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@@ -1,28 +1,42 @@
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"""
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-混合全文提取实现 (HybridFullTextExtractor) - 飞浆版面分析版
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+混合全文提取实现 (HybridFullTextExtractor) - GLM-OCR 版
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-基于飞浆 RapidLayout 版面分析，检测 table 区域判断扫描件：
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-1. 第一阶段：使用飞浆 RapidLayout 对所有页面进行版面分析
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-2. 第二阶段：含有 table 区域的页面走 MinerU OCR，其余走本地提取
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+【修改日期】2025-03-27
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+【修改说明】OCR 引擎从 MinerU 替换为 GLM-OCR 本地 API
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+- 版面分析阶段：保持不变（飞浆 RapidLayout）
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+- OCR 阶段：改为 GLM-OCR 单页请求
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+- 删除所有 MinerU 相关代码
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+
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+【请求格式】参考 glm_ocr_api_extractor.py 最终实现版本
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+【API 地址】http://183.220.37.46:25429/v1/chat/completions
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"""
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from __future__ import annotations
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+import base64
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import io
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+import time
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+from typing import Any, Dict, List, Optional, Set
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+
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import fitz # PyMuPDF
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-import os
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-import tempfile
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import numpy as np
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-from typing import Any, Dict, List, Optional, Set
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+import requests
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from foundation.observability.logger.loggering import review_logger as logger
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from ..config.provider import default_config_provider
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from ..interfaces import DocumentSource, FullTextExtractor
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from .fulltext_extractor import PdfFullTextExtractor
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-from .mineru_extractor import LocalMinerUFullTextExtractor
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-# 尝试导入 RapidLayout，如果未安装则给出友好提示
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+# 尝试导入 PIL 用于图片压缩
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+try:
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+ from PIL import Image
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+ PIL_AVAILABLE = True
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+except ImportError:
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+ PIL_AVAILABLE = False
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+ logger.warning("PIL 未安装，GLM-OCR 图片压缩功能将不可用")
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+
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+# 尝试导入 RapidLayout
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try:
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from rapid_layout import RapidLayout
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RAPID_LAYOUT_AVAILABLE = True
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@@ -33,32 +47,44 @@ except ImportError:
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class HybridFullTextExtractor(FullTextExtractor):
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"""
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- 混合提取器：基于飞浆版面分析检测 table 区域，智能路由扫描页到 MinerU OCR。
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+ 混合提取器：基于飞浆版面分析检测 table 区域，智能路由扫描页到 GLM-OCR。
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+
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+ 【变更记录】
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+ - 2025-03-27: OCR 引擎从 MinerU 切换为 GLM-OCR 本地 API
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"""
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+ # GLM-OCR 图片尺寸限制
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+ MAX_SHORT_EDGE = 1024 # 短边最大 1024px
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+ JPEG_QUALITY = 90 # 提高质量到 90，平衡识别效果和传输大小
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+
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def __init__(
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self,
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layout_dpi: int = 180,
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ocr_dpi: int = 220,
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- jpg_quality: int = 90
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+ jpg_quality: int = 85, # 降低为 85 配合 GLM-OCR
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+ api_url: Optional[str] = None,
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+ timeout: int = 600
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) -> None:
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self._cfg = default_config_provider
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- # 复用已有的提取器
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self.local_extractor = PdfFullTextExtractor()
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- self.mineru_extractor = LocalMinerUFullTextExtractor() # 使用本地 MinerU
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-
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- # 飞浆版面分析配置（保守版优化参数）
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- self.layout_dpi = layout_dpi # 版面分析 DPI：180（平衡检测精度和速度）
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- self.ocr_dpi = ocr_dpi # OCR阶段 DPI：220（表格识别甜点值）
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- self.jpg_quality = jpg_quality # JPEG质量：90（几乎无损，文件可控）
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- self._layout_engine: Optional[Any] = None # 延迟初始化
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-
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- # 外部注入的进度状态字典（由 DocumentWorkflow 设置，心跳协程读取）
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- # 格式：{'current': int(0-100), 'message': str}
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- # 阶段一（版面分析）：current 0→50，阶段二（OCR提取）：current 50→100
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+
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+ # GLM-OCR 配置
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+ self.api_url = api_url or self._cfg.get(
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+ "glm_ocr.api_url",
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+ "http://183.220.37.46:25429/v1/chat/completions"
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+ )
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+ self.timeout = timeout
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+ self.headers = {"Content-Type": "application/json"}
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+
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+ # 飞浆版面分析配置
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+ self.layout_dpi = layout_dpi
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+ self.ocr_dpi = ocr_dpi
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+ self.jpg_quality = jpg_quality
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+ self._layout_engine: Optional[Any] = None
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+
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+ # 外部注入的进度状态字典
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self._progress_state: Optional[dict] = None
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- # 检查 RapidLayout 是否可用
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if not RAPID_LAYOUT_AVAILABLE:
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raise ImportError(
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"RapidLayout 未安装。请在 doc_worker_venv 虚拟环境中运行：\n"
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@@ -75,13 +101,7 @@ class HybridFullTextExtractor(FullTextExtractor):
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def _detect_table_pages(self, doc: fitz.Document, dpi: int = 150) -> Set[int]:
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"""
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使用飞浆 RapidLayout 检测所有页面，返回包含 table 区域的页码集合。
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-
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- Args:
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- doc: PyMuPDF 文档对象
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- dpi: PDF 转图片的分辨率
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-
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- Returns:
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- 包含 table 区域的页码集合 (1-based)
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+ 【保持不变】
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"""
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table_pages: Set[int] = set()
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layout_engine = self._get_layout_engine()
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@@ -90,41 +110,39 @@ class HybridFullTextExtractor(FullTextExtractor):
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logger.debug(f" [飞浆分析] 开始版面分析，共 {total_pages} 页...")
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for page_num in range(1, total_pages + 1):
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- page = doc[page_num - 1] # PyMuPDF 使用 0-based 索引
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+ page = doc[page_num - 1]
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- # 1. 将页面转换为图片
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+ # 将页面转换为图片
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pix = page.get_pixmap(dpi=dpi)
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img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, 3)
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- # 2. 飞浆版面分析
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+ # 飞浆版面分析
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try:
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layout_output = layout_engine(img)
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- # 3. 解析版面结果，检查是否有 table 区域
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+ # 解析版面结果，检查是否有 table 区域
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labels = []
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if hasattr(layout_output, 'class_names'):
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labels = list(layout_output.class_names)
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elif hasattr(layout_output, 'boxes'):
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- # 兼容不同版本的输出格式
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labels = [
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label for _, label, _
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in zip(layout_output.boxes, layout_output.class_names, layout_output.scores)
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]
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- # 4. 判断是否包含 table
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+ # 判断是否包含 table
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if "table" in labels:
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table_pages.add(page_num)
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- logger.debug(f" 第 {page_num} 页: 检测到 table 区域 -> 将走 MinerU OCR")
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+ logger.debug(f" 第 {page_num} 页: 检测到 table 区域 -> 将走 GLM-OCR")
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else:
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region_types = ", ".join(set(labels)) if labels else "无"
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logger.debug(f" 第 {page_num} 页: {region_types}")
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except Exception as e:
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logger.error(f" 第 {page_num} 页: 版面分析失败 ({e})，默认不走 OCR")
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- # 分析失败时，保守起见不走 OCR
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pass
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- # 阶段一进度：已分析页 / 总页数 → 0% ~ 50%
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+ # 阶段一进度
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if self._progress_state is not None:
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self._progress_state['current'] = int(page_num / total_pages * 50)
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self._progress_state['message'] = f"版面分析中：已分析 {page_num}/{total_pages} 页"
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@@ -136,10 +154,10 @@ class HybridFullTextExtractor(FullTextExtractor):
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"""
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执行混合提取流程：
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1. 首先用飞浆 RapidLayout 检测所有页面的 table 区域
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- 2. 含有 table 的页面走 MinerU OCR
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+ 2. 含有 table 的页面走 GLM-OCR
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3. 其他页面走本地 PyMuPDF 提取
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"""
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- # 1. 打开文档
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+ # 打开文档
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if source.content is not None:
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doc = fitz.open(stream=io.BytesIO(source.content))
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source_file = "bytes_stream"
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@@ -154,14 +172,25 @@ class HybridFullTextExtractor(FullTextExtractor):
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try:
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total_pages = len(doc)
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- logger.debug(f"开始混合提取（飞浆版面分析 + 本地 MinerU），共 {total_pages} 页...")
