# -*- coding: utf-8 -*-
"""
Windows aggregator resize each item
in a batch output and save as an image.
"""
from __future__ import absolute_import, print_function, division
import os
import numpy as np
import niftynet.io.misc_io as misc_io
from niftynet.engine.sampler_resize import zoom_3d
from niftynet.engine.windows_aggregator_base import ImageWindowsAggregator
from niftynet.layer.discrete_label_normalisation import \
DiscreteLabelNormalisationLayer
from niftynet.layer.pad import PadLayer
[docs]class ResizeSamplesAggregator(ImageWindowsAggregator):
"""
This class decodes each item in a batch by resizing each image
window and save as a new image volume.
"""
def __init__(self,
image_reader,
name='image',
output_path=os.path.join('.', 'output'),
window_border=(),
interp_order=0):
ImageWindowsAggregator.__init__(self, image_reader=image_reader)
self.name = name
self.output_path = os.path.abspath(output_path)
self.window_border = window_border
self.output_interp_order = interp_order
[docs] def decode_batch(self, window, location):
"""
Resizing each output image window in the batch as an image volume
location specifies the original input image (so that the
interpolation order, original shape information retained in the
generated outputs).
"""
n_samples = location.shape[0]
window, location = self.crop_batch(window, location, self.window_border)
for batch_id in range(n_samples):
if self._is_stopping_signal(location[batch_id]):
return False
self.image_id = location[batch_id, 0]
resize_to_shape = self._initialise_image_shape(
image_id=self.image_id,
n_channels=window.shape[-1])
self._save_current_image(window[batch_id, ...], resize_to_shape)
return True
def _initialise_image_shape(self, image_id, n_channels):
self.image_id = image_id
spatial_shape = self.input_image[self.name].shape[:3]
output_image_shape = spatial_shape + (1, n_channels,)
empty_image = np.zeros(output_image_shape, dtype=np.bool)
for layer in self.reader.preprocessors:
if isinstance(layer, PadLayer):
empty_image, _ = layer(empty_image)
return empty_image.shape
def _save_current_image(self, image_out, resize_to):
if self.input_image is None:
return
window_shape = resize_to
while image_out.ndim < 5:
image_out = image_out[..., np.newaxis, :]
if self.window_border and any([b > 0 for b in self.window_border]):
np_border = self.window_border
while len(np_border) < 5:
np_border = np_border + (0,)
np_border = [(b,) for b in np_border]
image_out = np.pad(image_out, np_border, mode='edge')
image_shape = image_out.shape
zoom_ratio = \
[float(p) / float(d) for p, d in zip(window_shape, image_shape)]
image_shape = list(image_shape[:3]) + [1, image_shape[-1]]
image_out = np.reshape(image_out, image_shape)
image_out = zoom_3d(image=image_out,
ratio=zoom_ratio,
interp_order=self.output_interp_order)
for layer in reversed(self.reader.preprocessors):
if isinstance(layer, PadLayer):
image_out, _ = layer.inverse_op(image_out)
if isinstance(layer, DiscreteLabelNormalisationLayer):
image_out, _ = layer.inverse_op(image_out)
subject_name = self.reader.get_subject_id(self.image_id)
filename = "{}_niftynet_out.nii.gz".format(subject_name)
source_image_obj = self.input_image[self.name]
misc_io.save_data_array(self.output_path,
filename,
image_out,
source_image_obj,
self.output_interp_order)
return
