Method __call__

monai/transforms/utility/array.py:921–947

Args: select_labels: labels to generate mask from. for 1 channel label, the `select_labels` is the expected label values, like: [1, 2, 3]. for One-Hot format label, the `select_labels` is the expected channel indices. merge_channels: w

(
        self, img: NdarrayOrTensor, select_labels: Sequence[int] | int | None = None, merge_channels: bool = False
    )

        self.merge_channels = merge_channels

    def __call__(
        self, img: NdarrayOrTensor, select_labels: Sequence[int] | int | None = None, merge_channels: bool = False
    ) -> NdarrayOrTensor:
        """
        Args:
            select_labels: labels to generate mask from. for 1 channel label, the `select_labels`
                is the expected label values, like: [1, 2, 3]. for One-Hot format label, the
                `select_labels` is the expected channel indices.
            merge_channels: whether to use `np.any()` to merge the result on channel dim. if yes,
                will return a single channel mask with binary data.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if select_labels is None:
            select_labels = self.select_labels
        else:
            select_labels = ensure_tuple(select_labels)

        if img.shape[0] > 1:
            data = img[[*select_labels]]
        else:
            where: Callable = np.where if isinstance(img, np.ndarray) else torch.where  # type: ignore
            data = where(in1d(img, select_labels), True, False).reshape(img.shape)

        if merge_channels or self.merge_channels:
            return data.any(0)[None]  # type: ignore

        return data


class FgBgToIndices(Transform, MultiSampleTrait):