report-detect/test_accuracy_batch_full.py

"""
CMA Code Extraction & Institution Name - Batch Accuracy Testing Script (Enhanced)

This script implements comprehensive batch accuracy testing for BOTH:
1. CMA code extraction
2. Institution name extraction from seals

Uses the complete workflow from v_verify_logic.py including:
- Layout detection (Paddlex PP-DocLayout-L)
- Seal detection and refinement
- Polar unwarping
- OCR text recognition for institution names

Author: Claude Code
Date: 2025-02-05
Version: 2.0 (Enhanced with seal/institution extraction)
"""

import os
import sys
import json
import time
import logging
import re
import math
from pathlib import Path
from datetime import datetime
from typing import Dict, List, Tuple, Optional, Any
import numpy as np

# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
    import codecs
    try:
        sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, 'strict')
        sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, 'strict')
    except:
        pass

os.environ["DISABLE_MODEL_SOURCE_CHECK"] = "True"


class NumpyEncoder(json.JSONEncoder):
    """Custom JSON encoder for numpy types"""
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        if isinstance(obj, np.floating):
            return float(obj)
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        return super().default(obj)


try:
    import fitz  # PyMuPDF
    import cv2
    from paddleocr import PaddleOCR, SealTextDetection, TextRecognition
    try:
        from paddleocr import PaddleOCRVL
        PADDLEOCRVL_AVAILABLE = True
    except ImportError:
        PADDLEOCRVL_AVAILABLE = False
        print("Warning: PaddleOCRVL not available. Install with: pip install paddleocr[doc-parser]")
    import paddlex as px
    from Levenshtein import distance as levenshtein_distance
except ImportError as e:
    print(f"Error: Required dependency not found: {e}")
    print("Please install: pip install python-Levenshtein paddleocr paddlex pymupdf-ng opencv-python numpy")
    sys.exit(1)

# Import CMA extraction module
try:
    from cma_extraction_final import extract_cma_code_fullpage, imread_unicode
except ImportError:
    print("Error: cma_extraction_final.py not found in current directory")
    sys.exit(1)

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('test_accuracy_full.log', encoding='utf-8'),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

# Constants
PDF_DIR = Path(r"src/test/resources/data/pdfs")
RESULTS_JSON = Path(r"src/test/resources/data/results.json")
OUTPUT_DIR = Path("test_reports_full")
BATCH_SIZE = 20
SIMILARITY_THRESHOLD = 85.0

# OCR Model Configuration
# Options: "ppocr_v5" (default), "paddleocr_vl"
OCR_MODEL = os.environ.get("OCR_MODEL", "ppocr_v5")


# ============ Helper Functions ============

def imwrite_safe(file_path, img):
    """
    Write image file safely, handling Chinese paths on Windows.

    On Windows, cv2.imwrite fails with Chinese paths. This function uses
    cv2.imencode + tofile as a fallback.

    Args:
        file_path: Path to save the image
        img: Image data (numpy array)

    Returns:
        bool: True if successful, False otherwise
    """
    try:
        # Try standard cv2.imwrite first
        success = cv2.imwrite(file_path, img)
        if success:
            return True

        # Fallback: Use imencode + tofile for Chinese paths
        is_success, buffer = cv2.imencode(".png", img)
        if is_success:
            buffer.tofile(file_path)
            return True
        return False
    except Exception as e:
        logger.error(f"Failed to write image to {file_path}: {e}")
        return False


# ============ Seal Processing Functions (from v_verify_logic.py) ============

def polar_unwarp(img, center, radius, start_theta, angular_extent):
    """
    Polar Unwarp with Canvas Padding for Partial Seals

    Extended version:
    - Creates a padded canvas to handle partial seals (seals cut off at edges)
    - Samples both inward (toward center) and outward (away from center)
    - Uses white padding for areas outside the original image boundary
    - This ensures we can always sample at the full radius even if seal is cut off
    """
    if angular_extent <= 0: return None

    strip_w = int(angular_extent * radius)

    # Extended sampling range:
    # - Inward: 100% of radius (toward center) - all the way to center
    # - Outward: 20% beyond radius (away from center)
    inward_range = int(radius * 0.85)  # 向内到圆心
    outward_range = int(radius * 0.2)  # 向外20%
    strip_h = inward_range + outward_range

    if strip_w <= 0 or strip_h <= 0: return None

    ch, cw = img.shape[:2]

    # Calculate padding needed to ensure all sampling points are within bounds
    # Maximum distance from center will be radius + outward_range
    max_distance = radius + outward_range

    # Calculate padding needed on each side
    pad_top = max(0, max_distance - center[1])
    pad_bottom = max(0, max_distance - (ch - center[1]))
    pad_left = max(0, max_distance - center[0])
    pad_right = max(0, max_distance - (cw - center[0]))

    # Create padded canvas with white background
    padded_h = ch + pad_top + pad_bottom
    padded_w = cw + pad_left + pad_right
    padded_canvas = np.ones((padded_h, padded_w, 3), dtype=np.uint8) * 255

    # Place original image in center
    padded_canvas[pad_top:pad_top+ch, pad_left:pad_left+cw] = img

    # Adjust center position for padded canvas
    center_padded = [center[0] + pad_left, center[1] + pad_top]

    strip = np.zeros((strip_h, strip_w, 3), dtype=np.uint8)

    for y in range(strip_h):
        # Calculate radius at this row
        # Start from radius + outward_range (outside)
        # Move inward toward center
        r = radius + outward_range - y

        for x in range(strip_w):
            theta = start_theta + angular_extent * (x / strip_w)
            src_x = center_padded[0] + r * math.cos(theta)
            src_y = center_padded[1] + r * math.sin(theta)

            # Sample from padded canvas (all points should be within bounds now)
            sx, sy = int(src_x), int(src_y)
            if 0 <= sx < padded_w and 0 <= sy < padded_h:
                strip[y, x] = padded_canvas[sy, sx]
            else:
                strip[y, x] = [255, 255, 255]

    return strip


def calculate_precise_arc(polygons, center):
    """Calculate precise arc parameters for seal text"""
    initial_clusters = []
    gap_thresh = math.radians(15)
    for poly in polygons:
        thetas = sorted([math.atan2(p[1] - center[1], p[0] - center[0]) for i, p in enumerate(poly)])
        if not thetas: continue
        max_gap = 0
        gap_idx = -1
        for i in range(len(thetas)):
            gap = (thetas[0] + 2*math.pi - thetas[i]) if i == len(thetas)-1 else (thetas[i+1]-thetas[i])
            if gap > max_gap: max_gap = gap; gap_idx = i
        if gap_idx == len(thetas) - 1:
            t_arc = thetas
        else:
            t_arc = thetas[gap_idx+1:] + [t + 2*math.pi for t in thetas[:gap_idx+1]]
        if not t_arc: continue
        curr = [t_arc[0]]
        for i in range(1, len(t_arc)):
            if t_arc[i] - t_arc[i-1] > gap_thresh:
                initial_clusters.append({'start': curr[0], 'end': curr[-1]})
                curr = [t_arc[i]]
            else:
                curr.append(t_arc[i])
        initial_clusters.append({'start': curr[0], 'end': curr[-1]})
    if not initial_clusters: return 0.0, 0.0
    initial_clusters.sort(key=lambda x: x['start'])
    merged = []
    merge_thresh = math.radians(45)
    if initial_clusters:
        curr = initial_clusters[0]
        for i in range(1, len(initial_clusters)):
            nxt = initial_clusters[i]
            if nxt['start'] - curr['end'] < merge_thresh:
                curr['end'] = max(curr['end'], nxt['end'])
            else:
                merged.append(curr)
                curr = nxt
        merged.append(curr)
    candidates = []
    for m in merged:
        st, en = m['start'], m['end']
        ex = en - st
        mid = (st + en) / 2
        dist_to_top = abs(((mid + math.pi/2 + math.pi) % (2*math.pi)) - math.pi)
        weight = math.exp(-0.5 * (dist_to_top / (math.pi/2))**2)
        candidates.append({'start': st, 'end': en, 'extent': ex, 'score': ex * weight})
    candidates.sort(key=lambda x: x['score'], reverse=True)
    best = candidates[0]

    # FIX: Limit extent to max 350° to avoid overlap and distortion
    # Extent > 360° causes severe image distortion in polar unwarping
    MAX_EXTENT_DEG = 350.0
    start_theta = best['start']
    extent = best['end'] - best['start']

    if math.degrees(extent) > MAX_EXTENT_DEG:
        logger.warning(f"Arc extent {math.degrees(extent):.2f}° exceeds {MAX_EXTENT_DEG}°, clamping to avoid distortion")
        extent = math.radians(MAX_EXTENT_DEG)

    return start_theta, extent


def fit_circle_from_text_polygons(all_polygons):
    """
    Fit circle from text polygons using least squares method.

    Equation: (x - a)² + (y - b)² = r²
    Expanded: x² + y² - 2ax - 2by + (a² + b² - r²) = 0
    Let: c = a² + b² - r²
    Then: x² + y² = 2ax + 2by - c

    This is a linear system: [2x, 2y, -1] * [a, b, c]ᵀ = x² + y²
    """
    if len(all_polygons) == 0:
        return None, None, None

    # Collect all points from polygons
    points = []
    for poly in all_polygons:
        for p in poly:
            points.append([float(p[0]), float(p[1])])

    if len(points) < 5:
        return None, None, None

    points = np.array(points)

    # Build linear system
    # A * [a, b, c]ᵀ = b
    A = np.column_stack([2 * points[:, 0], 2 * points[:, 1], -np.ones(len(points))])
    b_vec = np.sum(points ** 2, axis=1)

    try:
        # Solve least squares
        sol, residuals, rank, singular_values = np.linalg.lstsq(A, b_vec, rcond=None)

        a, b, c = sol
        center_x = a
        center_y = b
        radius = np.sqrt(a**2 + b**2 - c)

        # Calculate fitting error (RMSE)
        if len(residuals) > 0:
            rmse = np.sqrt(residuals[0] / len(points))
        else:
            # Calculate manually
            predicted = A @ sol
            errors = predicted - b_vec
            rmse = np.sqrt(np.mean(errors ** 2))

        return (int(center_x), int(center_y)), int(radius), rmse

    except Exception as e:
        logger.error(f"Circle fitting failed: {e}")
        return None, None, None


def detect_seal_center_dual_method(seal_crop, all_polygons):
    """
    Dual strategy: Automatically select the best center detection method.

    Strategy:
    1. Try circle fitting
    2. Check fitting quality (RMSE, offset distance)
    3. If fitting quality is good → use fitted center
    4. Otherwise → use crop center

    Returns:
        center: [x, y] - detected center
        radius: int - detected radius
        method: str - "crop_center" or "circle_fitting"
    """
    ch, cw = seal_crop.shape[:2]

    # Method 1: Crop center (default method)
    center_crop = [cw // 2, ch // 2]
    radius_crop = min(cw, ch) // 2 - 10

    # Method 2: Circle fitting
    center_fit, radius_fit, rmse = fit_circle_from_text_polygons(all_polygons)

    if center_fit is None:
        logger.info("  Circle fitting failed, using crop center")
        return center_crop, radius_crop, "crop_center"

    # Calculate offset between fitted center and crop center
    offset = math.sqrt((center_fit[0] - center_crop[0])**2 +
                       (center_fit[1] - center_crop[1])**2)
    offset_ratio = offset / min(cw, ch)

    # Quality check criteria
    # 1. RMSE should be low (good fit)
    # 2. Offset should not be too large (center should be reasonable)
    # 3. Need enough polygons for reliable fitting
    rmse_threshold = 3000
    offset_threshold = 0.2  # 20% of crop size
    min_polygons = 3

    is_fit_good = (
        rmse < rmse_threshold and
        offset_ratio < offset_threshold and
        len(all_polygons) >= min_polygons
    )

    if is_fit_good:
        logger.info(f"  Using circle fitting: RMSE={rmse:.2f}, offset_ratio={offset_ratio:.2f}")
        return center_fit, radius_fit, "circle_fitting"
    else:
        reasons = []
        if rmse >= rmse_threshold:
            reasons.append(f"RMSE too high ({rmse:.2f} >= {rmse_threshold})")
        if offset_ratio >= offset_threshold:
            reasons.append(f"offset too large ({offset_ratio:.2f} >= {offset_threshold})")
        if len(all_polygons) < min_polygons:
            reasons.append(f"not enough polygons ({len(all_polygons)} < {min_polygons})")
        logger.info(f"  Circle fitting unreliable ({', '.join(reasons)}), using crop center")
        return center_crop, radius_crop, "crop_center"


def run_layout_detection(image_path):
    """Run Paddlex PP-DocLayout-L for layout analysis"""
    try:
        model = px.create_model("PP-DocLayout-L")
        output = model.predict(image_path, batch_size=1)
        all_regions = []
        for res in output:
            boxes = res.get('boxes', [])
            for box in boxes:
                label_name = box.get('label_name', box.get('label', 'unknown'))
                score = box.get('score', 0.0)
                coords = box.get('coordinate')
                all_regions.append({
                    'label': label_name,
                    'score': score,
                    'box': coords
                })
        return all_regions
    except Exception as e:
        logger.error(f"Layout detection failed: {e}")
        return []


def run_ocr_recognition(image_path, rec_model):
    """Run OCR recognition on unwarp seal image"""
    try:
        output = rec_model.predict(input=image_path, batch_size=1)
        if output and len(output) > 0:
            res = output[0]
            text = res.get('rec_text', '').strip()
            score = res.get('rec_score', 0.0)
            return {
                'text': text,
                'score': score,
                'success': len(text) > 0
            }
        else:
            return {'text': '', 'score': 0.0, 'success': False}
    except Exception as e:
        logger.error(f"OCR recognition failed: {e}")
        return {'text': '', 'score': 0.0, 'success': False}


def run_ocr_recognition_vl(image_path, vl_pipeline):
    """
    Run OCR recognition using PaddleOCRVL on seal image.

    Can be used on both unwarp images and crop images (backup mode).

    Args:
        image_path: Path to seal image (unwarp or crop)
        vl_pipeline: Initialized PaddleOCRVL pipeline

    Returns:
        Dict with 'text', 'score', 'success' keys
    """
    try:
        # Create temp output directory for VL results
        temp_output_dir = Path("temp_paddleocr_vl")
        temp_output_dir.mkdir(exist_ok=True)

        # Run prediction
        output = vl_pipeline.predict(image_path)

        if output and len(output) > 0:
            res = output[0]

            # Save JSON to extract text
            res.save_to_json(save_path=str(temp_output_dir))

            # Read JSON to find seal text
            json_file = temp_output_dir / f"{Path(image_path).stem}_res.json"

            if json_file.exists():
                with open(json_file, 'r', encoding='utf-8') as f:
                    data = json.load(f)

                # Find seal block and extract content
                for block in data.get('parsing_res_list', []):
                    if block.get('block_label') == 'seal':
                        text = block.get('block_content', '').strip()
                        # Clean up temp files
                        import shutil
                        if temp_output_dir.exists():
                            shutil.rmtree(temp_output_dir, ignore_errors=True)

                        return {
                            'text': text,
                            'score': 1.0,  # PaddleOCRVL doesn't provide confidence score
                            'success': len(text) > 0
                        }

            # Clean up temp files
            import shutil
            if temp_output_dir.exists():
                shutil.rmtree(temp_output_dir, ignore_errors=True)

            return {'text': '', 'score': 0.0, 'success': False}
        else:
            return {'text': '', 'score': 0.0, 'success': False}

    except Exception as e:
        logger.error(f"PaddleOCRVL recognition failed: {e}")
        import traceback
        logger.error(traceback.format_exc())
        return {'text': '', 'score': 0.0, 'success': False}


def extract_seals_and_institutions(page_img, output_dir, ocr_model="ppocr_v5", vl_pipeline=None):
    """
    Extract seals and recognize institution names from page image.

