Pad

`Pad`

Bases: BoundsTransform

Pad an image.

Parameters:

Name	Type	Description	Default
`padding`	`TypeBounds`	Tuple \((w_{ini}, w_{fin}, h_{ini}, h_{fin}, d_{ini}, d_{fin})\) defining the number of values padded to the edges of each axis. If the initial shape of the image is \(W \times H \times D\), the final shape will be \((w_{ini} + W + w_{fin}) \times (h_{ini} + H + h_{fin}) \times (d_{ini} + D + d_{fin})\). If only three values \((w, h, d)\) are provided, then \(w_{ini} = w_{fin} = w\), \(h_{ini} = h_{fin} = h\) and \(d_{ini} = d_{fin} = d\). If only one value \(n\) is provided, then \(w_{ini} = w_{fin} = h_{ini} = h_{fin} = d_{ini} = d_{fin} = n\).	required
`padding_mode`	`str \| float`	See possible modes in NumPy docs. If it is a number, the mode will be set to `'constant'`. If it is `'mean'`, `'maximum'`, `'median'` or `'minimum'`, the statistic will be computed from the whole volume, unlike in NumPy, which computes it along the padded axis.	`0`
`**kwargs`		See `Transform` for additional keyword arguments.	`{}`

If you want to pass the output shape instead, please use

__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

Return a dictionary representation of the transform for Hydra instantiation.