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+ ocr_page_count = 0 # 统计需要OCR的页数
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+
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+ # INFO级别：开始文档提取（方便查看主要流程）
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+ logger.info(f"[文档提取] 开始处理，共 {total_pages} 页，使用混合模式（GLM-OCR）")
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+ logger.debug(f"开始混合提取（飞浆版面分析 + GLM-OCR），共 {total_pages} 页...")
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if self._progress_state is not None:
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self._progress_state['current'] = 0
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self._progress_state['message'] = f"版面分析中：已分析 0/{total_pages} 页"
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- # ========== 第一阶段：飞浆版面分析，检测 table 页 ==========
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+ # ========== 第一阶段：飞浆版面分析 ==========
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table_pages = self._detect_table_pages(doc, dpi=self.layout_dpi)
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+ ocr_page_count = len(table_pages)
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+
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+ # INFO级别：版面分析完成，显示OCR页数
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+ if ocr_page_count > 0:
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+ logger.info(f"[文档提取] 版面分析完成，共 {ocr_page_count} 页需要OCR识别，{total_pages - ocr_page_count} 页直接提取")
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+ else:
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+ logger.info(f"[文档提取] 版面分析完成，无扫描页，全部直接提取")
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# ========== 第二阶段：分流处理 ==========
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logger.debug(f"\n开始分流处理...")
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@@ -169,25 +198,23 @@ class HybridFullTextExtractor(FullTextExtractor):
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for i, page in enumerate(doc):
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page_num = i + 1
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- # 判断是否为 table 页（即扫描件）
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if page_num in table_pages:
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- logger.debug(f" [第 {page_num} 页] 检测到 table -> 走本地 MinerU OCR")
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+ logger.debug(f" [第 {page_num} 页] 检测到 table -> 走 GLM-OCR")
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- # --- 扫描件处理 (MinerU OCR) ---
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try:
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- page_text = self._ocr_page(page, page_num, source_file)
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+ # 调用 GLM-OCR
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+ page_text = self._ocr_page_with_glm(page, page_num, source_file)
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except Exception as e:
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- logger.error(f" MinerU OCR 失败，回退到本地提取: {e}")
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+ logger.error(f" GLM-OCR 失败，回退到本地提取: {e}")
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raw_text = page.get_text()
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page_text = self.local_extractor._filter_header_footer(raw_text)
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else:
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logger.debug(f" [第 {page_num} 页] 无 table -> 走本地 PyMuPDF 提取")
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- # --- 电子版处理 (本地 PyMuPDF) ---
