340 lines
13 KiB
Python
340 lines
13 KiB
Python
# Copyright (c) Opendatalab. All rights reserved.
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import copy
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import os
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import statistics
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import warnings
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from typing import List
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import torch
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from loguru import logger
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from mineru.utils.config_reader import get_device
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from mineru.utils.enum_class import BlockType, ModelPath
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from mineru.utils.models_download_utils import auto_download_and_get_model_root_path
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def sort_blocks_by_bbox(blocks, page_w, page_h, footnote_blocks):
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"""获取所有line并计算正文line的高度"""
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line_height = get_line_height(blocks)
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"""获取所有line并对line排序"""
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sorted_bboxes = sort_lines_by_model(blocks, page_w, page_h, line_height, footnote_blocks)
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"""根据line的中位数算block的序列关系"""
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blocks = cal_block_index(blocks, sorted_bboxes)
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"""将image和table的block还原回group形式参与后续流程"""
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blocks = revert_group_blocks(blocks)
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"""重排block"""
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sorted_blocks = sorted(blocks, key=lambda b: b['index'])
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"""block内重排(img和table的block内多个caption或footnote的排序)"""
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for block in sorted_blocks:
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if block['type'] in [BlockType.IMAGE, BlockType.TABLE]:
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block['blocks'] = sorted(block['blocks'], key=lambda b: b['index'])
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return sorted_blocks
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def get_line_height(blocks):
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page_line_height_list = []
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for block in blocks:
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if block['type'] in [
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BlockType.TEXT, BlockType.TITLE,
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BlockType.IMAGE_CAPTION, BlockType.IMAGE_FOOTNOTE,
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BlockType.TABLE_CAPTION, BlockType.TABLE_FOOTNOTE
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]:
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for line in block['lines']:
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bbox = line['bbox']
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page_line_height_list.append(int(bbox[3] - bbox[1]))
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if len(page_line_height_list) > 0:
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return statistics.median(page_line_height_list)
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else:
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return 10
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def sort_lines_by_model(fix_blocks, page_w, page_h, line_height, footnote_blocks):
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page_line_list = []
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def add_lines_to_block(b):
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line_bboxes = insert_lines_into_block(b['bbox'], line_height, page_w, page_h)
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b['lines'] = []
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for line_bbox in line_bboxes:
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b['lines'].append({'bbox': line_bbox, 'spans': []})
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page_line_list.extend(line_bboxes)
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for block in fix_blocks:
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if block['type'] in [
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BlockType.TEXT, BlockType.TITLE,
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BlockType.IMAGE_CAPTION, BlockType.IMAGE_FOOTNOTE,
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BlockType.TABLE_CAPTION, BlockType.TABLE_FOOTNOTE
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]:
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if len(block['lines']) == 0:
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add_lines_to_block(block)
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elif block['type'] in [BlockType.TITLE] and len(block['lines']) == 1 and (block['bbox'][3] - block['bbox'][1]) > line_height * 2:
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block['real_lines'] = copy.deepcopy(block['lines'])
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add_lines_to_block(block)
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else:
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for line in block['lines']:
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bbox = line['bbox']
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page_line_list.append(bbox)
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elif block['type'] in [BlockType.IMAGE_BODY, BlockType.TABLE_BODY, BlockType.INTERLINE_EQUATION]:
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block['real_lines'] = copy.deepcopy(block['lines'])
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add_lines_to_block(block)
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for block in footnote_blocks:
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footnote_block = {'bbox': block[:4]}
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add_lines_to_block(footnote_block)
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if len(page_line_list) > 200: # layoutreader最高支持512line
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return None
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# 使用layoutreader排序
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x_scale = 1000.0 / page_w
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y_scale = 1000.0 / page_h
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boxes = []
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# logger.info(f"Scale: {x_scale}, {y_scale}, Boxes len: {len(page_line_list)}")
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for left, top, right, bottom in page_line_list:
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if left < 0:
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logger.warning(
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f'left < 0, left: {left}, right: {right}, top: {top}, bottom: {bottom}, page_w: {page_w}, page_h: {page_h}'
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) # noqa: E501
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left = 0
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if right > page_w:
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logger.warning(
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f'right > page_w, left: {left}, right: {right}, top: {top}, bottom: {bottom}, page_w: {page_w}, page_h: {page_h}'
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) # noqa: E501
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right = page_w
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if top < 0:
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logger.warning(
