Method __call__

monai/apps/detection/transforms/array.py:515–543

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 Returns: - cropped boxes, does not share memory wit

(  # type: ignore[override]
        self, boxes: NdarrayTensor, labels: Sequence[NdarrayOrTensor] | NdarrayOrTensor
    )

                raise ValueError("Currently negative indexing is not supported for SpatialCropBox.")

    def __call__(  # type: ignore[override]
        self, boxes: NdarrayTensor, labels: Sequence[NdarrayOrTensor] | NdarrayOrTensor
    ) -> tuple[NdarrayTensor, 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

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

        Example:
            .. code-block:: python

                box_cropper = SpatialCropPadBox(roi_start=[0, 1, 4], roi_end=[21, 15, 8])
                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)
                boxes_crop, labels_crop_tuple = box_cropper(boxes, labels)
        """
        spatial_dims = min(len(self.slices), get_spatial_dims(boxes=boxes))  # spatial dims
        boxes_crop, keep = spatial_crop_boxes(
            boxes,
            [self.slices[axis].start for axis in range(spatial_dims)],
            [self.slices[axis].stop for axis in range(spatial_dims)],
        )
        return boxes_crop, select_labels(labels, keep)


class RotateBox90(Rotate90):