Method __init__

monai/transforms/post/array.py:293–325

Args: applied_labels: Labels for applying the connected component analysis on. If given, voxels whose value is in this list will be analyzed. If `None`, all non-zero values will be analyzed. is_onehot: if `True`, treat the input data a

(
        self,
        applied_labels: Sequence[int] | int | None = None,
        is_onehot: bool | None = None,
        independent: bool = True,
        connectivity: int | None = None,
        num_components: int = 1,
    )

    backend = [TransformBackends.NUMPY, TransformBackends.CUPY]

    def __init__(
        self,
        applied_labels: Sequence[int] | int | None = None,
        is_onehot: bool | None = None,
        independent: bool = True,
        connectivity: int | None = None,
        num_components: int = 1,
    ) -> None:
        """
        Args:
            applied_labels: Labels for applying the connected component analysis on.
                If given, voxels whose value is in this list will be analyzed.
                If `None`, all non-zero values will be analyzed.
            is_onehot: if `True`, treat the input data as OneHot format data, otherwise, not OneHot format data.
                default to None, which treats multi-channel data as OneHot and single channel data as not OneHot.
            independent: whether to treat ``applied_labels`` as a union of foreground labels.
                If ``True``, the connected component analysis will be performed on each foreground label independently
                and return the intersection of the largest components.
                If ``False``, the analysis will be performed on the union of foreground labels.
                default is `True`.
            connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.
                Accepted values are ranging from  1 to input.ndim. If ``None``, a full
                connectivity of ``input.ndim`` is used. for more details:
                https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.label.
            num_components: The number of largest components to preserve.

        """
        super().__init__()
        self.applied_labels = ensure_tuple(applied_labels) if applied_labels is not None else None
        self.is_onehot = is_onehot
        self.independent = independent
        self.connectivity = connectivity
        self.num_components = num_components

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """