TextEntity(text=\"Dog barking in background\")\n", ")\n", @@ -202,17 +202,17 @@ "# Multiple transcription segments\n", "transcription_segments = [\n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=0.0, end_sec=2.3,\n", + " start_ms=0, end_ms=2300,\n", " name=\"transcription\",\n", " value=lb_types.TextEntity(text=\"Welcome to our podcast.\")\n", " ),\n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=2.5, end_sec=5.8,\n", + " start_ms=2500, end_ms=5800,\n", " name=\"transcription\", \n", " value=lb_types.TextEntity(text=\"Today we're discussing AI advancements.\")\n", " ),\n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=6.0, end_sec=9.2,\n", + " start_ms=6000, end_ms=9200,\n", " name=\"transcription\",\n", " value=lb_types.TextEntity(text=\"Let's start with machine learning basics.\")\n", " )\n", @@ -238,31 +238,31 @@ "podcast_annotations = [\n", " # Host introduction\n", " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=0.0, end_sec=5.0,\n", + " start_ms=0, end_ms=5000,\n", " name=\"speaker_id\",\n", " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"host\"))\n", " ),\n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=0.0, end_sec=5.0,\n", + " start_ms=0, end_ms=5000,\n", " name=\"transcription\",\n", " value=lb_types.TextEntity(text=\"Welcome to Tech Talk, I'm your host Sarah.\")\n", " ),\n", " \n", " # Guest response\n", " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=5.2, end_sec=8.5,\n", + " start_ms=5200, end_ms=8500,\n", " name=\"speaker_id\",\n", " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"guest\"))\n", " ),\n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=5.2, end_sec=8.5,\n", + " start_ms=5200, end_ms=8500,\n", " name=\"transcription\",\n", " value=lb_types.TextEntity(text=\"Thanks for having me, Sarah!\")\n", " ),\n", " \n", " # Audio quality assessment\n", " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=0.0, end_sec=10.0,\n", + " start_ms=0, end_ms=10000,\n", " name=\"audio_quality\",\n", " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"excellent\"))\n", " )\n", @@ -288,14 +288,14 @@ "call_center_annotations = [\n", " # Customer sentiment analysis\n", " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=0.0, end_sec=30.0,\n", + " start_ms=0, end_ms=30000,\n", " name=\"customer_sentiment\",\n", " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"frustrated\"))\n", " ),\n", " \n", " # Agent performance\n", " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=30.0, end_sec=60.0,\n", + " start_ms=30000, end_ms=60000,\n", " name=\"agent_performance\",\n", " value=lb_types.Checklist(answer=[\n", " lb_types.ClassificationAnswer(name=\"professional_tone\"),\n", @@ -306,13 +306,13 @@ " \n", " # Key phrases extraction\n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=15.0, end_sec=18.0,\n", + " start_ms=15000, end_ms=18000,\n", " name=\"key_phrase\",\n", " value=lb_types.TextEntity(text=\"I want to speak to your manager\")\n", " ),\n", " \n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=45.0, end_sec=48.0,\n", + " start_ms=45000, end_ms=48000,\n", " name=\"key_phrase\",\n", " value=lb_types.TextEntity(text=\"Thank you for your patience\")\n", " )\n", @@ -338,7 +338,7 @@ "music_annotations = [\n", " # Musical instruments\n", " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=0.0, end_sec=30.0,\n", + " start_ms=0, end_ms=30000,\n", " name=\"instruments\",\n", " value=lb_types.Checklist(answer=[\n", " lb_types.ClassificationAnswer(name=\"piano\"),\n", @@ -349,20 +349,20 @@ " \n", " # Genre classification\n", " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=0.0, end_sec=60.0,\n", + " start_ms=0, end_ms=60000,\n", " name=\"genre\",\n", " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"classical\"))\n", " ),\n", " \n", " # Sound events\n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=25.0, end_sec=27.0,\n", + " start_ms=25000, end_ms=27000,\n", " name=\"sound_event\",\n", " value=lb_types.TextEntity(text=\"Applause from audience\")\n", " ),\n", " \n", " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_sec=45.0, end_sec=46.5,\n", + " start_ms=45000, end_ms=46500,\n", " name=\"sound_event\",\n", " value=lb_types.TextEntity(text=\"Door closing in background\")\n", " )\n", @@ -681,12 +681,12 @@ "\n", "# Audio: 1 frame = 1 millisecond\n", "audio_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_sec=2.5, end_sec=4.1,\n", + " start_ms=2500, end_ms=4100,\n", " name=\"test\", value=lb_types.Text(answer=\"test\")\n", ")\n", "\n", "print(f\"Audio Annotation:\")\n", - "print(f\" Time: 2.5s → Frame: {audio_annotation.frame} (milliseconds)\")\n", + "print(f\" Time: 2500ms → Frame: {audio_annotation.frame} (milliseconds)\")\n", "print(f\" Frame rate: 1000 frames/second (1 frame = 1ms)\")\n", "\n", "print(f\"\\nVideo Annotation (for comparison):\")\n", @@ -704,8 +704,8 @@ "\n", "### 1. Time Precision\n", "- Audio temporal annotations use millisecond precision (1 frame = 1ms)\n", - "- Always use the `from_time_range()` method for user-friendly second-based input\n", - "- Frame values are automatically calculated: `frame = int(start_sec * 1000)`\n", + "- Use the `from_time_range()` method with millisecond-based input for precise timing control\n", + "- Frame values are set directly: `frame = start_ms`\n", "\n", "### 2. Ontology Alignment\n", "- Ensure annotation `name` fields match your ontology tool/classification names\n", @@ -751,7 +751,7 @@ "This notebook demonstrated:\n", "\n", "1. **Creating temporal audio annotations** using `AudioClassificationAnnotation` and `AudioObjectAnnotation`\n", - "2. **Time-based API** with `from_time_range()` for user-friendly input\n", + "2. **Millisecond-based API** with `from_time_range()` for precise timing control\n", "3. **Multiple use cases**: podcasts, call centers, music analysis\n", "4. **MAL import pipeline** for uploading temporal prelabels\n", "5. **NDJSON serialization** compatible with existing video infrastructure\n", @@ -762,6 +762,7 @@ "- **Frame-based precision** - 1ms accuracy for audio timing\n", "- **Seamless integration** - works with existing MAL and Label Import pipelines\n", "- **Flexible annotation types** - supports classifications and text entities with timestamps\n", + "- **Direct millisecond input** - precise timing control without conversion overhead\n", "\n", "### Next Steps:\n", "1. Upload your temporal audio annotations using this notebook as a template\n", diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index 35866f62a..e332b76d4 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -8,7 +8,7 @@ class AudioClassificationAnnotation(ClassificationAnnotation): """Audio classification for specific time range Examples: - - Speaker identification from 2.5s to 4.1s + - Speaker identification from 2500ms to 4100ms - Audio quality assessment for a segment - Language detection for audio segments @@ -25,25 +25,25 @@ class AudioClassificationAnnotation(ClassificationAnnotation): segment_index: Optional[int] = None @classmethod - def from_time_range(cls, start_sec: float, end_sec: float, **kwargs): - """Create from seconds (user-friendly) to frames (internal) + def from_time_range(cls, start_ms: int, end_ms: int, **kwargs): + """Create from milliseconds (user-friendly) to frames (internal) Args: - start_sec (float): Start time in seconds - end_sec (float): End time in seconds + start_ms (int): Start time in milliseconds + end_ms (int): End time in milliseconds **kwargs: Additional arguments for the annotation Returns: - AudioClassificationAnnotation: Annotation with frame set to start_sec * 1000 + AudioClassificationAnnotation: Annotation with frame set to start_ms Example: >>> AudioClassificationAnnotation.from_time_range( - ... start_sec=2.5, end_sec=4.1, + ... start_ms=2500, end_ms=4100, ... name="speaker_id", ... value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="john")) ... ) """ - return cls(frame=int(start_sec * 1000), **kwargs) + return cls(frame=start_ms, **kwargs) @property def start_time(self) -> float: @@ -59,8 +59,8 @@ class AudioObjectAnnotation(ObjectAnnotation, ConfidenceNotSupportedMixin, Custo """Audio object annotation for specific time range Examples: - - Transcription: "Hello world" from 2.5s to 4.1s - - Sound events: "Dog barking" from 10s to 12s + - Transcription: "Hello world" from 2500ms to 4100ms + - Sound events: "Dog barking" from 10000ms to 12000ms - Audio segments with metadata Args: @@ -79,25 +79,25 @@ class AudioObjectAnnotation(ObjectAnnotation, ConfidenceNotSupportedMixin, Custo segment_index: Optional[int] = None @classmethod - def from_time_range(cls, start_sec: float, end_sec: float, **kwargs): - """Create from seconds (user-friendly) to frames (internal) + def from_time_range(cls, start_ms: int, end_ms: int, **kwargs): + """Create from milliseconds (user-friendly) to frames (internal) Args: - start_sec (float): Start time in seconds - end_sec (float): End time in seconds + start_ms (int): Start time in milliseconds + end_ms (int): End time in milliseconds **kwargs: Additional arguments for the annotation Returns: - AudioObjectAnnotation: Annotation with frame set to start_sec * 1000 + AudioObjectAnnotation: Annotation with frame set to start_ms Example: >>> AudioObjectAnnotation.from_time_range( - ... start_sec=10.0, end_sec=12.5, + ... start_ms=10000, end_ms=12500, ... name="transcription", ... value=lb_types.TextEntity(text="Hello world") ... ) """ - return cls(frame=int(start_sec * 1000), **kwargs) + return cls(frame=start_ms, **kwargs) @property def start_time(self) -> float: diff --git a/libs/labelbox/tests/data/annotation_types/test_audio.py b/libs/labelbox/tests/data/annotation_types/test_audio.py index 3163f1079..017c960ab 100644 --- a/libs/labelbox/tests/data/annotation_types/test_audio.py +++ b/libs/labelbox/tests/data/annotation_types/test_audio.py @@ -16,13 +16,13 @@ def test_audio_classification_creation(): """Test creating audio classification with time range""" annotation = AudioClassificationAnnotation.from_time_range( - start_sec=2.5, - end_sec=4.1, + start_ms=2500, + end_ms=4100, name="speaker_id", value=Radio(answer=ClassificationAnswer(name="john")) ) - assert annotation.frame == 2500 # 2.5 seconds * 1000 + assert annotation.frame == 2500 # 2.5 seconds in milliseconds assert annotation.start_time == 2.5 assert annotation.segment_index is None assert annotation.name == "speaker_id" @@ -33,8 +33,8 @@ def test_audio_classification_creation(): def test_audio_classification_creation_with_segment(): """Test creating audio classification with segment index""" annotation = AudioClassificationAnnotation.from_time_range( - start_sec=10.0, - end_sec=15.0, + start_ms=10000, + end_ms=15000, name="language", value=Radio(answer=ClassificationAnswer(name="english")), segment_index=1 @@ -63,8 +63,8 @@ def test_audio_classification_direct_creation(): def test_audio_object_creation(): """Test creating audio object annotation""" annotation = AudioObjectAnnotation.from_time_range( - start_sec=10.0, - end_sec=12.5, + start_ms=10000, + end_ms=12500, name="transcription", value=lb_types.TextEntity(start=0, end=11) # "Hello world" has 11 characters ) @@ -88,8 +88,8 @@ def test_audio_object_creation_with_classifications(): ) annotation = AudioObjectAnnotation.from_time_range( - start_sec=10.0, - end_sec=12.5, + start_ms=10000, + end_ms=12500, name="transcription", value=lb_types.TextEntity(start=0, end=11), # "Hello world" has 11 characters classifications=[sub_classification] @@ -118,37 +118,37 @@ def test_audio_object_direct_creation(): def test_time_conversion_precision(): """Test time conversion maintains precision""" - # Test various time values + # Test various time values in milliseconds test_cases = [ - (0.0, 0), - (0.001, 1), # 1 millisecond - (1.0, 1000), # 1 second - (1.5, 1500), # 1.5 seconds - (10.123, 10123), # 10.123 seconds - (60.0, 60000), # 1 minute + (0, 0.0), + (1, 0.001), # 1 millisecond + (1000, 1.0), # 1 second + (1500, 1.5), # 1.5 seconds + (10123, 10.123), # 10.123 seconds + (60000, 60.0), # 1 minute ] - for seconds, expected_milliseconds in test_cases: + for milliseconds, expected_seconds in test_cases: annotation = AudioClassificationAnnotation.from_time_range( - start_sec=seconds, - end_sec=seconds + 1.0, + start_ms=milliseconds, + end_ms=milliseconds + 1000, name="test", value=Text(answer="test") ) - assert annotation.frame == expected_milliseconds - assert annotation.start_time == seconds + assert annotation.frame == milliseconds + assert annotation.start_time == expected_seconds def test_audio_label_integration(): """Test audio annotations in Label container""" # Create audio annotations speaker_annotation = AudioClassificationAnnotation.from_time_range( - start_sec=1.0, end_sec=2.0, + start_ms=1000, end_ms=2000, name="speaker", value=Radio(answer=ClassificationAnswer(name="john")) ) transcription_annotation = AudioObjectAnnotation.from_time_range( - start_sec=1.0, end_sec=2.0, + start_ms=1000, end_ms=2000, name="transcription", value=lb_types.TextEntity(start=0, end=5) # "Hello" has 5 characters ) @@ -371,8 +371,8 @@ def test_audio_annotation_edge_cases(): """Test audio annotation edge cases""" # Test very long audio (many hours) long_annotation = AudioClassificationAnnotation.from_time_range( - start_sec=3600.0, # 1 hour - end_sec=7200.0, # 2 hours + start_ms=3600000, # 1 hour in milliseconds + end_ms=7200000, # 2 hours in milliseconds name="long_audio", value=Text(answer="very long") ) @@ -382,8 +382,8 @@ def test_audio_annotation_edge_cases(): # Test very short audio (milliseconds) short_annotation = AudioClassificationAnnotation.from_time_range( - start_sec=0.001, # 1 millisecond - end_sec=0.002, # 2 milliseconds + start_ms=1, # 1 millisecond + end_ms=2, # 2 milliseconds name="short_audio", value=Text(answer="very short") ) @@ -393,8 +393,8 @@ def test_audio_annotation_edge_cases(): # Test zero time zero_annotation = AudioClassificationAnnotation.from_time_range( - start_sec=0.0, - end_sec=0.0, + start_ms=0, + end_ms=0, name="zero_time", value=Text(answer="zero") ) From dbb592fb279517b69fdb0f2e893f575034581c19 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 8 Sep 2025 17:52:46 +0000 Subject: [PATCH 003/103] :art: Cleaned --- .../annotation_import/audio_temporal.ipynb | 624 +++--------------- 1 file changed, 110 insertions(+), 514 deletions(-) diff --git a/examples/annotation_import/audio_temporal.ipynb b/examples/annotation_import/audio_temporal.ipynb index 73ac01004..1c77a6928 100644 --- a/examples/annotation_import/audio_temporal.ipynb +++ b/examples/annotation_import/audio_temporal.ipynb @@ -1,14 +1,18 @@ { + "nbformat": 4, + "nbformat_minor": 2, + "metadata": {}, "cells": [ { - "cell_type": "markdown", "metadata": {}, "source": [ - " \n" - ] + "", + " ", + "\n" + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "\n", @@ -19,11 +23,11 @@ "\n", "\n", - "\n" - ] + "" + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Audio Temporal Annotation Import\n", @@ -54,57 +58,46 @@ "\n", "- **Model-Assisted Labeling (MAL)**: Upload pre-annotations for labeler review\n", "- **Label Import**: Upload ground truth labels directly\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", "outputs": [], - "source": [ - "%pip install -q \"labelbox[data]\"\n" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nfrom typing import List", + "cell_type": "code", "outputs": [], - "source": [ - "import labelbox as lb\n", - "import labelbox.types as lb_types\n", - "import uuid\n", - "from typing import List\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Replace with your API key\n", "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", "outputs": [], - "source": [ - "# Add your api key\n", - "API_KEY = \"\"\n", - "client = lb.Client(api_key=API_KEY)\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Creating Temporal Audio Annotations\n", @@ -112,592 +105,206 @@ "### Audio Classification Annotations\n", "\n", "Use `AudioClassificationAnnotation` for classifications tied to specific time ranges. The interface now accepts milliseconds directly for precise timing control.\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Speaker identification for a time range\nspeaker_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=2500, # Start at 2500 milliseconds (2.5 seconds)\n end_ms=4100, # End at 4100 milliseconds (4.1 seconds)\n name=\"speaker_id\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"john\")),\n)\n\nprint(f\"Speaker annotation frame: {speaker_annotation.frame}ms\")\nprint(f\"Speaker annotation start time: {speaker_annotation.start_time}s\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Speaker identification for a time range\n", - "speaker_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=2500, # Start at 2500 milliseconds (2.5 seconds)\n", - " end_ms=4100, # End at 4100 milliseconds (4.1 seconds)\n", - " name=\"speaker_id\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"john\"))\n", - ")\n", - "\n", - "print(f\"Speaker annotation frame: {speaker_annotation.frame}ms\")\n", - "print(f\"Speaker annotation start time: {speaker_annotation.start_time}s\")\n" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Audio quality assessment for a segment\nquality_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=10000,\n name=\"audio_quality\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"clear_audio\"),\n lb_types.ClassificationAnswer(name=\"no_background_noise\"),\n ]),\n)\n\n# Emotion detection for a segment\nemotion_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=5200,\n end_ms=8700,\n name=\"emotion\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"happy\")),\n)", + "cell_type": "code", "outputs": [], - "source": [ - "# Audio quality assessment for a segment\n", - "quality_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=0,\n", - " end_ms=10000,\n", - " name=\"audio_quality\",\n", - " value=lb_types.Checklist(answer=[\n", - " lb_types.ClassificationAnswer(name=\"clear_audio\"),\n", - " lb_types.ClassificationAnswer(name=\"no_background_noise\")\n", - " ])\n", - ")\n", - "\n", - "# Emotion detection for a segment\n", - "emotion_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=5200,\n", - " end_ms=8700,\n", - " name=\"emotion\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"happy\"))\n", - ")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Audio Object Annotations\n", "\n", "Use `AudioObjectAnnotation` for text entities like transcriptions tied to specific time ranges. The interface now accepts milliseconds directly for precise timing control.\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Transcription with precise timestamps\ntranscription_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=2500,\n end_ms=4100,\n name=\"transcription\",\n value=lb_types.TextEntity(text=\"Hello, how are you doing today?\"),\n)\n\nprint(f\"Transcription frame: {transcription_annotation.frame}ms\")\nprint(f\"Transcription text: {transcription_annotation.value.text}\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Transcription with precise timestamps\n", - "transcription_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=2500,\n", - " end_ms=4100,\n", - " name=\"transcription\",\n", - " value=lb_types.TextEntity(text=\"Hello, how are you doing today?\")\n", - ")\n", - "\n", - "print(f\"Transcription frame: {transcription_annotation.frame}ms\")\n", - "print(f\"Transcription text: {transcription_annotation.value.text}\")\n" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Sound event detection\nsound_event_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=10000,\n end_ms=12500,\n name=\"sound_event\",\n value=lb_types.TextEntity(text=\"Dog barking in background\"),\n)\n\n# Multiple transcription segments\ntranscription_segments = [\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=0,\n end_ms=2300,\n name=\"transcription\",\n value=lb_types.TextEntity(text=\"Welcome to our podcast.\"),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=2500,\n end_ms=5800,\n name=\"transcription\",\n value=lb_types.TextEntity(\n text=\"Today we're discussing AI advancements.\"),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=6000,\n end_ms=9200,\n name=\"transcription\",\n value=lb_types.TextEntity(\n text=\"Let's start with machine learning basics.\"),\n ),\n]", + "cell_type": "code", "outputs": [], - "source": [ - "# Sound event detection\n", - "sound_event_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=10000,\n", - " end_ms=12500,\n", - " name=\"sound_event\",\n", - " value=lb_types.TextEntity(text=\"Dog barking in background\")\n", - ")\n", - "\n", - "# Multiple transcription segments\n", - "transcription_segments = [\n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=0, end_ms=2300,\n", - " name=\"transcription\",\n", - " value=lb_types.TextEntity(text=\"Welcome to our podcast.\")\n", - " ),\n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=2500, end_ms=5800,\n", - " name=\"transcription\", \n", - " value=lb_types.TextEntity(text=\"Today we're discussing AI advancements.\")\n", - " ),\n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=6000, end_ms=9200,\n", - " name=\"transcription\",\n", - " value=lb_types.TextEntity(text=\"Let's start with machine learning basics.\")\n", - " )\n", - "]\n" - ] - }, - { - "cell_type": "markdown", + "execution_count": null + }, + { "metadata": {}, "source": [ "## Use Cases and Examples\n", "\n", "### Use Case 1: Podcast Transcription with Speaker Identification\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Complete podcast annotation with speakers and transcriptions\npodcast_annotations = [\n # Host introduction\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=5000,\n name=\"speaker_id\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"host\")),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=0,\n end_ms=5000,\n name=\"transcription\",\n value=lb_types.TextEntity(\n text=\"Welcome to Tech Talk, I'm your host Sarah.\"),\n ),\n # Guest response\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=5200,\n end_ms=8500,\n name=\"speaker_id\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"guest\")),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=5200,\n end_ms=8500,\n name=\"transcription\",\n value=lb_types.TextEntity(text=\"Thanks for having me, Sarah!\"),\n ),\n # Audio quality assessment\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=10000,\n name=\"audio_quality\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"excellent\")),\n ),\n]\n\nprint(f\"Created {len(podcast_annotations)} podcast annotations\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Complete podcast annotation with speakers and transcriptions\n", - "podcast_annotations = [\n", - " # Host introduction\n", - " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=0, end_ms=5000,\n", - " name=\"speaker_id\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"host\"))\n", - " ),\n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=0, end_ms=5000,\n", - " name=\"transcription\",\n", - " value=lb_types.TextEntity(text=\"Welcome to Tech Talk, I'm your host Sarah.\")\n", - " ),\n", - " \n", - " # Guest response\n", - " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=5200, end_ms=8500,\n", - " name=\"speaker_id\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"guest\"))\n", - " ),\n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=5200, end_ms=8500,\n", - " name=\"transcription\",\n", - " value=lb_types.TextEntity(text=\"Thanks for having me, Sarah!\")\n", - " ),\n", - " \n", - " # Audio quality assessment\n", - " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=0, end_ms=10000,\n", - " name=\"audio_quality\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"excellent\"))\n", - " )\n", - "]\n", - "\n", - "print(f\"Created {len(podcast_annotations)} podcast annotations\")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Use Case 2: Call Center Quality Analysis\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Call center analysis with sentiment and quality metrics\ncall_center_annotations = [\n # Customer sentiment analysis\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=30000,\n name=\"customer_sentiment\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"frustrated\")),\n ),\n # Agent performance\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=30000,\n end_ms=60000,\n name=\"agent_performance\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"professional_tone\"),\n lb_types.ClassificationAnswer(name=\"resolved_issue\"),\n lb_types.ClassificationAnswer(name=\"followed_script\"),\n ]),\n ),\n # Key phrases extraction\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=15000,\n end_ms=18000,\n name=\"key_phrase\",\n value=lb_types.TextEntity(text=\"I want to speak to your manager\"),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=45000,\n end_ms=48000,\n name=\"key_phrase\",\n value=lb_types.TextEntity(text=\"Thank you for your patience\"),\n ),\n]\n\nprint(f\"Created {len(call_center_annotations)} call center annotations\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Call center analysis with sentiment and quality metrics\n", - "call_center_annotations = [\n", - " # Customer sentiment analysis\n", - " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=0, end_ms=30000,\n", - " name=\"customer_sentiment\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"frustrated\"))\n", - " ),\n", - " \n", - " # Agent performance\n", - " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=30000, end_ms=60000,\n", - " name=\"agent_performance\",\n", - " value=lb_types.Checklist(answer=[\n", - " lb_types.ClassificationAnswer(name=\"professional_tone\"),\n", - " lb_types.ClassificationAnswer(name=\"resolved_issue\"),\n", - " lb_types.ClassificationAnswer(name=\"followed_script\")\n", - " ])\n", - " ),\n", - " \n", - " # Key phrases extraction\n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=15000, end_ms=18000,\n", - " name=\"key_phrase\",\n", - " value=lb_types.TextEntity(text=\"I want to speak to your manager\")\n", - " ),\n", - " \n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=45000, end_ms=48000,\n", - " name=\"key_phrase\",\n", - " value=lb_types.TextEntity(text=\"Thank you for your patience\")\n", - " )\n", - "]\n", - "\n", - "print(f\"Created {len(call_center_annotations)} call center annotations\")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Use Case 3: Music and Sound Event Detection\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Music analysis and sound event detection\nmusic_annotations = [\n # Musical instruments\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=30000,\n name=\"instruments\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"piano\"),\n lb_types.ClassificationAnswer(name=\"violin\"),\n lb_types.ClassificationAnswer(name=\"drums\"),\n ]),\n ),\n # Genre classification\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=60000,\n name=\"genre\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"classical\")),\n ),\n # Sound events\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=25000,\n end_ms=27000,\n name=\"sound_event\",\n value=lb_types.TextEntity(text=\"Applause from audience\"),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=45000,\n end_ms=46500,\n name=\"sound_event\",\n value=lb_types.TextEntity(text=\"Door closing in background\"),\n ),\n]\n\nprint(f\"Created {len(music_annotations)} music annotations\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Music analysis and sound event detection\n", - "music_annotations = [\n", - " # Musical instruments\n", - " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=0, end_ms=30000,\n", - " name=\"instruments\",\n", - " value=lb_types.Checklist(answer=[\n", - " lb_types.ClassificationAnswer(name=\"piano\"),\n", - " lb_types.ClassificationAnswer(name=\"violin\"),\n", - " lb_types.ClassificationAnswer(name=\"drums\")\n", - " ])\n", - " ),\n", - " \n", - " # Genre classification\n", - " lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=0, end_ms=60000,\n", - " name=\"genre\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"classical\"))\n", - " ),\n", - " \n", - " # Sound events\n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=25000, end_ms=27000,\n", - " name=\"sound_event\",\n", - " value=lb_types.TextEntity(text=\"Applause from audience\")\n", - " ),\n", - " \n", - " lb_types.AudioObjectAnnotation.from_time_range(\n", - " start_ms=45000, end_ms=46500,\n", - " name=\"sound_event\",\n", - " value=lb_types.TextEntity(text=\"Door closing in background\")\n", - " )\n", - "]\n", - "\n", - "print(f\"Created {len(music_annotations)} music annotations\")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Uploading Audio Temporal Prelabels\n", "\n", "### Step 1: Import Audio Data into Catalog\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create dataset with audio file\nglobal_key = \"sample-audio-temporal-\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_temporal_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)", + "cell_type": "code", "outputs": [], - "source": [ - "# Create dataset with audio file\n", - "global_key = \"sample-audio-temporal-\" + str(uuid.uuid4())\n", - "\n", - "asset = {\n", - " \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", - " \"global_key\": global_key,\n", - "}\n", - "\n", - "dataset = client.create_dataset(name=\"audio_temporal_demo_dataset\")\n", - "task = dataset.create_data_rows([asset])\n", - "task.wait_till_done()\n", - "print(\"Errors:\", task.errors)\n", - "print(\"Failed data rows:\", task.failed_data_rows)\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: Create Ontology with Temporal Audio Tools\n", "\n", "Your ontology must include the tools and classifications that match your annotation names.\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(\n tools=[\n # Text entity tools for transcriptions and sound events\n lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"transcription\"),\n lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"sound_event\"),\n lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"key_phrase\"),\n ],\n classifications=[\n # Speaker identification\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"speaker_id\",\n scope=lb.Classification.Scope.INDEX, # Frame-based classification\n options=[\n lb.Option(value=\"host\"),\n lb.Option(value=\"guest\"),\n lb.Option(value=\"john\"),\n lb.Option(value=\"sarah\"),\n ],\n ),\n # Audio quality assessment\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"audio_quality\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"clear_audio\"),\n lb.Option(value=\"no_background_noise\"),\n lb.Option(value=\"good_volume\"),\n lb.Option(value=\"excellent\"),\n ],\n ),\n # Emotion detection\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"emotion\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"happy\"),\n lb.Option(value=\"sad\"),\n lb.Option(value=\"angry\"),\n lb.Option(value=\"neutral\"),\n ],\n ),\n # Customer sentiment (for call center example)\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"customer_sentiment\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"satisfied\"),\n lb.Option(value=\"frustrated\"),\n lb.Option(value=\"angry\"),\n lb.Option(value=\"neutral\"),\n ],\n ),\n # Agent performance (for call center example)\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"agent_performance\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"professional_tone\"),\n lb.Option(value=\"resolved_issue\"),\n lb.Option(value=\"followed_script\"),\n lb.Option(value=\"empathetic_response\"),\n ],\n ),\n # Music instruments (for music example)\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"instruments\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"piano\"),\n lb.Option(value=\"violin\"),\n lb.Option(value=\"drums\"),\n lb.Option(value=\"guitar\"),\n ],\n ),\n # Music genre\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"genre\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"classical\"),\n lb.Option(value=\"jazz\"),\n lb.Option(value=\"rock\"),\n lb.Option(value=\"pop\"),\n ],\n ),\n ],\n)\n\nontology = client.create_ontology(\n \"Audio Temporal Annotations Ontology\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)\n\nprint(f\"Created ontology: {ontology.name}\")", + "cell_type": "code", "outputs": [], - "source": [ - "ontology_builder = lb.OntologyBuilder(\n", - " tools=[\n", - " # Text entity tools for transcriptions and sound events\n", - " lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"transcription\"),\n", - " lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"sound_event\"),\n", - " lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"key_phrase\"),\n", - " ],\n", - " classifications=[\n", - " # Speaker identification\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"speaker_id\",\n", - " scope=lb.Classification.Scope.INDEX, # Frame-based classification\n", - " options=[\n", - " lb.Option(value=\"host\"),\n", - " lb.Option(value=\"guest\"),\n", - " lb.Option(value=\"john\"),\n", - " lb.Option(value=\"sarah\"),\n", - " ],\n", - " ),\n", - " \n", - " # Audio quality assessment\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"audio_quality\",\n", - " scope=lb.Classification.Scope.INDEX,\n", - " options=[\n", - " lb.Option(value=\"clear_audio\"),\n", - " lb.Option(value=\"no_background_noise\"),\n", - " lb.Option(value=\"good_volume\"),\n", - " lb.Option(value=\"excellent\"),\n", - " ],\n", - " ),\n", - " \n", - " # Emotion detection\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"emotion\",\n", - " scope=lb.Classification.Scope.INDEX,\n", - " options=[\n", - " lb.Option(value=\"happy\"),\n", - " lb.Option(value=\"sad\"),\n", - " lb.Option(value=\"angry\"),\n", - " lb.Option(value=\"neutral\"),\n", - " ],\n", - " ),\n", - " \n", - " # Customer sentiment (for call center example)\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"customer_sentiment\",\n", - " scope=lb.Classification.Scope.INDEX,\n", - " options=[\n", - " lb.Option(value=\"satisfied\"),\n", - " lb.Option(value=\"frustrated\"),\n", - " lb.Option(value=\"angry\"),\n", - " lb.Option(value=\"neutral\"),\n", - " ],\n", - " ),\n", - " \n", - " # Agent performance (for call center example)\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"agent_performance\",\n", - " scope=lb.Classification.Scope.INDEX,\n", - " options=[\n", - " lb.Option(value=\"professional_tone\"),\n", - " lb.Option(value=\"resolved_issue\"),\n", - " lb.Option(value=\"followed_script\"),\n", - " lb.Option(value=\"empathetic_response\"),\n", - " ],\n", - " ),\n", - " \n", - " # Music instruments (for music example)\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"instruments\",\n", - " scope=lb.Classification.Scope.INDEX,\n", - " options=[\n", - " lb.Option(value=\"piano\"),\n", - " lb.Option(value=\"violin\"),\n", - " lb.Option(value=\"drums\"),\n", - " lb.Option(value=\"guitar\"),\n", - " ],\n", - " ),\n", - " \n", - " # Music genre\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"genre\",\n", - " scope=lb.Classification.Scope.INDEX,\n", - " options=[\n", - " lb.Option(value=\"classical\"),\n", - " lb.Option(value=\"jazz\"),\n", - " lb.Option(value=\"rock\"),\n", - " lb.Option(value=\"pop\"),\n", - " ],\n", - " ),\n", - " ],\n", - ")\n", - "\n", - "ontology = client.create_ontology(\n", - " \"Audio Temporal Annotations Ontology\",\n", - " ontology_builder.asdict(),\n", - " media_type=lb.MediaType.Audio,\n", - ")\n", - "\n", - "print(f\"Created ontology: {ontology.name}\")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Create Project and Setup Editor\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create project\nproject = client.create_project(name=\"Audio Temporal Annotations Demo\",\n media_type=lb.MediaType.Audio)\n\n# Connect ontology to project\nproject.setup_editor(ontology)\n\nprint(f\"Created project: {project.name}\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Create project\n", - "project = client.create_project(\n", - " name=\"Audio Temporal Annotations Demo\",\n", - " media_type=lb.MediaType.Audio\n", - ")\n", - "\n", - "# Connect ontology to project\n", - "project.setup_editor(ontology)\n", - "\n", - "print(f\"Created project: {project.name}\")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4: Create Batch and Add Data\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create batch\nbatch = project.create_batch(\n \"audio-temporal-batch-\" + str(uuid.uuid4())[:8],\n global_keys=[global_key],\n priority=5,\n)\n\nprint(f\"Created batch: {batch.name}\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Create batch\n", - "batch = project.create_batch(\n", - " \"audio-temporal-batch-\" + str(uuid.uuid4())[:8],\n", - " global_keys=[global_key],\n", - " priority=5,\n", - ")\n", - "\n", - "print(f\"Created batch: {batch.name}\")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5: Upload Temporal Audio Annotations via MAL\n", "\n", "Now we'll upload our temporal audio annotations using the Model-Assisted Labeling pipeline.\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create label with temporal audio annotations\n# Using the podcast example annotations\nlabel = lb_types.Label(data={\"global_key\": global_key},\n annotations=podcast_annotations)\n\nprint(f\"Created label with {len(podcast_annotations)} temporal annotations\")\nprint(\"Annotation types:\")\nfor i, annotation in enumerate(podcast_annotations):\n ann_type = type(annotation).__name__\n if hasattr(annotation, \"frame\"):\n time_info = f\"at {annotation.start_time}s (frame {annotation.frame})\"\n else:\n time_info = \"global\"\n print(f\" {i+1}. {ann_type} '{annotation.name}' {time_info}\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Create label with temporal audio annotations\n", - "# Using the podcast example annotations\n", - "label = lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=podcast_annotations\n", - ")\n", - "\n", - "print(f\"Created label with {len(podcast_annotations)} temporal annotations\")\n", - "print(\"Annotation types:\")\n", - "for i, annotation in enumerate(podcast_annotations):\n", - " ann_type = type(annotation).__name__\n", - " if hasattr(annotation, 'frame'):\n", - " time_info = f\"at {annotation.start_time}s (frame {annotation.frame})\"\n", - " else:\n", - " time_info = \"global\"\n", - " print(f\" {i+1}. {ann_type} '{annotation.name}' {time_info}\")\n" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload via MAL (Model-Assisted Labeling)\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"audio_temporal_mal_{str(uuid.uuid4())[:8]}\",\n predictions=[label],\n)\n\nupload_job.wait_until_done()\nprint(\"Upload completed!\")\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status:\", upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload via MAL (Model-Assisted Labeling)\n", - "upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"audio_temporal_mal_{str(uuid.uuid4())[:8]}\",\n", - " predictions=[label],\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Upload completed!\")\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status:\", upload_job.statuses)\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## NDJSON Format Examples\n", "\n", "Temporal audio annotations serialize to NDJSON format similar to video annotations, with frame-based timing.\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Let's examine how temporal audio annotations serialize to NDJSON\nfrom labelbox.data.serialization.ndjson.label import NDLabel\nimport json\n\n# Serialize our label to NDJSON format\nndjson_generator = NDLabel.from_common([label])\nndjson_objects = list(ndjson_generator)\n\nprint(f\"Generated {len(ndjson_objects)} NDJSON objects\")\nprint(\"\\nNDJSON Examples:\")\nprint(\"=\" * 50)\n\nfor i, obj in enumerate(ndjson_objects[:3]): # Show first 3 examples\n print(f\"\\nObject {i+1}:\")\n # Convert to dict for pretty printing\n obj_dict = obj.dict(exclude_none=True)\n print(json.dumps(obj_dict, indent=2))", + "cell_type": "code", "outputs": [], - "source": [ - "# Let's examine how temporal audio annotations serialize to NDJSON\n", - "from labelbox.data.serialization.ndjson.label import NDLabel\n", - "import json\n", - "\n", - "# Serialize our label to NDJSON format\n", - "ndjson_generator = NDLabel.from_common([label])\n", - "ndjson_objects = list(ndjson_generator)\n", - "\n", - "print(f\"Generated {len(ndjson_objects)} NDJSON objects\")\n", - "print(\"\\nNDJSON Examples:\")\n", - "print(\"=\" * 50)\n", - "\n", - "for i, obj in enumerate(ndjson_objects[:3]): # Show first 3 examples\n", - " print(f\"\\nObject {i+1}:\")\n", - " # Convert to dict for pretty printing\n", - " obj_dict = obj.dict(exclude_none=True)\n", - " print(json.dumps(obj_dict, indent=2))\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Comparison with Video Annotations\n", "\n", "Audio temporal annotations use the same frame-based structure as video annotations:\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "print(\"Frame-based Structure Comparison:\")\nprint(\"=\" * 40)\n\n# Audio: 1 frame = 1 millisecond\naudio_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=2500, end_ms=4100, name=\"test\", value=lb_types.Text(answer=\"test\"))\n\nprint(f\"Audio Annotation:\")\nprint(f\" Time: 2500ms \u2192 Frame: {audio_annotation.frame} (milliseconds)\")\nprint(f\" Frame rate: 1000 frames/second (1 frame = 1ms)\")\n\nprint(f\"\\nVideo Annotation (for comparison):\")\nprint(f\" Time: 2.5s \u2192 Frame: depends on video frame rate\")\nprint(f\" Frame rate: varies (e.g., 30 fps = 30 frames/second)\")\n\nprint(f\"\\nBoth use the same NDJSON structure with 'frame' field\")", + "cell_type": "code", "outputs": [], - "source": [ - "print(\"Frame-based Structure Comparison:\")\n", - "print(\"=\" * 40)\n", - "\n", - "# Audio: 1 frame = 1 millisecond\n", - "audio_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n", - " start_ms=2500, end_ms=4100,\n", - " name=\"test\", value=lb_types.Text(answer=\"test\")\n", - ")\n", - "\n", - "print(f\"Audio Annotation:\")\n", - "print(f\" Time: 2500ms → Frame: {audio_annotation.frame} (milliseconds)\")\n", - "print(f\" Frame rate: 1000 frames/second (1 frame = 1ms)\")\n", - "\n", - "print(f\"\\nVideo Annotation (for comparison):\")\n", - "print(f\" Time: 2.5s → Frame: depends on video frame rate\")\n", - "print(f\" Frame rate: varies (e.g., 30 fps = 30 frames/second)\")\n", - "\n", - "print(f\"\\nBoth use the same NDJSON structure with 'frame' field\")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Best Practices\n", @@ -721,29 +328,24 @@ "- Batch multiple labels in a single MAL import for better performance\n", "- Use appropriate time ranges - avoid overly granular segments\n", "- Consider audio file length when planning annotation density\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Cleanup (Optional)\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Uncomment to clean up resources\n# project.delete()\n# dataset.delete()\n# ontology.delete()", + "cell_type": "code", "outputs": [], - "source": [ - "# Uncomment to clean up resources\n", - "# project.delete()\n", - "# dataset.delete()\n", - "# ontology.delete()\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", @@ -769,19 +371,13 @@ "2. Review annotations in the Labelbox editor (uses video timeline UI)\n", "3. Export annotated data for model training or analysis\n", "4. Integrate with your audio processing pipeline\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [], + "cell_type": "markdown" } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + ] +} \ No newline at end of file From ff298d44022a50cf12556b07b5172a6f717a5194 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 8 Sep 2025 17:53:17 +0000 Subject: [PATCH 004/103] :memo: README updated --- examples/README.md | 183 +++++++++++++++++++++++---------------------- 1 file changed, 94 insertions(+), 89 deletions(-) diff --git a/examples/README.md b/examples/README.md index 924d1017d..6cae49593 100644 --- a/examples/README.md +++ b/examples/README.md @@ -16,25 +16,20 @@ - - Ontologies - Open In Github - Open In Colab - - - Quick Start - Open In Github - Open In Colab - Data Rows Open In Github Open In Colab - Basics - Open In Github - Open In Colab + Custom Embeddings + Open In Github + Open In Colab + + + User Management + Open In Github + Open In Colab Batches @@ -47,19 +42,24 @@ Open In Colab - Data Row Metadata - Open In Github - Open In Colab + Quick Start + Open In Github + Open In Colab - Custom Embeddings - Open In Github - Open In Colab + Basics + Open In Github + Open In Colab - User Management - Open In Github - Open In Colab + Ontologies + Open In Github + Open In Colab + + + Data Row Metadata + Open In Github + Open In Colab @@ -80,11 +80,6 @@ Open In Github Open In Colab - - Exporting to CSV - Open In Github - Open In Colab - Composite Mask Export Open In Github @@ -95,6 +90,11 @@ Open In Github Open In Colab + + Exporting to CSV + Open In Github + Open In Colab + @@ -110,9 +110,9 @@ - Queue Management - Open In Github - Open In Colab + Multimodal Chat Project + Open In Github + Open In Colab Project Setup @@ -125,9 +125,9 @@ Open In Colab - Multimodal Chat Project - Open In Github - Open In Colab + Queue Management + Open In Github + Open In Colab @@ -144,34 +144,39 @@ - Tiled - Open In Github - Open In Colab - - - Text - Open In Github - Open In Colab + Conversational + Open In Github + Open In Colab PDF Open In Github Open In Colab - - Video - Open In Github - Open In Colab - Audio Open In Github Open In Colab - Conversational - Open In Github - Open In Colab + Conversational LLM Data Generation + Open In Github + Open In Colab + + + Text + Open In Github + Open In Colab + + + Audio Temporal + Open In Github + Open In Colab + + + Tiled + Open In Github + Open In Colab HTML @@ -179,9 +184,9 @@ Open In Colab - Conversational LLM Data Generation - Open In Github - Open In Colab + Conversational LLM + Open In Github + Open In Colab Image @@ -189,9 +194,9 @@ Open In Colab - Conversational LLM - Open In Github - Open In Colab + Video + Open In Github + Open In Colab @@ -207,15 +212,20 @@ + + Huggingface Custom Embeddings + Open In Github + Open In Colab + Langchain Open In Github Open In Colab - Meta SAM Video - Open In Github - Open In Colab + Import YOLOv8 Annotations + Open In Github + Open In Colab Meta SAM @@ -223,14 +233,9 @@ Open In Colab - Import YOLOv8 Annotations - Open In Github - Open In Colab - - - Huggingface Custom Embeddings - Open In Github - Open In Colab + Meta SAM Video + Open In Github + Open In Colab @@ -246,6 +251,11 @@ + + Model Slices + Open In Github + Open In Colab + Model Predictions to Project Open In Github @@ -261,11 +271,6 @@ Open In Github Open In Colab - - Model Slices - Open In Github - Open In Colab - @@ -280,6 +285,16 @@ + + PDF Predictions + Open In Github + Open In Colab + + + Conversational Predictions + Open In Github + Open In Colab + HTML Predictions Open In Github @@ -290,36 +305,26 @@ Open In Github Open In Colab - - Video Predictions - Open In Github - Open In Colab - - - Conversational Predictions - Open In Github - Open In Colab - Geospatial Predictions Open In Github Open In Colab - PDF Predictions - Open In Github - Open In Colab - - - Image Predictions - Open In Github - Open In Colab + Video Predictions + Open In Github + Open In Colab Conversational LLM Predictions Open In Github Open In Colab + + Image Predictions + Open In Github + Open In Colab + From 16896fd9296881b2219e4078166e01d3408ca2a1 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 11 Sep 2025 12:11:22 -0700 Subject: [PATCH 005/103] chore: it works for temporal text/radio/checklist classifications --- .../annotation_import/audio_temporal.ipynb | 7 +- .../labelbox/data/annotation_types/audio.py | 64 ++----------------- .../serialization/ndjson/classification.py | 3 +- .../data/serialization/ndjson/label.py | 55 ++++++++++++++-- 4 files changed, 60 insertions(+), 69 deletions(-) diff --git a/examples/annotation_import/audio_temporal.ipynb b/examples/annotation_import/audio_temporal.ipynb index 1c77a6928..52f574f15 100644 --- a/examples/annotation_import/audio_temporal.ipynb +++ b/examples/annotation_import/audio_temporal.ipynb @@ -49,10 +49,11 @@ "\n", "## Key Features\n", "\n", - "- **Time-based API**: Use seconds for user-friendly input\n", - "- **Frame-based storage**: Internally uses milliseconds (1 frame = 1ms)\n", + "- **Millisecond-based API**: Direct millisecond input for precise timing control\n", + "- **Video-compatible structure**: Matches video temporal annotation pattern exactly\n", + "- **Keyframe serialization**: Proper NDJSON structure for frontend timeline display\n", "- **MAL compatible**: Works with existing Model-Assisted Labeling pipeline\n", - "- **UI compatible**: Uses existing video timeline components\n", + "- **UI compatible**: Uses existing video timeline components seamlessly\n", "\n", "## Import Methods\n", "\n", diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index e332b76d4..db4d7a8ae 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -17,42 +17,14 @@ class AudioClassificationAnnotation(ClassificationAnnotation): feature_schema_id (Optional[Cuid]): Feature schema identifier value (Union[Text, Checklist, Radio]): Classification value frame (int): The frame index in milliseconds (e.g., 2500 = 2.5 seconds) + end_frame (Optional[int]): End frame in milliseconds (for time ranges) segment_index (Optional[int]): Index of audio segment this annotation belongs to extra (Dict[str, Any]): Additional metadata """ frame: int + end_frame: Optional[int] = None segment_index: Optional[int] = None - - @classmethod - def from_time_range(cls, start_ms: int, end_ms: int, **kwargs): - """Create from milliseconds (user-friendly) to frames (internal) - - Args: - start_ms (int): Start time in milliseconds - end_ms (int): End time in milliseconds - **kwargs: Additional arguments for the annotation - - Returns: - AudioClassificationAnnotation: Annotation with frame set to start_ms - - Example: - >>> AudioClassificationAnnotation.from_time_range( - ... start_ms=2500, end_ms=4100, - ... name="speaker_id", - ... value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="john")) - ... ) - """ - return cls(frame=start_ms, **kwargs) - - @property - def start_time(self) -> float: - """Convert frame to seconds for user-facing APIs - - Returns: - float: Time in seconds (e.g., 2500 -> 2.5) - """ - return self.frame / 1000.0 class AudioObjectAnnotation(ObjectAnnotation, ConfidenceNotSupportedMixin, CustomMetricsNotSupportedMixin): @@ -68,6 +40,7 @@ class AudioObjectAnnotation(ObjectAnnotation, ConfidenceNotSupportedMixin, Custo feature_schema_id (Optional[Cuid]): Feature schema identifier value (Union[TextEntity, Geometry]): Localization or text content frame (int): The frame index in milliseconds (e.g., 10000 = 10.0 seconds) + end_frame (Optional[int]): End frame in milliseconds (for time ranges) keyframe (bool): Whether this is a keyframe annotation (default: True) segment_index (Optional[int]): Index of audio segment this annotation belongs to classifications (Optional[List[ClassificationAnnotation]]): Optional sub-classifications @@ -75,35 +48,6 @@ class AudioObjectAnnotation(ObjectAnnotation, ConfidenceNotSupportedMixin, Custo """ frame: int + end_frame: Optional[int] = None keyframe: bool = True segment_index: Optional[int] = None - - @classmethod - def from_time_range(cls, start_ms: int, end_ms: int, **kwargs): - """Create from milliseconds (user-friendly) to frames (internal) - - Args: - start_ms (int): Start time in milliseconds - end_ms (int): End time in milliseconds - **kwargs: Additional arguments for the annotation - - Returns: - AudioObjectAnnotation: Annotation with frame set to start_ms - - Example: - >>> AudioObjectAnnotation.from_time_range( - ... start_ms=10000, end_ms=12500, - ... name="transcription", - ... value=lb_types.TextEntity(text="Hello world") - ... ) - """ - return cls(frame=start_ms, **kwargs) - - @property - def start_time(self) -> float: - """Convert frame to seconds for user-facing APIs - - Returns: - float: Time in seconds (e.g., 10000 -> 10.0) - """ - return self.frame / 1000.0 diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index 302231b7a..befb5130d 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -224,7 +224,7 @@ def from_common( # ====== End of subclasses -class NDText(NDAnnotation, NDTextSubclass): +class NDText(NDAnnotation, NDTextSubclass, VideoSupported): @classmethod def from_common( cls, @@ -243,6 +243,7 @@ def from_common( name=name, schema_id=feature_schema_id, uuid=uuid, + frames=extra.get("frames"), message_id=message_id, confidence=text.confidence, custom_metrics=text.custom_metrics, diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 31a9d32b0..0b70d8741 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -186,12 +186,57 @@ def _create_audio_annotations( ) for annotation_group in audio_annotations.values(): - # For audio, treat each annotation as a single frame (no segments needed) if isinstance(annotation_group[0], AudioClassificationAnnotation): - annotation = annotation_group[0] - # Add frame information to extra (milliseconds) - annotation.extra.update({"frame": annotation.frame}) - yield NDClassification.from_common(annotation, label.data) + # For TEXT classifications, group them into one feature with multiple keyframes + from ...annotation_types.classification.classification import Text + if isinstance(annotation_group[0].value, Text): + + # Group all annotations into one feature with multiple keyframes + # Use first annotation as template but create combined content + annotation = annotation_group[0] + frames_data = [] + all_tokens = [] + + for individual_annotation in annotation_group: + frame = individual_annotation.frame + end_frame = individual_annotation.end_frame if hasattr(individual_annotation, 'end_frame') and individual_annotation.end_frame is not None else frame + frames_data.append({"start": frame, "end": end_frame}) + all_tokens.append(individual_annotation.value.answer) + + # For per-token annotations, embed token mapping in the content + # Create a JSON structure that includes both the default text and token mapping + import json + token_mapping = {} + for individual_annotation in annotation_group: + frame = individual_annotation.frame + token_mapping[str(frame)] = individual_annotation.value.answer + + # Embed token mapping in the answer field as JSON + content_with_mapping = { + "default_text": " ".join(all_tokens), # Fallback text + "token_mapping": token_mapping # Per-keyframe content + } + from ...annotation_types.classification.classification import Text + annotation.value = Text(answer=json.dumps(content_with_mapping)) + + # Update the annotation with frames data + annotation.extra = {"frames": frames_data} + yield NDClassification.from_common(annotation, label.data) + else: + # For non-TEXT classifications, process each individually + for annotation in annotation_group: + + # Ensure frame data is properly formatted in extra field + if hasattr(annotation, 'frame') and annotation.frame is not None: + if not annotation.extra: + annotation.extra = {} + + if 'frames' not in annotation.extra: + end_frame = annotation.end_frame if hasattr(annotation, 'end_frame') and annotation.end_frame is not None else annotation.frame + frames_data = [{"start": annotation.frame, "end": end_frame}] + annotation.extra.update({"frames": frames_data}) + + yield NDClassification.from_common(annotation, label.data) elif isinstance(annotation_group[0], AudioObjectAnnotation): # For audio objects, treat like single video frame From 7a666cc24f2f6a92e1c71c7f52276955c3de6899 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 11 Sep 2025 13:46:09 -0700 Subject: [PATCH 006/103] chore: clean up and organize code --- .../data/serialization/ndjson/label.py | 117 ++---------- .../data/serialization/ndjson/objects.py | 6 +- .../serialization/ndjson/utils/__init__.py | 1 + .../ndjson/utils/temporal_processor.py | 177 ++++++++++++++++++ 4 files changed, 198 insertions(+), 103 deletions(-) create mode 100644 libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py create mode 100644 libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 0b70d8741..ba6184226 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -24,6 +24,7 @@ VideoMaskAnnotation, VideoObjectAnnotation, ) +from typing import List from ...annotation_types.audio import ( AudioClassificationAnnotation, AudioObjectAnnotation, @@ -128,47 +129,21 @@ def _get_segment_frame_ranges( def _create_video_annotations( cls, label: Label ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]: - video_annotations = defaultdict(list) + # Handle video mask annotations separately (special case) for annot in label.annotations: - if isinstance( - annot, (VideoClassificationAnnotation, VideoObjectAnnotation) - ): - video_annotations[annot.feature_schema_id or annot.name].append( - annot - ) - elif isinstance(annot, VideoMaskAnnotation): + if isinstance(annot, VideoMaskAnnotation): yield NDObject.from_common(annotation=annot, data=label.data) - - for annotation_group in video_annotations.values(): - segment_frame_ranges = cls._get_segment_frame_ranges( - annotation_group - ) - if isinstance(annotation_group[0], VideoClassificationAnnotation): - annotation = annotation_group[0] - frames_data = [] - for frames in segment_frame_ranges: - frames_data.append({"start": frames[0], "end": frames[-1]}) - annotation.extra.update({"frames": frames_data}) - yield NDClassification.from_common(annotation, label.data) - - elif isinstance(annotation_group[0], VideoObjectAnnotation): - segments = [] - for start_frame, end_frame in segment_frame_ranges: - segment = [] - for annotation in annotation_group: - if ( - annotation.keyframe - and start_frame <= annotation.frame <= end_frame - ): - segment.append(annotation) - segments.append(segment) - yield NDObject.from_common(segments, label.data) + + # Use temporal processor for video classifications and objects + from .utils.temporal_processor import VideoTemporalProcessor + processor = VideoTemporalProcessor() + yield from processor.process_annotations(label) @classmethod def _create_audio_annotations( cls, label: Label ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]: - """Create audio annotations + """Create audio annotations using generic temporal processor Args: label: Label containing audio annotations to be processed @@ -176,72 +151,14 @@ def _create_audio_annotations( Yields: NDClassification or NDObject: Audio annotations in NDJSON format """ - audio_annotations = defaultdict(list) - for annot in label.annotations: - if isinstance( - annot, (AudioClassificationAnnotation, AudioObjectAnnotation) - ): - audio_annotations[annot.feature_schema_id or annot.name].append( - annot - ) - - for annotation_group in audio_annotations.values(): - if isinstance(annotation_group[0], AudioClassificationAnnotation): - # For TEXT classifications, group them into one feature with multiple keyframes - from ...annotation_types.classification.classification import Text - if isinstance(annotation_group[0].value, Text): - - # Group all annotations into one feature with multiple keyframes - # Use first annotation as template but create combined content - annotation = annotation_group[0] - frames_data = [] - all_tokens = [] - - for individual_annotation in annotation_group: - frame = individual_annotation.frame - end_frame = individual_annotation.end_frame if hasattr(individual_annotation, 'end_frame') and individual_annotation.end_frame is not None else frame - frames_data.append({"start": frame, "end": end_frame}) - all_tokens.append(individual_annotation.value.answer) - - # For per-token annotations, embed token mapping in the content - # Create a JSON structure that includes both the default text and token mapping - import json - token_mapping = {} - for individual_annotation in annotation_group: - frame = individual_annotation.frame - token_mapping[str(frame)] = individual_annotation.value.answer - - # Embed token mapping in the answer field as JSON - content_with_mapping = { - "default_text": " ".join(all_tokens), # Fallback text - "token_mapping": token_mapping # Per-keyframe content - } - from ...annotation_types.classification.classification import Text - annotation.value = Text(answer=json.dumps(content_with_mapping)) - - # Update the annotation with frames data - annotation.extra = {"frames": frames_data} - yield NDClassification.from_common(annotation, label.data) - else: - # For non-TEXT classifications, process each individually - for annotation in annotation_group: - - # Ensure frame data is properly formatted in extra field - if hasattr(annotation, 'frame') and annotation.frame is not None: - if not annotation.extra: - annotation.extra = {} - - if 'frames' not in annotation.extra: - end_frame = annotation.end_frame if hasattr(annotation, 'end_frame') and annotation.end_frame is not None else annotation.frame - frames_data = [{"start": annotation.frame, "end": end_frame}] - annotation.extra.update({"frames": frames_data}) - - yield NDClassification.from_common(annotation, label.data) - - elif isinstance(annotation_group[0], AudioObjectAnnotation): - # For audio objects, treat like single video frame - annotation = annotation_group[0] - yield NDObject.from_common(annotation, label.data) + from .utils.temporal_processor import AudioTemporalProcessor + + # Use processor with configurable behavior + processor = AudioTemporalProcessor( + group_text_annotations=True, # Group multiple TEXT annotations into one feature + enable_token_mapping=True # Enable per-keyframe token content + ) + yield from processor.process_annotations(label) @classmethod def _create_non_video_annotations(cls, label: Label): diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py index 3c9def746..f543a786d 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py @@ -748,7 +748,7 @@ def from_common( return obj.from_common(annotation, data) elif isinstance(annotation, AudioObjectAnnotation): # Handle audio object annotation like single video frame - return cls._handle_single_audio_annotation(annotation, data) + return cls._serialize_audio_object_annotation(annotation, data) subclasses = [ NDSubclassification.from_common(annot) @@ -773,8 +773,8 @@ def from_common( ) @classmethod - def _handle_single_audio_annotation(cls, annotation: AudioObjectAnnotation, data: GenericDataRowData): - """Handle single audio annotation like video frame + def _serialize_audio_object_annotation(cls, annotation: AudioObjectAnnotation, data: GenericDataRowData): + """Serialize audio object annotation with temporal information Args: annotation: Audio object annotation to process diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py new file mode 100644 index 000000000..8959af847 --- /dev/null +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py @@ -0,0 +1 @@ +# Utils package for NDJSON serialization helpers \ No newline at end of file diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py new file mode 100644 index 000000000..44a4ed978 --- /dev/null +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py @@ -0,0 +1,177 @@ +""" +Generic temporal annotation processor for frame-based media (video, audio) +""" +from abc import ABC, abstractmethod +from collections import defaultdict +from typing import Any, Dict, Generator, List, Union + +from ...annotation_types.annotation import ClassificationAnnotation, ObjectAnnotation +from ...annotation_types.label import Label +from .classification import NDClassificationType, NDClassification +from .objects import NDObject + + +class TemporalAnnotationProcessor(ABC): + """Abstract base class for processing temporal annotations (video, audio, etc.)""" + + @abstractmethod + def get_annotation_types(self) -> tuple: + """Return tuple of annotation types this processor handles""" + pass + + @abstractmethod + def should_group_annotations(self, annotation_group: List) -> bool: + """Determine if annotations should be grouped into one feature""" + pass + + @abstractmethod + def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: + """Extract frame data from annotation group""" + pass + + @abstractmethod + def prepare_grouped_content(self, annotation_group: List) -> Any: + """Prepare content for grouped annotations (may modify annotation.value)""" + pass + + def process_annotations(self, label: Label) -> Generator[Union[NDClassificationType, Any], None, None]: + """Main processing method - generic for all temporal media""" + temporal_annotations = defaultdict(list) + classification_types, object_types = self.get_annotation_types() + + # Group annotations by feature name/schema + for annot in label.annotations: + if isinstance(annot, classification_types + object_types): + temporal_annotations[annot.feature_schema_id or annot.name].append(annot) + + # Process each group + for annotation_group in temporal_annotations.values(): + if isinstance(annotation_group[0], classification_types): + yield from self._process_classification_group(annotation_group, label.data) + elif isinstance(annotation_group[0], object_types): + yield from self._process_object_group(annotation_group, label.data) + + def _process_classification_group(self, annotation_group, data): + """Process classification annotations""" + if self.should_group_annotations(annotation_group): + # Group into single feature with multiple keyframes + annotation = annotation_group[0] # Use first as template + + # Build frame data + frames_data = self.build_frame_data(annotation_group) + + # Prepare content (may modify annotation.value) + self.prepare_grouped_content(annotation_group) + + # Update with frame data + annotation.extra = {"frames": frames_data} + yield NDClassification.from_common(annotation, data) + else: + # Process individually + for annotation in annotation_group: + frames_data = self.build_frame_data([annotation]) + if frames_data: + if not annotation.extra: + annotation.extra = {} + annotation.extra.update({"frames": frames_data}) + yield NDClassification.from_common(annotation, data) + + def _process_object_group(self, annotation_group, data): + """Process object annotations - default to individual processing""" + for annotation in annotation_group: + yield NDObject.from_common(annotation, data) + + +class AudioTemporalProcessor(TemporalAnnotationProcessor): + """Processor for audio temporal annotations""" + + def __init__(self, + group_text_annotations: bool = True, + enable_token_mapping: bool = True): + self.group_text_annotations = group_text_annotations + self.enable_token_mapping = enable_token_mapping + + def get_annotation_types(self) -> tuple: + from ...annotation_types.audio import AudioClassificationAnnotation, AudioObjectAnnotation + return (AudioClassificationAnnotation,), (AudioObjectAnnotation,) + + def should_group_annotations(self, annotation_group: List) -> bool: + """Group TEXT classifications with multiple temporal instances""" + if not self.group_text_annotations: + return False + + from ...annotation_types.classification.classification import Text + return (isinstance(annotation_group[0].value, Text) and + len(annotation_group) > 1 and + all(hasattr(ann, 'frame') for ann in annotation_group)) + + def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: + """Extract frame ranges from audio annotations""" + frames_data = [] + for annotation in annotation_group: + if hasattr(annotation, 'frame'): + frame = annotation.frame + end_frame = (annotation.end_frame + if hasattr(annotation, 'end_frame') and annotation.end_frame is not None + else frame) + frames_data.append({"start": frame, "end": end_frame}) + return frames_data + + def prepare_grouped_content(self, annotation_group: List) -> None: + """Prepare content for grouped audio annotations""" + from ...annotation_types.classification.classification import Text + + if not isinstance(annotation_group[0].value, Text) or not self.enable_token_mapping: + return + + # Build token mapping for TEXT annotations + import json + + all_content = [ann.value.answer for ann in annotation_group] + token_mapping = {str(ann.frame): ann.value.answer for ann in annotation_group} + + content_structure = json.dumps({ + "default_text": " ".join(all_content), + "token_mapping": token_mapping + }) + + # Update the template annotation + annotation_group[0].value = Text(answer=content_structure) + + +class VideoTemporalProcessor(TemporalAnnotationProcessor): + """Processor for video temporal annotations - matches existing behavior""" + + def get_annotation_types(self) -> tuple: + from ...annotation_types.video import VideoClassificationAnnotation, VideoObjectAnnotation + return (VideoClassificationAnnotation,), (VideoObjectAnnotation,) + + def should_group_annotations(self, annotation_group: List) -> bool: + """Video always groups by segment ranges""" + return True + + def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: + """Build frame data using existing video segment logic""" + from .label import NDLabel # Import here to avoid circular import + + segment_frame_ranges = NDLabel._get_segment_frame_ranges(annotation_group) + return [{"start": frames[0], "end": frames[-1]} for frames in segment_frame_ranges] + + def prepare_grouped_content(self, annotation_group: List) -> None: + """Video doesn't modify content - uses existing value""" + pass + + def _process_object_group(self, annotation_group, data): + """Video objects use segment-based processing""" + from .label import NDLabel + + segment_frame_ranges = NDLabel._get_segment_frame_ranges(annotation_group) + segments = [] + for start_frame, end_frame in segment_frame_ranges: + segment = [] + for annotation in annotation_group: + if (annotation.keyframe and + start_frame <= annotation.frame <= end_frame): + segment.append(annotation) + segments.append(segment) + yield NDObject.from_common(segments, data) \ No newline at end of file From ac58ad0dd1e84e90942a732051dc20bef63fcf4d Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 11 Sep 2025 14:22:42 -0700 Subject: [PATCH 007/103] chore: update tests fail and documentation update --- .python-version | 2 +- examples/README.md | 2 +- examples/annotation_import/audio.ipynb | 469 ++++++++++++++---- .../annotation_import/audio_temporal.ipynb | 384 -------------- .../ndjson/utils/temporal_processor.py | 20 +- .../tests/data/annotation_types/test_audio.py | 297 ++++++----- 6 files changed, 537 insertions(+), 637 deletions(-) delete mode 100644 examples/annotation_import/audio_temporal.ipynb diff --git a/.python-version b/.python-version index 43077b246..56d91d353 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.9.18 +3.10.12 diff --git a/examples/README.md b/examples/README.md index 6cae49593..cb1c1cebc 100644 --- a/examples/README.md +++ b/examples/README.md @@ -169,7 +169,7 @@ Open In Colab - Audio Temporal + Audio Temporal NEW! Open In Github Open In Colab diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index 437130a9e..f152f2d32 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,18 +1,16 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {}, "cells": [ { + "cell_type": "markdown", "metadata": {}, "source": [ - "", - " ", + "\n", + " \n", "\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n", @@ -24,10 +22,10 @@ "\n", "" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Audio Annotation Import\n", @@ -53,111 +51,188 @@ "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", "\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "* For information on what types of annotations are supported per data type, refer to this documentation:\n", " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "* Notes:\n", " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "%pip install -q \"labelbox[data]\"" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "import labelbox as lb\n", + "import uuid\n", + "import labelbox.types as lb_types" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Replace with your API key\n", "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Add your api key\n", + "API_KEY = \"\"\n", + "client = lb.Client(api_key=API_KEY)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Supported annotations for Audio" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "##### Classification free text #####\n", + "\n", + "text_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"text_audio\",\n", + " value=lb_types.Text(answer=\"free text audio annotation\"),\n", + ")\n", + "\n", + "text_annotation_ndjson = {\n", + " \"name\": \"text_audio\",\n", + " \"answer\": \"free text audio annotation\",\n", + "}" + ] }, { - "metadata": {}, - "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "##### Checklist Classification #######\n", + "\n", + "checklist_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"checklist_audio\",\n", + " value=lb_types.Checklist(answer=[\n", + " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", + " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", + " ]),\n", + ")\n", + "\n", + "checklist_annotation_ndjson = {\n", + " \"name\":\n", + " \"checklist_audio\",\n", + " \"answers\": [\n", + " {\n", + " \"name\": \"first_checklist_answer\"\n", + " },\n", + " {\n", + " \"name\": \"second_checklist_answer\"\n", + " },\n", + " ],\n", + "}" + ] }, { - "metadata": {}, - "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "######## Radio Classification ######\n", + "\n", + "radio_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"radio_audio\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", + " name=\"second_radio_answer\")),\n", + ")\n", + "\n", + "radio_annotation_ndjson = {\n", + " \"name\": \"radio_audio\",\n", + " \"answer\": {\n", + " \"name\": \"first_radio_answer\"\n", + " },\n", + "}" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Import data rows into Catalog" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create one Labelbox dataset\n", + "\n", + "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", + "\n", + "asset = {\n", + " \"row_data\":\n", + " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", + " \"global_key\":\n", + " global_key,\n", + "}\n", + "\n", + "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", + "task = dataset.create_data_rows([asset])\n", + "task.wait_till_done()\n", + "print(\"Errors:\", task.errors)\n", + "print(\"Failed data rows: \", task.failed_data_rows)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Create/select an ontology\n", @@ -165,135 +240,349 @@ "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", "\n", "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "ontology_builder = lb.OntologyBuilder(classifications=[\n", + " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", + " name=\"text_audio\"),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"checklist_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_checklist_answer\"),\n", + " lb.Option(value=\"second_checklist_answer\"),\n", + " ],\n", + " ),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"radio_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_radio_answer\"),\n", + " lb.Option(value=\"second_radio_answer\"),\n", + " ],\n", + " ),\n", + " # Temporal classification for token-level annotations\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.TEXT,\n", + " name=\"User Speaker\",\n", + " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", + " ),\n", + "])\n", + "\n", + "ontology = client.create_ontology(\n", + " \"Ontology Audio Annotations\",\n", + " ontology_builder.asdict(),\n", + " media_type=lb.MediaType.Audio,\n", + ")" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Step 3: Create a labeling project\n", "Connect the ontology to the labeling project" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create Labelbox project\n", + "project = client.create_project(name=\"audio_project\",\n", + " media_type=lb.MediaType.Audio)\n", + "\n", + "# Setup your ontology\n", + "project.setup_editor(\n", + " ontology) # Connect your ontology and editor to your project" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Setup Batches and Ontology\n", + "\n", + "# Create a batch to send to your MAL project\n", + "batch = project.create_batch(\n", + " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", + " global_keys=[\n", + " global_key\n", + " ], # Paginated collection of data row objects, list of data row ids or global keys\n", + " priority=5, # priority between 1(Highest) - 5(lowest)\n", + ")\n", + "\n", + "print(\"Batch: \", batch)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Create the annotations payload\n", "Create the annotations payload using the snippets of code above\n", "\n", "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Python annotation\n", "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ], - "cell_type": "markdown" + ] + }, + { + "cell_type": "markdown", + "id": "6b53669e", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9af095e", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] }, { + "cell_type": "code", + "execution_count": null, + "id": "64f229a3", "metadata": {}, - "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", + "outputs": [], + "source": [ + "\n" + ] + }, + { "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "label = []\n", + "label.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", + " ))" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### NDJSON annotations \n", "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "label_ndjson = []\n", + "for annotations in [\n", + " text_annotation_ndjson,\n", + " checklist_annotation_ndjson,\n", + " radio_annotation_ndjson,\n", + "]:\n", + " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", + " label_ndjson.append(annotations)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "3d3f11a1", + "metadata": {}, + "source": [ + "## Temporal Audio Annotations\n", + "\n", + "You can create temporal annotations for individual tokens (words) with precise timing:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5e7d34b", + "metadata": {}, + "outputs": [], + "source": [ + "# Define tokens with precise timing (from demo script)\n", + "tokens_data = [\n", + " (\"Hello\", 586, 770), # Hello: frames 586-770\n", + " (\"AI\", 771, 955), # AI: frames 771-955 \n", + " (\"how\", 956, 1140), # how: frames 956-1140\n", + " (\"are\", 1141, 1325), # are: frames 1141-1325\n", + " (\"you\", 1326, 1510), # you: frames 1326-1510\n", + " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", + " (\"today\", 1696, 1880), # today: frames 1696-1880\n", + "]\n", + "\n", + "# Create temporal annotations for each token\n", + "temporal_annotations = []\n", + "for token, start_frame, end_frame in tokens_data:\n", + " token_annotation = lb_types.AudioClassificationAnnotation(\n", + " frame=start_frame,\n", + " end_frame=end_frame,\n", + " name=\"User Speaker\",\n", + " value=lb_types.Text(answer=token)\n", + " )\n", + " temporal_annotations.append(token_annotation)\n", + "\n", + "print(f\"Created {len(temporal_annotations)} temporal token annotations\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42c5d52a", + "metadata": {}, + "outputs": [], + "source": [ + "# Create label with both regular and temporal annotations\n", + "label_with_temporal = []\n", + "label_with_temporal.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation] + temporal_annotations,\n", + " ))\n", + "\n", + "print(f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\")\n", + "print(f\" - Regular annotations: 3\")\n", + "print(f\" - Temporal annotations: {len(temporal_annotations)}\")\n" + ] + }, + { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Model Assisted Labeling (MAL)\n", "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", + "execution_count": null, + "id": "2473670f", "metadata": {}, - "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "outputs": [], + "source": [ + "# Upload temporal annotations via MAL\n", + "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label_with_temporal,\n", + ")\n", + "\n", + "temporal_upload_job.wait_until_done()\n", + "print(\"Temporal upload completed!\")\n", + "print(\"Errors:\", temporal_upload_job.errors)\n", + "print(\"Status:\", temporal_upload_job.statuses)\n" + ] + }, + { "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload our label using Model-Assisted Labeling\n", + "upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Label Import" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload label for this data row in project\n", + "upload_job = lb.LabelImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=\"label_import_job\" + str(uuid.uuid4()),\n", + " labels=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### Optional deletions for cleanup " - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# project.delete()\n# dataset.delete()", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# project.delete()\n", + "# dataset.delete()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/annotation_import/audio_temporal.ipynb b/examples/annotation_import/audio_temporal.ipynb deleted file mode 100644 index 52f574f15..000000000 --- a/examples/annotation_import/audio_temporal.ipynb +++ /dev/null @@ -1,384 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 2, - "metadata": {}, - "cells": [ - { - "metadata": {}, - "source": [ - "", - " ", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# Audio Temporal Annotation Import\n", - "\n", - "This notebook demonstrates how to create and upload **temporal audio annotations** - annotations that are tied to specific time ranges in audio files.\n", - "\n", - "## What are Temporal Audio Annotations?\n", - "\n", - "Temporal audio annotations allow you to:\n", - "- **Transcribe speech** with precise timestamps (\"Hello world\" from 2.5s to 4.1s)\n", - "- **Identify speakers** in specific segments (\"John speaking\" from 10s to 15s)\n", - "- **Detect sound events** with time ranges (\"Dog barking\" from 30s to 32s)\n", - "- **Classify audio quality** for segments (\"Clear audio\" from 0s to 10s)\n", - "\n", - "## Supported Temporal Annotations\n", - "\n", - "- **AudioClassificationAnnotation**: Radio, checklist, and text classifications for time ranges\n", - "- **AudioObjectAnnotation**: Text entities (transcriptions) for time ranges\n", - "\n", - "## Key Features\n", - "\n", - "- **Millisecond-based API**: Direct millisecond input for precise timing control\n", - "- **Video-compatible structure**: Matches video temporal annotation pattern exactly\n", - "- **Keyframe serialization**: Proper NDJSON structure for frontend timeline display\n", - "- **MAL compatible**: Works with existing Model-Assisted Labeling pipeline\n", - "- **UI compatible**: Uses existing video timeline components seamlessly\n", - "\n", - "## Import Methods\n", - "\n", - "- **Model-Assisted Labeling (MAL)**: Upload pre-annotations for labeler review\n", - "- **Label Import**: Upload ground truth labels directly\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Setup\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nfrom typing import List", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Replace with your API key\n", - "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Creating Temporal Audio Annotations\n", - "\n", - "### Audio Classification Annotations\n", - "\n", - "Use `AudioClassificationAnnotation` for classifications tied to specific time ranges. The interface now accepts milliseconds directly for precise timing control.\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Speaker identification for a time range\nspeaker_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=2500, # Start at 2500 milliseconds (2.5 seconds)\n end_ms=4100, # End at 4100 milliseconds (4.1 seconds)\n name=\"speaker_id\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"john\")),\n)\n\nprint(f\"Speaker annotation frame: {speaker_annotation.frame}ms\")\nprint(f\"Speaker annotation start time: {speaker_annotation.start_time}s\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Audio quality assessment for a segment\nquality_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=10000,\n name=\"audio_quality\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"clear_audio\"),\n lb_types.ClassificationAnswer(name=\"no_background_noise\"),\n ]),\n)\n\n# Emotion detection for a segment\nemotion_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=5200,\n end_ms=8700,\n name=\"emotion\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"happy\")),\n)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Audio Object Annotations\n", - "\n", - "Use `AudioObjectAnnotation` for text entities like transcriptions tied to specific time ranges. The interface now accepts milliseconds directly for precise timing control.\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Transcription with precise timestamps\ntranscription_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=2500,\n end_ms=4100,\n name=\"transcription\",\n value=lb_types.TextEntity(text=\"Hello, how are you doing today?\"),\n)\n\nprint(f\"Transcription frame: {transcription_annotation.frame}ms\")\nprint(f\"Transcription text: {transcription_annotation.value.text}\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Sound event detection\nsound_event_annotation = lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=10000,\n end_ms=12500,\n name=\"sound_event\",\n value=lb_types.TextEntity(text=\"Dog barking in background\"),\n)\n\n# Multiple transcription segments\ntranscription_segments = [\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=0,\n end_ms=2300,\n name=\"transcription\",\n value=lb_types.TextEntity(text=\"Welcome to our podcast.\"),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=2500,\n end_ms=5800,\n name=\"transcription\",\n value=lb_types.TextEntity(\n text=\"Today we're discussing AI advancements.\"),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=6000,\n end_ms=9200,\n name=\"transcription\",\n value=lb_types.TextEntity(\n text=\"Let's start with machine learning basics.\"),\n ),\n]", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Use Cases and Examples\n", - "\n", - "### Use Case 1: Podcast Transcription with Speaker Identification\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Complete podcast annotation with speakers and transcriptions\npodcast_annotations = [\n # Host introduction\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=5000,\n name=\"speaker_id\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"host\")),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=0,\n end_ms=5000,\n name=\"transcription\",\n value=lb_types.TextEntity(\n text=\"Welcome to Tech Talk, I'm your host Sarah.\"),\n ),\n # Guest response\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=5200,\n end_ms=8500,\n name=\"speaker_id\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"guest\")),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=5200,\n end_ms=8500,\n name=\"transcription\",\n value=lb_types.TextEntity(text=\"Thanks for having me, Sarah!\"),\n ),\n # Audio quality assessment\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=10000,\n name=\"audio_quality\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"excellent\")),\n ),\n]\n\nprint(f\"Created {len(podcast_annotations)} podcast annotations\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Use Case 2: Call Center Quality Analysis\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Call center analysis with sentiment and quality metrics\ncall_center_annotations = [\n # Customer sentiment analysis\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=30000,\n name=\"customer_sentiment\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"frustrated\")),\n ),\n # Agent performance\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=30000,\n end_ms=60000,\n name=\"agent_performance\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"professional_tone\"),\n lb_types.ClassificationAnswer(name=\"resolved_issue\"),\n lb_types.ClassificationAnswer(name=\"followed_script\"),\n ]),\n ),\n # Key phrases extraction\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=15000,\n end_ms=18000,\n name=\"key_phrase\",\n value=lb_types.TextEntity(text=\"I want to speak to your manager\"),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=45000,\n end_ms=48000,\n name=\"key_phrase\",\n value=lb_types.TextEntity(text=\"Thank you for your patience\"),\n ),\n]\n\nprint(f\"Created {len(call_center_annotations)} call center annotations\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Use Case 3: Music and Sound Event Detection\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Music analysis and sound event detection\nmusic_annotations = [\n # Musical instruments\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=30000,\n name=\"instruments\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"piano\"),\n lb_types.ClassificationAnswer(name=\"violin\"),\n lb_types.ClassificationAnswer(name=\"drums\"),\n ]),\n ),\n # Genre classification\n lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=0,\n end_ms=60000,\n name=\"genre\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"classical\")),\n ),\n # Sound events\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=25000,\n end_ms=27000,\n name=\"sound_event\",\n value=lb_types.TextEntity(text=\"Applause from audience\"),\n ),\n lb_types.AudioObjectAnnotation.from_time_range(\n start_ms=45000,\n end_ms=46500,\n name=\"sound_event\",\n value=lb_types.TextEntity(text=\"Door closing in background\"),\n ),\n]\n\nprint(f\"Created {len(music_annotations)} music annotations\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Uploading Audio Temporal Prelabels\n", - "\n", - "### Step 1: Import Audio Data into Catalog\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create dataset with audio file\nglobal_key = \"sample-audio-temporal-\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_temporal_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 2: Create Ontology with Temporal Audio Tools\n", - "\n", - "Your ontology must include the tools and classifications that match your annotation names.\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(\n tools=[\n # Text entity tools for transcriptions and sound events\n lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"transcription\"),\n lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"sound_event\"),\n lb.Tool(tool=lb.Tool.Type.TEXT_ENTITY, name=\"key_phrase\"),\n ],\n classifications=[\n # Speaker identification\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"speaker_id\",\n scope=lb.Classification.Scope.INDEX, # Frame-based classification\n options=[\n lb.Option(value=\"host\"),\n lb.Option(value=\"guest\"),\n lb.Option(value=\"john\"),\n lb.Option(value=\"sarah\"),\n ],\n ),\n # Audio quality assessment\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"audio_quality\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"clear_audio\"),\n lb.Option(value=\"no_background_noise\"),\n lb.Option(value=\"good_volume\"),\n lb.Option(value=\"excellent\"),\n ],\n ),\n # Emotion detection\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"emotion\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"happy\"),\n lb.Option(value=\"sad\"),\n lb.Option(value=\"angry\"),\n lb.Option(value=\"neutral\"),\n ],\n ),\n # Customer sentiment (for call center example)\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"customer_sentiment\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"satisfied\"),\n lb.Option(value=\"frustrated\"),\n lb.Option(value=\"angry\"),\n lb.Option(value=\"neutral\"),\n ],\n ),\n # Agent performance (for call center example)\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"agent_performance\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"professional_tone\"),\n lb.Option(value=\"resolved_issue\"),\n lb.Option(value=\"followed_script\"),\n lb.Option(value=\"empathetic_response\"),\n ],\n ),\n # Music instruments (for music example)\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"instruments\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"piano\"),\n lb.Option(value=\"violin\"),\n lb.Option(value=\"drums\"),\n lb.Option(value=\"guitar\"),\n ],\n ),\n # Music genre\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"genre\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(value=\"classical\"),\n lb.Option(value=\"jazz\"),\n lb.Option(value=\"rock\"),\n lb.Option(value=\"pop\"),\n ],\n ),\n ],\n)\n\nontology = client.create_ontology(\n \"Audio Temporal Annotations Ontology\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)\n\nprint(f\"Created ontology: {ontology.name}\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 3: Create Project and Setup Editor\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create project\nproject = client.create_project(name=\"Audio Temporal Annotations Demo\",\n media_type=lb.MediaType.Audio)\n\n# Connect ontology to project\nproject.setup_editor(ontology)\n\nprint(f\"Created project: {project.name}\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 4: Create Batch and Add Data\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create batch\nbatch = project.create_batch(\n \"audio-temporal-batch-\" + str(uuid.uuid4())[:8],\n global_keys=[global_key],\n priority=5,\n)\n\nprint(f\"Created batch: {batch.name}\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 5: Upload Temporal Audio Annotations via MAL\n", - "\n", - "Now we'll upload our temporal audio annotations using the Model-Assisted Labeling pipeline.\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create label with temporal audio annotations\n# Using the podcast example annotations\nlabel = lb_types.Label(data={\"global_key\": global_key},\n annotations=podcast_annotations)\n\nprint(f\"Created label with {len(podcast_annotations)} temporal annotations\")\nprint(\"Annotation types:\")\nfor i, annotation in enumerate(podcast_annotations):\n ann_type = type(annotation).__name__\n if hasattr(annotation, \"frame\"):\n time_info = f\"at {annotation.start_time}s (frame {annotation.frame})\"\n else:\n time_info = \"global\"\n print(f\" {i+1}. {ann_type} '{annotation.name}' {time_info}\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Upload via MAL (Model-Assisted Labeling)\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"audio_temporal_mal_{str(uuid.uuid4())[:8]}\",\n predictions=[label],\n)\n\nupload_job.wait_until_done()\nprint(\"Upload completed!\")\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status:\", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## NDJSON Format Examples\n", - "\n", - "Temporal audio annotations serialize to NDJSON format similar to video annotations, with frame-based timing.\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Let's examine how temporal audio annotations serialize to NDJSON\nfrom labelbox.data.serialization.ndjson.label import NDLabel\nimport json\n\n# Serialize our label to NDJSON format\nndjson_generator = NDLabel.from_common([label])\nndjson_objects = list(ndjson_generator)\n\nprint(f\"Generated {len(ndjson_objects)} NDJSON objects\")\nprint(\"\\nNDJSON Examples:\")\nprint(\"=\" * 50)\n\nfor i, obj in enumerate(ndjson_objects[:3]): # Show first 3 examples\n print(f\"\\nObject {i+1}:\")\n # Convert to dict for pretty printing\n obj_dict = obj.dict(exclude_none=True)\n print(json.dumps(obj_dict, indent=2))", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Comparison with Video Annotations\n", - "\n", - "Audio temporal annotations use the same frame-based structure as video annotations:\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "print(\"Frame-based Structure Comparison:\")\nprint(\"=\" * 40)\n\n# Audio: 1 frame = 1 millisecond\naudio_annotation = lb_types.AudioClassificationAnnotation.from_time_range(\n start_ms=2500, end_ms=4100, name=\"test\", value=lb_types.Text(answer=\"test\"))\n\nprint(f\"Audio Annotation:\")\nprint(f\" Time: 2500ms \u2192 Frame: {audio_annotation.frame} (milliseconds)\")\nprint(f\" Frame rate: 1000 frames/second (1 frame = 1ms)\")\n\nprint(f\"\\nVideo Annotation (for comparison):\")\nprint(f\" Time: 2.5s \u2192 Frame: depends on video frame rate\")\nprint(f\" Frame rate: varies (e.g., 30 fps = 30 frames/second)\")\n\nprint(f\"\\nBoth use the same NDJSON structure with 'frame' field\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Best Practices\n", - "\n", - "### 1. Time Precision\n", - "- Audio temporal annotations use millisecond precision (1 frame = 1ms)\n", - "- Use the `from_time_range()` method with millisecond-based input for precise timing control\n", - "- Frame values are set directly: `frame = start_ms`\n", - "\n", - "### 2. Ontology Alignment\n", - "- Ensure annotation `name` fields match your ontology tool/classification names\n", - "- Use `scope=lb.Classification.Scope.INDEX` for frame-based classifications\n", - "- Text entity tools work for transcriptions and sound event descriptions\n", - "\n", - "### 3. Segment Organization\n", - "- Use `segment_index` to group related annotations\n", - "- Segments help organize timeline view in the UI\n", - "- Each segment can contain multiple annotation types\n", - "\n", - "### 4. Performance Optimization\n", - "- Batch multiple labels in a single MAL import for better performance\n", - "- Use appropriate time ranges - avoid overly granular segments\n", - "- Consider audio file length when planning annotation density\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Cleanup (Optional)\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Uncomment to clean up resources\n# project.delete()\n# dataset.delete()\n# ontology.delete()", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "This notebook demonstrated:\n", - "\n", - "1. **Creating temporal audio annotations** using `AudioClassificationAnnotation` and `AudioObjectAnnotation`\n", - "2. **Millisecond-based API** with `from_time_range()` for precise timing control\n", - "3. **Multiple use cases**: podcasts, call centers, music analysis\n", - "4. **MAL import pipeline** for uploading temporal prelabels\n", - "5. **NDJSON serialization** compatible with existing video infrastructure\n", - "6. **Best practices** for ontology setup and performance optimization\n", - "\n", - "### Key Benefits:\n", - "- **No UI changes needed** - uses existing video timeline components\n", - "- **Frame-based precision** - 1ms accuracy for audio timing\n", - "- **Seamless integration** - works with existing MAL and Label Import pipelines\n", - "- **Flexible annotation types** - supports classifications and text entities with timestamps\n", - "- **Direct millisecond input** - precise timing control without conversion overhead\n", - "\n", - "### Next Steps:\n", - "1. Upload your temporal audio annotations using this notebook as a template\n", - "2. Review annotations in the Labelbox editor (uses video timeline UI)\n", - "3. Export annotated data for model training or analysis\n", - "4. Integrate with your audio processing pipeline\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [], - "cell_type": "markdown" - } - ] -} \ No newline at end of file diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py index 44a4ed978..97a35f5f3 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py @@ -5,10 +5,10 @@ from collections import defaultdict from typing import Any, Dict, Generator, List, Union -from ...annotation_types.annotation import ClassificationAnnotation, ObjectAnnotation -from ...annotation_types.label import Label -from .classification import NDClassificationType, NDClassification -from .objects import NDObject +from ....annotation_types.annotation import ClassificationAnnotation, ObjectAnnotation +from ....annotation_types.label import Label +from ..classification import NDClassificationType, NDClassification +from ..objects import NDObject class TemporalAnnotationProcessor(ABC): @@ -92,7 +92,7 @@ def __init__(self, self.enable_token_mapping = enable_token_mapping def get_annotation_types(self) -> tuple: - from ...annotation_types.audio import AudioClassificationAnnotation, AudioObjectAnnotation + from ....annotation_types.audio import AudioClassificationAnnotation, AudioObjectAnnotation return (AudioClassificationAnnotation,), (AudioObjectAnnotation,) def should_group_annotations(self, annotation_group: List) -> bool: @@ -100,7 +100,7 @@ def should_group_annotations(self, annotation_group: List) -> bool: if not self.group_text_annotations: return False - from ...annotation_types.classification.classification import Text + from ....annotation_types.classification.classification import Text return (isinstance(annotation_group[0].value, Text) and len(annotation_group) > 1 and all(hasattr(ann, 'frame') for ann in annotation_group)) @@ -119,7 +119,7 @@ def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: def prepare_grouped_content(self, annotation_group: List) -> None: """Prepare content for grouped audio annotations""" - from ...annotation_types.classification.classification import Text + from ....annotation_types.classification.classification import Text if not isinstance(annotation_group[0].value, Text) or not self.enable_token_mapping: return @@ -143,7 +143,7 @@ class VideoTemporalProcessor(TemporalAnnotationProcessor): """Processor for video temporal annotations - matches existing behavior""" def get_annotation_types(self) -> tuple: - from ...annotation_types.video import VideoClassificationAnnotation, VideoObjectAnnotation + from ....annotation_types.video import VideoClassificationAnnotation, VideoObjectAnnotation return (VideoClassificationAnnotation,), (VideoObjectAnnotation,) def should_group_annotations(self, annotation_group: List) -> bool: @@ -152,7 +152,7 @@ def should_group_annotations(self, annotation_group: List) -> bool: def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: """Build frame data using existing video segment logic""" - from .label import NDLabel # Import here to avoid circular import + from ..label import NDLabel # Import here to avoid circular import segment_frame_ranges = NDLabel._get_segment_frame_ranges(annotation_group) return [{"start": frames[0], "end": frames[-1]} for frames in segment_frame_ranges] @@ -163,7 +163,7 @@ def prepare_grouped_content(self, annotation_group: List) -> None: def _process_object_group(self, annotation_group, data): """Video objects use segment-based processing""" - from .label import NDLabel + from ..label import NDLabel segment_frame_ranges = NDLabel._get_segment_frame_ranges(annotation_group) segments = [] diff --git a/libs/labelbox/tests/data/annotation_types/test_audio.py b/libs/labelbox/tests/data/annotation_types/test_audio.py index 017c960ab..6c312abec 100644 --- a/libs/labelbox/tests/data/annotation_types/test_audio.py +++ b/libs/labelbox/tests/data/annotation_types/test_audio.py @@ -14,39 +14,52 @@ def test_audio_classification_creation(): - """Test creating audio classification with time range""" - annotation = AudioClassificationAnnotation.from_time_range( - start_ms=2500, - end_ms=4100, + """Test creating audio classification with direct frame specification""" + annotation = AudioClassificationAnnotation( + frame=2500, # 2.5 seconds in milliseconds name="speaker_id", value=Radio(answer=ClassificationAnswer(name="john")) ) - assert annotation.frame == 2500 # 2.5 seconds in milliseconds - assert annotation.start_time == 2.5 + assert annotation.frame == 2500 + assert annotation.end_frame is None assert annotation.segment_index is None assert annotation.name == "speaker_id" assert isinstance(annotation.value, Radio) assert annotation.value.answer.name == "john" +def test_audio_classification_with_time_range(): + """Test creating audio classification with start and end frames""" + annotation = AudioClassificationAnnotation( + frame=2500, # Start at 2.5 seconds + end_frame=4100, # End at 4.1 seconds + name="speaker_id", + value=Radio(answer=ClassificationAnswer(name="john")) + ) + + assert annotation.frame == 2500 + assert annotation.end_frame == 4100 + assert annotation.name == "speaker_id" + + def test_audio_classification_creation_with_segment(): """Test creating audio classification with segment index""" - annotation = AudioClassificationAnnotation.from_time_range( - start_ms=10000, - end_ms=15000, + annotation = AudioClassificationAnnotation( + frame=10000, + end_frame=15000, name="language", value=Radio(answer=ClassificationAnswer(name="english")), segment_index=1 ) assert annotation.frame == 10000 - assert annotation.start_time == 10.0 + assert annotation.end_frame == 15000 assert annotation.segment_index == 1 -def test_audio_classification_direct_creation(): - """Test creating audio classification directly with frame""" +def test_audio_classification_text_type(): + """Test creating audio classification with Text value""" annotation = AudioClassificationAnnotation( frame=5000, # 5.0 seconds name="quality", @@ -54,7 +67,6 @@ def test_audio_classification_direct_creation(): ) assert annotation.frame == 5000 - assert annotation.start_time == 5.0 assert annotation.name == "quality" assert isinstance(annotation.value, Text) assert annotation.value.answer == "excellent" @@ -62,15 +74,15 @@ def test_audio_classification_direct_creation(): def test_audio_object_creation(): """Test creating audio object annotation""" - annotation = AudioObjectAnnotation.from_time_range( - start_ms=10000, - end_ms=12500, + annotation = AudioObjectAnnotation( + frame=10000, + end_frame=12500, name="transcription", value=lb_types.TextEntity(start=0, end=11) # "Hello world" has 11 characters ) assert annotation.frame == 10000 - assert annotation.start_time == 10.0 + assert annotation.end_frame == 12500 assert annotation.keyframe is True assert annotation.segment_index is None assert annotation.name == "transcription" @@ -87,11 +99,11 @@ def test_audio_object_creation_with_classifications(): value=Radio(answer=ClassificationAnswer(name="high")) ) - annotation = AudioObjectAnnotation.from_time_range( - start_ms=10000, - end_ms=12500, + annotation = AudioObjectAnnotation( + frame=10000, + end_frame=12500, name="transcription", - value=lb_types.TextEntity(start=0, end=11), # "Hello world" has 11 characters + value=lb_types.TextEntity(start=0, end=11), classifications=[sub_classification] ) @@ -101,55 +113,48 @@ def test_audio_object_creation_with_classifications(): def test_audio_object_direct_creation(): - """Test creating audio object directly with frame""" + """Test creating audio object directly with various options""" annotation = AudioObjectAnnotation( frame=7500, # 7.5 seconds name="sound_event", - value=lb_types.TextEntity(start=0, end=11), # "Dog barking" has 11 characters + value=lb_types.TextEntity(start=0, end=11), keyframe=False, segment_index=2 ) assert annotation.frame == 7500 - assert annotation.start_time == 7.5 + assert annotation.end_frame is None assert annotation.keyframe is False assert annotation.segment_index == 2 -def test_time_conversion_precision(): - """Test time conversion maintains precision""" +def test_frame_precision(): + """Test frame values maintain precision""" # Test various time values in milliseconds - test_cases = [ - (0, 0.0), - (1, 0.001), # 1 millisecond - (1000, 1.0), # 1 second - (1500, 1.5), # 1.5 seconds - (10123, 10.123), # 10.123 seconds - (60000, 60.0), # 1 minute - ] - - for milliseconds, expected_seconds in test_cases: - annotation = AudioClassificationAnnotation.from_time_range( - start_ms=milliseconds, - end_ms=milliseconds + 1000, + test_cases = [0, 1, 1000, 1500, 10123, 60000] + + for milliseconds in test_cases: + annotation = AudioClassificationAnnotation( + frame=milliseconds, + end_frame=milliseconds + 1000, name="test", value=Text(answer="test") ) assert annotation.frame == milliseconds - assert annotation.start_time == expected_seconds + assert annotation.end_frame == milliseconds + 1000 def test_audio_label_integration(): - """Test audio annotations in Label container""" + """Test audio annotations work with Label container""" # Create audio annotations - speaker_annotation = AudioClassificationAnnotation.from_time_range( - start_ms=1000, end_ms=2000, + speaker_annotation = AudioClassificationAnnotation( + frame=1000, end_frame=2000, name="speaker", value=Radio(answer=ClassificationAnswer(name="john")) ) - transcription_annotation = AudioObjectAnnotation.from_time_range( - start_ms=1000, end_ms=2000, - name="transcription", value=lb_types.TextEntity(start=0, end=5) # "Hello" has 5 characters + transcription_annotation = AudioObjectAnnotation( + frame=1000, end_frame=2000, + name="transcription", value=lb_types.TextEntity(start=0, end=5) ) # Create label with audio annotations @@ -158,77 +163,17 @@ def test_audio_label_integration(): annotations=[speaker_annotation, transcription_annotation] ) - # Test audio annotations by frame - audio_frames = label.audio_annotations_by_frame() - assert 1000 in audio_frames - assert len(audio_frames[1000]) == 2 + # Verify annotations are accessible + assert len(label.annotations) == 2 - # Verify both annotations are in the same frame - frame_annotations = audio_frames[1000] - assert any(isinstance(ann, AudioClassificationAnnotation) for ann in frame_annotations) - assert any(isinstance(ann, AudioObjectAnnotation) for ann in frame_annotations) - - -def test_audio_annotations_by_frame_empty(): - """Test audio_annotations_by_frame with no audio annotations""" - label = lb_types.Label( - data={"global_key": "image_file.jpg"}, - annotations=[ - lb_types.ObjectAnnotation( - name="bbox", - value=lb_types.Rectangle( - start=lb_types.Point(x=0, y=0), - end=lb_types.Point(x=100, y=100) - ) - ) - ] - ) + # Check annotation types + audio_classifications = [ann for ann in label.annotations if isinstance(ann, AudioClassificationAnnotation)] + audio_objects = [ann for ann in label.annotations if isinstance(ann, AudioObjectAnnotation)] - audio_frames = label.audio_annotations_by_frame() - assert audio_frames == {} - - -def test_audio_annotations_by_frame_multiple_frames(): - """Test audio_annotations_by_frame with multiple time frames""" - # Create annotations at different times - annotation1 = AudioClassificationAnnotation( - frame=1000, # 1.0 seconds - name="speaker1", - value=Radio(answer=ClassificationAnswer(name="john")) - ) - - annotation2 = AudioClassificationAnnotation( - frame=5000, # 5.0 seconds - name="speaker2", - value=Radio(answer=ClassificationAnswer(name="jane")) - ) - - annotation3 = AudioObjectAnnotation( - frame=1000, # 1.0 seconds (same as annotation1) - name="transcription1", - value=lb_types.TextEntity(start=0, end=5) # "Hello" has 5 characters - ) - - label = lb_types.Label( - data={"global_key": "audio_file.mp3"}, - annotations=[annotation1, annotation2, annotation3] - ) - - audio_frames = label.audio_annotations_by_frame() - - # Should have 2 frames: 1000ms and 5000ms - assert len(audio_frames) == 2 - assert 1000 in audio_frames - assert 5000 in audio_frames - - # Frame 1000 should have 2 annotations - assert len(audio_frames[1000]) == 2 - assert any(ann.name == "speaker1" for ann in audio_frames[1000]) - assert any(ann.name == "transcription1" for ann in audio_frames[1000]) - - # Frame 5000 should have 1 annotation - assert len(audio_frames[5000]) == 1 - assert audio_frames[5000][0].name == "speaker2" + assert len(audio_classifications) == 1 + assert len(audio_objects) == 1 + assert audio_classifications[0].name == "speaker" + assert audio_objects[0].name == "transcription" def test_audio_annotation_validation(): @@ -240,15 +185,6 @@ def test_audio_annotation_validation(): name="test", value=Text(answer="test") ) - - # Test frame must be non-negative (Pydantic handles this automatically) - # Negative frames are allowed by Pydantic, so we test that they work - annotation = AudioClassificationAnnotation( - frame=-1000, # Negative frames are allowed - name="test", - value=Text(answer="test") - ) - assert annotation.frame == -1000 def test_audio_annotation_extra_fields(): @@ -272,14 +208,14 @@ def test_audio_annotation_feature_schema(): frame=4000, name="language", value=Radio(answer=ClassificationAnswer(name="spanish")), - feature_schema_id="1234567890123456789012345" # Exactly 25 characters + feature_schema_id="1234567890123456789012345" ) assert annotation.feature_schema_id == "1234567890123456789012345" def test_audio_annotation_mixed_types(): - """Test label with mixed audio, video, and image annotations""" + """Test label with mixed audio and other annotation types""" # Audio annotation audio_annotation = AudioClassificationAnnotation( frame=2000, @@ -309,26 +245,24 @@ def test_audio_annotation_mixed_types(): annotations=[audio_annotation, video_annotation, image_annotation] ) - # Test audio-specific method - audio_frames = label.audio_annotations_by_frame() - assert 2000 in audio_frames - assert len(audio_frames[2000]) == 1 + # Verify all annotations are present + assert len(label.annotations) == 3 - # Test video-specific method (should still work) - video_frames = label.frame_annotations() - assert 10 in video_frames - assert len(video_frames[10]) == 1 + # Check types + audio_annotations = [ann for ann in label.annotations if isinstance(ann, AudioClassificationAnnotation)] + video_annotations = [ann for ann in label.annotations if isinstance(ann, lb_types.VideoClassificationAnnotation)] + object_annotations = [ann for ann in label.annotations if isinstance(ann, lb_types.ObjectAnnotation)] - # Test general object annotations (should still work) - object_annotations = label.object_annotations() + assert len(audio_annotations) == 1 + assert len(video_annotations) == 1 assert len(object_annotations) == 1 - assert object_annotations[0].name == "bbox" def test_audio_annotation_serialization(): """Test audio annotations can be serialized to dict""" annotation = AudioClassificationAnnotation( frame=6000, + end_frame=8000, name="emotion", value=Radio(answer=ClassificationAnswer(name="happy")), segment_index=3, @@ -338,6 +272,7 @@ def test_audio_annotation_serialization(): # Test model_dump serialized = annotation.model_dump() assert serialized["frame"] == 6000 + assert serialized["end_frame"] == 8000 assert serialized["name"] == "emotion" assert serialized["segment_index"] == 3 assert serialized["extra"]["confidence"] == 0.9 @@ -346,6 +281,7 @@ def test_audio_annotation_serialization(): serialized_excluded = annotation.model_dump(exclude_none=True) assert "frame" in serialized_excluded assert "name" in serialized_excluded + assert "end_frame" in serialized_excluded assert "segment_index" in serialized_excluded @@ -353,6 +289,7 @@ def test_audio_annotation_from_dict(): """Test audio annotations can be created from dict""" annotation_data = { "frame": 7000, + "end_frame": 9000, "name": "topic", "value": Text(answer="technology"), "segment_index": 2, @@ -362,6 +299,7 @@ def test_audio_annotation_from_dict(): annotation = AudioClassificationAnnotation(**annotation_data) assert annotation.frame == 7000 + assert annotation.end_frame == 9000 assert annotation.name == "topic" assert annotation.segment_index == 2 assert annotation.extra["source"] == "manual" @@ -370,34 +308,91 @@ def test_audio_annotation_from_dict(): def test_audio_annotation_edge_cases(): """Test audio annotation edge cases""" # Test very long audio (many hours) - long_annotation = AudioClassificationAnnotation.from_time_range( - start_ms=3600000, # 1 hour in milliseconds - end_ms=7200000, # 2 hours in milliseconds + long_annotation = AudioClassificationAnnotation( + frame=3600000, # 1 hour in milliseconds + end_frame=7200000, # 2 hours in milliseconds name="long_audio", value=Text(answer="very long") ) - assert long_annotation.frame == 3600000 # 1 hour in milliseconds - assert long_annotation.start_time == 3600.0 + assert long_annotation.frame == 3600000 + assert long_annotation.end_frame == 7200000 # Test very short audio (milliseconds) - short_annotation = AudioClassificationAnnotation.from_time_range( - start_ms=1, # 1 millisecond - end_ms=2, # 2 milliseconds + short_annotation = AudioClassificationAnnotation( + frame=1, # 1 millisecond + end_frame=2, # 2 milliseconds name="short_audio", value=Text(answer="very short") ) - assert short_annotation.frame == 1 # 1 millisecond - assert short_annotation.start_time == 0.001 + assert short_annotation.frame == 1 + assert short_annotation.end_frame == 2 # Test zero time - zero_annotation = AudioClassificationAnnotation.from_time_range( - start_ms=0, - end_ms=0, + zero_annotation = AudioClassificationAnnotation( + frame=0, name="zero_time", value=Text(answer="zero") ) assert zero_annotation.frame == 0 - assert zero_annotation.start_time == 0.0 + assert zero_annotation.end_frame is None + + +def test_temporal_annotation_grouping(): + """Test that annotations with same name can be grouped for temporal processing""" + # Create multiple annotations with same name (like tokens) + tokens = ["Hello", "world", "this", "is", "audio"] + annotations = [] + + for i, token in enumerate(tokens): + start_frame = i * 1000 # 1 second apart + end_frame = start_frame + 900 # 900ms duration each + + annotation = AudioClassificationAnnotation( + frame=start_frame, + end_frame=end_frame, + name="tokens", # Same name for grouping + value=Text(answer=token) + ) + annotations.append(annotation) + + # Verify all have same name but different content and timing + assert len(annotations) == 5 + assert all(ann.name == "tokens" for ann in annotations) + assert annotations[0].value.answer == "Hello" + assert annotations[1].value.answer == "world" + assert annotations[0].frame == 0 + assert annotations[1].frame == 1000 + assert annotations[0].end_frame == 900 + assert annotations[1].end_frame == 1900 + + +def test_audio_object_types(): + """Test different types of audio object annotations""" + # Text entity (transcription) + text_obj = AudioObjectAnnotation( + frame=1000, + name="transcription", + value=TextEntity(start=0, end=5) # "hello" + ) + + assert isinstance(text_obj.value, TextEntity) + assert text_obj.value.start == 0 + assert text_obj.value.end == 5 + + # Test with keyframe and segment settings + keyframe_obj = AudioObjectAnnotation( + frame=2000, + end_frame=3000, + name="segment", + value=TextEntity(start=10, end=15), + keyframe=True, + segment_index=1 + ) + + assert keyframe_obj.keyframe is True + assert keyframe_obj.segment_index == 1 + assert keyframe_obj.frame == 2000 + assert keyframe_obj.end_frame == 3000 \ No newline at end of file From 67dd14a4b933f5906390a03e1c93bb48291c102b Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Thu, 11 Sep 2025 21:23:34 +0000 Subject: [PATCH 008/103] :art: Cleaned --- examples/annotation_import/audio.ipynb | 460 ++++++------------------- 1 file changed, 111 insertions(+), 349 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index f152f2d32..2463af769 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,16 +1,18 @@ { + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {}, "cells": [ { - "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - " \n", + "", + " ", "\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "\n", @@ -22,10 +24,10 @@ "\n", "" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Audio Annotation Import\n", @@ -51,188 +53,111 @@ "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", "\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "* For information on what types of annotations are supported per data type, refer to this documentation:\n", " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "* Notes:\n", " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", "outputs": [], - "source": [ - "%pip install -q \"labelbox[data]\"" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", + "cell_type": "code", "outputs": [], - "source": [ - "import labelbox as lb\n", - "import uuid\n", - "import labelbox.types as lb_types" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Replace with your API key\n", "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", "outputs": [], - "source": [ - "# Add your api key\n", - "API_KEY = \"\"\n", - "client = lb.Client(api_key=API_KEY)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Supported annotations for Audio" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", + "cell_type": "code", "outputs": [], - "source": [ - "##### Classification free text #####\n", - "\n", - "text_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"text_audio\",\n", - " value=lb_types.Text(answer=\"free text audio annotation\"),\n", - ")\n", - "\n", - "text_annotation_ndjson = {\n", - " \"name\": \"text_audio\",\n", - " \"answer\": \"free text audio annotation\",\n", - "}" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", + "cell_type": "code", "outputs": [], - "source": [ - "##### Checklist Classification #######\n", - "\n", - "checklist_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"checklist_audio\",\n", - " value=lb_types.Checklist(answer=[\n", - " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", - " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", - " ]),\n", - ")\n", - "\n", - "checklist_annotation_ndjson = {\n", - " \"name\":\n", - " \"checklist_audio\",\n", - " \"answers\": [\n", - " {\n", - " \"name\": \"first_checklist_answer\"\n", - " },\n", - " {\n", - " \"name\": \"second_checklist_answer\"\n", - " },\n", - " ],\n", - "}" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", + "cell_type": "code", "outputs": [], - "source": [ - "######## Radio Classification ######\n", - "\n", - "radio_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"radio_audio\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", - " name=\"second_radio_answer\")),\n", - ")\n", - "\n", - "radio_annotation_ndjson = {\n", - " \"name\": \"radio_audio\",\n", - " \"answer\": {\n", - " \"name\": \"first_radio_answer\"\n", - " },\n", - "}" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Upload Annotations - putting it all together " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Import data rows into Catalog" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", + "cell_type": "code", "outputs": [], - "source": [ - "# Create one Labelbox dataset\n", - "\n", - "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", - "\n", - "asset = {\n", - " \"row_data\":\n", - " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", - " \"global_key\":\n", - " global_key,\n", - "}\n", - "\n", - "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", - "task = dataset.create_data_rows([asset])\n", - "task.wait_till_done()\n", - "print(\"Errors:\", task.errors)\n", - "print(\"Failed data rows: \", task.failed_data_rows)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Create/select an ontology\n", @@ -240,349 +165,186 @@ "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", "\n", "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", + "cell_type": "code", "outputs": [], - "source": [ - "ontology_builder = lb.OntologyBuilder(classifications=[\n", - " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", - " name=\"text_audio\"),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"checklist_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_checklist_answer\"),\n", - " lb.Option(value=\"second_checklist_answer\"),\n", - " ],\n", - " ),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"radio_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_radio_answer\"),\n", - " lb.Option(value=\"second_radio_answer\"),\n", - " ],\n", - " ),\n", - " # Temporal classification for token-level annotations\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.TEXT,\n", - " name=\"User Speaker\",\n", - " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", - " ),\n", - "])\n", - "\n", - "ontology = client.create_ontology(\n", - " \"Ontology Audio Annotations\",\n", - " ontology_builder.asdict(),\n", - " media_type=lb.MediaType.Audio,\n", - ")" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Step 3: Create a labeling project\n", "Connect the ontology to the labeling project" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", + "cell_type": "code", "outputs": [], - "source": [ - "# Create Labelbox project\n", - "project = client.create_project(name=\"audio_project\",\n", - " media_type=lb.MediaType.Audio)\n", - "\n", - "# Setup your ontology\n", - "project.setup_editor(\n", - " ontology) # Connect your ontology and editor to your project" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Send a batch of data rows to the project" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", + "cell_type": "code", "outputs": [], - "source": [ - "# Setup Batches and Ontology\n", - "\n", - "# Create a batch to send to your MAL project\n", - "batch = project.create_batch(\n", - " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", - " global_keys=[\n", - " global_key\n", - " ], # Paginated collection of data row objects, list of data row ids or global keys\n", - " priority=5, # priority between 1(Highest) - 5(lowest)\n", - ")\n", - "\n", - "print(\"Batch: \", batch)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Create the annotations payload\n", "Create the annotations payload using the snippets of code above\n", "\n", "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Python annotation\n", "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", - "id": "6b53669e", "metadata": {}, "source": [ "\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, - "id": "f9af095e", "metadata": {}, + "source": "", + "cell_type": "code", "outputs": [], - "source": [ - "\n" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, - "id": "64f229a3", "metadata": {}, + "source": "", + "cell_type": "code", "outputs": [], - "source": [ - "\n" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", + "cell_type": "code", "outputs": [], - "source": [ - "label = []\n", - "label.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", - " ))" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### NDJSON annotations \n", "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", + "cell_type": "code", "outputs": [], - "source": [ - "label_ndjson = []\n", - "for annotations in [\n", - " text_annotation_ndjson,\n", - " checklist_annotation_ndjson,\n", - " radio_annotation_ndjson,\n", - "]:\n", - " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", - " label_ndjson.append(annotations)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", - "id": "3d3f11a1", "metadata": {}, "source": [ "## Temporal Audio Annotations\n", "\n", "You can create temporal annotations for individual tokens (words) with precise timing:\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, - "id": "f5e7d34b", "metadata": {}, + "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Define tokens with precise timing (from demo script)\n", - "tokens_data = [\n", - " (\"Hello\", 586, 770), # Hello: frames 586-770\n", - " (\"AI\", 771, 955), # AI: frames 771-955 \n", - " (\"how\", 956, 1140), # how: frames 956-1140\n", - " (\"are\", 1141, 1325), # are: frames 1141-1325\n", - " (\"you\", 1326, 1510), # you: frames 1326-1510\n", - " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", - " (\"today\", 1696, 1880), # today: frames 1696-1880\n", - "]\n", - "\n", - "# Create temporal annotations for each token\n", - "temporal_annotations = []\n", - "for token, start_frame, end_frame in tokens_data:\n", - " token_annotation = lb_types.AudioClassificationAnnotation(\n", - " frame=start_frame,\n", - " end_frame=end_frame,\n", - " name=\"User Speaker\",\n", - " value=lb_types.Text(answer=token)\n", - " )\n", - " temporal_annotations.append(token_annotation)\n", - "\n", - "print(f\"Created {len(temporal_annotations)} temporal token annotations\")\n" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, - "id": "42c5d52a", "metadata": {}, + "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Create label with both regular and temporal annotations\n", - "label_with_temporal = []\n", - "label_with_temporal.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation] + temporal_annotations,\n", - " ))\n", - "\n", - "print(f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\")\n", - "print(f\" - Regular annotations: 3\")\n", - "print(f\" - Temporal annotations: {len(temporal_annotations)}\")\n" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Model Assisted Labeling (MAL)\n", "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, - "id": "2473670f", "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload temporal annotations via MAL\n", - "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label_with_temporal,\n", - ")\n", - "\n", - "temporal_upload_job.wait_until_done()\n", - "print(\"Temporal upload completed!\")\n", - "print(\"Errors:\", temporal_upload_job.errors)\n", - "print(\"Status:\", temporal_upload_job.statuses)\n" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload our label using Model-Assisted Labeling\n", - "upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Label Import" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload label for this data row in project\n", - "upload_job = lb.LabelImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=\"label_import_job\" + str(uuid.uuid4()),\n", - " labels=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Optional deletions for cleanup " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# project.delete()\n# dataset.delete()", + "cell_type": "code", "outputs": [], - "source": [ - "# project.delete()\n", - "# dataset.delete()" - ] + "execution_count": null } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + ] +} \ No newline at end of file From a1600e5449d457b3fb754bd70d1bd1f5ea5067a3 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Thu, 11 Sep 2025 21:24:05 +0000 Subject: [PATCH 009/103] :memo: README updated --- examples/README.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/examples/README.md b/examples/README.md index cb1c1cebc..f6d505641 100644 --- a/examples/README.md +++ b/examples/README.md @@ -168,11 +168,6 @@ Open In Github Open In Colab - - Audio Temporal NEW! - Open In Github - Open In Colab - Tiled Open In Github From b4d2f422e7c785d227abc200fe8e7eb9740f59fd Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 11 Sep 2025 16:55:11 -0700 Subject: [PATCH 010/103] chore: improve imports --- libs/labelbox/src/labelbox/data/serialization/ndjson/label.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index ba6184226..0c65f5584 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -48,6 +48,7 @@ NDVideoMasks, ) from .relationship import NDRelationship +from .utils.temporal_processor import VideoTemporalProcessor, AudioTemporalProcessor AnnotationType = Union[ NDObjectType, @@ -135,7 +136,6 @@ def _create_video_annotations( yield NDObject.from_common(annotation=annot, data=label.data) # Use temporal processor for video classifications and objects - from .utils.temporal_processor import VideoTemporalProcessor processor = VideoTemporalProcessor() yield from processor.process_annotations(label) @@ -151,8 +151,6 @@ def _create_audio_annotations( Yields: NDClassification or NDObject: Audio annotations in NDJSON format """ - from .utils.temporal_processor import AudioTemporalProcessor - # Use processor with configurable behavior processor = AudioTemporalProcessor( group_text_annotations=True, # Group multiple TEXT annotations into one feature From fadb14e96ced46d4ca332617e7f88c290a263cd4 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 11 Sep 2025 16:57:12 -0700 Subject: [PATCH 011/103] chore: restore py version --- .python-version | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.python-version b/.python-version index 56d91d353..33a87347a 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.10.12 +3.9.18 \ No newline at end of file From 1e1259621ff95710e54335a61af1189589b7927b Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 11 Sep 2025 16:57:33 -0700 Subject: [PATCH 012/103] chore: restore py version --- .python-version | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.python-version b/.python-version index 33a87347a..43077b246 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.9.18 \ No newline at end of file +3.9.18 From c2a7b4cfd1b1b8639dd8afa35099e2e31eab6242 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Fri, 12 Sep 2025 10:00:07 -0700 Subject: [PATCH 013/103] chore: cleanup --- examples/README.md | 178 +++++++++--------- .../data/serialization/ndjson/label.py | 41 +++- .../ndjson/utils/temporal_processor.py | 37 ---- 3 files changed, 123 insertions(+), 133 deletions(-) diff --git a/examples/README.md b/examples/README.md index f6d505641..924d1017d 100644 --- a/examples/README.md +++ b/examples/README.md @@ -16,20 +16,25 @@ + + Ontologies + Open In Github + Open In Colab + + + Quick Start + Open In Github + Open In Colab + Data Rows Open In Github Open In Colab - Custom Embeddings - Open In Github - Open In Colab - - - User Management - Open In Github - Open In Colab + Basics + Open In Github + Open In Colab Batches @@ -42,24 +47,19 @@ Open In Colab - Quick Start - Open In Github - Open In Colab - - - Basics - Open In Github - Open In Colab + Data Row Metadata + Open In Github + Open In Colab - Ontologies - Open In Github - Open In Colab + Custom Embeddings + Open In Github + Open In Colab - Data Row Metadata - Open In Github - Open In Colab + User Management + Open In Github + Open In Colab @@ -80,6 +80,11 @@ Open In Github Open In Colab + + Exporting to CSV + Open In Github + Open In Colab + Composite Mask Export Open In Github @@ -90,11 +95,6 @@ Open In Github Open In Colab - - Exporting to CSV - Open In Github - Open In Colab - @@ -110,9 +110,9 @@ - Multimodal Chat Project - Open In Github - Open In Colab + Queue Management + Open In Github + Open In Colab Project Setup @@ -125,9 +125,9 @@ Open In Colab - Queue Management - Open In Github - Open In Colab + Multimodal Chat Project + Open In Github + Open In Colab @@ -144,34 +144,34 @@ - Conversational - Open In Github - Open In Colab + Tiled + Open In Github + Open In Colab + + + Text + Open In Github + Open In Colab PDF Open In Github Open In Colab + + Video + Open In Github + Open In Colab + Audio Open In Github Open In Colab - Conversational LLM Data Generation - Open In Github - Open In Colab - - - Text - Open In Github - Open In Colab - - - Tiled - Open In Github - Open In Colab + Conversational + Open In Github + Open In Colab HTML @@ -179,9 +179,9 @@ Open In Colab - Conversational LLM - Open In Github - Open In Colab + Conversational LLM Data Generation + Open In Github + Open In Colab Image @@ -189,9 +189,9 @@ Open In Colab - Video - Open In Github - Open In Colab + Conversational LLM + Open In Github + Open In Colab @@ -207,20 +207,15 @@ - - Huggingface Custom Embeddings - Open In Github - Open In Colab - Langchain Open In Github Open In Colab - Import YOLOv8 Annotations - Open In Github - Open In Colab + Meta SAM Video + Open In Github + Open In Colab Meta SAM @@ -228,9 +223,14 @@ Open In Colab - Meta SAM Video - Open In Github - Open In Colab + Import YOLOv8 Annotations + Open In Github + Open In Colab + + + Huggingface Custom Embeddings + Open In Github + Open In Colab @@ -246,11 +246,6 @@ - - Model Slices - Open In Github - Open In Colab - Model Predictions to Project Open In Github @@ -266,6 +261,11 @@ Open In Github Open In Colab + + Model Slices + Open In Github + Open In Colab + @@ -280,16 +280,6 @@ - - PDF Predictions - Open In Github - Open In Colab - - - Conversational Predictions - Open In Github - Open In Colab - HTML Predictions Open In Github @@ -300,26 +290,36 @@ Open In Github Open In Colab - - Geospatial Predictions - Open In Github - Open In Colab - Video Predictions Open In Github Open In Colab - Conversational LLM Predictions - Open In Github - Open In Colab + Conversational Predictions + Open In Github + Open In Colab + + + Geospatial Predictions + Open In Github + Open In Colab + + + PDF Predictions + Open In Github + Open In Colab Image Predictions Open In Github Open In Colab + + Conversational LLM Predictions + Open In Github + Open In Colab + diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 0c65f5584..6d7f016e5 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -48,7 +48,7 @@ NDVideoMasks, ) from .relationship import NDRelationship -from .utils.temporal_processor import VideoTemporalProcessor, AudioTemporalProcessor +from .utils.temporal_processor import AudioTemporalProcessor AnnotationType = Union[ NDObjectType, @@ -130,14 +130,41 @@ def _get_segment_frame_ranges( def _create_video_annotations( cls, label: Label ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]: - # Handle video mask annotations separately (special case) + video_annotations = defaultdict(list) for annot in label.annotations: - if isinstance(annot, VideoMaskAnnotation): + if isinstance( + annot, (VideoClassificationAnnotation, VideoObjectAnnotation) + ): + video_annotations[annot.feature_schema_id or annot.name].append( + annot + ) + elif isinstance(annot, VideoMaskAnnotation): yield NDObject.from_common(annotation=annot, data=label.data) - - # Use temporal processor for video classifications and objects - processor = VideoTemporalProcessor() - yield from processor.process_annotations(label) + + for annotation_group in video_annotations.values(): + segment_frame_ranges = cls._get_segment_frame_ranges( + annotation_group + ) + if isinstance(annotation_group[0], VideoClassificationAnnotation): + annotation = annotation_group[0] + frames_data = [] + for frames in segment_frame_ranges: + frames_data.append({"start": frames[0], "end": frames[-1]}) + annotation.extra.update({"frames": frames_data}) + yield NDClassification.from_common(annotation, label.data) + + elif isinstance(annotation_group[0], VideoObjectAnnotation): + segments = [] + for start_frame, end_frame in segment_frame_ranges: + segment = [] + for annotation in annotation_group: + if ( + annotation.keyframe + and start_frame <= annotation.frame <= end_frame + ): + segment.append(annotation) + segments.append(segment) + yield NDObject.from_common(segments, label.data) @classmethod def _create_audio_annotations( diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py index 97a35f5f3..76cc11146 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py @@ -138,40 +138,3 @@ def prepare_grouped_content(self, annotation_group: List) -> None: # Update the template annotation annotation_group[0].value = Text(answer=content_structure) - -class VideoTemporalProcessor(TemporalAnnotationProcessor): - """Processor for video temporal annotations - matches existing behavior""" - - def get_annotation_types(self) -> tuple: - from ....annotation_types.video import VideoClassificationAnnotation, VideoObjectAnnotation - return (VideoClassificationAnnotation,), (VideoObjectAnnotation,) - - def should_group_annotations(self, annotation_group: List) -> bool: - """Video always groups by segment ranges""" - return True - - def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: - """Build frame data using existing video segment logic""" - from ..label import NDLabel # Import here to avoid circular import - - segment_frame_ranges = NDLabel._get_segment_frame_ranges(annotation_group) - return [{"start": frames[0], "end": frames[-1]} for frames in segment_frame_ranges] - - def prepare_grouped_content(self, annotation_group: List) -> None: - """Video doesn't modify content - uses existing value""" - pass - - def _process_object_group(self, annotation_group, data): - """Video objects use segment-based processing""" - from ..label import NDLabel - - segment_frame_ranges = NDLabel._get_segment_frame_ranges(annotation_group) - segments = [] - for start_frame, end_frame in segment_frame_ranges: - segment = [] - for annotation in annotation_group: - if (annotation.keyframe and - start_frame <= annotation.frame <= end_frame): - segment.append(annotation) - segments.append(segment) - yield NDObject.from_common(segments, data) \ No newline at end of file From 26a35fd31065995b230acac4a6cdff6203ae3cda Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Fri, 12 Sep 2025 12:06:14 -0700 Subject: [PATCH 014/103] chore: lint --- .../labelbox/data/annotation_types/audio.py | 24 ++- .../labelbox/data/annotation_types/label.py | 8 +- .../serialization/ndjson/classification.py | 37 +++- .../data/serialization/ndjson/label.py | 8 +- .../data/serialization/ndjson/objects.py | 14 +- .../serialization/ndjson/utils/__init__.py | 2 +- .../ndjson/utils/temporal_processor.py | 118 ++++++----- .../tests/data/annotation_types/test_audio.py | 191 ++++++++++-------- 8 files changed, 241 insertions(+), 161 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index db4d7a8ae..7a5c5f40c 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -1,17 +1,23 @@ from typing import Optional -from labelbox.data.annotation_types.annotation import ClassificationAnnotation, ObjectAnnotation -from labelbox.data.mixins import ConfidenceNotSupportedMixin, CustomMetricsNotSupportedMixin +from labelbox.data.annotation_types.annotation import ( + ClassificationAnnotation, + ObjectAnnotation, +) +from labelbox.data.mixins import ( + ConfidenceNotSupportedMixin, + CustomMetricsNotSupportedMixin, +) class AudioClassificationAnnotation(ClassificationAnnotation): """Audio classification for specific time range - + Examples: - Speaker identification from 2500ms to 4100ms - Audio quality assessment for a segment - Language detection for audio segments - + Args: name (Optional[str]): Name of the classification feature_schema_id (Optional[Cuid]): Feature schema identifier @@ -27,14 +33,18 @@ class AudioClassificationAnnotation(ClassificationAnnotation): segment_index: Optional[int] = None -class AudioObjectAnnotation(ObjectAnnotation, ConfidenceNotSupportedMixin, CustomMetricsNotSupportedMixin): +class AudioObjectAnnotation( + ObjectAnnotation, + ConfidenceNotSupportedMixin, + CustomMetricsNotSupportedMixin, +): """Audio object annotation for specific time range - + Examples: - Transcription: "Hello world" from 2500ms to 4100ms - Sound events: "Dog barking" from 10000ms to 12000ms - Audio segments with metadata - + Args: name (Optional[str]): Name of the annotation feature_schema_id (Optional[Cuid]): Feature schema identifier diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index 6f20b175e..b01d51d54 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -90,12 +90,14 @@ def frame_annotations( def audio_annotations_by_frame( self, - ) -> Dict[int, List[Union[AudioObjectAnnotation, AudioClassificationAnnotation]]]: + ) -> Dict[ + int, List[Union[AudioObjectAnnotation, AudioClassificationAnnotation]] + ]: """Get audio annotations organized by frame (millisecond) - + Returns: Dict[int, List]: Dictionary mapping frame (milliseconds) to list of audio annotations - + Example: >>> label.audio_annotations_by_frame() {2500: [AudioClassificationAnnotation(...)], 10000: [AudioObjectAnnotation(...)]} diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index befb5130d..980457c74 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -401,7 +401,11 @@ class NDClassification: @staticmethod def to_common( annotation: "NDClassificationType", - ) -> Union[ClassificationAnnotation, VideoClassificationAnnotation]: + ) -> Union[ + ClassificationAnnotation, + VideoClassificationAnnotation, + AudioClassificationAnnotation, + ]: common = ClassificationAnnotation( value=annotation.to_common(), name=annotation.name, @@ -416,18 +420,35 @@ def to_common( results = [] for frame in annotation.frames: for idx in range(frame.start, frame.end + 1, 1): - results.append( - VideoClassificationAnnotation( - frame=idx, **common.model_dump(exclude_none=True) + # Check if this is an audio annotation by looking at the extra data + # Audio annotations will have frame/end_frame in extra, video annotations won't + if ( + hasattr(annotation, "extra") + and annotation.extra + and "frames" in annotation.extra + ): + # This is likely an audio temporal annotation + results.append( + AudioClassificationAnnotation( + frame=idx, **common.model_dump(exclude_none=True) + ) + ) + else: + # This is a video temporal annotation + results.append( + VideoClassificationAnnotation( + frame=idx, **common.model_dump(exclude_none=True) + ) ) - ) return results @classmethod def from_common( cls, annotation: Union[ - ClassificationAnnotation, VideoClassificationAnnotation, AudioClassificationAnnotation + ClassificationAnnotation, + VideoClassificationAnnotation, + AudioClassificationAnnotation, ], data: GenericDataRowData, ) -> Union[NDTextSubclass, NDChecklistSubclass, NDRadioSubclass]: @@ -450,7 +471,9 @@ def from_common( @staticmethod def lookup_classification( annotation: Union[ - ClassificationAnnotation, VideoClassificationAnnotation, AudioClassificationAnnotation + ClassificationAnnotation, + VideoClassificationAnnotation, + AudioClassificationAnnotation, ], ) -> Union[NDText, NDChecklist, NDRadio]: return {Text: NDText, Checklist: NDChecklist, Radio: NDRadio}.get( diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 6d7f016e5..fe80f2d74 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -171,17 +171,17 @@ def _create_audio_annotations( cls, label: Label ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]: """Create audio annotations using generic temporal processor - + Args: label: Label containing audio annotations to be processed - + Yields: NDClassification or NDObject: Audio annotations in NDJSON format """ # Use processor with configurable behavior processor = AudioTemporalProcessor( group_text_annotations=True, # Group multiple TEXT annotations into one feature - enable_token_mapping=True # Enable per-keyframe token content + enable_token_mapping=True, # Enable per-keyframe token content ) yield from processor.process_annotations(label) @@ -215,7 +215,7 @@ def _create_non_video_annotations(cls, label: Label): yield NDMessageTask.from_common(annotation, label.data) else: raise TypeError( - f"Unable to convert object to MAL format. `{type(getattr(annotation, 'value',annotation))}`" + f"Unable to convert object to MAL format. `{type(getattr(annotation, 'value', annotation))}`" ) @classmethod diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py index f543a786d..51825cd4b 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py @@ -773,29 +773,31 @@ def from_common( ) @classmethod - def _serialize_audio_object_annotation(cls, annotation: AudioObjectAnnotation, data: GenericDataRowData): + def _serialize_audio_object_annotation( + cls, annotation: AudioObjectAnnotation, data: GenericDataRowData + ): """Serialize audio object annotation with temporal information - + Args: annotation: Audio object annotation to process data: Data row data - + Returns: NDObject: Serialized audio object annotation """ # Get the appropriate NDObject subclass based on the annotation value type obj = cls.lookup_object(annotation) - + # Process sub-classifications if any subclasses = [ NDSubclassification.from_common(annot) for annot in annotation.classifications ] - + # Add frame information to extra (milliseconds) extra = annotation.extra.copy() if annotation.extra else {} extra.update({"frame": annotation.frame}) - + # Create the NDObject with frame information return obj.from_common( str(annotation._uuid), diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py index 8959af847..33f132b74 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py @@ -1 +1 @@ -# Utils package for NDJSON serialization helpers \ No newline at end of file +# Utils package for NDJSON serialization helpers diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py index 76cc11146..3eae9a1a4 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py @@ -1,11 +1,11 @@ """ Generic temporal annotation processor for frame-based media (video, audio) """ + from abc import ABC, abstractmethod from collections import defaultdict from typing import Any, Dict, Generator, List, Union -from ....annotation_types.annotation import ClassificationAnnotation, ObjectAnnotation from ....annotation_types.label import Label from ..classification import NDClassificationType, NDClassification from ..objects import NDObject @@ -13,56 +13,64 @@ class TemporalAnnotationProcessor(ABC): """Abstract base class for processing temporal annotations (video, audio, etc.)""" - + @abstractmethod def get_annotation_types(self) -> tuple: """Return tuple of annotation types this processor handles""" pass - + @abstractmethod def should_group_annotations(self, annotation_group: List) -> bool: """Determine if annotations should be grouped into one feature""" pass - + @abstractmethod def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: """Extract frame data from annotation group""" pass - + @abstractmethod def prepare_grouped_content(self, annotation_group: List) -> Any: """Prepare content for grouped annotations (may modify annotation.value)""" pass - - def process_annotations(self, label: Label) -> Generator[Union[NDClassificationType, Any], None, None]: + + def process_annotations( + self, label: Label + ) -> Generator[Union[NDClassificationType, Any], None, None]: """Main processing method - generic for all temporal media""" temporal_annotations = defaultdict(list) classification_types, object_types = self.get_annotation_types() - + # Group annotations by feature name/schema for annot in label.annotations: if isinstance(annot, classification_types + object_types): - temporal_annotations[annot.feature_schema_id or annot.name].append(annot) - + temporal_annotations[ + annot.feature_schema_id or annot.name + ].append(annot) + # Process each group for annotation_group in temporal_annotations.values(): if isinstance(annotation_group[0], classification_types): - yield from self._process_classification_group(annotation_group, label.data) + yield from self._process_classification_group( + annotation_group, label.data + ) elif isinstance(annotation_group[0], object_types): - yield from self._process_object_group(annotation_group, label.data) - + yield from self._process_object_group( + annotation_group, label.data + ) + def _process_classification_group(self, annotation_group, data): """Process classification annotations""" if self.should_group_annotations(annotation_group): # Group into single feature with multiple keyframes annotation = annotation_group[0] # Use first as template - + # Build frame data frames_data = self.build_frame_data(annotation_group) - + # Prepare content (may modify annotation.value) self.prepare_grouped_content(annotation_group) - + # Update with frame data annotation.extra = {"frames": frames_data} yield NDClassification.from_common(annotation, data) @@ -75,7 +83,7 @@ def _process_classification_group(self, annotation_group, data): annotation.extra = {} annotation.extra.update({"frames": frames_data}) yield NDClassification.from_common(annotation, data) - + def _process_object_group(self, annotation_group, data): """Process object annotations - default to individual processing""" for annotation in annotation_group: @@ -84,57 +92,75 @@ def _process_object_group(self, annotation_group, data): class AudioTemporalProcessor(TemporalAnnotationProcessor): """Processor for audio temporal annotations""" - - def __init__(self, - group_text_annotations: bool = True, - enable_token_mapping: bool = True): + + def __init__( + self, + group_text_annotations: bool = True, + enable_token_mapping: bool = True, + ): self.group_text_annotations = group_text_annotations self.enable_token_mapping = enable_token_mapping - + def get_annotation_types(self) -> tuple: - from ....annotation_types.audio import AudioClassificationAnnotation, AudioObjectAnnotation + from ....annotation_types.audio import ( + AudioClassificationAnnotation, + AudioObjectAnnotation, + ) + return (AudioClassificationAnnotation,), (AudioObjectAnnotation,) - + def should_group_annotations(self, annotation_group: List) -> bool: """Group TEXT classifications with multiple temporal instances""" if not self.group_text_annotations: return False - + from ....annotation_types.classification.classification import Text - return (isinstance(annotation_group[0].value, Text) and - len(annotation_group) > 1 and - all(hasattr(ann, 'frame') for ann in annotation_group)) - + + return ( + isinstance(annotation_group[0].value, Text) + and len(annotation_group) > 1 + and all(hasattr(ann, "frame") for ann in annotation_group) + ) + def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: """Extract frame ranges from audio annotations""" frames_data = [] for annotation in annotation_group: - if hasattr(annotation, 'frame'): + if hasattr(annotation, "frame"): frame = annotation.frame - end_frame = (annotation.end_frame - if hasattr(annotation, 'end_frame') and annotation.end_frame is not None - else frame) + end_frame = ( + annotation.end_frame + if hasattr(annotation, "end_frame") + and annotation.end_frame is not None + else frame + ) frames_data.append({"start": frame, "end": end_frame}) return frames_data - + def prepare_grouped_content(self, annotation_group: List) -> None: """Prepare content for grouped audio annotations""" from ....annotation_types.classification.classification import Text - - if not isinstance(annotation_group[0].value, Text) or not self.enable_token_mapping: + + if ( + not isinstance(annotation_group[0].value, Text) + or not self.enable_token_mapping + ): return - + # Build token mapping for TEXT annotations import json - + all_content = [ann.value.answer for ann in annotation_group] - token_mapping = {str(ann.frame): ann.value.answer for ann in annotation_group} - - content_structure = json.dumps({ - "default_text": " ".join(all_content), - "token_mapping": token_mapping - }) - + token_mapping = { + str(ann.frame): ann.value.answer for ann in annotation_group + } + + content_structure = json.dumps( + { + "default_text": " ".join(all_content), + "token_mapping": token_mapping, + } + ) + # Update the template annotation annotation_group[0].value = Text(answer=content_structure) - diff --git a/libs/labelbox/tests/data/annotation_types/test_audio.py b/libs/labelbox/tests/data/annotation_types/test_audio.py index 6c312abec..2703524f2 100644 --- a/libs/labelbox/tests/data/annotation_types/test_audio.py +++ b/libs/labelbox/tests/data/annotation_types/test_audio.py @@ -8,7 +8,6 @@ ClassificationAnswer, Radio, Text, - Checklist, ) from labelbox.data.annotation_types.ner import TextEntity @@ -18,9 +17,9 @@ def test_audio_classification_creation(): annotation = AudioClassificationAnnotation( frame=2500, # 2.5 seconds in milliseconds name="speaker_id", - value=Radio(answer=ClassificationAnswer(name="john")) + value=Radio(answer=ClassificationAnswer(name="john")), ) - + assert annotation.frame == 2500 assert annotation.end_frame is None assert annotation.segment_index is None @@ -32,12 +31,12 @@ def test_audio_classification_creation(): def test_audio_classification_with_time_range(): """Test creating audio classification with start and end frames""" annotation = AudioClassificationAnnotation( - frame=2500, # Start at 2.5 seconds + frame=2500, # Start at 2.5 seconds end_frame=4100, # End at 4.1 seconds name="speaker_id", - value=Radio(answer=ClassificationAnswer(name="john")) + value=Radio(answer=ClassificationAnswer(name="john")), ) - + assert annotation.frame == 2500 assert annotation.end_frame == 4100 assert annotation.name == "speaker_id" @@ -50,9 +49,9 @@ def test_audio_classification_creation_with_segment(): end_frame=15000, name="language", value=Radio(answer=ClassificationAnswer(name="english")), - segment_index=1 + segment_index=1, ) - + assert annotation.frame == 10000 assert annotation.end_frame == 15000 assert annotation.segment_index == 1 @@ -63,9 +62,9 @@ def test_audio_classification_text_type(): annotation = AudioClassificationAnnotation( frame=5000, # 5.0 seconds name="quality", - value=Text(answer="excellent") + value=Text(answer="excellent"), ) - + assert annotation.frame == 5000 assert annotation.name == "quality" assert isinstance(annotation.value, Text) @@ -78,9 +77,11 @@ def test_audio_object_creation(): frame=10000, end_frame=12500, name="transcription", - value=lb_types.TextEntity(start=0, end=11) # "Hello world" has 11 characters + value=lb_types.TextEntity( + start=0, end=11 + ), # "Hello world" has 11 characters ) - + assert annotation.frame == 10000 assert annotation.end_frame == 12500 assert annotation.keyframe is True @@ -96,17 +97,17 @@ def test_audio_object_creation_with_classifications(): sub_classification = AudioClassificationAnnotation( frame=10000, name="confidence", - value=Radio(answer=ClassificationAnswer(name="high")) + value=Radio(answer=ClassificationAnswer(name="high")), ) - + annotation = AudioObjectAnnotation( frame=10000, end_frame=12500, name="transcription", value=lb_types.TextEntity(start=0, end=11), - classifications=[sub_classification] + classifications=[sub_classification], ) - + assert len(annotation.classifications) == 1 assert annotation.classifications[0].name == "confidence" assert annotation.classifications[0].frame == 10000 @@ -119,9 +120,9 @@ def test_audio_object_direct_creation(): name="sound_event", value=lb_types.TextEntity(start=0, end=11), keyframe=False, - segment_index=2 + segment_index=2, ) - + assert annotation.frame == 7500 assert annotation.end_frame is None assert annotation.keyframe is False @@ -132,13 +133,13 @@ def test_frame_precision(): """Test frame values maintain precision""" # Test various time values in milliseconds test_cases = [0, 1, 1000, 1500, 10123, 60000] - + for milliseconds in test_cases: annotation = AudioClassificationAnnotation( frame=milliseconds, end_frame=milliseconds + 1000, name="test", - value=Text(answer="test") + value=Text(answer="test"), ) assert annotation.frame == milliseconds assert annotation.end_frame == milliseconds + 1000 @@ -148,28 +149,40 @@ def test_audio_label_integration(): """Test audio annotations work with Label container""" # Create audio annotations speaker_annotation = AudioClassificationAnnotation( - frame=1000, end_frame=2000, - name="speaker", value=Radio(answer=ClassificationAnswer(name="john")) + frame=1000, + end_frame=2000, + name="speaker", + value=Radio(answer=ClassificationAnswer(name="john")), ) - + transcription_annotation = AudioObjectAnnotation( - frame=1000, end_frame=2000, - name="transcription", value=lb_types.TextEntity(start=0, end=5) + frame=1000, + end_frame=2000, + name="transcription", + value=lb_types.TextEntity(start=0, end=5), ) - + # Create label with audio annotations label = lb_types.Label( data={"global_key": "audio_file.mp3"}, - annotations=[speaker_annotation, transcription_annotation] + annotations=[speaker_annotation, transcription_annotation], ) - + # Verify annotations are accessible assert len(label.annotations) == 2 - + # Check annotation types - audio_classifications = [ann for ann in label.annotations if isinstance(ann, AudioClassificationAnnotation)] - audio_objects = [ann for ann in label.annotations if isinstance(ann, AudioObjectAnnotation)] - + audio_classifications = [ + ann + for ann in label.annotations + if isinstance(ann, AudioClassificationAnnotation) + ] + audio_objects = [ + ann + for ann in label.annotations + if isinstance(ann, AudioObjectAnnotation) + ] + assert len(audio_classifications) == 1 assert len(audio_objects) == 1 assert audio_classifications[0].name == "speaker" @@ -183,21 +196,18 @@ def test_audio_annotation_validation(): AudioClassificationAnnotation( frame="invalid", # Should be int name="test", - value=Text(answer="test") + value=Text(answer="test"), ) def test_audio_annotation_extra_fields(): """Test audio annotations can have extra metadata""" extra_data = {"source": "automatic", "confidence_score": 0.95} - + annotation = AudioClassificationAnnotation( - frame=3000, - name="quality", - value=Text(answer="good"), - extra=extra_data + frame=3000, name="quality", value=Text(answer="good"), extra=extra_data ) - + assert annotation.extra["source"] == "automatic" assert annotation.extra["confidence_score"] == 0.95 @@ -208,9 +218,9 @@ def test_audio_annotation_feature_schema(): frame=4000, name="language", value=Radio(answer=ClassificationAnswer(name="spanish")), - feature_schema_id="1234567890123456789012345" + feature_schema_id="1234567890123456789012345", ) - + assert annotation.feature_schema_id == "1234567890123456789012345" @@ -220,39 +230,48 @@ def test_audio_annotation_mixed_types(): audio_annotation = AudioClassificationAnnotation( frame=2000, name="speaker", - value=Radio(answer=ClassificationAnswer(name="john")) + value=Radio(answer=ClassificationAnswer(name="john")), ) - + # Video annotation video_annotation = lb_types.VideoClassificationAnnotation( - frame=10, - name="quality", - value=Text(answer="good") + frame=10, name="quality", value=Text(answer="good") ) - + # Image annotation image_annotation = lb_types.ObjectAnnotation( name="bbox", value=lb_types.Rectangle( - start=lb_types.Point(x=0, y=0), - end=lb_types.Point(x=100, y=100) - ) + start=lb_types.Point(x=0, y=0), end=lb_types.Point(x=100, y=100) + ), ) - + # Create label with mixed types label = lb_types.Label( data={"global_key": "mixed_media"}, - annotations=[audio_annotation, video_annotation, image_annotation] + annotations=[audio_annotation, video_annotation, image_annotation], ) - + # Verify all annotations are present assert len(label.annotations) == 3 - + # Check types - audio_annotations = [ann for ann in label.annotations if isinstance(ann, AudioClassificationAnnotation)] - video_annotations = [ann for ann in label.annotations if isinstance(ann, lb_types.VideoClassificationAnnotation)] - object_annotations = [ann for ann in label.annotations if isinstance(ann, lb_types.ObjectAnnotation)] - + audio_annotations = [ + ann + for ann in label.annotations + if isinstance(ann, AudioClassificationAnnotation) + ] + video_annotations = [ + ann + for ann in label.annotations + if isinstance(ann, lb_types.VideoClassificationAnnotation) + ] + object_annotations = [ + ann + for ann in label.annotations + if isinstance(ann, lb_types.ObjectAnnotation) + ] + assert len(audio_annotations) == 1 assert len(video_annotations) == 1 assert len(object_annotations) == 1 @@ -266,9 +285,9 @@ def test_audio_annotation_serialization(): name="emotion", value=Radio(answer=ClassificationAnswer(name="happy")), segment_index=3, - extra={"confidence": 0.9} + extra={"confidence": 0.9}, ) - + # Test model_dump serialized = annotation.model_dump() assert serialized["frame"] == 6000 @@ -276,7 +295,7 @@ def test_audio_annotation_serialization(): assert serialized["name"] == "emotion" assert serialized["segment_index"] == 3 assert serialized["extra"]["confidence"] == 0.9 - + # Test model_dump with exclusions serialized_excluded = annotation.model_dump(exclude_none=True) assert "frame" in serialized_excluded @@ -293,11 +312,11 @@ def test_audio_annotation_from_dict(): "name": "topic", "value": Text(answer="technology"), "segment_index": 2, - "extra": {"source": "manual"} + "extra": {"source": "manual"}, } - + annotation = AudioClassificationAnnotation(**annotation_data) - + assert annotation.frame == 7000 assert annotation.end_frame == 9000 assert annotation.name == "topic" @@ -310,32 +329,30 @@ def test_audio_annotation_edge_cases(): # Test very long audio (many hours) long_annotation = AudioClassificationAnnotation( frame=3600000, # 1 hour in milliseconds - end_frame=7200000, # 2 hours in milliseconds + end_frame=7200000, # 2 hours in milliseconds name="long_audio", - value=Text(answer="very long") + value=Text(answer="very long"), ) - + assert long_annotation.frame == 3600000 assert long_annotation.end_frame == 7200000 - + # Test very short audio (milliseconds) short_annotation = AudioClassificationAnnotation( frame=1, # 1 millisecond - end_frame=2, # 2 milliseconds + end_frame=2, # 2 milliseconds name="short_audio", - value=Text(answer="very short") + value=Text(answer="very short"), ) - + assert short_annotation.frame == 1 assert short_annotation.end_frame == 2 - + # Test zero time zero_annotation = AudioClassificationAnnotation( - frame=0, - name="zero_time", - value=Text(answer="zero") + frame=0, name="zero_time", value=Text(answer="zero") ) - + assert zero_annotation.frame == 0 assert zero_annotation.end_frame is None @@ -345,19 +362,19 @@ def test_temporal_annotation_grouping(): # Create multiple annotations with same name (like tokens) tokens = ["Hello", "world", "this", "is", "audio"] annotations = [] - + for i, token in enumerate(tokens): start_frame = i * 1000 # 1 second apart end_frame = start_frame + 900 # 900ms duration each - + annotation = AudioClassificationAnnotation( frame=start_frame, end_frame=end_frame, name="tokens", # Same name for grouping - value=Text(answer=token) + value=Text(answer=token), ) annotations.append(annotation) - + # Verify all have same name but different content and timing assert len(annotations) == 5 assert all(ann.name == "tokens" for ann in annotations) @@ -375,24 +392,24 @@ def test_audio_object_types(): text_obj = AudioObjectAnnotation( frame=1000, name="transcription", - value=TextEntity(start=0, end=5) # "hello" + value=TextEntity(start=0, end=5), # "hello" ) - + assert isinstance(text_obj.value, TextEntity) assert text_obj.value.start == 0 assert text_obj.value.end == 5 - + # Test with keyframe and segment settings keyframe_obj = AudioObjectAnnotation( frame=2000, end_frame=3000, - name="segment", + name="segment", value=TextEntity(start=10, end=15), keyframe=True, - segment_index=1 + segment_index=1, ) - + assert keyframe_obj.keyframe is True assert keyframe_obj.segment_index == 1 assert keyframe_obj.frame == 2000 - assert keyframe_obj.end_frame == 3000 \ No newline at end of file + assert keyframe_obj.end_frame == 3000 From b16f2ea5aac7e4d490fc7e54b3b8a73ee31bf4cb Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Fri, 12 Sep 2025 12:32:39 -0700 Subject: [PATCH 015/103] fix: failing build issue due to lint --- libs/labelbox/tests/conftest.py | 12 +++--- .../test_generic_data_types.py | 38 ++++++++----------- 2 files changed, 21 insertions(+), 29 deletions(-) diff --git a/libs/labelbox/tests/conftest.py b/libs/labelbox/tests/conftest.py index a2ffdd49d..8eb3807ca 100644 --- a/libs/labelbox/tests/conftest.py +++ b/libs/labelbox/tests/conftest.py @@ -688,12 +688,12 @@ def create_label(): predictions, ) upload_task.wait_until_done(sleep_time_seconds=5) - assert ( - upload_task.state == AnnotationImportState.FINISHED - ), "Label Import did not finish" - assert ( - len(upload_task.errors) == 0 - ), f"Label Import {upload_task.name} failed with errors {upload_task.errors}" + assert upload_task.state == AnnotationImportState.FINISHED, ( + "Label Import did not finish" + ) + assert len(upload_task.errors) == 0, ( + f"Label Import {upload_task.name} failed with errors {upload_task.errors}" + ) project.create_label = create_label project.create_label() diff --git a/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py b/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py index 4a86fd834..73e8f4976 100644 --- a/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py +++ b/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py @@ -271,46 +271,38 @@ def test_import_mal_annotations( def test_audio_temporal_annotations_fixtures(): """Test that audio temporal annotation fixtures are properly structured""" # This test verifies our fixtures work without requiring the full integration environment - + # Mock prediction_id_mapping structure that our fixtures expect mock_prediction_id_mapping = [ { "checklist": { "tool": "checklist_tool", "name": "checklist", - "value": "checklist" - }, - "text": { - "tool": "text_tool", - "name": "text", - "value": "text" + "value": "checklist", }, - "radio": { - "tool": "radio_tool", - "name": "radio", - "value": "radio" - } + "text": {"tool": "text_tool", "name": "text", "value": "text"}, + "radio": {"tool": "radio_tool", "name": "radio", "value": "radio"}, } ] - + # Test that our fixtures can process the mock data # Note: We can't actually call the fixtures directly in a unit test, # but we can verify the structure is correct by checking the fixture definitions - + # Verify that our fixtures are properly defined and accessible from .conftest import ( audio_checklist_inference, - audio_text_inference, + audio_text_inference, audio_radio_inference, - audio_text_entity_inference + audio_text_entity_inference, ) - + # Check that all required fixtures exist assert audio_checklist_inference is not None assert audio_text_inference is not None assert audio_radio_inference is not None assert audio_text_entity_inference is not None - + # Verify the fixtures are callable (they should be functions) assert callable(audio_checklist_inference) assert callable(audio_text_inference) @@ -327,10 +319,10 @@ def test_audio_temporal_annotations_integration( """Test that audio temporal annotations work correctly in the integration framework""" # Filter to only audio annotations audio_annotations = annotations_by_media_type[MediaType.Audio] - + # Verify we have the expected audio temporal annotations assert len(audio_annotations) == 4 # checklist, text, radio, text_entity - + # Check that temporal annotations have frame information for annotation in audio_annotations: if "frame" in annotation: @@ -338,7 +330,7 @@ def test_audio_temporal_annotations_integration( assert annotation["frame"] >= 0 # Verify frame values are in milliseconds (reasonable range for audio) assert annotation["frame"] <= 600000 # 10 minutes max - + # Test import with audio temporal annotations label_import = lb.LabelImport.create_from_objects( client, @@ -347,11 +339,11 @@ def test_audio_temporal_annotations_integration( audio_annotations, ) label_import.wait_until_done() - + # Verify import was successful assert label_import.state == AnnotationImportState.FINISHED assert len(label_import.errors) == 0 - + # Verify all annotations were imported successfully all_annotations = sorted([a["uuid"] for a in audio_annotations]) successful_annotations = sorted( From 943cb7370342c0e00c07b7943094643c57e5edbf Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Fri, 19 Sep 2025 11:28:13 -0700 Subject: [PATCH 016/103] chore: simplify --- .../data/serialization/ndjson/label.py | 80 +++++++-- .../serialization/ndjson/utils/__init__.py | 1 - .../ndjson/utils/temporal_processor.py | 166 ------------------ 3 files changed, 67 insertions(+), 180 deletions(-) delete mode 100644 libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py delete mode 100644 libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index fe80f2d74..cbb463671 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -48,7 +48,6 @@ NDVideoMasks, ) from .relationship import NDRelationship -from .utils.temporal_processor import AudioTemporalProcessor AnnotationType = Union[ NDObjectType, @@ -87,6 +86,46 @@ def _get_consecutive_frames( consecutive.append((group[0], group[-1])) return consecutive + @classmethod + def _get_audio_frame_ranges(cls, annotation_group: List[Union[AudioClassificationAnnotation, AudioObjectAnnotation]]) -> List[Tuple[int, int]]: + """Get frame ranges for audio annotations (simpler than video segments)""" + return [(ann.frame, getattr(ann, 'end_frame', None) or ann.frame) for ann in annotation_group] + + @classmethod + def _has_changing_values(cls, annotation_group: List[AudioClassificationAnnotation]) -> bool: + """Check if annotations have different values (multi-value per instance)""" + if len(annotation_group) <= 1: + return False + first_value = annotation_group[0].value.answer + return any(ann.value.answer != first_value for ann in annotation_group) + + @classmethod + def _create_multi_value_annotation(cls, annotation_group: List[AudioClassificationAnnotation], data): + """Create annotation with frame-value mapping for changing values""" + import json + + # Build frame data and mapping in one pass + frames_data = [] + frame_mapping = {} + + for ann in annotation_group: + start, end = ann.frame, getattr(ann, 'end_frame', None) or ann.frame + frames_data.append({"start": start, "end": end}) + frame_mapping[str(start)] = ann.value.answer + + # Create content structure + content = json.dumps({ + "frame_mapping": frame_mapping, + }) + + # Update template annotation + template = annotation_group[0] + from ...annotation_types.classification.classification import Text + template.value = Text(answer=content) + template.extra = {"frames": frames_data} + + yield NDClassification.from_common(template, data) + @classmethod def _get_segment_frame_ranges( cls, @@ -170,20 +209,35 @@ def _create_video_annotations( def _create_audio_annotations( cls, label: Label ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]: - """Create audio annotations using generic temporal processor + """Create audio annotations with multi-value support""" + audio_annotations = defaultdict(list) + + # Collect audio annotations + for annot in label.annotations: + if isinstance(annot, (AudioClassificationAnnotation, AudioObjectAnnotation)): + audio_annotations[annot.feature_schema_id or annot.name].append(annot) - Args: - label: Label containing audio annotations to be processed + for annotation_group in audio_annotations.values(): + frame_ranges = cls._get_audio_frame_ranges(annotation_group) + + # Process classifications + if isinstance(annotation_group[0], AudioClassificationAnnotation): + if cls._has_changing_values(annotation_group): + # For audio with changing values, create frame-value mapping + yield from cls._create_multi_value_annotation(annotation_group, label.data) + else: + # Standard processing for audio with same values + annotation = annotation_group[0] + frames_data = [{"start": start, "end": end} for start, end in frame_ranges] + annotation.extra.update({"frames": frames_data}) + yield NDClassification.from_common(annotation, label.data) + + # Process objects + elif isinstance(annotation_group[0], AudioObjectAnnotation): + # For audio objects, process individually (simpler than video segments) + for annotation in annotation_group: + yield NDObject.from_common(annotation, label.data) - Yields: - NDClassification or NDObject: Audio annotations in NDJSON format - """ - # Use processor with configurable behavior - processor = AudioTemporalProcessor( - group_text_annotations=True, # Group multiple TEXT annotations into one feature - enable_token_mapping=True, # Enable per-keyframe token content - ) - yield from processor.process_annotations(label) @classmethod def _create_non_video_annotations(cls, label: Label): diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py deleted file mode 100644 index 33f132b74..000000000 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Utils package for NDJSON serialization helpers diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py deleted file mode 100644 index 3eae9a1a4..000000000 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/utils/temporal_processor.py +++ /dev/null @@ -1,166 +0,0 @@ -""" -Generic temporal annotation processor for frame-based media (video, audio) -""" - -from abc import ABC, abstractmethod -from collections import defaultdict -from typing import Any, Dict, Generator, List, Union - -from ....annotation_types.label import Label -from ..classification import NDClassificationType, NDClassification -from ..objects import NDObject - - -class TemporalAnnotationProcessor(ABC): - """Abstract base class for processing temporal annotations (video, audio, etc.)""" - - @abstractmethod - def get_annotation_types(self) -> tuple: - """Return tuple of annotation types this processor handles""" - pass - - @abstractmethod - def should_group_annotations(self, annotation_group: List) -> bool: - """Determine if annotations should be grouped into one feature""" - pass - - @abstractmethod - def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: - """Extract frame data from annotation group""" - pass - - @abstractmethod - def prepare_grouped_content(self, annotation_group: List) -> Any: - """Prepare content for grouped annotations (may modify annotation.value)""" - pass - - def process_annotations( - self, label: Label - ) -> Generator[Union[NDClassificationType, Any], None, None]: - """Main processing method - generic for all temporal media""" - temporal_annotations = defaultdict(list) - classification_types, object_types = self.get_annotation_types() - - # Group annotations by feature name/schema - for annot in label.annotations: - if isinstance(annot, classification_types + object_types): - temporal_annotations[ - annot.feature_schema_id or annot.name - ].append(annot) - - # Process each group - for annotation_group in temporal_annotations.values(): - if isinstance(annotation_group[0], classification_types): - yield from self._process_classification_group( - annotation_group, label.data - ) - elif isinstance(annotation_group[0], object_types): - yield from self._process_object_group( - annotation_group, label.data - ) - - def _process_classification_group(self, annotation_group, data): - """Process classification annotations""" - if self.should_group_annotations(annotation_group): - # Group into single feature with multiple keyframes - annotation = annotation_group[0] # Use first as template - - # Build frame data - frames_data = self.build_frame_data(annotation_group) - - # Prepare content (may modify annotation.value) - self.prepare_grouped_content(annotation_group) - - # Update with frame data - annotation.extra = {"frames": frames_data} - yield NDClassification.from_common(annotation, data) - else: - # Process individually - for annotation in annotation_group: - frames_data = self.build_frame_data([annotation]) - if frames_data: - if not annotation.extra: - annotation.extra = {} - annotation.extra.update({"frames": frames_data}) - yield NDClassification.from_common(annotation, data) - - def _process_object_group(self, annotation_group, data): - """Process object annotations - default to individual processing""" - for annotation in annotation_group: - yield NDObject.from_common(annotation, data) - - -class AudioTemporalProcessor(TemporalAnnotationProcessor): - """Processor for audio temporal annotations""" - - def __init__( - self, - group_text_annotations: bool = True, - enable_token_mapping: bool = True, - ): - self.group_text_annotations = group_text_annotations - self.enable_token_mapping = enable_token_mapping - - def get_annotation_types(self) -> tuple: - from ....annotation_types.audio import ( - AudioClassificationAnnotation, - AudioObjectAnnotation, - ) - - return (AudioClassificationAnnotation,), (AudioObjectAnnotation,) - - def should_group_annotations(self, annotation_group: List) -> bool: - """Group TEXT classifications with multiple temporal instances""" - if not self.group_text_annotations: - return False - - from ....annotation_types.classification.classification import Text - - return ( - isinstance(annotation_group[0].value, Text) - and len(annotation_group) > 1 - and all(hasattr(ann, "frame") for ann in annotation_group) - ) - - def build_frame_data(self, annotation_group: List) -> List[Dict[str, Any]]: - """Extract frame ranges from audio annotations""" - frames_data = [] - for annotation in annotation_group: - if hasattr(annotation, "frame"): - frame = annotation.frame - end_frame = ( - annotation.end_frame - if hasattr(annotation, "end_frame") - and annotation.end_frame is not None - else frame - ) - frames_data.append({"start": frame, "end": end_frame}) - return frames_data - - def prepare_grouped_content(self, annotation_group: List) -> None: - """Prepare content for grouped audio annotations""" - from ....annotation_types.classification.classification import Text - - if ( - not isinstance(annotation_group[0].value, Text) - or not self.enable_token_mapping - ): - return - - # Build token mapping for TEXT annotations - import json - - all_content = [ann.value.answer for ann in annotation_group] - token_mapping = { - str(ann.frame): ann.value.answer for ann in annotation_group - } - - content_structure = json.dumps( - { - "default_text": " ".join(all_content), - "token_mapping": token_mapping, - } - ) - - # Update the template annotation - annotation_group[0].value = Text(answer=content_structure) From a838513434d33566bacee884ca9ed50dc1de0eab Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Fri, 19 Sep 2025 14:05:11 -0700 Subject: [PATCH 017/103] chore: update examples - all tests passing --- examples/annotation_import/audio.ipynb | 452 +++++++++++++++++++------ 1 file changed, 341 insertions(+), 111 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index 2463af769..f085c0f13 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,18 +1,16 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {}, "cells": [ { + "cell_type": "markdown", "metadata": {}, "source": [ - "", - " ", + "\n", + " \n", "\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n", @@ -24,10 +22,10 @@ "\n", "" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Audio Annotation Import\n", @@ -53,111 +51,188 @@ "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", "\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "* For information on what types of annotations are supported per data type, refer to this documentation:\n", " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "* Notes:\n", " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "%pip install -q \"labelbox[data]\"" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "import labelbox as lb\n", + "import uuid\n", + "import labelbox.types as lb_types" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Replace with your API key\n", "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Add your api key\n", + "API_KEY = \"\"\n", + "client = lb.Client(api_key=API_KEY)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Supported annotations for Audio" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "##### Classification free text #####\n", + "\n", + "text_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"text_audio\",\n", + " value=lb_types.Text(answer=\"free text audio annotation\"),\n", + ")\n", + "\n", + "text_annotation_ndjson = {\n", + " \"name\": \"text_audio\",\n", + " \"answer\": \"free text audio annotation\",\n", + "}" + ] }, { - "metadata": {}, - "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "##### Checklist Classification #######\n", + "\n", + "checklist_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"checklist_audio\",\n", + " value=lb_types.Checklist(answer=[\n", + " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", + " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", + " ]),\n", + ")\n", + "\n", + "checklist_annotation_ndjson = {\n", + " \"name\":\n", + " \"checklist_audio\",\n", + " \"answers\": [\n", + " {\n", + " \"name\": \"first_checklist_answer\"\n", + " },\n", + " {\n", + " \"name\": \"second_checklist_answer\"\n", + " },\n", + " ],\n", + "}" + ] }, { - "metadata": {}, - "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "######## Radio Classification ######\n", + "\n", + "radio_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"radio_audio\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", + " name=\"second_radio_answer\")),\n", + ")\n", + "\n", + "radio_annotation_ndjson = {\n", + " \"name\": \"radio_audio\",\n", + " \"answer\": {\n", + " \"name\": \"first_radio_answer\"\n", + " },\n", + "}" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Import data rows into Catalog" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create one Labelbox dataset\n", + "\n", + "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", + "\n", + "asset = {\n", + " \"row_data\":\n", + " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", + " \"global_key\":\n", + " global_key,\n", + "}\n", + "\n", + "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", + "task = dataset.create_data_rows([asset])\n", + "task.wait_till_done()\n", + "print(\"Errors:\", task.errors)\n", + "print(\"Failed data rows: \", task.failed_data_rows)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Create/select an ontology\n", @@ -165,186 +240,341 @@ "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", "\n", "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "ontology_builder = lb.OntologyBuilder(classifications=[\n", + " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", + " name=\"text_audio\"),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"checklist_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_checklist_answer\"),\n", + " lb.Option(value=\"second_checklist_answer\"),\n", + " ],\n", + " ),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"radio_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_radio_answer\"),\n", + " lb.Option(value=\"second_radio_answer\"),\n", + " ],\n", + " ),\n", + " # Temporal classification for token-level annotations\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.TEXT,\n", + " name=\"User Speaker\",\n", + " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", + " ),\n", + "])\n", + "\n", + "ontology = client.create_ontology(\n", + " \"Ontology Audio Annotations\",\n", + " ontology_builder.asdict(),\n", + " media_type=lb.MediaType.Audio,\n", + ")" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Step 3: Create a labeling project\n", "Connect the ontology to the labeling project" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create Labelbox project\n", + "project = client.create_project(name=\"audio_project\",\n", + " media_type=lb.MediaType.Audio)\n", + "\n", + "# Setup your ontology\n", + "project.setup_editor(\n", + " ontology) # Connect your ontology and editor to your project" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Setup Batches and Ontology\n", + "\n", + "# Create a batch to send to your MAL project\n", + "batch = project.create_batch(\n", + " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", + " global_keys=[\n", + " global_key\n", + " ], # Paginated collection of data row objects, list of data row ids or global keys\n", + " priority=5, # priority between 1(Highest) - 5(lowest)\n", + ")\n", + "\n", + "print(\"Batch: \", batch)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Create the annotations payload\n", "Create the annotations payload using the snippets of code above\n", "\n", "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Python annotation\n", "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [] }, { - "metadata": {}, - "source": "", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [] }, { - "metadata": {}, - "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "label = []\n", + "label.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", + " ))" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### NDJSON annotations \n", "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "label_ndjson = []\n", + "for annotations in [\n", + " text_annotation_ndjson,\n", + " checklist_annotation_ndjson,\n", + " radio_annotation_ndjson,\n", + "]:\n", + " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", + " label_ndjson.append(annotations)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Temporal Audio Annotations\n", "\n", "You can create temporal annotations for individual tokens (words) with precise timing:\n" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Define tokens with precise timing (from demo script)\n", + "tokens_data = [\n", + " (\"Hello\", 586, 770), # Hello: frames 586-770\n", + " (\"AI\", 771, 955), # AI: frames 771-955\n", + " (\"how\", 956, 1140), # how: frames 956-1140\n", + " (\"are\", 1141, 1325), # are: frames 1141-1325\n", + " (\"you\", 1326, 1510), # you: frames 1326-1510\n", + " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", + " (\"today\", 1696, 1880), # today: frames 1696-1880\n", + "]\n", + "\n", + "# Create temporal annotations for each token\n", + "temporal_annotations = []\n", + "for token, start_frame, end_frame in tokens_data:\n", + " token_annotation = lb_types.AudioClassificationAnnotation(\n", + " frame=start_frame,\n", + " end_frame=end_frame,\n", + " name=\"User Speaker\",\n", + " value=lb_types.Text(answer=token),\n", + " )\n", + " temporal_annotations.append(token_annotation)\n", + "\n", + "print(f\"Created {len(temporal_annotations)} temporal token annotations\")" + ] }, { - "metadata": {}, - "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create label with both regular and temporal annotations\n", + "label_with_temporal = []\n", + "label_with_temporal.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation] +\n", + " temporal_annotations,\n", + " ))\n", + "\n", + "print(\n", + " f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n", + ")\n", + "print(f\" - Regular annotations: 3\")\n", + "print(f\" - Temporal annotations: {len(temporal_annotations)}\")" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Model Assisted Labeling (MAL)\n", "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload temporal annotations via MAL\n", + "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label_with_temporal,\n", + ")\n", + "\n", + "temporal_upload_job.wait_until_done()\n", + "print(\"Temporal upload completed!\")\n", + "print(\"Errors:\", temporal_upload_job.errors)\n", + "print(\"Status:\", temporal_upload_job.statuses)" + ] }, { - "metadata": {}, - "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload our label using Model-Assisted Labeling\n", + "upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Label Import" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload label for this data row in project\n", + "upload_job = lb.LabelImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=\"label_import_job\" + str(uuid.uuid4()),\n", + " labels=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### Optional deletions for cleanup " - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# project.delete()\n# dataset.delete()", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# project.delete()\n", + "# dataset.delete()" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 0ca9cd652d6780a074e3accad91984102a8ab719 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 22 Sep 2025 13:51:36 -0700 Subject: [PATCH 018/103] chore: use start frame instead of frame --- .../labelbox/data/annotation_types/audio.py | 8 +- .../serialization/ndjson/classification.py | 2 +- .../data/serialization/ndjson/label.py | 6 +- .../tests/data/annotation_types/test_audio.py | 76 +++++++++---------- 4 files changed, 46 insertions(+), 46 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index 7a5c5f40c..3188f7c92 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -22,13 +22,13 @@ class AudioClassificationAnnotation(ClassificationAnnotation): name (Optional[str]): Name of the classification feature_schema_id (Optional[Cuid]): Feature schema identifier value (Union[Text, Checklist, Radio]): Classification value - frame (int): The frame index in milliseconds (e.g., 2500 = 2.5 seconds) + start_frame (int): The frame index in milliseconds (e.g., 2500 = 2.5 seconds) end_frame (Optional[int]): End frame in milliseconds (for time ranges) segment_index (Optional[int]): Index of audio segment this annotation belongs to extra (Dict[str, Any]): Additional metadata """ - frame: int + start_frame: int end_frame: Optional[int] = None segment_index: Optional[int] = None @@ -49,7 +49,7 @@ class AudioObjectAnnotation( name (Optional[str]): Name of the annotation feature_schema_id (Optional[Cuid]): Feature schema identifier value (Union[TextEntity, Geometry]): Localization or text content - frame (int): The frame index in milliseconds (e.g., 10000 = 10.0 seconds) + start_frame (int): The frame index in milliseconds (e.g., 10000 = 10.0 seconds) end_frame (Optional[int]): End frame in milliseconds (for time ranges) keyframe (bool): Whether this is a keyframe annotation (default: True) segment_index (Optional[int]): Index of audio segment this annotation belongs to @@ -57,7 +57,7 @@ class AudioObjectAnnotation( extra (Dict[str, Any]): Additional metadata """ - frame: int + start_frame: int end_frame: Optional[int] = None keyframe: bool = True segment_index: Optional[int] = None diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index 980457c74..786fe06ea 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -421,7 +421,7 @@ def to_common( for frame in annotation.frames: for idx in range(frame.start, frame.end + 1, 1): # Check if this is an audio annotation by looking at the extra data - # Audio annotations will have frame/end_frame in extra, video annotations won't + # Audio annotations will have start_frame/end_frame in extra, video annotations won't if ( hasattr(annotation, "extra") and annotation.extra diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index cbb463671..205d6fa75 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -89,7 +89,7 @@ def _get_consecutive_frames( @classmethod def _get_audio_frame_ranges(cls, annotation_group: List[Union[AudioClassificationAnnotation, AudioObjectAnnotation]]) -> List[Tuple[int, int]]: """Get frame ranges for audio annotations (simpler than video segments)""" - return [(ann.frame, getattr(ann, 'end_frame', None) or ann.frame) for ann in annotation_group] + return [(ann.start_frame, getattr(ann, 'end_frame', None) or ann.start_frame) for ann in annotation_group] @classmethod def _has_changing_values(cls, annotation_group: List[AudioClassificationAnnotation]) -> bool: @@ -109,7 +109,7 @@ def _create_multi_value_annotation(cls, annotation_group: List[AudioClassificati frame_mapping = {} for ann in annotation_group: - start, end = ann.frame, getattr(ann, 'end_frame', None) or ann.frame + start, end = ann.start_frame, getattr(ann, 'end_frame', None) or ann.start_frame frames_data.append({"start": start, "end": end}) frame_mapping[str(start)] = ann.value.answer @@ -199,7 +199,7 @@ def _create_video_annotations( for annotation in annotation_group: if ( annotation.keyframe - and start_frame <= annotation.frame <= end_frame + and start_frame <= annotation.start_frame <= end_frame ): segment.append(annotation) segments.append(segment) diff --git a/libs/labelbox/tests/data/annotation_types/test_audio.py b/libs/labelbox/tests/data/annotation_types/test_audio.py index 2703524f2..476383669 100644 --- a/libs/labelbox/tests/data/annotation_types/test_audio.py +++ b/libs/labelbox/tests/data/annotation_types/test_audio.py @@ -15,12 +15,12 @@ def test_audio_classification_creation(): """Test creating audio classification with direct frame specification""" annotation = AudioClassificationAnnotation( - frame=2500, # 2.5 seconds in milliseconds + start_frame=2500, # 2.5 seconds in milliseconds name="speaker_id", value=Radio(answer=ClassificationAnswer(name="john")), ) - assert annotation.frame == 2500 + assert annotation.start_frame == 2500 assert annotation.end_frame is None assert annotation.segment_index is None assert annotation.name == "speaker_id" @@ -31,13 +31,13 @@ def test_audio_classification_creation(): def test_audio_classification_with_time_range(): """Test creating audio classification with start and end frames""" annotation = AudioClassificationAnnotation( - frame=2500, # Start at 2.5 seconds + start_frame=2500, # Start at 2.5 seconds end_frame=4100, # End at 4.1 seconds name="speaker_id", value=Radio(answer=ClassificationAnswer(name="john")), ) - assert annotation.frame == 2500 + assert annotation.start_frame == 2500 assert annotation.end_frame == 4100 assert annotation.name == "speaker_id" @@ -45,14 +45,14 @@ def test_audio_classification_with_time_range(): def test_audio_classification_creation_with_segment(): """Test creating audio classification with segment index""" annotation = AudioClassificationAnnotation( - frame=10000, + start_frame=10000, end_frame=15000, name="language", value=Radio(answer=ClassificationAnswer(name="english")), segment_index=1, ) - assert annotation.frame == 10000 + assert annotation.start_frame == 10000 assert annotation.end_frame == 15000 assert annotation.segment_index == 1 @@ -60,12 +60,12 @@ def test_audio_classification_creation_with_segment(): def test_audio_classification_text_type(): """Test creating audio classification with Text value""" annotation = AudioClassificationAnnotation( - frame=5000, # 5.0 seconds + start_frame=5000, # 5.0 seconds name="quality", value=Text(answer="excellent"), ) - assert annotation.frame == 5000 + assert annotation.start_frame == 5000 assert annotation.name == "quality" assert isinstance(annotation.value, Text) assert annotation.value.answer == "excellent" @@ -74,7 +74,7 @@ def test_audio_classification_text_type(): def test_audio_object_creation(): """Test creating audio object annotation""" annotation = AudioObjectAnnotation( - frame=10000, + start_frame=10000, end_frame=12500, name="transcription", value=lb_types.TextEntity( @@ -82,7 +82,7 @@ def test_audio_object_creation(): ), # "Hello world" has 11 characters ) - assert annotation.frame == 10000 + assert annotation.start_frame == 10000 assert annotation.end_frame == 12500 assert annotation.keyframe is True assert annotation.segment_index is None @@ -95,13 +95,13 @@ def test_audio_object_creation(): def test_audio_object_creation_with_classifications(): """Test creating audio object with sub-classifications""" sub_classification = AudioClassificationAnnotation( - frame=10000, + start_frame=10000, name="confidence", value=Radio(answer=ClassificationAnswer(name="high")), ) annotation = AudioObjectAnnotation( - frame=10000, + start_frame=10000, end_frame=12500, name="transcription", value=lb_types.TextEntity(start=0, end=11), @@ -110,20 +110,20 @@ def test_audio_object_creation_with_classifications(): assert len(annotation.classifications) == 1 assert annotation.classifications[0].name == "confidence" - assert annotation.classifications[0].frame == 10000 + assert annotation.classifications[0].start_frame == 10000 def test_audio_object_direct_creation(): """Test creating audio object directly with various options""" annotation = AudioObjectAnnotation( - frame=7500, # 7.5 seconds + start_frame=7500, # 7.5 seconds name="sound_event", value=lb_types.TextEntity(start=0, end=11), keyframe=False, segment_index=2, ) - assert annotation.frame == 7500 + assert annotation.start_frame == 7500 assert annotation.end_frame is None assert annotation.keyframe is False assert annotation.segment_index == 2 @@ -136,12 +136,12 @@ def test_frame_precision(): for milliseconds in test_cases: annotation = AudioClassificationAnnotation( - frame=milliseconds, + start_frame=milliseconds, end_frame=milliseconds + 1000, name="test", value=Text(answer="test"), ) - assert annotation.frame == milliseconds + assert annotation.start_frame == milliseconds assert annotation.end_frame == milliseconds + 1000 @@ -149,14 +149,14 @@ def test_audio_label_integration(): """Test audio annotations work with Label container""" # Create audio annotations speaker_annotation = AudioClassificationAnnotation( - frame=1000, + start_frame=1000, end_frame=2000, name="speaker", value=Radio(answer=ClassificationAnswer(name="john")), ) transcription_annotation = AudioObjectAnnotation( - frame=1000, + start_frame=1000, end_frame=2000, name="transcription", value=lb_types.TextEntity(start=0, end=5), @@ -194,7 +194,7 @@ def test_audio_annotation_validation(): # Test frame must be int with pytest.raises(ValueError): AudioClassificationAnnotation( - frame="invalid", # Should be int + start_frame="invalid", # Should be int name="test", value=Text(answer="test"), ) @@ -205,7 +205,7 @@ def test_audio_annotation_extra_fields(): extra_data = {"source": "automatic", "confidence_score": 0.95} annotation = AudioClassificationAnnotation( - frame=3000, name="quality", value=Text(answer="good"), extra=extra_data + start_frame=3000, name="quality", value=Text(answer="good"), extra=extra_data ) assert annotation.extra["source"] == "automatic" @@ -215,7 +215,7 @@ def test_audio_annotation_extra_fields(): def test_audio_annotation_feature_schema(): """Test audio annotations with feature schema IDs""" annotation = AudioClassificationAnnotation( - frame=4000, + start_frame=4000, name="language", value=Radio(answer=ClassificationAnswer(name="spanish")), feature_schema_id="1234567890123456789012345", @@ -228,14 +228,14 @@ def test_audio_annotation_mixed_types(): """Test label with mixed audio and other annotation types""" # Audio annotation audio_annotation = AudioClassificationAnnotation( - frame=2000, + start_frame=2000, name="speaker", value=Radio(answer=ClassificationAnswer(name="john")), ) # Video annotation video_annotation = lb_types.VideoClassificationAnnotation( - frame=10, name="quality", value=Text(answer="good") + start_frame=10, name="quality", value=Text(answer="good") ) # Image annotation @@ -280,7 +280,7 @@ def test_audio_annotation_mixed_types(): def test_audio_annotation_serialization(): """Test audio annotations can be serialized to dict""" annotation = AudioClassificationAnnotation( - frame=6000, + start_frame=6000, end_frame=8000, name="emotion", value=Radio(answer=ClassificationAnswer(name="happy")), @@ -317,7 +317,7 @@ def test_audio_annotation_from_dict(): annotation = AudioClassificationAnnotation(**annotation_data) - assert annotation.frame == 7000 + assert annotation.start_frame == 7000 assert annotation.end_frame == 9000 assert annotation.name == "topic" assert annotation.segment_index == 2 @@ -328,32 +328,32 @@ def test_audio_annotation_edge_cases(): """Test audio annotation edge cases""" # Test very long audio (many hours) long_annotation = AudioClassificationAnnotation( - frame=3600000, # 1 hour in milliseconds + start_frame=3600000, # 1 hour in milliseconds end_frame=7200000, # 2 hours in milliseconds name="long_audio", value=Text(answer="very long"), ) - assert long_annotation.frame == 3600000 + assert long_annotation.start_frame == 3600000 assert long_annotation.end_frame == 7200000 # Test very short audio (milliseconds) short_annotation = AudioClassificationAnnotation( - frame=1, # 1 millisecond + start_frame=1, # 1 millisecond end_frame=2, # 2 milliseconds name="short_audio", value=Text(answer="very short"), ) - assert short_annotation.frame == 1 + assert short_annotation.start_frame == 1 assert short_annotation.end_frame == 2 # Test zero time zero_annotation = AudioClassificationAnnotation( - frame=0, name="zero_time", value=Text(answer="zero") + start_frame=0, name="zero_time", value=Text(answer="zero") ) - assert zero_annotation.frame == 0 + assert zero_annotation.start_frame == 0 assert zero_annotation.end_frame is None @@ -368,7 +368,7 @@ def test_temporal_annotation_grouping(): end_frame = start_frame + 900 # 900ms duration each annotation = AudioClassificationAnnotation( - frame=start_frame, + start_frame=start_frame, end_frame=end_frame, name="tokens", # Same name for grouping value=Text(answer=token), @@ -380,8 +380,8 @@ def test_temporal_annotation_grouping(): assert all(ann.name == "tokens" for ann in annotations) assert annotations[0].value.answer == "Hello" assert annotations[1].value.answer == "world" - assert annotations[0].frame == 0 - assert annotations[1].frame == 1000 + assert annotations[0].start_frame == 0 + assert annotations[1].start_frame == 1000 assert annotations[0].end_frame == 900 assert annotations[1].end_frame == 1900 @@ -390,7 +390,7 @@ def test_audio_object_types(): """Test different types of audio object annotations""" # Text entity (transcription) text_obj = AudioObjectAnnotation( - frame=1000, + start_frame=1000, name="transcription", value=TextEntity(start=0, end=5), # "hello" ) @@ -401,7 +401,7 @@ def test_audio_object_types(): # Test with keyframe and segment settings keyframe_obj = AudioObjectAnnotation( - frame=2000, + start_frame=2000, end_frame=3000, name="segment", value=TextEntity(start=10, end=15), @@ -411,5 +411,5 @@ def test_audio_object_types(): assert keyframe_obj.keyframe is True assert keyframe_obj.segment_index == 1 - assert keyframe_obj.frame == 2000 + assert keyframe_obj.start_frame == 2000 assert keyframe_obj.end_frame == 3000 From 78615372a90cfb8a599b1961dd0aa3e7912ab741 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 22 Sep 2025 13:58:50 -0700 Subject: [PATCH 019/103] chore: remove audio object annotation --- .../data/annotation_types/__init__.py | 1 - .../labelbox/data/annotation_types/audio.py | 33 ------ .../labelbox/data/annotation_types/label.py | 11 +- .../data/serialization/ndjson/label.py | 11 +- .../data/serialization/ndjson/objects.py | 44 -------- .../tests/data/annotation_types/test_audio.py | 106 +----------------- 6 files changed, 9 insertions(+), 197 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index 455535c09..9f59b5197 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -20,7 +20,6 @@ from .video import VideoMaskAnnotation from .audio import AudioClassificationAnnotation -from .audio import AudioObjectAnnotation from .ner import ConversationEntity from .ner import DocumentEntity diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index 3188f7c92..b2f36d654 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -2,11 +2,6 @@ from labelbox.data.annotation_types.annotation import ( ClassificationAnnotation, - ObjectAnnotation, -) -from labelbox.data.mixins import ( - ConfidenceNotSupportedMixin, - CustomMetricsNotSupportedMixin, ) @@ -33,31 +28,3 @@ class AudioClassificationAnnotation(ClassificationAnnotation): segment_index: Optional[int] = None -class AudioObjectAnnotation( - ObjectAnnotation, - ConfidenceNotSupportedMixin, - CustomMetricsNotSupportedMixin, -): - """Audio object annotation for specific time range - - Examples: - - Transcription: "Hello world" from 2500ms to 4100ms - - Sound events: "Dog barking" from 10000ms to 12000ms - - Audio segments with metadata - - Args: - name (Optional[str]): Name of the annotation - feature_schema_id (Optional[Cuid]): Feature schema identifier - value (Union[TextEntity, Geometry]): Localization or text content - start_frame (int): The frame index in milliseconds (e.g., 10000 = 10.0 seconds) - end_frame (Optional[int]): End frame in milliseconds (for time ranges) - keyframe (bool): Whether this is a keyframe annotation (default: True) - segment_index (Optional[int]): Index of audio segment this annotation belongs to - classifications (Optional[List[ClassificationAnnotation]]): Optional sub-classifications - extra (Dict[str, Any]): Additional metadata - """ - - start_frame: int - end_frame: Optional[int] = None - keyframe: bool = True - segment_index: Optional[int] = None diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index b01d51d54..b50416b6a 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -13,7 +13,7 @@ from .metrics import ScalarMetric, ConfusionMatrixMetric from .video import VideoClassificationAnnotation from .video import VideoObjectAnnotation, VideoMaskAnnotation -from .audio import AudioClassificationAnnotation, AudioObjectAnnotation +from .audio import AudioClassificationAnnotation from .mmc import MessageEvaluationTaskAnnotation from pydantic import BaseModel, field_validator @@ -46,7 +46,6 @@ class Label(BaseModel): ObjectAnnotation, VideoMaskAnnotation, AudioClassificationAnnotation, - AudioObjectAnnotation, ScalarMetric, ConfusionMatrixMetric, RelationshipAnnotation, @@ -91,7 +90,7 @@ def frame_annotations( def audio_annotations_by_frame( self, ) -> Dict[ - int, List[Union[AudioObjectAnnotation, AudioClassificationAnnotation]] + int, List[AudioClassificationAnnotation] ]: """Get audio annotations organized by frame (millisecond) @@ -100,15 +99,15 @@ def audio_annotations_by_frame( Example: >>> label.audio_annotations_by_frame() - {2500: [AudioClassificationAnnotation(...)], 10000: [AudioObjectAnnotation(...)]} + {2500: [AudioClassificationAnnotation(...)]} """ frame_dict = defaultdict(list) for annotation in self.annotations: if isinstance( annotation, - (AudioObjectAnnotation, AudioClassificationAnnotation), + AudioClassificationAnnotation, ): - frame_dict[annotation.frame].append(annotation) + frame_dict[annotation.start_frame].append(annotation) return dict(frame_dict) def add_url_to_masks(self, signer) -> "Label": diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 205d6fa75..444b0ab5b 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -27,7 +27,6 @@ from typing import List from ...annotation_types.audio import ( AudioClassificationAnnotation, - AudioObjectAnnotation, ) from labelbox.types import DocumentRectangle, DocumentEntity from .classification import ( @@ -87,7 +86,7 @@ def _get_consecutive_frames( return consecutive @classmethod - def _get_audio_frame_ranges(cls, annotation_group: List[Union[AudioClassificationAnnotation, AudioObjectAnnotation]]) -> List[Tuple[int, int]]: + def _get_audio_frame_ranges(cls, annotation_group: List[AudioClassificationAnnotation]) -> List[Tuple[int, int]]: """Get frame ranges for audio annotations (simpler than video segments)""" return [(ann.start_frame, getattr(ann, 'end_frame', None) or ann.start_frame) for ann in annotation_group] @@ -214,7 +213,7 @@ def _create_audio_annotations( # Collect audio annotations for annot in label.annotations: - if isinstance(annot, (AudioClassificationAnnotation, AudioObjectAnnotation)): + if isinstance(annot, AudioClassificationAnnotation): audio_annotations[annot.feature_schema_id or annot.name].append(annot) for annotation_group in audio_annotations.values(): @@ -232,11 +231,6 @@ def _create_audio_annotations( annotation.extra.update({"frames": frames_data}) yield NDClassification.from_common(annotation, label.data) - # Process objects - elif isinstance(annotation_group[0], AudioObjectAnnotation): - # For audio objects, process individually (simpler than video segments) - for annotation in annotation_group: - yield NDObject.from_common(annotation, label.data) @classmethod @@ -251,7 +245,6 @@ def _create_non_video_annotations(cls, label: Label): VideoObjectAnnotation, VideoMaskAnnotation, AudioClassificationAnnotation, - AudioObjectAnnotation, RelationshipAnnotation, ), ) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py index 51825cd4b..55d6b5e62 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/objects.py @@ -14,9 +14,6 @@ from labelbox.data.annotation_types.video import ( VideoObjectAnnotation, ) -from labelbox.data.annotation_types.audio import ( - AudioObjectAnnotation, -) from labelbox.data.mixins import ( ConfidenceMixin, CustomMetric, @@ -718,7 +715,6 @@ def from_common( ObjectAnnotation, List[List[VideoObjectAnnotation]], VideoMaskAnnotation, - AudioObjectAnnotation, ], data: GenericDataRowData, ) -> Union[ @@ -746,9 +742,6 @@ def from_common( return obj.from_common(**args) elif obj == NDVideoMasks: return obj.from_common(annotation, data) - elif isinstance(annotation, AudioObjectAnnotation): - # Handle audio object annotation like single video frame - return cls._serialize_audio_object_annotation(annotation, data) subclasses = [ NDSubclassification.from_common(annot) @@ -772,43 +765,6 @@ def from_common( **optional_kwargs, ) - @classmethod - def _serialize_audio_object_annotation( - cls, annotation: AudioObjectAnnotation, data: GenericDataRowData - ): - """Serialize audio object annotation with temporal information - - Args: - annotation: Audio object annotation to process - data: Data row data - - Returns: - NDObject: Serialized audio object annotation - """ - # Get the appropriate NDObject subclass based on the annotation value type - obj = cls.lookup_object(annotation) - - # Process sub-classifications if any - subclasses = [ - NDSubclassification.from_common(annot) - for annot in annotation.classifications - ] - - # Add frame information to extra (milliseconds) - extra = annotation.extra.copy() if annotation.extra else {} - extra.update({"frame": annotation.frame}) - - # Create the NDObject with frame information - return obj.from_common( - str(annotation._uuid), - annotation.value, - subclasses, - annotation.name, - annotation.feature_schema_id, - extra, - data, - ) - @staticmethod def lookup_object( annotation: Union[ObjectAnnotation, List], diff --git a/libs/labelbox/tests/data/annotation_types/test_audio.py b/libs/labelbox/tests/data/annotation_types/test_audio.py index 476383669..ef818cfc7 100644 --- a/libs/labelbox/tests/data/annotation_types/test_audio.py +++ b/libs/labelbox/tests/data/annotation_types/test_audio.py @@ -2,7 +2,6 @@ import labelbox.types as lb_types from labelbox.data.annotation_types.audio import ( AudioClassificationAnnotation, - AudioObjectAnnotation, ) from labelbox.data.annotation_types.classification.classification import ( ClassificationAnswer, @@ -71,64 +70,6 @@ def test_audio_classification_text_type(): assert annotation.value.answer == "excellent" -def test_audio_object_creation(): - """Test creating audio object annotation""" - annotation = AudioObjectAnnotation( - start_frame=10000, - end_frame=12500, - name="transcription", - value=lb_types.TextEntity( - start=0, end=11 - ), # "Hello world" has 11 characters - ) - - assert annotation.start_frame == 10000 - assert annotation.end_frame == 12500 - assert annotation.keyframe is True - assert annotation.segment_index is None - assert annotation.name == "transcription" - assert isinstance(annotation.value, lb_types.TextEntity) - assert annotation.value.start == 0 - assert annotation.value.end == 11 - - -def test_audio_object_creation_with_classifications(): - """Test creating audio object with sub-classifications""" - sub_classification = AudioClassificationAnnotation( - start_frame=10000, - name="confidence", - value=Radio(answer=ClassificationAnswer(name="high")), - ) - - annotation = AudioObjectAnnotation( - start_frame=10000, - end_frame=12500, - name="transcription", - value=lb_types.TextEntity(start=0, end=11), - classifications=[sub_classification], - ) - - assert len(annotation.classifications) == 1 - assert annotation.classifications[0].name == "confidence" - assert annotation.classifications[0].start_frame == 10000 - - -def test_audio_object_direct_creation(): - """Test creating audio object directly with various options""" - annotation = AudioObjectAnnotation( - start_frame=7500, # 7.5 seconds - name="sound_event", - value=lb_types.TextEntity(start=0, end=11), - keyframe=False, - segment_index=2, - ) - - assert annotation.start_frame == 7500 - assert annotation.end_frame is None - assert annotation.keyframe is False - assert annotation.segment_index == 2 - - def test_frame_precision(): """Test frame values maintain precision""" # Test various time values in milliseconds @@ -155,21 +96,14 @@ def test_audio_label_integration(): value=Radio(answer=ClassificationAnswer(name="john")), ) - transcription_annotation = AudioObjectAnnotation( - start_frame=1000, - end_frame=2000, - name="transcription", - value=lb_types.TextEntity(start=0, end=5), - ) - # Create label with audio annotations label = lb_types.Label( data={"global_key": "audio_file.mp3"}, - annotations=[speaker_annotation, transcription_annotation], + annotations=[speaker_annotation], ) # Verify annotations are accessible - assert len(label.annotations) == 2 + assert len(label.annotations) == 1 # Check annotation types audio_classifications = [ @@ -177,16 +111,9 @@ def test_audio_label_integration(): for ann in label.annotations if isinstance(ann, AudioClassificationAnnotation) ] - audio_objects = [ - ann - for ann in label.annotations - if isinstance(ann, AudioObjectAnnotation) - ] assert len(audio_classifications) == 1 - assert len(audio_objects) == 1 assert audio_classifications[0].name == "speaker" - assert audio_objects[0].name == "transcription" def test_audio_annotation_validation(): @@ -384,32 +311,3 @@ def test_temporal_annotation_grouping(): assert annotations[1].start_frame == 1000 assert annotations[0].end_frame == 900 assert annotations[1].end_frame == 1900 - - -def test_audio_object_types(): - """Test different types of audio object annotations""" - # Text entity (transcription) - text_obj = AudioObjectAnnotation( - start_frame=1000, - name="transcription", - value=TextEntity(start=0, end=5), # "hello" - ) - - assert isinstance(text_obj.value, TextEntity) - assert text_obj.value.start == 0 - assert text_obj.value.end == 5 - - # Test with keyframe and segment settings - keyframe_obj = AudioObjectAnnotation( - start_frame=2000, - end_frame=3000, - name="segment", - value=TextEntity(start=10, end=15), - keyframe=True, - segment_index=1, - ) - - assert keyframe_obj.keyframe is True - assert keyframe_obj.segment_index == 1 - assert keyframe_obj.start_frame == 2000 - assert keyframe_obj.end_frame == 3000 From 6c3c50a3de83e5104398e5e48f0fb2f917e63fc5 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 22 Sep 2025 14:01:28 -0700 Subject: [PATCH 020/103] chore: change class shape for text and radio/checklist --- .../labelbox/data/annotation_types/audio.py | 23 ++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index b2f36d654..bb4072c90 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -24,7 +24,28 @@ class AudioClassificationAnnotation(ClassificationAnnotation): """ start_frame: int - end_frame: Optional[int] = None segment_index: Optional[int] = None +class AudioTextClassificationAnnotation(ClassificationAnnotation): + """Audio classification for specific time range + + Examples: + - Speaker identification from 2500ms to 4100ms + - Audio quality assessment for a segment + - Language detection for audio segments + + Args: + name (Optional[str]): Name of the classification + feature_schema_id (Optional[Cuid]): Feature schema identifier + value (Union[Text, Checklist, Radio]): Classification value + start_frame (int): The frame index in milliseconds (e.g., 2500 = 2.5 seconds) + end_frame (Optional[int]): End frame in milliseconds (for time ranges) + segment_index (Optional[int]): Index of audio segment this annotation belongs to + extra (Dict[str, Any]): Additional metadata + """ + + start_frame: int + end_frame: int = None + + From 68773cfdb0fc93f6e4b7f15eb2354f0b7c87b870 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 25 Sep 2025 15:46:51 -0700 Subject: [PATCH 021/103] chore: stan comments --- .../labelbox/data/annotation_types/audio.py | 22 +++++- .../labelbox/data/annotation_types/label.py | 35 +++------- .../serialization/ndjson/classification.py | 32 ++------- .../data/serialization/ndjson/label.py | 67 ++++--------------- 4 files changed, 46 insertions(+), 110 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index bb4072c90..14c9265fd 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -1,4 +1,5 @@ from typing import Optional +from pydantic import Field, AliasChoices from labelbox.data.annotation_types.annotation import ( ClassificationAnnotation, @@ -23,7 +24,15 @@ class AudioClassificationAnnotation(ClassificationAnnotation): extra (Dict[str, Any]): Additional metadata """ - start_frame: int + start_frame: int = Field( + validation_alias=AliasChoices("start_frame", "frame"), + serialization_alias="frame", + ) + end_frame: Optional[int] = Field( + default=None, + validation_alias=AliasChoices("end_frame", "endFrame"), + serialization_alias="end_frame", + ) segment_index: Optional[int] = None @@ -45,7 +54,14 @@ class AudioTextClassificationAnnotation(ClassificationAnnotation): extra (Dict[str, Any]): Additional metadata """ - start_frame: int - end_frame: int = None + start_frame: int = Field( + validation_alias=AliasChoices("start_frame", "frame"), + serialization_alias="frame", + ) + end_frame: Optional[int] = Field( + default=None, + validation_alias=AliasChoices("end_frame", "endFrame"), + serialization_alias="end_frame", + ) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index b50416b6a..228512a5d 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -77,36 +77,21 @@ def _get_annotations_by_type(self, annotation_type): def frame_annotations( self, - ) -> Dict[str, Union[VideoObjectAnnotation, VideoClassificationAnnotation]]: - frame_dict = defaultdict(list) - for annotation in self.annotations: - if isinstance( - annotation, - (VideoObjectAnnotation, VideoClassificationAnnotation), - ): - frame_dict[annotation.frame].append(annotation) - return frame_dict - - def audio_annotations_by_frame( - self, - ) -> Dict[ - int, List[AudioClassificationAnnotation] - ]: - """Get audio annotations organized by frame (millisecond) - + ) -> Dict[int, Union[VideoObjectAnnotation, VideoClassificationAnnotation, AudioClassificationAnnotation]]: + """Get temporal annotations organized by frame + Returns: - Dict[int, List]: Dictionary mapping frame (milliseconds) to list of audio annotations - + Dict[int, List]: Dictionary mapping frame (milliseconds) to list of temporal annotations + Example: - >>> label.audio_annotations_by_frame() - {2500: [AudioClassificationAnnotation(...)]} + >>> label.frame_annotations() + {2500: [VideoClassificationAnnotation(...), AudioClassificationAnnotation(...)]} """ frame_dict = defaultdict(list) for annotation in self.annotations: - if isinstance( - annotation, - AudioClassificationAnnotation, - ): + if isinstance(annotation, (VideoObjectAnnotation, VideoClassificationAnnotation)): + frame_dict[annotation.frame].append(annotation) + elif isinstance(annotation, AudioClassificationAnnotation): frame_dict[annotation.start_frame].append(annotation) return dict(frame_dict) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index 786fe06ea..3f67c511a 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -12,7 +12,6 @@ from ...annotation_types.annotation import ClassificationAnnotation from ...annotation_types.video import VideoClassificationAnnotation -from ...annotation_types.audio import AudioClassificationAnnotation from ...annotation_types.llm_prompt_response.prompt import ( PromptClassificationAnnotation, PromptText, @@ -401,11 +400,7 @@ class NDClassification: @staticmethod def to_common( annotation: "NDClassificationType", - ) -> Union[ - ClassificationAnnotation, - VideoClassificationAnnotation, - AudioClassificationAnnotation, - ]: + ) -> Union[ClassificationAnnotation, VideoClassificationAnnotation]: common = ClassificationAnnotation( value=annotation.to_common(), name=annotation.name, @@ -420,26 +415,11 @@ def to_common( results = [] for frame in annotation.frames: for idx in range(frame.start, frame.end + 1, 1): - # Check if this is an audio annotation by looking at the extra data - # Audio annotations will have start_frame/end_frame in extra, video annotations won't - if ( - hasattr(annotation, "extra") - and annotation.extra - and "frames" in annotation.extra - ): - # This is likely an audio temporal annotation - results.append( - AudioClassificationAnnotation( - frame=idx, **common.model_dump(exclude_none=True) - ) - ) - else: - # This is a video temporal annotation - results.append( - VideoClassificationAnnotation( - frame=idx, **common.model_dump(exclude_none=True) - ) + results.append( + VideoClassificationAnnotation( + frame=idx, **common.model_dump(exclude_none=True) ) + ) return results @classmethod @@ -448,7 +428,6 @@ def from_common( annotation: Union[ ClassificationAnnotation, VideoClassificationAnnotation, - AudioClassificationAnnotation, ], data: GenericDataRowData, ) -> Union[NDTextSubclass, NDChecklistSubclass, NDRadioSubclass]: @@ -473,7 +452,6 @@ def lookup_classification( annotation: Union[ ClassificationAnnotation, VideoClassificationAnnotation, - AudioClassificationAnnotation, ], ) -> Union[NDText, NDChecklist, NDRadio]: return {Text: NDText, Checklist: NDChecklist, Radio: NDRadio}.get( diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 444b0ab5b..f0b32b076 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -85,45 +85,6 @@ def _get_consecutive_frames( consecutive.append((group[0], group[-1])) return consecutive - @classmethod - def _get_audio_frame_ranges(cls, annotation_group: List[AudioClassificationAnnotation]) -> List[Tuple[int, int]]: - """Get frame ranges for audio annotations (simpler than video segments)""" - return [(ann.start_frame, getattr(ann, 'end_frame', None) or ann.start_frame) for ann in annotation_group] - - @classmethod - def _has_changing_values(cls, annotation_group: List[AudioClassificationAnnotation]) -> bool: - """Check if annotations have different values (multi-value per instance)""" - if len(annotation_group) <= 1: - return False - first_value = annotation_group[0].value.answer - return any(ann.value.answer != first_value for ann in annotation_group) - - @classmethod - def _create_multi_value_annotation(cls, annotation_group: List[AudioClassificationAnnotation], data): - """Create annotation with frame-value mapping for changing values""" - import json - - # Build frame data and mapping in one pass - frames_data = [] - frame_mapping = {} - - for ann in annotation_group: - start, end = ann.start_frame, getattr(ann, 'end_frame', None) or ann.start_frame - frames_data.append({"start": start, "end": end}) - frame_mapping[str(start)] = ann.value.answer - - # Create content structure - content = json.dumps({ - "frame_mapping": frame_mapping, - }) - - # Update template annotation - template = annotation_group[0] - from ...annotation_types.classification.classification import Text - template.value = Text(answer=content) - template.extra = {"frames": frames_data} - - yield NDClassification.from_common(template, data) @classmethod def _get_segment_frame_ranges( @@ -208,28 +169,24 @@ def _create_video_annotations( def _create_audio_annotations( cls, label: Label ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]: - """Create audio annotations with multi-value support""" + """Create audio annotations serialized in Video NDJSON classification format.""" audio_annotations = defaultdict(list) - - # Collect audio annotations + + # Collect audio annotations by name/schema_id for annot in label.annotations: if isinstance(annot, AudioClassificationAnnotation): audio_annotations[annot.feature_schema_id or annot.name].append(annot) for annotation_group in audio_annotations.values(): - frame_ranges = cls._get_audio_frame_ranges(annotation_group) - - # Process classifications - if isinstance(annotation_group[0], AudioClassificationAnnotation): - if cls._has_changing_values(annotation_group): - # For audio with changing values, create frame-value mapping - yield from cls._create_multi_value_annotation(annotation_group, label.data) - else: - # Standard processing for audio with same values - annotation = annotation_group[0] - frames_data = [{"start": start, "end": end} for start, end in frame_ranges] - annotation.extra.update({"frames": frames_data}) - yield NDClassification.from_common(annotation, label.data) + # Simple grouping: one NDJSON entry per annotation group (same as video) + annotation = annotation_group[0] + frames_data = [] + for ann in annotation_group: + start = ann.start_frame + end = getattr(ann, "end_frame", None) or ann.start_frame + frames_data.append({"start": start, "end": end}) + annotation.extra.update({"frames": frames_data}) + yield NDClassification.from_common(annotation, label.data) From 58b30f7a965ecf93c0fe75f390bde25d90e2f6dd Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Fri, 26 Sep 2025 11:45:12 -0700 Subject: [PATCH 022/103] chore: top level + nested working --- .../labelbox/data/annotation_types/audio.py | 32 +------- .../serialization/ndjson/classification.py | 22 +----- .../data/serialization/ndjson/label.py | 78 ++++++++++++++++--- 3 files changed, 70 insertions(+), 62 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index 14c9265fd..5043a91f8 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -26,7 +26,7 @@ class AudioClassificationAnnotation(ClassificationAnnotation): start_frame: int = Field( validation_alias=AliasChoices("start_frame", "frame"), - serialization_alias="frame", + serialization_alias="startframe", ) end_frame: Optional[int] = Field( default=None, @@ -35,33 +35,3 @@ class AudioClassificationAnnotation(ClassificationAnnotation): ) segment_index: Optional[int] = None - -class AudioTextClassificationAnnotation(ClassificationAnnotation): - """Audio classification for specific time range - - Examples: - - Speaker identification from 2500ms to 4100ms - - Audio quality assessment for a segment - - Language detection for audio segments - - Args: - name (Optional[str]): Name of the classification - feature_schema_id (Optional[Cuid]): Feature schema identifier - value (Union[Text, Checklist, Radio]): Classification value - start_frame (int): The frame index in milliseconds (e.g., 2500 = 2.5 seconds) - end_frame (Optional[int]): End frame in milliseconds (for time ranges) - segment_index (Optional[int]): Index of audio segment this annotation belongs to - extra (Dict[str, Any]): Additional metadata - """ - - start_frame: int = Field( - validation_alias=AliasChoices("start_frame", "frame"), - serialization_alias="frame", - ) - end_frame: Optional[int] = Field( - default=None, - validation_alias=AliasChoices("end_frame", "endFrame"), - serialization_alias="end_frame", - ) - - diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index 3f67c511a..00cb91aa0 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -60,22 +60,6 @@ def serialize_model(self, handler): return res -class FrameLocation(BaseModel): - end: int - start: int - - -class VideoSupported(BaseModel): - # Note that frames are only allowed as top level inferences for video - frames: Optional[List[FrameLocation]] = None - - @model_serializer(mode="wrap") - def serialize_model(self, handler): - res = handler(self) - # This means these are no video frames .. - if self.frames is None: - res.pop("frames") - return res class NDTextSubclass(NDAnswer): @@ -223,7 +207,7 @@ def from_common( # ====== End of subclasses -class NDText(NDAnnotation, NDTextSubclass, VideoSupported): +class NDText(NDAnnotation, NDTextSubclass): @classmethod def from_common( cls, @@ -249,7 +233,7 @@ def from_common( ) -class NDChecklist(NDAnnotation, NDChecklistSubclass, VideoSupported): +class NDChecklist(NDAnnotation, NDChecklistSubclass): @model_serializer(mode="wrap") def serialize_model(self, handler): res = handler(self) @@ -296,7 +280,7 @@ def from_common( ) -class NDRadio(NDAnnotation, NDRadioSubclass, VideoSupported): +class NDRadio(NDAnnotation, NDRadioSubclass): @classmethod def from_common( cls, diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index f0b32b076..1dec5934e 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -2,7 +2,7 @@ import copy from itertools import groupby from operator import itemgetter -from typing import Generator, List, Tuple, Union +from typing import Any, Dict, Generator, List, Tuple, Union from uuid import uuid4 from pydantic import BaseModel @@ -168,8 +168,8 @@ def _create_video_annotations( @classmethod def _create_audio_annotations( cls, label: Label - ) -> Generator[Union[NDChecklistSubclass, NDRadioSubclass], None, None]: - """Create audio annotations serialized in Video NDJSON classification format.""" + ) -> Generator[BaseModel, None, None]: + """Create audio annotations grouped by classification name in v2.py format.""" audio_annotations = defaultdict(list) # Collect audio annotations by name/schema_id @@ -177,16 +177,70 @@ def _create_audio_annotations( if isinstance(annot, AudioClassificationAnnotation): audio_annotations[annot.feature_schema_id or annot.name].append(annot) - for annotation_group in audio_annotations.values(): - # Simple grouping: one NDJSON entry per annotation group (same as video) - annotation = annotation_group[0] - frames_data = [] + # Create v2.py format for each classification group + for classification_name, annotation_group in audio_annotations.items(): + # Group annotations by value (like v2.py does) + value_groups = defaultdict(list) + for ann in annotation_group: - start = ann.start_frame - end = getattr(ann, "end_frame", None) or ann.start_frame - frames_data.append({"start": start, "end": end}) - annotation.extra.update({"frames": frames_data}) - yield NDClassification.from_common(annotation, label.data) + # Extract value based on classification type for grouping + if hasattr(ann.value, 'answer'): + if isinstance(ann.value.answer, list): + # Checklist classification - convert list to string for grouping + value = str(sorted([item.name for item in ann.value.answer])) + elif hasattr(ann.value.answer, 'name'): + # Radio classification - ann.value.answer is ClassificationAnswer with name + value = ann.value.answer.name + else: + # Text classification + value = ann.value.answer + else: + value = str(ann.value) + + # Group by value + value_groups[value].append(ann) + + # Create answer items with grouped frames (like v2.py) + answer_items = [] + for value, annotations_with_same_value in value_groups.items(): + frames = [] + for ann in annotations_with_same_value: + frames.append({"start": ann.start_frame, "end": ann.end_frame}) + + # Extract the actual value for the output (not the grouping key) + first_ann = annotations_with_same_value[0] + + # Use different field names based on classification type + if hasattr(first_ann.value, 'answer') and isinstance(first_ann.value.answer, list): + # Checklist - use "name" field (like v2.py) + answer_items.append({ + "name": first_ann.value.answer[0].name, # Single item for now + "frames": frames + }) + elif hasattr(first_ann.value, 'answer') and hasattr(first_ann.value.answer, 'name'): + # Radio - use "name" field (like v2.py) + answer_items.append({ + "name": first_ann.value.answer.name, + "frames": frames + }) + else: + # Text - use "value" field (like v2.py) + answer_items.append({ + "value": first_ann.value.answer, + "frames": frames + }) + + # Create a simple Pydantic model for the v2.py format + class AudioNDJSON(BaseModel): + name: str + answer: List[Dict[str, Any]] + dataRow: Dict[str, str] + + yield AudioNDJSON( + name=classification_name, + answer=answer_items, + dataRow={"globalKey": label.data.global_key} + ) From 0a63def213c2044982a6ffb548af19e41205321d Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 29 Sep 2025 11:29:01 -0700 Subject: [PATCH 023/103] feat: nested class for temporal annotations support --- .../data/serialization/ndjson/label.py | 238 +++++++++++++----- 1 file changed, 175 insertions(+), 63 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 1dec5934e..9fdf77fc7 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -169,77 +169,189 @@ def _create_video_annotations( def _create_audio_annotations( cls, label: Label ) -> Generator[BaseModel, None, None]: - """Create audio annotations grouped by classification name in v2.py format.""" - audio_annotations = defaultdict(list) + """Create audio annotations with nested classifications (v3-like), + while preserving v2 behavior for non-nested cases. - # Collect audio annotations by name/schema_id + Strategy: + - Group audio annotations by classification (schema_id or name) + - Identify root groups (not fully contained by another group's frames) + - For each root group, build answer items grouped by value with frames + - Recursively attach nested classifications by time containment + """ + + # 1) Collect all audio annotations grouped by classification key + # Use feature_schema_id when present, otherwise fall back to name + audio_by_group: Dict[str, List[AudioClassificationAnnotation]] = defaultdict(list) for annot in label.annotations: if isinstance(annot, AudioClassificationAnnotation): - audio_annotations[annot.feature_schema_id or annot.name].append(annot) - - # Create v2.py format for each classification group - for classification_name, annotation_group in audio_annotations.items(): - # Group annotations by value (like v2.py does) - value_groups = defaultdict(list) - - for ann in annotation_group: - # Extract value based on classification type for grouping - if hasattr(ann.value, 'answer'): + audio_by_group[annot.feature_schema_id or annot.name].append(annot) + + if not audio_by_group: + return + + # Helper: produce a user-facing classification name for a group + def group_display_name(group_key: str, anns: List[AudioClassificationAnnotation]) -> str: + # Prefer the first non-empty annotation name + for a in anns: + if a.name: + return a.name + # Fallback to group key (may be schema id) + return group_key + + # Helper: compute whether group A is fully contained by any other group by time + def is_group_nested(group_key: str) -> bool: + anns = audio_by_group[group_key] + for ann in anns: + # An annotation is considered nested if there exists any container in other groups + contained = False + for other_key, other_anns in audio_by_group.items(): + if other_key == group_key: + continue + for parent in other_anns: + if parent.start_frame <= ann.start_frame and ( + parent.end_frame is not None + and ann.end_frame is not None + and parent.end_frame >= ann.end_frame + ): + contained = True + break + if contained: + break + if not contained: + # If any annotation in this group is not contained, group is a root + return False + # All annotations were contained somewhere → nested group + return True + + # Helper: group annotations by logical value and produce answer entries + def group_by_value(annotations: List[AudioClassificationAnnotation]) -> List[Dict[str, Any]]: + value_buckets: Dict[str, List[AudioClassificationAnnotation]] = defaultdict(list) + + for ann in annotations: + # Compute grouping key depending on classification type + if hasattr(ann.value, "answer"): if isinstance(ann.value.answer, list): - # Checklist classification - convert list to string for grouping - value = str(sorted([item.name for item in ann.value.answer])) - elif hasattr(ann.value.answer, 'name'): - # Radio classification - ann.value.answer is ClassificationAnswer with name - value = ann.value.answer.name + # Checklist: stable key from selected option names + key = str(sorted([opt.name for opt in ann.value.answer])) + elif hasattr(ann.value.answer, "name"): + # Radio: option name + key = ann.value.answer.name else: - # Text classification - value = ann.value.answer + # Text: the string value + key = ann.value.answer else: - value = str(ann.value) - - # Group by value - value_groups[value].append(ann) - - # Create answer items with grouped frames (like v2.py) - answer_items = [] - for value, annotations_with_same_value in value_groups.items(): - frames = [] - for ann in annotations_with_same_value: - frames.append({"start": ann.start_frame, "end": ann.end_frame}) - - # Extract the actual value for the output (not the grouping key) - first_ann = annotations_with_same_value[0] - - # Use different field names based on classification type - if hasattr(first_ann.value, 'answer') and isinstance(first_ann.value.answer, list): - # Checklist - use "name" field (like v2.py) - answer_items.append({ - "name": first_ann.value.answer[0].name, # Single item for now - "frames": frames - }) - elif hasattr(first_ann.value, 'answer') and hasattr(first_ann.value.answer, 'name'): - # Radio - use "name" field (like v2.py) - answer_items.append({ - "name": first_ann.value.answer.name, - "frames": frames - }) + key = str(ann.value) + value_buckets[key].append(ann) + + entries: List[Dict[str, Any]] = [] + for _, anns in value_buckets.items(): + first = anns[0] + frames = [{"start": a.start_frame, "end": a.end_frame} for a in anns] + + if hasattr(first.value, "answer") and isinstance(first.value.answer, list): + # Checklist: emit one entry per distinct option present in this bucket + # Since bucket is keyed by the combination, take names from first + for opt_name in sorted([o.name for o in first.value.answer]): + entries.append({"name": opt_name, "frames": frames}) + elif hasattr(first.value, "answer") and hasattr(first.value.answer, "name"): + # Radio + entries.append({"name": first.value.answer.name, "frames": frames}) else: - # Text - use "value" field (like v2.py) - answer_items.append({ - "value": first_ann.value.answer, - "frames": frames - }) - - # Create a simple Pydantic model for the v2.py format - class AudioNDJSON(BaseModel): - name: str - answer: List[Dict[str, Any]] - dataRow: Dict[str, str] - + # Text + entries.append({"value": first.value.answer, "frames": frames}) + + return entries + + # Helper: check if child ann is inside any of the parent frames list + def ann_within_frames(ann: AudioClassificationAnnotation, frames: List[Dict[str, int]]) -> bool: + for fr in frames: + if fr["start"] <= ann.start_frame and ( + ann.end_frame is not None and fr["end"] is not None and fr["end"] >= ann.end_frame + ): + return True + return False + + # Helper: recursively build nested classifications for a specific parent frames list + def build_nested_for_frames(parent_frames: List[Dict[str, int]], exclude_group: str) -> List[Dict[str, Any]]: + nested: List[Dict[str, Any]] = [] + + # Collect all annotations within parent frames across all groups except the excluded one + all_contained: List[AudioClassificationAnnotation] = [] + for gk, ga in audio_by_group.items(): + if gk == exclude_group: + continue + all_contained.extend([a for a in ga if ann_within_frames(a, parent_frames)]) + + def strictly_contains(container: AudioClassificationAnnotation, inner: AudioClassificationAnnotation) -> bool: + if container is inner: + return False + if container.end_frame is None or inner.end_frame is None: + return False + return container.start_frame <= inner.start_frame and container.end_frame >= inner.end_frame and ( + container.start_frame < inner.start_frame or container.end_frame > inner.end_frame + ) + + for group_key, anns in audio_by_group.items(): + if group_key == exclude_group: + continue + # Do not nest groups that are roots themselves to avoid duplicating top-level groups inside others + if group_key in root_group_keys: + continue + + # Filter annotations that are contained by any parent frame + candidate_anns = [a for a in anns if ann_within_frames(a, parent_frames)] + if not candidate_anns: + continue + + # Keep only immediate children (those not strictly contained by another contained annotation) + child_anns = [] + for a in candidate_anns: + has_closer_container = any(strictly_contains(b, a) for b in all_contained) + if not has_closer_container: + child_anns.append(a) + if not child_anns: + continue + + # Build this child classification block + child_entries = group_by_value(child_anns) + # Recurse: for each answer entry, compute further nested + for entry in child_entries: + entry_frames = entry.get("frames", []) + child_nested = build_nested_for_frames(entry_frames, group_key) + if child_nested: + entry["classifications"] = child_nested + + nested.append({ + "name": group_display_name(group_key, anns), + "answer": child_entries, + }) + + return nested + + # 2) Determine root groups (not fully contained by other groups) + root_group_keys = [k for k in audio_by_group.keys() if not is_group_nested(k)] + + # 3) Emit one NDJSON object per root classification group + class AudioNDJSON(BaseModel): + name: str + answer: List[Dict[str, Any]] + dataRow: Dict[str, str] + + for group_key in root_group_keys: + anns = audio_by_group[group_key] + top_entries = group_by_value(anns) + + # Attach nested to each top-level answer entry + for entry in top_entries: + frames = entry.get("frames", []) + children = build_nested_for_frames(frames, group_key) + if children: + entry["classifications"] = children + yield AudioNDJSON( - name=classification_name, - answer=answer_items, - dataRow={"globalKey": label.data.global_key} + name=group_display_name(group_key, anns), + answer=top_entries, + dataRow={"globalKey": label.data.global_key}, ) From 538ba66ba16a1a4395a72492e24f372b4e27382c Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 29 Sep 2025 12:38:56 -0700 Subject: [PATCH 024/103] chore: revert old change --- libs/labelbox/src/labelbox/data/serialization/ndjson/label.py | 2 +- requirements-dev.lock | 2 ++ requirements.lock | 2 ++ 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 9fdf77fc7..c8ae80e05 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -159,7 +159,7 @@ def _create_video_annotations( for annotation in annotation_group: if ( annotation.keyframe - and start_frame <= annotation.start_frame <= end_frame + and start_frame <= annotation.frame <= end_frame ): segment.append(annotation) segments.append(segment) diff --git a/requirements-dev.lock b/requirements-dev.lock index 4dceb50ea..16352cfee 100644 --- a/requirements-dev.lock +++ b/requirements-dev.lock @@ -6,6 +6,8 @@ # features: [] # all-features: true # with-sources: false +# generate-hashes: false +# universal: false -e file:libs/labelbox -e file:libs/lbox-clients diff --git a/requirements.lock b/requirements.lock index bc7d7303e..fdf76ce9b 100644 --- a/requirements.lock +++ b/requirements.lock @@ -6,6 +6,8 @@ # features: [] # all-features: true # with-sources: false +# generate-hashes: false +# universal: false -e file:libs/labelbox -e file:libs/lbox-clients From 9675c7366fd027947f5f5ba302e1722563f728c0 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 29 Sep 2025 12:55:24 -0700 Subject: [PATCH 025/103] chore: update tests --- .../tests/data/annotation_import/conftest.py | 113 +------ .../test_generic_data_types.py | 88 ----- .../tests/data/annotation_types/test_audio.py | 313 ------------------ 3 files changed, 2 insertions(+), 512 deletions(-) delete mode 100644 libs/labelbox/tests/data/annotation_types/test_audio.py diff --git a/libs/labelbox/tests/data/annotation_import/conftest.py b/libs/labelbox/tests/data/annotation_import/conftest.py index 75a748459..e3c9c8b98 100644 --- a/libs/labelbox/tests/data/annotation_import/conftest.py +++ b/libs/labelbox/tests/data/annotation_import/conftest.py @@ -1630,82 +1630,6 @@ def video_checklist_inference(prediction_id_mapping): return checklists -@pytest.fixture -def audio_checklist_inference(prediction_id_mapping): - """Audio temporal checklist inference with frame-based timing""" - checklists = [] - for feature in prediction_id_mapping: - if "checklist" not in feature: - continue - checklist = feature["checklist"].copy() - checklist.update( - { - "answers": [ - {"name": "first_checklist_answer"}, - {"name": "second_checklist_answer"}, - ], - "frame": 2500, # 2.5 seconds in milliseconds - } - ) - del checklist["tool"] - checklists.append(checklist) - return checklists - - -@pytest.fixture -def audio_text_inference(prediction_id_mapping): - """Audio temporal text inference with frame-based timing""" - texts = [] - for feature in prediction_id_mapping: - if "text" not in feature: - continue - text = feature["text"].copy() - text.update({ - "answer": "free form text...", - "frame": 5000, # 5.0 seconds in milliseconds - }) - del text["tool"] - texts.append(text) - return texts - - -@pytest.fixture -def audio_radio_inference(prediction_id_mapping): - """Audio temporal radio inference with frame-based timing""" - radios = [] - for feature in prediction_id_mapping: - if "radio" not in feature: - continue - radio = feature["radio"].copy() - radio.update({ - "answer": {"name": "first_radio_answer"}, - "frame": 7500, # 7.5 seconds in milliseconds - }) - del radio["tool"] - radios.append(radio) - return radios - - -@pytest.fixture -def audio_text_entity_inference(prediction_id_mapping): - """Audio temporal text entity inference with frame-based timing""" - entities = [] - for feature in prediction_id_mapping: - if "text" not in feature: - continue - entity = feature["text"].copy() - entity.update({ - "frame": 3000, # 3.0 seconds in milliseconds - "location": { - "start": 0, - "end": 11, - } - }) - del entity["tool"] - entities.append(entity) - return entities - - @pytest.fixture def message_single_selection_inference( prediction_id_mapping, mmc_example_data_row_message_ids @@ -1843,18 +1767,9 @@ def annotations_by_media_type( radio_inference, radio_inference_index_mmc, text_inference_index_mmc, - audio_checklist_inference, - audio_text_inference, - audio_radio_inference, - audio_text_entity_inference, ): return { - MediaType.Audio: [ - audio_checklist_inference, - audio_text_inference, - audio_radio_inference, - audio_text_entity_inference - ], + MediaType.Audio: [checklist_inference, text_inference], MediaType.Conversational: [ checklist_inference_index, text_inference_index, @@ -2094,7 +2009,7 @@ def _convert_to_plain_object(obj): @pytest.fixture def annotation_import_test_helpers() -> Type[AnnotationImportTestHelpers]: - return AnnotationImportTestHelpers + return AnnotationImportTestHelpers() @pytest.fixture() @@ -2176,7 +2091,6 @@ def expected_export_v2_audio(): { "name": "checklist", "value": "checklist", - "frame": 2500, "checklist_answers": [ { "name": "first_checklist_answer", @@ -2193,34 +2107,11 @@ def expected_export_v2_audio(): { "name": "text", "value": "text", - "frame": 5000, "text_answer": { "content": "free form text...", "classifications": [], }, }, - { - "name": "radio", - "value": "radio", - "frame": 7500, - "radio_answer": { - "name": "first_radio_answer", - "classifications": [], - }, - }, - ], - "objects": [ - { - "name": "text", - "value": "text", - "frame": 3000, - "annotation_kind": "TextEntity", - "classifications": [], - "location": { - "start": 0, - "end": 11, - }, - } ], "segments": {}, "timestamp": {}, diff --git a/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py b/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py index 73e8f4976..805c24edf 100644 --- a/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py +++ b/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py @@ -268,94 +268,6 @@ def test_import_mal_annotations( # MAL Labels cannot be exported and compared to input labels -def test_audio_temporal_annotations_fixtures(): - """Test that audio temporal annotation fixtures are properly structured""" - # This test verifies our fixtures work without requiring the full integration environment - - # Mock prediction_id_mapping structure that our fixtures expect - mock_prediction_id_mapping = [ - { - "checklist": { - "tool": "checklist_tool", - "name": "checklist", - "value": "checklist", - }, - "text": {"tool": "text_tool", "name": "text", "value": "text"}, - "radio": {"tool": "radio_tool", "name": "radio", "value": "radio"}, - } - ] - - # Test that our fixtures can process the mock data - # Note: We can't actually call the fixtures directly in a unit test, - # but we can verify the structure is correct by checking the fixture definitions - - # Verify that our fixtures are properly defined and accessible - from .conftest import ( - audio_checklist_inference, - audio_text_inference, - audio_radio_inference, - audio_text_entity_inference, - ) - - # Check that all required fixtures exist - assert audio_checklist_inference is not None - assert audio_text_inference is not None - assert audio_radio_inference is not None - assert audio_text_entity_inference is not None - - # Verify the fixtures are callable (they should be functions) - assert callable(audio_checklist_inference) - assert callable(audio_text_inference) - assert callable(audio_radio_inference) - assert callable(audio_text_entity_inference) - - -def test_audio_temporal_annotations_integration( - client: Client, - configured_project: Project, - annotations_by_media_type, - media_type=MediaType.Audio, -): - """Test that audio temporal annotations work correctly in the integration framework""" - # Filter to only audio annotations - audio_annotations = annotations_by_media_type[MediaType.Audio] - - # Verify we have the expected audio temporal annotations - assert len(audio_annotations) == 4 # checklist, text, radio, text_entity - - # Check that temporal annotations have frame information - for annotation in audio_annotations: - if "frame" in annotation: - assert isinstance(annotation["frame"], int) - assert annotation["frame"] >= 0 - # Verify frame values are in milliseconds (reasonable range for audio) - assert annotation["frame"] <= 600000 # 10 minutes max - - # Test import with audio temporal annotations - label_import = lb.LabelImport.create_from_objects( - client, - configured_project.uid, - f"test-import-audio-temporal-{uuid.uuid4()}", - audio_annotations, - ) - label_import.wait_until_done() - - # Verify import was successful - assert label_import.state == AnnotationImportState.FINISHED - assert len(label_import.errors) == 0 - - # Verify all annotations were imported successfully - all_annotations = sorted([a["uuid"] for a in audio_annotations]) - successful_annotations = sorted( - [ - status["uuid"] - for status in label_import.statuses - if status["status"] == "SUCCESS" - ] - ) - assert successful_annotations == all_annotations - - @pytest.mark.parametrize( "configured_project_by_global_key, media_type", [ diff --git a/libs/labelbox/tests/data/annotation_types/test_audio.py b/libs/labelbox/tests/data/annotation_types/test_audio.py deleted file mode 100644 index ef818cfc7..000000000 --- a/libs/labelbox/tests/data/annotation_types/test_audio.py +++ /dev/null @@ -1,313 +0,0 @@ -import pytest -import labelbox.types as lb_types -from labelbox.data.annotation_types.audio import ( - AudioClassificationAnnotation, -) -from labelbox.data.annotation_types.classification.classification import ( - ClassificationAnswer, - Radio, - Text, -) -from labelbox.data.annotation_types.ner import TextEntity - - -def test_audio_classification_creation(): - """Test creating audio classification with direct frame specification""" - annotation = AudioClassificationAnnotation( - start_frame=2500, # 2.5 seconds in milliseconds - name="speaker_id", - value=Radio(answer=ClassificationAnswer(name="john")), - ) - - assert annotation.start_frame == 2500 - assert annotation.end_frame is None - assert annotation.segment_index is None - assert annotation.name == "speaker_id" - assert isinstance(annotation.value, Radio) - assert annotation.value.answer.name == "john" - - -def test_audio_classification_with_time_range(): - """Test creating audio classification with start and end frames""" - annotation = AudioClassificationAnnotation( - start_frame=2500, # Start at 2.5 seconds - end_frame=4100, # End at 4.1 seconds - name="speaker_id", - value=Radio(answer=ClassificationAnswer(name="john")), - ) - - assert annotation.start_frame == 2500 - assert annotation.end_frame == 4100 - assert annotation.name == "speaker_id" - - -def test_audio_classification_creation_with_segment(): - """Test creating audio classification with segment index""" - annotation = AudioClassificationAnnotation( - start_frame=10000, - end_frame=15000, - name="language", - value=Radio(answer=ClassificationAnswer(name="english")), - segment_index=1, - ) - - assert annotation.start_frame == 10000 - assert annotation.end_frame == 15000 - assert annotation.segment_index == 1 - - -def test_audio_classification_text_type(): - """Test creating audio classification with Text value""" - annotation = AudioClassificationAnnotation( - start_frame=5000, # 5.0 seconds - name="quality", - value=Text(answer="excellent"), - ) - - assert annotation.start_frame == 5000 - assert annotation.name == "quality" - assert isinstance(annotation.value, Text) - assert annotation.value.answer == "excellent" - - -def test_frame_precision(): - """Test frame values maintain precision""" - # Test various time values in milliseconds - test_cases = [0, 1, 1000, 1500, 10123, 60000] - - for milliseconds in test_cases: - annotation = AudioClassificationAnnotation( - start_frame=milliseconds, - end_frame=milliseconds + 1000, - name="test", - value=Text(answer="test"), - ) - assert annotation.start_frame == milliseconds - assert annotation.end_frame == milliseconds + 1000 - - -def test_audio_label_integration(): - """Test audio annotations work with Label container""" - # Create audio annotations - speaker_annotation = AudioClassificationAnnotation( - start_frame=1000, - end_frame=2000, - name="speaker", - value=Radio(answer=ClassificationAnswer(name="john")), - ) - - # Create label with audio annotations - label = lb_types.Label( - data={"global_key": "audio_file.mp3"}, - annotations=[speaker_annotation], - ) - - # Verify annotations are accessible - assert len(label.annotations) == 1 - - # Check annotation types - audio_classifications = [ - ann - for ann in label.annotations - if isinstance(ann, AudioClassificationAnnotation) - ] - - assert len(audio_classifications) == 1 - assert audio_classifications[0].name == "speaker" - - -def test_audio_annotation_validation(): - """Test audio annotation field validation""" - # Test frame must be int - with pytest.raises(ValueError): - AudioClassificationAnnotation( - start_frame="invalid", # Should be int - name="test", - value=Text(answer="test"), - ) - - -def test_audio_annotation_extra_fields(): - """Test audio annotations can have extra metadata""" - extra_data = {"source": "automatic", "confidence_score": 0.95} - - annotation = AudioClassificationAnnotation( - start_frame=3000, name="quality", value=Text(answer="good"), extra=extra_data - ) - - assert annotation.extra["source"] == "automatic" - assert annotation.extra["confidence_score"] == 0.95 - - -def test_audio_annotation_feature_schema(): - """Test audio annotations with feature schema IDs""" - annotation = AudioClassificationAnnotation( - start_frame=4000, - name="language", - value=Radio(answer=ClassificationAnswer(name="spanish")), - feature_schema_id="1234567890123456789012345", - ) - - assert annotation.feature_schema_id == "1234567890123456789012345" - - -def test_audio_annotation_mixed_types(): - """Test label with mixed audio and other annotation types""" - # Audio annotation - audio_annotation = AudioClassificationAnnotation( - start_frame=2000, - name="speaker", - value=Radio(answer=ClassificationAnswer(name="john")), - ) - - # Video annotation - video_annotation = lb_types.VideoClassificationAnnotation( - start_frame=10, name="quality", value=Text(answer="good") - ) - - # Image annotation - image_annotation = lb_types.ObjectAnnotation( - name="bbox", - value=lb_types.Rectangle( - start=lb_types.Point(x=0, y=0), end=lb_types.Point(x=100, y=100) - ), - ) - - # Create label with mixed types - label = lb_types.Label( - data={"global_key": "mixed_media"}, - annotations=[audio_annotation, video_annotation, image_annotation], - ) - - # Verify all annotations are present - assert len(label.annotations) == 3 - - # Check types - audio_annotations = [ - ann - for ann in label.annotations - if isinstance(ann, AudioClassificationAnnotation) - ] - video_annotations = [ - ann - for ann in label.annotations - if isinstance(ann, lb_types.VideoClassificationAnnotation) - ] - object_annotations = [ - ann - for ann in label.annotations - if isinstance(ann, lb_types.ObjectAnnotation) - ] - - assert len(audio_annotations) == 1 - assert len(video_annotations) == 1 - assert len(object_annotations) == 1 - - -def test_audio_annotation_serialization(): - """Test audio annotations can be serialized to dict""" - annotation = AudioClassificationAnnotation( - start_frame=6000, - end_frame=8000, - name="emotion", - value=Radio(answer=ClassificationAnswer(name="happy")), - segment_index=3, - extra={"confidence": 0.9}, - ) - - # Test model_dump - serialized = annotation.model_dump() - assert serialized["frame"] == 6000 - assert serialized["end_frame"] == 8000 - assert serialized["name"] == "emotion" - assert serialized["segment_index"] == 3 - assert serialized["extra"]["confidence"] == 0.9 - - # Test model_dump with exclusions - serialized_excluded = annotation.model_dump(exclude_none=True) - assert "frame" in serialized_excluded - assert "name" in serialized_excluded - assert "end_frame" in serialized_excluded - assert "segment_index" in serialized_excluded - - -def test_audio_annotation_from_dict(): - """Test audio annotations can be created from dict""" - annotation_data = { - "frame": 7000, - "end_frame": 9000, - "name": "topic", - "value": Text(answer="technology"), - "segment_index": 2, - "extra": {"source": "manual"}, - } - - annotation = AudioClassificationAnnotation(**annotation_data) - - assert annotation.start_frame == 7000 - assert annotation.end_frame == 9000 - assert annotation.name == "topic" - assert annotation.segment_index == 2 - assert annotation.extra["source"] == "manual" - - -def test_audio_annotation_edge_cases(): - """Test audio annotation edge cases""" - # Test very long audio (many hours) - long_annotation = AudioClassificationAnnotation( - start_frame=3600000, # 1 hour in milliseconds - end_frame=7200000, # 2 hours in milliseconds - name="long_audio", - value=Text(answer="very long"), - ) - - assert long_annotation.start_frame == 3600000 - assert long_annotation.end_frame == 7200000 - - # Test very short audio (milliseconds) - short_annotation = AudioClassificationAnnotation( - start_frame=1, # 1 millisecond - end_frame=2, # 2 milliseconds - name="short_audio", - value=Text(answer="very short"), - ) - - assert short_annotation.start_frame == 1 - assert short_annotation.end_frame == 2 - - # Test zero time - zero_annotation = AudioClassificationAnnotation( - start_frame=0, name="zero_time", value=Text(answer="zero") - ) - - assert zero_annotation.start_frame == 0 - assert zero_annotation.end_frame is None - - -def test_temporal_annotation_grouping(): - """Test that annotations with same name can be grouped for temporal processing""" - # Create multiple annotations with same name (like tokens) - tokens = ["Hello", "world", "this", "is", "audio"] - annotations = [] - - for i, token in enumerate(tokens): - start_frame = i * 1000 # 1 second apart - end_frame = start_frame + 900 # 900ms duration each - - annotation = AudioClassificationAnnotation( - start_frame=start_frame, - end_frame=end_frame, - name="tokens", # Same name for grouping - value=Text(answer=token), - ) - annotations.append(annotation) - - # Verify all have same name but different content and timing - assert len(annotations) == 5 - assert all(ann.name == "tokens" for ann in annotations) - assert annotations[0].value.answer == "Hello" - assert annotations[1].value.answer == "world" - assert annotations[0].start_frame == 0 - assert annotations[1].start_frame == 1000 - assert annotations[0].end_frame == 900 - assert annotations[1].end_frame == 1900 From 327800b7e7ffa86ba9fcc381dc98eb3a96741be0 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 29 Sep 2025 12:59:21 -0700 Subject: [PATCH 026/103] chore: clean up and track test files --- .../serialization/ndjson/classification.py | 27 +- libs/labelbox/tests/conftest.py | 12 +- .../data/serialization/ndjson/test_audio.py | 363 ++++++++++++++++++ requirements-dev.lock | 2 - requirements.lock | 2 - 5 files changed, 389 insertions(+), 17 deletions(-) create mode 100644 libs/labelbox/tests/data/serialization/ndjson/test_audio.py diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index 00cb91aa0..fedf4d91b 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -60,6 +60,22 @@ def serialize_model(self, handler): return res +class FrameLocation(BaseModel): + end: int + start: int + + +class VideoSupported(BaseModel): + # Note that frames are only allowed as top level inferences for video + frames: Optional[List[FrameLocation]] = None + + @model_serializer(mode="wrap") + def serialize_model(self, handler): + res = handler(self) + # This means these are no video frames .. + if self.frames is None: + res.pop("frames") + return res class NDTextSubclass(NDAnswer): @@ -226,14 +242,13 @@ def from_common( name=name, schema_id=feature_schema_id, uuid=uuid, - frames=extra.get("frames"), message_id=message_id, confidence=text.confidence, custom_metrics=text.custom_metrics, ) -class NDChecklist(NDAnnotation, NDChecklistSubclass): +class NDChecklist(NDAnnotation, NDChecklistSubclass, VideoSupported): @model_serializer(mode="wrap") def serialize_model(self, handler): res = handler(self) @@ -280,7 +295,7 @@ def from_common( ) -class NDRadio(NDAnnotation, NDRadioSubclass): +class NDRadio(NDAnnotation, NDRadioSubclass, VideoSupported): @classmethod def from_common( cls, @@ -410,8 +425,7 @@ def to_common( def from_common( cls, annotation: Union[ - ClassificationAnnotation, - VideoClassificationAnnotation, + ClassificationAnnotation, VideoClassificationAnnotation ], data: GenericDataRowData, ) -> Union[NDTextSubclass, NDChecklistSubclass, NDRadioSubclass]: @@ -434,8 +448,7 @@ def from_common( @staticmethod def lookup_classification( annotation: Union[ - ClassificationAnnotation, - VideoClassificationAnnotation, + ClassificationAnnotation, VideoClassificationAnnotation ], ) -> Union[NDText, NDChecklist, NDRadio]: return {Text: NDText, Checklist: NDChecklist, Radio: NDRadio}.get( diff --git a/libs/labelbox/tests/conftest.py b/libs/labelbox/tests/conftest.py index 8eb3807ca..a2ffdd49d 100644 --- a/libs/labelbox/tests/conftest.py +++ b/libs/labelbox/tests/conftest.py @@ -688,12 +688,12 @@ def create_label(): predictions, ) upload_task.wait_until_done(sleep_time_seconds=5) - assert upload_task.state == AnnotationImportState.FINISHED, ( - "Label Import did not finish" - ) - assert len(upload_task.errors) == 0, ( - f"Label Import {upload_task.name} failed with errors {upload_task.errors}" - ) + assert ( + upload_task.state == AnnotationImportState.FINISHED + ), "Label Import did not finish" + assert ( + len(upload_task.errors) == 0 + ), f"Label Import {upload_task.name} failed with errors {upload_task.errors}" project.create_label = create_label project.create_label() diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py new file mode 100644 index 000000000..e392c2577 --- /dev/null +++ b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py @@ -0,0 +1,363 @@ +import labelbox.types as lb_types +from labelbox.data.serialization.ndjson.converter import NDJsonConverter + + +def test_audio_nested_text_radio_checklist_structure(): + # Purpose: verify that class-based AudioClassificationAnnotation inputs serialize + # into v3-style nested NDJSON with: + # - exactly three top-level groups (text_class, radio_class, checklist_class) + # - children nested only under their closest containing parent frames + # - correct field shapes per type (Text uses "value", Radio/Checklist use "name") + + # Build annotations mirroring exec/v3.py shapes using class-based annotations + anns = [] + + # text_class top-level with multiple values + # Expect: produces an NDJSON object named "text_class" with four answer entries; + # the long segment (1500-2400) will carry nested children below. + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1000, + end_frame=1100, + name="text_class", + value=lb_types.Text(answer="A"), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1500, + end_frame=2400, + name="text_class", + value=lb_types.Text(answer="text_class value"), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=2500, + end_frame=2700, + name="text_class", + value=lb_types.Text(answer="C"), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=2900, + end_frame=2999, + name="text_class", + value=lb_types.Text(answer="D"), + ) + ) + + # nested under text_class + # Expect: nested_text_class (1600-2000) nests under the 1500-2400 parent; + # nested_text_class_2 nests under nested_text_class only (no duplicates at parent level). + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1600, + end_frame=2000, + name="nested_text_class", + value=lb_types.Text(answer="nested_text_class value"), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1800, + end_frame=2000, + name="nested_text_class_2", + value=lb_types.Text(answer="nested_text_class_2 value"), + ) + ) + + # radio_class top-level + # Expect: two answer entries for first_radio_answer (two frame segments) and + # two for second_radio_answer; children attach only to their closest container answer. + anns.append( + lb_types.AudioClassificationAnnotation( + frame=200, + end_frame=1500, + name="radio_class", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer(name="first_radio_answer") + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=2000, + end_frame=2500, + name="radio_class", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer(name="first_radio_answer") + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1550, + end_frame=1700, + name="radio_class", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer(name="second_radio_answer") + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=2700, + end_frame=3000, + name="radio_class", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer(name="second_radio_answer") + ), + ) + ) + + # nested radio + # Expect: sub_radio_question nests under first_radio_answer (1000-1500), and + # sub_radio_question_2 nests under sub_radio_question's first_sub_radio_answer only. + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1000, + end_frame=1500, + name="sub_radio_question", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer( + name="first_sub_radio_answer" + ) + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1300, + end_frame=1500, + name="sub_radio_question_2", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer( + name="first_sub_radio_answer_2" + ) + ), + ) + ) + + # checklist_class top-level + # Expect: three answer entries (first/second/third_checklist_option) and + # nested checklist children attach to the first option segments where contained. + anns.append( + lb_types.AudioClassificationAnnotation( + frame=300, + end_frame=800, + name="checklist_class", + value=lb_types.Checklist( + answer=[ + lb_types.ClassificationAnswer(name="first_checklist_option") + ] + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1200, + end_frame=1800, + name="checklist_class", + value=lb_types.Checklist( + answer=[ + lb_types.ClassificationAnswer(name="first_checklist_option") + ] + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=2200, + end_frame=2900, + name="checklist_class", + value=lb_types.Checklist( + answer=[ + lb_types.ClassificationAnswer( + name="second_checklist_option" + ) + ] + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=2500, + end_frame=3500, + name="checklist_class", + value=lb_types.Checklist( + answer=[ + lb_types.ClassificationAnswer(name="third_checklist_option") + ] + ), + ) + ) + + # nested checklist + # Expect: nested_checklist options 1/2/3 attach to their containing parent frames; + # checklist_nested_text attaches under nested_option_1 only. + anns.append( + lb_types.AudioClassificationAnnotation( + frame=400, + end_frame=700, + name="nested_checklist", + value=lb_types.Checklist( + answer=[lb_types.ClassificationAnswer(name="nested_option_1")] + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1200, + end_frame=1600, + name="nested_checklist", + value=lb_types.Checklist( + answer=[lb_types.ClassificationAnswer(name="nested_option_2")] + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=1400, + end_frame=1800, + name="nested_checklist", + value=lb_types.Checklist( + answer=[lb_types.ClassificationAnswer(name="nested_option_3")] + ), + ) + ) + anns.append( + lb_types.AudioClassificationAnnotation( + frame=500, + end_frame=700, + name="checklist_nested_text", + value=lb_types.Text(answer="checklist_nested_text value"), + ) + ) + + # Serialize a single Label containing all of the above annotations + label = lb_types.Label( + data={"global_key": "audio_nested_test_key"}, annotations=anns + ) + ndjson = list(NDJsonConverter.serialize([label])) + + # Assert: exactly three top-level groups, matching v3 root objects + assert {obj["name"] for obj in ndjson} == { + "text_class", + "radio_class", + "checklist_class", + } + + # Validate text_class structure: children appear under the long segment only, + # and grandchildren only under their immediate parent + text_nd = next(obj for obj in ndjson if obj["name"] == "text_class") + parent = next( + item + for item in text_nd["answer"] + if item.get("value") == "text_class value" + ) + nested = parent.get("classifications", []) + names = {c["name"] for c in nested} + assert "nested_text_class" in names + nt = next(c for c in nested if c["name"] == "nested_text_class") + nt_ans = nt["answer"][0] + assert nt_ans["value"] == "nested_text_class value" + nt_nested = nt_ans.get("classifications", []) + assert any(c["name"] == "nested_text_class_2" for c in nt_nested) + + # Validate radio_class structure and immediate-child only + radio_nd = next(obj for obj in ndjson if obj["name"] == "radio_class") + first_radio = next( + a for a in radio_nd["answer"] if a["name"] == "first_radio_answer" + ) + assert any( + c["name"] == "sub_radio_question" + for c in first_radio.get("classifications", []) + ) + # sub_radio_question_2 is nested under sub_radio_question only + sub_radio = next( + c + for c in first_radio["classifications"] + if c["name"] == "sub_radio_question" + ) + sr_first = next( + a for a in sub_radio["answer"] if a["name"] == "first_sub_radio_answer" + ) + assert any( + c["name"] == "sub_radio_question_2" + for c in sr_first.get("classifications", []) + ) + + # Validate checklist_class structure: nested_checklist exists, and nested text + # appears only under nested_option_1 (closest container) + checklist_nd = next( + obj for obj in ndjson if obj["name"] == "checklist_class" + ) + first_opt = next( + a + for a in checklist_nd["answer"] + if a["name"] == "first_checklist_option" + ) + assert any( + c["name"] == "nested_checklist" + for c in first_opt.get("classifications", []) + ) + nested_checklist = next( + c + for c in first_opt["classifications"] + if c["name"] == "nested_checklist" + ) + # Ensure nested text present under nested_checklist → nested_option_1 + opt1 = next( + a for a in nested_checklist["answer"] if a["name"] == "nested_option_1" + ) + assert any( + c["name"] == "checklist_nested_text" + for c in opt1.get("classifications", []) + ) + + +def test_audio_top_level_only_basic(): + anns = [ + lb_types.AudioClassificationAnnotation( + frame=200, + end_frame=1500, + name="radio_class", + value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="first_radio_answer")), + ), + lb_types.AudioClassificationAnnotation( + frame=1550, + end_frame=1700, + name="radio_class", + value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="second_radio_answer")), + ), + lb_types.AudioClassificationAnnotation( + frame=1200, + end_frame=1800, + name="checklist_class", + value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="angry")]), + ), + ] + + label = lb_types.Label(data={"global_key": "audio_top_level_only"}, annotations=anns) + ndjson = list(NDJsonConverter.serialize([label])) + + names = {o["name"] for o in ndjson} + assert names == {"radio_class", "checklist_class"} + + radio = next(o for o in ndjson if o["name"] == "radio_class") + r_answers = sorted(radio["answer"], key=lambda x: x["frames"][0]["start"]) + assert r_answers[0]["name"] == "first_radio_answer" + assert r_answers[0]["frames"] == [{"start": 200, "end": 1500}] + assert "classifications" not in r_answers[0] + assert r_answers[1]["name"] == "second_radio_answer" + assert r_answers[1]["frames"] == [{"start": 1550, "end": 1700}] + assert "classifications" not in r_answers[1] + + checklist = next(o for o in ndjson if o["name"] == "checklist_class") + c_answers = checklist["answer"] + assert len(c_answers) == 1 + assert c_answers[0]["name"] == "angry" + assert c_answers[0]["frames"] == [{"start": 1200, "end": 1800}] + assert "classifications" not in c_answers[0] diff --git a/requirements-dev.lock b/requirements-dev.lock index 16352cfee..4dceb50ea 100644 --- a/requirements-dev.lock +++ b/requirements-dev.lock @@ -6,8 +6,6 @@ # features: [] # all-features: true # with-sources: false -# generate-hashes: false -# universal: false -e file:libs/labelbox -e file:libs/lbox-clients diff --git a/requirements.lock b/requirements.lock index fdf76ce9b..bc7d7303e 100644 --- a/requirements.lock +++ b/requirements.lock @@ -6,8 +6,6 @@ # features: [] # all-features: true # with-sources: false -# generate-hashes: false -# universal: false -e file:libs/labelbox -e file:libs/lbox-clients From 1174ad8c485d09b390fa3f470714a6e093b814f7 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 29 Sep 2025 13:07:52 -0700 Subject: [PATCH 027/103] chore: update audio.ipynb to reflect breadth of use cases --- examples/annotation_import/audio.ipynb | 71 ++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index f085c0f13..b47440eb4 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,5 +1,76 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "137b71f2", + "metadata": {}, + "source": [ + "## Brief temporal audio examples (Text, Radio, Checklist, Nested)\n", + "\n", + "- This section shows minimal, class-based examples that serialize to NDJSON:\n", + " - Text: `value` with `frames`\n", + " - Radio: `name` with `frames`\n", + " - Checklist: `name` with `frames`\n", + " - Nested (1 level): child nested under closest containing parent `frames`\n", + "\n", + "Run this cell and the next one to see the NDJSON output only (no API calls).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f58dd5db", + "metadata": {}, + "outputs": [], + "source": [ + "import labelbox.types as lb_types\n", + "from labelbox.data.serialization.ndjson.converter import NDJsonConverter\n", + "\n", + "# Minimal Text temporal example\n", + "text_anns = [\n", + " lb_types.AudioClassificationAnnotation(\n", + " start_frame=1000, end_frame=1100, name=\"text_class\", value=lb_types.Text(answer=\"Hello\")\n", + " ),\n", + " lb_types.AudioClassificationAnnotation(\n", + " start_frame=1200, end_frame=1300, name=\"text_class\", value=lb_types.Text(answer=\"World\")\n", + " ),\n", + "]\n", + "\n", + "# Minimal Radio temporal example\n", + "radio_anns = [\n", + " lb_types.AudioClassificationAnnotation(\n", + " start_frame=200, end_frame=1500, name=\"radio_class\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_radio_answer\")),\n", + " ),\n", + "]\n", + "\n", + "# Minimal Checklist temporal example\n", + "checklist_anns = [\n", + " lb_types.AudioClassificationAnnotation(\n", + " start_frame=1200, end_frame=1800, name=\"checklist_class\",\n", + " value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name=\"angry\")]),\n", + " ),\n", + "]\n", + "\n", + "# Minimal Nested (1 level) example: nested_text under parent text segment\n", + "nested_anns = [\n", + " lb_types.AudioClassificationAnnotation(\n", + " start_frame=1500, end_frame=2400, name=\"text_class\", value=lb_types.Text(answer=\"parent\")\n", + " ),\n", + " lb_types.AudioClassificationAnnotation(\n", + " start_frame=1600, end_frame=2000, name=\"nested_text\", value=lb_types.Text(answer=\"child\")\n", + " ),\n", + "]\n", + "\n", + "label = lb_types.Label(\n", + " data={\"global_key\": \"audio_examples_demo\"},\n", + " annotations=text_anns + radio_anns + checklist_anns + nested_anns,\n", + ")\n", + "ndjson = list(NDJsonConverter.serialize([label]))\n", + "for i, obj in enumerate(ndjson, 1):\n", + " print(f\"{i}. {obj}\")\n" + ] + }, { "cell_type": "markdown", "metadata": {}, From 2361ca3e74c817a26cd9b111e9bb7f62a0d2e874 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 29 Sep 2025 13:51:11 -0700 Subject: [PATCH 028/103] chore: cursor reported bug --- libs/labelbox/src/labelbox/data/annotation_types/audio.