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f'top < 0, left: {left}, right: {right}, top: {top}, bottom: {bottom}, page_w: {page_w}, page_h: {page_h}'
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) # noqa: E501
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top = 0
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if bottom > page_h:
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logger.warning(
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f'bottom > page_h, left: {left}, right: {right}, top: {top}, bottom: {bottom}, page_w: {page_w}, page_h: {page_h}'
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) # noqa: E501
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bottom = page_h
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left = round(left * x_scale)
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top = round(top * y_scale)
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right = round(right * x_scale)
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bottom = round(bottom * y_scale)
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assert (
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1000 >= right >= left >= 0 and 1000 >= bottom >= top >= 0
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), f'Invalid box. right: {right}, left: {left}, bottom: {bottom}, top: {top}' # noqa: E126, E121
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boxes.append([left, top, right, bottom])
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model_manager = ModelSingleton()
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model = model_manager.get_model('layoutreader')
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with torch.no_grad():
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orders = do_predict(boxes, model)
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sorted_bboxes = [page_line_list[i] for i in orders]
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return sorted_bboxes
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def insert_lines_into_block(block_bbox, line_height, page_w, page_h):
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# block_bbox是一个元组(x0, y0, x1, y1)，其中(x0, y0)是左下角坐标，(x1, y1)是右上角坐标
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x0, y0, x1, y1 = block_bbox
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block_height = y1 - y0
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block_weight = x1 - x0
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# 如果block高度小于n行正文，则直接返回block的bbox
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if line_height * 2 < block_height:
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if (
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block_height > page_h * 0.25 and page_w * 0.5 > block_weight > page_w * 0.25
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): # 可能是双列结构，可以切细点
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lines = int(block_height / line_height)
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else:
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# 如果block的宽度超过0.4页面宽度，则将block分成3行(是一种复杂布局，图不能切的太细)
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if block_weight > page_w * 0.4:
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lines = 3
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elif block_weight > page_w * 0.25: # （可能是三列结构，也切细点）
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lines = int(block_height / line_height)
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else: # 判断长宽比
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if block_height / block_weight > 1.2: # 细长的不分
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return [[x0, y0, x1, y1]]
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else: # 不细长的还是分成两行
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lines = 2
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line_height = (y1 - y0) / lines
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# 确定从哪个y位置开始绘制线条
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current_y = y0
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# 用于存储线条的位置信息[(x0, y), ...]
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lines_positions = []
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for i in range(lines):
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lines_positions.append([x0, current_y, x1, current_y + line_height])
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current_y += line_height
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return lines_positions
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else:
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return [[x0, y0, x1, y1]]
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def model_init(model_name: str):
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from transformers import LayoutLMv3ForTokenClassification
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device_name = get_device()
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device = torch.device(device_name)
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bf_16_support = False
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if device_name.startswith("cuda"):
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if torch.cuda.get_device_properties(device).major >= 8:
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bf_16_support = True
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elif device_name.startswith("mps"):
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bf_16_support = True
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if model_name == 'layoutreader':
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# 检测modelscope的缓存目录是否存在
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layoutreader_model_dir = os.path.join(auto_download_and_get_model_root_path(ModelPath.layout_reader), ModelPath.layout_reader)
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if os.path.exists(layoutreader_model_dir):
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model = LayoutLMv3ForTokenClassification.from_pretrained(
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layoutreader_model_dir
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)
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else:
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logger.warning(
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'local layoutreader model not exists, use online model from huggingface'
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)
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model = LayoutLMv3ForTokenClassification.from_pretrained(
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'hantian/layoutreader'
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)
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if bf_16_support:
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model.to(device).eval().bfloat16()
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else:
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model.to(device).eval()
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else:
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logger.error('model name not allow')
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exit(1)
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return model
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class ModelSingleton:
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_instance = None
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_models = {}
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def __new__(cls, *args, **kwargs):
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def get_model(self, model_name: str):
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if model_name not in self._models:
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self._models[model_name] = model_init(model_name=model_name)
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return self._models[model_name]
