                              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)):
 
</s> remove     aligner = Aligner(shapePredictor, humanFaceDetector)
 </s> add     if use_aligner:
        aligner = Aligner(shapePredictor, humanFaceDetector) </s> remove     # result = aligner.align(image.copy(), new_face)
    # if result is None:
    #     return superpose(image, new_face, crop)
    # else:
    #     return result
 </s> add     if use_aligner:
        return aligner.align(image.copy(), new_face)
    else:
        return superpose(image, new_face, crop) </s> remove     return superpose(image, new_face, crop)
 </s> add  </s> remove     decoder_A.load_weights(model_dir + "/decoder_A.h5")
    decoder_B.load_weights(model_dir + "/decoder_B.h5")
 </s> add     decoder_A.load_weights(model_dir + face_A)
    decoder_B.load_weights(model_dir + face_B) </s> remove def convert_one_image(image, model_dir="models"):
 </s> add def convert_one_image(image, model_dir="models", swap_model=False, use_aligner=False):

    face_A = '/decoder_A.h5' if not swap_model else '/decoder_B.h5'
    face_B = '/decoder_B.h5' if not swap_model else '/decoder_A.h5'
        parser.add_argument('-s', '--swap-model',
                            action="store_true",
                            dest="swap_model",
                            default=False,
                            help="Swap the model. Instead of A -> B, swap B -> A.")
                              help="Model directory. A directory containing the trained model \
                      you wish to process. Defaults to 'models'")
        parser.add_argument('-s', '--swap-model',
                            action="store_true",
                            dest="swap_model",
                            default=False,
                            help="Swap the model. Instead of A -> B, swap B -> A.")
          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 keep keep
 Allow users to swap the A/B models (#29)

* Allow users to swap the conversion models. 
scripts/convert.py