Source code for niftynet.engine.windows_aggregator_resize

# -*- 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 as misc_io
from niftynet.engine.sampler_resize_v2 import zoom_3d
from niftynet.engine.windows_aggregator_base import ImageWindowsAggregator
from niftynet.layer.discrete_label_normalisation import \
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, postfix='_niftynet_out'): ImageWindowsAggregator.__init__( self, image_reader=image_reader, output_path=output_path) = name self.window_border = window_border self.output_interp_order = interp_order self.postfix = postfix
[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). each network output window is first cropped to:: size=input_window_size - inference_border then resized to original_image_size + volume_padding_size then cropped by volume_padding_size then written to file """ 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[].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 = "{}{}.nii.gz".format(subject_name, self.postfix) source_image_obj = self.input_image[] misc_io.save_data_array(self.output_path, filename, image_out, source_image_obj, self.output_interp_order) self.log_inferred(subject_name, filename) return