from sklearn.cluster import DBSCAN import numpy as np from loguru import logger from magic_pdf.libs.boxbase import _is_in LINE_STOP_FLAG = ['.', '!', '?', '。', '！', '？',"：", ":", ")", "）", ";"] INLINE_EQUATION = 'inline_equation' INTER_EQUATION = "displayed_equation" TEXT = "text" def __add_line_period(blocks, layout_bboxes): """ 为每行添加句号 如果这个行 1. 以行内公式结尾，但没有任何标点符号,此时加个句号，认为他就是段落结尾。 """ for block in blocks: for line in block['lines']: last_span = line['spans'][-1] span_type = last_span['type'] if span_type in [TEXT, INLINE_EQUATION]: span_content = last_span['content'].strip() if span_type==INLINE_EQUATION and span_content[-1] not in LINE_STOP_FLAG: if span_type in [INLINE_EQUATION, INTER_EQUATION]: last_span['content'] = span_content + '.' def __valign_lines(blocks, layout_bboxes): """ 对齐行的左侧和右侧。 扫描行的左侧和右侧，如果x0, x1差距不超过3就强行对齐到所处layout的左右两侧（和layout有一段距离）。 3是个经验值，TODO，计算得来 """ min_distance = 3 min_sample = 2 for layout_box in layout_bboxes: blocks_in_layoutbox = [b for b in blocks if _is_in(b['bbox'], layout_box['layout_bbox'])] if len(blocks_in_layoutbox)==0: continue x0_lst = np.array([[line['bbox'][0], 0] for block in blocks_in_layoutbox for line in block['lines']]) x1_lst = np.array([[line['bbox'][2], 0] for block in blocks_in_layoutbox for line in block['lines']]) x0_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x0_lst) x1_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x1_lst) x0_uniq_label = np.unique(x0_clusters.labels_) x1_uniq_label = np.unique(x1_clusters.labels_) x0_2_new_val = {} # 存储旧值对应的新值映射 x1_2_new_val = {} for label in x0_uniq_label: if label==-1: continue x0_index_of_label = np.where(x0_clusters.labels_==label) x0_raw_val = x0_lst[x0_index_of_label][:,0] x0_new_val = np.min(x0_lst[x0_index_of_label][:,0]) x0_2_new_val.update({idx: x0_new_val for idx in x0_raw_val}) for label in x1_uniq_label: if label==-1: continue x1_index_of_label = np.where(x1_clusters.labels_==label) x1_raw_val = x1_lst[x1_index_of_label][:,0] x1_new_val = np.max(x1_lst[x1_index_of_label][:,0]) x1_2_new_val.update({idx: x1_new_val for idx in x1_raw_val}) for block in blocks_in_layoutbox: for line in block['lines']: x0, x1 = line['bbox'][0], line['bbox'][2] if x0 in x0_2_new_val: line['bbox'][0] = int(x0_2_new_val[x0]) if x1 in x1_2_new_val: line['bbox'][2] = int(x1_2_new_val[x1]) # 其余对不齐的保持不动 # 由于修改了block里的line长度，现在需要重新计算block的bbox for block in blocks_in_layoutbox: block['bbox'] = [min([line['bbox'][0] for line in block['lines']]), min([line['bbox'][1] for line in block['lines']]), max([line['bbox'][2] for line in block['lines']]), max([line['bbox'][3] for line in block['lines']])] def __common_pre_proc(blocks, layout_bboxes): """ 不分语言的，对文本进行预处理 """ __add_line_period(blocks, layout_bboxes) __valign_lines(blocks, layout_bboxes) def __pre_proc_zh_blocks(blocks, layout_bboxes): """ 对中文文本进行分段预处理 """ pass def __pre_proc_en_blocks(blocks, layout_bboxes): """ 对英文文本进行分段预处理 """ pass def __group_line_by_layout(blocks, layout_bboxes, lang="en"): """ 每个layout内的行进行聚合 """ # 因为只是一个block一行目前, 一个block就是一个段落 lines_group = [] for lyout in layout_bboxes: lines = [line for block in blocks if _is_in(block['bbox'], lyout['layout_bbox']) for line in block['lines']] lines_group.append(lines) return lines_group def __split_para_in_layoutbox(lines_group, layout_bboxes, lang="en", char_avg_len=10): """ lines_group 进行行分段——layout内部进行分段。 1. 先计算每个group的左右边界。 2. 然后根据行末尾特征进行分段。 末尾特征：以句号等结束符结尾。并且距离右侧边界有一定距离。 """ def get_span_text(span): c = span.get('content', '') if len(c)==0: c = span.get('image-path', '') return c paras = [] right_tail_distance = 1.5 * char_avg_len for lines in lines_group: if len(lines)==0: continue layout_right = max([line['bbox'][2] for line in lines]) para = [] # 元素是line for line in lines: line_text = ''.join([get_span_text(span) for span in line['spans']]) #logger.info(line_text) last_span_type = line['spans'][-1]['type'] if last_span_type in [TEXT, INLINE_EQUATION]: last_char = line['spans'][-1]['content'][-1] if last_char in LINE_STOP_FLAG or line['bbox'][2] < layout_right - right_tail_distance: para.append(line) paras.append(para) # para_text = ''.join([span['content'] for line in para for span in line['spans']]) # logger.info(para_text) para = [] else: para.append(line) else: # 其他，图片、表格、行间公式，各自占一段 para.append(line) paras.append(para) # para_text = ''.join([get_span_text(span) for line in para for span in line['spans']]) # logger.info(para_text) para = [] if len(para)>0: paras.append(para) # para_text = ''.join([get_span_text(span) for line in para for span in line['spans']]) # logger.info(para_text) para = [] return paras def __do_split(blocks, layout_bboxes, lang="en"): """ 根据line和layout情况进行分段 先实现一个根据行末尾特征分段的简单方法。 """ """ 算法思路： 1. 扫描layout里每一行，找出来行尾距离layout有边界有一定距离的行。 2. 从上述行中找到末尾是句号等可作为断行标志的行。 3. 参照上述行尾特征进行分段。 4. 图、表，目前独占一行，不考虑分段。 """ lines_group = __group_line_by_layout(blocks, layout_bboxes, lang) # block内分段 layout_paras = __split_para_in_layoutbox(lines_group, layout_bboxes, lang) # block间连接分段 return layout_paras def para_split(blocks, layout_bboxes, lang="en"): """ 根据line和layout情况进行分段 """ __common_pre_proc(blocks, layout_bboxes) if lang=='en': __do_split(blocks, layout_bboxes, lang) elif lang=='zh': __do_split(blocks, layout_bboxes, lang) splited_blocks = __do_split(blocks, layout_bboxes, lang) return splited_blocks