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text_with_tables = self.local_extractor._extract_text_with_table_placeholders(page)
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page_text = self.local_extractor._filter_header_footer(text_with_tables)
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- # --- 组装结果 ---
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+ # 组装结果
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pages.append({
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"page_num": page_num,
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"text": page_text,
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@@ -197,7 +224,7 @@ class HybridFullTextExtractor(FullTextExtractor):
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})
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current_pos += len(page_text)
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- # 阶段二进度：已处理页 / 总页数 → 50% ~ 100%
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+ # 阶段二进度
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if self._progress_state is not None:
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self._progress_state['current'] = 50 + int(page_num / total_pages * 50)
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ocr_flag = "（OCR）" if page_num in table_pages else ""
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@@ -205,53 +232,334 @@ class HybridFullTextExtractor(FullTextExtractor):
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finally:
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doc.close()
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+
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+ # INFO级别：文档提取完成
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+ total_chars = sum(len(page['text']) for page in pages)
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+ logger.info(f"[文档提取] 完成，共 {total_pages} 页，总字符数: {total_chars}")
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return pages
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- def _ocr_page(self, page: fitz.Page, page_num: int, original_filename: str) -> str:
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+ def _ocr_page_with_glm(self, page: fitz.Page, page_num: int, original_filename: str) -> str:
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"""
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- 将单页转为图片并调用本地 MinerU OCR。
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- 使用 JPEG 格式以减小文件大小，提高传输效率。
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+ 将单页转为图片并调用 GLM-OCR 本地 API 识别
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+
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+ 【逻辑来源】glm_ocr_api_extractor.py 最终实现版本
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+
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+ 流程：
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+ 1. PyMuPDF 渲染页面为图片（220 DPI）
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+ 2. PIL 压缩图片（短边限制 1024px，JPEG 质量 85）
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+ 3. Base64 编码
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+ 4. 构建 OpenAI 兼容格式请求
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+ 5. POST 请求 GLM-OCR API
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+ 6. 解析响应并转换 HTML→Markdown
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+
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+ 请求格式：
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+ {
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+ "model": "GLM-OCR",
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+ "messages": [{
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+ "role": "user",
