使用 RANSAC 估计两组点之间的二维变换

Estimate 2D transformation between two sets of points using RANSAC

据我所知,OpenCV 使用 RANSAC 来解决 findHomography 的问题,它 return 提供了一些有用的参数,例如 homograph_mask

但是,如果我只想估计 2D 变换,这意味着仿射矩阵,有没有办法使用与 findHomography 相同的方法,它使用 RANSAC 和 return 掩码?

estimateRigidTransform does use RANSAC internally, though the parameters are fixed at the moment - see the code here - https://github.com/opencv/opencv/blob/master/modules/video/src/lkpyramid.cpp

cv::Mat cv::estimateRigidTransform( InputArray src1, InputArray src2, bool fullAffine )
{
    const int RANSAC_MAX_ITERS = 500;
    const int RANSAC_SIZE0 = 3;
    const double RANSAC_GOOD_RATIO = 0.5;

    // ...

    // RANSAC stuff:
    // 1. find the consensus
    for( k = 0; k < RANSAC_MAX_ITERS; k++ )
    {
        int idx[RANSAC_SIZE0];
        Point2f a[RANSAC_SIZE0];
        Point2f b[RANSAC_SIZE0];

        // choose random 3 non-complanar points from A & B
        for( i = 0; i < RANSAC_SIZE0; i++ )
        {
            for( k1 = 0; k1 < RANSAC_MAX_ITERS; k1++ )
            {

您可以直接使用 estimateAffinePartial2D : https://docs.opencv.org/4.0.0/d9/d0c/group__calib3d.html#gad767faff73e9cbd8b9d92b955b50062d

cv::Mat cv::estimateAffinePartial2D (   
    InputArray  from,
    InputArray  to,
    OutputArray     inliers = noArray(),
    int     method = RANSAC,
    double  ransacReprojThreshold = 3,
    size_t  maxIters = 2000,
    double  confidence = 0.99,
    size_t  refineIters = 10 
)   

例如:

        src_pts = np.float32([pic1.key_points[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
        dst_pts = np.float32([pic2.key_points[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)

        # Find the transformation between points, standard RANSAC
        transformation_matrix, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

        # Compute a rigid transformation (without depth, only scale + rotation + translation) and RANSAC
        transformation_rigid_matrix, rigid_mask = cv2.estimateAffinePartial2D(src_pts, dst_pts)