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

scripts/debug/test_col_sums.py

@@ -0,0 +1,47 @@
+import cv2
+import numpy as np
+import glob
+
+videos = glob.glob('output/*.mp4')
+cap = cv2.VideoCapture(videos[0])
+cap.set(cv2.CAP_PROP_POS_FRAMES, 500)
+ret, frame = cap.read()
+cap.release()
+
+def _find_white_tab_strip(bgr: np.ndarray):
+    gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
+    _, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)
+    h, w = binary.shape
+    row_white_counts = np.sum(binary, axis=1) / 255
+    threshold = w * 0.1
+    white_rows = np.where(row_white_counts > threshold)[0]
+    if len(white_rows) < 2: return None
+    return white_rows[0], white_rows[-1]
+
+strip = _find_white_tab_strip(frame)
+if strip:
+    y1, y2 = strip
+    roi = frame[y1:y2, :]
+    
+    gray_roi = np.max(roi, axis=2)
+    _, binary = cv2.threshold(gray_roi, 200, 255, cv2.THRESH_BINARY)
+    
+    col_sums = np.sum(binary, axis=0) / 255
+    h_roi = y2 - y1
+    
+    # Relaxed to 40% to survive hand occlusions. Note stems max out at ~20-30%.
+    bars = np.where(col_sums > h_roi * 0.4)[0]
+    
+    clean_bars = []
+    for x in bars:
+        if not clean_bars or x - clean_bars[-1] > 20: # 20px min distance
+            clean_bars.append(int(x))
+            
+    # Include edges
+    if not clean_bars or clean_bars[0] > 50: clean_bars.insert(0, 0)
+    if clean_bars[-1] < binary.shape[1] - 50: clean_bars.append(binary.shape[1])
+    
+    print(f"Top: {y1}, Bottom: {y2}, Height: {h_roi}")
+    print(f"Detected Clean Measure Bars: {clean_bars}")
+else:
+    print("Could not find tab strip.")