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+ "content": [
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+ {"type": "text", "text": "提示词"},
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+ {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
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+ ]
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+ }],
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+ "max_tokens": 2048,
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+ "temperature": 0.1
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+ }
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"""
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- # 1. 渲染为图片（保守版优化：220 DPI 提升表格识别精度）
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- pix = page.get_pixmap(dpi=self.ocr_dpi)
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+ start_time = time.time()
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+
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+ # INFO级别：开始调用GLM-OCR识别（方便查看主要流程）
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+ logger.info(f"[GLM-OCR] 开始识别第 {page_num} 页（扫描页）")
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- # 2. 保存为临时 JPEG 文件（比 PNG 更小）
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- tmp_path = None
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try:
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- with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp_file:
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- tmp_path = tmp_file.name
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+ # 1. 渲染为图片
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+ pix = page.get_pixmap(dpi=self.ocr_dpi)
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+ img_bytes = pix.tobytes("jpeg")
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+ original_kb = len(img_bytes) / 1024
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- # 保存为 JPEG 格式，质量 90%，几乎无损且文件可控
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- pix.save(tmp_path, "jpeg", jpg_quality=self.jpg_quality)
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+ logger.debug(f" [GLM-OCR] 第 {page_num} 页图片: {original_kb:.1f} KB ({pix.width}x{pix.height})")
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- # 检查文件是否正确生成
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- if not os.path.exists(tmp_path) or os.path.getsize(tmp_path) == 0:
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- logger.error(f" [WARN] 无法创建第 {page_num} 页的临时图片")
|
|
|
- return ""
|
|
|
+ # 2. 压缩图片
|
|
|
+ compressed_bytes = self._compress_image(img_bytes)
|
|
|
+ compressed_kb = len(compressed_bytes) / 1024
|
|
|
+
|
|
|
+ # 3. Base64 编码
|
|
|
+ img_base64 = base64.b64encode(compressed_bytes).decode('utf-8').replace('\n', '').replace('\r', '')
|
|
|
+
|
|
|
+ # 4. 构建 OpenAI 兼容格式请求
|
|
|
+ payload = {
|
|
|
+ "model": "GLM-OCR",
|
|
|
+ "messages": [
|
|
|
+ {
|
|
|
+ "role": "user",
|
|
|
+ "content": [
|
|
|
+ {
|
|
|
+ "type": "text",
|
|
|
+ "text": "请详细识别图片中的所有文字内容，保留原始排版格式，以 Markdown 格式输出。"
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "type": "image_url",
|
|
|
+ "image_url": {
|
|
|
+ "url": f"data:image/jpeg;base64,{img_base64}"
|
|
|
+ }
|
|
|
+ }
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "max_tokens": 2048,
|
|
|
+ "temperature": 0.1
|
|
|
+ }
|
|
|
+
|
|
|
+ # 5. 调用 GLM-OCR API
|
|
|
+ response = requests.post(
|
|
|
+ self.api_url,
|
|
|
+ headers=self.headers,
|
|
|
+ json=payload,
|
|
|
+ timeout=self.timeout
|
|
|
+ )
|
|
|
+ response.raise_for_status()
|
|
|
+
|
|
|
+ # 6. 解析结果
|
|
|
+ result = response.json()
|
|
|
+ content = self._extract_content(result)
