def normalize_homography(dst_pix_trans_src_pix: torch.Tensor,
dsize_src: Tuple[int, int],
dsize_dst: Tuple[int, int]) -> torch.Tensor:
__doc__ = HMW.normalize_homography.__doc__
warnings.warn(
"`normalize_homography` is deprecated and will be removed > 0.6.0. "
"Please use `kornia.geometry.transform.normalize_homography instead.`",
DeprecationWarning,
stacklevel=2)
return HMW.normalize_homography(dst_pix_trans_src_pix, dsize_src,
dsize_dst)
def transform_warp_impl(src: torch.Tensor, dst_pix_trans_src_pix: torch.Tensor,
dsize_src: Tuple[int, int], dsize_dst: Tuple[int, int],
grid_mode: str, padding_mode: str,
align_corners: bool) -> torch.Tensor:
"""Compute the transform in normalized coordinates and perform the warping.
"""
dst_norm_trans_src_norm: torch.Tensor = normalize_homography(
dst_pix_trans_src_pix, dsize_src, dsize_dst)
src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm)
return homography_warp(src, src_norm_trans_dst_norm, dsize_dst, grid_mode,
padding_mode, align_corners, True)
def warp_perspective(
src: torch.Tensor,
M: torch.Tensor,
dsize: Tuple[int, int],
mode: str = 'bilinear',
padding_mode: str = 'zeros',
align_corners: Optional[bool] = None,
) -> torch.Tensor:
r"""Applies a perspective transformation to an image.
.. image:: https://kornia-tutorials.readthedocs.io/en/latest/_images/warp_perspective_10_2.png
The function warp_perspective transforms the source image using
the specified matrix:
.. math::
\text{dst} (x, y) = \text{src} \left(
\frac{M^{-1}_{11} x + M^{-1}_{12} y + M^{-1}_{13}}{M^{-1}_{31} x + M^{-1}_{32} y + M^{-1}_{33}} ,
\frac{M^{-1}_{21} x + M^{-1}_{22} y + M^{-1}_{23}}{M^{-1}_{31} x + M^{-1}_{32} y + M^{-1}_{33}}
\right )
Args:
src: input image with shape :math:`(B, C, H, W)`.
M: transformation matrix with shape :math:`(B, 3, 3)`.
dsize: size of the output image (height, width).
mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``.
padding_mode: padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'``.
align_corners(bool, optional): interpolation flag.
Returns:
the warped input image :math:`(B, C, H, W)`.
Example:
>>> img = torch.rand(1, 4, 5, 6)
>>> H = torch.eye(3)[None]
>>> out = warp_perspective(img, H, (4, 2), align_corners=True)
>>> print(out.shape)
torch.Size([1, 4, 4, 2])
.. note::
This function is often used in conjuntion with :func:`get_perspective_transform`.
.. note::
See a working example `here <https://kornia-tutorials.readthedocs.io/en/
latest/warp_perspective.html>`_.
"""
if not isinstance(src, torch.Tensor):
raise TypeError("Input src type is not a torch.Tensor. Got {}".format(
type(src)))
if not isinstance(M, torch.Tensor):
raise TypeError("Input M type is not a torch.Tensor. Got {}".format(
type(M)))
if not len(src.shape) == 4:
raise ValueError("Input src must be a BxCxHxW tensor. Got {}".format(
src.shape))
if not (len(M.shape) == 3 and M.shape[-2:] == (3, 3)):
raise ValueError("Input M must be a Bx3x3 tensor. Got {}".format(
M.shape))
# 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.warpPerspective. 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
B, C, H, W = src.size()
h_out, w_out = dsize
# we normalize the 3x3 transformation matrix and convert to 3x4
dst_norm_trans_src_norm: torch.Tensor = normalize_homography(
M, (H, W), (h_out, w_out)) # Bx3x3
src_norm_trans_dst_norm = _torch_inverse_cast(
dst_norm_trans_src_norm) # Bx3x3
# this piece of code substitutes F.affine_grid since it does not support 3x3
grid = (create_meshgrid(h_out,
w_out,
normalized_coordinates=True,
device=src.device).to(src.dtype).repeat(
B, 1, 1, 1))
grid = transform_points(src_norm_trans_dst_norm[:, None, None], grid)
return F.grid_sample(src,
grid,
align_corners=align_corners,
mode=mode,
padding_mode=padding_mode)
def warp_affine(
src: torch.Tensor,
M: torch.Tensor,
dsize: Tuple[int, int],
mode: str = 'bilinear',
padding_mode: str = 'zeros',
align_corners: Optional[bool] = None,
) -> torch.Tensor:
r"""Applies an affine transformation to a tensor.
