def warp_affine3d(
src: torch.Tensor,
M: torch.Tensor,
dsize: Tuple[int, int, int],
flags: str = 'bilinear',
padding_mode: str = 'zeros',
align_corners: bool = True,
) -> torch.Tensor:
r"""Apply a projective transformation a to 3d tensor.
.. warning::
This API signature it is experimental and might suffer some changes in the future.
Args:
src : input tensor of shape :math:`(B, C, D, H, W)`.
M: projective transformation matrix of shape :math:`(B, 3, 4)`.
dsize: size of the output image (depth, height, width).
mode: interpolation mode to calculate output values
``'bilinear'`` | ``'nearest'``.
padding_mode: padding mode for outside grid values
``'zeros'`` | ``'border'`` | ``'reflection'``.
align_corners : mode for grid_generation.
Returns:
torch.Tensor: the warped 3d tensor with shape :math:`(B, C, D, H, W)`.
.. note::
This function is often used in conjunction with :func:`get_perspective_transform3d`.
"""
if len(src.shape) != 5:
raise AssertionError(src.shape)
if not (len(M.shape) == 3 and M.shape[-2:] == (3, 4)):
raise AssertionError(M.shape)
if len(dsize) != 3:
raise AssertionError(dsize)
B, C, D, H, W = src.size()
size_src: Tuple[int, int, int] = (D, H, W)
size_out: Tuple[int, int, int] = dsize
M_4x4 = convert_affinematrix_to_homography3d(M) # Bx4x4
# we need to normalize the transformation since grid sample needs -1/1 coordinates
dst_norm_trans_src_norm: torch.Tensor = normalize_homography3d(
M_4x4, size_src, size_out) # Bx4x4
src_norm_trans_dst_norm = _torch_inverse_cast(dst_norm_trans_src_norm)
P_norm: torch.Tensor = src_norm_trans_dst_norm[:, :3] # Bx3x4
# compute meshgrid and apply to input
dsize_out: List[int] = [B, C] + list(size_out)
grid = torch.nn.functional.affine_grid(P_norm,
dsize_out,
align_corners=align_corners)
return torch.nn.functional.grid_sample(src,
grid,
align_corners=align_corners,
mode=flags,
padding_mode=padding_mode)
def normalize_homography3d(dst_pix_trans_src_pix: torch.Tensor,
dsize_src: Tuple[int, int, int],
dsize_dst: Tuple[int, int, int]) -> torch.Tensor:
__doc__ = HMW.normalize_homography3d.__doc__
warnings.warn(
"`normalize_homography3d` is deprecated and will be removed > 0.6.0. "
"Please use `kornia.geometry.transform.normalize_homography3d instead.`",
DeprecationWarning,
stacklevel=2)
return HMW.normalize_homography3d(dst_pix_trans_src_pix, dsize_src,
dsize_dst)
def warp_affine3d(src: torch.Tensor,
M: torch.Tensor,
dsize: Tuple[int, int, int],
flags: str = 'bilinear',
padding_mode: str = 'zeros',
align_corners: bool = True) -> torch.Tensor:
r"""Applies a projective transformation a to 3d tensor.
.. warning::
This API signature it is experimental and might suffer some changes in the future.
Args:
src (torch.Tensor): input tensor of shape :math:`(B, C, D, H, W)`.
M (torch.Tensor): projective transformation matrix of shape :math:`(B, 3, 4)`.
dsize (Tuple[int, int, int]): size of the output image (depth, height, width).
mode (str): interpolation mode to calculate output values
'bilinear' | 'nearest'. Default: 'bilinear'.
padding_mode (str): padding mode for outside grid values
'zeros' | 'border' | 'reflection'. Default: 'zeros'.
align_corners (bool): mode for grid_generation. Default: True.
Returns:
torch.Tensor: the warped 3d tensor with shape :math:`(B, C, D, H, W)`.
