def _get_pad(self, image: np.ndarray) -> Transform:
# Compute the image scale and scaled size.
input_size = image.shape[:2]
output_size = self.crop_size
# Add padding if the image is scaled down.
pad_size = np.subtract(output_size, input_size)
pad_size = np.maximum(pad_size, 0)
original_size = np.minimum(input_size, output_size)
return PadTransform(0, 0, pad_size[1], pad_size[0], original_size[1],
original_size[0], self.pad_value)
def get_transform(self, image: np.ndarray) -> TransformList:
# Compute the image scale and scaled size.
input_size = image.shape[:2]
output_size = self.crop_size
# Add random crop if the image is scaled up.
max_offset = np.subtract(input_size, output_size)
max_offset = np.maximum(max_offset, 0)
offset = np.multiply(max_offset, np.random.uniform(0.0, 1.0))
offset = np.round(offset).astype(int)
crop_transform = CropTransform(offset[1], offset[0], output_size[1],
output_size[0], input_size[1],
input_size[0])
# Add padding if the image is scaled down.
pad_size = np.subtract(output_size, input_size)
pad_size = np.maximum(pad_size, 0)
original_size = np.minimum(input_size, output_size)
pad_transform = PadTransform(0, 0, pad_size[1], pad_size[0],
original_size[1], original_size[0],
self.pad_value)
return TransformList([crop_transform, pad_transform])