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- """
- 布局处理工具模块
- 提供布局相关处理功能:
- - 重叠框检测与去重
- - 阅读顺序排序
- - OCR Span 与 Layout Block 匹配
- - 大面积文本块转换为表格(后处理)
- 注:底层坐标计算方法(IoU、重叠比例、poly_to_bbox 等)已统一到 coordinate_utils.py
- """
- from typing import Dict, List, Any, Tuple, Optional
- from loguru import logger
- import statistics
- # 导入坐标工具(底层坐标计算方法)
- try:
- from .coordinate_utils import CoordinateUtils
- except ImportError:
- from coordinate_utils import CoordinateUtils
- class LayoutUtils:
- """布局处理工具类"""
-
- # ==================== 坐标计算方法(委托给 CoordinateUtils)====================
-
- @staticmethod
- def calculate_iou(bbox1: List[float], bbox2: List[float]) -> float:
- """计算两个 bbox 的 IoU(交并比)- 委托给 CoordinateUtils"""
- return CoordinateUtils.calculate_iou(bbox1, bbox2)
-
- @staticmethod
- def calculate_overlap_ratio(bbox1: List[float], bbox2: List[float]) -> float:
- """计算重叠面积占小框面积的比例 - 委托给 CoordinateUtils"""
- return CoordinateUtils.calculate_overlap_ratio(bbox1, bbox2)
-
- # ==================== 布局处理方法 ====================
-
- @staticmethod
- def remove_overlapping_boxes(
- layout_results: List[Dict[str, Any]],
- iou_threshold: float = 0.8,
- overlap_ratio_threshold: float = 0.8
- ) -> List[Dict[str, Any]]:
- """
- 处理重叠的布局框(参考 MinerU 的去重策略)
-
- 策略:
- 1. 高 IoU 重叠:保留置信度高的框
- 2. 包含关系:小框被大框高度包含时,保留大框并扩展边界
- 3. 同类型优先合并
-
- Args:
- layout_results: Layout 检测结果列表
- iou_threshold: IoU 阈值,超过此值认为高度重叠
- overlap_ratio_threshold: 重叠面积占小框面积的比例阈值
-
- Returns:
- 去重后的布局结果列表
- """
- if not layout_results or len(layout_results) <= 1:
- return layout_results
-
- # 复制列表避免修改原数据
- results = [item.copy() for item in layout_results]
- need_remove = set()
-
- for i in range(len(results)):
- if i in need_remove:
- continue
-
- for j in range(i + 1, len(results)):
- if j in need_remove:
- continue
-
- bbox1 = results[i].get('bbox', [0, 0, 0, 0])
- bbox2 = results[j].get('bbox', [0, 0, 0, 0])
-
- if len(bbox1) < 4 or len(bbox2) < 4:
- continue
-
- # 计算 IoU
- iou = LayoutUtils.calculate_iou(bbox1, bbox2)
-
- if iou > iou_threshold:
- # 高度重叠,保留置信度高的
- score1 = results[i].get('confidence', results[i].get('score', 0))
- score2 = results[j].get('confidence', results[j].get('score', 0))
-
- if score1 >= score2:
- need_remove.add(j)
- else:
- need_remove.add(i)
- break # i 被移除,跳出内层循环
- else:
- # 检查包含关系
- overlap_ratio = LayoutUtils.calculate_overlap_ratio(bbox1, bbox2)
-
- if overlap_ratio > overlap_ratio_threshold:
- # 小框被大框高度包含
- area1 = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1])
- area2 = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
-
- if area1 <= area2:
- small_idx, large_idx = i, j
- else:
- small_idx, large_idx = j, i
-
- # 扩展大框的边界
- small_bbox = results[small_idx]['bbox']
- large_bbox = results[large_idx]['bbox']
- results[large_idx]['bbox'] = [
- min(small_bbox[0], large_bbox[0]),
- min(small_bbox[1], large_bbox[1]),
- max(small_bbox[2], large_bbox[2]),
- max(small_bbox[3], large_bbox[3])
- ]
- need_remove.add(small_idx)
