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+from pathlib import Path
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+from paddlex import create_pipeline
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+
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+# 统计耗时
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+import time
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+start_time = time.time()
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+
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+pipeline = create_pipeline(pipeline="./PP-StructureV3-zhch.yaml")
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+
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+input_file = "./sample_data/300674-财报数据-扫描.pdf"
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+output_path = Path("./sample_data") / f"{Path(input_file).stem}_output"
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+
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+output = pipeline.predict(
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+ input=input_file,
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+ device="gpu", # 或者 "gpu" 如果你有 GPU 支持
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+ enable_mkldnn=False, # 如果你不需要 MKLDNN 优化，可以设置为 False
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+ use_doc_orientation_classify=True, # 开启文档方向分类
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+ use_doc_unwarping=False, # 开启文档去畸变
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+ # layout_detection_model_name=None, # 如果要禁用版面分析，可以这样设置，或者依赖其默认行为
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+ use_seal_recognition=True, # 跳过印章识别
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+ use_formula_recognition=False, # 跳过公式识别
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+ use_chart_recognition=True, # 跳过图表识别
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+ use_table_recognition=True, # 开启表格识别
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+)
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+markdown_list = []
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+markdown_images = []
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+
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+for res in output:
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+ res.save_all(save_path=output_path) # 保存所有结果到指定路径
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+ md_info = res.markdown
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+ markdown_list.append(md_info)
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+ markdown_images.append(md_info.get("markdown_images", {}))
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+
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+markdown_texts = pipeline.concatenate_markdown_pages(markdown_list)
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+
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+mkd_file_path = output_path / f"{Path(input_file).stem}.md"
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+mkd_file_path.parent.mkdir(parents=True, exist_ok=True)
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+
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+with open(mkd_file_path, "w", encoding="utf-8") as f:
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+ f.write(markdown_texts)
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+
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+for item in markdown_images:
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+ if item:
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+ for path, image in item.items():
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+ file_path = output_path / path
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+ file_path.parent.mkdir(parents=True, exist_ok=True)
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+ image.save(file_path)
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+
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+# 统计耗时
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+end_time = time.time()
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+elapsed_time = end_time - start_time
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+print(f"Total time taken: {elapsed_time:.2f} seconds")
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