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Transpose

Transpose

Bases: SpatialTransform

Swap the first and last spatial dimensions of the image.

The spatial metadata is updated accordingly, so the world coordinates of all voxels in the input and output spaces match.

Examples:

>>> import torchio as tio
>>> image = tio.datasets.FPG().t1
>>> image
ScalarImage(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; ...)
>>> transposed = tio.Transpose()(image)
>>> transposed
ScalarImage(shape: (1, 176, 256, 256); spacing: (1.00, 1.00, 1.00); orientation: RIP+; ...)

__call__(data) 

__call__(data: Subject) -> Subject

__call__(data: ImageT) -> ImageT

__call__(data: torch.Tensor) -> torch.Tensor

__call__(data: np.ndarray) -> np.ndarray

__call__(data: sitk.Image) -> sitk.Image

__call__(data: dict[str, object]) -> dict[str, object]

__call__(data: nib.Nifti1Image) -> nib.Nifti1Image

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data TypeTransformInput

Instance of torchio.Subject, 4D torch.Tensor or numpy.ndarray with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a torchio.Image, a NiBabel Nifti1 image or a dict. The output type is the same as the input type.

 required

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.