.. image:: _static/img/warp_affine.png
The function warp_affine transforms the source tensor using
the specified matrix:
.. math::
\text{dst}(x, y) = \text{src} \left( M_{11} x + M_{12} y + M_{13} ,
M_{21} x + M_{22} y + M_{23} \right )
Args:
src: input tensor of shape :math:`(B, C, H, W)`.
M: affine transformation of shape :math:`(B, 2, 3)`.
dsize: size of the output image (height, width).
mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``.
padding_mode (str): padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'``.
align_corners : mode for grid_generation.
Returns:
the warped tensor with shape :math:`(B, C, H, W)`.
Example:
>>> img = torch.rand(1, 4, 5, 6)
>>> A = torch.eye(2, 3)[None]
>>> out = warp_affine(img, A, (4, 2), align_corners=True)
>>> print(out.shape)
torch.Size([1, 4, 4, 2])
.. note::
This function is often used in conjuntion with :func:`get_rotation_matrix2d`,
:func:`get_shear_matrix2d`, :func:`get_affine_matrix2d`, :func:`invert_affine_transform`.
.. note::
See a working example `here <https://kornia.readthedocs.io/en/latest/
tutorials/warp_affine.html>`__.
"""
if not isinstance(src, torch.Tensor):
raise TypeError("Input src type is not a torch.Tensor. Got {}".format(
type(src)))
if not isinstance(M, torch.Tensor):
raise TypeError("Input M type is not a torch.Tensor. Got {}".format(
type(M)))
if not len(src.shape) == 4:
raise ValueError("Input src must be a BxCxHxW tensor. Got {}".format(
src.shape))
if not (len(M.shape) == 3 or M.shape[-2:] == (2, 3)):
raise ValueError("Input M must be a Bx2x3 tensor. Got {}".format(
M.shape))
# 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
B, C, H, W = src.size()
# we generate a 3x3 transformation matrix from 2x3 affine
M_3x3: torch.Tensor = convert_affinematrix_to_homography(M)
dst_norm_trans_src_norm: torch.Tensor = normalize_homography(
M_3x3, (H, W), dsize)
# src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm)
src_norm_trans_dst_norm = _torch_inverse_cast(dst_norm_trans_src_norm)
grid = F.affine_grid(src_norm_trans_dst_norm[:, :2, :],
[B, C, dsize[0], dsize[1]],
align_corners=align_corners)
return F.grid_sample(src,
grid,
align_corners=align_corners,
mode=mode,
padding_mode=padding_mode)
def warp_affine(
src: torch.Tensor,
M: torch.Tensor,
dsize: Tuple[int, int],
mode: str = 'bilinear',
padding_mode: str = 'zeros',
align_corners: bool = True,
) -> torch.Tensor:
r"""Apply an affine transformation to a tensor.
.. image:: _static/img/warp_affine.png
The function warp_affine transforms the source tensor using
the specified matrix:
.. math::
\text{dst}(x, y) = \text{src} \left( M_{11} x + M_{12} y + M_{13} ,
M_{21} x + M_{22} y + M_{23} \right )
Args:
src: input tensor of shape :math:`(B, C, H, W)`.
M: affine transformation of shape :math:`(B, 2, 3)`.
dsize: size of the output image (height, width).
mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``.
padding_mode (str): padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'``.
align_corners : mode for grid_generation.
