246 lines
9.7 KiB
Python
246 lines
9.7 KiB
Python
import re
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import os
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import requests
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import time
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import PyPDF2
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import multiprocessing as mp
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import math
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import sys
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import torch
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from loguru import logger
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from glob import glob
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from tqdm import tqdm
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from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
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from magic_pdf.data.dataset import PymuDocDataset
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from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
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from magic_pdf.config.enums import SupportedPdfParseMethod
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# 图床配置
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IMGBED_URL = "http://localhost:40027/"
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# 检查imgbed url是否以/结尾
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if not IMGBED_URL.endswith('/'):
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IMGBED_URL += '/'
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token_endpoint = f"{IMGBED_URL}api/v1/tokens"
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upload_endpoint = f"{IMGBED_URL}api/v1/upload"
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# 通过如下方式获取token
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# curl -X POST http://localhost:40027/api/v1/tokens -H "Content-Type: application/json" -d '{"email":"yt.li2@siat.ac.cn", "password":"lyt20000414."}'
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IMGBED_TOKEN = "6|QsBh5H7txY3Hd7ju1nzYKOBSdFQeL0YberydSFIH"
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def replace_image_links(md_content: str, images_urls: dict) -> str:
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# 匹配 Markdown 中的图像链接形式，即: 
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pattern = r'!\[(.*?)\]\((.*?)\)'
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def replace_link(match):
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# 提取出当前匹配到的图片路径
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image_path = match.group(2)
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# 检查该路径是否在字典中
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if image_path in images_urls:
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# 从字典中获取新的 URL
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new_url = images_urls[image_path]
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return f""
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return match.group(0)
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# 使用 sub 函数进行替换
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updated_md_content = re.sub(pattern, replace_link, md_content)
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return updated_md_content
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# 上传图片到LSKY Pro
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def upload_image(img_dir):
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headers = {
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"Authorization": f"Bearer {IMGBED_TOKEN}",
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'Accept': 'application/json'
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}
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image_urls = {}
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os.makedirs(img_dir, exist_ok=True)
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img_names = os.listdir(img_dir)
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for image_name in img_names:
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retry = 0
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image_path = os.path.join(img_dir, image_name)
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while retry < 5: # 最大重试次数
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try:
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with open(image_path, 'rb') as image_file: # 确保文件在上传时是打开状态
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files = {'file': image_file}
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# 上传文件
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response = requests.post(upload_endpoint, headers=headers, files=files)
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if response.status_code == 200:
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result = response.json()
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if result['status']:
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image_url = result['data']['links']['url']
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image_urls['images/'+image_name] = image_url
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break # 上传成功，退出重试循环
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else:
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raise Exception(f"图片上传失败: {result['message']}")
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elif response.status_code == 429:
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# 429 响应，等待一段时间再重试
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wait_time = min(2 ** retry, 60) # 指数退避，最大等待 60 秒
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logger.warning(f"请求过于频繁，等待 {wait_time} 秒...")
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time.sleep(wait_time)
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else:
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raise Exception(f"HTTP请求出错: {response.status_code}")
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retry += 1 # 增加重试次数
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time.sleep(1) # 在重试失败后稍等一下
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except FileNotFoundError:
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logger.error(f"文件 {image_path} 不存在，请检查路径是否正确")
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return
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return image_urls
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def pdf_parse_main(
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pdf_path: str,
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output_dir: str = None
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):
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try:
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name_without_suff = os.path.basename(pdf_path).replace('.pdf', '')
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# prepare env
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local_md_dir = os.path.join(output_dir, name_without_suff)
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local_image_dir = os.path.join(local_md_dir, 'images')
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image_dir = str(os.path.basename(local_image_dir))
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os.makedirs(local_image_dir, exist_ok=True)
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image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
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local_md_dir
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)
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# read bytes
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reader1 = FileBasedDataReader("")
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pdf_bytes = reader1.read(pdf_path) # read the pdf content
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# proc
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## Create Dataset Instance
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ds = PymuDocDataset(pdf_bytes)
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## inference
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if ds.classify() == SupportedPdfParseMethod.OCR:
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infer_result = ds.apply(doc_analyze, ocr=True)
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## pipeline
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pipe_result = infer_result.pipe_ocr_mode(image_writer)
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else:
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infer_result = ds.apply(doc_analyze, ocr=False)
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## pipeline
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pipe_result = infer_result.pipe_txt_mode(image_writer)
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### draw model result on each page
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infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
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### draw layout result on each page
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pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
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### draw spans result on each page
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pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
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### dump markdown
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md_content = pipe_result.dump_md(md_writer, os.path.join(local_md_dir, f"{name_without_suff}.md"), image_dir)
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### dump content list
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pipe_result.dump_content_list(md_writer, os.path.join(local_md_dir, f"{name_without_suff}_content_list.json"), image_dir)
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# print(md_content)
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# 上传图像到图床
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image_urls = upload_image(local_image_dir)
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md_content = replace_image_links(md_content, image_urls)
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md_writer.write_string(os.path.join(local_md_dir, f"{name_without_suff}.md"), md_content)
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except Exception as e:
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logger.exception(e)
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return 'error'
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def init_worker(pdfs, gpu_index, output_dir): # 添加output_dir参数
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"""
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Initialize a worker process to process a chunk of PDFs with a specific GPU.
