# Copyright (c) Opendatalab. All rights reserved. import collections import re import statistics import cv2 import numpy as np from loguru import logger from mineru.utils.boxbase import calculate_overlap_area_in_bbox1_area_ratio, calculate_iou, \ get_minbox_if_overlap_by_ratio from mineru.utils.enum_class import BlockType, ContentType from mineru.utils.pdf_image_tools import get_crop_img from mineru.utils.pdf_text_tool import get_page def remove_outside_spans(spans, all_bboxes, all_discarded_blocks): def get_block_bboxes(blocks, block_type_list): return [block[0:4] for block in blocks if block[7] in block_type_list] image_bboxes = get_block_bboxes(all_bboxes, [BlockType.IMAGE_BODY]) table_bboxes = get_block_bboxes(all_bboxes, [BlockType.TABLE_BODY]) other_block_type = [] for block_type in BlockType.__dict__.values(): if not isinstance(block_type, str): continue if block_type not in [BlockType.IMAGE_BODY, BlockType.TABLE_BODY]: other_block_type.append(block_type) other_block_bboxes = get_block_bboxes(all_bboxes, other_block_type) discarded_block_bboxes = get_block_bboxes(all_discarded_blocks, [BlockType.DISCARDED]) new_spans = [] for span in spans: span_bbox = span['bbox'] span_type = span['type'] if any(calculate_overlap_area_in_bbox1_area_ratio(span_bbox, block_bbox) > 0.4 for block_bbox in discarded_block_bboxes): new_spans.append(span) continue if span_type == ContentType.IMAGE: if any(calculate_overlap_area_in_bbox1_area_ratio(span_bbox, block_bbox) > 0.5 for block_bbox in image_bboxes): new_spans.append(span) elif span_type == ContentType.TABLE: if any(calculate_overlap_area_in_bbox1_area_ratio(span_bbox, block_bbox) > 0.5 for block_bbox in table_bboxes): new_spans.append(span) else: if any(calculate_overlap_area_in_bbox1_area_ratio(span_bbox, block_bbox) > 0.5 for block_bbox in other_block_bboxes): new_spans.append(span) return new_spans def remove_overlaps_low_confidence_spans(spans): dropped_spans = [] # 删除重叠spans中置信度低的的那些 for span1 in spans: for span2 in spans: if span1 != span2: # span1 或 span2 任何一个都不应该在 dropped_spans 中 if span1 in dropped_spans or span2 in dropped_spans: continue else: if calculate_iou(span1['bbox'], span2['bbox']) > 0.9: if span1['score'] < span2['score']: span_need_remove = span1 else: span_need_remove = span2 if ( span_need_remove is not None and span_need_remove not in dropped_spans ): dropped_spans.append(span_need_remove) if len(dropped_spans) > 0: for span_need_remove in dropped_spans: spans.remove(span_need_remove) return spans, dropped_spans def remove_overlaps_min_spans(spans): dropped_spans = [] # 删除重叠spans中较小的那些 for span1 in spans: for span2 in spans: if span1 != span2: # span1 或 span2 任何一个都不应该在 dropped_spans 中 if span1 in dropped_spans or span2 in dropped_spans: continue else: overlap_box = get_minbox_if_overlap_by_ratio(span1['bbox'], span2['bbox'], 0.65) if overlap_box is not None: span_need_remove = next((span for span in spans if span['bbox'] == overlap_box), None) if span_need_remove is not None and span_need_remove not in dropped_spans: dropped_spans.append(span_need_remove) if len(dropped_spans) > 0: for span_need_remove in dropped_spans: spans.remove(span_need_remove) return spans, dropped_spans def __replace_ligatures(text: str): ligatures = { 'fi': 'fi', 'fl': 'fl', 'ff': 'ff', 'ffi': 'ffi', 'ffl': 'ffl', 'ſt': 'ft', 'st': 'st' } return