      decoder_A.load_weights(model_dir + "/decoder_A.h5")
      decoder_B.load_weights(model_dir + "/decoder_B.h5")
  
  
      assert image.shape == (256, 256, 3)
      crop = slice(48, 208)
      face = image[crop, crop]
      face = cv2.resize(face, (64, 64))
      face = numpy.expand_dims(face, 0)
 
</s> remove def convert_one_image(image):
 </s> add  </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) </s> remove autoencoder = autoencoder_B
 </s> add def convert_one_image(image, model_dir="models"):
    autoencoder = autoencoder_B

    # 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")
      decoder_A.load_weights(model_dir + "/decoder_A.h5")
      decoder_B.load_weights(model_dir + "/decoder_B.h5")
  
    autoencoder = autoencoder_B

    # 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")
  
      assert image.shape == (256, 256, 3)
      crop = slice(48, 208)
      face = image[crop, crop]
      face = cv2.resize(face, (64, 64))
      face = numpy.expand_dims(face, 0)
keep keep add keep keep keep keep keep keep
 Add optional model path to convert.py (#14) 
lib/faces_process.py