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def do_predict(boxes: List[List[int]], model) -> List[int]:
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from mineru.model.reading_order.layout_reader import (
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boxes2inputs, parse_logits, prepare_inputs)
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=FutureWarning, module="transformers")
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inputs = boxes2inputs(boxes)
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inputs = prepare_inputs(inputs, model)
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logits = model(**inputs).logits.cpu().squeeze(0)
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return parse_logits(logits, len(boxes))
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def cal_block_index(fix_blocks, sorted_bboxes):
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if sorted_bboxes is not None:
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# 使用layoutreader排序
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for block in fix_blocks:
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line_index_list = []
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if len(block['lines']) == 0:
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block['index'] = sorted_bboxes.index(block['bbox'])
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else:
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for line in block['lines']:
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line['index'] = sorted_bboxes.index(line['bbox'])
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line_index_list.append(line['index'])
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median_value = statistics.median(line_index_list)
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block['index'] = median_value
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# 删除图表body block中的虚拟line信息, 并用real_lines信息回填
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if block['type'] in [BlockType.IMAGE_BODY, BlockType.TABLE_BODY, BlockType.TITLE, BlockType.INTERLINE_EQUATION]:
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if 'real_lines' in block:
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block['virtual_lines'] = copy.deepcopy(block['lines'])
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block['lines'] = copy.deepcopy(block['real_lines'])
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del block['real_lines']
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else:
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# 使用xycut排序
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block_bboxes = []
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for block in fix_blocks:
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# 如果block['bbox']任意值小于0，将其置为0
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block['bbox'] = [max(0, x) for x in block['bbox']]
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block_bboxes.append(block['bbox'])
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# 删除图表body block中的虚拟line信息, 并用real_lines信息回填
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if block['type'] in [BlockType.IMAGE_BODY, BlockType.TABLE_BODY, BlockType.TITLE, BlockType.INTERLINE_EQUATION]:
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if 'real_lines' in block:
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block['virtual_lines'] = copy.deepcopy(block['lines'])
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block['lines'] = copy.deepcopy(block['real_lines'])
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del block['real_lines']
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import numpy as np
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from mineru.model.reading_order.xycut import recursive_xy_cut
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random_boxes = np.array(block_bboxes)
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np.random.shuffle(random_boxes)
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res = []
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recursive_xy_cut(np.asarray(random_boxes).astype(int), np.arange(len(block_bboxes)), res)
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assert len(res) == len(block_bboxes)
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sorted_boxes = random_boxes[np.array(res)].tolist()
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for i, block in enumerate(fix_blocks):
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block['index'] = sorted_boxes.index(block['bbox'])
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# 生成line index
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sorted_blocks = sorted(fix_blocks, key=lambda b: b['index'])
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line_inedx = 1
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for block in sorted_blocks:
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for line in block['lines']:
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line['index'] = line_inedx
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line_inedx += 1
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return fix_blocks
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def revert_group_blocks(blocks):
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image_groups = {}
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table_groups = {}
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new_blocks = []
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for block in blocks:
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if block['type'] in [BlockType.IMAGE_BODY, BlockType.IMAGE_CAPTION, BlockType.IMAGE_FOOTNOTE]:
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group_id = block['group_id']
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if group_id not in image_groups:
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image_groups[group_id] = []
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image_groups[group_id].append(block)
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elif block['type'] in [BlockType.TABLE_BODY, BlockType.TABLE_CAPTION, BlockType.TABLE_FOOTNOTE]:
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group_id = block['group_id']
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if group_id not in table_groups:
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table_groups[group_id] = []
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table_groups[group_id].append(block)
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else:
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new_blocks.append(block)
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for group_id, blocks in image_groups.items():
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new_blocks.append(process_block_list(blocks, BlockType.IMAGE_BODY, BlockType.IMAGE))
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for group_id, blocks in table_groups.items():
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new_blocks.append(process_block_list(blocks, BlockType.TABLE_BODY, BlockType.TABLE))
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return new_blocks
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def process_block_list(blocks, body_type, block_type):
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indices = [block['index'] for block in blocks]
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median_index = statistics.median(indices)
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body_bbox = next((block['bbox'] for block in blocks if block.get('type') == body_type), [])
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return {
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'type': block_type,
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'bbox': body_bbox,
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'blocks': blocks,
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'index': median_index,
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} |