stuff

detect_loops.py

@@ -16,8 +16,6 @@ def extract_frames(webp_path):
             pass
     return frames
 
-
-
 def compute_sim(f1, f2):
     # f1 and f2 are already grayscale and downscaled
     mse = np.mean((f1.astype(np.float32) - f2.astype(np.float32)) ** 2)
@@ -47,12 +45,9 @@ def detect_loops(frames, min_len=6, max_len=40, top_k=3):
     for i in range(n):
         for j in range(i+min_len, min(i+max_len, n)):
             t0 = time.time()
-            # Composite start: average composite frames i, i+1, ..., i+composite_window-1
-            start_idxs = range(i, min(i+composite_window, n))
-            end_idxs = range(max(j-composite_window+1, i), j+1)
-            start_comp = np.mean([composite_frames[idx].astype(np.float32) for idx in start_idxs], axis=0)
-            end_comp = np.mean([composite_frames[idx].astype(np.float32) for idx in end_idxs], axis=0)
-            # No uint8 conversion; keep float32 for MSE
+            # Compare composite frames directly
+            start_comp = composite_frames[i].astype(np.float32)
+            end_comp = composite_frames[j].astype(np.float32)
             sim = compute_sim(start_comp, end_comp)
             t1 = time.time()
             score = -sim  # Lower MSE is better, so negate for sorting