Method __call__

monai/apps/detection/transforms/array.py:293–320

Args: boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode`` src_spatial_size: original image spatial size before resizing. Raises: ValueError: When ``self.spatial_size`` length is less th

(self, boxes: NdarrayOrTensor, src_spatial_size: Sequence[int] | int)

        self.spatial_size = spatial_size

    def __call__(self, boxes: NdarrayOrTensor, src_spatial_size: Sequence[int] | int) -> NdarrayOrTensor:  # type: ignore[override]
        """
        Args:
            boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
            src_spatial_size: original image spatial size before resizing.

        Raises:
            ValueError: When ``self.spatial_size`` length is less than ``boxes`` spatial dimensions.
        """
        input_ndim = get_spatial_dims(boxes=boxes)  # spatial ndim
        src_spatial_size_ = ensure_tuple_rep(src_spatial_size, input_ndim)

        if self.size_mode == "all":
            # spatial_size must be a Sequence if size_mode is 'all'
            output_ndim = len(ensure_tuple(self.spatial_size))
            if output_ndim != input_ndim:
                raise ValueError(
                    "len(spatial_size) must be greater or equal to img spatial dimensions, "
                    f"got spatial_size={output_ndim} img={input_ndim}."
                )
            spatial_size_ = fall_back_tuple(self.spatial_size, src_spatial_size_)
        else:  # for the "longest" mode
            if not isinstance(self.spatial_size, int):
                raise ValueError("spatial_size must be an int number if size_mode is 'longest'.")
            scale = self.spatial_size / max(src_spatial_size_)
            spatial_size_ = tuple(int(round(s * scale)) for s in src_spatial_size_)

        return resize_boxes(boxes, src_spatial_size_, spatial_size_)


class FlipBox(Transform):