      def __call__(self, parser, namespace, values, option_string=None):
          setattr(namespace, self.dest, os.path.abspath(os.path.expanduser(values)))
  
  
  class DirectoryProcessor():
      '''
      Abstract class that processes a directory of images
      and writes output to the specified folder
      '''
      arguments = None
 
</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                 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) </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")
class DirectoryProcessor(object):
      def __call__(self, parser, namespace, values, option_string=None):
          setattr(namespace, self.dest, os.path.abspath(os.path.expanduser(values)))
  
  
class DirectoryProcessor(object):
      '''
      Abstract class that processes a directory of images
      and writes output to the specified folder
      '''
      arguments = None
keep keep keep keep replace keep keep keep keep keep
 Add optional model path to convert.py (#14) 
lib/cli.py