    Args:
        page_img: Input page image
        output_dir: Directory to save intermediate results
        ocr_model: OCR model to use ("ppocr_v5" or "paddleocr_vl")
        vl_pipeline: PaddleOCRVL pipeline (required if ocr_model="paddleocr_vl")

    Returns:
        Dict with:
            - 'seals': list of seal results
            - 'institutions': list of recognized institution names
            - 'processing_time': time taken
    """
    start_time = time.time()
    result = {
        'seals': [],
        'institutions': [],
        'processing_time': 0.0
    }

    # Validate input image
    if page_img is None:
        logger.error("Input page_img is None")
        result['processing_time'] = time.time() - start_time
        return result

    if not isinstance(page_img, np.ndarray):
        logger.error(f"Input page_img is not numpy array, type: {type(page_img)}")
        result['processing_time'] = time.time() - start_time
        return result

    if page_img.size == 0:
        logger.error("Input page_img is empty")
        result['processing_time'] = time.time() - start_time
        return result

    logger.info(f"Input image shape: {page_img.shape}, dtype: {page_img.dtype}")

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Save page image
    doc_path = os.path.join(output_dir, "doc_page.png")
    try:
        success = imwrite_safe(doc_path, page_img)
        if not success:
            logger.error(f"imwrite_safe returned False for {doc_path}")
            # Try alternative save method using PIL
            try:
                from PIL import Image
                img_rgb = cv2.cvtColor(page_img, cv2.COLOR_BGR2RGB)
                pil_img = Image.fromarray(img_rgb)
                pil_img.save(doc_path)
                logger.info(f"Saved using PIL as fallback: {doc_path}")

                # Verify PIL save worked
                if not os.path.exists(doc_path):
                    logger.error(f"PIL save also failed, file not found: {doc_path}")
                    result['processing_time'] = time.time() - start_time
                    return result
            except Exception as pil_e:
                logger.error(f"PIL fallback also failed: {pil_e}")
                result['processing_time'] = time.time() - start_time
                return result
    except Exception as e:
        logger.error(f"Failed to save page image: {e}")
        result['processing_time'] = time.time() - start_time
        return result

    # Verify file exists before proceeding
    if not os.path.exists(doc_path):
        logger.error(f"Page image file not found after save: {doc_path}")
        result['processing_time'] = time.time() - start_time
        return result

    # Run layout detection
    logger.info("Running layout detection...")
    all_regions = run_layout_detection(doc_path)

    # Extract seal boxes
    seal_boxes = []
    page_viz = page_img.copy()
    for reg in all_regions:
        box = reg.get('box')
        label = reg.get('label')
        score = reg.get('score', 0.0)
        is_seal = (label == 'seal')

        if score > 0.2:
            x1, y1, x2, y2 = [int(v) for v in box]
            color = (0, 0, 255) if is_seal else (0, 255, 0)
            cv2.rectangle(page_viz, (x1, y1), (x2, y2), color, 2)

            if is_seal:
                seal_boxes.append(box)

    imwrite_safe(os.path.join(output_dir, "doc_layout_viz.png"), page_viz)

    if not seal_boxes:
        logger.warning("No seals detected")
        result['processing_time'] = time.time() - start_time
        return result

    # Process each seal
    logger.info(f"Processing {len(seal_boxes)} detected seals...")
    det_model = SealTextDetection(model_name="PP-OCRv4_server_seal_det")

    # Initialize OCR model based on selection
    if ocr_model == "paddleocr_vl":
        if not PADDLEOCRVL_AVAILABLE:
            logger.error("PaddleOCRVL requested but not available. Falling back to PP-OCRv5.")
            ocr_model = "ppocr_v5"
            rec_model = TextRecognition(model_name="PP-OCRv5_server_rec")
        elif vl_pipeline is None:
            logger.error("PaddleOCRVL requested but vl_pipeline is None. Falling back to PP-OCRv5.")
            ocr_model = "ppocr_v5"
            rec_model = TextRecognition(model_name="PP-OCRv5_server_rec")
        else:
            logger.info("Using PaddleOCRVL for seal text recognition")
            rec_model = None  # Not used for PaddleOCRVL
    else:
        logger.info("Using PP-OCRv5_server_rec for seal text recognition")
        rec_model = TextRecognition(model_name="PP-OCRv5_server_rec")

    for i, box in enumerate(seal_boxes):
        x1, y1, x2, y2 = [int(v) for v in box]
        pad = 40
        y1_p, y2_p = max(0, y1-pad), min(page_img.shape[0], y2+pad)
        x1_p, x2_p = max(0, x1-pad), min(page_img.shape[1], x2+pad)
        seal_crop = page_img[y1_p:y2_p, x1_p:x2_p]

        # Validate crop
        if seal_crop.size == 0 or seal_crop.shape[0] == 0 or seal_crop.shape[1] == 0:
            logger.warning(f"Invalid seal crop dimensions: {seal_crop.shape}, skipping seal {i}")
            continue

        crop_path = os.path.join(output_dir, f"seal_crop_{i}.png")
        success = imwrite_safe(crop_path, seal_crop)
        if not success:
            # Try PIL fallback
            try:
                from PIL import Image
                crop_rgb = cv2.cvtColor(seal_crop, cv2.COLOR_BGR2RGB)
                pil_img = Image.fromarray(crop_rgb)
                pil_img.save(crop_path)
                logger.info(f"Saved seal crop using PIL fallback: {crop_path}")
            except Exception as pil_e:
                logger.error(f"Failed to save seal crop to {crop_path}: {pil_e}, skipping seal {i}")
                continue

        # Verify file exists
        if not os.path.exists(crop_path):
            logger.error(f"Seal crop file not found after save: {crop_path}, skipping seal {i}")
            continue

        # Detect text polygons
        output = det_model.predict(crop_path, batch_size=1)
        all_polygons = []
        for res in output:
            polys = res.get('dt_polys') if isinstance(res, dict) else None
            if polys:
                all_polygons.extend(polys)

        ch, cw = seal_crop.shape[:2]

        # ============ DUAL STRATEGY: Choose best center detection method ============
        logger.info(f"  Seal #{i} Geometry:")
        logger.info(f"    - Crop size: {cw}x{ch}")
        logger.info(f"    - Text polygons detected: {len(all_polygons)}")

        center, radius, method_used = detect_seal_center_dual_method(seal_crop, all_polygons)
        logger.info(f"    - Method used: {method_used}")
        logger.info(f"    - Center: ({center[0]}, {center[1]})")
        logger.info(f"    - Radius: {radius}")

        # ============ INSUFFICIENT POLYGONS CHECK ============
        # If too few text polygons detected, polar unwarping will likely fail
        # Skip directly to PaddleOCRVL backup in this case
        MIN_POLYGONS_FOR_UNWARP = 3
        if len(all_polygons) < MIN_POLYGONS_FOR_UNWARP:
            logger.warning(f"  Seal #{i}: Only {len(all_polygons)} text polygons detected (< {MIN_POLYGONS_FOR_UNWARP})")
            logger.warning(f"  Seal #{i}: Skipping polar unwarping (insufficient polygon data)")
            logger.info(f"  Seal #{i}: Using PaddleOCRVL backup instead")

            # Save crop image
            imwrite_safe(crop_path, seal_crop)

            # Use PaddleOCRVL directly on crop (no unwarp)
            if vl_pipeline is not None and PADDLEOCRVL_AVAILABLE:
                ocr_result = run_ocr_recognition_vl(crop_path, vl_pipeline)
                logger.info(f"  Seal #{i} PaddleOCRVL Result (direct crop):")
                logger.info(f"    - Text: '{ocr_result['text']}'")
                logger.info(f"    - Score: {ocr_result['score']:.4f}")
                logger.info(f"    - Success: {ocr_result['success']}")
                logger.info(f"    - ** Used PaddleOCRVL (insufficient polygons for unwarping) **")

