text: import os
  import cv2
  import torch
  import numpy as np
  from models import ResnetGenerator
  import argparse
  from utils import Preprocess
  
  
  #parser = argparse.ArgumentParser()
  #parser.add_argument('--photo_path', type=str, help='input photo path')
  #parser.add_argument('--save_path', type=str, help='cartoon save path')
  #args = parser.parse_args()
  
  #os.makedirs(os.path.dirname(args.save_path), exist_ok=True)
  
  class Photo2Cartoon:
      def __init__(self):
          self.pre = Preprocess()
          self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
          self.net = ResnetGenerator(ngf=32, img_size=256, light=True).to(self.device)
  
          assert os.path.exists('./models/photo2cartoon_weights.pt'), "[Step1: load weights] Can not find 'photo2cartoon_weights.pt' in folder 'models!!!'"
          params = torch.load('./models/photo2cartoon_weights.pt', map_location=self.device)
          self.net.load_state_dict(params['genA2B'])
          print('[Step1: load weights] success!')
  
      def inference(self, img):
          # face alignment and segmentation
          face_rgba = self.pre.process(img)
          if face_rgba is None:
              print('[Step2: face detect] can not detect face!!!')
              return None
  
          print('[Step2: face detect] success!')
          face_rgba = cv2.resize(face_rgba, (256, 256), interpolation=cv2.INTER_AREA)
          face = face_rgba[:, :, :3].copy()
          mask = face_rgba[:, :, 3][:, :, np.newaxis].copy() / 255.
          face = (face*mask + (1-mask)*255) / 127.5 - 1
  
          face = np.transpose(face[np.newaxis, :, :, :], (0, 3, 1, 2)).astype(np.float32)
          face = torch.from_numpy(face).to(self.device)
  
          # inference
          with torch.no_grad():
              cartoon = self.net(face)[0][0]
  
          # post-process
          cartoon = np.transpose(cartoon.cpu().numpy(), (1, 2, 0))
          cartoon = (cartoon + 1) * 127.5
          cartoon = (cartoon * mask + 255 * (1 - mask)).astype(np.uint8)
          cartoon = cv2.cvtColor(cartoon, cv2.COLOR_RGB2BGR)
          print('[Step3: photo to cartoon] success!')
          return cartoon
  
  
  #if __name__ == '__main__':
      #img = cv2.cvtColor(cv2.imread(args.photo_path), cv2.COLOR_BGR2RGB)
      #c2p = Photo2Cartoon()
      #cartoon = c2p.inference(img)
      #if cartoon is not None:
      #    cv2.imwrite(args.save_path, cartoon)
      #    print('Cartoon portrait has been saved successfully!')
  