def swapaxes_boxes(boxes: NdarrayOrTensor, axis1: int, axis2: int):
"""
Interchange two axes of boxes.
Args:
boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
axis1: First axis.
axis2: Second axis.
Returns:
boxes with two axes interchanged.
"""
spatial_dims: int = get_spatial_dims(boxes=boxes)
if isinstance(boxes, torch.Tensor):
boxes_swap = boxes.clone()
else:
boxes_swap = deepcopy(boxes) # type: ignore
boxes_swap[:, [axis1, axis2]] = boxes_swap[:, [axis2,
axis1]] # type: ignore
boxes_swap[:, [spatial_dims + axis1, spatial_dims +
axis2]] = boxes_swap[ # type: ignore
:, [spatial_dims + axis2, spatial_dims + axis1]]
return boxes_swap
def flip_boxes(
boxes: NdarrayOrTensor,
spatial_size: Union[Sequence[int], int],
flip_axes: Optional[Union[Sequence[int], int]] = None,
):
"""
Flip boxes when the corresponding image is flipped
Args:
boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
spatial_size: image spatial size.
flip_axes: spatial axes along which to flip over. Default is None.
The default `axis=None` will flip over all of the axes of the input array.
If axis is negative it counts from the last to the first axis.
If axis is a tuple of ints, flipping is performed on all of the axes
specified in the tuple.
Returns:
flipped boxes, with same data type as ``boxes``, does not share memory with ``boxes``
"""
spatial_dims: int = get_spatial_dims(boxes=boxes)
spatial_size = ensure_tuple_rep(spatial_size, spatial_dims)
if flip_axes is None:
flip_axes = tuple(range(0, spatial_dims))
flip_axes = ensure_tuple(flip_axes)
# flip box
if isinstance(boxes, torch.Tensor):
_flip_boxes = boxes.clone()
else:
_flip_boxes = deepcopy(boxes) # type: ignore
for axis in flip_axes:
_flip_boxes[:, axis + spatial_dims] = spatial_size[
axis] - boxes[:, axis] - TO_REMOVE
_flip_boxes[:, axis] = spatial_size[
axis] - boxes[:, axis + spatial_dims] - TO_REMOVE
return _flip_boxes