niftynet.io.image_type module¶

This module defines images used by image reader, image properties are set by user or read from image header.

class Loadable[source]¶

Bases: object

interface of loadable data

get_data()[source]¶: loads a numpy array from the image object if the array has less than 5 dimensions it extends the array to 5d (corresponding to 3 spatial dimensions, temporal dim, modalities) ndims > 5 not currently supported

class DataFromFile(file_path, name=('loadable_data', ), loader=None)[source]¶

Bases: niftynet.io.image_type.Loadable

Data from file should have a valid file path (are files on hard drive) and a name.

dtype¶

data type property of the input images.

Returns:	a tuple of input image data types `len(self.dtype) == len(self.file_path)`

file_path¶

A tuple of valid image filenames, this property always returns a tuple, length of the tuple is one for single image, length of the tuple is larger than one for single image from multiple files.

Returns:	a tuple of file paths

loader¶: A tuple of valid image loaders. Always returns a tuple

name¶

A tuple of image names, this property always returns a tuple, length of the tuple is one for single image, length of the tuple is larger than one for single image from multiple files.

Returns:	a tuple of image name tags

get_data()[source]¶: loads a numpy array from the image object if the array has less than 5 dimensions it extends the array to 5d (corresponding to 3 spatial dimensions, temporal dim, modalities) ndims > 5 not currently supported

class SpatialImage2D(file_path, name, interp_order, output_pixdim, output_axcodes, loader)[source]¶

Bases: niftynet.io.image_type.DataFromFile

2D images, axcodes specifications are ignored when loading. (Resampling to new pixdims is currently not supported).

spatial_rank¶

volume [x, y, 1, m, n] will have a spatial rank 2 volume [x, y, z, m, n] will have a spatial rank 3

if z > 1

(resampling/reorientation will not be done when spatial rank is 2).

shape¶

This function read image shape info from the headers The lengths in the fifth dim of multiple images are summed as a multi-mod representation. The fourth dim corresponding to different time sequences is ignored.

Returns:	a tuple of integers as image shape

original_pixdim¶

pixdim info from the image header.

Returns:	a tuple of pixdims, with each element as pixdims of an image file

original_affine¶

affine info from the image header.

Returns:	a tuple of affine, with each element as an affine matrix of an image file

original_axcodes¶

axcodes info from the image header more info: http://nipy.org/nibabel/image_orientation.html

Returns:	a tuple of axcodes, with each element as axcodes of an image file

interp_order¶

interpolation order specified by user.

Returns:	a tuple of integers, with each element as an interpolation order of an image file

dtype¶

data type property of the input images.

Returns:	a tuple of input image data types `len(self.dtype) == len(self.file_path)`

output_pixdim¶

output pixdim info specified by user set to None for using the original pixdim in image header otherwise get_data() transforms image array according to this value.

Returns:	a tuple of pixdims, with each element as pixdims of an image file

output_axcodes¶

output axcodes info specified by user set to None for using the original axcodes in image header, otherwise get_data() change axes of the image array according to this value.

Returns:	a tuple of pixdims, with each element as pixdims of an image file

get_data()[source]¶: loads a numpy array from the image object if the array has less than 5 dimensions it extends the array to 5d (corresponding to 3 spatial dimensions, temporal dim, modalities) ndims > 5 not currently supported

class SpatialImage3D(file_path, name, interp_order, output_pixdim, output_axcodes, loader)[source]¶

Bases: niftynet.io.image_type.SpatialImage2D

3D image from a single, supports resampling and reorientation (3D image from a set of 2D slices is currently not supported).

output_pixdim¶

output_axcodes¶

shape¶

class SpatialImage4D(file_path, name, interp_order, output_pixdim, output_axcodes, loader)[source]¶

Bases: niftynet.io.image_type.SpatialImage3D

4D image from a set of 3D volumes, supports resampling and reorientation.

The 3D volumes are concatenated in the fifth dim (modality dim)

spatial_rank¶

Inferring spatial rank from array shape.

In the case of concatenating M volumes of [x, y, 1] the outcome [x, y, 1, 1, M] will have a spatial rank 2 (resampling/reorientation will not be done in this case).

Returns:	an integer

get_data()[source]¶: loads a numpy array from the image object if the array has less than 5 dimensions it extends the array to 5d (corresponding to 3 spatial dimensions, temporal dim, modalities) ndims > 5 not currently supported

class SpatialImage5D(file_path, name, interp_order, output_pixdim, output_axcodes, loader)[source]¶

Bases: niftynet.io.image_type.SpatialImage4D

5D image from a single file, resampling and reorientation are implemented as operations on each 3D slice individually.

(5D image from a set of 4D files is currently not supported)

class ImageFactory[source]¶

Bases: object

Create image instance according to number of dimensions specified in image headers.

INSTANCE_DICT = {2: <class 'niftynet.io.image_type.SpatialImage2D'>, 3: <class 'niftynet.io.image_type.SpatialImage3D'>, 4: <class 'niftynet.io.image_type.SpatialImage4D'>, 5: <class 'niftynet.io.image_type.SpatialImage5D'>, 6: <class 'niftynet.io.image_type.SpatialImage5D'>}¶

classmethod create_instance(file_path, **kwargs)[source]¶

Read image headers and create image instance.

Parameters:	file_path – a file path or a sequence of file paths kwargs – output properties for transforming the image data array into a desired format
Returns:	an image instance