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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

CropOrPad instead.

__call__(data)

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data InputType

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

get_base_args()

Provides easy access to the arguments used to instantiate the base class (Transform) of any transform.

This method is particularly useful when a new transform can be represented as a variant of an existing transform (e.g. all random transforms), allowing for seamless instantiation of the existing transform with the same arguments as the new transform during apply_transform.

Note

The p argument (probability of applying the transform) is excluded to avoid multiplying the probability of both existing and new transform.

add_base_args(arguments, overwrite_on_existing=False)

Add the init args to existing arguments

validate_keys_sequence(keys, name) staticmethod

Ensure that the input is not a string but a sequence of strings.

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.