# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function
import numpy as np
import tensorflow as tf
from niftynet.layer.base_layer import Layer, Invertible
[docs]class PadLayer(Layer, Invertible):
"""
This class defines a padding operation:
pad `2*border` pixels from spatial dims of the input (numpy array),
and return the padded input.
This function is used at volume level (as a preprocessor in image reader)
therefore assumes the input has at least three spatial dims.
"""
def __init__(self, image_name, border, name='pad', mode='minimum'):
super(PadLayer, self).__init__(name=name)
try:
spatial_border = tuple(map(lambda x: (x,), border))
except (ValueError, TypeError):
tf.logging.fatal("unknown padding param. {}".format(border))
raise
self.border = spatial_border
self.image_name = set(image_name)
self.mode = mode
[docs] def layer_op(self, input_image, mask=None):
if not isinstance(input_image, dict):
full_border = match_ndim(self.border, input_image.ndim)
input_image = np.pad(input_image, full_border, mode=self.mode)
return input_image, mask
for name, image in input_image.items():
if name not in self.image_name:
tf.logging.warning(
'could not pad, dict name %s not in %s',
name, self.image_name)
continue
full_border = match_ndim(self.border, image.ndim)
input_image[name] = np.pad(image, full_border, mode=self.mode)
return input_image, mask
[docs] def inverse_op(self, input_image, mask=None):
if not isinstance(input_image, dict):
full_border = match_ndim(self.border, input_image.ndim)
outputs = _crop_numpy_array(input_image, full_border)
return outputs, mask
for name, image in input_image.items():
if name not in self.image_name:
continue
full_border = match_ndim(self.border, image.ndim)
input_image[name] = _crop_numpy_array(image, full_border)
return input_image, mask
def _crop_numpy_array(image, border):
try:
assert image.ndim >= 3, \
"input image must have at least 3 spatial dims"
x_ = border[0][0] if image.shape[0] / 2 > border[0][0] > 0 else 0
y_ = border[1][0] if image.shape[1] / 2 > border[1][0] > 0 else 0
z_ = border[2][0] if image.shape[2] / 2 > border[2][0] > 0 else 0
_x = -border[0][0] if image.shape[0] / 2 > border[0][0] > 0 \
else image.shape[0]
_y = -border[1][0] if image.shape[1] / 2 > border[1][0] > 0 \
else image.shape[1]
_z = -border[2][0] if image.shape[2] / 2 > border[2][0] > 0 \
else image.shape[2]
return image[x_:_x, y_:_y, z_:_z, ...]
except (IndexError, AssertionError):
tf.logging.fatal(
"unable to inverse the padding "
"input: {}, pad param. {}".format(
image.shape, border))
raise
[docs]def match_ndim(border, image_ndim):
full_border = border
while len(full_border) < image_ndim:
full_border = full_border + ((0,),)
return full_border[:image_ndim]
