kornia.geometry.homography¶
Module with useful functionalities for homographies manipulation.
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find_homography_dlt(points1: torch.Tensor, points2: torch.Tensor, weights: Optional[torch.Tensor] = None) → torch.Tensor[source]¶ Computes 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 (torch.Tensor) – A set of points in the first image with a tensor shape \((B, N, 2)\).
points2 (torch.Tensor) – A set of points in the second image with a tensor shape \((B, N, 2)\).
weights (torch.Tensor, optional) – Tensor containing the weights per point correspondence with a shape of \((B, N)\). Defaults to all ones.
- Returns
the computed homography matrix with shape \((B, 3, 3)\).
- Return type
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find_homography_dlt_iterated(points1: torch.Tensor, points2: torch.Tensor, weights: torch.Tensor, soft_inl_th: float = 3.0, n_iter: int = 5) → torch.Tensor[source]¶ Computes 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 (torch.Tensor) – A set of points in the first image with a tensor shape \((B, N, 2)\).
points2 (torch.Tensor) – A set of points in the second image with a tensor shape \((B, N, 2)\).
weights (torch.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) – Soft inlier threshold used for weight calculation.
n_iter (int) – number of iterations. Default is 5.
- Returns
the computed homography matrix with shape \((B, 3, 3)\).
- Return type