Commit f5bde135 authored by myhloli's avatar myhloli

Merge remote-tracking branch 'origin/master'

parents 277e8e1c c753b41e
import os
import json
import copy
from loguru import logger
from magic_pdf.pipe.UNIPipe import UNIPipe
from magic_pdf.pipe.OCRPipe import OCRPipe
from magic_pdf.pipe.TXTPipe import TXTPipe
from magic_pdf.rw.DiskReaderWriter import DiskReaderWriter
import magic_pdf.model as model_config
model_config.__use_inside_model__ = True
# todo: 设备类型选择 (?)
def json_md_dump(
pipe,
md_writer,
pdf_name,
content_list,
md_content,
):
# 写入模型结果到 model.json
orig_model_list = copy.deepcopy(pipe.model_list)
md_writer.write(
content=json.dumps(orig_model_list, ensure_ascii=False, indent=4),
path=f"{pdf_name}_model.json"
)
# 写入中间结果到 middle.json
md_writer.write(
content=json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4),
path=f"{pdf_name}_middle.json"
)
# text文本结果写入到 conent_list.json
md_writer.write(
content=json.dumps(content_list, ensure_ascii=False, indent=4),
path=f"{pdf_name}_content_list.json"
)
# 写入结果到 .md 文件中
md_writer.write(
content=md_content,
path=f"{pdf_name}.md"
)
def pdf_parse_main(
pdf_path: str,
parse_method: str = 'auto',
model_json_path: str = None,
is_json_md_dump: bool = True,
output_dir: str = None
):
"""
执行从 pdf 转换到 json、md 的过程,输出 md 和 json 文件到 pdf 文件所在的目录
:param pdf_path: .pdf 文件的路径,可以是相对路径,也可以是绝对路径
:param parse_method: 解析方法, 共 auto、ocr、txt 三种,默认 auto,如果效果不好,可以尝试 ocr
:param model_json_path: 已经存在的模型数据文件,如果为空则使用内置模型,pdf 和 model_json 务必对应
:param is_json_md_dump: 是否将解析后的数据写入到 .json 和 .md 文件中,默认 True,会将不同阶段的数据写入到不同的 .json 文件中(共3个.json文件),md内容会保存到 .md 文件中
:param output_dir: 输出结果的目录地址,会生成一个以 pdf 文件名命名的文件夹并保存所有结果
"""
try:
pdf_name = os.path.basename(pdf_path).split(".")[0]
pdf_path_parent = os.path.dirname(pdf_path)
if output_dir:
output_path = os.path.join(output_dir, pdf_name)
else:
output_path = os.path.join(pdf_path_parent, pdf_name)
output_image_path = os.path.join(output_path, 'images')
# 获取图片的父路径,为的是以相对路径保存到 .md 和 conent_list.json 文件中
image_path_parent = os.path.basename(output_image_path)
pdf_bytes = open(pdf_path, "rb").read() # 读取 pdf 文件的二进制数据
if model_json_path:
# 读取已经被模型解析后的pdf文件的 json 原始数据,list 类型
model_json = json.loads(open(model_json_path, "r", encoding="utf-8").read())
else:
model_json = []
# 执行解析步骤
# image_writer = DiskReaderWriter(output_image_path)
image_writer, md_writer = DiskReaderWriter(output_image_path), DiskReaderWriter(output_path)
# 选择解析方式
# jso_useful_key = {"_pdf_type": "", "model_list": model_json}
# pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
if parse_method == "auto":
jso_useful_key = {"_pdf_type": "", "model_list": model_json}
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
elif parse_method == "txt":
pipe = TXTPipe(pdf_bytes, model_json, image_writer)
elif parse_method == "ocr":
pipe = OCRPipe(pdf_bytes, model_json, image_writer)
else:
logger.error("unknown parse method, only auto, ocr, txt allowed")
exit(1)
# 执行分类
pipe.pipe_classify()
# 如果没有传入模型数据,则使用内置模型解析
if not model_json:
if model_config.__use_inside_model__:
pipe.pipe_analyze() # 解析
else:
logger.error("need model list input")
exit(1)
# 执行解析
pipe.pipe_parse()
# 保存 text 和 md 格式的结果
content_list = pipe.pipe_mk_uni_format(image_path_parent, drop_mode="none")
md_content = pipe.pipe_mk_markdown(image_path_parent, drop_mode="none")
if is_json_md_dump:
json_md_dump(pipe, md_writer, pdf_name, content_list, md_content)
except Exception as e:
logger.exception(e)
# 测试
if __name__ == '__main__':
pdf_path = r"C:\Users\XYTK2\Desktop\2024-2016-gb-cd-300.pdf"
pdf_parse_main(pdf_path)
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