ocr_verify/compare_ocr_results.py

import sys
import time
import re
import difflib
import json
import argparse
from typing import Dict, List, Tuple
import markdown
from bs4 import BeautifulSoup
from fuzzywuzzy import fuzz

class OCRResultComparator:
    def __init__(self):
        self.differences = []
        self.similarity_threshold = 85  # 相似度阈值，超过85%认为是匹配的
        self.max_paragraph_window = 6  # 最大合并段落数
    
    def normalize_text(self, text: str) -> str:
        """标准化文本：去除多余空格、回车等无效字符"""
        if not text:
            return ""
        # 去除多余的空白字符
        text = re.sub(r'\s+', ' ', text.strip())
        # 去除标点符号周围的空格
        text = re.sub(r'\s*([，。：；！？、])\s*', r'\1', text)
        return text
    
    def is_image_reference(self, text: str) -> bool:
        """判断是否为图片引用或描述"""
        image_keywords = [
            '图', '图片', '图像', 'image', 'figure', 'fig',
            '照片', '截图', '示意图', '流程图', '结构图'
        ]
        # 检查是否包含图片相关关键词
        for keyword in image_keywords:
            if keyword in text.lower():
                return True
        
        # 检查是否为Markdown图片语法
        if re.search(r'!\[.*?\]\(.*?\)', text):
            return True
            
        # 检查是否为HTML图片标签
        if re.search(r'<img[^>]*>', text, re.IGNORECASE):
            return True
            
        return False
    
    def extract_table_data(self, md_content: str) -> List[List[List[str]]]:
        """从Markdown中提取表格数据"""
        tables = []
        
        # 使用BeautifulSoup解析HTML表格
        soup = BeautifulSoup(md_content, 'html.parser')
        html_tables = soup.find_all('table')
        
        for table in html_tables:
            table_data = []
            rows = table.find_all('tr')
            
            for row in rows:
                cells = row.find_all(['td', 'th'])
                row_data = []
                for cell in cells:
                    cell_text = self.normalize_text(cell.get_text())
                    # 跳过图片内容
                    if not self.is_image_reference(cell_text):
                        row_data.append(cell_text)
                    else:
                        row_data.append("[图片内容-忽略]")
                        
                if row_data:  # 只添加非空行
                    table_data.append(row_data)
            
            if table_data:
                tables.append(table_data)
        
        return tables
    
    def merge_split_paragraphs(self, lines: List[str]) -> List[str]:
        # 合并连续的非空行作为一个段落，且过滤图片内容
        merged_lines = []
        current_paragraph = ""
        for i, line in enumerate(lines):
            # 跳过空行
            if not line:
                if current_paragraph:
                    merged_lines.append(current_paragraph)
                    current_paragraph = ""
                continue
            # 跳过图片内容
            if self.is_image_reference(line):
                continue

            # 检查是否是标题（以数字、中文数字或特殊标记开头）
            is_title = (
                line.startswith(('一、', '二、', '三、', '四、', '五、', '六、', '七、', '八、', '九、', '十、')) or
                line.startswith(('1.', '2.', '3.', '4.', '5.', '6.', '7.', '8.', '9.')) or
                line.startswith('#')
            )
                        # 如果是标题，结束当前段落
            if is_title:
                if current_paragraph:
                    merged_lines.append(current_paragraph)
                    current_paragraph = ""
                merged_lines.append(line)
            else:
                # 检查是否应该与前一行合并 # 如果当前段落不为空，且当前段落最后一个字符非空白字符
                if current_paragraph and not current_paragraph.endswith((' ', '\t')):
                    current_paragraph += line
                else:
                    current_paragraph = line
        
        # 处理最后一个段落
        if current_paragraph:
            merged_lines.append(current_paragraph)
        
        return merged_lines

    def extract_paragraphs(self, md_content: str) -> List[str]:
        """提取段落文本"""
        # 移除表格
        content = re.sub(r'<table>.*?</table>', '', md_content, flags=re.DOTALL)
        # 移除HTML标签
        content = re.sub(r'<[^>]+>', '', content)
        # 移除Markdown注释
        content = re.sub(r'<!--.*?-->', '', content, flags=re.DOTALL)
        