-
- if small_idx == i:
- break # i 被移除,跳出内层循环
-
- # 返回去重后的结果
- return [results[i] for i in range(len(results)) if i not in need_remove]
-
- @staticmethod
- def convert_large_text_to_table(
- layout_results: List[Dict[str, Any]],
- image_shape: Tuple[int, int],
- min_area_ratio: float = 0.25,
- min_width_ratio: float = 0.4,
- min_height_ratio: float = 0.3
- ) -> List[Dict[str, Any]]:
- """
- 将大面积的文本块转换为表格
-
- 判断规则:
- 1. 面积占比:占页面面积超过 min_area_ratio(默认25%)
- 2. 尺寸比例:宽度和高度都超过一定比例(避免细长条)
- 3. 不与其他表格重叠:如果已有表格,不转换
-
- Args:
- layout_results: Layout 检测结果列表
- image_shape: 图像尺寸 (height, width)
- min_area_ratio: 最小面积占比(0-1),默认0.25(25%)
- min_width_ratio: 最小宽度占比(0-1),默认0.4(40%)
- min_height_ratio: 最小高度占比(0-1),默认0.3(30%)
-
- Returns:
- 转换后的布局结果列表
- """
- if not layout_results:
- return layout_results
-
- img_height, img_width = image_shape
- img_area = img_height * img_width
-
- # 检查是否已有表格
- has_table = any(
- item.get('category', '').lower() in ['table', 'table_body']
- for item in layout_results
- )
-
- # 如果已有表格,不进行转换(避免误判)
- if has_table:
- logger.debug("📋 Page already has table elements, skipping text-to-table conversion")
- return layout_results
-
- # 复制列表避免修改原数据
- results = [item.copy() for item in layout_results]
- converted_count = 0
-
- for item in results:
- category = item.get('category', '').lower()
-
- # 只处理文本类型的元素
- if category not in ['text', 'ocr_text']:
- continue
-
- bbox = item.get('bbox', [0, 0, 0, 0])
- if len(bbox) < 4:
- continue
-
- x1, y1, x2, y2 = bbox[:4]
- width = x2 - x1
- height = y2 - y1
- area = width * height
-
- # 计算占比
- area_ratio = area / img_area if img_area > 0 else 0
- width_ratio = width / img_width if img_width > 0 else 0
- height_ratio = height / img_height if img_height > 0 else 0
-
- # 判断是否满足转换条件
- if (area_ratio >= min_area_ratio and
- width_ratio >= min_width_ratio and
- height_ratio >= min_height_ratio):
-
- # 转换为表格
- item['category'] = 'table'
- item['original_category'] = category # 保留原始类别
- converted_count += 1
-
- logger.info(
- f"🔄 Converted large text block to table: "
- f"area={area_ratio:.1%}, size={width_ratio:.1%}×{height_ratio:.1%}, "
- f"bbox=[{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}]"
- )
-
- if converted_count > 0:
- logger.info(f"✅ Converted {converted_count} large text block(s) to table(s)")
-
- return results
-
- @staticmethod
- def sort_elements_by_reading_order(
- elements: List[Dict[str, Any]],
- y_tolerance: float = 15.0
- ) -> List[Dict[str, Any]]:
- """
- 根据阅读顺序对元素进行排序,并添加 reading_order 字段
-
- 排序规则:
- 1. 按Y坐标分行(考虑容差,Y坐标相近的元素视为同一行)
- 2. 同一行内按X坐标从左到右排序
- 3. 行与行之间按Y坐标从上到下排序
-
- Args:
- elements: 元素列表(坐标已转换为原始图片坐标系)
- y_tolerance: Y坐标容差,在此范围内的元素被视为同一行
-
- Returns:
- 排序后的元素列表,每个元素都添加了 reading_order 字段
- """
- if not elements:
- return elements
-
- # 为每个元素提取排序用的坐标
- elements_with_coords = []
- for elem in elements:
- bbox = elem.get('bbox', [0, 0, 0, 0])
- if len(bbox) >= 4:
- y_top = bbox[1] # 上边界
- x_left = bbox[0] # 左边界
- else:
- y_top = 0
