98381d2893
- HSV-aware _trim_to_content (white ratio 30-97%) - pHash cluster dedup: dHash 32x32(1024bit), max_hamming=20 - Panoramic stitching: template matching scroll offset detection - 4-stage pipeline: MSE -> Panorama -> pHash - 1080p download priority + MAX_FRAME_WIDTH=1280 cap - test_pipeline.py with YouTube URLs and --download mode - 3 new known-issues documented - devlog + STATUS.md updated
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
"""video_2 진단: 왜 0 프레임인지 각 단계별 확인"""
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import sys
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sys.stdout.reconfigure(encoding="utf-8", errors="replace")
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import cv2
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import numpy as np
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from pathlib import Path
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import importlib.util
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spec = importlib.util.spec_from_file_location("p", "youtube_tab_to_pdf.py")
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p = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(p)
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mp4 = 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"
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cap = cv2.VideoCapture(str(mp4))
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total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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fps = cap.get(cv2.CAP_PROP_FPS)
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# 10개 프레임 샘플
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indices = np.linspace(total * 0.1, total * 0.8, 10, dtype=int)
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for idx in indices:
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cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
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ret, frame = cap.read()
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if not ret:
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continue
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h, w = frame.shape[:2]
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# 행별 밝기
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margin_x = int(w * 0.1)
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row_br = np.mean(gray[:, margin_x:w-margin_x], axis=1)
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strip = p._find_white_tab_strip(frame)
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has_tab = False
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if strip:
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top, bottom = strip
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crop = frame[top:bottom, :]
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has_tab = p._has_tab_content(crop)
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print(f"Frame {idx:5d}: strip={strip}, has_tab={has_tab}, "
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f"top_br={np.mean(row_br[:h//3]):.0f}, "
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f"mid_br={np.mean(row_br[h//3:2*h//3]):.0f}, "
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f"bot_br={np.mean(row_br[2*h//3:]):.0f}")
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# strip이 있지만 has_tab=False인 경우 상세 진단
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if strip and not has_tab:
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top, bottom = strip
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crop = frame[top:bottom, :]
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g = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
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ch, cw = g.shape
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_, binary = cv2.threshold(g, 180, 255, cv2.THRESH_BINARY_INV)
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horiz_k = cv2.getStructuringElement(cv2.MORPH_RECT, (max(cw//3, 30), 1))
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horiz = cv2.morphologyEx(binary, cv2.MORPH_OPEN, horiz_k)
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lines = cv2.HoughLinesP(horiz, 1, np.pi/180, threshold=40,
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minLineLength=cw//4, maxLineGap=20)
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nlines = 0 if lines is None else len(lines)
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ys = []
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if lines is not None:
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for l in lines:
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x1,y1,x2,y2 = l[0]
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if abs(y2-y1) < max(5, abs(x2-x1)*0.05):
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ys.append((y1+y2)/2)
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ys.sort()
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clusters = []
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for y in ys:
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if not clusters or y - clusters[-1] > ch * 0.02:
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clusters.append(y)
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print(f" → 크롭크기: {cw}x{ch}, 라인수: {nlines}, "
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f"수평ys: {len(ys)}, 클러스터: {len(clusters)}")
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# 디버그: 크롭 저장
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cv2.imwrite(f"output/raw_dump/v2_diag_{idx}.png", crop)
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cap.release()
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