Method __call__

monai/apps/detection/transforms/array.py:365–397

Args: boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode`` labels: Sequence of array. Each element represents classification labels or scores corresponding to ``boxes``, sized (N,). spat

(  # type: ignore
        self,
        boxes: NdarrayOrTensor,
        labels: Sequence[NdarrayOrTensor] | NdarrayOrTensor,
        spatial_size: Sequence[int] | int,
    )

        self.remove_empty = remove_empty

    def __call__(  # type: ignore
        self,
        boxes: NdarrayOrTensor,
        labels: Sequence[NdarrayOrTensor] | NdarrayOrTensor,
        spatial_size: Sequence[int] | int,
    ) -> tuple[NdarrayOrTensor, tuple | NdarrayOrTensor]:
        """
        Args:
            boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
            labels: Sequence of array. Each element represents classification labels or scores
                corresponding to ``boxes``, sized (N,).
            spatial_size: The spatial size of the image where the boxes are attached. len(spatial_size) should be in [2, 3].

        Returns:
            - clipped boxes, does not share memory with original boxes
            - clipped labels, does not share memory with original labels

        Example:
            .. code-block:: python

                box_clipper = ClipBoxToImage(remove_empty=True)
                boxes = torch.ones(2, 6)
                class_labels = torch.Tensor([0, 1])
                pred_scores = torch.Tensor([[0.4,0.3,0.3], [0.5,0.1,0.4]])
                labels = (class_labels, pred_scores)
                spatial_size = [32, 32, 32]
                boxes_clip, labels_clip_tuple = box_clipper(boxes, labels, spatial_size)
        """
        spatial_dims: int = get_spatial_dims(boxes=boxes)
        spatial_size = ensure_tuple_rep(spatial_size, spatial_dims)  # match the spatial image dim

        boxes_clip, keep = clip_boxes_to_image(boxes, spatial_size, self.remove_empty)
        return boxes_clip, select_labels(labels, keep)


class BoxToMask(Transform):