        # 分割段落
        paragraphs = []
        lines = content.split('\n')
        merged_lines = self.merge_split_paragraphs(lines)
        
        for line in merged_lines:
            normalized = self.normalize_text(line)
            if normalized:
                paragraphs.append(normalized)
            else:
                print(f"跳过的内容无效或图片段落: {line[0:30]}...")
        
        return paragraphs
    
    def compare_tables(self, table1: List[List[str]], table2: List[List[str]]) -> List[Dict]:
        """比较表格数据"""
        differences = []
        
        # 确定最大行数
        max_rows = max(len(table1), len(table2))
        
        for i in range(max_rows):
            row1 = table1[i] if i < len(table1) else []
            row2 = table2[i] if i < len(table2) else []
            
            # 确定最大列数
            max_cols = max(len(row1), len(row2))
            
            for j in range(max_cols):
                cell1 = row1[j] if j < len(row1) else ""
                cell2 = row2[j] if j < len(row2) else ""
                
                # 跳过图片内容比较
                if "[图片内容-忽略]" in cell1 or "[图片内容-忽略]" in cell2:
                    continue
                
                if cell1 != cell2:
                    # 特别处理数字金额
                    if self.is_numeric(cell1) and self.is_numeric(cell2):
                        num1 = self.parse_number(cell1)
                        num2 = self.parse_number(cell2)
                        if abs(num1 - num2) > 0.001:  # 允许小数精度误差
                            differences.append({
                                'type': 'table_amount',
                                'position': f'行{i+1}列{j+1}',
                                'file1_value': cell1,
                                'file2_value': cell2,
                                'description': f'金额不一致: {cell1} vs {cell2}',
                                'row_index': i,
                                'col_index': j
                            })
                    else:
                        differences.append({
                            'type': 'table_text',
                            'position': f'行{i+1}列{j+1}',
                            'file1_value': cell1,
                            'file2_value': cell2,
                            'description': f'文本不一致: {cell1} vs {cell2}',
                            'row_index': i,
                            'col_index': j
                        })
        
        return differences
    
    def is_numeric(self, text: str) -> bool:
        """判断文本是否为数字"""
        if not text:
            return False
        # 移除千分位分隔符和负号
        clean_text = re.sub(r'[,，-]', '', text)
        try:
            float(clean_text)
            return True
        except ValueError:
            return False
    
    def parse_number(self, text: str) -> float:
        """解析数字"""
        if not text:
            return 0.0
        clean_text = re.sub(r'[,，]', '', text)
        try:
            return float(clean_text)
        except ValueError:
            return 0.0
    
    def normalize_text_for_comparison(self, text: str) -> str:
        """增强的文本标准化 - 用于语义比较"""
        if not text:
            return ""
        
        # 移除Markdown格式标记
        text = re.sub(r'#{1,6}\s*', '', text)  # 移除标题标记
        text = re.sub(r'\*\*(.+?)\*\*', r'\1', text)  # 移除粗体标记
        text = re.sub(r'\*(.+?)\*', r'\1', text)  # 移除斜体标记
        text = re.sub(r'`(.+?)`', r'\1', text)  # 移除代码标记
        text = re.sub(r'<!--.*?-->', '', text, flags=re.DOTALL)  # 移除注释
        
        # 统一标点符号
        punctuation_map = {
            '，': ',', '。': '.', '：': ':', '；': ';', 
            '！': '!', '？': '?', '（': '(', '）': ')',
            '【': '[', '】': ']', '《': '<', '》': '>',
            '"': '"', '"': '"', ''': "'", ''': "'",
            '、': ',', '…': '...'
        }
        
        for chinese_punct, english_punct in punctuation_map.items():
            text = text.replace(chinese_punct, english_punct)
        
        # 移除多余的空白字符
        text = re.sub(r'\s+', ' ', text.strip())
        
        # 移除标点符号周围的空格
        text = re.sub(r'\s*([,.():;!?])\s*', r'\1', text)
        
        return text
    
    def calculate_text_similarity(self, text1: str, text2: str) -> float:
        """改进的相似度计算"""
        if not text1 and not text2:
            return 100.0
        if not text1 or not text2:
            return 0.0
        
        # 如果标准化后完全相同，返回100%
        if text1 == text2:
            return 100.0
        
        # 使用多种相似度算法
        similarity_scores = [
            fuzz.ratio(text1, text2),
            fuzz.partial_ratio(text1, text2),
            fuzz.token_sort_ratio(text1, text2),
            fuzz.token_set_ratio(text1, text2)
        ]
        
        # 对于包含关系，给予更高的权重
        if text1 in text2 or text2 in text1:
            max_score = max(similarity_scores)
            # 提升包含关系的相似度
            return min(100.0, max_score + 10)
        
        return max(similarity_scores)
    
    def compare_paragraphs_with_flexible_matching(self, paras1: List[str], paras2: List[str]) -> List[Dict]:
        """改进的段落匹配算法 - 更好地处理段落重组"""
        differences = []
        
        # 直接调用normalize_text_for_comparison进行预处理
        meaningful_paras1 = [self.normalize_text_for_comparison(p) for p in paras1]
        meaningful_paras2 = [self.normalize_text_for_comparison(p) for p in paras2]

        # 使用预处理后的段落进行匹配
        used_paras1 = set()
        used_paras2 = set()
        
        best_match = {'similarity': 0.0}  # 初始化best_match
        # 文件1和文件2同时向下遍历，当有匹配项时，文件2的窗口从匹配项的下一个位置开始
        paras2_idx = 0
        for window_size1 in range(1, min(self.max_paragraph_window, len(meaningful_paras1) + 1)):  # 增加到6个段落
            for i in range(len(meaningful_paras1) - window_size1 + 1):
                if any(idx in used_paras1 for idx in range(i, i + window_size1)):
                    continue
                    
                # 合并文件1中的段落
                combined_para1 = "".join(meaningful_paras1[i:i+window_size1])
                
                # 在文件2中寻找最佳匹配
                best_match = self._find_best_match_in_paras2_improved(
                    combined_para1, 
                    meaningful_paras2[paras2_idx: min(paras2_idx + self.max_paragraph_window, len(meaningful_paras2))], 
                    paras2_idx
                )
                
                if best_match and best_match['similarity'] >= self.similarity_threshold:
                    paras2_idx = best_match['indices'][-1] + 1  # 更新文件2的起始索引
                    # 记录匹配
                    for idx in range(i, i + window_size1):
                        used_paras1.add(idx)
                    for idx in best_match['indices']:
                        used_paras2.add(idx)
                    
                    # 只有当相似度明显不同时才记录差异
                    if best_match['similarity'] < 95.0:  # 提高阈值到95%
                        severity = 'low' if best_match['similarity'] >= 90 else 'medium'
                        differences.append({
                            'type': 'paragraph',
                            'position': f'段落{i+1}' + (f'-{i+window_size1}' if window_size1 > 1 else ''),
                            'file1_value': combined_para1,
                            'file2_value': best_match['text'],
                            'description': f'段落格式差异 (相似度: {best_match["similarity"]:.1f}%)',
                            'similarity': best_match['similarity'],
                            'severity': severity
                        })
                    
            if paras2_idx >= len(meaningful_paras2):
                break  # 文件2已全部匹配完，退出
        
        # 处理未匹配的有意义段落
        for i, para in enumerate(meaningful_paras1):
            if i not in used_paras1:
                differences.append({
                    'type': 'paragraph',
                    'position': f'段落{i+1}',
                    'file1_value': para,
                    'file2_value': "",
                    'description': '文件1中独有的段落',
                    'similarity': 0.0,
                    'severity': 'medium'
                })
        
        for j, para in enumerate(meaningful_paras2):
            if j not in used_paras2:
                differences.append({
                    'type': 'paragraph',
                    'position': f'段落{j+1}',
                    'file1_value': "",
                    'file2_value': para,
                    'description': '文件2中独有的段落',
                    'similarity': 0.0,
                    'severity': 'medium'
                })
        
        return differences

    def _find_best_match_in_paras2_improved(self, target_text: str, paras2: List[str], 
                                       paras2_idx: int) -> Dict:
        """改进的段落匹配方法"""
        best_match = None
        
        for window_size in range(1, len(paras2) + 1):
            for j in range(len(paras2) - window_size + 1):
                combined_para2 = "".join(paras2[j:j+window_size])
                similarity = self.calculate_text_similarity(target_text, combined_para2)

                if best_match and best_match['similarity'] == 100.0:
                    break  # 找到完美匹配，提前退出
                
                if not best_match or similarity > best_match['similarity']:
                    best_match = {
                        'text': combined_para2,
                        'similarity': similarity,
                        'indices': list(range(j + paras2_idx, j + paras2_idx + window_size))
                    }
            if best_match and best_match['similarity'] == 100.0:
                break  # 找到完美匹配，提前退出
        
        # Return empty dict if no match found
        if best_match is None:
            return {
                'text': '',
                'similarity': 0.0,
                'indices': []
            }
        
        return best_match
    
    def compare_files(self, file1_path: str, file2_path: str) -> Dict:
        """改进的文件比较方法"""
        # 读取文件
        with open(file1_path, 'r', encoding='utf-8') as f:
            content1 = f.read()
        
        with open(file2_path, 'r', encoding='utf-8') as f:
            content2 = f.read()
        
        # 提取表格和段落
        tables1 = self.extract_table_data(content1)
        tables2 = self.extract_table_data(content2)
        
        paras1 = self.extract_paragraphs(content1)
        paras2 = self.extract_paragraphs(content2)
        
        # 比较结果
        all_differences = []
        
        # 比较表格 (保持原有逻辑)
        if tables1 and tables2:
            table_diffs = self.compare_tables(tables1[0], tables2[0])
            all_differences.extend(table_diffs)
        elif tables1 and not tables2:
            all_differences.append({
                'type': 'table_structure',
                'position': '表格结构',
                'file1_value': f'包含{len(tables1)}个表格',
                'file2_value': '无表格',
                'description': '文件1包含表格但文件2无表格',
                'severity': 'high'
            })
        elif not tables1 and tables2:
            all_differences.append({
                'type': 'table_structure',
                'position': '表格结构',
                'file1_value': '无表格',
                'file2_value': f'包含{len(tables2)}个表格',
                'description': '文件2包含表格但文件1无表格',
                'severity': 'high'
            })
        
        # 使用增强的段落比较
        para_diffs = self.compare_paragraphs_with_flexible_matching(paras1, paras2)
        all_differences.extend(para_diffs)
        
        # # 生成unified diff报告
        # unified_diff_data = self.generate_unified_diff_report(
        #     paras1, paras2, file1_path, file2_path, 
        #     "./output/pre_validation/unified_diff_comparison"
        # )

        # 统计信息
        stats = {
            'total_differences': len(all_differences),
            'table_differences': len([d for d in all_differences if d['type'].startswith('table')]),
            'paragraph_differences': len([d for d in all_differences if d['type'] == 'paragraph']),
            'amount_differences': len([d for d in all_differences if d['type'] == 'table_amount']),
            'high_severity': len([d for d in all_differences if d.get('severity') == 'high']),
            'medium_severity': len([d for d in all_differences if d.get('severity') == 'medium']),
            'low_severity': len([d for d in all_differences if d.get('severity') == 'low'])
        }
        
        # 在返回结果中添加unified diff数据
        result = {
            'differences': all_differences,
            'statistics': stats,
            'file1_tables': len(tables1),
            'file2_tables': len(tables2),
            'file1_paragraphs': len(paras1),
            'file2_paragraphs': len(paras2),
            'file1_path': file1_path,
            'file2_path': file2_path,
            # 'unified_diff': unified_diff_data  # 添加unified diff数据
        }
        
        return result

    def generate_unified_diff(self, paras1: List[str], paras2: List[str], file1_path: str, file2_path: str) -> Dict:
        """
        生成类似git diff的统一差异格式，并返回结构化数据
        """
        # 直接调用normalize_text_for_comparison进行预处理
        file1_lines = [self.normalize_text_for_comparison(p) for p in paras1]
        file2_lines = [self.normalize_text_for_comparison(p) for p in paras2]

        # 使用unified_diff生成差异
        diff = difflib.unified_diff(
            file1_lines, 
            file2_lines, 
            fromfile=file1_path, 
            tofile=file2_path,
            lineterm=''  # 确保每行末尾不添加额外字符
        )
        
