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@@ -2,7 +2,7 @@
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数据处理模块
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负责处理 MinerU/PaddleOCR_VL/DotsOCR 数据，添加 bbox 信息
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"""
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-from typing import List, Dict, Tuple
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+from typing import List, Dict, Tuple, Optional
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from bs4 import BeautifulSoup
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try:
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@@ -16,14 +16,17 @@ except ImportError:
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class DataProcessor:
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"""数据处理器"""
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- def __init__(self, text_matcher: TextMatcher, look_ahead_window: int = 10):
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+ def __init__(self, text_matcher: TextMatcher, look_ahead_window: int = 10, x_tolerance: int = 3):
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"""
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Args:
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text_matcher: 文本匹配器
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look_ahead_window: 向前查找窗口
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+ x_tolerance: x轴容差
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"""
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self.text_matcher = text_matcher
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self.look_ahead_window = look_ahead_window
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+ # X轴容差, 用于判断文本框是否在同一列
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+ self.x_tolerance = x_tolerance
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def process_mineru_data(self, mineru_data: List[Dict],
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paddle_text_boxes: List[Dict]) -> List[Dict]:
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@@ -102,7 +105,7 @@ class DataProcessor:
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# 🎯 根据类型处理
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if category.lower() == 'table':
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- merged_item, paddle_pointer = self._process_dotsocr_table(
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+ merged_item, paddle_pointer = self._process_table(
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mineru_item, paddle_text_boxes, paddle_pointer
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)
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merged_data.append(merged_item)
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@@ -185,31 +188,6 @@ class DataProcessor:
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return mineru_item
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- def _process_dotsocr_table(self, item: Dict, paddle_text_boxes: List[Dict],
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- start_pointer: int) -> Tuple[Dict, int]:
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- """
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- 🎯 处理 DotsOCR 表格（已转换为 MinerU 格式）
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-
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- DotsOCR 的表格 HTML 已经在 text 字段中，需要转移到 table_body
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- """
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- merged_item = item.copy()
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- table_html = item.get('table_body', '')
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-
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- if not table_html:
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- return merged_item, start_pointer
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-
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- # 🎯 复用表格处理逻辑
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- enhanced_html, cells, new_pointer = self._enhance_table_html_with_bbox(
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- table_html, paddle_text_boxes, start_pointer
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- )
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-
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- merged_item['table_body'] = enhanced_html
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- merged_item['table_body_with_bbox'] = enhanced_html
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- merged_item['bbox_mapping'] = 'merged_from_paddle_ocr'
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- merged_item['table_cells'] = cells if cells else []
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-
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- return merged_item, new_pointer
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-
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def process_paddleocr_vl_data(self, paddleocr_vl_data: Dict,
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paddle_text_boxes: List[Dict]) -> List[Dict]:
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"""
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@@ -400,22 +378,50 @@ class DataProcessor:
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return mineru_item
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def _process_table(self, item: Dict, paddle_text_boxes: List[Dict],
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- start_pointer: int) -> Tuple[Dict, int]:
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- """处理 MinerU 表格"""
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- merged_item = item.copy()
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- table_html = item.get('table_body', '')
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+ start_pointer: int) -> Tuple[Dict, int]:
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+ """
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+ 处理表格类型（MinerU 格式）
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- enhanced_html, cells, new_pointer = self._enhance_table_html_with_bbox(
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- table_html, paddle_text_boxes, start_pointer
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- )
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+ 策略：
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+ - 解析 HTML 表格
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+ - 为每个单元格匹配 PaddleOCR 的 bbox
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+ - 返回处理后的表格和新指针位置
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+ """
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+ table_body = item.get('table_body', '')
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- merged_item['table_body'] = enhanced_html
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- merged_item['table_body_with_bbox'] = enhanced_html
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- merged_item['bbox_mapping'] = 'merged_from_paddle_ocr'
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- merged_item['table_cells'] = cells if cells else []
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+ if not table_body:
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+ print(f"⚠️ 表格内容为空，跳过")
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+ return item, start_pointer
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+
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+ try:
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+ # 🔑 传入 table_bbox 用于筛选
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+ table_bbox = item.get('bbox') # MinerU 提供的表格边界
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+
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+ enhanced_html, cells, new_pointer = self._enhance_table_html_with_bbox(
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+ table_body,
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+ paddle_text_boxes,
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+ start_pointer,
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+ table_bbox # ✅ 传入边界框
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+ )
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+
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+ # 更新 item
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+ item['table_body'] = enhanced_html
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+ item['table_cells'] = cells
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+
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+ # 统计信息
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+ matched_count = len(cells)
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+ total_cells = len(BeautifulSoup(table_body, 'html.parser').find_all(['td', 'th']))
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+
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+ print(f" 表格单元格: {matched_count}/{total_cells} 匹配")
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+
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+ return item, new_pointer
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+
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+ except Exception as e:
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+ print(f"⚠️ 表格处理失败: {e}")
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+ import traceback
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+ traceback.print_exc()
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+ return item, start_pointer
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- return merged_item, new_pointer
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-
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def _process_text(self, item: Dict, paddle_text_boxes: List[Dict],
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paddle_pointer: int, last_matched_index: int) -> Tuple[Dict, int, int]:
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"""处理文本"""
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@@ -452,44 +458,829 @@ class DataProcessor:
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return merged_item, paddle_pointer, last_matched_index
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def _enhance_table_html_with_bbox(self, html: str, paddle_text_boxes: List[Dict],
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- start_pointer: int) -> Tuple[str, List[Dict], int]:
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- """为 HTML 表格添加 bbox 信息"""
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+ start_pointer: int, table_bbox: Optional[List[int]] = None) -> Tuple[str, List[Dict], int]:
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+ """
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+ 为 HTML 表格添加 bbox 信息（优化版：先筛选表格区域）
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+
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+ 策略：
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+ 1. 根据 table_bbox 筛选出表格区域内的 paddle_text_boxes
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+ 2. 将筛选后的 boxes 按行分组
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+ 3. 智能匹配 HTML 行与 paddle 行组
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+ 4. 在匹配的组内查找单元格
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+
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+ Args:
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+ html: HTML 表格
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+ paddle_text_boxes: 全部 paddle OCR 结果
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+ start_pointer: 开始位置
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+ table_bbox: 表格边界框 [x1, y1, x2, y2]
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+ """
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soup = BeautifulSoup(html, 'html.parser')
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- current_pointer = start_pointer
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- last_matched_index = start_pointer
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cells = []
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-
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- for row_idx, row in enumerate(soup.find_all('tr')):
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- for col_idx, cell in enumerate(row.find_all(['td', 'th'])):
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+
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+ # 🔑 第一步：筛选表格区域内的 paddle boxes
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+ table_region_boxes, actual_table_bbox = self._filter_boxes_in_table_region(
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+ paddle_text_boxes[start_pointer:],
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+ table_bbox,
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+ html
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+ )
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+
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+ if not table_region_boxes:
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+ print(f"⚠️ 未在表格区域找到 paddle boxes")
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+ return str(soup), cells, start_pointer
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+
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+ print(f"📊 表格区域: {len(table_region_boxes)} 个文本框")
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+ print(f" 边界: {actual_table_bbox}")
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+
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+ # 🔑 第二步：将表格区域的 boxes 按行分组
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+ grouped_boxes = self._group_paddle_boxes_by_rows(
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+ table_region_boxes,
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+ y_tolerance=20
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+ )
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+
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+ # 🔑 第三步：在每组内按 x 坐标排序
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+ for group in grouped_boxes:
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+ group['boxes'].sort(key=lambda x: x['bbox'][0])
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+
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+ grouped_boxes.sort(key=lambda g: g['y_center'])
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+
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+ print(f" 分组: {len(grouped_boxes)} 行")
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+
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+ # 🔑 第四步：智能匹配 HTML 行与 paddle 行组
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+ html_rows = soup.find_all('tr')
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+ row_mapping = self._match_html_rows_to_paddle_groups(html_rows, grouped_boxes)
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+
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+ print(f" HTML行: {len(html_rows)} 行")
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+ print(f" 映射: {len([v for v in row_mapping.values() if v])} 个有效映射")
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+
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+ # 🔑 第五步：遍历 HTML 表格，使用映射关系查找
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+ for row_idx, row in enumerate(html_rows):
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+ group_indices = row_mapping.get(row_idx, [])
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+
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+ if not group_indices:
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+ continue
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+
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+ # 合并多个组的 boxes
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+ current_boxes = []
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+ for group_idx in group_indices:
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+ if group_idx < len(grouped_boxes):
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+ current_boxes.extend(grouped_boxes[group_idx]['boxes'])
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+
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+ current_boxes.sort(key=lambda x: x['bbox'][0])
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+
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+ # 🎯 关键改进：提取 HTML 单元格并预先确定列边界
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+ html_cells = row.find_all(['td', 'th'])
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+
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+ if not html_cells:
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+ continue
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+
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+ # 🔑 预估列边界（基于 x 坐标分布）
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+ col_boundaries = self._estimate_column_boundaries(
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+ current_boxes,
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+ len(html_cells)
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+ )
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+
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+ print(f" 行 {row_idx + 1}: {len(html_cells)} 列，边界: {col_boundaries}")
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+
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+ # 🎯 关键改进：顺序指针匹配
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+ box_pointer = 0 # 当前行的 boxes 指针
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+
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+ for col_idx, cell in enumerate(html_cells):
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cell_text = cell.get_text(strip=True)
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if not cell_text:
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continue
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- matched_bbox, current_pointer, last_matched_index = \
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- self.text_matcher.find_matching_bbox(
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- cell_text, paddle_text_boxes, current_pointer,
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- last_matched_index, self.look_ahead_window
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- )
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+ # 🔑 从当前指针开始匹配
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+ matched_result = self._match_cell_sequential(
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+ cell_text,
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+ current_boxes,
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+ col_boundaries,
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+ box_pointer
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+ )
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- if matched_bbox:
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- bbox = matched_bbox['bbox']
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- cell['data-bbox'] = f"[{bbox[0]},{bbox[1]},{bbox[2]},{bbox[3]}]"
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- cell['data-score'] = f"{matched_bbox['score']:.4f}"
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- cell['data-paddle-index'] = str(matched_bbox['paddle_bbox_index'])
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-
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- # ✅ 完整记录单元格信息
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+ if matched_result:
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+ merged_bbox = matched_result['bbox']
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+ merged_text = matched_result['text']
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+
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+ cell['data-bbox'] = f"[{merged_bbox[0]},{merged_bbox[1]},{merged_bbox[2]},{merged_bbox[3]}]"
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+ cell['data-score'] = f"{matched_result['score']:.4f}"
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+ cell['data-paddle-indices'] = str(matched_result['paddle_indices'])
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+
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cells.append({
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'type': 'table_cell',
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'text': cell_text,
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- 'bbox': bbox,
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+ 'matched_text': merged_text,
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+ 'bbox': merged_bbox,
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'row': row_idx + 1,
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'col': col_idx + 1,
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- 'score': matched_bbox['score'],
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- 'paddle_bbox_index': matched_bbox['paddle_bbox_index']
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+ 'score': matched_result['score'],
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+ 'paddle_bbox_indices': matched_result['paddle_indices']
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})
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- matched_bbox['used'] = True
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- # ✅ 如果匹配失败，不应该添加到 cells 中
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+ # 标记已使用
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+ for box in matched_result['used_boxes']:
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+ box['used'] = True
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+
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+ # 🎯 移动指针到最后使用的 box 之后
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+ box_pointer = matched_result['last_used_index'] + 1
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+
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+ print(f" 列 {col_idx + 1}: '{cell_text[:20]}...' 匹配 {len(matched_result['used_boxes'])} 个box (指针: {box_pointer})")
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+
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+ # 计算新的指针位置
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+ used_count = sum(1 for box in table_region_boxes if box.get('used'))
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+ new_pointer = start_pointer + used_count
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+
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+ print(f" 匹配: {len(cells)} 个单元格")
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+
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+ return str(soup), cells, new_pointer
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+
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+
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+ def _estimate_column_boundaries(self, boxes: List[Dict],
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+ num_cols: int) -> List[Tuple[int, int]]:
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+ """
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+ 估算列边界（改进版：处理同列多文本框）
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+
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+ Args:
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+ boxes: 当前行的所有 boxes（已按 x 排序）
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+ num_cols: HTML 表格的列数
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+
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+ Returns:
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+ 列边界列表 [(x_start, x_end), ...]
