niftynet.engine.image_window module

This module provides an interface for data elements passed from sampler to network.

class niftynet.engine.image_window.ImageWindow(names, shapes, dtypes)

Bases: object

Each window is associated with a tuple of coordinates. These data properties are used to create TF placeholders when constructing a TF graph. Samplers read the data specifications and fill the placeholder with data.

coordinates_placeholder(name)

get coordinates placeholder, location name is formed using LOCATION_FORMAT

Parameters:name – input name string
Returns:coordinates placeholder
classmethod from_data_reader_properties(source_names, image_shapes, image_dtypes, data_param)

Create a window instance with input data properties each property is grouped into dict, with pairs of image_name: data_value. Some input images is a concatenated data array from multiple data sources. example of input: source_names={

‘image’: (u’modality1’, u’modality2’), ‘label’: (u’modality3’,)},
image_shapes={
‘image’: (192, 160, 192, 1, 2), ‘label’: (192, 160, 192, 1, 1)},
image_dtypes={
‘image’: tf.float32, ‘label’: tf.float32},
data_param={
‘modality1’: ParserNamespace(spatial_window_size=(10, 10, 2)), ‘modality2’: ParserNamespace(spatial_window_size=(10, 10, 2)), ‘modality3’: ParserNamespace(spatial_window_size=(5, 5, 1))}

see niftynet.io.ImageReader for more details.

Parameters:
  • source_names – input image names
  • image_shapes – tuple of image window shapes
  • image_dtypes – tuple of image window data types
  • data_param – dict of each input source specifications
Returns:

an ImageWindow instance
image_data_placeholder(name)

get the image data placeholder by name

Parameters:name – input name string
Returns:image placeholder
match_image_shapes(image_shapes)

if the window has dynamic shapes, this function infers the fully specified shape from the image_shapes

Parameters:image_shapes –
Returns:dict of fully specified window shapes
placeholders_dict(n_samples=1)

This function create a dictionary with items of {name: placeholders} name should match the queue input names placeholders corresponds to the image window data for each of these items an additional {location_name: placeholders} is created to hold the spatial location of the image window

Parameters:n_samples – specifies the number of image windows
Returns:a dictionary with window data and locations placeholders
set_spatial_shape(spatial_window)

overrides 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

Parameters:spatial_window – tuple of integers specifying new shape
Returns: