Class GaussianSmooth

monai/transforms/intensity/array.py:1614–1646

Apply Gaussian smooth to the input data based on specified `sigma` parameter. A default value `sigma=1.0` is provided for reference. Args: 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 sp

class GaussianSmooth(Transform):
    """
    Apply Gaussian smooth to the input data based on specified `sigma` parameter.
    A default value `sigma=1.0` is provided for reference.

    Args:
        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`.

    """

    backend = [TransformBackends.TORCH]

    def __init__(self, sigma: Sequence[float] | float = 1.0, approx: str = "erf") -> None:
        self.sigma = sigma
        self.approx = approx

    def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)
        sigma: Sequence[torch.Tensor] | torch.Tensor
        if isinstance(self.sigma, Sequence):
            sigma = [torch.as_tensor(s, device=img_t.device) for s in self.sigma]
        else:
            sigma = torch.as_tensor(self.sigma, device=img_t.device)
        gaussian_filter = GaussianFilter(img_t.ndim - 1, sigma, approx=self.approx)
        out_t: torch.Tensor = gaussian_filter(img_t.unsqueeze(0)).squeeze(0)
        out, *_ = convert_to_dst_type(out_t, dst=img, dtype=out_t.dtype)

        return out


class RandGaussianSmooth(RandomizableTransform):