loop detect using my own method

detect_loops.py

@@ -2,7 +2,12 @@ import cv2
 import numpy as np
 from pathlib import Path
 from PIL import Image
-from skimage.metrics import structural_similarity as ssim
+import time
+import json
+
+shots_dir = Path('data/shots')
+files = sorted(shots_dir.glob('sample-*.webp'))
+    
 
 def extract_frames(webp_path):
     frames = []
@@ -29,10 +34,8 @@ def compute_sim(f1, f2):
     return cos_sim
 
 def detect_loops(frames, min_len=6, max_len=40, top_k=3):
-    import time
     n = len(frames)
     candidates = []
-    composite_window = 3
     # Preprocess frames: grayscale float32 and downscale to 32x32 (float32)
     processed_frames = [
         cv2.resize(cv2.cvtColor(f, cv2.COLOR_BGR2GRAY).astype(np.float32), (32, 32), interpolation=cv2.INTER_AREA)
@@ -58,7 +61,7 @@ def detect_loops(frames, min_len=6, max_len=40, top_k=3):
             cos_sim = compute_sim(start_comp, end_comp)
             t1 = time.time()
             score = cos_sim  # Higher cosine similarity is better
-            print(f"Loop ({i},{j}): Cosine similarity={cos_sim:.4f} (t={t1-t0:.3f}s)")
+            # print(f"Loop ({i},{j}): Cosine similarity={cos_sim:.4f} (t={t1-t0:.3f}s)")
             candidates.append((score, i, j, cos_sim))
     # Sort by score descending
     candidates.sort(reverse=True)
@@ -70,8 +73,6 @@ if __name__ == "__main__":
     from glob import glob
 
     # For batch processing, change the pattern below to 'sample-*.webp' or similar
-    shots_dir = Path('data/shots')
-    files = sorted(shots_dir.glob('sample-000-*.webp'))
     if not files:
         print('No files found.')
         sys.exit(1)
@@ -87,24 +88,7 @@ if __name__ == "__main__":
         print(f"Processing {webp_path}")
         frames = extract_frames(webp_path)
         print(f"Extracted {len(frames)} frames from {webp_path}")
-        # # Show composite image for first frame (3-channel: R=prev, G=curr, B=next)
-        # processed_frames = [cv2.resize(cv2.cvtColor(f, cv2.COLOR_BGR2GRAY), (64, 64), interpolation=cv2.INTER_AREA) for f in frames]
-        # n = len(processed_frames)
-        # if n > 0:
-        #     prev_idx = (0 - 1) % n
-        #     next_idx = (0 + 1) % n
-        #     r = processed_frames[prev_idx]
-        #     g = processed_frames[0]
-        #     b = processed_frames[next_idx]
-        #     composite = np.stack([r, g, b], axis=-1)
-        #     plt.imshow(composite)
-        #     plt.title('Composite: R=prev, G=curr, B=next (grayscale, downscaled)')
-        #     plt.axis('off')
-        #     plt.show()
-        # Unindent following code to match main block
         loops = detect_loops(frames)
-        # Save all candidates and their scores to JSON
-        import json
         loop_json = []
         for score, i, j, cos_sim in loops:
             loop_json.append({
@@ -120,12 +104,14 @@ if __name__ == "__main__":
         print(f"Saved loop candidates: {json_path}")
         for idx, (score, i, j, cos_sim) in enumerate(loops):
             print(f"Loop candidate: start={i}, end={j}, score={score:.4f}, COS_SIM={cos_sim:.4f}")
+            if idx != 0:
+                continue  # For now, only save the top candidate
             # Extract loop frames (seamless looping: frames[i:j])
             loop_frames = frames[i:j]
             # Convert BGR (OpenCV) to RGB for PIL
             pil_frames = [Image.fromarray(cv2.cvtColor(f, cv2.COLOR_BGR2RGB)) for f in loop_frames]
             # Save as animated webp
-            out_name = f"{webp_path.stem}.loop-{idx}.webp"
+            out_name = f"{webp_path.stem}.loop.webp"
             out_path = output_dir / out_name
             pil_frames[0].save(
                 out_path,