Function get_spatial_dims

monai/data/box_utils.py:365–454

Get spatial dimension for the giving setting and check the validity of them. Missing input is allowed. But at least one of the input value should be given. It raises ValueError if the dimensions of multiple inputs do not match with each other. Args: boxes: bounding boxes, N

(
    boxes: torch.Tensor | np.ndarray | None = None,
    points: torch.Tensor | np.ndarray | None = None,
    corners: Sequence | None = None,
    spatial_size: Sequence[int] | torch.Tensor | np.ndarray | None = None,
)

def get_spatial_dims(
    boxes: torch.Tensor | np.ndarray | None = None,
    points: torch.Tensor | np.ndarray | None = None,
    corners: Sequence | None = None,
    spatial_size: Sequence[int] | torch.Tensor | np.ndarray | None = None,
) -> int:
    """
    Get spatial dimension for the giving setting and check the validity of them.
    Missing input is allowed. But at least one of the input value should be given.
    It raises ValueError if the dimensions of multiple inputs do not match with each other.

    Args:
        boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray
        points: point coordinates, [x, y] or [x, y, z], Nx2 or Nx3 torch tensor or ndarray
        corners: corners of boxes, 4-element or 6-element tuple, each element is a Nx1 torch tensor or ndarray
        spatial_size: The spatial size of the image where the boxes are attached.
                len(spatial_size) should be in [2, 3].

    Returns:
        ``int``: spatial_dims, number of spatial dimensions of the bounding boxes.

    Example:
        .. code-block:: python

            boxes = torch.ones(10,6)
            get_spatial_dims(boxes, spatial_size=[100,200,200]) # will return 3
            get_spatial_dims(boxes, spatial_size=[100,200]) # will raise ValueError
            get_spatial_dims(boxes) # will return 3
    """
    spatial_dims_set = set()

    # Check the validity of each input and add its corresponding spatial_dims to spatial_dims_set
    if boxes is not None:
        if len(boxes.shape) != 2:
            if boxes.shape[0] == 0:
                raise ValueError(
                    f"Currently we support only boxes with shape [N,4] or [N,6], "
                    f"got boxes with shape {boxes.shape}. "
                    f"Please reshape it with boxes = torch.reshape(boxes, [0, 4]) or torch.reshape(boxes, [0, 6])."
                )
            else:
                raise ValueError(
                    f"Currently we support only boxes with shape [N,4] or [N,6], got boxes with shape {boxes.shape}."
                )
        if int(boxes.shape[1] / 2) not in SUPPORTED_SPATIAL_DIMS:
            raise ValueError(
                f"Currently we support only boxes with shape [N,4] or [N,6], got boxes with shape {boxes.shape}."
            )
        spatial_dims_set.add(int(boxes.shape[1] / 2))
    if points is not None:
        if len(points.shape) != 2:
            if points.shape[0] == 0:
                raise ValueError(
                    f"Currently we support only points with shape [N,2] or [N,3], "
                    f"got points with shape {points.shape}. "
                    f"Please reshape it with points = torch.reshape(points, [0, 2]) or torch.reshape(points, [0, 3])."
                )
            else: