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RandomBlur

RandomBlur

Bases: RandomTransform, IntensityTransform

Blur an image using a random-sized Gaussian filter.

Parameters:

Name Type Description Default
std float | tuple[float, float]

Tuple \((a_1, b_1, a_2, b_2, a_3, b_3)\) representing the ranges (in mm) of the standard deviations \((\sigma_1, \sigma_2, \sigma_3)\) of the Gaussian kernels used to blur the image along each axis, where \(\sigma_i \sim \mathcal{U}(a_i, b_i)\). If two values \((a, b)\) are provided, then \(\sigma_i \sim \mathcal{U}(a, b)\). If only one value \(x\) is provided, then \(\sigma_i \sim \mathcal{U}(0, x)\). If three values \((x_1, x_2, x_3)\) are provided, then \(\sigma_i \sim \mathcal{U}(0, x_i)\).

 (0, 2)
**kwargs

See Transform for additional keyword arguments.

 {}

__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.

arguments_are_dict()

Check if main arguments are dict.

Return True if the type of all attributes specified in the args_names have dict type.