                # Create debug info without unwarp
                seal_data = {
                    'index': i,
                    'box': box,
                    'crop_path': Path(crop_path).name,
                    'unwarp_path': None,  # No unwarp performed
                    'marked_path': None,  # No marked image
                    'polar_viz_path': None,  # No polar visualization
                    'text': ocr_result['text'],
                    'confidence': float(ocr_result['score']),
                    'success': bool(ocr_result['success']),
                    'method_used': f'{method_used}_skip_unwarp',
                    'used_fallback': True,
                    'debug_info': {
                        'center': center,
                        'radius': radius,
                        'start_theta_deg': None,
                        'extent_deg': None,
                        'num_polygons': len(all_polygons),
                        'crop_size': (cw, ch),
                        'unwarp_size': None,
                        'skip_reason': f'Insufficient polygons ({len(all_polygons)} < {MIN_POLYGONS_FOR_UNWARP})'
                    }
                }
                result['seals'].append(seal_data)

                if ocr_result['success']:
                    result['institutions'].append(ocr_result['text'])
                    logger.info(f"  ✓ Seal #{i} SUCCESS: {ocr_result['text'][:50]}... (confidence: {ocr_result['score']:.4f})")
                else:
                    logger.warning(f"  ✗ Seal #{i} FAILED: Could not extract institution name")

                continue  # Skip to next seal
            else:
                logger.error(f"  Seal #{i}: PaddleOCRVL not available, cannot extract text")
                seal_data = {
                    'index': i,
                    'box': box,
                    'crop_path': Path(crop_path).name,
                    'unwarp_path': None,
                    'marked_path': None,
                    'polar_viz_path': None,
                    'text': '',
                    'confidence': 0.0,
                    'success': False,
                    'method_used': f'{method_used}_skip_unwarp',
                    'used_fallback': True,
                    'debug_info': {
                        'center': center,
                        'radius': radius,
                        'start_theta_deg': None,
                        'extent_deg': None,
                        'num_polygons': len(all_polygons),
                        'crop_size': (cw, ch),
                        'unwarp_size': None,
                        'skip_reason': f'Insufficient polygons and no PaddleOCRVL backup'
                    }
                }
                result['seals'].append(seal_data)
                continue

        # Calculate arc and unwarp
        start_theta, extent = calculate_precise_arc(all_polygons, center)
        logger.info(f"  Seal #{i} Arc Parameters:")
        logger.info(f"    - Start theta: {math.degrees(start_theta):.2f}°")
        logger.info(f"    - Extent: {math.degrees(extent):.2f}° ({math.degrees(extent)*radius:.1f} pixels width)")

        marked = seal_crop.copy()

        # Draw all text polygons in green
        for p in all_polygons:
            cv2.polylines(marked, [np.array(p, dtype=np.int32)], True, (0, 255, 0), 2)

        # Draw center point (yellow cross)
        center_x, center_y = int(center[0]), int(center[1])
        cv2.drawMarker(marked, (center_x, center_y), (0, 255, 255),
                      markerType=cv2.MARKER_CROSS, markerSize=20, thickness=2)
        cv2.circle(marked, (center_x, center_y), 5, (0, 255, 255), -1)

        # Draw estimated radius circle (cyan)
        cv2.circle(marked, (center_x, center_y), radius, (255, 255, 0), 2)

        # Draw polar sampling visualization
        polar_viz = seal_crop.copy()
        cv2.drawMarker(polar_viz, (center_x, center_y), (0, 255, 255),
                      markerType=cv2.MARKER_CROSS, markerSize=20, thickness=2)
        cv2.circle(polar_viz, (center_x, center_y), radius, (255, 255, 0), 2)

        unwarp_path = os.path.join(output_dir, f"seal_unwarp_{i}.png")
        unwarp = None
        used_fallback = False

        if extent > 0:
            logger.info(f"  Seal #{i}: Performing polar unwarping with detected text polygons...")
            unwarp = polar_unwarp(seal_crop, center, radius, start_theta, extent)
            if unwarp is not None:
                imwrite_safe(unwarp_path, unwarp)
                logger.info(f"    - Unwarp size: {unwarp.shape[1]}x{unwarp.shape[0]}")

                def draw_line(m, theta, color):
                    x = center[0] + radius * math.cos(theta)
                    y = center[1] + radius * math.sin(theta)
                    cv2.line(m, (int(center[0]), int(center[1])), (int(x), int(y)), color, 2)

                # Draw start angle line (blue)
                draw_line(marked, start_theta, (255, 0, 0))
                # Draw end angle line (red)
                draw_line(marked, start_theta + extent, (0, 0, 255))

                # Draw sampling points on polar_viz (show where polar samples come from)
                num_sample_points = min(50, int(extent * radius))  # Show up to 50 sample points
                for r_idx in range(5):  # 5 different radii
                    r = radius - r_idx * (radius * 0.6 / 5)
                    for theta_idx in range(num_sample_points):
                        theta = start_theta + extent * (theta_idx / num_sample_points)
                        src_x = center[0] + r * math.cos(theta)
                        src_y = center[1] + r * math.sin(theta)
                        if 0 <= src_x < cw and 0 <= src_y < ch:
                            cv2.circle(polar_viz, (int(src_x), int(src_y)), 1, (255, 0, 255), -1)

                # Save polar visualization
                polar_viz_path = os.path.join(output_dir, f"seal_polar_viz_{i}.png")
                imwrite_safe(polar_viz_path, polar_viz)
                logger.info(f"    - Polar visualization saved: seal_polar_viz_{i}.png")
            else:
                logger.warning(f"  Seal #{i}: Polar unwarp returned None")

        # ============ FALLBACK: Use fixed angle range when no text detected ============
        if unwarp is None and extent <= 0 and len(all_polygons) == 0:
            logger.warning(f"  Seal #{i}: No text polygons detected, using fallback angle range (7:30 to 4:30 clockwise)")
            used_fallback = True

            # 7:30 direction (left-bottom) to 4:30 direction (right-bottom) clockwise
            # In standard math angle (0 = 3 o'clock, CCW):
            # 7:30 = 225 degrees = 3.927 rad
            # 4:30 = 135 degrees = 2.356 rad
            # Clockwise from 7:30 to 4:30 covers 270 degrees
            # We start at 4:30 (135 degrees) and go counter-clockwise 270 degrees
            fallback_start_theta = math.radians(135)  # 4:30 position
            fallback_extent = math.radians(270)  # 270 degree coverage

            logger.info(f"  Seal #{i}: Fallback - Start: 135.00° (4:30), Extent: 270.00°")

            unwarp = polar_unwarp(seal_crop, center, radius, fallback_start_theta, fallback_extent)
            if unwarp is not None:
                imwrite_safe(unwarp_path, unwarp)
                logger.info(f"    - Fallback unwarp size: {unwarp.shape[1]}x{unwarp.shape[0]}")

                # Update start_theta and extent for visualization
                start_theta = fallback_start_theta
                extent = fallback_extent

                def draw_line(m, theta, color):
                    x = center[0] + radius * math.cos(theta)
                    y = center[1] + radius * math.sin(theta)
                    cv2.line(m, (int(center[0]), int(center[1])), (int(x), int(y)), color, 2)

                # Draw start angle line (blue) - 4:30 position
                draw_line(marked, start_theta, (255, 0, 0))
                # Draw end angle line (red) - 7:30 position
                draw_line(marked, start_theta + extent, (0, 0, 255))

                # Draw sampling points
                num_sample_points = 50
                for r_idx in range(5):
                    r = radius - r_idx * (radius * 0.6 / 5)
                    for theta_idx in range(num_sample_points):
                        theta = start_theta + extent * (theta_idx / num_sample_points)
                        src_x = center[0] + r * math.cos(theta)
                        src_y = center[1] + r * math.sin(theta)
                        if 0 <= src_x < cw and 0 <= src_y < ch:
                            cv2.circle(polar_viz, (int(src_x), int(src_y)), 1, (255, 0, 255), -1)

                polar_viz_path = os.path.join(output_dir, f"seal_polar_viz_{i}.png")
                imwrite_safe(polar_viz_path, polar_viz)
                logger.info(f"    - Fallback polar visualization saved: seal_polar_viz_{i}.png")
            else:
                logger.warning(f"  Seal #{i}: Fallback polar unwarp also returned None")

        marked_path = os.path.join(output_dir, f"seal_marked_{i}.png")
        imwrite_safe(marked_path, marked)

        # OCR recognition
        ocr_result = {'text': '', 'score': 0.0, 'success': False}

        if unwarp is not None:
            # Standard path: Recognize unwarp image
            method_str = "FALLBACK" if used_fallback else "Standard"
            logger.info(f"  Seal #{i}: Running OCR ({method_str}, model={ocr_model}) on unwarp image...")

