Initial commit: Wood knot detection model and GUI

convert_to_label_studio.py

@@ -0,0 +1,128 @@
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from PIL import Image
+
+
+def coco_to_label_studio(
+    image_paths: list[Path],
+    coco_predictions_path: Path,
+    category_name: str = "knot",
+) -> list[dict]:
+    """Convert COCO predictions to Label Studio import format with pre-annotations."""
+    
+    # Load COCO predictions
+    with coco_predictions_path.open("r", encoding="utf-8") as f:
+        coco_data = json.load(f)
+    
+    # Build image_id -> annotations mapping
+    annotations_by_image = {}
+    for ann in coco_data.get("annotations", []):
+        img_id = ann["image_id"]
+        if img_id not in annotations_by_image:
+            annotations_by_image[img_id] = []
+        annotations_by_image[img_id].append(ann)
+    
+    # Build image_id -> image info mapping
+    image_info = {img["id"]: img for img in coco_data.get("images", [])}
+    
+    # Convert to Label Studio format
+    ls_tasks = []
+    for img_id, img_data in image_info.items():
+        file_name = img_data["file_name"]
+        width = img_data["width"]
+        height = img_data["height"]
+        
+        # Find the actual file path
+        matching_paths = [p for p in image_paths if p.name == file_name]
+        if not matching_paths:
+            continue
+        
+        img_path = matching_paths[0]
+        
+        # Build predictions (pre-annotations)
+        predictions = []
+        for ann in annotations_by_image.get(img_id, []):
+            # COCO bbox: [x_min, y_min, width, height]
+            x, y, w, h = ann["bbox"]
+            
+            # Label Studio uses percentages
+            x_percent = (x / width) * 100
+            y_percent = (y / height) * 100
+            w_percent = (w / width) * 100
+            h_percent = (h / height) * 100
+            
+            predictions.append({
+                "value": {
+                    "x": x_percent,
+                    "y": y_percent,
+                    "width": w_percent,
+                    "height": h_percent,
+                    "rectanglelabels": [category_name]
+                },
+                "from_name": "label",
+                "to_name": "image",
+                "type": "rectanglelabels",
+                "score": ann.get("score", 1.0)
+            })
+        
+        # Create Label Studio task with predictions
+        task = {
+            "data": {
+                "image": f"/data/local-files/?d={img_path.absolute()}"
+            },
+            "predictions": [{
+                "result": predictions,
+                "model_version": "rfdetr-auto-label"
+            }]
+        }
+        
+        ls_tasks.append(task)
+    
+    return ls_tasks
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Convert COCO predictions to Label Studio format for review."
+    )
+    parser.add_argument("--coco-json", type=Path, required=True, help="COCO predictions JSON from auto_label_images.py")
+    parser.add_argument("--images-dir", type=Path, required=True, help="Directory with the images")
+    parser.add_argument("--output-json", type=Path, required=True, help="Output Label Studio tasks JSON")
+    parser.add_argument("--category-name", default="knot", help="Label name in Label Studio")
+    args = parser.parse_args()
+    
+    if not args.coco_json.exists():
+        raise SystemExit(f"COCO JSON not found: {args.coco_json}")
+    if not args.images_dir.exists():
+        raise SystemExit(f"Images dir not found: {args.images_dir}")
+    
+    # Collect all images
+    image_paths = list(args.images_dir.glob("*.jpg")) + list(args.images_dir.glob("*.png"))
+    if not image_paths:
+        raise SystemExit(f"No images found in {args.images_dir}")
+    
+    # Convert
+    ls_tasks = coco_to_label_studio(image_paths, args.coco_json, args.category_name)
+    
+    # Write Label Studio import format
+    with args.output_json.open("w", encoding="utf-8") as f:
+        json.dump(ls_tasks, f, indent=2)
+    
+    print(f"✓ Converted {len(ls_tasks)} tasks with predictions")
+    print(f"✓ Output: {args.output_json}")
+    print(f"\nImport into Label Studio:")
+    print(f"  1. Open your project")
+    print(f"  2. Settings → Storage → Add Local Storage")
+    print(f"     Absolute path: {args.images_dir.absolute()}")
+    print(f"  3. Import → Upload Files → select {args.output_json.name}")
+    print(f"  4. Start reviewing predictions!")
+    
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())