text: import argparse
  import cv2
  import glob
  import os
  from basicsr.archs.rrdbnet_arch import RRDBNet
  
  from realesrgan import RealESRGANer
  
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('--input', type=str, default='inputs', help='Input image or folder')
      parser.add_argument(
          '--model_path',
          type=str,
          default='experiments/pretrained_models/RealESRGAN_x4plus.pth',
          help='Path to the pre-trained model')
      parser.add_argument('--output', type=str, default='results', help='Output folder')
      parser.add_argument('--netscale', type=int, default=4, help='Upsample scale factor of the network')
      parser.add_argument('--outscale', type=float, default=4, help='The final upsampling scale of the image')
      parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
      parser.add_argument('--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
      parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
      parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
      parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
      parser.add_argument('--half', action='store_true', help='Use half precision during inference')
      parser.add_argument('--block', type=int, default=23, help='num_block in RRDB')
      parser.add_argument(
          '--alpha_upsampler',
          type=str,
          default='realesrgan',
          help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
      parser.add_argument(
          '--ext',
          type=str,
          default='auto',
          help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
      args = parser.parse_args()
  
      if 'RealESRGAN_x4plus_anime_6B.pth' in args.model_path:
          args.block = 6
      elif 'RealESRGAN_x2plus.pth' in args.model_path:
          args.netscale = 2
  
      model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=args.block, num_grow_ch=32, scale=args.netscale)
  
      upsampler = RealESRGANer(
          scale=args.netscale,
          model_path=args.model_path,
          model=model,
          tile=args.tile,
          tile_pad=args.tile_pad,
          pre_pad=args.pre_pad,
          half=args.half)
  
      if args.face_enhance:
          from gfpgan import GFPGANer
          face_enhancer = GFPGANer(
              model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
              upscale=args.outscale,
              arch='clean',
              channel_multiplier=2,
              bg_upsampler=upsampler)
      os.makedirs(args.output, exist_ok=True)
  
      if os.path.isfile(args.input):
          paths = [args.input]
      else:
          paths = sorted(glob.glob(os.path.join(args.input, '*')))
  
      for idx, path in enumerate(paths):
          imgname, extension = os.path.splitext(os.path.basename(path))
          print('Testing', idx, imgname)
  
          img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
          if len(img.shape) == 3 and img.shape[2] == 4:
              img_mode = 'RGBA'
          else:
              img_mode = None
  
          h, w = img.shape[0:2]
          if max(h, w) > 1000 and args.netscale == 4:
              import warnings
              warnings.warn('The input image is large, try X2 model for better performace.')
          if max(h, w) < 500 and args.netscale == 2:
              import warnings
              warnings.warn('The input image is small, try X4 model for better performace.')
  
          try:
              if args.face_enhance:
                  _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
              else:
                  output, _ = upsampler.enhance(img, outscale=args.outscale)
          except Exception as error:
              print('Error', error)
              print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
          else:
              if args.ext == 'auto':
                  extension = extension[1:]
              else:
                  extension = args.ext
              if img_mode == 'RGBA':  # RGBA images should be saved in png format
                  extension = 'png'
              save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
              cv2.imwrite(save_path, output)
  
  
  if __name__ == '__main__':
      main()
  