e18e682858c57124d46c40e8ab1d136c009a1f69,kornia/geometry/homography.py,,find_homography_dlt,#Any#Any#Any#,10

Before Change

    ax = torch.cat([zeros, zeros, zeros, -x1, -y1, -ones, y2 * x1, y2 * y1, y2], dim=-1)
    ay = torch.cat([x1, y1, ones, zeros, zeros, zeros, -x2 * x1, -x2 * y1, -x2], dim=-1)

    w_list = []
    axy_list = []
    for i in range(points1.shape[1]):
        axy_list.append(ax[:, i])
        axy_list.append(ay[:, i])
        w_list.append(weights[:, i])
        w_list.append(weights[:, i])
    A = torch.stack(axy_list, dim=1)
    w = torch.stack(w_list, dim=1)

    // apply weights

After Change

    // DIAPO 11: https://www.uio.no/studier/emner/matnat/its/nedlagte-emner/UNIK4690/v16/forelesninger/lecture_4_3-estimating-homographies-from-feature-correspondences.pdf  // noqa: E501
    ax = torch.cat([zeros, zeros, zeros, -x1, -y1, -ones, y2 * x1, y2 * y1, y2], dim=-1)
    ay = torch.cat([x1, y1, ones, zeros, zeros, zeros, -x2 * x1, -x2 * y1, -x2], dim=-1)
    A = torch.cat((ax, ay), dim=-1).reshape(ax.shape[0], -1, ax.shape[-1])

    if weights is None:
        // All points are equally important
        A = A.transpose(-2, -1) @ A
    else:
        // We should use provided weights
        assert len(weights.shape) == 2 and weights.shape == points1.shape[:2], weights.shape
        w_diag = torch.diag_embed(weights.repeat(1, 2))

Instances