  import cv2
  import numpy
  
  from lib.utils import get_image_paths, load_images, stack_images
  from lib.training_data import get_training_data
  
  from lib.model import autoencoder_A
  from lib.model import autoencoder_B
  from lib.model import encoder, decoder_A, decoder_B
  
  try:
      encoder.load_weights('models/encoder.h5')
      decoder_A.load_weights('models/decoder_A.h5')
      decoder_B.load_weights('models/decoder_B.h5')
  except:
      pass
  
  
  def save_model_weights():
      encoder.save_weights('models/encoder.h5')
      decoder_A.save_weights('models/decoder_A.h5')
      decoder_B.save_weights('models/decoder_B.h5')
      print('save model weights')
  
  
  def show_sample(test_A, test_B):
      figure_A = numpy.stack([
          test_A,
          autoencoder_A.predict(test_A),
          autoencoder_B.predict(test_A),
          ], axis=1)
      figure_B = numpy.stack([
          test_B,
          autoencoder_B.predict(test_B),
          autoencoder_A.predict(test_B),
          ], axis=1)
  
      figure = numpy.concatenate([figure_A, figure_B], axis=0)
      figure = figure.reshape((4, 7) + figure.shape[1:])
      figure = stack_images(figure)
  
      figure = numpy.clip(figure * 255, 0, 255).astype('uint8')
  
      cv2.imwrite('_sample.jpg', figure)
  
  
  images_A = get_image_paths('data/trump')
  images_B = get_image_paths('data/cage')
  images_A = load_images(images_A) / 255.0
  images_B = load_images(images_B) / 255.0
  
  images_A += images_B.mean(axis=(0, 1, 2)) - images_A.mean(axis=(0, 1, 2))
  
  print('press "q" to stop training and save model')
  
  BATCH_SIZE = 64
  
  for epoch in range(1000000):
      warped_A, target_A = get_training_data(images_A, BATCH_SIZE)
      warped_B, target_B = get_training_data(images_B, BATCH_SIZE)
  
      loss_A = autoencoder_A.train_on_batch(warped_A, target_A)
      loss_B = autoencoder_B.train_on_batch(warped_B, target_B)
      print(loss_A, loss_B)
  
      if epoch % 100 == 0:
          save_model_weights()
          show_sample(target_A[0:14], target_B[0:14])
  
      key = cv2.waitKey(1)
      if key == ord('q'):
          save_model_weights()
          exit()
  
 
</s> remove         setattr(namespace, self.dest, os.path.abspath(os.path.expanduser(values)))
 </s> add         setattr(namespace, self.dest, os.path.abspath(
            os.path.expanduser(values)))


class TrainingProcessor(object):
    arguments = None

    def __init__(self, description='default'):
        print('Initializing')
        self.parse_arguments(description)

        print("Model A Directory: {}".format(self.arguments.input_A))
        print("Model B Directory: {}".format(self.arguments.input_B))
        print("Training data directory: {}".format(self.arguments.model_dir))
        print('Starting, this may take a while...')

        try:
            encoder.load_weights(self.arguments.model_dir + '/encoder.h5')
            decoder_A.load_weights(self.arguments.model_dir + '/decoder_A.h5')
            decoder_B.load_weights(self.arguments.model_dir + '/decoder_B.h5')
        except Exception as e:
            print('Not loading existing training data.')

        self.process()

    def parse_arguments(self, description):
        parser = argparse.ArgumentParser(
            description=description,
            epilog="Questions and feedback: \
            https://github.com/deepfakes/faceswap-playground"
        )

        parser.add_argument('-A', '--input-A',
                            action=FullPaths,
                            dest="input_A",
                            default="input_A",
                            help="Input directory. A directory containing training images for face A.\
                             Defaults to 'input'")
        parser.add_argument('-B', '--input-B',
                            action=FullPaths,
                            dest="input_B",
                            default="input_B",
                            help="Input directory. A directory containing training images for face B.\
                             Defaults to 'input'")
        parser.add_argument('-m', '--model-dir',
                            action=FullPaths,
                            dest="model_dir",
                            default="model",
                            help="Model directory. This is where the training data will \
                                be stored. Defaults to 'model'")
        parser.add_argument('-p', '--preview',
                            action="store_true",
                            dest="preview",
                            default=False,
                            help="Show preview output. If not specified, write progress \
                            to file.")
        parser.add_argument('-v', '--verbose',
                            action="store_true",
                            dest="verbose",
                            default=False,
                            help="Show verbose output")
        parser = self.add_optional_arguments(parser)
        self.arguments = parser.parse_args()

    def add_optional_arguments(self, parser):
        # Override this for custom arguments
        return parser

    def save_model_weights(self):
        encoder.save_weights(self.arguments.model_dir + '/encoder.h5')
        decoder_A.save_weights(self.arguments.model_dir + '/decoder_A.h5')
        decoder_B.save_weights(self.arguments.model_dir + '/decoder_B.h5')
        print('save model weights')

    def show_sample(self, test_A, test_B):
        figure_A = numpy.stack([
            test_A,
            autoencoder_A.predict(test_A),
            autoencoder_B.predict(test_A),
        ], axis=1)
        figure_B = numpy.stack([
            test_B,
            autoencoder_B.predict(test_B),
            autoencoder_A.predict(test_B),
        ], axis=1)

        figure = numpy.concatenate([figure_A, figure_B], axis=0)
        figure = figure.reshape((4, 7) + figure.shape[1:])
        figure = stack_images(figure)

        figure = numpy.clip(figure * 255, 0, 255).astype('uint8')

        if self.arguments.preview is True:
            cv2.imshow('', figure)
        else:
            cv2.imwrite('_sample.jpg', figure)

    def process(self):
        images_A = get_image_paths(self.arguments.images_A)
        images_B = get_image_paths(self.arguments.images_B)
        images_A = load_images(images_A) / 255.0
        images_B = load_images(images_B) / 255.0

        images_A += images_B.mean(axis=(0, 1, 2)) - \
            images_A.mean(axis=(0, 1, 2))

        print('press "q" to stop training and save model')

        BATCH_SIZE = 64

        for epoch in range(1000000):
            warped_A, target_A = get_training_data(images_A, BATCH_SIZE)
            warped_B, target_B = get_training_data(images_B, BATCH_SIZE)

            loss_A = autoencoder_A.train_on_batch(warped_A, target_A)
            loss_B = autoencoder_B.train_on_batch(warped_B, target_B)
            print(loss_A, loss_B)

            if epoch % 100 == 0:
                self.save_model_weights()
                self.show_sample(target_A[0:14], target_B[0:14])

            key = cv2.waitKey(1)
            if key == ord('q'):
                self.save_model_weights()
                exit() </s> remove from lib.utils import get_image_paths, get_folder
 </s> add from lib.utils import get_image_paths, get_folder, load_images, stack_images
from lib.cli import TrainingProcessor

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replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace keep
 Add command line options for train.py 
train.py