# Copyright (c) Opendatalab. All rights reserved. import copy import json import os from pathlib import Path from loguru import logger from mineru.cli.common import convert_pdf_bytes_to_bytes_by_pypdfium2, prepare_env, read_fn from mineru.data.data_reader_writer import FileBasedDataWriter from mineru.utils.draw_bbox import draw_layout_bbox, draw_span_bbox from mineru.utils.enum_class import MakeMode from mineru.backend.vlm.vlm_analyze import doc_analyze as vlm_doc_analyze from mineru.backend.pipeline.pipeline_analyze import doc_analyze as pipeline_doc_analyze from mineru.backend.pipeline.pipeline_middle_json_mkcontent import union_make as pipeline_union_make from mineru.backend.pipeline.model_json_to_middle_json import result_to_middle_json as pipeline_result_to_middle_json from mineru.backend.vlm.vlm_middle_json_mkcontent import union_make as vlm_union_make from mineru.utils.models_download_utils import auto_download_and_get_model_root_path def do_parse( output_dir, # Output directory for storing parsing results pdf_file_names: list[str], # List of PDF file names to be parsed pdf_bytes_list: list[bytes], # List of PDF bytes to be parsed p_lang_list: list[str], # List of languages for each PDF, default is 'ch' (Chinese) backend="pipeline", # The backend for parsing PDF, default is 'pipeline' parse_method="auto", # The method for parsing PDF, default is 'auto' formula_enable=True, # Enable formula parsing table_enable=True, # Enable table parsing server_url=None, # Server URL for vlm-sglang-client backend f_draw_layout_bbox=True, # Whether to draw layout bounding boxes f_draw_span_bbox=True, # Whether to draw span bounding boxes f_dump_md=True, # Whether to dump markdown files f_dump_middle_json=True, # Whether to dump middle JSON files f_dump_model_output=True, # Whether to dump model output files f_dump_orig_pdf=True, # Whether to dump original PDF files f_dump_content_list=True, # Whether to dump content list files f_make_md_mode=MakeMode.MM_MD, # The mode for making markdown content, default is MM_MD start_page_id=0, # Start page ID for parsing, default is 0 end_page_id=None, # End page ID for parsing, default is None (parse all pages until the end of the document) ): if backend == "pipeline": for idx, pdf_bytes in enumerate(pdf_bytes_list): new_pdf_bytes = convert_pdf_bytes_to_bytes_by_pypdfium2(pdf_bytes, start_page_id, end_page_id) pdf_bytes_list[idx] = new_pdf_bytes infer_results, all_image_lists, all_pdf_docs, lang_list, ocr_enabled_list = pipeline_doc_analyze(pdf_bytes_list, p_lang_list, parse_method=parse_method, formula_enable=formula_enable,table_enable=table_enable) for idx, model_list in enumerate(infer_results): model_json = copy.deepcopy(model_list) pdf_file_name = pdf_file_names[idx] local_image_dir, local_md_dir = prepare_env(output_dir, pdf_file_name, parse_method) image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(local_md_dir) images_list = all_image_lists[idx] pdf_doc = all_pdf_docs[idx] _lang = lang_list[idx] _ocr_enable = ocr_enabled_list[idx] middle_json = pipeline_result_to_middle_json(model_list, images_list, pdf_doc, image_writer, _lang, _ocr_enable, formula_enable) pdf_info = middle_json["pdf_info"] pdf_bytes = pdf_bytes_list[idx] if f_draw_layout_bbox: draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_layout.pdf") if f_draw_span_bbox: draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_span.pdf") if f_dump_orig_pdf: md_writer.write( f"{pdf_file_name}_origin.pdf", pdf_bytes, ) if f_dump_md: image_dir = str(os.path.basename(local_image_dir)) md_content_str = pipeline_union_make(pdf_info, f_make_md_mode, image_dir) md_writer.write_string( f"{pdf_file_name}.md", md_content_str, ) if f_dump_content_list: image_dir = str(os.path.basename(local_image_dir)) content_list = pipeline_union_make(pdf_info, MakeMode.CONTENT_LIST, image_dir) md_writer.write_string( f"{pdf_file_name}_content_list.json", json.dumps(content_list, ensure_ascii=False, indent=4), ) if f_dump_middle_json: md_writer.write_string( f"{pdf_file_name}_middle.json", json.dumps(middle_json, ensure_ascii=False, indent=4), ) if f_dump_model_output: md_writer.write_string( f"{pdf_file_name}_model.json", json.dumps(model_json, ensure_ascii=False, indent=4), ) logger.info(f"local output dir is {local_md_dir}") else: if backend.startswith("vlm-"): backend = backend[4:] f_draw_span_bbox = False parse_method = "vlm" for idx, pdf_bytes in