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

scripts/debug/test_math.py

@@ -0,0 +1,43 @@
+import cv2
+import numpy as np
+from youtube_tab_to_pdf import _find_white_tab_strip, _detect_measure_bars, _extract_print_channel
+
+def get_clean_binary(img):
+    gray = np.max(img, axis=2)
+    _, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)
+    return binary
+
+cap = cv2.VideoCapture(r"output/サカナクション／新宝島(エレキギターTAB) 難易度★★★ sakanaction shintakarajima.mp4")
+cap.set(cv2.CAP_PROP_POS_FRAMES, 50)
+ret, f1 = cap.read()
+cap.set(cv2.CAP_PROP_POS_FRAMES, 65) # Next second
+ret, f2 = cap.read()
+cap.release()
+
+def process(frame):
+    s = _find_white_tab_strip(frame)
+    crop = frame[s[0]:s[1], :]
+    gray = _extract_print_channel(crop)
+    bars = _detect_measure_bars(gray)
+    coords = [0] + bars + [crop.shape[1]]
+    m = crop[:, coords[1]:coords[2]] # Get M2 just in case M1 is a clef
+    return m
+
+m1 = process(f1)
+m2 = process(f2)
+
+cv2.imwrite("test_m1.png", m1)
+cv2.imwrite("test_m2.png", m2)
+
+bin1 = get_clean_binary(m1)
+bin2 = get_clean_binary(m2)
+
+h = min(bin1.shape[0], bin2.shape[0])
+w = min(bin1.shape[1], bin2.shape[1])
+s1 = bin1[:h, :w]
+s2 = bin2[:h, :w]
+
+diff = cv2.absdiff(s1, s2)
+error_ratio = np.sum(diff > 0) / s1.size
+
+print(f"Error Ratio: {error_ratio:.4f}")