|
|
|
+
|
|
|
+ # 7. 处理 HTML 转 Markdown
|
|
|
+ md_content = self._process_raw_content(content)
|
|
|
+
|
|
|
+ elapsed = time.time() - start_time
|
|
|
+ # INFO级别：识别完成（方便查看主要流程）
|
|
|
+ logger.info(f"[GLM-OCR] 第 {page_num} 页识别完成，耗时: {elapsed:.2f}s，字符数: {len(md_content)}")
|
|
|
+ logger.debug(f" [GLM-OCR] 第 {page_num} 页详细耗时: {elapsed:.2f}s")
|
|
|
+
|
|
|
+ return md_content
|
|
|
+
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f" [GLM-OCR] 第 {page_num} 页识别失败: {e}")
|
|
|
+ raise
|
|
|
|
|
|
- # 输出文件大小信息（用于调试）
|
|
|
- file_size_kb = os.path.getsize(tmp_path) / 1024
|
|
|
- logger.debug(f" [INFO] 第 {page_num} 页图片: {file_size_kb:.1f} KB ({pix.width}x{pix.height})")
|
|
|
+ def _compress_image(self, img_bytes: bytes) -> bytes:
|
|
|
+ """
|
|
|
+ 压缩图片至 GLM-OCR 要求的尺寸限制内
|
|
|
+
|
|
|
+ 【逻辑来源】glm_ocr_api_extractor.py _compress_image 方法
|
|
|
+
|
|
|
+ 压缩规则：
|
|
|
+ - 短边最大 1024px
|
|
|
+ - JPEG 质量 85
|
|
|
+ - 等比缩放
|
|
|
+ """
|
|
|
+ if not PIL_AVAILABLE:
|
|
|
+ logger.debug(" [压缩] PIL 不可用，使用原始图片")
|
|
|
+ return img_bytes
|
|
|
+
|
|
|
+ try:
|
|
|
+ img = Image.open(io.BytesIO(img_bytes))
|
|
|
|
|
|
- # 3. 构造一个临时的 DocumentSource
|
|
|
- tmp_source = DocumentSource(path=tmp_path)
|
|
|
+ # 转为 RGB
|
|
|
+ if img.mode in ('RGBA', 'LA', 'P'):
|
|
|
+ background = Image.new('RGB', img.size, (255, 255, 255))
|
|
|
+ if img.mode == 'P':
|
|
|
+ img = img.convert('RGBA')
|
|
|
+ if img.mode in ('RGBA', 'LA'):
|
|
|
+ background.paste(img, mask=img.split()[-1])
|
|
|
+ img = background
|
|
|
+ elif img.mode != 'RGB':
|
|
|
+ img = img.convert('RGB')
|
|
|
|
|
|
- # 4. 调用本地 MinerU
|
|
|
- results = self.mineru_extractor.extract_full_text(tmp_source)
|
|
|
+ original_size = img.size
|
|
|
|
|
|
- if results and len(results) > 0:
|
|
|
- return results[0]["text"]
|
|
|
- return ""
|
|
|
+ # 检查是否需要缩放（短边 > 1024px）
|
|
|
+ min_edge = min(img.size)
|
|
|
+ if min_edge > self.MAX_SHORT_EDGE:
|
|
|
+ ratio = self.MAX_SHORT_EDGE / min_edge
|
|
|
+ new_size = (int(img.width * ratio), int(img.height * ratio))
|
|
|
+ img = img.resize(new_size, Image.Resampling.LANCZOS)
|
|
|
+ logger.debug(f" [压缩] 图片缩放: {original_size} -> {img.size}")
|
|
|
+
|
|
|
+ # 压缩为 JPEG
|
|
|
+ buffer = io.BytesIO()
|
|
|
+ img.save(buffer, format='JPEG', quality=self.JPEG_QUALITY, optimize=True)
|
|
|
+
|
|
|
+ compressed_kb = len(buffer.getvalue()) / 1024
|
|
|
+ original_kb = len(img_bytes) / 1024
|
|
|
+ logger.debug(f" [压缩] {original_kb:.1f} KB -> {compressed_kb:.1f} KB")
|
|
|
+
|
|
|
+ return buffer.getvalue()
|
|
|
|
|
|
except Exception as e:
|
|
|
- logger.error(f" [WARN] 第 {page_num} 页 OCR 失败: {e}")
|
|
|
+ logger.warning(f" [压缩] 主流程压缩失败，使用兜底压缩: {e}")
|
|
|
+ # 兜底：简化流程，但保持相同质量
|
|
|
+ try:
|
|
|
+ img = Image.open(io.BytesIO(img_bytes))
|
|
|
+ if img.mode != 'RGB':
|
|
|
+ img = img.convert('RGB')
|
|
|
+ # 确保尺寸符合要求（短边 <= 1024）
|
|
|
+ min_edge = min(img.size)
|
|
|
+ if min_edge > self.MAX_SHORT_EDGE:
|
|
|
+ ratio = self.MAX_SHORT_EDGE / min_edge
|
|
|
+ new_size = (int(img.width * ratio), int(img.height * ratio))
|
|
|
+ img = img.resize(new_size, Image.Resampling.LANCZOS)
|
|
|
+ buffer = io.BytesIO()
|
|
|
+ # 兜底也使用相同质量，确保识别效果
|
|
|
+ img.save(buffer, format='JPEG', quality=self.JPEG_QUALITY, optimize=True)
|
|
|
+ logger.debug(f" [压缩] 兜底压缩成功: {len(buffer.getvalue())/1024:.1f} KB")
|
|
|
+ return buffer.getvalue()
|
|
|
+ except Exception as e2:
|
|
|
+ logger.error(f" [压缩] 兜底压缩也失败: {e2}")
|
|
|
+ # 最后兜底：使用原始图片（可能导致API错误）
|
|
|
+ return img_bytes
|
|
|
+
|
|
|
+ def _extract_content(self, result: Dict[str, Any]) -> str:
|
|
|
+ """
|
|
|
+ 从 OpenAI 兼容响应中提取内容
|
|
|
+
|
|
|
+ 响应格式：
|
|
|
+ {
|
|
|
+ "choices": [{
|
|
|
+ "message": {
|
|
|
+ "content": "识别结果..."
