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 tensorflow as tf
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. Multiple output image can be proposed and csv output can be performed as well """ 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.image_out = None self.csv_out = None self.window_border = window_border self.output_interp_order = interp_order self.postfix = postfix self.current_out = {}
[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).For the fields that have the keyword 'window' in the dictionary key, it will be saved as image. The rest will be saved as csv. CSV files will contain at saving a first line of 0 (to be changed into the header by the user), the first column being the index of the window, followed by the list of output. """ n_samples = location.shape[0] location_init = np.copy(location) test = None for i in window: if 'window' in i: test = np.ones_like(window[i]) window[i], _ = self.crop_batch(window[i], location_init, self.window_border) location_init = np.copy(location) print(i, np.sum(window[i]), np.max(window[i])) _, location = self.crop_batch(test, location_init, 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] if self._is_stopping_signal(location[batch_id]): return False self.image_out = {} self.csv_out = {} for i in window: if 'window' in i: while window[i].ndim < 5: window[i] = window[i][..., np.newaxis, :] self.image_out[i] = window[i][batch_id, ...] else: if not isinstance(window[i], (list, tuple, np.ndarray)): window_loc = np.reshape(window[i], [1, 1]) self.csv_out[i] = self._initialise_empty_csv(1) else: window[i] = np.asarray(window[i]) try: assert window[i].ndim <= 2 except (TypeError, AssertionError): tf.logging.error( "The output you are trying to " "save as csv is more than " "bidimensional. Did you want " "to save an image instead? " "Put the keyword window " "in the output dictionary" " in your application file") if window[i].ndim < 2: window[i] = np.expand_dims(window[i], 0) window_loc = window[i] self.csv_out[i] = self._initialise_empty_csv( n_channel=window[i][0].shape[-1]) self.csv_out[i] = np.concatenate([self.csv_out[i], window_loc], 0) self._save_current_image() self._save_current_csv() return True
def _initialise_image_shape(self, image_id, n_channels): ''' Return the shape of the empty image to be saved :param image_id: index to find the appropriate input image from the reader :param n_channels: number of channels of the image :return: shape of the empty image ''' 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): ''' Loop through the dictionary of images output and resize and reverse the preprocessing prior to saving :return: ''' if self.input_image is None: return self.current_out = {} for i in self.image_out: resize_to_shape = self._initialise_image_shape( image_id=self.image_id, n_channels=self.image_out[i].shape[-1]) window_shape = resize_to_shape current_out = self.image_out[i] while current_out.ndim < 5: current_out = current_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] current_out = np.pad(current_out, np_border, mode='edge') image_shape = current_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]] print(np.sum(self.image_out[i]), " is sum of image out %s before" % i) current_out = np.reshape(current_out, image_shape) current_out = zoom_3d(image=current_out, ratio=zoom_ratio, interp_order=self.output_interp_order) self.current_out[i] = current_out for layer in reversed(self.reader.preprocessors): if isinstance(layer, PadLayer): for i in self.image_out: self.current_out[i], _ = layer.inverse_op( self.current_out[i]) if isinstance(layer, DiscreteLabelNormalisationLayer): for i in self.image_out: self.image_out[i], _ = layer.inverse_op(self.image_out[i]) subject_name = self.reader.get_subject_id(self.image_id) for i in self.image_out: print(np.sum(self.image_out[i]), " is sum of image out %s after" % i) for i in self.image_out: filename = "{}_{}_{}.nii.gz".format(i, subject_name, self.postfix) source_image_obj = self.input_image[] misc_io.save_data_array(self.output_path, filename, self.current_out[i], source_image_obj, self.output_interp_order) self.log_inferred(subject_name, filename) return def _save_current_csv(self): ''' Save all csv output present in the dictionary of csv_output. :return: ''' if self.input_image is None: return subject_name = self.reader.get_subject_id(self.image_id) for i in self.csv_out: filename = "{}_{}_{}.csv".format(i, subject_name, self.postfix) misc_io.save_csv_array(self.output_path, filename, self.csv_out[i]) self.log_inferred(subject_name, filename) return def _initialise_empty_csv(self, n_channel): ''' Initialise the array to be saved as csv as a line of zeros according to the number of elements to be saved :param n_channel: :return: ''' return np.zeros([1, n_channel])