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+ """
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+ if not boxes:
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+ return []
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+
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+ # 🔑 关键改进：先按 x 坐标聚类（合并同列的多个文本框）
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+ x_clusters = self._cluster_boxes_by_x(boxes, x_tolerance=self.x_tolerance)
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+
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+ print(f" X聚类: {len(boxes)} 个boxes -> {len(x_clusters)} 个列簇")
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+
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+ # 获取所有 x 坐标范围
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+ x_min = min(cluster['x_min'] for cluster in x_clusters)
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+ x_max = max(cluster['x_max'] for cluster in x_clusters)
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+
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+ # 🎯 策略 1: 如果聚类数量<=列数接近
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+ if len(x_clusters) <= num_cols:
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+ # 直接使用聚类边界
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+ boundaries = [(cluster['x_min'], cluster['x_max'])
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+ for cluster in x_clusters]
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+ return boundaries
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+
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+ # 🎯 策略 2: 聚类数多于列数（某些列有多个文本簇）
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+ if len(x_clusters) > num_cols:
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+ print(f" ℹ️ 聚类数 {len(x_clusters)} > 列数 {num_cols}，合并相近簇")
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+
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+ # 合并相近的簇
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+ merged_clusters = self._merge_close_clusters(x_clusters, num_cols)
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+
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+ boundaries = [(cluster['x_min'], cluster['x_max'])
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+ for cluster in merged_clusters]
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+ return boundaries
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+
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+ return []
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+
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+
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+ def _cluster_boxes_by_x(self, boxes: List[Dict],
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+ x_tolerance: int = 3) -> List[Dict]:
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+ """
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+ 按 x 坐标聚类（合并同列的多个文本框）
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+
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+ Args:
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+ boxes: 文本框列表
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+ x_tolerance: X坐标容忍度
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+
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+ Returns:
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+ 聚类列表 [{'x_min': int, 'x_max': int, 'boxes': List[Dict]}, ...]
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+ """
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+ if not boxes:
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+ return []
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+
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+ # 按左边界 x 坐标排序
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+ sorted_boxes = sorted(boxes, key=lambda b: b['bbox'][0])
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+
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+ clusters = []
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+ current_cluster = None
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+
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+ for box in sorted_boxes:
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+ bbox = box['bbox']
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+ x_start = bbox[0]
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+ x_end = bbox[2]
|
|
|
+
|
|
|
+ if current_cluster is None:
|
|
|
+ # 开始新簇
|
|
|
+ current_cluster = {
|
|
|
+ 'x_min': x_start,
|
|
|
+ 'x_max': x_end,
|
|
|
+ 'boxes': [box]
|
|
|
+ }
|
|
|
+ else:
|
|
|
