chore(docs): document ScoreExtractor tiling and refactor debug scripts (#563)

scripts/debug/test_y_crop.py

@@ -0,0 +1,52 @@
+import cv2
+import numpy as np
+
+def find_white_tab_bounds(video_path):
+    cap = cv2.VideoCapture(video_path)
+    cap.set(cv2.CAP_PROP_POS_FRAMES, 30 * cap.get(cv2.CAP_PROP_FPS))
+    ret, frame = cap.read()
+    cap.release()
+    
+    if not ret: return None
+    
+    scale = 1280 / frame.shape[1]
+    frame = cv2.resize(frame, (1280, int(frame.shape[0] * scale)))
+    
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    
+    # Calculate row-wise mean brightness
+    row_means = np.mean(gray, axis=1)
+    
+    # We are looking for the white paper background which has brightness > 230 on average
+    # Wait, notes and black lines reduce the mean of a row.
+    # A single black horizontal line on white reduces mean by (255 - 0) * (width/width) -> It drops to ~180 if it's thick.
+    # Let's say any row with mean > 180 is part of the white strip.
+    is_white_row = row_means > 180
+    
+    # Find contiguous blocks of True
+    # Pad with False to handle edges cleanly
+    padded = np.concatenate(([False], is_white_row, [False]))
+    diffs = np.diff(padded.astype(int))
+    
+    starts = np.where(diffs == 1)[0]
+    ends = np.where(diffs == -1)[0]
+    
+    best_start, best_end, max_len = 0, 0, 0
+    
+    for s, e in zip(starts, ends):
+        length = e - s
+        if length > max_len:
+            max_len = length
+            best_start = s
+            best_end = e
+            
+    return best_start, best_end, frame.shape[0]
+
+if __name__ == "__main__":
+    video_path = "output/サカナクション／新宝島(エレキギターTAB) 難易度★★★ sakanaction shintakarajima.mp4"
+    bounds = find_white_tab_bounds(video_path)
+    if bounds:
+        s, e, h = bounds
+        print(f"Mathematically found White Tab Strip: Y_START={s}, Y_END={e}. Total Height={h}")
+    else:
+        print("Failed to find bound")