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- # Copyright (c) Opendatalab. All rights reserved.
- import os
- from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
- from magic_pdf.data.dataset import PymuDocDataset
- from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
- from magic_pdf.config.enums import SupportedPdfParseMethod
- # args
- pdf_file_name = "demo1.pdf" # replace with the real pdf path
- name_without_suff = pdf_file_name.split(".")[0]
- # prepare env
- local_image_dir, local_md_dir = "output/images", "output"
- image_dir = str(os.path.basename(local_image_dir))
- os.makedirs(local_image_dir, exist_ok=True)
- image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
- local_md_dir
- )
- image_dir = str(os.path.basename(local_image_dir))
- # read bytes
- reader1 = FileBasedDataReader("")
- pdf_bytes = reader1.read(pdf_file_name) # read the pdf content
- # proc
- ## Create Dataset Instance
- ds = PymuDocDataset(pdf_bytes)
- ## inference
- if ds.classify() == SupportedPdfParseMethod.OCR:
- infer_result = ds.apply(doc_analyze, ocr=True)
- ## pipeline
- pipe_result = infer_result.pipe_ocr_mode(image_writer)
- else:
- infer_result = ds.apply(doc_analyze, ocr=False)
- ## pipeline
- pipe_result = infer_result.pipe_txt_mode(image_writer)
- ### draw model result on each page
- infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
- ### draw layout result on each page
- pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
- ### draw spans result on each page
- pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
- ### dump markdown
- pipe_result.dump_md(md_writer, f"{name_without_suff}.md", image_dir)
- ### dump content list
- pipe_result.dump_content_list(md_writer, f"{name_without_suff}_content_list.json", image_dir)
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