Skill: kaiyuelv/data-labeling-studio (version 1.0.0)

# Data Labeling Studio ## Metadata - **Name**: data-labeling-studio - **Display Name**: Data Labeling Studio | 数据标注工作室 - **Description**: - EN: Intelligent data labeling and annotation toolkit supporting image, text, audio, and video with active learning and quality control. - ZH: 智能数据标注和注释工具包，支持图像、文本、音频和视频，包含主动学习和质量控制。 - **Version**: 1.0.0 - **Author**: Kimi Claw - **Tags**: data-labeling, annotation, image-annotation, text-annotation, active-learning, quality-control, dataset, ml-training - **Category**: Data Processing - **Icon**: 🏷️ ## Capabilities ### Actions #### image_annotate Perform image annotation - **image_dir**: Image directory path (string, required) - **annotation_type**: Type of annotation (string, required) - bounding_box, polygon, keypoint, segmentation - **labels**: Label categories (array, required) - **output_format**: Output format (string) - coco, pascal_voc, yolo - **active_learning**: Enable active learning suggestions (boolean, default: true) #### text_annotate Perform text annotation - **text_data**: Text data source (string/object, required) - **annotation_task**: Task type (string, required) - classification, ner, sentiment, summarization - **labels**: Label categories (array, required) - **output_format**: Output format (string) - json, csv, spacy #### audio_annotate Perform audio annotation - **audio_dir**: Audio directory path (string, required) - **annotation_type**: Type (string, required) - transcription, speaker_id, emotion, event - **segment_duration**: Segment duration in seconds (float, default: 5.0) #### video_annotate Perform video annotation - **video_path**: Video file path (string, required) - **annotation_type**: Type (string, required) - object_tracking, action_recognition, scene_detection - **frame_sample_rate**: Frame sampling rate (int, default: 1) #### quality_check Check annotation quality and consistency - **annotations**: Annotation file path (string, required) - **ground_truth**: Ground truth file path (string, optional) - **metrics**: Quality metrics (array) - iou, accuracy, consistency, coverage #### dataset_export Export labeled dataset to ML format - **annotations**: Annotation source (string, required) - **format**: Target format (string, required) - coco, yolo, tfrecord, huggingface - **output_dir**: Output directory (string, required) - **split_ratios**: Train/val/test split (object) - {train: 0.8, val: 0.1, test: 0.1} ## Requirements - Python 3.8+ - Pillow >= 10.0.0 (for image processing) - OpenCV >= 4.8.0 (for image/video annotation) - NumPy >= 1.24.0 - Pandas >= 2.0.0 - LabelImg >= 1.8.0 (optional) - Librosa >= 0.10.0 (for audio processing) - scikit-learn >= 1.3.0 (for active learning) ## Examples ### Image Annotation ```python from labeling_studio import ImageAnnotator # Initialize annotator annotator = ImageAnnotator( annotation_type="bounding_box", labels=["person", "car", "dog", "cat"], output_format="coco" ) # Annotate images with active learning annotator.annotate( image_dir="./images", output_file="./annotations/coco.json", active_learning=True # AI suggests uncertain samples ) # Export to YOLO format annotator.export("./annotations", format="yolo") ``` ### Text Annotation ```python from labeling_studio import TextAnnotator # NER annotation annotator = TextAnnotator( annotation_task="ner", labels=["PERSON", "ORG", "LOC", "DATE"] ) # Annotate from file annotations = annotator.annotate( text_data="./data/corpus.txt", output_file="./annotations/ner.json" ) ``` ### Quality Check ```python from labeling_studio import QualityChecker # Check annotation quality checker = QualityChecker() report = checker.check( annotations="./annotations/coco.json", ground_truth="./annotations/ground_truth.json", metrics=["iou", "consistency", "coverage"] ) print(f"Average IoU: {report['iou']:.2f}") print(f"Consistency Score: {report['consistency']:.2f}") print(f"Coverage: {report['coverage']:.2f}") ``` ## Scripts - `scripts/annotate_images.py`: 图像标注工具 - `scripts/annotate_text.py`: 文本标注工具 - `scripts/annotate_audio.py`: 音频标注工具 - `scripts/annotate_video.py`: 视频标注工具 - `scripts/quality_check.py`: 质量检查工具 - `scripts/export_dataset.py`: 数据集导出工具 ## Installation ```bash pip install -r requirements.txt ``` ## Usage ```bash # Image annotation with active learning python scripts/annotate_images.py --input ./images --type bbox --labels person,car --format coco # Text NER annotation python scripts/annotate_text.py --input ./texts.txt --task ner --labels PERSON,ORG,LOC # Quality check python scripts/quality_check.py --annotations ./coco.json --ground-truth ./gt.json # Export to YOLO python scripts/export_dataset.py --input ./coco.json --format yolo --output ./yolo_dataset ``` ## License MIT License


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