+ # 🔑 检查是否属于当前簇（修正后的逻辑）
|
|
|
+ # 1. x 坐标有重叠：x_start <= current_x_max 且 x_end >= current_x_min
|
|
|
+ # 2. 或者距离在容忍度内
|
|
|
+
|
|
|
+ has_overlap = (x_start <= current_cluster['x_max'] and
|
|
|
+ x_end >= current_cluster['x_min'])
|
|
|
+
|
|
|
+ is_close = abs(x_start - current_cluster['x_max']) <= x_tolerance
|
|
|
+
|
|
|
+ if has_overlap or is_close:
|
|
|
+ # 合并到当前簇
|
|
|
+ current_cluster['boxes'].append(box)
|
|
|
+ current_cluster['x_min'] = min(current_cluster['x_min'], x_start)
|
|
|
+ current_cluster['x_max'] = max(current_cluster['x_max'], x_end)
|
|
|
+ else:
|
|
|
+ # 保存当前簇，开始新簇
|
|
|
+ clusters.append(current_cluster)
|
|
|
+ current_cluster = {
|
|
|
+ 'x_min': x_start,
|
|
|
+ 'x_max': x_end,
|
|
|
+ 'boxes': [box]
|
|
|
+ }
|
|
|
+
|
|
|
+ # 添加最后一簇
|
|
|
+ if current_cluster:
|
|
|
+ clusters.append(current_cluster)
|
|
|
+
|
|
|
+ return clusters
|
|
|
+
|
|
|
+
|
|
|
+ def _merge_close_clusters(self, clusters: List[Dict],
|
|
|
+ target_count: int) -> List[Dict]:
|
|
|
+ """
|
|
|
+ 合并相近的簇，直到数量等于目标列数
|
|
|
+
|
|
|
+ Args:
|
|
|
+ clusters: 聚类列表
|
|
|
+ target_count: 目标列数
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ 合并后的聚类列表
|
|
|
+ """
|
|
|
+ if len(clusters) <= target_count:
|
|
|
+ return clusters
|
|
|
+
|
|
|
+ # 复制一份，避免修改原数据
|
|
|
+ working_clusters = [c.copy() for c in clusters]
|
|
|
+
|
|
|
+ while len(working_clusters) > target_count:
|
|
|
+ # 找到距离最近的两个簇
|
|
|
+ min_distance = float('inf')
|
|
|
+ merge_idx = 0
|
|
|
+
|
|
|
+ for i in range(len(working_clusters) - 1):
|
|
|
+ distance = working_clusters[i + 1]['x_min'] - working_clusters[i]['x_max']
|
|
|
+ if distance < min_distance:
|
|
|
+ min_distance = distance
|
|
|
+ merge_idx = i
|
|
|
+
|
|
|
+ # 合并
|
|
|
+ cluster1 = working_clusters[merge_idx]
|
|
|
+ cluster2 = working_clusters[merge_idx + 1]
|
|
|
+
|
|
|
+ merged_cluster = {
|
|
|
+ 'x_min': cluster1['x_min'],
|
|
|
+ 'x_max': cluster2['x_max'],
|
|
|
+ 'boxes': cluster1['boxes'] + cluster2['boxes']
|
|
|
+ }
|
|
|
+
|
|
|
+ # 替换
|
|
|
+ working_clusters[merge_idx] = merged_cluster
|
|
|
+ working_clusters.pop(merge_idx + 1)
|
|
|
+
|
|
|
+ return working_clusters
|
|
|
+
|
|
|
+
|
|
|
+ def _get_boxes_in_column(self, boxes: List[Dict],
|
|
|
+ boundaries: List[Tuple[int, int]],
|
|
|
+ col_idx: int) -> List[Dict]:
|
|
|
+ """
|
|
|
+ 获取指定列范围内的 boxes（改进版：包含重叠）
|
|
|
+
|
|
|
+ Args:
|
|
|
+ boxes: 当前行的所有 boxes
|
|
|
+ boundaries: 列边界
|
|
|
+ col_idx: 列索引
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ 该列的 boxes
|
|
|
+ """
|
|
|
+ if col_idx >= len(boundaries):
|
|
|
+ return []
|
|
|
+
|
|
|
+ x_start, x_end = boundaries[col_idx]
|
|
|
+
|
|
|
+ col_boxes = []
|
|
|
+ for box in boxes:
|
|
|
+ bbox = box['bbox']
|
|
|
+ box_x_start = bbox[0]
|
|
|
+ box_x_end = bbox[2]
|
|
|
+
|
|
|
+ # 🔑 改进：检查是否有重叠（不只是中心点）
|
|
|
+ overlap = not (box_x_start > x_end or box_x_end < x_start)
|
|
|
+
|
|
|
+ if overlap:
|
|
|
+ col_boxes.append(box)
|
|
|
+
|
|
|
+ return col_boxes
|
|
|
+
|
|
|
+
|
|
|
+ def _filter_boxes_in_table_region(self, paddle_boxes: List[Dict],
|
|
|
+ table_bbox: Optional[List[int]],
|
|
|
+ html: str) -> Tuple[List[Dict], List[int]]:
|
|
|
+ """
|
|
|
+ 筛选表格区域内的 paddle boxes
|
|
|
+
|
|
|
+ 策略：
|
|
|
+ 1. 如果有 table_bbox，使用边界框筛选（扩展边界）
|
|
|
+ 2. 如果没有 table_bbox，通过内容匹配推断区域
|
|
|
+
|
|
|
+ Args:
|
|
|
+ paddle_boxes: paddle OCR 结果
|
|
|
+ table_bbox: 表格边界框 [x1, y1, x2, y2]
|
|
|
+ html: HTML 内容（用于内容验证）
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ (筛选后的 boxes, 实际表格边界框)
|
|
|
+ """
|
|
|
+ if not paddle_boxes:
|
|
|
+ return [], [0, 0, 0, 0]
|
|
|
+
|
|
|
+ # 🎯 策略 1: 使用提供的 table_bbox（扩展边界）
|
|
|
+ if table_bbox and len(table_bbox) == 4:
|
|
|
+ x1, y1, x2, y2 = table_bbox
|
|
|
+
|
|
|
+ # 扩展边界（考虑边框外的文本）
|
|
|
+ margin = 20
|
|
|
+ expanded_bbox = [
|
|
|
+ max(0, x1 - margin),
|
|
|
+ max(0, y1 - margin),
|
|
|
+ x2 + margin,
|
|
|
+ y2 + margin
|
|
|
+ ]
|
|
|
+
|
|
|
+ filtered = []
|
|
|
+ for box in paddle_boxes:
|
|
|
+ bbox = box['bbox']
|
|
|
+ box_center_x = (bbox[0] + bbox[2]) / 2
|
|
|
+ box_center_y = (bbox[1] + bbox[3]) / 2
|
|
|
+
|
|
|
+ # 中心点在扩展区域内
|
|
|
+ if (expanded_bbox[0] <= box_center_x <= expanded_bbox[2] and
|
|
|
+ expanded_bbox[1] <= box_center_y <= expanded_bbox[3]):
|
|
|
+ filtered.append(box)
|
|
|
+
|
|
|
+ if filtered:
|
|
|
+ # 计算实际边界框
|
|
|
+ actual_bbox = [
|
|
|
+ min(b['bbox'][0] for b in filtered),
|
|
|
+ min(b['bbox'][1] for b in filtered),
|
|
|
+ max(b['bbox'][2] for b in filtered),
|
|
|
+ max(b['bbox'][3] for b in filtered)
|
|
|
+ ]
|
|
|
+ return filtered, actual_bbox
|
|
|
+
|
|
|
+ # 🎯 策略 2: 通过内容匹配推断区域
|
|
|
+ print(" ℹ️ 无 table_bbox，使用内容匹配推断表格区域...")