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index 5043a91f8..c86fba668 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -26,7 +26,7 @@ class AudioClassificationAnnotation(ClassificationAnnotation): start_frame: int = Field( validation_alias=AliasChoices("start_frame", "frame"), - serialization_alias="startframe", + serialization_alias="start_frame", ) end_frame: Optional[int] = Field( default=None, From 59f0cd8f2713570cec275fb2670fbe23c1d36e0c Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 29 Sep 2025 14:25:00 -0700 Subject: [PATCH 029/103] chore: extract generic temporal nested logic --- .../data/serialization/ndjson/label.py | 202 +---------- .../data/serialization/ndjson/temporal.py | 339 ++++++++++++++++++ 2 files changed, 358 insertions(+), 183 deletions(-) create mode 100644 libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index c8ae80e05..5fc19c004 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -28,6 +28,7 @@ from ...annotation_types.audio import ( AudioClassificationAnnotation, ) +from .temporal import create_audio_ndjson_annotations from labelbox.types import DocumentRectangle, DocumentEntity from .classification import ( NDChecklistSubclass, @@ -169,190 +170,25 @@ def _create_video_annotations( def _create_audio_annotations( cls, label: Label ) -> Generator[BaseModel, None, None]: - """Create audio annotations with nested classifications (v3-like), - while preserving v2 behavior for non-nested cases. - - Strategy: - - Group audio annotations by classification (schema_id or name) - - Identify root groups (not fully contained by another group's frames) - - For each root group, build answer items grouped by value with frames - - Recursively attach nested classifications by time containment - """ - - # 1) Collect all audio annotations grouped by classification key - # Use feature_schema_id when present, otherwise fall back to name - audio_by_group: Dict[str, List[AudioClassificationAnnotation]] = defaultdict(list) - for annot in label.annotations: - if isinstance(annot, AudioClassificationAnnotation): - audio_by_group[annot.feature_schema_id or annot.name].append(annot) - - if not audio_by_group: + """Create audio annotations with nested classifications using modular hierarchy builder.""" + # Extract audio annotations from the label + audio_annotations = [ + annot for annot in label.annotations + if isinstance(annot, AudioClassificationAnnotation) + ] + + if not audio_annotations: return - - # Helper: produce a user-facing classification name for a group - def group_display_name(group_key: str, anns: List[AudioClassificationAnnotation]) -> str: - # Prefer the first non-empty annotation name - for a in anns: - if a.name: - return a.name - # Fallback to group key (may be schema id) - return group_key - - # Helper: compute whether group A is fully contained by any other group by time - def is_group_nested(group_key: str) -> bool: - anns = audio_by_group[group_key] - for ann in anns: - # An annotation is considered nested if there exists any container in other groups - contained = False - for other_key, other_anns in audio_by_group.items(): - if other_key == group_key: - continue - for parent in other_anns: - if parent.start_frame <= ann.start_frame and ( - parent.end_frame is not None - and ann.end_frame is not None - and parent.end_frame >= ann.end_frame - ): - contained = True - break - if contained: - break - if not contained: - # If any annotation in this group is not contained, group is a root - return False - # All annotations were contained somewhere → nested group - return True - - # Helper: group annotations by logical value and produce answer entries - def group_by_value(annotations: List[AudioClassificationAnnotation]) -> List[Dict[str, Any]]: - value_buckets: Dict[str, List[AudioClassificationAnnotation]] = defaultdict(list) - - for ann in annotations: - # Compute grouping key depending on classification type - if hasattr(ann.value, "answer"): - if isinstance(ann.value.answer, list): - # Checklist: stable key from selected option names - key = str(sorted([opt.name for opt in ann.value.answer])) - elif hasattr(ann.value.answer, "name"): - # Radio: option name - key = ann.value.answer.name - else: - # Text: the string value - key = ann.value.answer - else: - key = str(ann.value) - value_buckets[key].append(ann) - - entries: List[Dict[str, Any]] = [] - for _, anns in value_buckets.items(): - first = anns[0] - frames = [{"start": a.start_frame, "end": a.end_frame} for a in anns] - - if hasattr(first.value, "answer") and isinstance(first.value.answer, list): - # Checklist: emit one entry per distinct option present in this bucket - # Since bucket is keyed by the combination, take names from first - for opt_name in sorted([o.name for o in first.value.answer]): - entries.append({"name": opt_name, "frames": frames}) - elif hasattr(first.value, "answer") and hasattr(first.value.answer, "name"): - # Radio - entries.append({"name": first.value.answer.name, "frames": frames}) - else: - # Text - entries.append({"value": first.value.answer, "frames": frames}) - - return entries - - # Helper: check if child ann is inside any of the parent frames list - def ann_within_frames(ann: AudioClassificationAnnotation, frames: List[Dict[str, int]]) -> bool: - for fr in frames: - if fr["start"] <= ann.start_frame and ( - ann.end_frame is not None and fr["end"] is not None and fr["end"] >= ann.end_frame - ): - return True - return False - - # Helper: recursively build nested classifications for a specific parent frames list - def build_nested_for_frames(parent_frames: List[Dict[str, int]], exclude_group: str) -> List[Dict[str, Any]]: - nested: List[Dict[str, Any]] = [] - - # Collect all annotations within parent frames across all groups except the excluded one - all_contained: List[AudioClassificationAnnotation] = [] - for gk, ga in audio_by_group.items(): - if gk == exclude_group: - continue - all_contained.extend([a for a in ga if ann_within_frames(a, parent_frames)]) - - def strictly_contains(container: AudioClassificationAnnotation, inner: AudioClassificationAnnotation) -> bool: - if container is inner: - return False - if container.end_frame is None or inner.end_frame is None: - return False - return container.start_frame <= inner.start_frame and container.end_frame >= inner.end_frame and ( - container.start_frame < inner.start_frame or container.end_frame > inner.end_frame - ) - - for group_key, anns in audio_by_group.items(): - if group_key == exclude_group: - continue - # Do not nest groups that are roots themselves to avoid duplicating top-level groups inside others - if group_key in root_group_keys: - continue - - # Filter annotations that are contained by any parent frame - candidate_anns = [a for a in anns if ann_within_frames(a, parent_frames)] - if not candidate_anns: - continue - - # Keep only immediate children (those not strictly contained by another contained annotation) - child_anns = [] - for a in candidate_anns: - has_closer_container = any(strictly_contains(b, a) for b in all_contained) - if not has_closer_container: - child_anns.append(a) - if not child_anns: - continue - - # Build this child classification block - child_entries = group_by_value(child_anns) - # Recurse: for each answer entry, compute further nested - for entry in child_entries: - entry_frames = entry.get("frames", []) - child_nested = build_nested_for_frames(entry_frames, group_key) - if child_nested: - entry["classifications"] = child_nested - - nested.append({ - "name": group_display_name(group_key, anns), - "answer": child_entries, - }) - - return nested - - # 2) Determine root groups (not fully contained by other groups) - root_group_keys = [k for k in audio_by_group.keys() if not is_group_nested(k)] - - # 3) Emit one NDJSON object per root classification group - class AudioNDJSON(BaseModel): - name: str - answer: List[Dict[str, Any]] - dataRow: Dict[str, str] - - for group_key in root_group_keys: - anns = audio_by_group[group_key] - top_entries = group_by_value(anns) - - # Attach nested to each top-level answer entry - for entry in top_entries: - frames = entry.get("frames", []) - children = build_nested_for_frames(frames, group_key) - if children: - entry["classifications"] = children - - yield AudioNDJSON( - name=group_display_name(group_key, anns), - answer=top_entries, - dataRow={"globalKey": label.data.global_key}, - ) + + # Use the modular hierarchy builder to create NDJSON annotations + ndjson_annotations = create_audio_ndjson_annotations( + audio_annotations, + label.data.global_key + ) + + # Yield each NDJSON annotation + for annotation in ndjson_annotations: + yield annotation diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py new file mode 100644 index 000000000..da9af289d --- /dev/null +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -0,0 +1,339 @@ +""" +Generic hierarchical classification builder for NDJSON serialization. + +This module provides reusable components for constructing nested hierarchical +classifications from temporal annotations (audio, video, etc.), separating the +complex logic from the main serialization code. +""" + +from collections import defaultdict +from typing import Any, Dict, List, Set, Tuple, Protocol, TypeVar, Generic +from pydantic import BaseModel + +from ...annotation_types.audio import AudioClassificationAnnotation + +# Generic type for temporal annotations +TemporalAnnotation = TypeVar('TemporalAnnotation', bound=Any) + + +class TemporalFrame: + """Represents a time frame in temporal annotations (audio, video, etc.).""" + + def __init__(self, start: int, end: int = None): + self.start = start + self.end = end or start + + def contains(self, other: "TemporalFrame") -> bool: + """Check if this frame contains another frame.""" + return (self.start <= other.start and + self.end is not None and other.end is not None and + self.end >= other.end) + + def strictly_contains(self, other: "TemporalFrame") -> bool: + """Check if this frame strictly contains another frame (not equal).""" + return (self.contains(other) and + (self.start < other.start or self.end > other.end)) + + def overlaps(self, other: "TemporalFrame") -> bool: + """Check if this frame overlaps with another frame.""" + return not (self.end < other.start or other.end < self.start) + + def to_dict(self) -> Dict[str, int]: + """Convert to dictionary format.""" + return {"start": self.start, "end": self.end} + + +class AnnotationGroupManager(Generic[TemporalAnnotation]): + """Manages grouping of temporal annotations by classification type.""" + + def __init__(self, annotations: List[TemporalAnnotation], frame_extractor: callable): + self.annotations = annotations + self.frame_extractor = frame_extractor # Function to extract (start, end) from annotation + self.groups = self._group_annotations() + self.root_groups = self._identify_root_groups() + + def _group_annotations(self) -> Dict[str, List[TemporalAnnotation]]: + """Group annotations by classification key (schema_id or name).""" + groups = defaultdict(list) + for annot in self.annotations: + key = annot.feature_schema_id or annot.name + groups[key].append(annot) + return dict(groups) + + def _identify_root_groups(self) -> Set[str]: + """Identify root groups that are not fully contained by other groups.""" + root_groups = set() + + for group_key, group_anns in self.groups.items(): + if not self._is_group_nested(group_key): + root_groups.add(group_key) + + return root_groups + + def _is_group_nested(self, group_key: str) -> bool: + """Check if a group is fully contained by other groups.""" + group_anns = self.groups[group_key] + + for ann in group_anns: + start, end = self.frame_extractor(ann) + ann_frame = TemporalFrame(start, end) + + # Check if this annotation is contained by any other group + contained = False + for other_key, other_anns in self.groups.items(): + if other_key == group_key: + continue + + for parent in other_anns: + parent_start, parent_end = self.frame_extractor(parent) + parent_frame = TemporalFrame(parent_start, parent_end) + if parent_frame.contains(ann_frame): + contained = True + break + + if contained: + break + + if not contained: + return False # Group is not fully nested + + return True # All annotations were contained somewhere + + def get_group_display_name(self, group_key: str) -> str: + """Get display name for a group.""" + group_anns = self.groups[group_key] + # Prefer the first non-empty annotation name + for ann in group_anns: + if ann.name: + return ann.name + return group_key + + def get_annotations_within_frames(self, frames: List[TemporalFrame], exclude_group: str = None) -> List[TemporalAnnotation]: + """Get all annotations within the given frames, excluding specified group.""" + contained = [] + + for group_key, group_anns in self.groups.items(): + if group_key == exclude_group: + continue + + for ann in group_anns: + start, end = self.frame_extractor(ann) + ann_frame = TemporalFrame(start, end) + if any(frame.contains(ann_frame) for frame in frames): + contained.append(ann) + + return contained + + +class ValueGrouper(Generic[TemporalAnnotation]): + """Handles grouping of annotations by their values and answer construction.""" + + def __init__(self, frame_extractor: callable): + self.frame_extractor = frame_extractor # Function to extract (start, end) from annotation + + def group_by_value(self, annotations: List[TemporalAnnotation]) -> List[Dict[str, Any]]: + """Group annotations by logical value and produce answer entries.""" + value_buckets = defaultdict(list) + + for ann in annotations: + key = self._get_value_key(ann) + value_buckets[key].append(ann) + + entries = [] + for _, anns in value_buckets.items(): + first = anns[0] + frames = [self.frame_extractor(a) for a in anns] + frame_dicts = [{"start": start, "end": end} for start, end in frames] + + entry = self._create_answer_entry(first, frame_dicts) + entries.append(entry) + + return entries + + def _get_value_key(self, ann: TemporalAnnotation) -> str: + """Get a stable key for grouping annotations by value.""" + if hasattr(ann.value, "answer"): + if isinstance(ann.value.answer, list): + # Checklist: stable key from selected option names + return str(sorted([opt.name for opt in ann.value.answer])) + elif hasattr(ann.value.answer, "name"): + # Radio: option name + return ann.value.answer.name + else: + # Text: the string value + return ann.value.answer + else: + return str(ann.value) + + def _create_answer_entry(self, first_ann: TemporalAnnotation, frames: List[Dict[str, int]]) -> Dict[str, Any]: + """Create an answer entry from the first annotation and frames.""" + if hasattr(first_ann.value, "answer") and isinstance(first_ann.value.answer, list): + # Checklist: emit one entry per distinct option present in this bucket + entries = [] + for opt_name in sorted([o.name for o in first_ann.value.answer]): + entries.append({"name": opt_name, "frames": frames}) + return entries[0] if len(entries) == 1 else {"options": entries, "frames": frames} + elif hasattr(first_ann.value, "answer") and hasattr(first_ann.value.answer, "name"): + # Radio + return {"name": first_ann.value.answer.name, "frames": frames} + else: + # Text + return {"value": first_ann.value.answer, "frames": frames} + + +class HierarchyBuilder(Generic[TemporalAnnotation]): + """Builds hierarchical nested classifications from temporal annotations.""" + + def __init__(self, group_manager: AnnotationGroupManager[TemporalAnnotation], value_grouper: ValueGrouper[TemporalAnnotation]): + self.group_manager = group_manager + self.value_grouper = value_grouper + + def build_hierarchy(self) -> List[Dict[str, Any]]: + """Build the complete hierarchical structure.""" + results = [] + + for group_key in self.group_manager.root_groups: + group_anns = self.group_manager.groups[group_key] + top_entries = self.value_grouper.group_by_value(group_anns) + + # Attach nested classifications to each top-level entry + for entry in top_entries: + frames = [TemporalFrame(f["start"], f["end"]) for f in entry.get("frames", [])] + nested = self._build_nested_for_frames(frames, group_key) + if nested: + entry["classifications"] = nested + + results.append({ + "name": self.group_manager.get_group_display_name(group_key), + "answer": top_entries, + }) + + return results + + def _build_nested_for_frames(self, parent_frames: List[TemporalFrame], exclude_group: str) -> List[Dict[str, Any]]: + """Recursively build nested classifications for specific parent frames.""" + nested = [] + + # Get all annotations within parent frames + all_contained = self.group_manager.get_annotations_within_frames(parent_frames, exclude_group) + + # Group by classification type and process each group + for group_key, group_anns in self.group_manager.groups.items(): + if group_key == exclude_group or group_key in self.group_manager.root_groups: + continue + + # Filter annotations that are contained by parent frames + candidate_anns = [] + for ann in group_anns: + start, end = self.group_manager.frame_extractor(ann) + ann_frame = TemporalFrame(start, end) + if any(frame.contains(ann_frame) for frame in parent_frames): + candidate_anns.append(ann) + + if not candidate_anns: + continue + + # Keep only immediate children (not strictly contained by other contained annotations) + child_anns = self._filter_immediate_children(candidate_anns, all_contained) + if not child_anns: + continue + + # Build this child classification block + child_entries = self.value_grouper.group_by_value(child_anns) + + # Recursively attach further nested classifications + for entry in child_entries: + entry_frames = [TemporalFrame(f["start"], f["end"]) for f in entry.get("frames", [])] + child_nested = self._build_nested_for_frames(entry_frames, group_key) + if child_nested: + entry["classifications"] = child_nested + + nested.append({ + "name": self.group_manager.get_group_display_name(group_key), + "answer": child_entries, + }) + + return nested + + def _filter_immediate_children(self, candidates: List[TemporalAnnotation], + all_contained: List[TemporalAnnotation]) -> List[TemporalAnnotation]: + """Filter to keep only immediate children (not strictly contained by others).""" + immediate_children = [] + + for candidate in candidates: + start, end = self.group_manager.frame_extractor(candidate) + candidate_frame = TemporalFrame(start, end) + + # Check if this candidate is strictly contained by any other contained annotation + has_closer_container = False + for other in all_contained: + if other is candidate: + continue + other_start, other_end = self.group_manager.frame_extractor(other) + other_frame = TemporalFrame(other_start, other_end) + if other_frame.strictly_contains(candidate_frame): + has_closer_container = True + break + + if not has_closer_container: + immediate_children.append(candidate) + + return immediate_children + + +class TemporalNDJSON(BaseModel): + """NDJSON format for temporal annotations (audio, video, etc.).""" + name: str + answer: List[Dict[str, Any]] + dataRow: Dict[str, str] + + +def create_temporal_ndjson_annotations(annotations: List[TemporalAnnotation], + data_global_key: str, + frame_extractor: callable) -> List[TemporalNDJSON]: + """ + Create NDJSON temporal annotations with hierarchical structure. + + Args: + annotations: List of temporal classification annotations + data_global_key: Global key for the data row + frame_extractor: Function that extracts (start, end) from annotation + + Returns: + List of TemporalNDJSON objects + """ + if not annotations: + return [] + + group_manager = AnnotationGroupManager(annotations, frame_extractor) + value_grouper = ValueGrouper(frame_extractor) + hierarchy_builder = HierarchyBuilder(group_manager, value_grouper) + hierarchy = hierarchy_builder.build_hierarchy() + + return [ + TemporalNDJSON( + name=item["name"], + answer=item["answer"], + dataRow={"globalKey": data_global_key} + ) + for item in hierarchy + ] + + +# Audio-specific convenience function +def create_audio_ndjson_annotations(annotations: List[AudioClassificationAnnotation], + data_global_key: str) -> List[TemporalNDJSON]: + """ + Create NDJSON audio annotations with hierarchical structure. + + Args: + annotations: List of audio classification annotations + data_global_key: Global key for the data row + + Returns: + List of TemporalNDJSON objects + """ + def audio_frame_extractor(ann: AudioClassificationAnnotation) -> Tuple[int, int]: + return (ann.start_frame, ann.end_frame or ann.start_frame) + + return create_temporal_ndjson_annotations(annotations, data_global_key, audio_frame_extractor) From b1863595b4b08bf28a625bff6e0e38ba9cec57b7 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Tue, 30 Sep 2025 09:11:01 -0700 Subject: [PATCH 030/103] chore: update temporal logic to be 1:1 with v3 script --- .../data/serialization/ndjson/temporal.py | 123 +++++++++++++++--- 1 file changed, 105 insertions(+), 18 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index da9af289d..3d0531940 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -183,45 +183,132 @@ def _create_answer_entry(self, first_ann: TemporalAnnotation, frames: List[Dict[ class HierarchyBuilder(Generic[TemporalAnnotation]): """Builds hierarchical nested classifications from temporal annotations.""" - + def __init__(self, group_manager: AnnotationGroupManager[TemporalAnnotation], value_grouper: ValueGrouper[TemporalAnnotation]): self.group_manager = group_manager self.value_grouper = value_grouper - + self.parent_assignments = self._compute_parent_assignments() + + def _compute_parent_assignments(self) -> Dict[str, str]: + """ + Compute best parent assignment for each group based on temporal containment and hierarchy depth. + Returns mapping of child_group_key -> parent_group_key. + """ + assignments = {} + assignment_depth = {} # Track depth of each assignment (0 = root) + + # Assign depth 0 to roots + for root_key in self.group_manager.root_groups: + assignment_depth[root_key] = 0 + + # Build assignments level by level + remaining_groups = set(self.group_manager.groups.keys()) - self.group_manager.root_groups + + max_iterations = len(remaining_groups) + 1 # Prevent infinite loops + iteration = 0 + + while remaining_groups and iteration < max_iterations: + iteration += 1 + assigned_this_round = set() + + for child_key in remaining_groups: + child_anns = self.group_manager.groups[child_key] + + # Find all potential parents (groups that contain this child's annotations) + potential_parents = [] + + for parent_key, parent_anns in self.group_manager.groups.items(): + if parent_key == child_key: + continue + + # Check if all child annotations are contained by at least one parent annotation + all_contained = True + for child_ann in child_anns: + child_start, child_end = self.group_manager.frame_extractor(child_ann) + child_frame = TemporalFrame(child_start, child_end) + + contained_by_parent = False + for parent_ann in parent_anns: + parent_start, parent_end = self.group_manager.frame_extractor(parent_ann) + parent_frame = TemporalFrame(parent_start, parent_end) + if parent_frame.contains(child_frame): + contained_by_parent = True + break + + if not contained_by_parent: + all_contained = False + break + + if all_contained: + # Calculate average container size for this parent + avg_size = sum((self.group_manager.frame_extractor(ann)[1] - self.group_manager.frame_extractor(ann)[0]) + for ann in parent_anns) / len(parent_anns) + + # Get depth of this parent (lower depth = closer to root = prefer) + parent_depth = assignment_depth.get(parent_key, 999) + + # Name similarity heuristic: if child name contains parent name as prefix/substring, + # it's likely related (e.g., "sub_radio_question_2" contains "sub_radio_question") + name_similarity = 1 if parent_key in child_key else 0 + + potential_parents.append((parent_key, avg_size, parent_depth, name_similarity)) + + # Choose best parent: prefer name similarity, then higher depth, then smallest size + if potential_parents: + # Sort by: 1) prefer name similarity, 2) prefer higher depth, 3) smallest size + potential_parents.sort(key=lambda x: (-x[3], -x[2], x[1])) + best_parent = potential_parents[0][0] + assignments[child_key] = best_parent + assignment_depth[child_key] = assignment_depth.get(best_parent, 0) + 1 + assigned_this_round.add(child_key) + + # Remove assigned groups from remaining + remaining_groups -= assigned_this_round + + # If no progress, break to avoid infinite loop + if not assigned_this_round: + break + + return assignments + def build_hierarchy(self) -> List[Dict[str, Any]]: """Build the complete hierarchical structure.""" results = [] - + for group_key in self.group_manager.root_groups: group_anns = self.group_manager.groups[group_key] top_entries = self.value_grouper.group_by_value(group_anns) - + # Attach nested classifications to each top-level entry for entry in top_entries: frames = [TemporalFrame(f["start"], f["end"]) for f in entry.get("frames", [])] nested = self._build_nested_for_frames(frames, group_key) if nested: entry["classifications"] = nested - + results.append({ "name": self.group_manager.get_group_display_name(group_key), "answer": top_entries, }) - + return results - def _build_nested_for_frames(self, parent_frames: List[TemporalFrame], exclude_group: str) -> List[Dict[str, Any]]: + def _build_nested_for_frames(self, parent_frames: List[TemporalFrame], parent_group_key: str) -> List[Dict[str, Any]]: """Recursively build nested classifications for specific parent frames.""" nested = [] - + # Get all annotations within parent frames - all_contained = self.group_manager.get_annotations_within_frames(parent_frames, exclude_group) - + all_contained = self.group_manager.get_annotations_within_frames(parent_frames, parent_group_key) + # Group by classification type and process each group for group_key, group_anns in self.group_manager.groups.items(): - if group_key == exclude_group or group_key in self.group_manager.root_groups: + if group_key == parent_group_key or group_key in self.group_manager.root_groups: continue - + + # Only process groups that are assigned to this parent + if self.parent_assignments.get(group_key) != parent_group_key: + continue + # Filter annotations that are contained by parent frames candidate_anns = [] for ann in group_anns: @@ -229,30 +316,30 @@ def _build_nested_for_frames(self, parent_frames: List[TemporalFrame], exclude_g ann_frame = TemporalFrame(start, end) if any(frame.contains(ann_frame) for frame in parent_frames): candidate_anns.append(ann) - + if not candidate_anns: continue - + # Keep only immediate children (not strictly contained by other contained annotations) child_anns = self._filter_immediate_children(candidate_anns, all_contained) if not child_anns: continue - + # Build this child classification block child_entries = self.value_grouper.group_by_value(child_anns) - + # Recursively attach further nested classifications for entry in child_entries: entry_frames = [TemporalFrame(f["start"], f["end"]) for f in entry.get("frames", [])] child_nested = self._build_nested_for_frames(entry_frames, group_key) if child_nested: entry["classifications"] = child_nested - + nested.append({ "name": self.group_manager.get_group_display_name(group_key), "answer": child_entries, }) - + return nested def _filter_immediate_children(self, candidates: List[TemporalAnnotation], From e63b306ae8776745eb000ea0f95adfc2002538f6 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Tue, 30 Sep 2025 10:21:50 -0700 Subject: [PATCH 031/103] chore: simplifiy drastically --- .../classification/classification.py | 10 + .../data/serialization/ndjson/temporal.py | 398 +++++++----------- .../data/serialization/ndjson/test_audio.py | 378 ++++++++++------- 3 files changed, 396 insertions(+), 390 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py index d6a6448dd..aca1827a9 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py @@ -17,11 +17,17 @@ class ClassificationAnswer(FeatureSchema, ConfidenceMixin, CustomMetricsMixin): Each answer can have a keyframe independent of the others. So unlike object annotations, classification annotations track keyframes at a classification answer level. + + - For temporal classifications (audio/video), optional start_frame/end_frame can specify + the time range for this answer. Must be within root annotation's frame range. + Defaults to root frame range if not specified. """ extra: Dict[str, Any] = {} keyframe: Optional[bool] = None classifications: Optional[List["ClassificationAnnotation"]] = None + start_frame: Optional[int] = None + end_frame: Optional[int] = None class Radio(ConfidenceMixin, CustomMetricsMixin, BaseModel): @@ -69,8 +75,12 @@ class ClassificationAnnotation( classifications (Optional[List[ClassificationAnnotation]]): Optional sub classification of the annotation feature_schema_id (Optional[Cuid]) value (Union[Text, Checklist, Radio]) + start_frame (Optional[int]): Start frame for temporal classifications (audio/video). Must be within root annotation's frame range. Defaults to root start_frame if not specified. + end_frame (Optional[int]): End frame for temporal classifications (audio/video). Must be within root annotation's frame range. Defaults to root end_frame if not specified. extra (Dict[str, Any]) """ value: Union[Text, Checklist, Radio] message_id: Optional[str] = None + start_frame: Optional[int] = None + end_frame: Optional[int] = None diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index 3d0531940..c13a9665d 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -2,12 +2,15 @@ Generic hierarchical classification builder for NDJSON serialization. This module provides reusable components for constructing nested hierarchical -classifications from temporal annotations (audio, video, etc.), separating the -complex logic from the main serialization code. +classifications from temporal annotations (audio, video, etc.). + +IMPORTANT: This module ONLY supports explicit nesting via ClassificationAnswer.classifications. +Annotations must define their hierarchy structure explicitly in the annotation objects. +Temporal containment-based inference is NOT supported. """ from collections import defaultdict -from typing import Any, Dict, List, Set, Tuple, Protocol, TypeVar, Generic +from typing import Any, Dict, List, Tuple, TypeVar, Generic from pydantic import BaseModel from ...annotation_types.audio import AudioClassificationAnnotation @@ -44,14 +47,18 @@ def to_dict(self) -> Dict[str, int]: class AnnotationGroupManager(Generic[TemporalAnnotation]): - """Manages grouping of temporal annotations by classification type.""" - + """Manages grouping of temporal annotations by classification type. + + NOTE: Since we only support explicit nesting via ClassificationAnswer.classifications, + all top-level AudioClassificationAnnotation objects are considered roots. + """ + def __init__(self, annotations: List[TemporalAnnotation], frame_extractor: callable): self.annotations = annotations self.frame_extractor = frame_extractor # Function to extract (start, end) from annotation self.groups = self._group_annotations() - self.root_groups = self._identify_root_groups() - + self.root_groups = set(self.groups.keys()) # All groups are roots with explicit nesting + def _group_annotations(self) -> Dict[str, List[TemporalAnnotation]]: """Group annotations by classification key (schema_id or name).""" groups = defaultdict(list) @@ -59,46 +66,7 @@ def _group_annotations(self) -> Dict[str, List[TemporalAnnotation]]: key = annot.feature_schema_id or annot.name groups[key].append(annot) return dict(groups) - - def _identify_root_groups(self) -> Set[str]: - """Identify root groups that are not fully contained by other groups.""" - root_groups = set() - - for group_key, group_anns in self.groups.items(): - if not self._is_group_nested(group_key): - root_groups.add(group_key) - - return root_groups - - def _is_group_nested(self, group_key: str) -> bool: - """Check if a group is fully contained by other groups.""" - group_anns = self.groups[group_key] - - for ann in group_anns: - start, end = self.frame_extractor(ann) - ann_frame = TemporalFrame(start, end) - - # Check if this annotation is contained by any other group - contained = False - for other_key, other_anns in self.groups.items(): - if other_key == group_key: - continue - - for parent in other_anns: - parent_start, parent_end = self.frame_extractor(parent) - parent_frame = TemporalFrame(parent_start, parent_end) - if parent_frame.contains(ann_frame): - contained = True - break - - if contained: - break - - if not contained: - return False # Group is not fully nested - - return True # All annotations were contained somewhere - + def get_group_display_name(self, group_key: str) -> str: """Get display name for a group.""" group_anns = self.groups[group_key] @@ -107,47 +75,35 @@ def get_group_display_name(self, group_key: str) -> str: if ann.name: return ann.name return group_key - - def get_annotations_within_frames(self, frames: List[TemporalFrame], exclude_group: str = None) -> List[TemporalAnnotation]: - """Get all annotations within the given frames, excluding specified group.""" - contained = [] - - for group_key, group_anns in self.groups.items(): - if group_key == exclude_group: - continue - - for ann in group_anns: - start, end = self.frame_extractor(ann) - ann_frame = TemporalFrame(start, end) - if any(frame.contains(ann_frame) for frame in frames): - contained.append(ann) - - return contained class ValueGrouper(Generic[TemporalAnnotation]): """Handles grouping of annotations by their values and answer construction.""" - + def __init__(self, frame_extractor: callable): self.frame_extractor = frame_extractor # Function to extract (start, end) from annotation - + def group_by_value(self, annotations: List[TemporalAnnotation]) -> List[Dict[str, Any]]: """Group annotations by logical value and produce answer entries.""" value_buckets = defaultdict(list) - + for ann in annotations: key = self._get_value_key(ann) value_buckets[key].append(ann) - + entries = [] for _, anns in value_buckets.items(): first = anns[0] + # Extract frames from each annotation (root frames) frames = [self.frame_extractor(a) for a in anns] frame_dicts = [{"start": start, "end": end} for start, end in frames] - - entry = self._create_answer_entry(first, frame_dicts) + + # Get root frames for passing to nested classifications + root_frames = frames[0] if frames else (None, None) + + entry = self._create_answer_entry(first, frame_dicts, root_frames) entries.append(entry) - + return entries def _get_value_key(self, ann: TemporalAnnotation) -> str: @@ -165,207 +121,163 @@ def _get_value_key(self, ann: TemporalAnnotation) -> str: else: return str(ann.value) - def _create_answer_entry(self, first_ann: TemporalAnnotation, frames: List[Dict[str, int]]) -> Dict[str, Any]: - """Create an answer entry from the first annotation and frames.""" + def _get_nested_frames(self, obj: Any, parent_frames: List[Dict[str, int]], root_frames: Tuple[int, int]) -> List[Dict[str, int]]: + """Get frame range for nested classification object. + + If obj has start_frame/end_frame specified, use those. Otherwise default to root frames. + + Args: + obj: ClassificationAnswer or ClassificationAnnotation + parent_frames: Parent's frame list (for fallback) + root_frames: Root annotation's (start, end) tuple + + Returns: + List of frame dictionaries + """ + if hasattr(obj, 'start_frame') and obj.start_frame is not None and hasattr(obj, 'end_frame') and obj.end_frame is not None: + # Use explicitly specified frames + return [{"start": obj.start_frame, "end": obj.end_frame}] + else: + # Default to root frames + if root_frames and root_frames[0] is not None and root_frames[1] is not None: + return [{"start": root_frames[0], "end": root_frames[1]}] + else: + # Fall back to parent frames if root not available + return parent_frames + + def _create_answer_entry(self, first_ann: TemporalAnnotation, frames: List[Dict[str, int]], root_frames: Tuple[int, int]) -> Dict[str, Any]: + """Create an answer entry from the first annotation and frames. + + Args: + first_ann: The first annotation in the value group + frames: List of frame dictionaries for this answer + root_frames: Tuple of (start, end) from the root AudioClassificationAnnotation + """ if hasattr(first_ann.value, "answer") and isinstance(first_ann.value.answer, list): # Checklist: emit one entry per distinct option present in this bucket entries = [] - for opt_name in sorted([o.name for o in first_ann.value.answer]): - entries.append({"name": opt_name, "frames": frames}) + for opt in first_ann.value.answer: + # Get frames for this specific checklist option (from opt or parent) + opt_frames = self._get_nested_frames(opt, frames, root_frames) + entry = {"name": opt.name, "frames": opt_frames} + # Handle explicit nesting for this checklist option + if hasattr(opt, 'classifications') and opt.classifications: + entry["classifications"] = self._serialize_explicit_classifications(opt.classifications, root_frames) + entries.append(entry) return entries[0] if len(entries) == 1 else {"options": entries, "frames": frames} elif hasattr(first_ann.value, "answer") and hasattr(first_ann.value.answer, "name"): # Radio - return {"name": first_ann.value.answer.name, "frames": frames} + opt = first_ann.value.answer + # Get frames for this radio answer (from answer or parent) + opt_frames = self._get_nested_frames(opt, frames, root_frames) + entry = {"name": opt.name, "frames": opt_frames} + # Handle explicit nesting via ClassificationAnswer.classifications + if hasattr(opt, 'classifications') and opt.classifications: + entry["classifications"] = self._serialize_explicit_classifications(opt.classifications, root_frames) + return entry else: - # Text - return {"value": first_ann.value.answer, "frames": frames} + # Text - nesting is at the annotation level, not answer level + entry = {"value": first_ann.value.answer, "frames": frames} + # Handle explicit nesting via AudioClassificationAnnotation.classifications + if hasattr(first_ann, 'classifications') and first_ann.classifications: + entry["classifications"] = self._serialize_explicit_classifications(first_ann.classifications, root_frames) + return entry + + def _serialize_explicit_classifications(self, classifications: List[Any], root_frames: Tuple[int, int]) -> List[Dict[str, Any]]: + """Serialize explicitly nested ClassificationAnnotation objects. + + Args: + classifications: List of ClassificationAnnotation objects + root_frames: Tuple of (start, end) from root AudioClassificationAnnotation + + Returns: + List of serialized classification dictionaries + """ + result = [] + + # Group nested classifications by name + grouped = defaultdict(list) + for cls in classifications: + name = cls.feature_schema_id or cls.name + grouped[name].append(cls) + + # Serialize each group + for name, cls_list in grouped.items(): + # Get display name from first annotation + display_name = cls_list[0].name if cls_list[0].name else name + + # Create answer entries for this nested classification + answers = [] + for cls in cls_list: + # Get frames for this ClassificationAnnotation (from cls or root) + cls_frames = self._get_nested_frames(cls, [], root_frames) + + if hasattr(cls.value, "answer"): + if isinstance(cls.value.answer, list): + # Checklist + for opt in cls.value.answer: + # Get frames for this checklist option (from opt or cls or root) + opt_frames = self._get_nested_frames(opt, cls_frames, root_frames) + answer = {"name": opt.name, "frames": opt_frames} + # Recursively handle deeper nesting + if hasattr(opt, 'classifications') and opt.classifications: + answer["classifications"] = self._serialize_explicit_classifications(opt.classifications, root_frames) + answers.append(answer) + elif hasattr(cls.value.answer, "name"): + # Radio + opt = cls.value.answer + # Get frames for this radio answer (from opt or cls or root) + opt_frames = self._get_nested_frames(opt, cls_frames, root_frames) + answer = {"name": opt.name, "frames": opt_frames} + # Recursively handle deeper nesting + if hasattr(opt, 'classifications') and opt.classifications: + answer["classifications"] = self._serialize_explicit_classifications(opt.classifications, root_frames) + answers.append(answer) + else: + # Text - check for annotation-level nesting + answer = {"value": cls.value.answer, "frames": cls_frames} + # Recursively handle deeper nesting at ClassificationAnnotation level + if hasattr(cls, 'classifications') and cls.classifications: + answer["classifications"] = self._serialize_explicit_classifications(cls.classifications, root_frames) + answers.append(answer) + + result.append({ + "name": display_name, + "answer": answers + }) + + return result class HierarchyBuilder(Generic[TemporalAnnotation]): - """Builds hierarchical nested classifications from temporal annotations.""" + """Builds hierarchical nested classifications from temporal annotations. + + NOTE: This builder only handles explicit nesting via ClassificationAnswer.classifications. + All nesting must be defined in the annotation structure itself, not inferred from temporal containment. + """ def __init__(self, group_manager: AnnotationGroupManager[TemporalAnnotation], value_grouper: ValueGrouper[TemporalAnnotation]): self.group_manager = group_manager self.value_grouper = value_grouper - self.parent_assignments = self._compute_parent_assignments() - - def _compute_parent_assignments(self) -> Dict[str, str]: - """ - Compute best parent assignment for each group based on temporal containment and hierarchy depth. - Returns mapping of child_group_key -> parent_group_key. - """ - assignments = {} - assignment_depth = {} # Track depth of each assignment (0 = root) - - # Assign depth 0 to roots - for root_key in self.group_manager.root_groups: - assignment_depth[root_key] = 0 - - # Build assignments level by level - remaining_groups = set(self.group_manager.groups.keys()) - self.group_manager.root_groups - - max_iterations = len(remaining_groups) + 1 # Prevent infinite loops - iteration = 0 - - while remaining_groups and iteration < max_iterations: - iteration += 1 - assigned_this_round = set() - - for child_key in remaining_groups: - child_anns = self.group_manager.groups[child_key] - - # Find all potential parents (groups that contain this child's annotations) - potential_parents = [] - - for parent_key, parent_anns in self.group_manager.groups.items(): - if parent_key == child_key: - continue - - # Check if all child annotations are contained by at least one parent annotation - all_contained = True - for child_ann in child_anns: - child_start, child_end = self.group_manager.frame_extractor(child_ann) - child_frame = TemporalFrame(child_start, child_end) - - contained_by_parent = False - for parent_ann in parent_anns: - parent_start, parent_end = self.group_manager.frame_extractor(parent_ann) - parent_frame = TemporalFrame(parent_start, parent_end) - if parent_frame.contains(child_frame): - contained_by_parent = True - break - - if not contained_by_parent: - all_contained = False - break - - if all_contained: - # Calculate average container size for this parent - avg_size = sum((self.group_manager.frame_extractor(ann)[1] - self.group_manager.frame_extractor(ann)[0]) - for ann in parent_anns) / len(parent_anns) - - # Get depth of this parent (lower depth = closer to root = prefer) - parent_depth = assignment_depth.get(parent_key, 999) - - # Name similarity heuristic: if child name contains parent name as prefix/substring, - # it's likely related (e.g., "sub_radio_question_2" contains "sub_radio_question") - name_similarity = 1 if parent_key in child_key else 0 - - potential_parents.append((parent_key, avg_size, parent_depth, name_similarity)) - - # Choose best parent: prefer name similarity, then higher depth, then smallest size - if potential_parents: - # Sort by: 1) prefer name similarity, 2) prefer higher depth, 3) smallest size - potential_parents.sort(key=lambda x: (-x[3], -x[2], x[1])) - best_parent = potential_parents[0][0] - assignments[child_key] = best_parent - assignment_depth[child_key] = assignment_depth.get(best_parent, 0) + 1 - assigned_this_round.add(child_key) - - # Remove assigned groups from remaining - remaining_groups -= assigned_this_round - - # If no progress, break to avoid infinite loop - if not assigned_this_round: - break - - return assignments def build_hierarchy(self) -> List[Dict[str, Any]]: - """Build the complete hierarchical structure.""" + """Build the complete hierarchical structure. + + All nesting is handled via explicit ClassificationAnswer.classifications, + so we simply group by value and let the ValueGrouper serialize the nested structure. + """ results = [] for group_key in self.group_manager.root_groups: group_anns = self.group_manager.groups[group_key] top_entries = self.value_grouper.group_by_value(group_anns) - # Attach nested classifications to each top-level entry - for entry in top_entries: - frames = [TemporalFrame(f["start"], f["end"]) for f in entry.get("frames", [])] - nested = self._build_nested_for_frames(frames, group_key) - if nested: - entry["classifications"] = nested - results.append({ "name": self.group_manager.get_group_display_name(group_key), "answer": top_entries, }) return results - - def _build_nested_for_frames(self, parent_frames: List[TemporalFrame], parent_group_key: str) -> List[Dict[str, Any]]: - """Recursively build nested classifications for specific parent frames.""" - nested = [] - - # Get all annotations within parent frames - all_contained = self.group_manager.get_annotations_within_frames(parent_frames, parent_group_key) - - # Group by classification type and process each group - for group_key, group_anns in self.group_manager.groups.items(): - if group_key == parent_group_key or group_key in self.group_manager.root_groups: - continue - - # Only process groups that are assigned to this parent - if self.parent_assignments.get(group_key) != parent_group_key: - continue - - # Filter annotations that are contained by parent frames - candidate_anns = [] - for ann in group_anns: - start, end = self.group_manager.frame_extractor(ann) - ann_frame = TemporalFrame(start, end) - if any(frame.contains(ann_frame) for frame in parent_frames): - candidate_anns.append(ann) - - if not candidate_anns: - continue - - # Keep only immediate children (not strictly contained by other contained annotations) - child_anns = self._filter_immediate_children(candidate_anns, all_contained) - if not child_anns: - continue - - # Build this child classification block - child_entries = self.value_grouper.group_by_value(child_anns) - - # Recursively attach further nested classifications - for entry in child_entries: - entry_frames = [TemporalFrame(f["start"], f["end"]) for f in entry.get("frames", [])] - child_nested = self._build_nested_for_frames(entry_frames, group_key) - if child_nested: - entry["classifications"] = child_nested - - nested.append({ - "name": self.group_manager.get_group_display_name(group_key), - "answer": child_entries, - }) - - return nested - - def _filter_immediate_children(self, candidates: List[TemporalAnnotation], - all_contained: List[TemporalAnnotation]) -> List[TemporalAnnotation]: - """Filter to keep only immediate children (not strictly contained by others).""" - immediate_children = [] - - for candidate in candidates: - start, end = self.group_manager.frame_extractor(candidate) - candidate_frame = TemporalFrame(start, end) - - # Check if this candidate is strictly contained by any other contained annotation - has_closer_container = False - for other in all_contained: - if other is candidate: - continue - other_start, other_end = self.group_manager.frame_extractor(other) - other_frame = TemporalFrame(other_start, other_end) - if other_frame.strictly_contains(candidate_frame): - has_closer_container = True - break - - if not has_closer_container: - immediate_children.append(candidate) - - return immediate_children class TemporalNDJSON(BaseModel): diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py index e392c2577..b275d7580 100644 --- a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py +++ b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py @@ -3,18 +3,17 @@ def test_audio_nested_text_radio_checklist_structure(): - # Purpose: verify that class-based AudioClassificationAnnotation inputs serialize - # into v3-style nested NDJSON with: + # Purpose: verify that class-based AudioClassificationAnnotation inputs with explicit + # nesting serialize into v3-style nested NDJSON with: # - exactly three top-level groups (text_class, radio_class, checklist_class) - # - children nested only under their closest containing parent frames + # - explicit nesting via ClassificationAnnotation.classifications and ClassificationAnswer.classifications + # - nested classifications can specify their own start_frame/end_frame (subset of root) # - correct field shapes per type (Text uses "value", Radio/Checklist use "name") - # Build annotations mirroring exec/v3.py shapes using class-based annotations + # Build annotations using explicit nesting (NEW interface) matching exec/v3.py output shape anns = [] - # text_class top-level with multiple values - # Expect: produces an NDJSON object named "text_class" with four answer entries; - # the long segment (1500-2400) will carry nested children below. + # text_class: simple value without nesting anns.append( lb_types.AudioClassificationAnnotation( frame=1000, @@ -23,14 +22,38 @@ def test_audio_nested_text_radio_checklist_structure(): value=lb_types.Text(answer="A"), ) ) + + # text_class: value WITH explicit nested classifications + # This annotation has nested classifications at the annotation level (for Text type) anns.append( lb_types.AudioClassificationAnnotation( frame=1500, - end_frame=2400, + end_frame=2400, # Root frame range name="text_class", value=lb_types.Text(answer="text_class value"), + classifications=[ # Explicit nesting via classifications field + lb_types.ClassificationAnnotation( + name="nested_text_class", + start_frame=1600, end_frame=2000, # Nested frame range (subset of root) + value=lb_types.Text(answer="nested_text_class value"), + classifications=[ # Deeper nesting + lb_types.ClassificationAnnotation( + name="nested_text_class_2", + start_frame=1800, end_frame=2000, # Even more specific nested range + value=lb_types.Text(answer="nested_text_class_2 value") + ) + ] + ), + lb_types.ClassificationAnnotation( + name="nested_text_class", + start_frame=2001, end_frame=2400, # Different nested frame range + value=lb_types.Text(answer="nested_text_class value2") + ) + ] ) ) + + # Additional text_class segments anns.append( lb_types.AudioClassificationAnnotation( frame=2500, @@ -48,49 +71,77 @@ def test_audio_nested_text_radio_checklist_structure(): ) ) - # nested under text_class - # Expect: nested_text_class (1600-2000) nests under the 1500-2400 parent; - # nested_text_class_2 nests under nested_text_class only (no duplicates at parent level). - anns.append( - lb_types.AudioClassificationAnnotation( - frame=1600, - end_frame=2000, - name="nested_text_class", - value=lb_types.Text(answer="nested_text_class value"), - ) - ) - anns.append( - lb_types.AudioClassificationAnnotation( - frame=1800, - end_frame=2000, - name="nested_text_class_2", - value=lb_types.Text(answer="nested_text_class_2 value"), - ) - ) - - # radio_class top-level - # Expect: two answer entries for first_radio_answer (two frame segments) and - # two for second_radio_answer; children attach only to their closest container answer. + # radio_class: Explicit nesting via ClassificationAnswer.classifications + # First segment with nested classifications anns.append( lb_types.AudioClassificationAnnotation( frame=200, - end_frame=1500, + end_frame=1500, # Root frame range name="radio_class", value=lb_types.Radio( - answer=lb_types.ClassificationAnswer(name="first_radio_answer") + answer=lb_types.ClassificationAnswer( + name="first_radio_answer", + classifications=[ # Explicit nesting at answer level for Radio + lb_types.ClassificationAnnotation( + name="sub_radio_question", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer( + name="first_sub_radio_answer", + start_frame=1000, end_frame=1500, # Nested frame range + classifications=[ # Deeper nesting + lb_types.ClassificationAnnotation( + name="sub_radio_question_2", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer( + name="first_sub_radio_answer_2", + start_frame=1300, end_frame=1500 # Even more specific nested range + ) + ) + ) + ] + ) + ) + ), + lb_types.ClassificationAnnotation( + name="sub_radio_question", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer( + name="second_sub_radio_answer", + start_frame=2100, end_frame=2500 # Nested frame range for second segment + ) + ) + ) + ] + ) ), ) ) + + # Second segment for first_radio_answer (will merge frames in output) anns.append( lb_types.AudioClassificationAnnotation( frame=2000, end_frame=2500, name="radio_class", value=lb_types.Radio( - answer=lb_types.ClassificationAnswer(name="first_radio_answer") + answer=lb_types.ClassificationAnswer( + name="first_radio_answer", + classifications=[ + lb_types.ClassificationAnnotation( + name="sub_radio_question", + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer( + name="second_sub_radio_answer" + ) + ) + ) + ] + ) ), ) ) + + # radio_class: second_radio_answer without nesting anns.append( lb_types.AudioClassificationAnnotation( frame=1550, @@ -112,61 +163,77 @@ def test_audio_nested_text_radio_checklist_structure(): ) ) - # nested radio - # Expect: sub_radio_question nests under first_radio_answer (1000-1500), and - # sub_radio_question_2 nests under sub_radio_question's first_sub_radio_answer only. - anns.append( - lb_types.AudioClassificationAnnotation( - frame=1000, - end_frame=1500, - name="sub_radio_question", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer( - name="first_sub_radio_answer" - ) - ), - ) - ) - anns.append( - lb_types.AudioClassificationAnnotation( - frame=1300, - end_frame=1500, - name="sub_radio_question_2", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer( - name="first_sub_radio_answer_2" - ) - ), - ) - ) - - # checklist_class top-level - # Expect: three answer entries (first/second/third_checklist_option) and - # nested checklist children attach to the first option segments where contained. + # checklist_class: Explicit nesting via ClassificationAnswer.classifications + # First segment with nested checklist anns.append( lb_types.AudioClassificationAnnotation( frame=300, - end_frame=800, + end_frame=800, # Root frame range (first segment) name="checklist_class", value=lb_types.Checklist( answer=[ - lb_types.ClassificationAnswer(name="first_checklist_option") + lb_types.ClassificationAnswer( + name="first_checklist_option", + classifications=[ # Explicit nesting at answer level for Checklist + lb_types.ClassificationAnnotation( + name="nested_checklist", + value=lb_types.Checklist( + answer=[ + lb_types.ClassificationAnswer( + name="nested_option_1", + start_frame=400, end_frame=700, # Nested frame range + classifications=[ # Deeper nesting + lb_types.ClassificationAnnotation( + name="checklist_nested_text", + start_frame=500, end_frame=700, # Even more specific nested range + value=lb_types.Text(answer="checklist_nested_text value") + ) + ] + ) + ] + ) + ) + ] + ) ] ), ) ) + + # Second segment for first_checklist_option with different nested options anns.append( lb_types.AudioClassificationAnnotation( frame=1200, - end_frame=1800, + end_frame=1800, # Root frame range (second segment) name="checklist_class", value=lb_types.Checklist( answer=[ - lb_types.ClassificationAnswer(name="first_checklist_option") + lb_types.ClassificationAnswer( + name="first_checklist_option", + classifications=[ + lb_types.ClassificationAnnotation( + name="nested_checklist", + value=lb_types.Checklist( + answer=[ + lb_types.ClassificationAnswer( + name="nested_option_2", + start_frame=1200, end_frame=1600 # Nested frame range + ), + lb_types.ClassificationAnswer( + name="nested_option_3", + start_frame=1400, end_frame=1800 # Nested frame range + ) + ] + ) + ) + ] + ) ] ), ) ) + + # checklist_class: other options without nesting anns.append( lb_types.AudioClassificationAnnotation( frame=2200, @@ -174,9 +241,7 @@ def test_audio_nested_text_radio_checklist_structure(): name="checklist_class", value=lb_types.Checklist( answer=[ - lb_types.ClassificationAnswer( - name="second_checklist_option" - ) + lb_types.ClassificationAnswer(name="second_checklist_option") ] ), ) @@ -194,48 +259,6 @@ def test_audio_nested_text_radio_checklist_structure(): ) ) - # nested checklist - # Expect: nested_checklist options 1/2/3 attach to their containing parent frames; - # checklist_nested_text attaches under nested_option_1 only. - anns.append( - lb_types.AudioClassificationAnnotation( - frame=400, - end_frame=700, - name="nested_checklist", - value=lb_types.Checklist( - answer=[lb_types.ClassificationAnswer(name="nested_option_1")] - ), - ) - ) - anns.append( - lb_types.AudioClassificationAnnotation( - frame=1200, - end_frame=1600, - name="nested_checklist", - value=lb_types.Checklist( - answer=[lb_types.ClassificationAnswer(name="nested_option_2")] - ), - ) - ) - anns.append( - lb_types.AudioClassificationAnnotation( - frame=1400, - end_frame=1800, - name="nested_checklist", - value=lb_types.Checklist( - answer=[lb_types.ClassificationAnswer(name="nested_option_3")] - ), - ) - ) - anns.append( - lb_types.AudioClassificationAnnotation( - frame=500, - end_frame=700, - name="checklist_nested_text", - value=lb_types.Text(answer="checklist_nested_text value"), - ) - ) - # Serialize a single Label containing all of the above annotations label = lb_types.Label( data={"global_key": "audio_nested_test_key"}, annotations=anns @@ -249,73 +272,134 @@ def test_audio_nested_text_radio_checklist_structure(): "checklist_class", } - # Validate text_class structure: children appear under the long segment only, - # and grandchildren only under their immediate parent + # Validate text_class structure with explicit nesting and frame ranges text_nd = next(obj for obj in ndjson if obj["name"] == "text_class") + + # Check that we have 4 text_class answers (A, text_class value, C, D) + assert len(text_nd["answer"]) == 4 + + # Find the parent answer with nested classifications parent = next( item for item in text_nd["answer"] if item.get("value") == "text_class value" ) + assert parent["frames"] == [{"start": 1500, "end": 2400}] + + # Check explicit nested classifications nested = parent.get("classifications", []) - names = {c["name"] for c in nested} - assert "nested_text_class" in names - nt = next(c for c in nested if c["name"] == "nested_text_class") - nt_ans = nt["answer"][0] - assert nt_ans["value"] == "nested_text_class value" - nt_nested = nt_ans.get("classifications", []) - assert any(c["name"] == "nested_text_class_2" for c in nt_nested) - - # Validate radio_class structure and immediate-child only + assert len(nested) == 1 # One nested_text_class group + nt = nested[0] + assert nt["name"] == "nested_text_class" + + # Check nested_text_class has 2 answers with different frame ranges + assert len(nt["answer"]) == 2 + nt_ans_1 = nt["answer"][0] + assert nt_ans_1["value"] == "nested_text_class value" + assert nt_ans_1["frames"] == [{"start": 1600, "end": 2000}] # Nested frame range + + # Check nested_text_class_2 is nested under nested_text_class + nt_nested = nt_ans_1.get("classifications", []) + assert len(nt_nested) == 1 + nt2 = nt_nested[0] + assert nt2["name"] == "nested_text_class_2" + assert nt2["answer"][0]["value"] == "nested_text_class_2 value" + assert nt2["answer"][0]["frames"] == [{"start": 1800, "end": 2000}] # Even more specific nested range + + # Check second nested_text_class answer + nt_ans_2 = nt["answer"][1] + assert nt_ans_2["value"] == "nested_text_class value2" + assert nt_ans_2["frames"] == [{"start": 2001, "end": 2400}] # Different nested frame range + + # Validate radio_class structure with explicit nesting and frame ranges radio_nd = next(obj for obj in ndjson if obj["name"] == "radio_class") - first_radio = next( + + # Check first_radio_answer + # Note: The two annotation segments have different nested structures, so they create separate answer entries + first_radios = [ a for a in radio_nd["answer"] if a["name"] == "first_radio_answer" - ) - assert any( - c["name"] == "sub_radio_question" - for c in first_radio.get("classifications", []) - ) - # sub_radio_question_2 is nested under sub_radio_question only + ] + # We get only first segment (200-1500) because second segment has different nested structure + assert len(first_radios) >= 1 + first_radio = first_radios[0] + # First segment frames + assert first_radio["frames"] == [{"start": 200, "end": 1500}] + + # Check explicit nested sub_radio_question + assert "classifications" in first_radio sub_radio = next( c for c in first_radio["classifications"] if c["name"] == "sub_radio_question" ) + + # Check sub_radio_question has 2 answers with specific frame ranges + assert len(sub_radio["answer"]) == 2 sr_first = next( a for a in sub_radio["answer"] if a["name"] == "first_sub_radio_answer" ) - assert any( - c["name"] == "sub_radio_question_2" - for c in sr_first.get("classifications", []) + assert sr_first["frames"] == [{"start": 1000, "end": 1500}] # Nested frame range + + # Check sub_radio_question_2 is nested under first_sub_radio_answer + assert "classifications" in sr_first + sr2 = next( + c + for c in sr_first["classifications"] + if c["name"] == "sub_radio_question_2" + ) + assert sr2["answer"][0]["name"] == "first_sub_radio_answer_2" + assert sr2["answer"][0]["frames"] == [{"start": 1300, "end": 1500}] # Even more specific nested range + + # Check second_sub_radio_answer + sr_second = next( + a for a in sub_radio["answer"] if a["name"] == "second_sub_radio_answer" ) + # Has specific nested frame range from first segment + assert sr_second["frames"] == [{"start": 2100, "end": 2500}] - # Validate checklist_class structure: nested_checklist exists, and nested text - # appears only under nested_option_1 (closest container) + # Validate checklist_class structure with explicit nesting and frame ranges checklist_nd = next( obj for obj in ndjson if obj["name"] == "checklist_class" ) - first_opt = next( + + # Check first_checklist_option + # Note: segments with different nested structures don't merge + first_opts = [ a for a in checklist_nd["answer"] if a["name"] == "first_checklist_option" - ) - assert any( - c["name"] == "nested_checklist" - for c in first_opt.get("classifications", []) - ) + ] + assert len(first_opts) >= 1 + first_opt = first_opts[0] + # First segment frames + assert first_opt["frames"] == [{"start": 300, "end": 800}] + + # Check explicit nested_checklist + assert "classifications" in first_opt nested_checklist = next( c for c in first_opt["classifications"] if c["name"] == "nested_checklist" ) - # Ensure nested text present under nested_checklist → nested_option_1 + + # Check nested_checklist has nested_option_1 from first segment + assert len(nested_checklist["answer"]) >= 1 + + # Check nested_option_1 with specific frame range opt1 = next( a for a in nested_checklist["answer"] if a["name"] == "nested_option_1" ) - assert any( - c["name"] == "checklist_nested_text" - for c in opt1.get("classifications", []) + assert opt1["frames"] == [{"start": 400, "end": 700}] # Nested frame range + + # Check checklist_nested_text is nested under nested_option_1 + assert "classifications" in opt1 + nested_text = next( + c + for c in opt1["classifications"] + if c["name"] == "checklist_nested_text" ) + assert nested_text["answer"][0]["value"] == "checklist_nested_text value" + assert nested_text["answer"][0]["frames"] == [{"start": 500, "end": 700}] # Even more specific nested range def test_audio_top_level_only_basic(): From 6b54e26482665dd390aa9f6bd96510c992753b5f Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Tue, 30 Sep 2025 11:24:38 -0700 Subject: [PATCH 032/103] chore: works perfectly --- .../data/serialization/ndjson/temporal.py | 164 +++++++++++++----- .../data/serialization/ndjson/test_audio.py | 22 +-- 2 files changed, 135 insertions(+), 51 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index c13a9665d..860432230 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -93,15 +93,15 @@ def group_by_value(self, annotations: List[TemporalAnnotation]) -> List[Dict[str entries = [] for _, anns in value_buckets.items(): - first = anns[0] # Extract frames from each annotation (root frames) frames = [self.frame_extractor(a) for a in anns] frame_dicts = [{"start": start, "end": end} for start, end in frames] - # Get root frames for passing to nested classifications + # Get root frames for passing to nested classifications (use first annotation's frames) root_frames = frames[0] if frames else (None, None) - entry = self._create_answer_entry(first, frame_dicts, root_frames) + # Pass ALL annotations so we can merge their nested classifications + entry = self._create_answer_entry(anns, frame_dicts, root_frames) entries.append(entry) return entries @@ -138,49 +138,80 @@ def _get_nested_frames(self, obj: Any, parent_frames: List[Dict[str, int]], root # Use explicitly specified frames return [{"start": obj.start_frame, "end": obj.end_frame}] else: - # Default to root frames - if root_frames and root_frames[0] is not None and root_frames[1] is not None: + # Default to parent frames first, then root frames + if parent_frames: + return parent_frames + elif root_frames and root_frames[0] is not None and root_frames[1] is not None: return [{"start": root_frames[0], "end": root_frames[1]}] else: - # Fall back to parent frames if root not available - return parent_frames + return [] - def _create_answer_entry(self, first_ann: TemporalAnnotation, frames: List[Dict[str, int]], root_frames: Tuple[int, int]) -> Dict[str, Any]: - """Create an answer entry from the first annotation and frames. + def _create_answer_entry(self, anns: List[TemporalAnnotation], frames: List[Dict[str, int]], root_frames: Tuple[int, int]) -> Dict[str, Any]: + """Create an answer entry from all annotations with the same value, merging their nested classifications. Args: - first_ann: The first annotation in the value group + anns: All annotations in the value group frames: List of frame dictionaries for this answer root_frames: Tuple of (start, end) from the root AudioClassificationAnnotation """ + first_ann = anns[0] + if hasattr(first_ann.value, "answer") and isinstance(first_ann.value.answer, list): - # Checklist: emit one entry per distinct option present in this bucket + # Checklist: emit one entry per distinct option present across ALL annotations + # First, collect all unique option names across all annotations + all_option_names = set() + for ann in anns: + if hasattr(ann.value, "answer") and isinstance(ann.value.answer, list): + for opt in ann.value.answer: + all_option_names.add(opt.name) + entries = [] - for opt in first_ann.value.answer: - # Get frames for this specific checklist option (from opt or parent) - opt_frames = self._get_nested_frames(opt, frames, root_frames) - entry = {"name": opt.name, "frames": opt_frames} - # Handle explicit nesting for this checklist option - if hasattr(opt, 'classifications') and opt.classifications: - entry["classifications"] = self._serialize_explicit_classifications(opt.classifications, root_frames) + for opt_name in sorted(all_option_names): # Sort for consistent ordering + # For each unique option, collect frames and nested classifications from all annotations + opt_frames = [] + all_nested = [] + for ann in anns: + if hasattr(ann.value, "answer") and isinstance(ann.value.answer, list): + for ann_opt in ann.value.answer: + if ann_opt.name == opt_name: + # Get this annotation's root frame range + ann_start, ann_end = self.frame_extractor(ann) + ann_frame_dict = [{"start": ann_start, "end": ann_end}] + # Collect this option's frame range (from option or parent annotation) + frames_for_this_opt = self._get_nested_frames(ann_opt, ann_frame_dict, root_frames) + opt_frames.extend(frames_for_this_opt) + # Collect nested classifications + if hasattr(ann_opt, 'classifications') and ann_opt.classifications: + all_nested.extend(ann_opt.classifications) + + entry = {"name": opt_name, "frames": opt_frames} + if all_nested: + entry["classifications"] = self._serialize_explicit_classifications(all_nested, root_frames) entries.append(entry) return entries[0] if len(entries) == 1 else {"options": entries, "frames": frames} elif hasattr(first_ann.value, "answer") and hasattr(first_ann.value.answer, "name"): # Radio opt = first_ann.value.answer - # Get frames for this radio answer (from answer or parent) - opt_frames = self._get_nested_frames(opt, frames, root_frames) - entry = {"name": opt.name, "frames": opt_frames} - # Handle explicit nesting via ClassificationAnswer.classifications - if hasattr(opt, 'classifications') and opt.classifications: - entry["classifications"] = self._serialize_explicit_classifications(opt.classifications, root_frames) + # Use the merged frames from all annotations (already passed in) + entry = {"name": opt.name, "frames": frames} + # Collect nested classifications from all annotations + all_nested = [] + for ann in anns: + if hasattr(ann.value, "answer") and hasattr(ann.value.answer, "classifications") and ann.value.answer.classifications: + all_nested.extend(ann.value.answer.classifications) + if all_nested: + entry["classifications"] = self._serialize_explicit_classifications(all_nested, root_frames) return entry else: # Text - nesting is at the annotation level, not answer level entry = {"value": first_ann.value.answer, "frames": frames} - # Handle explicit nesting via AudioClassificationAnnotation.classifications - if hasattr(first_ann, 'classifications') and first_ann.classifications: - entry["classifications"] = self._serialize_explicit_classifications(first_ann.classifications, root_frames) + # Collect nested classifications from all annotations + all_nested = [] + for ann in anns: + if hasattr(ann, 'classifications') and ann.classifications: + all_nested.extend(ann.classifications) + if all_nested: + entry["classifications"] = self._serialize_explicit_classifications(all_nested, root_frames) return entry def _serialize_explicit_classifications(self, classifications: List[Any], root_frames: Tuple[int, int]) -> List[Dict[str, Any]]: @@ -207,10 +238,12 @@ def _serialize_explicit_classifications(self, classifications: List[Any], root_f display_name = cls_list[0].name if cls_list[0].name else name # Create answer entries for this nested classification - answers = [] + # De-duplicate by answer value + seen_values = {} # value_key -> (answer_dict, nested_classifications) for cls in cls_list: # Get frames for this ClassificationAnnotation (from cls or root) cls_frames = self._get_nested_frames(cls, [], root_frames) + value_key = self._get_value_key(cls) if hasattr(cls.value, "answer"): if isinstance(cls.value.answer, list): @@ -219,27 +252,78 @@ def _serialize_explicit_classifications(self, classifications: List[Any], root_f # Get frames for this checklist option (from opt or cls or root) opt_frames = self._get_nested_frames(opt, cls_frames, root_frames) answer = {"name": opt.name, "frames": opt_frames} - # Recursively handle deeper nesting + # Collect nested for recursion + opt_nested = [] if hasattr(opt, 'classifications') and opt.classifications: - answer["classifications"] = self._serialize_explicit_classifications(opt.classifications, root_frames) - answers.append(answer) + opt_nested = opt.classifications + if opt_nested: + answer["classifications"] = self._serialize_explicit_classifications(opt_nested, root_frames) + # Note: Checklist options don't need de-duplication + # (they're already handled at the parent level) + if value_key not in seen_values: + seen_values[value_key] = [] + seen_values[value_key].append(answer) elif hasattr(cls.value.answer, "name"): - # Radio + # Radio - de-duplicate by name opt = cls.value.answer + # Check if this answer has explicit frames + has_explicit_frames = (hasattr(opt, 'start_frame') and opt.start_frame is not None and + hasattr(opt, 'end_frame') and opt.end_frame is not None) # Get frames for this radio answer (from opt or cls or root) opt_frames = self._get_nested_frames(opt, cls_frames, root_frames) - answer = {"name": opt.name, "frames": opt_frames} - # Recursively handle deeper nesting - if hasattr(opt, 'classifications') and opt.classifications: - answer["classifications"] = self._serialize_explicit_classifications(opt.classifications, root_frames) - answers.append(answer) + + # Check if we've already seen this answer name + if value_key in seen_values: + # Only merge frames if both have explicit frames, or neither does + existing_has_explicit = seen_values[value_key].get("_has_explicit", False) + if has_explicit_frames and existing_has_explicit: + # Both explicit - merge + seen_values[value_key]["frames"].extend(opt_frames) + elif has_explicit_frames and not existing_has_explicit: + # Current is explicit, existing is implicit - replace with explicit + seen_values[value_key]["frames"] = opt_frames + seen_values[value_key]["_has_explicit"] = True + elif not has_explicit_frames and existing_has_explicit: + # Current is implicit, existing is explicit - keep existing (don't merge) + pass + else: + # Both implicit - merge + seen_values[value_key]["frames"].extend(opt_frames) + + # Always merge nested classifications + if hasattr(opt, 'classifications') and opt.classifications: + seen_values[value_key]["_nested"].extend(opt.classifications) + else: + answer = {"name": opt.name, "frames": opt_frames, "_nested": [], "_has_explicit": has_explicit_frames} + if hasattr(opt, 'classifications') and opt.classifications: + answer["_nested"] = list(opt.classifications) + seen_values[value_key] = answer else: # Text - check for annotation-level nesting answer = {"value": cls.value.answer, "frames": cls_frames} - # Recursively handle deeper nesting at ClassificationAnnotation level + # Collect nested + text_nested = [] if hasattr(cls, 'classifications') and cls.classifications: - answer["classifications"] = self._serialize_explicit_classifications(cls.classifications, root_frames) - answers.append(answer) + text_nested = cls.classifications + if text_nested: + answer["classifications"] = self._serialize_explicit_classifications(text_nested, root_frames) + if value_key not in seen_values: + seen_values[value_key] = [] + seen_values[value_key].append(answer) + + # Convert seen_values to answers list + answers = [] + for value_key, value_data in seen_values.items(): + if isinstance(value_data, list): + answers.extend(value_data) + else: + # Radio case - handle nested classifications + if value_data.get("_nested"): + value_data["classifications"] = self._serialize_explicit_classifications(value_data["_nested"], root_frames) + # Clean up internal fields + value_data.pop("_nested", None) + value_data.pop("_has_explicit", None) + answers.append(value_data) result.append({ "name": display_name, diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py index b275d7580..038d4d526 100644 --- a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py +++ b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py @@ -315,15 +315,15 @@ def test_audio_nested_text_radio_checklist_structure(): radio_nd = next(obj for obj in ndjson if obj["name"] == "radio_class") # Check first_radio_answer - # Note: The two annotation segments have different nested structures, so they create separate answer entries + # Note: Segments with the same answer value are merged (both segments have "first_radio_answer") first_radios = [ a for a in radio_nd["answer"] if a["name"] == "first_radio_answer" ] - # We get only first segment (200-1500) because second segment has different nested structure - assert len(first_radios) >= 1 + # We get one merged answer with both frame ranges + assert len(first_radios) == 1 first_radio = first_radios[0] - # First segment frames - assert first_radio["frames"] == [{"start": 200, "end": 1500}] + # Merged frames from both segments: [200-1500] and [2000-2500] + assert first_radio["frames"] == [{"start": 200, "end": 1500}, {"start": 2000, "end": 2500}] # Check explicit nested sub_radio_question assert "classifications" in first_radio @@ -363,16 +363,16 @@ def test_audio_nested_text_radio_checklist_structure(): ) # Check first_checklist_option - # Note: segments with different nested structures don't merge + # Note: segments with the same answer value are merged first_opts = [ a for a in checklist_nd["answer"] if a["name"] == "first_checklist_option" ] - assert len(first_opts) >= 1 + assert len(first_opts) == 1 first_opt = first_opts[0] - # First segment frames - assert first_opt["frames"] == [{"start": 300, "end": 800}] + # Merged frames from both segments: [300-800] and [1200-1800] + assert first_opt["frames"] == [{"start": 300, "end": 800}, {"start": 1200, "end": 1800}] # Check explicit nested_checklist assert "classifications" in first_opt @@ -382,8 +382,8 @@ def test_audio_nested_text_radio_checklist_structure(): if c["name"] == "nested_checklist" ) - # Check nested_checklist has nested_option_1 from first segment - assert len(nested_checklist["answer"]) >= 1 + # Check nested_checklist has all 3 options (nested_option_1, 2, 3) from both segments + assert len(nested_checklist["answer"]) == 3 # Check nested_option_1 with specific frame range opt1 = next( From ccad765fa34bef9fbd2c72aa8e8ac30e00d99ddc Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 30 Sep 2025 18:25:31 +0000 Subject: [PATCH 033/103] :art: Cleaned --- examples/annotation_import/audio.ipynb | 525 ++++++------------------- 1 file changed, 124 insertions(+), 401 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index b47440eb4..4f20127ee 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,87 +1,42 @@ { + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {}, "cells": [ { - "cell_type": "markdown", - "id": "137b71f2", "metadata": {}, "source": [ - "## Brief temporal audio examples (Text, Radio, Checklist, Nested)\n", - "\n", - "- This section shows minimal, class-based examples that serialize to NDJSON:\n", - " - Text: `value` with `frames`\n", - " - Radio: `name` with `frames`\n", - " - Checklist: `name` with `frames`\n", - " - Nested (1 level): child nested under closest containing parent `frames`\n", - "\n", - "Run this cell and the next one to see the NDJSON output only (no API calls).\n" - ] + "", + " ", + "\n" + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, - "id": "f58dd5db", "metadata": {}, - "outputs": [], "source": [ - "import labelbox.types as lb_types\n", - "from labelbox.data.serialization.ndjson.converter import NDJsonConverter\n", - "\n", - "# Minimal Text temporal example\n", - "text_anns = [\n", - " lb_types.AudioClassificationAnnotation(\n", - " start_frame=1000, end_frame=1100, name=\"text_class\", value=lb_types.Text(answer=\"Hello\")\n", - " ),\n", - " lb_types.AudioClassificationAnnotation(\n", - " start_frame=1200, end_frame=1300, name=\"text_class\", value=lb_types.Text(answer=\"World\")\n", - " ),\n", - "]\n", - "\n", - "# Minimal Radio temporal example\n", - "radio_anns = [\n", - " lb_types.AudioClassificationAnnotation(\n", - " start_frame=200, end_frame=1500, name=\"radio_class\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_radio_answer\")),\n", - " ),\n", - "]\n", - "\n", - "# Minimal Checklist temporal example\n", - "checklist_anns = [\n", - " lb_types.AudioClassificationAnnotation(\n", - " start_frame=1200, end_frame=1800, name=\"checklist_class\",\n", - " value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name=\"angry\")]),\n", - " ),\n", - "]\n", - "\n", - "# Minimal Nested (1 level) example: nested_text under parent text segment\n", - "nested_anns = [\n", - " lb_types.AudioClassificationAnnotation(\n", - " start_frame=1500, end_frame=2400, name=\"text_class\", value=lb_types.Text(answer=\"parent\")\n", - " ),\n", - " lb_types.AudioClassificationAnnotation(\n", - " start_frame=1600, end_frame=2000, name=\"nested_text\", value=lb_types.Text(answer=\"child\")\n", - " ),\n", - "]\n", + "\n", + "\n", + "\n", "\n", - "label = lb_types.Label(\n", - " data={\"global_key\": \"audio_examples_demo\"},\n", - " annotations=text_anns + radio_anns + checklist_anns + nested_anns,\n", - ")\n", - "ndjson = list(NDJsonConverter.serialize([label]))\n", - "for i, obj in enumerate(ndjson, 1):\n", - " print(f\"{i}. {obj}\")\n" - ] + "\n", + "\n", + "" + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", "\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "\n", @@ -93,10 +48,10 @@ "\n", "" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Audio Annotation Import\n", @@ -122,188 +77,111 @@ "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", "\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "* For information on what types of annotations are supported per data type, refer to this documentation:\n", " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "* Notes:\n", " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", "outputs": [], - "source": [ - "%pip install -q \"labelbox[data]\"" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", + "cell_type": "code", "outputs": [], - "source": [ - "import labelbox as lb\n", - "import uuid\n", - "import labelbox.types as lb_types" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Replace with your API key\n", "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", "outputs": [], - "source": [ - "# Add your api key\n", - "API_KEY = \"\"\n", - "client = lb.Client(api_key=API_KEY)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Supported annotations for Audio" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", + "cell_type": "code", "outputs": [], - "source": [ - "##### Classification free text #####\n", - "\n", - "text_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"text_audio\",\n", - " value=lb_types.Text(answer=\"free text audio annotation\"),\n", - ")\n", - "\n", - "text_annotation_ndjson = {\n", - " \"name\": \"text_audio\",\n", - " \"answer\": \"free text audio annotation\",\n", - "}" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", + "cell_type": "code", "outputs": [], - "source": [ - "##### Checklist Classification #######\n", - "\n", - "checklist_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"checklist_audio\",\n", - " value=lb_types.Checklist(answer=[\n", - " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", - " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", - " ]),\n", - ")\n", - "\n", - "checklist_annotation_ndjson = {\n", - " \"name\":\n", - " \"checklist_audio\",\n", - " \"answers\": [\n", - " {\n", - " \"name\": \"first_checklist_answer\"\n", - " },\n", - " {\n", - " \"name\": \"second_checklist_answer\"\n", - " },\n", - " ],\n", - "}" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", + "cell_type": "code", "outputs": [], - "source": [ - "######## Radio Classification ######\n", - "\n", - "radio_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"radio_audio\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", - " name=\"second_radio_answer\")),\n", - ")\n", - "\n", - "radio_annotation_ndjson = {\n", - " \"name\": \"radio_audio\",\n", - " \"answer\": {\n", - " \"name\": \"first_radio_answer\"\n", - " },\n", - "}" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Upload Annotations - putting it all together " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Import data rows into Catalog" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", + "cell_type": "code", "outputs": [], - "source": [ - "# Create one Labelbox dataset\n", - "\n", - "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", - "\n", - "asset = {\n", - " \"row_data\":\n", - " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", - " \"global_key\":\n", - " global_key,\n", - "}\n", - "\n", - "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", - "task = dataset.create_data_rows([asset])\n", - "task.wait_till_done()\n", - "print(\"Errors:\", task.errors)\n", - "print(\"Failed data rows: \", task.failed_data_rows)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Create/select an ontology\n", @@ -311,341 +189,186 @@ "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", "\n", "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", + "cell_type": "code", "outputs": [], - "source": [ - "ontology_builder = lb.OntologyBuilder(classifications=[\n", - " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", - " name=\"text_audio\"),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"checklist_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_checklist_answer\"),\n", - " lb.Option(value=\"second_checklist_answer\"),\n", - " ],\n", - " ),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"radio_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_radio_answer\"),\n", - " lb.Option(value=\"second_radio_answer\"),\n", - " ],\n", - " ),\n", - " # Temporal classification for token-level annotations\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.TEXT,\n", - " name=\"User Speaker\",\n", - " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", - " ),\n", - "])\n", - "\n", - "ontology = client.create_ontology(\n", - " \"Ontology Audio Annotations\",\n", - " ontology_builder.asdict(),\n", - " media_type=lb.MediaType.Audio,\n", - ")" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Step 3: Create a labeling project\n", "Connect the ontology to the labeling project" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", + "cell_type": "code", "outputs": [], - "source": [ - "# Create Labelbox project\n", - "project = client.create_project(name=\"audio_project\",\n", - " media_type=lb.MediaType.Audio)\n", - "\n", - "# Setup your ontology\n", - "project.setup_editor(\n", - " ontology) # Connect your ontology and editor to your project" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Send a batch of data rows to the project" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", + "cell_type": "code", "outputs": [], - "source": [ - "# Setup Batches and Ontology\n", - "\n", - "# Create a batch to send to your MAL project\n", - "batch = project.create_batch(\n", - " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", - " global_keys=[\n", - " global_key\n", - " ], # Paginated collection of data row objects, list of data row ids or global keys\n", - " priority=5, # priority between 1(Highest) - 5(lowest)\n", - ")\n", - "\n", - "print(\"Batch: \", batch)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Create the annotations payload\n", "Create the annotations payload using the snippets of code above\n", "\n", "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Python annotation\n", "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "", + "cell_type": "code", "outputs": [], - "source": [] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "", + "cell_type": "code", "outputs": [], - "source": [] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", + "cell_type": "code", "outputs": [], - "source": [ - "label = []\n", - "label.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", - " ))" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### NDJSON annotations \n", "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", + "cell_type": "code", "outputs": [], - "source": [ - "label_ndjson = []\n", - "for annotations in [\n", - " text_annotation_ndjson,\n", - " checklist_annotation_ndjson,\n", - " radio_annotation_ndjson,\n", - "]:\n", - " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", - " label_ndjson.append(annotations)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Temporal Audio Annotations\n", "\n", "You can create temporal annotations for individual tokens (words) with precise timing:\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Define tokens with precise timing (from demo script)\n", - "tokens_data = [\n", - " (\"Hello\", 586, 770), # Hello: frames 586-770\n", - " (\"AI\", 771, 955), # AI: frames 771-955\n", - " (\"how\", 956, 1140), # how: frames 956-1140\n", - " (\"are\", 1141, 1325), # are: frames 1141-1325\n", - " (\"you\", 1326, 1510), # you: frames 1326-1510\n", - " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", - " (\"today\", 1696, 1880), # today: frames 1696-1880\n", - "]\n", - "\n", - "# Create temporal annotations for each token\n", - "temporal_annotations = []\n", - "for token, start_frame, end_frame in tokens_data:\n", - " token_annotation = lb_types.AudioClassificationAnnotation(\n", - " frame=start_frame,\n", - " end_frame=end_frame,\n", - " name=\"User Speaker\",\n", - " value=lb_types.Text(answer=token),\n", - " )\n", - " temporal_annotations.append(token_annotation)\n", - "\n", - "print(f\"Created {len(temporal_annotations)} temporal token annotations\")" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Create label with both regular and temporal annotations\n", - "label_with_temporal = []\n", - "label_with_temporal.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation] +\n", - " temporal_annotations,\n", - " ))\n", - "\n", - "print(\n", - " f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n", - ")\n", - "print(f\" - Regular annotations: 3\")\n", - "print(f\" - Temporal annotations: {len(temporal_annotations)}\")" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Model Assisted Labeling (MAL)\n", "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload temporal annotations via MAL\n", - "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label_with_temporal,\n", - ")\n", - "\n", - "temporal_upload_job.wait_until_done()\n", - "print(\"Temporal upload completed!\")\n", - "print(\"Errors:\", temporal_upload_job.errors)\n", - "print(\"Status:\", temporal_upload_job.statuses)" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload our label using Model-Assisted Labeling\n", - "upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Label Import" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload label for this data row in project\n", - "upload_job = lb.LabelImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=\"label_import_job\" + str(uuid.uuid4()),\n", - " labels=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Optional deletions for cleanup " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# project.delete()\n# dataset.delete()", + "cell_type": "code", "outputs": [], - "source": [ - "# project.delete()\n", - "# dataset.delete()" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" + "execution_count": null } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + ] +} \ No newline at end of file From 735bb098d93286247ab33ae1fa292589900114fd Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 30 Sep 2025 18:26:06 +0000 Subject: [PATCH 034/103] :memo: README updated --- examples/README.md | 168 ++++++++++++++++++++++----------------------- 1 file changed, 84 insertions(+), 84 deletions(-) diff --git a/examples/README.md b/examples/README.md index 924d1017d..842286b2d 100644 --- a/examples/README.md +++ b/examples/README.md @@ -16,15 +16,25 @@ + + Projects + Open In Github + Open In Colab + Ontologies Open In Github Open In Colab - Quick Start - Open In Github - Open In Colab + Batches + Open In Github + Open In Colab + + + Custom Embeddings + Open In Github + Open In Colab Data Rows @@ -37,25 +47,15 @@ Open In Colab - Batches - Open In Github - Open In Colab - - - Projects - Open In Github - Open In Colab + Quick Start + Open In Github + Open In Colab Data Row Metadata Open In Github Open In Colab - - Custom Embeddings - Open In Github - Open In Colab - User Management Open In Github @@ -75,25 +75,25 @@ + + Export Data + Open In Github + Open In Colab + Export V1 to V2 Migration Support Open In Github Open In Colab - - Exporting to CSV - Open In Github - Open In Colab - Composite Mask Export Open In Github Open In Colab - Export Data - Open In Github - Open In Colab + Exporting to CSV + Open In Github + Open In Colab @@ -143,36 +143,11 @@ - - Tiled - Open In Github - Open In Colab - Text Open In Github Open In Colab - - PDF - Open In Github - Open In Colab - - - Video - Open In Github - Open In Colab - - - Audio - Open In Github - Open In Colab - - - Conversational - Open In Github - Open In Colab - HTML Open In Github @@ -188,11 +163,36 @@ Open In Github Open In Colab + + Video + Open In Github + Open In Colab + + + Audio + Open In Github + Open In Colab + Conversational LLM Open In Github Open In Colab + + Tiled + Open In Github + Open In Colab + + + PDF + Open In Github + Open In Colab + + + Conversational + Open In Github + Open In Colab + @@ -208,9 +208,9 @@ - Langchain - Open In Github - Open In Colab + Meta SAM + Open In Github + Open In Colab Meta SAM Video @@ -218,20 +218,20 @@ Open In Colab - Meta SAM - Open In Github - Open In Colab + Huggingface Custom Embeddings + Open In Github + Open In Colab + + + Langchain + Open In Github + Open In Colab Import YOLOv8 Annotations Open In Github Open In Colab - - Huggingface Custom Embeddings - Open In Github - Open In Colab - @@ -247,25 +247,25 @@ - Model Predictions to Project - Open In Github - Open In Colab + Custom Metrics Basics + Open In Github + Open In Colab Custom Metrics Demo Open In Github Open In Colab - - Custom Metrics Basics - Open In Github - Open In Colab - Model Slices Open In Github Open In Colab + + Model Predictions to Project + Open In Github + Open In Colab + @@ -280,25 +280,15 @@ - - HTML Predictions - Open In Github - Open In Colab - Text Predictions Open In Github Open In Colab - Video Predictions - Open In Github - Open In Colab - - - Conversational Predictions - Open In Github - Open In Colab + PDF Predictions + Open In Github + Open In Colab Geospatial Predictions @@ -306,9 +296,14 @@ Open In Colab - PDF Predictions - Open In Github - Open In Colab + Conversational Predictions + Open In Github + Open In Colab + + + Video Predictions + Open In Github + Open In Colab Image Predictions @@ -320,6 +315,11 @@ Open In Github Open In Colab + + HTML Predictions + Open In Github + Open In Colab + From db3fb5eb4912380d2b095e4bd02aacd0e1687f77 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Tue, 30 Sep 2025 11:36:06 -0700 Subject: [PATCH 035/103] chore: update audio.ipynb --- examples/annotation_import/audio.ipynb | 477 +++++++++++++++++-------- 1 file changed, 332 insertions(+), 145 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index 4f20127ee..2faf0162c 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,42 +1,18 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {}, "cells": [ { - "metadata": {}, - "source": [ - "", - " ", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "cell_type": "markdown" - }, - { + "cell_type": "markdown", + "id": "d5df30ad", "metadata": {}, "source": [ "\n", " \n", "\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "4ece4169", "metadata": {}, "source": [ "\n", @@ -48,10 +24,10 @@ "\n", "" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Audio Annotation Import\n", @@ -77,111 +53,188 @@ "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", "\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "* For information on what types of annotations are supported per data type, refer to this documentation:\n", " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "* Notes:\n", " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "%pip install -q \"labelbox[data]\"" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "import labelbox as lb\n", + "import uuid\n", + "import labelbox.types as lb_types" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "# Replace with your API key\n", "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Add your api key\n", + "API_KEY = \"\"\n", + "client = lb.Client(api_key=API_KEY)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Supported annotations for Audio" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "##### Classification free text #####\n", + "\n", + "text_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"text_audio\",\n", + " value=lb_types.Text(answer=\"free text audio annotation\"),\n", + ")\n", + "\n", + "text_annotation_ndjson = {\n", + " \"name\": \"text_audio\",\n", + " \"answer\": \"free text audio annotation\",\n", + "}" + ] }, { - "metadata": {}, - "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "##### Checklist Classification #######\n", + "\n", + "checklist_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"checklist_audio\",\n", + " value=lb_types.Checklist(answer=[\n", + " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", + " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", + " ]),\n", + ")\n", + "\n", + "checklist_annotation_ndjson = {\n", + " \"name\":\n", + " \"checklist_audio\",\n", + " \"answers\": [\n", + " {\n", + " \"name\": \"first_checklist_answer\"\n", + " },\n", + " {\n", + " \"name\": \"second_checklist_answer\"\n", + " },\n", + " ],\n", + "}" + ] }, { - "metadata": {}, - "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "######## Radio Classification ######\n", + "\n", + "radio_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"radio_audio\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", + " name=\"second_radio_answer\")),\n", + ")\n", + "\n", + "radio_annotation_ndjson = {\n", + " \"name\": \"radio_audio\",\n", + " \"answer\": {\n", + " \"name\": \"first_radio_answer\"\n", + " },\n", + "}" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Import data rows into Catalog" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create one Labelbox dataset\n", + "\n", + "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", + "\n", + "asset = {\n", + " \"row_data\":\n", + " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", + " \"global_key\":\n", + " global_key,\n", + "}\n", + "\n", + "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", + "task = dataset.create_data_rows([asset])\n", + "task.wait_till_done()\n", + "print(\"Errors:\", task.errors)\n", + "print(\"Failed data rows: \", task.failed_data_rows)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Create/select an ontology\n", @@ -189,186 +242,320 @@ "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", "\n", "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "ontology_builder = lb.OntologyBuilder(classifications=[\n", + " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", + " name=\"text_audio\"),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"checklist_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_checklist_answer\"),\n", + " lb.Option(value=\"second_checklist_answer\"),\n", + " ],\n", + " ),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"radio_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_radio_answer\"),\n", + " lb.Option(value=\"second_radio_answer\"),\n", + " ],\n", + " ),\n", + " # Temporal classification for token-level annotations\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.TEXT,\n", + " name=\"User Speaker\",\n", + " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", + " ),\n", + "])\n", + "\n", + "ontology = client.create_ontology(\n", + " \"Ontology Audio Annotations\",\n", + " ontology_builder.asdict(),\n", + " media_type=lb.MediaType.Audio,\n", + ")" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Step 3: Create a labeling project\n", "Connect the ontology to the labeling project" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create Labelbox project\n", + "project = client.create_project(name=\"audio_project\",\n", + " media_type=lb.MediaType.Audio)\n", + "\n", + "# Setup your ontology\n", + "project.setup_editor(\n", + " ontology) # Connect your ontology and editor to your project" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Setup Batches and Ontology\n", + "\n", + "# Create a batch to send to your MAL project\n", + "batch = project.create_batch(\n", + " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", + " global_keys=[\n", + " global_key\n", + " ], # Paginated collection of data row objects, list of data row ids or global keys\n", + " priority=5, # priority between 1(Highest) - 5(lowest)\n", + ")\n", + "\n", + "print(\"Batch: \", batch)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Create the annotations payload\n", "Create the annotations payload using the snippets of code above\n", "\n", "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Python annotation\n", "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": [ - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "", "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { + "execution_count": null, "metadata": {}, - "source": "", - "cell_type": "code", "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", - "cell_type": "code", - "outputs": [], - "execution_count": null + "source": [ + "label = []\n", + "label.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", + " ))" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### NDJSON annotations \n", "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "label_ndjson = []\n", + "for annotations in [\n", + " text_annotation_ndjson,\n", + " checklist_annotation_ndjson,\n", + " radio_annotation_ndjson,\n", + "]:\n", + " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", + " label_ndjson.append(annotations)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "## Temporal Audio Annotations\n", "\n", "You can create temporal annotations for individual tokens (words) with precise timing:\n" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Define tokens with precise timing (from demo script)\n", + "tokens_data = [\n", + " (\"Hello\", 586, 770), # Hello: frames 586-770\n", + " (\"AI\", 771, 955), # AI: frames 771-955\n", + " (\"how\", 956, 1140), # how: frames 956-1140\n", + " (\"are\", 1141, 1325), # are: frames 1141-1325\n", + " (\"you\", 1326, 1510), # you: frames 1326-1510\n", + " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", + " (\"today\", 1696, 1880), # today: frames 1696-1880\n", + "]\n", + "\n", + "# Create temporal annotations for each token\n", + "temporal_annotations = []\n", + "for token, start_frame, end_frame in tokens_data:\n", + " token_annotation = lb_types.AudioClassificationAnnotation(\n", + " frame=start_frame,\n", + " end_frame=end_frame,\n", + " name=\"User Speaker\",\n", + " value=lb_types.Text(answer=token),\n", + " )\n", + " temporal_annotations.append(token_annotation)\n", + "\n", + "print(f\"Created {len(temporal_annotations)} temporal token annotations\")" + ] }, { - "metadata": {}, - "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create label with both regular and temporal annotations\n", + "label_with_temporal = []\n", + "label_with_temporal.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation] +\n", + " temporal_annotations,\n", + " ))\n", + "\n", + "print(\n", + " f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n", + ")\n", + "print(f\" - Regular annotations: 3\")\n", + "print(f\" - Temporal annotations: {len(temporal_annotations)}\")" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Model Assisted Labeling (MAL)\n", "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload temporal annotations via MAL\n", + "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label_with_temporal,\n", + ")\n", + "\n", + "temporal_upload_job.wait_until_done()\n", + "print(\"Temporal upload completed!\")\n", + "print(\"Errors:\", temporal_upload_job.errors)\n", + "print(\"Status:\", temporal_upload_job.statuses)" + ] }, { - "metadata": {}, - "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload our label using Model-Assisted Labeling\n", + "upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "#### Label Import" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload label for this data row in project\n", + "upload_job = lb.LabelImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=\"label_import_job\" + str(uuid.uuid4()),\n", + " labels=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "### Optional deletions for cleanup " - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# project.delete()\n# dataset.delete()", "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# project.delete()\n", + "# dataset.delete()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b0d5ee4147330f5db5e7d8a4977c89902d699e7e Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 30 Sep 2025 18:37:03 +0000 Subject: [PATCH 036/103] :art: Cleaned --- examples/annotation_import/audio.ipynb | 438 ++++++------------------- 1 file changed, 103 insertions(+), 335 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index 2faf0162c..615ac7c86 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,18 +1,18 @@ { + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {}, "cells": [ { - "cell_type": "markdown", - "id": "d5df30ad", "metadata": {}, "source": [ - "\n", - " \n", + "", + " ", "\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", - "id": "4ece4169", "metadata": {}, "source": [ "\n", @@ -24,10 +24,10 @@ "\n", "" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Audio Annotation Import\n", @@ -53,188 +53,111 @@ "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", "\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "* For information on what types of annotations are supported per data type, refer to this documentation:\n", " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "* Notes:\n", " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", "outputs": [], - "source": [ - "%pip install -q \"labelbox[data]\"" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", + "cell_type": "code", "outputs": [], - "source": [ - "import labelbox as lb\n", - "import uuid\n", - "import labelbox.types as lb_types" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "# Replace with your API key\n", "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", "outputs": [], - "source": [ - "# Add your api key\n", - "API_KEY = \"\"\n", - "client = lb.Client(api_key=API_KEY)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Supported annotations for Audio" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", + "cell_type": "code", "outputs": [], - "source": [ - "##### Classification free text #####\n", - "\n", - "text_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"text_audio\",\n", - " value=lb_types.Text(answer=\"free text audio annotation\"),\n", - ")\n", - "\n", - "text_annotation_ndjson = {\n", - " \"name\": \"text_audio\",\n", - " \"answer\": \"free text audio annotation\",\n", - "}" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", + "cell_type": "code", "outputs": [], - "source": [ - "##### Checklist Classification #######\n", - "\n", - "checklist_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"checklist_audio\",\n", - " value=lb_types.Checklist(answer=[\n", - " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", - " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", - " ]),\n", - ")\n", - "\n", - "checklist_annotation_ndjson = {\n", - " \"name\":\n", - " \"checklist_audio\",\n", - " \"answers\": [\n", - " {\n", - " \"name\": \"first_checklist_answer\"\n", - " },\n", - " {\n", - " \"name\": \"second_checklist_answer\"\n", - " },\n", - " ],\n", - "}" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", + "cell_type": "code", "outputs": [], - "source": [ - "######## Radio Classification ######\n", - "\n", - "radio_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"radio_audio\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", - " name=\"second_radio_answer\")),\n", - ")\n", - "\n", - "radio_annotation_ndjson = {\n", - " \"name\": \"radio_audio\",\n", - " \"answer\": {\n", - " \"name\": \"first_radio_answer\"\n", - " },\n", - "}" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Upload Annotations - putting it all together " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Import data rows into Catalog" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", + "cell_type": "code", "outputs": [], - "source": [ - "# Create one Labelbox dataset\n", - "\n", - "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", - "\n", - "asset = {\n", - " \"row_data\":\n", - " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", - " \"global_key\":\n", - " global_key,\n", - "}\n", - "\n", - "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", - "task = dataset.create_data_rows([asset])\n", - "task.wait_till_done()\n", - "print(\"Errors:\", task.errors)\n", - "print(\"Failed data rows: \", task.failed_data_rows)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Create/select an ontology\n", @@ -242,320 +165,165 @@ "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", "\n", "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", + "cell_type": "code", "outputs": [], - "source": [ - "ontology_builder = lb.OntologyBuilder(classifications=[\n", - " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", - " name=\"text_audio\"),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"checklist_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_checklist_answer\"),\n", - " lb.Option(value=\"second_checklist_answer\"),\n", - " ],\n", - " ),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"radio_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_radio_answer\"),\n", - " lb.Option(value=\"second_radio_answer\"),\n", - " ],\n", - " ),\n", - " # Temporal classification for token-level annotations\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.TEXT,\n", - " name=\"User Speaker\",\n", - " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", - " ),\n", - "])\n", - "\n", - "ontology = client.create_ontology(\n", - " \"Ontology Audio Annotations\",\n", - " ontology_builder.asdict(),\n", - " media_type=lb.MediaType.Audio,\n", - ")" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Step 3: Create a labeling project\n", "Connect the ontology to the labeling project" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", + "cell_type": "code", "outputs": [], - "source": [ - "# Create Labelbox project\n", - "project = client.create_project(name=\"audio_project\",\n", - " media_type=lb.MediaType.Audio)\n", - "\n", - "# Setup your ontology\n", - "project.setup_editor(\n", - " ontology) # Connect your ontology and editor to your project" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Send a batch of data rows to the project" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", + "cell_type": "code", "outputs": [], - "source": [ - "# Setup Batches and Ontology\n", - "\n", - "# Create a batch to send to your MAL project\n", - "batch = project.create_batch(\n", - " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", - " global_keys=[\n", - " global_key\n", - " ], # Paginated collection of data row objects, list of data row ids or global keys\n", - " priority=5, # priority between 1(Highest) - 5(lowest)\n", - ")\n", - "\n", - "print(\"Batch: \", batch)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Create the annotations payload\n", "Create the annotations payload using the snippets of code above\n", "\n", "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Python annotation\n", "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", + "cell_type": "code", "outputs": [], - "source": [ - "label = []\n", - "label.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", - " ))" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### NDJSON annotations \n", "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", + "cell_type": "code", "outputs": [], - "source": [ - "label_ndjson = []\n", - "for annotations in [\n", - " text_annotation_ndjson,\n", - " checklist_annotation_ndjson,\n", - " radio_annotation_ndjson,\n", - "]:\n", - " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", - " label_ndjson.append(annotations)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "markdown", "metadata": {}, "source": [ "## Temporal Audio Annotations\n", "\n", "You can create temporal annotations for individual tokens (words) with precise timing:\n" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Define tokens with precise timing (from demo script)\n", - "tokens_data = [\n", - " (\"Hello\", 586, 770), # Hello: frames 586-770\n", - " (\"AI\", 771, 955), # AI: frames 771-955\n", - " (\"how\", 956, 1140), # how: frames 956-1140\n", - " (\"are\", 1141, 1325), # are: frames 1141-1325\n", - " (\"you\", 1326, 1510), # you: frames 1326-1510\n", - " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", - " (\"today\", 1696, 1880), # today: frames 1696-1880\n", - "]\n", - "\n", - "# Create temporal annotations for each token\n", - "temporal_annotations = []\n", - "for token, start_frame, end_frame in tokens_data:\n", - " token_annotation = lb_types.AudioClassificationAnnotation(\n", - " frame=start_frame,\n", - " end_frame=end_frame,\n", - " name=\"User Speaker\",\n", - " value=lb_types.Text(answer=token),\n", - " )\n", - " temporal_annotations.append(token_annotation)\n", - "\n", - "print(f\"Created {len(temporal_annotations)} temporal token annotations\")" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", + "cell_type": "code", "outputs": [], - "source": [ - "# Create label with both regular and temporal annotations\n", - "label_with_temporal = []\n", - "label_with_temporal.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation] +\n", - " temporal_annotations,\n", - " ))\n", - "\n", - "print(\n", - " f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n", - ")\n", - "print(f\" - Regular annotations: 3\")\n", - "print(f\" - Temporal annotations: {len(temporal_annotations)}\")" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Model Assisted Labeling (MAL)\n", "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload temporal annotations via MAL\n", - "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label_with_temporal,\n", - ")\n", - "\n", - "temporal_upload_job.wait_until_done()\n", - "print(\"Temporal upload completed!\")\n", - "print(\"Errors:\", temporal_upload_job.errors)\n", - "print(\"Status:\", temporal_upload_job.statuses)" - ] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload our label using Model-Assisted Labeling\n", - "upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "#### Label Import" - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", "outputs": [], - "source": [ - "# Upload label for this data row in project\n", - "upload_job = lb.LabelImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=\"label_import_job\" + str(uuid.uuid4()),\n", - " labels=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] + "execution_count": null }, { - "cell_type": "markdown", "metadata": {}, "source": [ "### Optional deletions for cleanup " - ] + ], + "cell_type": "markdown" }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# project.delete()\n# dataset.delete()", + "cell_type": "code", "outputs": [], - "source": [ - "# project.delete()\n", - "# dataset.delete()" - ] + "execution_count": null } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + ] +} \ No newline at end of file From 1266338998f3fb9fa720509764bba4719ed407ef Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Wed, 1 Oct 2025 12:18:16 -0700 Subject: [PATCH 037/103] chore: drastically simplify --- .../data/serialization/ndjson/temporal.py | 700 ++++++++---------- 1 file changed, 329 insertions(+), 371 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index 860432230..d25db3e80 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -1,7 +1,7 @@ """ -Generic hierarchical classification builder for NDJSON serialization. +Simplified temporal NDJSON serialization. -This module provides reusable components for constructing nested hierarchical +This module provides a streamlined approach for constructing nested hierarchical classifications from temporal annotations (audio, video, etc.). IMPORTANT: This module ONLY supports explicit nesting via ClassificationAnswer.classifications. @@ -10,413 +10,371 @@ """ from collections import defaultdict -from typing import Any, Dict, List, Tuple, TypeVar, Generic +from typing import Any, Dict, List, Tuple from pydantic import BaseModel from ...annotation_types.audio import AudioClassificationAnnotation -# Generic type for temporal annotations -TemporalAnnotation = TypeVar('TemporalAnnotation', bound=Any) - - -class TemporalFrame: - """Represents a time frame in temporal annotations (audio, video, etc.).""" - - def __init__(self, start: int, end: int = None): - self.start = start - self.end = end or start - - def contains(self, other: "TemporalFrame") -> bool: - """Check if this frame contains another frame.""" - return (self.start <= other.start and - self.end is not None and other.end is not None and - self.end >= other.end) - - def strictly_contains(self, other: "TemporalFrame") -> bool: - """Check if this frame strictly contains another frame (not equal).""" - return (self.contains(other) and - (self.start < other.start or self.end > other.end)) - - def overlaps(self, other: "TemporalFrame") -> bool: - """Check if this frame overlaps with another frame.""" - return not (self.end < other.start or other.end < self.start) - - def to_dict(self) -> Dict[str, int]: - """Convert to dictionary format.""" - return {"start": self.start, "end": self.end} - - -class AnnotationGroupManager(Generic[TemporalAnnotation]): - """Manages grouping of temporal annotations by classification type. - - NOTE: Since we only support explicit nesting via ClassificationAnswer.classifications, - all top-level AudioClassificationAnnotation objects are considered roots. + +def create_temporal_ndjson_annotations( + annotations: List[Any], + data_global_key: str, + frame_extractor: callable +) -> List['TemporalNDJSON']: """ + Create NDJSON temporal annotations with hierarchical structure. - def __init__(self, annotations: List[TemporalAnnotation], frame_extractor: callable): - self.annotations = annotations - self.frame_extractor = frame_extractor # Function to extract (start, end) from annotation - self.groups = self._group_annotations() - self.root_groups = set(self.groups.keys()) # All groups are roots with explicit nesting - - def _group_annotations(self) -> Dict[str, List[TemporalAnnotation]]: - """Group annotations by classification key (schema_id or name).""" - groups = defaultdict(list) - for annot in self.annotations: - key = annot.feature_schema_id or annot.name - groups[key].append(annot) - return dict(groups) - - def get_group_display_name(self, group_key: str) -> str: - """Get display name for a group.""" - group_anns = self.groups[group_key] - # Prefer the first non-empty annotation name - for ann in group_anns: - if ann.name: - return ann.name - return group_key - - -class ValueGrouper(Generic[TemporalAnnotation]): - """Handles grouping of annotations by their values and answer construction.""" - - def __init__(self, frame_extractor: callable): - self.frame_extractor = frame_extractor # Function to extract (start, end) from annotation - - def group_by_value(self, annotations: List[TemporalAnnotation]) -> List[Dict[str, Any]]: - """Group annotations by logical value and produce answer entries.""" - value_buckets = defaultdict(list) - - for ann in annotations: - key = self._get_value_key(ann) - value_buckets[key].append(ann) - - entries = [] - for _, anns in value_buckets.items(): - # Extract frames from each annotation (root frames) - frames = [self.frame_extractor(a) for a in anns] - frame_dicts = [{"start": start, "end": end} for start, end in frames] - - # Get root frames for passing to nested classifications (use first annotation's frames) - root_frames = frames[0] if frames else (None, None) - - # Pass ALL annotations so we can merge their nested classifications - entry = self._create_answer_entry(anns, frame_dicts, root_frames) - entries.append(entry) - - return entries - - def _get_value_key(self, ann: TemporalAnnotation) -> str: - """Get a stable key for grouping annotations by value.""" - if hasattr(ann.value, "answer"): - if isinstance(ann.value.answer, list): - # Checklist: stable key from selected option names - return str(sorted([opt.name for opt in ann.value.answer])) - elif hasattr(ann.value.answer, "name"): - # Radio: option name - return ann.value.answer.name - else: - # Text: the string value - return ann.value.answer - else: - return str(ann.value) - - def _get_nested_frames(self, obj: Any, parent_frames: List[Dict[str, int]], root_frames: Tuple[int, int]) -> List[Dict[str, int]]: - """Get frame range for nested classification object. - - If obj has start_frame/end_frame specified, use those. Otherwise default to root frames. - - Args: - obj: ClassificationAnswer or ClassificationAnnotation - parent_frames: Parent's frame list (for fallback) - root_frames: Root annotation's (start, end) tuple - - Returns: - List of frame dictionaries - """ - if hasattr(obj, 'start_frame') and obj.start_frame is not None and hasattr(obj, 'end_frame') and obj.end_frame is not None: - # Use explicitly specified frames - return [{"start": obj.start_frame, "end": obj.end_frame}] - else: - # Default to parent frames first, then root frames - if parent_frames: - return parent_frames - elif root_frames and root_frames[0] is not None and root_frames[1] is not None: - return [{"start": root_frames[0], "end": root_frames[1]}] + Args: + annotations: List of temporal classification annotations + data_global_key: Global key for the data row + frame_extractor: Function that extracts (start, end) tuple from annotation + + Returns: + List of TemporalNDJSON objects + """ + if not annotations: + return [] + + # Group by classification name/schema_id + groups = defaultdict(list) + for ann in annotations: + key = ann.feature_schema_id or ann.name + groups[key].append(ann) + + results = [] + for group_key, group_anns in groups.items(): + # Get display name (prefer first non-empty name) + display_name = next((a.name for a in group_anns if a.name), group_key) + + # Process this group recursively + answers = _process_annotation_group(group_anns, frame_extractor) + + results.append( + TemporalNDJSON( + name=display_name, + answer=answers, + dataRow={"globalKey": data_global_key} + ) + ) + + return results + + +def _process_annotation_group(annotations: List[Any], frame_extractor: callable) -> List[Dict[str, Any]]: + """ + Process a group of annotations with the same name/schema_id. + Groups by answer value and handles nested classifications recursively. + """ + # Group by answer value + value_groups = defaultdict(list) + for ann in annotations: + value_key = _get_value_key(ann) + value_groups[value_key].append(ann) + + results = [] + for _, anns in value_groups.items(): + first = anns[0] + + # Handle different annotation types + if hasattr(first.value, "answer"): + answer = first.value.answer + + if isinstance(answer, list): + # Checklist - process each option + results.extend(_process_checklist(anns, frame_extractor)) + elif hasattr(answer, "name"): + # Radio - merge frames and nested classifications + results.append(_process_radio(anns, frame_extractor)) else: - return [] - - def _create_answer_entry(self, anns: List[TemporalAnnotation], frames: List[Dict[str, int]], root_frames: Tuple[int, int]) -> Dict[str, Any]: - """Create an answer entry from all annotations with the same value, merging their nested classifications. - - Args: - anns: All annotations in the value group - frames: List of frame dictionaries for this answer - root_frames: Tuple of (start, end) from the root AudioClassificationAnnotation - """ - first_ann = anns[0] - - if hasattr(first_ann.value, "answer") and isinstance(first_ann.value.answer, list): - # Checklist: emit one entry per distinct option present across ALL annotations - # First, collect all unique option names across all annotations - all_option_names = set() - for ann in anns: - if hasattr(ann.value, "answer") and isinstance(ann.value.answer, list): - for opt in ann.value.answer: - all_option_names.add(opt.name) - - entries = [] - for opt_name in sorted(all_option_names): # Sort for consistent ordering - # For each unique option, collect frames and nested classifications from all annotations - opt_frames = [] - all_nested = [] - for ann in anns: - if hasattr(ann.value, "answer") and isinstance(ann.value.answer, list): - for ann_opt in ann.value.answer: - if ann_opt.name == opt_name: - # Get this annotation's root frame range - ann_start, ann_end = self.frame_extractor(ann) - ann_frame_dict = [{"start": ann_start, "end": ann_end}] - # Collect this option's frame range (from option or parent annotation) - frames_for_this_opt = self._get_nested_frames(ann_opt, ann_frame_dict, root_frames) - opt_frames.extend(frames_for_this_opt) - # Collect nested classifications - if hasattr(ann_opt, 'classifications') and ann_opt.classifications: - all_nested.extend(ann_opt.classifications) - - entry = {"name": opt_name, "frames": opt_frames} - if all_nested: - entry["classifications"] = self._serialize_explicit_classifications(all_nested, root_frames) - entries.append(entry) - return entries[0] if len(entries) == 1 else {"options": entries, "frames": frames} - elif hasattr(first_ann.value, "answer") and hasattr(first_ann.value.answer, "name"): - # Radio - opt = first_ann.value.answer - # Use the merged frames from all annotations (already passed in) - entry = {"name": opt.name, "frames": frames} - # Collect nested classifications from all annotations - all_nested = [] - for ann in anns: - if hasattr(ann.value, "answer") and hasattr(ann.value.answer, "classifications") and ann.value.answer.classifications: - all_nested.extend(ann.value.answer.classifications) - if all_nested: - entry["classifications"] = self._serialize_explicit_classifications(all_nested, root_frames) - return entry + # Text - simple value with potential nesting + results.append(_process_text(anns, frame_extractor)) else: - # Text - nesting is at the annotation level, not answer level - entry = {"value": first_ann.value.answer, "frames": frames} - # Collect nested classifications from all annotations - all_nested = [] - for ann in anns: - if hasattr(ann, 'classifications') and ann.classifications: - all_nested.extend(ann.classifications) - if all_nested: - entry["classifications"] = self._serialize_explicit_classifications(all_nested, root_frames) - return entry - - def _serialize_explicit_classifications(self, classifications: List[Any], root_frames: Tuple[int, int]) -> List[Dict[str, Any]]: - """Serialize explicitly nested ClassificationAnnotation objects. - - Args: - classifications: List of ClassificationAnnotation objects - root_frames: Tuple of (start, end) from root AudioClassificationAnnotation - - Returns: - List of serialized classification dictionaries - """ - result = [] - - # Group nested classifications by name - grouped = defaultdict(list) - for cls in classifications: - name = cls.feature_schema_id or cls.name - grouped[name].append(cls) - - # Serialize each group - for name, cls_list in grouped.items(): - # Get display name from first annotation - display_name = cls_list[0].name if cls_list[0].name else name - - # Create answer entries for this nested classification - # De-duplicate by answer value - seen_values = {} # value_key -> (answer_dict, nested_classifications) - for cls in cls_list: - # Get frames for this ClassificationAnnotation (from cls or root) - cls_frames = self._get_nested_frames(cls, [], root_frames) - value_key = self._get_value_key(cls) - - if hasattr(cls.value, "answer"): - if isinstance(cls.value.answer, list): - # Checklist - for opt in cls.value.answer: - # Get frames for this checklist option (from opt or cls or root) - opt_frames = self._get_nested_frames(opt, cls_frames, root_frames) - answer = {"name": opt.name, "frames": opt_frames} - # Collect nested for recursion - opt_nested = [] - if hasattr(opt, 'classifications') and opt.classifications: - opt_nested = opt.classifications - if opt_nested: - answer["classifications"] = self._serialize_explicit_classifications(opt_nested, root_frames) - # Note: Checklist options don't need de-duplication - # (they're already handled at the parent level) - if value_key not in seen_values: - seen_values[value_key] = [] - seen_values[value_key].append(answer) - elif hasattr(cls.value.answer, "name"): - # Radio - de-duplicate by name - opt = cls.value.answer - # Check if this answer has explicit frames - has_explicit_frames = (hasattr(opt, 'start_frame') and opt.start_frame is not None and - hasattr(opt, 'end_frame') and opt.end_frame is not None) - # Get frames for this radio answer (from opt or cls or root) - opt_frames = self._get_nested_frames(opt, cls_frames, root_frames) - - # Check if we've already seen this answer name - if value_key in seen_values: - # Only merge frames if both have explicit frames, or neither does - existing_has_explicit = seen_values[value_key].get("_has_explicit", False) - if has_explicit_frames and existing_has_explicit: - # Both explicit - merge - seen_values[value_key]["frames"].extend(opt_frames) - elif has_explicit_frames and not existing_has_explicit: - # Current is explicit, existing is implicit - replace with explicit - seen_values[value_key]["frames"] = opt_frames - seen_values[value_key]["_has_explicit"] = True - elif not has_explicit_frames and existing_has_explicit: - # Current is implicit, existing is explicit - keep existing (don't merge) - pass - else: - # Both implicit - merge - seen_values[value_key]["frames"].extend(opt_frames) - - # Always merge nested classifications - if hasattr(opt, 'classifications') and opt.classifications: - seen_values[value_key]["_nested"].extend(opt.classifications) - else: - answer = {"name": opt.name, "frames": opt_frames, "_nested": [], "_has_explicit": has_explicit_frames} - if hasattr(opt, 'classifications') and opt.classifications: - answer["_nested"] = list(opt.classifications) - seen_values[value_key] = answer - else: - # Text - check for annotation-level nesting - answer = {"value": cls.value.answer, "frames": cls_frames} - # Collect nested - text_nested = [] - if hasattr(cls, 'classifications') and cls.classifications: - text_nested = cls.classifications - if text_nested: - answer["classifications"] = self._serialize_explicit_classifications(text_nested, root_frames) - if value_key not in seen_values: - seen_values[value_key] = [] - seen_values[value_key].append(answer) - - # Convert seen_values to answers list - answers = [] - for value_key, value_data in seen_values.items(): - if isinstance(value_data, list): - answers.extend(value_data) - else: - # Radio case - handle nested classifications - if value_data.get("_nested"): - value_data["classifications"] = self._serialize_explicit_classifications(value_data["_nested"], root_frames) - # Clean up internal fields - value_data.pop("_nested", None) - value_data.pop("_has_explicit", None) - answers.append(value_data) + # Fallback for unexpected structure + results.append(_process_text(anns, frame_extractor)) + + return results + + +def _process_checklist(annotations: List[Any], frame_extractor: callable) -> List[Dict[str, Any]]: + """Process checklist annotations - collect all unique options across all annotations.""" + # Collect all unique option names and their data + option_data = defaultdict(lambda: {"frames": [], "nested": []}) + + for ann in annotations: + ann_start, ann_end = frame_extractor(ann) + ann_frames = [{"start": ann_start, "end": ann_end}] + + if hasattr(ann.value, "answer") and isinstance(ann.value.answer, list): + for opt in ann.value.answer: + opt_name = opt.name + + # Get frames for this option (use explicit if available, else annotation frames) + opt_frames = _extract_frames(opt, ann_frames) + option_data[opt_name]["frames"].extend(opt_frames) + + # Collect nested classifications + if hasattr(opt, 'classifications') and opt.classifications: + option_data[opt_name]["nested"].extend(opt.classifications) + + # Build answer entries + results = [] + for opt_name in sorted(option_data.keys()): + entry = { + "name": opt_name, + "frames": option_data[opt_name]["frames"] + } + + # Recursively process nested classifications + if option_data[opt_name]["nested"]: + nested = _process_nested_classifications(option_data[opt_name]["nested"]) + if nested: + entry["classifications"] = nested + + results.append(entry) + + return results + + +def _process_radio(annotations: List[Any], frame_extractor: callable) -> Dict[str, Any]: + """Process radio annotations - merge frames and nested classifications.""" + first = annotations[0] + opt_name = first.value.answer.name + + # Collect all frames and nested classifications + all_frames = [] + all_nested = [] + + for ann in annotations: + ann_start, ann_end = frame_extractor(ann) + ann_frames = [{"start": ann_start, "end": ann_end}] + + # Get frames for this radio answer + opt_frames = _extract_frames(ann.value.answer, ann_frames) + all_frames.extend(opt_frames) + + # Collect nested + if hasattr(ann.value.answer, 'classifications') and ann.value.answer.classifications: + all_nested.extend(ann.value.answer.classifications) + + entry = {"name": opt_name, "frames": all_frames} + + # Recursively process nested + if all_nested: + nested = _process_nested_classifications(all_nested) + if nested: + entry["classifications"] = nested + + return entry + + +def _process_text(annotations: List[Any], frame_extractor: callable) -> Dict[str, Any]: + """Process text annotations - collect frames and nested classifications.""" + first = annotations[0] + text_value = first.value.answer if hasattr(first.value, "answer") else str(first.value) + + # Collect all frames and nested + all_frames = [] + all_nested = [] + + for ann in annotations: + start, end = frame_extractor(ann) + all_frames.append({"start": start, "end": end}) + + # Text nesting is at annotation level + if hasattr(ann, 'classifications') and ann.classifications: + all_nested.extend(ann.classifications) - result.append({ - "name": display_name, - "answer": answers - }) + entry = {"value": text_value, "frames": all_frames} - return result + # Recursively process nested + if all_nested: + nested = _process_nested_classifications(all_nested) + if nested: + entry["classifications"] = nested + return entry -class HierarchyBuilder(Generic[TemporalAnnotation]): - """Builds hierarchical nested classifications from temporal annotations. - NOTE: This builder only handles explicit nesting via ClassificationAnswer.classifications. - All nesting must be defined in the annotation structure itself, not inferred from temporal containment. +def _process_nested_classifications(classifications: List[Any]) -> List[Dict[str, Any]]: """ + Recursively process nested ClassificationAnnotation objects. + This uses the same grouping logic as top-level annotations. + """ + # Group by name/schema_id + groups = defaultdict(list) + for cls in classifications: + key = cls.feature_schema_id or cls.name + groups[key].append(cls) + + results = [] + for group_key, cls_list in groups.items(): + display_name = next((c.name for c in cls_list if c.name), group_key) + + # Group by value and process + value_groups = defaultdict(list) + for cls in cls_list: + value_key = _get_value_key(cls) + value_groups[value_key].append(cls) + + answers = [] + for _, cls_group in value_groups.items(): + first_cls = cls_group[0] + + if hasattr(first_cls.value, "answer"): + answer = first_cls.value.answer + + if isinstance(answer, list): + # Checklist + answers.extend(_process_nested_checklist(cls_group)) + elif hasattr(answer, "name"): + # Radio + answers.append(_process_nested_radio(cls_group)) + else: + # Text + answers.append(_process_nested_text(cls_group)) - def __init__(self, group_manager: AnnotationGroupManager[TemporalAnnotation], value_grouper: ValueGrouper[TemporalAnnotation]): - self.group_manager = group_manager - self.value_grouper = value_grouper + results.append({ + "name": display_name, + "answer": answers + }) - def build_hierarchy(self) -> List[Dict[str, Any]]: - """Build the complete hierarchical structure. + return results - All nesting is handled via explicit ClassificationAnswer.classifications, - so we simply group by value and let the ValueGrouper serialize the nested structure. - """ - results = [] - for group_key in self.group_manager.root_groups: - group_anns = self.group_manager.groups[group_key] - top_entries = self.value_grouper.group_by_value(group_anns) +def _process_nested_checklist(classifications: List[Any]) -> List[Dict[str, Any]]: + """Process nested checklist classifications.""" + option_data = defaultdict(lambda: {"frames": [], "nested": []}) - results.append({ - "name": self.group_manager.get_group_display_name(group_key), - "answer": top_entries, - }) + for cls in classifications: + cls_frames = _extract_frames(cls, []) - return results + if hasattr(cls.value, "answer") and isinstance(cls.value.answer, list): + for opt in cls.value.answer: + opt_frames = _extract_frames(opt, cls_frames) + option_data[opt.name]["frames"].extend(opt_frames) + if hasattr(opt, 'classifications') and opt.classifications: + option_data[opt.name]["nested"].extend(opt.classifications) -class TemporalNDJSON(BaseModel): - """NDJSON format for temporal annotations (audio, video, etc.).""" - name: str - answer: List[Dict[str, Any]] - dataRow: Dict[str, str] + results = [] + for opt_name in sorted(option_data.keys()): + entry = {"name": opt_name, "frames": option_data[opt_name]["frames"]} + + if option_data[opt_name]["nested"]: + nested = _process_nested_classifications(option_data[opt_name]["nested"]) + if nested: + entry["classifications"] = nested + + results.append(entry) + + return results + + +def _process_nested_radio(classifications: List[Any]) -> Dict[str, Any]: + """Process nested radio classifications - merge frames.""" + first = classifications[0] + opt_name = first.value.answer.name + + all_frames = [] + all_nested = [] + + for cls in classifications: + cls_frames = _extract_frames(cls, []) + opt_frames = _extract_frames(cls.value.answer, cls_frames) + all_frames.extend(opt_frames) + + if hasattr(cls.value.answer, 'classifications') and cls.value.answer.classifications: + all_nested.extend(cls.value.answer.classifications) + + entry = {"name": opt_name, "frames": all_frames} + + if all_nested: + nested = _process_nested_classifications(all_nested) + if nested: + entry["classifications"] = nested + + return entry + + +def _process_nested_text(classifications: List[Any]) -> Dict[str, Any]: + """Process nested text classifications.""" + first = classifications[0] + text_value = first.value.answer if hasattr(first.value, "answer") else str(first.value) + + all_frames = [] + all_nested = [] + + for cls in classifications: + frames = _extract_frames(cls, []) + all_frames.extend(frames) + + if hasattr(cls, 'classifications') and cls.classifications: + all_nested.extend(cls.classifications) + + entry = {"value": text_value, "frames": all_frames} + if all_nested: + nested = _process_nested_classifications(all_nested) + if nested: + entry["classifications"] = nested -def create_temporal_ndjson_annotations(annotations: List[TemporalAnnotation], - data_global_key: str, - frame_extractor: callable) -> List[TemporalNDJSON]: + return entry + + +def _extract_frames(obj: Any, fallback_frames: List[Dict[str, int]]) -> List[Dict[str, int]]: """ - Create NDJSON temporal annotations with hierarchical structure. - - Args: - annotations: List of temporal classification annotations - data_global_key: Global key for the data row - frame_extractor: Function that extracts (start, end) from annotation - - Returns: - List of TemporalNDJSON objects + Extract frame range from an object (annotation, answer, or classification). + Uses explicit frames if available, otherwise falls back to provided frames. """ - if not annotations: + if (hasattr(obj, 'start_frame') and obj.start_frame is not None and + hasattr(obj, 'end_frame') and obj.end_frame is not None): + return [{"start": obj.start_frame, "end": obj.end_frame}] + elif fallback_frames: + return fallback_frames + else: return [] - - group_manager = AnnotationGroupManager(annotations, frame_extractor) - value_grouper = ValueGrouper(frame_extractor) - hierarchy_builder = HierarchyBuilder(group_manager, value_grouper) - hierarchy = hierarchy_builder.build_hierarchy() - - return [ - TemporalNDJSON( - name=item["name"], - answer=item["answer"], - dataRow={"globalKey": data_global_key} - ) - for item in hierarchy - ] + + +def _get_value_key(obj: Any) -> str: + """Get a stable key for grouping by answer value.""" + if hasattr(obj.value, "answer"): + answer = obj.value.answer + if isinstance(answer, list): + # Checklist: stable key from selected option names + return str(sorted([opt.name for opt in answer])) + elif hasattr(answer, "name"): + # Radio: option name + return answer.name + else: + # Text: the string value + return str(answer) + else: + return str(obj.value) + + +class TemporalNDJSON(BaseModel): + """NDJSON format for temporal annotations (audio, video, etc.).""" + name: str + answer: List[Dict[str, Any]] + dataRow: Dict[str, str] # Audio-specific convenience function -def create_audio_ndjson_annotations(annotations: List[AudioClassificationAnnotation], - data_global_key: str) -> List[TemporalNDJSON]: +def create_audio_ndjson_annotations( + annotations: List[AudioClassificationAnnotation], + data_global_key: str +) -> List[TemporalNDJSON]: """ Create NDJSON audio annotations with hierarchical structure. - + Args: annotations: List of audio classification annotations data_global_key: Global key for the data row - + Returns: List of TemporalNDJSON objects """ def audio_frame_extractor(ann: AudioClassificationAnnotation) -> Tuple[int, int]: return (ann.start_frame, ann.end_frame or ann.start_frame) - + return create_temporal_ndjson_annotations(annotations, data_global_key, audio_frame_extractor) From 66e4c44eae4a29c214b32aaed0a4d89979ce2f03 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Wed, 1 Oct 2025 13:05:50 -0700 Subject: [PATCH 038/103] chore: lint --- .../labelbox/data/annotation_types/audio.py | 1 - .../labelbox/data/annotation_types/label.py | 18 ++- .../data/serialization/ndjson/label.py | 17 +-- .../data/serialization/ndjson/temporal.py | 105 +++++++++----- .../data/serialization/ndjson/test_audio.py | 130 ++++++++++++------ 5 files changed, 176 insertions(+), 95 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index c86fba668..588085962 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -34,4 +34,3 @@ class AudioClassificationAnnotation(ClassificationAnnotation): serialization_alias="end_frame", ) segment_index: Optional[int] = None - diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index 228512a5d..9170ebbe4 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -77,19 +77,29 @@ def _get_annotations_by_type(self, annotation_type): def frame_annotations( self, - ) -> Dict[int, Union[VideoObjectAnnotation, VideoClassificationAnnotation, AudioClassificationAnnotation]]: + ) -> Dict[ + int, + Union[ + VideoObjectAnnotation, + VideoClassificationAnnotation, + AudioClassificationAnnotation, + ], + ]: """Get temporal annotations organized by frame - + Returns: Dict[int, List]: Dictionary mapping frame (milliseconds) to list of temporal annotations - + Example: >>> label.frame_annotations() {2500: [VideoClassificationAnnotation(...), AudioClassificationAnnotation(...)]} """ frame_dict = defaultdict(list) for annotation in self.annotations: - if isinstance(annotation, (VideoObjectAnnotation, VideoClassificationAnnotation)): + if isinstance( + annotation, + (VideoObjectAnnotation, VideoClassificationAnnotation), + ): frame_dict[annotation.frame].append(annotation) elif isinstance(annotation, AudioClassificationAnnotation): frame_dict[annotation.start_frame].append(annotation) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 5fc19c004..9974c9aa0 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -2,7 +2,7 @@ import copy from itertools import groupby from operator import itemgetter -from typing import Any, Dict, Generator, List, Tuple, Union +from typing import Generator, List, Tuple, Union from uuid import uuid4 from pydantic import BaseModel @@ -86,7 +86,6 @@ def _get_consecutive_frames( consecutive.append((group[0], group[-1])) return consecutive - @classmethod def _get_segment_frame_ranges( cls, @@ -173,25 +172,23 @@ def _create_audio_annotations( """Create audio annotations with nested classifications using modular hierarchy builder.""" # Extract audio annotations from the label audio_annotations = [ - annot for annot in label.annotations + annot + for annot in label.annotations if isinstance(annot, AudioClassificationAnnotation) ] - + if not audio_annotations: return - + # Use the modular hierarchy builder to create NDJSON annotations ndjson_annotations = create_audio_ndjson_annotations( - audio_annotations, - label.data.global_key + audio_annotations, label.data.global_key ) - + # Yield each NDJSON annotation for annotation in ndjson_annotations: yield annotation - - @classmethod def _create_non_video_annotations(cls, label: Label): non_video_annotations = [ diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index d25db3e80..fc51fd0b9 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -17,10 +17,8 @@ def create_temporal_ndjson_annotations( - annotations: List[Any], - data_global_key: str, - frame_extractor: callable -) -> List['TemporalNDJSON']: + annotations: List[Any], data_global_key: str, frame_extractor: callable +) -> List["TemporalNDJSON"]: """ Create NDJSON temporal annotations with hierarchical structure. @@ -53,14 +51,16 @@ def create_temporal_ndjson_annotations( TemporalNDJSON( name=display_name, answer=answers, - dataRow={"globalKey": data_global_key} + dataRow={"globalKey": data_global_key}, ) ) return results -def _process_annotation_group(annotations: List[Any], frame_extractor: callable) -> List[Dict[str, Any]]: +def _process_annotation_group( + annotations: List[Any], frame_extractor: callable +) -> List[Dict[str, Any]]: """ Process a group of annotations with the same name/schema_id. Groups by answer value and handles nested classifications recursively. @@ -95,7 +95,9 @@ def _process_annotation_group(annotations: List[Any], frame_extractor: callable) return results -def _process_checklist(annotations: List[Any], frame_extractor: callable) -> List[Dict[str, Any]]: +def _process_checklist( + annotations: List[Any], frame_extractor: callable +) -> List[Dict[str, Any]]: """Process checklist annotations - collect all unique options across all annotations.""" # Collect all unique option names and their data option_data = defaultdict(lambda: {"frames": [], "nested": []}) @@ -113,20 +115,19 @@ def _process_checklist(annotations: List[Any], frame_extractor: callable) -> Lis option_data[opt_name]["frames"].extend(opt_frames) # Collect nested classifications - if hasattr(opt, 'classifications') and opt.classifications: + if hasattr(opt, "classifications") and opt.classifications: option_data[opt_name]["nested"].extend(opt.classifications) # Build answer entries results = [] for opt_name in sorted(option_data.keys()): - entry = { - "name": opt_name, - "frames": option_data[opt_name]["frames"] - } + entry = {"name": opt_name, "frames": option_data[opt_name]["frames"]} # Recursively process nested classifications if option_data[opt_name]["nested"]: - nested = _process_nested_classifications(option_data[opt_name]["nested"]) + nested = _process_nested_classifications( + option_data[opt_name]["nested"] + ) if nested: entry["classifications"] = nested @@ -135,7 +136,9 @@ def _process_checklist(annotations: List[Any], frame_extractor: callable) -> Lis return results -def _process_radio(annotations: List[Any], frame_extractor: callable) -> Dict[str, Any]: +def _process_radio( + annotations: List[Any], frame_extractor: callable +) -> Dict[str, Any]: """Process radio annotations - merge frames and nested classifications.""" first = annotations[0] opt_name = first.value.answer.name @@ -153,7 +156,10 @@ def _process_radio(annotations: List[Any], frame_extractor: callable) -> Dict[st all_frames.extend(opt_frames) # Collect nested - if hasattr(ann.value.answer, 'classifications') and ann.value.answer.classifications: + if ( + hasattr(ann.value.answer, "classifications") + and ann.value.answer.classifications + ): all_nested.extend(ann.value.answer.classifications) entry = {"name": opt_name, "frames": all_frames} @@ -167,10 +173,16 @@ def _process_radio(annotations: List[Any], frame_extractor: callable) -> Dict[st return entry -def _process_text(annotations: List[Any], frame_extractor: callable) -> Dict[str, Any]: +def _process_text( + annotations: List[Any], frame_extractor: callable +) -> Dict[str, Any]: """Process text annotations - collect frames and nested classifications.""" first = annotations[0] - text_value = first.value.answer if hasattr(first.value, "answer") else str(first.value) + text_value = ( + first.value.answer + if hasattr(first.value, "answer") + else str(first.value) + ) # Collect all frames and nested all_frames = [] @@ -181,7 +193,7 @@ def _process_text(annotations: List[Any], frame_extractor: callable) -> Dict[str all_frames.append({"start": start, "end": end}) # Text nesting is at annotation level - if hasattr(ann, 'classifications') and ann.classifications: + if hasattr(ann, "classifications") and ann.classifications: all_nested.extend(ann.classifications) entry = {"value": text_value, "frames": all_frames} @@ -195,7 +207,9 @@ def _process_text(annotations: List[Any], frame_extractor: callable) -> Dict[str return entry -def _process_nested_classifications(classifications: List[Any]) -> List[Dict[str, Any]]: +def _process_nested_classifications( + classifications: List[Any], +) -> List[Dict[str, Any]]: """ Recursively process nested ClassificationAnnotation objects. This uses the same grouping logic as top-level annotations. @@ -233,15 +247,14 @@ def _process_nested_classifications(classifications: List[Any]) -> List[Dict[str # Text answers.append(_process_nested_text(cls_group)) - results.append({ - "name": display_name, - "answer": answers - }) + results.append({"name": display_name, "answer": answers}) return results -def _process_nested_checklist(classifications: List[Any]) -> List[Dict[str, Any]]: +def _process_nested_checklist( + classifications: List[Any], +) -> List[Dict[str, Any]]: """Process nested checklist classifications.""" option_data = defaultdict(lambda: {"frames": [], "nested": []}) @@ -253,7 +266,7 @@ def _process_nested_checklist(classifications: List[Any]) -> List[Dict[str, Any] opt_frames = _extract_frames(opt, cls_frames) option_data[opt.name]["frames"].extend(opt_frames) - if hasattr(opt, 'classifications') and opt.classifications: + if hasattr(opt, "classifications") and opt.classifications: option_data[opt.name]["nested"].extend(opt.classifications) results = [] @@ -261,7 +274,9 @@ def _process_nested_checklist(classifications: List[Any]) -> List[Dict[str, Any] entry = {"name": opt_name, "frames": option_data[opt_name]["frames"]} if option_data[opt_name]["nested"]: - nested = _process_nested_classifications(option_data[opt_name]["nested"]) + nested = _process_nested_classifications( + option_data[opt_name]["nested"] + ) if nested: entry["classifications"] = nested @@ -283,7 +298,10 @@ def _process_nested_radio(classifications: List[Any]) -> Dict[str, Any]: opt_frames = _extract_frames(cls.value.answer, cls_frames) all_frames.extend(opt_frames) - if hasattr(cls.value.answer, 'classifications') and cls.value.answer.classifications: + if ( + hasattr(cls.value.answer, "classifications") + and cls.value.answer.classifications + ): all_nested.extend(cls.value.answer.classifications) entry = {"name": opt_name, "frames": all_frames} @@ -299,7 +317,11 @@ def _process_nested_radio(classifications: List[Any]) -> Dict[str, Any]: def _process_nested_text(classifications: List[Any]) -> Dict[str, Any]: """Process nested text classifications.""" first = classifications[0] - text_value = first.value.answer if hasattr(first.value, "answer") else str(first.value) + text_value = ( + first.value.answer + if hasattr(first.value, "answer") + else str(first.value) + ) all_frames = [] all_nested = [] @@ -308,7 +330,7 @@ def _process_nested_text(classifications: List[Any]) -> Dict[str, Any]: frames = _extract_frames(cls, []) all_frames.extend(frames) - if hasattr(cls, 'classifications') and cls.classifications: + if hasattr(cls, "classifications") and cls.classifications: all_nested.extend(cls.classifications) entry = {"value": text_value, "frames": all_frames} @@ -321,13 +343,19 @@ def _process_nested_text(classifications: List[Any]) -> Dict[str, Any]: return entry -def _extract_frames(obj: Any, fallback_frames: List[Dict[str, int]]) -> List[Dict[str, int]]: +def _extract_frames( + obj: Any, fallback_frames: List[Dict[str, int]] +) -> List[Dict[str, int]]: """ Extract frame range from an object (annotation, answer, or classification). Uses explicit frames if available, otherwise falls back to provided frames. """ - if (hasattr(obj, 'start_frame') and obj.start_frame is not None and - hasattr(obj, 'end_frame') and obj.end_frame is not None): + if ( + hasattr(obj, "start_frame") + and obj.start_frame is not None + and hasattr(obj, "end_frame") + and obj.end_frame is not None + ): return [{"start": obj.start_frame, "end": obj.end_frame}] elif fallback_frames: return fallback_frames @@ -354,6 +382,7 @@ def _get_value_key(obj: Any) -> str: class TemporalNDJSON(BaseModel): """NDJSON format for temporal annotations (audio, video, etc.).""" + name: str answer: List[Dict[str, Any]] dataRow: Dict[str, str] @@ -361,8 +390,7 @@ class TemporalNDJSON(BaseModel): # Audio-specific convenience function def create_audio_ndjson_annotations( - annotations: List[AudioClassificationAnnotation], - data_global_key: str + annotations: List[AudioClassificationAnnotation], data_global_key: str ) -> List[TemporalNDJSON]: """ Create NDJSON audio annotations with hierarchical structure. @@ -374,7 +402,12 @@ def create_audio_ndjson_annotations( Returns: List of TemporalNDJSON objects """ - def audio_frame_extractor(ann: AudioClassificationAnnotation) -> Tuple[int, int]: + + def audio_frame_extractor( + ann: AudioClassificationAnnotation, + ) -> Tuple[int, int]: return (ann.start_frame, ann.end_frame or ann.start_frame) - return create_temporal_ndjson_annotations(annotations, data_global_key, audio_frame_extractor) + return create_temporal_ndjson_annotations( + annotations, data_global_key, audio_frame_extractor + ) diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py index 038d4d526..66bf7e2ff 100644 --- a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py +++ b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py @@ -34,22 +34,27 @@ def test_audio_nested_text_radio_checklist_structure(): classifications=[ # Explicit nesting via classifications field lb_types.ClassificationAnnotation( name="nested_text_class", - start_frame=1600, end_frame=2000, # Nested frame range (subset of root) + start_frame=1600, + end_frame=2000, # Nested frame range (subset of root) value=lb_types.Text(answer="nested_text_class value"), classifications=[ # Deeper nesting lb_types.ClassificationAnnotation( name="nested_text_class_2", - start_frame=1800, end_frame=2000, # Even more specific nested range - value=lb_types.Text(answer="nested_text_class_2 value") + start_frame=1800, + end_frame=2000, # Even more specific nested range + value=lb_types.Text( + answer="nested_text_class_2 value" + ), ) - ] + ], ), lb_types.ClassificationAnnotation( name="nested_text_class", - start_frame=2001, end_frame=2400, # Different nested frame range - value=lb_types.Text(answer="nested_text_class value2") - ) - ] + start_frame=2001, + end_frame=2400, # Different nested frame range + value=lb_types.Text(answer="nested_text_class value2"), + ), + ], ) ) @@ -87,31 +92,34 @@ def test_audio_nested_text_radio_checklist_structure(): value=lb_types.Radio( answer=lb_types.ClassificationAnswer( name="first_sub_radio_answer", - start_frame=1000, end_frame=1500, # Nested frame range + start_frame=1000, + end_frame=1500, # Nested frame range classifications=[ # Deeper nesting lb_types.ClassificationAnnotation( name="sub_radio_question_2", value=lb_types.Radio( answer=lb_types.ClassificationAnswer( name="first_sub_radio_answer_2", - start_frame=1300, end_frame=1500 # Even more specific nested range + start_frame=1300, + end_frame=1500, # Even more specific nested range ) - ) + ), ) - ] + ], ) - ) + ), ), lb_types.ClassificationAnnotation( name="sub_radio_question", value=lb_types.Radio( answer=lb_types.ClassificationAnswer( name="second_sub_radio_answer", - start_frame=2100, end_frame=2500 # Nested frame range for second segment + start_frame=2100, + end_frame=2500, # Nested frame range for second segment ) - ) - ) - ] + ), + ), + ], ) ), ) @@ -133,9 +141,9 @@ def test_audio_nested_text_radio_checklist_structure(): answer=lb_types.ClassificationAnswer( name="second_sub_radio_answer" ) - ) + ), ) - ] + ], ) ), ) @@ -181,19 +189,23 @@ def test_audio_nested_text_radio_checklist_structure(): answer=[ lb_types.ClassificationAnswer( name="nested_option_1", - start_frame=400, end_frame=700, # Nested frame range + start_frame=400, + end_frame=700, # Nested frame range classifications=[ # Deeper nesting lb_types.ClassificationAnnotation( name="checklist_nested_text", - start_frame=500, end_frame=700, # Even more specific nested range - value=lb_types.Text(answer="checklist_nested_text value") + start_frame=500, + end_frame=700, # Even more specific nested range + value=lb_types.Text( + answer="checklist_nested_text value" + ), ) - ] + ], ) ] - ) + ), ) - ] + ], ) ] ), @@ -217,16 +229,18 @@ def test_audio_nested_text_radio_checklist_structure(): answer=[ lb_types.ClassificationAnswer( name="nested_option_2", - start_frame=1200, end_frame=1600 # Nested frame range + start_frame=1200, + end_frame=1600, # Nested frame range ), lb_types.ClassificationAnswer( name="nested_option_3", - start_frame=1400, end_frame=1800 # Nested frame range - ) + start_frame=1400, + end_frame=1800, # Nested frame range + ), ] - ) + ), ) - ] + ], ) ] ), @@ -241,7 +255,9 @@ def test_audio_nested_text_radio_checklist_structure(): name="checklist_class", value=lb_types.Checklist( answer=[ - lb_types.ClassificationAnswer(name="second_checklist_option") + lb_types.ClassificationAnswer( + name="second_checklist_option" + ) ] ), ) @@ -296,7 +312,9 @@ def test_audio_nested_text_radio_checklist_structure(): assert len(nt["answer"]) == 2 nt_ans_1 = nt["answer"][0] assert nt_ans_1["value"] == "nested_text_class value" - assert nt_ans_1["frames"] == [{"start": 1600, "end": 2000}] # Nested frame range + assert nt_ans_1["frames"] == [ + {"start": 1600, "end": 2000} + ] # Nested frame range # Check nested_text_class_2 is nested under nested_text_class nt_nested = nt_ans_1.get("classifications", []) @@ -304,12 +322,16 @@ def test_audio_nested_text_radio_checklist_structure(): nt2 = nt_nested[0] assert nt2["name"] == "nested_text_class_2" assert nt2["answer"][0]["value"] == "nested_text_class_2 value" - assert nt2["answer"][0]["frames"] == [{"start": 1800, "end": 2000}] # Even more specific nested range + assert nt2["answer"][0]["frames"] == [ + {"start": 1800, "end": 2000} + ] # Even more specific nested range # Check second nested_text_class answer nt_ans_2 = nt["answer"][1] assert nt_ans_2["value"] == "nested_text_class value2" - assert nt_ans_2["frames"] == [{"start": 2001, "end": 2400}] # Different nested frame range + assert nt_ans_2["frames"] == [ + {"start": 2001, "end": 2400} + ] # Different nested frame range # Validate radio_class structure with explicit nesting and frame ranges radio_nd = next(obj for obj in ndjson if obj["name"] == "radio_class") @@ -323,7 +345,10 @@ def test_audio_nested_text_radio_checklist_structure(): assert len(first_radios) == 1 first_radio = first_radios[0] # Merged frames from both segments: [200-1500] and [2000-2500] - assert first_radio["frames"] == [{"start": 200, "end": 1500}, {"start": 2000, "end": 2500}] + assert first_radio["frames"] == [ + {"start": 200, "end": 1500}, + {"start": 2000, "end": 2500}, + ] # Check explicit nested sub_radio_question assert "classifications" in first_radio @@ -338,7 +363,9 @@ def test_audio_nested_text_radio_checklist_structure(): sr_first = next( a for a in sub_radio["answer"] if a["name"] == "first_sub_radio_answer" ) - assert sr_first["frames"] == [{"start": 1000, "end": 1500}] # Nested frame range + assert sr_first["frames"] == [ + {"start": 1000, "end": 1500} + ] # Nested frame range # Check sub_radio_question_2 is nested under first_sub_radio_answer assert "classifications" in sr_first @@ -348,7 +375,9 @@ def test_audio_nested_text_radio_checklist_structure(): if c["name"] == "sub_radio_question_2" ) assert sr2["answer"][0]["name"] == "first_sub_radio_answer_2" - assert sr2["answer"][0]["frames"] == [{"start": 1300, "end": 1500}] # Even more specific nested range + assert sr2["answer"][0]["frames"] == [ + {"start": 1300, "end": 1500} + ] # Even more specific nested range # Check second_sub_radio_answer sr_second = next( @@ -372,7 +401,10 @@ def test_audio_nested_text_radio_checklist_structure(): assert len(first_opts) == 1 first_opt = first_opts[0] # Merged frames from both segments: [300-800] and [1200-1800] - assert first_opt["frames"] == [{"start": 300, "end": 800}, {"start": 1200, "end": 1800}] + assert first_opt["frames"] == [ + {"start": 300, "end": 800}, + {"start": 1200, "end": 1800}, + ] # Check explicit nested_checklist assert "classifications" in first_opt @@ -399,7 +431,9 @@ def test_audio_nested_text_radio_checklist_structure(): if c["name"] == "checklist_nested_text" ) assert nested_text["answer"][0]["value"] == "checklist_nested_text value" - assert nested_text["answer"][0]["frames"] == [{"start": 500, "end": 700}] # Even more specific nested range + assert nested_text["answer"][0]["frames"] == [ + {"start": 500, "end": 700} + ] # Even more specific nested range def test_audio_top_level_only_basic(): @@ -408,30 +442,38 @@ def test_audio_top_level_only_basic(): frame=200, end_frame=1500, name="radio_class", - value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="first_radio_answer")), + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer(name="first_radio_answer") + ), ), lb_types.AudioClassificationAnnotation( frame=1550, end_frame=1700, name="radio_class", - value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="second_radio_answer")), + value=lb_types.Radio( + answer=lb_types.ClassificationAnswer(name="second_radio_answer") + ), ), lb_types.AudioClassificationAnnotation( frame=1200, end_frame=1800, name="checklist_class", - value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="angry")]), + value=lb_types.Checklist( + answer=[lb_types.ClassificationAnswer(name="angry")] + ), ), ] - label = lb_types.Label(data={"global_key": "audio_top_level_only"}, annotations=anns) + label = lb_types.Label( + data={"global_key": "audio_top_level_only"}, annotations=anns + ) ndjson = list(NDJsonConverter.serialize([label])) names = {o["name"] for o in ndjson} assert names == {"radio_class", "checklist_class"} radio = next(o for o in ndjson if o["name"] == "radio_class") - r_answers = sorted(radio["answer"], key=lambda x: x["frames"][0]["start"]) + r_answers = sorted(radio["answer"], key=lambda x: x["frames"][0]["start"]) assert r_answers[0]["name"] == "first_radio_answer" assert r_answers[0]["frames"] == [{"start": 200, "end": 1500}] assert "classifications" not in r_answers[0] From 471c6189a18b35dabcb8a2af3e67f1777e2ffb3f Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 2 Oct 2025 16:19:55 -0700 Subject: [PATCH 039/103] chore: new new interface --- examples/annotation_import/audio.ipynb | 650 +++++++++--------- .../data/annotation_types/__init__.py | 1 + .../labelbox/data/annotation_types/audio.py | 18 +- .../classification/__init__.py | 2 +- .../classification/classification.py | 21 +- .../data/serialization/ndjson/temporal.py | 68 +- .../data/serialization/ndjson/test_audio.py | 75 +- 7 files changed, 433 insertions(+), 402 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index 615ac7c86..52ac57dbd 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,329 +1,325 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {}, - "cells": [ - { - "metadata": {}, - "source": [ - "", - " ", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# Audio Annotation Import\n", - "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", - "\n", - "Suported annotations that can be uploaded through the SDK\n", - "\n", - "* Classification Radio \n", - "* Classification Checklist \n", - "* Classification Free Text \n", - "\n", - "**Not** supported annotations\n", - "\n", - "* Bouding box\n", - "* NER\n", - "* Polygon \n", - "* Point\n", - "* Polyline \n", - "* Segmentation Mask\n", - "\n", - "MAL and Label Import:\n", - "\n", - "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", - "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "* For information on what types of annotations are supported per data type, refer to this documentation:\n", - " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "* Notes:\n", - " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# Setup" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# Replace with your API key\n", - "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Supported annotations for Audio" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Step 1: Import data rows into Catalog" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 2: Create/select an ontology\n", - "\n", - "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", - "\n", - "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "\n", - "## Step 3: Create a labeling project\n", - "Connect the ontology to the labeling project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 5: Create the annotations payload\n", - "Create the annotations payload using the snippets of code above\n", - "\n", - "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "#### Python annotation\n", - "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### NDJSON annotations \n", - "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Temporal Audio Annotations\n", - "\n", - "You can create temporal annotations for individual tokens (words) with precise timing:\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Model Assisted Labeling (MAL)\n", - "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Label Import" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Optional deletions for cleanup " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# project.delete()\n# dataset.delete()", - "cell_type": "code", - "outputs": [], - "execution_count": null - } - ] + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {}, + "cells": [ + { + "metadata": {}, + "source": [ + "", + " ", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "# Audio Annotation Import\n", + "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", + "\n", + "Suported annotations that can be uploaded through the SDK\n", + "\n", + "* Classification Radio \n", + "* Classification Checklist \n", + "* Classification Free Text \n", + "\n", + "**Not** supported annotations\n", + "\n", + "* Bouding box\n", + "* NER\n", + "* Polygon \n", + "* Point\n", + "* Polyline \n", + "* Segmentation Mask\n", + "\n", + "MAL and Label Import:\n", + "\n", + "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", + "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* For information on what types of annotations are supported per data type, refer to this documentation:\n", + " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* Notes:\n", + " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Setup" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Replace with your API key\n", + "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Supported annotations for Audio" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Upload Annotations - putting it all together " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "## Step 1: Import data rows into Catalog" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 2: Create/select an ontology\n", + "\n", + "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", + "\n", + "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "\n", + "## Step 3: Create a labeling project\n", + "Connect the ontology to the labeling project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 4: Send a batch of data rows to the project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 5: Create the annotations payload\n", + "Create the annotations payload using the snippets of code above\n", + "\n", + "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "#### Python annotation\n", + "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### NDJSON annotations \n", + "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Step 6: Upload annotations to a project as pre-labels or complete labels" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "## Temporal Audio Annotations\n\nYou can create temporal annotations for individual tokens (words) with precise timing.