code: def main():
      args = parse_args()
      sbd_path = download_file_and_uncompress(
          url=URL,
          savepath=DATA_HOME,
          extrapath=DATA_HOME,
          extraname="benchmark_RELEASE",
      )
      with open(os.path.join(sbd_path, "dataset/train.txt"), "r") as f:
          sbd_file_list = [line.strip() for line in f]
      with open(os.path.join(sbd_path, "dataset/val.txt"), "r") as f:
          sbd_file_list += [line.strip() for line in f]
      if not os.path.exists(args.voc_path):
          raise FileNotFoundError(
              "There is no voc_path: {}. Please ensure that the Pascal VOC dataset has been downloaded correctly"
          )
      with open(
          os.path.join(
              args.voc_path,
              "VOC2012/ImageSets/Segmentation/trainval.txt",
          ),
          "r",
      ) as f:
          voc_file_list = [line.strip() for line in f]
  
      aug_file_list = list(set(sbd_file_list) - set(voc_file_list))
      with open(
          os.path.join(
              args.voc_path, "VOC2012/ImageSets/Segmentation/aug.txt"
          ),
          "w",
      ) as f:
          f.writelines("".join([line, "\n"]) for line in aug_file_list)
  
      sbd_cls_dir = os.path.join(sbd_path, "dataset/cls")
      save_dir = os.path.join(
          args.voc_path, "VOC2012/SegmentationClassAug"
      )
      if not os.path.exists(save_dir):
          os.mkdir(save_dir)
      mat_file_list = os.listdir(sbd_cls_dir)
      p = Pool(args.num_workers)
      for f in tqdm.tqdm(mat_file_list):
          p.apply_async(mat_to_png, args=(f, sbd_cls_dir, save_dir))
      p.close()
      p.join()
  
file_path: PaddleSeg/tools/data/voc_augment.py
imports: import os
  import argparse
  from multiprocessing import Pool, cpu_count
  import cv2
  import numpy
  from scipy.io import loadmat
  import tqdm
  from paddleseg.utils.download import download_file_and_uncompress
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types