Class GaussianSmoothd

monai/transforms/intensity/dictionary.py:1196–1228

Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSmooth`. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` sigma: if a list of values, must match the count of spatial dimens

class GaussianSmoothd(MapTransform):
    """
    Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSmooth`.

    Args:
        keys: keys of the corresponding items to be transformed.
            See also: :py:class:`monai.transforms.compose.MapTransform`
        sigma: if a list of values, must match the count of spatial dimensions of input data,
            and apply every value in the list to 1 spatial dimension. if only 1 value provided,
            use it for all spatial dimensions.
        approx: discrete Gaussian kernel type, available options are "erf", "sampled", and "scalespace".
            see also :py:meth:`monai.networks.layers.GaussianFilter`.
        allow_missing_keys: don't raise exception if key is missing.

    """

    backend = GaussianSmooth.backend

    def __init__(
        self,
        keys: KeysCollection,
        sigma: Sequence[float] | float,
        approx: str = "erf",
        allow_missing_keys: bool = False,
    ) -> None:
        super().__init__(keys, allow_missing_keys)
        self.converter = GaussianSmooth(sigma, approx=approx)

    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
        d = dict(data)
        for key in self.key_iterator(d):
            d[key] = self.converter(d[key])
        return d


class RandGaussianSmoothd(RandomizableTransform, MapTransform):