enumerate(pdf_bytes_list): pdf_file_name = pdf_file_names[idx] pdf_bytes = convert_pdf_bytes_to_bytes_by_pypdfium2(pdf_bytes, start_page_id, end_page_id) local_image_dir, local_md_dir = prepare_env(output_dir, pdf_file_name, parse_method) image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(local_md_dir) middle_json, infer_result = vlm_doc_analyze(pdf_bytes, image_writer=image_writer, backend=backend, server_url=server_url) pdf_info = middle_json["pdf_info"] if f_draw_layout_bbox: draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_layout.pdf") if f_draw_span_bbox: draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_span.pdf") if f_dump_orig_pdf: md_writer.write( f"{pdf_file_name}_origin.pdf", pdf_bytes, ) if f_dump_md: image_dir = str(os.path.basename(local_image_dir)) md_content_str = vlm_union_make(pdf_info, f_make_md_mode, image_dir) md_writer.write_string( f"{pdf_file_name}.md", md_content_str, ) if f_dump_content_list: image_dir = str(os.path.basename(local_image_dir)) content_list = vlm_union_make(pdf_info, MakeMode.CONTENT_LIST, image_dir) md_writer.write_string( f"{pdf_file_name}_content_list.json", json.dumps(content_list, ensure_ascii=False, indent=4), ) if f_dump_middle_json: md_writer.write_string( f"{pdf_file_name}_middle.json", json.dumps(middle_json, ensure_ascii=False, indent=4), ) if f_dump_model_output: model_output = ("\n" + "-" * 50 + "\n").join(infer_result) md_writer.write_string( f"{pdf_file_name}_model_output.txt", model_output, ) logger.info(f"local output dir is {local_md_dir}") def parse_doc( path_list: list[Path], output_dir, lang="ch", backend="pipeline", method="auto", server_url=None, start_page_id=0, end_page_id=None ): """ Parameter description: path_list: List of document paths to be parsed, can be PDF or image files. output_dir: Output directory for storing parsing results. lang: Language option, default is 'ch', optional values include['ch', 'ch_server', 'ch_lite', 'en', 'korean', 'japan', 'chinese_cht', 'ta', 'te', 'ka']。 Input the languages in the pdf (if known) to improve OCR accuracy. Optional. Adapted only for the case where the backend is set to "pipeline" backend: the backend for parsing pdf: pipeline: More general. vlm-transformers: More general. vlm-sglang-engine: Faster(engine). vlm-sglang-client: Faster(client). without method specified, pipeline will be used by default. method: the method for parsing pdf: auto: Automatically determine the method based on the file type. txt: Use text extraction method. ocr: Use OCR method for image-based PDFs. Without method specified, 'auto' will be used by default. Adapted only for the case where the backend is set to "pipeline". server_url: When the backend is `sglang-client`, you need to specify the server_url, for example:`http://127.0.0.1:30000` start_page_id: Start page ID for parsing, default is 0 end_page_id: End page ID for parsing, default is None (parse all pages until the end of the document) """ try: file_name_list = [] pdf_bytes_list = [] lang_list = [] for path in path_list: file_name = str(Path(path).stem) pdf_bytes = read_fn(path) file_name_list.append(file_name) pdf_bytes_list.append(pdf_bytes) lang_list.append(lang) do_parse( output_dir=output_dir, pdf_file_names=file_name_list, pdf_bytes_list=pdf_bytes_list, p_lang_list=lang_list, backend=backend, parse_method=method, server_url=server_url, start_page_id=start_page_id, end_page_id=end_page_id ) except Exception as e: logger.exception(e) if __name__ == '__main__': # args __dir__ = os.path.dirname(os.path.abspath(__file__)) pdf_files_dir = os.path.join(__dir__, "pdfs") output_dir = os.path.join(__dir__, "output") pdf_suffixes = [".pdf"] image_suffixes = [".png", ".jpeg", ".jpg"] doc_path_list = [] for doc_path in Path(pdf_files_dir).glob('*'): if doc_path.suffix in pdf_suffixes + image_suffixes: doc_path_list.append(doc_path) """如果您由于网络问题无法下载模型，可以设置环境变量MINERU_MODEL_SOURCE为modelscope使用免代理仓库下载模型""" # os.environ['MINERU_MODEL_SOURCE'] = "modelscope" """Use pipeline mode if your environment does not support VLM""" parse_doc(doc_path_list, output_dir, backend="pipeline") """To enable VLM mode, change the backend to 'vlm-xxx'""" # parse_doc(doc_path_list, output_dir, backend="vlm-transformers") # more general. # parse_doc(doc_path_list, output_dir, backend="vlm-sglang-engine") # faster(engine). # parse_doc(doc_path_list, output_dir, backend="vlm-sglang-client", server_url="http://127.0.0.1:30000") # faster(client).