re.sub('|'.join(map(re.escape, ligatures.keys())), lambda m: ligatures[m.group()], text) def __replace_unicode(text: str): ligatures = { '\r\n': '', '\u0002': '-', } return re.sub('|'.join(map(re.escape, ligatures.keys())), lambda m: ligatures[m.group()], text) """pdf_text dict方案 char级别""" def txt_spans_extract(pdf_page, spans, pil_img, scale, all_bboxes, all_discarded_blocks): page_dict = get_page(pdf_page) page_all_chars = [] page_all_lines = [] for block in page_dict['blocks']: for line in block['lines']: if 0 < abs(line['rotation']) < 90: # 旋转角度在0-90度之间的行，直接跳过 continue page_all_lines.append(line) for span in line['spans']: for char in span['chars']: page_all_chars.append(char) # 计算所有sapn的高度的中位数 span_height_list = [] for span in spans: if span['type'] in [ContentType.TEXT]: span_height = span['bbox'][3] - span['bbox'][1] span['height'] = span_height span['width'] = span['bbox'][2] - span['bbox'][0] span_height_list.append(span_height) if len(span_height_list) == 0: return spans else: median_span_height = statistics.median(span_height_list) useful_spans = [] unuseful_spans = [] # 纵向span的两个特征：1. 高度超过多个line 2. 高宽比超过某个值 vertical_spans = [] for span in spans: if span['type'] in [ContentType.TEXT]: for block in all_bboxes + all_discarded_blocks: if block[7] in [BlockType.IMAGE_BODY, BlockType.TABLE_BODY, BlockType.INTERLINE_EQUATION]: continue if calculate_overlap_area_in_bbox1_area_ratio(span['bbox'], block[0:4]) > 0.5: if span['height'] > median_span_height * 3 and span['height'] > span['width'] * 3: vertical_spans.append(span) elif block in all_bboxes: useful_spans.append(span) else: unuseful_spans.append(span) break """垂直的span框直接用line进行填充""" if len(vertical_spans) > 0: for pdfium_line in page_all_lines: for span in vertical_spans: if calculate_overlap_area_in_bbox1_area_ratio(pdfium_line['bbox'].bbox, span['bbox']) > 0.5: for pdfium_span in pdfium_line['spans']: span['content'] += pdfium_span['text'] break for span in vertical_spans: if len(span['content']) == 0: spans.remove(span) """水平的span框先用char填充，再用ocr填充空的span框""" new_spans = [] for span in useful_spans + unuseful_spans: if span['type'] in [ContentType.TEXT]: span['chars'] = [] new_spans.append(span) need_ocr_spans = fill_char_in_spans(new_spans, page_all_chars, median_span_height) """对未填充的span进行ocr""" if len(need_ocr_spans) > 0: for span in need_ocr_spans: # 对span的bbox截图再ocr span_pil_img = get_crop_img(span['bbox'], pil_img, scale) span_img = cv2.cvtColor(np.array(span_pil_img), cv2.COLOR_RGB2BGR) # 计算span的对比度，低于0.20的span不进行ocr if calculate_contrast(span_img, img_mode='bgr') <= 0.17: spans.remove(span) continue span['content'] = '' span['score'] = 1.0 span['np_img'] = span_img return spans def fill_char_in_spans(spans, all_chars, median_span_height): # 简单从上到下排一下序 spans = sorted(spans, key=lambda x: x['bbox'][1]) grid_size = median_span_height grid = collections.defaultdict(list) for i, span in enumerate(spans): start_cell = int(span['bbox'][1] / grid_size) end_cell = int(span['bbox'][3] / grid_size) for cell_idx in range(start_cell, end_cell + 1): grid[cell_idx].append(i) for char in all_chars: char_center_y = (char['bbox'][1] + char['bbox'][3]) / 2 cell_idx = int(char_center_y / grid_size) candidate_span_indices = grid.get(cell_idx, []) for span_idx in candidate_span_indices: span = spans[span_idx] if calculate_char_in_span(char['bbox'], span['bbox'], char['char']): span['chars'].append(char) break