Function convert_box_mode

monai/data/box_utils.py:546–603

This function converts the boxes in src_mode to the dst_mode. Args: boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. src_mode: source box mode. If it is not given, this func will assume it is ``StandardMode()``. It follows the same format with `

(
    boxes: NdarrayOrTensor,
    src_mode: str | BoxMode | type[BoxMode] | None = None,
    dst_mode: str | BoxMode | type[BoxMode] | None = None,
)

def convert_box_mode(
    boxes: NdarrayOrTensor,
    src_mode: str | BoxMode | type[BoxMode] | None = None,
    dst_mode: str | BoxMode | type[BoxMode] | None = None,
) -> NdarrayOrTensor:
    """
    This function converts the boxes in src_mode to the dst_mode.

    Args:
        boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray.
        src_mode: source box mode. If it is not given, this func will assume it is ``StandardMode()``.
            It follows the same format with ``mode`` in :func:`~monai.data.box_utils.get_boxmode`.
        dst_mode: target box mode. If it is not given, this func will assume it is ``StandardMode()``.
            It follows the same format with ``mode`` in :func:`~monai.data.box_utils.get_boxmode`.

    Returns:
        bounding boxes with target mode, with same data type as ``boxes``, does not share memory with ``boxes``

    Example:
        .. code-block:: python

            boxes = torch.ones(10,4)
            # The following three lines are equivalent
            # They convert boxes with format [xmin, ymin, xmax, ymax] to [xcenter, ycenter, xsize, ysize].
            convert_box_mode(boxes=boxes, src_mode="xyxy", dst_mode="ccwh")
            convert_box_mode(boxes=boxes, src_mode="xyxy", dst_mode=monai.data.box_utils.CenterSizeMode)
            convert_box_mode(boxes=boxes, src_mode="xyxy", dst_mode=monai.data.box_utils.CenterSizeMode())
    """
    # handle empty box
    if boxes.shape[0] == 0:
        return boxes

    src_boxmode = get_boxmode(src_mode)
    dst_boxmode = get_boxmode(dst_mode)

    # if mode not changed, deepcopy the original boxes
    if isinstance(src_boxmode, type(dst_boxmode)):
        return deepcopy(boxes)

    # convert box mode
    # convert numpy to tensor if needed
    boxes_t, *_ = convert_data_type(boxes, torch.Tensor)

    # convert boxes to corners
    corners = src_boxmode.boxes_to_corners(boxes_t)

    # check validity of corners
    spatial_dims = get_spatial_dims(boxes=boxes_t)
    for axis in range(spatial_dims):
        if (corners[spatial_dims + axis] < corners[axis]).sum() > 0:
            warnings.warn("Given boxes has invalid values. The box size must be non-negative.")

    # convert corners to boxes
    boxes_t_dst = dst_boxmode.corners_to_boxes(corners)

    # convert tensor back to numpy if needed
    boxes_dst, *_ = convert_to_dst_type(src=boxes_t_dst, dst=boxes)
    return boxes_dst