\n\nAdditionally, you can create **nested temporal annotations** with hierarchical classifications at different frame ranges.\n", + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token using NEW frames interface\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frames=[lb_types.FrameLocation(start=start_frame, end=end_frame)],\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Create label with regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")\n\n# Example: Nested temporal annotation with explicit frame matching\n# Structure: Speaker -> Transcription -> Emotion -> Intensity\n# Each level can have different frame ranges (subsets of parent)\nnested_temporal_annotation = lb_types.AudioClassificationAnnotation(\n frames=[lb_types.FrameLocation(start=100, end=500)],\n name=\"Speaker Analysis\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"User\",\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Transcription\",\n value=lb_types.Text(answer=\"Hello there\"),\n frames=[lb_types.FrameLocation(start=100, end=500)],\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Emotion\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"happy\",\n frames=[lb_types.FrameLocation(start=150, end=450)],\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Intensity\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"high\",\n frames=[lb_types.FrameLocation(start=200, end=400)]\n )\n )\n )\n ]\n )\n ),\n frames=[lb_types.FrameLocation(start=150, end=450)]\n )\n ]\n )\n ]\n )\n )\n)\n\nprint(\"\\nNested temporal annotation created:\")\nprint(\" - Speaker: 100-500ms\")\nprint(\" → Transcription: 100-500ms\")\nprint(\" → Emotion: 150-450ms (subset)\")\nprint(\" → Intensity: 200-400ms (subset)\")\n", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Model Assisted Labeling (MAL)\n", + "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Label Import" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Optional deletions for cleanup " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# project.delete()\n# dataset.delete()", + "cell_type": "code", + "outputs": [], + "execution_count": null + } + ] } \ No newline at end of file diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index 9f59b5197..6dcb0cb20 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -30,6 +30,7 @@ from .classification import ClassificationAnswer from .classification import Radio from .classification import Text +from .classification import FrameLocation from .data import GenericDataRowData from .data import MaskData diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index 588085962..6aef4c231 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -1,13 +1,14 @@ -from typing import Optional +from typing import Optional, List from pydantic import Field, AliasChoices from labelbox.data.annotation_types.annotation import ( ClassificationAnnotation, ) +from labelbox.data.annotation_types.classification.classification import FrameLocation class AudioClassificationAnnotation(ClassificationAnnotation): - """Audio classification for specific time range + """Audio classification for specific time range(s) Examples: - Speaker identification from 2500ms to 4100ms @@ -18,19 +19,10 @@ class AudioClassificationAnnotation(ClassificationAnnotation): name (Optional[str]): Name of the classification feature_schema_id (Optional[Cuid]): Feature schema identifier value (Union[Text, Checklist, Radio]): Classification value - start_frame (int): The frame index in milliseconds (e.g., 2500 = 2.5 seconds) - end_frame (Optional[int]): End frame in milliseconds (for time ranges) + frames (Optional[List[FrameLocation]]): List of frame ranges (in milliseconds) segment_index (Optional[int]): Index of audio segment this annotation belongs to extra (Dict[str, Any]): Additional metadata """ - start_frame: int = Field( - validation_alias=AliasChoices("start_frame", "frame"), - serialization_alias="start_frame", - ) - end_frame: Optional[int] = Field( - default=None, - validation_alias=AliasChoices("end_frame", "endFrame"), - serialization_alias="end_frame", - ) + frames: Optional[List[FrameLocation]] = None segment_index: Optional[int] = None diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py index a814336e4..fc00c9410 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py @@ -1 +1 @@ -from .classification import Checklist, ClassificationAnswer, Radio, Text +from .classification import Checklist, ClassificationAnswer, Radio, Text, FrameLocation diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py index aca1827a9..921817b3e 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py @@ -7,6 +7,12 @@ from ..feature import FeatureSchema +class FrameLocation(BaseModel): + """Represents a temporal frame range with start and end times (in milliseconds).""" + start: int + end: int + + class ClassificationAnswer(FeatureSchema, ConfidenceMixin, CustomMetricsMixin): """ - Represents a classification option. @@ -18,14 +24,17 @@ class ClassificationAnswer(FeatureSchema, ConfidenceMixin, CustomMetricsMixin): So unlike object annotations, classification annotations track keyframes at a classification answer level. - - For temporal classifications (audio/video), optional start_frame/end_frame can specify - the time range for this answer. Must be within root annotation's frame range. - Defaults to root frame range if not specified. + - For temporal classifications (audio/video), optional frames can specify + one or more time ranges for this answer. Must be within root annotation's frame ranges. + Defaults to root frame ranges if not specified. """ extra: Dict[str, Any] = {} keyframe: Optional[bool] = None classifications: Optional[List["ClassificationAnnotation"]] = None + frames: Optional[List[FrameLocation]] = None + + # Deprecated: use frames instead start_frame: Optional[int] = None end_frame: Optional[int] = None @@ -75,12 +84,14 @@ class ClassificationAnnotation( classifications (Optional[List[ClassificationAnnotation]]): Optional sub classification of the annotation feature_schema_id (Optional[Cuid]) value (Union[Text, Checklist, Radio]) - start_frame (Optional[int]): Start frame for temporal classifications (audio/video). Must be within root annotation's frame range. Defaults to root start_frame if not specified. - end_frame (Optional[int]): End frame for temporal classifications (audio/video). Must be within root annotation's frame range. Defaults to root end_frame if not specified. + frames (Optional[List[FrameLocation]]): Frame ranges for temporal classifications (audio/video). Must be within root annotation's frame ranges. Defaults to root frames if not specified. extra (Dict[str, Any]) """ value: Union[Text, Checklist, Radio] message_id: Optional[str] = None + frames: Optional[List[FrameLocation]] = None + + # Deprecated: use frames instead start_frame: Optional[int] = None end_frame: Optional[int] = None diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index fc51fd0b9..d4c54ca00 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -162,7 +162,16 @@ def _process_radio( ): all_nested.extend(ann.value.answer.classifications) - entry = {"name": opt_name, "frames": all_frames} + # Deduplicate frames + seen = set() + unique_frames = [] + for frame in all_frames: + frame_tuple = (frame["start"], frame["end"]) + if frame_tuple not in seen: + seen.add(frame_tuple) + unique_frames.append(frame) + + entry = {"name": opt_name, "frames": unique_frames} # Recursively process nested if all_nested: @@ -196,7 +205,16 @@ def _process_text( if hasattr(ann, "classifications") and ann.classifications: all_nested.extend(ann.classifications) - entry = {"value": text_value, "frames": all_frames} + # Deduplicate frames + seen = set() + unique_frames = [] + for frame in all_frames: + frame_tuple = (frame["start"], frame["end"]) + if frame_tuple not in seen: + seen.add(frame_tuple) + unique_frames.append(frame) + + entry = {"value": text_value, "frames": unique_frames} # Recursively process nested if all_nested: @@ -304,7 +322,16 @@ def _process_nested_radio(classifications: List[Any]) -> Dict[str, Any]: ): all_nested.extend(cls.value.answer.classifications) - entry = {"name": opt_name, "frames": all_frames} + # Deduplicate frames + seen = set() + unique_frames = [] + for frame in all_frames: + frame_tuple = (frame["start"], frame["end"]) + if frame_tuple not in seen: + seen.add(frame_tuple) + unique_frames.append(frame) + + entry = {"name": opt_name, "frames": unique_frames} if all_nested: nested = _process_nested_classifications(all_nested) @@ -333,7 +360,16 @@ def _process_nested_text(classifications: List[Any]) -> Dict[str, Any]: if hasattr(cls, "classifications") and cls.classifications: all_nested.extend(cls.classifications) - entry = {"value": text_value, "frames": all_frames} + # Deduplicate frames + seen = set() + unique_frames = [] + for frame in all_frames: + frame_tuple = (frame["start"], frame["end"]) + if frame_tuple not in seen: + seen.add(frame_tuple) + unique_frames.append(frame) + + entry = {"value": text_value, "frames": unique_frames} if all_nested: nested = _process_nested_classifications(all_nested) @@ -347,18 +383,30 @@ def _extract_frames( obj: Any, fallback_frames: List[Dict[str, int]] ) -> List[Dict[str, int]]: """ - Extract frame range from an object (annotation, answer, or classification). + Extract frame ranges from an object (annotation, answer, or classification). Uses explicit frames if available, otherwise falls back to provided frames. + + Supports both: + - New format: frames: List[FrameLocation] + - Legacy format: start_frame/end_frame (single range) """ - if ( + # New format: frames list + if hasattr(obj, "frames") and obj.frames is not None: + return [{"start": frame.start, "end": frame.end} for frame in obj.frames] + + # Legacy format: single start_frame/end_frame + elif ( hasattr(obj, "start_frame") and obj.start_frame is not None and hasattr(obj, "end_frame") and obj.end_frame is not None ): return [{"start": obj.start_frame, "end": obj.end_frame}] + + # Fallback to parent frames elif fallback_frames: return fallback_frames + else: return [] @@ -406,6 +454,14 @@ def create_audio_ndjson_annotations( def audio_frame_extractor( ann: AudioClassificationAnnotation, ) -> Tuple[int, int]: + """ + Legacy frame extractor for AudioClassificationAnnotation. + Only used when frames list is not provided. + """ + # Return first frame if frames list exists + if ann.frames and len(ann.frames) > 0: + return (ann.frames[0].start, ann.frames[0].end) + # Fall back to legacy start_frame/end_frame return (ann.start_frame, ann.end_frame or ann.start_frame) return create_temporal_ndjson_annotations( diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py index 66bf7e2ff..031c0c078 100644 --- a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py +++ b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py @@ -16,8 +16,7 @@ def test_audio_nested_text_radio_checklist_structure(): # text_class: simple value without nesting anns.append( lb_types.AudioClassificationAnnotation( - frame=1000, - end_frame=1100, + frames=[lb_types.FrameLocation(start=1000, end=1100)], name="text_class", value=lb_types.Text(answer="A"), ) @@ -27,21 +26,18 @@ def test_audio_nested_text_radio_checklist_structure(): # This annotation has nested classifications at the annotation level (for Text type) anns.append( lb_types.AudioClassificationAnnotation( - frame=1500, - end_frame=2400, # Root frame range + frames=[lb_types.FrameLocation(start=1500, end=2400)], # Root frame range name="text_class", value=lb_types.Text(answer="text_class value"), classifications=[ # Explicit nesting via classifications field lb_types.ClassificationAnnotation( name="nested_text_class", - start_frame=1600, - end_frame=2000, # Nested frame range (subset of root) + frames=[lb_types.FrameLocation(start=1600, end=2000)], # Nested frame range (subset of root) value=lb_types.Text(answer="nested_text_class value"), classifications=[ # Deeper nesting lb_types.ClassificationAnnotation( name="nested_text_class_2", - start_frame=1800, - end_frame=2000, # Even more specific nested range + frames=[lb_types.FrameLocation(start=1800, end=2000)], # Even more specific nested range value=lb_types.Text( answer="nested_text_class_2 value" ), @@ -50,8 +46,7 @@ def test_audio_nested_text_radio_checklist_structure(): ), lb_types.ClassificationAnnotation( name="nested_text_class", - start_frame=2001, - end_frame=2400, # Different nested frame range + frames=[lb_types.FrameLocation(start=2001, end=2400)], # Different nested frame range value=lb_types.Text(answer="nested_text_class value2"), ), ], @@ -61,16 +56,14 @@ def test_audio_nested_text_radio_checklist_structure(): # Additional text_class segments anns.append( lb_types.AudioClassificationAnnotation( - frame=2500, - end_frame=2700, + frames=[lb_types.FrameLocation(start=2500, end=2700)], name="text_class", value=lb_types.Text(answer="C"), ) ) anns.append( lb_types.AudioClassificationAnnotation( - frame=2900, - end_frame=2999, + frames=[lb_types.FrameLocation(start=2900, end=2999)], name="text_class", value=lb_types.Text(answer="D"), ) @@ -80,8 +73,7 @@ def test_audio_nested_text_radio_checklist_structure(): # First segment with nested classifications anns.append( lb_types.AudioClassificationAnnotation( - frame=200, - end_frame=1500, # Root frame range + frames=[lb_types.FrameLocation(start=200, end=1500)], # Root frame range name="radio_class", value=lb_types.Radio( answer=lb_types.ClassificationAnswer( @@ -92,16 +84,14 @@ def test_audio_nested_text_radio_checklist_structure(): value=lb_types.Radio( answer=lb_types.ClassificationAnswer( name="first_sub_radio_answer", - start_frame=1000, - end_frame=1500, # Nested frame range + frames=[lb_types.FrameLocation(start=1000, end=1500)], # Nested frame range classifications=[ # Deeper nesting lb_types.ClassificationAnnotation( name="sub_radio_question_2", value=lb_types.Radio( answer=lb_types.ClassificationAnswer( name="first_sub_radio_answer_2", - start_frame=1300, - end_frame=1500, # Even more specific nested range + frames=[lb_types.FrameLocation(start=1300, end=1500)], # Even more specific nested range ) ), ) @@ -114,8 +104,7 @@ def test_audio_nested_text_radio_checklist_structure(): value=lb_types.Radio( answer=lb_types.ClassificationAnswer( name="second_sub_radio_answer", - start_frame=2100, - end_frame=2500, # Nested frame range for second segment + frames=[lb_types.FrameLocation(start=2100, end=2500)], # Nested frame range for second segment ) ), ), @@ -128,8 +117,7 @@ def test_audio_nested_text_radio_checklist_structure(): # Second segment for first_radio_answer (will merge frames in output) anns.append( lb_types.AudioClassificationAnnotation( - frame=2000, - end_frame=2500, + frames=[lb_types.FrameLocation(start=2000, end=2500)], name="radio_class", value=lb_types.Radio( answer=lb_types.ClassificationAnswer( @@ -152,8 +140,7 @@ def test_audio_nested_text_radio_checklist_structure(): # radio_class: second_radio_answer without nesting anns.append( lb_types.AudioClassificationAnnotation( - frame=1550, - end_frame=1700, + frames=[lb_types.FrameLocation(start=1550, end=1700)], name="radio_class", value=lb_types.Radio( answer=lb_types.ClassificationAnswer(name="second_radio_answer") @@ -162,8 +149,7 @@ def test_audio_nested_text_radio_checklist_structure(): ) anns.append( lb_types.AudioClassificationAnnotation( - frame=2700, - end_frame=3000, + frames=[lb_types.FrameLocation(start=2700, end=3000)], name="radio_class", value=lb_types.Radio( answer=lb_types.ClassificationAnswer(name="second_radio_answer") @@ -175,8 +161,7 @@ def test_audio_nested_text_radio_checklist_structure(): # First segment with nested checklist anns.append( lb_types.AudioClassificationAnnotation( - frame=300, - end_frame=800, # Root frame range (first segment) + frames=[lb_types.FrameLocation(start=300, end=800)], # Root frame range (first segment) name="checklist_class", value=lb_types.Checklist( answer=[ @@ -189,13 +174,11 @@ def test_audio_nested_text_radio_checklist_structure(): answer=[ lb_types.ClassificationAnswer( name="nested_option_1", - start_frame=400, - end_frame=700, # Nested frame range + frames=[lb_types.FrameLocation(start=400, end=700)], # Nested frame range classifications=[ # Deeper nesting lb_types.ClassificationAnnotation( name="checklist_nested_text", - start_frame=500, - end_frame=700, # Even more specific nested range + frames=[lb_types.FrameLocation(start=500, end=700)], # Even more specific nested range value=lb_types.Text( answer="checklist_nested_text value" ), @@ -215,8 +198,7 @@ def test_audio_nested_text_radio_checklist_structure(): # Second segment for first_checklist_option with different nested options anns.append( lb_types.AudioClassificationAnnotation( - frame=1200, - end_frame=1800, # Root frame range (second segment) + frames=[lb_types.FrameLocation(start=1200, end=1800)], # Root frame range (second segment) name="checklist_class", value=lb_types.Checklist( answer=[ @@ -229,13 +211,11 @@ def test_audio_nested_text_radio_checklist_structure(): answer=[ lb_types.ClassificationAnswer( name="nested_option_2", - start_frame=1200, - end_frame=1600, # Nested frame range + frames=[lb_types.FrameLocation(start=1200, end=1600)], # Nested frame range ), lb_types.ClassificationAnswer( name="nested_option_3", - start_frame=1400, - end_frame=1800, # Nested frame range + frames=[lb_types.FrameLocation(start=1400, end=1800)], # Nested frame range ), ] ), @@ -250,8 +230,7 @@ def test_audio_nested_text_radio_checklist_structure(): # checklist_class: other options without nesting anns.append( lb_types.AudioClassificationAnnotation( - frame=2200, - end_frame=2900, + frames=[lb_types.FrameLocation(start=2200, end=2900)], name="checklist_class", value=lb_types.Checklist( answer=[ @@ -264,8 +243,7 @@ def test_audio_nested_text_radio_checklist_structure(): ) anns.append( lb_types.AudioClassificationAnnotation( - frame=2500, - end_frame=3500, + frames=[lb_types.FrameLocation(start=2500, end=3500)], name="checklist_class", value=lb_types.Checklist( answer=[ @@ -439,24 +417,21 @@ def test_audio_nested_text_radio_checklist_structure(): def test_audio_top_level_only_basic(): anns = [ lb_types.AudioClassificationAnnotation( - frame=200, - end_frame=1500, + frames=[lb_types.FrameLocation(start=200, end=1500)], name="radio_class", value=lb_types.Radio( answer=lb_types.ClassificationAnswer(name="first_radio_answer") ), ), lb_types.AudioClassificationAnnotation( - frame=1550, - end_frame=1700, + frames=[lb_types.FrameLocation(start=1550, end=1700)], name="radio_class", value=lb_types.Radio( answer=lb_types.ClassificationAnswer(name="second_radio_answer") ), ), lb_types.AudioClassificationAnnotation( - frame=1200, - end_frame=1800, + frames=[lb_types.FrameLocation(start=1200, end=1800)], name="checklist_class", value=lb_types.Checklist( answer=[lb_types.ClassificationAnswer(name="angry")] From 478fb23fec5b188036813307b1d43017b5ae0633 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 2 Oct 2025 21:26:11 -0700 Subject: [PATCH 040/103] chore: final nail; interface is simple and works with frame arg --- examples/annotation_import/audio.ipynb | 4 ++-- .../src/labelbox/data/annotation_types/audio.py | 15 ++++++++++++--- .../classification/classification.py | 7 ------- 3 files changed, 14 insertions(+), 12 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index 52ac57dbd..e6bb2b6c0 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -259,14 +259,14 @@ }, { "metadata": {}, - "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token using NEW frames interface\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frames=[lb_types.FrameLocation(start=start_frame, end=end_frame)],\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", + "source": "# Define tokens with precise timing\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n start_frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", "cell_type": "code", "outputs": [], "execution_count": null }, { "metadata": {}, - "source": "# Create label with regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")\n\n# Example: Nested temporal annotation with explicit frame matching\n# Structure: Speaker -> Transcription -> Emotion -> Intensity\n# Each level can have different frame ranges (subsets of parent)\nnested_temporal_annotation = lb_types.AudioClassificationAnnotation(\n frames=[lb_types.FrameLocation(start=100, end=500)],\n name=\"Speaker Analysis\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"User\",\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Transcription\",\n value=lb_types.Text(answer=\"Hello there\"),\n frames=[lb_types.FrameLocation(start=100, end=500)],\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Emotion\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"happy\",\n frames=[lb_types.FrameLocation(start=150, end=450)],\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Intensity\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"high\",\n frames=[lb_types.FrameLocation(start=200, end=400)]\n )\n )\n )\n ]\n )\n ),\n frames=[lb_types.FrameLocation(start=150, end=450)]\n )\n ]\n )\n ]\n )\n )\n)\n\nprint(\"\\nNested temporal annotation created:\")\nprint(\" - Speaker: 100-500ms\")\nprint(\" → Transcription: 100-500ms\")\nprint(\" → Emotion: 150-450ms (subset)\")\nprint(\" → Intensity: 200-400ms (subset)\")\n", + "source": "# Create label with regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")\n\n# Example: Nested temporal annotation with hierarchical classifications\n# Structure: Speaker -> Transcription -> Emotion -> Intensity\n# Parent uses start_frame/end_frame, nested items use frames for discontinuous ranges\nnested_temporal_annotation = lb_types.AudioClassificationAnnotation(\n start_frame=100,\n end_frame=500,\n name=\"Speaker Analysis\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"User\",\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Transcription\",\n value=lb_types.Text(answer=\"Hello there\"),\n frames=[lb_types.FrameLocation(start=100, end=500)],\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Emotion\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"happy\",\n frames=[lb_types.FrameLocation(start=150, end=450)],\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Intensity\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"high\",\n frames=[lb_types.FrameLocation(start=200, end=400)]\n )\n )\n )\n ]\n )\n ),\n frames=[lb_types.FrameLocation(start=150, end=450)]\n )\n ]\n )\n ]\n )\n )\n)\n\nprint(\"\\nNested temporal annotation created:\")\nprint(\" - Speaker: 100-500ms (parent range)\")\nprint(\" → Transcription: 100-500ms\")\nprint(\" → Emotion: 150-450ms (nested subset)\")\nprint(\" → Intensity: 200-400ms (nested subset)\")\n", "cell_type": "code", "outputs": [], "execution_count": null diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index 6aef4c231..92a19c8e2 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -8,7 +8,7 @@ class AudioClassificationAnnotation(ClassificationAnnotation): - """Audio classification for specific time range(s) + """Audio classification for specific time range Examples: - Speaker identification from 2500ms to 4100ms @@ -19,10 +19,19 @@ class AudioClassificationAnnotation(ClassificationAnnotation): name (Optional[str]): Name of the classification feature_schema_id (Optional[Cuid]): Feature schema identifier value (Union[Text, Checklist, Radio]): Classification value - frames (Optional[List[FrameLocation]]): List of frame ranges (in milliseconds) + start_frame (Optional[int]): Start frame in milliseconds + end_frame (Optional[int]): End frame in milliseconds segment_index (Optional[int]): Index of audio segment this annotation belongs to extra (Dict[str, Any]): Additional metadata + + Note: + Parent AudioClassificationAnnotation uses start_frame/end_frame (single range). + Nested classifications/answers use frames: List[FrameLocation] for discontinuous ranges. + Multiple time ranges for same classification = multiple separate annotation objects. """ - frames: Optional[List[FrameLocation]] = None + start_frame: Optional[int] = Field( + default=None, validation_alias=AliasChoices("start_frame", "frame") + ) + end_frame: Optional[int] = None segment_index: Optional[int] = None diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py index 921817b3e..99f9817e8 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py @@ -34,10 +34,6 @@ class ClassificationAnswer(FeatureSchema, ConfidenceMixin, CustomMetricsMixin): classifications: Optional[List["ClassificationAnnotation"]] = None frames: Optional[List[FrameLocation]] = None - # Deprecated: use frames instead - start_frame: Optional[int] = None - end_frame: Optional[int] = None - class Radio(ConfidenceMixin, CustomMetricsMixin, BaseModel): """A classification with only one selected option allowed @@ -92,6 +88,3 @@ class ClassificationAnnotation( message_id: Optional[str] = None frames: Optional[List[FrameLocation]] = None - # Deprecated: use frames instead - start_frame: Optional[int] = None - end_frame: Optional[int] = None From 82e90e1c799f644dab389f6806985da7476eeedd Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 2 Oct 2025 21:47:25 -0700 Subject: [PATCH 041/103] chore: lint --- examples/annotation_import/audio.ipynb | 481 ++++++++++++++---- .../data/annotation_types/__init__.py | 100 ++-- .../labelbox/data/annotation_types/audio.py | 3 +- .../classification/__init__.py | 2 +- 4 files changed, 430 insertions(+), 156 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index e6bb2b6c0..663f9fe26 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,18 +1,18 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {}, "cells": [ { + "cell_type": "markdown", + "id": "7fb27b941602401d91542211134fc71a", "metadata": {}, "source": [ - "", - " ", + "\n", + " \n", "\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "acae54e37e7d407bbb7b55eff062a284", "metadata": {}, "source": [ "\n", @@ -24,10 +24,11 @@ "\n", "" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "9a63283cbaf04dbcab1f6479b197f3a8", "metadata": {}, "source": [ "# Audio Annotation Import\n", @@ -53,111 +54,203 @@ "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", "\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "8dd0d8092fe74a7c96281538738b07e2", "metadata": {}, "source": [ "* For information on what types of annotations are supported per data type, refer to this documentation:\n", " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "72eea5119410473aa328ad9291626812", "metadata": {}, "source": [ "* Notes:\n", " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", "cell_type": "code", + "execution_count": null, + "id": "8edb47106e1a46a883d545849b8ab81b", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "%pip install -q \"labelbox[data]\"" + ] }, { + "cell_type": "markdown", + "id": "10185d26023b46108eb7d9f57d49d2b3", "metadata": {}, "source": [ "# Setup" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", "cell_type": "code", + "execution_count": null, + "id": "8763a12b2bbd4a93a75aff182afb95dc", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "import labelbox as lb\n", + "import uuid\n", + "import labelbox.types as lb_types" + ] }, { + "cell_type": "markdown", + "id": "7623eae2785240b9bd12b16a66d81610", "metadata": {}, "source": [ "# Replace with your API key\n", "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", "cell_type": "code", + "execution_count": null, + "id": "7cdc8c89c7104fffa095e18ddfef8986", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Add your api key\n", + "API_KEY = \"\"\n", + "client = lb.Client(api_key=API_KEY)" + ] }, { + "cell_type": "markdown", + "id": "b118ea5561624da68c537baed56e602f", "metadata": {}, "source": [ "## Supported annotations for Audio" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", "cell_type": "code", + "execution_count": null, + "id": "938c804e27f84196a10c8828c723f798", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "##### Classification free text #####\n", + "\n", + "text_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"text_audio\",\n", + " value=lb_types.Text(answer=\"free text audio annotation\"),\n", + ")\n", + "\n", + "text_annotation_ndjson = {\n", + " \"name\": \"text_audio\",\n", + " \"answer\": \"free text audio annotation\",\n", + "}" + ] }, { - "metadata": {}, - "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", "cell_type": "code", + "execution_count": null, + "id": "504fb2a444614c0babb325280ed9130a", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "##### Checklist Classification #######\n", + "\n", + "checklist_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"checklist_audio\",\n", + " value=lb_types.Checklist(answer=[\n", + " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", + " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", + " ]),\n", + ")\n", + "\n", + "checklist_annotation_ndjson = {\n", + " \"name\":\n", + " \"checklist_audio\",\n", + " \"answers\": [\n", + " {\n", + " \"name\": \"first_checklist_answer\"\n", + " },\n", + " {\n", + " \"name\": \"second_checklist_answer\"\n", + " },\n", + " ],\n", + "}" + ] }, { - "metadata": {}, - "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", "cell_type": "code", + "execution_count": null, + "id": "59bbdb311c014d738909a11f9e486628", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "######## Radio Classification ######\n", + "\n", + "radio_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"radio_audio\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", + " name=\"second_radio_answer\")),\n", + ")\n", + "\n", + "radio_annotation_ndjson = {\n", + " \"name\": \"radio_audio\",\n", + " \"answer\": {\n", + " \"name\": \"first_radio_answer\"\n", + " },\n", + "}" + ] }, { + "cell_type": "markdown", + "id": "b43b363d81ae4b689946ece5c682cd59", "metadata": {}, "source": [ "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "8a65eabff63a45729fe45fb5ade58bdc", "metadata": {}, "source": [ "## Step 1: Import data rows into Catalog" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", "cell_type": "code", + "execution_count": null, + "id": "c3933fab20d04ec698c2621248eb3be0", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create one Labelbox dataset\n", + "\n", + "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", + "\n", + "asset = {\n", + " \"row_data\":\n", + " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", + " \"global_key\":\n", + " global_key,\n", + "}\n", + "\n", + "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", + "task = dataset.create_data_rows([asset])\n", + "task.wait_till_done()\n", + "print(\"Errors:\", task.errors)\n", + "print(\"Failed data rows: \", task.failed_data_rows)" + ] }, { + "cell_type": "markdown", + "id": "4dd4641cc4064e0191573fe9c69df29b", "metadata": {}, "source": [ "## Step 2: Create/select an ontology\n", @@ -165,161 +258,343 @@ "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", "\n", "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", "cell_type": "code", + "execution_count": null, + "id": "8309879909854d7188b41380fd92a7c3", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "ontology_builder = lb.OntologyBuilder(classifications=[\n", + " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", + " name=\"text_audio\"),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"checklist_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_checklist_answer\"),\n", + " lb.Option(value=\"second_checklist_answer\"),\n", + " ],\n", + " ),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"radio_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_radio_answer\"),\n", + " lb.Option(value=\"second_radio_answer\"),\n", + " ],\n", + " ),\n", + " # Temporal classification for token-level annotations\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.TEXT,\n", + " name=\"User Speaker\",\n", + " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", + " ),\n", + "])\n", + "\n", + "ontology = client.create_ontology(\n", + " \"Ontology Audio Annotations\",\n", + " ontology_builder.asdict(),\n", + " media_type=lb.MediaType.Audio,\n", + ")" + ] }, { + "cell_type": "markdown", + "id": "3ed186c9a28b402fb0bc4494df01f08d", "metadata": {}, "source": [ "\n", "## Step 3: Create a labeling project\n", "Connect the ontology to the labeling project" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", "cell_type": "code", + "execution_count": null, + "id": "cb1e1581032b452c9409d6c6813c49d1", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create Labelbox project\n", + "project = client.create_project(name=\"audio_project\",\n", + " media_type=lb.MediaType.Audio)\n", + "\n", + "# Setup your ontology\n", + "project.setup_editor(\n", + " ontology) # Connect your ontology and editor to your project" + ] }, { + "cell_type": "markdown", + "id": "379cbbc1e968416e875cc15c1202d7eb", "metadata": {}, "source": [ "## Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", "cell_type": "code", + "execution_count": null, + "id": "277c27b1587741f2af2001be3712ef0d", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Setup Batches and Ontology\n", + "\n", + "# Create a batch to send to your MAL project\n", + "batch = project.create_batch(\n", + " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", + " global_keys=[\n", + " global_key\n", + " ], # Paginated collection of data row objects, list of data row ids or global keys\n", + " priority=5, # priority between 1(Highest) - 5(lowest)\n", + ")\n", + "\n", + "print(\"Batch: \", batch)" + ] }, { + "cell_type": "markdown", + "id": "db7b79bc585a40fcaf58bf750017e135", "metadata": {}, "source": [ "## Step 5: Create the annotations payload\n", "Create the annotations payload using the snippets of code above\n", "\n", "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "916684f9a58a4a2aa5f864670399430d", "metadata": {}, "source": [ "#### Python annotation\n", "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", "cell_type": "code", + "execution_count": null, + "id": "1671c31a24314836a5b85d7ef7fbf015", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "label = []\n", + "label.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", + " ))" + ] }, { + "cell_type": "markdown", + "id": "33b0902fd34d4ace834912fa1002cf8e", "metadata": {}, "source": [ "### NDJSON annotations \n", "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", "cell_type": "code", + "execution_count": null, + "id": "f6fa52606d8c4a75a9b52967216f8f3f", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "label_ndjson = []\n", + "for annotations in [\n", + " text_annotation_ndjson,\n", + " checklist_annotation_ndjson,\n", + " radio_annotation_ndjson,\n", + "]:\n", + " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", + " label_ndjson.append(annotations)" + ] }, { + "cell_type": "markdown", + "id": "f5a1fa73e5044315a093ec459c9be902", "metadata": {}, "source": [ "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "markdown", + "id": "3ee572b6", "metadata": {}, - "source": "## Temporal Audio Annotations\n\nYou can create temporal annotations for individual tokens (words) with precise timing.\n\nAdditionally, you can create **nested temporal annotations** with hierarchical classifications at different frame ranges.\n", - "cell_type": "markdown" + "source": [ + "## Temporal Audio Annotations\n", + "\n", + "You can create temporal annotations for individual tokens (words) with precise timing.\n", + "\n", + "Additionally, you can create **nested temporal annotations** with hierarchical classifications at different frame ranges.\n" + ] }, { - "metadata": {}, - "source": "# Define tokens with precise timing\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n start_frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", "cell_type": "code", + "execution_count": null, + "id": "cdf66aed5cc84ca1b48e60bad68798a8", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Define tokens with precise timing (from demo script)\n", + "tokens_data = [\n", + " (\"Hello\", 586, 770), # Hello: frames 586-770\n", + " (\"AI\", 771, 955), # AI: frames 771-955\n", + " (\"how\", 956, 1140), # how: frames 956-1140\n", + " (\"are\", 1141, 1325), # are: frames 1141-1325\n", + " (\"you\", 1326, 1510), # you: frames 1326-1510\n", + " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", + " (\"today\", 1696, 1880), # today: frames 1696-1880\n", + "]\n", + "\n", + "# Create temporal annotations for each token\n", + "temporal_annotations = []\n", + "for token, start_frame, end_frame in tokens_data:\n", + " token_annotation = lb_types.AudioClassificationAnnotation(\n", + " frame=start_frame,\n", + " end_frame=end_frame,\n", + " name=\"User Speaker\",\n", + " value=lb_types.Text(answer=token),\n", + " )\n", + " temporal_annotations.append(token_annotation)\n", + "\n", + "print(f\"Created {len(temporal_annotations)} temporal token annotations\")" + ] }, { - "metadata": {}, - "source": "# Create label with regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(f\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")\n\n# Example: Nested temporal annotation with hierarchical classifications\n# Structure: Speaker -> Transcription -> Emotion -> Intensity\n# Parent uses start_frame/end_frame, nested items use frames for discontinuous ranges\nnested_temporal_annotation = lb_types.AudioClassificationAnnotation(\n start_frame=100,\n end_frame=500,\n name=\"Speaker Analysis\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"User\",\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Transcription\",\n value=lb_types.Text(answer=\"Hello there\"),\n frames=[lb_types.FrameLocation(start=100, end=500)],\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Emotion\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"happy\",\n frames=[lb_types.FrameLocation(start=150, end=450)],\n classifications=[\n lb_types.ClassificationAnnotation(\n name=\"Intensity\",\n value=lb_types.Radio(\n answer=lb_types.ClassificationAnswer(\n name=\"high\",\n frames=[lb_types.FrameLocation(start=200, end=400)]\n )\n )\n )\n ]\n )\n ),\n frames=[lb_types.FrameLocation(start=150, end=450)]\n )\n ]\n )\n ]\n )\n )\n)\n\nprint(\"\\nNested temporal annotation created:\")\nprint(\" - Speaker: 100-500ms (parent range)\")\nprint(\" → Transcription: 100-500ms\")\nprint(\" → Emotion: 150-450ms (nested subset)\")\nprint(\" → Intensity: 200-400ms (nested subset)\")\n", "cell_type": "code", + "execution_count": null, + "id": "28d3efd5258a48a79c179ea5c6759f01", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Create label with both regular and temporal annotations\n", + "label_with_temporal = []\n", + "label_with_temporal.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation] +\n", + " temporal_annotations,\n", + " ))\n", + "\n", + "print(\n", + " f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n", + ")\n", + "print(\" - Regular annotations: 3\")\n", + "print(f\" - Temporal annotations: {len(temporal_annotations)}\")" + ] }, { + "cell_type": "markdown", + "id": "3f9bc0b9dd2c44919cc8dcca39b469f8", "metadata": {}, "source": [ "#### Model Assisted Labeling (MAL)\n", "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "id": "0e382214b5f147d187d36a2058b9c724", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload temporal annotations via MAL\n", + "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label_with_temporal,\n", + ")\n", + "\n", + "temporal_upload_job.wait_until_done()\n", + "print(\"Temporal upload completed!\")\n", + "print(\"Errors:\", temporal_upload_job.errors)\n", + "print(\"Status:\", temporal_upload_job.statuses)" + ] }, { - "metadata": {}, - "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "id": "5b09d5ef5b5e4bb6ab9b829b10b6a29f", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload our label using Model-Assisted Labeling\n", + "upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] }, { + "cell_type": "markdown", + "id": "a50416e276a0479cbe66534ed1713a40", "metadata": {}, "source": [ "#### Label Import" - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", "cell_type": "code", + "execution_count": null, + "id": "46a27a456b804aa2a380d5edf15a5daf", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# Upload label for this data row in project\n", + "upload_job = lb.LabelImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=\"label_import_job\" + str(uuid.uuid4()),\n", + " labels=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] }, { + "cell_type": "markdown", + "id": "1944c39560714e6e80c856f20744a8e5", "metadata": {}, "source": [ "### Optional deletions for cleanup " - ], - "cell_type": "markdown" + ] }, { - "metadata": {}, - "source": "# project.delete()\n# dataset.delete()", "cell_type": "code", + "execution_count": null, + "id": "d6ca27006b894b04b6fc8b79396e2797", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "# project.delete()\n", + "# dataset.delete()" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index 6dcb0cb20..be6c0d195 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -1,64 +1,64 @@ -from .geometry import Line -from .geometry import Point -from .geometry import Mask -from .geometry import Polygon -from .geometry import Rectangle -from .geometry import Geometry -from .geometry import DocumentRectangle -from .geometry import RectangleUnit +from .geometry import Line as Line +from .geometry import Point as Point +from .geometry import Mask as Mask +from .geometry import Polygon as Polygon +from .geometry import Rectangle as Rectangle +from .geometry import Geometry as Geometry +from .geometry import DocumentRectangle as DocumentRectangle +from .geometry import RectangleUnit as RectangleUnit -from .annotation import ClassificationAnnotation -from .annotation import ObjectAnnotation +from .annotation import ClassificationAnnotation as ClassificationAnnotation +from .annotation import ObjectAnnotation as ObjectAnnotation -from .relationship import RelationshipAnnotation -from .relationship import Relationship +from .relationship import RelationshipAnnotation as RelationshipAnnotation +from .relationship import Relationship as Relationship -from .video import VideoClassificationAnnotation -from .video import VideoObjectAnnotation -from .video import MaskFrame -from .video import MaskInstance -from .video import VideoMaskAnnotation +from .video import VideoClassificationAnnotation as VideoClassificationAnnotation +from .video import VideoObjectAnnotation as VideoObjectAnnotation +from .video import MaskFrame as MaskFrame +from .video import MaskInstance as MaskInstance +from .video import VideoMaskAnnotation as VideoMaskAnnotation -from .audio import AudioClassificationAnnotation +from .audio import AudioClassificationAnnotation as AudioClassificationAnnotation -from .ner import ConversationEntity -from .ner import DocumentEntity -from .ner import DocumentTextSelection -from .ner import TextEntity +from .ner import ConversationEntity as ConversationEntity +from .ner import DocumentEntity as DocumentEntity +from .ner import DocumentTextSelection as DocumentTextSelection +from .ner import TextEntity as TextEntity -from .classification import Checklist -from .classification import ClassificationAnswer -from .classification import Radio -from .classification import Text -from .classification import FrameLocation +from .classification import Checklist as Checklist +from .classification import ClassificationAnswer as ClassificationAnswer +from .classification import Radio as Radio +from .classification import Text as Text +from .classification import FrameLocation as FrameLocation -from .data import GenericDataRowData -from .data import MaskData +from .data import GenericDataRowData as GenericDataRowData +from .data import MaskData as MaskData -from .label import Label -from .collection import LabelGenerator +from .label import Label as Label +from .collection import LabelGenerator as LabelGenerator -from .metrics import ScalarMetric -from .metrics import ScalarMetricAggregation -from .metrics import ConfusionMatrixMetric -from .metrics import ConfusionMatrixAggregation -from .metrics import ScalarMetricValue -from .metrics import ConfusionMatrixMetricValue +from .metrics import ScalarMetric as ScalarMetric +from .metrics import ScalarMetricAggregation as ScalarMetricAggregation +from .metrics import ConfusionMatrixMetric as ConfusionMatrixMetric +from .metrics import ConfusionMatrixAggregation as ConfusionMatrixAggregation +from .metrics import ScalarMetricValue as ScalarMetricValue +from .metrics import ConfusionMatrixMetricValue as ConfusionMatrixMetricValue -from .data.tiled_image import EPSG -from .data.tiled_image import EPSGTransformer -from .data.tiled_image import TiledBounds -from .data.tiled_image import TiledImageData -from .data.tiled_image import TileLayer +from .data.tiled_image import EPSG as EPSG +from .data.tiled_image import EPSGTransformer as EPSGTransformer +from .data.tiled_image import TiledBounds as TiledBounds +from .data.tiled_image import TiledImageData as TiledImageData +from .data.tiled_image import TileLayer as TileLayer -from .llm_prompt_response.prompt import PromptText -from .llm_prompt_response.prompt import PromptClassificationAnnotation +from .llm_prompt_response.prompt import PromptText as PromptText +from .llm_prompt_response.prompt import PromptClassificationAnnotation as PromptClassificationAnnotation from .mmc import ( - MessageInfo, - OrderedMessageInfo, - MessageSingleSelectionTask, - MessageMultiSelectionTask, - MessageRankingTask, - MessageEvaluationTaskAnnotation, + MessageInfo as MessageInfo, + OrderedMessageInfo as OrderedMessageInfo, + MessageSingleSelectionTask as MessageSingleSelectionTask, + MessageMultiSelectionTask as MessageMultiSelectionTask, + MessageRankingTask as MessageRankingTask, + MessageEvaluationTaskAnnotation as MessageEvaluationTaskAnnotation, ) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py index 92a19c8e2..997a7e550 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/audio.py @@ -1,10 +1,9 @@ -from typing import Optional, List +from typing import Optional from pydantic import Field, AliasChoices from labelbox.data.annotation_types.annotation import ( ClassificationAnnotation, ) -from labelbox.data.annotation_types.classification.classification import FrameLocation class AudioClassificationAnnotation(ClassificationAnnotation): diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py index fc00c9410..f518e7095 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py @@ -1 +1 @@ -from .classification import Checklist, ClassificationAnswer, Radio, Text, FrameLocation +from .classification import Checklist as Checklist, ClassificationAnswer as ClassificationAnswer, Radio as Radio, Text as Text, FrameLocation as FrameLocation From fb8df4a70c1a6d5e1cac1bb0405683cb9f49fa9a Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 3 Oct 2025 04:48:21 +0000 Subject: [PATCH 042/103] :art: Cleaned --- examples/annotation_import/audio.ipynb | 929 +++++++++---------------- 1 file changed, 330 insertions(+), 599 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index 663f9fe26..c22095c13 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,600 +1,331 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "7fb27b941602401d91542211134fc71a", - "metadata": {}, - "source": [ - "\n", - " \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "acae54e37e7d407bbb7b55eff062a284", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "9a63283cbaf04dbcab1f6479b197f3a8", - "metadata": {}, - "source": [ - "# Audio Annotation Import\n", - "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", - "\n", - "Suported annotations that can be uploaded through the SDK\n", - "\n", - "* Classification Radio \n", - "* Classification Checklist \n", - "* Classification Free Text \n", - "\n", - "**Not** supported annotations\n", - "\n", - "* Bouding box\n", - "* NER\n", - "* Polygon \n", - "* Point\n", - "* Polyline \n", - "* Segmentation Mask\n", - "\n", - "MAL and Label Import:\n", - "\n", - "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", - "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "8dd0d8092fe74a7c96281538738b07e2", - "metadata": {}, - "source": [ - "* For information on what types of annotations are supported per data type, refer to this documentation:\n", - " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ] - }, - { - "cell_type": "markdown", - "id": "72eea5119410473aa328ad9291626812", - "metadata": {}, - "source": [ - "* Notes:\n", - " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8edb47106e1a46a883d545849b8ab81b", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q \"labelbox[data]\"" - ] - }, - { - "cell_type": "markdown", - "id": "10185d26023b46108eb7d9f57d49d2b3", - "metadata": {}, - "source": [ - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8763a12b2bbd4a93a75aff182afb95dc", - "metadata": {}, - "outputs": [], - "source": [ - "import labelbox as lb\n", - "import uuid\n", - "import labelbox.types as lb_types" - ] - }, - { - "cell_type": "markdown", - "id": "7623eae2785240b9bd12b16a66d81610", - "metadata": {}, - "source": [ - "# Replace with your API key\n", - "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cdc8c89c7104fffa095e18ddfef8986", - "metadata": {}, - "outputs": [], - "source": [ - "# Add your api key\n", - "API_KEY = \"\"\n", - "client = lb.Client(api_key=API_KEY)" - ] - }, - { - "cell_type": "markdown", - "id": "b118ea5561624da68c537baed56e602f", - "metadata": {}, - "source": [ - "## Supported annotations for Audio" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "938c804e27f84196a10c8828c723f798", - "metadata": {}, - "outputs": [], - "source": [ - "##### Classification free text #####\n", - "\n", - "text_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"text_audio\",\n", - " value=lb_types.Text(answer=\"free text audio annotation\"),\n", - ")\n", - "\n", - "text_annotation_ndjson = {\n", - " \"name\": \"text_audio\",\n", - " \"answer\": \"free text audio annotation\",\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "504fb2a444614c0babb325280ed9130a", - "metadata": {}, - "outputs": [], - "source": [ - "##### Checklist Classification #######\n", - "\n", - "checklist_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"checklist_audio\",\n", - " value=lb_types.Checklist(answer=[\n", - " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", - " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", - " ]),\n", - ")\n", - "\n", - "checklist_annotation_ndjson = {\n", - " \"name\":\n", - " \"checklist_audio\",\n", - " \"answers\": [\n", - " {\n", - " \"name\": \"first_checklist_answer\"\n", - " },\n", - " {\n", - " \"name\": \"second_checklist_answer\"\n", - " },\n", - " ],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59bbdb311c014d738909a11f9e486628", - "metadata": {}, - "outputs": [], - "source": [ - "######## Radio Classification ######\n", - "\n", - "radio_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"radio_audio\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", - " name=\"second_radio_answer\")),\n", - ")\n", - "\n", - "radio_annotation_ndjson = {\n", - " \"name\": \"radio_audio\",\n", - " \"answer\": {\n", - " \"name\": \"first_radio_answer\"\n", - " },\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "b43b363d81ae4b689946ece5c682cd59", - "metadata": {}, - "source": [ - "## Upload Annotations - putting it all together " - ] - }, - { - "cell_type": "markdown", - "id": "8a65eabff63a45729fe45fb5ade58bdc", - "metadata": {}, - "source": [ - "## Step 1: Import data rows into Catalog" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3933fab20d04ec698c2621248eb3be0", - "metadata": {}, - "outputs": [], - "source": [ - "# Create one Labelbox dataset\n", - "\n", - "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", - "\n", - "asset = {\n", - " \"row_data\":\n", - " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", - " \"global_key\":\n", - " global_key,\n", - "}\n", - "\n", - "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", - "task = dataset.create_data_rows([asset])\n", - "task.wait_till_done()\n", - "print(\"Errors:\", task.errors)\n", - "print(\"Failed data rows: \", task.failed_data_rows)" - ] - }, - { - "cell_type": "markdown", - "id": "4dd4641cc4064e0191573fe9c69df29b", - "metadata": {}, - "source": [ - "## Step 2: Create/select an ontology\n", - "\n", - "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", - "\n", - "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8309879909854d7188b41380fd92a7c3", - "metadata": {}, - "outputs": [], - "source": [ - "ontology_builder = lb.OntologyBuilder(classifications=[\n", - " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", - " name=\"text_audio\"),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"checklist_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_checklist_answer\"),\n", - " lb.Option(value=\"second_checklist_answer\"),\n", - " ],\n", - " ),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"radio_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_radio_answer\"),\n", - " lb.Option(value=\"second_radio_answer\"),\n", - " ],\n", - " ),\n", - " # Temporal classification for token-level annotations\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.TEXT,\n", - " name=\"User Speaker\",\n", - " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", - " ),\n", - "])\n", - "\n", - "ontology = client.create_ontology(\n", - " \"Ontology Audio Annotations\",\n", - " ontology_builder.asdict(),\n", - " media_type=lb.MediaType.Audio,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3ed186c9a28b402fb0bc4494df01f08d", - "metadata": {}, - "source": [ - "\n", - "## Step 3: Create a labeling project\n", - "Connect the ontology to the labeling project" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb1e1581032b452c9409d6c6813c49d1", - "metadata": {}, - "outputs": [], - "source": [ - "# Create Labelbox project\n", - "project = client.create_project(name=\"audio_project\",\n", - " media_type=lb.MediaType.Audio)\n", - "\n", - "# Setup your ontology\n", - "project.setup_editor(\n", - " ontology) # Connect your ontology and editor to your project" - ] - }, - { - "cell_type": "markdown", - "id": "379cbbc1e968416e875cc15c1202d7eb", - "metadata": {}, - "source": [ - "## Step 4: Send a batch of data rows to the project" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "277c27b1587741f2af2001be3712ef0d", - "metadata": {}, - "outputs": [], - "source": [ - "# Setup Batches and Ontology\n", - "\n", - "# Create a batch to send to your MAL project\n", - "batch = project.create_batch(\n", - " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", - " global_keys=[\n", - " global_key\n", - " ], # Paginated collection of data row objects, list of data row ids or global keys\n", - " priority=5, # priority between 1(Highest) - 5(lowest)\n", - ")\n", - "\n", - "print(\"Batch: \", batch)" - ] - }, - { - "cell_type": "markdown", - "id": "db7b79bc585a40fcaf58bf750017e135", - "metadata": {}, - "source": [ - "## Step 5: Create the annotations payload\n", - "Create the annotations payload using the snippets of code above\n", - "\n", - "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ] - }, - { - "cell_type": "markdown", - "id": "916684f9a58a4a2aa5f864670399430d", - "metadata": {}, - "source": [ - "#### Python annotation\n", - "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1671c31a24314836a5b85d7ef7fbf015", - "metadata": {}, - "outputs": [], - "source": [ - "label = []\n", - "label.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", - " ))" - ] - }, - { - "cell_type": "markdown", - "id": "33b0902fd34d4ace834912fa1002cf8e", - "metadata": {}, - "source": [ - "### NDJSON annotations \n", - "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6fa52606d8c4a75a9b52967216f8f3f", - "metadata": {}, - "outputs": [], - "source": [ - "label_ndjson = []\n", - "for annotations in [\n", - " text_annotation_ndjson,\n", - " checklist_annotation_ndjson,\n", - " radio_annotation_ndjson,\n", - "]:\n", - " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", - " label_ndjson.append(annotations)" - ] - }, - { - "cell_type": "markdown", - "id": "f5a1fa73e5044315a093ec459c9be902", - "metadata": {}, - "source": [ - "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ] - }, - { - "cell_type": "markdown", - "id": "3ee572b6", - "metadata": {}, - "source": [ - "## Temporal Audio Annotations\n", - "\n", - "You can create temporal annotations for individual tokens (words) with precise timing.\n", - "\n", - "Additionally, you can create **nested temporal annotations** with hierarchical classifications at different frame ranges.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cdf66aed5cc84ca1b48e60bad68798a8", - "metadata": {}, - "outputs": [], - "source": [ - "# Define tokens with precise timing (from demo script)\n", - "tokens_data = [\n", - " (\"Hello\", 586, 770), # Hello: frames 586-770\n", - " (\"AI\", 771, 955), # AI: frames 771-955\n", - " (\"how\", 956, 1140), # how: frames 956-1140\n", - " (\"are\", 1141, 1325), # are: frames 1141-1325\n", - " (\"you\", 1326, 1510), # you: frames 1326-1510\n", - " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", - " (\"today\", 1696, 1880), # today: frames 1696-1880\n", - "]\n", - "\n", - "# Create temporal annotations for each token\n", - "temporal_annotations = []\n", - "for token, start_frame, end_frame in tokens_data:\n", - " token_annotation = lb_types.AudioClassificationAnnotation(\n", - " frame=start_frame,\n", - " end_frame=end_frame,\n", - " name=\"User Speaker\",\n", - " value=lb_types.Text(answer=token),\n", - " )\n", - " temporal_annotations.append(token_annotation)\n", - "\n", - "print(f\"Created {len(temporal_annotations)} temporal token annotations\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28d3efd5258a48a79c179ea5c6759f01", - "metadata": {}, - "outputs": [], - "source": [ - "# Create label with both regular and temporal annotations\n", - "label_with_temporal = []\n", - "label_with_temporal.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation] +\n", - " temporal_annotations,\n", - " ))\n", - "\n", - "print(\n", - " f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n", - ")\n", - "print(\" - Regular annotations: 3\")\n", - "print(f\" - Temporal annotations: {len(temporal_annotations)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "3f9bc0b9dd2c44919cc8dcca39b469f8", - "metadata": {}, - "source": [ - "#### Model Assisted Labeling (MAL)\n", - "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e382214b5f147d187d36a2058b9c724", - "metadata": {}, - "outputs": [], - "source": [ - "# Upload temporal annotations via MAL\n", - "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label_with_temporal,\n", - ")\n", - "\n", - "temporal_upload_job.wait_until_done()\n", - "print(\"Temporal upload completed!\")\n", - "print(\"Errors:\", temporal_upload_job.errors)\n", - "print(\"Status:\", temporal_upload_job.statuses)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b09d5ef5b5e4bb6ab9b829b10b6a29f", - "metadata": {}, - "outputs": [], - "source": [ - "# Upload our label using Model-Assisted Labeling\n", - "upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] - }, - { - "cell_type": "markdown", - "id": "a50416e276a0479cbe66534ed1713a40", - "metadata": {}, - "source": [ - "#### Label Import" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46a27a456b804aa2a380d5edf15a5daf", - "metadata": {}, - "outputs": [], - "source": [ - "# Upload label for this data row in project\n", - "upload_job = lb.LabelImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=\"label_import_job\" + str(uuid.uuid4()),\n", - " labels=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] - }, - { - "cell_type": "markdown", - "id": "1944c39560714e6e80c856f20744a8e5", - "metadata": {}, - "source": [ - "### Optional deletions for cleanup " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6ca27006b894b04b6fc8b79396e2797", - "metadata": {}, - "outputs": [], - "source": [ - "# project.delete()\n", - "# dataset.delete()" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {}, + "cells": [ + { + "metadata": {}, + "source": [ + "", + " ", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "# Audio Annotation Import\n", + "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", + "\n", + "Suported annotations that can be uploaded through the SDK\n", + "\n", + "* Classification Radio \n", + "* Classification Checklist \n", + "* Classification Free Text \n", + "\n", + "**Not** supported annotations\n", + "\n", + "* Bouding box\n", + "* NER\n", + "* Polygon \n", + "* Point\n", + "* Polyline \n", + "* Segmentation Mask\n", + "\n", + "MAL and Label Import:\n", + "\n", + "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", + "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* For information on what types of annotations are supported per data type, refer to this documentation:\n", + " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* Notes:\n", + " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Setup" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Replace with your API key\n", + "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Supported annotations for Audio" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Upload Annotations - putting it all together " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "## Step 1: Import data rows into Catalog" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 2: Create/select an ontology\n", + "\n", + "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", + "\n", + "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "\n", + "## Step 3: Create a labeling project\n", + "Connect the ontology to the labeling project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 4: Send a batch of data rows to the project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 5: Create the annotations payload\n", + "Create the annotations payload using the snippets of code above\n", + "\n", + "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "#### Python annotation\n", + "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### NDJSON annotations \n", + "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Step 6: Upload annotations to a project as pre-labels or complete labels" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "## Temporal Audio Annotations\n", + "\n", + "You can create temporal annotations for individual tokens (words) with precise timing.\n", + "\n", + "Additionally, you can create **nested temporal annotations** with hierarchical classifications at different frame ranges.\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Model Assisted Labeling (MAL)\n", + "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Label Import" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Optional deletions for cleanup " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# project.delete()\n# dataset.delete()", + "cell_type": "code", + "outputs": [], + "execution_count": null + } + ] +} \ No newline at end of file From f202586cb7b6b6f5bb6f4870ebbf36e2101f88f6 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Thu, 2 Oct 2025 22:05:03 -0700 Subject: [PATCH 043/103] chore: revert init py file --- .../data/annotation_types/__init__.py | 121 +++++++++--------- .../classification/__init__.py | 2 +- 2 files changed, 61 insertions(+), 62 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index be6c0d195..9f59b5197 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -1,64 +1,63 @@ -from .geometry import Line as Line -from .geometry import Point as Point -from .geometry import Mask as Mask -from .geometry import Polygon as Polygon -from .geometry import Rectangle as Rectangle -from .geometry import Geometry as Geometry -from .geometry import DocumentRectangle as DocumentRectangle -from .geometry import RectangleUnit as RectangleUnit - -from .annotation import ClassificationAnnotation as ClassificationAnnotation -from .annotation import ObjectAnnotation as ObjectAnnotation - -from .relationship import RelationshipAnnotation as RelationshipAnnotation -from .relationship import Relationship as Relationship - -from .video import VideoClassificationAnnotation as VideoClassificationAnnotation -from .video import VideoObjectAnnotation as VideoObjectAnnotation -from .video import MaskFrame as MaskFrame -from .video import MaskInstance as MaskInstance -from .video import VideoMaskAnnotation as VideoMaskAnnotation - -from .audio import AudioClassificationAnnotation as AudioClassificationAnnotation - -from .ner import ConversationEntity as ConversationEntity -from .ner import DocumentEntity as DocumentEntity -from .ner import DocumentTextSelection as DocumentTextSelection -from .ner import TextEntity as TextEntity - -from .classification import Checklist as Checklist -from .classification import ClassificationAnswer as ClassificationAnswer -from .classification import Radio as Radio -from .classification import Text as Text -from .classification import FrameLocation as FrameLocation - -from .data import GenericDataRowData as GenericDataRowData -from .data import MaskData as MaskData - -from .label import Label as Label -from .collection import LabelGenerator as LabelGenerator - -from .metrics import ScalarMetric as ScalarMetric -from .metrics import ScalarMetricAggregation as ScalarMetricAggregation -from .metrics import ConfusionMatrixMetric as ConfusionMatrixMetric -from .metrics import ConfusionMatrixAggregation as ConfusionMatrixAggregation -from .metrics import ScalarMetricValue as ScalarMetricValue -from .metrics import ConfusionMatrixMetricValue as ConfusionMatrixMetricValue - -from .data.tiled_image import EPSG as EPSG -from .data.tiled_image import EPSGTransformer as EPSGTransformer -from .data.tiled_image import TiledBounds as TiledBounds -from .data.tiled_image import TiledImageData as TiledImageData -from .data.tiled_image import TileLayer as TileLayer - -from .llm_prompt_response.prompt import PromptText as PromptText -from .llm_prompt_response.prompt import PromptClassificationAnnotation as PromptClassificationAnnotation +from .geometry import Line +from .geometry import Point +from .geometry import Mask +from .geometry import Polygon +from .geometry import Rectangle +from .geometry import Geometry +from .geometry import DocumentRectangle +from .geometry import RectangleUnit + +from .annotation import ClassificationAnnotation +from .annotation import ObjectAnnotation + +from .relationship import RelationshipAnnotation +from .relationship import Relationship + +from .video import VideoClassificationAnnotation +from .video import VideoObjectAnnotation +from .video import MaskFrame +from .video import MaskInstance +from .video import VideoMaskAnnotation + +from .audio import AudioClassificationAnnotation + +from .ner import ConversationEntity +from .ner import DocumentEntity +from .ner import DocumentTextSelection +from .ner import TextEntity + +from .classification import Checklist +from .classification import ClassificationAnswer +from .classification import Radio +from .classification import Text + +from .data import GenericDataRowData +from .data import MaskData + +from .label import Label +from .collection import LabelGenerator + +from .metrics import ScalarMetric +from .metrics import ScalarMetricAggregation +from .metrics import ConfusionMatrixMetric +from .metrics import ConfusionMatrixAggregation +from .metrics import ScalarMetricValue +from .metrics import ConfusionMatrixMetricValue + +from .data.tiled_image import EPSG +from .data.tiled_image import EPSGTransformer +from .data.tiled_image import TiledBounds +from .data.tiled_image import TiledImageData +from .data.tiled_image import TileLayer + +from .llm_prompt_response.prompt import PromptText +from .llm_prompt_response.prompt import PromptClassificationAnnotation from .mmc import ( - MessageInfo as MessageInfo, - OrderedMessageInfo as OrderedMessageInfo, - MessageSingleSelectionTask as MessageSingleSelectionTask, - MessageMultiSelectionTask as MessageMultiSelectionTask, - MessageRankingTask as MessageRankingTask, - MessageEvaluationTaskAnnotation as MessageEvaluationTaskAnnotation, + MessageInfo, + OrderedMessageInfo, + MessageSingleSelectionTask, + MessageMultiSelectionTask, + MessageRankingTask, + MessageEvaluationTaskAnnotation, ) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py index f518e7095..fc00c9410 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py @@ -1 +1 @@ -from .classification import Checklist as Checklist, ClassificationAnswer as ClassificationAnswer, Radio as Radio, Text as Text, FrameLocation as FrameLocation +from .classification import Checklist, ClassificationAnswer, Radio, Text, FrameLocation From 1e424efa1d6db6c5c61f508ddef2050ec375ea4d Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Fri, 3 Oct 2025 14:48:47 -0700 Subject: [PATCH 044/103] chore: new new new interface for tempral classes --- .../data/annotation_types/__init__.py | 5 +- .../labelbox/data/annotation_types/audio.py | 36 -- .../labelbox/data/annotation_types/label.py | 26 +- .../data/annotation_types/temporal.py | 194 ++++++ .../data/serialization/ndjson/label.py | 27 +- .../data/serialization/ndjson/temporal.py | 557 ++++++------------ .../data/serialization/ndjson/test_audio.py | 464 --------------- .../serialization/ndjson/test_temporal.py | 308 ++++++++++ 8 files changed, 719 insertions(+), 898 deletions(-) delete mode 100644 libs/labelbox/src/labelbox/data/annotation_types/audio.py create mode 100644 libs/labelbox/src/labelbox/data/annotation_types/temporal.py delete mode 100644 libs/labelbox/tests/data/serialization/ndjson/test_audio.py create mode 100644 libs/labelbox/tests/data/serialization/ndjson/test_temporal.py diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index 9f59b5197..5a5cf339f 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -19,7 +19,9 @@ from .video import MaskInstance from .video import VideoMaskAnnotation -from .audio import AudioClassificationAnnotation +from .temporal import TemporalClassificationText +from .temporal import TemporalClassificationQuestion +from .temporal import TemporalClassificationAnswer from .ner import ConversationEntity from .ner import DocumentEntity @@ -30,6 +32,7 @@ from .classification import ClassificationAnswer from .classification import Radio from .classification import Text +from .classification import FrameLocation from .data import GenericDataRowData from .data import MaskData diff --git a/libs/labelbox/src/labelbox/data/annotation_types/audio.py b/libs/labelbox/src/labelbox/data/annotation_types/audio.py deleted file mode 100644 index 997a7e550..000000000 --- a/libs/labelbox/src/labelbox/data/annotation_types/audio.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Optional -from pydantic import Field, AliasChoices - -from labelbox.data.annotation_types.annotation import ( - ClassificationAnnotation, -) - - -class AudioClassificationAnnotation(ClassificationAnnotation): - """Audio classification for specific time range - - Examples: - - Speaker identification from 2500ms to 4100ms - - Audio quality assessment for a segment - - Language detection for audio segments - - Args: - name (Optional[str]): Name of the classification - feature_schema_id (Optional[Cuid]): Feature schema identifier - value (Union[Text, Checklist, Radio]): Classification value - start_frame (Optional[int]): Start frame in milliseconds - end_frame (Optional[int]): End frame in milliseconds - segment_index (Optional[int]): Index of audio segment this annotation belongs to - extra (Dict[str, Any]): Additional metadata - - Note: - Parent AudioClassificationAnnotation uses start_frame/end_frame (single range). - Nested classifications/answers use frames: List[FrameLocation] for discontinuous ranges. - Multiple time ranges for same classification = multiple separate annotation objects. - """ - - start_frame: Optional[int] = Field( - default=None, validation_alias=AliasChoices("start_frame", "frame") - ) - end_frame: Optional[int] = None - segment_index: Optional[int] = None diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index 9170ebbe4..98f4a11aa 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -13,7 +13,10 @@ from .metrics import ScalarMetric, ConfusionMatrixMetric from .video import VideoClassificationAnnotation from .video import VideoObjectAnnotation, VideoMaskAnnotation -from .audio import AudioClassificationAnnotation +from .temporal import ( + TemporalClassificationText, + TemporalClassificationQuestion, +) from .mmc import MessageEvaluationTaskAnnotation from pydantic import BaseModel, field_validator @@ -45,7 +48,8 @@ class Label(BaseModel): ClassificationAnnotation, ObjectAnnotation, VideoMaskAnnotation, - AudioClassificationAnnotation, + TemporalClassificationText, + TemporalClassificationQuestion, ScalarMetric, ConfusionMatrixMetric, RelationshipAnnotation, @@ -82,7 +86,8 @@ def frame_annotations( Union[ VideoObjectAnnotation, VideoClassificationAnnotation, - AudioClassificationAnnotation, + TemporalClassificationText, + TemporalClassificationQuestion, ], ]: """Get temporal annotations organized by frame @@ -92,7 +97,11 @@ def frame_annotations( Example: >>> label.frame_annotations() - {2500: [VideoClassificationAnnotation(...), AudioClassificationAnnotation(...)]} + {2500: [VideoClassificationAnnotation(...), TemporalClassificationText(...)]} + + Note: + For TemporalClassificationText/Question, returns dictionary mapping to start of first frame range. + These annotations may have multiple discontinuous frame ranges. """ frame_dict = defaultdict(list) for annotation in self.annotations: @@ -101,8 +110,13 @@ def frame_annotations( (VideoObjectAnnotation, VideoClassificationAnnotation), ): frame_dict[annotation.frame].append(annotation) - elif isinstance(annotation, AudioClassificationAnnotation): - frame_dict[annotation.start_frame].append(annotation) + elif isinstance(annotation, (TemporalClassificationText, TemporalClassificationQuestion)): + # For temporal annotations with multiple values/answers, use first frame + if isinstance(annotation, TemporalClassificationText) and annotation.value: + frame_dict[annotation.value[0][0]].append(annotation) # value[0][0] is start_frame + elif isinstance(annotation, TemporalClassificationQuestion) and annotation.value: + if annotation.value[0].frames: + frame_dict[annotation.value[0].frames[0][0]].append(annotation) # frames[0][0] is start_frame return dict(frame_dict) def add_url_to_masks(self, signer) -> "Label": diff --git a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py new file mode 100644 index 000000000..a6e261702 --- /dev/null +++ b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py @@ -0,0 +1,194 @@ +""" +Temporal classification annotations for audio, video, and other time-based media. + +These classes provide a unified, recursive structure for temporal annotations with +frame-level precision. All temporal classifications support nested hierarchies. +""" + +from typing import List, Optional, Tuple, Union +from pydantic import Field + +from labelbox.data.annotation_types.annotation import ClassificationAnnotation +from labelbox.data.annotation_types.classification.classification import ( + ClassificationAnswer, + FrameLocation, +) + + +class TemporalClassificationAnswer(ClassificationAnswer): + """ + Temporal answer for Radio/Checklist questions with frame ranges. + + Represents a single answer option that can exist at multiple discontinuous + time ranges and contain nested classifications. + + Args: + name (str): Name of the answer option + frames (List[Tuple[int, int]]): List of (start_frame, end_frame) ranges in milliseconds + classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): + Nested classifications within this answer + feature_schema_id (Optional[str]): Feature schema identifier + extra (dict): Additional metadata + + Example: + >>> # Radio answer with nested classifications + >>> answer = TemporalClassificationAnswer( + >>> name="user", + >>> frames=[(200, 1600)], + >>> classifications=[ + >>> TemporalClassificationQuestion( + >>> name="tone", + >>> answers=[ + >>> TemporalClassificationAnswer( + >>> name="professional", + >>> frames=[(1000, 1600)] + >>> ) + >>> ] + >>> ) + >>> ] + >>> ) + """ + + frames: List[Tuple[int, int]] = Field( + default_factory=list, + description="List of (start_frame, end_frame) tuples in milliseconds", + ) + classifications: Optional[ + List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] + ] = None + + +class TemporalClassificationText(ClassificationAnnotation): + """ + Temporal text classification with multiple text values at different frame ranges. + + Allows multiple text annotations at different time segments, each with precise + frame ranges. Supports recursive nesting of text and question classifications. + + Args: + name (str): Name of the text classification + values (List[Tuple[int, int, str]]): List of (start_frame, end_frame, text_value) tuples + classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): + Nested classifications + feature_schema_id (Optional[str]): Feature schema identifier + extra (dict): Additional metadata + + Example: + >>> # Simple text with multiple temporal values + >>> transcription = TemporalClassificationText( + >>> name="transcription", + >>> values=[ + >>> (1600, 2000, "Hello, how can I help you?"), + >>> (2500, 3000, "Thank you for calling!"), + >>> ] + >>> ) + >>> + >>> # Text with nested classifications + >>> transcription_with_notes = TemporalClassificationText( + >>> name="transcription", + >>> values=[ + >>> (1600, 2000, "Hello, how can I help you?"), + >>> ], + >>> classifications=[ + >>> TemporalClassificationText( + >>> name="speaker_notes", + >>> values=[ + >>> (1600, 2000, "Polite greeting"), + >>> ] + >>> ) + >>> ] + >>> ) + """ + + # Override parent's value field + value: List[Tuple[int, int, str]] = Field( + default_factory=list, + description="List of (start_frame, end_frame, text_value) tuples", + ) + classifications: Optional[ + List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] + ] = None + + +class TemporalClassificationQuestion(ClassificationAnnotation): + """ + Temporal Radio/Checklist question with multiple answer options. + + Represents a question with one or more answer options, each having their own + frame ranges. Radio questions have a single answer, Checklist can have multiple. + + Args: + name (str): Name of the question/classification + answers (List[TemporalClassificationAnswer]): List of answer options with frame ranges + feature_schema_id (Optional[str]): Feature schema identifier + extra (dict): Additional metadata + + Note: + - Radio: Single answer in the answers list + - Checklist: Multiple answers in the answers list + The serializer automatically handles the distinction based on the number of answers. + + Example: + >>> # Radio question (single answer) + >>> speaker = TemporalClassificationQuestion( + >>> name="speaker", + >>> answers=[ + >>> TemporalClassificationAnswer( + >>> name="user", + >>> frames=[(200, 1600)] + >>> ) + >>> ] + >>> ) + >>> + >>> # Checklist question (multiple answers) + >>> audio_quality = TemporalClassificationQuestion( + >>> name="audio_quality", + >>> answers=[ + >>> TemporalClassificationAnswer( + >>> name="background_noise", + >>> frames=[(0, 1500), (2000, 3000)] + >>> ), + >>> TemporalClassificationAnswer( + >>> name="echo", + >>> frames=[(2200, 2900)] + >>> ) + >>> ] + >>> ) + >>> + >>> # Nested structure: Radio > Radio > Radio + >>> speaker_with_tone = TemporalClassificationQuestion( + >>> name="speaker", + >>> answers=[ + >>> TemporalClassificationAnswer( + >>> name="user", + >>> frames=[(200, 1600)], + >>> classifications=[ + >>> TemporalClassificationQuestion( + >>> name="tone", + >>> answers=[ + >>> TemporalClassificationAnswer( + >>> name="professional", + >>> frames=[(1000, 1600)] + >>> ) + >>> ] + >>> ) + >>> ] + >>> ) + >>> ] + >>> ) + """ + + # Override parent's value field + value: List[TemporalClassificationAnswer] = Field( + default_factory=list, + description="List of temporal answer options", + ) + classifications: Optional[ + List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] + ] = None + + +# Update forward references for recursive types +TemporalClassificationAnswer.model_rebuild() +TemporalClassificationText.model_rebuild() +TemporalClassificationQuestion.model_rebuild() diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 9974c9aa0..05a247f61 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -25,10 +25,12 @@ VideoObjectAnnotation, ) from typing import List -from ...annotation_types.audio import ( - AudioClassificationAnnotation, +from ...annotation_types.temporal import ( + TemporalClassificationText, + TemporalClassificationQuestion, + TemporalClassificationAnswer, ) -from .temporal import create_audio_ndjson_annotations +from .temporal import create_temporal_ndjson_annotations from labelbox.types import DocumentRectangle, DocumentEntity from .classification import ( NDChecklistSubclass, @@ -169,20 +171,20 @@ def _create_video_annotations( def _create_audio_annotations( cls, label: Label ) -> Generator[BaseModel, None, None]: - """Create audio annotations with nested classifications using modular hierarchy builder.""" - # Extract audio annotations from the label - audio_annotations = [ + """Create temporal annotations with nested classifications using new temporal classes.""" + # Extract temporal annotations from the label + temporal_annotations = [ annot for annot in label.annotations - if isinstance(annot, AudioClassificationAnnotation) + if isinstance(annot, (TemporalClassificationText, TemporalClassificationQuestion)) ] - if not audio_annotations: + if not temporal_annotations: return - # Use the modular hierarchy builder to create NDJSON annotations - ndjson_annotations = create_audio_ndjson_annotations( - audio_annotations, label.data.global_key + # Use the new temporal serializer to create NDJSON annotations + ndjson_annotations = create_temporal_ndjson_annotations( + temporal_annotations, label.data.global_key ) # Yield each NDJSON annotation @@ -200,7 +202,8 @@ def _create_non_video_annotations(cls, label: Label): VideoClassificationAnnotation, VideoObjectAnnotation, VideoMaskAnnotation, - AudioClassificationAnnotation, + TemporalClassificationText, + TemporalClassificationQuestion, RelationshipAnnotation, ), ) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index d4c54ca00..100d2db21 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -1,34 +1,47 @@ """ -Simplified temporal NDJSON serialization. +Temporal NDJSON serialization for new temporal classification structure. -This module provides a streamlined approach for constructing nested hierarchical -classifications from temporal annotations (audio, video, etc.). - -IMPORTANT: This module ONLY supports explicit nesting via ClassificationAnswer.classifications. -Annotations must define their hierarchy structure explicitly in the annotation objects. -Temporal containment-based inference is NOT supported. +Handles TemporalClassificationText, TemporalClassificationQuestion, and TemporalClassificationAnswer +with frame validation and recursive nesting support. """ +import logging from collections import defaultdict -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Tuple, Union from pydantic import BaseModel -from ...annotation_types.audio import AudioClassificationAnnotation +from ...annotation_types.temporal import ( + TemporalClassificationText, + TemporalClassificationQuestion, + TemporalClassificationAnswer, +) + +logger = logging.getLogger(__name__) + + +class TemporalNDJSON(BaseModel): + """NDJSON structure for temporal annotations""" + + name: str + answer: List[Dict[str, Any]] + dataRow: Dict[str, str] def create_temporal_ndjson_annotations( - annotations: List[Any], data_global_key: str, frame_extractor: callable -) -> List["TemporalNDJSON"]: + annotations: List[ + Union[TemporalClassificationText, TemporalClassificationQuestion] + ], + data_global_key: str, +) -> List[TemporalNDJSON]: """ - Create NDJSON temporal annotations with hierarchical structure. + Create NDJSON temporal annotations from new temporal classification types. Args: - annotations: List of temporal classification annotations + annotations: List of TemporalClassificationText or TemporalClassificationQuestion data_global_key: Global key for the data row - frame_extractor: Function that extracts (start, end) tuple from annotation Returns: - List of TemporalNDJSON objects + List of TemporalNDJSON objects ready for serialization """ if not annotations: return [] @@ -44,90 +57,72 @@ def create_temporal_ndjson_annotations( # Get display name (prefer first non-empty name) display_name = next((a.name for a in group_anns if a.name), group_key) - # Process this group recursively - answers = _process_annotation_group(group_anns, frame_extractor) + # Process based on annotation type + first_ann = group_anns[0] - results.append( - TemporalNDJSON( - name=display_name, - answer=answers, - dataRow={"globalKey": data_global_key}, + if isinstance(first_ann, TemporalClassificationText): + answers = _process_text_group(group_anns, parent_frames=None) + elif isinstance(first_ann, TemporalClassificationQuestion): + answers = _process_question_group(group_anns, parent_frames=None) + else: + logger.warning(f"Unknown temporal annotation type: {type(first_ann)}") + continue + + if answers: # Only add if we have valid answers + results.append( + TemporalNDJSON( + name=display_name, + answer=answers, + dataRow={"globalKey": data_global_key}, + ) ) - ) return results -def _process_annotation_group( - annotations: List[Any], frame_extractor: callable +def _process_text_group( + annotations: List[TemporalClassificationText], + parent_frames: List[Tuple[int, int]] = None, ) -> List[Dict[str, Any]]: """ - Process a group of annotations with the same name/schema_id. - Groups by answer value and handles nested classifications recursively. - """ - # Group by answer value - value_groups = defaultdict(list) - for ann in annotations: - value_key = _get_value_key(ann) - value_groups[value_key].append(ann) - - results = [] - for _, anns in value_groups.items(): - first = anns[0] - - # Handle different annotation types - if hasattr(first.value, "answer"): - answer = first.value.answer - - if isinstance(answer, list): - # Checklist - process each option - results.extend(_process_checklist(anns, frame_extractor)) - elif hasattr(answer, "name"): - # Radio - merge frames and nested classifications - results.append(_process_radio(anns, frame_extractor)) - else: - # Text - simple value with potential nesting - results.append(_process_text(anns, frame_extractor)) - else: - # Fallback for unexpected structure - results.append(_process_text(anns, frame_extractor)) - - return results - + Process TemporalClassificationText annotations. -def _process_checklist( - annotations: List[Any], frame_extractor: callable -) -> List[Dict[str, Any]]: - """Process checklist annotations - collect all unique options across all annotations.""" - # Collect all unique option names and their data - option_data = defaultdict(lambda: {"frames": [], "nested": []}) + Each annotation can have multiple (start, end, text) tuples. + Groups by text value and merges frames. + """ + # Collect all text values with their frames + text_data = defaultdict(lambda: {"frames": [], "nested": []}) for ann in annotations: - ann_start, ann_end = frame_extractor(ann) - ann_frames = [{"start": ann_start, "end": ann_end}] + for start, end, text_value in ann.value: + # Validate frames against parent if provided + if parent_frames and not _is_frame_subset([(start, end)], parent_frames): + logger.warning( + f"Text value frames ({start}, {end}) not subset of parent frames {parent_frames}. Discarding." + ) + continue - if hasattr(ann.value, "answer") and isinstance(ann.value.answer, list): - for opt in ann.value.answer: - opt_name = opt.name + text_data[text_value]["frames"].append({"start": start, "end": end}) - # Get frames for this option (use explicit if available, else annotation frames) - opt_frames = _extract_frames(opt, ann_frames) - option_data[opt_name]["frames"].extend(opt_frames) - - # Collect nested classifications - if hasattr(opt, "classifications") and opt.classifications: - option_data[opt_name]["nested"].extend(opt.classifications) + # Collect nested classifications + if ann.classifications: + text_data[text_value]["nested"].extend(ann.classifications) - # Build answer entries + # Build results results = [] - for opt_name in sorted(option_data.keys()): - entry = {"name": opt_name, "frames": option_data[opt_name]["frames"]} - - # Recursively process nested classifications - if option_data[opt_name]["nested"]: - nested = _process_nested_classifications( - option_data[opt_name]["nested"] - ) + for text_value, data in text_data.items(): + # Deduplicate frames + unique_frames = _deduplicate_frames(data["frames"]) + + entry = { + "value": text_value, + "frames": unique_frames, + } + + # Process nested classifications recursively + if data["nested"]: + parent_frame_tuples = [(f["start"], f["end"]) for f in unique_frames] + nested = _process_nested_classifications(data["nested"], parent_frame_tuples) if nested: entry["classifications"] = nested @@ -136,334 +131,138 @@ def _process_checklist( return results -def _process_radio( - annotations: List[Any], frame_extractor: callable -) -> Dict[str, Any]: - """Process radio annotations - merge frames and nested classifications.""" - first = annotations[0] - opt_name = first.value.answer.name - - # Collect all frames and nested classifications - all_frames = [] - all_nested = [] - - for ann in annotations: - ann_start, ann_end = frame_extractor(ann) - ann_frames = [{"start": ann_start, "end": ann_end}] - - # Get frames for this radio answer - opt_frames = _extract_frames(ann.value.answer, ann_frames) - all_frames.extend(opt_frames) - - # Collect nested - if ( - hasattr(ann.value.answer, "classifications") - and ann.value.answer.classifications - ): - all_nested.extend(ann.value.answer.classifications) - - # Deduplicate frames - seen = set() - unique_frames = [] - for frame in all_frames: - frame_tuple = (frame["start"], frame["end"]) - if frame_tuple not in seen: - seen.add(frame_tuple) - unique_frames.append(frame) - - entry = {"name": opt_name, "frames": unique_frames} - - # Recursively process nested - if all_nested: - nested = _process_nested_classifications(all_nested) - if nested: - entry["classifications"] = nested - - return entry - - -def _process_text( - annotations: List[Any], frame_extractor: callable -) -> Dict[str, Any]: - """Process text annotations - collect frames and nested classifications.""" - first = annotations[0] - text_value = ( - first.value.answer - if hasattr(first.value, "answer") - else str(first.value) - ) +def _process_question_group( + annotations: List[TemporalClassificationQuestion], + parent_frames: List[Tuple[int, int]] = None, +) -> List[Dict[str, Any]]: + """ + Process TemporalClassificationQuestion annotations. - # Collect all frames and nested - all_frames = [] - all_nested = [] + Each annotation has a list of TemporalClassificationAnswer objects. + Groups by answer name and merges frames. + """ + # Collect all answers + answer_data = defaultdict(lambda: {"frames": [], "nested": []}) for ann in annotations: - start, end = frame_extractor(ann) - all_frames.append({"start": start, "end": end}) - - # Text nesting is at annotation level - if hasattr(ann, "classifications") and ann.classifications: - all_nested.extend(ann.classifications) + for answer in ann.value: # value contains list of answers + # Validate and collect frames + valid_frames = [] + for start, end in answer.frames: + if parent_frames and not _is_frame_subset([(start, end)], parent_frames): + logger.warning( + f"Answer '{answer.name}' frames ({start}, {end}) not subset of parent frames {parent_frames}. Discarding." + ) + continue + valid_frames.append({"start": start, "end": end}) + + if valid_frames: # Only add if we have valid frames + answer_data[answer.name]["frames"].extend(valid_frames) - # Deduplicate frames - seen = set() - unique_frames = [] - for frame in all_frames: - frame_tuple = (frame["start"], frame["end"]) - if frame_tuple not in seen: - seen.add(frame_tuple) - unique_frames.append(frame) + # Collect nested classifications + if answer.classifications: + answer_data[answer.name]["nested"].extend(answer.classifications) - entry = {"value": text_value, "frames": unique_frames} + # Build results + results = [] + for answer_name, data in answer_data.items(): + # Deduplicate frames + unique_frames = _deduplicate_frames(data["frames"]) + + if not unique_frames: # Skip if no valid frames + continue + + entry = { + "name": answer_name, + "frames": unique_frames, + } + + # Process nested classifications recursively + if data["nested"]: + parent_frame_tuples = [(f["start"], f["end"]) for f in unique_frames] + nested = _process_nested_classifications(data["nested"], parent_frame_tuples) + if nested: + entry["classifications"] = nested - # Recursively process nested - if all_nested: - nested = _process_nested_classifications(all_nested) - if nested: - entry["classifications"] = nested + results.append(entry) - return entry + return results def _process_nested_classifications( - classifications: List[Any], + classifications: List[Union[TemporalClassificationText, TemporalClassificationQuestion]], + parent_frames: List[Tuple[int, int]], ) -> List[Dict[str, Any]]: """ - Recursively process nested ClassificationAnnotation objects. - This uses the same grouping logic as top-level annotations. + Process nested classifications recursively. + + Groups by name/schema_id and processes each group. """ - # Group by name/schema_id + # Group by name groups = defaultdict(list) for cls in classifications: key = cls.feature_schema_id or cls.name groups[key].append(cls) results = [] - for group_key, cls_list in groups.items(): - display_name = next((c.name for c in cls_list if c.name), group_key) - - # Group by value and process - value_groups = defaultdict(list) - for cls in cls_list: - value_key = _get_value_key(cls) - value_groups[value_key].append(cls) - - answers = [] - for _, cls_group in value_groups.items(): - first_cls = cls_group[0] - - if hasattr(first_cls.value, "answer"): - answer = first_cls.value.answer - - if isinstance(answer, list): - # Checklist - answers.extend(_process_nested_checklist(cls_group)) - elif hasattr(answer, "name"): - # Radio - answers.append(_process_nested_radio(cls_group)) - else: - # Text - answers.append(_process_nested_text(cls_group)) - - results.append({"name": display_name, "answer": answers}) - - return results - - -def _process_nested_checklist( - classifications: List[Any], -) -> List[Dict[str, Any]]: - """Process nested checklist classifications.""" - option_data = defaultdict(lambda: {"frames": [], "nested": []}) - - for cls in classifications: - cls_frames = _extract_frames(cls, []) - - if hasattr(cls.value, "answer") and isinstance(cls.value.answer, list): - for opt in cls.value.answer: - opt_frames = _extract_frames(opt, cls_frames) - option_data[opt.name]["frames"].extend(opt_frames) + for group_key, group_items in groups.items(): + # Get display name + display_name = next((c.name for c in group_items if c.name), group_key) - if hasattr(opt, "classifications") and opt.classifications: - option_data[opt.name]["nested"].extend(opt.classifications) + # Process based on type + first_item = group_items[0] - results = [] - for opt_name in sorted(option_data.keys()): - entry = {"name": opt_name, "frames": option_data[opt_name]["frames"]} - - if option_data[opt_name]["nested"]: - nested = _process_nested_classifications( - option_data[opt_name]["nested"] - ) - if nested: - entry["classifications"] = nested + if isinstance(first_item, TemporalClassificationText): + answers = _process_text_group(group_items, parent_frames) + elif isinstance(first_item, TemporalClassificationQuestion): + answers = _process_question_group(group_items, parent_frames) + else: + logger.warning(f"Unknown nested classification type: {type(first_item)}") + continue - results.append(entry) + if answers: # Only add if we have valid answers + results.append({ + "name": display_name, + "answer": answers, + }) return results -def _process_nested_radio(classifications: List[Any]) -> Dict[str, Any]: - """Process nested radio classifications - merge frames.""" - first = classifications[0] - opt_name = first.value.answer.name - - all_frames = [] - all_nested = [] - - for cls in classifications: - cls_frames = _extract_frames(cls, []) - opt_frames = _extract_frames(cls.value.answer, cls_frames) - all_frames.extend(opt_frames) - - if ( - hasattr(cls.value.answer, "classifications") - and cls.value.answer.classifications - ): - all_nested.extend(cls.value.answer.classifications) - - # Deduplicate frames - seen = set() - unique_frames = [] - for frame in all_frames: - frame_tuple = (frame["start"], frame["end"]) - if frame_tuple not in seen: - seen.add(frame_tuple) - unique_frames.append(frame) - - entry = {"name": opt_name, "frames": unique_frames} - - if all_nested: - nested = _process_nested_classifications(all_nested) - if nested: - entry["classifications"] = nested - - return entry - - -def _process_nested_text(classifications: List[Any]) -> Dict[str, Any]: - """Process nested text classifications.""" - first = classifications[0] - text_value = ( - first.value.answer - if hasattr(first.value, "answer") - else str(first.value) - ) - - all_frames = [] - all_nested = [] - - for cls in classifications: - frames = _extract_frames(cls, []) - all_frames.extend(frames) - - if hasattr(cls, "classifications") and cls.classifications: - all_nested.extend(cls.classifications) - - # Deduplicate frames - seen = set() - unique_frames = [] - for frame in all_frames: - frame_tuple = (frame["start"], frame["end"]) - if frame_tuple not in seen: - seen.add(frame_tuple) - unique_frames.append(frame) - - entry = {"value": text_value, "frames": unique_frames} - - if all_nested: - nested = _process_nested_classifications(all_nested) - if nested: - entry["classifications"] = nested - - return entry - - -def _extract_frames( - obj: Any, fallback_frames: List[Dict[str, int]] -) -> List[Dict[str, int]]: +def _is_frame_subset( + child_frames: List[Tuple[int, int]], + parent_frames: List[Tuple[int, int]], +) -> bool: """ - Extract frame ranges from an object (annotation, answer, or classification). - Uses explicit frames if available, otherwise falls back to provided frames. + Check if all child frames are subsets of at least one parent frame. - Supports both: - - New format: frames: List[FrameLocation] - - Legacy format: start_frame/end_frame (single range) + A child frame (cs, ce) is a subset of parent frame (ps, pe) if: + ps <= cs and ce <= pe """ - # New format: frames list - if hasattr(obj, "frames") and obj.frames is not None: - return [{"start": frame.start, "end": frame.end} for frame in obj.frames] - - # Legacy format: single start_frame/end_frame - elif ( - hasattr(obj, "start_frame") - and obj.start_frame is not None - and hasattr(obj, "end_frame") - and obj.end_frame is not None - ): - return [{"start": obj.start_frame, "end": obj.end_frame}] - - # Fallback to parent frames - elif fallback_frames: - return fallback_frames - - else: - return [] + for child_start, child_end in child_frames: + is_subset = False + for parent_start, parent_end in parent_frames: + if parent_start <= child_start and child_end <= parent_end: + is_subset = True + break + if not is_subset: + return False # At least one child frame is not a subset -def _get_value_key(obj: Any) -> str: - """Get a stable key for grouping by answer value.""" - if hasattr(obj.value, "answer"): - answer = obj.value.answer - if isinstance(answer, list): - # Checklist: stable key from selected option names - return str(sorted([opt.name for opt in answer])) - elif hasattr(answer, "name"): - # Radio: option name - return answer.name - else: - # Text: the string value - return str(answer) - else: - return str(obj.value) + return True -class TemporalNDJSON(BaseModel): - """NDJSON format for temporal annotations (audio, video, etc.).""" - - name: str - answer: List[Dict[str, Any]] - dataRow: Dict[str, str] - - -# Audio-specific convenience function -def create_audio_ndjson_annotations( - annotations: List[AudioClassificationAnnotation], data_global_key: str -) -> List[TemporalNDJSON]: +def _deduplicate_frames(frames: List[Dict[str, int]]) -> List[Dict[str, int]]: """ - Create NDJSON audio annotations with hierarchical structure. - - Args: - annotations: List of audio classification annotations - data_global_key: Global key for the data row - - Returns: - List of TemporalNDJSON objects + Remove duplicate frame ranges. """ + seen = set() + unique = [] + + for frame in frames: + frame_tuple = (frame["start"], frame["end"]) + if frame_tuple not in seen: + seen.add(frame_tuple) + unique.append(frame) - def audio_frame_extractor( - ann: AudioClassificationAnnotation, - ) -> Tuple[int, int]: - """ - Legacy frame extractor for AudioClassificationAnnotation. - Only used when frames list is not provided. - """ - # Return first frame if frames list exists - if ann.frames and len(ann.frames) > 0: - return (ann.frames[0].start, ann.frames[0].end) - # Fall back to legacy start_frame/end_frame - return (ann.start_frame, ann.end_frame or ann.start_frame) - - return create_temporal_ndjson_annotations( - annotations, data_global_key, audio_frame_extractor - ) + return unique diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py b/libs/labelbox/tests/data/serialization/ndjson/test_audio.py deleted file mode 100644 index 031c0c078..000000000 --- a/libs/labelbox/tests/data/serialization/ndjson/test_audio.py +++ /dev/null @@ -1,464 +0,0 @@ -import labelbox.types as lb_types -from labelbox.data.serialization.ndjson.converter import NDJsonConverter - - -def test_audio_nested_text_radio_checklist_structure(): - # Purpose: verify that class-based AudioClassificationAnnotation inputs with explicit - # nesting serialize into v3-style nested NDJSON with: - # - exactly three top-level groups (text_class, radio_class, checklist_class) - # - explicit nesting via ClassificationAnnotation.classifications and ClassificationAnswer.classifications - # - nested classifications can specify their own start_frame/end_frame (subset of root) - # - correct field shapes per type (Text uses "value", Radio/Checklist use "name") - - # Build annotations using explicit nesting (NEW interface) matching exec/v3.py output shape - anns = [] - - # text_class: simple value without nesting - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=1000, end=1100)], - name="text_class", - value=lb_types.Text(answer="A"), - ) - ) - - # text_class: value WITH explicit nested classifications - # This annotation has nested classifications at the annotation level (for Text type) - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=1500, end=2400)], # Root frame range - name="text_class", - value=lb_types.Text(answer="text_class value"), - classifications=[ # Explicit nesting via classifications field - lb_types.ClassificationAnnotation( - name="nested_text_class", - frames=[lb_types.FrameLocation(start=1600, end=2000)], # Nested frame range (subset of root) - value=lb_types.Text(answer="nested_text_class value"), - classifications=[ # Deeper nesting - lb_types.ClassificationAnnotation( - name="nested_text_class_2", - frames=[lb_types.FrameLocation(start=1800, end=2000)], # Even more specific nested range - value=lb_types.Text( - answer="nested_text_class_2 value" - ), - ) - ], - ), - lb_types.ClassificationAnnotation( - name="nested_text_class", - frames=[lb_types.FrameLocation(start=2001, end=2400)], # Different nested frame range - value=lb_types.Text(answer="nested_text_class value2"), - ), - ], - ) - ) - - # Additional text_class segments - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=2500, end=2700)], - name="text_class", - value=lb_types.Text(answer="C"), - ) - ) - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=2900, end=2999)], - name="text_class", - value=lb_types.Text(answer="D"), - ) - ) - - # radio_class: Explicit nesting via ClassificationAnswer.classifications - # First segment with nested classifications - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=200, end=1500)], # Root frame range - name="radio_class", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer( - name="first_radio_answer", - classifications=[ # Explicit nesting at answer level for Radio - lb_types.ClassificationAnnotation( - name="sub_radio_question", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer( - name="first_sub_radio_answer", - frames=[lb_types.FrameLocation(start=1000, end=1500)], # Nested frame range - classifications=[ # Deeper nesting - lb_types.ClassificationAnnotation( - name="sub_radio_question_2", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer( - name="first_sub_radio_answer_2", - frames=[lb_types.FrameLocation(start=1300, end=1500)], # Even more specific nested range - ) - ), - ) - ], - ) - ), - ), - lb_types.ClassificationAnnotation( - name="sub_radio_question", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer( - name="second_sub_radio_answer", - frames=[lb_types.FrameLocation(start=2100, end=2500)], # Nested frame range for second segment - ) - ), - ), - ], - ) - ), - ) - ) - - # Second segment for first_radio_answer (will merge frames in output) - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=2000, end=2500)], - name="radio_class", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer( - name="first_radio_answer", - classifications=[ - lb_types.ClassificationAnnotation( - name="sub_radio_question", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer( - name="second_sub_radio_answer" - ) - ), - ) - ], - ) - ), - ) - ) - - # radio_class: second_radio_answer without nesting - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=1550, end=1700)], - name="radio_class", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer(name="second_radio_answer") - ), - ) - ) - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=2700, end=3000)], - name="radio_class", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer(name="second_radio_answer") - ), - ) - ) - - # checklist_class: Explicit nesting via ClassificationAnswer.classifications - # First segment with nested checklist - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=300, end=800)], # Root frame range (first segment) - name="checklist_class", - value=lb_types.Checklist( - answer=[ - lb_types.ClassificationAnswer( - name="first_checklist_option", - classifications=[ # Explicit nesting at answer level for Checklist - lb_types.ClassificationAnnotation( - name="nested_checklist", - value=lb_types.Checklist( - answer=[ - lb_types.ClassificationAnswer( - name="nested_option_1", - frames=[lb_types.FrameLocation(start=400, end=700)], # Nested frame range - classifications=[ # Deeper nesting - lb_types.ClassificationAnnotation( - name="checklist_nested_text", - frames=[lb_types.FrameLocation(start=500, end=700)], # Even more specific nested range - value=lb_types.Text( - answer="checklist_nested_text value" - ), - ) - ], - ) - ] - ), - ) - ], - ) - ] - ), - ) - ) - - # Second segment for first_checklist_option with different nested options - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=1200, end=1800)], # Root frame range (second segment) - name="checklist_class", - value=lb_types.Checklist( - answer=[ - lb_types.ClassificationAnswer( - name="first_checklist_option", - classifications=[ - lb_types.ClassificationAnnotation( - name="nested_checklist", - value=lb_types.Checklist( - answer=[ - lb_types.ClassificationAnswer( - name="nested_option_2", - frames=[lb_types.FrameLocation(start=1200, end=1600)], # Nested frame range - ), - lb_types.ClassificationAnswer( - name="nested_option_3", - frames=[lb_types.FrameLocation(start=1400, end=1800)], # Nested frame range - ), - ] - ), - ) - ], - ) - ] - ), - ) - ) - - # checklist_class: other options without nesting - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=2200, end=2900)], - name="checklist_class", - value=lb_types.Checklist( - answer=[ - lb_types.ClassificationAnswer( - name="second_checklist_option" - ) - ] - ), - ) - ) - anns.append( - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=2500, end=3500)], - name="checklist_class", - value=lb_types.Checklist( - answer=[ - lb_types.ClassificationAnswer(name="third_checklist_option") - ] - ), - ) - ) - - # Serialize a single Label containing all of the above annotations - label = lb_types.Label( - data={"global_key": "audio_nested_test_key"}, annotations=anns - ) - ndjson = list(NDJsonConverter.serialize([label])) - - # Assert: exactly three top-level groups, matching v3 root objects - assert {obj["name"] for obj in ndjson} == { - "text_class", - "radio_class", - "checklist_class", - } - - # Validate text_class structure with explicit nesting and frame ranges - text_nd = next(obj for obj in ndjson if obj["name"] == "text_class") - - # Check that we have 4 text_class answers (A, text_class value, C, D) - assert len(text_nd["answer"]) == 4 - - # Find the parent answer with nested classifications - parent = next( - item - for item in text_nd["answer"] - if item.get("value") == "text_class value" - ) - assert parent["frames"] == [{"start": 1500, "end": 2400}] - - # Check explicit nested classifications - nested = parent.get("classifications", []) - assert len(nested) == 1 # One nested_text_class group - nt = nested[0] - assert nt["name"] == "nested_text_class" - - # Check nested_text_class has 2 answers with different frame ranges - assert len(nt["answer"]) == 2 - nt_ans_1 = nt["answer"][0] - assert nt_ans_1["value"] == "nested_text_class value" - assert nt_ans_1["frames"] == [ - {"start": 1600, "end": 2000} - ] # Nested frame range - - # Check nested_text_class_2 is nested under nested_text_class - nt_nested = nt_ans_1.get("classifications", []) - assert len(nt_nested) == 1 - nt2 = nt_nested[0] - assert nt2["name"] == "nested_text_class_2" - assert nt2["answer"][0]["value"] == "nested_text_class_2 value" - assert nt2["answer"][0]["frames"] == [ - {"start": 1800, "end": 2000} - ] # Even more specific nested range - - # Check second nested_text_class answer - nt_ans_2 = nt["answer"][1] - assert nt_ans_2["value"] == "nested_text_class value2" - assert nt_ans_2["frames"] == [ - {"start": 2001, "end": 2400} - ] # Different nested frame range - - # Validate radio_class structure with explicit nesting and frame ranges - radio_nd = next(obj for obj in ndjson if obj["name"] == "radio_class") - - # Check first_radio_answer - # Note: Segments with the same answer value are merged (both segments have "first_radio_answer") - first_radios = [ - a for a in radio_nd["answer"] if a["name"] == "first_radio_answer" - ] - # We get one merged answer with both frame ranges - assert len(first_radios) == 1 - first_radio = first_radios[0] - # Merged frames from both segments: [200-1500] and [2000-2500] - assert first_radio["frames"] == [ - {"start": 200, "end": 1500}, - {"start": 2000, "end": 2500}, - ] - - # Check explicit nested sub_radio_question - assert "classifications" in first_radio - sub_radio = next( - c - for c in first_radio["classifications"] - if c["name"] == "sub_radio_question" - ) - - # Check sub_radio_question has 2 answers with specific frame ranges - assert len(sub_radio["answer"]) == 2 - sr_first = next( - a for a in sub_radio["answer"] if a["name"] == "first_sub_radio_answer" - ) - assert sr_first["frames"] == [ - {"start": 1000, "end": 1500} - ] # Nested frame range - - # Check sub_radio_question_2 is nested under first_sub_radio_answer - assert "classifications" in sr_first - sr2 = next( - c - for c in sr_first["classifications"] - if c["name"] == "sub_radio_question_2" - ) - assert sr2["answer"][0]["name"] == "first_sub_radio_answer_2" - assert sr2["answer"][0]["frames"] == [ - {"start": 1300, "end": 1500} - ] # Even more specific nested range - - # Check second_sub_radio_answer - sr_second = next( - a for a in sub_radio["answer"] if a["name"] == "second_sub_radio_answer" - ) - # Has specific nested frame range from first segment - assert sr_second["frames"] == [{"start": 2100, "end": 2500}] - - # Validate checklist_class structure with explicit nesting and frame ranges - checklist_nd = next( - obj for obj in ndjson if obj["name"] == "checklist_class" - ) - - # Check first_checklist_option - # Note: segments with the same answer value are merged - first_opts = [ - a - for a in checklist_nd["answer"] - if a["name"] == "first_checklist_option" - ] - assert len(first_opts) == 1 - first_opt = first_opts[0] - # Merged frames from both segments: [300-800] and [1200-1800] - assert first_opt["frames"] == [ - {"start": 300, "end": 800}, - {"start": 1200, "end": 1800}, - ] - - # Check explicit nested_checklist - assert "classifications" in first_opt - nested_checklist = next( - c - for c in first_opt["classifications"] - if c["name"] == "nested_checklist" - ) - - # Check nested_checklist has all 3 options (nested_option_1, 2, 3) from both segments - assert len(nested_checklist["answer"]) == 3 - - # Check nested_option_1 with specific frame range - opt1 = next( - a for a in nested_checklist["answer"] if a["name"] == "nested_option_1" - ) - assert opt1["frames"] == [{"start": 400, "end": 700}] # Nested frame range - - # Check checklist_nested_text is nested under nested_option_1 - assert "classifications" in opt1 - nested_text = next( - c - for c in opt1["classifications"] - if c["name"] == "checklist_nested_text" - ) - assert nested_text["answer"][0]["value"] == "checklist_nested_text value" - assert nested_text["answer"][0]["frames"] == [ - {"start": 500, "end": 700} - ] # Even more specific nested range - - -def test_audio_top_level_only_basic(): - anns = [ - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=200, end=1500)], - name="radio_class", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer(name="first_radio_answer") - ), - ), - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=1550, end=1700)], - name="radio_class", - value=lb_types.Radio( - answer=lb_types.ClassificationAnswer(name="second_radio_answer") - ), - ), - lb_types.AudioClassificationAnnotation( - frames=[lb_types.FrameLocation(start=1200, end=1800)], - name="checklist_class", - value=lb_types.Checklist( - answer=[lb_types.ClassificationAnswer(name="angry")] - ), - ), - ] - - label = lb_types.Label( - data={"global_key": "audio_top_level_only"}, annotations=anns - ) - ndjson = list(NDJsonConverter.serialize([label])) - - names = {o["name"] for o in ndjson} - assert names == {"radio_class", "checklist_class"} - - radio = next(o for o in ndjson if o["name"] == "radio_class") - r_answers = sorted(radio["answer"], key=lambda x: x["frames"][0]["start"]) - assert r_answers[0]["name"] == "first_radio_answer" - assert r_answers[0]["frames"] == [{"start": 200, "end": 1500}] - assert "classifications" not in r_answers[0] - assert r_answers[1]["name"] == "second_radio_answer" - assert r_answers[1]["frames"] == [{"start": 1550, "end": 1700}] - assert "classifications" not in r_answers[1] - - checklist = next(o for o in ndjson if o["name"] == "checklist_class") - c_answers = checklist["answer"] - assert len(c_answers) == 1 - assert c_answers[0]["name"] == "angry" - assert c_answers[0]["frames"] == [{"start": 1200, "end": 1800}] - assert "classifications" not in c_answers[0] diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py b/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py new file mode 100644 index 000000000..83a850dc2 --- /dev/null +++ b/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py @@ -0,0 +1,308 @@ +"""Tests for new temporal classification serialization""" + +import labelbox.types as lb_types +from labelbox.data.serialization.ndjson.temporal import ( + create_temporal_ndjson_annotations, +) + + +def test_temporal_text_simple(): + """Test simple TemporalClassificationText serialization""" + annotations = [ + lb_types.TemporalClassificationText( + name="transcription", + value=[ + (1000, 1100, "Hello"), + (1500, 2400, "How can I help you?"), + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + assert len(result) == 1 + assert result[0].name == "transcription" + assert len(result[0].answer) == 2 + + # Check first text value + answer0 = result[0].answer[0] + assert answer0["value"] == "Hello" + assert answer0["frames"] == [{"start": 1000, "end": 1100}] + + # Check second text value + answer1 = result[0].answer[1] + assert answer1["value"] == "How can I help you?" + assert answer1["frames"] == [{"start": 1500, "end": 2400}] + + +def test_temporal_question_radio(): + """Test TemporalClassificationQuestion with single answer (Radio)""" + annotations = [ + lb_types.TemporalClassificationQuestion( + name="speaker", + value=[ + lb_types.TemporalClassificationAnswer( + name="user", + frames=[(200, 1600)], + ) + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + assert len(result) == 1 + assert result[0].name == "speaker" + assert len(result[0].answer) == 1 + + answer = result[0].answer[0] + assert answer["name"] == "user" + assert answer["frames"] == [{"start": 200, "end": 1600}] + + +def test_temporal_question_checklist(): + """Test TemporalClassificationQuestion with multiple answers (Checklist)""" + annotations = [ + lb_types.TemporalClassificationQuestion( + name="audio_quality", + value=[ + lb_types.TemporalClassificationAnswer( + name="background_noise", + frames=[(0, 1500), (2000, 3000)], + ), + lb_types.TemporalClassificationAnswer( + name="echo", + frames=[(2200, 2900)], + ), + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + assert len(result) == 1 + assert result[0].name == "audio_quality" + assert len(result[0].answer) == 2 + + # Check background_noise answer + bg_noise = next(a for a in result[0].answer if a["name"] == "background_noise") + assert bg_noise["frames"] == [ + {"start": 0, "end": 1500}, + {"start": 2000, "end": 3000}, + ] + + # Check echo answer + echo = next(a for a in result[0].answer if a["name"] == "echo") + assert echo["frames"] == [{"start": 2200, "end": 2900}] + + +def test_temporal_text_nested(): + """Test TemporalClassificationText with nested classifications""" + annotations = [ + lb_types.TemporalClassificationText( + name="transcription", + value=[ + (1600, 2000, "Hello, how can I help you?"), + ], + classifications=[ + lb_types.TemporalClassificationText( + name="speaker_notes", + value=[ + (1600, 2000, "Polite greeting"), + ], + classifications=[ + lb_types.TemporalClassificationText( + name="context_tags", + value=[ + (1800, 2000, "customer service tone"), + ], + ) + ], + ) + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + assert len(result) == 1 + assert result[0].name == "transcription" + assert len(result[0].answer) == 1 + + answer = result[0].answer[0] + assert answer["value"] == "Hello, how can I help you?" + assert answer["frames"] == [{"start": 1600, "end": 2000}] + + # Check nested classifications + assert "classifications" in answer + assert len(answer["classifications"]) == 1 + + nested1 = answer["classifications"][0] + assert nested1["name"] == "speaker_notes" + assert len(nested1["answer"]) == 1 + assert nested1["answer"][0]["value"] == "Polite greeting" + + # Check deeper nesting + assert "classifications" in nested1["answer"][0] + nested2 = nested1["answer"][0]["classifications"][0] + assert nested2["name"] == "context_tags" + assert nested2["answer"][0]["value"] == "customer service tone" + + +def test_temporal_question_nested(): + """Test TemporalClassificationQuestion with nested classifications""" + annotations = [ + lb_types.TemporalClassificationQuestion( + name="speaker", + value=[ + lb_types.TemporalClassificationAnswer( + name="user", + frames=[(200, 1600)], + classifications=[ + lb_types.TemporalClassificationQuestion( + name="tone", + value=[ + lb_types.TemporalClassificationAnswer( + name="professional", + frames=[(1000, 1600)], + classifications=[ + lb_types.TemporalClassificationQuestion( + name="clarity", + value=[ + lb_types.TemporalClassificationAnswer( + name="clear", + frames=[(1300, 1600)], + ) + ], + ) + ], + ) + ], + ) + ], + ) + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + assert len(result) == 1 + answer = result[0].answer[0] + assert answer["name"] == "user" + + # Check nested tone + assert "classifications" in answer + tone = answer["classifications"][0] + assert tone["name"] == "tone" + assert tone["answer"][0]["name"] == "professional" + + # Check deeper nested clarity + clarity = tone["answer"][0]["classifications"][0] + assert clarity["name"] == "clarity" + assert clarity["answer"][0]["name"] == "clear" + assert clarity["answer"][0]["frames"] == [{"start": 1300, "end": 1600}] + + +def test_frame_validation_discard_invalid(): + """Test that invalid frames (not subset of parent) are discarded""" + annotations = [ + lb_types.TemporalClassificationQuestion( + name="speaker", + value=[ + lb_types.TemporalClassificationAnswer( + name="user", + frames=[(200, 1600)], # Parent range + classifications=[ + lb_types.TemporalClassificationText( + name="notes", + value=[ + (300, 800, "Valid note"), # Within parent range + (1700, 2000, "Invalid note"), # Outside parent range + ], + ) + ], + ) + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + # Find the nested notes classification + answer = result[0].answer[0] + notes = answer["classifications"][0] + + # Only the valid note should be present + assert len(notes["answer"]) == 1 + assert notes["answer"][0]["value"] == "Valid note" + assert notes["answer"][0]["frames"] == [{"start": 300, "end": 800}] + + +def test_frame_deduplication(): + """Test that duplicate frames are removed""" + annotations = [ + lb_types.TemporalClassificationText( + name="transcription", + value=[ + (1000, 1100, "Hello"), + (1000, 1100, "Hello"), # Duplicate + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + # Should only have one entry + assert len(result[0].answer) == 1 + assert result[0].answer[0]["frames"] == [{"start": 1000, "end": 1100}] + + +def test_mixed_text_and_question_nesting(): + """Test Checklist > Text > Radio nesting""" + annotations = [ + lb_types.TemporalClassificationQuestion( + name="checklist_class", + value=[ + lb_types.TemporalClassificationAnswer( + name="quality_check", + frames=[(0, 1500)], + classifications=[ + lb_types.TemporalClassificationText( + name="notes_text", + value=[ + (0, 1500, "Audio quality is excellent"), + ], + classifications=[ + lb_types.TemporalClassificationQuestion( + name="severity_radio", + value=[ + lb_types.TemporalClassificationAnswer( + name="minor", + frames=[(0, 1500)], + ) + ], + ) + ], + ) + ], + ) + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + assert len(result) == 1 + answer = result[0].answer[0] + assert answer["name"] == "quality_check" + + # Check text classification + text_cls = answer["classifications"][0] + assert text_cls["name"] == "notes_text" + assert text_cls["answer"][0]["value"] == "Audio quality is excellent" + + # Check radio classification + radio_cls = text_cls["answer"][0]["classifications"][0] + assert radio_cls["name"] == "severity_radio" + assert radio_cls["answer"][0]["name"] == "minor" From fb209f0055e9e60d2e9dfa8706ff98b4de206d65 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 6 Oct 2025 10:50:23 -0700 Subject: [PATCH 045/103] chore: cleanup --- .../data/annotation_types/__init__.py | 1 - .../classification/__init__.py | 2 +- .../classification/classification.py | 13 ---- .../labelbox/data/annotation_types/label.py | 4 +- .../data/annotation_types/temporal.py | 61 ++++++++++--------- 5 files changed, 35 insertions(+), 46 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index 5a5cf339f..9595810e5 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -32,7 +32,6 @@ from .classification import ClassificationAnswer from .classification import Radio from .classification import Text -from .classification import FrameLocation from .data import GenericDataRowData from .data import MaskData diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py index fc00c9410..a814336e4 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py @@ -1 +1 @@ -from .classification import Checklist, ClassificationAnswer, Radio, Text, FrameLocation +from .classification import Checklist, ClassificationAnswer, Radio, Text diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py index 99f9817e8..35428ee9d 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py @@ -7,12 +7,6 @@ from ..feature import FeatureSchema -class FrameLocation(BaseModel): - """Represents a temporal frame range with start and end times (in milliseconds).""" - start: int - end: int - - class ClassificationAnswer(FeatureSchema, ConfidenceMixin, CustomMetricsMixin): """ - Represents a classification option. @@ -23,16 +17,11 @@ class ClassificationAnswer(FeatureSchema, ConfidenceMixin, CustomMetricsMixin): Each answer can have a keyframe independent of the others. So unlike object annotations, classification annotations track keyframes at a classification answer level. - - - For temporal classifications (audio/video), optional frames can specify - one or more time ranges for this answer. Must be within root annotation's frame ranges. - Defaults to root frame ranges if not specified. """ extra: Dict[str, Any] = {} keyframe: Optional[bool] = None classifications: Optional[List["ClassificationAnnotation"]] = None - frames: Optional[List[FrameLocation]] = None class Radio(ConfidenceMixin, CustomMetricsMixin, BaseModel): @@ -80,11 +69,9 @@ class ClassificationAnnotation( classifications (Optional[List[ClassificationAnnotation]]): Optional sub classification of the annotation feature_schema_id (Optional[Cuid]) value (Union[Text, Checklist, Radio]) - frames (Optional[List[FrameLocation]]): Frame ranges for temporal classifications (audio/video). Must be within root annotation's frame ranges. Defaults to root frames if not specified. extra (Dict[str, Any]) """ value: Union[Text, Checklist, Radio] message_id: Optional[str] = None - frames: Optional[List[FrameLocation]] = None diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index 98f4a11aa..f8b431edc 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -69,8 +69,8 @@ def validate_data(cls, data): def object_annotations(self) -> List[ObjectAnnotation]: return self._get_annotations_by_type(ObjectAnnotation) - def classification_annotations(self) -> List[ClassificationAnnotation]: - return self._get_annotations_by_type(ClassificationAnnotation) + def classification_annotations(self) -> List[Union[ClassificationAnnotation, TemporalClassificationText, TemporalClassificationQuestion]]: + return self._get_annotations_by_type((ClassificationAnnotation, TemporalClassificationText, TemporalClassificationQuestion)) def _get_annotations_by_type(self, annotation_type): return [ diff --git a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py index a6e261702..7fcd40e78 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py @@ -5,17 +5,13 @@ frame-level precision. All temporal classifications support nested hierarchies. """ -from typing import List, Optional, Tuple, Union -from pydantic import Field +from typing import Any, Dict, List, Optional, Tuple, Union +from pydantic import BaseModel, Field -from labelbox.data.annotation_types.annotation import ClassificationAnnotation -from labelbox.data.annotation_types.classification.classification import ( - ClassificationAnswer, - FrameLocation, -) +from ...annotated_types import Cuid -class TemporalClassificationAnswer(ClassificationAnswer): +class TemporalClassificationAnswer(BaseModel): """ Temporal answer for Radio/Checklist questions with frame ranges. @@ -27,8 +23,8 @@ class TemporalClassificationAnswer(ClassificationAnswer): frames (List[Tuple[int, int]]): List of (start_frame, end_frame) ranges in milliseconds classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): Nested classifications within this answer - feature_schema_id (Optional[str]): Feature schema identifier - extra (dict): Additional metadata + feature_schema_id (Optional[Cuid]): Feature schema identifier + extra (Dict[str, Any]): Additional metadata Example: >>> # Radio answer with nested classifications @@ -49,6 +45,7 @@ class TemporalClassificationAnswer(ClassificationAnswer): >>> ) """ + name: str frames: List[Tuple[int, int]] = Field( default_factory=list, description="List of (start_frame, end_frame) tuples in milliseconds", @@ -56,9 +53,11 @@ class TemporalClassificationAnswer(ClassificationAnswer): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None + feature_schema_id: Optional[Cuid] = None + extra: Dict[str, Any] = Field(default_factory=dict) -class TemporalClassificationText(ClassificationAnnotation): +class TemporalClassificationText(BaseModel): """ Temporal text classification with multiple text values at different frame ranges. @@ -67,17 +66,17 @@ class TemporalClassificationText(ClassificationAnnotation): Args: name (str): Name of the text classification - values (List[Tuple[int, int, str]]): List of (start_frame, end_frame, text_value) tuples + value (List[Tuple[int, int, str]]): List of (start_frame, end_frame, text_value) tuples classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): Nested classifications - feature_schema_id (Optional[str]): Feature schema identifier - extra (dict): Additional metadata + feature_schema_id (Optional[Cuid]): Feature schema identifier + extra (Dict[str, Any]): Additional metadata Example: >>> # Simple text with multiple temporal values >>> transcription = TemporalClassificationText( >>> name="transcription", - >>> values=[ + >>> value=[ >>> (1600, 2000, "Hello, how can I help you?"), >>> (2500, 3000, "Thank you for calling!"), >>> ] @@ -86,13 +85,13 @@ class TemporalClassificationText(ClassificationAnnotation): >>> # Text with nested classifications >>> transcription_with_notes = TemporalClassificationText( >>> name="transcription", - >>> values=[ + >>> value=[ >>> (1600, 2000, "Hello, how can I help you?"), >>> ], >>> classifications=[ >>> TemporalClassificationText( >>> name="speaker_notes", - >>> values=[ + >>> value=[ >>> (1600, 2000, "Polite greeting"), >>> ] >>> ) @@ -100,7 +99,7 @@ class TemporalClassificationText(ClassificationAnnotation): >>> ) """ - # Override parent's value field + name: str value: List[Tuple[int, int, str]] = Field( default_factory=list, description="List of (start_frame, end_frame, text_value) tuples", @@ -108,9 +107,11 @@ class TemporalClassificationText(ClassificationAnnotation): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None + feature_schema_id: Optional[Cuid] = None + extra: Dict[str, Any] = Field(default_factory=dict) -class TemporalClassificationQuestion(ClassificationAnnotation): +class TemporalClassificationQuestion(BaseModel): """ Temporal Radio/Checklist question with multiple answer options. @@ -119,20 +120,20 @@ class TemporalClassificationQuestion(ClassificationAnnotation): Args: name (str): Name of the question/classification - answers (List[TemporalClassificationAnswer]): List of answer options with frame ranges - feature_schema_id (Optional[str]): Feature schema identifier - extra (dict): Additional metadata + value (List[TemporalClassificationAnswer]): List of answer options with frame ranges + feature_schema_id (Optional[Cuid]): Feature schema identifier + extra (Dict[str, Any]): Additional metadata Note: - - Radio: Single answer in the answers list - - Checklist: Multiple answers in the answers list + - Radio: Single answer in the value list + - Checklist: Multiple answers in the value list The serializer automatically handles the distinction based on the number of answers. Example: >>> # Radio question (single answer) >>> speaker = TemporalClassificationQuestion( >>> name="speaker", - >>> answers=[ + >>> value=[ >>> TemporalClassificationAnswer( >>> name="user", >>> frames=[(200, 1600)] @@ -143,7 +144,7 @@ class TemporalClassificationQuestion(ClassificationAnnotation): >>> # Checklist question (multiple answers) >>> audio_quality = TemporalClassificationQuestion( >>> name="audio_quality", - >>> answers=[ + >>> value=[ >>> TemporalClassificationAnswer( >>> name="background_noise", >>> frames=[(0, 1500), (2000, 3000)] @@ -158,14 +159,14 @@ class TemporalClassificationQuestion(ClassificationAnnotation): >>> # Nested structure: Radio > Radio > Radio >>> speaker_with_tone = TemporalClassificationQuestion( >>> name="speaker", - >>> answers=[ + >>> value=[ >>> TemporalClassificationAnswer( >>> name="user", >>> frames=[(200, 1600)], >>> classifications=[ >>> TemporalClassificationQuestion( >>> name="tone", - >>> answers=[ + >>> value=[ >>> TemporalClassificationAnswer( >>> name="professional", >>> frames=[(1000, 1600)] @@ -178,7 +179,7 @@ class TemporalClassificationQuestion(ClassificationAnnotation): >>> ) """ - # Override parent's value field + name: str value: List[TemporalClassificationAnswer] = Field( default_factory=list, description="List of temporal answer options", @@ -186,6 +187,8 @@ class TemporalClassificationQuestion(ClassificationAnnotation): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None + feature_schema_id: Optional[Cuid] = None + extra: Dict[str, Any] = Field(default_factory=dict) # Update forward references for recursive types From 15bb17bdca388f22f59ebd9a7845b0c1b985536b Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 6 Oct 2025 23:39:32 -0700 Subject: [PATCH 046/103] chore: final nail --- .../data/serialization/ndjson/label.py | 4 +- .../data/serialization/ndjson/temporal.py | 169 +++++++++++++++--- 2 files changed, 148 insertions(+), 25 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 05a247f61..ffc021799 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -76,7 +76,7 @@ def from_common( yield from cls._create_relationship_annotations(label) yield from cls._create_non_video_annotations(label) yield from cls._create_video_annotations(label) - yield from cls._create_audio_annotations(label) + yield from cls._create_temporal_annotations(label) @staticmethod def _get_consecutive_frames( @@ -168,7 +168,7 @@ def _create_video_annotations( yield NDObject.from_common(segments, label.data) @classmethod - def _create_audio_annotations( + def _create_temporal_annotations( cls, label: Label ) -> Generator[BaseModel, None, None]: """Create temporal annotations with nested classifications using new temporal classes.""" diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index 100d2db21..ec1e2ecec 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -89,9 +89,14 @@ def _process_text_group( Each annotation can have multiple (start, end, text) tuples. Groups by text value and merges frames. + + Nested classifications are assigned to text values based on frame overlap. """ # Collect all text values with their frames - text_data = defaultdict(lambda: {"frames": [], "nested": []}) + text_data = defaultdict(lambda: {"frames": []}) + + # Collect all nested classifications from all annotations + all_nested_classifications = [] for ann in annotations: for start, end, text_value in ann.value: @@ -104,9 +109,12 @@ def _process_text_group( text_data[text_value]["frames"].append({"start": start, "end": end}) - # Collect nested classifications - if ann.classifications: - text_data[text_value]["nested"].extend(ann.classifications) + # Collect nested classifications at annotation level (not per text value) + if ann.classifications: + all_nested_classifications.extend(ann.classifications) + + # Track which nested classifications were assigned + assigned_nested = set() # Build results results = [] @@ -119,15 +127,40 @@ def _process_text_group( "frames": unique_frames, } - # Process nested classifications recursively - if data["nested"]: + # Assign nested classifications based on frame overlap + if all_nested_classifications: parent_frame_tuples = [(f["start"], f["end"]) for f in unique_frames] - nested = _process_nested_classifications(data["nested"], parent_frame_tuples) - if nested: - entry["classifications"] = nested + # Filter nested classifications that overlap with this text value's frames + relevant_nested = _filter_classifications_by_overlap( + all_nested_classifications, parent_frame_tuples + ) + if relevant_nested: + # Track that these were assigned + for cls in relevant_nested: + assigned_nested.add(id(cls)) + + # Pass ONLY THIS text value's frames so nested answers are filtered correctly + nested = _process_nested_classifications(relevant_nested, parent_frame_tuples) + if nested: + entry["classifications"] = nested results.append(entry) + # Log orphaned nested classifications (not assigned to any parent) + if all_nested_classifications: + for cls in all_nested_classifications: + if id(cls) not in assigned_nested: + if isinstance(cls, TemporalClassificationText): + frames_info = cls.value[0][:2] if cls.value else "no frames" + elif isinstance(cls, TemporalClassificationQuestion): + frames_info = cls.value[0].frames if cls.value and cls.value[0].frames else "no frames" + else: + frames_info = "unknown" + logger.warning( + f"Orphaned nested classification '{cls.name}' with frames {frames_info} - " + f"no parent text value found with overlapping frames." + ) + return results @@ -140,28 +173,43 @@ def _process_question_group( Each annotation has a list of TemporalClassificationAnswer objects. Groups by answer name and merges frames. + + Nested classifications are assigned to answers based on frame overlap. """ - # Collect all answers - answer_data = defaultdict(lambda: {"frames": [], "nested": []}) + # Collect all answers with their frames + answer_data = defaultdict(lambda: {"frames": []}) + + # Collect all nested classifications from all answers + all_nested_by_answer = defaultdict(list) for ann in annotations: for answer in ann.value: # value contains list of answers # Validate and collect frames valid_frames = [] for start, end in answer.frames: - if parent_frames and not _is_frame_subset([(start, end)], parent_frames): - logger.warning( - f"Answer '{answer.name}' frames ({start}, {end}) not subset of parent frames {parent_frames}. Discarding." - ) - continue + # If parent_frames provided, check if answer frames are subset of ANY parent frame + # A child frame is a subset if: parent_start <= child_start AND child_end <= parent_end + if parent_frames: + is_valid = False + for parent_start, parent_end in parent_frames: + if parent_start <= start and end <= parent_end: + is_valid = True + break + if not is_valid: + # Don't log here - this is expected when processing inductive structures + # Only log orphaned classifications that are never assigned to any parent + continue valid_frames.append({"start": start, "end": end}) if valid_frames: # Only add if we have valid frames answer_data[answer.name]["frames"].extend(valid_frames) - # Collect nested classifications + # Collect nested classifications at answer level if answer.classifications: - answer_data[answer.name]["nested"].extend(answer.classifications) + all_nested_by_answer[answer.name].extend(answer.classifications) + + # Track which nested classifications were assigned + assigned_nested = set() # Build results results = [] @@ -177,15 +225,39 @@ def _process_question_group( "frames": unique_frames, } - # Process nested classifications recursively - if data["nested"]: + # Assign nested classifications based on frame overlap + if all_nested_by_answer[answer_name]: parent_frame_tuples = [(f["start"], f["end"]) for f in unique_frames] - nested = _process_nested_classifications(data["nested"], parent_frame_tuples) - if nested: - entry["classifications"] = nested + # Filter nested classifications that overlap with this answer's frames + relevant_nested = _filter_classifications_by_overlap( + all_nested_by_answer[answer_name], parent_frame_tuples + ) + if relevant_nested: + # Track that these were assigned + for cls in relevant_nested: + assigned_nested.add(id(cls)) + + nested = _process_nested_classifications(relevant_nested, parent_frame_tuples) + if nested: + entry["classifications"] = nested results.append(entry) + # Log orphaned nested classifications (not assigned to any answer) + for answer_name, nested_list in all_nested_by_answer.items(): + for cls in nested_list: + if id(cls) not in assigned_nested: + if isinstance(cls, TemporalClassificationText): + frames_info = cls.value[0][:2] if cls.value else "no frames" + elif isinstance(cls, TemporalClassificationQuestion): + frames_info = cls.value[0].frames if cls.value and cls.value[0].frames else "no frames" + else: + frames_info = "unknown" + logger.warning( + f"Orphaned nested classification '{cls.name}' in answer '{answer_name}' with frames {frames_info} - " + f"no overlapping frames found with parent answer." + ) + return results @@ -229,6 +301,57 @@ def _process_nested_classifications( return results +def _filter_classifications_by_overlap( + classifications: List[Union[TemporalClassificationText, TemporalClassificationQuestion]], + parent_frames: List[Tuple[int, int]], +) -> List[Union[TemporalClassificationText, TemporalClassificationQuestion]]: + """ + Filter classifications to only include those with frames that overlap with parent frames. + + A classification is included if ANY of its frame ranges overlap with ANY parent frame range. + """ + relevant = [] + + for cls in classifications: + has_overlap = False + + # Check frames based on classification type + if isinstance(cls, TemporalClassificationText): + # Check text value frames + for start, end, _ in cls.value: + if _frames_overlap([(start, end)], parent_frames): + has_overlap = True + break + elif isinstance(cls, TemporalClassificationQuestion): + # Check answer frames + for answer in cls.value: + if _frames_overlap(answer.frames, parent_frames): + has_overlap = True + break + + if has_overlap: + relevant.append(cls) + + return relevant + + +def _frames_overlap( + frames1: List[Tuple[int, int]], + frames2: List[Tuple[int, int]], +) -> bool: + """ + Check if any frame in frames1 overlaps with any frame in frames2. + + Two frames (s1, e1) and (s2, e2) overlap if: + max(s1, s2) <= min(e1, e2) + """ + for start1, end1 in frames1: + for start2, end2 in frames2: + if max(start1, start2) <= min(end1, end2): + return True + return False + + def _is_frame_subset( child_frames: List[Tuple[int, int]], parent_frames: List[Tuple[int, int]], From c28a7cab3c4a5045c1ee2b2bba9aee40b4b50b72 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Mon, 6 Oct 2025 23:50:09 -0700 Subject: [PATCH 047/103] chore: docs and tests --- examples/annotation_import/audio.ipynb | 684 +++++++++--------- .../serialization/ndjson/test_temporal.py | 146 +++- 2 files changed, 498 insertions(+), 332 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index c22095c13..b243b722e 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,331 +1,357 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {}, - "cells": [ - { - "metadata": {}, - "source": [ - "", - " ", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# Audio Annotation Import\n", - "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", - "\n", - "Suported annotations that can be uploaded through the SDK\n", - "\n", - "* Classification Radio \n", - "* Classification Checklist \n", - "* Classification Free Text \n", - "\n", - "**Not** supported annotations\n", - "\n", - "* Bouding box\n", - "* NER\n", - "* Polygon \n", - "* Point\n", - "* Polyline \n", - "* Segmentation Mask\n", - "\n", - "MAL and Label Import:\n", - "\n", - "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", - "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "* For information on what types of annotations are supported per data type, refer to this documentation:\n", - " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "* Notes:\n", - " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# Setup" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# Replace with your API key\n", - "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Supported annotations for Audio" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Step 1: Import data rows into Catalog" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 2: Create/select an ontology\n", - "\n", - "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", - "\n", - "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "\n", - "## Step 3: Create a labeling project\n", - "Connect the ontology to the labeling project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 5: Create the annotations payload\n", - "Create the annotations payload using the snippets of code above\n", - "\n", - "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "#### Python annotation\n", - "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### NDJSON annotations \n", - "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Temporal Audio Annotations\n", - "\n", - "You can create temporal annotations for individual tokens (words) with precise timing.\n", - "\n", - "Additionally, you can create **nested temporal annotations** with hierarchical classifications at different frame ranges.\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Model Assisted Labeling (MAL)\n", - "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Label Import" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Optional deletions for cleanup " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# project.delete()\n# dataset.delete()", - "cell_type": "code", - "outputs": [], - "execution_count": null - } - ] + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {}, + "cells": [ + { + "metadata": {}, + "source": [ + "", + " ", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "# Audio Annotation Import\n", + "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", + "\n", + "Suported annotations that can be uploaded through the SDK\n", + "\n", + "* Classification Radio \n", + "* Classification Checklist \n", + "* Classification Free Text \n", + "\n", + "**Not** supported annotations\n", + "\n", + "* Bouding box\n", + "* NER\n", + "* Polygon \n", + "* Point\n", + "* Polyline \n", + "* Segmentation Mask\n", + "\n", + "MAL and Label Import:\n", + "\n", + "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", + "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* For information on what types of annotations are supported per data type, refer to this documentation:\n", + " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* Notes:\n", + " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Setup" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Replace with your API key\n", + "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Supported annotations for Audio" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Upload Annotations - putting it all together " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "## Step 1: Import data rows into Catalog" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 2: Create/select an ontology\n", + "\n", + "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", + "\n", + "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n # Global (non-temporal) classifications\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n \n # Temporal classifications (scope=INDEX for frame-based annotations)\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"transcription\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"speaker_notes\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"context_tags\",\n )\n ]\n )\n ]\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"speaker\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\"user\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"audio_quality\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\"background_noise\"),\n lb.Option(\"echo\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"content_notes\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"clarity_radio\",\n options=[\n lb.Option(\"very_clear\"),\n lb.Option(\"slightly_clear\"),\n ],\n )\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_class\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\n \"quality_check\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"notes_text\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"severity_radio\",\n options=[\n lb.Option(\"minor\"),\n ],\n )\n ],\n )\n ],\n )\n ],\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "\n", + "## Step 3: Create a labeling project\n", + "Connect the ontology to the labeling project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 4: Send a batch of data rows to the project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 5: Create the annotations payload\n", + "Create the annotations payload using the snippets of code above\n", + "\n", + "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "#### Python annotation\n", + "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label = []\n\n# Regular (global) annotations\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))\n\n# Temporal annotations (using new API)\ntemporal_label = []\ntemporal_label.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[\n temporal_text_annotation,\n temporal_radio_annotation, \n temporal_checklist_annotation,\n nested_text_annotation,\n inductive_annotation,\n complex_annotation,\n ],\n ))\n\nprint(f\"Created {len(label)} label with regular annotations\")\nprint(f\"Created {len(temporal_label)} label with {len(temporal_label[0].annotations)} temporal annotations\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### NDJSON annotations \n", + "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Step 6: Upload annotations to a project as pre-labels or complete labels" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "## Temporal Audio Annotations\n\nLabelbox supports temporal annotations for audio/video with frame-level precision using the new temporal classification API.\n\n### Key Features:\n- **Frame-based timing**: All annotations use millisecond precision\n- **Deep nesting**: Support for multi-level nested classifications (Text > Text > Text, Radio > Radio > Radio, etc.)\n- **Inductive structures**: Multiple parent values can share nested classifications that are automatically split based on frame overlap\n- **Frame validation**: Frames start at 1 (not 0) and must be non-overlapping for Text and Radio siblings\n\n### Important Constraints:\n1. **Frame indexing**: Frames are 1-based (frame 0 is invalid)\n2. **Non-overlapping siblings**: Text and Radio classifications at the same level cannot have overlapping frame ranges\n3. **Overlapping checklists**: Only Checklist answers can have overlapping frame ranges with their siblings", + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "### Example 1: Simple Temporal Text Classification\n\n# Create temporal text annotation with multiple values at different frame ranges\ntemporal_text_annotation = lb_types.TemporalClassificationText(\n name=\"transcription\",\n value=[\n (1000, 1500, \"Hello AI\"),\n (1501, 2000, \"How are you today?\"),\n ],\n)\n\nprint(\"Created temporal text annotation with 2 text values\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "### Example 2: Temporal Radio Question (single answer)\n\n# Create temporal radio annotation with frame range\ntemporal_radio_annotation = lb_types.TemporalClassificationQuestion(\n name=\"speaker\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"user\",\n frames=[(1000, 2000)],\n )\n ],\n)\n\nprint(\"Created temporal radio annotation\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Model Assisted Labeling (MAL)\n", + "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" + ], + "cell_type": "markdown" + }, + { + "cell_type": "code", + "id": "m6vpbezpb3i", + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=temporal_label, # Use the new temporal_label\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)\nprint(\"\\nTemporal annotations uploaded:\")\nprint(\" - Simple text classification\")\nprint(\" - Radio classification\")\nprint(\" - Checklist with overlapping answers\")\nprint(\" - Nested text (3 levels)\")\nprint(\" - Inductive structure (shared nested radio)\")\nprint(\" - Complex nesting (Checklist > Text > Radio)\")", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "id": "f5zkedpjd56", + "source": "### Example 4: Nested Temporal Classifications (Text > Text > Text)\n\n# Create deeply nested text classifications\nnested_text_annotation = lb_types.TemporalClassificationText(\n name=\"transcription\",\n value=[\n (1000, 2000, \"Hello, how can I help you?\"),\n ],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"speaker_notes\",\n value=[\n (1000, 2000, \"Polite greeting\"),\n ],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"context_tags\",\n value=[\n (1500, 2000, \"customer service tone\"),\n ],\n )\n ],\n )\n ],\n)\n\nprint(\"Created 3-level nested text classification\")", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "id": "92fx23aaam", + "source": "### Example 5: Inductive Structure (Multiple text values sharing nested classifications)\n\n# This demonstrates an \"inductive structure\" where multiple parent text values\n# share the same nested radio classification. The serializer will automatically\n# split the nested radio so each text value gets only the radio answers that\n# overlap with its frame range.\n\ninductive_annotation = lb_types.TemporalClassificationText(\n name=\"content_notes\",\n value=[\n (1000, 1500, \"Topic is relevant\"),\n (1501, 2000, \"Good pacing\"),\n ],\n classifications=[\n # This nested radio has answers for BOTH parent text values\n lb_types.TemporalClassificationQuestion(\n name=\"clarity_radio\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"very_clear\",\n frames=[(1000, 1500)], # Will be assigned to \"Topic is relevant\"\n ),\n lb_types.TemporalClassificationAnswer(\n name=\"slightly_clear\",\n frames=[(1501, 2000)], # Will be assigned to \"Good pacing\"\n ),\n ],\n )\n ],\n)\n\nprint(\"Created inductive structure annotation\")", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "id": "f5xbp02gnyd", + "source": "### Example 6: Complex Nesting (Checklist > Text > Radio)\n\n# This demonstrates deep nesting with mixed types\ncomplex_annotation = lb_types.TemporalClassificationQuestion(\n name=\"checklist_class\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"quality_check\",\n frames=[(1, 1500), (2000, 3000)],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"notes_text\",\n value=[\n (1, 1500, \"Audio quality is excellent\"),\n (2000, 2500, \"Some background noise detected\"),\n ],\n classifications=[\n lb_types.TemporalClassificationQuestion(\n name=\"severity_radio\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"minor\",\n frames=[(2000, 2500)],\n )\n ],\n )\n ],\n )\n ],\n )\n ],\n)\n\nprint(\"Created complex nested annotation: Checklist > Text > Radio\")", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Label Import" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Optional deletions for cleanup " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# project.delete()\n# dataset.delete()", + "cell_type": "code", + "outputs": [], + "execution_count": null + } + ] } \ No newline at end of file diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py b/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py index 83a850dc2..9638dc27c 100644 --- a/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py +++ b/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py @@ -266,12 +266,12 @@ def test_mixed_text_and_question_nesting(): value=[ lb_types.TemporalClassificationAnswer( name="quality_check", - frames=[(0, 1500)], + frames=[(1, 1500)], classifications=[ lb_types.TemporalClassificationText( name="notes_text", value=[ - (0, 1500, "Audio quality is excellent"), + (1, 1500, "Audio quality is excellent"), ], classifications=[ lb_types.TemporalClassificationQuestion( @@ -279,7 +279,7 @@ def test_mixed_text_and_question_nesting(): value=[ lb_types.TemporalClassificationAnswer( name="minor", - frames=[(0, 1500)], + frames=[(1, 1500)], ) ], ) @@ -306,3 +306,143 @@ def test_mixed_text_and_question_nesting(): radio_cls = text_cls["answer"][0]["classifications"][0] assert radio_cls["name"] == "severity_radio" assert radio_cls["answer"][0]["name"] == "minor" + + +def test_inductive_structure_text_with_shared_nested_radio(): + """ + Test inductive structure where multiple text values share the same nested radio classification. + + Each text value should get its own instance of the nested radio with only the radio answers + that overlap with that text value's frames. + """ + annotations = [ + lb_types.TemporalClassificationText( + name="content_notes", + value=[ + (1000, 1500, "Topic is relevant"), + (1501, 2000, "Good pacing"), + ], + classifications=[ + # Shared nested radio with answers for BOTH text values + lb_types.TemporalClassificationQuestion( + name="clarity_radio", + value=[ + lb_types.TemporalClassificationAnswer( + name="very_clear", + frames=[(1000, 1500)], + ), + lb_types.TemporalClassificationAnswer( + name="slightly_clear", + frames=[(1501, 2000)], + ), + ], + ) + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + assert len(result) == 1 + assert result[0].name == "content_notes" + assert len(result[0].answer) == 2 + + # Check first text value: "Topic is relevant" + text1 = next(a for a in result[0].answer if a["value"] == "Topic is relevant") + assert text1["frames"] == [{"start": 1000, "end": 1500}] + assert "classifications" in text1 + assert len(text1["classifications"]) == 1 + + # Should only have "very_clear" radio answer (overlaps with 1000-1500) + radio1 = text1["classifications"][0] + assert radio1["name"] == "clarity_radio" + assert len(radio1["answer"]) == 1 + assert radio1["answer"][0]["name"] == "very_clear" + assert radio1["answer"][0]["frames"] == [{"start": 1000, "end": 1500}] + + # Check second text value: "Good pacing" + text2 = next(a for a in result[0].answer if a["value"] == "Good pacing") + assert text2["frames"] == [{"start": 1501, "end": 2000}] + assert "classifications" in text2 + assert len(text2["classifications"]) == 1 + + # Should only have "slightly_clear" radio answer (overlaps with 1501-2000) + radio2 = text2["classifications"][0] + assert radio2["name"] == "clarity_radio" + assert len(radio2["answer"]) == 1 + assert radio2["answer"][0]["name"] == "slightly_clear" + assert radio2["answer"][0]["frames"] == [{"start": 1501, "end": 2000}] + + +def test_inductive_structure_checklist_with_multiple_text_values(): + """ + Test inductive structure with Checklist > Text > Radio where text has multiple values + and nested radio has answers that map to different text values. + """ + annotations = [ + lb_types.TemporalClassificationQuestion( + name="checklist_class", + value=[ + lb_types.TemporalClassificationAnswer( + name="content_check", + frames=[(1000, 2000)], + classifications=[ + lb_types.TemporalClassificationText( + name="content_notes_text", + value=[ + (1000, 1500, "Topic is relevant"), + (1501, 2000, "Good pacing"), + ], + classifications=[ + # Nested radio with multiple answers covering different text value frames + lb_types.TemporalClassificationQuestion( + name="clarity_radio", + value=[ + lb_types.TemporalClassificationAnswer( + name="very_clear", + frames=[(1000, 1500)], + ), + lb_types.TemporalClassificationAnswer( + name="slightly_clear", + frames=[(1501, 2000)], + ), + ], + ) + ], + ) + ], + ) + ], + ) + ] + + result = create_temporal_ndjson_annotations(annotations, "test-global-key") + + assert len(result) == 1 + assert result[0].name == "checklist_class" + + # Get the content_check answer + content_check = result[0].answer[0] + assert content_check["name"] == "content_check" + assert content_check["frames"] == [{"start": 1000, "end": 2000}] + + # Get the nested text classification + text_cls = content_check["classifications"][0] + assert text_cls["name"] == "content_notes_text" + assert len(text_cls["answer"]) == 2 + + # Check first text value and its nested radio + text1 = next(a for a in text_cls["answer"] if a["value"] == "Topic is relevant") + assert text1["frames"] == [{"start": 1000, "end": 1500}] + radio1 = text1["classifications"][0] + assert radio1["name"] == "clarity_radio" + assert len(radio1["answer"]) == 1 + assert radio1["answer"][0]["name"] == "very_clear" + + # Check second text value and its nested radio + text2 = next(a for a in text_cls["answer"] if a["value"] == "Good pacing") + assert text2["frames"] == [{"start": 1501, "end": 2000}] + radio2 = text2["classifications"][0] + assert radio2["name"] == "clarity_radio" + assert len(radio2["answer"]) == 1 + assert radio2["answer"][0]["name"] == "slightly_clear" From d2dc6580ea4756fdca2fa5ed089afc596f9cc30e Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 7 Oct 2025 06:51:00 +0000 Subject: [PATCH 048/103] :art: Cleaned --- examples/annotation_import/audio.ipynb | 706 ++++++++++++------------- 1 file changed, 351 insertions(+), 355 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index b243b722e..ed1b2b13d 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,357 +1,353 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {}, - "cells": [ - { - "metadata": {}, - "source": [ - "", - " ", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# Audio Annotation Import\n", - "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", - "\n", - "Suported annotations that can be uploaded through the SDK\n", - "\n", - "* Classification Radio \n", - "* Classification Checklist \n", - "* Classification Free Text \n", - "\n", - "**Not** supported annotations\n", - "\n", - "* Bouding box\n", - "* NER\n", - "* Polygon \n", - "* Point\n", - "* Polyline \n", - "* Segmentation Mask\n", - "\n", - "MAL and Label Import:\n", - "\n", - "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", - "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "* For information on what types of annotations are supported per data type, refer to this documentation:\n", - " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "* Notes:\n", - " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# Setup" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# Replace with your API key\n", - "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Supported annotations for Audio" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Step 1: Import data rows into Catalog" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 2: Create/select an ontology\n", - "\n", - "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", - "\n", - "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n # Global (non-temporal) classifications\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n \n # Temporal classifications (scope=INDEX for frame-based annotations)\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"transcription\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"speaker_notes\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"context_tags\",\n )\n ]\n )\n ]\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"speaker\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\"user\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"audio_quality\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\"background_noise\"),\n lb.Option(\"echo\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"content_notes\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"clarity_radio\",\n options=[\n lb.Option(\"very_clear\"),\n lb.Option(\"slightly_clear\"),\n ],\n )\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_class\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\n \"quality_check\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"notes_text\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"severity_radio\",\n options=[\n lb.Option(\"minor\"),\n ],\n )\n ],\n )\n ],\n )\n ],\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "\n", - "## Step 3: Create a labeling project\n", - "Connect the ontology to the labeling project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 5: Create the annotations payload\n", - "Create the annotations payload using the snippets of code above\n", - "\n", - "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "#### Python annotation\n", - "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "label = []\n\n# Regular (global) annotations\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))\n\n# Temporal annotations (using new API)\ntemporal_label = []\ntemporal_label.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[\n temporal_text_annotation,\n temporal_radio_annotation, \n temporal_checklist_annotation,\n nested_text_annotation,\n inductive_annotation,\n complex_annotation,\n ],\n ))\n\nprint(f\"Created {len(label)} label with regular annotations\")\nprint(f\"Created {len(temporal_label)} label with {len(temporal_label[0].annotations)} temporal annotations\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### NDJSON annotations \n", - "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "## Temporal Audio Annotations\n\nLabelbox supports temporal annotations for audio/video with frame-level precision using the new temporal classification API.\n\n### Key Features:\n- **Frame-based timing**: All annotations use millisecond precision\n- **Deep nesting**: Support for multi-level nested classifications (Text > Text > Text, Radio > Radio > Radio, etc.)\n- **Inductive structures**: Multiple parent values can share nested classifications that are automatically split based on frame overlap\n- **Frame validation**: Frames start at 1 (not 0) and must be non-overlapping for Text and Radio siblings\n\n### Important Constraints:\n1. **Frame indexing**: Frames are 1-based (frame 0 is invalid)\n2. **Non-overlapping siblings**: Text and Radio classifications at the same level cannot have overlapping frame ranges\n3. **Overlapping checklists**: Only Checklist answers can have overlapping frame ranges with their siblings", - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "### Example 1: Simple Temporal Text Classification\n\n# Create temporal text annotation with multiple values at different frame ranges\ntemporal_text_annotation = lb_types.TemporalClassificationText(\n name=\"transcription\",\n value=[\n (1000, 1500, \"Hello AI\"),\n (1501, 2000, \"How are you today?\"),\n ],\n)\n\nprint(\"Created temporal text annotation with 2 text values\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "### Example 2: Temporal Radio Question (single answer)\n\n# Create temporal radio annotation with frame range\ntemporal_radio_annotation = lb_types.TemporalClassificationQuestion(\n name=\"speaker\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"user\",\n frames=[(1000, 2000)],\n )\n ],\n)\n\nprint(\"Created temporal radio annotation\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Model Assisted Labeling (MAL)\n", - "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ], - "cell_type": "markdown" - }, - { - "cell_type": "code", - "id": "m6vpbezpb3i", - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=temporal_label, # Use the new temporal_label\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)\nprint(\"\\nTemporal annotations uploaded:\")\nprint(\" - Simple text classification\")\nprint(\" - Radio classification\")\nprint(\" - Checklist with overlapping answers\")\nprint(\" - Nested text (3 levels)\")\nprint(\" - Inductive structure (shared nested radio)\")\nprint(\" - Complex nesting (Checklist > Text > Radio)\")", - "metadata": {}, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "id": "f5zkedpjd56", - "source": "### Example 4: Nested Temporal Classifications (Text > Text > Text)\n\n# Create deeply nested text classifications\nnested_text_annotation = lb_types.TemporalClassificationText(\n name=\"transcription\",\n value=[\n (1000, 2000, \"Hello, how can I help you?\"),\n ],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"speaker_notes\",\n value=[\n (1000, 2000, \"Polite greeting\"),\n ],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"context_tags\",\n value=[\n (1500, 2000, \"customer service tone\"),\n ],\n )\n ],\n )\n ],\n)\n\nprint(\"Created 3-level nested text classification\")", - "metadata": {}, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "id": "92fx23aaam", - "source": "### Example 5: Inductive Structure (Multiple text values sharing nested classifications)\n\n# This demonstrates an \"inductive structure\" where multiple parent text values\n# share the same nested radio classification. The serializer will automatically\n# split the nested radio so each text value gets only the radio answers that\n# overlap with its frame range.\n\ninductive_annotation = lb_types.TemporalClassificationText(\n name=\"content_notes\",\n value=[\n (1000, 1500, \"Topic is relevant\"),\n (1501, 2000, \"Good pacing\"),\n ],\n classifications=[\n # This nested radio has answers for BOTH parent text values\n lb_types.TemporalClassificationQuestion(\n name=\"clarity_radio\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"very_clear\",\n frames=[(1000, 1500)], # Will be assigned to \"Topic is relevant\"\n ),\n lb_types.TemporalClassificationAnswer(\n name=\"slightly_clear\",\n frames=[(1501, 2000)], # Will be assigned to \"Good pacing\"\n ),\n ],\n )\n ],\n)\n\nprint(\"Created inductive structure annotation\")", - "metadata": {}, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "id": "f5xbp02gnyd", - "source": "### Example 6: Complex Nesting (Checklist > Text > Radio)\n\n# This demonstrates deep nesting with mixed types\ncomplex_annotation = lb_types.TemporalClassificationQuestion(\n name=\"checklist_class\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"quality_check\",\n frames=[(1, 1500), (2000, 3000)],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"notes_text\",\n value=[\n (1, 1500, \"Audio quality is excellent\"),\n (2000, 2500, \"Some background noise detected\"),\n ],\n classifications=[\n lb_types.TemporalClassificationQuestion(\n name=\"severity_radio\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"minor\",\n frames=[(2000, 2500)],\n )\n ],\n )\n ],\n )\n ],\n )\n ],\n)\n\nprint(\"Created complex nested annotation: Checklist > Text > Radio\")", - "metadata": {}, - "execution_count": null, - "outputs": [] - }, - { - "metadata": {}, - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Label Import" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Optional deletions for cleanup " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# project.delete()\n# dataset.delete()", - "cell_type": "code", - "outputs": [], - "execution_count": null - } - ] + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {}, + "cells": [ + { + "metadata": {}, + "source": [ + "", + " ", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "# Audio Annotation Import\n", + "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", + "\n", + "Suported annotations that can be uploaded through the SDK\n", + "\n", + "* Classification Radio \n", + "* Classification Checklist \n", + "* Classification Free Text \n", + "\n", + "**Not** supported annotations\n", + "\n", + "* Bouding box\n", + "* NER\n", + "* Polygon \n", + "* Point\n", + "* Polyline \n", + "* Segmentation Mask\n", + "\n", + "MAL and Label Import:\n", + "\n", + "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", + "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* For information on what types of annotations are supported per data type, refer to this documentation:\n", + " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* Notes:\n", + " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Setup" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Replace with your API key\n", + "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Supported annotations for Audio" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Upload Annotations - putting it all together " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "## Step 1: Import data rows into Catalog" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 2: Create/select an ontology\n", + "\n", + "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", + "\n", + "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n # Global (non-temporal) classifications\n lb.Classification(class_type=lb.Classification.Type.TEXT, name=\"text_audio\"\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classifications (scope=INDEX for frame-based annotations)\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"transcription\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"speaker_notes\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"context_tags\",\n )\n ],\n )\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"speaker\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\"user\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"audio_quality\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\"background_noise\"),\n lb.Option(\"echo\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"content_notes\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"clarity_radio\",\n options=[\n lb.Option(\"very_clear\"),\n lb.Option(\"slightly_clear\"),\n ],\n )\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_class\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\n \"quality_check\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"notes_text\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"severity_radio\",\n options=[\n lb.Option(\"minor\"),\n ],\n )\n ],\n )\n ],\n )\n ],\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "\n", + "## Step 3: Create a labeling project\n", + "Connect the ontology to the labeling project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 4: Send a batch of data rows to the project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 5: Create the annotations payload\n", + "Create the annotations payload using the snippets of code above\n", + "\n", + "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "#### Python annotation\n", + "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label = []\n\n# Regular (global) annotations\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))\n\n# Temporal annotations (using new API)\ntemporal_label = []\ntemporal_label.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[\n temporal_text_annotation,\n temporal_radio_annotation,\n temporal_checklist_annotation,\n nested_text_annotation,\n inductive_annotation,\n complex_annotation,\n ],\n ))\n\nprint(f\"Created {len(label)} label with regular annotations\")\nprint(\n f\"Created {len(temporal_label)} label with {len(temporal_label[0].annotations)} temporal annotations\"\n)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### NDJSON annotations \n", + "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Step 6: Upload annotations to a project as pre-labels or complete labels" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "## Temporal Audio Annotations\n\nLabelbox supports temporal annotations for audio/video with frame-level precision using the new temporal classification API.\n\n### Key Features:\n- **Frame-based timing**: All annotations use millisecond precision\n- **Deep nesting**: Support for multi-level nested classifications (Text > Text > Text, Radio > Radio > Radio, etc.)\n- **Inductive structures**: Multiple parent values can share nested classifications that are automatically split based on frame overlap\n- **Frame validation**: Frames start at 1 (not 0) and must be non-overlapping for Text and Radio siblings\n\n### Important Constraints:\n1. **Frame indexing**: Frames are 1-based (frame 0 is invalid)\n2. **Non-overlapping siblings**: Text and Radio classifications at the same level cannot have overlapping frame ranges\n3. **Overlapping checklists**: Only Checklist answers can have overlapping frame ranges with their siblings", + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "### Example 1: Simple Temporal Text Classification\n\n# Create temporal text annotation with multiple values at different frame ranges\ntemporal_text_annotation = lb_types.TemporalClassificationText(\n name=\"transcription\",\n value=[\n (1000, 1500, \"Hello AI\"),\n (1501, 2000, \"How are you today?\"),\n ],\n)\n\nprint(\"Created temporal text annotation with 2 text values\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "### Example 2: Temporal Radio Question (single answer)\n\n# Create temporal radio annotation with frame range\ntemporal_radio_annotation = lb_types.TemporalClassificationQuestion(\n name=\"speaker\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"user\",\n frames=[(1000, 2000)],\n )\n ],\n)\n\nprint(\"Created temporal radio annotation\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Model Assisted Labeling (MAL)\n", + "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=temporal_label, # Use the new temporal_label\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)\nprint(\"\\nTemporal annotations uploaded:\")\nprint(\" - Simple text classification\")\nprint(\" - Radio classification\")\nprint(\" - Checklist with overlapping answers\")\nprint(\" - Nested text (3 levels)\")\nprint(\" - Inductive structure (shared nested radio)\")\nprint(\" - Complex nesting (Checklist > Text > Radio)\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "### Example 4: Nested Temporal Classifications (Text > Text > Text)\n\n# Create deeply nested text classifications\nnested_text_annotation = lb_types.TemporalClassificationText(\n name=\"transcription\",\n value=[\n (1000, 2000, \"Hello, how can I help you?\"),\n ],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"speaker_notes\",\n value=[\n (1000, 2000, \"Polite greeting\"),\n ],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"context_tags\",\n value=[\n (1500, 2000, \"customer service tone\"),\n ],\n )\n ],\n )\n ],\n)\n\nprint(\"Created 3-level nested text classification\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "### Example 5: Inductive Structure (Multiple text values sharing nested classifications)\n\n# This demonstrates an \"inductive structure\" where multiple parent text values\n# share the same nested radio classification. The serializer will automatically\n# split the nested radio so each text value gets only the radio answers that\n# overlap with its frame range.\n\ninductive_annotation = lb_types.TemporalClassificationText(\n name=\"content_notes\",\n value=[\n (1000, 1500, \"Topic is relevant\"),\n (1501, 2000, \"Good pacing\"),\n ],\n classifications=[\n # This nested radio has answers for BOTH parent text values\n lb_types.TemporalClassificationQuestion(\n name=\"clarity_radio\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"very_clear\",\n frames=[(1000, 1500)\n ], # Will be assigned to \"Topic is relevant\"\n ),\n lb_types.TemporalClassificationAnswer(\n name=\"slightly_clear\",\n frames=[(1501, 2000)], # Will be assigned to \"Good pacing\"\n ),\n ],\n )\n ],\n)\n\nprint(\"Created inductive structure annotation\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "### Example 6: Complex Nesting (Checklist > Text > Radio)\n\n# This demonstrates deep nesting with mixed types\ncomplex_annotation = lb_types.TemporalClassificationQuestion(\n name=\"checklist_class\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"quality_check\",\n frames=[(1, 1500), (2000, 3000)],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"notes_text\",\n value=[\n (1, 1500, \"Audio quality is excellent\"),\n (2000, 2500, \"Some background noise detected\"),\n ],\n classifications=[\n lb_types.TemporalClassificationQuestion(\n name=\"severity_radio\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"minor\",\n frames=[(2000, 2500)],\n )\n ],\n )\n ],\n )\n ],\n )\n ],\n)\n\nprint(\"Created complex nested annotation: Checklist > Text > Radio\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Label Import" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Optional deletions for cleanup " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# project.delete()\n# dataset.delete()", + "cell_type": "code", + "outputs": [], + "execution_count": null + } + ] } \ No newline at end of file From 76bdf35fc39fc7bf2bc127dc64ffea47c02f467d Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Tue, 7 Oct 2025 08:44:43 -0700 Subject: [PATCH 049/103] chore: lint --- .../data/annotation_types/__init__.py | 67 +++++++++++++++++++ .../data/serialization/ndjson/label.py | 1 - .../data/serialization/ndjson/temporal.py | 1 - 3 files changed, 67 insertions(+), 2 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index 9595810e5..64d0675a2 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -63,3 +63,70 @@ MessageRankingTask, MessageEvaluationTaskAnnotation, ) + +__all__ = [ + # Geometry + "Line", + "Point", + "Mask", + "Polygon", + "Rectangle", + "Geometry", + "DocumentRectangle", + "RectangleUnit", + # Annotation + "ClassificationAnnotation", + "ObjectAnnotation", + # Relationship + "RelationshipAnnotation", + "Relationship", + # Video + "VideoClassificationAnnotation", + "VideoObjectAnnotation", + "MaskFrame", + "MaskInstance", + "VideoMaskAnnotation", + # Temporal + "TemporalClassificationText", + "TemporalClassificationQuestion", + "TemporalClassificationAnswer", + # NER + "ConversationEntity", + "DocumentEntity", + "DocumentTextSelection", + "TextEntity", + # Classification + "Checklist", + "ClassificationAnswer", + "Radio", + "Text", + # Data + "GenericDataRowData", + "MaskData", + # Label + "Label", + "LabelGenerator", + # Metrics + "ScalarMetric", + "ScalarMetricAggregation", + "ConfusionMatrixMetric", + "ConfusionMatrixAggregation", + "ScalarMetricValue", + "ConfusionMatrixMetricValue", + # Tiled Image + "EPSG", + "EPSGTransformer", + "TiledBounds", + "TiledImageData", + "TileLayer", + # LLM Prompt Response + "PromptText", + "PromptClassificationAnnotation", + # MMC + "MessageInfo", + "OrderedMessageInfo", + "MessageSingleSelectionTask", + "MessageMultiSelectionTask", + "MessageRankingTask", + "MessageEvaluationTaskAnnotation", +] diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index ffc021799..317a79012 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -28,7 +28,6 @@ from ...annotation_types.temporal import ( TemporalClassificationText, TemporalClassificationQuestion, - TemporalClassificationAnswer, ) from .temporal import create_temporal_ndjson_annotations from labelbox.types import DocumentRectangle, DocumentEntity diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index ec1e2ecec..974535cdc 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -13,7 +13,6 @@ from ...annotation_types.temporal import ( TemporalClassificationText, TemporalClassificationQuestion, - TemporalClassificationAnswer, ) logger = logging.getLogger(__name__) From 58aaf62b1751705a43098e0eb29fa475fb319231 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Tue, 7 Oct 2025 14:43:57 -0700 Subject: [PATCH 050/103] chore: stan + cursor bugbot changes --- .../labelbox/data/annotation_types/label.py | 14 +++++++----- .../data/annotation_types/temporal.py | 10 ++------- .../data/serialization/ndjson/label.py | 8 +++---- .../data/serialization/ndjson/temporal.py | 12 +++++----- .../serialization/ndjson/test_temporal.py | 22 +++++++++---------- 5 files changed, 30 insertions(+), 36 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index f8b431edc..dd56495a2 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -82,12 +82,14 @@ def _get_annotations_by_type(self, annotation_type): def frame_annotations( self, ) -> Dict[ - int, - Union[ - VideoObjectAnnotation, - VideoClassificationAnnotation, - TemporalClassificationText, - TemporalClassificationQuestion, + Union[int, None], + List[ + Union[ + VideoObjectAnnotation, + VideoClassificationAnnotation, + TemporalClassificationText, + TemporalClassificationQuestion, + ] ], ]: """Get temporal annotations organized by frame diff --git a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py index 7fcd40e78..929e2ff19 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py @@ -8,8 +8,6 @@ from typing import Any, Dict, List, Optional, Tuple, Union from pydantic import BaseModel, Field -from ...annotated_types import Cuid - class TemporalClassificationAnswer(BaseModel): """ @@ -23,7 +21,6 @@ class TemporalClassificationAnswer(BaseModel): frames (List[Tuple[int, int]]): List of (start_frame, end_frame) ranges in milliseconds classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): Nested classifications within this answer - feature_schema_id (Optional[Cuid]): Feature schema identifier extra (Dict[str, Any]): Additional metadata Example: @@ -53,7 +50,6 @@ class TemporalClassificationAnswer(BaseModel): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None - feature_schema_id: Optional[Cuid] = None extra: Dict[str, Any] = Field(default_factory=dict) @@ -69,7 +65,6 @@ class TemporalClassificationText(BaseModel): value (List[Tuple[int, int, str]]): List of (start_frame, end_frame, text_value) tuples classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): Nested classifications - feature_schema_id (Optional[Cuid]): Feature schema identifier extra (Dict[str, Any]): Additional metadata Example: @@ -107,7 +102,6 @@ class TemporalClassificationText(BaseModel): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None - feature_schema_id: Optional[Cuid] = None extra: Dict[str, Any] = Field(default_factory=dict) @@ -121,7 +115,8 @@ class TemporalClassificationQuestion(BaseModel): Args: name (str): Name of the question/classification value (List[TemporalClassificationAnswer]): List of answer options with frame ranges - feature_schema_id (Optional[Cuid]): Feature schema identifier + classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): + Nested classifications (typically not used at question level) extra (Dict[str, Any]): Additional metadata Note: @@ -187,7 +182,6 @@ class TemporalClassificationQuestion(BaseModel): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None - feature_schema_id: Optional[Cuid] = None extra: Dict[str, Any] = Field(default_factory=dict) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 317a79012..a83414ace 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -29,7 +29,7 @@ TemporalClassificationText, TemporalClassificationQuestion, ) -from .temporal import create_temporal_ndjson_annotations +from .temporal import create_temporal_ndjson_classifications from labelbox.types import DocumentRectangle, DocumentEntity from .classification import ( NDChecklistSubclass, @@ -75,7 +75,7 @@ def from_common( yield from cls._create_relationship_annotations(label) yield from cls._create_non_video_annotations(label) yield from cls._create_video_annotations(label) - yield from cls._create_temporal_annotations(label) + yield from cls._create_temporal_classifications(label) @staticmethod def _get_consecutive_frames( @@ -167,7 +167,7 @@ def _create_video_annotations( yield NDObject.from_common(segments, label.data) @classmethod - def _create_temporal_annotations( + def _create_temporal_classifications( cls, label: Label ) -> Generator[BaseModel, None, None]: """Create temporal annotations with nested classifications using new temporal classes.""" @@ -182,7 +182,7 @@ def _create_temporal_annotations( return # Use the new temporal serializer to create NDJSON annotations - ndjson_annotations = create_temporal_ndjson_annotations( + ndjson_annotations = create_temporal_ndjson_classifications( temporal_annotations, label.data.global_key ) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index 974535cdc..6ba787390 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -26,7 +26,7 @@ class TemporalNDJSON(BaseModel): dataRow: Dict[str, str] -def create_temporal_ndjson_annotations( +def create_temporal_ndjson_classifications( annotations: List[ Union[TemporalClassificationText, TemporalClassificationQuestion] ], @@ -45,11 +45,10 @@ def create_temporal_ndjson_annotations( if not annotations: return [] - # Group by classification name/schema_id + # Group by classification name groups = defaultdict(list) for ann in annotations: - key = ann.feature_schema_id or ann.name - groups[key].append(ann) + groups[ann.name].append(ann) results = [] for group_key, group_anns in groups.items(): @@ -267,13 +266,12 @@ def _process_nested_classifications( """ Process nested classifications recursively. - Groups by name/schema_id and processes each group. + Groups by name and processes each group. """ # Group by name groups = defaultdict(list) for cls in classifications: - key = cls.feature_schema_id or cls.name - groups[key].append(cls) + groups[cls.name].append(cls) results = [] for group_key, group_items in groups.items(): diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py b/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py index 9638dc27c..24b61e067 100644 --- a/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py +++ b/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py @@ -2,7 +2,7 @@ import labelbox.types as lb_types from labelbox.data.serialization.ndjson.temporal import ( - create_temporal_ndjson_annotations, + create_temporal_ndjson_classifications, ) @@ -18,7 +18,7 @@ def test_temporal_text_simple(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") assert len(result) == 1 assert result[0].name == "transcription" @@ -49,7 +49,7 @@ def test_temporal_question_radio(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") assert len(result) == 1 assert result[0].name == "speaker" @@ -78,7 +78,7 @@ def test_temporal_question_checklist(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") assert len(result) == 1 assert result[0].name == "audio_quality" @@ -123,7 +123,7 @@ def test_temporal_text_nested(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") assert len(result) == 1 assert result[0].name == "transcription" @@ -185,7 +185,7 @@ def test_temporal_question_nested(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") assert len(result) == 1 answer = result[0].answer[0] @@ -227,7 +227,7 @@ def test_frame_validation_discard_invalid(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") # Find the nested notes classification answer = result[0].answer[0] @@ -251,7 +251,7 @@ def test_frame_deduplication(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") # Should only have one entry assert len(result[0].answer) == 1 @@ -291,7 +291,7 @@ def test_mixed_text_and_question_nesting(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") assert len(result) == 1 answer = result[0].answer[0] @@ -341,7 +341,7 @@ def test_inductive_structure_text_with_shared_nested_radio(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") assert len(result) == 1 assert result[0].name == "content_notes" @@ -416,7 +416,7 @@ def test_inductive_structure_checklist_with_multiple_text_values(): ) ] - result = create_temporal_ndjson_annotations(annotations, "test-global-key") + result = create_temporal_ndjson_classifications(annotations, "test-global-key") assert len(result) == 1 assert result[0].name == "checklist_class" From 9afd82d6f0fd144b9c59f6c2f580dcd420adca4b Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Tue, 7 Oct 2025 15:03:29 -0700 Subject: [PATCH 051/103] chore: remove extra keyword (unused) --- .../src/labelbox/data/annotation_types/temporal.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py index 929e2ff19..ca887c4d8 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py @@ -5,7 +5,7 @@ frame-level precision. All temporal classifications support nested hierarchies. """ -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import List, Optional, Tuple, Union from pydantic import BaseModel, Field @@ -21,7 +21,6 @@ class TemporalClassificationAnswer(BaseModel): frames (List[Tuple[int, int]]): List of (start_frame, end_frame) ranges in milliseconds classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): Nested classifications within this answer - extra (Dict[str, Any]): Additional metadata Example: >>> # Radio answer with nested classifications @@ -50,7 +49,6 @@ class TemporalClassificationAnswer(BaseModel): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None - extra: Dict[str, Any] = Field(default_factory=dict) class TemporalClassificationText(BaseModel): @@ -65,7 +63,6 @@ class TemporalClassificationText(BaseModel): value (List[Tuple[int, int, str]]): List of (start_frame, end_frame, text_value) tuples classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): Nested classifications - extra (Dict[str, Any]): Additional metadata Example: >>> # Simple text with multiple temporal values @@ -102,7 +99,6 @@ class TemporalClassificationText(BaseModel): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None - extra: Dict[str, Any] = Field(default_factory=dict) class TemporalClassificationQuestion(BaseModel): @@ -117,7 +113,6 @@ class TemporalClassificationQuestion(BaseModel): value (List[TemporalClassificationAnswer]): List of answer options with frame ranges classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]): Nested classifications (typically not used at question level) - extra (Dict[str, Any]): Additional metadata Note: - Radio: Single answer in the value list @@ -182,7 +177,6 @@ class TemporalClassificationQuestion(BaseModel): classifications: Optional[ List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] ] = None - extra: Dict[str, Any] = Field(default_factory=dict) # Update forward references for recursive types From ad2223ce5dff9784238fb5d52835ef40d5734828 Mon Sep 17 00:00:00 2001 From: Rishi Surana Date: Fri, 10 Oct 2025 00:17:43 -0700 Subject: [PATCH 052/103] chore: lint --- examples/annotation_import/audio.ipynb | 922 +++++++++++------- .../data/annotation_types/__init__.py | 2 +- .../classification/classification.py | 1 - .../labelbox/data/annotation_types/label.py | 41 +- .../data/annotation_types/temporal.py | 18 +- .../data/serialization/ndjson/label.py | 5 +- .../data/serialization/ndjson/temporal.py | 62 +- .../serialization/ndjson/test_temporal.py | 58 +- 8 files changed, 714 insertions(+), 395 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index ed1b2b13d..885fb2b6e 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,353 +1,571 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {}, - "cells": [ - { - "metadata": {}, - "source": [ - "", - " ", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# Audio Annotation Import\n", - "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", - "\n", - "Suported annotations that can be uploaded through the SDK\n", - "\n", - "* Classification Radio \n", - "* Classification Checklist \n", - "* Classification Free Text \n", - "\n", - "**Not** supported annotations\n", - "\n", - "* Bouding box\n", - "* NER\n", - "* Polygon \n", - "* Point\n", - "* Polyline \n", - "* Segmentation Mask\n", - "\n", - "MAL and Label Import:\n", - "\n", - "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", - "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", - "\n" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "* For information on what types of annotations are supported per data type, refer to this documentation:\n", - " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "* Notes:\n", - " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "%pip install -q \"labelbox[data]\"", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# Setup" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# Replace with your API key\n", - "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Supported annotations for Audio" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Step 1: Import data rows into Catalog" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 2: Create/select an ontology\n", - "\n", - "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", - "\n", - "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n # Global (non-temporal) classifications\n lb.Classification(class_type=lb.Classification.Type.TEXT, name=\"text_audio\"\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classifications (scope=INDEX for frame-based annotations)\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"transcription\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"speaker_notes\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"context_tags\",\n )\n ],\n )\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"speaker\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\"user\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"audio_quality\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\"background_noise\"),\n lb.Option(\"echo\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"content_notes\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"clarity_radio\",\n options=[\n lb.Option(\"very_clear\"),\n lb.Option(\"slightly_clear\"),\n ],\n )\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_class\",\n scope=lb.Classification.Scope.INDEX,\n options=[\n lb.Option(\n \"quality_check\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"notes_text\",\n options=[\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"severity_radio\",\n options=[\n lb.Option(\"minor\"),\n ],\n )\n ],\n )\n ],\n )\n ],\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "\n", - "## Step 3: Create a labeling project\n", - "Connect the ontology to the labeling project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Step 5: Create the annotations payload\n", - "Create the annotations payload using the snippets of code above\n", - "\n", - "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "#### Python annotation\n", - "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "label = []\n\n# Regular (global) annotations\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))\n\n# Temporal annotations (using new API)\ntemporal_label = []\ntemporal_label.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[\n temporal_text_annotation,\n temporal_radio_annotation,\n temporal_checklist_annotation,\n nested_text_annotation,\n inductive_annotation,\n complex_annotation,\n ],\n ))\n\nprint(f\"Created {len(label)} label with regular annotations\")\nprint(\n f\"Created {len(temporal_label)} label with {len(temporal_label[0].annotations)} temporal annotations\"\n)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### NDJSON annotations \n", - "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "## Temporal Audio Annotations\n\nLabelbox supports temporal annotations for audio/video with frame-level precision using the new temporal classification API.\n\n### Key Features:\n- **Frame-based timing**: All annotations use millisecond precision\n- **Deep nesting**: Support for multi-level nested classifications (Text > Text > Text, Radio > Radio > Radio, etc.)\n- **Inductive structures**: Multiple parent values can share nested classifications that are automatically split based on frame overlap\n- **Frame validation**: Frames start at 1 (not 0) and must be non-overlapping for Text and Radio siblings\n\n### Important Constraints:\n1. **Frame indexing**: Frames are 1-based (frame 0 is invalid)\n2. **Non-overlapping siblings**: Text and Radio classifications at the same level cannot have overlapping frame ranges\n3. **Overlapping checklists**: Only Checklist answers can have overlapping frame ranges with their siblings", - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "### Example 1: Simple Temporal Text Classification\n\n# Create temporal text annotation with multiple values at different frame ranges\ntemporal_text_annotation = lb_types.TemporalClassificationText(\n name=\"transcription\",\n value=[\n (1000, 1500, \"Hello AI\"),\n (1501, 2000, \"How are you today?\"),\n ],\n)\n\nprint(\"Created temporal text annotation with 2 text values\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "### Example 2: Temporal Radio Question (single answer)\n\n# Create temporal radio annotation with frame range\ntemporal_radio_annotation = lb_types.TemporalClassificationQuestion(\n name=\"speaker\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"user\",\n frames=[(1000, 2000)],\n )\n ],\n)\n\nprint(\"Created temporal radio annotation\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Model Assisted Labeling (MAL)\n", - "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=temporal_label, # Use the new temporal_label\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)\nprint(\"\\nTemporal annotations uploaded:\")\nprint(\" - Simple text classification\")\nprint(\" - Radio classification\")\nprint(\" - Checklist with overlapping answers\")\nprint(\" - Nested text (3 levels)\")\nprint(\" - Inductive structure (shared nested radio)\")\nprint(\" - Complex nesting (Checklist > Text > Radio)\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "### Example 4: Nested Temporal Classifications (Text > Text > Text)\n\n# Create deeply nested text classifications\nnested_text_annotation = lb_types.TemporalClassificationText(\n name=\"transcription\",\n value=[\n (1000, 2000, \"Hello, how can I help you?\"),\n ],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"speaker_notes\",\n value=[\n (1000, 2000, \"Polite greeting\"),\n ],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"context_tags\",\n value=[\n (1500, 2000, \"customer service tone\"),\n ],\n )\n ],\n )\n ],\n)\n\nprint(\"Created 3-level nested text classification\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "### Example 5: Inductive Structure (Multiple text values sharing nested classifications)\n\n# This demonstrates an \"inductive structure\" where multiple parent text values\n# share the same nested radio classification. The serializer will automatically\n# split the nested radio so each text value gets only the radio answers that\n# overlap with its frame range.\n\ninductive_annotation = lb_types.TemporalClassificationText(\n name=\"content_notes\",\n value=[\n (1000, 1500, \"Topic is relevant\"),\n (1501, 2000, \"Good pacing\"),\n ],\n classifications=[\n # This nested radio has answers for BOTH parent text values\n lb_types.TemporalClassificationQuestion(\n name=\"clarity_radio\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"very_clear\",\n frames=[(1000, 1500)\n ], # Will be assigned to \"Topic is relevant\"\n ),\n lb_types.TemporalClassificationAnswer(\n name=\"slightly_clear\",\n frames=[(1501, 2000)], # Will be assigned to \"Good pacing\"\n ),\n ],\n )\n ],\n)\n\nprint(\"Created inductive structure annotation\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "### Example 6: Complex Nesting (Checklist > Text > Radio)\n\n# This demonstrates deep nesting with mixed types\ncomplex_annotation = lb_types.TemporalClassificationQuestion(\n name=\"checklist_class\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"quality_check\",\n frames=[(1, 1500), (2000, 3000)],\n classifications=[\n lb_types.TemporalClassificationText(\n name=\"notes_text\",\n value=[\n (1, 1500, \"Audio quality is excellent\"),\n (2000, 2500, \"Some background noise detected\"),\n ],\n classifications=[\n lb_types.TemporalClassificationQuestion(\n name=\"severity_radio\",\n value=[\n lb_types.TemporalClassificationAnswer(\n name=\"minor\",\n frames=[(2000, 2500)],\n )\n ],\n )\n ],\n )\n ],\n )\n ],\n)\n\nprint(\"Created complex nested annotation: Checklist > Text > Radio\")", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Label Import" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Optional deletions for cleanup " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": "# project.delete()\n# dataset.delete()", - "cell_type": "code", - "outputs": [], - "execution_count": null - } - ] -} \ No newline at end of file + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Audio Annotation Import\n", + "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", + "\n", + "Suported annotations that can be uploaded through the SDK\n", + "\n", + "* Classification Radio \n", + "* Classification Checklist \n", + "* Classification Free Text \n", + "\n", + "**Not** supported annotations\n", + "\n", + "* Bouding box\n", + "* NER\n", + "* Polygon \n", + "* Point\n", + "* Polyline \n", + "* Segmentation Mask\n", + "\n", + "MAL and Label Import:\n", + "\n", + "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", + "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* For information on what types of annotations are supported per data type, refer to this documentation:\n", + " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Notes:\n", + " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q \"labelbox[data]\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import labelbox as lb\n", + "import uuid\n", + "import labelbox.types as lb_types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Replace with your API key\n", + "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add your api key\n", + "API_KEY = \"\"\n", + "client = lb.Client(api_key=API_KEY)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Supported annotations for Audio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##### Classification free text #####\n", + "\n", + "text_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"text_audio\",\n", + " value=lb_types.Text(answer=\"free text audio annotation\"),\n", + ")\n", + "\n", + "text_annotation_ndjson = {\n", + " \"name\": \"text_audio\",\n", + " \"answer\": \"free text audio annotation\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##### Checklist Classification #######\n", + "\n", + "checklist_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"checklist_audio\",\n", + " value=lb_types.Checklist(answer=[\n", + " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", + " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", + " ]),\n", + ")\n", + "\n", + "checklist_annotation_ndjson = {\n", + " \"name\":\n", + " \"checklist_audio\",\n", + " \"answers\": [\n", + " {\n", + " \"name\": \"first_checklist_answer\"\n", + " },\n", + " {\n", + " \"name\": \"second_checklist_answer\"\n", + " },\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "######## Radio Classification ######\n", + "\n", + "radio_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"radio_audio\",\n", + " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", + " name=\"second_radio_answer\")),\n", + ")\n", + "\n", + "radio_annotation_ndjson = {\n", + " \"name\": \"radio_audio\",\n", + " \"answer\": {\n", + " \"name\": \"first_radio_answer\"\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upload Annotations - putting it all together " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Import data rows into Catalog" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create one Labelbox dataset\n", + "\n", + "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", + "\n", + "asset = {\n", + " \"row_data\":\n", + " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", + " \"global_key\":\n", + " global_key,\n", + "}\n", + "\n", + "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", + "task = dataset.create_data_rows([asset])\n", + "task.wait_till_done()\n", + "print(\"Errors:\", task.errors)\n", + "print(\"Failed data rows: \", task.failed_data_rows)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Create/select an ontology\n", + "\n", + "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", + "\n", + "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ontology_builder = lb.OntologyBuilder(classifications=[\n", + " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", + " name=\"text_audio\"),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"checklist_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_checklist_answer\"),\n", + " lb.Option(value=\"second_checklist_answer\"),\n", + " ],\n", + " ),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"radio_audio\",\n", + " options=[\n", + " lb.Option(value=\"first_radio_answer\"),\n", + " lb.Option(value=\"second_radio_answer\"),\n", + " ],\n", + " ),\n", + " # Temporal classification for token-level annotations\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.TEXT,\n", + " name=\"User Speaker\",\n", + " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", + " ),\n", + "])\n", + "\n", + "ontology = client.create_ontology(\n", + " \"Ontology Audio Annotations\",\n", + " ontology_builder.asdict(),\n", + " media_type=lb.MediaType.Audio,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Step 3: Create a labeling project\n", + "Connect the ontology to the labeling project" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Labelbox project\n", + "project = client.create_project(name=\"audio_project\",\n", + " media_type=lb.MediaType.Audio)\n", + "\n", + "# Setup your ontology\n", + "project.setup_editor(\n", + " ontology) # Connect your ontology and editor to your project" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Send a batch of data rows to the project" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup Batches and Ontology\n", + "\n", + "# Create a batch to send to your MAL project\n", + "batch = project.create_batch(\n", + " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", + " global_keys=[\n", + " global_key\n", + " ], # Paginated collection of data row objects, list of data row ids or global keys\n", + " priority=5, # priority between 1(Highest) - 5(lowest)\n", + ")\n", + "\n", + "print(\"Batch: \", batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Create the annotations payload\n", + "Create the annotations payload using the snippets of code above\n", + "\n", + "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python annotation\n", + "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "label = []\n", + "label.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", + " ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NDJSON annotations \n", + "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "label_ndjson = []\n", + "for annotations in [\n", + " text_annotation_ndjson,\n", + " checklist_annotation_ndjson,\n", + " radio_annotation_ndjson,\n", + "]:\n", + " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", + " label_ndjson.append(annotations)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6: Upload annotations to a project as pre-labels or complete labels" + ] + }, + { + "cell_type": "markdown", + "id": "a612e9bd", + "metadata": {}, + "source": [ + "## Temporal Audio Annotations\n", + "\n", + "Labelbox supports temporal annotations for audio/video with frame-level precision using the new temporal classification API.\n", + "\n", + "### Key Features:\n", + "- **Frame-based timing**: All annotations use millisecond precision\n", + "- **Deep nesting**: Support for multi-level nested classifications (Text > Text > Text, Radio > Radio > Radio, etc.)\n", + "- **Inductive structures**: Multiple parent values can share nested classifications that are automatically split based on frame overlap\n", + "- **Frame validation**: Frames start at 1 (not 0) and must be non-overlapping for Text and Radio siblings\n", + "\n", + "### Important Constraints:\n", + "1. **Frame indexing**: Frames are 1-based (frame 0 is invalid)\n", + "2. **Non-overlapping siblings**: Text and Radio classifications at the same level cannot have overlapping frame ranges\n", + "3. **Overlapping checklists**: Only Checklist answers can have overlapping frame ranges with their siblings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define tokens with precise timing (from demo script)\n", + "tokens_data = [\n", + " (\"Hello\", 586, 770), # Hello: frames 586-770\n", + " (\"AI\", 771, 955), # AI: frames 771-955\n", + " (\"how\", 956, 1140), # how: frames 956-1140\n", + " (\"are\", 1141, 1325), # are: frames 1141-1325\n", + " (\"you\", 1326, 1510), # you: frames 1326-1510\n", + " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", + " (\"today\", 1696, 1880), # today: frames 1696-1880\n", + "]\n", + "\n", + "# Create temporal annotations for each token\n", + "temporal_annotations = []\n", + "for token, start_frame, end_frame in tokens_data:\n", + " token_annotation = lb_types.AudioClassificationAnnotation(\n", + " frame=start_frame,\n", + " end_frame=end_frame,\n", + " name=\"User Speaker\",\n", + " value=lb_types.Text(answer=token),\n", + " )\n", + " temporal_annotations.append(token_annotation)\n", + "\n", + "print(f\"Created {len(temporal_annotations)} temporal token annotations\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create label with both regular and temporal annotations\n", + "label_with_temporal = []\n", + "label_with_temporal.append(\n", + " lb_types.Label(\n", + " data={\"global_key\": global_key},\n", + " annotations=[text_annotation, checklist_annotation, radio_annotation] +\n", + " temporal_annotations,\n", + " ))\n", + "\n", + "print(\n", + " f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n", + ")\n", + "print(\" - Regular annotations: 3\")\n", + "print(f\" - Temporal annotations: {len(temporal_annotations)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Model Assisted Labeling (MAL)\n", + "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload temporal annotations via MAL\n", + "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label_with_temporal,\n", + ")\n", + "\n", + "temporal_upload_job.wait_until_done()\n", + "print(\"Temporal upload completed!\")\n", + "print(\"Errors:\", temporal_upload_job.errors)\n", + "print(\"Status:\", temporal_upload_job.statuses)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload our label using Model-Assisted Labeling\n", + "upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=f\"mal_job-{str(uuid.uuid4())}\",\n", + " predictions=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Label Import" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload label for this data row in project\n", + "upload_job = lb.LabelImport.create_from_objects(\n", + " client=client,\n", + " project_id=project.uid,\n", + " name=\"label_import_job\" + str(uuid.uuid4()),\n", + " labels=label,\n", + ")\n", + "\n", + "upload_job.wait_until_done()\n", + "print(\"Errors:\", upload_job.errors)\n", + "print(\"Status of uploads: \", upload_job.statuses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional deletions for cleanup " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# project.delete()\n", + "# dataset.delete()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py index 64d0675a2..addfb8836 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/__init__.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/__init__.py @@ -67,7 +67,7 @@ __all__ = [ # Geometry "Line", - "Point", + "Point", "Mask", "Polygon", "Rectangle", diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py index 35428ee9d..d6a6448dd 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py @@ -74,4 +74,3 @@ class ClassificationAnnotation( value: Union[Text, Checklist, Radio] message_id: Optional[str] = None - diff --git a/libs/labelbox/src/labelbox/data/annotation_types/label.py b/libs/labelbox/src/labelbox/data/annotation_types/label.py index dd56495a2..2650e2e06 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/label.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/label.py @@ -69,8 +69,22 @@ def validate_data(cls, data): def object_annotations(self) -> List[ObjectAnnotation]: return self._get_annotations_by_type(ObjectAnnotation) - def classification_annotations(self) -> List[Union[ClassificationAnnotation, TemporalClassificationText, TemporalClassificationQuestion]]: - return self._get_annotations_by_type((ClassificationAnnotation, TemporalClassificationText, TemporalClassificationQuestion)) + def classification_annotations( + self, + ) -> List[ + Union[ + ClassificationAnnotation, + TemporalClassificationText, + TemporalClassificationQuestion, + ] + ]: + return self._get_annotations_by_type( + ( + ClassificationAnnotation, + TemporalClassificationText, + TemporalClassificationQuestion, + ) + ) def _get_annotations_by_type(self, annotation_type): return [ @@ -112,13 +126,26 @@ def frame_annotations( (VideoObjectAnnotation, VideoClassificationAnnotation), ): frame_dict[annotation.frame].append(annotation) - elif isinstance(annotation, (TemporalClassificationText, TemporalClassificationQuestion)): + elif isinstance( + annotation, + (TemporalClassificationText, TemporalClassificationQuestion), + ): # For temporal annotations with multiple values/answers, use first frame - if isinstance(annotation, TemporalClassificationText) and annotation.value: - frame_dict[annotation.value[0][0]].append(annotation) # value[0][0] is start_frame - elif isinstance(annotation, TemporalClassificationQuestion) and annotation.value: + if ( + isinstance(annotation, TemporalClassificationText) + and annotation.value + ): + frame_dict[annotation.value[0][0]].append( + annotation + ) # value[0][0] is start_frame + elif ( + isinstance(annotation, TemporalClassificationQuestion) + and annotation.value + ): if annotation.value[0].frames: - frame_dict[annotation.value[0].frames[0][0]].append(annotation) # frames[0][0] is start_frame + frame_dict[annotation.value[0].frames[0][0]].append( + annotation + ) # frames[0][0] is start_frame return dict(frame_dict) def add_url_to_masks(self, signer) -> "Label": diff --git a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py index ca887c4d8..d52656859 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/temporal.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/temporal.py @@ -47,7 +47,11 @@ class TemporalClassificationAnswer(BaseModel): description="List of (start_frame, end_frame) tuples in milliseconds", ) classifications: Optional[ - List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] + List[ + Union[ + "TemporalClassificationText", "TemporalClassificationQuestion" + ] + ] ] = None @@ -97,7 +101,11 @@ class TemporalClassificationText(BaseModel): description="List of (start_frame, end_frame, text_value) tuples", ) classifications: Optional[ - List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] + List[ + Union[ + "TemporalClassificationText", "TemporalClassificationQuestion" + ] + ] ] = None @@ -175,7 +183,11 @@ class TemporalClassificationQuestion(BaseModel): description="List of temporal answer options", ) classifications: Optional[ - List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]] + List[ + Union[ + "TemporalClassificationText", "TemporalClassificationQuestion" + ] + ] ] = None diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index a83414ace..39deafa64 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -175,7 +175,10 @@ def _create_temporal_classifications( temporal_annotations = [ annot for annot in label.annotations - if isinstance(annot, (TemporalClassificationText, TemporalClassificationQuestion)) + if isinstance( + annot, + (TemporalClassificationText, TemporalClassificationQuestion), + ) ] if not temporal_annotations: diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py index 6ba787390..eb281fdd1 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/temporal.py @@ -63,7 +63,9 @@ def create_temporal_ndjson_classifications( elif isinstance(first_ann, TemporalClassificationQuestion): answers = _process_question_group(group_anns, parent_frames=None) else: - logger.warning(f"Unknown temporal annotation type: {type(first_ann)}") + logger.warning( + f"Unknown temporal annotation type: {type(first_ann)}" + ) continue if answers: # Only add if we have valid answers @@ -99,7 +101,9 @@ def _process_text_group( for ann in annotations: for start, end, text_value in ann.value: # Validate frames against parent if provided - if parent_frames and not _is_frame_subset([(start, end)], parent_frames): + if parent_frames and not _is_frame_subset( + [(start, end)], parent_frames + ): logger.warning( f"Text value frames ({start}, {end}) not subset of parent frames {parent_frames}. Discarding." ) @@ -127,7 +131,9 @@ def _process_text_group( # Assign nested classifications based on frame overlap if all_nested_classifications: - parent_frame_tuples = [(f["start"], f["end"]) for f in unique_frames] + parent_frame_tuples = [ + (f["start"], f["end"]) for f in unique_frames + ] # Filter nested classifications that overlap with this text value's frames relevant_nested = _filter_classifications_by_overlap( all_nested_classifications, parent_frame_tuples @@ -138,7 +144,9 @@ def _process_text_group( assigned_nested.add(id(cls)) # Pass ONLY THIS text value's frames so nested answers are filtered correctly - nested = _process_nested_classifications(relevant_nested, parent_frame_tuples) + nested = _process_nested_classifications( + relevant_nested, parent_frame_tuples + ) if nested: entry["classifications"] = nested @@ -151,7 +159,11 @@ def _process_text_group( if isinstance(cls, TemporalClassificationText): frames_info = cls.value[0][:2] if cls.value else "no frames" elif isinstance(cls, TemporalClassificationQuestion): - frames_info = cls.value[0].frames if cls.value and cls.value[0].frames else "no frames" + frames_info = ( + cls.value[0].frames + if cls.value and cls.value[0].frames + else "no frames" + ) else: frames_info = "unknown" logger.warning( @@ -204,7 +216,9 @@ def _process_question_group( # Collect nested classifications at answer level if answer.classifications: - all_nested_by_answer[answer.name].extend(answer.classifications) + all_nested_by_answer[answer.name].extend( + answer.classifications + ) # Track which nested classifications were assigned assigned_nested = set() @@ -225,7 +239,9 @@ def _process_question_group( # Assign nested classifications based on frame overlap if all_nested_by_answer[answer_name]: - parent_frame_tuples = [(f["start"], f["end"]) for f in unique_frames] + parent_frame_tuples = [ + (f["start"], f["end"]) for f in unique_frames + ] # Filter nested classifications that overlap with this answer's frames relevant_nested = _filter_classifications_by_overlap( all_nested_by_answer[answer_name], parent_frame_tuples @@ -235,7 +251,9 @@ def _process_question_group( for cls in relevant_nested: assigned_nested.add(id(cls)) - nested = _process_nested_classifications(relevant_nested, parent_frame_tuples) + nested = _process_nested_classifications( + relevant_nested, parent_frame_tuples + ) if nested: entry["classifications"] = nested @@ -248,7 +266,11 @@ def _process_question_group( if isinstance(cls, TemporalClassificationText): frames_info = cls.value[0][:2] if cls.value else "no frames" elif isinstance(cls, TemporalClassificationQuestion): - frames_info = cls.value[0].frames if cls.value and cls.value[0].frames else "no frames" + frames_info = ( + cls.value[0].frames + if cls.value and cls.value[0].frames + else "no frames" + ) else: frames_info = "unknown" logger.warning( @@ -260,7 +282,9 @@ def _process_question_group( def _process_nested_classifications( - classifications: List[Union[TemporalClassificationText, TemporalClassificationQuestion]], + classifications: List[ + Union[TemporalClassificationText, TemporalClassificationQuestion] + ], parent_frames: List[Tuple[int, int]], ) -> List[Dict[str, Any]]: """ @@ -286,20 +310,26 @@ def _process_nested_classifications( elif isinstance(first_item, TemporalClassificationQuestion): answers = _process_question_group(group_items, parent_frames) else: - logger.warning(f"Unknown nested classification type: {type(first_item)}") + logger.warning( + f"Unknown nested classification type: {type(first_item)}" + ) continue if answers: # Only add if we have valid answers - results.append({ - "name": display_name, - "answer": answers, - }) + results.append( + { + "name": display_name, + "answer": answers, + } + ) return results def _filter_classifications_by_overlap( - classifications: List[Union[TemporalClassificationText, TemporalClassificationQuestion]], + classifications: List[ + Union[TemporalClassificationText, TemporalClassificationQuestion] + ], parent_frames: List[Tuple[int, int]], ) -> List[Union[TemporalClassificationText, TemporalClassificationQuestion]]: """ diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py b/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py index 24b61e067..1c0a361c1 100644 --- a/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py +++ b/libs/labelbox/tests/data/serialization/ndjson/test_temporal.py @@ -18,7 +18,9 @@ def test_temporal_text_simple(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) assert len(result) == 1 assert result[0].name == "transcription" @@ -49,7 +51,9 @@ def test_temporal_question_radio(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) assert len(result) == 1 assert result[0].name == "speaker" @@ -78,14 +82,18 @@ def test_temporal_question_checklist(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) assert len(result) == 1 assert result[0].name == "audio_quality" assert len(result[0].answer) == 2 # Check background_noise answer - bg_noise = next(a for a in result[0].answer if a["name"] == "background_noise") + bg_noise = next( + a for a in result[0].answer if a["name"] == "background_noise" + ) assert bg_noise["frames"] == [ {"start": 0, "end": 1500}, {"start": 2000, "end": 3000}, @@ -123,7 +131,9 @@ def test_temporal_text_nested(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) assert len(result) == 1 assert result[0].name == "transcription" @@ -185,7 +195,9 @@ def test_temporal_question_nested(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) assert len(result) == 1 answer = result[0].answer[0] @@ -218,7 +230,11 @@ def test_frame_validation_discard_invalid(): name="notes", value=[ (300, 800, "Valid note"), # Within parent range - (1700, 2000, "Invalid note"), # Outside parent range + ( + 1700, + 2000, + "Invalid note", + ), # Outside parent range ], ) ], @@ -227,7 +243,9 @@ def test_frame_validation_discard_invalid(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) # Find the nested notes classification answer = result[0].answer[0] @@ -251,7 +269,9 @@ def test_frame_deduplication(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) # Should only have one entry assert len(result[0].answer) == 1 @@ -291,7 +311,9 @@ def test_mixed_text_and_question_nesting(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) assert len(result) == 1 answer = result[0].answer[0] @@ -341,14 +363,18 @@ def test_inductive_structure_text_with_shared_nested_radio(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) assert len(result) == 1 assert result[0].name == "content_notes" assert len(result[0].answer) == 2 # Check first text value: "Topic is relevant" - text1 = next(a for a in result[0].answer if a["value"] == "Topic is relevant") + text1 = next( + a for a in result[0].answer if a["value"] == "Topic is relevant" + ) assert text1["frames"] == [{"start": 1000, "end": 1500}] assert "classifications" in text1 assert len(text1["classifications"]) == 1 @@ -416,7 +442,9 @@ def test_inductive_structure_checklist_with_multiple_text_values(): ) ] - result = create_temporal_ndjson_classifications(annotations, "test-global-key") + result = create_temporal_ndjson_classifications( + annotations, "test-global-key" + ) assert len(result) == 1 assert result[0].name == "checklist_class" @@ -432,7 +460,9 @@ def test_inductive_structure_checklist_with_multiple_text_values(): assert len(text_cls["answer"]) == 2 # Check first text value and its nested radio - text1 = next(a for a in text_cls["answer"] if a["value"] == "Topic is relevant") + text1 = next( + a for a in text_cls["answer"] if a["value"] == "Topic is relevant" + ) assert text1["frames"] == [{"start": 1000, "end": 1500}] radio1 = text1["classifications"][0] assert radio1["name"] == "clarity_radio" From f49a1d8c679ce456f6f67ac917f7ffbae016c4b4 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 10 Oct 2025 07:18:56 +0000 Subject: [PATCH 053/103] :art: Cleaned --- examples/annotation_import/audio.ipynb | 909 +++++++++---------------- 1 file changed, 339 insertions(+), 570 deletions(-) diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb index 885fb2b6e..0de1b193e 100644 --- a/examples/annotation_import/audio.ipynb +++ b/examples/annotation_import/audio.ipynb @@ -1,571 +1,340 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Audio Annotation Import\n", - "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", - "\n", - "Suported annotations that can be uploaded through the SDK\n", - "\n", - "* Classification Radio \n", - "* Classification Checklist \n", - "* Classification Free Text \n", - "\n", - "**Not** supported annotations\n", - "\n", - "* Bouding box\n", - "* NER\n", - "* Polygon \n", - "* Point\n", - "* Polyline \n", - "* Segmentation Mask\n", - "\n", - "MAL and Label Import:\n", - "\n", - "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", - "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* For information on what types of annotations are supported per data type, refer to this documentation:\n", - " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Notes:\n", - " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q \"labelbox[data]\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import labelbox as lb\n", - "import uuid\n", - "import labelbox.types as lb_types" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Replace with your API key\n", - "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your api key\n", - "API_KEY = \"\"\n", - "client = lb.Client(api_key=API_KEY)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Supported annotations for Audio" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##### Classification free text #####\n", - "\n", - "text_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"text_audio\",\n", - " value=lb_types.Text(answer=\"free text audio annotation\"),\n", - ")\n", - "\n", - "text_annotation_ndjson = {\n", - " \"name\": \"text_audio\",\n", - " \"answer\": \"free text audio annotation\",\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##### Checklist Classification #######\n", - "\n", - "checklist_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"checklist_audio\",\n", - " value=lb_types.Checklist(answer=[\n", - " lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n", - " lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n", - " ]),\n", - ")\n", - "\n", - "checklist_annotation_ndjson = {\n", - " \"name\":\n", - " \"checklist_audio\",\n", - " \"answers\": [\n", - " {\n", - " \"name\": \"first_checklist_answer\"\n", - " },\n", - " {\n", - " \"name\": \"second_checklist_answer\"\n", - " },\n", - " ],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "######## Radio Classification ######\n", - "\n", - "radio_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"radio_audio\",\n", - " value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n", - " name=\"second_radio_answer\")),\n", - ")\n", - "\n", - "radio_annotation_ndjson = {\n", - " \"name\": \"radio_audio\",\n", - " \"answer\": {\n", - " \"name\": \"first_radio_answer\"\n", - " },\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Upload Annotations - putting it all together " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Import data rows into Catalog" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create one Labelbox dataset\n", - "\n", - "global_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n", - "\n", - "asset = {\n", - " \"row_data\":\n", - " \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n", - " \"global_key\":\n", - " global_key,\n", - "}\n", - "\n", - "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n", - "task = dataset.create_data_rows([asset])\n", - "task.wait_till_done()\n", - "print(\"Errors:\", task.errors)\n", - "print(\"Failed data rows: \", task.failed_data_rows)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Create/select an ontology\n", - "\n", - "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", - "\n", - "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ontology_builder = lb.OntologyBuilder(classifications=[\n", - " lb.Classification(class_type=lb.Classification.Type.TEXT,\n", - " name=\"text_audio\"),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"checklist_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_checklist_answer\"),\n", - " lb.Option(value=\"second_checklist_answer\"),\n", - " ],\n", - " ),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"radio_audio\",\n", - " options=[\n", - " lb.Option(value=\"first_radio_answer\"),\n", - " lb.Option(value=\"second_radio_answer\"),\n", - " ],\n", - " ),\n", - " # Temporal classification for token-level annotations\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.TEXT,\n", - " name=\"User Speaker\",\n", - " scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n", - " ),\n", - "])\n", - "\n", - "ontology = client.create_ontology(\n", - " \"Ontology Audio Annotations\",\n", - " ontology_builder.asdict(),\n", - " media_type=lb.MediaType.Audio,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Step 3: Create a labeling project\n", - "Connect the ontology to the labeling project" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create Labelbox project\n", - "project = client.create_project(name=\"audio_project\",\n", - " media_type=lb.MediaType.Audio)\n", - "\n", - "# Setup your ontology\n", - "project.setup_editor(\n", - " ontology) # Connect your ontology and editor to your project" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Send a batch of data rows to the project" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Setup Batches and Ontology\n", - "\n", - "# Create a batch to send to your MAL project\n", - "batch = project.create_batch(\n", - " \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n", - " global_keys=[\n", - " global_key\n", - " ], # Paginated collection of data row objects, list of data row ids or global keys\n", - " priority=5, # priority between 1(Highest) - 5(lowest)\n", - ")\n", - "\n", - "print(\"Batch: \", batch)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Create the annotations payload\n", - "Create the annotations payload using the snippets of code above\n", - "\n", - "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python annotation\n", - "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "label = []\n", - "label.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation],\n", - " ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NDJSON annotations \n", - "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "label_ndjson = []\n", - "for annotations in [\n", - " text_annotation_ndjson,\n", - " checklist_annotation_ndjson,\n", - " radio_annotation_ndjson,\n", - "]:\n", - " annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n", - " label_ndjson.append(annotations)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step 6: Upload annotations to a project as pre-labels or complete labels" - ] - }, - { - "cell_type": "markdown", - "id": "a612e9bd", - "metadata": {}, - "source": [ - "## Temporal Audio Annotations\n", - "\n", - "Labelbox supports temporal annotations for audio/video with frame-level precision using the new temporal classification API.\n", - "\n", - "### Key Features:\n", - "- **Frame-based timing**: All annotations use millisecond precision\n", - "- **Deep nesting**: Support for multi-level nested classifications (Text > Text > Text, Radio > Radio > Radio, etc.)\n", - "- **Inductive structures**: Multiple parent values can share nested classifications that are automatically split based on frame overlap\n", - "- **Frame validation**: Frames start at 1 (not 0) and must be non-overlapping for Text and Radio siblings\n", - "\n", - "### Important Constraints:\n", - "1. **Frame indexing**: Frames are 1-based (frame 0 is invalid)\n", - "2. **Non-overlapping siblings**: Text and Radio classifications at the same level cannot have overlapping frame ranges\n", - "3. **Overlapping checklists**: Only Checklist answers can have overlapping frame ranges with their siblings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define tokens with precise timing (from demo script)\n", - "tokens_data = [\n", - " (\"Hello\", 586, 770), # Hello: frames 586-770\n", - " (\"AI\", 771, 955), # AI: frames 771-955\n", - " (\"how\", 956, 1140), # how: frames 956-1140\n", - " (\"are\", 1141, 1325), # are: frames 1141-1325\n", - " (\"you\", 1326, 1510), # you: frames 1326-1510\n", - " (\"doing\", 1511, 1695), # doing: frames 1511-1695\n", - " (\"today\", 1696, 1880), # today: frames 1696-1880\n", - "]\n", - "\n", - "# Create temporal annotations for each token\n", - "temporal_annotations = []\n", - "for token, start_frame, end_frame in tokens_data:\n", - " token_annotation = lb_types.AudioClassificationAnnotation(\n", - " frame=start_frame,\n", - " end_frame=end_frame,\n", - " name=\"User Speaker\",\n", - " value=lb_types.Text(answer=token),\n", - " )\n", - " temporal_annotations.append(token_annotation)\n", - "\n", - "print(f\"Created {len(temporal_annotations)} temporal token annotations\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create label with both regular and temporal annotations\n", - "label_with_temporal = []\n", - "label_with_temporal.append(\n", - " lb_types.Label(\n", - " data={\"global_key\": global_key},\n", - " annotations=[text_annotation, checklist_annotation, radio_annotation] +\n", - " temporal_annotations,\n", - " ))\n", - "\n", - "print(\n", - " f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n", - ")\n", - "print(\" - Regular annotations: 3\")\n", - "print(f\" - Temporal annotations: {len(temporal_annotations)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Model Assisted Labeling (MAL)\n", - "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload temporal annotations via MAL\n", - "temporal_upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label_with_temporal,\n", - ")\n", - "\n", - "temporal_upload_job.wait_until_done()\n", - "print(\"Temporal upload completed!\")\n", - "print(\"Errors:\", temporal_upload_job.errors)\n", - "print(\"Status:\", temporal_upload_job.statuses)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload our label using Model-Assisted Labeling\n", - "upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=f\"mal_job-{str(uuid.uuid4())}\",\n", - " predictions=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Label Import" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload label for this data row in project\n", - "upload_job = lb.LabelImport.create_from_objects(\n", - " client=client,\n", - " project_id=project.uid,\n", - " name=\"label_import_job\" + str(uuid.uuid4()),\n", - " labels=label,\n", - ")\n", - "\n", - "upload_job.wait_until_done()\n", - "print(\"Errors:\", upload_job.errors)\n", - "print(\"Status of uploads: \", upload_job.statuses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional deletions for cleanup " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# project.delete()\n", - "# dataset.delete()" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {}, + "cells": [ + { + "metadata": {}, + "source": [ + "", + " ", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "# Audio Annotation Import\n", + "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n", + "\n", + "Suported annotations that can be uploaded through the SDK\n", + "\n", + "* Classification Radio \n", + "* Classification Checklist \n", + "* Classification Free Text \n", + "\n", + "**Not** supported annotations\n", + "\n", + "* Bouding box\n", + "* NER\n", + "* Polygon \n", + "* Point\n", + "* Polyline \n", + "* Segmentation Mask\n", + "\n", + "MAL and Label Import:\n", + "\n", + "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n", + "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n", + "\n" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* For information on what types of annotations are supported per data type, refer to this documentation:\n", + " * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "* Notes:\n", + " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "%pip install -q \"labelbox[data]\"", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Setup" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "# Replace with your API key\n", + "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Supported annotations for Audio" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n name=\"text_audio\",\n value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n \"name\": \"text_audio\",\n \"answer\": \"free text audio annotation\",\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n name=\"checklist_audio\",\n value=lb_types.Checklist(answer=[\n lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n ]),\n)\n\nchecklist_annotation_ndjson = {\n \"name\":\n \"checklist_audio\",\n \"answers\": [\n {\n \"name\": \"first_checklist_answer\"\n },\n {\n \"name\": \"second_checklist_answer\"\n },\n ],\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n name=\"radio_audio\",\n value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n \"name\": \"radio_audio\",\n \"answer\": {\n \"name\": \"first_radio_answer\"\n },\n}", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Upload Annotations - putting it all together " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "## Step 1: Import data rows into Catalog" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\" + str(uuid.uuid4())\n\nasset = {\n \"row_data\":\n \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n \"global_key\":\n global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 2: Create/select an ontology\n", + "\n", + "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n", + "\n", + "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n lb.Classification(class_type=lb.Classification.Type.TEXT,\n name=\"text_audio\"),\n lb.Classification(\n class_type=lb.Classification.Type.CHECKLIST,\n name=\"checklist_audio\",\n options=[\n lb.Option(value=\"first_checklist_answer\"),\n lb.Option(value=\"second_checklist_answer\"),\n ],\n ),\n lb.Classification(\n class_type=lb.Classification.Type.RADIO,\n name=\"radio_audio\",\n options=[\n lb.Option(value=\"first_radio_answer\"),\n lb.Option(value=\"second_radio_answer\"),\n ],\n ),\n # Temporal classification for token-level annotations\n lb.Classification(\n class_type=lb.Classification.Type.TEXT,\n name=\"User Speaker\",\n scope=lb.Classification.Scope.INDEX, # INDEX scope for temporal\n ),\n])\n\nontology = client.create_ontology(\n \"Ontology Audio Annotations\",\n ontology_builder.asdict(),\n media_type=lb.MediaType.Audio,\n)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "\n", + "## Step 3: Create a labeling project\n", + "Connect the ontology to the labeling project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n ontology) # Connect your ontology and editor to your project", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 4: Send a batch of data rows to the project" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n global_keys=[\n global_key\n ], # Paginated collection of data row objects, list of data row ids or global keys\n priority=5, # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "## Step 5: Create the annotations payload\n", + "Create the annotations payload using the snippets of code above\n", + "\n", + "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "#### Python annotation\n", + "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label = []\nlabel.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation],\n ))", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### NDJSON annotations \n", + "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)." + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "label_ndjson = []\nfor annotations in [\n text_annotation_ndjson,\n checklist_annotation_ndjson,\n radio_annotation_ndjson,\n]:\n annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n label_ndjson.append(annotations)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Step 6: Upload annotations to a project as pre-labels or complete labels" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": [ + "## Temporal Audio Annotations\n", + "\n", + "Labelbox supports temporal annotations for audio/video with frame-level precision using the new temporal classification API.\n", + "\n", + "### Key Features:\n", + "- **Frame-based timing**: All annotations use millisecond precision\n", + "- **Deep nesting**: Support for multi-level nested classifications (Text > Text > Text, Radio > Radio > Radio, etc.)\n", + "- **Inductive structures**: Multiple parent values can share nested classifications that are automatically split based on frame overlap\n", + "- **Frame validation**: Frames start at 1 (not 0) and must be non-overlapping for Text and Radio siblings\n", + "\n", + "### Important Constraints:\n", + "1. **Frame indexing**: Frames are 1-based (frame 0 is invalid)\n", + "2. **Non-overlapping siblings**: Text and Radio classifications at the same level cannot have overlapping frame ranges\n", + "3. **Overlapping checklists**: Only Checklist answers can have overlapping frame ranges with their siblings" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Define tokens with precise timing (from demo script)\ntokens_data = [\n (\"Hello\", 586, 770), # Hello: frames 586-770\n (\"AI\", 771, 955), # AI: frames 771-955\n (\"how\", 956, 1140), # how: frames 956-1140\n (\"are\", 1141, 1325), # are: frames 1141-1325\n (\"you\", 1326, 1510), # you: frames 1326-1510\n (\"doing\", 1511, 1695), # doing: frames 1511-1695\n (\"today\", 1696, 1880), # today: frames 1696-1880\n]\n\n# Create temporal annotations for each token\ntemporal_annotations = []\nfor token, start_frame, end_frame in tokens_data:\n token_annotation = lb_types.AudioClassificationAnnotation(\n frame=start_frame,\n end_frame=end_frame,\n name=\"User Speaker\",\n value=lb_types.Text(answer=token),\n )\n temporal_annotations.append(token_annotation)\n\nprint(f\"Created {len(temporal_annotations)} temporal token annotations\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Create label with both regular and temporal annotations\nlabel_with_temporal = []\nlabel_with_temporal.append(\n lb_types.Label(\n data={\"global_key\": global_key},\n annotations=[text_annotation, checklist_annotation, radio_annotation] +\n temporal_annotations,\n ))\n\nprint(\n f\"Created label with {len(label_with_temporal[0].annotations)} total annotations\"\n)\nprint(\" - Regular annotations: 3\")\nprint(f\" - Temporal annotations: {len(temporal_annotations)}\")", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Model Assisted Labeling (MAL)\n", + "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload temporal annotations via MAL\ntemporal_upload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"temporal_mal_job-{str(uuid.uuid4())}\",\n predictions=label_with_temporal,\n)\n\ntemporal_upload_job.wait_until_done()\nprint(\"Temporal upload completed!\")\nprint(\"Errors:\", temporal_upload_job.errors)\nprint(\"Status:\", temporal_upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=f\"mal_job-{str(uuid.uuid4())}\",\n predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "#### Label Import" + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n client=client,\n project_id=project.uid,\n name=\"label_import_job\" + str(uuid.uuid4()),\n labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)", + "cell_type": "code", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "source": [ + "### Optional deletions for cleanup " + ], + "cell_type": "markdown" + }, + { + "metadata": {}, + "source": "# project.delete()\n# dataset.delete()", + "cell_type": "code", + "outputs": [], + "execution_count": null + } + ] +} \ No newline at end of file From 77a7fd9770557598970680a5af283bca7d5d14ce Mon Sep 17 00:00:00 2001 From: Midhun M Date: Wed, 22 Oct 2025 11:09:13 -0700 Subject: [PATCH 054/103] Add comment --- .github/actions/lbox-matrix/index.js | 1 + libs/labelbox/tests/integration/test_api_keys.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/actions/lbox-matrix/index.js b/.github/actions/lbox-matrix/index.js index 58e04c678..5dd4197e7 100644 --- a/.github/actions/lbox-matrix/index.js +++ b/.github/actions/lbox-matrix/index.js @@ -26811,6 +26811,7 @@ const core = __nccwpck_require__(8611); try { const files = JSON.parse(core.getInput('files-changed')); const startingMatrix = [ + // To be updated with the new API keys { "python-version": "3.9", "api-key": "STAGING_LABELBOX_API_KEY_3", diff --git a/libs/labelbox/tests/integration/test_api_keys.py b/libs/labelbox/tests/integration/test_api_keys.py index dba8c8e77..77be1881c 100644 --- a/libs/labelbox/tests/integration/test_api_keys.py +++ b/libs/labelbox/tests/integration/test_api_keys.py @@ -55,7 +55,7 @@ def test_create_api_key_failed(client): client.create_api_key( name=f"Test Key {uuid.uuid4()}", user=client.get_user().email, - role="LABELER", # This string should fail since role strings are case sensitive + role="LABELER", # This string should fail because role strings are case sensitive validity=5, time_unit=TimeUnit.MINUTE, ) From 623df67e771e84bc690f5f1ec9dcf6fdb0b6563f Mon Sep 17 00:00:00 2001 From: Midhun M Date: Wed, 22 Oct 2025 11:59:03 -0700 Subject: [PATCH 055/103] Update all api keys --- .github/actions/lbox-matrix/index.js | 10 +++++----- .github/workflows/python-package-develop.yml | 10 +++++----- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/actions/lbox-matrix/index.js b/.github/actions/lbox-matrix/index.js index 5dd4197e7..98785e9b8 100644 --- a/.github/actions/lbox-matrix/index.js +++ b/.github/actions/lbox-matrix/index.js @@ -26814,27 +26814,27 @@ try { // To be updated with the new API keys { "python-version": "3.9", - "api-key": "STAGING_LABELBOX_API_KEY_3", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.10", - "api-key": "STAGING_LABELBOX_API_KEY_4", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.11", - "api-key": "STAGING_LABELBOX_API_KEY", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.12", - "api-key": "STAGING_LABELBOX_API_KEY_5", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.13", - "api-key": "STAGING_LABELBOX_API_KEY_2", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, ]; diff --git a/.github/workflows/python-package-develop.yml b/.github/workflows/python-package-develop.yml index 19e7a294d..60fc6c01a 100644 --- a/.github/workflows/python-package-develop.yml +++ b/.github/workflows/python-package-develop.yml @@ -59,19 +59,19 @@ jobs: matrix: include: - python-version: "3.9" - api-key: STAGING_LABELBOX_API_KEY_3 + api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.10" - api-key: STAGING_LABELBOX_API_KEY_4 + api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.11" - api-key: STAGING_LABELBOX_API_KEY + api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.12" - api-key: STAGING_LABELBOX_API_KEY_5 + api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.13" - api-key: STAGING_LABELBOX_API_KEY_2 + api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 da-test-key: DA_GCP_LABELBOX_API_KEY uses: ./.github/workflows/python-package-shared.yml with: From 35eba4930dd616e93df7cda6ebd8ac700258e901 Mon Sep 17 00:00:00 2001 From: Midhun M Date: Wed, 22 Oct 2025 13:47:23 -0700 Subject: [PATCH 056/103] Update keys --- .github/actions/lbox-matrix/index.js | 10 +++++----- .github/workflows/python-package-develop.yml | 10 +++++----- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/actions/lbox-matrix/index.js b/.github/actions/lbox-matrix/index.js index 98785e9b8..733584289 100644 --- a/.github/actions/lbox-matrix/index.js +++ b/.github/actions/lbox-matrix/index.js @@ -26814,27 +26814,27 @@ try { // To be updated with the new API keys { "python-version": "3.9", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2FKAOZ032H0735C32V1U63", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.10", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2G1557037K0726H50N3JQK", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.11", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2G7STM04WC071F73LG8RSD", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.12", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2GEJW9033B07299RKLAOFM", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.13", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3", + "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2GGV3X04QK071X1EJCH8W0", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, ]; diff --git a/.github/workflows/python-package-develop.yml b/.github/workflows/python-package-develop.yml index 60fc6c01a..a9718f300 100644 --- a/.github/workflows/python-package-develop.yml +++ b/.github/workflows/python-package-develop.yml @@ -59,19 +59,19 @@ jobs: matrix: include: - python-version: "3.9" - api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 + api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2FKAOZ032H0735C32V1U63 da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.10" - api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 + api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2G1557037K0726H50N3JQK da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.11" - api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 + api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2G7STM04WC071F73LG8RSD da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.12" - api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 + api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2GEJW9033B07299RKLAOFM da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.13" - api-key: STAGING_LABELBOX_API_KEY_ORG_CLURHDFY8022607W648WZ1NF3 + api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2GGV3X04QK071X1EJCH8W0 da-test-key: DA_GCP_LABELBOX_API_KEY uses: ./.github/workflows/python-package-shared.yml with: From 6e2affdf34a15bace1a663c6202246120536be1f Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan Date: Wed, 24 Sep 2025 14:32:50 -0700 Subject: [PATCH 057/103] Alignerr project creation --- libs/labelbox/pyproject.toml | 1 + libs/labelbox/src/labelbox/__init__.py | 2 +- .../src/labelbox/alignerr/__init__.py | 3 + .../src/labelbox/alignerr/alignerr_project.py | 91 +++ .../alignerr/alignerr_project_builder.py | 178 ++++++ .../alignerr/alignerr_project_factory.py | 154 +++++ .../src/labelbox/alignerr/schema/__init__.py | 0 .../alignerr/schema/project_domain.py | 283 +++++++++ .../labelbox/alignerr/schema/project_rate.py | 119 ++++ libs/labelbox/src/labelbox/client.py | 5 + libs/labelbox/src/labelbox/orm/model.py | 1 + libs/labelbox/src/labelbox/schema/role.py | 7 + .../integration/test_alignerr_project.py | 106 ++++ .../test_alignerr_project_builder.py | 109 ++++ .../test_alignerr_project_factory.py | 128 ++++ .../tests/integration/test_project_domain.py | 165 +++++ requirements-dev.lock | 4 + requirements.lock | 4 + uv.lock | 568 ++++++++++++++++++ 19 files changed, 1927 insertions(+), 1 deletion(-) create mode 100644 libs/labelbox/src/labelbox/alignerr/__init__.py create mode 100644 libs/labelbox/src/labelbox/alignerr/alignerr_project.py create mode 100644 libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py create mode 100644 libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py create mode 100644 libs/labelbox/src/labelbox/alignerr/schema/__init__.py create mode 100644 libs/labelbox/src/labelbox/alignerr/schema/project_domain.py create mode 100644 libs/labelbox/src/labelbox/alignerr/schema/project_rate.py create mode 100644 libs/labelbox/tests/integration/test_alignerr_project.py create mode 100644 libs/labelbox/tests/integration/test_alignerr_project_builder.py create mode 100644 libs/labelbox/tests/integration/test_alignerr_project_factory.py create mode 100644 libs/labelbox/tests/integration/test_project_domain.py create mode 100644 uv.lock diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml index 3ec33bf17..aa7376d91 100644 --- a/libs/labelbox/pyproject.toml +++ b/libs/labelbox/pyproject.toml @@ -12,6 +12,7 @@ dependencies = [ "tqdm>=4.66.2", "geojson>=3.1.0", "lbox-clients==1.1.2", + "PyYAML>=6.0", ] readme = "README.md" requires-python = ">=3.9,<3.14" diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index b543894f1..4e3f348d5 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -78,6 +78,7 @@ from labelbox.schema.ontology_kind import OntologyKind from labelbox.schema.organization import Organization from labelbox.schema.project import Project +from labelbox.alignerr.schema.project_rate import ProjectRateV2 as ProjectRate from labelbox.schema.project_model_config import ProjectModelConfig from labelbox.schema.project_overview import ( ProjectOverview, @@ -98,7 +99,6 @@ ResponseOption, PromptResponseClassification, ) -from lbox.exceptions import * from labelbox.schema.taskstatus import TaskStatus from labelbox.schema.api_key import ApiKey from labelbox.schema.timeunit import TimeUnit diff --git a/libs/labelbox/src/labelbox/alignerr/__init__.py b/libs/labelbox/src/labelbox/alignerr/__init__.py new file mode 100644 index 000000000..0f77eb997 --- /dev/null +++ b/libs/labelbox/src/labelbox/alignerr/__init__.py @@ -0,0 +1,3 @@ +from .alignerr_project import AlignerrWorkspace + +__all__ = ["AlignerrWorkspace"] diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py new file mode 100644 index 000000000..e738de7ff --- /dev/null +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py @@ -0,0 +1,91 @@ +from enum import Enum +from typing import TYPE_CHECKING, Optional + +import logging + +from labelbox.alignerr.schema.project_rate import ProjectRateV2 +from labelbox.alignerr.schema.project_domain import ProjectDomain +from labelbox.pagination import PaginatedCollection + +logger = logging.getLogger(__name__) + + +if TYPE_CHECKING: + from labelbox import Client + from labelbox.schema.project import Project + from labelbox.alignerr.schema.project_domain import ProjectDomain + + +class AlignerrRole(Enum): + Labeler = "LABELER" + Reviewer = "REVIEWER" + Admin = "ADMIN" + + +class AlignerrProject: + def __init__( + self, client: "Client", project: "Project", _internal: bool = False + ): + if not _internal: + raise RuntimeError( + "AlignerrProject cannot be initialized directly. " + "Use AlignerrProjectBuilder or AlignerrProjectFactory to create instances." + ) + self.client = client + self.project = project + + @property + def project(self) -> Optional["Project"]: + return self._project + + @project.setter + def project(self, project: "Project"): + self._project = project + + def domains(self) -> PaginatedCollection: + """Get all domains associated with this project. + + Returns: + PaginatedCollection of ProjectDomain instances + """ + return ProjectDomain.get_by_project_id( + client=self.client, project_id=self.project.uid + ) + + def add_domain(self, project_domain: ProjectDomain): + return ProjectDomain.connect_project_to_domains( + client=self.client, + project_id=self.project.uid, + domain_ids=[project_domain.uid], + ) + + def get_project_rate(self) -> Optional["ProjectRateV2"]: + return ProjectRateV2.get_by_project_id( + client=self.client, project_id=self.project.uid + ) + + def set_project_rate(self, project_rate_input): + return ProjectRateV2.set_project_rate( + client=self.client, + project_id=self.project.uid, + project_rate_input=project_rate_input, + ) + + +class AlignerrWorkspace: + def __init__(self, client: "Client"): + self.client = client + + def project_builder(self): + from labelbox.alignerr.alignerr_project_builder import ( + AlignerrProjectBuilder, + ) + + return AlignerrProjectBuilder(self.client) + + def project_prototype(self): + from labelbox.alignerr.alignerr_project_factory import ( + AlignerrProjectFactory, + ) + + return AlignerrProjectFactory(self.client) diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py new file mode 100644 index 000000000..2c10815a5 --- /dev/null +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py @@ -0,0 +1,178 @@ +import datetime +from typing import TYPE_CHECKING, Optional +import logging + +from labelbox.alignerr.schema.project_rate import BillingMode +from labelbox.alignerr.schema.project_rate import ProjectRateInput +from labelbox.alignerr.schema.project_rate import ProjectRateV2 +from labelbox.alignerr.schema.project_domain import ProjectDomain +from labelbox.schema.media_type import MediaType + +logger = logging.getLogger(__name__) + + +if TYPE_CHECKING: + from labelbox import Client + from labelbox.alignerr.alignerr_project import AlignerrProject, AlignerrRole + + +class AlignerrProjectBuilder: + def __init__(self, client: "Client"): + self.client = client + self._alignerr_rates: dict[str, ProjectRateInput] = {} + self._customer_rate: ProjectRateInput = None + self._domains: list[ProjectDomain] = [] + self.role_name_to_id = self._get_role_name_to_id() + + def set_name(self, name: str): + self.project_name = name + return self + + def set_media_type(self, media_type: "MediaType"): + self.project_media_type = media_type + return self + + def set_alignerr_role_rate( + self, + *, + role_name: "AlignerrRole", + rate: float, + billing_mode: BillingMode, + effective_since: datetime.datetime, + effective_until: Optional[datetime.datetime] = None, + ): + if role_name.value not in self.role_name_to_id: + raise ValueError(f"Role {role_name.value} not found") + + role_id = self.role_name_to_id[role_name.value] + role_name = role_name.value + + # Convert datetime objects to ISO format strings + effective_since_str = ( + effective_since.isoformat() + if isinstance(effective_since, datetime.datetime) + else effective_since + ) + effective_until_str = ( + effective_until.isoformat() + if isinstance(effective_until, datetime.datetime) + else effective_until + ) + + self._alignerr_rates[role_name] = ProjectRateInput( + rateForId=role_id, + isBillRate=False, + billingMode=billing_mode, + rate=rate, + effectiveSince=effective_since_str, + effectiveUntil=effective_until_str, + ) + return self + + def set_customer_rate( + self, + *, + rate: float, + billing_mode: BillingMode, + effective_since: datetime.datetime, + effective_until: Optional[datetime.datetime] = None, + ): + # Convert datetime objects to ISO format strings + effective_since_str = ( + effective_since.isoformat() + if isinstance(effective_since, datetime.datetime) + else effective_since + ) + effective_until_str = ( + effective_until.isoformat() + if isinstance(effective_until, datetime.datetime) + else effective_until + ) + + self._customer_rate = ProjectRateInput( + rateForId="", # Empty string for customer rate + isBillRate=True, + billingMode=billing_mode, + rate=rate, + effectiveSince=effective_since_str, + effectiveUntil=effective_until_str, + ) + return self + + def set_domains(self, domains: list[str]): + for domain in domains: + project_domain_page = ProjectDomain.search( + self.client, search_by_name=domain + ) + domain_result = project_domain_page.get_one() + if domain_result is None: + raise ValueError(f"Domain {domain} not found") + self._domains.append(domain_result) + return self + + def create(self, skip_validation: bool = False): + if not skip_validation: + self._validate() + logger.info("Creating project") + + project_data = { + "name": self.project_name, + "media_type": self.project_media_type, + } + labelbox_project = self.client.create_project(**project_data) + + # Import here to avoid circular imports + from labelbox.alignerr.alignerr_project import AlignerrProject + + alignerr_project = AlignerrProject( + self.client, labelbox_project, _internal=True + ) + + self._create_rates(alignerr_project) + self._create_domains(alignerr_project) + + return alignerr_project + + def _create_rates(self, alignerr_project: "AlignerrProject"): + for alignerr_role, project_rate in self._alignerr_rates.items(): + logger.info(f"Setting project rate for {alignerr_role}") + alignerr_project.set_project_rate(project_rate) + + def _create_domains(self, alignerr_project: "AlignerrProject"): + if self._domains: + logger.info( + f"Setting domains: {[domain.name for domain in self._domains]}" + ) + domain_ids = [domain.uid for domain in self._domains] + ProjectDomain.connect_project_to_domains( + client=self.client, + project_id=alignerr_project.project.uid, + domain_ids=domain_ids, + ) + + def _validate_alignerr_rates(self): + # Import here to avoid circular imports + from labelbox.alignerr.alignerr_project import AlignerrRole + + required_role_rates = set( + [AlignerrRole.Labeler.value, AlignerrRole.Reviewer.value] + ) + + for role_name in self._alignerr_rates.keys(): + required_role_rates.remove(role_name) + if len(required_role_rates) > 0: + raise ValueError( + f"Required role rates are not set: {required_role_rates}" + ) + + def _validate_customer_rate(self): + if self._customer_rate is None: + raise ValueError("Customer rate is not set") + + def _validate(self): + self._validate_alignerr_rates() + self._validate_customer_rate() + + def _get_role_name_to_id(self) -> dict[str, str]: + roles = self.client.get_roles() + return {role.name: role.uid for role in roles.values()} diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py new file mode 100644 index 000000000..43d479c8b --- /dev/null +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py @@ -0,0 +1,154 @@ +import datetime +from typing import TYPE_CHECKING +import yaml +from pathlib import Path +import logging + +from labelbox.alignerr.schema.project_rate import BillingMode +from labelbox.schema.media_type import MediaType + +logger = logging.getLogger(__name__) + + +if TYPE_CHECKING: + from labelbox import Client + + +class AlignerrProjectFactory: + def __init__(self, client: "Client"): + self.client = client + + def create(self, yaml_file_path: str, skip_validation: bool = False): + """ + Create an AlignerrProject from a YAML configuration file. + + Args: + yaml_file_path: Path to the YAML configuration file + skip_validation: Whether to skip validation of required fields + + Returns: + AlignerrProject: The created project with configured rates + + Raises: + FileNotFoundError: If the YAML file doesn't exist + yaml.YAMLError: If the YAML file is invalid + ValueError: If required fields are missing or invalid + """ + logger.info(f"Creating project from YAML file: {yaml_file_path}") + + # Load and parse YAML file + yaml_path = Path(yaml_file_path) + if not yaml_path.exists(): + raise FileNotFoundError(f"YAML file not found: {yaml_file_path}") + + try: + with open(yaml_path, "r") as file: + config = yaml.safe_load(file) + except yaml.YAMLError as e: + raise yaml.YAMLError(f"Invalid YAML file: {e}") + + # Validate required fields + if not config: + raise ValueError("YAML file is empty") + + required_fields = ["name", "media_type"] + for field in required_fields: + if field not in config: + raise ValueError( + f"Required field '{field}' is missing from YAML configuration" + ) + + # Import here to avoid circular imports + from labelbox.alignerr.alignerr_project_builder import ( + AlignerrProjectBuilder, + ) + from labelbox.alignerr.alignerr_project import AlignerrRole + + # Create project builder + builder = AlignerrProjectBuilder(self.client) + + # Set basic project properties + builder.set_name(config["name"]) + + # Set media type + media_type_str = config["media_type"] + media_type = MediaType(media_type_str) + + # Check if the media type is supported + if not MediaType.is_supported(media_type): + supported_members = MediaType.get_supported_members() + raise ValueError( + f"Invalid media_type '{media_type_str}'. Must be one of: {supported_members}" + ) + + builder.set_media_type(media_type) + + # Set project rates if provided + if "rates" in config: + rates_config = config["rates"] + if not isinstance(rates_config, dict): + raise ValueError("'rates' must be a dictionary") + + for role_name, rate_config in rates_config.items(): + try: + alignerr_role = AlignerrRole(role_name.upper()) + except ValueError: + raise ValueError( + f"Invalid role '{role_name}'. Must be one of: {[r.value for r in AlignerrRole]}" + ) + + # Validate rate configuration + required_rate_fields = [ + "rate", + "billing_mode", + "effective_since", + ] + for field in required_rate_fields: + if field not in rate_config: + raise ValueError( + f"Required field '{field}' is missing for role '{role_name}'" + ) + + # Parse billing mode + try: + billing_mode = BillingMode(rate_config["billing_mode"]) + except ValueError: + raise ValueError( + f"Invalid billing_mode '{rate_config['billing_mode']}' for role '{role_name}'. Must be one of: {[e.value for e in BillingMode]}" + ) + + # Parse effective dates + try: + effective_since = datetime.datetime.fromisoformat( + rate_config["effective_since"] + ) + except ValueError: + raise ValueError( + f"Invalid effective_since date format for role '{role_name}'. Use ISO format (YYYY-MM-DDTHH:MM:SS)" + ) + + effective_until = None + if ( + "effective_until" in rate_config + and rate_config["effective_until"] + ): + try: + effective_until = datetime.datetime.fromisoformat( + rate_config["effective_until"] + ) + except ValueError: + raise ValueError( + f"Invalid effective_until date format for role '{role_name}'. Use ISO format (YYYY-MM-DDTHH:MM:SS)" + ) + + # Set the rate + builder.set_alignerr_role_rate( + role_name=alignerr_role, + rate=float(rate_config["rate"]), + billing_mode=billing_mode, + effective_since=effective_since, + effective_until=effective_until, + ) + + # Create the project + return builder.create(skip_validation=skip_validation) diff --git a/libs/labelbox/src/labelbox/alignerr/schema/__init__.py b/libs/labelbox/src/labelbox/alignerr/schema/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_domain.py b/libs/labelbox/src/labelbox/alignerr/schema/project_domain.py new file mode 100644 index 000000000..7ef56b553 --- /dev/null +++ b/libs/labelbox/src/labelbox/alignerr/schema/project_domain.py @@ -0,0 +1,283 @@ +from typing import List, Optional +from labelbox.orm.db_object import Deletable, DbObject +from labelbox.orm.model import Field +from labelbox.pagination import PaginatedCollection +from pydantic import BaseModel + + +class CreateProjectDomainInput(BaseModel): + """Input for creating a new project domain.""" + + name: str + projectId: Optional[str] = None + + +class ProjectDomainsPaginationInput(BaseModel): + """Input for paginating project domains query.""" + + limit: int = 30 + offset: int = 0 + searchByName: Optional[str] = None + projectIds: Optional[List[str]] = None + includeArchived: bool = False + + +class ConnectProjectToDomainsInput(BaseModel): + """Input for connecting a project to domains.""" + + projectId: str + domainIds: List[str] + + +class ProjectDomainsPage(BaseModel): + """Response model for paginated project domains.""" + + nodes: List["ProjectDomain"] + totalCount: int + + +class ProjectDomain(DbObject, Deletable): + """A project domain represents a categorization for projects.""" + + # Fields matching the GraphQL schema + id = Field.String("id") + name = Field.String("name") + createdAt = Field.DateTime("createdAt") + updatedAt = Field.DateTime("updatedAt") + deactivatedAt = Field.DateTime("deactivatedAt") + ratingsCount = Field.Int("ratingsCount") + + @classmethod + def create( + cls, client, name: str, project_id: Optional[str] = None + ) -> "ProjectDomain": + """Create a new project domain. + + Args: + client: Labelbox client instance + name: Name of the project domain + project_id: Optional project ID to associate with + + Returns: + Created ProjectDomain instance + """ + input_data = CreateProjectDomainInput(name=name, projectId=project_id) + + query_str = """ + mutation CreateProjectDomainPyApi($input: CreateProjectDomainInput!) { + createProjectDomain(input: $input) { + id + name + createdAt + updatedAt + deactivatedAt + ratingsCount + } + }""" + + result = client.execute(query_str, {"input": input_data.model_dump()}) + return cls(client, result["createProjectDomain"]) + + def activate(self) -> "ProjectDomain": + """Activate this project domain. + + Returns: + Updated ProjectDomain instance + """ + query_str = """ + mutation ActivateProjectDomainPyApi($id: ID!) { + activateProjectDomain(id: $id) { + id + name + createdAt + updatedAt + deactivatedAt + ratingsCount + } + }""" + + result = self.client.execute(query_str, {"id": self.uid}) + return self.__class__(self.client, result["activateProjectDomain"]) + + def deactivate(self) -> "ProjectDomain": + """Deactivate this project domain. + + Returns: + Updated ProjectDomain instance + """ + query_str = """ + mutation DeactivateProjectDomainPyApi($id: ID!) { + deactivateProjectDomain(id: $id) { + id + name + createdAt + updatedAt + deactivatedAt + ratingsCount + } + }""" + + result = self.client.execute(query_str, {"id": self.uid}) + return self.__class__(self.client, result["deactivateProjectDomain"]) + + @classmethod + def connect_project_to_domains( + cls, client, project_id: str, domain_ids: List[str] + ) -> bool: + """Connect a project to multiple domains. + + Args: + client: Labelbox client instance + project_id: ID of the project to connect + domain_ids: List of domain IDs to connect to the project + + Returns: + True if successful + """ + input_data = ConnectProjectToDomainsInput( + projectId=project_id, domainIds=domain_ids + ) + + query_str = """ + mutation ConnectProjectToDomainsPyApi($input: ConnectProjectToDomainsInput!) { + connectProjectToDomains(input: $input) + }""" + + result = client.execute(query_str, {"input": input_data.model_dump()}) + return result["connectProjectToDomains"] + + @classmethod + def query_by_project_id( + cls, + project_id: str, + limit: int = 30, + offset: int = 0, + include_archived: bool = False, + ) -> str: + """Get GraphQL query string for fetching project domains by project ID. + + Args: + project_id: ID of the project to fetch domains for + limit: Maximum number of results to return + offset: Number of results to skip + include_archived: Whether to include archived domains + + Returns: + GraphQL query string + """ + return """ + query ProjectDomainsPyApi($projectId: ID!, $includeArchived: Boolean!) { + projectDomains( + pagination: { + offset: %d + limit: %d + projectIds: [$projectId] + includeArchived: $includeArchived + } + ) { + nodes { + id + name + createdAt + updatedAt + deactivatedAt + ratingsCount + } + totalCount + } + }""" + + @classmethod + def get_by_project_id( + cls, + client, + project_id: str, + limit: int = 30, + offset: int = 0, + include_archived: bool = False, + ) -> PaginatedCollection: + """Get project domains for a specific project with pagination. + + Args: + client: Labelbox client instance + project_id: ID of the project to fetch domains for + limit: Maximum number of results to return + offset: Number of results to skip + include_archived: Whether to include archived domains + + Returns: + PaginatedCollection of ProjectDomain instances + """ + query_str = cls.query_by_project_id( + project_id, limit, offset, include_archived + ) + + params = {"projectId": project_id, "includeArchived": include_archived} + + return PaginatedCollection( + client=client, + query=query_str, + params=params, + dereferencing=["projectDomains", "nodes"], + obj_class=cls, + ) + + @classmethod + def search( + cls, + client, + search_by_name: Optional[str] = None, + project_ids: Optional[List[str]] = None, + limit: int = 30, + offset: int = 0, + include_archived: bool = False, + ) -> PaginatedCollection: + """Search project domains with various filters. + + Args: + client: Labelbox client instance + search_by_name: Optional name to search for + project_ids: Optional list of project IDs to filter by + limit: Maximum number of results to return + offset: Number of results to skip + include_archived: Whether to include archived domains + + Returns: + PaginatedCollection of ProjectDomain instances + """ + query_str = """ + query SearchProjectDomainsPyApi($includeArchived: Boolean!, $searchByName: String, $projectIds: [ID!]) { + projectDomains( + pagination: { + offset: %d + limit: %d + searchByName: $searchByName + projectIds: $projectIds + includeArchived: $includeArchived + } + ) { + nodes { + id + name + createdAt + updatedAt + deactivatedAt + ratingsCount + } + totalCount + } + }""" + + params = { + "includeArchived": include_archived, + "searchByName": search_by_name, + "projectIds": project_ids, + } + + return PaginatedCollection( + client=client, + query=query_str, + params=params, + dereferencing=["projectDomains", "nodes"], + obj_class=cls, + ) diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py b/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py new file mode 100644 index 000000000..46223298a --- /dev/null +++ b/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py @@ -0,0 +1,119 @@ +from enum import Enum +from typing import Optional +from labelbox.orm.db_object import DbObject, Deletable +from labelbox.orm.model import Relationship, Field +from pydantic import BaseModel, model_validator + + +class BillingMode(Enum): + BY_TASK = "BY_TASK" + BY_HOUR = "BY_HOUR" + BY_TASK_PER_TURN = "BY_TASK_PER_TURN" + BY_ACCEPTED_TASK = "BY_ACCEPTED_TASK" + + +class ProjectRateInput(BaseModel): + rateForId: str + isBillRate: bool + billingMode: BillingMode + rate: float + effectiveSince: str # DateTime as string + effectiveUntil: Optional[str] = None # Optional DateTime as string + + @model_validator(mode="after") + def validate_fields(self): + if self.rate < 0: + raise ValueError("Rate must be greater than or equal to 0") + + if self.isBillRate and self.rateForId != "": + raise ValueError( + "isBillRate indicates that this is a customer bill rate. rateForId must be empty if isBillRate is true" + ) + + if not self.isBillRate and self.rateForId == "": + raise ValueError( + "rateForId must be set to the id of the Alignerr Role" + ) + + return self + + +class ProjectRateV2(DbObject, Deletable): + # Relationships + userRole = Relationship.ToOne("UserRole", False) + updatedBy = Relationship.ToOne("User", False) + + # Fields matching the GraphQL schema + isBillRate = Field.Boolean("isBillRate") + billingMode = Field.Enum(BillingMode, "billingMode") + rate = Field.Float("rate") + createdAt = Field.DateTime("createdAt") + updatedAt = Field.DateTime("updatedAt") + effectiveSince = Field.DateTime("effectiveSince") + effectiveUntil = Field.DateTime("effectiveUntil") + + @classmethod + def get_by_project_id(cls, client, project_id: str) -> "ProjectRateV2": + query_str = """ + query GetAllProjectRatesPyApi($projectId: ID!) { + project(where: { id: $projectId }) { + id + ratesV2 { + id + userRole { + id + name + } + isBillRate + billingMode + rate + effectiveSince + effectiveUntil + createdAt + updatedAt + updatedBy { + id + email + name + } + } + } + } + """ + result = client.execute(query_str, {"projectId": project_id}) + rates_data = result["project"]["ratesV2"] + + if not rates_data: + return None + + # Return the first rate as a ProjectRateV2 object + return cls(client, rates_data[0]) + + @classmethod + def set_project_rate( + cls, client, project_id: str, project_rate_input: ProjectRateInput + ): + mutation_str = """mutation SetProjectRateV2PyApi($input: SetProjectRateV2Input!) { + setProjectRateV2(input: $input) { + success + } + }""" + + params = { + "projectId": project_id, + "input": { + "projectId": project_id, + "userRoleId": project_rate_input.rateForId, + "isBillRate": project_rate_input.isBillRate, + "billingMode": project_rate_input.billingMode.value + if hasattr(project_rate_input.billingMode, "value") + else project_rate_input.billingMode, + "rate": project_rate_input.rate, + "effectiveSince": project_rate_input.effectiveSince, + "effectiveUntil": project_rate_input.effectiveUntil, + }, + } + + result = client.execute(mutation_str, params) + + return result["setProjectRateV2"]["success"] diff --git a/libs/labelbox/src/labelbox/client.py b/libs/labelbox/src/labelbox/client.py index b48db006e..e5c914fd3 100644 --- a/libs/labelbox/src/labelbox/client.py +++ b/libs/labelbox/src/labelbox/client.py @@ -82,6 +82,7 @@ from labelbox.schema.taskstatus import TaskStatus from labelbox.schema.api_key import ApiKey from labelbox.schema.timeunit import TimeUnit +from labelbox.alignerr import AlignerrWorkspace logger = logging.getLogger(__name__) @@ -161,6 +162,10 @@ def enable_experimental(self, value: bool): def app_url(self) -> str: return self._request_client.app_url + @property + def alignerr_workspace(self) -> AlignerrWorkspace: + return AlignerrWorkspace(self) + def set_sdk_method(self, sdk_method: str): self._request_client.sdk_method = sdk_method diff --git a/libs/labelbox/src/labelbox/orm/model.py b/libs/labelbox/src/labelbox/orm/model.py index b4ec7c2c2..260b3a7d9 100644 --- a/libs/labelbox/src/labelbox/orm/model.py +++ b/libs/labelbox/src/labelbox/orm/model.py @@ -395,6 +395,7 @@ class Entity(metaclass=EntityMeta): ProjectRole: Type[labelbox.ProjectRole] ProjectModelConfig: Type[labelbox.ProjectModelConfig] Project: Type[labelbox.Project] + ProjectRate: Type[labelbox.ProjectRate] Batch: Type[labelbox.Batch] CatalogSlice: Type[labelbox.CatalogSlice] ModelSlice: Type[labelbox.ModelSlice] diff --git a/libs/labelbox/src/labelbox/schema/role.py b/libs/labelbox/src/labelbox/schema/role.py index d22e2a78e..4d555ac06 100644 --- a/libs/labelbox/src/labelbox/schema/role.py +++ b/libs/labelbox/src/labelbox/schema/role.py @@ -29,11 +29,18 @@ def format_role(name: str): class Role(DbObject): name = Field.String("name") + @classmethod + def from_name(cls, client: "Client", name: str) -> "Role": + roles = get_roles(client) + return roles.get(name.upper()) + + class OrgRole(Role): ... class UserRole(Role): ... + @dataclass diff --git a/libs/labelbox/tests/integration/test_alignerr_project.py b/libs/labelbox/tests/integration/test_alignerr_project.py new file mode 100644 index 000000000..decb37a8f --- /dev/null +++ b/libs/labelbox/tests/integration/test_alignerr_project.py @@ -0,0 +1,106 @@ +"""Integration tests for AlignerrProject functionality. + +These tests interact with the actual Labelbox API to verify AlignerrProject operations. +""" + +import datetime +import uuid + +import pytest +from labelbox.alignerr.alignerr_project import AlignerrProject +from labelbox.alignerr.schema.project_rate import BillingMode, ProjectRateInput +from labelbox.schema.media_type import MediaType + + +@pytest.fixture +def test_project(client): + """Create a test project for AlignerrProject testing.""" + project_name = f"Test AlignerrProject {uuid.uuid4()}" + project = client.create_project( + name=project_name, media_type=MediaType.Image + ) + + yield project + + # Cleanup + try: + project.delete() + except Exception: + pass # Project may already be deleted + + +@pytest.fixture +def test_alignerr_project(client, test_project): + """Create a test AlignerrProject instance using the builder pattern.""" + return ( + client.alignerr_workspace.project_builder() + .set_name(test_project.name) + .set_media_type(test_project.media_type) + .create(skip_validation=True) + ) + + +def test_alignerr_project_initialization_error(client, test_project): + """Test that direct AlignerrProject initialization raises an error.""" + with pytest.raises( + RuntimeError, match="AlignerrProject cannot be initialized directly" + ): + AlignerrProject(client, test_project) + + +def test_alignerr_project_property_setter(client, test_alignerr_project): + """Test AlignerrProject property setter.""" + # Create a new project to test property setting + new_project_name = f"New Test Project {uuid.uuid4()}" + new_project = client.create_project( + name=new_project_name, media_type=MediaType.Image + ) + + try: + # Test property setter + test_alignerr_project.project = new_project + assert test_alignerr_project.project == new_project + assert test_alignerr_project.project.name == new_project_name + finally: + new_project.delete() + + +def test_alignerr_project_domains(client, test_alignerr_project): + """Test AlignerrProject domains() method.""" + # Test that domains() returns a PaginatedCollection + domains = test_alignerr_project.domains() + assert domains is not None + # The collection might be empty for a new project, which is expected + + +def test_alignerr_project_get_project_rate_no_rates( + client, test_alignerr_project +): + """Test get_project_rate() when no rates are set.""" + # For a new project without rates, this should return None + project_rate = test_alignerr_project.get_project_rate() + assert project_rate is None + + +def test_alignerr_project_set_and_get_project_rate( + client, test_alignerr_project +): + """Test setting and getting project rates.""" + # Create a project rate input for a customer rate (isBillRate=True requires empty rateForId) + project_rate_input = ProjectRateInput( + rateForId="", # Empty string for customer rate + isBillRate=True, + billingMode=BillingMode.BY_HOUR, + rate=25.0, + effectiveSince=datetime.datetime.now().isoformat(), + effectiveUntil=None, + ) + + # Set the project rate + result = test_alignerr_project.set_project_rate(project_rate_input) + assert result is True # Should return success status + + # Get the project rate back + project_rate = test_alignerr_project.get_project_rate() + # Note: The actual rate retrieval might depend on the API implementation + # This test verifies the method calls work without errors diff --git a/libs/labelbox/tests/integration/test_alignerr_project_builder.py b/libs/labelbox/tests/integration/test_alignerr_project_builder.py new file mode 100644 index 000000000..76daa4ed3 --- /dev/null +++ b/libs/labelbox/tests/integration/test_alignerr_project_builder.py @@ -0,0 +1,109 @@ +"""Integration tests for ProjectRateV2 functionality.""" + +import datetime +from labelbox import Client +from labelbox.alignerr.alignerr_project import AlignerrRole +from labelbox.alignerr.schema.project_rate import BillingMode +from labelbox.schema.media_type import MediaType +import pytest + + +def test_skip_validation(client: Client): + alignerr_project = ( + client.alignerr_workspace.project_builder() + .set_name("TestAlignerrProject") + .set_media_type(MediaType.Image) + .set_alignerr_role_rate( + role_name=AlignerrRole.Labeler, + rate=10.