        # 将差异生成器转换为列表
        diff_output = list(diff)
        
        # 解析diff输出并生成结构化数据
        structured_diff = self._parse_unified_diff(diff_output, file1_lines, file2_lines, file1_path, file2_path)
        
        return structured_diff

    def _parse_unified_diff(self, diff_lines: List[str], file1_lines: List[str], file2_lines: List[str], 
                       file1_path: str, file2_path: str) -> Dict:
        """解析unified diff输出并生成结构化数据"""
        differences = []
        current_hunk = None
        file1_line_num = 0
        file2_line_num = 0
        
        for line in diff_lines:
            if line.startswith('---') or line.startswith('+++'):
                continue
            elif line.startswith('@@'):
                # 解析hunk头部，例如: @@ -1,5 +1,4 @@
                import re
                match = re.match(r'@@ -(\d+)(?:,(\d+))? \+(\d+)(?:,(\d+))? @@', line)
                if match:
                    file1_start = int(match.group(1))
                    file1_count = int(match.group(2)) if match.group(2) else 1
                    file2_start = int(match.group(3))
                    file2_count = int(match.group(4)) if match.group(4) else 1
                    
                    current_hunk = {
                        'file1_start': file1_start,
                        'file1_count': file1_count,
                        'file2_start': file2_start,
                        'file2_count': file2_count
                    }
                    file1_line_num = file1_start - 1  # 转为0基索引
                    file2_line_num = file2_start - 1
            elif line.startswith(' '):
                # 未改变的行
                file1_line_num += 1
                file2_line_num += 1
            elif line.startswith('-'):
                # 文件1中删除的行
                content = line[1:]  # 去掉'-'前缀
                differences.append({
                    'type': 'paragraph',
                    'position': f'段落{file1_line_num + 1}',
                    'file1_value': content,
                    'file2_value': "",
                    'description': '文件1中独有的段落',
                    'similarity': 0.0,
                    'severity': 'medium',
                    'line_number': file1_line_num + 1,
                    'change_type': 'deletion'
                })
                file1_line_num += 1
            elif line.startswith('+'):
                # 文件2中添加的行
                content = line[1:]  # 去掉'+'前缀
                differences.append({
                    'type': 'paragraph',
                    'position': f'段落{file2_line_num + 1}',
                    'file1_value': "",
                    'file2_value': content,
                    'description': '文件2中独有的段落',
                    'similarity': 0.0,
                    'severity': 'medium',
                    'line_number': file2_line_num + 1,
                    'change_type': 'addition'
                })
                file2_line_num += 1
    
        # 计算统计信息
        stats = {
            'total_differences': len(differences),
            'table_differences': 0,  # diff不包含表格差异
            'paragraph_differences': len(differences),
            'amount_differences': 0,
            'high_severity': len([d for d in differences if d.get('severity') == 'high']),
            'medium_severity': len([d for d in differences if d.get('severity') == 'medium']),
            'low_severity': len([d for d in differences if d.get('severity') == 'low']),
            'deletions': len([d for d in differences if d.get('change_type') == 'deletion']),
            'additions': len([d for d in differences if d.get('change_type') == 'addition'])
        }
        
        return {
            'differences': differences,
            'statistics': stats,
            'file1_tables': 0,
            'file2_tables': 0,
            'file1_paragraphs': len(file1_lines),
            'file2_paragraphs': len(file2_lines),
            'file1_path': file1_path,
            'file2_path': file2_path,
            'diff_type': 'unified_diff'
        }

    def generate_unified_diff_report(self, paras1: List[str], paras2: List[str], file1_path: str, file2_path: str, output_file: str):
        """生成unified diff的JSON和Markdown报告"""
        # 生成结构化diff数据
        diff_data = self.generate_unified_diff(paras1, paras2, file1_path, file2_path)
        
        # 添加时间戳
        import datetime
        diff_data['timestamp'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        
        # 生成JSON报告
        json_file = f"{output_file}_unified_diff.json"
        with open(json_file, 'w', encoding='utf-8') as f:
            json.dump(diff_data, f, ensure_ascii=False, indent=2)
        
        # 生成Markdown报告
        md_file = f"{output_file}_unified_diff.md"
        self._generate_unified_diff_markdown(diff_data, md_file)
        
        print(f"📄 Unified Diff JSON报告: {json_file}")
        print(f"📝 Unified Diff Markdown报告: {md_file}")
        
        return diff_data

    def _generate_unified_diff_markdown(self, diff_data: Dict, output_file: str):
        """生成unified diff的Markdown报告"""
        with open(output_file, 'w', encoding='utf-8') as f:
            f.write("# OCR结果Unified Diff对比报告\n\n")
            
            # 基本信息
            f.write("## 基本信息\n\n")
            f.write(f"- **文件1**: `{diff_data['file1_path']}`\n")
            f.write(f"- **文件2**: `{diff_data['file2_path']}`\n")
            f.write(f"- **比较时间**: {diff_data.get('timestamp', 'N/A')}\n")
            f.write(f"- **对比方式**: Unified Diff\n\n")
            
            # 统计信息
            stats = diff_data['statistics']
            f.write("## 统计信息\n\n")
            f.write(f"- 总差异数量: **{stats['total_differences']}**\n")
            f.write(f"- 删除行数: **{stats['deletions']}**\n")
            f.write(f"- 添加行数: **{stats['additions']}**\n")
            f.write(f"- 文件1段落数: {diff_data['file1_paragraphs']}\n")
            f.write(f"- 文件2段落数: {diff_data['file2_paragraphs']}\n\n")
            
            # 差异详情
            if diff_data['differences']:
                f.write("## 差异详情\n\n")
                
                # 按变更类型分组
                deletions = [d for d in diff_data['differences'] if d['change_type'] == 'deletion']
                additions = [d for d in diff_data['differences'] if d['change_type'] == 'addition']
                
                if deletions:
                    f.write(f"### 🗑️ 删除内容 ({len(deletions)}项)\n\n")
                    for i, diff in enumerate(deletions, 1):
                        f.write(f"**{i}. 第{diff['line_number']}行**\n")
                        f.write(f"```\n{diff['file1_value']}\n```\n\n")
                
                if additions:
                    f.write(f"### ➕ 新增内容 ({len(additions)}项)\n\n")
                    for i, diff in enumerate(additions, 1):
                        f.write(f"**{i}. 第{diff['line_number']}行**\n")
                        f.write(f"```\n{diff['file2_value']}\n```\n\n")
                
                # 详细差异表格
                f.write("## 详细差异列表\n\n")
                f.write("| 序号 | 类型 | 行号 | 变更类型 | 内容 | 描述 |\n")
                f.write("| --- | --- | --- | --- | --- | --- |\n")
                
                for i, diff in enumerate(diff_data['differences'], 1):
                    change_icon = "🗑️" if diff['change_type'] == 'deletion' else "➕"
                    content = diff['file1_value'] if diff['change_type'] == 'deletion' else diff['file2_value']
                    f.write(f"| {i} | {change_icon} | {diff['line_number']} | {diff['change_type']} | ")
                    f.write(f"`{content[:50]}{'...' if len(content) > 50 else ''}` | ")
                    f.write(f"{diff['description']} |\n")
            else:
                f.write("## 结论\n\n")
                f.write("🎉 **完美匹配！没有发现任何差异。**\n\n")
        
    def generate_json_report(self, comparison_result: Dict, output_file: str):
        """生成JSON格式的比较报告"""
        # report_data = {
        #     'comparison_summary': {
        #         'timestamp': re.sub(r'[^\w\-_\.]', '_', str(comparison_result.get('timestamp', ''))),
        #         'file1': comparison_result['file1_path'],
        #         'file2': comparison_result['file2_path'],
        #         'statistics': comparison_result['statistics'],
        #         'file_info': {
        #             'file1_tables': comparison_result['file1_tables'],
        #             'file2_tables': comparison_result['file2_tables'],
        #             'file1_paragraphs': comparison_result['file1_paragraphs'],
        #             'file2_paragraphs': comparison_result['file2_paragraphs']
        #         }
        #     },
        #     'differences': comparison_result['differences']
        # }
        
        with open(output_file, 'w', encoding='utf-8') as f:
            json.dump(comparison_result, f, ensure_ascii=False, indent=2)
    
    def generate_markdown_report(self, comparison_result: Dict, output_file: str):
        """生成Markdown格式的比较报告"""
        with open(output_file, 'w', encoding='utf-8') as f:
            f.write("# OCR结果对比报告\n\n")
            