Returns:
the warped tensor with shape :math:`(B, C, H, W)`.
.. note::
This function is often used in conjunction with :func:`get_rotation_matrix2d`,
:func:`get_shear_matrix2d`, :func:`get_affine_matrix2d`, :func:`invert_affine_transform`.
.. note::
See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
rotate_affine.html>`__.
Example:
>>> img = torch.rand(1, 4, 5, 6)
>>> A = torch.eye(2, 3)[None]
>>> out = warp_affine(img, A, (4, 2), align_corners=True)
>>> print(out.shape)
torch.Size([1, 4, 4, 2])
"""
if not isinstance(src, torch.Tensor):
raise TypeError(f"Input src type is not a torch.Tensor. Got {type(src)}")
if not isinstance(M, torch.Tensor):
raise TypeError(f"Input M type is not a torch.Tensor. Got {type(M)}")
if not len(src.shape) == 4:
raise ValueError(f"Input src must be a BxCxHxW tensor. Got {src.shape}")
if not (len(M.shape) == 3 or M.shape[-2:] == (2, 3)):
raise ValueError(f"Input M must be a Bx2x3 tensor. Got {M.shape}")
B, C, H, W = src.size()
# we generate a 3x3 transformation matrix from 2x3 affine
M_3x3: torch.Tensor = convert_affinematrix_to_homography(M)
dst_norm_trans_src_norm: torch.Tensor = normalize_homography(M_3x3, (H, W), dsize)
# src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm)
src_norm_trans_dst_norm = _torch_inverse_cast(dst_norm_trans_src_norm)
grid = F.affine_grid(src_norm_trans_dst_norm[:, :2, :], [B, C, dsize[0], dsize[1]], align_corners=align_corners)
return F.grid_sample(src, grid, align_corners=align_corners, mode=mode, padding_mode=padding_mode)
def warp_affine(src: torch.Tensor,
M: torch.Tensor,
dsize: Tuple[int, int],
flags: str = 'bilinear',
padding_mode: str = 'zeros',
align_corners: bool = False) -> torch.Tensor:
r"""Applies an affine transformation to a tensor.
The function warp_affine transforms the source tensor using
the specified matrix:
.. math::
\text{dst}(x, y) = \text{src} \left( M_{11} x + M_{12} y + M_{13} ,
M_{21} x + M_{22} y + M_{23} \right )
Args:
src (torch.Tensor): input tensor of shape :math:`(B, C, H, W)`.
M (torch.Tensor): affine transformation of shape :math:`(B, 2, 3)`.
dsize (Tuple[int, int]): size of the output image (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: False.
Returns:
torch.Tensor: the warped tensor with shape :math:`(B, C, H, W)`.
.. note::
See a working example `here <https://kornia.readthedocs.io/en/latest/
tutorials/warp_affine.html>`__.
"""
if not isinstance(src, torch.Tensor):
raise TypeError("Input src type is not a torch.Tensor. Got {}".format(
type(src)))
if not isinstance(M, torch.Tensor):
raise TypeError("Input M type is not a torch.Tensor. Got {}".format(
type(M)))
if not len(src.shape) == 4:
raise ValueError("Input src must be a BxCxHxW tensor. Got {}".format(
src.shape))
if not (len(M.shape) == 3 or M.shape[-2:] == (2, 3)):
raise ValueError("Input M must be a Bx2x3 tensor. Got {}".format(
M.shape))
B, C, H, W = src.size()
dsize_src = (H, W)
out_size = dsize
# we generate a 3x3 transformation matrix from 2x3 affine
M_3x3: torch.Tensor = convert_affinematrix_to_homography(M)
dst_norm_trans_src_norm: torch.Tensor = normalize_homography(
M_3x3, dsize_src, out_size)
src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm)
grid = F.affine_grid(src_norm_trans_dst_norm[:, :2, :],
[B, C, out_size[0], out_size[1]],
align_corners=align_corners)
return F.grid_sample(src,
grid,
align_corners=align_corners,
mode=flags,
padding_mode=padding_mode)