  encoder.load_weights("models/encoder.h5")
  decoder_A.load_weights("models/decoder_A.h5")
  decoder_B.load_weights("models/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")
  
 
</s> remove encoder.load_weights("models/encoder.h5")
decoder_A.load_weights("models/decoder_A.h5")
decoder_B.load_weights("models/decoder_B.h5")
 </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 def convert_one_image(image):
 </s> add 
def convert_one_image(image, model_dir="models"):
  encoder.load_weights("models/encoder.h5")
  decoder_A.load_weights("models/decoder_A.h5")
  decoder_B.load_weights("models/decoder_B.h5")
  
def convert_one_image(image, model_dir="models"):
  
  # 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")
  
keep keep keep keep replace keep keep keep keep keep
 Add optional model path to convert.py (#14) 
lib/faces_process.py