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .create(skip_validation=True) + ) + assert alignerr_project is not None + assert alignerr_project.project.name == "TestAlignerrProject" + + alignerr_project.project.delete() + + +def test_create_alignerr_project_using_builder_validate_input(client: Client): + with pytest.raises(ValueError): + client.alignerr_workspace.project_builder().set_name( + "TestAlignerrProject" + ).set_media_type(MediaType.Image).set_alignerr_role_rate( + role_name=AlignerrRole.Labeler, + rate=10.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ).create() + + alignerr_project = ( + client.alignerr_workspace.project_builder() + .set_name("TestAlignerrProject2") + .set_media_type(MediaType.Image) + .set_alignerr_role_rate( + role_name=AlignerrRole.Labeler, + rate=10.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .set_alignerr_role_rate( + role_name=AlignerrRole.Reviewer, + rate=10.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .set_customer_rate( + rate=15.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .create() + ) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestAlignerrProject2" + + alignerr_project.project.delete() + + +def test_create_alignerr_project_using_builder_add_domains(client: Client): + from labelbox.alignerr.schema.project_domain import ProjectDomain + import uuid + + # Create test domains first + domain1_name = f"TestDomain1_{uuid.uuid4()}" + domain2_name = f"TestDomain2_{uuid.uuid4()}" + + domain1 = ProjectDomain.create(client, name=domain1_name) + domain2 = ProjectDomain.create(client, name=domain2_name) + + # Add a small delay to allow domains to be searchable + import time + + time.sleep(0.5) + + try: + # Add domains using set_domains method + alignerr_project = ( + client.alignerr_workspace.project_builder() + .set_name("TestAlignerrProject3") + .set_media_type(MediaType.Image) + .set_domains([domain1_name, domain2_name]) + .create(skip_validation=True) + ) + assert alignerr_project is not None + assert alignerr_project.project.name == "TestAlignerrProject3" + + # Count domains by iterating through the collection + domain_count = sum(1 for _ in alignerr_project.domains()) + assert domain_count == 2 + alignerr_project.project.delete() + finally: + # Cleanup domains + try: + domain1.deactivate() + domain2.deactivate() + except Exception: + pass diff --git a/libs/labelbox/tests/integration/test_alignerr_project_factory.py b/libs/labelbox/tests/integration/test_alignerr_project_factory.py new file mode 100644 index 000000000..43b5ff1b9 --- /dev/null +++ b/libs/labelbox/tests/integration/test_alignerr_project_factory.py @@ -0,0 +1,128 @@ +"""Integration tests for AlignerrProjectFactory functionality.""" + +import tempfile +import os +import yaml + +import pytest + +from labelbox import Client +from labelbox.alignerr.alignerr_project_factory import AlignerrProjectFactory +from labelbox.schema.media_type import MediaType + + +def test_create_alignerr_project_from_yaml_basic(client: Client): + """Test creating an AlignerrProject from a basic YAML configuration.""" + config = {"name": "TestFactoryProject", "media_type": "IMAGE"} + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + alignerr_project = factory.create(yaml_file_path, skip_validation=True) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestFactoryProject" + assert alignerr_project.project.media_type == MediaType.Image + + alignerr_project.project.delete() + finally: + os.unlink(yaml_file_path) + + +def test_create_alignerr_project_from_yaml_with_rates(client: Client): + """Test creating an AlignerrProject from YAML with rate configurations.""" + config = { + "name": "TestFactoryProjectWithRates", + "media_type": "IMAGE", + "rates": { + "labeler": { + "rate": 0.50, + "billing_mode": "BY_TASK", + "effective_since": "2024-01-01T00:00:00", + "effective_until": "2024-12-31T23:59:59", + }, + "reviewer": { + "rate": 0.75, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + }, + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + alignerr_project = factory.create(yaml_file_path, skip_validation=True) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestFactoryProjectWithRates" + assert alignerr_project.project.media_type == MediaType.Image + + # Verify rates were set by checking project rates + project_rate = alignerr_project.get_project_rate() + assert project_rate is not None + + alignerr_project.project.delete() + finally: + os.unlink(yaml_file_path) + + +def test_create_alignerr_project_from_yaml_validation_error(client: Client): + """Test that validation errors are raised for incomplete configurations.""" + config = { + "name": "TestProject" + # Missing media_type + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + + with pytest.raises( + ValueError, match="Required field 'media_type' is missing" + ): + factory.create(yaml_file_path) + finally: + os.unlink(yaml_file_path) + + +def test_create_alignerr_project_from_yaml_invalid_media_type(client: Client): + """Test that invalid media types raise appropriate errors.""" + config = {"name": "TestProject", "media_type": "INVALID_MEDIA_TYPE"} + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + + with pytest.raises(ValueError, match="Invalid media_type"): + factory.create(yaml_file_path, skip_validation=True) + finally: + os.unlink(yaml_file_path) + + +def test_create_alignerr_project_from_yaml_file_not_found(client: Client): + """Test that missing YAML files raise appropriate errors.""" + factory = AlignerrProjectFactory(client) + + with pytest.raises(FileNotFoundError, match="YAML file not found"): + factory.create("nonexistent_file.yaml") diff --git a/libs/labelbox/tests/integration/test_project_domain.py b/libs/labelbox/tests/integration/test_project_domain.py new file mode 100644 index 000000000..e45ef339f --- /dev/null +++ b/libs/labelbox/tests/integration/test_project_domain.py @@ -0,0 +1,165 @@ +"""Integration tests for ProjectDomain functionality. + +These tests interact with the actual Labelbox API to verify ProjectDomain operations. +""" + +import pytest +import uuid + +from labelbox.alignerr.schema.project_domain import ProjectDomain +from labelbox.schema.media_type import MediaType + + +@pytest.fixture +def test_project(client): + """Create a test project for domain testing.""" + project_name = f"Test Project Domain {uuid.uuid4()}" + project = client.create_project( + name=project_name, + media_type=MediaType.Image + ) + + yield project + + # Cleanup + try: + project.delete() + except Exception: + pass # Project may already be deleted + + +@pytest.fixture +def test_domains(client): + """Create test domains for testing.""" + domains = [] + + # Create multiple test domains + for i in range(3): + domain_name = f"Test Domain {i+1} {uuid.uuid4()}" + domain = ProjectDomain.create(client, name=domain_name) + domains.append(domain) + + yield domains + + # Cleanup - deactivate domains + for domain in domains: + try: + domain.deactivate() + except Exception: + pass # Domain may already be deactivated + + +def test_create_project_domain(client): + """Test creating a new project domain.""" + domain_name = f"Test Create Domain {uuid.uuid4()}" + + # Create domain + domain = ProjectDomain.create(client, name=domain_name) + + assert domain is not None + assert domain.name == domain_name + assert domain.id is not None + assert domain.createdAt is not None + # Cleanup + try: + domain.deactivate() + except Exception: + pass + + +def test_activate_project_domain(client, test_domains): + """Test activating a project domain.""" + domain = test_domains[0] + + # Initially, domain should be active (created domains are active by default) + assert domain.deactivatedAt is None + + # Deactivate first + deactivated_domain = domain.deactivate() + assert deactivated_domain.deactivatedAt is not None + + # Then activate + activated_domain = deactivated_domain.activate() + assert activated_domain.deactivatedAt is None + assert activated_domain.id == domain.id + + +def test_deactivate_project_domain(client, test_domains): + """Test deactivating a project domain.""" + domain = test_domains[0] + + # Initially, domain should be active + assert domain.deactivatedAt is None + + # Deactivate + deactivated_domain = domain.deactivate() + assert deactivated_domain.deactivatedAt is not None + assert deactivated_domain.id == domain.id + + +def test_connect_project_to_domains(client, test_project, test_domains): + """Test connecting a project to multiple domains.""" + domain_ids = [domain.id for domain in test_domains] + + # Connect project to domains + result = ProjectDomain.connect_project_to_domains( + client, + project_id=test_project.uid, + domain_ids=domain_ids + ) + + assert result is True + + +def test_search_project_domains(client, test_domains): + """Test searching project domains with various filters.""" + # Test 1: Search without filters - should return all domains + results = ProjectDomain.search(client) + assert results is not None + domain_list = list(results) + assert isinstance(domain_list, list) + # Should find at least our test domains + assert len(domain_list) >= len(test_domains) + + # Test 2: Search by specific name - should find exact match + target_domain = test_domains[0] + search_results = ProjectDomain.search(client, search_by_name=target_domain.name) + found_domains = list(search_results) + assert len(found_domains) >= 1 + assert any(domain.name == target_domain.name for domain in found_domains) + + # Test 3: Search by partial name - should find matches + partial_name = "Test Domain" + partial_results = ProjectDomain.search(client, search_by_name=partial_name) + partial_domains = list(partial_results) + assert len(partial_domains) >= len(test_domains) + assert all("Test Domain" in domain.name for domain in partial_domains) + + # Test 4: Search for non-existent domain - should return empty + non_existent_results = ProjectDomain.search(client, search_by_name="NonExistentDomain12345") + non_existent_domains = list(non_existent_results) + assert len(non_existent_domains) == 0 + + # Test 5: Search with pagination parameters + # Note: PaginatedCollection automatically fetches all pages, so limit only affects individual page size + paginated_results = ProjectDomain.search(client, limit=2, offset=0) + paginated_domains = list(paginated_results) + # Should still find all domains since PaginatedCollection fetches all pages + assert len(paginated_domains) >= len(test_domains) + + # Test 6: Search with include_archived parameter + archived_results = ProjectDomain.search(client, include_archived=True) + archived_domains = list(archived_results) + assert isinstance(archived_domains, list) + + # Test 7: Verify domain properties in search results + if found_domains: + domain = found_domains[0] + assert hasattr(domain, 'id') + assert hasattr(domain, 'name') + assert hasattr(domain, 'createdAt') + assert hasattr(domain, 'updatedAt') + assert hasattr(domain, 'deactivatedAt') + assert hasattr(domain, 'ratingsCount') + assert domain.id is not None + assert domain.name is not None diff --git a/requirements-dev.lock b/requirements-dev.lock index 4dceb50ea..0081696f1 100644 --- a/requirements-dev.lock +++ b/requirements-dev.lock @@ -6,6 +6,8 @@ # features: [] # all-features: true # with-sources: false +# generate-hashes: false +# universal: false -e file:libs/labelbox -e file:libs/lbox-clients @@ -219,6 +221,8 @@ python-dateutil==2.9.0.post0 # via pandas pytz==2024.1 # via pandas +pyyaml==6.0.3 + # via labelbox pyzmq==26.0.3 # via jupyter-client referencing==0.35.1 diff --git a/requirements.lock b/requirements.lock index bc7d7303e..65ed91f9f 100644 --- a/requirements.lock +++ b/requirements.lock @@ -6,6 +6,8 @@ # features: [] # all-features: true # with-sources: false +# generate-hashes: false +# universal: false -e 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139 +++++++++++++ .../labelbox/alignerr/schema/project_rate.py | 8 +- .../integration/test_alignerr_project.py | 19 +- .../test_alignerr_project_builder.py | 95 +++++++++ .../test_alignerr_project_factory.py | 5 +- .../test_enhanced_resource_tags.py | 186 ++++++++++++++++++ .../tests/integration/test_project_rate.py | 148 ++++++++++++++ 9 files changed, 706 insertions(+), 21 deletions(-) create mode 100644 libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py create mode 100644 libs/labelbox/tests/integration/test_enhanced_resource_tags.py create mode 100644 libs/labelbox/tests/integration/test_project_rate.py diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py index e738de7ff..0501e83a5 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py @@ -5,6 +5,7 @@ from labelbox.alignerr.schema.project_rate import ProjectRateV2 from labelbox.alignerr.schema.project_domain import ProjectDomain +from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType from labelbox.pagination import PaginatedCollection logger = logging.getLogger(__name__) @@ -43,11 +44,6 @@ def project(self, project: "Project"): self._project = project def domains(self) -> PaginatedCollection: - """Get all domains associated with this project. - - Returns: - PaginatedCollection of ProjectDomain instances - """ return ProjectDomain.get_by_project_id( client=self.client, project_id=self.project.uid ) @@ -59,7 +55,7 @@ def add_domain(self, project_domain: ProjectDomain): domain_ids=[project_domain.uid], ) - def get_project_rate(self) -> Optional["ProjectRateV2"]: + def get_project_rates(self) -> list["ProjectRateV2"]: return ProjectRateV2.get_by_project_id( client=self.client, project_id=self.project.uid ) @@ -71,6 +67,71 @@ def set_project_rate(self, project_rate_input): project_rate_input=project_rate_input, ) + def set_tags(self, tag_names: list[str], tag_type: ResourceTagType): + # Convert tag names to tag IDs + tag_ids = [] + for tag_name in tag_names: + # Search for the tag by text to get its ID + found_tags = EnhancedResourceTag.search_by_text(self.client, search_text=tag_name, tag_type=tag_type) + if found_tags: + tag_ids.append(found_tags[0].id) + + # Use the existing project resource tag functionality with IDs + self.project.update_project_resource_tags(tag_ids) + return self + + def get_tags(self) -> list[EnhancedResourceTag]: + """Get enhanced resource tags associated with this project. + + Returns: + List of EnhancedResourceTag instances + """ + # Get project resource tags and convert to EnhancedResourceTag instances + project_resource_tags = self.project.get_resource_tags() + enhanced_tags = [] + for tag in project_resource_tags: + # Search for the corresponding EnhancedResourceTag by text (try different types) + found_tags = [] + for tag_type in [ResourceTagType.Default, ResourceTagType.Billing]: + found_tags = EnhancedResourceTag.search_by_text(self.client, search_text=tag.text, tag_type=tag_type) + if found_tags: + break + if found_tags: + enhanced_tags.extend(found_tags) + return enhanced_tags + + def add_tag(self, tag: EnhancedResourceTag): + """Add a single enhanced resource tag to the project. + + Args: + tag: EnhancedResourceTag instance to add + + Returns: + Self for method chaining + """ + current_tags = self.get_tags() + current_tag_names = [t.text for t in current_tags] + + if tag.text not in current_tag_names: + current_tag_names.append(tag.text) + self.set_tags(current_tag_names) + + return self + + def remove_tag(self, tag: EnhancedResourceTag): + """Remove a single enhanced resource tag from the project. + + Args: + tag: EnhancedResourceTag instance to remove + + Returns: + Self for method chaining + """ + current_tags = self.get_tags() + current_tag_names = [t.text for t in current_tags if t.uid != tag.uid] + self.set_tags(current_tag_names) + return self + class AlignerrWorkspace: def __init__(self, client: "Client"): diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py index 2c10815a5..42ace0178 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py @@ -4,8 +4,8 @@ from labelbox.alignerr.schema.project_rate import BillingMode from labelbox.alignerr.schema.project_rate import ProjectRateInput -from labelbox.alignerr.schema.project_rate import ProjectRateV2 from labelbox.alignerr.schema.project_domain import ProjectDomain +from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType from labelbox.schema.media_type import MediaType logger = logging.getLogger(__name__) @@ -22,6 +22,7 @@ def __init__(self, client: "Client"): self._alignerr_rates: dict[str, ProjectRateInput] = {} self._customer_rate: ProjectRateInput = None self._domains: list[ProjectDomain] = [] + self._enhanced_resource_tags: list[EnhancedResourceTag] = [] self.role_name_to_id = self._get_role_name_to_id() def set_name(self, name: str): @@ -110,6 +111,37 @@ def set_domains(self, domains: list[str]): self._domains.append(domain_result) return self + def set_tags(self, tag_texts: list[str], tag_type: ResourceTagType): + """Set enhanced resource tags for the project. + + Args: + tag_texts: List of tag text values to search for and attach + tag_type: Type filter for searching tags + + Returns: + Self for method chaining + """ + for tag_text in tag_texts: + # Search for existing tags by text + existing_tags = EnhancedResourceTag.search_by_text( + self.client, search_text=tag_text, tag_type=tag_type + ) + + if existing_tags: + # Use the first matching tag + self._enhanced_resource_tags.append(existing_tags[0]) + else: + # Create new tag if not found + new_tag = EnhancedResourceTag.create( + self.client, + text=tag_text, + color="#007bff", # Default blue color + tag_type=tag_type + ) + self._enhanced_resource_tags.append(new_tag) + return self + + def create(self, skip_validation: bool = False): if not skip_validation: self._validate() @@ -130,6 +162,7 @@ def create(self, skip_validation: bool = False): self._create_rates(alignerr_project) self._create_domains(alignerr_project) + self._create_resource_tags(alignerr_project) return alignerr_project @@ -150,6 +183,25 @@ def _create_domains(self, alignerr_project: "AlignerrProject"): domain_ids=domain_ids, ) + def _create_resource_tags(self, alignerr_project: "AlignerrProject"): + if self._enhanced_resource_tags: + logger.info( + f"Setting enhanced resource tags: {[tag.text for tag in self._enhanced_resource_tags]}" + ) + # Group tags by type and set them accordingly + tags_by_type = {} + for tag in self._enhanced_resource_tags: + tag_type = tag.type + if tag_type not in tags_by_type: + tags_by_type[tag_type] = [] + tags_by_type[tag_type].append(tag.text) + + # Set tags for each type + for tag_type_str, tag_names in tags_by_type.items(): + # Convert string back to enum + tag_type_enum = ResourceTagType(tag_type_str) + alignerr_project.set_tags(tag_names, tag_type_enum) + def _validate_alignerr_rates(self): # Import here to avoid circular imports from labelbox.alignerr.alignerr_project import AlignerrRole diff --git a/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py b/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py new file mode 100644 index 000000000..a1f448a96 --- /dev/null +++ b/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py @@ -0,0 +1,139 @@ +from enum import Enum +from typing import List, Optional +from labelbox.orm.db_object import DbObject, Updateable +from labelbox.orm.model import Field +from pydantic import BaseModel + + +class ResourceTagType(Enum): + """Enum for resource tag types.""" + Default = "Default" + System = "System" + Request = "Request" + Migration = "Migration" + Billing = "Billing" + + +class CreateResourceTagInput(BaseModel): + """Input for creating a new resource tag.""" + + text: str + color: str + type: Optional[str] = None + + +class UpdateResourceTagInput(BaseModel): + """Input for updating a resource tag.""" + + id: str + text: str + color: str + type: Optional[str] = None + + +class DeleteResourceTagInput(BaseModel): + """Input for deleting a resource tag.""" + + id: str + type: Optional[str] = None + + +class ResourceTagsInput(BaseModel): + """Input for querying resource tags.""" + + type: str + + +class EnhancedResourceTag(DbObject, Updateable): + """Enhanced resource tag with additional functionality and type support.""" + + # Fields matching the DDL schema + id = Field.String("id") + createdAt = Field.DateTime("createdAt") + updatedAt = Field.DateTime("updatedAt") + organizationId = Field.String("organizationId") + text = Field.String("text") + color = Field.String("color") + createdById = Field.String("createdById") + type = Field.String("type") + + @classmethod + def create( + cls, client, text: str, color: str, tag_type: Optional[ResourceTagType] = None + ) -> "EnhancedResourceTag": + """Create a new enhanced resource tag. + + Args: + client: Labelbox client instance + text: Text content of the resource tag + color: Color of the resource tag + tag_type: Optional type of the resource tag + + Returns: + Created EnhancedResourceTag instance + """ + # Use the existing organization create_resource_tag method + # Get the organization + org = client.get_organization() + + # Create the tag using existing API + tag_data = {"text": text, "color": color} + created_tag = org.create_resource_tag(tag_data) + + # Create EnhancedResourceTag with the same data plus defaults for missing fields + enhanced_tag = cls(client, { + "id": created_tag.uid, + "text": created_tag.text, + "color": created_tag.color, + "createdAt": None, + "updatedAt": None, + "organizationId": None, + "createdById": None, + "type": tag_type.value if tag_type else None + }) + + return enhanced_tag + + + + @classmethod + def search_by_text( + cls, client, search_text: str, tag_type: ResourceTagType + ) -> List["EnhancedResourceTag"]: + """Search resource tags by text content. + + Args: + client: Labelbox client instance + search_text: Text to search for + tag_type: Type filter + + Returns: + List of matching EnhancedResourceTag instances + """ + # Use the existing organization get_resource_tags method + # Get the organization + org = client.get_organization() + + # Get all resource tags + regular_tags = org.get_resource_tags() + + # Convert to EnhancedResourceTag instances and filter by search text and type + matching_tags = [] + for tag in regular_tags: + if search_text.lower() in tag.text.lower(): + enhanced_tag = cls(client, { + "id": tag.uid, + "text": tag.text, + "color": tag.color, + "createdAt": None, + "updatedAt": None, + "organizationId": None, + "createdById": None, + "type": tag_type.value + }) + + # Apply type filter + if enhanced_tag.type == tag_type.value: + matching_tags.append(enhanced_tag) + + return matching_tags diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py b/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py index 46223298a..22ecaa7e5 100644 --- a/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py +++ b/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py @@ -53,7 +53,7 @@ class ProjectRateV2(DbObject, Deletable): effectiveUntil = Field.DateTime("effectiveUntil") @classmethod - def get_by_project_id(cls, client, project_id: str) -> "ProjectRateV2": + def get_by_project_id(cls, client, project_id: str) -> list["ProjectRateV2"]: query_str = """ query GetAllProjectRatesPyApi($projectId: ID!) { project(where: { id: $projectId }) { @@ -84,10 +84,10 @@ def get_by_project_id(cls, client, project_id: str) -> "ProjectRateV2": rates_data = result["project"]["ratesV2"] if not rates_data: - return None + return [] - # Return the first rate as a ProjectRateV2 object - return cls(client, rates_data[0]) + # Return all rates as ProjectRateV2 objects + return [cls(client, rate_data) for rate_data in rates_data] @classmethod def set_project_rate( diff --git a/libs/labelbox/tests/integration/test_alignerr_project.py b/libs/labelbox/tests/integration/test_alignerr_project.py index decb37a8f..cd2513c3d 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project.py +++ b/libs/labelbox/tests/integration/test_alignerr_project.py @@ -73,16 +73,16 @@ def test_alignerr_project_domains(client, test_alignerr_project): # The collection might be empty for a new project, which is expected -def test_alignerr_project_get_project_rate_no_rates( +def test_alignerr_project_get_project_rates_no_rates( client, test_alignerr_project ): - """Test get_project_rate() when no rates are set.""" - # For a new project without rates, this should return None - project_rate = test_alignerr_project.get_project_rate() - assert project_rate is None + """Test get_project_rates() when no rates are set.""" + # For a new project without rates, this should return an empty list + project_rates = test_alignerr_project.get_project_rates() + assert project_rates == [] -def test_alignerr_project_set_and_get_project_rate( +def test_alignerr_project_set_and_get_project_rates( client, test_alignerr_project ): """Test setting and getting project rates.""" @@ -100,7 +100,10 @@ def test_alignerr_project_set_and_get_project_rate( result = test_alignerr_project.set_project_rate(project_rate_input) assert result is True # Should return success status - # Get the project rate back - project_rate = test_alignerr_project.get_project_rate() + # Get the project rates back + project_rates = test_alignerr_project.get_project_rates() + # Should return a list with at least one rate + assert isinstance(project_rates, list) + assert len(project_rates) >= 1 # Note: The actual rate retrieval might depend on the API implementation # This test verifies the method calls work without errors diff --git a/libs/labelbox/tests/integration/test_alignerr_project_builder.py b/libs/labelbox/tests/integration/test_alignerr_project_builder.py index 76daa4ed3..3b9e3f918 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project_builder.py +++ b/libs/labelbox/tests/integration/test_alignerr_project_builder.py @@ -107,3 +107,98 @@ def test_create_alignerr_project_using_builder_add_domains(client: Client): domain2.deactivate() except Exception: pass + + +def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Client): + """Test creating an Alignerr project with rates, domains, and enhanced resource tags.""" + from labelbox.alignerr.schema.project_domain import ProjectDomain + from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType + import uuid + import time + + # Create test domains first + domain1_name = f"TestDomain1_{uuid.uuid4()}" + domain2_name = f"TestDomain2_{uuid.uuid4()}" + + domain1 = ProjectDomain.create(client, name=domain1_name) + domain2 = ProjectDomain.create(client, name=domain2_name) + + # Create test resource tags + tag1_text = f"TestTag1_{uuid.uuid4().hex[:8]}" + tag2_text = f"TestTag2_{uuid.uuid4().hex[:8]}" + + tag1 = EnhancedResourceTag.create( + client, + text=tag1_text, + color="#FF5733", + tag_type=ResourceTagType.Default + ) + tag2 = EnhancedResourceTag.create( + client, + text=tag2_text, + color="#33FF57", + tag_type=ResourceTagType.Billing + ) + + # Add a small delay to allow domains to be searchable + time.sleep(0.5) + + try: + # Create project with rates, domains, and resource tags + alignerr_project = ( + client.alignerr_workspace.project_builder() + .set_name("TestAlignerrProjectWithAll") + .set_media_type(MediaType.Image) + .set_alignerr_role_rate( + role_name=AlignerrRole.Labeler, + rate=12.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .set_alignerr_role_rate( + role_name=AlignerrRole.Reviewer, + rate=15.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .set_customer_rate( + rate=20.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .set_domains([domain1_name, domain2_name]) + .set_tags([tag1_text, tag2_text], ResourceTagType.Default) + .create() + ) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestAlignerrProjectWithAll" + + # Verify domains were added + domain_count = sum(1 for _ in alignerr_project.domains()) + assert domain_count == 2 + + # Verify resource tags were added + enhanced_tags = alignerr_project.get_tags() + assert len(enhanced_tags) >= 2 + + # Check that our specific tags are present + tag_texts = [tag.text for tag in enhanced_tags] + assert tag1_text in tag_texts + assert tag2_text in tag_texts + + alignerr_project.project.delete() + finally: + # Cleanup domains + try: + domain1.deactivate() + domain2.deactivate() + except Exception: + pass + + # Cleanup resource tags + try: + tag1.delete() + tag2.delete() + except Exception: + pass diff --git a/libs/labelbox/tests/integration/test_alignerr_project_factory.py b/libs/labelbox/tests/integration/test_alignerr_project_factory.py index 43b5ff1b9..82dd1a4d9 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project_factory.py +++ b/libs/labelbox/tests/integration/test_alignerr_project_factory.py @@ -69,8 +69,9 @@ def test_create_alignerr_project_from_yaml_with_rates(client: Client): assert alignerr_project.project.media_type == MediaType.Image # Verify rates were set by checking project rates - project_rate = alignerr_project.get_project_rate() - assert project_rate is not None + project_rates = alignerr_project.get_project_rates() + assert isinstance(project_rates, list) + assert len(project_rates) >= 1 alignerr_project.project.delete() finally: diff --git a/libs/labelbox/tests/integration/test_enhanced_resource_tags.py b/libs/labelbox/tests/integration/test_enhanced_resource_tags.py new file mode 100644 index 000000000..6d2981760 --- /dev/null +++ b/libs/labelbox/tests/integration/test_enhanced_resource_tags.py @@ -0,0 +1,186 @@ +"""Integration tests for EnhancedResourceTag functionality. + +These tests interact with the actual Labelbox API to verify EnhancedResourceTag operations. +""" + +import pytest +import uuid + +from labelbox.alignerr.schema.enchanced_resource_tags import ( + EnhancedResourceTag, + ResourceTagType, +) + + +@pytest.fixture +def test_resource_tags(client): + """Create test resource tags for testing.""" + tags = [] + + # Create multiple test tags with different types + for i, tag_type in enumerate([ResourceTagType.Default, ResourceTagType.Billing]): + tag_text = f"Test_Tag_{i+1}_{uuid.uuid4().hex[:8]}" + tag_color = f"#{i:06x}" # Generate different colors + tag = EnhancedResourceTag.create( + client, + text=tag_text, + color=tag_color, + tag_type=tag_type + ) + tags.append(tag) + + yield tags + + # Cleanup - delete tags + for tag in tags: + try: + tag.delete() + except Exception: + pass # Tag may already be deleted + + +def test_create_enhanced_resource_tag(client): + """Test creating a new enhanced resource tag.""" + tag_text = f"Test_Create_Tag_{uuid.uuid4().hex[:8]}" + tag_color = "#FF5733" + + # Create tag + tag = EnhancedResourceTag.create( + client, + text=tag_text, + color=tag_color, + tag_type=ResourceTagType.Default + ) + + assert tag is not None + assert tag.text == tag_text + assert tag.color == tag_color.lstrip('#') # API returns color without # + assert tag.type == ResourceTagType.Default.value + assert tag.id is not None + # Note: createdAt and organizationId are not available in current API + # assert tag.createdAt is not None + # assert tag.organizationId is not None + + # Cleanup + try: + tag.delete() + except Exception: + pass + + +def test_create_enhanced_resource_tag_without_type(client): + """Test creating a resource tag without specifying type.""" + tag_text = f"Test_Create_Tag_No_Type_{uuid.uuid4().hex[:8]}" + tag_color = "#33FF57" + + # Create tag without type + tag = EnhancedResourceTag.create( + client, + text=tag_text, + color=tag_color + ) + + assert tag is not None + assert tag.text == tag_text + assert tag.color == tag_color.lstrip('#') # API returns color without # + assert tag.id is not None + # Note: createdAt is not available in current API + # assert tag.createdAt is not None + + # Cleanup + try: + tag.delete() + except Exception: + pass + + + + + + +def test_search_by_text(client, test_resource_tags): + """Test searching resource tags by text content.""" + # Test 1: Search for exact text match + target_tag = test_resource_tags[0] + search_results = EnhancedResourceTag.search_by_text( + client, search_text=target_tag.text, tag_type=ResourceTagType.Default + ) + assert isinstance(search_results, list) + assert len(search_results) >= 1 + assert any(tag.text == target_tag.text for tag in search_results) + + # Test 2: Search for partial text match + partial_text = "Test_Tag" + partial_results = EnhancedResourceTag.search_by_text( + client, search_text=partial_text, tag_type=ResourceTagType.Default + ) + assert isinstance(partial_results, list) + assert len(partial_results) >= 1 # At least one Default type tag + assert all(partial_text in tag.text for tag in partial_results) + + # Test 3: Search with type filter + type_filtered_results = EnhancedResourceTag.search_by_text( + client, + search_text="Test_Tag", + tag_type=ResourceTagType.Default + ) + assert isinstance(type_filtered_results, list) + # All results should contain the search text and match the type + for tag in type_filtered_results: + assert "Test_Tag" in tag.text + assert tag.type == ResourceTagType.Default.value + + # Test 4: Search for non-existent text + non_existent_results = EnhancedResourceTag.search_by_text( + client, search_text="NonExistentTag12345", tag_type=ResourceTagType.Default + ) + assert isinstance(non_existent_results, list) + assert len(non_existent_results) == 0 + + +def test_resource_tag_types_enum(client): + """Test that all resource tag types are properly defined.""" + # Test creating tags with each supported type + supported_types = [ResourceTagType.Default, ResourceTagType.Billing] + for tag_type in supported_types: + tag_text = f"Test_{tag_type.value}_Tag_{uuid.uuid4().hex[:8]}" + tag_color = "#123456" + + tag = EnhancedResourceTag.create( + client, + text=tag_text, + color=tag_color, + tag_type=tag_type + ) + + assert tag.type == tag_type.value + + # Cleanup + try: + tag.delete() + except Exception: + pass + + +def test_enhanced_resource_tag_properties(client, test_resource_tags): + """Test that enhanced resource tags have all expected properties.""" + tag = test_resource_tags[0] + + # Test all expected properties exist + expected_properties = [ + 'id', 'createdAt', 'updatedAt', 'organizationId', 'text', + 'color', 'createdById', 'type' + ] + + for prop in expected_properties: + assert hasattr(tag, prop), f"Tag missing property: {prop}" + + # Test that required properties are not None + assert tag.id is not None + assert tag.text is not None + assert tag.color is not None + # Note: Some properties are not available in current API + # assert tag.createdAt is not None + # assert tag.organizationId is not None + + diff --git a/libs/labelbox/tests/integration/test_project_rate.py b/libs/labelbox/tests/integration/test_project_rate.py new file mode 100644 index 000000000..285ef04f5 --- /dev/null +++ b/libs/labelbox/tests/integration/test_project_rate.py @@ -0,0 +1,148 @@ +"""Integration tests for ProjectRateV2 functionality.""" + +import datetime +import uuid + +import pytest +from labelbox.alignerr.schema.project_rate import ( + BillingMode, + ProjectRateInput, + ProjectRateV2, +) +from labelbox.schema.media_type import MediaType + + +@pytest.fixture +def test_project(client): + """Create a test project for ProjectRateV2 testing.""" + project_name = f"Test ProjectRateV2 {uuid.uuid4()}" + project = client.create_project( + name=project_name, media_type=MediaType.Image + ) + + yield project + + # Cleanup + try: + project.delete() + except Exception: + pass # Project may already be deleted + + +def test_project_rate_input_validation(): + """Test ProjectRateInput validation logic.""" + # Test negative rate validation + with pytest.raises(ValueError, match="Rate must be greater than or equal to 0"): + ProjectRateInput( + rateForId="", + isBillRate=True, + billingMode=BillingMode.BY_HOUR, + rate=-10.0, + effectiveSince=datetime.datetime.now().isoformat(), + ) + + # Test isBillRate=True with non-empty rateForId + with pytest.raises( + ValueError, + match="isBillRate indicates that this is a customer bill rate. rateForId must be empty if isBillRate is true" + ): + ProjectRateInput( + rateForId="some-id", + isBillRate=True, + billingMode=BillingMode.BY_HOUR, + rate=25.0, + effectiveSince=datetime.datetime.now().isoformat(), + ) + + +def test_get_by_project_id_no_rates(client, test_project): + """Test get_by_project_id when no rates are set.""" + rates = ProjectRateV2.get_by_project_id(client, test_project.uid) + assert rates == [] + + +def test_set_and_get_project_rate_customer(client, test_project): + """Test setting and getting a customer project rate.""" + # Create customer rate input + rate_input = ProjectRateInput( + rateForId="", # Empty string for customer rate + isBillRate=True, + billingMode=BillingMode.BY_HOUR, + rate=25.0, + effectiveSince=datetime.datetime.now().isoformat(), + ) + + # Set the project rate + result = ProjectRateV2.set_project_rate( + client, test_project.uid, rate_input + ) + assert result is True + + # Get the project rates back + rates = ProjectRateV2.get_by_project_id(client, test_project.uid) + assert isinstance(rates, list) + assert len(rates) >= 1 + + # Find the customer rate + customer_rate = None + for rate in rates: + if rate.isBillRate: + customer_rate = rate + break + + assert customer_rate is not None + assert customer_rate.isBillRate is True + assert customer_rate.billingMode == BillingMode.BY_HOUR + assert customer_rate.rate == 25.0 + + +def test_multiple_project_rates(client, test_project): + """Test setting multiple project rates for the same project.""" + # Set customer rate + customer_rate_input = ProjectRateInput( + rateForId="", + isBillRate=True, + billingMode=BillingMode.BY_HOUR, + rate=30.0, + effectiveSince=datetime.datetime.now().isoformat(), + ) + + result1 = ProjectRateV2.set_project_rate( + client, test_project.uid, customer_rate_input + ) + assert result1 is True + + # Get available roles for role rate + roles = client.get_roles() + role_id = None + for role in roles.values(): + if role.name == "REVIEWER": + role_id = role.uid + break + + if role_id: + # Set role rate + role_rate_input = ProjectRateInput( + rateForId=role_id, + isBillRate=False, + billingMode=BillingMode.BY_TASK, + rate=1.25, + effectiveSince=datetime.datetime.now().isoformat(), + ) + + result2 = ProjectRateV2.set_project_rate( + client, test_project.uid, role_rate_input + ) + assert result2 is True + + # Get all project rates + rates = ProjectRateV2.get_by_project_id(client, test_project.uid) + assert isinstance(rates, list) + assert len(rates) >= 2 + + # Verify we have both customer and role rates + customer_rates = [r for r in rates if r.isBillRate] + role_rates = [r for r in rates if not r.isBillRate] + + assert len(customer_rates) >= 1 + assert len(role_rates) >= 1 From 42ca9954c8a07c7c37a7db8b5f192c62c1387812 Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan Date: Thu, 9 Oct 2025 11:16:57 -0700 Subject: [PATCH 059/103] Set project owner during project creation --- .../src/labelbox/alignerr/alignerr_project.py | 12 + .../alignerr/alignerr_project_builder.py | 96 ++++++- .../schema/project_boost_workforce.py | 240 ++++++++++++++++++ .../test_alignerr_project_builder.py | 93 +++++++ 4 files changed, 439 insertions(+), 2 deletions(-) create mode 100644 libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py index 0501e83a5..3c2873b1f 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py @@ -6,6 +6,7 @@ from labelbox.alignerr.schema.project_rate import ProjectRateV2 from labelbox.alignerr.schema.project_domain import ProjectDomain from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType +from labelbox.alignerr.schema.project_boost_workforce import ProjectBoostWorkforce from labelbox.pagination import PaginatedCollection logger = logging.getLogger(__name__) @@ -132,6 +133,17 @@ def remove_tag(self, tag: EnhancedResourceTag): self.set_tags(current_tag_names) return self + def get_project_owner(self) -> Optional[ProjectBoostWorkforce]: + """Get the ProjectBoostWorkforce for this project. + + Returns: + ProjectBoostWorkforce instance or None if not found + """ + return ProjectBoostWorkforce.get_by_project_id( + client=self.client, + project_id=self.project.uid + ) + class AlignerrWorkspace: def __init__(self, client: "Client"): diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py index 42ace0178..7219a3445 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py @@ -1,16 +1,25 @@ import datetime -from typing import TYPE_CHECKING, Optional +from enum import Enum +from typing import TYPE_CHECKING, Optional, Union, List import logging from labelbox.alignerr.schema.project_rate import BillingMode from labelbox.alignerr.schema.project_rate import ProjectRateInput from labelbox.alignerr.schema.project_domain import ProjectDomain from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType +from labelbox.alignerr.schema.project_boost_workforce import ProjectBoostWorkforce from labelbox.schema.media_type import MediaType logger = logging.getLogger(__name__) +class ValidationType(Enum): + """Enum for validation types that can be selectively skipped.""" + ALIGNERR_RATE = "AlignerrRate" + CUSTOMER_RATE = "CustomerRate" + PROJECT_OWNER = "ProjectOwner" + + if TYPE_CHECKING: from labelbox import Client from labelbox.alignerr.alignerr_project import AlignerrProject, AlignerrRole @@ -23,6 +32,7 @@ def __init__(self, client: "Client"): self._customer_rate: ProjectRateInput = None self._domains: list[ProjectDomain] = [] self._enhanced_resource_tags: list[EnhancedResourceTag] = [] + self._project_owner_email: Optional[str] = None self.role_name_to_id = self._get_role_name_to_id() def set_name(self, name: str): @@ -141,10 +151,24 @@ def set_tags(self, tag_texts: list[str], tag_type: ResourceTagType): self._enhanced_resource_tags.append(new_tag) return self + def set_project_owner(self, project_owner_email: str): + """Set the project owner for the ProjectBoostWorkforce. + + Args: + project_owner_email: Email of the user to set as project owner + + Returns: + Self for method chaining + """ + self._project_owner_email = project_owner_email + return self - def create(self, skip_validation: bool = False): + + def create(self, skip_validation: Union[bool, List[ValidationType]] = False): if not skip_validation: self._validate() + elif isinstance(skip_validation, list): + self._validate_selective(skip_validation) logger.info("Creating project") project_data = { @@ -163,6 +187,7 @@ def create(self, skip_validation: bool = False): self._create_rates(alignerr_project) self._create_domains(alignerr_project) self._create_resource_tags(alignerr_project) + self._create_project_owner(alignerr_project) return alignerr_project @@ -202,6 +227,22 @@ def _create_resource_tags(self, alignerr_project: "AlignerrProject"): tag_type_enum = ResourceTagType(tag_type_str) alignerr_project.set_tags(tag_names, tag_type_enum) + def _create_project_owner(self, alignerr_project: "AlignerrProject"): + if self._project_owner_email: + logger.info(f"Setting project owner: {self._project_owner_email}") + + # Find user by email in the organization + user_id = self._find_user_by_email(self._project_owner_email) + if not user_id: + current_org = self.client.get_organization() + raise ValueError(f"User with email {self._project_owner_email} not found in organization {current_org.uid}") + + ProjectBoostWorkforce.set_project_owner( + client=self.client, + project_id=alignerr_project.project.uid, + project_owner_user_id=user_id + ) + def _validate_alignerr_rates(self): # Import here to avoid circular imports from labelbox.alignerr.alignerr_project import AlignerrRole @@ -221,10 +262,61 @@ def _validate_customer_rate(self): if self._customer_rate is None: raise ValueError("Customer rate is not set") + def _validate_project_owner(self): + if self._project_owner_email is None: + raise ValueError("Project owner is not set") + def _validate(self): self._validate_alignerr_rates() self._validate_customer_rate() + self._validate_project_owner() + + def _validate_selective(self, skip_validations: List[ValidationType]): + """Run validations selectively, skipping those in the provided list. + + Args: + skip_validations: List of ValidationType enums to skip + """ + if ValidationType.ALIGNERR_RATE not in skip_validations: + self._validate_alignerr_rates() + + if ValidationType.CUSTOMER_RATE not in skip_validations: + self._validate_customer_rate() + + if ValidationType.PROJECT_OWNER not in skip_validations: + self._validate_project_owner() def _get_role_name_to_id(self) -> dict[str, str]: roles = self.client.get_roles() return {role.name: role.uid for role in roles.values()} + + def _find_user_by_email(self, email: str) -> Optional[str]: + """Find user ID by email in the organization. + + Args: + email: Email address to search for + + Returns: + User ID if found, None otherwise + """ + try: + # Import here to avoid circular imports + from labelbox.schema.user import User + + # Get the current organization + current_org = self.client.get_organization() + + # Use client.get_users with where clause to find user by email + users = self.client.get_users(where=User.email == email) + + # Get the first matching user and verify they belong to the same organization + user = next(users, None) + if user and user.organization().uid == current_org.uid: + return user.uid + else: + logger.warning(f"User with email {email} not found in organization {current_org.uid}") + return None + + except Exception as e: + logger.error(f"Error finding user by email {email}: {e}") + return None diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py b/libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py new file mode 100644 index 000000000..af486b420 --- /dev/null +++ b/libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py @@ -0,0 +1,240 @@ +from enum import Enum +from typing import Optional +from labelbox.orm.db_object import DbObject +from labelbox.orm.model import Relationship, Field +from pydantic import BaseModel + + +class ProjectBoostWorkforceStatus(Enum): + """Enum for ProjectBoostWorkforce status.""" + SET_UP = "SET_UP" + REQUESTED = "REQUESTED" + ACCEPTED = "ACCEPTED" + CALIBRATION = "CALIBRATION" + PRODUCTION = "PRODUCTION" + COMPLETE = "COMPLETE" + PAUSED = "PAUSED" + + +class ProjectBoostType(Enum): + """Enum for ProjectBoost type.""" + SELF_SERVE = "SELF_SERVE" + MANAGED = "MANAGED" + + +class ProjectDifficulty(Enum): + """Enum for project difficulty levels.""" + EASY = "easy" + MEDIUM = "medium" + HARD = "hard" + + +class BillingMode(Enum): + """Enum for billing modes.""" + BY_TASK = "BY_TASK" + BY_HOUR = "BY_HOUR" + BY_TASK_PER_TURN = "BY_TASK_PER_TURN" + + +class UpsertProjectBoostWorkforceInput(BaseModel): + """Input for upserting a ProjectBoostWorkforce.""" + projectId: str + + +class UpdateProjectBoostWorkforceStatusInput(BaseModel): + """Input for updating ProjectBoostWorkforce status.""" + projectId: str + status: ProjectBoostWorkforceStatus + + +class UpdateProjectBoostWorkforceCountryMultiplierInput(BaseModel): + """Input for updating country rate multipliers.""" + projectId: str + disabledCountryRateMultipliers: bool + + +class UpdateProjectBoostWorkforceBillingModeInput(BaseModel): + """Input for updating billing mode.""" + projectId: str + billingMode: BillingMode + customerBillingMode: Optional[BillingMode] = None + + +class ValidateAndRequestProjectBoostWorkforceInput(BaseModel): + """Input for validating and requesting ProjectBoostWorkforce.""" + projectId: str + + +class UpdateProjectBoostWorkforceInput(BaseModel): + """Input for updating ProjectBoostWorkforce.""" + projectId: str + status: Optional[ProjectBoostWorkforceStatus] = None + calibrationDatarows: Optional[int] = None + reworkThreshold: Optional[float] = None + jiraTicketUrl: Optional[str] = None + slackChannelUrl: Optional[str] = None + sampleVideoUrl: Optional[str] = None + projectDifficulty: Optional[ProjectDifficulty] = None + estimatedTimePerLabel: Optional[float] = None + projectDescription: Optional[str] = None + pilotStatus: Optional[bool] = None + discordLandingChannelId: Optional[str] = None + discordLabelerRoleId: Optional[str] = None + discordGuildId: Optional[str] = None + discordReviewerChannelId: Optional[str] = None + discordReviewerRoleId: Optional[str] = None + projectOwnerUserId: Optional[str] = None + + +class FindProjectBoostWorkforceInput(BaseModel): + """Input for finding ProjectBoostWorkforce.""" + projectId: str + + +class ProjectBoostWorkforceResult(BaseModel): + """Result model for ProjectBoostWorkforce operations.""" + success: bool + + +class ProjectBoostWorkforceStatusHistoryFields(BaseModel): + """Model for ProjectBoostWorkforce status history fields.""" + id: str + projectId: str + updatedAt: str + updatedById: str + status: ProjectBoostWorkforceStatus + + +class ProjectBoostWorkforce(DbObject): + """A ProjectBoostWorkforce represents workforce management for a project.""" + + # Relationships + projectOwner = Relationship.ToOne("User", False) + + # Fields matching the GraphQL schema + id = Field.String("id") + projectId = Field.String("projectId") + createdAt = Field.DateTime("createdAt") + updatedAt = Field.DateTime("updatedAt") + createdById = Field.String("createdById") + createdByEmail = Field.String("createdByEmail") + updatedById = Field.String("updatedById") + status = Field.Enum(ProjectBoostWorkforceStatus, "status") + calibrationDatarows = Field.Int("calibrationDatarows") + reworkThreshold = Field.Float("reworkThreshold") + jiraTicketUrl = Field.String("jiraTicketUrl") + slackChannelUrl = Field.String("slackChannelUrl") + pilotStatus = Field.Boolean("pilotStatus") + discourseCategoryUrl = Field.String("discourseCategoryUrl") + sampleVideoUrl = Field.String("sampleVideoUrl") + projectDifficulty = Field.Enum(ProjectDifficulty, "projectDifficulty") + projectDescription = Field.String("projectDescription") + estimatedTimePerLabel = Field.Float("estimatedTimePerLabel") + disabledCountryRateMultipliers = Field.Boolean("disabledCountryRateMultipliers") + billingMode = Field.Enum(BillingMode, "billingMode") + customerBillingMode = Field.Enum(BillingMode, "customerBillingMode") + type = Field.Enum(ProjectBoostType, "type") + isPaused = Field.Boolean("isPaused") + isAnnotatingPausedForUser = Field.Boolean("isAnnotatingPausedForUser") + isPayPerTaskEnabled = Field.Boolean("isPayPerTaskEnabled") + codeId = Field.String("codeId") + projectOwnerUserId = Field.String("projectOwnerUserId") + + @classmethod + def get_by_project_id(cls, client, project_id: str) -> Optional["ProjectBoostWorkforce"]: + """Get ProjectBoostWorkforce by project ID. + + Args: + client: Labelbox client instance + project_id: ID of the project + + Returns: + ProjectBoostWorkforce instance or None if not found + """ + input_data = FindProjectBoostWorkforceInput(projectId=project_id) + + query_str = """ + query GetProjectBoostWorkforcePyApi($data: FindProjectBoostWorkforceInput!) { + projectBoostWorkforce(data: $data) { + id + projectId + createdAt + updatedAt + createdById + createdByEmail + updatedById + status + calibrationDatarows + reworkThreshold + jiraTicketUrl + slackChannelUrl + pilotStatus + discourseCategoryUrl + sampleVideoUrl + projectDifficulty + projectDescription + estimatedTimePerLabel + disabledCountryRateMultipliers + billingMode + customerBillingMode + type + isPaused + isAnnotatingPausedForUser + isPayPerTaskEnabled + codeId + projectOwnerUserId + projectOwner { + id + email + name + } + } + }""" + + result = client.execute(query_str, {"data": input_data.model_dump()}) + workforce_data = result.get("projectBoostWorkforce") + + if not workforce_data: + return None + + return cls(client, workforce_data) + + @classmethod + def update(cls, client, update_input: UpdateProjectBoostWorkforceInput) -> ProjectBoostWorkforceResult: + """Update ProjectBoostWorkforce with various fields. + + Args: + client: Labelbox client instance + update_input: UpdateProjectBoostWorkforceInput with fields to update + + Returns: + ProjectBoostWorkforceResult indicating success + """ + mutation_str = """ + mutation UpdateProjectBoostWorkforcePyApi($data: UpdateProjectBoostWorkforceInput!) { + updateProjectBoostWorkforce(data: $data) { + success + } + }""" + + result = client.execute(mutation_str, {"data": update_input.model_dump()}) + return ProjectBoostWorkforceResult(**result["updateProjectBoostWorkforce"]) + + @classmethod + def set_project_owner(cls, client, project_id: str, project_owner_user_id: str) -> ProjectBoostWorkforceResult: + """Set the project owner for ProjectBoostWorkforce. + + Args: + client: Labelbox client instance + project_id: ID of the project + project_owner_user_id: ID of the user to set as project owner + + Returns: + ProjectBoostWorkforceResult indicating success + """ + update_input = UpdateProjectBoostWorkforceInput( + projectId=project_id, + projectOwnerUserId=project_owner_user_id + ) + + return cls.update(client, update_input) diff --git a/libs/labelbox/tests/integration/test_alignerr_project_builder.py b/libs/labelbox/tests/integration/test_alignerr_project_builder.py index 3b9e3f918..8ae71fe28 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project_builder.py +++ b/libs/labelbox/tests/integration/test_alignerr_project_builder.py @@ -38,6 +38,9 @@ def test_create_alignerr_project_using_builder_validate_input(client: Client): effective_since=datetime.datetime.now().isoformat(), ).create() + # Get current user for project owner + current_user = client.get_user() + alignerr_project = ( client.alignerr_workspace.project_builder() .set_name("TestAlignerrProject2") @@ -59,6 +62,7 @@ def test_create_alignerr_project_using_builder_validate_input(client: Client): billing_mode=BillingMode.BY_HOUR, effective_since=datetime.datetime.now().isoformat(), ) + .set_project_owner(current_user.email) .create() ) @@ -144,6 +148,9 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Cl time.sleep(0.5) try: + # Get current user for project owner + current_user = client.get_user() + # Create project with rates, domains, and resource tags alignerr_project = ( client.alignerr_workspace.project_builder() @@ -168,6 +175,7 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Cl ) .set_domains([domain1_name, domain2_name]) .set_tags([tag1_text, tag2_text], ResourceTagType.Default) + .set_project_owner(current_user.email) .create() ) @@ -202,3 +210,88 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Cl tag2.delete() except Exception: pass + + +def test_create_alignerr_project_with_project_owner(client: Client): + """Test creating an Alignerr project with project owner set.""" + # Get the current user as the project owner + current_user = client.get_user() + + try: + # Create project with project owner using email + alignerr_project = ( + client.alignerr_workspace.project_builder() + .set_name("TestAlignerrProjectWithOwner") + .set_media_type(MediaType.Image) + .set_alignerr_role_rate( + role_name=AlignerrRole.Labeler, + rate=10.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .set_alignerr_role_rate( + role_name=AlignerrRole.Reviewer, + rate=12.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .set_customer_rate( + rate=15.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + .set_project_owner(current_user.email) + .create() + ) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestAlignerrProjectWithOwner" + + # Verify project owner was set using the AlignerrProject method + project_boost_workforce = alignerr_project.get_project_owner() + + if project_boost_workforce: + assert project_boost_workforce.projectOwnerUserId == current_user.uid + assert project_boost_workforce.projectOwner.uid == current_user.uid + + alignerr_project.project.delete() + except Exception as e: + # Clean up if test fails + try: + alignerr_project.project.delete() + except: + pass + raise e + + +def test_create_alignerr_project_selective_validation_skip_multiple(client: Client): + """Test creating an Alignerr project with selective validation - skipping multiple validations.""" + from labelbox.alignerr.alignerr_project_builder import ValidationType + + try: + # Create project skipping multiple validations + alignerr_project = ( + client.alignerr_workspace.project_builder() + .set_name("TestAlignerrProjectSkipMultiple") + .set_media_type(MediaType.Image) + .set_alignerr_role_rate( + role_name=AlignerrRole.Labeler, + rate=10.0, + billing_mode=BillingMode.BY_HOUR, + effective_since=datetime.datetime.now().isoformat(), + ) + # Note: Missing reviewer rate, customer rate, and project owner, but we skip those validations + .create(skip_validation=[ValidationType.ALIGNERR_RATE, ValidationType.CUSTOMER_RATE, ValidationType.PROJECT_OWNER]) + ) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestAlignerrProjectSkipMultiple" + + alignerr_project.project.delete() + except Exception as e: + # Clean up if test fails + try: + alignerr_project.project.delete() + except: + pass + raise e From f7eedc001975bbef3eb276b1edf2d9fb6669d4da Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan Date: Thu, 9 Oct 2025 12:12:47 -0700 Subject: [PATCH 060/103] Update alignerr project factory and tests --- .../alignerr/alignerr_project_factory.py | 136 +++++- .../assets/test_project_comprehensive.yaml | 38 ++ .../test_alignerr_project_factory.py | 397 ++++++++++++++++++ 3 files changed, 567 insertions(+), 4 deletions(-) create mode 100644 libs/labelbox/tests/assets/test_project_comprehensive.yaml diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py index 43d479c8b..85d341cd6 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py @@ -1,10 +1,11 @@ import datetime -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Union, List import yaml from pathlib import Path import logging from labelbox.alignerr.schema.project_rate import BillingMode +from labelbox.alignerr.schema.enchanced_resource_tags import ResourceTagType from labelbox.schema.media_type import MediaType logger = logging.getLogger(__name__) @@ -18,21 +19,43 @@ class AlignerrProjectFactory: def __init__(self, client: "Client"): self.client = client - def create(self, yaml_file_path: str, skip_validation: bool = False): + def create(self, yaml_file_path: str, skip_validation: Union[bool, List] = False): """ Create an AlignerrProject from a YAML configuration file. Args: yaml_file_path: Path to the YAML configuration file - skip_validation: Whether to skip validation of required fields + skip_validation: Whether to skip validation of required fields. Can be: + - bool: Skip all validations (True) or run all validations (False) + - List: Skip specific validations (e.g., [ValidationType.PROJECT_OWNER]) Returns: - AlignerrProject: The created project with configured rates + AlignerrProject: The created project with all configured attributes Raises: FileNotFoundError: If the YAML file doesn't exist yaml.YAMLError: If the YAML file is invalid ValueError: If required fields are missing or invalid + + YAML Configuration Structure: + name: str (required) - Project name + media_type: str (required) - Media type (e.g., "Image", "Video", "Text") + rates: dict (optional) - Alignerr role rates + role_name: + rate: float + billing_mode: str + effective_since: str (ISO datetime) + effective_until: str (optional, ISO datetime) + customer_rate: dict (optional) - Customer billing rate + rate: float + billing_mode: str + effective_since: str (ISO datetime) + effective_until: str (optional, ISO datetime) + domains: list[str] (optional) - Project domain names + tags: list[dict] (optional) - Enhanced resource tags + - text: str + type: str (ResourceTagType enum value) + project_owner: str (optional) - Project owner email address """ logger.info(f"Creating project from YAML file: {yaml_file_path}") @@ -150,5 +173,110 @@ def create(self, yaml_file_path: str, skip_validation: bool = False): effective_until=effective_until, ) + # Set customer rate if provided + if "customer_rate" in config: + customer_rate_config = config["customer_rate"] + if not isinstance(customer_rate_config, dict): + raise ValueError("'customer_rate' must be a dictionary") + + # Validate customer rate configuration + required_customer_rate_fields = [ + "rate", + "billing_mode", + "effective_since", + ] + for field in required_customer_rate_fields: + if field not in customer_rate_config: + raise ValueError( + f"Required field '{field}' is missing for customer_rate" + ) + + # Parse billing mode + try: + billing_mode = BillingMode(customer_rate_config["billing_mode"]) + except ValueError: + raise ValueError( + f"Invalid billing_mode '{customer_rate_config['billing_mode']}' for customer_rate. Must be one of: {[e.value for e in BillingMode]}" + ) + + # Parse effective dates + try: + effective_since = datetime.datetime.fromisoformat( + customer_rate_config["effective_since"] + ) + except ValueError: + raise ValueError( + f"Invalid effective_since date format for customer_rate. Use ISO format (YYYY-MM-DDTHH:MM:SS)" + ) + + effective_until = None + if ( + "effective_until" in customer_rate_config + and customer_rate_config["effective_until"] + ): + try: + effective_until = datetime.datetime.fromisoformat( + customer_rate_config["effective_until"] + ) + except ValueError: + raise ValueError( + f"Invalid effective_until date format for customer_rate. Use ISO format (YYYY-MM-DDTHH:MM:SS)" + ) + + # Set the customer rate + builder.set_customer_rate( + rate=float(customer_rate_config["rate"]), + billing_mode=billing_mode, + effective_since=effective_since, + effective_until=effective_until, + ) + + # Set domains if provided + if "domains" in config: + domains_config = config["domains"] + if not isinstance(domains_config, list): + raise ValueError("'domains' must be a list") + + if not all(isinstance(domain, str) for domain in domains_config): + raise ValueError("All domain names must be strings") + + builder.set_domains(domains_config) + + # Set enhanced resource tags if provided + if "tags" in config: + tags_config = config["tags"] + if not isinstance(tags_config, list): + raise ValueError("'tags' must be a list") + + for tag_config in tags_config: + if not isinstance(tag_config, dict): + raise ValueError("Each tag must be a dictionary") + + required_tag_fields = ["text", "type"] + for field in required_tag_fields: + if field not in tag_config: + raise ValueError( + f"Required field '{field}' is missing for tag" + ) + + # Validate tag type + try: + tag_type = ResourceTagType(tag_config["type"]) + except ValueError: + raise ValueError( + f"Invalid tag type '{tag_config['type']}'. Must be one of: {[e.value for e in ResourceTagType]}" + ) + + # Set the tag + builder.set_tags([tag_config["text"]], tag_type) + + # Set project owner if provided + if "project_owner" in config: + project_owner_config = config["project_owner"] + if not isinstance(project_owner_config, str): + raise ValueError("'project_owner' must be a string (email address)") + + builder.set_project_owner(project_owner_config) + # Create the project return builder.create(skip_validation=skip_validation) diff --git a/libs/labelbox/tests/assets/test_project_comprehensive.yaml b/libs/labelbox/tests/assets/test_project_comprehensive.yaml new file mode 100644 index 000000000..f341602df --- /dev/null +++ b/libs/labelbox/tests/assets/test_project_comprehensive.yaml @@ -0,0 +1,38 @@ +name: "TestComprehensiveProject" +media_type: "Image" + +# Alignerr role rates +rates: + LABELER: + rate: 15.0 + billing_mode: "BY_HOUR" + effective_since: "2024-01-01T00:00:00" + effective_until: "2024-12-31T23:59:59" + REVIEWER: + rate: 20.0 + billing_mode: "BY_HOUR" + effective_since: "2024-01-01T00:00:00" + +# Customer billing rate +customer_rate: + rate: 25.0 + billing_mode: "BY_HOUR" + effective_since: "2024-01-01T00:00:00" + effective_until: "2024-12-31T23:59:59" + +# Project domains (will be created if they don't exist) +domains: + - "TestDomain1" + - "TestDomain2" + +# Enhanced resource tags +tags: + - text: "TestTag1" + type: "Default" + - text: "TestTag2" + type: "Billing" + - text: "TestTag3" + type: "System" + +# Project owner (will use current user's email in tests) +project_owner: "test@example.com" diff --git a/libs/labelbox/tests/integration/test_alignerr_project_factory.py b/libs/labelbox/tests/integration/test_alignerr_project_factory.py index 82dd1a4d9..b3f41a63b 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project_factory.py +++ b/libs/labelbox/tests/integration/test_alignerr_project_factory.py @@ -3,11 +3,13 @@ import tempfile import os import yaml +from pathlib import Path import pytest from labelbox import Client from labelbox.alignerr.alignerr_project_factory import AlignerrProjectFactory +from labelbox.alignerr.alignerr_project_builder import ValidationType from labelbox.schema.media_type import MediaType @@ -127,3 +129,398 @@ def test_create_alignerr_project_from_yaml_file_not_found(client: Client): with pytest.raises(FileNotFoundError, match="YAML file not found"): factory.create("nonexistent_file.yaml") + + +def test_create_alignerr_project_from_yaml_with_customer_rate(client: Client): + """Test creating an AlignerrProject from YAML with customer rate configuration.""" + config = { + "name": "TestFactoryProjectWithCustomerRate", + "media_type": "IMAGE", + "rates": { + "LABELER": { + "rate": 15.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + "REVIEWER": { + "rate": 20.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + }, + "customer_rate": { + "rate": 25.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + "effective_until": "2024-12-31T23:59:59", + }, + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + alignerr_project = factory.create(yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER]) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestFactoryProjectWithCustomerRate" + assert alignerr_project.project.media_type == MediaType.Image + + # Verify rates were set + project_rates = alignerr_project.get_project_rates() + assert isinstance(project_rates, list) + assert len(project_rates) >= 2 # Should have both labeler and reviewer rates + + alignerr_project.project.delete() + finally: + os.unlink(yaml_file_path) + + +def test_create_alignerr_project_from_yaml_with_domains(client: Client): + """Test creating an AlignerrProject from YAML with domains configuration.""" + from labelbox.alignerr.schema.project_domain import ProjectDomain + import uuid + import time + + # Create test domains first + domain1_name = f"TestDomain1_{uuid.uuid4()}" + domain2_name = f"TestDomain2_{uuid.uuid4()}" + + domain1 = ProjectDomain.create(client, name=domain1_name) + domain2 = ProjectDomain.create(client, name=domain2_name) + + # Add a small delay to allow domains to be searchable + time.sleep(0.5) + + config = { + "name": "TestFactoryProjectWithDomains", + "media_type": "IMAGE", + "rates": { + "LABELER": { + "rate": 15.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + "REVIEWER": { + "rate": 20.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + }, + "customer_rate": { + "rate": 25.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + "domains": [domain1_name, domain2_name], + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + alignerr_project = factory.create(yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER]) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestFactoryProjectWithDomains" + + # Verify domains were added + domain_count = sum(1 for _ in alignerr_project.domains()) + assert domain_count == 2 + + alignerr_project.project.delete() + finally: + os.unlink(yaml_file_path) + # Cleanup domains + try: + domain1.deactivate() + domain2.deactivate() + except Exception: + pass + + +def test_create_alignerr_project_from_yaml_with_tags(client: Client): + """Test creating an AlignerrProject from YAML with enhanced resource tags configuration.""" + from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType + import uuid + + # Create test resource tags + tag1_text = f"TestTag1_{uuid.uuid4().hex[:8]}" + tag2_text = f"TestTag2_{uuid.uuid4().hex[:8]}" + + tag1 = EnhancedResourceTag.create( + client, + text=tag1_text, + color="#FF5733", + tag_type=ResourceTagType.Default + ) + tag2 = EnhancedResourceTag.create( + client, + text=tag2_text, + color="#33FF57", + tag_type=ResourceTagType.Billing + ) + + config = { + "name": "TestFactoryProjectWithTags", + "media_type": "IMAGE", + "rates": { + "LABELER": { + "rate": 15.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + "REVIEWER": { + "rate": 20.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + }, + "customer_rate": { + "rate": 25.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + "tags": [ + {"text": tag1_text, "type": "Default"}, + {"text": tag2_text, "type": "Billing"}, + ], + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + alignerr_project = factory.create(yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER]) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestFactoryProjectWithTags" + + # Verify resource tags were added + enhanced_tags = alignerr_project.get_tags() + assert len(enhanced_tags) >= 1 # At least one tag should be present + + # Check that our specific tags are present (if any) + tag_texts = [tag.text for tag in enhanced_tags] + # Note: The tag matching might not work perfectly due to how the builder processes tags + # So we just verify that tags were processed + + alignerr_project.project.delete() + finally: + os.unlink(yaml_file_path) + # Cleanup resource tags + try: + tag1.delete() + tag2.delete() + except Exception: + pass + + +def test_create_alignerr_project_from_yaml_with_project_owner(client: Client): + """Test creating an AlignerrProject from YAML with project owner configuration.""" + # Get the current user as the project owner + current_user = client.get_user() + + config = { + "name": "TestFactoryProjectWithOwner", + "media_type": "IMAGE", + "rates": { + "LABELER": { + "rate": 15.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + "REVIEWER": { + "rate": 20.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + }, + "customer_rate": { + "rate": 25.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + "project_owner": current_user.email, + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + alignerr_project = factory.create(yaml_file_path) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestFactoryProjectWithOwner" + + # Verify project owner was set + project_boost_workforce = alignerr_project.get_project_owner() + if project_boost_workforce: + assert project_boost_workforce.projectOwnerUserId == current_user.uid + assert project_boost_workforce.projectOwner.uid == current_user.uid + + alignerr_project.project.delete() + finally: + os.unlink(yaml_file_path) + + +def test_create_alignerr_project_from_yaml_comprehensive(client: Client): + """Test creating an AlignerrProject from the comprehensive YAML asset file.""" + # Get the current user for project owner + current_user = client.get_user() + + # Path to the comprehensive test YAML file + yaml_file_path = Path(__file__).parent.parent / "assets" / "test_project_comprehensive.yaml" + + # Read and modify the YAML to use current user's email and remove domains/tags that require existing resources + with open(yaml_file_path, 'r') as f: + config = yaml.safe_load(f) + + # Update project owner to current user's email + config['project_owner'] = current_user.email + + # Remove domains and tags that require existing resources for this test + if 'domains' in config: + del config['domains'] + if 'tags' in config: + del config['tags'] + + # Create temporary YAML file with updated config + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + temp_yaml_path = f.name + + try: + factory = AlignerrProjectFactory(client) + alignerr_project = factory.create(temp_yaml_path) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestComprehensiveProject" + assert alignerr_project.project.media_type == MediaType.Image + + # Verify rates were set + project_rates = alignerr_project.get_project_rates() + assert isinstance(project_rates, list) + assert len(project_rates) >= 2 + + # Verify project owner was set + project_boost_workforce = alignerr_project.get_project_owner() + if project_boost_workforce: + assert project_boost_workforce.projectOwnerUserId == current_user.uid + + alignerr_project.project.delete() + finally: + os.unlink(temp_yaml_path) + + +def test_create_alignerr_project_from_yaml_selective_validation(client: Client): + """Test creating an AlignerrProject from YAML with selective validation.""" + config = { + "name": "TestFactoryProjectSelectiveValidation", + "media_type": "IMAGE", + "rates": { + "LABELER": { + "rate": 15.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + "REVIEWER": { + "rate": 20.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + }, + "customer_rate": { + "rate": 25.0, + "billing_mode": "BY_HOUR", + "effective_since": "2024-01-01T00:00:00", + }, + # Note: No project owner set, but we skip that validation + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + # Skip project owner validation + alignerr_project = factory.create(yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER]) + + assert alignerr_project is not None + assert alignerr_project.project.name == "TestFactoryProjectSelectiveValidation" + + alignerr_project.project.delete() + finally: + os.unlink(yaml_file_path) + + +def test_create_alignerr_project_from_yaml_invalid_customer_rate(client: Client): + """Test that invalid customer rate configurations raise appropriate errors.""" + config = { + "name": "TestProject", + "media_type": "IMAGE", + "customer_rate": { + "rate": 25.0, + # Missing billing_mode and effective_since + }, + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + + with pytest.raises(ValueError, match="Required field 'billing_mode' is missing for customer_rate"): + factory.create(yaml_file_path, skip_validation=True) + finally: + os.unlink(yaml_file_path) + + +def test_create_alignerr_project_from_yaml_invalid_tags(client: Client): + """Test that invalid tag configurations raise appropriate errors.""" + config = { + "name": "TestProject", + "media_type": "IMAGE", + "tags": [ + {"text": "TestTag1", "type": "InvalidType"}, # Invalid tag type + ], + } + + with tempfile.NamedTemporaryFile( + mode="w", suffix=".yaml", delete=False + ) as f: + yaml.dump(config, f) + yaml_file_path = f.name + + try: + factory = AlignerrProjectFactory(client) + + with pytest.raises(ValueError, match="Invalid tag type 'InvalidType'"): + factory.create(yaml_file_path, skip_validation=True) + finally: + os.unlink(yaml_file_path) From 6a1a7780da464ab1b91acee62e4555ad17c895a4 Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan Date: Thu, 9 Oct 2025 12:35:31 -0700 Subject: [PATCH 061/103] lint --- .../src/labelbox/alignerr/alignerr_project.py | 38 ++++--- .../alignerr/alignerr_project_builder.py | 61 ++++++----- .../alignerr/alignerr_project_factory.py | 22 ++-- .../schema/enchanced_resource_tags.py | 69 ++++++------ .../schema/project_boost_workforce.py | 42 ++++++-- .../labelbox/alignerr/schema/project_rate.py | 4 +- libs/labelbox/src/labelbox/schema/role.py | 2 - .../test_alignerr_project_builder.py | 59 +++++++---- .../test_alignerr_project_factory.py | 100 ++++++++++++------ .../test_enhanced_resource_tags.py | 91 +++++++--------- .../tests/integration/test_project_domain.py | 69 ++++++------ .../tests/integration/test_project_rate.py | 8 +- 12 files changed, 331 insertions(+), 234 deletions(-) diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py index 3c2873b1f..e3a8835d4 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py @@ -5,8 +5,13 @@ from labelbox.alignerr.schema.project_rate import ProjectRateV2 from labelbox.alignerr.schema.project_domain import ProjectDomain -from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType -from labelbox.alignerr.schema.project_boost_workforce import ProjectBoostWorkforce +from labelbox.alignerr.schema.enchanced_resource_tags import ( + EnhancedResourceTag, + ResourceTagType, +) +from labelbox.alignerr.schema.project_boost_workforce import ( + ProjectBoostWorkforce, +) from labelbox.pagination import PaginatedCollection logger = logging.getLogger(__name__) @@ -73,17 +78,19 @@ def set_tags(self, tag_names: list[str], tag_type: ResourceTagType): tag_ids = [] for tag_name in tag_names: # Search for the tag by text to get its ID - found_tags = EnhancedResourceTag.search_by_text(self.client, search_text=tag_name, tag_type=tag_type) + found_tags = EnhancedResourceTag.search_by_text( + self.client, search_text=tag_name, tag_type=tag_type + ) if found_tags: tag_ids.append(found_tags[0].id) - + # Use the existing project resource tag functionality with IDs self.project.update_project_resource_tags(tag_ids) return self def get_tags(self) -> list[EnhancedResourceTag]: """Get enhanced resource tags associated with this project. - + Returns: List of EnhancedResourceTag instances """ @@ -94,7 +101,9 @@ def get_tags(self) -> list[EnhancedResourceTag]: # Search for the corresponding EnhancedResourceTag by text (try different types) found_tags = [] for tag_type in [ResourceTagType.Default, ResourceTagType.Billing]: - found_tags = EnhancedResourceTag.search_by_text(self.client, search_text=tag.text, tag_type=tag_type) + found_tags = EnhancedResourceTag.search_by_text( + self.client, search_text=tag.text, tag_type=tag_type + ) if found_tags: break if found_tags: @@ -103,28 +112,28 @@ def get_tags(self) -> list[EnhancedResourceTag]: def add_tag(self, tag: EnhancedResourceTag): """Add a single enhanced resource tag to the project. - + Args: tag: EnhancedResourceTag instance to add - + Returns: Self for method chaining """ current_tags = self.get_tags() current_tag_names = [t.text for t in current_tags] - + if tag.text not in current_tag_names: current_tag_names.append(tag.text) self.set_tags(current_tag_names) - + return self def remove_tag(self, tag: EnhancedResourceTag): """Remove a single enhanced resource tag from the project. - + Args: tag: EnhancedResourceTag instance to remove - + Returns: Self for method chaining """ @@ -135,13 +144,12 @@ def remove_tag(self, tag: EnhancedResourceTag): def get_project_owner(self) -> Optional[ProjectBoostWorkforce]: """Get the ProjectBoostWorkforce for this project. - + Returns: ProjectBoostWorkforce instance or None if not found """ return ProjectBoostWorkforce.get_by_project_id( - client=self.client, - project_id=self.project.uid + client=self.client, project_id=self.project.uid ) diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py index 7219a3445..9f1c8bd72 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py @@ -6,8 +6,13 @@ from labelbox.alignerr.schema.project_rate import BillingMode from labelbox.alignerr.schema.project_rate import ProjectRateInput from labelbox.alignerr.schema.project_domain import ProjectDomain -from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType -from labelbox.alignerr.schema.project_boost_workforce import ProjectBoostWorkforce +from labelbox.alignerr.schema.enchanced_resource_tags import ( + EnhancedResourceTag, + ResourceTagType, +) +from labelbox.alignerr.schema.project_boost_workforce import ( + ProjectBoostWorkforce, +) from labelbox.schema.media_type import MediaType logger = logging.getLogger(__name__) @@ -15,6 +20,7 @@ class ValidationType(Enum): """Enum for validation types that can be selectively skipped.""" + ALIGNERR_RATE = "AlignerrRate" CUSTOMER_RATE = "CustomerRate" PROJECT_OWNER = "ProjectOwner" @@ -123,11 +129,11 @@ def set_domains(self, domains: list[str]): def set_tags(self, tag_texts: list[str], tag_type: ResourceTagType): """Set enhanced resource tags for the project. - + Args: tag_texts: List of tag text values to search for and attach tag_type: Type filter for searching tags - + Returns: Self for method chaining """ @@ -136,7 +142,7 @@ def set_tags(self, tag_texts: list[str], tag_type: ResourceTagType): existing_tags = EnhancedResourceTag.search_by_text( self.client, search_text=tag_text, tag_type=tag_type ) - + if existing_tags: # Use the first matching tag self._enhanced_resource_tags.append(existing_tags[0]) @@ -146,25 +152,26 @@ def set_tags(self, tag_texts: list[str], tag_type: ResourceTagType): self.client, text=tag_text, color="#007bff", # Default blue color - tag_type=tag_type + tag_type=tag_type, ) self._enhanced_resource_tags.append(new_tag) return self def set_project_owner(self, project_owner_email: str): """Set the project owner for the ProjectBoostWorkforce. - + Args: project_owner_email: Email of the user to set as project owner - + Returns: Self for method chaining """ self._project_owner_email = project_owner_email return self - - def create(self, skip_validation: Union[bool, List[ValidationType]] = False): + def create( + self, skip_validation: Union[bool, List[ValidationType]] = False + ): if not skip_validation: self._validate() elif isinstance(skip_validation, list): @@ -220,7 +227,7 @@ def _create_resource_tags(self, alignerr_project: "AlignerrProject"): if tag_type not in tags_by_type: tags_by_type[tag_type] = [] tags_by_type[tag_type].append(tag.text) - + # Set tags for each type for tag_type_str, tag_names in tags_by_type.items(): # Convert string back to enum @@ -230,17 +237,19 @@ def _create_resource_tags(self, alignerr_project: "AlignerrProject"): def _create_project_owner(self, alignerr_project: "AlignerrProject"): if self._project_owner_email: logger.info(f"Setting project owner: {self._project_owner_email}") - + # Find user by email in the organization user_id = self._find_user_by_email(self._project_owner_email) if not user_id: current_org = self.client.get_organization() - raise ValueError(f"User with email {self._project_owner_email} not found in organization {current_org.uid}") - + raise ValueError( + f"User with email {self._project_owner_email} not found in organization {current_org.uid}" + ) + ProjectBoostWorkforce.set_project_owner( client=self.client, project_id=alignerr_project.project.uid, - project_owner_user_id=user_id + project_owner_user_id=user_id, ) def _validate_alignerr_rates(self): @@ -273,16 +282,16 @@ def _validate(self): def _validate_selective(self, skip_validations: List[ValidationType]): """Run validations selectively, skipping those in the provided list. - + Args: skip_validations: List of ValidationType enums to skip """ if ValidationType.ALIGNERR_RATE not in skip_validations: self._validate_alignerr_rates() - + if ValidationType.CUSTOMER_RATE not in skip_validations: self._validate_customer_rate() - + if ValidationType.PROJECT_OWNER not in skip_validations: self._validate_project_owner() @@ -292,31 +301,33 @@ def _get_role_name_to_id(self) -> dict[str, str]: def _find_user_by_email(self, email: str) -> Optional[str]: """Find user ID by email in the organization. - + Args: email: Email address to search for - + Returns: User ID if found, None otherwise """ try: # Import here to avoid circular imports from labelbox.schema.user import User - + # Get the current organization current_org = self.client.get_organization() - + # Use client.get_users with where clause to find user by email users = self.client.get_users(where=User.email == email) - + # Get the first matching user and verify they belong to the same organization user = next(users, None) if user and user.organization().uid == current_org.uid: return user.uid else: - logger.warning(f"User with email {email} not found in organization {current_org.uid}") + logger.warning( + f"User with email {email} not found in organization {current_org.uid}" + ) return None - + except Exception as e: logger.error(f"Error finding user by email {email}: {e}") return None diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py index 85d341cd6..d49799528 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py @@ -19,7 +19,9 @@ class AlignerrProjectFactory: def __init__(self, client: "Client"): self.client = client - def create(self, yaml_file_path: str, skip_validation: Union[bool, List] = False): + def create( + self, yaml_file_path: str, skip_validation: Union[bool, List] = False + ): """ Create an AlignerrProject from a YAML configuration file. @@ -236,10 +238,10 @@ def create(self, yaml_file_path: str, skip_validation: Union[bool, List] = False domains_config = config["domains"] if not isinstance(domains_config, list): raise ValueError("'domains' must be a list") - + if not all(isinstance(domain, str) for domain in domains_config): raise ValueError("All domain names must be strings") - + builder.set_domains(domains_config) # Set enhanced resource tags if provided @@ -247,18 +249,18 @@ def create(self, yaml_file_path: str, skip_validation: Union[bool, List] = False tags_config = config["tags"] if not isinstance(tags_config, list): raise ValueError("'tags' must be a list") - + for tag_config in tags_config: if not isinstance(tag_config, dict): raise ValueError("Each tag must be a dictionary") - + required_tag_fields = ["text", "type"] for field in required_tag_fields: if field not in tag_config: raise ValueError( f"Required field '{field}' is missing for tag" ) - + # Validate tag type try: tag_type = ResourceTagType(tag_config["type"]) @@ -266,7 +268,7 @@ def create(self, yaml_file_path: str, skip_validation: Union[bool, List] = False raise ValueError( f"Invalid tag type '{tag_config['type']}'. Must be one of: {[e.value for e in ResourceTagType]}" ) - + # Set the tag builder.set_tags([tag_config["text"]], tag_type) @@ -274,8 +276,10 @@ def create(self, yaml_file_path: str, skip_validation: Union[bool, List] = False if "project_owner" in config: project_owner_config = config["project_owner"] if not isinstance(project_owner_config, str): - raise ValueError("'project_owner' must be a string (email address)") - + raise ValueError( + "'project_owner' must be a string (email address)" + ) + builder.set_project_owner(project_owner_config) # Create the project diff --git a/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py b/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py index a1f448a96..4c0a482ed 100644 --- a/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py +++ b/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py @@ -7,6 +7,7 @@ class ResourceTagType(Enum): """Enum for resource tag types.""" + Default = "Default" System = "System" Request = "Request" @@ -59,7 +60,11 @@ class EnhancedResourceTag(DbObject, Updateable): @classmethod def create( - cls, client, text: str, color: str, tag_type: Optional[ResourceTagType] = None + cls, + client, + text: str, + color: str, + tag_type: Optional[ResourceTagType] = None, ) -> "EnhancedResourceTag": """Create a new enhanced resource tag. @@ -75,26 +80,27 @@ def create( # Use the existing organization create_resource_tag method # Get the organization org = client.get_organization() - + # Create the tag using existing API tag_data = {"text": text, "color": color} created_tag = org.create_resource_tag(tag_data) - - # Create EnhancedResourceTag with the same data plus defaults for missing fields - enhanced_tag = cls(client, { - "id": created_tag.uid, - "text": created_tag.text, - "color": created_tag.color, - "createdAt": None, - "updatedAt": None, - "organizationId": None, - "createdById": None, - "type": tag_type.value if tag_type else None - }) - - return enhanced_tag + # Create EnhancedResourceTag with the same data plus defaults for missing fields + enhanced_tag = cls( + client, + { + "id": created_tag.uid, + "text": created_tag.text, + "color": created_tag.color, + "createdAt": None, + "updatedAt": None, + "organizationId": None, + "createdById": None, + "type": tag_type.value if tag_type else None, + }, + ) + return enhanced_tag @classmethod def search_by_text( @@ -113,27 +119,30 @@ def search_by_text( # Use the existing organization get_resource_tags method # Get the organization org = client.get_organization() - + # Get all resource tags regular_tags = org.get_resource_tags() - + # Convert to EnhancedResourceTag instances and filter by search text and type matching_tags = [] for tag in regular_tags: if search_text.lower() in tag.text.lower(): - enhanced_tag = cls(client, { - "id": tag.uid, - "text": tag.text, - "color": tag.color, - "createdAt": None, - "updatedAt": None, - "organizationId": None, - "createdById": None, - "type": tag_type.value - }) - + enhanced_tag = cls( + client, + { + "id": tag.uid, + "text": tag.text, + "color": tag.color, + "createdAt": None, + "updatedAt": None, + "organizationId": None, + "createdById": None, + "type": tag_type.value, + }, + ) + # Apply type filter if enhanced_tag.type == tag_type.value: matching_tags.append(enhanced_tag) - + return matching_tags diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py b/libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py index af486b420..fdd9e937a 100644 --- a/libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py +++ b/libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py @@ -7,6 +7,7 @@ class ProjectBoostWorkforceStatus(Enum): """Enum for ProjectBoostWorkforce status.""" + SET_UP = "SET_UP" REQUESTED = "REQUESTED" ACCEPTED = "ACCEPTED" @@ -18,12 +19,14 @@ class ProjectBoostWorkforceStatus(Enum): class ProjectBoostType(Enum): """Enum for ProjectBoost type.""" + SELF_SERVE = "SELF_SERVE" MANAGED = "MANAGED" class ProjectDifficulty(Enum): """Enum for project difficulty levels.""" + EASY = "easy" MEDIUM = "medium" HARD = "hard" @@ -31,6 +34,7 @@ class ProjectDifficulty(Enum): class BillingMode(Enum): """Enum for billing modes.""" + BY_TASK = "BY_TASK" BY_HOUR = "BY_HOUR" BY_TASK_PER_TURN = "BY_TASK_PER_TURN" @@ -38,23 +42,27 @@ class BillingMode(Enum): class UpsertProjectBoostWorkforceInput(BaseModel): """Input for upserting a ProjectBoostWorkforce.""" + projectId: str class UpdateProjectBoostWorkforceStatusInput(BaseModel): """Input for updating ProjectBoostWorkforce status.""" + projectId: str status: ProjectBoostWorkforceStatus class UpdateProjectBoostWorkforceCountryMultiplierInput(BaseModel): """Input for updating country rate multipliers.""" + projectId: str disabledCountryRateMultipliers: bool class UpdateProjectBoostWorkforceBillingModeInput(BaseModel): """Input for updating billing mode.""" + projectId: str billingMode: BillingMode customerBillingMode: Optional[BillingMode] = None @@ -62,11 +70,13 @@ class UpdateProjectBoostWorkforceBillingModeInput(BaseModel): class ValidateAndRequestProjectBoostWorkforceInput(BaseModel): """Input for validating and requesting ProjectBoostWorkforce.""" + projectId: str class UpdateProjectBoostWorkforceInput(BaseModel): """Input for updating ProjectBoostWorkforce.""" + projectId: str status: Optional[ProjectBoostWorkforceStatus] = None calibrationDatarows: Optional[int] = None @@ -88,16 +98,19 @@ class UpdateProjectBoostWorkforceInput(BaseModel): class FindProjectBoostWorkforceInput(BaseModel): """Input for finding ProjectBoostWorkforce.""" + projectId: str class ProjectBoostWorkforceResult(BaseModel): """Result model for ProjectBoostWorkforce operations.""" + success: bool class ProjectBoostWorkforceStatusHistoryFields(BaseModel): """Model for ProjectBoostWorkforce status history fields.""" + id: str projectId: str updatedAt: str @@ -130,7 +143,9 @@ class ProjectBoostWorkforce(DbObject): projectDifficulty = Field.Enum(ProjectDifficulty, "projectDifficulty") projectDescription = Field.String("projectDescription") estimatedTimePerLabel = Field.Float("estimatedTimePerLabel") - disabledCountryRateMultipliers = Field.Boolean("disabledCountryRateMultipliers") + disabledCountryRateMultipliers = Field.Boolean( + "disabledCountryRateMultipliers" + ) billingMode = Field.Enum(BillingMode, "billingMode") customerBillingMode = Field.Enum(BillingMode, "customerBillingMode") type = Field.Enum(ProjectBoostType, "type") @@ -141,7 +156,9 @@ class ProjectBoostWorkforce(DbObject): projectOwnerUserId = Field.String("projectOwnerUserId") @classmethod - def get_by_project_id(cls, client, project_id: str) -> Optional["ProjectBoostWorkforce"]: + def get_by_project_id( + cls, client, project_id: str + ) -> Optional["ProjectBoostWorkforce"]: """Get ProjectBoostWorkforce by project ID. Args: @@ -193,14 +210,16 @@ def get_by_project_id(cls, client, project_id: str) -> Optional["ProjectBoostWor result = client.execute(query_str, {"data": input_data.model_dump()}) workforce_data = result.get("projectBoostWorkforce") - + if not workforce_data: return None return cls(client, workforce_data) @classmethod - def update(cls, client, update_input: UpdateProjectBoostWorkforceInput) -> ProjectBoostWorkforceResult: + def update( + cls, client, update_input: UpdateProjectBoostWorkforceInput + ) -> ProjectBoostWorkforceResult: """Update ProjectBoostWorkforce with various fields. Args: @@ -217,11 +236,17 @@ def update(cls, client, update_input: UpdateProjectBoostWorkforceInput) -> Proje } }""" - result = client.execute(mutation_str, {"data": update_input.model_dump()}) - return ProjectBoostWorkforceResult(**result["updateProjectBoostWorkforce"]) + result = client.execute( + mutation_str, {"data": update_input.model_dump()} + ) + return ProjectBoostWorkforceResult( + **result["updateProjectBoostWorkforce"] + ) @classmethod - def set_project_owner(cls, client, project_id: str, project_owner_user_id: str) -> ProjectBoostWorkforceResult: + def set_project_owner( + cls, client, project_id: str, project_owner_user_id: str + ) -> ProjectBoostWorkforceResult: """Set the project owner for ProjectBoostWorkforce. Args: @@ -233,8 +258,7 @@ def set_project_owner(cls, client, project_id: str, project_owner_user_id: str) ProjectBoostWorkforceResult indicating success """ update_input = UpdateProjectBoostWorkforceInput( - projectId=project_id, - projectOwnerUserId=project_owner_user_id + projectId=project_id, projectOwnerUserId=project_owner_user_id ) return cls.update(client, update_input) diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py b/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py index 22ecaa7e5..85788b7d6 100644 --- a/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py +++ b/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py @@ -53,7 +53,9 @@ class ProjectRateV2(DbObject, Deletable): effectiveUntil = Field.DateTime("effectiveUntil") @classmethod - def get_by_project_id(cls, client, project_id: str) -> list["ProjectRateV2"]: + def get_by_project_id( + cls, client, project_id: str + ) -> list["ProjectRateV2"]: query_str = """ query GetAllProjectRatesPyApi($projectId: ID!) { project(where: { id: $projectId }) { diff --git a/libs/labelbox/src/labelbox/schema/role.py b/libs/labelbox/src/labelbox/schema/role.py index 4d555ac06..83c3e5ac0 100644 --- a/libs/labelbox/src/labelbox/schema/role.py +++ b/libs/labelbox/src/labelbox/schema/role.py @@ -35,12 +35,10 @@ def from_name(cls, client: "Client", name: str) -> "Role": return roles.get(name.upper()) - class OrgRole(Role): ... class UserRole(Role): ... - @dataclass diff --git a/libs/labelbox/tests/integration/test_alignerr_project_builder.py b/libs/labelbox/tests/integration/test_alignerr_project_builder.py index 8ae71fe28..108f61944 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project_builder.py +++ b/libs/labelbox/tests/integration/test_alignerr_project_builder.py @@ -40,7 +40,7 @@ def test_create_alignerr_project_using_builder_validate_input(client: Client): # Get current user for project owner current_user = client.get_user() - + alignerr_project = ( client.alignerr_workspace.project_builder() .set_name("TestAlignerrProject2") @@ -113,10 +113,15 @@ def test_create_alignerr_project_using_builder_add_domains(client: Client): pass -def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Client): +def test_create_alignerr_project_with_rates_domains_and_resource_tags( + client: Client, +): """Test creating an Alignerr project with rates, domains, and enhanced resource tags.""" from labelbox.alignerr.schema.project_domain import ProjectDomain - from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType + from labelbox.alignerr.schema.enchanced_resource_tags import ( + EnhancedResourceTag, + ResourceTagType, + ) import uuid import time @@ -132,16 +137,16 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Cl tag2_text = f"TestTag2_{uuid.uuid4().hex[:8]}" tag1 = EnhancedResourceTag.create( - client, - text=tag1_text, - color="#FF5733", - tag_type=ResourceTagType.Default + client, + text=tag1_text, + color="#FF5733", + tag_type=ResourceTagType.Default, ) tag2 = EnhancedResourceTag.create( - client, - text=tag2_text, - color="#33FF57", - tag_type=ResourceTagType.Billing + client, + text=tag2_text, + color="#33FF57", + tag_type=ResourceTagType.Billing, ) # Add a small delay to allow domains to be searchable @@ -150,7 +155,7 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Cl try: # Get current user for project owner current_user = client.get_user() - + # Create project with rates, domains, and resource tags alignerr_project = ( client.alignerr_workspace.project_builder() @@ -189,7 +194,7 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Cl # Verify resource tags were added enhanced_tags = alignerr_project.get_tags() assert len(enhanced_tags) >= 2 - + # Check that our specific tags are present tag_texts = [tag.text for tag in enhanced_tags] assert tag1_text in tag_texts @@ -203,7 +208,7 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags(client: Cl domain2.deactivate() except Exception: pass - + # Cleanup resource tags try: tag1.delete() @@ -216,7 +221,7 @@ def test_create_alignerr_project_with_project_owner(client: Client): """Test creating an Alignerr project with project owner set.""" # Get the current user as the project owner current_user = client.get_user() - + try: # Create project with project owner using email alignerr_project = ( @@ -249,9 +254,11 @@ def test_create_alignerr_project_with_project_owner(client: Client): # Verify project owner was set using the AlignerrProject method project_boost_workforce = alignerr_project.get_project_owner() - + if project_boost_workforce: - assert project_boost_workforce.projectOwnerUserId == current_user.uid + assert ( + project_boost_workforce.projectOwnerUserId == current_user.uid + ) assert project_boost_workforce.projectOwner.uid == current_user.uid alignerr_project.project.delete() @@ -264,10 +271,12 @@ def test_create_alignerr_project_with_project_owner(client: Client): raise e -def test_create_alignerr_project_selective_validation_skip_multiple(client: Client): +def test_create_alignerr_project_selective_validation_skip_multiple( + client: Client, +): """Test creating an Alignerr project with selective validation - skipping multiple validations.""" from labelbox.alignerr.alignerr_project_builder import ValidationType - + try: # Create project skipping multiple validations alignerr_project = ( @@ -281,11 +290,19 @@ def test_create_alignerr_project_selective_validation_skip_multiple(client: Clie effective_since=datetime.datetime.now().isoformat(), ) # Note: Missing reviewer rate, customer rate, and project owner, but we skip those validations - .create(skip_validation=[ValidationType.ALIGNERR_RATE, ValidationType.CUSTOMER_RATE, ValidationType.PROJECT_OWNER]) + .create( + skip_validation=[ + ValidationType.ALIGNERR_RATE, + ValidationType.CUSTOMER_RATE, + ValidationType.PROJECT_OWNER, + ] + ) ) assert alignerr_project is not None - assert alignerr_project.project.name == "TestAlignerrProjectSkipMultiple" + assert ( + alignerr_project.project.name == "TestAlignerrProjectSkipMultiple" + ) alignerr_project.project.delete() except Exception as e: diff --git a/libs/labelbox/tests/integration/test_alignerr_project_factory.py b/libs/labelbox/tests/integration/test_alignerr_project_factory.py index b3f41a63b..e25a78e17 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project_factory.py +++ b/libs/labelbox/tests/integration/test_alignerr_project_factory.py @@ -164,16 +164,23 @@ def test_create_alignerr_project_from_yaml_with_customer_rate(client: Client): try: factory = AlignerrProjectFactory(client) - alignerr_project = factory.create(yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER]) + alignerr_project = factory.create( + yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER] + ) assert alignerr_project is not None - assert alignerr_project.project.name == "TestFactoryProjectWithCustomerRate" + assert ( + alignerr_project.project.name + == "TestFactoryProjectWithCustomerRate" + ) assert alignerr_project.project.media_type == MediaType.Image # Verify rates were set project_rates = alignerr_project.get_project_rates() assert isinstance(project_rates, list) - assert len(project_rates) >= 2 # Should have both labeler and reviewer rates + assert ( + len(project_rates) >= 2 + ) # Should have both labeler and reviewer rates alignerr_project.project.delete() finally: @@ -227,7 +234,9 @@ def test_create_alignerr_project_from_yaml_with_domains(client: Client): try: factory = AlignerrProjectFactory(client) - alignerr_project = factory.create(yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER]) + alignerr_project = factory.create( + yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER] + ) assert alignerr_project is not None assert alignerr_project.project.name == "TestFactoryProjectWithDomains" @@ -249,7 +258,10 @@ def test_create_alignerr_project_from_yaml_with_domains(client: Client): def test_create_alignerr_project_from_yaml_with_tags(client: Client): """Test creating an AlignerrProject from YAML with enhanced resource tags configuration.""" - from labelbox.alignerr.schema.enchanced_resource_tags import EnhancedResourceTag, ResourceTagType + from labelbox.alignerr.schema.enchanced_resource_tags import ( + EnhancedResourceTag, + ResourceTagType, + ) import uuid # Create test resource tags @@ -257,16 +269,16 @@ def test_create_alignerr_project_from_yaml_with_tags(client: Client): tag2_text = f"TestTag2_{uuid.uuid4().hex[:8]}" tag1 = EnhancedResourceTag.create( - client, - text=tag1_text, - color="#FF5733", - tag_type=ResourceTagType.Default + client, + text=tag1_text, + color="#FF5733", + tag_type=ResourceTagType.Default, ) tag2 = EnhancedResourceTag.create( - client, - text=tag2_text, - color="#33FF57", - tag_type=ResourceTagType.Billing + client, + text=tag2_text, + color="#33FF57", + tag_type=ResourceTagType.Billing, ) config = { @@ -303,7 +315,9 @@ def test_create_alignerr_project_from_yaml_with_tags(client: Client): try: factory = AlignerrProjectFactory(client) - alignerr_project = factory.create(yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER]) + alignerr_project = factory.create( + yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER] + ) assert alignerr_project is not None assert alignerr_project.project.name == "TestFactoryProjectWithTags" @@ -311,7 +325,7 @@ def test_create_alignerr_project_from_yaml_with_tags(client: Client): # Verify resource tags were added enhanced_tags = alignerr_project.get_tags() assert len(enhanced_tags) >= 1 # At least one tag should be present - + # Check that our specific tags are present (if any) tag_texts = [tag.text for tag in enhanced_tags] # Note: The tag matching might not work perfectly due to how the builder processes tags @@ -332,7 +346,7 @@ def test_create_alignerr_project_from_yaml_with_project_owner(client: Client): """Test creating an AlignerrProject from YAML with project owner configuration.""" # Get the current user as the project owner current_user = client.get_user() - + config = { "name": "TestFactoryProjectWithOwner", "media_type": "IMAGE", @@ -372,7 +386,9 @@ def test_create_alignerr_project_from_yaml_with_project_owner(client: Client): # Verify project owner was set project_boost_workforce = alignerr_project.get_project_owner() if project_boost_workforce: - assert project_boost_workforce.projectOwnerUserId == current_user.uid + assert ( + project_boost_workforce.projectOwnerUserId == current_user.uid + ) assert project_boost_workforce.projectOwner.uid == current_user.uid alignerr_project.project.delete() @@ -384,23 +400,27 @@ def test_create_alignerr_project_from_yaml_comprehensive(client: Client): """Test creating an AlignerrProject from the comprehensive YAML asset file.""" # Get the current user for project owner current_user = client.get_user() - + # Path to the comprehensive test YAML file - yaml_file_path = Path(__file__).parent.parent / "assets" / "test_project_comprehensive.yaml" - + yaml_file_path = ( + Path(__file__).parent.parent + / "assets" + / "test_project_comprehensive.yaml" + ) + # Read and modify the YAML to use current user's email and remove domains/tags that require existing resources - with open(yaml_file_path, 'r') as f: + with open(yaml_file_path, "r") as f: config = yaml.safe_load(f) - + # Update project owner to current user's email - config['project_owner'] = current_user.email - + config["project_owner"] = current_user.email + # Remove domains and tags that require existing resources for this test - if 'domains' in config: - del config['domains'] - if 'tags' in config: - del config['tags'] - + if "domains" in config: + del config["domains"] + if "tags" in config: + del config["tags"] + # Create temporary YAML file with updated config with tempfile.NamedTemporaryFile( mode="w", suffix=".yaml", delete=False @@ -424,7 +444,9 @@ def test_create_alignerr_project_from_yaml_comprehensive(client: Client): # Verify project owner was set project_boost_workforce = alignerr_project.get_project_owner() if project_boost_workforce: - assert project_boost_workforce.projectOwnerUserId == current_user.uid + assert ( + project_boost_workforce.projectOwnerUserId == current_user.uid + ) alignerr_project.project.delete() finally: @@ -465,17 +487,24 @@ def test_create_alignerr_project_from_yaml_selective_validation(client: Client): try: factory = AlignerrProjectFactory(client) # Skip project owner validation - alignerr_project = factory.create(yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER]) + alignerr_project = factory.create( + yaml_file_path, skip_validation=[ValidationType.PROJECT_OWNER] + ) assert alignerr_project is not None - assert alignerr_project.project.name == "TestFactoryProjectSelectiveValidation" + assert ( + alignerr_project.project.name + == "TestFactoryProjectSelectiveValidation" + ) alignerr_project.project.delete() finally: os.unlink(yaml_file_path) -def test_create_alignerr_project_from_yaml_invalid_customer_rate(client: Client): +def test_create_alignerr_project_from_yaml_invalid_customer_rate( + client: Client, +): """Test that invalid customer rate configurations raise appropriate errors.""" config = { "name": "TestProject", @@ -495,7 +524,10 @@ def test_create_alignerr_project_from_yaml_invalid_customer_rate(client: Client) try: factory = AlignerrProjectFactory(client) - with pytest.raises(ValueError, match="Required field 'billing_mode' is missing for customer_rate"): + with pytest.raises( + ValueError, + match="Required field 'billing_mode' is missing for customer_rate", + ): factory.create(yaml_file_path, skip_validation=True) finally: os.unlink(yaml_file_path) diff --git a/libs/labelbox/tests/integration/test_enhanced_resource_tags.py b/libs/labelbox/tests/integration/test_enhanced_resource_tags.py index 6d2981760..6e0884334 100644 --- a/libs/labelbox/tests/integration/test_enhanced_resource_tags.py +++ b/libs/labelbox/tests/integration/test_enhanced_resource_tags.py @@ -16,21 +16,20 @@ def test_resource_tags(client): """Create test resource tags for testing.""" tags = [] - + # Create multiple test tags with different types - for i, tag_type in enumerate([ResourceTagType.Default, ResourceTagType.Billing]): - tag_text = f"Test_Tag_{i+1}_{uuid.uuid4().hex[:8]}" + for i, tag_type in enumerate( + [ResourceTagType.Default, ResourceTagType.Billing] + ): + tag_text = f"Test_Tag_{i + 1}_{uuid.uuid4().hex[:8]}" tag_color = f"#{i:06x}" # Generate different colors tag = EnhancedResourceTag.create( - client, - text=tag_text, - color=tag_color, - tag_type=tag_type + client, text=tag_text, color=tag_color, tag_type=tag_type ) tags.append(tag) - + yield tags - + # Cleanup - delete tags for tag in tags: try: @@ -43,24 +42,21 @@ def test_create_enhanced_resource_tag(client): """Test creating a new enhanced resource tag.""" tag_text = f"Test_Create_Tag_{uuid.uuid4().hex[:8]}" tag_color = "#FF5733" - + # Create tag tag = EnhancedResourceTag.create( - client, - text=tag_text, - color=tag_color, - tag_type=ResourceTagType.Default + client, text=tag_text, color=tag_color, tag_type=ResourceTagType.Default ) - + assert tag is not None assert tag.text == tag_text - assert tag.color == tag_color.lstrip('#') # API returns color without # + assert tag.color == tag_color.lstrip("#") # API returns color without # assert tag.type == ResourceTagType.Default.value assert tag.id is not None # Note: createdAt and organizationId are not available in current API # assert tag.createdAt is not None # assert tag.organizationId is not None - + # Cleanup try: tag.delete() @@ -72,21 +68,17 @@ def test_create_enhanced_resource_tag_without_type(client): """Test creating a resource tag without specifying type.""" tag_text = f"Test_Create_Tag_No_Type_{uuid.uuid4().hex[:8]}" tag_color = "#33FF57" - + # Create tag without type - tag = EnhancedResourceTag.create( - client, - text=tag_text, - color=tag_color - ) - + tag = EnhancedResourceTag.create(client, text=tag_text, color=tag_color) + assert tag is not None assert tag.text == tag_text - assert tag.color == tag_color.lstrip('#') # API returns color without # + assert tag.color == tag_color.lstrip("#") # API returns color without # assert tag.id is not None # Note: createdAt is not available in current API # assert tag.createdAt is not None - + # Cleanup try: tag.delete() @@ -94,10 +86,6 @@ def test_create_enhanced_resource_tag_without_type(client): pass - - - - def test_search_by_text(client, test_resource_tags): """Test searching resource tags by text content.""" # Test 1: Search for exact text match @@ -108,7 +96,7 @@ def test_search_by_text(client, test_resource_tags): assert isinstance(search_results, list) assert len(search_results) >= 1 assert any(tag.text == target_tag.text for tag in search_results) - + # Test 2: Search for partial text match partial_text = "Test_Tag" partial_results = EnhancedResourceTag.search_by_text( @@ -117,22 +105,22 @@ def test_search_by_text(client, test_resource_tags): assert isinstance(partial_results, list) assert len(partial_results) >= 1 # At least one Default type tag assert all(partial_text in tag.text for tag in partial_results) - + # Test 3: Search with type filter type_filtered_results = EnhancedResourceTag.search_by_text( - client, - search_text="Test_Tag", - tag_type=ResourceTagType.Default + client, search_text="Test_Tag", tag_type=ResourceTagType.Default ) assert isinstance(type_filtered_results, list) # All results should contain the search text and match the type for tag in type_filtered_results: assert "Test_Tag" in tag.text assert tag.type == ResourceTagType.Default.value - + # Test 4: Search for non-existent text non_existent_results = EnhancedResourceTag.search_by_text( - client, search_text="NonExistentTag12345", tag_type=ResourceTagType.Default + client, + search_text="NonExistentTag12345", + tag_type=ResourceTagType.Default, ) assert isinstance(non_existent_results, list) assert len(non_existent_results) == 0 @@ -145,16 +133,13 @@ def test_resource_tag_types_enum(client): for tag_type in supported_types: tag_text = f"Test_{tag_type.value}_Tag_{uuid.uuid4().hex[:8]}" tag_color = "#123456" - + tag = EnhancedResourceTag.create( - client, - text=tag_text, - color=tag_color, - tag_type=tag_type + client, text=tag_text, color=tag_color, tag_type=tag_type ) - + assert tag.type == tag_type.value - + # Cleanup try: tag.delete() @@ -165,16 +150,22 @@ def test_resource_tag_types_enum(client): def test_enhanced_resource_tag_properties(client, test_resource_tags): """Test that enhanced resource tags have all expected properties.""" tag = test_resource_tags[0] - + # Test all expected properties exist expected_properties = [ - 'id', 'createdAt', 'updatedAt', 'organizationId', 'text', - 'color', 'createdById', 'type' + "id", + "createdAt", + "updatedAt", + "organizationId", + "text", + "color", + "createdById", + "type", ] - + for prop in expected_properties: assert hasattr(tag, prop), f"Tag missing property: {prop}" - + # Test that required properties are not None assert tag.id is not None assert tag.text is not None @@ -182,5 +173,3 @@ def test_enhanced_resource_tag_properties(client, test_resource_tags): # Note: Some properties are not available in current API # assert tag.createdAt is not None # assert tag.organizationId is not None - - diff --git a/libs/labelbox/tests/integration/test_project_domain.py b/libs/labelbox/tests/integration/test_project_domain.py index e45ef339f..26783b1e5 100644 --- a/libs/labelbox/tests/integration/test_project_domain.py +++ b/libs/labelbox/tests/integration/test_project_domain.py @@ -15,12 +15,11 @@ def test_project(client): """Create a test project for domain testing.""" project_name = f"Test Project Domain {uuid.uuid4()}" project = client.create_project( - name=project_name, - media_type=MediaType.Image + name=project_name, media_type=MediaType.Image ) - + yield project - + # Cleanup try: project.delete() @@ -32,15 +31,15 @@ def test_project(client): def test_domains(client): """Create test domains for testing.""" domains = [] - + # Create multiple test domains for i in range(3): - domain_name = f"Test Domain {i+1} {uuid.uuid4()}" + domain_name = f"Test Domain {i + 1} {uuid.uuid4()}" domain = ProjectDomain.create(client, name=domain_name) domains.append(domain) - + yield domains - + # Cleanup - deactivate domains for domain in domains: try: @@ -52,10 +51,10 @@ def test_domains(client): def test_create_project_domain(client): """Test creating a new project domain.""" domain_name = f"Test Create Domain {uuid.uuid4()}" - + # Create domain domain = ProjectDomain.create(client, name=domain_name) - + assert domain is not None assert domain.name == domain_name assert domain.id is not None @@ -70,14 +69,14 @@ def test_create_project_domain(client): def test_activate_project_domain(client, test_domains): """Test activating a project domain.""" domain = test_domains[0] - + # Initially, domain should be active (created domains are active by default) assert domain.deactivatedAt is None - + # Deactivate first deactivated_domain = domain.deactivate() assert deactivated_domain.deactivatedAt is not None - + # Then activate activated_domain = deactivated_domain.activate() assert activated_domain.deactivatedAt is None @@ -87,10 +86,10 @@ def test_activate_project_domain(client, test_domains): def test_deactivate_project_domain(client, test_domains): """Test deactivating a project domain.""" domain = test_domains[0] - + # Initially, domain should be active assert domain.deactivatedAt is None - + # Deactivate deactivated_domain = domain.deactivate() assert deactivated_domain.deactivatedAt is not None @@ -100,14 +99,12 @@ def test_deactivate_project_domain(client, test_domains): def test_connect_project_to_domains(client, test_project, test_domains): """Test connecting a project to multiple domains.""" domain_ids = [domain.id for domain in test_domains] - + # Connect project to domains result = ProjectDomain.connect_project_to_domains( - client, - project_id=test_project.uid, - domain_ids=domain_ids + client, project_id=test_project.uid, domain_ids=domain_ids ) - + assert result is True @@ -120,46 +117,50 @@ def test_search_project_domains(client, test_domains): assert isinstance(domain_list, list) # Should find at least our test domains assert len(domain_list) >= len(test_domains) - + # Test 2: Search by specific name - should find exact match target_domain = test_domains[0] - search_results = ProjectDomain.search(client, search_by_name=target_domain.name) + search_results = ProjectDomain.search( + client, search_by_name=target_domain.name + ) found_domains = list(search_results) assert len(found_domains) >= 1 assert any(domain.name == target_domain.name for domain in found_domains) - + # Test 3: Search by partial name - should find matches partial_name = "Test Domain" partial_results = ProjectDomain.search(client, search_by_name=partial_name) partial_domains = list(partial_results) assert len(partial_domains) >= len(test_domains) assert all("Test Domain" in domain.name for domain in partial_domains) - + # Test 4: Search for non-existent domain - should return empty - non_existent_results = ProjectDomain.search(client, search_by_name="NonExistentDomain12345") + non_existent_results = ProjectDomain.search( + client, search_by_name="NonExistentDomain12345" + ) non_existent_domains = list(non_existent_results) assert len(non_existent_domains) == 0 - + # Test 5: Search with pagination parameters # Note: PaginatedCollection automatically fetches all pages, so limit only affects individual page size paginated_results = ProjectDomain.search(client, limit=2, offset=0) paginated_domains = list(paginated_results) # Should still find all domains since PaginatedCollection fetches all pages assert len(paginated_domains) >= len(test_domains) - + # Test 6: Search with include_archived parameter archived_results = ProjectDomain.search(client, include_archived=True) archived_domains = list(archived_results) assert isinstance(archived_domains, list) - + # Test 7: Verify domain properties in search results if found_domains: domain = found_domains[0] - assert hasattr(domain, 'id') - assert hasattr(domain, 'name') - assert hasattr(domain, 'createdAt') - assert hasattr(domain, 'updatedAt') - assert hasattr(domain, 'deactivatedAt') - assert hasattr(domain, 'ratingsCount') + assert hasattr(domain, "id") + assert hasattr(domain, "name") + assert hasattr(domain, "createdAt") + assert hasattr(domain, "updatedAt") + assert hasattr(domain, "deactivatedAt") + assert hasattr(domain, "ratingsCount") assert domain.id is not None assert domain.name is not None diff --git a/libs/labelbox/tests/integration/test_project_rate.py b/libs/labelbox/tests/integration/test_project_rate.py index 285ef04f5..e720a3f6c 100644 --- a/libs/labelbox/tests/integration/test_project_rate.py +++ b/libs/labelbox/tests/integration/test_project_rate.py @@ -32,7 +32,9 @@ def test_project(client): def test_project_rate_input_validation(): """Test ProjectRateInput validation logic.""" # Test negative rate validation - with pytest.raises(ValueError, match="Rate must be greater than or equal to 0"): + with pytest.raises( + ValueError, match="Rate must be greater than or equal to 0" + ): ProjectRateInput( rateForId="", isBillRate=True, @@ -44,7 +46,7 @@ def test_project_rate_input_validation(): # Test isBillRate=True with non-empty rateForId with pytest.raises( ValueError, - match="isBillRate indicates that this is a customer bill rate. rateForId must be empty if isBillRate is true" + match="isBillRate indicates that this is a customer bill rate. rateForId must be empty if isBillRate is true", ): ProjectRateInput( rateForId="some-id", @@ -119,7 +121,7 @@ def test_multiple_project_rates(client, test_project): if role.name == "REVIEWER": role_id = role.uid break - + if role_id: # Set role rate role_rate_input = ProjectRateInput( From b1966b2f69b9f53b628594c8bd0080d33ba2508f Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan Date: Thu, 9 Oct 2025 14:19:55 -0700 Subject: [PATCH 062/103] Fix types --- libs/labelbox/src/labelbox/alignerr/alignerr_project.py | 6 +++--- .../src/labelbox/alignerr/alignerr_project_builder.py | 8 ++++---- libs/labelbox/src/labelbox/schema/role.py | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py index e3a8835d4..65e03d896 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py @@ -42,7 +42,7 @@ def __init__( self.project = project @property - def project(self) -> Optional["Project"]: + def project(self) -> "Project": return self._project @project.setter @@ -124,7 +124,7 @@ def add_tag(self, tag: EnhancedResourceTag): if tag.text not in current_tag_names: current_tag_names.append(tag.text) - self.set_tags(current_tag_names) + self.set_tags(current_tag_names, tag.type) return self @@ -139,7 +139,7 @@ def remove_tag(self, tag: EnhancedResourceTag): """ current_tags = self.get_tags() current_tag_names = [t.text for t in current_tags if t.uid != tag.uid] - self.set_tags(current_tag_names) + self.set_tags(current_tag_names, tag.type) return self def get_project_owner(self) -> Optional[ProjectBoostWorkforce]: diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py index 9f1c8bd72..bebbd6748 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py @@ -35,7 +35,7 @@ class AlignerrProjectBuilder: def __init__(self, client: "Client"): self.client = client self._alignerr_rates: dict[str, ProjectRateInput] = {} - self._customer_rate: ProjectRateInput = None + self._customer_rate: Optional[ProjectRateInput] = None self._domains: list[ProjectDomain] = [] self._enhanced_resource_tags: list[EnhancedResourceTag] = [] self._project_owner_email: Optional[str] = None @@ -62,7 +62,7 @@ def set_alignerr_role_rate( raise ValueError(f"Role {role_name.value} not found") role_id = self.role_name_to_id[role_name.value] - role_name = role_name.value + role_name_str = role_name.value # Convert datetime objects to ISO format strings effective_since_str = ( @@ -76,7 +76,7 @@ def set_alignerr_role_rate( else effective_until ) - self._alignerr_rates[role_name] = ProjectRateInput( + self._alignerr_rates[role_name_str] = ProjectRateInput( rateForId=role_id, isBillRate=False, billingMode=billing_mode, @@ -221,7 +221,7 @@ def _create_resource_tags(self, alignerr_project: "AlignerrProject"): f"Setting enhanced resource tags: {[tag.text for tag in self._enhanced_resource_tags]}" ) # Group tags by type and set them accordingly - tags_by_type = {} + tags_by_type: dict[ResourceTagType, list[str]] = {} for tag in self._enhanced_resource_tags: tag_type = tag.type if tag_type not in tags_by_type: diff --git a/libs/labelbox/src/labelbox/schema/role.py b/libs/labelbox/src/labelbox/schema/role.py index 83c3e5ac0..0367d8f0c 100644 --- a/libs/labelbox/src/labelbox/schema/role.py +++ b/libs/labelbox/src/labelbox/schema/role.py @@ -30,7 +30,7 @@ class Role(DbObject): name = Field.String("name") @classmethod - def from_name(cls, client: "Client", name: str) -> "Role": + def from_name(cls, client: "Client", name: str) -> Optional["Role"]: roles = get_roles(client) return roles.get(name.upper()) From 6520316b173a873d77711318c53b96caf0f7b442 Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan Date: Thu, 9 Oct 2025 14:26:47 -0700 Subject: [PATCH 063/103] Fix types --- libs/labelbox/pyproject.toml | 1 + .../alignerr/schema/project_domain.py | 19 ++++++++++++++----- 2 files changed, 15 insertions(+), 5 deletions(-) diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml index aa7376d91..47e4be5d3 100644 --- a/libs/labelbox/pyproject.toml +++ b/libs/labelbox/pyproject.toml @@ -69,6 +69,7 @@ dev-dependencies = [ "types-python-dateutil>=2.9.0.20240316", "types-requests>=2.31.0.20240311", "types-tqdm>=4.66.0.20240106", + "types-PyYAML>=6.0.12.20240311", ] [tool.ruff] diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_domain.py b/libs/labelbox/src/labelbox/alignerr/schema/project_domain.py index 7ef56b553..1169c6872 100644 --- a/libs/labelbox/src/labelbox/alignerr/schema/project_domain.py +++ b/libs/labelbox/src/labelbox/alignerr/schema/project_domain.py @@ -1,4 +1,4 @@ -from typing import List, Optional +from typing import List, Optional, Dict, Any from labelbox.orm.db_object import Deletable, DbObject from labelbox.orm.model import Field from labelbox.pagination import PaginatedCollection @@ -212,7 +212,10 @@ def get_by_project_id( project_id, limit, offset, include_archived ) - params = {"projectId": project_id, "includeArchived": include_archived} + params: Dict[str, Any] = { + "projectId": project_id, + "includeArchived": include_archived, + } return PaginatedCollection( client=client, @@ -268,12 +271,18 @@ def search( } }""" - params = { + # Build params dictionary with proper types for GraphQL + params: Dict[str, Any] = { "includeArchived": include_archived, - "searchByName": search_by_name, - "projectIds": project_ids, } + # Only add non-None values to avoid type issues + if search_by_name is not None: + params["searchByName"] = search_by_name + if project_ids is not None: + # Keep as list for GraphQL - it will be properly serialized + params["projectIds"] = project_ids + return PaginatedCollection( client=client, query=query_str, From 67b146734e7ac0c958df5658f89c0c1fd0f25453 Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan Date: Thu, 9 Oct 2025 14:45:43 -0700 Subject: [PATCH 064/103] Add more roles --- libs/labelbox/src/labelbox/alignerr/alignerr_project.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py index 65e03d896..6dcaf5d9e 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py +++ b/libs/labelbox/src/labelbox/alignerr/alignerr_project.py @@ -27,6 +27,9 @@ class AlignerrRole(Enum): Labeler = "LABELER" Reviewer = "REVIEWER" Admin = "ADMIN" + ProjectCoordinator = "PROJECT_COORDINATOR" + AlignerrLabeler = "ALIGNERR_LABELER" + EndLabellingRole = "ENDLABELLINGROLE" class AlignerrProject: From 32f7bdcfa85cf8a3f27ba62e2acb7f8687ddaa87 Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan Date: Thu, 9 Oct 2025 14:50:59 -0700 Subject: [PATCH 065/103] Remove extra tags --- .../src/labelbox/alignerr/schema/enchanced_resource_tags.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py b/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py index 4c0a482ed..a500a2571 100644 --- a/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py +++ b/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py @@ -9,9 +9,6 @@ class ResourceTagType(Enum): """Enum for resource tag types.""" Default = "Default" - System = "System" - Request = "Request" - Migration = "Migration" Billing = "Billing" From 841322dd9e80d86614fb9df9466a3a862d15787a Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Fri, 10 Oct 2025 16:19:30 -0500 Subject: [PATCH 066/103] refactor to support lbox-alignerr as a rye workspace --- libs/labelbox/pyproject.toml | 5 +- libs/labelbox/src/labelbox/__init__.py | 1 - .../src/labelbox/alignerr/__init__.py | 3 - libs/labelbox/src/labelbox/client.py | 5 - libs/labelbox/src/labelbox/orm/model.py | 1 - libs/lbox-alignerr/README.md | 9 + libs/lbox-alignerr/pyproject.toml | 73 + libs/lbox-alignerr/src/alignerr/__init__.py | 3 + .../src}/alignerr/alignerr_project.py | 18 +- .../src}/alignerr/alignerr_project_builder.py | 16 +- .../src}/alignerr/alignerr_project_factory.py | 8 +- .../src}/alignerr/schema/__init__.py | 0 .../schema/enchanced_resource_tags.py | 0 .../schema/project_boost_workforce.py | 0 .../src}/alignerr/schema/project_domain.py | 0 .../src}/alignerr/schema/project_rate.py | 0 .../assets/test_project_comprehensive.yaml | 0 libs/lbox-alignerr/tests/conftest.py | 1309 +++++++++++++++++ .../integration/test_alignerr_project.py | 4 +- .../test_alignerr_project_builder.py | 12 +- .../test_alignerr_project_factory.py | 8 +- .../test_enhanced_resource_tags.py | 2 +- .../tests/integration/test_project_domain.py | 2 +- .../tests/integration/test_project_rate.py | 2 +- requirements-dev.lock | 8 +- requirements.lock | 7 +- 26 files changed, 1447 insertions(+), 49 deletions(-) delete mode 100644 libs/labelbox/src/labelbox/alignerr/__init__.py create mode 100644 libs/lbox-alignerr/README.md create mode 100644 libs/lbox-alignerr/pyproject.toml create mode 100644 libs/lbox-alignerr/src/alignerr/__init__.py rename libs/{labelbox/src/labelbox => lbox-alignerr/src}/alignerr/alignerr_project.py (91%) rename libs/{labelbox/src/labelbox => lbox-alignerr/src}/alignerr/alignerr_project_builder.py (95%) rename libs/{labelbox/src/labelbox => lbox-alignerr/src}/alignerr/alignerr_project_factory.py (97%) rename libs/{labelbox/src/labelbox => lbox-alignerr/src}/alignerr/schema/__init__.py (100%) rename libs/{labelbox/src/labelbox => lbox-alignerr/src}/alignerr/schema/enchanced_resource_tags.py (100%) rename libs/{labelbox/src/labelbox => lbox-alignerr/src}/alignerr/schema/project_boost_workforce.py (100%) rename libs/{labelbox/src/labelbox => lbox-alignerr/src}/alignerr/schema/project_domain.py (100%) rename libs/{labelbox/src/labelbox => lbox-alignerr/src}/alignerr/schema/project_rate.py (100%) rename libs/{labelbox => lbox-alignerr}/tests/assets/test_project_comprehensive.yaml (100%) create mode 100644 libs/lbox-alignerr/tests/conftest.py rename libs/{labelbox => lbox-alignerr}/tests/integration/test_alignerr_project.py (96%) rename libs/{labelbox => lbox-alignerr}/tests/integration/test_alignerr_project_builder.py (96%) rename libs/{labelbox => lbox-alignerr}/tests/integration/test_alignerr_project_factory.py (98%) rename libs/{labelbox => lbox-alignerr}/tests/integration/test_enhanced_resource_tags.py (98%) rename libs/{labelbox => lbox-alignerr}/tests/integration/test_project_domain.py (98%) rename libs/{labelbox => lbox-alignerr}/tests/integration/test_project_rate.py (98%) diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml index 47e4be5d3..4e5ae1d0a 100644 --- a/libs/labelbox/pyproject.toml +++ b/libs/labelbox/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "tqdm>=4.66.2", "geojson>=3.1.0", "lbox-clients==1.1.2", - "PyYAML>=6.0", + "PyYAML>=6.0" ] readme = "README.md" requires-python = ">=3.9,<3.14" @@ -56,6 +56,9 @@ data = [ "typing-extensions>=4.10.0", "opencv-python-headless>=4.9.0.80", ] +alignerr = [ + "lbox-alignerr>=0.1.0", +] [build-system] requires = ["hatchling"] diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index 4e3f348d5..24920786e 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -78,7 +78,6 @@ from labelbox.schema.ontology_kind import OntologyKind from labelbox.schema.organization import Organization from labelbox.schema.project import Project -from labelbox.alignerr.schema.project_rate import ProjectRateV2 as ProjectRate from labelbox.schema.project_model_config import ProjectModelConfig from labelbox.schema.project_overview import ( ProjectOverview, diff --git a/libs/labelbox/src/labelbox/alignerr/__init__.py b/libs/labelbox/src/labelbox/alignerr/__init__.py deleted file mode 100644 index 0f77eb997..000000000 --- a/libs/labelbox/src/labelbox/alignerr/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .alignerr_project import AlignerrWorkspace - -__all__ = ["AlignerrWorkspace"] diff --git a/libs/labelbox/src/labelbox/client.py b/libs/labelbox/src/labelbox/client.py index e5c914fd3..b48db006e 100644 --- a/libs/labelbox/src/labelbox/client.py +++ b/libs/labelbox/src/labelbox/client.py @@ -82,7 +82,6 @@ from labelbox.schema.taskstatus import TaskStatus from labelbox.schema.api_key import ApiKey from labelbox.schema.timeunit import TimeUnit -from labelbox.alignerr import AlignerrWorkspace logger = logging.getLogger(__name__) @@ -162,10 +161,6 @@ def enable_experimental(self, value: bool): def app_url(self) -> str: return self._request_client.app_url - @property - def alignerr_workspace(self) -> AlignerrWorkspace: - return AlignerrWorkspace(self) - def set_sdk_method(self, sdk_method: str): self._request_client.sdk_method = sdk_method diff --git a/libs/labelbox/src/labelbox/orm/model.py b/libs/labelbox/src/labelbox/orm/model.py index 260b3a7d9..b4ec7c2c2 100644 --- a/libs/labelbox/src/labelbox/orm/model.py +++ b/libs/labelbox/src/labelbox/orm/model.py @@ -395,7 +395,6 @@ class Entity(metaclass=EntityMeta): ProjectRole: Type[labelbox.ProjectRole] ProjectModelConfig: Type[labelbox.ProjectModelConfig] Project: Type[labelbox.Project] - ProjectRate: Type[labelbox.ProjectRate] Batch: Type[labelbox.Batch] CatalogSlice: Type[labelbox.CatalogSlice] ModelSlice: Type[labelbox.ModelSlice] diff --git a/libs/lbox-alignerr/README.md b/libs/lbox-alignerr/README.md new file mode 100644 index 000000000..3dc34b809 --- /dev/null +++ b/libs/lbox-alignerr/README.md @@ -0,0 +1,9 @@ +# Alignerr + +Alignerr workspace management for Labelbox. + +This package provides functionality for managing Alignerr projects, including: +- Project creation and configuration +- Rate management for labelers and reviewers +- Domain and tag management +- Workforce management diff --git a/libs/lbox-alignerr/pyproject.toml b/libs/lbox-alignerr/pyproject.toml new file mode 100644 index 000000000..0d25f4ab8 --- /dev/null +++ b/libs/lbox-alignerr/pyproject.toml @@ -0,0 +1,73 @@ +[project] +name = "lbox-alignerr" +version = "0.1.0" +description = "Alignerr workspace management for Labelbox" +authors = [ + { name = "Labelbox", email = "engineering@labelbox.com" } +] +dependencies = [ + "labelbox>=0.1.0", + "pydantic>=2.0.0", + "pyyaml>=6.0", +] +readme = "README.md" +requires-python = ">= 3.9" + +classifiers=[ + # How mature is this project? + "Development Status :: 2 - Pre-Alpha", + # Indicate who your project is intended for + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Software Development :: Libraries", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + # Pick your license as you wish + "License :: OSI Approved :: Apache Software License", + # Specify the Python versions you support here. + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +keywords = ["ml", "ai", "labelbox", "labeling", "llm", "machinelearning", "alignerr", "lbox-alignerr"] + +[project.urls] +Homepage = "https://labelbox.com/" +Documentation = "https://labelbox-python.readthedocs.io/en/latest/" +Repository = "https://github.com/Labelbox/labelbox-python" +Issues = "https://github.com/Labelbox/labelbox-python/issues" +Changelog = "https://github.com/Labelbox/labelbox-python/blob/develop/libs/labelbox/CHANGELOG.md" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.rye] +managed = true +dev-dependencies = [ + "pytest>=8.1.1", + "pytest-cases>=3.8.4", + "pytest-rerunfailures>=14.0", + "pytest-snapshot>=0.9.0", + "pytest-cov>=4.1.0", + "pytest-xdist>=3.5.0", + "faker>=25.5.0", + "pytest-timestamper>=0.0.10", + "pytest-timeout>=2.3.1", + "pytest-order>=1.2.1", + "pyjwt>=2.9.0", +] + +[tool.rye.scripts] +unit = "pytest tests/unit" +integration = "pytest tests/integration" + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.targets.wheel] +packages = ["src/alignerr"] + +[tool.pytest.ini_options] +addopts = "-rP -vvv --durations=20 --cov=alignerr --import-mode=importlib" diff --git a/libs/lbox-alignerr/src/alignerr/__init__.py b/libs/lbox-alignerr/src/alignerr/__init__.py new file mode 100644 index 000000000..eecedade9 --- /dev/null +++ b/libs/lbox-alignerr/src/alignerr/__init__.py @@ -0,0 +1,3 @@ +from alignerr.alignerr_project import AlignerrWorkspace + +__all__ = ["AlignerrWorkspace"] diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py b/libs/lbox-alignerr/src/alignerr/alignerr_project.py similarity index 91% rename from libs/labelbox/src/labelbox/alignerr/alignerr_project.py rename to libs/lbox-alignerr/src/alignerr/alignerr_project.py index 6dcaf5d9e..607db9a42 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project.py +++ b/libs/lbox-alignerr/src/alignerr/alignerr_project.py @@ -3,13 +3,13 @@ import logging -from labelbox.alignerr.schema.project_rate import ProjectRateV2 -from labelbox.alignerr.schema.project_domain import ProjectDomain -from labelbox.alignerr.schema.enchanced_resource_tags import ( +from alignerr.schema.project_rate import ProjectRateV2 +from alignerr.schema.project_domain import ProjectDomain +from alignerr.schema.enchanced_resource_tags import ( EnhancedResourceTag, ResourceTagType, ) -from labelbox.alignerr.schema.project_boost_workforce import ( +from alignerr.schema.project_boost_workforce import ( ProjectBoostWorkforce, ) from labelbox.pagination import PaginatedCollection @@ -20,7 +20,7 @@ if TYPE_CHECKING: from labelbox import Client from labelbox.schema.project import Project - from labelbox.alignerr.schema.project_domain import ProjectDomain + from alignerr.schema.project_domain import ProjectDomain class AlignerrRole(Enum): @@ -161,15 +161,19 @@ def __init__(self, client: "Client"): self.client = client def project_builder(self): - from labelbox.alignerr.alignerr_project_builder import ( + from alignerr.alignerr_project_builder import ( AlignerrProjectBuilder, ) return AlignerrProjectBuilder(self.client) def project_prototype(self): - from labelbox.alignerr.alignerr_project_factory import ( + from alignerr.alignerr_project_factory import ( AlignerrProjectFactory, ) return AlignerrProjectFactory(self.client) + + @classmethod + def from_labelbox(cls, client: "Client") -> "AlignerrWorkspace": + return cls(client) diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py b/libs/lbox-alignerr/src/alignerr/alignerr_project_builder.py similarity index 95% rename from libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py rename to libs/lbox-alignerr/src/alignerr/alignerr_project_builder.py index bebbd6748..d12d00cf3 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project_builder.py +++ b/libs/lbox-alignerr/src/alignerr/alignerr_project_builder.py @@ -3,14 +3,14 @@ from typing import TYPE_CHECKING, Optional, Union, List import logging -from labelbox.alignerr.schema.project_rate import BillingMode -from labelbox.alignerr.schema.project_rate import ProjectRateInput -from labelbox.alignerr.schema.project_domain import ProjectDomain -from labelbox.alignerr.schema.enchanced_resource_tags import ( +from alignerr.schema.project_rate import BillingMode +from alignerr.schema.project_rate import ProjectRateInput +from alignerr.schema.project_domain import ProjectDomain +from alignerr.schema.enchanced_resource_tags import ( EnhancedResourceTag, ResourceTagType, ) -from labelbox.alignerr.schema.project_boost_workforce import ( +from alignerr.schema.project_boost_workforce import ( ProjectBoostWorkforce, ) from labelbox.schema.media_type import MediaType @@ -28,7 +28,7 @@ class ValidationType(Enum): if TYPE_CHECKING: from labelbox import Client - from labelbox.alignerr.alignerr_project import AlignerrProject, AlignerrRole + from alignerr.alignerr_project import AlignerrProject, AlignerrRole class AlignerrProjectBuilder: @@ -185,7 +185,7 @@ def create( labelbox_project = self.client.create_project(**project_data) # Import here to avoid circular imports - from labelbox.alignerr.alignerr_project import AlignerrProject + from alignerr.alignerr_project import AlignerrProject alignerr_project = AlignerrProject( self.client, labelbox_project, _internal=True @@ -254,7 +254,7 @@ def _create_project_owner(self, alignerr_project: "AlignerrProject"): def _validate_alignerr_rates(self): # Import here to avoid circular imports - from labelbox.alignerr.alignerr_project import AlignerrRole + from alignerr.alignerr_project import AlignerrRole required_role_rates = set( [AlignerrRole.Labeler.value, AlignerrRole.Reviewer.value] diff --git a/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py b/libs/lbox-alignerr/src/alignerr/alignerr_project_factory.py similarity index 97% rename from libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py rename to libs/lbox-alignerr/src/alignerr/alignerr_project_factory.py index d49799528..e6c58eff8 100644 --- a/libs/labelbox/src/labelbox/alignerr/alignerr_project_factory.py +++ b/libs/lbox-alignerr/src/alignerr/alignerr_project_factory.py @@ -4,8 +4,8 @@ from pathlib import Path import logging -from labelbox.alignerr.schema.project_rate import BillingMode -from labelbox.alignerr.schema.enchanced_resource_tags import ResourceTagType +from alignerr.schema.project_rate import BillingMode +from alignerr.schema.enchanced_resource_tags import ResourceTagType from labelbox.schema.media_type import MediaType logger = logging.getLogger(__name__) @@ -84,10 +84,10 @@ def create( ) # Import here to avoid circular imports - from labelbox.alignerr.alignerr_project_builder import ( + from alignerr.alignerr_project_builder import ( AlignerrProjectBuilder, ) - from labelbox.alignerr.alignerr_project import AlignerrRole + from alignerr.alignerr_project import AlignerrRole # Create project builder builder = AlignerrProjectBuilder(self.client) diff --git a/libs/labelbox/src/labelbox/alignerr/schema/__init__.py b/libs/lbox-alignerr/src/alignerr/schema/__init__.py similarity index 100% rename from libs/labelbox/src/labelbox/alignerr/schema/__init__.py rename to libs/lbox-alignerr/src/alignerr/schema/__init__.py diff --git a/libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py b/libs/lbox-alignerr/src/alignerr/schema/enchanced_resource_tags.py similarity index 100% rename from libs/labelbox/src/labelbox/alignerr/schema/enchanced_resource_tags.py rename to libs/lbox-alignerr/src/alignerr/schema/enchanced_resource_tags.py diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py b/libs/lbox-alignerr/src/alignerr/schema/project_boost_workforce.py similarity index 100% rename from libs/labelbox/src/labelbox/alignerr/schema/project_boost_workforce.py rename to libs/lbox-alignerr/src/alignerr/schema/project_boost_workforce.py diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_domain.py b/libs/lbox-alignerr/src/alignerr/schema/project_domain.py similarity index 100% rename from libs/labelbox/src/labelbox/alignerr/schema/project_domain.py rename to libs/lbox-alignerr/src/alignerr/schema/project_domain.py diff --git a/libs/labelbox/src/labelbox/alignerr/schema/project_rate.py b/libs/lbox-alignerr/src/alignerr/schema/project_rate.py similarity index 100% rename from libs/labelbox/src/labelbox/alignerr/schema/project_rate.py rename to libs/lbox-alignerr/src/alignerr/schema/project_rate.py diff --git a/libs/labelbox/tests/assets/test_project_comprehensive.yaml b/libs/lbox-alignerr/tests/assets/test_project_comprehensive.yaml similarity index 100% rename from libs/labelbox/tests/assets/test_project_comprehensive.yaml rename to libs/lbox-alignerr/tests/assets/test_project_comprehensive.yaml diff --git a/libs/lbox-alignerr/tests/conftest.py b/libs/lbox-alignerr/tests/conftest.py new file mode 100644 index 000000000..a2ffdd49d --- /dev/null +++ b/libs/lbox-alignerr/tests/conftest.py @@ -0,0 +1,1309 @@ +import json +import os +import re +import time +import uuid +from datetime import datetime +from enum import Enum +from random import randint +from string import ascii_letters +from types import SimpleNamespace +from typing import Tuple, Type + +import pytest +import requests +from lbox.exceptions import LabelboxError + +from labelbox import ( + Classification, + Client, + DataRow, + Dataset, + MediaType, + OntologyBuilder, + Option, + Tool, +) +from labelbox.orm import query +from labelbox.pagination import PaginatedCollection +from labelbox.schema.annotation_import import LabelImport +from labelbox.schema.enums import AnnotationImportState +from labelbox.schema.invite import Invite +from labelbox.schema.ontology import Ontology +from labelbox.schema.project import Project +from labelbox.schema.quality_mode import QualityMode + +IMG_URL = "https://picsum.photos/200/300.jpg" +MASKABLE_IMG_URL = "https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg" +SMALL_DATASET_URL = "https://storage.googleapis.com/lb-artifacts-testing-public/sdk_integration_test/potato.jpeg" +DATA_ROW_PROCESSING_WAIT_TIMEOUT_SECONDS = 30 +DATA_ROW_PROCESSING_WAIT_SLEEP_INTERNAL_SECONDS = 3 +EPHEMERAL_BASE_URL = "http://lb-api-public" +IMAGE_URL = "https://storage.googleapis.com/diagnostics-demo-data/coco/COCO_train2014_000000000034.jpg" +EXTERNAL_ID = "my-image" + +pytest_plugins = [] + + +@pytest.fixture(scope="session") +def rand_gen(): + def gen(field_type): + if field_type is str: + return "".join( + ascii_letters[randint(0, len(ascii_letters) - 1)] + for _ in range(16) + ) + + if field_type is datetime: + return datetime.now() + + raise Exception( + "Can't random generate for field type '%r'" % field_type + ) + + return gen + + +class Environ(Enum): + LOCAL = "local" + PROD = "prod" + STAGING = "staging" + CUSTOM = "custom" + STAGING_EU = "staging-eu" + EPHEMERAL = "ephemeral" # Used for testing PRs with ephemeral environments + + +@pytest.fixture +def image_url() -> str: + return MASKABLE_IMG_URL + + +@pytest.fixture +def external_id() -> str: + return EXTERNAL_ID + + +def ephemeral_endpoint() -> str: + return os.getenv("LABELBOX_TEST_BASE_URL", EPHEMERAL_BASE_URL) + + +def graphql_url(environ: str) -> str: + if environ == Environ.LOCAL: + return "http://localhost:3000/api/graphql" + elif environ == Environ.PROD: + return "https://api.labelbox.com/graphql" + elif environ == Environ.STAGING: + return "https://api.lb-stage.xyz/graphql" + elif environ == Environ.CUSTOM: + graphql_api_endpoint = os.environ.get( + "LABELBOX_TEST_GRAPHQL_API_ENDPOINT" + ) + if graphql_api_endpoint is None: + raise Exception("Missing LABELBOX_TEST_GRAPHQL_API_ENDPOINT") + return graphql_api_endpoint + elif environ == Environ.EPHEMERAL: + return f"{ephemeral_endpoint()}/graphql" + return "http://host.docker.internal:8080/graphql" + + +def rest_url(environ: str) -> str: + if environ == Environ.LOCAL: + return "http://localhost:3000/api/v1" + elif environ == Environ.PROD: + return "https://api.labelbox.com/api/v1" + elif environ == Environ.STAGING: + return "https://api.lb-stage.xyz/api/v1" + elif environ == Environ.CUSTOM: + rest_api_endpoint = os.environ.get("LABELBOX_TEST_REST_API_ENDPOINT") + if rest_api_endpoint is None: + raise Exception("Missing LABELBOX_TEST_REST_API_ENDPOINT") + return rest_api_endpoint + elif environ == Environ.EPHEMERAL: + return f"{ephemeral_endpoint()}/api/v1" + return "http://host.docker.internal:8080/api/v1" + + +def testing_api_key(environ: Environ) -> str: + keys = [ + f"LABELBOX_TEST_API_KEY_{environ.value.upper()}", + "LABELBOX_TEST_API_KEY", + "LABELBOX_API_KEY", + ] + for key in keys: + value = os.environ.get(key) + if value is not None: + return value + raise Exception("Cannot find API to use for tests") + + +def service_api_key() -> str: + service_api_key = os.environ["SERVICE_API_KEY"] + if service_api_key is None: + raise Exception( + "SERVICE_API_KEY is missing and needed for admin client" + ) + return service_api_key + + +class IntegrationClient(Client): + def __init__(self, environ: str) -> None: + api_url = graphql_url(environ) + api_key = testing_api_key(environ) + rest_endpoint = rest_url(environ) + super().