user_api.py

"""
用户输入：
    model数组，每个元素代表一个页面
    pdf在s3的路径
    截图保存的s3位置

然后：
    1）根据s3路径，调用spark集群的api,拿到ak,sk,endpoint，构造出s3PDFReader
    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

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！！！

"""
import re

from loguru import logger

from magic_pdf.libs.version import __version__
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.rw import AbsReaderWriter
from magic_pdf.pdf_parse_by_ocr_v2 import parse_pdf_by_ocr
from magic_pdf.pdf_parse_by_txt_v2 import parse_pdf_by_txt

PARSE_TYPE_TXT = "txt"
PARSE_TYPE_OCR = "ocr"


def parse_txt_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0, *args,
                  **kwargs):
    """
    解析文本类pdf
    """
    pdf_info_dict = parse_pdf_by_txt(
        pdf_bytes,
        pdf_models,
        imageWriter,
        start_page_id=start_page,
        debug_mode=is_debug,
    )

    pdf_info_dict["_parse_type"] = PARSE_TYPE_TXT

    pdf_info_dict["_version_name"] = __version__

    return pdf_info_dict


def parse_ocr_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0, *args,
                  **kwargs):
    """
    解析ocr类pdf
    """
    pdf_info_dict = parse_pdf_by_ocr(
        pdf_bytes,
        pdf_models,
        imageWriter,
        start_page_id=start_page,
        debug_mode=is_debug,
    )

    pdf_info_dict["_parse_type"] = PARSE_TYPE_OCR

    pdf_info_dict["_version_name"] = __version__

    return pdf_info_dict


def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0,
                    input_model_is_empty: bool = False,
                    *args, **kwargs):
    """
    ocr和文本混合的pdf，全部解析出来
    """

    def parse_pdf(method):
        try:
            return method(
                pdf_bytes,
                pdf_models,
                imageWriter,
                start_page_id=start_page,
                debug_mode=is_debug,
            )
        except Exception as e:
            logger.exception(e)
            return None

    pdf_info_dict = parse_pdf(parse_pdf_by_txt)
    # text_all = ""
    # for page_dict in pdf_info_dict['pdf_info']:
    #     for para_block in page_dict['para_blocks']:
    #         if para_block['type'] in ['title', 'text']:
    #             for line in para_block['lines']:
    #                 for span in line['spans']:
    #                     text_all += span['content']

    # def calculate_not_common_character_rate(text):
    #     garbage_regex = re.compile(r'[^\u4e00-\u9fa5\u0030-\u0039\u0041-\u005a\u0061-\u007a\u3000-\u303f\uff00-\uffef]')
    #     # 计算乱码字符的数量
    #     garbage_count = len(garbage_regex.findall(text))
    #     total = len(text)
    #     if total == 0:
    #         return 0  # 避免除以零的错误
    #     return garbage_count / total
    #
    # def calculate_not_printable_rate(text):
    #     printable_text = ""
    #     for c in text:
    #         if c.isprintable():
    #             printable_text += c
    #     printable_total = len(printable_text)
    #     total = len(text)
    #     if total == 0:
    #         return 0  # 避免除以零的错误
    #     return (total - printable_total) / total
    #
    # not_common_character_rate = calculate_not_common_character_rate(text_all)
    # not_printable_rate = calculate_not_printable_rate(text_all)
    # pdf_info_dict["_not_common_character_rate"] = not_common_character_rate
    # pdf_info_dict["_not_printable_rate"] = not_printable_rate
    # logger.info(f"not_common_character_rate: {not_common_character_rate}, not_printable_rate: {not_printable_rate}")
    '''新逻辑使用pdfminer识别乱码pdf,准确率高且不会误伤,已在解析流程之前进行处理'''
    # 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
    if (pdf_info_dict is None
            or pdf_info_dict.get("_need_drop", False)
            # 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
    ):
        logger.warning(f"parse_pdf_by_txt drop or error, switch to parse_pdf_by_ocr")
        if input_model_is_empty:
            pdf_models = doc_analyze(pdf_bytes, ocr=True)
        pdf_info_dict = parse_pdf(parse_pdf_by_ocr)
        if pdf_info_dict is None:
            raise Exception("Both parse_pdf_by_txt and parse_pdf_by_ocr failed.")
        else:
            pdf_info_dict["_parse_type"] = PARSE_TYPE_OCR
    else:
        pdf_info_dict["_parse_type"] = PARSE_TYPE_TXT

    pdf_info_dict["_version_name"] = __version__

    return pdf_info_dict