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https://github.com/opendatalab/MinerU.git
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299 lines
9.8 KiB
Python
299 lines
9.8 KiB
Python
# Copyright (c) Opendatalab. All rights reserved.
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import os
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import signal
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import time
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from io import BytesIO
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import numpy as np
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import pypdfium2 as pdfium
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from loguru import logger
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from PIL import Image, ImageOps
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from mineru.data.data_reader_writer import FileBasedDataWriter
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from mineru.utils.check_sys_env import is_windows_environment
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from mineru.utils.os_env_config import get_load_images_timeout, get_load_images_threads
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from mineru.utils.pdf_reader import image_to_b64str, image_to_bytes, page_to_image
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from mineru.utils.enum_class import ImageType
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from mineru.utils.hash_utils import str_sha256
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from mineru.utils.pdf_page_id import get_end_page_id
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from concurrent.futures import ProcessPoolExecutor, wait, ALL_COMPLETED
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def pdf_page_to_image(page: pdfium.PdfPage, dpi=200, image_type=ImageType.PIL) -> dict:
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"""Convert pdfium.PdfDocument to image, Then convert the image to base64.
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Args:
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page (_type_): pdfium.PdfPage
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dpi (int, optional): reset the dpi of dpi. Defaults to 200.
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image_type (ImageType, optional): The type of image to return. Defaults to ImageType.PIL.
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Returns:
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dict: {'img_base64': str, 'img_pil': pil_img, 'scale': float }
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"""
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pil_img, scale = page_to_image(page, dpi=dpi)
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image_dict = {
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"scale": scale,
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}
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if image_type == ImageType.BASE64:
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image_dict["img_base64"] = image_to_b64str(pil_img)
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else:
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image_dict["img_pil"] = pil_img
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return image_dict
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def _load_images_from_pdf_worker(
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pdf_bytes, dpi, start_page_id, end_page_id, image_type
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):
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"""用于进程池的包装函数"""
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return load_images_from_pdf_core(
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pdf_bytes, dpi, start_page_id, end_page_id, image_type
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)
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def load_images_from_pdf(
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pdf_bytes: bytes,
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dpi=200,
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start_page_id=0,
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end_page_id=None,
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image_type=ImageType.PIL,
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timeout=None,
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threads=None,
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):
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"""带超时控制的 PDF 转图片函数,支持多进程加速
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Args:
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pdf_bytes (bytes): PDF 文件的 bytes
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dpi (int, optional): reset the dpi of dpi. Defaults to 200.
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start_page_id (int, optional): 起始页码. Defaults to 0.
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end_page_id (int | None, optional): 结束页码. Defaults to None.
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image_type (ImageType, optional): 图片类型. Defaults to ImageType.PIL.
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timeout (int | None, optional): 超时时间(秒)。如果为 None，则从环境变量 MINERU_PDF_RENDER_TIMEOUT 读取，若未设置则默认为 300 秒。
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threads (int): 进程数, 如果为 None，则从环境变量 MINERU_PDF_RENDER_THREADS 读取，若未设置则默认为 4.
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Raises:
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TimeoutError: 当转换超时时抛出
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"""
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pdf_doc = pdfium.PdfDocument(pdf_bytes)
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if is_windows_environment():
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# Windows 环境下不使用多进程
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return load_images_from_pdf_core(
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pdf_bytes,
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dpi,
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start_page_id,
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get_end_page_id(end_page_id, len(pdf_doc)),
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image_type,
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), pdf_doc
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else:
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if timeout is None:
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timeout = get_load_images_timeout()
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if threads is None:
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threads = get_load_images_threads()
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end_page_id = get_end_page_id(end_page_id, len(pdf_doc))
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# 计算总页数
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total_pages = end_page_id - start_page_id + 1
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# 实际使用的进程数不超过总页数
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actual_threads = min(os.cpu_count() or 1, threads, total_pages)
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# 根据实际进程数分组页面范围
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pages_per_thread = max(1, total_pages // actual_threads)
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page_ranges = []
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for i in range(actual_threads):
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range_start = start_page_id + i * pages_per_thread
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if i == actual_threads - 1:
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# 最后一个进程处理剩余所有页面
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range_end = end_page_id
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else:
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range_end = start_page_id + (i + 1) * pages_per_thread - 1
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page_ranges.append((range_start, range_end))
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logger.debug(f"PDF to images using {actual_threads} processes, page ranges: {page_ranges}")
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executor = ProcessPoolExecutor(max_workers=actual_threads)
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try:
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# 提交所有任务
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futures = []
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future_to_range = {}
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for range_start, range_end in page_ranges:
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future = executor.submit(
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_load_images_from_pdf_worker,
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pdf_bytes,
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dpi,
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range_start,
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range_end,
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image_type,
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)
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futures.append(future)
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future_to_range[future] = range_start
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# 使用 wait() 设置单一全局超时
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done, not_done = wait(futures, timeout=timeout, return_when=ALL_COMPLETED)
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# 检查是否有未完成的任务（超时情况）
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if not_done:
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# 超时：强制终止所有子进程
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_terminate_executor_processes(executor)
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pdf_doc.close()