|
|
|
+
|
|
|
+ # 提取 HTML 中的所有文本
|
|
|
+ from bs4 import BeautifulSoup
|
|
|
+ soup = BeautifulSoup(html, 'html.parser')
|
|
|
+ html_texts = set()
|
|
|
+ for cell in soup.find_all(['td', 'th']):
|
|
|
+ text = cell.get_text(strip=True)
|
|
|
+ if text:
|
|
|
+ html_texts.add(self.text_matcher.normalize_text(text))
|
|
|
+
|
|
|
+ if not html_texts:
|
|
|
+ return [], [0, 0, 0, 0]
|
|
|
+
|
|
|
+ # 找出与 HTML 内容匹配的 boxes
|
|
|
+ matched_boxes = []
|
|
|
+ for box in paddle_boxes:
|
|
|
+ normalized_text = self.text_matcher.normalize_text(box['text'])
|
|
|
+
|
|
|
+ # 检查是否匹配
|
|
|
+ if any(normalized_text in ht or ht in normalized_text
|
|
|
+ for ht in html_texts):
|
|
|
+ matched_boxes.append(box)
|
|
|
+
|
|
|
+ if not matched_boxes:
|
|
|
+ # 🔑 降级：如果精确匹配失败，使用模糊匹配
|
|
|
+ print(" ℹ️ 精确匹配失败，尝试模糊匹配...")
|
|
|
+
|
|
|
+ from fuzzywuzzy import fuzz
|
|
|
+ for box in paddle_boxes:
|
|
|
+ normalized_text = self.text_matcher.normalize_text(box['text'])
|
|
|
+
|
|
|
+ for ht in html_texts:
|
|
|
+ similarity = fuzz.partial_ratio(normalized_text, ht)
|
|
|
+ if similarity >= 70: # 降低阈值
|
|
|
+ matched_boxes.append(box)
|
|
|
+ break
|
|
|
|
|
|
- return str(soup), cells, current_pointer
|
|
|
+ if matched_boxes:
|
|
|
+ # 计算边界框
|
|
|
+ actual_bbox = [
|
|
|
+ min(b['bbox'][0] for b in matched_boxes),
|
|
|
+ min(b['bbox'][1] for b in matched_boxes),
|
|
|
+ max(b['bbox'][2] for b in matched_boxes),
|
|
|
+ max(b['bbox'][3] for b in matched_boxes)
|
|
|
+ ]
|
|
|
+
|
|
|
+ # 🔑 扩展边界，包含可能遗漏的文本
|
|
|
+ margin = 30
|
|
|
+ expanded_bbox = [
|
|
|
+ max(0, actual_bbox[0] - margin),
|
|
|
+ max(0, actual_bbox[1] - margin),
|
|
|
+ actual_bbox[2] + margin,
|
|
|
+ actual_bbox[3] + margin
|
|
|
+ ]
|
|
|
+
|
|
|
+ # 重新筛选（包含边界上的文本）
|
|
|
+ final_filtered = []
|
|
|
+ for box in paddle_boxes:
|
|
|
+ bbox = box['bbox']
|
|
|
+ box_center_x = (bbox[0] + bbox[2]) / 2
|
|
|
+ box_center_y = (bbox[1] + bbox[3]) / 2
|
|
|
+
|
|
|
+ if (expanded_bbox[0] <= box_center_x <= expanded_bbox[2] and
|
|
|
+ expanded_bbox[1] <= box_center_y <= expanded_bbox[3]):
|
|
|
+ final_filtered.append(box)
|
|
|
+
|
|
|
+ return final_filtered, actual_bbox
|
|
|
+
|
|
|
+ # 🔑 最后的降级：返回所有 boxes
|
|
|
+ print(" ⚠️ 无法确定表格区域，使用所有 paddle boxes")
|
|
|
+ if paddle_boxes:
|
|
|
+ actual_bbox = [
|
|
|
+ min(b['bbox'][0] for b in paddle_boxes),
|
|
|
+ min(b['bbox'][1] for b in paddle_boxes),
|
|
|
+ max(b['bbox'][2] for b in paddle_boxes),
|
|
|
+ max(b['bbox'][3] for b in paddle_boxes)
|
|
|
+ ]
|
|
|
+ return paddle_boxes, actual_bbox
|
|
|
+
|
|
|
+ return [], [0, 0, 0, 0]
|
|
|
+
|
|
|
+ def _group_paddle_boxes_by_rows(self, paddle_boxes: List[Dict],
|
|
|
+ y_tolerance: int = 20) -> List[Dict]:
|
|
|
+ """
|
|
|
+ 将 paddle_text_boxes 按 y 坐标分组（聚类）
|
|
|
+
|
|
|
+ Args:
|
|
|
+ paddle_boxes: Paddle OCR 文字框列表
|
|
|
+ y_tolerance: Y 坐标容忍度（像素）
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ 分组列表，每组包含 {'y_center': float, 'boxes': List[Dict]}
|
|
|
+ """
|
|
|
+ if not paddle_boxes:
|
|
|
+ return []
|
|
|
+
|
|
|
+ # 计算每个 box 的中心 y 坐标
|
|
|
+ boxes_with_y = []
|
|
|
+ for box in paddle_boxes:
|
|
|
+ bbox = box['bbox']
|
|
|
+ y_center = (bbox[1] + bbox[3]) / 2
|
|
|
+ boxes_with_y.append({
|
|
|
+ 'y_center': y_center,
|
|
|
+ 'box': box
|
|
|
+ })
|
|
|
+
|
|
|
+ # 按 y 坐标排序
|
|
|
+ boxes_with_y.sort(key=lambda x: x['y_center'])
|
|
|
+
|
|
|
+ # 聚类
|
|
|
+ groups = []
|
|
|
+ current_group = None
|
|
|
+
|
|
|
+ for item in boxes_with_y:
|
|
|
+ if current_group is None:
|
|
|
+ # 开始新组
|
|
|
+ current_group = {
|
|
|
+ 'y_center': item['y_center'],
|
|
|
+ 'boxes': [item['box']]
|
|
|
+ }
|
|
|
+ else:
|
