Function boxes_center_distance

monai/data/box_utils.py:680–720

Distance of center points between two sets of boxes Args: boxes1: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode`` boxes2: bounding boxes, Mx4 or Mx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``

(
    boxes1: NdarrayOrTensor, boxes2: NdarrayOrTensor, euclidean: bool = True
)

def boxes_center_distance(
    boxes1: NdarrayOrTensor, boxes2: NdarrayOrTensor, euclidean: bool = True
) -> tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor]:
    """
    Distance of center points between two sets of boxes

    Args:
        boxes1: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
        boxes2: bounding boxes, Mx4 or Mx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
        euclidean: computed the euclidean distance otherwise it uses the l1 distance

    Returns:
        - The pairwise distances for every element in boxes1 and boxes2,
          with size of (N,M) and same data type as ``boxes1``.
        - Center points of boxes1, with size of (N,spatial_dims) and same data type as ``boxes1``.
        - Center points of boxes2, with size of (M,spatial_dims) and same data type as ``boxes1``.

    Reference:
        https://github.com/MIC-DKFZ/nnDetection/blob/main/nndet/core/boxes/ops.py

    """

    if not isinstance(boxes1, type(boxes2)):
        warnings.warn(f"boxes1 is {type(boxes1)}, while boxes2 is {type(boxes2)}. The result will be {type(boxes1)}.")

    # convert numpy to tensor if needed
    boxes1_t, *_ = convert_data_type(boxes1, torch.Tensor)
    boxes2_t, *_ = convert_data_type(boxes2, torch.Tensor)

    center1 = box_centers(boxes1_t.to(COMPUTE_DTYPE))  # (N, spatial_dims)
    center2 = box_centers(boxes2_t.to(COMPUTE_DTYPE))  # (M, spatial_dims)

    if euclidean:
        dists = (center1[:, None] - center2[None]).pow(2).sum(-1).sqrt()  # type: ignore
    else:
        # before sum: (N, M, spatial_dims)
        dists = (center1[:, None] - center2[None]).sum(-1)

    # convert tensor back to numpy if needed
    (dists, center1, center2), *_ = convert_to_dst_type(src=(dists, center1, center2), dst=boxes1)
    return dists, center1, center2


def is_valid_box_values(boxes: NdarrayOrTensor) -> bool: