feat(pipeline): v3-v4 dedup + panorama stitching + 1080p support

- 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

diag_v2.py

@@ -0,0 +1,75 @@
+"""video_2 진단: 왜 0 프레임인지 각 단계별 확인"""
+import sys
+sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+
+import cv2
+import numpy as np
+from pathlib import Path
+import importlib.util
+
+spec = importlib.util.spec_from_file_location("p", "youtube_tab_to_pdf.py")
+p = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(p)
+
+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"
+cap = cv2.VideoCapture(str(mp4))
+total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+fps = cap.get(cv2.CAP_PROP_FPS)
+
+# 10개 프레임 샘플
+indices = np.linspace(total * 0.1, total * 0.8, 10, dtype=int)
+for idx in indices:
+    cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
+    ret, frame = cap.read()
+    if not ret:
+        continue
+
+    h, w = frame.shape[:2]
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # 행별 밝기
+    margin_x = int(w * 0.1)
+    row_br = np.mean(gray[:, margin_x:w-margin_x], axis=1)
+
+    strip = p._find_white_tab_strip(frame)
+    has_tab = False
+    if strip:
+        top, bottom = strip
+        crop = frame[top:bottom, :]
+        has_tab = p._has_tab_content(crop)
+
+    print(f"Frame {idx:5d}: strip={strip}, has_tab={has_tab}, "
+          f"top_br={np.mean(row_br[:h//3]):.0f}, "
+          f"mid_br={np.mean(row_br[h//3:2*h//3]):.0f}, "
+          f"bot_br={np.mean(row_br[2*h//3:]):.0f}")
+
+    # strip이 있지만 has_tab=False인 경우 상세 진단
+    if strip and not has_tab:
+        top, bottom = strip
+        crop = frame[top:bottom, :]
+        g = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
+        ch, cw = g.shape
+        _, binary = cv2.threshold(g, 180, 255, cv2.THRESH_BINARY_INV)
+        horiz_k = cv2.getStructuringElement(cv2.MORPH_RECT, (max(cw//3, 30), 1))
+        horiz = cv2.morphologyEx(binary, cv2.MORPH_OPEN, horiz_k)
+        lines = cv2.HoughLinesP(horiz, 1, np.pi/180, threshold=40,
+                                minLineLength=cw//4, maxLineGap=20)
+        nlines = 0 if lines is None else len(lines)
+        ys = []
+        if lines is not None:
+            for l in lines:
+                x1,y1,x2,y2 = l[0]
+                if abs(y2-y1) < max(5, abs(x2-x1)*0.05):
+                    ys.append((y1+y2)/2)
+        ys.sort()
+        clusters = []
+        for y in ys:
+            if not clusters or y - clusters[-1] > ch * 0.02:
+                clusters.append(y)
+        print(f"  → 크롭크기: {cw}x{ch}, 라인수: {nlines}, "
+              f"수평ys: {len(ys)}, 클러스터: {len(clusters)}")
+
+        # 디버그: 크롭 저장
+        cv2.imwrite(f"output/raw_dump/v2_diag_{idx}.png", crop)
+
+cap.release()