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raise TimeoutError(f"PDF to images conversion timeout after {timeout}s")
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# 所有任务完成，收集结果
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all_results = []
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for future in futures:
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range_start = future_to_range[future]
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# 这里不需要 timeout，因为任务已完成
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images_list = future.result()
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all_results.append((range_start, images_list))
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# 按起始页码排序并合并结果
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all_results.sort(key=lambda x: x[0])
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images_list = []
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for _, imgs in all_results:
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images_list.extend(imgs)
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return images_list, pdf_doc
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except Exception as e:
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# 发生任何异常时，确保清理子进程
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_terminate_executor_processes(executor)
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pdf_doc.close()
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if isinstance(e, TimeoutError):
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raise
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raise
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finally:
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executor.shutdown(wait=False, cancel_futures=True)
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def _terminate_executor_processes(executor):
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"""强制终止 ProcessPoolExecutor 中的所有子进程"""
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if hasattr(executor, '_processes'):
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for pid, process in executor._processes.items():
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if process.is_alive():
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try:
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# 先发送 SIGTERM 允许优雅退出
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os.kill(pid, signal.SIGTERM)
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except (ProcessLookupError, OSError):
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pass
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# 给子进程一点时间响应 SIGTERM
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time.sleep(0.1)
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# 对仍然存活的进程发送 SIGKILL 强制终止
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for pid, process in executor._processes.items():
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if process.is_alive():
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try:
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os.kill(pid, signal.SIGKILL)
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except (ProcessLookupError, OSError):
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pass
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def load_images_from_pdf_core(
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pdf_bytes: bytes,
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dpi=200,
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start_page_id=0,
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end_page_id=None,
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image_type=ImageType.PIL, # PIL or BASE64
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):
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images_list = []
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pdf_doc = pdfium.PdfDocument(pdf_bytes)
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pdf_page_num = len(pdf_doc)
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end_page_id = get_end_page_id(end_page_id, pdf_page_num)
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for index in range(start_page_id, end_page_id + 1):
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# logger.debug(f"Converting page {index}/{pdf_page_num} to image")
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page = pdf_doc[index]
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image_dict = pdf_page_to_image(page, dpi=dpi, image_type=image_type)
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images_list.append(image_dict)
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pdf_doc.close()
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return images_list
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def cut_image(
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bbox: tuple,
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page_num: int,
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page_pil_img,
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return_path,
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image_writer: FileBasedDataWriter,
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scale=2,
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):
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"""从第page_num页的page中，根据bbox进行裁剪出一张jpg图片，返回图片路径 save_path：需要同时支持s3和本地,
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图片存放在save_path下，文件名是:
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{page_num}_{bbox[0]}_{bbox[1]}_{bbox[2]}_{bbox[3]}.jpg , bbox内数字取整。"""
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# 拼接文件名
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filename = f"{page_num}_{int(bbox[0])}_{int(bbox[1])}_{int(bbox[2])}_{int(bbox[3])}"
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# 老版本返回不带bucket的路径
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img_path = f"{return_path}_{filename}" if return_path is not None else None
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# 新版本生成平铺路径
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img_hash256_path = f"{str_sha256(img_path)}.jpg"
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# img_hash256_path = f'{img_path}.jpg'
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crop_img = get_crop_img(bbox, page_pil_img, scale=scale)
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img_bytes = image_to_bytes(crop_img, image_format="JPEG")
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image_writer.write(img_hash256_path, img_bytes)
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return img_hash256_path
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def get_crop_img(bbox: tuple, pil_img, scale=2):
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scale_bbox = (
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int(bbox[0] * scale),
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int(bbox[1] * scale),
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int(bbox[2] * scale),
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int(bbox[3] * scale),
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)
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return pil_img.crop(scale_bbox)
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def get_crop_np_img(bbox: tuple, input_img, scale=2):
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if isinstance(input_img, Image.Image):
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np_img = np.asarray(input_img)
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elif isinstance(input_img, np.ndarray):
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np_img = input_img
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else:
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raise ValueError("Input must be a pillow object or a numpy array.")
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scale_bbox = (
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int(bbox[0] * scale),
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int(bbox[1] * scale),
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int(bbox[2] * scale),
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int(bbox[3] * scale),
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)
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return np_img[scale_bbox[1] : scale_bbox[3], scale_bbox[0] : scale_bbox[2]]
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def images_bytes_to_pdf_bytes(image_bytes):
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# 内存缓冲区
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pdf_buffer = BytesIO()
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# 载入并转换所有图像为 RGB 模式
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image = Image.open(BytesIO(image_bytes))
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# 根据 EXIF 信息自动转正（处理手机拍摄的带 Orientation 标记的图片）
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image = ImageOps.exif_transpose(image) or image
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# 只在必要时转换
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if image.mode != "RGB":
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image = image.convert("RGB")
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# 第一张图保存为 PDF，其余追加
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image.save(
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pdf_buffer,
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format="PDF",
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# save_all=True
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)
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# 获取 PDF bytes 并重置指针（可选）
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pdf_bytes = pdf_buffer.getvalue()
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pdf_buffer.close()
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return pdf_bytes
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