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"""
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try:
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# 设置CUDA设备
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os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_index)
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import torch
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device = torch.device('cuda:0')
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print(f"进程 {os.getpid()} 启动于GPU {gpu_index}")
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print(f"处理 {len(pdfs)} 个PDF文件")
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process_pdf_chunk(pdfs, device, output_dir) # 传递output_dir
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except Exception as e:
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print(f"进程 {os.getpid()} 在GPU {gpu_index} 上初始化失败: {str(e)}")
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raise e
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def process_pdf_chunk(pdf_paths, worker_id, output_dir):
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for pdf_path in tqdm(pdf_paths, desc=f"Worker {worker_id} Progress"):
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try:
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# 定期清理GPU内存
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torch.cuda.empty_cache()
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with open(pdf_path, 'rb') as file:
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pdf_reader = PyPDF2.PdfReader(file)
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print(os.path.basename(pdf_path).replace(".pdf", "").replace('_', '/'))
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pdf_parse_main(pdf_path, output_dir=output_dir)
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except PyPDF2.errors.PdfReadError:
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logger.error(f"{pdf_path} has been broken")
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except Exception as e:
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logger.error(f"{pdf_path} has an error: {e}")
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def multiprocessing_setup(pdf_paths, num_gpus, output_dir):
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# 计算每个GPU处理的文件数量
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chunk_size = math.ceil(len(pdf_paths) / num_gpus)
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processes = []
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# 为每个GPU创建一个进程
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for gpu_id in range(num_gpus):
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start_idx = gpu_id * chunk_size
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end_idx = min(len(pdf_paths), start_idx + chunk_size)
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chunk = pdf_paths[start_idx:end_idx]
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p = mp.Process(target=init_worker, args=(chunk, gpu_id, output_dir)) # 传递output_dir
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processes.append(p)
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p.start()
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time.sleep(2)
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# 等待所有进程完成
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for p in processes:
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p.join()
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if __name__ == "__main__":
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_cur_dir = os.path.dirname(os.path.abspath(__file__))
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# 此处更改路径
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# pdf_dir = os.path.join(_cur_dir, "二维材料剥离/二维材料剥离/石墨烯")
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# output_dir = os.path.join(_cur_dir, "二维材料剥离/mds/石墨烯")
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# pdf_dir = os.path.join(_cur_dir, "二维材料剥离/二维材料剥离/黑磷烯")
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# output_dir = os.path.join(_cur_dir, "二维材料剥离/mds/黑磷烯")
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pdf_dir = os.path.join(_cur_dir, "模型评估/模型评估")
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output_dir = os.path.join(_cur_dir, "模型评估/mds")
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# pdf_dir = os.path.join(_cur_dir, "金纳米棒/金纳米棒")
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# output_dir = os.path.join(_cur_dir, "金纳米棒/mds")
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# pdf_dir = os.path.join(_cur_dir, "钙钛矿/钙钛矿-复合材料")
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# output_dir = os.path.join(_cur_dir, "钙钛矿/mds/复合材料")
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# pdf_dir = os.path.join(_cur_dir, "钙钛矿/钙钛矿-LAPR/PDF论文")
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# output_dir = os.path.join(_cur_dir, "钙钛矿/mds/LAPR")
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os.makedirs(output_dir, exist_ok=True)
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pdf_paths = sorted(glob(os.path.join(pdf_dir, "*.pdf")))
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print("pdf数量：", len(pdf_paths))
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# 输出目录中md文件的数量
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md_paths = sorted(glob(os.path.join(output_dir, "**", "*.md"), recursive=True))
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md_names = [os.path.basename(md_path) for md_path in md_paths]
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pdf_paths = [pdf_path for pdf_path in pdf_paths if os.path.basename(pdf_path).replace('.pdf', '.md') not in md_names]
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print("过滤后pdf数量：", len(pdf_paths))
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# # 设置GPU数量
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# num_gpus = 2 # 先用2个GPU测试
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# # 设置多进程启动方法
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# mp.set_start_method('spawn', force=True)
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# try:
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# multiprocessing_setup(pdf_paths, num_gpus, output_dir)
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# except Exception as e:
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# print(f"程序执行出错: {str(e)}")
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# pdf_path = "black_phosphorus/参考文献/2015.03-ACS Nano-Barbaros Özyilmaz-石墨烯接触、全封装的超薄黑磷基场效应晶体管中的空气稳定传输.pdf"
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for pdf_path in tqdm(pdf_paths):
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pdf_parse_main(pdf_path, output_dir=output_dir)
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