return image
# New: replace random_crop with center crop
def center_crop(image, crop_size):
image_shape = image.get_shape().as_list()
offset_length = floor(float(crop_size / 2))
x_start = floor(image_shape[2] / 2 - offset_length)
y_start = floor(image_shape[1] / 2 - offset_length)
image = image[:, x_start:x_start + crop_size, y_start:y_start + crop_size]
image.set_shape((3, crop_size, crop_size))
return image
if __name__ == '__main__':
params = get_params(train)
parser = argparse.ArgumentParser(
description='AdaIN Style Transfer Training')
# general
parser.add_argument(
'--content_dir',
default=params['content_dir'],
help=
'A directory with TFRecords files containing content images for training'
)
parser.add_argument(
'--style_dir',
default=params['style_dir'],
help=
otherLayers = [vgg[i] for i in otherLayerNames]
if decoder_weights:
with open_weights(decoder_weights) as w:
decoder = build_decoder(combined_target, w, trainable=False,
data_format=data_format)
else:
decoder = build_decoder(combined_target, None, trainable=False,
data_format=data_format)
# Return other layers on top of original outputs
return image, content, style, target, encoder, decoder, otherLayers
if __name__ == '__main__':
params = get_params(style_transfer)
parser = argparse.ArgumentParser(description='AdaIN Style Transfer')
parser.add_argument('--content', help='File path to the content image')
parser.add_argument('--content_dir', help="""Directory path to a batch of
content images""")
parser.add_argument('--style', help="""File path to the style image,
or multiple style images separated by commas if you want to do style
interpolation or spatial control""")
parser.add_argument('--style_dir', help="""Directory path to a batch of
style images""")
parser.add_argument('--vgg_weights', default=params['vgg_weights'],
help='Path to the weights of the VGG19 network')
parser.add_argument('--decoder_weights', default=params['decoder_weights'],
help='Path to the decoder')