Method __call__

monai/transforms/utility/array.py:1113–1144

Args: img: the image that we want to add new channel to. label: label image to get extreme points from. Shape must be (1, spatial_dim1, [, spatial_dim2, ...]). Doesn't support one-hot labels. sigma: if a list of values, must match the coun

(
        self,
        img: NdarrayOrTensor,
        label: NdarrayOrTensor | None = None,
        sigma: Sequence[float] | float | Sequence[torch.Tensor] | torch.Tensor = 3.0,
        rescale_min: float = -1.0,
        rescale_max: float = 1.0,
    )

        self._points = get_extreme_points(label, rand_state=self.R, background=self._background, pert=self._pert)

    def __call__(
        self,
        img: NdarrayOrTensor,
        label: NdarrayOrTensor | None = None,
        sigma: Sequence[float] | float | Sequence[torch.Tensor] | torch.Tensor = 3.0,
        rescale_min: float = -1.0,
        rescale_max: float = 1.0,
    ) -> NdarrayOrTensor:
        """
        Args:
            img: the image that we want to add new channel to.
            label: label image to get extreme points from. Shape must be
                (1, spatial_dim1, [, spatial_dim2, ...]). Doesn't support one-hot labels.
            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.
            rescale_min: minimum value of output data.
            rescale_max: maximum value of output data.
        """
        if label is None:
            raise ValueError("This transform requires a label array!")
        if label.shape[0] != 1:
            raise ValueError("Only supports single channel labels!")

        # Generate extreme points
        self.randomize(label[0, :])

        points_image = extreme_points_to_image(
            points=self._points, label=label, sigma=sigma, rescale_min=rescale_min, rescale_max=rescale_max
        )
        points_image, *_ = convert_to_dst_type(points_image, img)  # type: ignore
        return concatenate((img, points_image), axis=0)


class TorchVision(Transform):