__init__( + api_key, + api_url, + enable_experimental=True, + rest_endpoint=rest_endpoint, + ) + self.queries = [] + + def execute(self, query=None, params=None, check_naming=True, **kwargs): + if check_naming and query is not None: + assert ( + re.match(r"\s*(?:query|mutation) \w+PyApi", query) is not None + ) + self.queries.append((query, params)) + if not kwargs.get("timeout"): + kwargs["timeout"] = 30.0 + return super().execute(query, params, **kwargs) + + +class AdminClient(Client): + def __init__(self, env): + """ + The admin client creates organizations and users using admin api described here https://labelbox.atlassian.net/wiki/spaces/AP/pages/2206564433/Internal+Admin+APIs. + """ + self._api_key = service_api_key() + self._admin_endpoint = f"{ephemeral_endpoint()}/admin/v1" + self._api_url = graphql_url(env) + self._rest_endpoint = rest_url(env) + + super().__init__( + self._api_key, + self._api_url, + enable_experimental=True, + rest_endpoint=self._rest_endpoint, + ) + + def _create_organization(self) -> str: + endpoint = f"{self._admin_endpoint}/organizations/" + response = requests.post( + endpoint, + headers=self.headers, + json={"name": f"Test Org {uuid.uuid4()}"}, + ) + + data = response.json() + if response.status_code not in [ + requests.codes.created, + requests.codes.ok, + ]: + raise Exception( + "Failed to create org, message: " + str(data["message"]) + ) + + return data["id"] + + def _create_user(self, organization_id=None) -> Tuple[str, str]: + if organization_id is None: + organization_id = self.organization_id + + endpoint = f"{self._admin_endpoint}/user-identities/" + identity_id = f"e2e+{uuid.uuid4()}" + + response = requests.post( + endpoint, + headers=self.headers, + json={ + "identityId": identity_id, + "email": "email@email.com", + "name": f"tester{uuid.uuid4()}", + "verificationStatus": "VERIFIED", + }, + ) + data = response.json() + if response.status_code not in [ + requests.codes.created, + requests.codes.ok, + ]: + raise Exception( + "Failed to create user, message: " + str(data["message"]) + ) + + user_identity_id = data["identityId"] + + endpoint = ( + f"{self._admin_endpoint}/organizations/{organization_id}/users/" + ) + response = requests.post( + endpoint, + headers=self.headers, + json={"identityId": user_identity_id, "organizationRole": "Admin"}, + ) + + data = response.json() + if response.status_code not in [ + requests.codes.created, + requests.codes.ok, + ]: + raise Exception( + "Failed to create link user to org, message: " + + str(data["message"]) + ) + + user_id = data["id"] + + endpoint = f"{self._admin_endpoint}/users/{user_id}/token" + response = requests.get( + endpoint, + headers=self.headers, + ) + data = response.json() + if response.status_code not in [ + requests.codes.created, + requests.codes.ok, + ]: + raise Exception( + "Failed to create ephemeral user, message: " + + str(data["message"]) + ) + + token = data["token"] + + return user_id, token + + def create_api_key_for_user(self) -> str: + organization_id = self._create_organization() + _, user_token = self._create_user(organization_id) + key_name = f"test-key+{uuid.uuid4()}" + query = """mutation CreateApiKeyPyApi($name: String!) { + createApiKey(data: {name: $name}) { + id + jwt + } + } + """ + params = {"name": key_name} + self.headers["Authorization"] = f"Bearer {user_token}" + res = self.execute(query, params, error_log_key="errors") + + return res["createApiKey"]["jwt"] + + +class EphemeralClient(Client): + def __init__(self, environ=Environ.EPHEMERAL): + self.admin_client = AdminClient(environ) + self.api_key = self.admin_client.create_api_key_for_user() + api_url = graphql_url(environ) + rest_endpoint = rest_url(environ) + + super().__init__( + self.api_key, + api_url, + enable_experimental=True, + rest_endpoint=rest_endpoint, + ) + + +@pytest.fixture +def ephmeral_client() -> EphemeralClient: + return EphemeralClient + + +@pytest.fixture +def admin_client() -> AdminClient: + return AdminClient + + +@pytest.fixture +def integration_client() -> IntegrationClient: + return IntegrationClient + + +@pytest.fixture(scope="session") +def environ() -> Environ: + """ + Checks environment variables for LABELBOX_ENVIRON to be + 'prod' or 'staging' + Make sure to set LABELBOX_TEST_ENVIRON in .github/workflows/python-package.yaml + """ + keys = ["LABELBOX_TEST_ENV", "LABELBOX_TEST_ENVIRON", "LABELBOX_ENV"] + for key in keys: + value = os.environ.get(key) + if value is not None: + return Environ(value) + raise Exception(f"Missing env key in: {os.environ}") + + +def cancel_invite(client, invite_id): + """ + Do not use. Only for testing. + """ + query_str = """mutation CancelInvitePyApi($where: WhereUniqueIdInput!) { + cancelInvite(where: $where) {id}}""" + client.execute(query_str, {"where": {"id": invite_id}}, experimental=True) + + +def get_project_invites(client, project_id): + """ + Do not use. Only for testing. + """ + id_param = "projectId" + query_str = """query GetProjectInvitationsPyApi($from: ID, $first: PageSize, $%s: ID!) { + project(where: {id: $%s}) {id + invites(from: $from, first: $first) { nodes { %s + projectInvites { projectId projectRoleName } } nextCursor}}} + """ % (id_param, id_param, query.results_query_part(Invite)) + return PaginatedCollection( + client, + query_str, + {id_param: project_id}, + ["project", "invites", "nodes"], + Invite, + cursor_path=["project", "invites", "nextCursor"], + ) + + +def get_invites(client): + """ + Do not use. Only for testing. + """ + query_str = """query GetOrgInvitationsPyApi($from: ID, $first: PageSize) { + organization { id invites(from: $from, first: $first) { + nodes { id createdAt organizationRoleName inviteeEmail } nextCursor }}}""" + invites = PaginatedCollection( + client, + query_str, + {}, + ["organization", "invites", "nodes"], + Invite, + cursor_path=["organization", "invites", "nextCursor"], + experimental=True, + ) + return invites + + +@pytest.fixture +def queries(): + return SimpleNamespace( + cancel_invite=cancel_invite, + get_project_invites=get_project_invites, + get_invites=get_invites, + ) + + +@pytest.fixture(scope="session") +def admin_client(environ: str): + return AdminClient(environ) + + +@pytest.fixture(scope="session") +def client(environ: str): + if environ == Environ.EPHEMERAL: + return EphemeralClient() + return IntegrationClient(environ) + + +@pytest.fixture(scope="session") +def pdf_url(client): + pdf_url = client.upload_file("tests/assets/loremipsum.pdf") + return { + "row_data": { + "pdf_url": pdf_url, + }, + "global_key": str(uuid.uuid4()), + } + + +@pytest.fixture(scope="session") +def pdf_entity_data_row(client): + pdf_url = client.upload_file( + "tests/assets/arxiv-pdf_data_99-word-token-pdfs_0801.3483.pdf" + ) + text_layer_url = client.upload_file( + "tests/assets/arxiv-pdf_data_99-word-token-pdfs_0801.3483-lb-textlayer.json" + ) + + return { + "row_data": {"pdf_url": pdf_url, "text_layer_url": text_layer_url}, + "global_key": str(uuid.uuid4()), + } + + +@pytest.fixture() +def conversation_entity_data_row(client, rand_gen): + return { + "row_data": "https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json", + "global_key": f"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json-{rand_gen(str)}", + } + + +@pytest.fixture +def project(client, rand_gen): + project = client.create_project( + name=rand_gen(str), + media_type=MediaType.Image, + ) + yield project + project.delete() + + +@pytest.fixture +def consensus_project(client, rand_gen): + project = client.create_project( + name=rand_gen(str), + quality_modes={QualityMode.Consensus}, + media_type=MediaType.Image, + ) + yield project + project.delete() + + +@pytest.fixture +def model_config(client, rand_gen, valid_model_id): + model_config = client.create_model_config( + name=rand_gen(str), + model_id=valid_model_id, + inference_params={"param": "value"}, + ) + yield model_config + client.delete_model_config(model_config.uid) + + +@pytest.fixture +def consensus_project_with_batch( + consensus_project, initial_dataset, rand_gen, image_url +): + project = consensus_project + dataset = initial_dataset + + data_rows = [] + for _ in range(3): + data_rows.append( + {DataRow.row_data: image_url, DataRow.global_key: str(uuid.uuid4())} + ) + task = dataset.create_data_rows(data_rows) + task.wait_till_done() + assert task.status == "COMPLETE" + + data_rows = list(dataset.data_rows()) + assert len(data_rows) == 3 + batch = project.create_batch( + rand_gen(str), + data_rows, # sample of data row objects + 5, # priority between 1(Highest) - 5(lowest) + ) + + yield [project, batch, data_rows] + batch.delete() + + +@pytest.fixture +def dataset(client, rand_gen): + # Handle invalid default IAM integrations in test environments gracefully + dataset = create_dataset_robust(client, name=rand_gen(str)) + yield dataset + dataset.delete() + + +@pytest.fixture(scope="function") +def unique_dataset(client, rand_gen): + # Handle invalid default IAM integrations in test environments gracefully + dataset = create_dataset_robust(client, name=rand_gen(str)) + yield dataset + dataset.delete() + + +@pytest.fixture +def small_dataset(dataset: Dataset): + task = dataset.create_data_rows( + [ + {"row_data": SMALL_DATASET_URL, "external_id": "my-image"}, + ] + * 2 + ) + task.wait_till_done() + + yield dataset + + +@pytest.fixture +def data_row(dataset, image_url, rand_gen): + global_key = f"global-key-{rand_gen(str)}" + task = dataset.create_data_rows( + [ + { + "row_data": image_url, + "external_id": "my-image", + "global_key": global_key, + }, + ] + ) + task.wait_till_done() + dr = dataset.data_rows().get_one() + yield dr + dr.delete() + + +@pytest.fixture +def data_row_and_global_key(dataset, image_url, rand_gen): + global_key = f"global-key-{rand_gen(str)}" + task = dataset.create_data_rows( + [ + { + "row_data": image_url, + "external_id": "my-image", + "global_key": global_key, + }, + ] + ) + task.wait_till_done() + dr = dataset.data_rows().get_one() + yield dr, global_key + dr.delete() + + +# can be used with +# @pytest.mark.parametrize('data_rows', [], indirect=True) +# if omitted, count defaults to 1 +@pytest.fixture +def data_rows( + dataset, image_url, request, wait_for_data_row_processing, client +): + count = 1 + if hasattr(request, "param"): + count = request.param + + datarows = [ + dict(row_data=image_url, global_key=f"global-key-{uuid.uuid4()}") + for _ in range(count) + ] + + task = dataset.create_data_rows(datarows) + task.wait_till_done() + datarows = dataset.data_rows().get_many(count) + for dr in dataset.data_rows(): + wait_for_data_row_processing(client, dr) + + yield datarows + + for datarow in datarows: + datarow.delete() + + +@pytest.fixture +def iframe_url(environ) -> str: + if environ in [Environ.PROD, Environ.LOCAL]: + return "https://editor.labelbox.com" + elif environ == Environ.STAGING: + return "https://editor.lb-stage.xyz" + + +@pytest.fixture +def sample_image() -> str: + path_to_video = "tests/integration/media/sample_image.jpg" + return path_to_video + + +@pytest.fixture +def sample_video() -> str: + path_to_video = "tests/integration/media/cat.mp4" + return path_to_video + + +@pytest.fixture +def sample_bulk_conversation() -> list: + path_to_conversation = "tests/integration/media/bulk_conversation.json" + with open(path_to_conversation) as json_file: + conversations = json.load(json_file) + return conversations + + +@pytest.fixture +def organization(client): + # Must have at least one seat open in your org to run these tests + org = client.get_organization() + + yield org + + +@pytest.fixture +def configured_project_with_label( + client, + rand_gen, + dataset, + data_row, + wait_for_label_processing, + teardown_helpers, +): + """Project with a connected dataset, having one datarow + + Project contains an ontology with 1 bbox tool + Additionally includes a create_label method for any needed extra labels + One label is already created and yielded when using fixture + """ + project = client.create_project( + name=rand_gen(str), + media_type=MediaType.Image, + ) + project._wait_until_data_rows_are_processed( + data_row_ids=[data_row.uid], + wait_processing_max_seconds=DATA_ROW_PROCESSING_WAIT_TIMEOUT_SECONDS, + sleep_interval=DATA_ROW_PROCESSING_WAIT_SLEEP_INTERNAL_SECONDS, + ) + + project.create_batch( + rand_gen(str), + [data_row.uid], # sample of data row objects + 5, # priority between 1(Highest) - 5(lowest) + ) + ontology = _setup_ontology(project, client) + label = _create_label( + project, data_row, ontology, wait_for_label_processing + ) + yield [project, dataset, data_row, label] + + teardown_helpers.teardown_project_labels_ontology_feature_schemas(project) + + +def _create_label(project, data_row, ontology, wait_for_label_processing): + predictions = [ + { + "uuid": str(uuid.uuid4()), + "schemaId": ontology.tools[0].feature_schema_id, + "dataRow": {"id": data_row.uid}, + "bbox": {"top": 20, "left": 20, "height": 50, "width": 50}, + } + ] + + def create_label(): + """Ad-hoc function to create a LabelImport + Creates a LabelImport task which will create a label + """ + upload_task = LabelImport.create_from_objects( + project.client, + project.uid, + f"label-import-{uuid.uuid4()}", + predictions, + ) + upload_task.wait_until_done(sleep_time_seconds=5) + assert ( + upload_task.state == AnnotationImportState.FINISHED + ), "Label Import did not finish" + assert ( + len(upload_task.errors) == 0 + ), f"Label Import {upload_task.name} failed with errors {upload_task.errors}" + + project.create_label = create_label + project.create_label() + label = wait_for_label_processing(project)[0] + return label + + +def _setup_ontology(project: Project, client: Client): + ontology_builder = OntologyBuilder( + tools=[ + Tool(tool=Tool.Type.BBOX, name="test-bbox-class"), + ] + ) + ontology = client.create_ontology( + name="ontology with features", + media_type=MediaType.Image, + normalized=ontology_builder.asdict(), + ) + project.connect_ontology(ontology) + + return OntologyBuilder.from_project(project) + + +@pytest.fixture +def big_dataset(dataset: Dataset): + task = dataset.create_data_rows( + [ + {"row_data": IMAGE_URL, "external_id": EXTERNAL_ID}, + ] + * 3 + ) + task.wait_till_done() + + yield dataset + + +@pytest.fixture +def configured_batch_project_with_label( + client, + dataset, + data_row, + wait_for_label_processing, + rand_gen, + teardown_helpers, +): + """Project with a batch having one datarow + Project contains an ontology with 1 bbox tool + Additionally includes a create_label method for any needed extra labels + One label is already created and yielded when using fixture + """ + project = client.create_project( + name=rand_gen(str), + media_type=MediaType.Image, + ) + data_rows = [dr.uid for dr in list(dataset.data_rows())] + project._wait_until_data_rows_are_processed( + data_row_ids=data_rows, sleep_interval=3 + ) + project.create_batch("test-batch", data_rows) + project.data_row_ids = data_rows + + ontology = _setup_ontology(project, client) + label = _create_label( + project, data_row, ontology, wait_for_label_processing + ) + + yield [project, dataset, data_row, label] + + teardown_helpers.teardown_project_labels_ontology_feature_schemas(project) + + +@pytest.fixture +def configured_batch_project_with_multiple_datarows( + client, + dataset, + data_rows, + wait_for_label_processing, + rand_gen, + teardown_helpers, +): + """Project with a batch having multiple datarows + Project contains an ontology with 1 bbox tool + Additionally includes a create_label method for any needed extra labels + """ + project = client.create_project( + name=rand_gen(str), + media_type=MediaType.Image, + ) + global_keys = [dr.global_key for dr in data_rows] + + batch_name = f"batch {uuid.uuid4()}" + project.create_batch(batch_name, global_keys=global_keys) + + ontology = _setup_ontology(project, client) + for datarow in data_rows: + _create_label(project, datarow, ontology, wait_for_label_processing) + + yield [project, dataset, data_rows] + + teardown_helpers.teardown_project_labels_ontology_feature_schemas(project) + + +# NOTE this is nice heuristics, also there is this logic _wait_until_data_rows_are_processed in Project +# in case we still have flakiness in the future, we can use it +@pytest.fixture +def wait_for_data_row_processing(): + """ + Do not use. Only for testing. + + Returns DataRow after waiting for it to finish processing media_attributes. + Some tests, specifically ones that rely on label export, rely on + DataRow be fully processed with media_attributes + """ + + def func(client, data_row, custom_check=None): + """ + added check_updated_at because when a data_row is updated from say + an image to pdf, it already has media_attributes and the loop does + not wait for processing to a pdf + """ + data_row_id = data_row.uid + timeout_seconds = 60 + while True: + data_row = client.get_data_row(data_row_id) + passed_custom_check = not custom_check or custom_check(data_row) + if data_row.media_attributes and passed_custom_check: + return data_row + timeout_seconds -= 2 + if timeout_seconds <= 0: + raise TimeoutError( + f"Timed out waiting for DataRow '{data_row_id}' to finish processing media_attributes" + ) + time.sleep(2) + + return func + + +@pytest.fixture +def wait_for_label_processing(): + """ + Do not use. Only for testing. + + Returns project's labels as a list after waiting for them to finish processing. + If `project.labels()` is called before label is fully processed, + it may return an empty set + """ + + def func(project): + timeout_seconds = 10 + while True: + labels = list(project.labels()) + if len(labels) > 0: + return labels + timeout_seconds -= 2 + if timeout_seconds <= 0: + raise TimeoutError( + f"Timed out waiting for label for project '{project.uid}' to finish processing" + ) + time.sleep(2) + + return func + + +@pytest.fixture +def initial_dataset(client, rand_gen): + # Handle invalid default IAM integrations in test environments gracefully + dataset = create_dataset_robust(client, name=rand_gen(str)) + yield dataset + + dataset.delete() + + +@pytest.fixture +def video_data(client, rand_gen, video_data_row, wait_for_data_row_processing): + # Handle invalid default IAM integrations in test environments gracefully + dataset = create_dataset_robust(client, name=rand_gen(str)) + data_row_ids = [] + data_row = dataset.create_data_row(video_data_row) + data_row = wait_for_data_row_processing(client, data_row) + data_row_ids.append(data_row.uid) + yield dataset, data_row_ids + dataset.delete() + + +def create_video_data_row(rand_gen): + return { + "row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-1.mp4", + "global_key": f"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-1.mp4-{rand_gen(str)}", + "media_type": "VIDEO", + } + + +@pytest.fixture +def video_data_100_rows(client, rand_gen, wait_for_data_row_processing): + # Handle invalid default IAM integrations in test environments gracefully + dataset = create_dataset_robust(client, name=rand_gen(str)) + data_row_ids = [] + for _ in range(100): + data_row = dataset.create_data_row(create_video_data_row(rand_gen)) + data_row = wait_for_data_row_processing(client, data_row) + data_row_ids.append(data_row.uid) + yield dataset, data_row_ids + dataset.delete() + + +@pytest.fixture() +def video_data_row(rand_gen): + return create_video_data_row(rand_gen) + + +class ExportV2Helpers: + @classmethod + def run_project_export_v2_task( + cls, project, num_retries=5, task_name=None, filters={}, params={} + ): + task = None + params = ( + params + if params + else { + "project_details": True, + "performance_details": False, + "data_row_details": True, + "label_details": True, + } + ) + while num_retries > 0: + task = project.export_v2( + task_name=task_name, filters=filters, params=params + ) + task.wait_till_done() + assert task.status == "COMPLETE" + assert task.errors is None + if len(task.result) == 0: + num_retries -= 1 + time.sleep(5) + else: + break + return task.result + + @classmethod + def run_dataset_export_v2_task( + cls, dataset, num_retries=5, task_name=None, filters={}, params={} + ): + task = None + params = ( + params + if params + else {"performance_details": False, "label_details": True} + ) + while num_retries > 0: + task = dataset.export_v2( + task_name=task_name, filters=filters, params=params + ) + task.wait_till_done() + assert task.status == "COMPLETE" + assert task.errors is None + if len(task.result) == 0: + num_retries -= 1 + time.sleep(5) + else: + break + + return task.result + + @classmethod + def run_catalog_export_v2_task( + cls, client, num_retries=5, task_name=None, filters={}, params={} + ): + task = None + params = ( + params + if params + else {"performance_details": False, "label_details": True} + ) + catalog = client.get_catalog() + while num_retries > 0: + task = catalog.export_v2( + task_name=task_name, filters=filters, params=params + ) + task.wait_till_done() + assert task.status == "COMPLETE" + assert task.errors is None + if len(task.result) == 0: + num_retries -= 1 + time.sleep(5) + else: + break + + return task.result + + +@pytest.fixture +def export_v2_test_helpers() -> Type[ExportV2Helpers]: + return ExportV2Helpers() + + +@pytest.fixture +def big_dataset_data_row_ids(big_dataset: Dataset): + export_task = big_dataset.export() + export_task.wait_till_done() + stream = export_task.get_buffered_stream() + yield [dr.json["data_row"]["id"] for dr in stream] + + +@pytest.fixture(scope="function") +def dataset_with_invalid_data_rows( + unique_dataset: Dataset, upload_invalid_data_rows_for_dataset +): + upload_invalid_data_rows_for_dataset(unique_dataset) + + yield unique_dataset + + +@pytest.fixture +def upload_invalid_data_rows_for_dataset(): + def _upload_invalid_data_rows_for_dataset(dataset: Dataset): + task = dataset.create_data_rows( + [ + { + "row_data": "gs://invalid-bucket/example.png", # forbidden + "external_id": "image-without-access.jpg", + }, + ] + * 2 + ) + task.wait_till_done() + + return _upload_invalid_data_rows_for_dataset + + +@pytest.fixture +def configured_project( + project_with_one_feature_ontology, initial_dataset, rand_gen, image_url +): + dataset = initial_dataset + data_row_id = dataset.create_data_row(row_data=image_url).uid + project = project_with_one_feature_ontology + + batch = project.create_batch( + rand_gen(str), + [data_row_id], # sample of data row objects + 5, # priority between 1(Highest) - 5(lowest) + ) + project.data_row_ids = [data_row_id] + + yield project + + batch.delete() + + +@pytest.fixture +def project_with_one_feature_ontology(project, client: Client): + tools = [ + Tool(tool=Tool.Type.BBOX, name="test-bbox-class").asdict(), + ] + empty_ontology = {"tools": tools, "classifications": []} + ontology = client.create_ontology( + "empty ontology", empty_ontology, MediaType.Image + ) + project.connect_ontology(ontology) + yield project + + +@pytest.fixture +def configured_project_with_complex_ontology( + client: Client, initial_dataset, rand_gen, image_url, teardown_helpers +): + project = client.create_project( + name=rand_gen(str), + media_type=MediaType.Image, + ) + dataset = initial_dataset + data_row = dataset.create_data_row(row_data=image_url) + data_row_ids = [data_row.uid] + + project.create_batch( + rand_gen(str), + data_row_ids, # sample of data row objects + 5, # priority between 1(Highest) - 5(lowest) + ) + project.data_row_ids = data_row_ids + + ontology = OntologyBuilder() + tools = [ + Tool(tool=Tool.Type.BBOX, name="test-bbox-class"), + Tool(tool=Tool.Type.LINE, name="test-line-class"), + Tool(tool=Tool.Type.POINT, name="test-point-class"), + Tool(tool=Tool.Type.POLYGON, name="test-polygon-class"), + ] + + options = [ + Option(value="first option answer"), + Option(value="second option answer"), + Option(value="third option answer"), + ] + + classifications = [ + Classification( + class_type=Classification.Type.TEXT, name="test-text-class" + ), + Classification( + class_type=Classification.Type.RADIO, + name="test-radio-class", + options=options, + ), + Classification( + class_type=Classification.Type.CHECKLIST, + name="test-checklist-class", + options=options, + ), + ] + + for t in tools: + for c in classifications: + t.add_classification(c) + ontology.add_tool(t) + for c in classifications: + ontology.add_classification(c) + + ontology = client.create_ontology( + "complex image ontology", ontology.asdict(), MediaType.Image + ) + + project.connect_ontology(ontology) + + yield [project, data_row] + teardown_helpers.teardown_project_labels_ontology_feature_schemas(project) + + +@pytest.fixture +def embedding(client: Client, environ): + uuid_str = uuid.uuid4().hex + time.sleep(randint(1, 5)) + embedding = client.create_embedding(f"sdk-int-{uuid_str}", 8) + yield embedding + + embedding.delete() + + +@pytest.fixture +def valid_model_id(): + return "2c903542-d1da-48fd-9db1-8c62571bd3d2" + + +@pytest.fixture +def requested_labeling_service( + rand_gen, client, chat_evaluation_ontology, model_config, teardown_helpers +): + project_name = f"test-model-evaluation-project-{rand_gen(str)}" + dataset_name = f"test-model-evaluation-dataset-{rand_gen(str)}" + project = client.create_model_evaluation_project( + name=project_name, dataset_name=dataset_name, data_row_count=1 + ) + project.connect_ontology(chat_evaluation_ontology) + + project.upsert_instructions("tests/integration/media/sample_pdf.pdf") + + labeling_service = project.get_labeling_service() + project.add_model_config(model_config.uid) + project.set_project_model_setup_complete() + + labeling_service.request() + + yield project, project.get_labeling_service() + + teardown_helpers.teardown_project_labels_ontology_feature_schemas(project) + + +class TearDownHelpers: + @staticmethod + def teardown_project_labels_ontology_feature_schemas(project: Project): + """ + Call this function to release project, labels, ontology and feature schemas in fixture teardown + + NOTE: exception handling is not required as this is a fixture teardown + """ + ontology = project.ontology() + ontology_id = ontology.uid + client = project.client + classification_feature_schema_ids = [ + feature["featureSchemaId"] + for feature in ontology.normalized["classifications"] + ] + tool_feature_schema_ids = [ + feature["featureSchemaId"] + for feature in ontology.normalized["tools"] + ] + + feature_schema_ids = ( + classification_feature_schema_ids + tool_feature_schema_ids + ) + labels = list(project.labels()) + for label in labels: + label.delete() + + project.delete() + client.delete_unused_ontology(ontology_id) + for feature_schema_id in feature_schema_ids: + try: + project.client.delete_unused_feature_schema(feature_schema_id) + except LabelboxError as e: + print( + f"Failed to delete feature schema {feature_schema_id}: {e}" + ) + + @staticmethod + def teardown_ontology_feature_schemas(ontology: Ontology): + """ + Call this function to release project, labels, ontology and feature schemas in fixture teardown + + NOTE: exception handling is not required as this is a fixture teardown + """ + ontology_id = ontology.uid + client = ontology.client + classification_feature_schema_ids = [ + feature["featureSchemaId"] + for feature in ontology.normalized["classifications"] + ] + [ + option["featureSchemaId"] + for feature in ontology.normalized["classifications"] + for option in feature.get("options", []) + ] + + tool_feature_schema_ids = ( + [ + feature["featureSchemaId"] + for feature in ontology.normalized["tools"] + ] + + [ + classification["featureSchemaId"] + for tool in ontology.normalized["tools"] + for classification in tool.get("classifications", []) + ] + + [ + option["featureSchemaId"] + for tool in ontology.normalized["tools"] + for classification in tool.get("classifications", []) + for option in classification.get("options", []) + ] + ) + + feature_schema_ids = ( + classification_feature_schema_ids + tool_feature_schema_ids + ) + + client.delete_unused_ontology(ontology_id) + for feature_schema_id in feature_schema_ids: + try: + project.client.delete_unused_feature_schema(feature_schema_id) + except LabelboxError as e: + print( + f"Failed to delete feature schema {feature_schema_id}: {e}" + ) + + +class ModuleTearDownHelpers(TearDownHelpers): ... + + +class LabelHelpers: + def wait_for_labels(self, project, number_of_labels=1): + timeout_seconds = 10 + while True: + labels = list(project.labels()) + if len(labels) >= number_of_labels: + return labels + timeout_seconds -= 2 + if timeout_seconds <= 0: + raise TimeoutError( + f"Timed out waiting for label for project '{project.uid}' to finish processing" + ) + time.sleep(2) + + +@pytest.fixture +def teardown_helpers(): + return TearDownHelpers() + + +@pytest.fixture(scope="module") +def module_teardown_helpers(): + return TearDownHelpers() + + +@pytest.fixture +def label_helpers(): + return LabelHelpers() + + +def create_dataset_robust(client, **kwargs): + """ + Robust dataset creation that handles invalid default IAM integrations gracefully. + + This is a helper function for tests that need to create datasets directly + instead of using fixtures. It falls back to creating datasets without + IAM integration when the default integration is invalid. + + Args: + client: Labelbox client instance + **kwargs: Arguments to pass to create_dataset + + Returns: + Dataset: Created dataset + """ + try: + return client.create_dataset(**kwargs) + except ValueError as e: + if "Integration is not valid" in str(e): + # Fallback to creating dataset without IAM integration for tests + kwargs["iam_integration"] = None + return client.create_dataset(**kwargs) + else: + raise e diff --git a/libs/labelbox/tests/integration/test_alignerr_project.py b/libs/lbox-alignerr/tests/integration/test_alignerr_project.py similarity index 96% rename from libs/labelbox/tests/integration/test_alignerr_project.py rename to libs/lbox-alignerr/tests/integration/test_alignerr_project.py index cd2513c3d..9d8569f45 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project.py +++ b/libs/lbox-alignerr/tests/integration/test_alignerr_project.py @@ -7,8 +7,8 @@ import uuid import pytest -from labelbox.alignerr.alignerr_project import AlignerrProject -from labelbox.alignerr.schema.project_rate import BillingMode, ProjectRateInput +from alignerr.alignerr_project import AlignerrProject +from alignerr.schema.project_rate import BillingMode, ProjectRateInput from labelbox.schema.media_type import MediaType diff --git a/libs/labelbox/tests/integration/test_alignerr_project_builder.py b/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py similarity index 96% rename from libs/labelbox/tests/integration/test_alignerr_project_builder.py rename to libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py index 108f61944..b3f993f4f 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project_builder.py +++ b/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py @@ -2,8 +2,8 @@ import datetime from labelbox import Client -from labelbox.alignerr.alignerr_project import AlignerrRole -from labelbox.alignerr.schema.project_rate import BillingMode +from alignerr.alignerr_project import AlignerrRole +from alignerr.schema.project_rate import BillingMode from labelbox.schema.media_type import MediaType import pytest @@ -73,7 +73,7 @@ def test_create_alignerr_project_using_builder_validate_input(client: Client): def test_create_alignerr_project_using_builder_add_domains(client: Client): - from labelbox.alignerr.schema.project_domain import ProjectDomain + from alignerr.schema.project_domain import ProjectDomain import uuid # Create test domains first @@ -117,8 +117,8 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags( client: Client, ): """Test creating an Alignerr project with rates, domains, and enhanced resource tags.""" - from labelbox.alignerr.schema.project_domain import ProjectDomain - from labelbox.alignerr.schema.enchanced_resource_tags import ( + from alignerr.schema.project_domain import ProjectDomain + from alignerr.schema.enchanced_resource_tags import ( EnhancedResourceTag, ResourceTagType, ) @@ -275,7 +275,7 @@ def test_create_alignerr_project_selective_validation_skip_multiple( client: Client, ): """Test creating an Alignerr project with selective validation - skipping multiple validations.""" - from labelbox.alignerr.alignerr_project_builder import ValidationType + from alignerr.alignerr_project_builder import ValidationType try: # Create project skipping multiple validations diff --git a/libs/labelbox/tests/integration/test_alignerr_project_factory.py b/libs/lbox-alignerr/tests/integration/test_alignerr_project_factory.py similarity index 98% rename from libs/labelbox/tests/integration/test_alignerr_project_factory.py rename to libs/lbox-alignerr/tests/integration/test_alignerr_project_factory.py index e25a78e17..d65c05b99 100644 --- a/libs/labelbox/tests/integration/test_alignerr_project_factory.py +++ b/libs/lbox-alignerr/tests/integration/test_alignerr_project_factory.py @@ -8,8 +8,8 @@ import pytest from labelbox import Client -from labelbox.alignerr.alignerr_project_factory import AlignerrProjectFactory -from labelbox.alignerr.alignerr_project_builder import ValidationType +from alignerr.alignerr_project_factory import AlignerrProjectFactory +from alignerr.alignerr_project_builder import ValidationType from labelbox.schema.media_type import MediaType @@ -189,7 +189,7 @@ def test_create_alignerr_project_from_yaml_with_customer_rate(client: Client): def test_create_alignerr_project_from_yaml_with_domains(client: Client): """Test creating an AlignerrProject from YAML with domains configuration.""" - from labelbox.alignerr.schema.project_domain import ProjectDomain + from alignerr.schema.project_domain import ProjectDomain import uuid import time @@ -258,7 +258,7 @@ def test_create_alignerr_project_from_yaml_with_domains(client: Client): def test_create_alignerr_project_from_yaml_with_tags(client: Client): """Test creating an AlignerrProject from YAML with enhanced resource tags configuration.""" - from labelbox.alignerr.schema.enchanced_resource_tags import ( + from alignerr.schema.enchanced_resource_tags import ( EnhancedResourceTag, ResourceTagType, ) diff --git a/libs/labelbox/tests/integration/test_enhanced_resource_tags.py b/libs/lbox-alignerr/tests/integration/test_enhanced_resource_tags.py similarity index 98% rename from libs/labelbox/tests/integration/test_enhanced_resource_tags.py rename to libs/lbox-alignerr/tests/integration/test_enhanced_resource_tags.py index 6e0884334..a9ecd8f6a 100644 --- a/libs/labelbox/tests/integration/test_enhanced_resource_tags.py +++ b/libs/lbox-alignerr/tests/integration/test_enhanced_resource_tags.py @@ -6,7 +6,7 @@ import pytest import uuid -from labelbox.alignerr.schema.enchanced_resource_tags import ( +from alignerr.schema.enchanced_resource_tags import ( EnhancedResourceTag, ResourceTagType, ) diff --git a/libs/labelbox/tests/integration/test_project_domain.py b/libs/lbox-alignerr/tests/integration/test_project_domain.py similarity index 98% rename from libs/labelbox/tests/integration/test_project_domain.py rename to libs/lbox-alignerr/tests/integration/test_project_domain.py index 26783b1e5..ed1bc1c69 100644 --- a/libs/labelbox/tests/integration/test_project_domain.py +++ b/libs/lbox-alignerr/tests/integration/test_project_domain.py @@ -6,7 +6,7 @@ import pytest import uuid -from labelbox.alignerr.schema.project_domain import ProjectDomain +from alignerr.schema.project_domain import ProjectDomain from labelbox.schema.media_type import MediaType diff --git a/libs/labelbox/tests/integration/test_project_rate.py b/libs/lbox-alignerr/tests/integration/test_project_rate.py similarity index 98% rename from libs/labelbox/tests/integration/test_project_rate.py rename to libs/lbox-alignerr/tests/integration/test_project_rate.py index e720a3f6c..691ea0303 100644 --- a/libs/labelbox/tests/integration/test_project_rate.py +++ b/libs/lbox-alignerr/tests/integration/test_project_rate.py @@ -4,7 +4,7 @@ import uuid import pytest -from labelbox.alignerr.schema.project_rate import ( +from alignerr.schema.project_rate import ( BillingMode, ProjectRateInput, ProjectRateV2, diff --git a/requirements-dev.lock b/requirements-dev.lock index 0081696f1..97cca34cd 100644 --- a/requirements-dev.lock +++ b/requirements-dev.lock @@ -6,10 +6,11 @@ # features: [] # all-features: true # with-sources: false -# generate-hashes: false -# universal: false -e file:libs/labelbox + # via lbox-alignerr +-e file:libs/lbox-alignerr + # via labelbox -e file:libs/lbox-clients # via labelbox -e file:libs/lbox-example @@ -187,6 +188,7 @@ pyasn1-modules==0.4.0 pydantic==2.8.2 # via databooks # via labelbox + # via lbox-alignerr pydantic-core==2.20.1 # via pydantic pygeotile==1.0.6 @@ -223,6 +225,7 @@ pytz==2024.1 # via pandas pyyaml==6.0.3 # via labelbox + # via lbox-alignerr pyzmq==26.0.3 # via jupyter-client referencing==0.35.1 @@ -313,6 +316,7 @@ typer==0.12.3 # via toml-cli types-pillow==10.2.0.20240520 types-python-dateutil==2.9.0.20240316 +types-pyyaml==6.0.12.20250915 types-requests==2.32.0.20240622 types-tqdm==4.66.0.20240417 typing-extensions==4.12.2 diff --git a/requirements.lock b/requirements.lock index 65ed91f9f..3bf1f2bd4 100644 --- a/requirements.lock +++ b/requirements.lock @@ -6,10 +6,11 @@ # features: [] # all-features: true # with-sources: false -# generate-hashes: false -# universal: false -e file:libs/labelbox + # via lbox-alignerr +-e file:libs/lbox-alignerr + # via labelbox -e file:libs/lbox-clients # via labelbox -e file:libs/lbox-example @@ -75,6 +76,7 @@ pyasn1-modules==0.4.0 # via google-auth pydantic==2.8.2 # via labelbox + # via lbox-alignerr pydantic-core==2.20.1 # via pydantic pygeotile==1.0.6 @@ -87,6 +89,7 @@ python-dateutil==2.9.0.post0 # via labelbox pyyaml==6.0.3 # via labelbox + # via lbox-alignerr requests==2.32.3 # via google-api-core # via labelbox From 726789674023af0d745850a9a6c09dc4bed39c3e Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Sun, 12 Oct 2025 21:21:45 -0500 Subject: [PATCH 067/103] fix GH Actions workflows for lbox --- .github/workflows/lbox-develop.yml | 3 +-- .github/workflows/lbox-publish.yml | 4 +--- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/.github/workflows/lbox-develop.yml b/.github/workflows/lbox-develop.yml index 309ea2969..c0f1773b3 100644 --- a/.github/workflows/lbox-develop.yml +++ b/.github/workflows/lbox-develop.yml @@ -49,7 +49,6 @@ jobs: steps: - uses: actions/checkout@v4 with: - token: ${{ secrets.ACTIONS_ACCESS_TOKEN }} ref: ${{ github.head_ref }} - uses: ./.github/actions/python-package-shared-setup with: @@ -163,4 +162,4 @@ jobs: - name: Build and push (Pull Request) Output if: github.event_name == 'pull_request' run: | - echo "ghcr.io/labelbox/${{ matrix.package }}:${{ github.sha }}" >> "$GITHUB_STEP_SUMMARY" \ No newline at end of file + echo "ghcr.io/labelbox/${{ matrix.package }}:${{ github.sha }}" >> "$GITHUB_STEP_SUMMARY" diff --git a/.github/workflows/lbox-publish.yml b/.github/workflows/lbox-publish.yml index 031207155..dcca8e561 100644 --- a/.github/workflows/lbox-publish.yml +++ b/.github/workflows/lbox-publish.yml @@ -104,8 +104,6 @@ jobs: steps: - uses: actions/checkout@v4 with: - token: ${{ secrets.ACTIONS_ACCESS_TOKEN }} - # ref: ${{ inputs.tag }} ref: ${{ inputs.tag }} - uses: ./.github/actions/python-package-shared-setup with: @@ -190,4 +188,4 @@ jobs: id: image run: | echo "ghcr.io/labelbox/${{ matrix.package }}:latest" >> "$GITHUB_STEP_SUMMARY" - echo "ghcr.io/labelbox/${{ matrix.package }}:${{ inputs.tag }}" >> "$GITHUB_STEP_SUMMARY" \ No newline at end of file + echo "ghcr.io/labelbox/${{ matrix.package }}:${{ inputs.tag }}" >> "$GITHUB_STEP_SUMMARY" From a6080103a1b36cf34f68dc0ab2de3b621c479575 Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Sun, 12 Oct 2025 21:32:40 -0500 Subject: [PATCH 068/103] fix rye format / lint errors --- .../src/alignerr/alignerr_project.py | 4 +- .../src/alignerr/alignerr_project_builder.py | 12 +-- .../src/alignerr/alignerr_project_factory.py | 21 ++--- .../schema/project_boost_workforce.py | 12 +-- .../src/alignerr/schema/project_domain.py | 4 +- .../src/alignerr/schema/project_rate.py | 8 +- libs/lbox-alignerr/tests/conftest.py | 94 +++++-------------- .../integration/test_alignerr_project.py | 12 +-- .../test_alignerr_project_builder.py | 12 +-- .../test_alignerr_project_factory.py | 83 ++++------------ .../test_enhanced_resource_tags.py | 4 +- .../tests/integration/test_project_domain.py | 8 +- .../tests/integration/test_project_rate.py | 12 +-- 13 files changed, 71 insertions(+), 215 deletions(-) diff --git a/libs/lbox-alignerr/src/alignerr/alignerr_project.py b/libs/lbox-alignerr/src/alignerr/alignerr_project.py index 607db9a42..cc2460f87 100644 --- a/libs/lbox-alignerr/src/alignerr/alignerr_project.py +++ b/libs/lbox-alignerr/src/alignerr/alignerr_project.py @@ -33,9 +33,7 @@ class AlignerrRole(Enum): class AlignerrProject: - def __init__( - self, client: "Client", project: "Project", _internal: bool = False - ): + def __init__(self, client: "Client", project: "Project", _internal: bool = False): if not _internal: raise RuntimeError( "AlignerrProject cannot be initialized directly. " diff --git a/libs/lbox-alignerr/src/alignerr/alignerr_project_builder.py b/libs/lbox-alignerr/src/alignerr/alignerr_project_builder.py index d12d00cf3..1edf00d31 100644 --- a/libs/lbox-alignerr/src/alignerr/alignerr_project_builder.py +++ b/libs/lbox-alignerr/src/alignerr/alignerr_project_builder.py @@ -169,9 +169,7 @@ def set_project_owner(self, project_owner_email: str): self._project_owner_email = project_owner_email return self - def create( - self, skip_validation: Union[bool, List[ValidationType]] = False - ): + def create(self, skip_validation: Union[bool, List[ValidationType]] = False): if not skip_validation: self._validate() elif isinstance(skip_validation, list): @@ -205,9 +203,7 @@ def _create_rates(self, alignerr_project: "AlignerrProject"): def _create_domains(self, alignerr_project: "AlignerrProject"): if self._domains: - logger.info( - f"Setting domains: {[domain.name for domain in self._domains]}" - ) + logger.info(f"Setting domains: {[domain.name for domain in self._domains]}") domain_ids = [domain.uid for domain in self._domains] ProjectDomain.connect_project_to_domains( client=self.client, @@ -263,9 +259,7 @@ def _validate_alignerr_rates(self): for role_name in self._alignerr_rates.keys(): required_role_rates.remove(role_name) if len(required_role_rates) > 0: - raise ValueError( - f"Required role rates are not set: {required_role_rates}" - ) + raise ValueError(f"Required role rates are not set: {required_role_rates}") def _validate_customer_rate(self): if self._customer_rate is None: diff --git a/libs/lbox-alignerr/src/alignerr/alignerr_project_factory.py b/libs/lbox-alignerr/src/alignerr/alignerr_project_factory.py index e6c58eff8..27d744b71 100644 --- a/libs/lbox-alignerr/src/alignerr/alignerr_project_factory.py +++ b/libs/lbox-alignerr/src/alignerr/alignerr_project_factory.py @@ -19,9 +19,7 @@ class AlignerrProjectFactory: def __init__(self, client: "Client"): self.client = client - def create( - self, yaml_file_path: str, skip_validation: Union[bool, List] = False - ): + def create(self, yaml_file_path: str, skip_validation: Union[bool, List] = False): """ Create an AlignerrProject from a YAML configuration file. @@ -153,10 +151,7 @@ def create( ) effective_until = None - if ( - "effective_until" in rate_config - and rate_config["effective_until"] - ): + if "effective_until" in rate_config and rate_config["effective_until"]: try: effective_until = datetime.datetime.fromisoformat( rate_config["effective_until"] @@ -208,7 +203,7 @@ def create( ) except ValueError: raise ValueError( - f"Invalid effective_since date format for customer_rate. Use ISO format (YYYY-MM-DDTHH:MM:SS)" + "Invalid effective_since date format for customer_rate. Use ISO format (YYYY-MM-DDTHH:MM:SS)" ) effective_until = None @@ -222,7 +217,7 @@ def create( ) except ValueError: raise ValueError( - f"Invalid effective_until date format for customer_rate. Use ISO format (YYYY-MM-DDTHH:MM:SS)" + "Invalid effective_until date format for customer_rate. Use ISO format (YYYY-MM-DDTHH:MM:SS)" ) # Set the customer rate @@ -257,9 +252,7 @@ def create( required_tag_fields = ["text", "type"] for field in required_tag_fields: if field not in tag_config: - raise ValueError( - f"Required field '{field}' is missing for tag" - ) + raise ValueError(f"Required field '{field}' is missing for tag") # Validate tag type try: @@ -276,9 +269,7 @@ def create( if "project_owner" in config: project_owner_config = config["project_owner"] if not isinstance(project_owner_config, str): - raise ValueError( - "'project_owner' must be a string (email address)" - ) + raise ValueError("'project_owner' must be a string (email address)") builder.set_project_owner(project_owner_config) diff --git a/libs/lbox-alignerr/src/alignerr/schema/project_boost_workforce.py b/libs/lbox-alignerr/src/alignerr/schema/project_boost_workforce.py index fdd9e937a..28ff27566 100644 --- a/libs/lbox-alignerr/src/alignerr/schema/project_boost_workforce.py +++ b/libs/lbox-alignerr/src/alignerr/schema/project_boost_workforce.py @@ -143,9 +143,7 @@ class ProjectBoostWorkforce(DbObject): projectDifficulty = Field.Enum(ProjectDifficulty, "projectDifficulty") projectDescription = Field.String("projectDescription") estimatedTimePerLabel = Field.Float("estimatedTimePerLabel") - disabledCountryRateMultipliers = Field.Boolean( - "disabledCountryRateMultipliers" - ) + disabledCountryRateMultipliers = Field.Boolean("disabledCountryRateMultipliers") billingMode = Field.Enum(BillingMode, "billingMode") customerBillingMode = Field.Enum(BillingMode, "customerBillingMode") type = Field.Enum(ProjectBoostType, "type") @@ -236,12 +234,8 @@ def update( } }""" - result = client.execute( - mutation_str, {"data": update_input.model_dump()} - ) - return ProjectBoostWorkforceResult( - **result["updateProjectBoostWorkforce"] - ) + result = client.execute(mutation_str, {"data": update_input.model_dump()}) + return ProjectBoostWorkforceResult(**result["updateProjectBoostWorkforce"]) @classmethod def set_project_owner( diff --git a/libs/lbox-alignerr/src/alignerr/schema/project_domain.py b/libs/lbox-alignerr/src/alignerr/schema/project_domain.py index 1169c6872..3a9c2b6e3 100644 --- a/libs/lbox-alignerr/src/alignerr/schema/project_domain.py +++ b/libs/lbox-alignerr/src/alignerr/schema/project_domain.py @@ -208,9 +208,7 @@ def get_by_project_id( Returns: PaginatedCollection of ProjectDomain instances """ - query_str = cls.query_by_project_id( - project_id, limit, offset, include_archived - ) + query_str = cls.query_by_project_id(project_id, limit, offset, include_archived) params: Dict[str, Any] = { "projectId": project_id, diff --git a/libs/lbox-alignerr/src/alignerr/schema/project_rate.py b/libs/lbox-alignerr/src/alignerr/schema/project_rate.py index 85788b7d6..1d0d25110 100644 --- a/libs/lbox-alignerr/src/alignerr/schema/project_rate.py +++ b/libs/lbox-alignerr/src/alignerr/schema/project_rate.py @@ -31,9 +31,7 @@ def validate_fields(self): ) if not self.isBillRate and self.rateForId == "": - raise ValueError( - "rateForId must be set to the id of the Alignerr Role" - ) + raise ValueError("rateForId must be set to the id of the Alignerr Role") return self @@ -53,9 +51,7 @@ class ProjectRateV2(DbObject, Deletable): effectiveUntil = Field.DateTime("effectiveUntil") @classmethod - def get_by_project_id( - cls, client, project_id: str - ) -> list["ProjectRateV2"]: + def get_by_project_id(cls, client, project_id: str) -> list["ProjectRateV2"]: query_str = """ query GetAllProjectRatesPyApi($projectId: ID!) { project(where: { id: $projectId }) { diff --git a/libs/lbox-alignerr/tests/conftest.py b/libs/lbox-alignerr/tests/conftest.py index a2ffdd49d..27e1140e1 100644 --- a/libs/lbox-alignerr/tests/conftest.py +++ b/libs/lbox-alignerr/tests/conftest.py @@ -50,16 +50,13 @@ def rand_gen(): def gen(field_type): if field_type is str: return "".join( - ascii_letters[randint(0, len(ascii_letters) - 1)] - for _ in range(16) + ascii_letters[randint(0, len(ascii_letters) - 1)] for _ in range(16) ) if field_type is datetime: return datetime.now() - raise Exception( - "Can't random generate for field type '%r'" % field_type - ) + raise Exception("Can't random generate for field type '%r'" % field_type) return gen @@ -95,9 +92,7 @@ def graphql_url(environ: str) -> str: elif environ == Environ.STAGING: return "https://api.lb-stage.xyz/graphql" elif environ == Environ.CUSTOM: - graphql_api_endpoint = os.environ.get( - "LABELBOX_TEST_GRAPHQL_API_ENDPOINT" - ) + graphql_api_endpoint = os.environ.get("LABELBOX_TEST_GRAPHQL_API_ENDPOINT") if graphql_api_endpoint is None: raise Exception("Missing LABELBOX_TEST_GRAPHQL_API_ENDPOINT") return graphql_api_endpoint @@ -139,9 +134,7 @@ def testing_api_key(environ: Environ) -> str: def service_api_key() -> str: service_api_key = os.environ["SERVICE_API_KEY"] if service_api_key is None: - raise Exception( - "SERVICE_API_KEY is missing and needed for admin client" - ) + raise Exception("SERVICE_API_KEY is missing and needed for admin client") return service_api_key @@ -160,9 +153,7 @@ def __init__(self, environ: str) -> None: def execute(self, query=None, params=None, check_naming=True, **kwargs): if check_naming and query is not None: - assert ( - re.match(r"\s*(?:query|mutation) \w+PyApi", query) is not None - ) + assert re.match(r"\s*(?:query|mutation) \w+PyApi", query) is not None self.queries.append((query, params)) if not kwargs.get("timeout"): kwargs["timeout"] = 30.0 @@ -199,9 +190,7 @@ def _create_organization(self) -> str: requests.codes.created, requests.codes.ok, ]: - raise Exception( - "Failed to create org, message: " + str(data["message"]) - ) + raise Exception("Failed to create org, message: " + str(data["message"])) return data["id"] @@ -227,15 +216,11 @@ def _create_user(self, organization_id=None) -> Tuple[str, str]: requests.codes.created, requests.codes.ok, ]: - raise Exception( - "Failed to create user, message: " + str(data["message"]) - ) + raise Exception("Failed to create user, message: " + str(data["message"])) user_identity_id = data["identityId"] - endpoint = ( - f"{self._admin_endpoint}/organizations/{organization_id}/users/" - ) + endpoint = f"{self._admin_endpoint}/organizations/{organization_id}/users/" response = requests.post( endpoint, headers=self.headers, @@ -248,8 +233,7 @@ def _create_user(self, organization_id=None) -> Tuple[str, str]: requests.codes.ok, ]: raise Exception( - "Failed to create link user to org, message: " - + str(data["message"]) + "Failed to create link user to org, message: " + str(data["message"]) ) user_id = data["id"] @@ -265,8 +249,7 @@ def _create_user(self, organization_id=None) -> Tuple[str, str]: requests.codes.ok, ]: raise Exception( - "Failed to create ephemeral user, message: " - + str(data["message"]) + "Failed to create ephemeral user, message: " + str(data["message"]) ) token = data["token"] @@ -311,11 +294,6 @@ def ephmeral_client() -> EphemeralClient: return EphemeralClient -@pytest.fixture -def admin_client() -> AdminClient: - return AdminClient - - @pytest.fixture def integration_client() -> IntegrationClient: return IntegrationClient @@ -568,9 +546,7 @@ def data_row_and_global_key(dataset, image_url, rand_gen): # @pytest.mark.parametrize('data_rows', [], indirect=True) # if omitted, count defaults to 1 @pytest.fixture -def data_rows( - dataset, image_url, request, wait_for_data_row_processing, client -): +def data_rows(dataset, image_url, request, wait_for_data_row_processing, client): count = 1 if hasattr(request, "param"): count = request.param @@ -659,9 +635,7 @@ def configured_project_with_label( 5, # priority between 1(Highest) - 5(lowest) ) ontology = _setup_ontology(project, client) - label = _create_label( - project, data_row, ontology, wait_for_label_processing - ) + label = _create_label(project, data_row, ontology, wait_for_label_processing) yield [project, dataset, data_row, label] teardown_helpers.teardown_project_labels_ontology_feature_schemas(project) @@ -756,9 +730,7 @@ def configured_batch_project_with_label( project.data_row_ids = data_rows ontology = _setup_ontology(project, client) - label = _create_label( - project, data_row, ontology, wait_for_label_processing - ) + label = _create_label(project, data_row, ontology, wait_for_label_processing) yield [project, dataset, data_row, label] @@ -940,9 +912,7 @@ def run_dataset_export_v2_task( ): task = None params = ( - params - if params - else {"performance_details": False, "label_details": True} + params if params else {"performance_details": False, "label_details": True} ) while num_retries > 0: task = dataset.export_v2( @@ -965,9 +935,7 @@ def run_catalog_export_v2_task( ): task = None params = ( - params - if params - else {"performance_details": False, "label_details": True} + params if params else {"performance_details": False, "label_details": True} ) catalog = client.get_catalog() while num_retries > 0: @@ -1051,9 +1019,7 @@ def project_with_one_feature_ontology(project, client: Client): Tool(tool=Tool.Type.BBOX, name="test-bbox-class").asdict(), ] empty_ontology = {"tools": tools, "classifications": []} - ontology = client.create_ontology( - "empty ontology", empty_ontology, MediaType.Image - ) + ontology = client.create_ontology("empty ontology", empty_ontology, MediaType.Image) project.connect_ontology(ontology) yield project @@ -1092,9 +1058,7 @@ def configured_project_with_complex_ontology( ] classifications = [ - Classification( - class_type=Classification.Type.TEXT, name="test-text-class" - ), + Classification(class_type=Classification.Type.TEXT, name="test-text-class"), Classification( class_type=Classification.Type.RADIO, name="test-radio-class", @@ -1179,13 +1143,10 @@ def teardown_project_labels_ontology_feature_schemas(project: Project): for feature in ontology.normalized["classifications"] ] tool_feature_schema_ids = [ - feature["featureSchemaId"] - for feature in ontology.normalized["tools"] + feature["featureSchemaId"] for feature in ontology.normalized["tools"] ] - feature_schema_ids = ( - classification_feature_schema_ids + tool_feature_schema_ids - ) + feature_schema_ids = classification_feature_schema_ids + tool_feature_schema_ids labels = list(project.labels()) for label in labels: label.delete() @@ -1196,9 +1157,7 @@ def teardown_project_labels_ontology_feature_schemas(project: Project): try: project.client.delete_unused_feature_schema(feature_schema_id) except LabelboxError as e: - print( - f"Failed to delete feature schema {feature_schema_id}: {e}" - ) + print(f"Failed to delete feature schema {feature_schema_id}: {e}") @staticmethod def teardown_ontology_feature_schemas(ontology: Ontology): @@ -1219,10 +1178,7 @@ def teardown_ontology_feature_schemas(ontology: Ontology): ] tool_feature_schema_ids = ( - [ - feature["featureSchemaId"] - for feature in ontology.normalized["tools"] - ] + [feature["featureSchemaId"] for feature in ontology.normalized["tools"]] + [ classification["featureSchemaId"] for tool in ontology.normalized["tools"] @@ -1236,18 +1192,14 @@ def teardown_ontology_feature_schemas(ontology: Ontology): ] ) - feature_schema_ids = ( - classification_feature_schema_ids + tool_feature_schema_ids - ) + feature_schema_ids = classification_feature_schema_ids + tool_feature_schema_ids client.delete_unused_ontology(ontology_id) for feature_schema_id in feature_schema_ids: try: project.client.delete_unused_feature_schema(feature_schema_id) except LabelboxError as e: - print( - f"Failed to delete feature schema {feature_schema_id}: {e}" - ) + print(f"Failed to delete feature schema {feature_schema_id}: {e}") class ModuleTearDownHelpers(TearDownHelpers): ... diff --git a/libs/lbox-alignerr/tests/integration/test_alignerr_project.py b/libs/lbox-alignerr/tests/integration/test_alignerr_project.py index 9d8569f45..966d44cad 100644 --- a/libs/lbox-alignerr/tests/integration/test_alignerr_project.py +++ b/libs/lbox-alignerr/tests/integration/test_alignerr_project.py @@ -16,9 +16,7 @@ def test_project(client): """Create a test project for AlignerrProject testing.""" project_name = f"Test AlignerrProject {uuid.uuid4()}" - project = client.create_project( - name=project_name, media_type=MediaType.Image - ) + project = client.create_project(name=project_name, media_type=MediaType.Image) yield project @@ -73,18 +71,14 @@ def test_alignerr_project_domains(client, test_alignerr_project): # The collection might be empty for a new project, which is expected -def test_alignerr_project_get_project_rates_no_rates( - client, test_alignerr_project -): +def test_alignerr_project_get_project_rates_no_rates(client, test_alignerr_project): """Test get_project_rates() when no rates are set.""" # For a new project without rates, this should return an empty list project_rates = test_alignerr_project.get_project_rates() assert project_rates == [] -def test_alignerr_project_set_and_get_project_rates( - client, test_alignerr_project -): +def test_alignerr_project_set_and_get_project_rates(client, test_alignerr_project): """Test setting and getting project rates.""" # Create a project rate input for a customer rate (isBillRate=True requires empty rateForId) project_rate_input = ProjectRateInput( diff --git a/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py b/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py index b3f993f4f..a1f378d6e 100644 --- a/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py +++ b/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py @@ -256,9 +256,7 @@ def test_create_alignerr_project_with_project_owner(client: Client): project_boost_workforce = alignerr_project.get_project_owner() if project_boost_workforce: - assert ( - project_boost_workforce.projectOwnerUserId == current_user.uid - ) + assert project_boost_workforce.projectOwnerUserId == current_user.uid assert project_boost_workforce.projectOwner.uid == current_user.uid alignerr_project.project.delete() @@ -266,7 +264,7 @@ def test_create_alignerr_project_with_project_owner(client: Client): # Clean up if test fails try: alignerr_project.project.delete() - except: + except Exception: pass raise e @@ -300,15 +298,13 @@ def test_create_alignerr_project_selective_validation_skip_multiple( ) assert alignerr_project is not None - assert ( - alignerr_project.project.name == "TestAlignerrProjectSkipMultiple" - ) + assert alignerr_project.project.name == "TestAlignerrProjectSkipMultiple" alignerr_project.project.delete() except Exception as e: # Clean up if test fails try: alignerr_project.project.delete() - except: + except Exception: pass raise e diff --git a/libs/lbox-alignerr/tests/integration/test_alignerr_project_factory.py b/libs/lbox-alignerr/tests/integration/test_alignerr_project_factory.py index d65c05b99..488bebc2d 100644 --- a/libs/lbox-alignerr/tests/integration/test_alignerr_project_factory.py +++ b/libs/lbox-alignerr/tests/integration/test_alignerr_project_factory.py @@ -17,9 +17,7 @@ def test_create_alignerr_project_from_yaml_basic(client: Client): """Test creating an AlignerrProject from a basic YAML configuration.""" config = {"name": "TestFactoryProject", "media_type": "IMAGE"} - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -56,9 +54,7 @@ def test_create_alignerr_project_from_yaml_with_rates(client: Client): }, } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -87,18 +83,14 @@ def test_create_alignerr_project_from_yaml_validation_error(client: Client): # Missing media_type } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name try: factory = AlignerrProjectFactory(client) - with pytest.raises( - ValueError, match="Required field 'media_type' is missing" - ): + with pytest.raises(ValueError, match="Required field 'media_type' is missing"): factory.create(yaml_file_path) finally: os.unlink(yaml_file_path) @@ -108,9 +100,7 @@ def test_create_alignerr_project_from_yaml_invalid_media_type(client: Client): """Test that invalid media types raise appropriate errors.""" config = {"name": "TestProject", "media_type": "INVALID_MEDIA_TYPE"} - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -156,9 +146,7 @@ def test_create_alignerr_project_from_yaml_with_customer_rate(client: Client): }, } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -169,18 +157,13 @@ def test_create_alignerr_project_from_yaml_with_customer_rate(client: Client): ) assert alignerr_project is not None - assert ( - alignerr_project.project.name - == "TestFactoryProjectWithCustomerRate" - ) + assert alignerr_project.project.name == "TestFactoryProjectWithCustomerRate" assert alignerr_project.project.media_type == MediaType.Image # Verify rates were set project_rates = alignerr_project.get_project_rates() assert isinstance(project_rates, list) - assert ( - len(project_rates) >= 2 - ) # Should have both labeler and reviewer rates + assert len(project_rates) >= 2 # Should have both labeler and reviewer rates alignerr_project.project.delete() finally: @@ -226,9 +209,7 @@ def test_create_alignerr_project_from_yaml_with_domains(client: Client): "domains": [domain1_name, domain2_name], } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -307,9 +288,7 @@ def test_create_alignerr_project_from_yaml_with_tags(client: Client): ], } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -326,11 +305,6 @@ def test_create_alignerr_project_from_yaml_with_tags(client: Client): enhanced_tags = alignerr_project.get_tags() assert len(enhanced_tags) >= 1 # At least one tag should be present - # Check that our specific tags are present (if any) - tag_texts = [tag.text for tag in enhanced_tags] - # Note: The tag matching might not work perfectly due to how the builder processes tags - # So we just verify that tags were processed - alignerr_project.project.delete() finally: os.unlink(yaml_file_path) @@ -370,9 +344,7 @@ def test_create_alignerr_project_from_yaml_with_project_owner(client: Client): "project_owner": current_user.email, } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -386,9 +358,7 @@ def test_create_alignerr_project_from_yaml_with_project_owner(client: Client): # Verify project owner was set project_boost_workforce = alignerr_project.get_project_owner() if project_boost_workforce: - assert ( - project_boost_workforce.projectOwnerUserId == current_user.uid - ) + assert project_boost_workforce.projectOwnerUserId == current_user.uid assert project_boost_workforce.projectOwner.uid == current_user.uid alignerr_project.project.delete() @@ -403,9 +373,7 @@ def test_create_alignerr_project_from_yaml_comprehensive(client: Client): # Path to the comprehensive test YAML file yaml_file_path = ( - Path(__file__).parent.parent - / "assets" - / "test_project_comprehensive.yaml" + Path(__file__).parent.parent / "assets" / "test_project_comprehensive.yaml" ) # Read and modify the YAML to use current user's email and remove domains/tags that require existing resources @@ -422,9 +390,7 @@ def test_create_alignerr_project_from_yaml_comprehensive(client: Client): del config["tags"] # Create temporary YAML file with updated config - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) temp_yaml_path = f.name @@ -444,9 +410,7 @@ def test_create_alignerr_project_from_yaml_comprehensive(client: Client): # Verify project owner was set project_boost_workforce = alignerr_project.get_project_owner() if project_boost_workforce: - assert ( - project_boost_workforce.projectOwnerUserId == current_user.uid - ) + assert project_boost_workforce.projectOwnerUserId == current_user.uid alignerr_project.project.delete() finally: @@ -478,9 +442,7 @@ def test_create_alignerr_project_from_yaml_selective_validation(client: Client): # Note: No project owner set, but we skip that validation } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -492,10 +454,7 @@ def test_create_alignerr_project_from_yaml_selective_validation(client: Client): ) assert alignerr_project is not None - assert ( - alignerr_project.project.name - == "TestFactoryProjectSelectiveValidation" - ) + assert alignerr_project.project.name == "TestFactoryProjectSelectiveValidation" alignerr_project.project.delete() finally: @@ -515,9 +474,7 @@ def test_create_alignerr_project_from_yaml_invalid_customer_rate( }, } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name @@ -543,9 +500,7 @@ def test_create_alignerr_project_from_yaml_invalid_tags(client: Client): ], } - with tempfile.NamedTemporaryFile( - mode="w", suffix=".yaml", delete=False - ) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: yaml.dump(config, f) yaml_file_path = f.name diff --git a/libs/lbox-alignerr/tests/integration/test_enhanced_resource_tags.py b/libs/lbox-alignerr/tests/integration/test_enhanced_resource_tags.py index a9ecd8f6a..5e15d5eeb 100644 --- a/libs/lbox-alignerr/tests/integration/test_enhanced_resource_tags.py +++ b/libs/lbox-alignerr/tests/integration/test_enhanced_resource_tags.py @@ -18,9 +18,7 @@ def test_resource_tags(client): tags = [] # Create multiple test tags with different types - for i, tag_type in enumerate( - [ResourceTagType.Default, ResourceTagType.Billing] - ): + for i, tag_type in enumerate([ResourceTagType.Default, ResourceTagType.Billing]): tag_text = f"Test_Tag_{i + 1}_{uuid.uuid4().hex[:8]}" tag_color = f"#{i:06x}" # Generate different colors tag = EnhancedResourceTag.create( diff --git a/libs/lbox-alignerr/tests/integration/test_project_domain.py b/libs/lbox-alignerr/tests/integration/test_project_domain.py index ed1bc1c69..7cd92348e 100644 --- a/libs/lbox-alignerr/tests/integration/test_project_domain.py +++ b/libs/lbox-alignerr/tests/integration/test_project_domain.py @@ -14,9 +14,7 @@ def test_project(client): """Create a test project for domain testing.""" project_name = f"Test Project Domain {uuid.uuid4()}" - project = client.create_project( - name=project_name, media_type=MediaType.Image - ) + project = client.create_project(name=project_name, media_type=MediaType.Image) yield project @@ -120,9 +118,7 @@ def test_search_project_domains(client, test_domains): # Test 2: Search by specific name - should find exact match target_domain = test_domains[0] - search_results = ProjectDomain.search( - client, search_by_name=target_domain.name - ) + search_results = ProjectDomain.search(client, search_by_name=target_domain.name) found_domains = list(search_results) assert len(found_domains) >= 1 assert any(domain.name == target_domain.name for domain in found_domains) diff --git a/libs/lbox-alignerr/tests/integration/test_project_rate.py b/libs/lbox-alignerr/tests/integration/test_project_rate.py index 691ea0303..8612c9f60 100644 --- a/libs/lbox-alignerr/tests/integration/test_project_rate.py +++ b/libs/lbox-alignerr/tests/integration/test_project_rate.py @@ -16,9 +16,7 @@ def test_project(client): """Create a test project for ProjectRateV2 testing.""" project_name = f"Test ProjectRateV2 {uuid.uuid4()}" - project = client.create_project( - name=project_name, media_type=MediaType.Image - ) + project = client.create_project(name=project_name, media_type=MediaType.Image) yield project @@ -32,9 +30,7 @@ def test_project(client): def test_project_rate_input_validation(): """Test ProjectRateInput validation logic.""" # Test negative rate validation - with pytest.raises( - ValueError, match="Rate must be greater than or equal to 0" - ): + with pytest.raises(ValueError, match="Rate must be greater than or equal to 0"): ProjectRateInput( rateForId="", isBillRate=True, @@ -75,9 +71,7 @@ def test_set_and_get_project_rate_customer(client, test_project): ) # Set the project rate - result = ProjectRateV2.set_project_rate( - client, test_project.uid, rate_input - ) + result = ProjectRateV2.set_project_rate(client, test_project.uid, rate_input) assert result is True # Get the project rates back From 212f67947c97e86e304958482f82faca70c433dc Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Sun, 12 Oct 2025 21:38:15 -0500 Subject: [PATCH 069/103] Add .gitkeep file to libs/lbox-alignerr/tests/unit --- libs/lbox-alignerr/tests/unit/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 libs/lbox-alignerr/tests/unit/.gitkeep diff --git a/libs/lbox-alignerr/tests/unit/.gitkeep b/libs/lbox-alignerr/tests/unit/.gitkeep new file mode 100644 index 000000000..e69de29bb From 1fb9031f647dcc8efa47410767edc140f434dc79 Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Sun, 12 Oct 2025 21:41:20 -0500 Subject: [PATCH 070/103] need to add an (dummy) actual test --- libs/lbox-alignerr/tests/unit/.gitkeep | 0 libs/lbox-alignerr/tests/unit/test_placeholder.py | 2 ++ 2 files changed, 2 insertions(+) delete mode 100644 libs/lbox-alignerr/tests/unit/.gitkeep create mode 100644 libs/lbox-alignerr/tests/unit/test_placeholder.py diff --git a/libs/lbox-alignerr/tests/unit/.gitkeep b/libs/lbox-alignerr/tests/unit/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/libs/lbox-alignerr/tests/unit/test_placeholder.py b/libs/lbox-alignerr/tests/unit/test_placeholder.py new file mode 100644 index 000000000..201975fcc --- /dev/null +++ b/libs/lbox-alignerr/tests/unit/test_placeholder.py @@ -0,0 +1,2 @@ +def test_placeholder(): + pass From 7fcd96f040c0fe6fb5873e14d5819907f976aa53 Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Sun, 12 Oct 2025 21:48:27 -0500 Subject: [PATCH 071/103] fix tests --- .../integration/test_alignerr_project.py | 5 +++-- .../test_alignerr_project_builder.py | 22 ++++++++++++------- 2 files changed, 17 insertions(+), 10 deletions(-) diff --git a/libs/lbox-alignerr/tests/integration/test_alignerr_project.py b/libs/lbox-alignerr/tests/integration/test_alignerr_project.py index 966d44cad..f3c38964d 100644 --- a/libs/lbox-alignerr/tests/integration/test_alignerr_project.py +++ b/libs/lbox-alignerr/tests/integration/test_alignerr_project.py @@ -7,7 +7,7 @@ import uuid import pytest -from alignerr.alignerr_project import AlignerrProject +from alignerr.alignerr_project import AlignerrProject, AlignerrWorkspace from alignerr.schema.project_rate import BillingMode, ProjectRateInput from labelbox.schema.media_type import MediaType @@ -31,7 +31,8 @@ def test_project(client): def test_alignerr_project(client, test_project): """Create a test AlignerrProject instance using the builder pattern.""" return ( - client.alignerr_workspace.project_builder() + AlignerrWorkspace.from_labelbox(client) + .project_builder() .set_name(test_project.name) .set_media_type(test_project.media_type) .create(skip_validation=True) diff --git a/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py b/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py index a1f378d6e..2bcdd9f08 100644 --- a/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py +++ b/libs/lbox-alignerr/tests/integration/test_alignerr_project_builder.py @@ -2,7 +2,7 @@ import datetime from labelbox import Client -from alignerr.alignerr_project import AlignerrRole +from alignerr.alignerr_project import AlignerrRole, AlignerrWorkspace from alignerr.schema.project_rate import BillingMode from labelbox.schema.media_type import MediaType import pytest @@ -10,7 +10,8 @@ def test_skip_validation(client: Client): alignerr_project = ( - client.alignerr_workspace.project_builder() + AlignerrWorkspace.from_labelbox(client) + .project_builder() .set_name("TestAlignerrProject") .set_media_type(MediaType.Image) .set_alignerr_role_rate( @@ -29,7 +30,7 @@ def test_skip_validation(client: Client): def test_create_alignerr_project_using_builder_validate_input(client: Client): with pytest.raises(ValueError): - client.alignerr_workspace.project_builder().set_name( + AlignerrWorkspace.from_labelbox(client).project_builder().set_name( "TestAlignerrProject" ).set_media_type(MediaType.Image).set_alignerr_role_rate( role_name=AlignerrRole.Labeler, @@ -42,7 +43,8 @@ def test_create_alignerr_project_using_builder_validate_input(client: Client): current_user = client.get_user() alignerr_project = ( - client.alignerr_workspace.project_builder() + AlignerrWorkspace.from_labelbox(client) + .project_builder() .set_name("TestAlignerrProject2") .set_media_type(MediaType.Image) .set_alignerr_role_rate( @@ -91,7 +93,8 @@ def test_create_alignerr_project_using_builder_add_domains(client: Client): try: # Add domains using set_domains method alignerr_project = ( - client.alignerr_workspace.project_builder() + AlignerrWorkspace.from_labelbox(client) + .project_builder() .set_name("TestAlignerrProject3") .set_media_type(MediaType.Image) .set_domains([domain1_name, domain2_name]) @@ -158,7 +161,8 @@ def test_create_alignerr_project_with_rates_domains_and_resource_tags( # Create project with rates, domains, and resource tags alignerr_project = ( - client.alignerr_workspace.project_builder() + AlignerrWorkspace.from_labelbox(client) + .project_builder() .set_name("TestAlignerrProjectWithAll") .set_media_type(MediaType.Image) .set_alignerr_role_rate( @@ -225,7 +229,8 @@ def test_create_alignerr_project_with_project_owner(client: Client): try: # Create project with project owner using email alignerr_project = ( - client.alignerr_workspace.project_builder() + AlignerrWorkspace.from_labelbox(client) + .project_builder() .set_name("TestAlignerrProjectWithOwner") .set_media_type(MediaType.Image) .set_alignerr_role_rate( @@ -278,7 +283,8 @@ def test_create_alignerr_project_selective_validation_skip_multiple( try: # Create project skipping multiple validations alignerr_project = ( - client.alignerr_workspace.project_builder() + AlignerrWorkspace.from_labelbox(client) + .project_builder() .set_name("TestAlignerrProjectSkipMultiple") .set_media_type(MediaType.Image) .set_alignerr_role_rate( From 70cecd5ec41ceda92599fe13cb04ccc1142e7096 Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Thu, 23 Oct 2025 14:20:43 -0500 Subject: [PATCH 072/103] [PLT-0] lbox-alignerr: add missing Dockerfile (#2024) --- .github/workflows/lbox-develop.yml | 1 + libs/lbox-alignerr/Dockerfile | 45 ++++++++++++++++++++++++++++++ libs/lbox-clients/Dockerfile | 9 +++--- libs/lbox-example/Dockerfile | 9 +++--- 4 files changed, 56 insertions(+), 8 deletions(-) create mode 100644 libs/lbox-alignerr/Dockerfile diff --git a/.github/workflows/lbox-develop.yml b/.github/workflows/lbox-develop.yml index c0f1773b3..cfad49232 100644 --- a/.github/workflows/lbox-develop.yml +++ b/.github/workflows/lbox-develop.yml @@ -104,6 +104,7 @@ jobs: with: packages-dir: dist/ repository-url: https://test.pypi.org/legacy/ + verbose: true test-container: runs-on: ubuntu-latest needs: ['build', 'path-filter'] diff --git a/libs/lbox-alignerr/Dockerfile b/libs/lbox-alignerr/Dockerfile new file mode 100644 index 000000000..64d585507 --- /dev/null +++ b/libs/lbox-alignerr/Dockerfile @@ -0,0 +1,45 @@ +# https://github.com/ucyo/python-package-template/blob/master/Dockerfile +FROM python:3.10-slim as rye + +ENV LANG="C.UTF-8" \ + LC_ALL="C.UTF-8" \ + PATH="/home/python/.local/bin:/home/python/.rye/shims:$PATH" \ + PIP_NO_CACHE_DIR="false" \ + RYE_VERSION="0.43.0" \ + RYE_INSTALL_OPTION="--yes" \ + LABELBOX_TEST_ENVIRON="prod" + +RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ + ca-certificates \ + curl \ + inotify-tools \ + make \ + # cv2 + libsm6 \ + libxext6 \ + ffmpeg \ + libfontconfig1 \ + libxrender1 \ + libgl1 \ + libglx-mesa0 \ + libgeos-dev \ + gcc \ + && rm -rf /var/lib/apt/lists/* + +RUN groupadd --gid 1000 python && \ + useradd --uid 1000 --gid python --shell /bin/bash --create-home python + +USER 1000 +WORKDIR /home/python/ + +RUN curl -sSf https://rye.astral.sh/get | bash - + +COPY --chown=python:python . /home/python/labelbox-python/ +WORKDIR /home/python/labelbox-python + +RUN rye config --set-bool behavior.global-python=true && \ + rye config --set-bool behavior.use-uv=true && \ + rye pin 3.10 && \ + rye sync + +CMD rye run unit && rye integration diff --git a/libs/lbox-clients/Dockerfile b/libs/lbox-clients/Dockerfile index 1d7a3642a..64d585507 100644 --- a/libs/lbox-clients/Dockerfile +++ b/libs/lbox-clients/Dockerfile @@ -1,5 +1,5 @@ # https://github.com/ucyo/python-package-template/blob/master/Dockerfile -FROM python:3.9-slim as rye +FROM python:3.10-slim as rye ENV LANG="C.UTF-8" \ LC_ALL="C.UTF-8" \ @@ -20,7 +20,8 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins ffmpeg \ libfontconfig1 \ libxrender1 \ - libgl1-mesa-glx \ + libgl1 \ + libglx-mesa0 \ libgeos-dev \ gcc \ && rm -rf /var/lib/apt/lists/* @@ -38,7 +39,7 @@ WORKDIR /home/python/labelbox-python RUN rye config --set-bool behavior.global-python=true && \ rye config --set-bool behavior.use-uv=true && \ - rye pin 3.9 && \ + rye pin 3.10 && \ rye sync -CMD rye run unit && rye integration \ No newline at end of file +CMD rye run unit && rye integration diff --git a/libs/lbox-example/Dockerfile b/libs/lbox-example/Dockerfile index 1d7a3642a..64d585507 100644 --- a/libs/lbox-example/Dockerfile +++ b/libs/lbox-example/Dockerfile @@ -1,5 +1,5 @@ # https://github.com/ucyo/python-package-template/blob/master/Dockerfile -FROM python:3.9-slim as rye +FROM python:3.10-slim as rye ENV LANG="C.UTF-8" \ LC_ALL="C.UTF-8" \ @@ -20,7 +20,8 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins ffmpeg \ libfontconfig1 \ libxrender1 \ - libgl1-mesa-glx \ + libgl1 \ + libglx-mesa0 \ libgeos-dev \ gcc \ && rm -rf /var/lib/apt/lists/* @@ -38,7 +39,7 @@ WORKDIR /home/python/labelbox-python RUN rye config --set-bool behavior.global-python=true && \ rye config --set-bool behavior.use-uv=true && \ - rye pin 3.9 && \ + rye pin 3.10 && \ rye sync -CMD rye run unit && rye integration \ No newline at end of file +CMD rye run unit && rye integration From 5d6c59a6fa3e85d65935f69212798a384771b7ba Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Mon, 27 Oct 2025 11:48:42 -0500 Subject: [PATCH 073/103] v7.3.0 - release preparation (#2025) --- docs/conf.py | 2 +- libs/labelbox/CHANGELOG.md | 4 ++++ libs/labelbox/pyproject.toml | 2 +- libs/labelbox/src/labelbox/__init__.py | 2 +- 4 files changed, 7 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index b87eb39d9..40afedce1 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ project = 'Python SDK reference' copyright = '2025, Labelbox' author = 'Labelbox' -release = '7.2.0' +release = '7.3.0' # -- General configuration --------------------------------------------------- diff --git a/libs/labelbox/CHANGELOG.md b/libs/labelbox/CHANGELOG.md index c4aebdf21..18f6ef023 100644 --- a/libs/labelbox/CHANGELOG.md +++ b/libs/labelbox/CHANGELOG.md @@ -1,4 +1,8 @@ # Changelog +# Version 7.3.0 (2025-11-27) +## Added +* Add support for Audio Temporal Annotations ([#2013](https://github.com/Labelbox/labelbox-python/pull/2013)) + # Version 7.2.0 (2025-08-28) ## Updated * DataRowMetadata: Remove enforcement of character limit ([#2010](https://github.com/Labelbox/labelbox-python/pull/2010)) diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml index 4e5ae1d0a..756c89eea 100644 --- a/libs/labelbox/pyproject.toml +++ b/libs/labelbox/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "labelbox" -version = "7.2.0" +version = "7.3.0" description = "Labelbox Python API" authors = [{ name = "Labelbox", email = "engineering@labelbox.com" }] dependencies = [ diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index 24920786e..a7b13e77a 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -1,6 +1,6 @@ name = "labelbox" -__version__ = "7.2.0" +__version__ = "7.3.0" from labelbox.client import Client from labelbox.schema.annotation_import import ( From 21a3a1c7feca4d3dcae90378258958ea379c23c5 Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Mon, 27 Oct 2025 11:54:43 -0500 Subject: [PATCH 074/103] Update CHANGELOG.md (#2026) --- libs/labelbox/CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/labelbox/CHANGELOG.md b/libs/labelbox/CHANGELOG.md index 18f6ef023..bed05788f 100644 --- a/libs/labelbox/CHANGELOG.md +++ b/libs/labelbox/CHANGELOG.md @@ -1,5 +1,5 @@ # Changelog -# Version 7.3.0 (2025-11-27) +# Version 7.3.0 (2025-10-27) ## Added * Add support for Audio Temporal Annotations ([#2013](https://github.com/Labelbox/labelbox-python/pull/2013)) From 681f6645ab8cdb8a222ddb60da9b874dc46a9c1c Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Mon, 27 Oct 2025 15:03:57 -0500 Subject: [PATCH 075/103] [PLT-0] decouple the publish workflows for lbox and the sdk (#2027) --- .github/workflows/lbox-publish.yml | 6 ++---- .github/workflows/publish.yml | 26 +++++++++++++------------- 2 files changed, 15 insertions(+), 17 deletions(-) diff --git a/.github/workflows/lbox-publish.yml b/.github/workflows/lbox-publish.yml index dcca8e561..58ccedcbc 100644 --- a/.github/workflows/lbox-publish.yml +++ b/.github/workflows/lbox-publish.yml @@ -1,16 +1,14 @@ name: LBox Publish on: - workflow_call: + workflow_dispatch: inputs: tag: description: 'Release Tag' required: true - type: string prev_sdk_tag: - description: 'Prev SDK Release Tag' + description: 'Prev SDK Release Tag, used to determine which lbox files have changed' required: true - type: string concurrency: group: ${{ github.workflow }}-${{ github.ref }} diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index f540d2a6d..11845e6d2 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -26,19 +26,19 @@ permissions: id-token: write jobs: - build-lbox: - permissions: - actions: read - contents: write - id-token: write # Needed to access the workflow's OIDC identity. - packages: write - uses: ./.github/workflows/lbox-publish.yml - with: - tag: ${{ inputs.tag }} - prev_sdk_tag: ${{ inputs.prev_sdk_tag }} - secrets: inherit +# build-lbox: +# permissions: +# actions: read +# contents: write +# id-token: write # Needed to access the workflow's OIDC identity. +# packages: write +# uses: ./.github/workflows/lbox-publish.yml +# with: +# tag: ${{ inputs.tag }} +# prev_sdk_tag: ${{ inputs.prev_sdk_tag }} +# secrets: inherit build: - needs: ['build-lbox'] +# needs: ['build-lbox'] runs-on: ubuntu-latest outputs: hashes: ${{ steps.hash.outputs.hashes }} @@ -252,4 +252,4 @@ jobs: digest: ${{ needs. container-publish.outputs.digest }} registry-username: ${{ github.actor }} secrets: - registry-password: ${{ secrets.GITHUB_TOKEN }} \ No newline at end of file + registry-password: ${{ secrets.GITHUB_TOKEN }} From 5e791175ba8f8fb3b0ae24409265309506f904a2 Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Tue, 28 Oct 2025 10:01:24 -0500 Subject: [PATCH 076/103] Revert "[PLT-0] decouple the publish workflows for lbox and the sdk" (#2028) --- .github/workflows/lbox-publish.yml | 6 ++++-- .github/workflows/publish.yml | 26 +++++++++++++------------- 2 files changed, 17 insertions(+), 15 deletions(-) diff --git a/.github/workflows/lbox-publish.yml b/.github/workflows/lbox-publish.yml index 58ccedcbc..dcca8e561 100644 --- a/.github/workflows/lbox-publish.yml +++ b/.github/workflows/lbox-publish.yml @@ -1,14 +1,16 @@ name: LBox Publish on: - workflow_dispatch: + workflow_call: inputs: tag: description: 'Release Tag' required: true + type: string prev_sdk_tag: - description: 'Prev SDK Release Tag, used to determine which lbox files have changed' + description: 'Prev SDK Release Tag' required: true + type: string concurrency: group: ${{ github.workflow }}-${{ github.ref }} diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 11845e6d2..f540d2a6d 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -26,19 +26,19 @@ permissions: id-token: write jobs: -# build-lbox: -# permissions: -# actions: read -# contents: write -# id-token: write # Needed to access the workflow's OIDC identity. -# packages: write -# uses: ./.github/workflows/lbox-publish.yml -# with: -# tag: ${{ inputs.tag }} -# prev_sdk_tag: ${{ inputs.prev_sdk_tag }} -# secrets: inherit + build-lbox: + permissions: + actions: read + contents: write + id-token: write # Needed to access the workflow's OIDC identity. + packages: write + uses: ./.github/workflows/lbox-publish.yml + with: + tag: ${{ inputs.tag }} + prev_sdk_tag: ${{ inputs.prev_sdk_tag }} + secrets: inherit build: -# needs: ['build-lbox'] + needs: ['build-lbox'] runs-on: ubuntu-latest outputs: hashes: ${{ steps.hash.outputs.hashes }} @@ -252,4 +252,4 @@ jobs: digest: ${{ needs. container-publish.outputs.digest }} registry-username: ${{ github.actor }} secrets: - registry-password: ${{ secrets.GITHUB_TOKEN }} + registry-password: ${{ secrets.GITHUB_TOKEN }} \ No newline at end of file From 3a7fb219a15f5a1adacc699378fc85a4f29206e0 Mon Sep 17 00:00:00 2001 From: kozikkamil <91909509+kozikkamil@users.noreply.github.com> Date: Tue, 2 Dec 2025 16:56:22 +0100 Subject: [PATCH 077/103] Add method to report fraud (#2029) --- libs/labelbox/src/labelbox/client.py | 45 ++++ libs/labelbox/src/labelbox/schema/project.py | 65 ++++- .../src/alignerr/alignerr_project.py | 233 ++++++++++++++++++ 3 files changed, 339 insertions(+), 4 deletions(-) diff --git a/libs/labelbox/src/labelbox/client.py b/libs/labelbox/src/labelbox/client.py index b48db006e..0d8c113a3 100644 --- a/libs/labelbox/src/labelbox/client.py +++ b/libs/labelbox/src/labelbox/client.py @@ -503,6 +503,51 @@ def delete_model_config(self, id: str) -> bool: raise ResourceNotFoundError(Entity.ModelConfig, params) return result["deleteModelConfig"]["success"] + def delete_project_memberships( + self, project_id: str, user_ids: list[str] + ) -> dict: + """Deletes project memberships for one or more users. + + Args: + project_id (str): ID of the project + user_ids (list[str]): List of user IDs to remove from the project + + Returns: + dict: Result containing: + - success (bool): True if operation succeeded + - errorMessage (str or None): Error message if operation failed + + Example: + >>> result = client.delete_project_memberships( + >>> project_id="project123", + >>> user_ids=["user1", "user2"] + >>> ) + >>> if result["success"]: + >>> print("Users removed successfully") + >>> else: + >>> print(f"Error: {result['errorMessage']}") + """ + mutation = """mutation DeleteProjectMembershipsPyApi( + $projectId: ID! + $userIds: [ID!]! + ) { + deleteProjectMemberships(where: { + projectId: $projectId + userIds: $userIds + }) { + success + errorMessage + } + }""" + + params = { + "projectId": project_id, + "userIds": user_ids, + } + + result = self.execute(mutation, params) + return result["deleteProjectMemberships"] + def create_dataset( self, iam_integration=IAMIntegration._DEFAULT, **kwargs ) -> Dataset: diff --git a/libs/labelbox/src/labelbox/schema/project.py b/libs/labelbox/src/labelbox/schema/project.py index 8c89b9ae4..f00a75cb2 100644 --- a/libs/labelbox/src/labelbox/schema/project.py +++ b/libs/labelbox/src/labelbox/schema/project.py @@ -317,7 +317,7 @@ def get_resource_tags(self) -> List[ResourceTag]: return [ResourceTag(self.client, tag) for tag in results] - def labels(self, datasets=None, order_by=None) -> PaginatedCollection: + def labels(self, datasets=None, order_by=None, created_by=None) -> PaginatedCollection: """Custom relationship expansion method to support limited filtering. Args: @@ -325,6 +325,20 @@ def labels(self, datasets=None, order_by=None) -> PaginatedCollection: whose Labels are sought. If not provided, all Labels in this Project are returned. order_by (None or (Field, Field.Order)): Ordering clause. + created_by (str or User): Optional. Filter labels by the user who created them. + Can be a user ID string or a User object. + + Returns: + PaginatedCollection of Labels matching the filters. + + Example: + >>> # Get all labels + >>> all_labels = project.labels() + >>> + >>> # Get labels by specific user + >>> user_labels = project.labels(created_by=user_id) + >>> # or + >>> user_labels = project.labels(created_by=user_object) """ Label = Entity.Label @@ -335,10 +349,20 @@ def labels(self, datasets=None, order_by=None) -> PaginatedCollection: stacklevel=2, ) + # Build where clause + where_clauses = [] + if datasets is not None: - where = " where:{dataRow: {dataset: {id_in: [%s]}}}" % ", ".join( - '"%s"' % dataset.uid for dataset in datasets - ) + dataset_ids = ", ".join('"%s"' % dataset.uid for dataset in datasets) + where_clauses.append(f"dataRow: {{dataset: {{id_in: [{dataset_ids}]}}}}") + + if created_by is not None: + # Handle both User object and user_id string + user_id = created_by.uid if hasattr(created_by, 'uid') else created_by + where_clauses.append(f'createdBy: {{id: "{user_id}"}}') + + if where_clauses: + where = " where:{" + ", ".join(where_clauses) + "}" else: where = "" @@ -370,6 +394,39 @@ def labels(self, datasets=None, order_by=None) -> PaginatedCollection: Label, ) + def delete_labels_by_user(self, user_id: str) -> int: + """Soft deletes all labels created by a specific user in this project. + + This performs a soft delete (sets deleted=true in the database). + The labels will no longer appear in queries but remain in the database. + Labels are deleted in chunks of 500 to avoid overwhelming the API. + + Args: + user_id (str): The ID of the user whose labels to delete. + + Returns: + int: Number of labels deleted. + + Example: + >>> project = client.get_project(project_id) + >>> deleted_count = project.delete_labels_by_user(user_id) + >>> print(f"Deleted {deleted_count} labels") + """ + labels_to_delete = list(self.labels(created_by=user_id)) + + if not labels_to_delete: + return 0 + + chunk_size = 500 + total_deleted = 0 + + for i in range(0, len(labels_to_delete), chunk_size): + chunk = labels_to_delete[i:i + chunk_size] + Entity.Label.bulk_delete(chunk) + total_deleted += len(chunk) + + return total_deleted + def export( self, task_name: Optional[str] = None, diff --git a/libs/lbox-alignerr/src/alignerr/alignerr_project.py b/libs/lbox-alignerr/src/alignerr/alignerr_project.py index cc2460f87..f9432ff3a 100644 --- a/libs/lbox-alignerr/src/alignerr/alignerr_project.py +++ b/libs/lbox-alignerr/src/alignerr/alignerr_project.py @@ -13,6 +13,7 @@ ProjectBoostWorkforce, ) from labelbox.pagination import PaginatedCollection +from labelbox.orm.model import Entity logger = logging.getLogger(__name__) @@ -153,6 +154,238 @@ def get_project_owner(self) -> Optional[ProjectBoostWorkforce]: client=self.client, project_id=self.project.uid ) + def _get_user_labels(self, user_id: str): + """Get all labels created by a user in this project. + + Args: + user_id: ID of the user + + Returns: + List of Label objects + + Raises: + Exception: If labels cannot be retrieved + """ + labels = list(self.project.labels(created_by=user_id)) + logger.info( + "Found %d labels created by user %s in project %s", + len(labels), + user_id, + self.project.uid + ) + return labels + + def _create_trust_safety_case(self, user_id: str, event_metadata: dict) -> bool: + """Create a Trust & Safety case for a user. + + Args: + user_id: ID of the user being reported + event_metadata: JSON metadata about the event + + Returns: + True if case was created successfully + + Raises: + Exception: If T&S case creation fails + """ + mutation = """mutation CreateTrustAndSafetyCasePyApi( + $subjectUserId: String! + $eventType: CaseEventGqlType! + $severity: CaseSeverityGqlType! + $eventMetadata: Json! + ) { + createTrustAndSafetyCase(input: { + subjectUserId: $subjectUserId + eventType: $eventType + severity: $severity + eventMetadata: $eventMetadata + }) { + success + } + }""" + + params = { + "subjectUserId": user_id, + "eventType": "manual", + "severity": "high", + "eventMetadata": event_metadata, + } + + result = self.client.execute(mutation, params) + success = result["createTrustAndSafetyCase"]["success"] + + if success: + logger.info( + "Created T&S case for user %s in project %s", + user_id, + self.project.uid + ) + + return success + + def _remove_user_from_project(self, user_id: str) -> None: + """Remove a user from this project. + + Args: + user_id: ID of the user to remove + + Raises: + ValueError: If user not found in project + Exception: If removal fails + """ + # Check if user is in project members + user_found = False + for member in self.project.members(): + if member.user().uid == user_id: + user_found = True + break + + if not user_found: + logger.warning("User %s not found in project %s members", user_id, self.project.uid) + raise ValueError(f"User {user_id} not found in project members") + + # Remove user using deleteProjectMemberships mutation + result = self.client.delete_project_memberships( + project_id=self.project.uid, + user_ids=[user_id] + ) + + if not result.get("success"): + error_message = result.get("errorMessage", "Unknown error") + logger.error("Failed to remove user: %s", error_message) + raise Exception(f"Failed to remove user: {error_message}") + + logger.info( + "Removed user %s from project %s", + user_id, + self.project.uid + ) + + def _delete_user_labels(self, labels) -> int: + """Delete a list of labels. + + Args: + labels: List of Label objects to delete + + Returns: + Number of labels deleted + + Raises: + Exception: If deletion fails + """ + if not labels: + return 0 + + Entity.Label.bulk_delete(labels) + logger.info( + "Deleted %d labels in project %s", + len(labels), + self.project.uid + ) + return len(labels) + + def report_fraud( + self, + user_id: str, + reason: str, + custom_metadata: dict = None + ) -> dict: + """Report potential fraud by a user in this project. + + This method performs the following actions: + 1. Gets all labels created by the user in this project + 2. Creates a Trust & Safety case for the user (MANUAL event type, HIGH severity) + 3. Removes the user from the project (prevents creating more labels) + 4. Deletes all the user's labels + + Args: + user_id (str): The ID of the user to report for fraud. + reason (str): Reason for reporting fraud (e.g., "Spam labels", "Low quality work"). + custom_metadata (dict, optional): Additional metadata to include in the T&S case. + Will be merged with automatic metadata (project_id, reason, label_count, label_ids). + + Returns: + dict: A dictionary containing: + - ts_case_id: Status of T&S case creation ("created" if successful) + - labels_found: Number of labels found by the user + - user_removed: Whether the user was successfully removed + - labels_deleted: Number of labels deleted + - error: Any error message if any step failed + + Example: + >>> from alignerr import AlignerrWorkspace + >>> from labelbox import Client + >>> + >>> client = Client(api_key="YOUR_API_KEY") + >>> workspace = AlignerrWorkspace.from_labelbox(client) + >>> project = workspace.project_builder().from_existing(project_id) + >>> + >>> # Report fraud with reason + >>> result = project.report_fraud(user_id, reason="Spam labels detected") + >>> print(f"Removed user: {result['user_removed']}, Deleted {result['labels_deleted']} labels") + >>> + >>> # With additional custom metadata + >>> result = project.report_fraud( + >>> user_id, + >>> reason="Production quality issues", + >>> custom_metadata={"ticket_id": "TICKET-123", "reviewer": "john@example.com"} + >>> ) + """ + result = { + "ts_case_id": None, + "labels_found": 0, + "user_removed": False, + "labels_deleted": 0, + "error": None, + } + + # Step 1: Get all labels cteated by this user in this project + try: + labels_to_delete = self._get_user_labels(user_id) + result["labels_found"] = len(labels_to_delete) + except Exception as e: + logger.error("Failed to get labels: %s", str(e)) + result["error"] = f"Failed to get labels: {str(e)}" + return result + + # Step 2: Create T&S case with label information + try: + event_metadata = { + "project_id": self.project.uid, + "reason": reason, + "label_count": len(labels_to_delete), + "label_ids": [label.uid for label in labels_to_delete], + } + if custom_metadata: + event_metadata.update(custom_metadata) + + ts_case_created = self._create_trust_safety_case(user_id, event_metadata) + if ts_case_created: + result["ts_case_id"] = "created" + except Exception as e: + logger.error("Failed to create T&S case: %s", str(e)) + result["error"] = f"Failed to create T&S case: {str(e)}" + return result + + # Step 3: Remove user from project (prevent creating more labels) + try: + self._remove_user_from_project(user_id) + result["user_removed"] = True + except Exception as e: + logger.error("Failed to remove user from project: %s", str(e)) + result["error"] = f"Failed to remove user: {str(e)}" + return result + + # Step 4: Delete all labels by this user + try: + result["labels_deleted"] = self._delete_user_labels(labels_to_delete) + except Exception as e: + logger.error("Failed to delete labels: %s", str(e)) + result["error"] = f"Failed to delete labels: {str(e)}" + return result + + return result + class AlignerrWorkspace: def __init__(self, client: "Client"): From 3a71bd1f8819b08b41e67bdb1c1eca25f1b8986b Mon Sep 17 00:00:00 2001 From: paulnoirel <87332996+paulnoirel@users.noreply.github.com> Date: Tue, 13 Jan 2026 09:27:35 +0000 Subject: [PATCH 078/103] Fix lint and integration errors (#2033) --- libs/labelbox/src/labelbox/client.py | 10 +++--- libs/labelbox/src/labelbox/schema/api_key.py | 28 ++++++++++++--- libs/labelbox/src/labelbox/schema/project.py | 36 +++++++++++-------- .../src/labelbox/schema/user_group.py | 9 +++++ .../test_project_set_model_setup_complete.py | 2 +- .../tests/unit/schema/test_user_group.py | 4 +-- 6 files changed, 62 insertions(+), 27 deletions(-) diff --git a/libs/labelbox/src/labelbox/client.py b/libs/labelbox/src/labelbox/client.py index 0d8c113a3..60fb8016d 100644 --- a/libs/labelbox/src/labelbox/client.py +++ b/libs/labelbox/src/labelbox/client.py @@ -507,16 +507,16 @@ def delete_project_memberships( self, project_id: str, user_ids: list[str] ) -> dict: """Deletes project memberships for one or more users. - + Args: project_id (str): ID of the project user_ids (list[str]): List of user IDs to remove from the project - + Returns: dict: Result containing: - success (bool): True if operation succeeded - errorMessage (str or None): Error message if operation failed - + Example: >>> result = client.delete_project_memberships( >>> project_id="project123", @@ -539,12 +539,12 @@ def delete_project_memberships( errorMessage } }""" - + params = { "projectId": project_id, "userIds": user_ids, } - + result = self.execute(mutation, params) return result["deleteProjectMemberships"] diff --git a/libs/labelbox/src/labelbox/schema/api_key.py b/libs/labelbox/src/labelbox/schema/api_key.py index c5dba9148..d297e1451 100644 --- a/libs/labelbox/src/labelbox/schema/api_key.py +++ b/libs/labelbox/src/labelbox/schema/api_key.py @@ -258,7 +258,9 @@ def _get_available_api_key_roles(client: "Client") -> List[str]: if role["name"] in ["None", "Tenant Admin"]: continue if all(perm in current_permissions for perm in role["permissions"]): - available_roles.append(format_role(role["name"])) + # Preserve server-provided role names (case-sensitive) so callers can + # pass them through without normalization. + available_roles.append(role["name"]) client._cached_available_api_key_roles = available_roles return available_roles @@ -332,9 +334,25 @@ def create_api_key( raise ValueError("role must be a Role object or a valid role name") allowed_roles = ApiKey._get_available_api_key_roles(client) - # Format the input role name consistently with available roles - formatted_role_name = format_role(role_name) - if formatted_role_name not in allowed_roles: + # Determine the exact server role name to pass through. + # + # - If caller provides a string, require exact match (case-sensitive). + # - If caller provides a Role object (which may be normalized by the SDK), + # map it back to the server role name. + server_role_name: Optional[str] = None + if hasattr(role, "name"): + # Role objects in the SDK are often normalized (e.g. "TENANT_ADMIN"). + # Map normalized name back to the server-provided role display name. + normalized_to_server = {format_role(r): r for r in allowed_roles} + server_role_name = ( + role_name + if role_name in allowed_roles + else normalized_to_server.get(format_role(role_name)) + ) + else: + server_role_name = role_name if role_name in allowed_roles else None + + if server_role_name is None: raise ValueError( f"Invalid role specified. Allowed roles are: {allowed_roles}" ) @@ -371,7 +389,7 @@ def create_api_key( params = { "name": name, "userEmail": user_email, - "role": role_name, + "role": server_role_name, "validitySeconds": validity_seconds, } diff --git a/libs/labelbox/src/labelbox/schema/project.py b/libs/labelbox/src/labelbox/schema/project.py index f00a75cb2..60d6b6258 100644 --- a/libs/labelbox/src/labelbox/schema/project.py +++ b/libs/labelbox/src/labelbox/schema/project.py @@ -317,7 +317,9 @@ def get_resource_tags(self) -> List[ResourceTag]: return [ResourceTag(self.client, tag) for tag in results] - def labels(self, datasets=None, order_by=None, created_by=None) -> PaginatedCollection: + def labels( + self, datasets=None, order_by=None, created_by=None + ) -> PaginatedCollection: """Custom relationship expansion method to support limited filtering. Args: @@ -334,7 +336,7 @@ def labels(self, datasets=None, order_by=None, created_by=None) -> PaginatedColl Example: >>> # Get all labels >>> all_labels = project.labels() - >>> + >>> >>> # Get labels by specific user >>> user_labels = project.labels(created_by=user_id) >>> # or @@ -351,16 +353,22 @@ def labels(self, datasets=None, order_by=None, created_by=None) -> PaginatedColl # Build where clause where_clauses = [] - + if datasets is not None: - dataset_ids = ", ".join('"%s"' % dataset.uid for dataset in datasets) - where_clauses.append(f"dataRow: {{dataset: {{id_in: [{dataset_ids}]}}}}") - + dataset_ids = ", ".join( + '"%s"' % dataset.uid for dataset in datasets + ) + where_clauses.append( + f"dataRow: {{dataset: {{id_in: [{dataset_ids}]}}}}" + ) + if created_by is not None: # Handle both User object and user_id string - user_id = created_by.uid if hasattr(created_by, 'uid') else created_by + user_id = ( + created_by.uid if hasattr(created_by, "uid") else created_by + ) where_clauses.append(f'createdBy: {{id: "{user_id}"}}') - + if where_clauses: where = " where:{" + ", ".join(where_clauses) + "}" else: @@ -396,7 +404,7 @@ def labels(self, datasets=None, order_by=None, created_by=None) -> PaginatedColl def delete_labels_by_user(self, user_id: str) -> int: """Soft deletes all labels created by a specific user in this project. - + This performs a soft delete (sets deleted=true in the database). The labels will no longer appear in queries but remain in the database. Labels are deleted in chunks of 500 to avoid overwhelming the API. @@ -413,18 +421,18 @@ def delete_labels_by_user(self, user_id: str) -> int: >>> print(f"Deleted {deleted_count} labels") """ labels_to_delete = list(self.labels(created_by=user_id)) - + if not labels_to_delete: return 0 - + chunk_size = 500 total_deleted = 0 - + for i in range(0, len(labels_to_delete), chunk_size): - chunk = labels_to_delete[i:i + chunk_size] + chunk = labels_to_delete[i : i + chunk_size] Entity.Label.bulk_delete(chunk) total_deleted += len(chunk) - + return total_deleted def export( diff --git a/libs/labelbox/src/labelbox/schema/user_group.py b/libs/labelbox/src/labelbox/schema/user_group.py index e247af1c7..9b98b588a 100644 --- a/libs/labelbox/src/labelbox/schema/user_group.py +++ b/libs/labelbox/src/labelbox/schema/user_group.py @@ -9,6 +9,7 @@ from collections import defaultdict from dataclasses import dataclass from enum import Enum +import uuid from typing import Any, Dict, Iterator, List, Optional, Set from lbox.exceptions import ( @@ -415,6 +416,14 @@ def delete(self) -> bool: if not self.id: raise ValueError("Group id is required") + # The API expects a UUID-formatted identifier and may respond with an + # internal server error if the value cannot be parsed. Validate client-side + # so callers get a consistent exception. + try: + uuid.UUID(str(self.id)) + except Exception as e: + raise MalformedQueryException("Invalid user group id") from e + query = """ mutation DeleteUserGroupPyApi($id: ID!) { deleteUserGroup(where: {id: $id}) { diff --git a/libs/labelbox/tests/integration/test_project_set_model_setup_complete.py b/libs/labelbox/tests/integration/test_project_set_model_setup_complete.py index 30e179028..2ab035d95 100644 --- a/libs/labelbox/tests/integration/test_project_set_model_setup_complete.py +++ b/libs/labelbox/tests/integration/test_project_set_model_setup_complete.py @@ -36,7 +36,7 @@ def test_live_chat_evaluation_project_delete_cofig( with pytest.raises( expected_exception=LabelboxError, - match="Cannot create model config for project because model setup is complete", + match="Cannot (create model config for project because model setup is complete|perform this action because model setup is complete)", ): project_model_config.delete() diff --git a/libs/labelbox/tests/unit/schema/test_user_group.py b/libs/labelbox/tests/unit/schema/test_user_group.py index c51e60c6a..1e54332f7 100644 --- a/libs/labelbox/tests/unit/schema/test_user_group.py +++ b/libs/labelbox/tests/unit/schema/test_user_group.py @@ -341,7 +341,7 @@ def test_delete(self): "deleteUserGroup": {"success": True} } group = self.group - group.id = "group_id" + group.id = "11111111-2222-3333-4444-555555555555" result = group.delete() assert result is True @@ -350,7 +350,7 @@ def test_delete_resource_not_found_error(self): message="Not found" ) group = self.group - group.id = "group_id" + group.id = "11111111-2222-3333-4444-555555555555" with pytest.raises(ResourceNotFoundError): group.delete() From a9cf6b57c37a07b6ed32bbc947cff5ef3273b1fc Mon Sep 17 00:00:00 2001 From: paulnoirel <87332996+paulnoirel@users.noreply.github.com> Date: Tue, 13 Jan 2026 10:36:59 +0000 Subject: [PATCH 079/103] [PLT-3393] Add support of groups to invite_user() (#2035) --- libs/labelbox/src/labelbox/__init__.py | 2 +- libs/labelbox/src/labelbox/orm/model.py | 1 + .../src/labelbox/schema/organization.py | 53 +++++- libs/labelbox/src/labelbox/schema/role.py | 7 + .../schema/test_organization_invite_user.py | 151 ++++++++++++++++++ 5 files changed, 212 insertions(+), 2 deletions(-) create mode 100644 libs/labelbox/tests/unit/schema/test_organization_invite_user.py diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index a7b13e77a..d8296d594 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -55,7 +55,7 @@ from labelbox.schema.tool_building.step_reasoning_tool import StepReasoningTool from labelbox.schema.tool_building.prompt_issue_tool import PromptIssueTool from labelbox.schema.tool_building.relationship_tool import RelationshipTool -from labelbox.schema.role import Role, ProjectRole +from labelbox.schema.role import Role, ProjectRole, UserGroupRole from labelbox.schema.invite import Invite, InviteLimit from labelbox.schema.data_row_metadata import ( DataRowMetadataOntology, diff --git a/libs/labelbox/src/labelbox/orm/model.py b/libs/labelbox/src/labelbox/orm/model.py index b4ec7c2c2..15a39fc72 100644 --- a/libs/labelbox/src/labelbox/orm/model.py +++ b/libs/labelbox/src/labelbox/orm/model.py @@ -399,6 +399,7 @@ class Entity(metaclass=EntityMeta): CatalogSlice: Type[labelbox.CatalogSlice] ModelSlice: Type[labelbox.ModelSlice] TaskQueue: Type[labelbox.TaskQueue] + UserGroupRole: Type[labelbox.UserGroupRole] @classmethod def _attributes_of_type(cls, attr_type): diff --git a/libs/labelbox/src/labelbox/schema/organization.py b/libs/labelbox/src/labelbox/schema/organization.py index 8d15cc1b2..4b84b5bba 100644 --- a/libs/labelbox/src/labelbox/schema/organization.py +++ b/libs/labelbox/src/labelbox/schema/organization.py @@ -1,4 +1,4 @@ -from typing import TYPE_CHECKING, Dict, List, Optional, Union +from typing import TYPE_CHECKING, Dict, List, Set, Optional, Union from lbox.exceptions import LabelboxError @@ -22,6 +22,7 @@ ProjectRole, Role, User, + UserGroupRole, ) @@ -65,6 +66,7 @@ def invite_user( email: str, role: "Role", project_roles: Optional[List["ProjectRole"]] = None, + user_group_roles: Optional[List["UserGroupRole"]] = None, ) -> "Invite": """ Invite a new member to the org. This will send the user an email invite @@ -88,6 +90,40 @@ def invite_user( f"Project roles cannot be set for a user with organization level permissions. Found role name `{role.name}`, expected `NONE`" ) + if user_group_roles and role.name != "NONE": + raise ValueError( + f"User Group roles cannot be set for a user with organization level permissions. Found role name `{role.name}`, expected `NONE`" + ) + + if user_group_roles: + # The backend can 500 if the same groupId appears more than once. + # We dedupe exact duplicates (same groupId+roleId), but reject + # conflicting assignments (same groupId with different roleId). + + deduped_user_group_roles: Dict[str, "UserGroupRole"] = {} + conflicting_user_group_ids: Set[str] = set() + + for user_group_role in user_group_roles: + user_group_id = user_group_role.user_group.id + role_id = user_group_role.role.uid + + existing = deduped_user_group_roles.get(user_group_id) + if existing is None: + deduped_user_group_roles[user_group_id] = user_group_role + else: + if existing.role.uid != role_id: + conflicting_user_group_ids.add(user_group_id) + + if conflicting_user_group_ids: + conflicts_str = ", ".join(sorted(conflicting_user_group_ids)) + raise ValueError( + "user_group_roles contains conflicting role assignments for " + "the same UserGroup. Each UserGroup may only appear once. " + f"Conflicting user_group.id values: {conflicts_str}" + ) + + user_group_roles = list(deduped_user_group_roles.values()) + data_param = "data" query_str = """mutation createInvitesPyApi($%s: [CreateInviteInput!]){ createInvites(data: $%s){ invite { id createdAt organizationRoleName inviteeEmail inviter { %s } }}}""" % ( @@ -104,6 +140,19 @@ def invite_user( for project_role in project_roles or [] ] + user_group_ids = [ + user_group_role.user_group.id + for user_group_role in user_group_roles or [] + ] + + user_group_with_role_ids = [ + { + "groupId": user_group_role.user_group.id, + "roleId": user_group_role.role.uid, + } + for user_group_role in user_group_roles or [] + ] + res = self.client.execute( query_str, { @@ -114,6 +163,8 @@ def invite_user( "organizationId": self.uid, "organizationRoleId": role.uid, "projects": projects, + "userGroupIds": user_group_ids, + "userGroupWithRoleIds": user_group_with_role_ids, } ] }, diff --git a/libs/labelbox/src/labelbox/schema/role.py b/libs/labelbox/src/labelbox/schema/role.py index 0367d8f0c..2ae6adc91 100644 --- a/libs/labelbox/src/labelbox/schema/role.py +++ b/libs/labelbox/src/labelbox/schema/role.py @@ -6,6 +6,7 @@ if TYPE_CHECKING: from labelbox import Client, Project + from labelbox.schema.user_group import UserGroup _ROLES: Optional[Dict[str, "Role"]] = None @@ -45,3 +46,9 @@ class UserRole(Role): ... class ProjectRole: project: "Project" role: Role + + +@dataclass +class UserGroupRole: + user_group: "UserGroup" + role: Role diff --git a/libs/labelbox/tests/unit/schema/test_organization_invite_user.py b/libs/labelbox/tests/unit/schema/test_organization_invite_user.py new file mode 100644 index 000000000..0e6ce5305 --- /dev/null +++ b/libs/labelbox/tests/unit/schema/test_organization_invite_user.py @@ -0,0 +1,151 @@ +import pytest +from types import SimpleNamespace +from unittest.mock import MagicMock + +from labelbox.schema.role import UserGroupRole +from labelbox.schema.organization import Organization + + +def test_invite_user_duplicate_user_group_roles_same_role_is_deduped(): + client = MagicMock() + client.get_user.return_value = SimpleNamespace(uid="inviter-id") + client.execute.return_value = { + "createInvites": [ + { + "invite": { + "id": "invite-id", + "createdAt": "2020-01-01T00:00:00.000Z", + "organizationRoleName": "NONE", + "inviteeEmail": "someone@example.com", + "inviter": {"id": "inviter-id"}, + } + } + ] + } + + organization = Organization( + client, + { + "id": "org-id", + "name": "Test Org", + "createdAt": "2020-01-01T00:00:00.000Z", + "updatedAt": "2020-01-01T00:00:00.000Z", + }, + ) + + org_role_none = SimpleNamespace(uid="org-role-none-id", name="NONE") + reviewer_role = SimpleNamespace(uid="reviewer-role-id", name="REVIEWER") + user_group = SimpleNamespace(id="user-group-id") + + user_group_roles = [ + UserGroupRole(user_group=user_group, role=reviewer_role), + UserGroupRole(user_group=user_group, role=reviewer_role), + ] + + organization.invite_user( + email="someone@example.com", + role=org_role_none, + user_group_roles=user_group_roles, + ) + + # ensure we only send one entry per group + args, kwargs = client.execute.call_args + assert kwargs == {} + payload = args[1]["data"][0] + assert payload["userGroupIds"] == ["user-group-id"] + assert payload["userGroupWithRoleIds"] == [ + {"groupId": "user-group-id", "roleId": "reviewer-role-id"} + ] + + +def test_invite_user_duplicate_user_group_roles_conflicting_roles_raises_value_error(): + client = MagicMock() + client.get_user.return_value = SimpleNamespace(uid="inviter-id") + + organization = Organization( + client, + { + "id": "org-id", + "name": "Test Org", + "createdAt": "2020-01-01T00:00:00.000Z", + "updatedAt": "2020-01-01T00:00:00.000Z", + }, + ) + + org_role_none = SimpleNamespace(uid="org-role-none-id", name="NONE") + reviewer_role = SimpleNamespace(uid="reviewer-role-id", name="REVIEWER") + team_manager_role = SimpleNamespace( + uid="team-manager-role-id", name="TEAM_MANAGER" + ) + user_group = SimpleNamespace(id="user-group-id") + + user_group_roles = [ + UserGroupRole(user_group=user_group, role=reviewer_role), + UserGroupRole(user_group=user_group, role=team_manager_role), + ] + + with pytest.raises(ValueError, match="conflicting role assignments"): + organization.invite_user( + email="someone@example.com", + role=org_role_none, + user_group_roles=user_group_roles, + ) + + client.execute.assert_not_called() + + +def test_invite_user_user_group_roles_payload_contains_all_groups(): + client = MagicMock() + client.get_user.return_value = SimpleNamespace(uid="inviter-id") + client.execute.return_value = { + "createInvites": [ + { + "invite": { + "id": "invite-id", + "createdAt": "2020-01-01T00:00:00.000Z", + "organizationRoleName": "NONE", + "inviteeEmail": "someone@example.com", + "inviter": {"id": "inviter-id"}, + } + } + ] + } + + organization = Organization( + client, + { + "id": "org-id", + "name": "Test Org", + "createdAt": "2020-01-01T00:00:00.000Z", + "updatedAt": "2020-01-01T00:00:00.000Z", + }, + ) + + org_role_none = SimpleNamespace(uid="org-role-none-id", name="NONE") + reviewer_role = SimpleNamespace(uid="reviewer-role-id", name="REVIEWER") + team_manager_role = SimpleNamespace( + uid="team-manager-role-id", name="TEAM_MANAGER" + ) + + ug1 = SimpleNamespace(id="user-group-1") + ug2 = SimpleNamespace(id="user-group-2") + + user_group_roles = [ + UserGroupRole(user_group=ug1, role=reviewer_role), + UserGroupRole(user_group=ug2, role=team_manager_role), + ] + + organization.invite_user( + email="someone@example.com", + role=org_role_none, + user_group_roles=user_group_roles, + ) + + args, kwargs = client.execute.call_args + assert kwargs == {} + payload = args[1]["data"][0] + assert payload["userGroupIds"] == ["user-group-1", "user-group-2"] + assert payload["userGroupWithRoleIds"] == [ + {"groupId": "user-group-1", "roleId": "reviewer-role-id"}, + {"groupId": "user-group-2", "roleId": "team-manager-role-id"}, + ] From 8cc60769f96629ac1fd8388cdf11dec80ab2e190 Mon Sep 17 00:00:00 2001 From: paulnoirel <87332996+paulnoirel@users.noreply.github.com> Date: Tue, 13 Jan 2026 10:37:39 +0000 Subject: [PATCH 080/103] [PLT-3337] SDK - Workflow Management Remove input tests (#2031) --- .../schema/workflow/workflow_utils.py | 25 -------- .../tests/integration/test_workflow.py | 34 ++++++++++ .../unit/test_workflow_utils_validation.py | 64 +++++++++++++++++++ 3 files changed, 98 insertions(+), 25 deletions(-) create mode 100644 libs/labelbox/tests/unit/test_workflow_utils_validation.py diff --git a/libs/labelbox/src/labelbox/schema/workflow/workflow_utils.py b/libs/labelbox/src/labelbox/schema/workflow/workflow_utils.py index bd2ca0ca0..1e71a576f 100644 --- a/libs/labelbox/src/labelbox/schema/workflow/workflow_utils.py +++ b/libs/labelbox/src/labelbox/schema/workflow/workflow_utils.py @@ -117,31 +117,6 @@ def validate_node_connections( "node_type": node_type, } ) - elif len(predecessors) > 1: - # Check if all predecessors are initial nodes - node_map = {n.id: n for n in nodes} - predecessor_nodes = [ - node_map.get(pred_id) for pred_id in predecessors - ] - all_initial = all( - pred_node - and pred_node.definition_id in initial_node_types - for pred_node in predecessor_nodes - if pred_node is not None - ) - - if not all_initial: - preds_info = ", ".join( - [p[:8] + "..." for p in predecessors] - ) - errors.append( - { - "reason": f"has multiple incoming connections ({len(predecessors)}) but not all are from initial nodes", - "node_id": node.id, - "node_type": node_type, - "details": f"Connected from: {preds_info}", - } - ) # Check outgoing connections (except terminal nodes) if node.definition_id not in terminal_node_types: diff --git a/libs/labelbox/tests/integration/test_workflow.py b/libs/labelbox/tests/integration/test_workflow.py index 96cb53b46..f09cac2a4 100644 --- a/libs/labelbox/tests/integration/test_workflow.py +++ b/libs/labelbox/tests/integration/test_workflow.py @@ -96,6 +96,40 @@ def test_workflow_creation(client, test_projects): assert WorkflowDefinitionId.Done in node_types +def test_workflow_allows_multiple_incoming_from_non_initial_nodes( + client, test_projects +): + """ + Nodes may have multiple incoming connections from any nodes (not only initial nodes). + + This used to fail validation when a node had >1 predecessor and at least one + predecessor was not an initial node. + """ + source_project, _ = test_projects + + workflow = source_project.get_workflow() + initial_nodes = workflow.reset_to_initial_nodes( + labeling_config=LabelingConfig(instructions="Start labeling here") + ) + + logic = workflow.add_node( + type=NodeType.Logic, + name="Gate", + filters=ProjectWorkflowFilter([labeled_by.is_one_of(["test-user"])]), + ) + review = workflow.add_node(type=NodeType.Review, name="Review Task") + done = workflow.add_node(type=NodeType.Done, name="Done") + + # Multiple incoming connections to review, including from a non-initial node (logic) + workflow.add_edge(initial_nodes.labeling, logic) + workflow.add_edge(logic, review, NodeOutput.If) + workflow.add_edge(initial_nodes.rework, review) + workflow.add_edge(review, done, NodeOutput.Approved) + + # Should validate and update successfully + workflow.update_config(reposition=False) + + def test_workflow_creation_simple(client): """Test creating a simple workflow with the working pattern.""" # Create a new project for this test diff --git a/libs/labelbox/tests/unit/test_workflow_utils_validation.py b/libs/labelbox/tests/unit/test_workflow_utils_validation.py new file mode 100644 index 000000000..6e608faa6 --- /dev/null +++ b/libs/labelbox/tests/unit/test_workflow_utils_validation.py @@ -0,0 +1,64 @@ +from dataclasses import dataclass + +from labelbox.schema.workflow.enums import WorkflowDefinitionId +from labelbox.schema.workflow.graph import ProjectWorkflowGraph +from labelbox.schema.workflow.workflow_utils import WorkflowValidator + + +@dataclass(frozen=True) +class _Node: + id: str + definition_id: WorkflowDefinitionId + + +def test_validate_node_connections_allows_multiple_incoming_from_non_initial_nodes(): + """ + Regression test: nodes may have multiple incoming connections from any nodes. + + Historically validation required that if a node had >1 predecessors, they all had + to be initial nodes. Workflow Management now allows multi-input nodes from any + nodes, so this must not error. + """ + initial_labeling = _Node( + id="initial_labeling", + definition_id=WorkflowDefinitionId.InitialLabelingTask, + ) + initial_rework = _Node( + id="initial_rework", + definition_id=WorkflowDefinitionId.InitialReworkTask, + ) + logic = _Node(id="logic", definition_id=WorkflowDefinitionId.Logic) + review = _Node(id="review", definition_id=WorkflowDefinitionId.ReviewTask) + done = _Node(id="done", definition_id=WorkflowDefinitionId.Done) + + nodes = [initial_labeling, initial_rework, logic, review, done] + + graph = ProjectWorkflowGraph() + graph.add_edge(initial_labeling.id, logic.id) + graph.add_edge(logic.id, review.id) + graph.add_edge(initial_rework.id, review.id) + graph.add_edge(review.id, done.id) + + errors = WorkflowValidator.validate_node_connections(nodes, graph) + assert errors == [] + + +def test_validate_node_connections_still_flags_missing_incoming_connections(): + """Non-initial nodes must still have at least one incoming connection.""" + initial_labeling = _Node( + id="initial_labeling", + definition_id=WorkflowDefinitionId.InitialLabelingTask, + ) + review = _Node(id="review", definition_id=WorkflowDefinitionId.ReviewTask) + done = _Node(id="done", definition_id=WorkflowDefinitionId.Done) + + nodes = [initial_labeling, review, done] + graph = ProjectWorkflowGraph() + graph.add_edge(initial_labeling.id, done.id) + + errors = WorkflowValidator.validate_node_connections(nodes, graph) + assert any( + e.get("node_id") == review.id + and e.get("reason") == "has no incoming connections" + for e in errors + ) From cc00906ba672d71d4dc06da52dc0026ccb044195 Mon Sep 17 00:00:00 2001 From: Tim Kerr <54248546+Tim-Kerr@users.noreply.github.com> Date: Mon, 19 Jan 2026 08:34:55 -0700 Subject: [PATCH 081/103] Group classification type (#2030) --- .../labelbox/src/labelbox/schema/tool_building/classification.py | 1 + 1 file changed, 1 insertion(+) diff --git a/libs/labelbox/src/labelbox/schema/tool_building/classification.py b/libs/labelbox/src/labelbox/schema/tool_building/classification.py index a6f4ebd1d..8e6d8be70 100644 --- a/libs/labelbox/src/labelbox/schema/tool_building/classification.py +++ b/libs/labelbox/src/labelbox/schema/tool_building/classification.py @@ -53,6 +53,7 @@ class Type(Enum): TEXT = "text" CHECKLIST = "checklist" RADIO = "radio" + GROUP = "group" class Scope(Enum): GLOBAL = "global" From ce50697f56ace0cf093d156be249c8cc159bf3af Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C5=81ukasz=20G=C5=82uszek?= Date: Wed, 21 Jan 2026 19:08:37 +0100 Subject: [PATCH 082/103] [PTDT-3784] Model config response count support (#2014) --- libs/labelbox/src/labelbox/schema/project.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/libs/labelbox/src/labelbox/schema/project.py b/libs/labelbox/src/labelbox/schema/project.py index 60d6b6258..206d3c43d 100644 --- a/libs/labelbox/src/labelbox/schema/project.py +++ b/libs/labelbox/src/labelbox/schema/project.py @@ -1183,18 +1183,21 @@ def get_label_count(self) -> int: res = self.client.execute(query_str, {"projectId": self.uid}) return res["project"]["labelCount"] - def add_model_config(self, model_config_id: str) -> str: + def add_model_config( + self, model_config_id: str, response_count: Optional[int] = None + ) -> str: """Adds a model config to this project. Args: model_config_id (str): ID of a model config to add to this project. + response_count (Optional[int]): Number of responses to generate. If not provided, uses the default. Returns: str, ID of the project model config association. This is needed for updating and deleting associations. """ - query = """mutation CreateProjectModelConfigPyApi($projectId: ID!, $modelConfigId: ID!) { - createProjectModelConfig(input: {projectId: $projectId, modelConfigId: $modelConfigId}) { + query = """mutation CreateProjectModelConfigPyApi($projectId: ID!, $modelConfigId: ID!, $responseCount: Int) { + createProjectModelConfig(input: {projectId: $projectId, modelConfigId: $modelConfigId, responseCount: $responseCount}) { projectModelConfigId } }""" @@ -1202,6 +1205,7 @@ def add_model_config(self, model_config_id: str) -> str: params = { "projectId": self.uid, "modelConfigId": model_config_id, + "responseCount": response_count, } try: result = self.client.execute(query, params) From b7120b0abb5cf79482fc27a6c8b2e2b56a0636f5 Mon Sep 17 00:00:00 2001 From: Chuck Terry Date: Wed, 21 Jan 2026 13:32:37 -0500 Subject: [PATCH 083/103] Correct Prompt Issue Tool Name (#2034) --- docs/labelbox/prompt-issue-tool.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/labelbox/prompt-issue-tool.rst b/docs/labelbox/prompt-issue-tool.rst index 7f1842e4b..40f919050 100644 --- a/docs/labelbox/prompt-issue-tool.rst +++ b/docs/labelbox/prompt-issue-tool.rst @@ -1,4 +1,4 @@ -Step Reasoning Tool +Prompt Issue Tool =============================================================================================== .. automodule:: labelbox.schema.tool_building.prompt_issue_tool From 1553e6630713809bf615d61574f5aa0c2aceb035 Mon Sep 17 00:00:00 2001 From: paulnoirel <87332996+paulnoirel@users.noreply.github.com> Date: Thu, 22 Jan 2026 10:18:07 +0000 Subject: [PATCH 084/103] v7.4.0 - release preparation (#2036) --- docs/conf.py | 2 +- libs/labelbox/CHANGELOG.md | 9 +++++++++ libs/labelbox/pyproject.toml | 2 +- libs/labelbox/src/labelbox/__init__.py | 2 +- 4 files changed, 12 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 40afedce1..325ab6b57 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ project = 'Python SDK reference' copyright = '2025, Labelbox' author = 'Labelbox' -release = '7.3.0' +release = '7.4.0' # -- General configuration --------------------------------------------------- diff --git a/libs/labelbox/CHANGELOG.md b/libs/labelbox/CHANGELOG.md index bed05788f..8d2bbf7b4 100644 --- a/libs/labelbox/CHANGELOG.md +++ b/libs/labelbox/CHANGELOG.md @@ -1,4 +1,13 @@ # Changelog +# Version 7.4.0 (2026-01-22) +## Added +* Add support of groups to invite_user() ([#2035](https://github.com/Labelbox/labelbox-python/pull/2035)) +* Add group classification type ([2030](https://github.com/Labelbox/labelbox-python/pull/2030)) +* Add model config response count support ([#2014](https://github.com/Labelbox/labelbox-python/pull/2014)) + +## Fixed +* Workflow Management allow multiple inputs ([#2031](https://github.com/Labelbox/labelbox-python/pull/2031)) + # Version 7.3.0 (2025-10-27) ## Added * Add support for Audio Temporal Annotations ([#2013](https://github.com/Labelbox/labelbox-python/pull/2013)) diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml index 756c89eea..92250d517 100644 --- a/libs/labelbox/pyproject.toml +++ b/libs/labelbox/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "labelbox" -version = "7.3.0" +version = "7.4.0" description = "Labelbox Python API" authors = [{ name = "Labelbox", email = "engineering@labelbox.com" }] dependencies = [ diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index d8296d594..9a6d9efc9 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -1,6 +1,6 @@ name = "labelbox" -__version__ = "7.3.0" +__version__ = "7.4.0" from labelbox.client import Client from labelbox.schema.annotation_import import ( From 043cec56be89b8ace43420cc9245829c3580a2c1 Mon Sep 17 00:00:00 2001 From: paulnoirel <87332996+paulnoirel@users.noreply.github.com> Date: Thu, 22 Jan 2026 10:28:57 +0000 Subject: [PATCH 085/103] Pno/fix linter alignerr project (#2037) --- .../src/alignerr/alignerr_project.py | 94 ++++++++----------- 1 file changed, 41 insertions(+), 53 deletions(-) diff --git a/libs/lbox-alignerr/src/alignerr/alignerr_project.py b/libs/lbox-alignerr/src/alignerr/alignerr_project.py index f9432ff3a..92a119f43 100644 --- a/libs/lbox-alignerr/src/alignerr/alignerr_project.py +++ b/libs/lbox-alignerr/src/alignerr/alignerr_project.py @@ -156,13 +156,13 @@ def get_project_owner(self) -> Optional[ProjectBoostWorkforce]: def _get_user_labels(self, user_id: str): """Get all labels created by a user in this project. - + Args: user_id: ID of the user - + Returns: List of Label objects - + Raises: Exception: If labels cannot be retrieved """ @@ -171,20 +171,20 @@ def _get_user_labels(self, user_id: str): "Found %d labels created by user %s in project %s", len(labels), user_id, - self.project.uid + self.project.uid, ) return labels def _create_trust_safety_case(self, user_id: str, event_metadata: dict) -> bool: """Create a Trust & Safety case for a user. - + Args: user_id: ID of the user being reported event_metadata: JSON metadata about the event - + Returns: True if case was created successfully - + Raises: Exception: If T&S case creation fails """ @@ -203,32 +203,30 @@ def _create_trust_safety_case(self, user_id: str, event_metadata: dict) -> bool: success } }""" - + params = { "subjectUserId": user_id, "eventType": "manual", "severity": "high", "eventMetadata": event_metadata, } - + result = self.client.execute(mutation, params) success = result["createTrustAndSafetyCase"]["success"] - + if success: logger.info( - "Created T&S case for user %s in project %s", - user_id, - self.project.uid + "Created T&S case for user %s in project %s", user_id, self.project.uid ) - + return success def _remove_user_from_project(self, user_id: str) -> None: """Remove a user from this project. - + Args: user_id: ID of the user to remove - + Raises: ValueError: If user not found in project Exception: If removal fails @@ -239,56 +237,46 @@ def _remove_user_from_project(self, user_id: str) -> None: if member.user().uid == user_id: user_found = True break - + if not user_found: - logger.warning("User %s not found in project %s members", user_id, self.project.uid) + logger.warning( + "User %s not found in project %s members", user_id, self.project.uid + ) raise ValueError(f"User {user_id} not found in project members") - + # Remove user using deleteProjectMemberships mutation result = self.client.delete_project_memberships( - project_id=self.project.uid, - user_ids=[user_id] + project_id=self.project.uid, user_ids=[user_id] ) - + if not result.get("success"): error_message = result.get("errorMessage", "Unknown error") logger.error("Failed to remove user: %s", error_message) raise Exception(f"Failed to remove user: {error_message}") - - logger.info( - "Removed user %s from project %s", - user_id, - self.project.uid - ) + + logger.info("Removed user %s from project %s", user_id, self.project.uid) def _delete_user_labels(self, labels) -> int: """Delete a list of labels. - + Args: labels: List of Label objects to delete - + Returns: Number of labels deleted - + Raises: Exception: If deletion fails """ if not labels: return 0 - + Entity.Label.bulk_delete(labels) - logger.info( - "Deleted %d labels in project %s", - len(labels), - self.project.uid - ) + logger.info("Deleted %d labels in project %s", len(labels), self.project.uid) return len(labels) def report_fraud( - self, - user_id: str, - reason: str, - custom_metadata: dict = None + self, user_id: str, reason: str, custom_metadata: dict = None ) -> dict: """Report potential fraud by a user in this project. @@ -297,13 +285,13 @@ def report_fraud( 2. Creates a Trust & Safety case for the user (MANUAL event type, HIGH severity) 3. Removes the user from the project (prevents creating more labels) 4. Deletes all the user's labels - + Args: user_id (str): The ID of the user to report for fraud. reason (str): Reason for reporting fraud (e.g., "Spam labels", "Low quality work"). custom_metadata (dict, optional): Additional metadata to include in the T&S case. Will be merged with automatic metadata (project_id, reason, label_count, label_ids). - + Returns: dict: A dictionary containing: - ts_case_id: Status of T&S case creation ("created" if successful) @@ -311,19 +299,19 @@ def report_fraud( - user_removed: Whether the user was successfully removed - labels_deleted: Number of labels deleted - error: Any error message if any step failed - + Example: >>> from alignerr import AlignerrWorkspace >>> from labelbox import Client - >>> + >>> >>> client = Client(api_key="YOUR_API_KEY") >>> workspace = AlignerrWorkspace.from_labelbox(client) >>> project = workspace.project_builder().from_existing(project_id) - >>> + >>> >>> # Report fraud with reason >>> result = project.report_fraud(user_id, reason="Spam labels detected") >>> print(f"Removed user: {result['user_removed']}, Deleted {result['labels_deleted']} labels") - >>> + >>> >>> # With additional custom metadata >>> result = project.report_fraud( >>> user_id, @@ -338,7 +326,7 @@ def report_fraud( "labels_deleted": 0, "error": None, } - + # Step 1: Get all labels cteated by this user in this project try: labels_to_delete = self._get_user_labels(user_id) @@ -347,7 +335,7 @@ def report_fraud( logger.error("Failed to get labels: %s", str(e)) result["error"] = f"Failed to get labels: {str(e)}" return result - + # Step 2: Create T&S case with label information try: event_metadata = { @@ -358,7 +346,7 @@ def report_fraud( } if custom_metadata: event_metadata.update(custom_metadata) - + ts_case_created = self._create_trust_safety_case(user_id, event_metadata) if ts_case_created: result["ts_case_id"] = "created" @@ -366,7 +354,7 @@ def report_fraud( logger.error("Failed to create T&S case: %s", str(e)) result["error"] = f"Failed to create T&S case: {str(e)}" return result - + # Step 3: Remove user from project (prevent creating more labels) try: self._remove_user_from_project(user_id) @@ -375,7 +363,7 @@ def report_fraud( logger.error("Failed to remove user from project: %s", str(e)) result["error"] = f"Failed to remove user: {str(e)}" return result - + # Step 4: Delete all labels by this user try: result["labels_deleted"] = self._delete_user_labels(labels_to_delete) @@ -383,7 +371,7 @@ def report_fraud( logger.error("Failed to delete labels: %s", str(e)) result["error"] = f"Failed to delete labels: {str(e)}" return result - + return result From 5cff6f296360b2234383dfdba883d9da12e3ede3 Mon Sep 17 00:00:00 2001 From: paulnoirel <87332996+paulnoirel@users.noreply.github.com> Date: Thu, 22 Jan 2026 11:28:01 +0000 Subject: [PATCH 086/103] Bump lbox-alignerr version (#2038) --- libs/lbox-alignerr/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/lbox-alignerr/pyproject.toml b/libs/lbox-alignerr/pyproject.toml index 0d25f4ab8..375d488c8 100644 --- a/libs/lbox-alignerr/pyproject.toml +++ b/libs/lbox-alignerr/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "lbox-alignerr" -version = "0.1.0" +version = "0.2.0" description = "Alignerr workspace management for Labelbox" authors = [ { name = "Labelbox", email = "engineering@labelbox.com" } From c75b19f6bb0087f06ffbbf4ce655868d745180c3 Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Thu, 22 Jan 2026 10:18:22 -0600 Subject: [PATCH 087/103] [PLT-0] remove build-lbox job from publish.yml (#2039) --- .github/workflows/publish.yml | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index f540d2a6d..11845e6d2 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -26,19 +26,19 @@ permissions: id-token: write jobs: - build-lbox: - permissions: - actions: read - contents: write - id-token: write # Needed to access the workflow's OIDC identity. - packages: write - uses: ./.github/workflows/lbox-publish.yml - with: - tag: ${{ inputs.tag }} - prev_sdk_tag: ${{ inputs.prev_sdk_tag }} - secrets: inherit +# build-lbox: +# permissions: +# actions: read +# contents: write +# id-token: write # Needed to access the workflow's OIDC identity. +# packages: write +# uses: ./.github/workflows/lbox-publish.yml +# with: +# tag: ${{ inputs.tag }} +# prev_sdk_tag: ${{ inputs.prev_sdk_tag }} +# secrets: inherit build: - needs: ['build-lbox'] +# needs: ['build-lbox'] runs-on: ubuntu-latest outputs: hashes: ${{ steps.hash.outputs.hashes }} @@ -252,4 +252,4 @@ jobs: digest: ${{ needs. container-publish.outputs.digest }} registry-username: ${{ github.actor }} secrets: - registry-password: ${{ secrets.GITHUB_TOKEN }} \ No newline at end of file + registry-password: ${{ secrets.GITHUB_TOKEN }} From 4d8931d640c5e27bba87009f3d043178a3f9edfb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C5=81ukasz=20G=C5=82uszek?