def preprocess_image(cls, image, is_training, normalize_image=True):
return VGGPreprocessing.preprocess_image(
image=image,
output_height=cls.INPUT_SHAPE[0],
output_width=cls.INPUT_SHAPE[1],
mean_center_image=normalize_image,
is_training=is_training)
def _resize_for_compression(self, _input_image, height, width):
""" resize image as in VGG preprocessing
args:
_input_image: 3-D tensor
returns:
resized image
"""
if self._dataset_name == 'kodak':
_input_image.set_shape([height, width, 3])
return _input_image
else:
return VGGPreprocessing.preprocess_image(image=_input_image,
output_height=height,
output_width=width,
mean_center_image=False)
def standardize_tensor(input_tensor):
return VGGPreprocessing.mean_image_subtraction(input_tensor)