model_json_to_middle_json.py 5.5 KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137
  1. # Copyright (c) Opendatalab. All rights reserved.
  2. from mineru.utils.block_pre_proc import prepare_block_bboxes, process_groups
  3. from mineru.utils.block_sort import sort_blocks_by_bbox
  4. from mineru.utils.cut_image import cut_image_and_table
  5. from mineru.utils.pipeline_magic_model import MagicModel
  6. from mineru.utils.span_block_fix import fill_spans_in_blocks, fix_discarded_block, fix_block_spans
  7. from mineru.utils.span_pre_proc import remove_outside_spans, remove_overlaps_low_confidence_spans, \
  8. remove_overlaps_min_spans, txt_spans_extract
  9. from mineru.version import __version__
  10. from mineru.utils.hash_utils import str_md5
  11. def page_model_info_to_page_info(page_model_info, image_dict, page, image_writer, page_index, ocr=False):
  12. scale = image_dict["scale"]
  13. page_pil_img = image_dict["img_pil"]
  14. page_img_md5 = str_md5(image_dict["img_base64"])
  15. page_w, page_h = map(int, page.get_size())
  16. magic_model = MagicModel(page_model_info, scale)
  17. """从magic_model对象中获取后面会用到的区块信息"""
  18. img_groups = magic_model.get_imgs()
  19. table_groups = magic_model.get_tables()
  20. """对image和table的区块分组"""
  21. img_body_blocks, img_caption_blocks, img_footnote_blocks = process_groups(
  22. img_groups, 'image_body', 'image_caption_list', 'image_footnote_list'
  23. )
  24. table_body_blocks, table_caption_blocks, table_footnote_blocks = process_groups(
  25. table_groups, 'table_body', 'table_caption_list', 'table_footnote_list'
  26. )
  27. discarded_blocks = magic_model.get_discarded()
  28. text_blocks = magic_model.get_text_blocks()
  29. title_blocks = magic_model.get_title_blocks()
  30. inline_equations, interline_equations, interline_equation_blocks = magic_model.get_equations()
  31. """将所有区块的bbox整理到一起"""
  32. interline_equation_blocks = []
  33. if len(interline_equation_blocks) > 0:
  34. all_bboxes, all_discarded_blocks, footnote_blocks = prepare_block_bboxes(
  35. img_body_blocks, img_caption_blocks, img_footnote_blocks,
  36. table_body_blocks, table_caption_blocks, table_footnote_blocks,
  37. discarded_blocks,
  38. text_blocks,
  39. title_blocks,
  40. interline_equation_blocks,
  41. page_w,
  42. page_h,
  43. )
  44. else:
  45. all_bboxes, all_discarded_blocks, footnote_blocks = prepare_block_bboxes(
  46. img_body_blocks, img_caption_blocks, img_footnote_blocks,
  47. table_body_blocks, table_caption_blocks, table_footnote_blocks,
  48. discarded_blocks,
  49. text_blocks,
  50. title_blocks,
  51. interline_equations,
  52. page_w,
  53. page_h,
  54. )
  55. """获取所有的spans信息"""
  56. spans = magic_model.get_all_spans()
  57. """在删除重复span之前,应该通过image_body和table_body的block过滤一下image和table的span"""
  58. """顺便删除大水印并保留abandon的span"""
  59. spans = remove_outside_spans(spans, all_bboxes, all_discarded_blocks)
  60. """删除重叠spans中置信度较低的那些"""
  61. spans, dropped_spans_by_confidence = remove_overlaps_low_confidence_spans(spans)
  62. """删除重叠spans中较小的那些"""
  63. spans, dropped_spans_by_span_overlap = remove_overlaps_min_spans(spans)
  64. """根据parse_mode,构造spans,主要是文本类的字符填充"""
  65. if ocr:
  66. pass
  67. else:
  68. """使用新版本的混合ocr方案."""
  69. spans = txt_spans_extract(page, spans, page_pil_img, scale)
  70. """先处理不需要排版的discarded_blocks"""
  71. discarded_block_with_spans, spans = fill_spans_in_blocks(
  72. all_discarded_blocks, spans, 0.4
  73. )
  74. fix_discarded_blocks = fix_discarded_block(discarded_block_with_spans)
  75. """如果当前页面没有有效的bbox则跳过"""
  76. if len(all_bboxes) == 0:
  77. return None
  78. """对image和table截图"""
  79. for span in spans:
  80. if span['type'] in ['image', 'table']:
  81. span = cut_image_and_table(
  82. span, page_pil_img, page_img_md5, page_index, image_writer, scale=scale
  83. )
  84. """span填充进block"""
  85. block_with_spans, spans = fill_spans_in_blocks(all_bboxes, spans, 0.5)
  86. """对block进行fix操作"""
  87. fix_blocks = fix_block_spans(block_with_spans)
  88. """同一行被断开的titile合并"""
  89. # merge_title_blocks(fix_blocks)
  90. """对block进行排序"""
  91. sorted_blocks = sort_blocks_by_bbox(fix_blocks, page_w, page_h, footnote_blocks)
  92. """构造page_info"""
  93. page_info = make_page_info_dict(sorted_blocks, page_index, page_w, page_h, fix_discarded_blocks)
  94. return page_info
  95. def result_to_middle_json(model_list, images_list, pdf_doc, image_writer, lang=None, ocr=False):
  96. middle_json = {"pdf_info": [], "_backend":"vlm", "_version_name": __version__}
  97. for page_index, page_model_info in enumerate(model_list):
  98. page = pdf_doc[page_index]
  99. image_dict = images_list[page_index]
  100. page_info = page_model_info_to_page_info(
  101. page_model_info, image_dict, page, image_writer, page_index, ocr=ocr
  102. )
  103. if page_info is None:
  104. page_w, page_h = map(int, page.get_size())
  105. page_info = make_page_info_dict([], page_index, page_w, page_h, [])
  106. middle_json["pdf_info"].append(page_info)
  107. return middle_json
  108. def make_page_info_dict(blocks, page_id, page_w, page_h, discarded_blocks):
  109. return_dict = {
  110. 'preproc_blocks': blocks,
  111. 'page_idx': page_id,
  112. 'page_size': [page_w, page_h],
  113. 'discarded_blocks': discarded_blocks,
  114. }
  115. return return_dict