"""
assert len(src.shape) == 5, src.shape
assert len(M.shape) == 3 and M.shape[-2:] == (3, 4), M.shape
assert len(dsize) == 3, dsize
B, C, D, H, W = src.size()
size_src: Tuple[int, int, int] = (D, H, W)
size_out: Tuple[int, int, int] = dsize
M_4x4 = convert_affinematrix_to_homography3d(M) # Bx4x4
# we need to normalize the transformation since grid sample needs -1/1 coordinates
dst_norm_trans_src_norm: torch.Tensor = normalize_homography3d(
M_4x4, size_src, size_out) # Bx4x4
src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm)
P_norm: torch.Tensor = src_norm_trans_dst_norm[:, :3] # Bx3x4
# compute meshgrid and apply to input
dsize_out: List[int] = [B, C] + list(size_out)
grid = torch.nn.functional.affine_grid(P_norm,
dsize_out,
align_corners=align_corners)
return torch.nn.functional.grid_sample(src,
grid,
align_corners=align_corners,
mode=flags,
padding_mode=padding_mode)
def transform_warp_impl3d(
src: torch.Tensor,
dst_pix_trans_src_pix: torch.Tensor,
dsize_src: Tuple[int, int, int],
dsize_dst: Tuple[int, int, int],
grid_mode: str,
padding_mode: str,
align_corners: bool,
) -> torch.Tensor:
"""Compute the transform in normalized cooridnates and perform the warping."""
dst_norm_trans_src_norm: torch.Tensor = normalize_homography3d(dst_pix_trans_src_pix, dsize_src, dsize_dst)
src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm)
return homography_warp3d(src, src_norm_trans_dst_norm, dsize_dst, grid_mode, padding_mode, align_corners, True)
def warp_affine3d(
src: torch.Tensor,
M: torch.Tensor,
dsize: Tuple[int, int, int],
flags: str = 'bilinear',
padding_mode: str = 'zeros',
align_corners: Optional[bool] = None,
) -> torch.Tensor:
r"""Applies a projective transformation a to 3d tensor.
.. warning::
This API signature it is experimental and might suffer some changes in the future.
Args:
src : input tensor of shape :math:`(B, C, D, H, W)`.
M: projective transformation matrix of shape :math:`(B, 3, 4)`.
dsize: size of the output image (depth, height, width).
mode: interpolation mode to calculate output values
``'bilinear'`` | ``'nearest'``.
padding_mode: padding mode for outside grid values
``'zeros'`` | ``'border'`` | ``'reflection'``.
align_corners : mode for grid_generation.
Returns:
torch.Tensor: the warped 3d tensor with shape :math:`(B, C, D, H, W)`.
.. note::
This function is often used in conjuntion with :func:`get_perspective_transform3d`.
"""
assert len(src.shape) == 5, src.shape
assert len(M.shape) == 3 and M.shape[-2:] == (3, 4), M.shape
assert len(dsize) == 3, dsize
B, C, D, H, W = src.size()
# TODO: remove the statement below in kornia v0.6
if align_corners is None:
message: str = (
"The align_corners default value has been changed. By default now is set True "
"in order to match cv2.warpAffine. In case you want to keep your previous "
"behaviour set it to False. This warning will disappear in kornia > v0.6."
)
warnings.warn(message)
# set default value for align corners
align_corners = True
size_src: Tuple[int, int, int] = (D, H, W)
size_out: Tuple[int, int, int] = dsize
M_4x4 = convert_affinematrix_to_homography3d(M) # Bx4x4
# we need to normalize the transformation since grid sample needs -1/1 coordinates
dst_norm_trans_src_norm: torch.Tensor = normalize_homography3d(
M_4x4, size_src, size_out) # Bx4x4
src_norm_trans_dst_norm = _torch_inverse_cast(dst_norm_trans_src_norm)
P_norm: torch.Tensor = src_norm_trans_dst_norm[:, :3] # Bx3x4
# compute meshgrid and apply to input
dsize_out: List[int] = [B, C] + list(size_out)
grid = torch.nn.functional.affine_grid(P_norm,
dsize_out,
align_corners=align_corners)
return torch.nn.functional.grid_sample(src,
grid,
align_corners=align_corners,
mode=flags,
padding_mode=padding_mode)