Method __call__

monai/transforms/utility/array.py:971–988

Args: label: input data to compute foreground and background indices. image: if image is not None, use ``label = 0 & image > image_threshold`` to define background. so the output items will not map to all the voxels in the label. output_sh

(
        self, label: NdarrayOrTensor, image: NdarrayOrTensor | None = None, output_shape: Sequence[int] | None = None
    )

        self.output_shape = output_shape

    def __call__(
        self, label: NdarrayOrTensor, image: NdarrayOrTensor | None = None, output_shape: Sequence[int] | None = None
    ) -> tuple[NdarrayOrTensor, NdarrayOrTensor]:
        """
        Args:
            label: input data to compute foreground and background indices.
            image: if image is not None, use ``label = 0 & image > image_threshold``
                to define background. so the output items will not map to all the voxels in the label.
            output_shape: expected shape of output indices. if None, use `self.output_shape` instead.

        """
        if output_shape is None:
            output_shape = self.output_shape
        fg_indices, bg_indices = map_binary_to_indices(label, image, self.image_threshold)
        if output_shape is not None:
            fg_indices = unravel_indices(fg_indices, output_shape)
            bg_indices = unravel_indices(bg_indices, output_shape)
        return fg_indices, bg_indices


class ClassesToIndices(Transform, MultiSampleTrait):