Crop

`Crop`

Bases: BoundsTransform

Crop an image.

Parameters:

Name	Type	Description	Default
`cropping`	`TypeBounds`	Tuple \((w_{ini}, w_{fin}, h_{ini}, h_{fin}, d_{ini}, d_{fin})\) defining the number of values cropped from 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
`copy`		If `True`, each image will be cropped and the patch copied to a new subject. If `False`, each image will be cropped in place. This transform overwrites the copy argument of the base transform and copies only the cropped patch instead of the whole image. This can provide a significant speedup when cropping small patches from large images.	`True`
`**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.