niftynet.io.image_type module¶
This module defines images used by image reader, image properties are set by user or read from image header.
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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.
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dtype
¶ data type property of the input images.
Returns: a tuple of input image data types len(self.dtype) == len(self.file_path)
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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
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loader
¶ A tuple of valid image loaders. Always returns a tuple
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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
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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).
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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).
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original_shape
¶ Shape with multi-modal concatenation, before any resampling.
Returns: a tuple of integers as the original image shape
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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
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original_pixdim
¶ pixdim info from the image header.
Returns: a tuple of pixdims, with each element as pixdims of an image file
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original_affine
¶ affine info from the image header.
Returns: a tuple of affine, with each element as an affine matrix of an image file
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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
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interp_order
¶ interpolation order specified by user.
Returns: a tuple of integers, with each element as an interpolation order of an image file
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dtype
¶ data type property of the input images.
Returns: a tuple of input image data types len(self.dtype) == len(self.file_path)
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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
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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
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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).
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output_pixdim
¶
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output_axcodes
¶
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shape
¶
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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)
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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
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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)
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class
ImageFactory
[source]¶ Bases:
object
Create image instance according to number of dimensions specified in image headers.
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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'>}¶
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