|
|
+ # 检查是否属于当前组
|
|
|
+ if abs(item['y_center'] - current_group['y_center']) <= y_tolerance:
|
|
|
+ current_group['boxes'].append(item['box'])
|
|
|
+ # 更新组的中心（使用平均值）
|
|
|
+ current_group['y_center'] = sum(
|
|
|
+ b['bbox'][1] + b['bbox'][3] for b in current_group['boxes']
|
|
|
+ ) / (2 * len(current_group['boxes']))
|
|
|
+ else:
|
|
|
+ # 保存当前组，开始新组
|
|
|
+ groups.append(current_group)
|
|
|
+ current_group = {
|
|
|
+ 'y_center': item['y_center'],
|
|
|
+ 'boxes': [item['box']]
|
|
|
+ }
|
|
|
+
|
|
|
+ # 添加最后一组
|
|
|
+ if current_group:
|
|
|
+ groups.append(current_group)
|
|
|
+
|
|
|
+ return groups
|
|
|
+
|
|
|
+
|
|
|
+ def _match_html_rows_to_paddle_groups(self, html_rows: List,
|
|
|
+ grouped_boxes: List[Dict]) -> Dict[int, List[int]]:
|
|
|
+ """
|
|
|
+ 智能匹配 HTML 行与 paddle 分组（改进版：处理跨行文本）
|
|
|
+
|
|
|
+ 策略：
|
|
|
+ 1. 第一遍：基于内容精确匹配
|
|
|
+ 2. 第二遍：将未使用的组合并到相邻已匹配的行
|
|
|
+ """
|
|
|
+ if not html_rows or not grouped_boxes:
|
|
|
+ return {}
|
|
|
+
|
|
|
+ mapping = {}
|
|
|
+
|
|
|
+ # 🎯 策略 1: 数量相等，简单 1:1 映射
|
|
|
+ if len(html_rows) == len(grouped_boxes):
|
|
|
+ for i in range(len(html_rows)):
|
|
|
+ mapping[i] = [i]
|
|
|
+ return mapping
|
|
|
+
|
|
|
+ # 🎯 策略 2: 第一遍 - 基于内容精确匹配
|
|
|
+ used_groups = set()
|
|
|
+
|
|
|
+ for row_idx, row in enumerate(html_rows):
|
|
|
+ row_texts = [cell.get_text(strip=True) for cell in row.find_all(['td', 'th'])]
|
|
|
+ row_texts = [t for t in row_texts if t]
|
|
|
+
|
|
|
+ if not row_texts:
|
|
|
+ mapping[row_idx] = []
|
|
|
+ continue
|
|
|
+
|
|
|
+ row_text_normalized = [self.text_matcher.normalize_text(t) for t in row_texts]
|
|
|
+
|
|
|
+ # 查找最匹配的 paddle 组
|
|
|
+ best_groups = []
|
|
|
+ best_score = 0
|
|
|
+
|
|
|
+ # 尝试匹配单个组
|
|
|
+ for group_idx, group in enumerate(grouped_boxes):
|
|
|
+ if group_idx in used_groups:
|
|
|
+ continue
|
|
|
+
|
|
|
+ group_texts = [self.text_matcher.normalize_text(b['text'])
|
|
|
+ for b in group['boxes'] if not b.get('used')]
|
|
|
+
|
|
|
+ match_count = sum(1 for rt in row_text_normalized
|
|
|
+ if any(rt in gt or gt in rt for gt in group_texts))
|
|
|
+
|
|
|
+ coverage = match_count / len(row_texts) if row_texts else 0
|
|
|
+
|
|
|
+ if coverage > best_score:
|
|
|
+ best_score = coverage
|
|
|
+ best_groups = [group_idx]
|
|
|
+
|
|
|
+ # 🔑 如果单组匹配度不高，尝试匹配多个连续组
|
|
|
+ if best_score < 0.5:
|
|
|
+ # 从当前位置向后查找
|
|
|
+ start_group = min([g for g in range(len(grouped_boxes)) if g not in used_groups],
|
|
|
+ default=0)
|
|
|
+ combined_texts = []
|
|
|
+ combined_groups = []
|
|
|
+
|
|
|
+ for group_idx in range(start_group, min(start_group + 5, len(grouped_boxes))):
|
|
|
+ if group_idx in used_groups:
|
|
|
+ continue
|
|
|
+
|
|
|
+ combined_groups.append(group_idx)
|
|
|
+ combined_texts.extend([
|
|
|
+ self.text_matcher.normalize_text(b['text'])
|
|
|
+ for b in grouped_boxes[group_idx]['boxes']
|
|
|
+ if not b.get('used')
|
|
|
+ ])
|
|
|
+
|
|
|
+ match_count = sum(1 for rt in row_text_normalized
|
|
|
+ if any(rt in gt or gt in rt for gt in combined_texts))
|
|
|
+ coverage = match_count / len(row_texts) if row_texts else 0
|
|
|
+
|
|
|
+ if coverage > best_score:
|
|
|
+ best_score = coverage
|
|
|
+ best_groups = combined_groups.copy()
|
|
|
+
|
|
|
+ # 记录映射
|
|
|
+ if best_groups and best_score > 0.3:
|
|
|
+ mapping[row_idx] = best_groups
|
|
|
+ used_groups.update(best_groups)
|
|
|
+ else:
|
|
|
+ # 降级策略：位置推测
|
|
|
+ estimated_group = min(row_idx, len(grouped_boxes) - 1)
|
|
|
+ if estimated_group not in used_groups:
|
|
|
+ mapping[row_idx] = [estimated_group]
|
|
|
+ used_groups.add(estimated_group)
|
|
|
+ else:
|
|
|
+ mapping[row_idx] = []