= Date: Sat, 31 Jan 2026 04:29:16 +0100 Subject: [PATCH 088/103] [PTDT-4832] Prelabels for text subclasses under global text classes (#2040) --- .../classification/classification.py | 1 + .../serialization/ndjson/classification.py | 18 ++++++++++++++++++ 2 files changed, 19 insertions(+) diff --git a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py index d6a6448dd..a749c212c 100644 --- a/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py +++ b/libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py @@ -52,6 +52,7 @@ class Text(ConfidenceMixin, CustomMetricsMixin, BaseModel): """ answer: str + classifications: Optional[List["ClassificationAnnotation"]] = None class ClassificationAnnotation( diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index fedf4d91b..49177bba2 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -86,6 +86,12 @@ def to_common(self) -> Text: answer=self.answer, confidence=self.confidence, custom_metrics=self.custom_metrics, + classifications=[ + NDSubclassification.to_common(annot) + for annot in self.classifications + ] + if self.classifications + else None, ) @classmethod @@ -98,6 +104,12 @@ def from_common( schema_id=feature_schema_id, confidence=text.confidence, custom_metrics=text.custom_metrics, + classifications=[ + NDSubclassification.from_common(annot) + for annot in text.classifications + ] + if text.classifications + else None, ) @@ -245,6 +257,12 @@ def from_common( message_id=message_id, confidence=text.confidence, custom_metrics=text.custom_metrics, + classifications=[ + NDSubclassification.from_common(annot) + for annot in text.classifications + ] + if text.classifications + else None, ) From b1b5f7c8a0bd8271775cc9fe1ae670a49c8e208f Mon Sep 17 00:00:00 2001 From: Matthew Roberson Date: Fri, 30 Jan 2026 22:26:49 -0600 Subject: [PATCH 089/103] v7.5.0 - release preparation (#2041) --- docs/conf.py | 2 +- libs/labelbox/CHANGELOG.md | 6 +++++- libs/labelbox/pyproject.toml | 2 +- libs/labelbox/src/labelbox/__init__.py | 2 +- 4 files changed, 8 insertions(+), 4 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 325ab6b57..ef07b50f5 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ project = 'Python SDK reference' copyright = '2025, Labelbox' author = 'Labelbox' -release = '7.4.0' +release = '7.5.0' # -- General configuration --------------------------------------------------- diff --git a/libs/labelbox/CHANGELOG.md b/libs/labelbox/CHANGELOG.md index 8d2bbf7b4..5be42483a 100644 --- a/libs/labelbox/CHANGELOG.md +++ b/libs/labelbox/CHANGELOG.md @@ -1,8 +1,12 @@ # Changelog +# Version 7.5.0 (2026-01-30) +## Added +* Add support for text subclasses under global text subclasses ([#2040](https://github.com/Labelbox/labelbox-python/pull/2040)) + # Version 7.4.0 (2026-01-22) ## Added * Add support of groups to invite_user() ([#2035](https://github.com/Labelbox/labelbox-python/pull/2035)) -* Add group classification type ([2030](https://github.com/Labelbox/labelbox-python/pull/2030)) +* Add group classification type ([#2030](https://github.com/Labelbox/labelbox-python/pull/2030)) * Add model config response count support ([#2014](https://github.com/Labelbox/labelbox-python/pull/2014)) ## Fixed diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml index 92250d517..5f11ff906 100644 --- a/libs/labelbox/pyproject.toml +++ b/libs/labelbox/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "labelbox" -version = "7.4.0" +version = "7.5.0" description = "Labelbox Python API" authors = [{ name = "Labelbox", email = "engineering@labelbox.com" }] dependencies = [ diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index 9a6d9efc9..0979d1590 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -1,6 +1,6 @@ name = "labelbox" -__version__ = "7.4.0" +__version__ = "7.5.0" from labelbox.client import Client from labelbox.schema.annotation_import import ( From 45fb47cd7724c7111aff04858dcc8277b1a9a71e Mon Sep 17 00:00:00 2001 From: kozikkamil <91909509+kozikkamil@users.noreply.github.com> Date: Mon, 23 Feb 2026 17:30:44 +0100 Subject: [PATCH 090/103] Add submit_external_metrics (#2042) --- libs/labelbox/src/labelbox/__init__.py | 12 ++ libs/labelbox/src/labelbox/schema/project.py | 38 ++++++ .../src/labelbox/schema/project_sync.py | 121 ++++++++++++++++++ 3 files changed, 171 insertions(+) create mode 100644 libs/labelbox/src/labelbox/schema/project_sync.py diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index 0979d1590..07c60ab11 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -22,6 +22,18 @@ ) from labelbox.schema.dataset import Dataset from labelbox.schema.enums import AnnotationImportState +from labelbox.schema.project_sync import ( + AutoQA, + AutoQaStatus, + CustomScore, + GranularRating, + ProjectSyncEntry, + ProjectSyncLabel, + ProjectSyncResult, + ProjectSyncReview, + ReviewedBy, + SubmittedBy, +) from labelbox.schema.export_task import ( BufferedJsonConverterOutput, ExportTask, diff --git a/libs/labelbox/src/labelbox/schema/project.py b/libs/labelbox/src/labelbox/schema/project.py index 206d3c43d..3a92db3f9 100644 --- a/libs/labelbox/src/labelbox/schema/project.py +++ b/libs/labelbox/src/labelbox/schema/project.py @@ -37,6 +37,11 @@ ProjectExportFilters, build_filters, ) +from labelbox.schema.project_sync import ( + ProjectSyncEntry, + ProjectSyncResult, + _to_gql_input, +) from labelbox.schema.export_params import ProjectExportParams from labelbox.schema.export_task import ExportTask from labelbox.schema.identifiable import DataRowIdentifier @@ -1001,6 +1006,39 @@ def create_batches( return CreateBatchesTask(self.client, self.uid, batch_ids, task_ids) + def sync_external_project( + self, + entries: List[ProjectSyncEntry], + ) -> ProjectSyncResult: + """Syncs external project data — labels, metrics, and workflow state. + + Processing is asynchronous. The returned submission ID can be used + to track the progress of the sync operation. + + Args: + entries: A list of ProjectSyncEntry objects. + + Returns: + A ProjectSyncResult containing the submission ID. + """ + mutation_str = """mutation syncExternalProjectPyApi($input: SyncExternalProjectInput!) { + syncExternalProject(input: $input) { + submissionId + } + }""" + + params = { + "input": { + "projectId": self.uid, + "entries": [_to_gql_input(e) for e in entries], + } + } + + response = self.client.execute(mutation_str, params) + payload = response["syncExternalProject"] + + return ProjectSyncResult(submission_id=payload["submissionId"]) + def create_batches_from_dataset( self, name_prefix: str, diff --git a/libs/labelbox/src/labelbox/schema/project_sync.py b/libs/labelbox/src/labelbox/schema/project_sync.py new file mode 100644 index 000000000..50dedaa6e --- /dev/null +++ b/libs/labelbox/src/labelbox/schema/project_sync.py @@ -0,0 +1,121 @@ +from enum import Enum +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel + + +class AutoQaStatus(str, Enum): + Approve = "Approve" + Reject = "Reject" + Neutral = "Neutral" + + +class SubmittedBy(BaseModel): + email: str + + +class CustomScore(BaseModel): + name: str + value: float + + +class AutoQA(BaseModel): + status: AutoQaStatus + score: Optional[float] = None + feedback: Optional[str] = None + custom_scores: Optional[List[CustomScore]] = None + + +class ProjectSyncLabel(BaseModel): + submitted_by: SubmittedBy + auto_qa: Optional[AutoQA] = None + seconds_to_completion: Optional[float] = None + submitted_on: Optional[str] = None + + +class ReviewedBy(BaseModel): + email: str + + +class GranularRating(BaseModel): + score: int + comment: Optional[str] = None + + +class ProjectSyncReview(BaseModel): + reviewed_by: ReviewedBy + rating: Optional[GranularRating] = None + custom_scores: Optional[List[CustomScore]] = None + + +class ProjectSyncEntry(BaseModel): + task_id: str + content_url: Optional[str] = None + label: Optional[ProjectSyncLabel] = None + review: Optional[ProjectSyncReview] = None + queue_type: Optional[str] = None + + +class ProjectSyncResult(BaseModel): + submission_id: str + + +def _to_gql_input(entry: ProjectSyncEntry) -> Dict[str, Any]: + """Convert a ProjectSyncEntry to a camelCase dict matching the GQL schema.""" + result: Dict[str, Any] = {"taskId": entry.task_id} + + if entry.content_url is not None: + result["contentUrl"] = entry.content_url + + if entry.label is not None: + label: Dict[str, Any] = { + "submittedBy": {"email": entry.label.submitted_by.email}, + } + + if entry.label.auto_qa is not None: + auto_qa: Dict[str, Any] = { + "status": entry.label.auto_qa.status.value, + } + if entry.label.auto_qa.score is not None: + auto_qa["score"] = entry.label.auto_qa.score + if entry.label.auto_qa.feedback is not None: + auto_qa["feedback"] = entry.label.auto_qa.feedback + if entry.label.auto_qa.custom_scores is not None: + auto_qa["customScores"] = [ + {"name": cs.name, "value": cs.value} + for cs in entry.label.auto_qa.custom_scores + ] + label["autoQA"] = auto_qa + + if entry.label.seconds_to_completion is not None: + label["secondsToCompletion"] = entry.label.seconds_to_completion + + if entry.label.submitted_on is not None: + label["submittedOn"] = entry.label.submitted_on + + result["label"] = label + elif "label" in entry.model_fields_set: + result["label"] = None + + if entry.review is not None: + review: Dict[str, Any] = { + "reviewedBy": {"email": entry.review.reviewed_by.email}, + } + if entry.review.rating is not None: + rating: Dict[str, Any] = {"score": entry.review.rating.score} + if entry.review.rating.comment is not None: + rating["comment"] = entry.review.rating.comment + review["rating"] = rating + if entry.review.custom_scores is not None: + review["customScores"] = [ + {"name": cs.name, "value": cs.value} + for cs in entry.review.custom_scores + ] + result["review"] = review + + if entry.queue_type is not None: + result["queueType"] = entry.queue_type + elif "queue_type" in entry.model_fields_set: + result["queueType"] = None + + return result From 78cfe93611e92278fbd5c1595f7a4252aa6c636a Mon Sep 17 00:00:00 2001 From: lb-pno <87332996+lb-pno@users.noreply.github.com> Date: Wed, 18 Mar 2026 13:13:37 +0000 Subject: [PATCH 091/103] Implement issue management (#2043) Co-authored-by: paulnoirel <87332996+paulnoirel@users.noreply.github.com> Co-authored-by: Cursor --- docs/labelbox/index.rst | 3 + docs/labelbox/issue-category.rst | 6 + docs/labelbox/issue-position.rst | 6 + docs/labelbox/issue.rst | 6 + libs/labelbox/src/labelbox/__init__.py | 10 + libs/labelbox/src/labelbox/schema/issue.py | 487 ++++++++++++++++++ .../src/labelbox/schema/issue_category.py | 89 ++++ .../src/labelbox/schema/issue_position.py | 260 ++++++++++ libs/labelbox/src/labelbox/schema/project.py | 308 +++++++++++ .../integration/test_issue_management.py | 329 ++++++++++++ libs/labelbox/tests/unit/schema/test_issue.py | 293 +++++++++++ .../tests/unit/schema/test_issue_category.py | 52 ++ .../tests/unit/schema/test_issue_position.py | 250 +++++++++ .../tests/unit/schema/test_project_issues.py | 376 ++++++++++++++ 14 files changed, 2475 insertions(+) create mode 100644 docs/labelbox/issue-category.rst create mode 100644 docs/labelbox/issue-position.rst create mode 100644 docs/labelbox/issue.rst create mode 100644 libs/labelbox/src/labelbox/schema/issue.py create mode 100644 libs/labelbox/src/labelbox/schema/issue_category.py create mode 100644 libs/labelbox/src/labelbox/schema/issue_position.py create mode 100644 libs/labelbox/tests/integration/test_issue_management.py create mode 100644 libs/labelbox/tests/unit/schema/test_issue.py create mode 100644 libs/labelbox/tests/unit/schema/test_issue_category.py create mode 100644 libs/labelbox/tests/unit/schema/test_issue_position.py create mode 100644 libs/labelbox/tests/unit/schema/test_project_issues.py diff --git a/docs/labelbox/index.rst b/docs/labelbox/index.rst index 347abf6b4..8069b6b62 100644 --- a/docs/labelbox/index.rst +++ b/docs/labelbox/index.rst @@ -24,6 +24,9 @@ Labelbox Python SDK Documentation foundry-model identifiable identifiables + issue + issue-category + issue-position label label-score labeling-frontend diff --git a/docs/labelbox/issue-category.rst b/docs/labelbox/issue-category.rst new file mode 100644 index 000000000..bd9f96372 --- /dev/null +++ b/docs/labelbox/issue-category.rst @@ -0,0 +1,6 @@ +Issue Category +=============================================================================================== + +.. automodule:: labelbox.schema.issue_category + :members: + :show-inheritance: diff --git a/docs/labelbox/issue-position.rst b/docs/labelbox/issue-position.rst new file mode 100644 index 000000000..2dd59b448 --- /dev/null +++ b/docs/labelbox/issue-position.rst @@ -0,0 +1,6 @@ +Issue Position +=============================================================================================== + +.. automodule:: labelbox.schema.issue_position + :members: + :show-inheritance: diff --git a/docs/labelbox/issue.rst b/docs/labelbox/issue.rst new file mode 100644 index 000000000..8937a13d8 --- /dev/null +++ b/docs/labelbox/issue.rst @@ -0,0 +1,6 @@ +Issue +=============================================================================================== + +.. automodule:: labelbox.schema.issue + :members: + :show-inheritance: diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index 07c60ab11..a83ce2e7a 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -44,6 +44,16 @@ from labelbox.schema.identifiable import GlobalKey, UniqueId from labelbox.schema.identifiables import DataRowIds, GlobalKeys, UniqueIds from labelbox.schema.invite import Invite, InviteLimit +from labelbox.schema.issue import Comment, Issue, IssueStatus +from labelbox.schema.issue_category import IssueCategory +from labelbox.schema.issue_position import ( + ImageIssuePosition, + IssuePosition, + PdfIssuePosition, + TextIssuePosition, + VideoFrameRange, + VideoIssuePosition, +) from labelbox.schema.label import Label from labelbox.schema.label_score import LabelScore from labelbox.schema.labeling_frontend import ( diff --git a/libs/labelbox/src/labelbox/schema/issue.py b/libs/labelbox/src/labelbox/schema/issue.py new file mode 100644 index 000000000..af5da716d --- /dev/null +++ b/libs/labelbox/src/labelbox/schema/issue.py @@ -0,0 +1,487 @@ +"""Issue and Comment models for the Labelbox Python SDK. + +Uses ``_CamelCaseMixin`` (Pydantic) instead of ``DbObject`` / ``Updateable`` +/ ``Deletable`` because the backend's GraphQL mutations use typed input objects +incompatible with the ORM's auto-generated mutations. +""" + +from datetime import datetime +from enum import Enum +from typing import TYPE_CHECKING, Any, List, Optional + +from pydantic import ConfigDict, PrivateAttr + +from labelbox.orm.model import Entity +from labelbox.schema.issue_category import IssueCategory +from labelbox.schema.issue_position import ( + IssuePosition, + _deserialize_position, +) +from labelbox.schema.user import User +from labelbox.utils import _CamelCaseMixin + +if TYPE_CHECKING: + from labelbox.schema.data_row import DataRow + from labelbox.schema.label import Label + + +class IssueStatus(str, Enum): + """Status of an issue.""" + + OPEN = "Open" + RESOLVED = "Resolved" + + +# --------------------------------------------------------------------------- +# GraphQL fragments +# --------------------------------------------------------------------------- + +_USER_FIELDS = ( + "id email nickname name picture isViewer isExternalUser createdAt updatedAt" +) + +_COMMENT_FIELDS = ( + """ + id + content + createdBy { %s } + createdAt + updatedAt +""" + % _USER_FIELDS +) + +_ISSUE_FIELDS = """ + id + friendlyId + labelId + dataRowId + categoryId + content + position + status + createdBy { %s } + resolvedBy { %s } + createdAt + updatedAt + resolvedAt + contentUpdatedAt + latestReplyAt +""" % (_USER_FIELDS, _USER_FIELDS) + + +# --------------------------------------------------------------------------- +# Helper: build a User DbObject from a raw dict +# --------------------------------------------------------------------------- + + +def _build_user(client: Any, raw: Optional[dict]) -> Optional[User]: + """Construct a :class:`User` DbObject from a GraphQL response fragment. + + Returns ``None`` when *raw* is ``None``. + """ + if raw is None: + return None + return User(client, raw) + + +# --------------------------------------------------------------------------- +# Comment +# --------------------------------------------------------------------------- + + +class Comment(_CamelCaseMixin): + """A comment attached to an :class:`Issue`. + + Attributes: + id: Unique identifier. + content: Comment body text. + created_by: The :class:`~labelbox.schema.user.User` who authored the + comment. + created_at: Creation timestamp. + updated_at: Last-modification timestamp. + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, + populate_by_name=True, + ) + + id: str + content: str + created_by: Any # User DbObject + created_at: datetime + updated_at: datetime + _client: Any = PrivateAttr(default=None) + + def __repr__(self) -> str: + return "" % self.id + + def update(self, content: str) -> "Comment": + """Update this comment's content. + + Args: + content: New body text. + + Returns: + Updated :class:`Comment` instance. + """ + query_str = ( + """mutation UpdateCommentPyApi( + $where: WhereUniqueIdInput!, + $data: UpdateCommentInput! + ) { + updateComment(where: $where, data: $data) { %s } + }""" + % _COMMENT_FIELDS + ) + + result = self._client.execute( + query_str, + { + "where": {"id": self.id}, + "data": {"content": content}, + }, + experimental=True, + ) + return _parse_comment(self._client, result["updateComment"]) + + def delete(self) -> bool: + """Delete this comment. + + Returns: + ``True`` when the deletion succeeds. + """ + query_str = """mutation DeleteCommentPyApi( + $where: WhereUniqueIdInput! + ) { + deleteComment(where: $where) + }""" + + self._client.execute( + query_str, {"where": {"id": self.id}}, experimental=True + ) + return True + + +# --------------------------------------------------------------------------- +# Issue +# --------------------------------------------------------------------------- + + +class Issue(_CamelCaseMixin): + """An issue pinned to a data row within a project. + + Attributes: + id: Unique identifier. + friendly_id: Human-readable short identifier. + label_id: Associated label ID (may be ``None``). + data_row_id: Associated data-row ID (may be ``None`` for legacy + issues). + category_id: Associated issue-category ID (may be ``None``). + content: Issue body text. + position: Typed position model or ``None``. + status: :class:`IssueStatus` (``OPEN`` / ``RESOLVED``). + created_by: The :class:`~labelbox.schema.user.User` who created the + issue. + resolved_by: The :class:`~labelbox.schema.user.User` who resolved it, + or ``None``. + created_at: Creation timestamp. + updated_at: Last-modification timestamp. + resolved_at: Resolution timestamp, or ``None``. + content_updated_at: Timestamp of last content edit, or ``None``. + latest_reply_at: Timestamp of the most recent comment, or ``None``. + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, + populate_by_name=True, + ) + + id: str + friendly_id: str + label_id: Optional[str] = None + data_row_id: Optional[str] = None + category_id: Optional[str] = None + content: str + position: Optional[Any] = None # IssuePosition (typed at runtime) + status: IssueStatus + created_by: Any # User DbObject + resolved_by: Optional[Any] = None # User DbObject or None + created_at: datetime + updated_at: datetime + resolved_at: Optional[datetime] = None + content_updated_at: Optional[datetime] = None + latest_reply_at: Optional[datetime] = None + _project_id: Optional[str] = PrivateAttr(default=None) + _client: Any = PrivateAttr(default=None) + + def __repr__(self) -> str: + return "" % self.id + + # ------------------------------------------------------------------ + # Methods that fetch related objects (each makes an API call) + # ------------------------------------------------------------------ + + def comments(self) -> List[Comment]: + """Fetch all comments for this issue. + + Returns: + List of :class:`Comment` instances. + """ + query_str = ( + """query GetIssueCommentsPyApi($where: WhereUniqueIdInput!) { + issue(where: $where) { + comments { %s } + } + }""" + % _COMMENT_FIELDS + ) + + result = self._client.execute( + query_str, {"where": {"id": self.id}}, experimental=True + ) + raw_comments = result.get("issue", {}).get("comments", []) + return [_parse_comment(self._client, c) for c in raw_comments] + + def data_row(self) -> Optional["DataRow"]: + """Fetch the associated :class:`~labelbox.schema.data_row.DataRow`. + + Returns: + The data row, or ``None`` if :attr:`data_row_id` is not set. + """ + if self.data_row_id is None: + return None + return self._client.get_data_row(self.data_row_id) + + def category(self) -> Optional[IssueCategory]: + """Fetch the associated :class:`IssueCategory`. + + Requires :attr:`project_id` to be set (automatically populated + when the issue is obtained via :class:`Project` methods). + + Returns: + The category, or ``None`` if :attr:`category_id` is not set + or the project context is unavailable. + """ + if self.category_id is None: + return None + if self._project_id is None: + return None + + query_str = """query GetIssueCategoriesPyApi($projectId: ID!) { + project(where: {id: $projectId}) { + issueCategories { id name description } + } + }""" + + result = self._client.execute( + query_str, {"projectId": self._project_id} + ) + raw_list = result.get("project", {}).get("issueCategories", []) + for raw in raw_list: + if raw["id"] == self.category_id: + cat = IssueCategory( + id=raw["id"], + name=raw["name"], + description=raw["description"], + ) + cat._client = self._client + return cat + return None + + def label(self) -> Optional["Label"]: + """Fetch the associated :class:`~labelbox.schema.label.Label`. + + Returns: + The label, or ``None`` if :attr:`label_id` is not set. + """ + if self.label_id is None: + return None + return self._client._get_single(Entity.Label, self.label_id) + + # ------------------------------------------------------------------ + # Mutation methods + # ------------------------------------------------------------------ + + def update( + self, + content: Optional[str] = None, + category_id: Optional[str] = None, + position: Optional[IssuePosition] = None, + label_id: Optional[str] = None, + ) -> "Issue": + """Update this issue. + + Only the provided fields are modified; pass ``None`` to leave a + field unchanged. + + Args: + content: New body text. + category_id: New category ID. + position: New position model. + label_id: New label ID. + + Returns: + Updated :class:`Issue` instance. + """ + data: dict = {} + if content is not None: + data["content"] = content + if category_id is not None: + data["categoryId"] = category_id + if position is not None: + data["position"] = position.to_dict() + if label_id is not None: + data["labelId"] = label_id + + if not data: + return self + + query_str = ( + """mutation UpdateIssuePyApi( + $where: WhereUniqueIdInput!, + $data: UpdateIssueInput! + ) { + updateIssue(where: $where, data: $data) { %s } + }""" + % _ISSUE_FIELDS + ) + + result = self._client.execute( + query_str, + {"where": {"id": self.id}, "data": data}, + experimental=True, + ) + return _parse_issue( + self._client, result["updateIssue"], project_id=self._project_id + ) + + def delete(self) -> bool: + """Delete this issue. + + Returns: + ``True`` when the deletion succeeds. + """ + query_str = """mutation DeleteIssuePyApi($data: DeleteIssueInput!) { + deleteIssue(data: $data) + }""" + + self._client.execute( + query_str, + {"data": {"issueIds": [self.id]}}, + experimental=True, + ) + return True + + def resolve(self) -> "Issue": + """Resolve this issue. + + Returns: + Updated :class:`Issue` with ``status == IssueStatus.RESOLVED``. + """ + query_str = ( + """mutation ResolveIssuePyApi($where: WhereUniqueIdInput!) { + resolveIssue(where: $where) { %s } + }""" + % _ISSUE_FIELDS + ) + + result = self._client.execute( + query_str, {"where": {"id": self.id}}, experimental=True + ) + return _parse_issue( + self._client, result["resolveIssue"], project_id=self._project_id + ) + + def reopen(self) -> "Issue": + """Re-open this issue. + + Returns: + Updated :class:`Issue` with ``status == IssueStatus.OPEN``. + """ + query_str = ( + """mutation OpenIssuePyApi($where: WhereUniqueIdInput!) { + openIssue(where: $where) { %s } + }""" + % _ISSUE_FIELDS + ) + + result = self._client.execute( + query_str, {"where": {"id": self.id}}, experimental=True + ) + return _parse_issue( + self._client, result["openIssue"], project_id=self._project_id + ) + + def create_comment(self, content: str) -> Comment: + """Create a new comment on this issue. + + Args: + content: Comment body text. + + Returns: + The newly created :class:`Comment`. + """ + query_str = ( + """mutation CreateCommentPyApi($data: CreateCommentInput!) { + createComment(data: $data) { %s } + }""" + % _COMMENT_FIELDS + ) + + result = self._client.execute( + query_str, + {"data": {"content": content, "issueId": self.id}}, + experimental=True, + ) + return _parse_comment(self._client, result["createComment"]) + + +# --------------------------------------------------------------------------- +# Factory functions +# --------------------------------------------------------------------------- + + +def _parse_comment(client: Any, data: dict) -> Comment: + """Build a :class:`Comment` from a raw GraphQL response dict.""" + created_by = _build_user(client, data.get("createdBy")) + comment = Comment( + id=data["id"], + content=data["content"], + created_by=created_by, + created_at=data["createdAt"], + updated_at=data["updatedAt"], + ) + comment._client = client + return comment + + +def _parse_issue( + client: Any, data: dict, project_id: Optional[str] = None +) -> Issue: + """Build an :class:`Issue` from a raw GraphQL response dict.""" + created_by = _build_user(client, data.get("createdBy")) + resolved_by = _build_user(client, data.get("resolvedBy")) + position = _deserialize_position(data.get("position")) + + issue = Issue( + id=data["id"], + friendly_id=data["friendlyId"], + label_id=data.get("labelId"), + data_row_id=data.get("dataRowId"), + category_id=data.get("categoryId"), + content=data["content"], + position=position, + status=IssueStatus(data["status"]), + created_by=created_by, + resolved_by=resolved_by, + created_at=data["createdAt"], + updated_at=data["updatedAt"], + resolved_at=data.get("resolvedAt"), + content_updated_at=data.get("contentUpdatedAt"), + latest_reply_at=data.get("latestReplyAt"), + ) + issue._client = client + issue._project_id = project_id + return issue diff --git a/libs/labelbox/src/labelbox/schema/issue_category.py b/libs/labelbox/src/labelbox/schema/issue_category.py new file mode 100644 index 000000000..d2bb601a0 --- /dev/null +++ b/libs/labelbox/src/labelbox/schema/issue_category.py @@ -0,0 +1,89 @@ +"""IssueCategory model for the Labelbox Python SDK. + +Uses ``_CamelCaseMixin`` (Pydantic) instead of ``DbObject`` / ``Updateable`` +/ ``Deletable`` because the backend's GraphQL mutations use typed input objects +incompatible with the ORM's auto-generated mutations. +""" + +from typing import Any + +from pydantic import ConfigDict, PrivateAttr + +from labelbox.utils import _CamelCaseMixin + + +class IssueCategory(_CamelCaseMixin): + """A category that can be assigned to issues within a project. + + Attributes: + id: Unique identifier. + name: Display name. + description: Human-readable description. + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, + populate_by_name=True, + ) + + id: str + name: str + description: str + _client: Any = PrivateAttr(default=None) + + def __repr__(self) -> str: + return "" % self.id + + def update(self, name: str, description: str) -> "IssueCategory": + """Update this issue category. + + Args: + name: New name for the category. + description: New description for the category. + + Returns: + Updated :class:`IssueCategory` instance. + """ + query_str = """mutation EditIssueCategoryPyApi( + $where: WhereUniqueIdInput!, + $data: EditIssueCategoryInput! + ) { + editIssueCategory(where: $where, data: $data) { + id name description + } + }""" + + result = self._client.execute( + query_str, + { + "where": {"id": self.id}, + "data": {"name": name, "description": description}, + }, + experimental=True, + ) + + data = result["editIssueCategory"] + cat = IssueCategory( + id=data["id"], + name=data["name"], + description=data["description"], + ) + cat._client = self._client + return cat + + def delete(self) -> bool: + """Delete this issue category. + + Returns: + ``True`` when the deletion succeeds. + """ + query_str = """mutation DeleteIssueCategoryPyApi( + $where: WhereUniqueIdInput! + ) { + deleteIssueCategory(where: $where) + }""" + + self._client.execute( + query_str, {"where": {"id": self.id}}, experimental=True + ) + return True diff --git a/libs/labelbox/src/labelbox/schema/issue_position.py b/libs/labelbox/src/labelbox/schema/issue_position.py new file mode 100644 index 000000000..0c798eb43 --- /dev/null +++ b/libs/labelbox/src/labelbox/schema/issue_position.py @@ -0,0 +1,260 @@ +"""Position models for issues, varying by media type. + +Each position model serializes to a GeoJSON-compatible dict for the +``position: Json`` GraphQL field. +""" + +import json +import logging +from typing import Any, Dict, List, Optional, Union + +from pydantic import BaseModel, field_validator + +from labelbox.schema.media_type import MediaType + +logger = logging.getLogger(__name__) + + +class ImageIssuePosition(BaseModel): + """Pin position on an image asset. + + Attributes: + x: Horizontal pixel coordinate. + y: Vertical pixel coordinate. + """ + + x: int + y: int + + def to_dict(self) -> dict: + return {"type": "Point", "coordinates": [self.x, self.y]} + + +class PdfIssuePosition(BaseModel): + """Pin position on a PDF page. + + Coordinates are expressed as percentages (0.0 – 1.0) of the page + dimensions, matching the backend ``PERCENT`` unit. + + Attributes: + x: Horizontal position as a fraction of page width (0.0 – 1.0). + y: Vertical position as a fraction of page height (0.0 – 1.0). + page: Zero-based page index. + """ + + x: float + y: float + page: int + + @field_validator("x", "y") + @classmethod + def _check_range(cls, v: float) -> float: + if not (0.0 <= v <= 1.0): + raise ValueError( + "PDF coordinates must be between 0.0 and 1.0 (percentage). " + f"Got {v}." + ) + return v + + def to_dict(self) -> dict: + return { + "type": "Point", + "coordinates": [self.x, self.y], + "page": self.page, + "unit": "PERCENT", + } + + +class TextIssuePosition(BaseModel): + """Character range within a text block. + + Attributes: + text_block_id: Identifier of the text block. + start_char_index: Start character index (inclusive). + end_char_index: End character index (exclusive). + """ + + text_block_id: str + start_char_index: int + end_char_index: int + + def to_dict(self) -> dict: + return { + "textBlockId": self.text_block_id, + "startCharIndex": self.start_char_index, + "endCharIndex": self.end_char_index, + } + + +class VideoFrameRange(BaseModel): + """A contiguous frame range with optional moving coordinates. + + For a single frame, set ``start == end``. When ``start == end`` the + ``end_x`` / ``end_y`` fields are ignored during serialization. + + Attributes: + start: Start frame number. + end: End frame number (equal to *start* for a single frame). + x: Horizontal pixel coordinate at *start*. + y: Vertical pixel coordinate at *start*. + end_x: Horizontal pixel coordinate at *end* (moving pin). Ignored + when ``start == end``. + end_y: Vertical pixel coordinate at *end* (moving pin). Ignored + when ``start == end``. + """ + + start: int + end: int + x: int + y: int + end_x: Optional[int] = None + end_y: Optional[int] = None + + +class VideoIssuePosition(BaseModel): + """Pin position(s) on a video asset. + + Supports single frames, contiguous ranges, and multiple separated + ranges (with optional moving coordinates). + + Attributes: + frames: One or more :class:`VideoFrameRange` entries. + """ + + frames: List[VideoFrameRange] + + def to_dict(self) -> dict: + """Serialize to KeyframesGeoJSONPoint format.""" + keyframes: list = [] + for fr in self.frames: + start_entry = { + "frame": fr.start, + "value": { + "type": "Point", + "coordinates": [fr.x, fr.y], + }, + } + keyframes.append(start_entry) + # Only emit a separate end keyframe when the range spans + # multiple frames. + if fr.end != fr.start: + end_entry = { + "frame": fr.end, + "value": { + "type": "Point", + "coordinates": [ + fr.end_x if fr.end_x is not None else fr.x, + fr.end_y if fr.end_y is not None else fr.y, + ], + }, + } + keyframes.append(end_entry) + return {"type": "KeyframesGeoJSONPoint", "keyframes": keyframes} + + +IssuePosition = Union[ + ImageIssuePosition, + PdfIssuePosition, + TextIssuePosition, + VideoIssuePosition, +] + +MEDIA_TYPE_POSITION_MAP: Dict[MediaType, type] = { + MediaType.Image: ImageIssuePosition, + MediaType.Video: VideoIssuePosition, + MediaType.Text: TextIssuePosition, + MediaType.Document: PdfIssuePosition, + MediaType.Pdf: PdfIssuePosition, +} + + +def _deserialize_position( + raw: Optional[Union[str, dict]], +) -> Optional[IssuePosition]: + """Convert a raw position value from GraphQL into a typed model. + + Returns ``None`` (with a warning) when the structure is unrecognized, + ensuring forward-compatibility with new media types. + """ + if raw is None: + return None + + data: Any # Use Any for safer checking after json.loads + if isinstance(raw, str): + try: + data = json.loads(raw) + if data is None: + return None + except (json.JSONDecodeError, TypeError): + return None + else: + data = raw + + if not isinstance(data, dict): + return None + + try: + # PDF – has "page" key + if "page" in data: + coords = data.get("coordinates", [0.0, 0.0]) + return PdfIssuePosition(x=coords[0], y=coords[1], page=data["page"]) + + # Text – has "textBlockId" key + if "textBlockId" in data: + return TextIssuePosition( + text_block_id=data["textBlockId"], + start_char_index=data["startCharIndex"], + end_char_index=data["endCharIndex"], + ) + + # Video – KeyframesGeoJSONPoint + if data.get("type") == "KeyframesGeoJSONPoint": + frames: List[VideoFrameRange] = [] + kf_list = data.get("keyframes", []) + i = 0 + while i < len(kf_list): + kf = kf_list[i] + start_frame = kf["frame"] + start_coords = kf["value"]["coordinates"] + # Look ahead for an end keyframe + if i + 1 < len(kf_list): + next_kf = kf_list[i + 1] + next_coords = next_kf["value"]["coordinates"] + end_frame = next_kf["frame"] + if end_frame != start_frame: + frames.append( + VideoFrameRange( + start=start_frame, + end=end_frame, + x=int(start_coords[0]), + y=int(start_coords[1]), + end_x=int(next_coords[0]), + end_y=int(next_coords[1]), + ) + ) + i += 2 + continue + # Single frame or last entry + frames.append( + VideoFrameRange( + start=start_frame, + end=start_frame, + x=int(start_coords[0]), + y=int(start_coords[1]), + ) + ) + i += 1 + return VideoIssuePosition(frames=frames) + + # Image – plain GeoJSON Point + if data.get("type") == "Point": + coords = data.get("coordinates", [0, 0]) + return ImageIssuePosition(x=int(coords[0]), y=int(coords[1])) + except (KeyError, IndexError, TypeError, ValueError) as exc: + logger.warning( + "Failed to deserialize issue position: %s (%s)", data, exc + ) + return None + + logger.warning("Unrecognized issue position structure: %s", data) + return None diff --git a/libs/labelbox/src/labelbox/schema/project.py b/libs/labelbox/src/labelbox/schema/project.py index 3a92db3f9..251001828 100644 --- a/libs/labelbox/src/labelbox/schema/project.py +++ b/libs/labelbox/src/labelbox/schema/project.py @@ -64,6 +64,18 @@ ProjectOverview, ProjectOverviewDetailed, ) +from labelbox.schema.issue import ( + Issue, + IssueStatus, + _ISSUE_FIELDS, + _parse_issue, +) +from labelbox.schema.issue_category import IssueCategory +from labelbox.schema.issue_position import ( + MEDIA_TYPE_POSITION_MAP, + IssuePosition, +) +from labelbox.schema.label import Label from labelbox.schema.workflow import ProjectWorkflow from labelbox.schema.resource_tag import ResourceTag from labelbox.schema.task import Task @@ -1857,6 +1869,302 @@ def clone_workflow_from(self, source_project_id: str) -> "ProjectWorkflow": target_project_id=self.uid, ) + # ------------------------------------------------------------------ + # Issue management + # ------------------------------------------------------------------ + + def create_issue( + self, + content: str, + data_row_id: Union[str, "DataRow"], + label_id: Optional[Union[str, "Label"]] = None, + category_id: Optional[Union[str, "IssueCategory"]] = None, + position: Optional[IssuePosition] = None, + ) -> Issue: + """Create a new issue in this project. + + Args: + content: Issue body text. + data_row_id: The data row to attach the issue to. Accepts a + string ID or a :class:`~labelbox.schema.data_row.DataRow` + instance. + label_id: Optional label to associate. Accepts a string ID or + a :class:`~labelbox.schema.label.Label` instance. Strongly + recommended: the backend only returns issues that have a + ``label_id`` from :meth:`get_issues`, so issues created + without one will not appear in paginated queries. + category_id: Optional issue category. Accepts a string ID or + an :class:`~labelbox.schema.issue_category.IssueCategory` + instance. + position: Optional typed position (e.g. + :class:`~labelbox.schema.issue_position.ImageIssuePosition`). + Must match the project's media type. + + Returns: + The newly created :class:`Issue`. + + Raises: + TypeError: If *position* does not match the project's media + type. + """ + # Resolve DbObject instances to string IDs + resolved_data_row_id = ( + data_row_id.uid if hasattr(data_row_id, "uid") else str(data_row_id) + ) + resolved_label_id: Optional[str] = None + if label_id is not None: + resolved_label_id = ( + label_id.uid if hasattr(label_id, "uid") else str(label_id) + ) + resolved_category_id: Optional[str] = None + if category_id is not None: + resolved_category_id = ( + category_id.uid + if hasattr(category_id, "uid") + else str(category_id) + ) + + # Validate position type against project media type + if position is not None and self.media_type is not None: + expected_cls = MEDIA_TYPE_POSITION_MAP.get(self.media_type) + if expected_cls is not None and not isinstance( + position, expected_cls + ): + raise TypeError( + f"Position type {type(position).__name__} is not valid " + f"for media type {self.media_type.name}. " + f"Expected {expected_cls.__name__}." + ) + + mutation_data: Dict[str, Any] = { + "content": content, + "projectId": self.uid, + "dataRowId": resolved_data_row_id, + "type": "Issue", + } + if resolved_label_id is not None: + mutation_data["labelId"] = resolved_label_id + if resolved_category_id is not None: + mutation_data["categoryId"] = resolved_category_id + if position is not None: + mutation_data["position"] = position.to_dict() + + query_str = ( + """mutation CreateIssuePyApi($data: CreateIssueInput!) { + createIssue(data: $data) { %s } + }""" + % _ISSUE_FIELDS + ) + + result = self.client.execute( + query_str, {"data": mutation_data}, experimental=True + ) + issue = _parse_issue( + self.client, result["createIssue"], project_id=self.uid + ) + + # The createIssue mutation may not return dataRowId / labelId / + # categoryId in its response. Since we know the values from the + # input, patch them onto the returned object so callers don't + # have to re-fetch. + if issue.data_row_id is None and resolved_data_row_id is not None: + issue.data_row_id = resolved_data_row_id + if issue.label_id is None and resolved_label_id is not None: + issue.label_id = resolved_label_id + if issue.category_id is None and resolved_category_id is not None: + issue.category_id = resolved_category_id + + return issue + + def get_issues( + self, + status: Optional[IssueStatus] = None, + data_row_id: Optional[str] = None, + category_id: Optional[str] = None, + created_by_ids: Optional[List[str]] = None, + content: Optional[str] = None, + ) -> PaginatedCollection: + """Fetch issues for this project with optional filters. + + Uses cursor-based pagination (``after`` / ``first``) as defined + by the ``IssueConnection`` return type. Returns a lazy + :class:`~labelbox.pagination.PaginatedCollection` that pages + transparently during iteration. + + .. note:: + The backend only returns issues that have a ``label_id``. + Issues created without a label will not appear in the + results. Use :meth:`get_issue` (by ID) or + :meth:`export_issues` to retrieve them. + + Args: + status: Filter by issue status. + data_row_id: Filter by data-row ID. + category_id: Filter by category ID. + created_by_ids: Filter by creator user IDs. + content: Full-text search on issue content. + + Returns: + A :class:`PaginatedCollection` of :class:`Issue` instances. + """ + # Build the where filter to match the backend ProjectIssueInput + where: Dict[str, Any] = {"type": "Issue"} + if status is not None: + where["status"] = status.value # "Open" or "Resolved" + if data_row_id is not None: + where["dataRow"] = {"id": data_row_id} + if category_id is not None: + where["categoryId"] = category_id + if created_by_ids is not None: + where["createdByIds"] = created_by_ids + if content is not None: + where["content"] = content + + query_str = ( + """query GetProjectIssuesPyApi( + $projectId: ID!, $where: ProjectIssueInput, + $from: ID, $first: PageSize + ) { + project(where: {id: $projectId}) { + issues(where: $where, after: $from, first: $first) { + nodes { %s } + nextCursor + } + } + }""" + % _ISSUE_FIELDS + ) + + project_id = self.uid + params: Dict[str, Any] = { + "projectId": self.uid, + "where": where, + } + + return PaginatedCollection( + client=self.client, + query=query_str, + params=params, # type: ignore[arg-type] + dereferencing=["project", "issues", "nodes"], + obj_class=lambda client, data: _parse_issue( + client, data, project_id=project_id + ), + cursor_path=["project", "issues", "nextCursor"], + experimental=True, + ) + + def get_issue(self, issue_id: str) -> Issue: + """Fetch a single issue by ID. + + Args: + issue_id: The issue's unique identifier. + + Returns: + An :class:`Issue` instance. + """ + query_str = ( + """query GetIssuePyApi($where: WhereUniqueIdInput!) { + issue(where: $where) { %s } + }""" + % _ISSUE_FIELDS + ) + + result = self.client.execute( + query_str, {"where": {"id": issue_id}}, experimental=True + ) + return _parse_issue(self.client, result["issue"], project_id=self.uid) + + def delete_issues(self, issue_ids: List[str]) -> bool: + """Delete one or more issues in bulk. + + The backend enforces creator-only authorization: the call will + fail if any of the listed issues belong to a different user. + Non-existent IDs are silently ignored. + + Args: + issue_ids: List of issue IDs to delete. + + Returns: + ``True`` when the mutation succeeds. + """ + query_str = """mutation DeleteIssuePyApi($data: DeleteIssueInput!) { + deleteIssue(data: $data) + }""" + + self.client.execute( + query_str, + {"data": {"issueIds": issue_ids}}, + experimental=True, + ) + return True + + # ------------------------------------------------------------------ + # Issue category management + # ------------------------------------------------------------------ + + def create_issue_category( + self, name: str, description: str + ) -> IssueCategory: + """Create a new issue category in this project. + + Args: + name: Category display name. + description: Human-readable description. + + Returns: + The newly created :class:`IssueCategory`. + """ + query_str = """mutation CreateIssueCategoryPyApi( + $data: CreateIssueCategoryInput! + ) { + createIssueCategory(data: $data) { id name description } + }""" + + result = self.client.execute( + query_str, + { + "data": { + "projectId": self.uid, + "name": name, + "description": description, + } + }, + experimental=True, + ) + data = result["createIssueCategory"] + cat = IssueCategory( + id=data["id"], + name=data["name"], + description=data["description"], + ) + cat._client = self.client + return cat + + def get_issue_categories(self) -> List[IssueCategory]: + """Fetch all issue categories for this project. + + Returns: + List of :class:`IssueCategory` instances. + """ + query_str = """query GetIssueCategoriesPyApi($projectId: ID!) { + project(where: {id: $projectId}) { + issueCategories { id name description } + } + }""" + + result = self.client.execute(query_str, {"projectId": self.uid}) + raw_list = result.get("project", {}).get("issueCategories", []) + categories = [] + for c in raw_list: + cat = IssueCategory( + id=c["id"], + name=c["name"], + description=c["description"], + ) + cat._client = self.client + categories.append(cat) + return categories + class ProjectMember(DbObject): user = Relationship.ToOne("User", cache=True) diff --git a/libs/labelbox/tests/integration/test_issue_management.py b/libs/labelbox/tests/integration/test_issue_management.py new file mode 100644 index 000000000..e0ee6913c --- /dev/null +++ b/libs/labelbox/tests/integration/test_issue_management.py @@ -0,0 +1,329 @@ +"""Integration tests for issue management (Issues, Comments, Issue Categories). + +Uses a single Image project with one data row to test the full CRUD +lifecycle, keeping setup cost minimal. +""" + +import time + +from labelbox import Project +from labelbox.schema.issue import Issue, IssueStatus, Comment +from labelbox.schema.issue_category import IssueCategory +from labelbox.schema.issue_position import ImageIssuePosition + + +# --------------------------------------------------------------------------- +# Issue Category CRUD +# --------------------------------------------------------------------------- + + +def test_create_issue_category(project: Project): + category = project.create_issue_category( + name="Quality", description="Quality-related issues" + ) + assert isinstance(category, IssueCategory) + assert category.id is not None + assert category.name == "Quality" + assert category.description == "Quality-related issues" + + # Cleanup + category.delete() + + +def test_get_issue_categories(project: Project): + cat1 = project.create_issue_category( + name="Cat A", description="First category" + ) + cat2 = project.create_issue_category( + name="Cat B", description="Second category" + ) + + categories = project.get_issue_categories() + cat_ids = {c.id for c in categories} + assert cat1.id in cat_ids + assert cat2.id in cat_ids + + # Cleanup + cat1.delete() + cat2.delete() + + +def test_update_issue_category(project: Project): + category = project.create_issue_category( + name="Original", description="Original description" + ) + updated = category.update(name="Renamed", description="New description") + assert updated.name == "Renamed" + assert updated.description == "New description" + + # Cleanup + updated.delete() + + +def test_delete_issue_category(project: Project): + category = project.create_issue_category( + name="ToDelete", description="Will be deleted" + ) + assert category.delete() is True + + +# --------------------------------------------------------------------------- +# Issue CRUD +# --------------------------------------------------------------------------- + + +def test_create_issue(project: Project, data_row): + issue = project.create_issue( + content="Something is wrong here", + data_row_id=data_row.uid, + ) + assert isinstance(issue, Issue) + assert issue.id is not None + assert issue.content == "Something is wrong here" + assert issue.status == IssueStatus.OPEN + assert issue.data_row_id == data_row.uid + assert issue.created_by is not None + + # Cleanup + issue.delete() + + +def test_create_issue_with_position(project: Project, data_row): + position = ImageIssuePosition(x=100, y=200) + issue = project.create_issue( + content="Pin on image", + data_row_id=data_row.uid, + position=position, + ) + assert issue.position is not None + + # Cleanup + issue.delete() + + +def test_create_issue_with_category(project: Project, data_row): + category = project.create_issue_category( + name="Test Category", description="For testing" + ) + issue = project.create_issue( + content="Categorized issue", + data_row_id=data_row.uid, + category_id=category.id, + ) + assert issue.category_id == category.id + + # Verify lazy-loaded category + fetched_cat = issue.category() + assert fetched_cat is not None + assert fetched_cat.id == category.id + + # Cleanup + issue.delete() + category.delete() + + +def test_get_issue(project: Project, data_row): + created = project.create_issue( + content="Fetch me", + data_row_id=data_row.uid, + ) + fetched = project.get_issue(created.id) + assert fetched.id == created.id + assert fetched.content == "Fetch me" + + # Cleanup + created.delete() + + +def test_get_issues(configured_project_with_label): + # get_issues() only returns issues that have a label_id (backend + # filters out labelId IS NULL), so we must attach a label. + project, _dataset, data_row, label = configured_project_with_label + + issue1 = project.create_issue( + content="First issue", + data_row_id=data_row.uid, + label_id=label.uid, + ) + issue2 = project.create_issue( + content="Second issue", + data_row_id=data_row.uid, + label_id=label.uid, + ) + + # Allow eventual consistency in the backend index + for _ in range(5): + issues = list(project.get_issues()) + issue_ids = {i.id for i in issues} + if issue1.id in issue_ids and issue2.id in issue_ids: + break + time.sleep(2) + + assert issue1.id in issue_ids + assert issue2.id in issue_ids + + # Cleanup + project.delete_issues([issue1.id, issue2.id]) + + +def test_get_issues_with_status_filter(configured_project_with_label): + # get_issues() only returns issues that have a label_id (backend + # filters out labelId IS NULL), so we must attach a label. + project, _dataset, data_row, label = configured_project_with_label + + issue = project.create_issue( + content="Filter test", + data_row_id=data_row.uid, + label_id=label.uid, + ) + + # Allow eventual consistency in the backend index + for _ in range(5): + open_issues = list(project.get_issues(status=IssueStatus.OPEN)) + if any(i.id == issue.id for i in open_issues): + break + time.sleep(2) + + assert any(i.id == issue.id for i in open_issues) + + # Give the backend a moment to ensure the index is consistent + # for the next query + time.sleep(2) + + resolved_issues = list(project.get_issues(status=IssueStatus.RESOLVED)) + assert not any(i.id == issue.id for i in resolved_issues) + + # Cleanup + issue.delete() + + +def test_update_issue(project: Project, data_row): + issue = project.create_issue( + content="Original content", + data_row_id=data_row.uid, + ) + updated = issue.update(content="Updated content") + assert updated.content == "Updated content" + + # Cleanup + updated.delete() + + +def test_resolve_and_reopen_issue(project: Project, data_row): + issue = project.create_issue( + content="Resolve me", + data_row_id=data_row.uid, + ) + resolved = issue.resolve() + assert resolved.status == IssueStatus.RESOLVED + assert resolved.resolved_by is not None + + reopened = resolved.reopen() + assert reopened.status == IssueStatus.OPEN + + # Cleanup + reopened.delete() + + +def test_delete_issue(project: Project, data_row): + issue = project.create_issue( + content="Delete me", + data_row_id=data_row.uid, + ) + assert issue.delete() is True + + +def test_delete_issues_bulk(project: Project, data_row): + issue1 = project.create_issue( + content="Bulk delete 1", + data_row_id=data_row.uid, + ) + issue2 = project.create_issue( + content="Bulk delete 2", + data_row_id=data_row.uid, + ) + assert project.delete_issues([issue1.id, issue2.id]) is True + + +# --------------------------------------------------------------------------- +# Issue accessor methods +# --------------------------------------------------------------------------- + + +def test_issue_data_row(project: Project, data_row): + issue = project.create_issue( + content="Data row test", + data_row_id=data_row.uid, + ) + fetched_dr = issue.data_row() + assert fetched_dr is not None + assert fetched_dr.uid == data_row.uid + + # Cleanup + issue.delete() + + +# --------------------------------------------------------------------------- +# Comment CRUD +# --------------------------------------------------------------------------- + + +def test_create_comment(project: Project, data_row): + issue = project.create_issue( + content="Comment test", + data_row_id=data_row.uid, + ) + comment = issue.create_comment(content="This is a comment") + assert isinstance(comment, Comment) + assert comment.id is not None + assert comment.content == "This is a comment" + assert comment.created_by is not None + + # Cleanup + issue.delete() + + +def test_get_comments(project: Project, data_row): + issue = project.create_issue( + content="Multi-comment test", + data_row_id=data_row.uid, + ) + comment1 = issue.create_comment(content="Comment 1") + comment2 = issue.create_comment(content="Comment 2") + + comments = issue.comments() + comment_ids = {c.id for c in comments} + assert comment1.id in comment_ids + assert comment2.id in comment_ids + + # Cleanup + issue.delete() + + +def test_update_comment(project: Project, data_row): + issue = project.create_issue( + content="Update comment test", + data_row_id=data_row.uid, + ) + comment = issue.create_comment(content="Original comment") + updated = comment.update(content="Revised comment") + assert updated.content == "Revised comment" + + # Cleanup + issue.delete() + + +def test_delete_comment(project: Project, data_row): + issue = project.create_issue( + content="Delete comment test", + data_row_id=data_row.uid, + ) + comment = issue.create_comment(content="Will be deleted") + assert comment.delete() is True + + # Verify comment is gone + remaining = issue.comments() + assert not any(c.id == comment.id for c in remaining) + + # Cleanup + issue.delete() diff --git a/libs/labelbox/tests/unit/schema/test_issue.py b/libs/labelbox/tests/unit/schema/test_issue.py new file mode 100644 index 000000000..47cdc772f --- /dev/null +++ b/libs/labelbox/tests/unit/schema/test_issue.py @@ -0,0 +1,293 @@ +from unittest.mock import MagicMock + +from labelbox.schema.issue import ( + Comment, + IssueStatus, + _parse_comment, + _parse_issue, +) + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +_NOW = "2025-01-15T10:00:00.000Z" + +_USER_RAW = { + "id": "user-1", + "email": "alice@example.com", + "nickname": "alice", + "name": "Alice", + "picture": "", + "isViewer": False, + "isExternalUser": False, + "createdAt": _NOW, + "updatedAt": _NOW, +} + +_COMMENT_RAW = { + "id": "comment-1", + "content": "Looks good", + "createdBy": _USER_RAW, + "createdAt": _NOW, + "updatedAt": _NOW, +} + +_ISSUE_RAW = { + "id": "issue-1", + "friendlyId": "I-42", + "labelId": "label-1", + "dataRowId": "dr-1", + "categoryId": "cat-1", + "content": "Something is wrong", + "position": None, + "status": "Open", + "createdBy": _USER_RAW, + "resolvedBy": None, + "createdAt": _NOW, + "updatedAt": _NOW, + "resolvedAt": None, + "contentUpdatedAt": None, + "latestReplyAt": None, +} + + +def _make_client(): + return MagicMock() + + +def _make_issue(client=None, overrides=None, project_id="proj-1"): + c = client or _make_client() + raw = {**_ISSUE_RAW, **(overrides or {})} + return _parse_issue(c, raw, project_id=project_id) + + +def _make_comment(client=None): + c = client or _make_client() + return _parse_comment(c, _COMMENT_RAW) + + +# --------------------------------------------------------------------------- +# _parse_issue / _parse_comment +# --------------------------------------------------------------------------- + + +class TestParseIssue: + def test_basic_fields(self): + issue = _make_issue() + assert issue.id == "issue-1" + assert issue.friendly_id == "I-42" + assert issue.content == "Something is wrong" + assert issue.status == IssueStatus.OPEN + assert issue.data_row_id == "dr-1" + assert issue.label_id == "label-1" + assert issue.category_id == "cat-1" + assert issue.position is None + + def test_created_by_is_user(self): + issue = _make_issue() + # User DbObject has .uid + assert issue.created_by.uid == "user-1" + + def test_resolved_by_none(self): + issue = _make_issue() + assert issue.resolved_by is None + + def test_resolved_by_present(self): + issue = _make_issue(overrides={"resolvedBy": _USER_RAW}) + assert issue.resolved_by.uid == "user-1" + + +class TestParseComment: + def test_basic_fields(self): + comment = _make_comment() + assert comment.id == "comment-1" + assert comment.content == "Looks good" + assert comment.created_by.uid == "user-1" + + +# --------------------------------------------------------------------------- +# Issue mutation methods +# --------------------------------------------------------------------------- + + +class TestIssueUpdate: + def test_update_content(self): + client = _make_client() + issue = _make_issue(client) + client.execute.return_value = { + "updateIssue": {**_ISSUE_RAW, "content": "Updated"} + } + updated = issue.update(content="Updated") + assert updated.content == "Updated" + # Verify GraphQL call + args, _ = client.execute.call_args + assert "UpdateIssuePyApi" in args[0] + assert args[1]["data"]["content"] == "Updated" + + def test_update_no_args_returns_self(self): + client = _make_client() + issue = _make_issue(client) + result = issue.update() + assert result is issue + client.execute.assert_not_called() + + +class TestIssueDelete: + def test_delete(self): + client = _make_client() + issue = _make_issue(client) + client.execute.return_value = {"deleteIssue": {"id": "issue-1"}} + assert issue.delete() is True + args, _ = client.execute.call_args + assert "DeleteIssuePyApi" in args[0] + assert args[1]["data"]["issueIds"] == ["issue-1"] + + +class TestIssueResolve: + def test_resolve(self): + client = _make_client() + issue = _make_issue(client) + client.execute.return_value = { + "resolveIssue": {**_ISSUE_RAW, "status": "Resolved"} + } + resolved = issue.resolve() + assert resolved.status == IssueStatus.RESOLVED + args, _ = client.execute.call_args + assert "ResolveIssuePyApi" in args[0] + + +class TestIssueReopen: + def test_reopen(self): + client = _make_client() + issue = _make_issue(client, overrides={"status": "Resolved"}) + client.execute.return_value = { + "openIssue": {**_ISSUE_RAW, "status": "Open"} + } + reopened = issue.reopen() + assert reopened.status == IssueStatus.OPEN + args, _ = client.execute.call_args + assert "OpenIssuePyApi" in args[0] + + +class TestIssueCreateComment: + def test_create_comment(self): + client = _make_client() + issue = _make_issue(client) + client.execute.return_value = {"createComment": _COMMENT_RAW} + comment = issue.create_comment(content="Nice work") + assert isinstance(comment, Comment) + args, _ = client.execute.call_args + assert "CreateCommentPyApi" in args[0] + assert args[1]["data"]["content"] == "Nice work" + assert args[1]["data"]["issueId"] == "issue-1" + + +# --------------------------------------------------------------------------- +# Issue accessor methods +# --------------------------------------------------------------------------- + + +class TestIssueComments: + def test_comments(self): + client = _make_client() + issue = _make_issue(client) + client.execute.return_value = {"issue": {"comments": [_COMMENT_RAW]}} + comments = issue.comments() + assert len(comments) == 1 + assert comments[0].id == "comment-1" + + def test_comments_empty(self): + client = _make_client() + issue = _make_issue(client) + client.execute.return_value = {"issue": {"comments": []}} + assert issue.comments() == [] + + +class TestIssueDataRow: + def test_data_row(self): + client = _make_client() + issue = _make_issue(client) + mock_dr = MagicMock() + client.get_data_row.return_value = mock_dr + result = issue.data_row() + assert result is mock_dr + client.get_data_row.assert_called_once_with("dr-1") + + def test_data_row_none_when_no_id(self): + client = _make_client() + issue = _make_issue(client, overrides={"dataRowId": None}) + assert issue.data_row() is None + client.get_data_row.assert_not_called() + + +class TestIssueCategory: + def test_category(self): + client = _make_client() + issue = _make_issue(client) + # category() queries project -> issueCategories and matches by id + client.execute.return_value = { + "project": { + "issueCategories": [ + { + "id": "cat-1", + "name": "Quality", + "description": "Quality issues", + } + ] + } + } + cat = issue.category() + assert cat is not None + assert cat.id == "cat-1" + assert cat.name == "Quality" + + def test_category_none_when_no_id(self): + client = _make_client() + issue = _make_issue(client, overrides={"categoryId": None}) + assert issue.category() is None + + +class TestIssueLabel: + def test_label(self): + client = _make_client() + issue = _make_issue(client) + mock_label = MagicMock() + client._get_single.return_value = mock_label + result = issue.label() + assert result is mock_label + client._get_single.assert_called_once() + + def test_label_none_when_no_id(self): + client = _make_client() + issue = _make_issue(client, overrides={"labelId": None}) + assert issue.label() is None + client._get_single.assert_not_called() + + +# --------------------------------------------------------------------------- +# Comment mutation methods +# --------------------------------------------------------------------------- + + +class TestCommentUpdate: + def test_update(self): + client = _make_client() + comment = _make_comment(client) + client.execute.return_value = { + "updateComment": {**_COMMENT_RAW, "content": "Revised"} + } + updated = comment.update(content="Revised") + assert updated.content == "Revised" + args, _ = client.execute.call_args + assert "UpdateCommentPyApi" in args[0] + + +class TestCommentDelete: + def test_delete(self): + client = _make_client() + comment = _make_comment(client) + client.execute.return_value = {"deleteComment": {"id": "comment-1"}} + assert comment.delete() is True + args, _ = client.execute.call_args + assert "DeleteCommentPyApi" in args[0] diff --git a/libs/labelbox/tests/unit/schema/test_issue_category.py b/libs/labelbox/tests/unit/schema/test_issue_category.py new file mode 100644 index 000000000..4f3c8d81c --- /dev/null +++ b/libs/labelbox/tests/unit/schema/test_issue_category.py @@ -0,0 +1,52 @@ +from unittest.mock import MagicMock + +from labelbox.schema.issue_category import IssueCategory + + +def _make_client(): + return MagicMock() + + +def _make_category(client=None): + c = client or _make_client() + cat = IssueCategory( + id="cat-1", + name="Quality", + description="Quality issues", + ) + cat._client = c + return cat + + +class TestIssueCategoryUpdate: + def test_update(self): + client = _make_client() + cat = _make_category(client) + client.execute.return_value = { + "editIssueCategory": { + "id": "cat-1", + "name": "Renamed", + "description": "New desc", + } + } + updated = cat.update(name="Renamed", description="New desc") + assert updated.name == "Renamed" + assert updated.description == "New desc" + args, _ = client.execute.call_args + assert "EditIssueCategoryPyApi" in args[0] + assert args[1]["where"] == {"id": "cat-1"} + assert args[1]["data"] == { + "name": "Renamed", + "description": "New desc", + } + + +class TestIssueCategoryDelete: + def test_delete(self): + client = _make_client() + cat = _make_category(client) + client.execute.return_value = {"deleteIssueCategory": {"id": "cat-1"}} + assert cat.delete() is True + args, _ = client.execute.call_args + assert "DeleteIssueCategoryPyApi" in args[0] + assert args[1]["where"] == {"id": "cat-1"} diff --git a/libs/labelbox/tests/unit/schema/test_issue_position.py b/libs/labelbox/tests/unit/schema/test_issue_position.py new file mode 100644 index 000000000..39998010e --- /dev/null +++ b/libs/labelbox/tests/unit/schema/test_issue_position.py @@ -0,0 +1,250 @@ +import logging + +import pytest + +from labelbox.schema.issue_position import ( + MEDIA_TYPE_POSITION_MAP, + ImageIssuePosition, + PdfIssuePosition, + TextIssuePosition, + VideoFrameRange, + VideoIssuePosition, + _deserialize_position, +) +from labelbox.schema.media_type import MediaType + + +# --------------------------------------------------------------------------- +# ImageIssuePosition +# --------------------------------------------------------------------------- + + +class TestImageIssuePosition: + def test_to_dict(self): + pos = ImageIssuePosition(x=100, y=200) + assert pos.to_dict() == { + "type": "Point", + "coordinates": [100, 200], + } + + def test_integer_coordinates(self): + pos = ImageIssuePosition(x=0, y=0) + assert isinstance(pos.x, int) + assert isinstance(pos.y, int) + + +# --------------------------------------------------------------------------- +# PdfIssuePosition +# --------------------------------------------------------------------------- + + +class TestPdfIssuePosition: + def test_to_dict(self): + pos = PdfIssuePosition(x=0.5, y=0.75, page=2) + assert pos.to_dict() == { + "type": "Point", + "coordinates": [0.5, 0.75], + "page": 2, + "unit": "PERCENT", + } + + def test_validation_x_out_of_range(self): + with pytest.raises(ValueError, match="0.0 and 1.0"): + PdfIssuePosition(x=1.5, y=0.5, page=0) + + def test_validation_y_out_of_range(self): + with pytest.raises(ValueError, match="0.0 and 1.0"): + PdfIssuePosition(x=0.5, y=-0.1, page=0) + + def test_boundary_values(self): + pos_min = PdfIssuePosition(x=0.0, y=0.0, page=0) + assert pos_min.x == 0.0 + pos_max = PdfIssuePosition(x=1.0, y=1.0, page=0) + assert pos_max.x == 1.0 + + +# --------------------------------------------------------------------------- +# TextIssuePosition +# --------------------------------------------------------------------------- + + +class TestTextIssuePosition: + def test_to_dict(self): + pos = TextIssuePosition( + text_block_id="block-1", + start_char_index=10, + end_char_index=25, + ) + assert pos.to_dict() == { + "textBlockId": "block-1", + "startCharIndex": 10, + "endCharIndex": 25, + } + + +# --------------------------------------------------------------------------- +# VideoIssuePosition +# --------------------------------------------------------------------------- + + +class TestVideoIssuePosition: + def test_single_frame(self): + pos = VideoIssuePosition( + frames=[VideoFrameRange(start=5, end=5, x=100, y=200)] + ) + result = pos.to_dict() + assert result["type"] == "KeyframesGeoJSONPoint" + assert len(result["keyframes"]) == 1 + kf = result["keyframes"][0] + assert kf["frame"] == 5 + assert kf["value"]["coordinates"] == [100, 200] + + def test_contiguous_range(self): + pos = VideoIssuePosition( + frames=[VideoFrameRange(start=5, end=11, x=450, y=300)] + ) + result = pos.to_dict() + assert len(result["keyframes"]) == 2 + assert result["keyframes"][0]["frame"] == 5 + assert result["keyframes"][1]["frame"] == 11 + # No end_x/end_y => coordinates repeat + assert result["keyframes"][1]["value"]["coordinates"] == [450, 300] + + def test_moving_coordinates(self): + pos = VideoIssuePosition( + frames=[ + VideoFrameRange( + start=5, end=11, x=450, y=300, end_x=500, end_y=350 + ) + ] + ) + result = pos.to_dict() + assert len(result["keyframes"]) == 2 + assert result["keyframes"][0]["value"]["coordinates"] == [450, 300] + assert result["keyframes"][1]["value"]["coordinates"] == [500, 350] + + def test_multiple_ranges(self): + pos = VideoIssuePosition( + frames=[ + VideoFrameRange(start=5, end=11, x=450, y=300), + VideoFrameRange(start=20, end=25, x=100, y=100), + ] + ) + result = pos.to_dict() + assert len(result["keyframes"]) == 4 + + def test_single_frame_ignores_end_coords(self): + """When start == end, end_x/end_y are not serialized.""" + pos = VideoIssuePosition( + frames=[ + VideoFrameRange( + start=5, end=5, x=100, y=200, end_x=999, end_y=999 + ) + ] + ) + result = pos.to_dict() + assert len(result["keyframes"]) == 1 + assert result["keyframes"][0]["value"]["coordinates"] == [100, 200] + + +# --------------------------------------------------------------------------- +# MEDIA_TYPE_POSITION_MAP +# --------------------------------------------------------------------------- + + +class TestMediaTypePositionMap: + def test_image(self): + assert MEDIA_TYPE_POSITION_MAP[MediaType.Image] is ImageIssuePosition + + def test_video(self): + assert MEDIA_TYPE_POSITION_MAP[MediaType.Video] is VideoIssuePosition + + def test_text(self): + assert MEDIA_TYPE_POSITION_MAP[MediaType.Text] is TextIssuePosition + + def test_document(self): + assert MEDIA_TYPE_POSITION_MAP[MediaType.Document] is PdfIssuePosition + + def test_pdf(self): + assert MEDIA_TYPE_POSITION_MAP[MediaType.Pdf] is PdfIssuePosition + + def test_audio_not_in_map(self): + assert MediaType.Audio not in MEDIA_TYPE_POSITION_MAP + + +# --------------------------------------------------------------------------- +# _deserialize_position +# --------------------------------------------------------------------------- + + +class TestDeserializePosition: + def test_none_input(self): + assert _deserialize_position(None) is None + + def test_image_geojson(self): + raw = {"type": "Point", "coordinates": [100, 200]} + result = _deserialize_position(raw) + assert isinstance(result, ImageIssuePosition) + assert result.x == 100 + assert result.y == 200 + + def test_pdf_geojson(self): + raw = { + "type": "Point", + "coordinates": [0.5, 0.75], + "page": 2, + "unit": "PERCENT", + } + result = _deserialize_position(raw) + assert isinstance(result, PdfIssuePosition) + assert result.page == 2 + + def test_text_position(self): + raw = { + "textBlockId": "block-1", + "startCharIndex": 10, + "endCharIndex": 25, + } + result = _deserialize_position(raw) + assert isinstance(result, TextIssuePosition) + assert result.text_block_id == "block-1" + + def test_video_position(self): + raw = { + "type": "KeyframesGeoJSONPoint", + "keyframes": [ + { + "frame": 5, + "value": {"type": "Point", "coordinates": [100, 200]}, + }, + { + "frame": 11, + "value": {"type": "Point", "coordinates": [150, 250]}, + }, + ], + } + result = _deserialize_position(raw) + assert isinstance(result, VideoIssuePosition) + assert len(result.frames) == 1 + assert result.frames[0].start == 5 + assert result.frames[0].end == 11 + + def test_json_string_input(self): + import json + + raw = json.dumps({"type": "Point", "coordinates": [10, 20]}) + result = _deserialize_position(raw) + assert isinstance(result, ImageIssuePosition) + assert result.x == 10 + + def test_unrecognized_structure_returns_none(self, caplog): + raw = {"unknown_key": "some_value"} + with caplog.at_level(logging.WARNING): + result = _deserialize_position(raw) + assert result is None + assert "Unrecognized issue position structure" in caplog.text + + def test_invalid_json_string_returns_none(self, caplog): + with caplog.at_level(logging.WARNING): + result = _deserialize_position("not-valid-json") + assert result is None diff --git a/libs/labelbox/tests/unit/schema/test_project_issues.py b/libs/labelbox/tests/unit/schema/test_project_issues.py new file mode 100644 index 000000000..2d814a0ff --- /dev/null +++ b/libs/labelbox/tests/unit/schema/test_project_issues.py @@ -0,0 +1,376 @@ +import logging +from types import SimpleNamespace +from unittest.mock import MagicMock + +import pytest + +from labelbox.schema.issue import Issue, IssueStatus +from labelbox.schema.issue_category import IssueCategory +from labelbox.schema.issue_position import ( + ImageIssuePosition, + PdfIssuePosition, + VideoFrameRange, + VideoIssuePosition, + _deserialize_position, +) +from labelbox.schema.project import Project + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +_NOW = "2025-01-15T10:00:00.000Z" + +_USER_RAW = { + "id": "user-1", + "email": "alice@example.com", + "nickname": "alice", + "name": "Alice", + "picture": "", + "isViewer": False, + "isExternalUser": False, + "createdAt": _NOW, + "updatedAt": _NOW, +} + +_ISSUE_RAW = { + "id": "issue-1", + "friendlyId": "I-42", + "labelId": "label-1", + "dataRowId": "dr-1", + "categoryId": "cat-1", + "content": "Something is wrong", + "position": None, + "status": "Open", + "createdBy": _USER_RAW, + "resolvedBy": None, + "createdAt": _NOW, + "updatedAt": _NOW, + "resolvedAt": None, + "contentUpdatedAt": None, + "latestReplyAt": None, +} + + +def _make_client(): + return MagicMock() + + +def _project_field_values(media_type="IMAGE"): + """Minimal field values needed to construct a ``Project`` DbObject.""" + return { + "id": "proj-1", + "name": "Test Project", + "description": "", + "updatedAt": _NOW, + "createdAt": _NOW, + "setupComplete": None, + "lastActivityTime": None, + "autoAuditNumberOfLabels": 1, + "autoAuditPercentage": 0.0, + "allowedMediaType": media_type, + "editorTaskType": None, + "dataRowCount": 0, + "modelSetupComplete": None, + "uploadType": None, + "isBenchmarkEnabled": False, + "isConsensusEnabled": False, + # Relationships with cache=True need a value + "ontology": {"id": "onto-1", "name": "test", "normalized": "{}"}, + } + + +def _make_project(client=None, media_type="IMAGE"): + c = client or _make_client() + return Project(c, _project_field_values(media_type)) + + +# --------------------------------------------------------------------------- +# create_issue +# --------------------------------------------------------------------------- + + +class TestCreateIssue: + def test_basic(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"createIssue": _ISSUE_RAW} + + issue = project.create_issue( + content="Something is wrong", + data_row_id="dr-1", + ) + assert isinstance(issue, Issue) + assert issue.id == "issue-1" + args, _ = client.execute.call_args + assert "CreateIssuePyApi" in args[0] + data = args[1]["data"] + assert data["content"] == "Something is wrong" + assert data["projectId"] == "proj-1" + assert data["dataRowId"] == "dr-1" + assert data["type"] == "Issue" + + def test_with_label_and_category(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"createIssue": _ISSUE_RAW} + + project.create_issue( + content="Issue", + data_row_id="dr-1", + label_id="label-1", + category_id="cat-1", + ) + args, _ = client.execute.call_args + data = args[1]["data"] + assert data["labelId"] == "label-1" + assert data["categoryId"] == "cat-1" + + def test_with_image_position(self): + client = _make_client() + project = _make_project(client, media_type="IMAGE") + client.execute.return_value = {"createIssue": _ISSUE_RAW} + + pos = ImageIssuePosition(x=100, y=200) + project.create_issue( + content="Pin here", + data_row_id="dr-1", + position=pos, + ) + args, _ = client.execute.call_args + data = args[1]["data"] + assert data["position"] == {"type": "Point", "coordinates": [100, 200]} + + def test_position_validation_wrong_type(self): + client = _make_client() + project = _make_project(client, media_type="IMAGE") + + with pytest.raises(TypeError, match="PdfIssuePosition"): + project.create_issue( + content="Wrong position", + data_row_id="dr-1", + position=PdfIssuePosition(x=0.5, y=0.5, page=0), + ) + + def test_accepts_datarow_object(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"createIssue": _ISSUE_RAW} + + mock_dr = SimpleNamespace(uid="dr-obj-1") + project.create_issue(content="Test", data_row_id=mock_dr) + args, _ = client.execute.call_args + assert args[1]["data"]["dataRowId"] == "dr-obj-1" + + def test_accepts_label_object(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"createIssue": _ISSUE_RAW} + + mock_label = SimpleNamespace(uid="label-obj-1") + project.create_issue( + content="Test", + data_row_id="dr-1", + label_id=mock_label, + ) + args, _ = client.execute.call_args + assert args[1]["data"]["labelId"] == "label-obj-1" + + def test_accepts_category_object(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"createIssue": _ISSUE_RAW} + + mock_cat = SimpleNamespace(uid="cat-obj-1") + project.create_issue( + content="Test", + data_row_id="dr-1", + category_id=mock_cat, + ) + args, _ = client.execute.call_args + assert args[1]["data"]["categoryId"] == "cat-obj-1" + + def test_no_position_validation_when_media_type_none(self): + """Projects with unknown media type should not raise on position.""" + client = _make_client() + project = _make_project(client, media_type=None) + client.execute.return_value = {"createIssue": _ISSUE_RAW} + + pos = ImageIssuePosition(x=10, y=20) + project.create_issue( + content="Test", + data_row_id="dr-1", + position=pos, + ) + # No error means success + + def test_video_position_on_video_project(self): + client = _make_client() + project = _make_project(client, media_type="VIDEO") + client.execute.return_value = {"createIssue": _ISSUE_RAW} + + pos = VideoIssuePosition( + frames=[VideoFrameRange(start=5, end=5, x=100, y=200)] + ) + project.create_issue( + content="Video issue", + data_row_id="dr-1", + position=pos, + ) + args, _ = client.execute.call_args + assert args[1]["data"]["position"]["type"] == "KeyframesGeoJSONPoint" + + +# --------------------------------------------------------------------------- +# get_issues +# --------------------------------------------------------------------------- + + +class TestGetIssues: + def test_returns_paginated_collection(self): + client = _make_client() + project = _make_project(client) + result = project.get_issues() + # PaginatedCollection is returned (lazy); no execute call yet + from labelbox.pagination import PaginatedCollection + + assert isinstance(result, PaginatedCollection) + + def test_with_status_filter(self): + client = _make_client() + project = _make_project(client) + result = project.get_issues(status=IssueStatus.OPEN) + # The params should contain the status filter + assert result.paginator.params["where"]["status"] == "Open" + + def test_with_data_row_filter(self): + client = _make_client() + project = _make_project(client) + result = project.get_issues(data_row_id="dr-1") + assert result.paginator.params["where"]["dataRow"] == {"id": "dr-1"} + + +# --------------------------------------------------------------------------- +# get_issue +# --------------------------------------------------------------------------- + + +class TestGetIssue: + def test_get_single_issue(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"issue": _ISSUE_RAW} + + issue = project.get_issue("issue-1") + assert issue.id == "issue-1" + args, _ = client.execute.call_args + assert "GetIssuePyApi" in args[0] + assert args[1]["where"] == {"id": "issue-1"} + + +# --------------------------------------------------------------------------- +# delete_issues +# --------------------------------------------------------------------------- + + +class TestDeleteIssues: + def test_delete_single(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"deleteIssue": {"id": "issue-1"}} + + assert project.delete_issues(["issue-1"]) is True + args, _ = client.execute.call_args + assert "DeleteIssuePyApi" in args[0] + assert args[1]["data"]["issueIds"] == ["issue-1"] + + def test_delete_multiple(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"deleteIssue": {"id": "issue-1"}} + + assert project.delete_issues(["issue-1", "issue-2"]) is True + args, _ = client.execute.call_args + assert args[1]["data"]["issueIds"] == ["issue-1", "issue-2"] + + +# --------------------------------------------------------------------------- +# create_issue_category +# --------------------------------------------------------------------------- + + +class TestCreateIssueCategory: + def test_create(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = { + "createIssueCategory": { + "id": "cat-1", + "name": "Quality", + "description": "Quality issues", + } + } + + cat = project.create_issue_category( + name="Quality", description="Quality issues" + ) + assert isinstance(cat, IssueCategory) + assert cat.id == "cat-1" + assert cat.name == "Quality" + args, _ = client.execute.call_args + assert "CreateIssueCategoryPyApi" in args[0] + assert args[1]["data"]["projectId"] == "proj-1" + + +# --------------------------------------------------------------------------- +# get_issue_categories +# --------------------------------------------------------------------------- + + +class TestGetIssueCategories: + def test_get_categories(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = { + "project": { + "issueCategories": [ + { + "id": "cat-1", + "name": "Quality", + "description": "Quality issues", + }, + { + "id": "cat-2", + "name": "Labeling", + "description": "Labeling issues", + }, + ] + } + } + + cats = project.get_issue_categories() + assert len(cats) == 2 + assert cats[0].name == "Quality" + assert cats[1].name == "Labeling" + + def test_empty_categories(self): + client = _make_client() + project = _make_project(client) + client.execute.return_value = {"project": {"issueCategories": []}} + + cats = project.get_issue_categories() + assert cats == [] + + +# --------------------------------------------------------------------------- +# _deserialize_position fallback +# --------------------------------------------------------------------------- + + +class TestDeserializePositionFallback: + def test_unrecognized_returns_none_and_warns(self, caplog): + raw = {"totally": "unknown", "structure": True} + with caplog.at_level(logging.WARNING): + result = _deserialize_position(raw) + assert result is None + assert "Unrecognized issue position structure" in caplog.text From 61c29f85a69dbf3e772bfb79d38e958cf3ebca07 Mon Sep 17 00:00:00 2001 From: lb-pno <87332996+lb-pno@users.noreply.github.com> Date: Wed, 18 Mar 2026 13:14:45 +0000 Subject: [PATCH 092/103] [PLT-0] Fix VideoClassificationText (#2044) Co-authored-by: paulnoirel <87332996+paulnoirel@users.noreply.github.com> --- .../serialization/ndjson/classification.py | 56 ++++++++++++ .../data/serialization/ndjson/label.py | 34 +++++++- .../data/serialization/ndjson/test_video.py | 87 +++++++++++++++++++ 3 files changed, 174 insertions(+), 3 deletions(-) diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py index 49177bba2..fc519edb2 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py @@ -209,6 +209,61 @@ def from_common( ) +class NDVideoTextAnswer(BaseModel): + value: str + frames: List[Dict[str, int]] + + model_config = ConfigDict(populate_by_name=True) + + +class NDVideoText(BaseModel): + """Video text classification with per-segment text values and frame ranges. + + Produces NDJSON like: + {"name": "...", "answer": [{"value": "text", "frames": [{"start": 1, "end": 5}]}], ...} + """ + + name: Optional[str] = None + schema_id: Optional[str] = Field(default=None, alias="schemaId") + answer: List[NDVideoTextAnswer] + data_row: DataRow = Field(alias="dataRow") + + model_config = ConfigDict(populate_by_name=True) + + @model_validator(mode="after") + def must_set_one(self): + if not self.name and not self.schema_id: + raise ValueError("Schema id or name are not set. Set either one.") + return self + + @model_serializer(mode="wrap") + def serialize_model(self, handler): + res = handler(self) + if "name" in res and res["name"] is None: + res.pop("name") + if "schemaId" in res and res["schemaId"] is None: + res.pop("schemaId") + return res + + @classmethod + def from_video_text_group( + cls, + annotation_group: List["VideoClassificationAnnotation"], + frame_ranges_by_text: Dict[str, List[Dict[str, int]]], + data: "GenericDataRowData", + ) -> "NDVideoText": + first = annotation_group[0] + return cls( + name=first.name, + schema_id=first.feature_schema_id, + data_row=DataRow(id=data.uid, global_key=data.global_key), + answer=[ + NDVideoTextAnswer(value=text_val, frames=ranges) + for text_val, ranges in frame_ranges_by_text.items() + ], + ) + + class NDPromptTextSubclass(NDAnswer): answer: str @@ -517,6 +572,7 @@ def from_common( NDRadioSubclass.model_rebuild() NDRadio.model_rebuild() NDText.model_rebuild() +NDVideoText.model_rebuild() NDPromptText.model_rebuild() NDTextSubclass.model_rebuild() diff --git a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py index 39deafa64..fc06fd959 100644 --- a/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py +++ b/libs/labelbox/src/labelbox/data/serialization/ndjson/label.py @@ -31,6 +31,7 @@ ) from .temporal import create_temporal_ndjson_classifications from labelbox.types import DocumentRectangle, DocumentEntity +from ...annotation_types.classification.classification import Text from .classification import ( NDChecklistSubclass, NDClassification, @@ -39,6 +40,7 @@ NDPromptClassificationType, NDPromptText, NDRadioSubclass, + NDVideoText, ) from .metric import NDConfusionMatrixMetric, NDMetricAnnotation, NDScalarMetric from .mmc import NDMessageTask @@ -61,6 +63,7 @@ NDRelationship, NDPromptText, NDMessageTask, + NDVideoText, ] @@ -142,11 +145,33 @@ def _create_video_annotations( yield NDObject.from_common(annotation=annot, data=label.data) for annotation_group in video_annotations.values(): - segment_frame_ranges = cls._get_segment_frame_ranges( - annotation_group - ) if isinstance(annotation_group[0], VideoClassificationAnnotation): annotation = annotation_group[0] + + if isinstance(annotation.value, Text): + by_text = defaultdict(list) + for ann in annotation_group: + by_text[ann.value.answer].append(ann) + + frame_ranges_by_text = {} + for text_val, anns in sorted( + by_text.items(), + key=lambda x: min(a.frame for a in x[1]), + ): + ranges = [ + {"start": s, "end": e} + for s, e in cls._get_segment_frame_ranges(anns) + ] + frame_ranges_by_text[text_val] = ranges + + yield NDVideoText.from_video_text_group( + annotation_group, frame_ranges_by_text, label.data + ) + continue + + segment_frame_ranges = cls._get_segment_frame_ranges( + annotation_group + ) frames_data = [] for frames in segment_frame_ranges: frames_data.append({"start": frames[0], "end": frames[-1]}) @@ -154,6 +179,9 @@ def _create_video_annotations( yield NDClassification.from_common(annotation, label.data) elif isinstance(annotation_group[0], VideoObjectAnnotation): + segment_frame_ranges = cls._get_segment_frame_ranges( + annotation_group + ) segments = [] for start_frame, end_frame in segment_frame_ranges: segment = [] diff --git a/libs/labelbox/tests/data/serialization/ndjson/test_video.py b/libs/labelbox/tests/data/serialization/ndjson/test_video.py index 6c14343a4..119a614e1 100644 --- a/libs/labelbox/tests/data/serialization/ndjson/test_video.py +++ b/libs/labelbox/tests/data/serialization/ndjson/test_video.py @@ -635,6 +635,93 @@ def test_video_classification_global_subclassifications(): assert res == [expected_first_annotation, expected_second_annotation] +def test_video_classification_text_produces_ndjson_with_frames(): + """VideoClassificationAnnotation + Text serializes with answer as a list of {value, frames}.""" + label = Label( + data=GenericDataRowData(global_key="sample-video-text"), + annotations=[ + VideoClassificationAnnotation( + name="free_text", + frame=9, + segment_index=0, + value=Text(answer="Looks like a hungry big cat"), + ), + VideoClassificationAnnotation( + name="free_text", + frame=15, + segment_index=0, + value=Text(answer="Looks like a hungry big cat"), + ), + VideoClassificationAnnotation( + name="free_text", + frame=40, + segment_index=1, + value=Text(answer="It's getting closer!"), + ), + VideoClassificationAnnotation( + name="free_text", + frame=50, + segment_index=1, + value=Text(answer="It's getting closer!"), + ), + ], + ) + serialized = list(NDJsonConverter.serialize([label])) + free_text_rows = [r for r in serialized if r.get("name") == "free_text"] + assert len(free_text_rows) == 1 + + row = free_text_rows[0] + assert row["dataRow"] == {"globalKey": "sample-video-text"} + assert "answer" in row + answer = row["answer"] + assert isinstance(answer, list) + assert len(answer) == 2 + + by_value = {a["value"]: a for a in answer} + assert "Looks like a hungry big cat" in by_value + assert "It's getting closer!" in by_value + assert by_value["Looks like a hungry big cat"]["frames"] == [ + {"start": 9, "end": 15} + ] + assert by_value["It's getting closer!"]["frames"] == [ + {"start": 40, "end": 50} + ] + + +def test_video_classification_text_single_text_across_frames(): + """VideoClassificationAnnotation + Text with same text across all frames.""" + label = Label( + data=GenericDataRowData(global_key="sample-video-single-text"), + annotations=[ + VideoClassificationAnnotation( + name="free_text_per_frame", + frame=9, + segment_index=0, + value=Text(answer="sample text"), + ), + VideoClassificationAnnotation( + name="free_text_per_frame", + frame=15, + segment_index=0, + value=Text(answer="sample text"), + ), + ], + ) + serialized = list(NDJsonConverter.serialize([label])) + free_text_rows = [ + r for r in serialized if r.get("name") == "free_text_per_frame" + ] + assert len(free_text_rows) == 1 + + row = free_text_rows[0] + assert row["dataRow"] == {"globalKey": "sample-video-single-text"} + answer = row["answer"] + assert isinstance(answer, list) + assert len(answer) == 1 + assert answer[0]["value"] == "sample text" + assert answer[0]["frames"] == [{"start": 9, "end": 15}] + + def test_video_classification_nesting_bbox(): bbox_annotation = [ VideoObjectAnnotation( From 85a4e935f6c0d02dfe0e83b8cef5d7c2d9347cca Mon Sep 17 00:00:00 2001 From: lb-pno <87332996+lb-pno@users.noreply.github.com> Date: Wed, 18 Mar 2026 13:15:23 +0000 Subject: [PATCH 093/103] [PLT-0] Remove GCP integration tests (#2045) Co-authored-by: paulnoirel <87332996+paulnoirel@users.noreply.github.com> --- .../integration/test_delegated_access.py | 67 ------------------- 1 file changed, 67 deletions(-) diff --git a/libs/labelbox/tests/integration/test_delegated_access.py b/libs/labelbox/tests/integration/test_delegated_access.py index 7d9d0d3cb..1b3f27018 100644 --- a/libs/labelbox/tests/integration/test_delegated_access.py +++ b/libs/labelbox/tests/integration/test_delegated_access.py @@ -8,7 +8,6 @@ from labelbox import Client from labelbox.schema.iam_integration import ( AwsIamIntegrationSettings, - GcpIamIntegrationSettings, AzureIamIntegrationSettings, ) from ..conftest import create_dataset_robust @@ -47,23 +46,6 @@ def aws_integration( delete_iam_integration(client, integration.uid) -@pytest.fixture -def gcp_integration( - client, test_integration_name -) -> Optional["IAMIntegration"]: - """Creates a test GCP integration and cleans it up after the test.""" - settings = GcpIamIntegrationSettings( - read_bucket="gs://test-bucket", - ) - integration = client.get_organization().create_iam_integration( - name=test_integration_name, - settings=settings, - ) - yield integration - # Proper cleanup using delete mutation - delete_iam_integration(client, integration.uid) - - @pytest.fixture def azure_integration( client, test_integration_name @@ -103,23 +85,6 @@ def test_create_aws_integration(client, test_integration_name): delete_iam_integration(client, integration.uid) -def test_create_gcp_integration(client, test_integration_name): - """Test creating a GCP IAM integration.""" - settings = GcpIamIntegrationSettings(read_bucket="gs://test-bucket") - integration = client.get_organization().create_iam_integration( - name=test_integration_name, settings=settings - ) - - try: - assert integration.name == test_integration_name - assert integration.provider == "GCP" - assert isinstance(integration.settings, GcpIamIntegrationSettings) - assert integration.settings.read_bucket == settings.read_bucket - finally: - # Ensure cleanup even if assertions fail - delete_iam_integration(client, integration.uid) - - def test_create_azure_integration(client, test_integration_name): """Test creating an Azure IAM integration.""" settings = AzureIamIntegrationSettings( @@ -180,38 +145,6 @@ def test_update_aws_integration(client, test_integration_name): delete_iam_integration(client, integration.uid) -def test_update_gcp_integration(client, test_integration_name): - """Test updating a GCP IAM integration.""" - # Create initial integration - settings = GcpIamIntegrationSettings(read_bucket="gs://test-bucket") - integration = client.get_organization().create_iam_integration( - name=test_integration_name, settings=settings - ) - - try: - # Update integration - new_settings = GcpIamIntegrationSettings( - read_bucket="gs://updated-bucket" - ) - integration.update( - name=f"updated-{test_integration_name}", settings=new_settings - ) - - # Verify update - find the specific integration by ID - updated_integration = None - for iam_int in client.get_organization().get_iam_integrations(): - if iam_int.uid == integration.uid: - updated_integration = iam_int - break - - assert updated_integration is not None - assert updated_integration.name == f"updated-{test_integration_name}" - # Note: Settings may not be returned immediately after update - finally: - # Ensure cleanup even if assertions fail - delete_iam_integration(client, integration.uid) - - def test_update_azure_integration(client, test_integration_name): """Test updating an Azure IAM integration.""" # Create initial integration From b10244fd574a3cdb089587ebe4b3ac4bd3f81cdf Mon Sep 17 00:00:00 2001 From: lb-pno <87332996+lb-pno@users.noreply.github.com> Date: Wed, 18 Mar 2026 13:15:58 +0000 Subject: [PATCH 094/103] =?UTF-8?q?[PLT-0]=20Mark=20ModelEvaluation=20MAL?= =?UTF-8?q?=20import=20tests=20as=20xfail=20and=20fix=20checklist=5Finfe?= =?UTF-8?q?=E2=80=A6=20(#2046)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: paulnoirel <87332996+paulnoirel@users.noreply.github.com> --- .../tests/data/annotation_import/conftest.py | 2 +- .../annotation_import/test_generic_data_types.py | 16 ++++++++++++++-- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/libs/labelbox/tests/data/annotation_import/conftest.py b/libs/labelbox/tests/data/annotation_import/conftest.py index e3c9c8b98..f43151f2b 100644 --- a/libs/labelbox/tests/data/annotation_import/conftest.py +++ b/libs/labelbox/tests/data/annotation_import/conftest.py @@ -1417,7 +1417,7 @@ def checklist_inference_index_mmc( checklists = [] for feature in prediction_id_mapping: if "checklist_index" not in feature: - return None + continue checklist = feature["checklist_index"].copy() checklist.update( { diff --git a/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py b/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py index 805c24edf..70759357c 100644 --- a/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py +++ b/libs/labelbox/tests/data/annotation_import/test_generic_data_types.py @@ -245,7 +245,13 @@ def test_import_media_types_by_global_key( ), (MediaType.LLMPromptCreation, MediaType.LLMPromptCreation), (OntologyKind.ResponseCreation, OntologyKind.ResponseCreation), - (OntologyKind.ModelEvaluation, OntologyKind.ModelEvaluation), + pytest.param( + OntologyKind.ModelEvaluation, + OntologyKind.ModelEvaluation, + marks=pytest.mark.xfail( + reason="Backend bug: lb-import-annot-pred-no-mta raises TypeError on ModelEvaluation MAL annotations (encoding without a string argument)" + ), + ), ], indirect=["configured_project"], ) @@ -279,7 +285,13 @@ def test_import_mal_annotations( (MediaType.Conversational, MediaType.Conversational), (MediaType.Document, MediaType.Document), (OntologyKind.ResponseCreation, OntologyKind.ResponseCreation), - (OntologyKind.ModelEvaluation, OntologyKind.ModelEvaluation), + pytest.param( + OntologyKind.ModelEvaluation, + OntologyKind.ModelEvaluation, + marks=pytest.mark.xfail( + reason="Backend bug: lb-import-annot-pred-no-mta raises TypeError on ModelEvaluation MAL annotations (encoding without a string argument)" + ), + ), ], indirect=["configured_project_by_global_key"], ) From 4f0e294a36910c8be48fc64d21dc8f5d9299342d Mon Sep 17 00:00:00 2001 From: lb-pno <87332996+lb-pno@users.noreply.github.com> Date: Wed, 18 Mar 2026 13:16:46 +0000 Subject: [PATCH 095/103] [PLT-0] Fix race conditions in test_task_filter and test_data_row_delete_and_create_with_same_global_key (#2047) Co-authored-by: paulnoirel <87332996+paulnoirel@users.noreply.github.com> --- .../test_export_data_rows_streamable.py | 55 ++++++++++++++----- .../tests/integration/test_data_rows.py | 19 +++++-- 2 files changed, 53 insertions(+), 21 deletions(-) diff --git a/libs/labelbox/tests/data/export/streamable/test_export_data_rows_streamable.py b/libs/labelbox/tests/data/export/streamable/test_export_data_rows_streamable.py index 233fc2144..4a3127dd8 100644 --- a/libs/labelbox/tests/data/export/streamable/test_export_data_rows_streamable.py +++ b/libs/labelbox/tests/data/export/streamable/test_export_data_rows_streamable.py @@ -146,22 +146,47 @@ def test_task_filter(self, client, data_row, wait_for_data_row_processing): task_name="TestExportDataRow:test_task_filter", ) - # Check if task is listed "in progress" in organization's tasks - org_tasks_in_progress = organization.tasks( - where=Task.status_as_enum == TaskStatus.In_Progress - ) - retrieved_task_in_progress = next( - (t for t in org_tasks_in_progress if t.uid == export_task.uid), "" - ) - assert getattr(retrieved_task_in_progress, "uid", "") == export_task.uid + # The task may complete before we query, so retry a few times + # and fall back to checking the Complete status instead. + found_in_progress = False + for _ in range(5): + try: + org_tasks_in_progress = organization.tasks( + where=Task.status_as_enum == TaskStatus.In_Progress + ) + retrieved = next( + ( + t + for t in org_tasks_in_progress + if t.uid == export_task.uid + ), + None, + ) + if retrieved is not None: + found_in_progress = True + break + except Exception: + pass + time.sleep(2) - export_task.wait_till_done() + export_task.wait_till_done(timeout_seconds=600) + + if not found_in_progress: + export_task.refresh() + assert ( + export_task.status == "COMPLETE" + ), f"Task was never seen as In_Progress and did not complete: {export_task.status}" # Check if task is listed "complete" in user's created tasks - user_tasks_complete = user.created_tasks( - where=Task.status_as_enum == TaskStatus.Complete - ) - retrieved_task_complete = next( - (t for t in user_tasks_complete if t.uid == export_task.uid), "" - ) + for _ in range(5): + user_tasks_complete = user.created_tasks( + where=Task.status_as_enum == TaskStatus.Complete + ) + retrieved_task_complete = next( + (t for t in user_tasks_complete if t.uid == export_task.uid), + None, + ) + if retrieved_task_complete is not None: + break + time.sleep(2) assert getattr(retrieved_task_complete, "uid", "") == export_task.uid diff --git a/libs/labelbox/tests/integration/test_data_rows.py b/libs/labelbox/tests/integration/test_data_rows.py index 485719575..fc668c05f 100644 --- a/libs/labelbox/tests/integration/test_data_rows.py +++ b/libs/labelbox/tests/integration/test_data_rows.py @@ -1024,6 +1024,16 @@ def test_data_row_bulk_creation_with_same_global_keys( assert len(all_results) == 1 +def _wait_for_task(task, timeout_seconds=600): + """Wait for a task to complete with a defensive refresh if timeout is exhausted.""" + task.wait_till_done(timeout_seconds=timeout_seconds) + if task.status == "IN_PROGRESS": + task.refresh() + assert ( + task.status == "COMPLETE" + ), f"Task {task.uid} did not complete within {timeout_seconds}s: {task.status}" + + def test_data_row_delete_and_create_with_same_global_key( client, dataset, sample_image ): @@ -1035,18 +1045,16 @@ def test_data_row_delete_and_create_with_same_global_key( # should successfully insert new datarow task = dataset.create_data_rows([data_row_payload]) - task.wait_till_done() + _wait_for_task(task) - assert task.status == "COMPLETE" assert task.result[0]["global_key"] == global_key_1 new_data_row_id = task.result[0]["id"] # same payload should fail due to duplicated global key task = dataset.create_data_rows([data_row_payload]) - task.wait_till_done() + _wait_for_task(task) - assert task.status == "COMPLETE" assert len(task.failed_data_rows) == 1 assert ( task.failed_data_rows[0]["message"] @@ -1058,9 +1066,8 @@ def test_data_row_delete_and_create_with_same_global_key( # should successfully insert new datarow now task = dataset.create_data_rows([data_row_payload]) - task.wait_till_done() + _wait_for_task(task) - assert task.status == "COMPLETE" assert task.result[0]["global_key"] == global_key_1 From 5500a59bc13d4d77f51099bf3c6b315b8ee4f37c Mon Sep 17 00:00:00 2001 From: lb-pno <87332996+lb-pno@users.noreply.github.com> Date: Wed, 18 Mar 2026 13:17:13 +0000 Subject: [PATCH 096/103] [PLT-0] Fix flaky test_get_user_groups_with_creation_deletion (#2048) Co-authored-by: paulnoirel <87332996+paulnoirel@users.noreply.github.com> --- .../integration/schema/test_user_group.py | 41 +++++++++++-------- 1 file changed, 24 insertions(+), 17 deletions(-) diff --git a/libs/labelbox/tests/integration/schema/test_user_group.py b/libs/labelbox/tests/integration/schema/test_user_group.py index 33af0784c..354335b6e 100644 --- a/libs/labelbox/tests/integration/schema/test_user_group.py +++ b/libs/labelbox/tests/integration/schema/test_user_group.py @@ -194,33 +194,40 @@ def test_update_user_group(user_group): def test_get_user_groups_with_creation_deletion(client): """Test user group creation, retrieval, and deletion.""" - # Get initial count - initial_groups = list(UserGroup.get_user_groups(client)) - initial_count = len(initial_groups) - - # Create a new group group_name = f"{data.name()}_{int(time.time())}" user_group = UserGroup(client) user_group.name = group_name user_group.color = UserGroupColor.CYAN user_group.create() - # Verify the group was created - updated_groups = list(UserGroup.get_user_groups(client)) - assert len(updated_groups) == initial_count + 1 - - # Find our group - our_group = next((g for g in updated_groups if g.name == group_name), None) - assert our_group is not None - assert our_group.id == user_group.id + # Verify the created group appears in the listing (retry for eventual + # consistency; count-based checks are racy with parallel test workers). + our_group = None + for _ in range(5): + updated_groups = list(UserGroup.get_user_groups(client)) + our_group = next( + (g for g in updated_groups if g.id == user_group.id), None + ) + if our_group is not None: + break + time.sleep(2) + assert ( + our_group is not None + ), f"Created group {user_group.id} not found in group listing" + assert our_group.name == group_name # Delete the group user_group.delete() - # Verify the group was deleted - final_groups = list(UserGroup.get_user_groups(client)) - assert len(final_groups) == initial_count - assert len(user_group.members) == 0 # V3 uses members + # Verify the deleted group no longer appears in the listing + gone = False + for _ in range(5): + final_groups = list(UserGroup.get_user_groups(client)) + if not any(g.id == user_group.id for g in final_groups): + gone = True + break + time.sleep(2) + assert gone, f"Deleted group {user_group.id} still appears in group listing" def test_update_user_group_members_projects(user_group, client, project_pack): From 23528c7e5cb9696addeedb1883933d3e5ae90624 Mon Sep 17 00:00:00 2001 From: lb-pno <87332996+lb-pno@users.noreply.github.com> Date: Wed, 18 Mar 2026 13:17:51 +0000 Subject: [PATCH 097/103] [PLT-0] Fix flaky test_request_labeling_service_moe_project (#2049) Co-authored-by: paulnoirel <87332996+paulnoirel@users.noreply.github.com> --- libs/labelbox/tests/integration/conftest.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/libs/labelbox/tests/integration/conftest.py b/libs/labelbox/tests/integration/conftest.py index aa4c411fb..58d94aa43 100644 --- a/libs/labelbox/tests/integration/conftest.py +++ b/libs/labelbox/tests/integration/conftest.py @@ -746,15 +746,25 @@ def live_chat_evaluation_project(client, rand_gen): def live_chat_evaluation_project_with_batch( client, rand_gen, - live_chat_evaluation_project, offline_conversational_data_row, ): project_name = f"test-model-evaluation-project-{rand_gen(str)}" - project = client.create_model_evaluation_project(name=project_name) + + # Retry to handle transient ER_LOCK_DEADLOCK from the backend when + # multiple parallel workers create model-evaluation projects. + project = None + for attempt in range(3): + try: + project = client.create_model_evaluation_project(name=project_name) + break + except Exception: + if attempt == 2: + raise + time.sleep(2 * (attempt + 1)) project.create_batch( rand_gen(str), - [offline_conversational_data_row.uid], # sample of data row objects + [offline_conversational_data_row.uid], ) yield project From 9549be402e64af9f26cfccc0002c5453be8f5c7f Mon Sep 17 00:00:00 2001 From: kozikkamil <91909509+kozikkamil@users.noreply.github.com> Date: Wed, 18 Mar 2026 15:30:40 +0100 Subject: [PATCH 098/103] v7.6.0 (#2050) Co-authored-by: Matthew Roberson --- docs/conf.py | 2 +- docs/labelbox/index.rst | 1 + docs/labelbox/project-sync.rst | 6 ++++++ libs/labelbox/CHANGELOG.md | 8 ++++++++ libs/labelbox/pyproject.toml | 2 +- libs/labelbox/src/labelbox/__init__.py | 2 +- 6 files changed, 18 insertions(+), 3 deletions(-) create mode 100644 docs/labelbox/project-sync.rst diff --git a/docs/conf.py b/docs/conf.py index ef07b50f5..da7f64238 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ project = 'Python SDK reference' copyright = '2025, Labelbox' author = 'Labelbox' -release = '7.5.0' +release = '7.6.0' # -- General configuration --------------------------------------------------- diff --git a/docs/labelbox/index.rst b/docs/labelbox/index.rst index 8069b6b62..1888cb233 100644 --- a/docs/labelbox/index.rst +++ b/docs/labelbox/index.rst @@ -43,6 +43,7 @@ Labelbox Python SDK Documentation pagination project project-model-config + project-sync prompt-issue-tool quality-mode request-client diff --git a/docs/labelbox/project-sync.rst b/docs/labelbox/project-sync.rst new file mode 100644 index 000000000..b9c3134f0 --- /dev/null +++ b/docs/labelbox/project-sync.rst @@ -0,0 +1,6 @@ +Project Sync +=============================================================================================== + +.. automodule:: labelbox.schema.project_sync + :members: + :show-inheritance: diff --git a/libs/labelbox/CHANGELOG.md b/libs/labelbox/CHANGELOG.md index 5be42483a..532427892 100644 --- a/libs/labelbox/CHANGELOG.md +++ b/libs/labelbox/CHANGELOG.md @@ -1,4 +1,12 @@ # Changelog +# Version 7.6.0 (2026-03-18) +## Added +* Add `Project.sync_external_project()` method for syncing external labels, metrics, and workflow state ([#2042](https://github.com/Labelbox/labelbox-python/pull/2042)) +* Add `ProjectSyncEntry`, `ProjectSyncResult`, `ProjectSyncLabel`, `ProjectSyncReview`, `AutoQA`, `AutoQaStatus`, `CustomScore`, `GranularRating`, `SubmittedBy`, `ReviewedBy` classes ([#2042](https://github.com/Labelbox/labelbox-python/pull/2042)) +* Add CRUD support for `Issues`, `Comments` and `Issue Categories` ([#2043](https://github.com/Labelbox/labelbox-python/pull/2043)) +## Fixed +* Fix NDJSON serialization for `VideoClassificationAnnotation` with `Text` values ([#2044](https://github.com/Labelbox/labelbox-python/pull/2044)) + # Version 7.5.0 (2026-01-30) ## Added * Add support for text subclasses under global text subclasses ([#2040](https://github.com/Labelbox/labelbox-python/pull/2040)) diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml index 5f11ff906..d15b2eb3a 100644 --- a/libs/labelbox/pyproject.toml +++ b/libs/labelbox/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "labelbox" -version = "7.5.0" +version = "7.6.0" description = "Labelbox Python API" authors = [{ name = "Labelbox", email = "engineering@labelbox.com" }] dependencies = [ diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index a83ce2e7a..5f48796c6 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -1,6 +1,6 @@ name = "labelbox" -__version__ = "7.5.0" +__version__ = "7.6.0" from labelbox.client import Client from labelbox.schema.annotation_import import ( From b1370db880b36dabc4e31cc96202137ec2d5393e Mon Sep 17 00:00:00 2001 From: Aaron Bacchi <146245419+abacchilb@users.noreply.github.com> Date: Mon, 23 Mar 2026 12:54:06 -0400 Subject: [PATCH 099/103] DEVOPS-7261 - security: pin GitHub Actions to commit SHAs (#2051) Co-authored-by: Cursor Agent Co-authored-by: Aaron Bacchi --- .github/workflows/lbox-develop.yml | 20 ++++++------ .github/workflows/lbox-publish.yml | 26 ++++++++-------- .github/workflows/notebooks.yml | 10 +++--- .github/workflows/publish.yml | 32 ++++++++++---------- .github/workflows/python-package-develop.yml | 18 +++++------ .github/workflows/python-package-shared.yml | 6 ++-- .github/workflows/secrets_scan.yml | 4 +-- 7 files changed, 58 insertions(+), 58 deletions(-) diff --git a/.github/workflows/lbox-develop.yml b/.github/workflows/lbox-develop.yml index cfad49232..68596abcc 100644 --- a/.github/workflows/lbox-develop.yml +++ b/.github/workflows/lbox-develop.yml @@ -21,10 +21,10 @@ jobs: test-matrix: ${{ steps.matrix.outputs.test-matrix }} package-matrix: ${{ steps.matrix.outputs.publish-matrix }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} - - uses: dorny/paths-filter@v3 + - uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3 id: filter with: list-files: 'json' @@ -47,7 +47,7 @@ jobs: group: lbox-staging-${{ matrix.python-version }}-${{ matrix.package }} cancel-in-progress: false steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} - uses: ./.github/actions/python-package-shared-setup @@ -83,7 +83,7 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing id-token: write steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} - uses: ./.github/actions/python-package-shared-setup @@ -100,7 +100,7 @@ jobs: rye run toml set --toml-path pyproject.toml project.name ${{ matrix.package }} rye build - name: Publish package distributions to Test PyPI - uses: pypa/gh-action-pypi-publish@release/v1 + uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1 with: packages-dir: dist/ repository-url: https://test.pypi.org/legacy/ @@ -117,20 +117,20 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing packages: write steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 + uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3 - name: Log in to the Container registry - uses: docker/login-action@v3 + uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3 with: registry: ghcr.io username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - name: Build and push (Develop) if: github.event_name == 'push' - uses: docker/build-push-action@v5 + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5 with: context: . file: ./libs/${{ matrix.package }}/Dockerfile @@ -149,7 +149,7 @@ jobs: echo "ghcr.io/labelbox/${{ matrix.package }}:${{ github.sha }}" >> "$GITHUB_STEP_SUMMARY" - name: Build and push (Pull Request) if: github.event_name == 'pull_request' - uses: docker/build-push-action@v5 + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5 with: context: . file: ./libs/${{ matrix.package }}/Dockerfile diff --git a/.github/workflows/lbox-publish.yml b/.github/workflows/lbox-publish.yml index dcca8e561..271e0799b 100644 --- a/.github/workflows/lbox-publish.yml +++ b/.github/workflows/lbox-publish.yml @@ -27,10 +27,10 @@ jobs: test-matrix: ${{ steps.matrix.outputs.test-matrix }} package-matrix: ${{ steps.matrix.outputs.publish-matrix }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.tag }} - - uses: dorny/paths-filter@v3 + - uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3 id: filter with: base: ${{ inputs.prev_sdk_tag }} @@ -52,11 +52,11 @@ jobs: matrix: include: ${{ fromJSON(needs.path-filter.outputs.package-matrix) }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.tag }} - name: Install the latest version of rye - uses: eifinger/setup-rye@v2 + uses: eifinger/setup-rye@787604a465b1696ad17eedf2f8101df9fc555c94 # v2 with: version: ${{ vars.RYE_VERSION }} enable-cache: true @@ -73,7 +73,7 @@ jobs: run: | cd dist && echo "hashes_${{ matrix.package }}=$(sha256sum * | base64 -w0)" >> $GITHUB_OUTPUT echo "hashes_${{ matrix.package }}=$(sha256sum * | base64 -w0)" - - uses: actions/upload-artifact@v4 + - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: build-${{ matrix.package }} path: ./dist @@ -83,7 +83,7 @@ jobs: actions: read contents: write id-token: write # Needed to access the workflow's OIDC identity. - uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@v2.0.0 + uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@5a775b367a56d5bd118a224a811bba288150a563 # v2.0.0 with: base64-subjects: "${{ needs.build.outputs.hashes }}" upload-assets: true @@ -102,7 +102,7 @@ jobs: group: lbox-staging-${{ matrix.python-version }}-${{ matrix.package }} cancel-in-progress: false steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.tag }} - uses: ./.github/actions/python-package-shared-setup @@ -137,12 +137,12 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing id-token: write steps: - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4 with: name: build-${{ matrix.package }} path: ./artifact - name: Publish package distributions to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 + uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1 with: packages-dir: artifact/ verbose: true @@ -158,20 +158,20 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing packages: write steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: # ref: ${{ inputs.tag }} ref: ${{ inputs.tag }} - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 + uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3 - name: Log in to the Container registry - uses: docker/login-action@v3 + uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3 with: registry: ghcr.io username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - name: Build and push - uses: docker/build-push-action@v5 + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5 id: build_container with: context: . diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml index 382176478..9b2b3e973 100644 --- a/.github/workflows/notebooks.yml +++ b/.github/workflows/notebooks.yml @@ -20,7 +20,7 @@ jobs: if: github.event.pull_request.merged == false runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} fetch-depth: 0 @@ -38,7 +38,7 @@ jobs: git add examples/. git commit -m ":art: Cleaned" || exit 0 - name: Push changes - uses: ad-m/github-push-action@master + uses: ad-m/github-push-action@4cc74773234f74829a8c21bc4d69dd4be9cfa599 # master with: github_token: ${{ secrets.GITHUB_TOKEN }} branch: ${{ github.head_ref }} @@ -50,7 +50,7 @@ jobs: outputs: addedOrModified: ${{ steps.filter.outputs.addedOrModified }} steps: - - uses: dorny/paths-filter@v3 + - uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3 id: filter with: filters: | @@ -62,7 +62,7 @@ jobs: if: ${{ needs.changes.outputs.addedOrModified == 'true' }} && github.event.pull_request.merged == false runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} fetch-depth: 0 @@ -80,7 +80,7 @@ jobs: git add examples/. git commit -m ":memo: README updated" || exit 0 - name: Push changes - uses: ad-m/github-push-action@master + uses: ad-m/github-push-action@4cc74773234f74829a8c21bc4d69dd4be9cfa599 # master with: github_token: ${{ secrets.GITHUB_TOKEN }} branch: ${{ github.head_ref }} diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 11845e6d2..8b6cb6ccb 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -43,11 +43,11 @@ jobs: outputs: hashes: ${{ steps.hash.outputs.hashes }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.tag }} - name: Install the latest version of rye - uses: eifinger/setup-rye@v2 + uses: eifinger/setup-rye@787604a465b1696ad17eedf2f8101df9fc555c94 # v2 with: version: ${{ vars.RYE_VERSION }} enable-cache: true @@ -63,7 +63,7 @@ jobs: id: hash run: | cd dist && echo "hashes=$(sha256sum * | base64 -w0)" >> $GITHUB_OUTPUT - - uses: actions/upload-artifact@v4 + - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4 with: name: build path: ./dist @@ -73,7 +73,7 @@ jobs: actions: read contents: write id-token: write # Needed to access the workflow's OIDC identity. - uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@v2.0.0 + uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@5a775b367a56d5bd118a224a811bba288150a563 # v2.0.0 with: base64-subjects: "${{ needs.build.outputs.hashes }}" upload-assets: true @@ -102,11 +102,11 @@ jobs: prod-key: PROD_LABELBOX_API_KEY_2 da-test-key: DA_GCP_LABELBOX_API_KEY steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.tag }} - name: Install the latest version of rye - uses: eifinger/setup-rye@v2 + uses: eifinger/setup-rye@787604a465b1696ad17eedf2f8101df9fc555c94 # v2 with: version: ${{ vars.RYE_VERSION }} enable-cache: true @@ -115,7 +115,7 @@ jobs: rye config --set-bool behavior.use-uv=true - name: Python setup run: rye pin ${{ matrix.python-version }} - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4 with: name: build path: ./dist @@ -151,10 +151,10 @@ jobs: permissions: contents: write steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.tag }} - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4 with: name: build path: ./artifact @@ -176,12 +176,12 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing id-token: write steps: - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4 with: name: build path: ./artifact - name: Publish package distributions to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 + uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1 with: packages-dir: artifact/ container-publish: @@ -198,7 +198,7 @@ jobs: env: CONTAINER_IMAGE: "ghcr.io/${{ github.repository }}" steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.tag }} @@ -207,17 +207,17 @@ jobs: echo "CONTAINER_IMAGE=${CONTAINER_IMAGE,,}" >> ${GITHUB_ENV} - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 + uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3 - name: Log in to the Container registry - uses: docker/login-action@v3 + uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3 with: registry: ghcr.io username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - name: Build and push - uses: docker/build-push-action@v5 + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5 id: build_container with: context: . @@ -246,7 +246,7 @@ jobs: actions: read # for detecting the Github Actions environment. id-token: write # for creating OIDC tokens for signing. packages: write # for uploading attestations. - uses: slsa-framework/slsa-github-generator/.github/workflows/generator_container_slsa3.yml@v2.0.0 + uses: slsa-framework/slsa-github-generator/.github/workflows/generator_container_slsa3.yml@5a775b367a56d5bd118a224a811bba288150a563 # v2.0.0 with: image: ${{ needs. container-publish.outputs.image }} digest: ${{ needs. container-publish.outputs.digest }} diff --git a/.github/workflows/python-package-develop.yml b/.github/workflows/python-package-develop.yml index a9718f300..d371f693a 100644 --- a/.github/workflows/python-package-develop.yml +++ b/.github/workflows/python-package-develop.yml @@ -19,10 +19,10 @@ jobs: outputs: labelbox: ${{ steps.filter.outputs.labelbox }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} - - uses: dorny/paths-filter@v3 + - uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3 id: filter with: filters: | @@ -93,7 +93,7 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing id-token: write steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} - uses: ./.github/actions/python-package-shared-setup @@ -110,7 +110,7 @@ jobs: rye run toml set --toml-path pyproject.toml project.name labelbox-test rye build - name: Publish package distributions to Test PyPI - uses: pypa/gh-action-pypi-publish@release/v1 + uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1 with: packages-dir: dist/ repository-url: https://test.pypi.org/legacy/ @@ -124,7 +124,7 @@ jobs: env: CONTAINER_IMAGE: "ghcr.io/${{ github.repository }}" steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} @@ -133,10 +133,10 @@ jobs: echo "CONTAINER_IMAGE=${CONTAINER_IMAGE,,}" >> ${GITHUB_ENV} - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 + uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3 - name: Log in to the Container registry - uses: docker/login-action@v3 + uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3 with: registry: ghcr.io username: ${{ github.actor }} @@ -144,7 +144,7 @@ jobs: - name: Build and push (Develop) if: github.event_name == 'push' - uses: docker/build-push-action@v5 + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5 with: context: . file: ./libs/labelbox/Dockerfile @@ -161,7 +161,7 @@ jobs: - name: Build and push (Pull Request) if: github.event_name == 'pull_request' - uses: docker/build-push-action@v5 + uses: docker/build-push-action@ca052bb54ab0790a636c9b5f226502c73d547a25 # v5 with: context: . file: ./libs/labelbox/Dockerfile diff --git a/.github/workflows/python-package-shared.yml b/.github/workflows/python-package-shared.yml index 4311020d8..cf13782db 100644 --- a/.github/workflows/python-package-shared.yml +++ b/.github/workflows/python-package-shared.yml @@ -26,7 +26,7 @@ jobs: lint: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.head_ref }} - uses: ./.github/actions/python-package-shared-setup @@ -42,7 +42,7 @@ jobs: group: labelbox-python-${{ inputs.test-env }}-${{ inputs.python-version }}-integration cancel-in-progress: false steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.sdk-version || github.head_ref }} - uses: ./.github/actions/python-package-shared-setup @@ -62,7 +62,7 @@ jobs: group: labelbox-python-${{ inputs.test-env }}-${{ inputs.python-version }}-unit-data cancel-in-progress: false steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ inputs.sdk-version || github.head_ref }} - uses: ./.github/actions/python-package-shared-setup diff --git a/.github/workflows/secrets_scan.yml b/.github/workflows/secrets_scan.yml index dbe4cc1b9..c123b0978 100644 --- a/.github/workflows/secrets_scan.yml +++ b/.github/workflows/secrets_scan.yml @@ -8,10 +8,10 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: fetch-depth: 0 - name: Secret Scanning - uses: trufflesecurity/trufflehog@main + uses: trufflesecurity/trufflehog@6c64db94d5b2e09d7e0948fb6bd3166cc6fffbc7 # main with: extra_args: --only-verified From cd744109545d0ac725fb174a4f4d95124a21322b Mon Sep 17 00:00:00 2001 From: Aaron Bacchi <146245419+abacchilb@users.noreply.github.com> Date: Thu, 26 Mar 2026 10:16:14 -0400 Subject: [PATCH 100/103] DEVOPS-7258 - action security updates (#2052) --- .github/actions/provenance/action.yml | 15 ---- .../python-package-shared-setup/action.yml | 2 +- .github/workflows/notebooks.yml | 86 ------------------- .github/workflows/python-package-develop.yml | 2 +- .github/workflows/python-package-prod.yml | 3 + 5 files changed, 5 insertions(+), 103 deletions(-) delete mode 100644 .github/actions/provenance/action.yml delete mode 100644 .github/workflows/notebooks.yml diff --git a/.github/actions/provenance/action.yml b/.github/actions/provenance/action.yml deleted file mode 100644 index ea809724c..000000000 --- a/.github/actions/provenance/action.yml +++ /dev/null @@ -1,15 +0,0 @@ -name: Labelbox Python SDK Provenance Generation - -inputs: - subjects: - required: true - type: string -runs: - using: "composite" - steps: - - name: upload - uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@v2.0.0 - with: - base64-subjects: "${{ inputs.subjects }}" - upload-assets: true - upload-tag-name: v.6.0.0 # Tag from the initiation of the workflow \ No newline at end of file diff --git a/.github/actions/python-package-shared-setup/action.yml b/.github/actions/python-package-shared-setup/action.yml index 4b9727737..d37559e36 100644 --- a/.github/actions/python-package-shared-setup/action.yml +++ b/.github/actions/python-package-shared-setup/action.yml @@ -10,7 +10,7 @@ runs: using: "composite" steps: - name: Install the latest version of rye - uses: eifinger/setup-rye@v2 + uses: eifinger/setup-rye@787604a465b1696ad17eedf2f8101df9fc555c94 # v2 with: version: ${{ inputs.rye-version }} enable-cache: true diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml deleted file mode 100644 index 9b2b3e973..000000000 --- a/.github/workflows/notebooks.yml +++ /dev/null @@ -1,86 +0,0 @@ -name: Labelbox Example Notebook Workflow - -on: - push: - branches: [develop] - paths: - - examples/** - pull_request: - branches: [develop] - paths: - - examples/** - -permissions: - contents: write - pull-requests: write - -jobs: - # Get installs from rye and run rye run clean to format - format: - if: github.event.pull_request.merged == false - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - with: - ref: ${{ github.head_ref }} - fetch-depth: 0 - - uses: ./.github/actions/python-package-shared-setup - with: - rye-version: ${{ vars.RYE_VERSION }} - python-version: 3.12 - - name: Format - working-directory: examples - run: rye run clean - - name: Commit changes - run: | - git config --local user.email "github-actions[bot]@users.noreply.github.com" - git config --local user.name "github-actions[bot]" - git add examples/. - git commit -m ":art: Cleaned" || exit 0 - - name: Push changes - uses: ad-m/github-push-action@4cc74773234f74829a8c21bc4d69dd4be9cfa599 # master - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - branch: ${{ github.head_ref }} - # See if notebooks were added or deleted (name change counts as both) - changes: - needs: format - if: github.event.pull_request.merged == false - runs-on: ubuntu-latest - outputs: - addedOrModified: ${{ steps.filter.outputs.addedOrModified }} - steps: - - uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3 - id: filter - with: - filters: | - addedOrModified: - - added|deleted: 'examples/**/*.ipynb' - # Create readme if the above job shows true using rye run create-readme - create: - needs: changes - if: ${{ needs.changes.outputs.addedOrModified == 'true' }} && github.event.pull_request.merged == false - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 - with: - ref: ${{ github.head_ref }} - fetch-depth: 0 - - uses: ./.github/actions/python-package-shared-setup - with: - rye-version: ${{ vars.RYE_VERSION }} - python-version: 3.12 - - name: Create readme - working-directory: examples - run: rye run create-readme - - name: Commit changes - run: | - git config --local user.email "github-actions[bot]@users.noreply.github.com" - git config --local user.name "github-actions[bot]" - git add examples/. - git commit -m ":memo: README updated" || exit 0 - - name: Push changes - uses: ad-m/github-push-action@4cc74773234f74829a8c21bc4d69dd4be9cfa599 # master - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - branch: ${{ github.head_ref }} diff --git a/.github/workflows/python-package-develop.yml b/.github/workflows/python-package-develop.yml index d371f693a..9142f1878 100644 --- a/.github/workflows/python-package-develop.yml +++ b/.github/workflows/python-package-develop.yml @@ -34,7 +34,7 @@ jobs: sdk_versions: ${{ steps.get_sdk_versions.outputs.sdk_versions }} steps: - name: Checkout repository - uses: actions/checkout@v2 + uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4 with: ref: ${{ github.event.repository.default_branch }} diff --git a/.github/workflows/python-package-prod.yml b/.github/workflows/python-package-prod.yml index c0e24536f..037c4e200 100644 --- a/.github/workflows/python-package-prod.yml +++ b/.github/workflows/python-package-prod.yml @@ -7,6 +7,9 @@ concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true +permissions: + contents: read + jobs: build: strategy: From 9e3a27467c9de195e9795e797b52517a882aafee Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan <6133708+midhun-pm@users.noreply.github.com> Date: Tue, 28 Apr 2026 19:44:53 +0530 Subject: [PATCH 101/103] Add support to assign multiple datarows to a user (#2054) --- .github/actions/lbox-matrix/index.js | 10 +- .github/workflows/python-package-develop.yml | 10 +- libs/labelbox/src/labelbox/__init__.py | 1 + libs/labelbox/src/labelbox/schema/api_key.py | 50 ++++---- .../schema/internal/data_row_upsert_item.py | 5 +- libs/labelbox/src/labelbox/schema/project.py | 46 +++++++ .../labelbox/schema/task_assignment_status.py | 12 ++ .../tests/integration/test_api_keys.py | 16 ++- .../tests/integration/test_bulk_assign.py | 41 +++++++ .../unit/test_unit_project_bulk_assign.py | 115 ++++++++++++++++++ 10 files changed, 261 insertions(+), 45 deletions(-) create mode 100644 libs/labelbox/src/labelbox/schema/task_assignment_status.py create mode 100644 libs/labelbox/tests/integration/test_bulk_assign.py create mode 100644 libs/labelbox/tests/unit/test_unit_project_bulk_assign.py diff --git a/.github/actions/lbox-matrix/index.js b/.github/actions/lbox-matrix/index.js index 733584289..ec0c974d4 100644 --- a/.github/actions/lbox-matrix/index.js +++ b/.github/actions/lbox-matrix/index.js @@ -26814,27 +26814,27 @@ try { // To be updated with the new API keys { "python-version": "3.9", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2FKAOZ032H0735C32V1U63", + "api-key": "STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.10", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2G1557037K0726H50N3JQK", + "api-key": "STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.11", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2G7STM04WC071F73LG8RSD", + "api-key": "STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.12", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2GEJW9033B07299RKLAOFM", + "api-key": "STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, { "python-version": "3.13", - "api-key": "STAGING_LABELBOX_API_KEY_ORG_CMH2GGV3X04QK071X1EJCH8W0", + "api-key": "STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH", "da-test-key": "DA_GCP_LABELBOX_API_KEY" }, ]; diff --git a/.github/workflows/python-package-develop.yml b/.github/workflows/python-package-develop.yml index 9142f1878..d5d828186 100644 --- a/.github/workflows/python-package-develop.yml +++ b/.github/workflows/python-package-develop.yml @@ -59,19 +59,19 @@ jobs: matrix: include: - python-version: "3.9" - api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2FKAOZ032H0735C32V1U63 + api-key: STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.10" - api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2G1557037K0726H50N3JQK + api-key: STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.11" - api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2G7STM04WC071F73LG8RSD + api-key: STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.12" - api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2GEJW9033B07299RKLAOFM + api-key: STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH da-test-key: DA_GCP_LABELBOX_API_KEY - python-version: "3.13" - api-key: STAGING_LABELBOX_API_KEY_ORG_CMH2GGV3X04QK071X1EJCH8W0 + api-key: STAGING_API_KEY_ORG_CMOI3PQ7801GM070D4TPG7FMH da-test-key: DA_GCP_LABELBOX_API_KEY uses: ./.github/workflows/python-package-shared.yml with: diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index 5f48796c6..7da5a9dcd 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -93,6 +93,7 @@ from labelbox.schema.project_resource_tag import ProjectResourceTag from labelbox.schema.media_type import MediaType from labelbox.schema.slice import Slice, CatalogSlice, ModelSlice +from labelbox.schema.task_assignment_status import TaskAssignmentStatus from labelbox.schema.task_queue import TaskQueue from labelbox.schema.label_score import LabelScore from labelbox.schema.identifiables import UniqueIds, GlobalKeys, DataRowIds diff --git a/libs/labelbox/src/labelbox/schema/api_key.py b/libs/labelbox/src/labelbox/schema/api_key.py index d297e1451..f15964871 100644 --- a/libs/labelbox/src/labelbox/schema/api_key.py +++ b/libs/labelbox/src/labelbox/schema/api_key.py @@ -322,6 +322,27 @@ def create_api_key( if not user_email or not isinstance(user_email, str): raise ValueError("user must be a User object or a valid email") + role_name = role.name if hasattr(role, "name") else role + if not role_name or not isinstance(role_name, str): + raise ValueError("role must be a Role object or a valid role name") + + if not isinstance(time_unit, TimeUnit): + raise ValueError("time_unit must be a valid TimeUnit enum value") + + if validity < 0: + raise ValueError("validity must be a positive integer") + + validity_seconds = validity * time_unit.value + + if validity_seconds < TimeUnit.MINUTE.value: + raise ValueError("Minimum validity period is 1 minute") + + max_seconds = 25 * TimeUnit.WEEK.value + if validity_seconds > max_seconds: + raise ValueError( + "Maximum validity period is 6 months (or 25 weeks)" + ) + # Check if the user exists in the organization user_id = ApiKey._get_user(client, user_email) if not user_id: @@ -329,20 +350,9 @@ def create_api_key( f"User with email '{user_email}' does not exist in the organization" ) - role_name = role.name if hasattr(role, "name") else role - if not role_name or not isinstance(role_name, str): - raise ValueError("role must be a Role object or a valid role name") - allowed_roles = ApiKey._get_available_api_key_roles(client) - # Determine the exact server role name to pass through. - # - # - If caller provides a string, require exact match (case-sensitive). - # - If caller provides a Role object (which may be normalized by the SDK), - # map it back to the server role name. server_role_name: Optional[str] = None if hasattr(role, "name"): - # Role objects in the SDK are often normalized (e.g. "TENANT_ADMIN"). - # Map normalized name back to the server-provided role display name. normalized_to_server = {format_role(r): r for r in allowed_roles} server_role_name = ( role_name @@ -357,24 +367,6 @@ def create_api_key( f"Invalid role specified. Allowed roles are: {allowed_roles}" ) - validity_seconds = 0 - if validity < 0: - raise ValueError("validity must be a positive integer") - - if not isinstance(time_unit, TimeUnit): - raise ValueError("time_unit must be a valid TimeUnit enum value") - - validity_seconds = validity * time_unit.value - - if validity_seconds < TimeUnit.MINUTE.value: - raise ValueError("Minimum validity period is 1 minute") - - max_seconds = 25 * TimeUnit.WEEK.value - if validity_seconds > max_seconds: - raise ValueError( - "Maximum validity period is 6 months (or 25 weeks)" - ) - query_str = """ mutation CreateUserApiKeyPyApi($name: String!, $userEmail: String!, $role: String, $validitySeconds: Int) { createApiKey( diff --git a/libs/labelbox/src/labelbox/schema/internal/data_row_upsert_item.py b/libs/labelbox/src/labelbox/schema/internal/data_row_upsert_item.py index cc9bbb2c3..2db844459 100644 --- a/libs/labelbox/src/labelbox/schema/internal/data_row_upsert_item.py +++ b/libs/labelbox/src/labelbox/schema/internal/data_row_upsert_item.py @@ -1,6 +1,6 @@ from abc import ABC, abstractmethod -from typing import List, Tuple, Optional +from typing import List, Tuple, Optional, Union from labelbox.schema.identifiable import UniqueId, GlobalKey from labelbox.schema.data_row import DataRow @@ -34,7 +34,8 @@ def build( if not key: key = {"type": "AUTO", "value": ""} elif isinstance(key, key_types): # type: ignore - key = {"type": key.id_type.value, "value": key.key} + typed_key: Union[UniqueId, GlobalKey] = key # type: ignore[assignment] + key = {"type": typed_key.id_type.value, "value": typed_key.key} else: if not key_types: raise ValueError( diff --git a/libs/labelbox/src/labelbox/schema/project.py b/libs/labelbox/src/labelbox/schema/project.py index 251001828..f39677a95 100644 --- a/libs/labelbox/src/labelbox/schema/project.py +++ b/libs/labelbox/src/labelbox/schema/project.py @@ -48,6 +48,7 @@ from labelbox.schema.identifiables import ( DataRowIdentifiers, ) +from labelbox.schema.task_assignment_status import TaskAssignmentStatus from labelbox.schema.labeling_service import ( LabelingService, LabelingServiceStatus, @@ -1466,6 +1467,51 @@ def extend_reservations(self, queue_type) -> int: res = self.client.execute(query_str, {id_param: self.uid}) return res["extendReservations"] + def bulk_assign_data_rows( + self, + user_id: str, + data_row_ids: List[str], + allowed_statuses: Optional[List[TaskAssignmentStatus]] = None, + ) -> bool: + """Assigns multiple data rows to a user in bulk. + + Reserves the specified data rows in the project's initial labeling + queue for the given user. Only data rows whose current assignment + status matches ``allowed_statuses`` will be assigned. + + Args: + user_id: The ID of the user to assign the data rows to. + data_row_ids: List of data row IDs to assign. + allowed_statuses: Optional list of statuses that a data row must + currently have in order to be assigned. Defaults to ``[FREE]`` + on the server (i.e. only unassigned rows). Pass + ``[TaskAssignmentStatus.FREE, TaskAssignmentStatus.RESERVED]`` + to allow reassignment of already-reserved rows. + Returns: + True if the bulk assignment succeeded. + Raises: + LabelboxError: If the GraphQL mutation fails. + """ + if not data_row_ids: + return True + + query_str = """mutation BulkAssignDataRowsPyApi($input: BulkAssignDataRowsInput!) { + bulkAssignDataRows(input: $input) { + success + } + }""" + + input_dict: Dict[str, Any] = { + "projectId": self.uid, + "userId": user_id, + "dataRowIds": data_row_ids, + } + if allowed_statuses is not None: + input_dict["allowedStatuses"] = [s.value for s in allowed_statuses] + + result = self.client.execute(query_str, {"input": input_dict}) + return result["bulkAssignDataRows"]["success"] + def enable_model_assisted_labeling(self, toggle: bool = True) -> bool: """Turns model assisted labeling either on or off based on input diff --git a/libs/labelbox/src/labelbox/schema/task_assignment_status.py b/libs/labelbox/src/labelbox/schema/task_assignment_status.py new file mode 100644 index 000000000..ffb5f8da7 --- /dev/null +++ b/libs/labelbox/src/labelbox/schema/task_assignment_status.py @@ -0,0 +1,12 @@ +from enum import Enum + + +class TaskAssignmentStatus(str, Enum): + """Status filter for bulk data row assignment. + + FREE - only assign data rows that are currently unassigned. + RESERVED - only assign data rows that are currently reserved by another user. + """ + + FREE = "FREE" + RESERVED = "RESERVED" diff --git a/libs/labelbox/tests/integration/test_api_keys.py b/libs/labelbox/tests/integration/test_api_keys.py index 77be1881c..1f48eaf59 100644 --- a/libs/labelbox/tests/integration/test_api_keys.py +++ b/libs/labelbox/tests/integration/test_api_keys.py @@ -151,8 +151,12 @@ def test_create_api_key_invalid_email_formats(client): def test_create_api_key_invalid_validity_values(client): - """Test that providing invalid validity values causes failure.""" - user_email = client.get_user().email + """Test that providing invalid validity values causes failure. + + Validity checks are pure input validation and run before any API calls, + so a dummy email is sufficient here. + """ + user_email = "placeholder@labelbox.com" # Test with negative validity with pytest.raises(ValueError) as excinfo: @@ -200,8 +204,12 @@ def test_create_api_key_invalid_validity_values(client): def test_create_api_key_invalid_time_unit(client): - """Test that providing invalid time unit causes failure.""" - user_email = client.get_user().email + """Test that providing invalid time unit causes failure. + + time_unit checks are pure input validation and run before any API calls, + so a dummy email is sufficient here. + """ + user_email = "placeholder@labelbox.com" # Test with None time unit with pytest.raises(ValueError) as excinfo: diff --git a/libs/labelbox/tests/integration/test_bulk_assign.py b/libs/labelbox/tests/integration/test_bulk_assign.py new file mode 100644 index 000000000..c65cb7221 --- /dev/null +++ b/libs/labelbox/tests/integration/test_bulk_assign.py @@ -0,0 +1,41 @@ +from labelbox.schema.task_assignment_status import TaskAssignmentStatus + + +def test_bulk_assign_data_rows( + configured_batch_project_with_label, project_based_user +): + project, _, data_row, _ = configured_batch_project_with_label + user = project_based_user + + result = project.bulk_assign_data_rows( + user_id=user.uid, + data_row_ids=[data_row.uid], + ) + assert result is True + + +def test_bulk_assign_data_rows_with_allowed_statuses( + configured_batch_project_with_label, project_based_user +): + project, _, data_row, _ = configured_batch_project_with_label + user = project_based_user + + result = project.bulk_assign_data_rows( + user_id=user.uid, + data_row_ids=[data_row.uid], + allowed_statuses=[ + TaskAssignmentStatus.FREE, + TaskAssignmentStatus.RESERVED, + ], + ) + assert result is True + + +def test_bulk_assign_empty_list(configured_batch_project_with_label): + project, _, _, _ = configured_batch_project_with_label + + result = project.bulk_assign_data_rows( + user_id="any_user_id", + data_row_ids=[], + ) + assert result is True diff --git a/libs/labelbox/tests/unit/test_unit_project_bulk_assign.py b/libs/labelbox/tests/unit/test_unit_project_bulk_assign.py new file mode 100644 index 000000000..af7802703 --- /dev/null +++ b/libs/labelbox/tests/unit/test_unit_project_bulk_assign.py @@ -0,0 +1,115 @@ +from unittest.mock import MagicMock + +import pytest + +from labelbox.schema.project import Project +from labelbox.schema.task_assignment_status import TaskAssignmentStatus + + +@pytest.fixture +def mock_client(): + return MagicMock() + + +@pytest.fixture +def project(mock_client): + return Project( + mock_client, + { + "id": "test_project_id", + "name": "test", + "createdAt": "2021-06-01T00:00:00.000Z", + "updatedAt": "2021-06-01T00:00:00.000Z", + "autoAuditNumberOfLabels": 1, + "autoAuditPercentage": 100, + "dataRowCount": 1, + "description": "test", + "editorTaskType": "MODEL_CHAT_EVALUATION", + "lastActivityTime": "2021-06-01T00:00:00.000Z", + "allowedMediaType": "IMAGE", + "setupComplete": "2021-06-01T00:00:00.000Z", + "modelSetupComplete": None, + "uploadType": "Auto", + "isBenchmarkEnabled": False, + "isConsensusEnabled": False, + }, + ) + + +def test_bulk_assign_sends_correct_mutation_and_variables(project, mock_client): + mock_client.execute.return_value = {"bulkAssignDataRows": {"success": True}} + data_row_ids = ["dr_1", "dr_2", "dr_3"] + + result = project.bulk_assign_data_rows("user_123", data_row_ids) + + assert result is True + mock_client.execute.assert_called_once() + args, _ = mock_client.execute.call_args + query_str, params = args + + assert "BulkAssignDataRowsPyApi" in query_str + assert "bulkAssignDataRows" in query_str + assert params == { + "input": { + "projectId": "test_project_id", + "userId": "user_123", + "dataRowIds": ["dr_1", "dr_2", "dr_3"], + } + } + + +def test_bulk_assign_omits_allowed_statuses_when_none(project, mock_client): + mock_client.execute.return_value = {"bulkAssignDataRows": {"success": True}} + + project.bulk_assign_data_rows("user_123", ["dr_1"]) + + _, params = mock_client.execute.call_args[0] + assert "allowedStatuses" not in params["input"] + + +def test_bulk_assign_serializes_allowed_statuses(project, mock_client): + mock_client.execute.return_value = {"bulkAssignDataRows": {"success": True}} + + project.bulk_assign_data_rows( + "user_123", + ["dr_1"], + allowed_statuses=[ + TaskAssignmentStatus.FREE, + TaskAssignmentStatus.RESERVED, + ], + ) + + _, params = mock_client.execute.call_args[0] + assert params["input"]["allowedStatuses"] == ["FREE", "RESERVED"] + + +def test_bulk_assign_single_status(project, mock_client): + mock_client.execute.return_value = {"bulkAssignDataRows": {"success": True}} + + project.bulk_assign_data_rows( + "user_123", + ["dr_1"], + allowed_statuses=[TaskAssignmentStatus.RESERVED], + ) + + _, params = mock_client.execute.call_args[0] + assert params["input"]["allowedStatuses"] == ["RESERVED"] + + +def test_bulk_assign_empty_data_rows_returns_true_without_execute( + project, mock_client +): + result = project.bulk_assign_data_rows("user_123", []) + + assert result is True + mock_client.execute.assert_not_called() + + +def test_bulk_assign_returns_false_on_server_failure(project, mock_client): + mock_client.execute.return_value = { + "bulkAssignDataRows": {"success": False} + } + + result = project.bulk_assign_data_rows("user_123", ["dr_1"]) + + assert result is False From b8bf13499823aa03385b1f918735894d7edf7a84 Mon Sep 17 00:00:00 2001 From: Midhun Pookkottil Madhusoodanan <6133708+midhun-pm@users.noreply.github.com> Date: Wed, 29 Apr 2026 19:24:10 +0530 Subject: [PATCH 102/103] Prep 7.7.0 (#2056) --- docs/conf.py | 2 +- docs/labelbox/index.rst | 1 + docs/labelbox/task-assignment-status.rst | 7 +++++++ libs/labelbox/CHANGELOG.md | 8 ++++++++ libs/labelbox/pyproject.toml | 2 +- libs/labelbox/src/labelbox/__init__.py | 2 +- 6 files changed, 19 insertions(+), 3 deletions(-) create mode 100644 docs/labelbox/task-assignment-status.rst diff --git a/docs/conf.py b/docs/conf.py index da7f64238..c5542aa52 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ project = 'Python SDK reference' copyright = '2025, Labelbox' author = 'Labelbox' -release = '7.6.0' +release = '7.7.0' # -- General configuration --------------------------------------------------- diff --git a/docs/labelbox/index.rst b/docs/labelbox/index.rst index 1888cb233..39e879191 100644 --- a/docs/labelbox/index.rst +++ b/docs/labelbox/index.rst @@ -54,6 +54,7 @@ Labelbox Python SDK Documentation slice step-reasoning-tool task + task-assignment-status task-queue user user-group-v2 diff --git a/docs/labelbox/task-assignment-status.rst b/docs/labelbox/task-assignment-status.rst new file mode 100644 index 000000000..9c196798d --- /dev/null +++ b/docs/labelbox/task-assignment-status.rst @@ -0,0 +1,7 @@ +TaskAssignmentStatus +==================== + +.. autoclass:: labelbox.schema.task_assignment_status.TaskAssignmentStatus + :members: + :undoc-members: + :show-inheritance: diff --git a/libs/labelbox/CHANGELOG.md b/libs/labelbox/CHANGELOG.md index 532427892..a499d12a8 100644 --- a/libs/labelbox/CHANGELOG.md +++ b/libs/labelbox/CHANGELOG.md @@ -1,4 +1,12 @@ # Changelog +# Version 7.7.0 (2026-04-29) +## Added +* Add `Project.bulk_assign_data_rows()` method for bulk assigning data rows to a user ([#2054](https://github.com/Labelbox/labelbox-python/pull/2054)) +* Add `TaskAssignmentStatus` enum for filtering assignable data row statuses ([#2054](https://github.com/Labelbox/labelbox-python/pull/2054)) +## Fixed +* Reorder `create_api_key` validation to check input params before making API calls ([#2054](https://github.com/Labelbox/labelbox-python/pull/2054)) +* Fix mypy type error in `DataRowUpsertItem.build` ([#2054](https://github.com/Labelbox/labelbox-python/pull/2054)) + # Version 7.6.0 (2026-03-18) ## Added * Add `Project.sync_external_project()` method for syncing external labels, metrics, and workflow state ([#2042](https://github.com/Labelbox/labelbox-python/pull/2042)) diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml index d15b2eb3a..1f06f2628 100644 --- a/libs/labelbox/pyproject.toml +++ b/libs/labelbox/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "labelbox" -version = "7.6.0" +version = "7.7.0" description = "Labelbox Python API" authors = [{ name = "Labelbox", email = "engineering@labelbox.com" }] dependencies = [ diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py index 7da5a9dcd..c86f0b95c 100644 --- a/libs/labelbox/src/labelbox/__init__.py +++ b/libs/labelbox/src/labelbox/__init__.py @@ -1,6 +1,6 @@ name = "labelbox" -__version__ = "7.6.0" +__version__ = "7.7.0" from labelbox.client import Client from labelbox.schema.annotation_import import ( From 68a77fedd84af99f2fe4d952208aac13e8316a17 Mon Sep 17 00:00:00 2001 From: Dmitriy Date: Wed, 10 Jun 2026 10:12:02 -0600 Subject: [PATCH 103/103] Add ModelRun.total_cost and total_data_rows (#2057) --- .../labelbox/src/labelbox/schema/model_run.py | 49 ++++++++ .../tests/unit/test_unit_model_run.py | 116 ++++++++++++++++++ 2 files changed, 165 insertions(+) create mode 100644 libs/labelbox/tests/unit/test_unit_model_run.py diff --git a/libs/labelbox/src/labelbox/schema/model_run.py b/libs/labelbox/src/labelbox/schema/model_run.py index a7712858e..0791e15e5 100644 --- a/libs/labelbox/src/labelbox/schema/model_run.py +++ b/libs/labelbox/src/labelbox/schema/model_run.py @@ -16,6 +16,8 @@ Union, ) +from lbox.exceptions import InternalServerError, ResourceNotFoundError + from labelbox.orm.db_object import DbObject, experimental from labelbox.orm.model import Entity, Field, Relationship from labelbox.orm.query import results_query_part @@ -65,6 +67,53 @@ class Status(Enum): COMPLETE = "COMPLETE" FAILED = "FAILED" + def _get_cost_and_usage(self) -> Dict[str, Any]: + """Lazily fetches and caches cost and data row count for this Model Run. + + Returns an empty dict when no cost/usage information is available. + """ + if getattr(self, "_cost_and_usage", None) is None: + query_str = """ + query GetModelRunCostInfoPyApi($modelRunId: ID!) { + modelFoundryModelRunInfo(where: {modelRunId: $modelRunId}) { + cost + status + totalDataRows + } + } + """ + try: + res = self.client.execute(query_str, {"modelRunId": self.uid}) + except (ResourceNotFoundError, InternalServerError): + # No cost/usage info available; cache the empty result. + # Transient errors (network, timeout, rate limit) are not + # caught so they propagate and the next access can retry. + res = None + self._cost_and_usage = (res or {}).get( + "modelFoundryModelRunInfo" + ) or {} + return self._cost_and_usage + + @property + def total_cost(self) -> Optional[float]: + """Total cost (USD) of this Model Run. + + ``None`` if cost is not available for this run. + """ + return self._get_cost_and_usage().get("cost") + + @property + def total_data_rows(self) -> Optional[int]: + """Number of data rows processed by this Model Run. + + ``None`` if not available for this run. + """ + return self._get_cost_and_usage().get("totalDataRows") + + def refresh_cost_and_usage(self) -> None: + """Clears the cached cost/usage so the next access re-fetches live data.""" + self._cost_and_usage = None + def upsert_labels( self, label_ids: Optional[List[str]] = None, diff --git a/libs/labelbox/tests/unit/test_unit_model_run.py b/libs/labelbox/tests/unit/test_unit_model_run.py new file mode 100644 index 000000000..747f1aa00 --- /dev/null +++ b/libs/labelbox/tests/unit/test_unit_model_run.py @@ -0,0 +1,116 @@ +from unittest.mock import MagicMock + +import pytest +from lbox.exceptions import ( + InternalServerError, + NetworkError, + ResourceNotFoundError, +) + +from labelbox.schema.model_run import ModelRun + + +def _make_model_run(client): + return ModelRun( + client, + { + "id": "model-run-1", + "name": "test run", + "createdAt": "2021-06-01T00:00:00.000Z", + "updatedAt": "2021-06-01T00:00:00.000Z", + "createdBy": "user-1", + "modelId": "model-1", + "trainingMetadata": {}, + "modelAppId": "app-1", + }, + ) + + +def test_total_cost_and_data_rows_are_fetched_and_cached(): + client = MagicMock() + client.execute.return_value = { + "modelFoundryModelRunInfo": { + "cost": 3.5, + "status": "finished", + "totalDataRows": 12, + } + } + model_run = _make_model_run(client) + + assert model_run.total_cost == 3.5 + assert model_run.total_data_rows == 12 + + # Cost/usage is rehydrated once and cached across property reads. + assert client.execute.call_count == 1 + # The model run id is passed to the query. + assert client.execute.call_args[0][1] == {"modelRunId": "model-run-1"} + + +def test_refresh_cost_and_usage_refetches(): + client = MagicMock() + client.execute.return_value = { + "modelFoundryModelRunInfo": { + "cost": 1.0, + "status": "finished", + "totalDataRows": 1, + } + } + model_run = _make_model_run(client) + + assert model_run.total_cost == 1.0 + model_run.refresh_cost_and_usage() + assert model_run.total_cost == 1.0 + assert client.execute.call_count == 2 + + +@pytest.mark.parametrize( + "error", + [ + ResourceNotFoundError(message="model run not found"), + InternalServerError("no model job for run"), + ], +) +def test_cost_and_usage_none_for_non_foundry_run(error): + client = MagicMock() + client.execute.side_effect = error + model_run = _make_model_run(client) + + assert model_run.total_cost is None + assert model_run.total_data_rows is None + + +@pytest.mark.parametrize( + "execute_result", + [ + None, # execute() can return None instead of a payload + {"modelFoundryModelRunInfo": None}, + ], +) +def test_cost_and_usage_none_when_payload_missing(execute_result): + client = MagicMock() + client.execute.return_value = execute_result + model_run = _make_model_run(client) + + assert model_run.total_cost is None + assert model_run.total_data_rows is None + + +def test_transient_errors_propagate_and_are_not_cached(): + client = MagicMock() + client.execute.side_effect = NetworkError(Exception("boom")) + model_run = _make_model_run(client) + + with pytest.raises(NetworkError): + _ = model_run.total_cost + + # The failure is not cached, so a later successful access recovers. + client.execute.side_effect = None + client.execute.return_value = { + "modelFoundryModelRunInfo": { + "cost": 2.0, + "status": "finished", + "totalDataRows": 5, + } + } + assert model_run.total_cost == 2.0 + assert model_run.total_data_rows == 5