44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
import cv2
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import numpy as np
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from youtube_tab_to_pdf import _find_white_tab_strip, _detect_measure_bars, _extract_print_channel
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def get_clean_binary(img):
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gray = np.max(img, axis=2)
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_, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)
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return binary
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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")
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cap.set(cv2.CAP_PROP_POS_FRAMES, 50)
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ret, f1 = cap.read()
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cap.set(cv2.CAP_PROP_POS_FRAMES, 65) # Next second
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ret, f2 = cap.read()
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cap.release()
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def process(frame):
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s = _find_white_tab_strip(frame)
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crop = frame[s[0]:s[1], :]
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gray = _extract_print_channel(crop)
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bars = _detect_measure_bars(gray)
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coords = [0] + bars + [crop.shape[1]]
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m = crop[:, coords[1]:coords[2]] # Get M2 just in case M1 is a clef
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return m
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m1 = process(f1)
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m2 = process(f2)
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cv2.imwrite("test_m1.png", m1)
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cv2.imwrite("test_m2.png", m2)
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bin1 = get_clean_binary(m1)
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bin2 = get_clean_binary(m2)
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h = min(bin1.shape[0], bin2.shape[0])
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w = min(bin1.shape[1], bin2.shape[1])
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s1 = bin1[:h, :w]
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s2 = bin2[:h, :w]
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diff = cv2.absdiff(s1, s2)
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error_ratio = np.sum(diff > 0) / s1.size
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print(f"Error Ratio: {error_ratio:.4f}")
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