|
|
|
+
|
|
|
+ # 🎯 策略 3: 第二遍 - 处理未使用的组（关键！）
|
|
|
+ unused_groups = [i for i in range(len(grouped_boxes)) if i not in used_groups]
|
|
|
+
|
|
|
+ if unused_groups:
|
|
|
+ print(f" ℹ️ 发现 {len(unused_groups)} 个未匹配的 paddle 组: {unused_groups}")
|
|
|
+
|
|
|
+ # 🔑 将未使用的组合并到相邻的已匹配行
|
|
|
+ for unused_idx in unused_groups:
|
|
|
+ # 🎯 关键改进：计算与相邻行的边界距离
|
|
|
+ unused_group = grouped_boxes[unused_idx]
|
|
|
+ unused_y_min = min(b['bbox'][1] for b in unused_group['boxes'])
|
|
|
+ unused_y_max = max(b['bbox'][3] for b in unused_group['boxes'])
|
|
|
+
|
|
|
+ # 🔑 查找上方和下方最近的已使用组
|
|
|
+ above_idx = None
|
|
|
+ below_idx = None
|
|
|
+ above_distance = float('inf')
|
|
|
+ below_distance = float('inf')
|
|
|
+
|
|
|
+ # 向上查找
|
|
|
+ for i in range(unused_idx - 1, -1, -1):
|
|
|
+ if i in used_groups:
|
|
|
+ above_idx = i
|
|
|
+ # 🎯 边界距离：unused 的最小 y - above 的最大 y
|
|
|
+ above_group = grouped_boxes[i]
|
|
|
+ max_y_box = max(
|
|
|
+ above_group['boxes'],
|
|
|
+ key=lambda b: b['bbox'][3]
|
|
|
+ )
|
|
|
+ above_y_center = (max_y_box['bbox'][1] + max_y_box['bbox'][3]) / 2
|
|
|
+ above_distance = abs(unused_y_min - above_y_center)
|
|
|
+ print(f" • 组 {unused_idx} 与上方组 {i} 距离: {above_distance:.1f}px")
|
|
|
+ break
|
|
|
+
|
|
|
+ # 向下查找
|
|
|
+ for i in range(unused_idx + 1, len(grouped_boxes)):
|
|
|
+ if i in used_groups:
|
|
|
+ below_idx = i
|
|
|
+ # 🎯 边界距离：below 的最小 y - unused 的最大 y
|
|
|
+ below_group = grouped_boxes[i]
|
|
|
+ min_y_box = min(
|
|
|
+ below_group['boxes'],
|
|
|
+ key=lambda b: b['bbox'][1]
|
|
|
+ )
|
|
|
+ below_y_center = (min_y_box['bbox'][1] + min_y_box['bbox'][3]) / 2
|
|
|
+ below_distance = abs(below_y_center - unused_y_max)
|
|
|
+ print(f" • 组 {unused_idx} 与下方组 {i} 距离: {below_distance:.1f}px")
|
|
|
+ break
|
|
|
+
|
|
|
+ # 🎯 选择距离更近的一侧
|
|
|
+ if above_idx is not None and below_idx is not None:
|
|
|
+ # 都存在，选择距离更近的
|
|
|
+ if above_distance < below_distance:
|
|
|
+ closest_used_idx = above_idx
|
|
|
+ merge_direction = "上方"
|
|
|
+ else:
|
|
|
+ closest_used_idx = below_idx
|
|
|
+ merge_direction = "下方"
|
|
|
+ print(f" ✓ 组 {unused_idx} 选择合并到{merge_direction}组 {closest_used_idx}")
|
|
|
+ elif above_idx is not None:
|
|
|
+ closest_used_idx = above_idx
|
|
|
+ merge_direction = "上方"
|
|
|
+ elif below_idx is not None:
|
|
|
+ closest_used_idx = below_idx
|
|
|
+ merge_direction = "下方"
|
|
|
+ else:
|
|
|
+ print(f" ⚠️ 组 {unused_idx} 无相邻已使用组，跳过")
|
|
|
+ continue
|
|
|
+
|
|
|
+ # 🔑 找到该组对应的 HTML 行
|
|
|
+ target_html_row = None
|
|
|
+ for html_row_idx, group_indices in mapping.items():
|
|
|
+ if closest_used_idx in group_indices:
|
|
|
+ target_html_row = html_row_idx
|
|
|
+ break
|
|
|
+
|
|
|
+ if target_html_row is not None:
|
|
|
+ # 🎯 根据合并方向决定目标行
|
|
|
+ if merge_direction == "上方":
|
|
|
+ # 合并到上方对应的 HTML 行
|
|
|
+ if target_html_row in mapping:
|
|
|
+ if unused_idx not in mapping[target_html_row]:
|
|
|
+ mapping[target_html_row].append(unused_idx)
|
|
|
+ print(f" • 组 {unused_idx} 合并到 HTML 行 {target_html_row}（上方行）")
|
|
|
+ else:
|
|
|
+ # 合并到下方对应的 HTML 行
|
|
|
+ if target_html_row in mapping:
|
|
|
+ if unused_idx not in mapping[target_html_row]:
|
|
|
+ mapping[target_html_row].append(unused_idx)
|
|
|
+ print(f" • 组 {unused_idx} 合并到 HTML 行 {target_html_row}（下方行）")
|
|
|
+
|
|
|
+ used_groups.add(unused_idx)
|
|
|
+
|
|
|
+ # 🔑 策略 4: 第三遍 - 按 y 坐标排序每行的组索引
|
|
|
+ for row_idx in mapping:
|
|
|
+ if mapping[row_idx]:
|
|
|
+ mapping[row_idx].sort(key=lambda idx: grouped_boxes[idx]['y_center'])
|
|
|
+
|
|
|
+ return mapping
|
|
|
+
|
|
|
+ def _match_cell_sequential(self, cell_text: str,
|
|
|
+ boxes: List[Dict],
