# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function
import tensorflow as tf
from niftynet.layer.base_layer import Layer
from niftynet.layer.layer_util import expand_spatial_params
from niftynet.layer.layer_util import infer_spatial_rank
[docs]class LinearResizeLayer(Layer):
"""
Resize 2D/3D images using ``tf.image.resize_bilinear``
(without trainable parameters).
"""
[docs] def __init__(self, new_size, name='trilinear_resize'):
"""
:param new_size: integer or a list of integers set the output
2D/3D spatial shape. If the parameter is an integer ``d``,
it'll be expanded to ``(d, d)`` and ``(d, d, d)`` for 2D and
3D inputs respectively.
:param name: layer name string
"""
super(LinearResizeLayer, self).__init__(name=name)
self.new_size = new_size
[docs] def layer_op(self, input_tensor):
"""
Resize the image by linearly interpolating the input
using TF ``resize_bilinear`` function.
:param input_tensor: 2D/3D image tensor, with shape:
``batch, X, Y, [Z,] Channels``
:return: interpolated volume
"""
input_spatial_rank = infer_spatial_rank(input_tensor)
assert input_spatial_rank in (2, 3), \
"linearly interpolation layer can only be applied to " \
"2D/3D images (4D or 5D tensor)."
self.new_size = expand_spatial_params(self.new_size, input_spatial_rank)
if input_spatial_rank == 2:
return tf.image.resize_bilinear(input_tensor, self.new_size)
b_size, x_size, y_size, z_size, c_size = \
input_tensor.shape.as_list()
x_size_new, y_size_new, z_size_new = self.new_size
if (x_size == x_size_new) and (y_size == y_size_new) and (
z_size == z_size_new):
# already in the target shape
return input_tensor
# resize y-z
squeeze_b_x = tf.reshape(
input_tensor, [-1, y_size, z_size, c_size])
resize_b_x = tf.image.resize_bilinear(
squeeze_b_x, [y_size_new, z_size_new])
resume_b_x = tf.reshape(
resize_b_x, [b_size, x_size, y_size_new, z_size_new, c_size])
# resize x
# first reorient
reoriented = tf.transpose(resume_b_x, [0, 3, 2, 1, 4])
# squeeze and 2d resize
squeeze_b_z = tf.reshape(
reoriented, [-1, y_size_new, x_size, c_size])
resize_b_z = tf.image.resize_bilinear(
squeeze_b_z, [y_size_new, x_size_new])
resume_b_z = tf.reshape(
resize_b_z, [b_size, z_size_new, y_size_new, x_size_new, c_size])
output_tensor = tf.transpose(resume_b_z, [0, 3, 2, 1, 4])
return output_tensor