            # 基本信息
            f.write("## 基本信息\n\n")
            f.write(f"- **文件1**: `{comparison_result['file1_path']}`\n")
            f.write(f"- **文件2**: `{comparison_result['file2_path']}`\n")
            f.write(f"- **比较时间**: {comparison_result.get('timestamp', 'N/A')}\n\n")
            
            # 统计信息
            stats = comparison_result['statistics']
            f.write("## 统计信息\n\n")
            f.write(f"- 总差异数量: **{stats['total_differences']}**\n")
            f.write(f"- 表格差异: **{stats['table_differences']}**\n")
            f.write(f"- 金额差异: **{stats['amount_differences']}**\n")
            f.write(f"- 段落差异: **{stats['paragraph_differences']}**\n")
            f.write(f"- 文件1表格数: {comparison_result['file1_tables']}\n")
            f.write(f"- 文件2表格数: {comparison_result['file2_tables']}\n")
            f.write(f"- 文件1段落数: {comparison_result['file1_paragraphs']}\n")
            f.write(f"- 文件2段落数: {comparison_result['file2_paragraphs']}\n\n")
            
            # 差异摘要
            if stats['total_differences'] == 0:
                f.write("## 结论\n\n")
                f.write("🎉 **完美匹配！没有发现任何差异。**\n\n")
            else:
                f.write("## 差异摘要\n\n")
                
                # 按类型分组显示差异
                diff_by_type = {}
                for diff in comparison_result['differences']:
                    diff_type = diff['type']
                    if diff_type not in diff_by_type:
                        diff_by_type[diff_type] = []
                    diff_by_type[diff_type].append(diff)
                
                for diff_type, diffs in diff_by_type.items():
                    type_name = {
                        'table_amount': '💰 表格金额差异',
                        'table_text': '📝 表格文本差异',
                        'paragraph': '📄 段落差异',
                        'table_structure': '🏗️ 表格结构差异'
                    }.get(diff_type, f'❓ {diff_type}')
                    
                    f.write(f"### {type_name} ({len(diffs)}个)\n\n")
                    
                    for i, diff in enumerate(diffs, 1):
                        f.write(f"**{i}. {diff['position']}**\n")
                        f.write(f"- 文件1: `{diff['file1_value']}`\n")
                        f.write(f"- 文件2: `{diff['file2_value']}`\n")
                        f.write(f"- 说明: {diff['description']}\n\n")
            
            # 详细差异列表
            if comparison_result['differences']:
                f.write("## 详细差异列表\n\n")
                f.write("| 序号 | 类型 | 位置 | 文件1内容 | 文件2内容 | 描述 |\n")
                f.write("| --- | --- | --- | --- | --- | --- |\n")
                
                for i, diff in enumerate(comparison_result['differences'], 1):
                    f.write(f"| {i} | {diff['type']} | {diff['position']} | ")
                    f.write(f"`{diff['file1_value'][:50]}{'...' if len(diff['file1_value']) > 50 else ''}` | ")
                    f.write(f"`{diff['file2_value'][:50]}{'...' if len(diff['file2_value']) > 50 else ''}` | ")
                    f.write(f"{diff['description']} |\n")

def compare_ocr_results(file1_path: str, file2_path: str, output_file: str = "comparison_report",
                       output_format: str = "markdown", ignore_images: bool = True):
    """
    比较两个OCR结果文件
    
    Args:
        file1_path: 第一个OCR结果文件路径
        file2_path: 第二个OCR结果文件路径
        output_file: 输出文件名（不含扩展名），默认为"comparison_report"
        output_format: 输出格式，选项: 'json', 'markdown', 'both'，默认为'markdown'
        ignore_images: 是否忽略图片内容，默认为True
    
