def warp_image(self, input_):
if input_ is None:
input_ = self.image_moving
return util.resample_linear(input_, self.grid_warped)
def warp_volumes_by_ddf(input_, ddf):
grid_warped = util.get_reference_grid(ddf.shape[1:4]) + ddf
warped = util.resample_linear(tf.convert_to_tensor(input_, dtype=tf.float32), grid_warped)
with tf.Session() as sess:
return sess.run(warped)
def warp_image(self, image, ddf):
return util.resample_linear(image, ddf)
def warp_FIX_image(self, input_):
return util.resample_linear(input_, self.grid_warped_FIX_MV)
