129 lines
4.4 KiB
Python
129 lines
4.4 KiB
Python
from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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from PIL import Image
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def coco_to_label_studio(
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image_paths: list[Path],
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coco_predictions_path: Path,
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category_name: str = "knot",
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) -> list[dict]:
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"""Convert COCO predictions to Label Studio import format with pre-annotations."""
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# Load COCO predictions
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with coco_predictions_path.open("r", encoding="utf-8") as f:
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coco_data = json.load(f)
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# Build image_id -> annotations mapping
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annotations_by_image = {}
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for ann in coco_data.get("annotations", []):
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img_id = ann["image_id"]
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if img_id not in annotations_by_image:
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annotations_by_image[img_id] = []
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annotations_by_image[img_id].append(ann)
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# Build image_id -> image info mapping
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image_info = {img["id"]: img for img in coco_data.get("images", [])}
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# Convert to Label Studio format
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ls_tasks = []
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for img_id, img_data in image_info.items():
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file_name = img_data["file_name"]
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width = img_data["width"]
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height = img_data["height"]
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# Find the actual file path
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matching_paths = [p for p in image_paths if p.name == file_name]
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if not matching_paths:
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continue
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img_path = matching_paths[0]
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# Build predictions (pre-annotations)
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predictions = []
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for ann in annotations_by_image.get(img_id, []):
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# COCO bbox: [x_min, y_min, width, height]
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x, y, w, h = ann["bbox"]
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# Label Studio uses percentages
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x_percent = (x / width) * 100
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y_percent = (y / height) * 100
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w_percent = (w / width) * 100
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h_percent = (h / height) * 100
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predictions.append({
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"value": {
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"x": x_percent,
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"y": y_percent,
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"width": w_percent,
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"height": h_percent,
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"rectanglelabels": [category_name]
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},
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"from_name": "label",
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"to_name": "image",
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"type": "rectanglelabels",
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"score": ann.get("score", 1.0)
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})
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# Create Label Studio task with predictions
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task = {
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"data": {
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"image": f"/data/local-files/?d={img_path.absolute()}"
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},
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"predictions": [{
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"result": predictions,
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"model_version": "rfdetr-auto-label"
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}]
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}
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ls_tasks.append(task)
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return ls_tasks
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def main() -> int:
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parser = argparse.ArgumentParser(
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description="Convert COCO predictions to Label Studio format for review."
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)
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parser.add_argument("--coco-json", type=Path, required=True, help="COCO predictions JSON from auto_label_images.py")
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parser.add_argument("--images-dir", type=Path, required=True, help="Directory with the images")
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parser.add_argument("--output-json", type=Path, required=True, help="Output Label Studio tasks JSON")
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parser.add_argument("--category-name", default="knot", help="Label name in Label Studio")
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args = parser.parse_args()
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if not args.coco_json.exists():
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raise SystemExit(f"COCO JSON not found: {args.coco_json}")
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if not args.images_dir.exists():
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raise SystemExit(f"Images dir not found: {args.images_dir}")
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# Collect all images
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image_paths = list(args.images_dir.glob("*.jpg")) + list(args.images_dir.glob("*.png"))
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if not image_paths:
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raise SystemExit(f"No images found in {args.images_dir}")
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# Convert
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ls_tasks = coco_to_label_studio(image_paths, args.coco_json, args.category_name)
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# Write Label Studio import format
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with args.output_json.open("w", encoding="utf-8") as f:
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json.dump(ls_tasks, f, indent=2)
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print(f"✓ Converted {len(ls_tasks)} tasks with predictions")
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print(f"✓ Output: {args.output_json}")
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print(f"\nImport into Label Studio:")
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print(f" 1. Open your project")
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print(f" 2. Settings → Storage → Add Local Storage")
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print(f" Absolute path: {args.images_dir.absolute()}")
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print(f" 3. Import → Upload Files → select {args.output_json.name}")
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print(f" 4. Start reviewing predictions!")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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