kornia.geometry.homography#
Module with useful functionalities for homographies manipulation.
- kornia.geometry.homography.find_homography_dlt(points1, points2, weights=None)[source]#
Compute the homography matrix using the DLT formulation.
The linear system is solved by using the Weighted Least Squares Solution for the 4 Points algorithm.
- Parameters
points1 (
Tensor) – A set of points in the first image with a tensor shape \((B, N, 2)\).points2 (
Tensor) – A set of points in the second image with a tensor shape \((B, N, 2)\).weights (
Optional[Tensor], optional) – Tensor containing the weights per point correspondence with a shape of \((B, N)\). Default:None
- Return type
- Returns
the computed homography matrix with shape \((B, 3, 3)\).
- kornia.geometry.homography.find_homography_dlt_iterated(points1, points2, weights, soft_inl_th=3.0, n_iter=5)[source]#
Compute the homography matrix using the iteratively-reweighted least squares (IRWLS).
The linear system is solved by using the Reweighted Least Squares Solution for the 4 Points algorithm.
- Parameters
points1 (
Tensor) – A set of points in the first image with a tensor shape \((B, N, 2)\).points2 (
Tensor) – A set of points in the second image with a tensor shape \((B, N, 2)\).weights (
Tensor) – Tensor containing the weights per point correspondence with a shape of \((B, N)\). Used for the first iteration of the IRWLS.soft_inl_th (
float, optional) – Soft inlier threshold used for weight calculation. Default:3.0n_iter (
int, optional) – number of iterations. Default:5
- Return type
- Returns
the computed homography matrix with shape \((B, 3, 3)\).
- kornia.geometry.homography.find_homography_lines_dlt(ls1, ls2, weights=None)[source]#
Compute the homography matrix using the DLT formulation for line correspondences.
See [JJC01] for details. The linear system is solved by using the Weighted Least Squares Solution for the 4 Line correspondences algorithm. :type ls1:
Tensor:param ls1: A set of line segments in the first image with a tensor shape \((B, N, 2, 2)\). :type ls2:Tensor:param ls2: A set of line segments in the second image with a tensor shape \((B, N, 2, 2)\). :type weights:Optional[Tensor], optional :param weights: Tensor containing the weights per point correspondence with a shape of \((B, N)\).Default:
None- Return type
- Returns
the computed homography matrix with shape \((B, 3, 3)\).
- kornia.geometry.homography.find_homography_lines_dlt_iterated(ls1, ls2, weights, soft_inl_th=4.0, n_iter=5)[source]#
Compute the homography matrix using the iteratively-reweighted least squares (IRWLS) from line segments. The linear system is solved by using the Reweighted Least Squares Solution for the 4 line segments algorithm.
- Parameters
ls1 (
Tensor) – A set of line segments in the first image with a tensor shape \((B, N, 2, 2)\).ls2 (
Tensor) – A set of line segments in the second image with a tensor shape \((B, N, 2, 2)\).weights (
Tensor) – Tensor containing the weights per point correspondence with a shape of \((B, N)\). Used for the first iteration of the IRWLS.soft_inl_th (
float, optional) – Soft inlier threshold used for weight calculation. Default:4.0n_iter (
int, optional) – number of iterations. Default:5
- Return type
- Returns
the computed homography matrix with shape \((B, 3, 3)\).
- kornia.geometry.homography.line_segment_transfer_error_one_way(ls1, ls2, H, squared=False)[source]#
Return transfer error in image 2 for line segment correspondences given the homography matrix. Line segment end points are reprojected into image 2, and point-to-line error is calculted w.r.t. line, induced by line segment in image 2. See [JJC01] for details.
- Parameters
ls1 (
Tensor) – line segment correspondences from the left images with shape (B, N, 2, 2).ls2 (
Tensor) – line segment correspondences from the right images with shape (B, N, 2, 2).H (
Tensor) – Homographies with shape \((B, 3, 3)\).squared (
bool, optional) – if True (default is False), the squared distance is returned. Default:False
- Return type
- Returns
the computed distance with shape \((B, N)\).
- kornia.geometry.homography.oneway_transfer_error(pts1, pts2, H, squared=True, eps=1e-08)[source]#
Return transfer error in image 2 for correspondences given the homography matrix.
- Parameters
pts1 (
Tensor) – correspondences from the left images with shape (B, N, 2 or 3). If they are homogeneous, converted automatically.pts2 (
Tensor) – correspondences from the right images with shape (B, N, 2 or 3). If they are homogeneous, converted automatically.H (
Tensor) – Homographies with shape \((B, 3, 3)\).squared (
bool, optional) – if True (default), the squared distance is returned. Default:Trueeps (
float, optional) – Small constant for safe sqrt. Default:1e-08
- Return type
- Returns
the computed distance with shape \((B, N)\).
- kornia.geometry.homography.sample_is_valid_for_homography(points1, points2)[source]#
Function, which implements oriented constraint check from [MarquezNLopezABB16].
Analogous to https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/usac/degeneracy.cpp#L88
- kornia.geometry.homography.symmetric_transfer_error(pts1, pts2, H, squared=True, eps=1e-08)[source]#
Return Symmetric transfer error for correspondences given the homography matrix.
- Parameters
pts1 (
Tensor) – correspondences from the left images with shape (B, N, 2 or 3). If they are homogeneous, converted automatically.pts2 (
Tensor) – correspondences from the right images with shape (B, N, 2 or 3). If they are homogeneous, converted automatically.H (
Tensor) – Homographies with shape \((B, 3, 3)\).squared (
bool, optional) – if True (default), the squared distance is returned. Default:Trueeps (
float, optional) – Small constant for safe sqrt. Default:1e-08
- Return type
- Returns
the computed distance with shape \((B, N)\).