|
|
|
+ }
|
|
|
+ }]
|
|
|
+ }
|
|
|
+ """
|
|
|
+ if "choices" in result and isinstance(result["choices"], list):
|
|
|
+ if len(result["choices"]) > 0:
|
|
|
+ message = result["choices"][0].get("message", {})
|
|
|
+ return message.get("content", "")
|
|
|
+ return ""
|
|
|
+
|
|
|
+ def _process_raw_content(self, raw_content: str) -> str:
|
|
|
+ """
|
|
|
+ 处理原始内容（HTML 转 Markdown）
|
|
|
+
|
|
|
+ 【逻辑来源】glm_ocr_api_extractor.py _process_raw_content 方法
|
|
|
+
|
|
|
+ 处理流程：
|
|
|
+ 1. 检测并转换 HTML 表格
|
|
|
+ 2. 检测 HTML 格式，使用 markdownify 转换
|
|
|
+ 3. 失败则返回原始内容
|
|
|
+ """
|
|
|
+ if not raw_content:
|
|
|
return ""
|
|
|
+
|
|
|
+ # 转换 HTML 表格
|
|
|
+ if "<table" in raw_content.lower():
|
|
|
+ raw_content = self._convert_html_tables_to_markdown(raw_content)
|
|
|
+
|
|
|
+ # HTML 转 Markdown
|
|
|
+ if self._is_html_content(raw_content):
|
|
|
+ try:
|
|
|
+ import markdownify
|
|
|
+ return markdownify.markdownify(raw_content, heading_style="ATX").strip()
|
|
|
+ except ImportError:
|
|
|
+ logger.debug(" [转换] markdownify 未安装，跳过 HTML 转换")
|
|
|
+
|
|
|
+ return raw_content.strip()
|
|
|
+
|
|
|
+ def _is_html_content(self, content: str) -> bool:
|
|
|
+ """检查内容是否为 HTML 格式"""
|
|
|
+ if not content:
|
|
|
+ return False
|
|
|
+
|
|
|
+ html_indicators = [
|
|
|
+ "<!DOCTYPE", "<html", "<body", "<div", "<p>", "<table",
|
|
|
+ "<h1", "<h2", "<span", "<br", " ", """
|
|
|
+ ]
|
|
|
+ content_lower = content.lower()
|
|
|
+ html_tag_count = sum(1 for indicator in html_indicators if indicator.lower() in content_lower)
|
|
|
+ return html_tag_count >= 2
|
|
|
+
|
|
|
+ def _convert_html_tables_to_markdown(self, content: str) -> str:
|
|
|
+ """
|
|
|
+ 将 HTML 表格转换为 Markdown 表格格式
|
|
|
+
|
|
|
+ 【逻辑来源】glm_ocr_api_extractor.py _convert_html_tables_to_markdown 方法
|
|
|
+ """
|
|
|
+ import re
|
|
|
+
|
|
|
+ def extract_cell_text(cell_html: str) -> str:
|
|
|
+ text = re.sub(r'<[^>]+>', '', cell_html)
|
|
|
+ text = text.replace(' ', ' ').replace('<', '<').replace('>', '>')
|
|
|
+ text = text.replace('&', '&').replace('"', '"').replace(''', "'")
|
|
|
+ return text.strip()
|
|
|
+
|
|
|
+ def parse_colspan(td_html: str) -> int:
|
|
|
+ match = re.search(r'colspan=["\']?(\d+)["\']?', td_html, re.IGNORECASE)
|
|
|
+ return int(match.group(1)) if match else 1
|
|
|
+
|
|
|
+ def convert_table_match(match):
|
|
|
+ table_html = match.group(0)
|
|
|
|
|
|
- finally:
|
|
|
- # 清理临时文件
|
|
|
- if tmp_path and os.path.exists(tmp_path):
|
|
|
- try:
|
|
|
- os.remove(tmp_path)
|
|
|
- except:
|
|
|
- pass
|
|
|
+ # 提取 thead 和 tbody
|
|
