work on well of souls and yolo detection

tools/python-detect/prelabel.py

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+"""
+Pre-label images using an existing YOLO model.
+Generates .txt labels for unlabeled images so the annotator can load them for review.
+
+Usage:
+    python prelabel.py [image_dir] [--model boss-v1] [--conf 0.20]
+"""
+import argparse
+import glob
+import os
+
+def run_prelabel(args):
+    """Run pre-labeling. Called from main() or manage.py."""
+    img_dir = os.path.abspath(args.img_dir)
+    model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models", f"{args.model}.pt")
+
+    if not os.path.exists(model_path):
+        print(f"Model not found: {model_path}")
+        return
+
+    from ultralytics import YOLO
+    model = YOLO(model_path)
+
+    extensions = ("*.jpg", "*.jpeg", "*.png")
+    files = []
+    for ext in extensions:
+        files.extend(glob.glob(os.path.join(img_dir, ext)))
+    files.sort()
+
+    # Only process unlabeled images
+    unlabeled = []
+    for f in files:
+        label_path = os.path.splitext(f)[0] + ".txt"
+        if not os.path.exists(label_path):
+            unlabeled.append(f)
+
+    print(f"Found {len(files)} images, {len(files) - len(unlabeled)} already labeled, {len(unlabeled)} to pre-label")
+
+    if not unlabeled:
+        print("All images already have labels!")
+        return
+
+    labeled = 0
+    skipped = 0
+    for filepath in unlabeled:
+        results = model(filepath, conf=args.conf, verbose=False)
+        boxes = results[0].boxes
+
+        if len(boxes) == 0:
+            skipped += 1
+            continue
+
+        label_path = os.path.splitext(filepath)[0] + ".txt"
+        with open(label_path, "w") as f:
+            for box in boxes:
+                cls = int(box.cls[0])
+                xywhn = box.xywhn[0]  # normalized center x, y, w, h
+                cx, cy, w, h = xywhn.tolist()
+                f.write(f"{cls} {cx:.6f} {cy:.6f} {w:.6f} {h:.6f}\n")
+
+        labeled += 1
+        fname = os.path.basename(filepath)
+        conf = boxes.conf[0].item()
+        print(f"  {fname}: {len(boxes)} box(es), best conf={conf:.2f}")
+
+    print(f"\nPre-labeled {labeled} images, skipped {skipped} (no detections)")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Pre-label images with YOLO model")
+    parser.add_argument("img_dir", nargs="?", default="../../training-data/kulemak/raw")
+    parser.add_argument("--model", default="boss-kulemak", help="Model name in models/")
+    parser.add_argument("--conf", type=float, default=0.20, help="Confidence threshold")
+    args = parser.parse_args()
+    run_prelabel(args)
+
+if __name__ == "__main__":
+    main()