- x_left = 0
- elements_with_coords.append((elem, y_top, x_left))
-
- # 先按Y坐标排序
- elements_with_coords.sort(key=lambda x: (x[1], x[2]))
-
- # 按Y坐标分行
- rows = []
- current_row = []
- current_row_y = None
-
- for elem, y_top, x_left in elements_with_coords:
- if current_row_y is None:
- # 第一个元素
- current_row.append((elem, x_left))
- current_row_y = y_top
- elif abs(y_top - current_row_y) <= y_tolerance:
- # 同一行
- current_row.append((elem, x_left))
- else:
- # 新的一行
- rows.append(current_row)
- current_row = [(elem, x_left)]
- current_row_y = y_top
-
- # 添加最后一行
- if current_row:
- rows.append(current_row)
-
- # 每行内按X坐标排序,然后展平
- sorted_elements = []
- reading_order = 0
-
- for row in rows:
- # 行内按X坐标排序
- row.sort(key=lambda x: x[1])
- for elem, _ in row:
- # 添加 reading_order 字段
- elem['reading_order'] = reading_order
- sorted_elements.append(elem)
- reading_order += 1
-
- logger.debug(f"📖 Elements sorted by reading order: {len(sorted_elements)} elements")
- return sorted_elements
- class SpanMatcher:
- """
- OCR Span 与 Layout Block 匹配器
-
- 参考 MinerU 的处理方式:
- 1. 整页 OCR 获取所有 spans
- 2. 将 spans 匹配到对应的 layout blocks
-
- 注:底层坐标计算方法已统一到 CoordinateUtils
- """
-
- # ==================== 坐标计算方法(委托给 CoordinateUtils)====================
-
- @staticmethod
- def calculate_overlap_area_in_bbox1_ratio(
- bbox1: List[float],
- bbox2: List[float]
- ) -> float:
- """计算 bbox1 被 bbox2 覆盖的面积比例 - 委托给 CoordinateUtils"""
- return CoordinateUtils.calculate_overlap_in_bbox1_ratio(bbox1, bbox2)
-
- @staticmethod
- def poly_to_bbox(poly: List) -> List[float]:
- """将多边形坐标转换为 bbox 格式 - 委托给 CoordinateUtils"""
- return CoordinateUtils.poly_to_bbox(poly)
-
- # ==================== Span 匹配方法 ====================
-
- @staticmethod
- def match_spans_to_blocks(
- ocr_spans: List[Dict[str, Any]],
- layout_blocks: List[Dict[str, Any]],
- overlap_threshold: float = 0.5
- ) -> Dict[int, List[Dict[str, Any]]]:
- """
- 将 OCR spans 匹配到 layout blocks
-
- Args:
- ocr_spans: OCR 识别结果列表,每项包含 'bbox'/'poly', 'text', 'confidence'
- layout_blocks: Layout 检测结果列表,每项包含 'bbox', 'category'
- overlap_threshold: 重叠比例阈值,span 被 block 覆盖超过此比例才算匹配
-
- Returns:
- 匹配结果字典 {block_index: [matched_spans]}
- """
- matched = {i: [] for i in range(len(layout_blocks))}
-
- for span in ocr_spans:
- # 获取 span 的 bbox
- span_bbox = span.get('bbox', [])
- if not span_bbox:
- continue
-
- # 转换为标准 bbox 格式
- span_bbox = SpanMatcher.poly_to_bbox(span_bbox)
-
- # 找到最佳匹配的 block
- best_match_idx = -1
- best_overlap = 0.0
-
- for block_idx, block in enumerate(layout_blocks):
- block_bbox = block.get('bbox', [0, 0, 0, 0])
-
- overlap = SpanMatcher.calculate_overlap_area_in_bbox1_ratio(
- span_bbox, block_bbox
- )
-
- if overlap > overlap_threshold and overlap > best_overlap:
- best_overlap = overlap
- best_match_idx = block_idx
-
- if best_match_idx >= 0:
- # 创建带绝对坐标的 span 副本
- matched_span = span.copy()
- matched_span['bbox'] = span_bbox # 确保是标准 bbox 格式
- matched[best_match_idx].append(matched_span)
-
