Function opencv::calib3d::recover_pose_camera

pub fn recover_pose_camera(
    e: &dyn ToInputArray, 
    points1: &dyn ToInputArray, 
    points2: &dyn ToInputArray, 
    camera_matrix: &dyn ToInputArray, 
    r: &mut dyn ToOutputArray, 
    t: &mut dyn ToOutputArray, 
    mask: &mut dyn ToInputOutputArray
) -> Result<i32>

Recovers the relative camera rotation and the translation from an estimated essential matrix and the corresponding points in two images, using cheirality check. Returns the number of inliers that pass the check.

Parameters

• E: The input essential matrix.
• points1: Array of N 2D points from the first image. The point coordinates should be floating-point (single or double precision).
• points2: Array of the second image points of the same size and format as points1 .
• cameraMatrix: Camera intrinsic matrix . Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
• R: Output rotation matrix. Together with the translation vector, this matrix makes up a tuple that performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Note that, in general, t can not be used for this tuple, see the parameter described below.
• t: Output translation vector. This vector is obtained by @ref decomposeEssentialMat and therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
• mask: Input/output mask for inliers in points1 and points2. If it is not empty, then it marks inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the cheirality check.

This function decomposes an essential matrix using @ref decomposeEssentialMat and then verifies possible pose hypotheses by doing cheirality check. The cheirality check means that the triangulated 3D points should have positive depth. Some details can be found in Nister03.

This function can be used to process the output E and mask from @ref findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat.:

   // Example. Estimation of fundamental matrix using the RANSAC algorithm
   int point_count = 100;
   vector<Point2f> points1(point_count);
   vector<Point2f> points2(point_count);
 
   // initialize the points here ...
   for( int i = 0; i < point_count; i++ )
   {
       points1[i] = ...;
       points2[i] = ...;
   }
 
   // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
   Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
 
   Mat E, R, t, mask;
 
   E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
   recoverPose(E, points1, points2, cameraMatrix, R, t, mask);

C++ default parameters

• mask: noArray()