Source code for niftynet.engine.sampler_resize_v2

# -*- coding: utf-8 -*-
Resize input image as output window.
from __future__ import absolute_import, print_function, division

import numpy as np
import scipy.ndimage
import tensorflow as tf

from niftynet.engine.image_window_dataset import ImageWindowDataset
from niftynet.engine.image_window import LOCATION_FORMAT

[docs]class ResizeSampler(ImageWindowDataset): """ This class generates samples by rescaling the whole image to the desired size Assuming the reader's output is 5d: ``Height x Width x Depth x time x Modality`` """ def __init__(self, reader, window_sizes, batch_size=1, spatial_window_size=None, windows_per_image=1, shuffle=True, queue_length=10, smaller_final_batch_mode='pad', name='resize_sampler_v2'):'reading size of preprocessed images') ImageWindowDataset.__init__( self, reader=reader, window_sizes=window_sizes, batch_size=batch_size, windows_per_image=windows_per_image, queue_length=queue_length, shuffle=shuffle, epoch=-1 if shuffle else 1, smaller_final_batch_mode=smaller_final_batch_mode, name=name) if spatial_window_size: # override all spatial window defined in input # modalities sections # this is useful when do inference with a spatial window # which is different from the training specifications self.window.set_spatial_shape(spatial_window_size)"initialised resize sampler %s ", self.window.shapes)
[docs] def layer_op(self, idx=None): """ This function generates sampling windows to the input buffer image data are from ``self.reader()``. It first completes window shapes based on image data, then resize each image as window and output a dictionary (required by input buffer) :return: output data dictionary ``{'image_modality': data_array}`` """ image_id, data, interp_orders = self.reader(idx=idx) image_shapes = \ dict((name, data[name].shape) for name in self.window.names) # window shapes can be dynamic, here they # are converted to static ones # as now we know the image shapes static_window_shapes = self.window.match_image_shapes(image_shapes) # for resize sampler the coordinates are not used # simply use the spatial dims of the input image output_dict = {} for name in list(data): # prepare output dictionary keys coordinates_key = LOCATION_FORMAT.format(name) image_data_key = name output_dict[coordinates_key] = self.dummy_coordinates( image_id, static_window_shapes[name], self.window.n_samples) image_array = [] for _ in range(self.window.n_samples): # prepare image data image_shape = image_shapes[name] window_shape = static_window_shapes[name] if image_shape == window_shape or interp_orders[name][0] < 0: # already in the same shape image_window = data[name] else: zoom_ratio = [float(p) / float(d) for p, d in zip(window_shape, image_shape)] image_window = zoom_3d(image=data[name], ratio=zoom_ratio, interp_order=interp_orders[name][0]) image_array.append(image_window[np.newaxis, ...]) if len(image_array) > 1: output_dict[image_data_key] = \ np.concatenate(image_array, axis=0) else: output_dict[image_data_key] = image_array[0] # the output image shape should be # [enqueue_batch_size, x, y, z, time, modality] # here enqueue_batch_size = 1 as we only have one sample # per image return output_dict
[docs]def zoom_3d(image, ratio, interp_order): """ Taking 5D image as input, and zoom each 3D slice independently """ assert image.ndim == 5, "input images should be 5D array" output = [] for time_pt in range(image.shape[3]): output_mod = [] for mod in range(image.shape[4]): zoomed = scipy.ndimage.zoom( image[..., time_pt, mod], ratio[:3], order=interp_order) output_mod.append(zoomed[..., np.newaxis, np.newaxis]) output.append(np.concatenate(output_mod, axis=-1)) return np.concatenate(output, axis=-2)