- return matched
-
- @staticmethod
- def merge_spans_to_text(
- spans: List[Dict[str, Any]],
- block_bbox: Optional[List[float]] = None
- ) -> Tuple[str, List[Dict[str, Any]]]:
- """
- 将多个 spans 合并为单个文本字符串
-
- 参考 MinerU 的 span 合并逻辑:
- 1. 按 Y 坐标分行
- 2. 同行内按 X 坐标排序
- 3. 行间添加换行,词间可能添加空格
-
- Args:
- spans: span 列表
- block_bbox: 所属 block 的 bbox(用于参考)
-
- Returns:
- (merged_text, sorted_spans)
- """
- if not spans:
- return "", []
-
- # 计算 spans 的高度中位数(用于判断同行)
- heights = []
- for span in spans:
- bbox = span.get('bbox', [0, 0, 0, 0])
- if len(bbox) >= 4:
- h = bbox[3] - bbox[1]
- if h > 0:
- heights.append(h)
-
- if heights:
- median_height = statistics.median(heights)
- y_tolerance = median_height * 0.5
- else:
- y_tolerance = 10
-
- # 为每个 span 添加坐标信息用于排序
- spans_with_coords = []
- for span in spans:
- bbox = span.get('bbox', [0, 0, 0, 0])
- if len(bbox) >= 4:
- y_center = (bbox[1] + bbox[3]) / 2
- x_left = bbox[0]
- else:
- y_center = 0
- x_left = 0
- spans_with_coords.append((span, y_center, x_left))
-
- # 按 Y 坐标分行
- spans_with_coords.sort(key=lambda x: (x[1], x[2]))
-
- lines = []
- current_line = []
- current_line_y = None
-
- for span, y_center, x_left in spans_with_coords:
- if current_line_y is None:
- current_line.append((span, x_left))
- current_line_y = y_center
- elif abs(y_center - current_line_y) <= y_tolerance:
- current_line.append((span, x_left))
- else:
- lines.append(current_line)
- current_line = [(span, x_left)]
- current_line_y = y_center
-
- if current_line:
- lines.append(current_line)
-
- # 合并文本
- text_parts = []
- sorted_spans = []
-
- for line in lines:
- # 行内按 X 坐标排序
- line.sort(key=lambda x: x[1])
-
- line_texts = []
- for span, _ in line:
- text = span.get('text', '')
- if text:
- line_texts.append(text)
- sorted_spans.append(span)
-
- if line_texts:
- text_parts.append(' '.join(line_texts))
-
- merged_text = '\n'.join(text_parts)
-
- return merged_text, sorted_spans
-
- @staticmethod
- def remove_duplicate_spans(
- spans: List[Dict[str, Any]],
- iou_threshold: float = 0.9
- ) -> List[Dict[str, Any]]:
- """
- 移除重复的 spans(高 IoU 重叠)
-
- Args:
- spans: span 列表
- iou_threshold: IoU 阈值
-
- Returns:
- 去重后的 spans
- """
- if len(spans) <= 1:
- return spans
-
- result = []
- removed = set()
-
- for i, span1 in enumerate(spans):
- if i in removed:
- continue
-
- bbox1 = span1.get('bbox', [0, 0, 0, 0])
- bbox1 = CoordinateUtils.poly_to_bbox(bbox1)
-
- for j in range(i + 1, len(spans)):
- if j in removed:
- continue
-
- bbox2 = spans[j].get('bbox', [0, 0, 0, 0])
- bbox2 = CoordinateUtils.poly_to_bbox(bbox2)
-
- iou = CoordinateUtils.calculate_iou(bbox1, bbox2)
-
- if iou > iou_threshold:
- # 保留置信度高的
- score1 = span1.get('confidence', span1.get('score', 0))
- score2 = spans[j].get('confidence', spans[j].get('score', 0))
-
- if score1 >= score2:
- removed.add(j)
- else:
- removed.add(i)
- break
-
- if i not in removed:
- result.append(span1)
-
- return result
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