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137 lines
4.6 KiB
Python
137 lines
4.6 KiB
Python
"""
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用户输入：
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model数组，每个元素代表一个页面
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pdf在s3的路径
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截图保存的s3位置
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然后：
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1）根据s3路径，调用spark集群的api,拿到ak,sk,endpoint，构造出s3PDFReader
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2）根据用户输入的s3地址，调用spark集群的api,拿到ak,sk,endpoint，构造出s3ImageWriter
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其余部分至于构造s3cli, 获取ak,sk都在code-clean里写代码完成。不要反向依赖！！！
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"""
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import re
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from loguru import logger
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from magic_pdf.libs.version import __version__
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from magic_pdf.model.doc_analyze_by_pp_structurev2 import doc_analyze
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from magic_pdf.rw import AbsReaderWriter
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from magic_pdf.pdf_parse_by_ocr_v2 import parse_pdf_by_ocr
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from magic_pdf.pdf_parse_by_txt_v2 import parse_pdf_by_txt
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PARSE_TYPE_TXT = "txt"
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PARSE_TYPE_OCR = "ocr"
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def parse_txt_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0, *args,
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**kwargs):
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"""
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解析文本类pdf
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"""
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pdf_info_dict = parse_pdf_by_txt(
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pdf_bytes,
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pdf_models,
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imageWriter,
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start_page_id=start_page,
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debug_mode=is_debug,
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)
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pdf_info_dict["_parse_type"] = PARSE_TYPE_TXT
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pdf_info_dict["_version_name"] = __version__
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return pdf_info_dict
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def parse_ocr_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0, *args,
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**kwargs):
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"""
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解析ocr类pdf
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"""
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pdf_info_dict = parse_pdf_by_ocr(
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pdf_bytes,
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pdf_models,
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imageWriter,
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start_page_id=start_page,
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debug_mode=is_debug,
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)
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pdf_info_dict["_parse_type"] = PARSE_TYPE_OCR
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pdf_info_dict["_version_name"] = __version__
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return pdf_info_dict
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def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0,
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input_model_is_empty: bool = False,
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*args, **kwargs):
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"""
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ocr和文本混合的pdf，全部解析出来
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"""
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def parse_pdf(method):
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try:
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return method(
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pdf_bytes,
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pdf_models,
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imageWriter,
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start_page_id=start_page,
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debug_mode=is_debug,
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)
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except Exception as e:
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logger.exception(e)
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return None
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pdf_info_dict = parse_pdf(parse_pdf_by_txt)
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text_all = ""
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for page_dict in pdf_info_dict['pdf_info']:
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for para_block in page_dict['para_blocks']:
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if para_block['type'] in ['title', 'text']:
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for line in para_block['lines']:
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for span in line['spans']:
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text_all += span['content']
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def calculate_not_common_character_rate(text):
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garbage_regex = re.compile(r'[^\u4e00-\u9fa5\u0030-\u0039\u0041-\u005a\u0061-\u007a\u3000-\u303f\uff00-\uffef]')
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# 计算乱码字符的数量
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garbage_count = len(garbage_regex.findall(text))
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total = len(text)
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if total == 0:
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return 0 # 避免除以零的错误
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return garbage_count / total
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def calculate_not_printable_rate(text):
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printable = sum(1 for c in text if c.isprintable())
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total = len(text)
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if total == 0:
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return 0 # 避免除以零的错误
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return (total - printable) / total
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not_common_character_rate = calculate_not_common_character_rate(text_all)
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not_printable_rate = calculate_not_printable_rate(text_all)
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pdf_info_dict["_not_common_character_rate"] = not_common_character_rate
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pdf_info_dict["_not_printable_rate"] = not_printable_rate
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logger.info(f"not_common_character_rate: {not_common_character_rate}, not_printable_rate: {not_printable_rate}")
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# not_common_character_rate对小语种可能会有误伤，not_printable_rate对小语种较为友好
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if (pdf_info_dict is None
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or pdf_info_dict.get("_need_drop", False)
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or not_printable_rate > 0.02 # 参考一些正常的pdf，这个值没有超过0.01的，阈值设为0.02
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):
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logger.warning(f"parse_pdf_by_txt drop or error or garbled_rate too large, switch to parse_pdf_by_ocr")
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if input_model_is_empty:
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pdf_models = doc_analyze(pdf_bytes, ocr=True)
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pdf_info_dict = parse_pdf(parse_pdf_by_ocr)
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if pdf_info_dict is None:
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raise Exception("Both parse_pdf_by_txt and parse_pdf_by_ocr failed.")
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else:
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pdf_info_dict["_parse_type"] = PARSE_TYPE_OCR
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else:
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pdf_info_dict["_parse_type"] = PARSE_TYPE_TXT
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pdf_info_dict["_version_name"] = __version__
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return pdf_info_dict
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