need_ocr_spans = [] for span in spans: chars_to_content(span) # 有的span中虽然没有字但有一两个空的占位符，用宽高和content长度过滤 if len(span['content']) * span['height'] < span['width'] * 0.5: # logger.info(f"maybe empty span: {len(span['content'])}, {span['height']}, {span['width']}") need_ocr_spans.append(span) del span['height'], span['width'] return need_ocr_spans LINE_STOP_FLAG = ('.', '!', '?', '。', '！', '？', ')', '）', '"', '”', ':', '：', ';', '；', ']', '】', '}', '}', '>', '》', '、', ',', '，', '-', '—', '–',) LINE_START_FLAG = ('(', '（', '"', '“', '【', '{', '《', '<', '「', '『', '【', '[',) Span_Height_Radio = 0.33 # 字符的中轴和span的中轴高度差不能超过1/3span高度 def calculate_char_in_span(char_bbox, span_bbox, char, span_height_radio=Span_Height_Radio): char_center_x = (char_bbox[0] + char_bbox[2]) / 2 char_center_y = (char_bbox[1] + char_bbox[3]) / 2 span_center_y = (span_bbox[1] + span_bbox[3]) / 2 span_height = span_bbox[3] - span_bbox[1] if ( span_bbox[0] < char_center_x < span_bbox[2] and span_bbox[1] < char_center_y < span_bbox[3] and abs(char_center_y - span_center_y) < span_height * span_height_radio # 字符的中轴和span的中轴高度差不能超过Span_Height_Radio ): return True else: # 如果char是LINE_STOP_FLAG，就不用中心点判定，换一种方案（左边界在span区域内，高度判定和之前逻辑一致） # 主要是给结尾符号一个进入span的机会，这个char还应该离span右边界较近 if char in LINE_STOP_FLAG: if ( (span_bbox[2] - span_height) < char_bbox[0] < span_bbox[2] and char_center_x > span_bbox[0] and span_bbox[1] < char_center_y < span_bbox[3] and abs(char_center_y - span_center_y) < span_height * span_height_radio ): return True elif char in LINE_START_FLAG: if ( span_bbox[0] < char_bbox[2] < (span_bbox[0] + span_height) and char_center_x < span_bbox[2] and span_bbox[1] < char_center_y < span_bbox[3] and abs(char_center_y - span_center_y) < span_height * span_height_radio ): return True else: return False def chars_to_content(span): # 检查span中的char是否为空 if len(span['chars']) == 0: pass else: # 给chars按char_idx排序 span['chars'] = sorted(span['chars'], key=lambda x: x['char_idx']) # Calculate the width of each character char_widths = [char['bbox'][2] - char['bbox'][0] for char in span['chars']] # Calculate the median width median_width = statistics.median(char_widths) content = '' for char in span['chars']: # 如果下一个char的x0和上一个char的x1距离超过0.25个字符宽度，则需要在中间插入一个空格 char1 = char char2 = span['chars'][span['chars'].index(char) + 1] if span['chars'].index(char) + 1 < len(span['chars']) else None if char2 and char2['bbox'][0] - char1['bbox'][2] > median_width * 0.25 and char['char'] != ' ' and char2['char'] != ' ': content += f"{char['char']} " else: content += char['char'] content = __replace_unicode(content) content = __replace_ligatures(content) content = __replace_ligatures(content) span['content'] = content.strip() del span['chars'] def calculate_contrast(img, img_mode) -> float: """ 计算给定图像的对比度。 :param img: 图像，类型为numpy.ndarray :Param img_mode = 图像的色彩通道，'rgb' 或 'bgr' :return: 图像的对比度值 """ if img_mode == 'rgb': # 将RGB图像转换为灰度图 gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) elif img_mode == 'bgr': # 将BGR图像转换为灰度图 gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) else: raise ValueError("Invalid image mode. Please provide 'rgb' or 'bgr'.") # 计算均值和标准差 mean_value = np.mean(gray_img) std_dev = np.std(gray_img) # 对比度定义为标准差除以平均值（加上小常数避免除零错误） contrast = std_dev / (mean_value + 1e-6) # logger.debug(f"contrast: {contrast}") return round(contrast, 2)