199 lines
6.4 KiB
Python
199 lines
6.4 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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批量提取参考文档目录中所有 .doc 文件的内容
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输出为 JSON 格式，方便后续处理
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"""
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import os
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import sys
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import json
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import olefile
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import re
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from pathlib import Path
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from datetime import datetime
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def extract_text_from_doc(filepath):
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"""从 .doc 文件提取文本内容"""
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try:
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ole = olefile.OleFileIO(filepath)
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if not ole.exists('WordDocument'):
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return None, "不是有效的Word文档"
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# 读取WordDocument流
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word_stream = ole.openstream('WordDocument')
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data = word_stream.read()
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# 尝试读取表格流
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table_name = '1Table' if ole.exists('1Table') else '0Table'
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table_data = b''
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if ole.exists(table_name):
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table_stream = ole.openstream(table_name)
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table_data = table_stream.read()
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# 提取文本
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text_parts = []
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# 从WordDocument流提取
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try:
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decoded = data.decode('utf-16-le', errors='ignore')
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# 提取中文和英文文本
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matches = re.findall(r'[\u4e00-\u9fff\u0020-\u007e\n\r\t\.,;:!?()（）。，；：！？、""''【】\[\]｛｝{}<>《》\-+×÷=±≡≈≠≤≥∞∩∪∈∉⊂⊃⊆⊇∠⊥∥→←↑↓↔\d]+', decoded)
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for m in matches:
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if len(m.strip()) > 1:
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text_parts.append(m.strip())
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except:
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pass
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# 从表格流提取
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try:
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decoded = table_data.decode('utf-16-le', errors='ignore')
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matches = re.findall(r'[\u4e00-\u9fff\u0020-\u007e\n\r\t\.,;:!?()（）。，；：！？、""''【】\[\]｛｝{}<>《》\-+×÷=±≡≈≠≤≥∞∩∪∈∉⊂⊃⊆⊇∠⊥∥→←↑↓↔\d]+', decoded)
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for m in matches:
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if len(m.strip()) > 1:
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text_parts.append(m.strip())
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except:
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pass
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# 简单提取（从整个文件）
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with open(filepath, 'rb') as f:
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raw_data = f.read()
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try:
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decoded = raw_data.decode('utf-16-le', errors='ignore')
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matches = re.findall(r'[\u4e00-\u9fff\u0020-\u007e\n\r\t\.,;:!?()（）。，；：！？、""''【】\[\]｛｝{}<>《》\-+×÷=±≡≈≠≤≥∞∩∪∈∉⊂⊃⊆⊇∠⊥∥→←↑↓↔\d]+', decoded)
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for m in matches:
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if len(m.strip()) > 1:
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text_parts.append(m.strip())
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except:
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pass
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# 去重
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seen = set()
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unique_parts = []
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for part in text_parts:
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if part not in seen and len(part) > 1:
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seen.add(part)
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unique_parts.append(part)
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# 合并文本
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full_text = '\n'.join(unique_parts)
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ole.close()
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return full_text, None
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except Exception as e:
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return None, str(e)
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def extract_tables_from_doc(filepath):
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"""尝试提取表格内容"""
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try:
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ole = olefile.OleFileIO(filepath)
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tables = []
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# 表格信息存储在表格流中
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# 这是一个简化的方法，实际需要解析复杂的二进制结构
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# 从整个文件中寻找表格特征
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with open(filepath, 'rb') as f:
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data = f.read()
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# 尝试提取表格单元格内容
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decoded = data.decode('utf-16-le', errors='ignore')
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# 寻找表格模式（连续的短文本行）
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lines = [l.strip() for l in decoded.split('\n') if l.strip()]
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# 检测可能的表格行
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table_rows = []
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for line in lines:
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if len(line) > 2 and len(line) < 200: # 表格单元格通常是短文本
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# 检查是否是表格分隔符
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if not re.match(r'^[\s\-\|]+$', line):
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table_rows.append(line)
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ole.close()
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return table_rows
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except Exception as e:
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return []
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def process_all_doc_files(directory):
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"""处理目录下所有 .doc 文件"""
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results = {}
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doc_files = []
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for root, dirs, files in os.walk(directory):
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for file in files:
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if file.lower().endswith('.doc') and not file.lower().endswith('.docx'):
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doc_files.append((file, os.path.join(root, file)))
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print(f"发现 {len(doc_files)} 个 .doc 文件")
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for i, (filename, filepath) in enumerate(doc_files):
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print(f"处理 [{i+1}/{len(doc_files)}]: {filename}")
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text, error = extract_text_from_doc(filepath)
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tables = extract_tables_from_doc(filepath)
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results[filename] = {
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'filepath': filepath,
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'text': text if text else '',
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'tables': tables,
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'error': error
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}
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return results
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def main():
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"""主函数"""
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# 默认处理参考文档目录
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doc_dir = r"D:\医院绩效系统\参考文档"
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if len(sys.argv) > 1:
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doc_dir = sys.argv[1]
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print(f"开始处理目录: {doc_dir}")
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print("=" * 60)
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# 提取所有 .doc 文件
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results = process_all_doc_files(doc_dir)
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# 保存结果
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output_file = r"D:\医院绩效系统\doc_extracted_content.json"
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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print(f"\n结果已保存到: {output_file}")
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# 统计
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success_count = sum(1 for v in results.values() if v.get('text'))
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error_count = sum(1 for v in results.values() if v.get('error'))
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print(f"成功提取: {success_count} 个文件")
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print(f"提取失败: {error_count} 个文件")
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# 显示部分结果
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print("\n" + "=" * 60)
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print("部分提取结果预览:")
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print("=" * 60)
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for filename, data in list(results.items())[:3]:
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print(f"\n【{filename}】")
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text = data.get('text', '')
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if text:
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print(text[:500] + "..." if len(text) > 500 else text)
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else:
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print(f" 错误: {data.get('error', '未知错误')}")
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if __name__ == '__main__':
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main()
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