text: import os
  from glob import glob
  
  import cv2
  import numpy as np
  from tqdm import tqdm
  
  
  def main():
      img_size = 96
  
      paths = glob('inputs/data-science-bowl-2018/stage1_train/*')
  
      os.makedirs('inputs/dsb2018_%d/images' % img_size, exist_ok=True)
      os.makedirs('inputs/dsb2018_%d/masks/0' % img_size, exist_ok=True)
  
      for i in tqdm(range(len(paths))):
          path = paths[i]
          img = cv2.imread(os.path.join(path, 'images',
                           os.path.basename(path) + '.png'))
          mask = np.zeros((img.shape[0], img.shape[1]))
          for mask_path in glob(os.path.join(path, 'masks', '*')):
              mask_ = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) > 127
              mask[mask_] = 1
          if len(img.shape) == 2:
              img = np.tile(img[..., None], (1, 1, 3))
          if img.shape[2] == 4:
              img = img[..., :3]
          img = cv2.resize(img, (img_size, img_size))
          mask = cv2.resize(mask, (img_size, img_size))
          cv2.imwrite(os.path.join('inputs/dsb2018_%d/images' % img_size,
                      os.path.basename(path) + '.png'), img)
          cv2.imwrite(os.path.join('inputs/dsb2018_%d/masks/0' % img_size,
                      os.path.basename(path) + '.png'), (mask * 255).astype('uint8'))
  
  
  if __name__ == '__main__':
      main()
  