      filename = ''
  
      def process_image(self, filename):
          try:
              image = cv2.imread(filename)
              for (idx, face) in enumerate(crop_faces(image)):
 
</s> remove def convert_one_image(image):
 </s> add  </s> remove autoencoder = autoencoder_B
 </s> add def convert_one_image(image, model_dir="models"): </s> remove # landmark file can be found in http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
# unzip it in the same folder as the main scripts
aligner = Aligner("shape_predictor_68_face_landmarks.dat", "mmod_human_face_detector.dat")
 </s> add     encoder.load_weights(model_dir + "/encoder.h5")
    decoder_A.load_weights(model_dir + "/decoder_A.h5")
    decoder_B.load_weights(model_dir + "/decoder_B.h5") </s> remove                 new_face = convert_one_image(cv2.resize(face.image, (256, 256)))
 </s> add                 new_face = convert_one_image(cv2.resize(face.image, (256, 256)), self.arguments.model_dir)
    def add_optional_arguments(self, parser):
        parser.add_argument('-m', '--model-dir',
                            action=FullPaths,
                            dest="model_dir",
                            default="models",
                            help="Model directory. A directory containing the trained model \
                    you wish to process. Defaults to 'models'")
        return parser

      filename = ''
  
    def add_optional_arguments(self, parser):
        parser.add_argument('-m', '--model-dir',
                            action=FullPaths,
                            dest="model_dir",
                            default="models",
                            help="Model directory. A directory containing the trained model \
                    you wish to process. Defaults to 'models'")
        return parser

      def process_image(self, filename):
          try:
              image = cv2.imread(filename)
              for (idx, face) in enumerate(crop_faces(image)):
keep add keep keep keep keep
 Add optional model path to convert.py (#14) 
convert.py