    Returns:
        Dict: 比较结果字典
    """
    comparator = OCRResultComparator()
    
    print("🔍 开始对比OCR结果...")
    print(f"📄 文件1: {file1_path}")
    print(f"📄 文件2: {file2_path}")
    print(f"📁 输出格式: {output_format}")
    print(f"🖼️  图片处理: {'忽略' if ignore_images else '对比'}")
    
    try:
        # 执行比较
        result = comparator.compare_files(file1_path, file2_path)
        
        # 添加时间戳
        import datetime
        result['timestamp'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        
        # 生成报告
        if output_format in ['json', 'both']:
            json_file = f"{output_file}.json"
            comparator.generate_json_report(result, json_file)
            print(f"📄 JSON报告已保存至: {json_file}")
        
        if output_format in ['markdown', 'both']:
            md_file = f"{output_file}.md"
            comparator.generate_markdown_report(result, md_file)
            print(f"📝 Markdown报告已保存至: {md_file}")
        
        # 打印简要结果
        print(f"\n📊 对比完成！")
        print(f"   总差异数: {result['statistics']['total_differences']}")
        print(f"   表格差异: {result['statistics']['table_differences']}")
        print(f"   金额差异: {result['statistics']['amount_differences']}")
        print(f"   段落差异: {result['statistics']['paragraph_differences']}")
        
        # 打印前几个重要差异
        if result['differences']:
            print(f"\n🔍 前3个重要差异:")
            for i, diff in enumerate(result['differences'][:3], 1):
                print(f"   {i}. {diff['position']}: {diff['description']}")
                print(f"      文件1: '{diff['file1_value'][:50]}{'...' if len(diff['file1_value']) > 50 else ''}'")
                print(f"      文件2: '{diff['file2_value'][:50]}{'...' if len(diff['file2_value']) > 50 else ''}'")
        else:
            print(f"\n🎉 恭喜！两个文件内容完全一致！")
        
        # 添加处理统计信息（模仿 ocr_by_vlm.py 的风格）
        print("\n📊 对比处理统计")
        print(f"   文件1路径: {result['file1_path']}")
        print(f"   文件2路径: {result['file2_path']}")
        print(f"   输出文件: {output_file}")
        print(f"   输出格式: {output_format}")
        print(f"   忽略图片: {ignore_images}")
        print(f"   处理时间: {result['timestamp']}")
        print(f"   文件1表格数: {result['file1_tables']}")
        print(f"   文件2表格数: {result['file2_tables']}")
        print(f"   文件1段落数: {result['file1_paragraphs']}")
        print(f"   文件2段落数: {result['file2_paragraphs']}")
        
        return result
            
    except Exception as e:
        import traceback
        traceback.print_exc()
        raise Exception(f"OCR对比任务失败: {e}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='OCR结果对比工具')
    parser.add_argument('file1', nargs= '?', help='第一个OCR结果文件路径')
    parser.add_argument('file2', nargs= '?', help='第二个OCR结果文件路径')
    parser.add_argument('-o', '--output', default='comparison_report', 
                       help='输出文件名（不含扩展名）')
    parser.add_argument('-f', '--format', choices=['json', 'markdown', 'both'], 
                       default='markdown', help='输出格式: json, markdown, 或 both')
    parser.add_argument('--ignore-images', action='store_true', 
                       help='忽略图片内容（默认已启用）')
    
    args = parser.parse_args()

    if args.file1 and args.file2:
        result = compare_ocr_results(
            file1_path=args.file1,
            file2_path=args.file2,
            output_file=args.output,
            output_format=args.format,
            ignore_images=args.ignore_images
        )
    else:
        # 如果sys.argv没有被传入参数，则提供默认参数用于测试
        result = compare_ocr_results(
            file1_path='/Users/zhch158/workspace/data/至远彩色印刷工业有限公司/data_DotsOCR_Results/2023年度报告母公司_page_001.md',
            file2_path='./output/pre_validation/2023年度报告母公司_page_001.md',
            # output_file=f'./output/comparison_result_{time.strftime("%Y%m%d_%H%M%S")}',
            output_file=f'./output/pre_validation/2023年度报告母公司_page_001_comparison_result',
            output_format='both',
            ignore_images=True
        )
    print("\n🎉 OCR对比完成！")