|
+ thead_match = re.search(r'<thead[^>]*>(.*?)</thead>', table_html, re.DOTALL | re.IGNORECASE)
|
|
|
+ tbody_match = re.search(r'<tbody[^>]*>(.*?)</tbody>', table_html, re.DOTALL | re.IGNORECASE)
|
|
|
+
|
|
|
+ all_rows = []
|
|
|
+
|
|
|
+ # 处理 thead 中的行
|
|
|
+ if thead_match:
|
|
|
+ thead_html = thead_match.group(1)
|
|
|
+ tr_matches = re.findall(r'<tr[^>]*>(.*?)</tr>', thead_html, re.DOTALL | re.IGNORECASE)
|
|
|
+ for tr in tr_matches:
|
|
|
+ all_rows.append(tr)
|
|
|
+
|
|
|
+ # 处理 tbody 中的行
|
|
|
+ if tbody_match:
|
|
|
+ tbody_html = tbody_match.group(1)
|
|
|
+ tr_matches = re.findall(r'<tr[^>]*>(.*?)</tr>', tbody_html, re.DOTALL | re.IGNORECASE)
|
|
|
+ for tr in tr_matches:
|
|
|
+ all_rows.append(tr)
|
|
|
+
|
|
|
+ # 如果没有 thead/tbody，直接提取所有 tr
|
|
|
+ if not all_rows:
|
|
|
+ all_rows = re.findall(r'<tr[^>]*>(.*?)</tr>', table_html, re.DOTALL | re.IGNORECASE)
|
|
|
+
|
|
|
+ # 解析所有行
|
|
|
+ parsed_rows = []
|
|
|
+ for tr_html in all_rows:
|
|
|
+ cells = re.findall(r'<(t[dh])[^>]*>(.*?)</\1>', tr_html, re.DOTALL | re.IGNORECASE)
|
|
|
+
|
|
|
+ row_data = []
|
|
|
+ for tag, cell_content in cells:
|
|
|
+ full_cell_match = re.search(rf'<{tag}[^>]*>', tr_html[tr_html.find(cell_content)-50:tr_html.find(cell_content)])
|
|
|
+ cell_start = full_cell_match.group(0) if full_cell_match else f'<{tag}>'
|
|
|
+
|
|
|
+ text = extract_cell_text(cell_content)
|
|
|
+ colspan = parse_colspan(cell_start)
|
|
|
+ row_data.append((text, colspan))
|
|
|
+
|
|
|
+ if row_data:
|
|
|
+ parsed_rows.append(row_data)
|
|
|
+
|
|
|
+ if not parsed_rows:
|
|
|
+ return ""
|
|
|
+
|
|
|
+ # 计算最大列数（考虑 colspan）
|
|
|
+ max_cols = 0
|
|
|
+ for row in parsed_rows:
|
|
|
+ cols = sum(colspan for _, colspan in row)
|
|
|
+ max_cols = max(max_cols, cols)
|
|
|
+
|
|
|
+ # 展开 colspan 并生成 Markdown
|
|
|
+ md_rows = []
|
|
|
+ for row in parsed_rows:
|
|
|
+ expanded_cells = []
|
|
|
+ for text, colspan in row:
|
|
|
+ expanded_cells.append(text)
|
|
|
+ for _ in range(colspan - 1):
|
|
|
+ expanded_cells.append("")
|
|
|
+
|
|
|
+ while len(expanded_cells) < max_cols:
|
|
|
+ expanded_cells.append("")
|
|
|
+
|
|
|
+ md_rows.append("| " + " | ".join(expanded_cells) + " |")
|
|
|
+
|
|
|
+ # 添加分隔行
|
|
|
+ if len(md_rows) > 0:
|
|
|
+ md_rows.insert(1, "| " + " | ".join(["---"] * max_cols) + " |")
|
|
|
+
|
|
|
+ return "\n".join(md_rows)
|
|
|
+
|
|
|
+ return re.sub(r'<table[^>]*>.*?</table>', convert_table_match, content,
|
|
|
+ flags=re.DOTALL | re.IGNORECASE)
|
suhua31
LingMin
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