Function box_area

monai/data/box_utils.py:740–779

This function computes the area (2D) or volume (3D) of each box. Half precision is not recommended for this function as it may cause overflow, especially for 3D images. Args: boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode

(boxes: NdarrayOrTensor)

def box_area(boxes: NdarrayOrTensor) -> NdarrayOrTensor:
    """
    This function computes the area (2D) or volume (3D) of each box.
    Half precision is not recommended for this function as it may cause overflow, especially for 3D images.

    Args:
        boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``

    Returns:
        area (2D) or volume (3D) of boxes, with size of (N,).

    Example:
        .. code-block:: python

            boxes = torch.ones(10,6)
            # we do computation with torch.float32 to avoid overflow
            compute_dtype = torch.float32
            area = box_area(boxes=boxes.to(dtype=compute_dtype))  # torch.float32, size of (10,)
    """

    if not is_valid_box_values(boxes):
        raise ValueError("Given boxes has invalid values. The box size must be non-negative.")

    spatial_dims = get_spatial_dims(boxes=boxes)

    area = boxes[:, spatial_dims] - boxes[:, 0] + TO_REMOVE
    for axis in range(1, spatial_dims):
        area = area * (boxes[:, axis + spatial_dims] - boxes[:, axis] + TO_REMOVE)

    # convert numpy to tensor if needed
    area_t, *_ = convert_data_type(area, torch.Tensor)

    # check if NaN or Inf, especially for half precision
    if area_t.isnan().any() or area_t.isinf().any():
        if area_t.dtype is torch.float16:
            raise ValueError("Box area is NaN or Inf. boxes is float16. Please change to float32 and test it again.")
        else:
            raise ValueError("Box area is NaN or Inf.")

    return area


def _box_inter_union(