|
|
|
+ col_boundaries: List[Tuple[int, int]],
|
|
|
+ start_idx: int) -> Optional[Dict]:
|
|
|
+ """
|
|
|
+ 🎯 顺序匹配单元格：从指定位置开始，逐步合并 boxes 直到匹配
|
|
|
+
|
|
|
+ 策略：
|
|
|
+ 1. 找到第一个未使用的 box
|
|
|
+ 2. 尝试单个 box 精确匹配
|
|
|
+ 3. 如果失败，尝试合并多个 boxes
|
|
|
+
|
|
|
+ Args:
|
|
|
+ cell_text: HTML 单元格文本
|
|
|
+ boxes: 候选 boxes（已按 x 坐标排序）
|
|
|
+ col_boundaries: 列边界列表
|
|
|
+ start_idx: 起始索引
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ {'bbox': [x1,y1,x2,y2], 'text': str, 'score': float,
|
|
|
+ 'paddle_indices': [idx1, idx2], 'used_boxes': [box1, box2],
|
|
|
+ 'last_used_index': int}
|
|
|
+ """
|
|
|
+ from fuzzywuzzy import fuzz
|
|
|
+
|
|
|
+ cell_text_normalized = self.text_matcher.normalize_text(cell_text)
|
|
|
+
|
|
|
+ if len(cell_text_normalized) < 2:
|
|
|
+ return None
|
|
|
+
|
|
|
+ # 🔑 找到第一个未使用的 box
|
|
|
+ first_unused_idx = start_idx
|
|
|
+ while first_unused_idx < len(boxes) and boxes[first_unused_idx].get('used'):
|
|
|
+ first_unused_idx += 1
|
|
|
+
|
|
|
+ if first_unused_idx >= len(boxes):
|
|
|
+ return None
|
|
|
+
|
|
|
+ # 🔑 策略 1: 单个 box 精确匹配
|
|
|
+ for box in boxes[first_unused_idx:]:
|
|
|
+ if box.get('used'):
|
|
|
+ continue
|
|
|
+
|
|
|
+ box_text = self.text_matcher.normalize_text(box['text'])
|
|
|
+
|
|
|
+ if cell_text_normalized == box_text:
|
|
|
+ return self._build_match_result([box], box['text'], 100.0, boxes.index(box))
|
|
|
+
|
|
|
+ # 🔑 策略 2: 多个 boxes 合并匹配
|
|
|
+ unused_boxes = [b for b in boxes if not b.get('used')]
|
|
|
+ # 合并同列的 boxes 合并
|
|
|
+ merged_bboxes = []
|
|
|
+ for col_idx in range(len(col_boundaries)):
|
|
|
+ combo_boxes = self._get_boxes_in_column(unused_boxes, col_boundaries, col_idx)
|
|
|
+ if len(combo_boxes) > 0:
|
|
|
+ sorted_combo = sorted(combo_boxes, key=lambda b: (b['bbox'][1], b['bbox'][0]))
|
|
|
+ merged_text = ''.join([b['text'] for b in sorted_combo])
|
|
|
+ merged_bboxes.append({
|
|
|
+ 'text': merged_text,
|
|
|
+ 'sorted_combo': sorted_combo
|
|
|
+ })
|
|
|
+
|
|
|
+ for box in merged_bboxes:
|
|
|
+ # 1. 精确匹配
|
|
|
+ merged_text_normalized = self.text_matcher.normalize_text(box['text'])
|
|
|
+ if cell_text_normalized == merged_text_normalized:
|
|
|
+ last_sort_idx = boxes.index(box['sorted_combo'][-1])
|
|
|
+ return self._build_match_result(box['sorted_combo'], box['text'], 100.0, last_sort_idx)
|
|
|
+
|
|
|
+ # 2. 子串匹配
|
|
|
+ is_substring = (cell_text_normalized in merged_text_normalized or
|
|
|
+ merged_text_normalized in cell_text_normalized)
|
|
|
+
|
|
|
+ # 3. 模糊匹配
|
|
|
+ similarity = fuzz.partial_ratio(cell_text_normalized, merged_text_normalized)
|
|
|
+
|
|
|
+ # 🎯 子串匹配加分
|
|
|
+ if is_substring:
|
|
|
+ similarity = min(100, similarity + 10)
|
|
|
+
|
|
|
+ if similarity >= self.text_matcher.similarity_threshold:
|
|
|
+ print(f" ✓ 匹配成功: '{cell_text[:15]}' vs '{merged_text[:15]}' (相似度: {similarity})")
|
|
|
+ return self._build_match_result(box['sorted_combo'], box['text'], similarity, start_idx)
|
|
|
+
|
|
|
+ print(f" ✗ 匹配失败: '{cell_text[:15]}'")
|
|
|
+ return None
|
|
|
+
|
|
|
+
|
|
|
+ def _build_match_result(self, boxes: List[Dict], text: str,
|
|
|
+ score: float, last_index: int) -> Dict:
|
|
|
+ """构建匹配结果"""
|
|
|
+ merged_bbox = [
|
|
|
+ min(b['bbox'][0] for b in boxes),
|
|
|
+ min(b['bbox'][1] for b in boxes),
|
|
|
+ max(b['bbox'][2] for b in boxes),
|
|
|
+ max(b['bbox'][3] for b in boxes)
|
|
|
+ ]
|
|
|
+
|
|
|
+ return {
|
|
|
+ 'bbox': merged_bbox,
|
|
|
+ 'text': text,
|
|
|
+ 'score': score,
|
|
|
+ 'paddle_indices': [b['paddle_bbox_index'] for b in boxes],
|
|
|
+ 'used_boxes': boxes,
|
|
|
+ 'last_used_index': last_index
|
|
|
+ }
|