            if ocr_model == "paddleocr_vl":
                ocr_result = run_ocr_recognition_vl(unwarp_path, vl_pipeline)
            else:
                ocr_result = run_ocr_recognition(unwarp_path, rec_model)

            logger.info(f"  Seal #{i} OCR Result:")
            logger.info(f"    - Text: '{ocr_result['text']}'")
            logger.info(f"    - Score: {ocr_result['score']:.4f}")
            logger.info(f"    - Success: {ocr_result['success']}")
            logger.info(f"    - Text length: {len(ocr_result['text'])} chars")
            if used_fallback:
                logger.info(f"    - ** Used fallback angle range (7:30 to 4:30) **")
        else:
            # ============ BACKUP: Use PaddleOCRVL directly on seal crop ============
            logger.warning(f"  Seal #{i}: No unwarp image available (polar unwarp failed)")

            if vl_pipeline is not None and PADDLEOCRVL_AVAILABLE:
                logger.info(f"  Seal #{i}: Using PaddleOCRVL backup - directly recognize seal crop image")
                seal_crop_path = os.path.join(output_dir, f"seal_crop_{i}.png")
                ocr_result = run_ocr_recognition_vl(seal_crop_path, vl_pipeline)
                logger.info(f"  Seal #{i} PaddleOCRVL Backup Result:")
                logger.info(f"    - Text: '{ocr_result['text']}'")
                logger.info(f"    - Score: {ocr_result['score']:.4f}")
                logger.info(f"    - Success: {ocr_result['success']}")
                logger.info(f"    - Text length: {len(ocr_result['text'])} chars")
                logger.info(f"    - ** Used PaddleOCRVL backup (direct crop recognition) **")
            else:
                logger.warning(f"  Seal #{i}: No backup available (vl_pipeline=None or PaddleOCRVL not installed), skipping OCR")

        seal_data = {
            'index': int(i),
            'box': [float(v) for v in box],
            'crop_path': f"seal_crop_{i}.png",
            'unwarp_path': f"seal_unwarp_{i}.png" if unwarp is not None else None,
            'marked_path': f"seal_marked_{i}.png",
            'polar_viz_path': f"seal_polar_viz_{i}.png" if unwarp is not None else None,
            'text': ocr_result['text'],
            'confidence': float(ocr_result['score']),
            'success': bool(ocr_result['success']),
            'method_used': method_used,  # Add method tracking
            'used_fallback': used_fallback,  # Track if fallback was used
            'debug_info': {
                'center': center,
                'radius': radius,
                'start_theta_deg': float(math.degrees(start_theta)),
                'extent_deg': float(math.degrees(extent)),
                'num_polygons': len(all_polygons),
                'crop_size': (cw, ch),
                'unwarp_size': (unwarp.shape[1], unwarp.shape[0]) if unwarp is not None else None
            }
        }
        result['seals'].append(seal_data)

        if ocr_result['success']:
            result['institutions'].append(ocr_result['text'])
            logger.info(f"  ✓ Seal #{i} SUCCESS: {ocr_result['text'][:50]}... (confidence: {ocr_result['score']:.4f})")
        else:
            logger.warning(f"  ✗ Seal #{i} FAILED: Could not extract institution name")

    result['processing_time'] = time.time() - start_time
    return result


# ============ Similarity and Matching Functions ============

def calculate_similarity(str1: str, str2: str) -> float:
    """Calculate similarity percentage using Levenshtein distance"""
    if not str1 or not str2:
        return 0.0
    max_len = max(len(str1), len(str2))
    if max_len == 0:
        return 100.0
    edit_dist = levenshtein_distance(str1, str2)
    similarity = (1 - edit_dist / max_len) * 100
    return round(similarity, 2)


def classify_match(extracted: Optional[str], expected: str) -> Dict[str, Any]:
    """Classify match type between extracted and expected values"""
    if extracted is None:
        return {
            'match_type': 'no_match',
            'similarity': 0.0,
            'edit_distance': len(expected)
        }

    similarity = calculate_similarity(extracted, expected)
    edit_dist = levenshtein_distance(extracted, expected)

    if similarity == 100.0:
        match_type = 'exact'
    elif similarity >= SIMILARITY_THRESHOLD:
        match_type = 'partial'
    else:
        match_type = 'no_match'

    return {
        'match_type': match_type,
        'similarity': similarity,
        'edit_distance': edit_dist
    }


# ============ PDF Processing Functions ============

def extract_pdf_page(pdf_path: str, page_num: int = 0) -> Optional[np.ndarray]:
    """Extract a page from PDF as image"""
    try:
        doc = fitz.open(pdf_path)
        page = doc.load_page(page_num)
        pix = page.get_pixmap(matrix=fitz.Matrix(2, 2))
        img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.h, pix.w, pix.n)

        # Convert to BGR format for OpenCV
        if pix.n == 4:  # RGBA
            img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR)
        elif pix.n == 3:  # RGB
            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        elif pix.n == 1:  # Grayscale
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        else:
            logger.warning(f"Unexpected number of channels: {pix.n}")
            # Assume RGB and convert
            if pix.n >= 3:
                img = img[:, :, :3]
                img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

        return img
    except Exception as e:
        logger.error(f"Failed to extract page from {pdf_path}: {e}")
        return None


def process_single_pdf(pdf_name: str, expected_cma: str, expected_inst: str,
                      pdf_dir: Path, output_dir: Path, ocr_engine,
                      ocr_model="ppocr_v5", vl_pipeline=None) -> Dict[str, Any]:
    """
    Process a single PDF for CMA and institution extraction.

    Args:
        pdf_name: Name of PDF file
        expected_cma: Expected CMA code from ground truth
        expected_inst: Expected institution name from ground truth
        pdf_dir: Directory containing PDFs
        output_dir: Output directory for results
        ocr_engine: Global PaddleOCR instance (not currently used)
        ocr_model: OCR model to use ("ppocr_v5" or "paddleocr_vl")
        vl_pipeline: PaddleOCRVL pipeline (required if ocr_model="paddleocr_vl")

    Returns:
        Result dictionary with extraction and comparison data
    """
    pdf_path = pdf_dir / pdf_name
    pdf_output_dir = output_dir / pdf_name

    result = {
        'pdf_name': pdf_name,
        'expected': {
            'cma': expected_cma,
            'institution': expected_inst
        },
        'extracted': {
            'cma': None,
            'institution': None,
            'cma_confidence': 0.0,
            'cma_success': False,
            'institutions_from_seals': []
        },
        'comparison': {
            'cma': {},
            'institution': {}
        },
        'performance': {
            'total_time': 0.0,
            'cma_time': 0.0,
            'seal_time': 0.0
        },
        'seal_results': [],
        'status': 'success',
        'error': None,
        'file_size': 0
    }

    # Check file exists
    if not pdf_path.exists():
        result['status'] = 'file_not_found'
        result['error'] = f"PDF file not found: {pdf_path}"
        logger.warning(result['error'])
        return result

    result['file_size'] = pdf_path.stat().st_size

    # Clean output directory to ensure fresh processing
    if pdf_output_dir.exists():
        import shutil
        try:
            shutil.rmtree(pdf_output_dir)
            logger.info(f"Cleaned existing output directory: {pdf_output_dir}")
        except Exception as e:
            logger.warning(f"Failed to clean output directory: {e}")

    # Create fresh output directory
    pdf_output_dir.mkdir(parents=True, exist_ok=True)
    total_start = time.time()

    # Extract page
    logger.info(f"Extracting page 1 from {pdf_name}...")
    page_img = extract_pdf_page(str(pdf_path), page_num=0)
    if page_img is None:
        result['status'] = 'extraction_failed'
        result['error'] = "Failed to extract page from PDF"
        return result

    # Extract CMA code
    logger.info(f"Running CMA extraction on {pdf_name}...")
    cma_start = time.time()
    cma_result = extract_cma_code_fullpage(page_img, ocr_engine, output_dir=str(pdf_output_dir))
    result['performance']['cma_time'] = time.time() - cma_start

    result['extracted']['cma'] = cma_result['code']
    result['extracted']['cma_confidence'] = cma_result['confidence']
    result['extracted']['cma_success'] = cma_result['success']

    # Compare CMA
    if expected_cma == "无":
        result['comparison']['cma']['notes'] = "Ground truth marked as 'None'"
    else:
        comparison = classify_match(cma_result['code'], expected_cma)
        result['comparison']['cma'] = comparison

    # Extract seals and institutions
    logger.info(f"Running seal extraction on {pdf_name}...")
    seal_start = time.time()
    seal_result = extract_seals_and_institutions(page_img, str(pdf_output_dir),
                                                   ocr_model=ocr_model, vl_pipeline=vl_pipeline)
    result['performance']['seal_time'] = time.time() - seal_start

    result['seal_results'] = seal_result['seals']
    result['extracted']['institutions_from_seals'] = seal_result['institutions']

    # Select best institution match
    if seal_result['institutions']:
        # Find best matching institution
        best_inst = None
        best_similarity = 0.0

        for inst in seal_result['institutions']:
            if expected_inst and expected_inst != "无":
                sim = calculate_similarity(inst, expected_inst)
                if sim > best_similarity:
                    best_similarity = sim
                    best_inst = inst
            elif not best_inst:
                best_inst = inst

        # Fallback: if best_inst is still None (all similarities were 0), use first institution
        if best_inst is None and seal_result['institutions']:
            best_inst = seal_result['institutions'][0]

        result['extracted']['institution'] = best_inst

        # Compare institution
        if expected_inst and expected_inst != "无":
            inst_comparison = classify_match(best_inst, expected_inst)
            result['comparison']['institution'] = inst_comparison
        else:
            result['comparison']['institution']['notes'] = "No expected institution"

    result['performance']['total_time'] = time.time() - total_start

    return result


def generate_individual_report(result: Dict[str, Any], output_dir: Path):
    """Generate individual HTML report for a single PDF"""
    pdf_name = result['pdf_name']
    expected_cma = result['expected']['cma']
    expected_inst = result['expected']['institution']
    extracted_cma = result['extracted']['cma']
    extracted_inst = result['extracted']['institution']

    cma_match = result['comparison'].get('cma', {}).get('match_type', 'no_match')
    cma_sim = result['comparison'].get('cma', {}).get('similarity', 0)
    inst_match = result['comparison'].get('institution', {}).get('match_type', 'no_match')
    inst_sim = result['comparison'].get('institution', {}).get('similarity', 0)

    total_time = result['performance']['total_time']

    # Colors
    cma_color = '#4caf50' if cma_match == 'exact' else '#ff9800' if cma_match == 'partial' else '#f44336'
    inst_color = '#4caf50' if inst_match == 'exact' else '#ff9800' if inst_match == 'partial' else '#f44336'

    # Build seals HTML
    seals_html = ""
    if result['seal_results']:
        seals_html = "<h2>Detected Seals and Institution Names</h2>"
        for seal in result['seal_results']:
            status = "[OK]" if seal['success'] else "[FAIL]"
            text = seal['text'] if seal['text'] else "No text recognized"
            seals_html += f"""
        <div style="background: white; padding: 15px; margin-bottom: 20px; border-radius: 6px; border-left: 4px solid #2196F3;">
            <h3>Seal #{seal['index']}</h3>
            <p><strong>Recognized Text:</strong> {text}</p>
            <p><strong>Confidence:</strong> {seal['confidence']:.2%}</p>
            <p><strong>Status:</strong> {status}</p>
            <div style="display: flex; gap: 10px; margin-top: 10px;">
                <div>
                    <p style="margin: 0;">Marked:</p>
                    <img src="{seal['marked_path']}" style="max-height: 200px; border: 1px solid #ddd;">
                </div>
                <div>
                    <p style="margin: 0;">Unwarped:</p>
                    {f'<img src="{seal["unwarp_path"]}" style="max-height: 200px; border: 1px solid #ddd;">' if seal.get('unwarp_path') else 'N/A'}
                </div>
            </div>
        </div>"""

    html = f"""<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <title>Extraction Report - {pdf_name}</title>
    <style>
        body {{ font-family: 'Segoe UI', sans-serif; margin: 0; padding: 20px; background: #f5f5f5; }}
        .container {{ max-width: 1200px; margin: 0 auto; background: white; padding: 30px; border-radius: 8px; }}
        h1 {{ color: #333; border-bottom: 3px solid #4caf50; padding-bottom: 10px; }}
        .info-grid {{ display: grid; grid-template-columns: repeat(2, 1fr); gap: 20px; margin: 20px 0; }}
        .info-box {{ background: #f9f9f9; padding: 15px; border-radius: 6px; }}
        .info-box label {{ display: block; font-weight: bold; color: #666; margin-bottom: 5px; }}
        .info-box .value {{ font-size: 18px; }}
        .cma-box {{ border-left: 4px solid {cma_color}; }}
        .inst-box {{ border-left: 4px solid {inst_color}; }}
        .similarity {{ text-align: center; margin: 20px 0; }}
        .similarity .score {{ font-size: 48px; font-weight: bold; }}
    </style>
</head>
<body>
    <div class="container">
        <h1>CMA & Institution Extraction Report</h1>
        <p><strong>PDF:</strong> {pdf_name}</p>
        <p><strong>Processing Time:</strong> {total_time:.2f}s</p>

        <h2>CMA Code Extraction</h2>
        <div class="info-grid">
            <div class="info-box cma-box">
                <label>Expected CMA</label>
                <div class="value">{expected_cma}</div>
            </div>
            <div class="info-box cma-box">
                <label>Extracted CMA</label>
                <div class="value">{extracted_cma if extracted_cma else 'N/A'}</div>
            </div>
            <div class="info-box">
                <label>Match Type</label>
                <div class="value" style="color: {cma_color};">{cma_match.upper()}</div>
            </div>
            <div class="info-box">
                <label>Similarity</label>
                <div class="value">{cma_sim:.1f}%</div>
            </div>
        </div>

        <h2>Institution Name Extraction</h2>
        <div class="info-grid">
            <div class="info-box inst-box">
                <label>Expected Institution</label>
                <div class="value">{expected_inst}</div>
            </div>
            <div class="info-box inst-box">
                <label>Extracted Institution</label>
                <div class="value">{extracted_inst if extracted_inst else 'N/A'}</div>
            </div>
            <div class="info-box">
                <label>Match Type</label>
                <div class="value" style="color: {inst_color};">{inst_match.upper()}</div>
            </div>
            <div class="info-box">
                <label>Similarity</label>
                <div class="value">{inst_sim:.1f}%</div>
            </div>
        </div>

        <h2>Performance</h2>
        <div class="info-grid">
            <div class="info-box">
                <label>Total Time</label>
                <div class="value">{total_time:.2f}s</div>
            </div>
            <div class="info-box">
                <label>CMA Extraction Time</label>
                <div class="value">{result['performance']['cma_time']:.2f}s</div>
            </div>
            <div class="info-box">
                <label>Seal Extraction Time</label>
                <div class="value">{result['performance']['seal_time']:.2f}s</div>
            </div>
            <div class="info-box">
                <label>Seals Detected</label>
                <div class="value">{len(result['seal_results'])}</div>
            </div>
        </div>

        {seals_html}

        <h2>Visualizations</h2>
        <div style="background: white; padding: 15px; border-radius: 6px;">
            <p style="margin: 0 0 10px 0;">CMA Detection:</p>
            <img src="cma_detection_fullpage.png" style="max-width: 100%; border: 1px solid #ddd;">
        </div>
        <div style="background: white; padding: 15px; border-radius: 6px; margin-top: 10px;">
            <p style="margin: 0 0 10px 0;">Layout Detection:</p>
            <img src="doc_layout_viz.png" style="max-width: 100%; border: 1px solid #ddd;">
        </div>
    </div>
</body>
</html>"""

    os.makedirs(output_dir, exist_ok=True)
    with open(output_dir / 'index.html', 'w', encoding='utf-8') as f:
        f.write(html)


def generate_summary_report(all_results: List[Dict[str, Any]], output_dir: Path):
    """Generate summary HTML report"""
    # Calculate statistics
    total = len(all_results)
    valid_cma = [r for r in all_results if r['expected']['cma'] not in ['无', None]]
    valid_inst = [r for r in all_results if r['expected']['institution'] not in ['无', None]]

    cma_exact = sum(1 for r in valid_cma if r['comparison']['cma'].get('match_type') == 'exact')
    cma_partial = sum(1 for r in valid_cma if r['comparison']['cma'].get('match_type') == 'partial')
    cma_no = len(valid_cma) - cma_exact - cma_partial

    inst_exact = sum(1 for r in valid_inst if r['comparison']['institution'].get('match_type') == 'exact')
    inst_partial = sum(1 for r in valid_inst if r['comparison']['institution'].get('match_type') == 'partial')
    inst_no = len(valid_inst) - inst_exact - inst_partial

    cma_acc = (cma_exact / len(valid_cma) * 100) if valid_cma else 0
    inst_acc = (inst_exact / len(valid_inst) * 100) if valid_inst else 0

    avg_time = np.mean([r['performance']['total_time'] for r in all_results])

    html = f"""<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <title>Batch Test Summary - CMA & Institution Extraction</title>
    <style>
        body {{ font-family: 'Segoe UI', sans-serif; margin: 0; padding: 20px; background: #f5f5f5; }}
        .container {{ max-width: 1400px; margin: 0 auto; background: white; padding: 30px; border-radius: 8px; }}
        h1 {{ color: #333; }}
        .summary {{ display: grid; grid-template-columns: repeat(4, 1fr); gap: 20px; margin: 20px 0; }}
        .summary-card {{ background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 20px; border-radius: 8px; color: white; text-align: center; }}
        .summary-card .label {{ font-size: 14px; opacity: 0.9; }}
        .summary-card .value {{ font-size: 32px; font-weight: bold; }}
        table {{ width: 100%; border-collapse: collapse; margin: 20px 0; }}
        th, td {{ padding: 12px; text-align: left; border-bottom: 1px solid #ddd; }}
        th {{ background: #f5f5f5; }}
    </style>
</head>
<body>
    <div class="container">
        <h1>CMA & Institution Extraction - Batch Test Summary</h1>
        <p>Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>

        <h2>CMA Code Results</h2>
        <div class="summary">
            <div class="summary-card" style="background: linear-gradient(135deg, #4caf50 0%, #45a049 100%);">
                <div class="label">Exact Match</div>
                <div class="value">{cma_exact}/{len(valid_cma)}</div>
            </div>
            <div class="summary-card" style="background: linear-gradient(135deg, #ff9800 0%, #f57c00 100%);">
                <div class="label">Partial Match</div>
                <div class="value">{cma_partial}/{len(valid_cma)}</div>
            </div>
            <div class="summary-card" style="background: linear-gradient(135deg, #f44336 0%, #d32f2f 100%);">
                <div class="label">No Match</div>
                <div class="value">{cma_no}/{len(valid_cma)}</div>
            </div>
            <div class="summary-card" style="background: linear-gradient(135deg, #2196F3 0%, #1976D2 100%);">
                <div class="label">Accuracy</div>
                <div class="value">{cma_acc:.1f}%</div>
            </div>
        </div>

        <h2>Institution Name Results</h2>
        <div class="summary">
            <div class="summary-card" style="background: linear-gradient(135deg, #4caf50 0%, #45a049 100%);">
                <div class="label">Exact Match</div>
                <div class="value">{inst_exact}/{len(valid_inst)}</div>
            </div>
            <div class="summary-card" style="background: linear-gradient(135deg, #ff9800 0%, #f57c00 100%);">
                <div class="label">Partial Match</div>
                <div class="value">{inst_partial}/{len(valid_inst)}</div>
            </div>
            <div class="summary-card" style="background: linear-gradient(135deg, #f44336 0%, #d32f2f 100%);">
                <div class="label">No Match</div>
                <div class="value">{inst_no}/{len(valid_inst)}</div>
            </div>
            <div class="summary-card" style="background: linear-gradient(135deg, #2196F3 0%, #1976D2 100%);">
                <div class="label">Accuracy</div>
                <div class="value">{inst_acc:.1f}%</div>
            </div>
        </div>

        <h2>Performance</h2>
        <p>Average processing time: {avg_time:.1f}s per PDF</p>

        <h2>Complete Results</h2>
        <table>
            <thead>
                <tr>
                    <th>PDF</th>
                    <th>Expected CMA</th>
                    <th>Extracted CMA</th>
                    <th>CMA Match</th>
                    <th>Expected Inst</th>
                    <th>Extracted Inst</th>
                    <th>Inst Match</th>
                    <th>Seals</th>
                    <th>Time</th>
                </tr>
            </thead>
            <tbody>"""

    for r in all_results:
        cma_symbol = {'exact': '[OK]', 'partial': '[PARTIAL]', 'no_match': '[FAIL]'}.get(r['comparison'].get('cma', {}).get('match_type', 'no_match'), '[?]')
        inst_symbol = {'exact': '[OK]', 'partial': '[PARTIAL]', 'no_match': '[FAIL]'}.get(r['comparison'].get('institution', {}).get('match_type', 'no_match'), '[?]')
        seals_count = len(r['seal_results'])

        html += f"""
                <tr>
                    <td>{r['pdf_name']}</td>
                    <td>{r['expected']['cma']}</td>
                    <td>{r['extracted']['cma'] or 'N/A'}</td>
                    <td>{cma_symbol}</td>
                    <td>{r['expected']['institution'][:30]}...</td>
                    <td>{(r['extracted']['institution'] or 'N/A')[:30]}...</td>
                    <td>{inst_symbol}</td>
                    <td>{seals_count}</td>
                    <td>{r['performance']['total_time']:.1f}s</td>
                </tr>"""

    html += """
            </tbody>
        </table>
    </div>
</body>
</html>"""

    with open(output_dir / 'summary.html', 'w', encoding='utf-8') as f:
        f.write(html)


def main():
    """Main execution function"""
    # Parse command line arguments
    import argparse
    parser = argparse.ArgumentParser(description='CMA & Institution Extraction - Batch Accuracy Test')
    parser.add_argument('--ocr-model', type=str, default=OCR_MODEL,
                       choices=['ppocr_v5', 'paddleocr_vl'],
                       help='OCR model to use (default: from OCR_MODEL env var or ppocr_v5)')
    parser.add_argument('--batch-size', type=int, default=BATCH_SIZE,
                       help=f'Number of PDFs to process (default: {BATCH_SIZE})')
    parser.add_argument('--pdf-names', type=str, default=None,
                       help='Comma-separated list of PDF names to process (e.g., "1.pdf,2.pdf"). Overrides --batch-size')
    args = parser.parse_args()

    # Use command line argument if provided
    ocr_model = args.ocr_model
    batch_size = args.batch_size
    pdf_names_filter = args.pdf_names

    print("=" * 80)
    print("CMA & INSTITUTION EXTRACTION - BATCH ACCURACY TEST")
    print("=" * 80)
    print(f"OCR Model: {ocr_model.upper()}")
    print(f"Processing first {batch_size} PDFs from results.json...")
    print(f"PDF directory: {PDF_DIR}")
    print(f"Output directory: {OUTPUT_DIR}")
    print()

    # Load ground truth
    if not RESULTS_JSON.exists():
        logger.error(f"Ground truth file not found: {RESULTS_JSON}")
        return

    with open(RESULTS_JSON, 'r', encoding='utf-8') as f:
        ground_truth = json.load(f)

    # Filter PDFs: either by name filter or by batch size
    if pdf_names_filter:
        # Split comma-separated names and strip whitespace
        requested_names = [name.strip() for name in pdf_names_filter.split(',')]
        pdf_list = [(name, ground_truth[name]) for name in requested_names if name in ground_truth]
        if not pdf_list:
            logger.error(f"None of the specified PDFs found in results.json: {requested_names}")
            print(f"ERROR: None of the specified PDFs found in results.json: {requested_names}")
            return
        print(f"Processing {len(pdf_list)} specified PDF(s): {[name for name, _ in pdf_list]}")
    else:
        # Get first N PDFs
        pdf_list = list(ground_truth.items())[:batch_size]

    # Initialize OCR engines
    # Note: We ALWAYS initialize ocr_engine for CMA recognition
    # We ALWAYS try to initialize vl_pipeline for backup seal recognition (when unwarp fails)
    ocr_engine = None
    vl_pipeline = None

    logger.info("Initializing PaddleOCR engine for CMA recognition...")
    print("Initializing PaddleOCR engine (required for CMA extraction)...")
    ocr_engine = PaddleOCR(use_angle_cls=True, lang='ch')
    logger.info("PaddleOCR initialized successfully")
    print("PaddleOCR initialized successfully\n")

    # Initialize PaddleOCRVL for backup seal recognition (always try if available)
    # This provides a fallback when polar unwarping fails
    if PADDLEOCRVL_AVAILABLE:
        logger.info("Initializing PaddleOCRVL for backup seal recognition...")
        print("Initializing PaddleOCRVL for backup seal recognition (this may take a while)...")
        try:
            vl_pipeline = PaddleOCRVL(
                use_seal_recognition=True,
                use_ocr_for_image_block=True,
                use_layout_detection=True
            )

            # Verify initialization
            if vl_pipeline is None:
                raise RuntimeError("PaddleOCRVL initialization returned None")

            logger.info("PaddleOCRVL initialized successfully (backup ready)")
            print("PaddleOCRVL backup ready - will be used when polar unwarping fails\n")
        except Exception as e:
            logger.error(f"Failed to initialize PaddleOCRVL: {e}")
            logger.error(f"Exception type: {type(e).__name__}")
            print(f"WARNING: Failed to initialize PaddleOCRVL: {e}")
            print("Polar unwarping failures will skip OCR (no backup available)\n")
    else:
        logger.info("PaddleOCRVL not available - polar unwarping failures will skip OCR")
        print("Note: PaddleOCRVL not installed - polar unwarping failures will skip OCR")
        print("      To enable backup: pip install paddleocr[doc-parser]\n")

    # Validate OCR model selection
    if ocr_model == "paddleocr_vl" and vl_pipeline is None:
        print("WARNING: PaddleOCRVL requested for primary seal recognition but not available!")
        print("Falling back to PP-OCRv5 for seal recognition")
        print("Please install: pip install paddleocr[doc-parser]")
        ocr_model = "ppocr_v5"

    # Create output directory
    OUTPUT_DIR.mkdir(exist_ok=True)

    # Process each PDF
    all_results = []
    start_time = time.time()

    total_pdfs = len(pdf_list)
    for i, (pdf_name, expected_data) in enumerate(pdf_list, 1):
        expected_cma = expected_data.get('CMA', '')
        expected_inst = expected_data.get('机构名', '')

        print(f"\n[{i}/{total_pdfs}] Processing: {pdf_name}")
        print("  + Loading PDF and extracting page...")

        result = process_single_pdf(
            pdf_name, expected_cma, expected_inst,
            PDF_DIR, OUTPUT_DIR, ocr_engine,
            ocr_model=ocr_model, vl_pipeline=vl_pipeline
        )

        all_results.append(result)

        # Print result summary
        if result['status'] == 'file_not_found':
            print(f"  + [!] File not found, skipping")
        else:
            cma_match = result['comparison']['cma'].get('match_type', 'unknown')
            cma_sim = result['comparison']['cma'].get('similarity', 0)
            cma_symbol = {'exact': '[OK]', 'partial': '[PARTIAL]', 'no_match': '[FAIL]'}.get(cma_match, '[?]')

            print(f"  + CMA Extraction:")
            print(f"    + Extracted: {result['extracted']['cma'] or 'N/A'}")
            print(f"    + Expected: {expected_cma}")
            print(f"    + Match: {cma_symbol} {cma_match.upper()} ({cma_sim:.1f}%)")

            if result['extracted']['institution']:
                inst_match = result['comparison']['institution'].get('match_type', 'unknown')
                inst_sim = result['comparison']['institution'].get('similarity', 0)
                inst_symbol = {'exact': '[OK]', 'partial': '[PARTIAL]', 'no_match': '[FAIL]'}.get(inst_match, '[?]')
                print(f"  + Institution Extraction:")
                print(f"    + Extracted: {result['extracted']['institution'][:50]}...")
                print(f"    + Expected: {expected_inst[:50]}...")
                print(f"    + Match: {inst_symbol} {inst_match.upper()} ({inst_sim:.1f}%)")

            print(f"  + Seals detected: {len(result['seal_results'])}")
            print(f"  + Completed in {result['performance']['total_time']:.2f}s")

        # Generate individual report
        generate_individual_report(result, OUTPUT_DIR / pdf_name)

        # Interim results every 5
        if i % 5 == 0:
            valid_cma = [r for r in all_results if r['expected']['cma'] not in ['无', None]]
            cma_exact = sum(1 for r in valid_cma if r['comparison']['cma'].get('match_type') == 'exact')
            cma_acc = (cma_exact / len(valid_cma) * 100) if valid_cma else 0

            valid_inst = [r for r in all_results if r['expected']['institution'] not in ['无', None] and r['extracted']['institution']]
            inst_exact = sum(1 for r in valid_inst if r['comparison']['institution'].get('match_type') == 'exact')
            inst_acc = (inst_exact / len(valid_inst) * 100) if valid_inst else 0

            print()
            print("=" * 80)
            print(f"INTERIM RESULTS ({i}/{BATCH_SIZE} completed)")
            print("=" * 80)
            print(f"CMA Accuracy: {cma_acc:.1f}% ({cma_exact}/{len(valid_cma)} exact)")
            print(f"Institution Accuracy: {inst_acc:.1f}% ({inst_exact}/{len(valid_inst)} exact)")
            print("=" * 80)
            print()

    total_time = time.time() - start_time

    # Calculate final statistics
    valid_cma = [r for r in all_results if r['expected']['cma'] not in ['无', None]]
    cma_exact = sum(1 for r in valid_cma if r['comparison']['cma'].get('match_type') == 'exact')
    cma_partial = sum(1 for r in valid_cma if r['comparison']['cma'].get('match_type') == 'partial')
    cma_no = len(valid_cma) - cma_exact - cma_partial
    cma_acc = (cma_exact / len(valid_cma) * 100) if valid_cma else 0

    valid_inst = [r for r in all_results if r['expected']['institution'] not in ['无', None] and r['extracted']['institution']]
    inst_exact = sum(1 for r in valid_inst if r['comparison']['institution'].get('match_type') == 'exact')
    inst_partial = sum(1 for r in valid_inst if r['comparison']['institution'].get('match_type') == 'partial')
    inst_no = len(valid_inst) - inst_exact - inst_partial
    inst_acc = (inst_exact / len(valid_inst) * 100) if valid_inst else 0

    # Generate summary report
    print("\nGenerating summary report...")
    generate_summary_report(all_results, OUTPUT_DIR)

    # Save JSON
    json_output = {
        'summary': {
            'total_processed': len(all_results),
            'cma': {
                'exact': cma_exact,
                'partial': cma_partial,
                'no_match': cma_no,
                'accuracy': cma_acc / 100
            },
            'institution': {
                'exact': inst_exact,
                'partial': inst_partial,
                'no_match': inst_no,
                'accuracy': inst_acc / 100
            },
            'avg_processing_time': np.mean([r['performance']['total_time'] for r in all_results])
        },
        'results': all_results
    }

    with open(OUTPUT_DIR / 'test_report.json', 'w', encoding='utf-8') as f:
        json.dump(json_output, f, ensure_ascii=False, indent=2, cls=NumpyEncoder)

    # Print final summary
    print("\n" + "=" * 80)
    print("BATCH TEST COMPLETED - FINAL RESULTS")
    print("=" * 80)
    print(f"Total Processed: {len(all_results)}")
    print()
    print("CMA Code Results:")
    print(f"  Exact Match: {cma_exact}/{len(valid_cma)} ({cma_exact/len(valid_cma)*100:.1f}%)")
    print(f"  Partial Match: {cma_partial}/{len(valid_cma)} ({cma_partial/len(valid_cma)*100:.1f}%)")
    print(f"  No Match: {cma_no}/{len(valid_cma)} ({cma_no/len(valid_cma)*100:.1f}%)")
    print(f"  ** CMA Accuracy: {cma_acc:.1f}% **")
    print()
    print("Institution Name Results:")
    print(f"  Exact Match: {inst_exact}/{len(valid_inst)} ({inst_exact/len(valid_inst)*100:.1f}%)")
    print(f"  Partial Match: {inst_partial}/{len(valid_inst)} ({inst_partial/len(valid_inst)*100:.1f}%)")
    print(f"  No Match: {inst_no}/{len(valid_inst)} ({inst_no/len(valid_inst)*100:.1f}%)")
    print(f"  ** Institution Accuracy: {inst_acc:.1f}% **")
    print()
    print("Performance:")
    print(f"  Total Time: {total_time:.1f}s ({total_time/60:.1f}min)")
    print(f"  Average Time: {total_time/len(all_results):.1f}s per PDF")
    print()
    print("Reports Generated:")
    print(f"  - {OUTPUT_DIR / 'summary.html'}")
    print(f"  - {OUTPUT_DIR / 'test_report.json'}")
    print(f"  - Individual reports: {OUTPUT_DIR / '{pdf_name}/'}")
    print()
    print("=" * 80)


if __name__ == "__main__":
    main()