import re

with open('youtube_tab_to_pdf.py', 'r', encoding='utf-8') as f:
    code = f.read()

new_func = """def extract_unique_scroll(frames: List[np.ndarray], threshold: float = SIMILARITY_THRESHOLD) -> List[np.ndarray]:
    print(f"[4/5] 순차 Stable Content Trigger 방식 추출 중...")
    
    strip_tops, strip_bottoms = [], []
    for frame in frames[:50]:
        strip = _find_white_tab_strip(frame)
        if strip:
            strip_tops.append(strip[0])
            strip_bottoms.append(strip[1])
            
    if not strip_tops: return []
        
    median_top = int(np.median(strip_tops))
    median_bottom = int(np.median(strip_bottoms))
    
    def get_clean_binary(img):
        gray = np.max(img, axis=2)
        _, binary = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)
        return binary

    unique_measures = []
    chunk_width = 1280
    
    last_1fps_bin = None
    last_solid_page = None

    for frame_idx, frame in enumerate(frames):
        h = frame.shape[0]
        tab_crop = frame[max(0, median_top):min(h, median_bottom), :]
        if not _has_tab_content(tab_crop): 
            continue
            
        clean_bin = get_clean_binary(tab_crop)
        
        if last_1fps_bin is not None:
            # Check stability compared to 1 second ago
            diff = cv2.absdiff(clean_bin, last_1fps_bin)
            error = np.count_nonzero(diff) / clean_bin.size
            if error < 0.05: # Page is fully stabilized (not a fading transition)
                has_changed_since_last_solid = True
                
                if last_solid_page is not None:
                    s_diff = cv2.absdiff(clean_bin, last_solid_page)
                    s_err = np.count_nonzero(s_diff) / clean_bin.size
                    if s_err < 0.05:
                        has_changed_since_last_solid = False
                
                # We only process this page if it's securely stable AND we haven't already processed it
                if has_changed_since_last_solid:
                    last_solid_page = clean_bin.copy()
                    
                    # Extract measures
                    gray_page = _extract_print_channel(tab_crop)
                    bar_coords = _detect_measure_bars(gray_page)
                    
                    if bar_coords:
                        coords = [0] + bar_coords + [tab_crop.shape[1]]
                        coords = sorted(list(set(coords)))
                        
                        page_measures = []
                        for i in range(len(coords) - 1):
                            x_start = coords[i]
                            x_end = coords[i+1]
                            if x_end - x_start < 40: continue
                            page_measures.append(tab_crop[:, x_start:x_end])
                            
                        if page_measures:
                            if not unique_measures:
                                unique_measures.extend(page_measures)
                            else:
                                first_m = page_measures[0]
                                bin_first = get_clean_binary(first_m)
                                
                                best_error = 1.0
                                best_offset = 0
                                anchored = False
                                
                                # scan_dist=4 ensures we never loop back to identical repeating choruses from 10 seconds ago!
                                for scan_dist in range(1, min(4, len(unique_measures) + 1)):
                                    past_idx = len(unique_measures) - scan_dist
                                    past_m = unique_measures[past_idx]
                                    bin_past = get_clean_binary(past_m)
                                    
                                    if abs(bin_first.shape[1] - bin_past.shape[1]) <= 25:
                                        hs = min(bin_first.shape[0], bin_past.shape[0])
                                        ws = min(bin_first.shape[1], bin_past.shape[1])
                                        s1 = bin_first[:hs, :ws]
                                        s2 = bin_past[:hs, :ws]
                                        
                                        m_diff = cv2.absdiff(s1, s2)
                                        error_ratio = np.sum(m_diff > 0) / s1.size
                                        
                                        if error_ratio < best_error:
                                            best_error = error_ratio
                                            best_offset = len(unique_measures) - past_idx
                                            
                                if best_error < 0.15:
                                    new_start_offset = best_offset
                                    if new_start_offset < len(page_measures):
                                        unique_measures.extend(page_measures[new_start_offset:])
                                else:
                                    unique_measures.extend(page_measures)
                                    
        last_1fps_bin = clean_bin.copy()
            
    print(f"  -> 동기화 중복 제거 완료: Stability 기반 {len(unique_measures)}개 마디 보존")

    final_chunks = []
    current_row_measures = []
    current_row_width = 0
    
    for measure_img in unique_measures:
        measure_w = measure_img.shape[1]
        
        if current_row_width + measure_w > chunk_width and len(current_row_measures) > 0:
            row_img = np.hstack(current_row_measures)
            pad_w = chunk_width - row_img.shape[1]
            if pad_w > 0:
                pad_img = np.full((row_img.shape[0], pad_w, 3), 255, dtype=np.uint8)
                row_img = np.hstack([row_img, pad_img])
            final_chunks.append(row_img)
            current_row_measures = [measure_img]
            current_row_width = measure_w
        else:
            current_row_measures.append(measure_img)
            current_row_width += measure_w
            
    if current_row_measures:
        row_img = np.hstack(current_row_measures)
        if row_img.shape[1] > chunk_width:
             row_img = row_img[:, :chunk_width]
        else:
            pad_w = chunk_width - row_img.shape[1]
            if pad_w > 0:
                pad_img = np.full((row_img.shape[0], pad_w, 3), 255, dtype=np.uint8)
                row_img = np.hstack([row_img, pad_img])
        final_chunks.append(row_img)
        
    print(f"  -> A4 분할 컷: {len(final_chunks)}개 줄(Row)")
    return final_chunks
"""

pattern = r'def extract_unique_scroll\(frames: List\[np\.ndarray\], threshold: float = SIMILARITY_THRESHOLD\) -> List\[np\.ndarray\]:.*?return final_chunks'
new_code = re.sub(pattern, new_func, code, flags=re.DOTALL)

with open('youtube_tab_to_pdf.py', 'w', encoding='utf-8') as f:
    f.write(new_code)
print("Stable Content Trigger Patched.")