Recover the rotation and translation between the cameras from the estimated essential matrix and corresponding points in the two images, checked using the beam law. Returns the number of inliers that pass the test.
#include <opencv2/calib3d.hpp>
int cv::recoverPose ( InputArray E,
InputArray points1,
InputArray points2,
InputArray cameraMatrix,
OutputArray R,
OutputArray t,
InputOutputArray mask = noArray()
)
int recoverPose( InputArray E, InputArray points1, InputArray points2,
OutputArray R, OutputArray t, double focal = 1.0,
Point2d pp = Point2d(0, 0), InputOutputArray mask = noArray() );
int recoverPose( InputArray E, InputArray points1, InputArray points2,
InputArray cameraMatrix, OutputArray R, OutputArray t,
double distanceThresh, InputOutputArray mask = noArray(),
OutputArray triangulatedPoints = noArray());
E: The essential matrix that has been solved, which is a 3x3 matrix;
points1: the points in the first picture;
points2: the points in the second picture;
cameraMatrix: the internal reference matrix of the camera, which is a 3x3 matrix;
R: solve The rotation matrix between the two frames of pictures;
t: the translation vector between the two frames of pictures;
focal: the focal length
of the camera; pp: the origin of the pixel coordinates;
Far points;
triangulatedPoints: restore points by triangulation;
official example
// 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);
**Description: ** 1. R , t R,t
calculated by this functionR,t , it represents the transformation from points1 to points2, that is,R 21 R_{21}R21 , t 21 t_{21} t21
2. The R 21 R_{21} obtained by this functionR21 , t 21 t_{21} t21, It is already the most suitable solution. The other three wrong
solutions have been removed through the internal code. The order of input is the same.
4. The method of use can directly include the corresponding header file, or directly copy the internal implementation of the function or implement it yourself (vins), as follows:
int recoverPose( InputArray E, InputArray _points1, InputArray _points2, InputArray _cameraMatrix,
OutputArray _R, OutputArray _t, InputOutputArray _mask)
{
Mat points1, points2, cameraMatrix;
_points1.getMat().convertTo(points1, CV_64F);
_points2.getMat().convertTo(points2, CV_64F);
_cameraMatrix.getMat().convertTo(cameraMatrix, CV_64F);
int npoints = points1.checkVector(2);
CV_Assert( npoints >= 0 && points2.checkVector(2) == npoints &&
points1.type() == points2.type());
CV_Assert(cameraMatrix.rows == 3 && cameraMatrix.cols == 3 && cameraMatrix.channels() == 1);
if (points1.channels() > 1)
{
points1 = points1.reshape(1, npoints);
points2 = points2.reshape(1, npoints);
}
double fx = cameraMatrix.at<double>(0,0);
double fy = cameraMatrix.at<double>(1,1);
double cx = cameraMatrix.at<double>(0,2);
double cy = cameraMatrix.at<double>(1,2);
points1.col(0) = (points1.col(0) - cx) / fx;
points2.col(0) = (points2.col(0) - cx) / fx;
points1.col(1) = (points1.col(1) - cy) / fy;
points2.col(1) = (points2.col(1) - cy) / fy;
points1 = points1.t();
points2 = points2.t();
Mat R1, R2, t;
decomposeEssentialMat(E, R1, R2, t);
Mat P0 = Mat::eye(3, 4, R1.type());
Mat P1(3, 4, R1.type()), P2(3, 4, R1.type()), P3(3, 4, R1.type()), P4(3, 4, R1.type());
P1(Range::all(), Range(0, 3)) = R1 * 1.0; P1.col(3) = t * 1.0;
P2(Range::all(), Range(0, 3)) = R2 * 1.0; P2.col(3) = t * 1.0;
P3(Range::all(), Range(0, 3)) = R1 * 1.0; P3.col(3) = -t * 1.0;
P4(Range::all(), Range(0, 3)) = R2 * 1.0; P4.col(3) = -t * 1.0;
// Do the cheirality check.
// Notice here a threshold dist is used to filter
// out far away points (i.e. infinite points) since
// there depth may vary between postive and negtive.
double dist = 50.0;
Mat Q;
triangulatePoints(P0, P1, points1, points2, Q);
Mat mask1 = Q.row(2).mul(Q.row(3)) > 0;
Q.row(0) /= Q.row(3);
Q.row(1) /= Q.row(3);
Q.row(2) /= Q.row(3);
Q.row(3) /= Q.row(3);
mask1 = (Q.row(2) < dist) & mask1;
Q = P1 * Q;
mask1 = (Q.row(2) > 0) & mask1;
mask1 = (Q.row(2) < dist) & mask1;
triangulatePoints(P0, P2, points1, points2, Q);
Mat mask2 = Q.row(2).mul(Q.row(3)) > 0;
Q.row(0) /= Q.row(3);
Q.row(1) /= Q.row(3);
Q.row(2) /= Q.row(3);
Q.row(3) /= Q.row(3);
mask2 = (Q.row(2) < dist) & mask2;
Q = P2 * Q;
mask2 = (Q.row(2) > 0) & mask2;
mask2 = (Q.row(2) < dist) & mask2;
triangulatePoints(P0, P3, points1, points2, Q);
Mat mask3 = Q.row(2).mul(Q.row(3)) > 0;
Q.row(0) /= Q.row(3);
Q.row(1) /= Q.row(3);
Q.row(2) /= Q.row(3);
Q.row(3) /= Q.row(3);
mask3 = (Q.row(2) < dist) & mask3;
Q = P3 * Q;
mask3 = (Q.row(2) > 0) & mask3;
mask3 = (Q.row(2) < dist) & mask3;
triangulatePoints(P0, P4, points1, points2, Q);
Mat mask4 = Q.row(2).mul(Q.row(3)) > 0;
Q.row(0) /= Q.row(3);
Q.row(1) /= Q.row(3);
Q.row(2) /= Q.row(3);
Q.row(3) /= Q.row(3);
mask4 = (Q.row(2) < dist) & mask4;
Q = P4 * Q;
mask4 = (Q.row(2) > 0) & mask4;
mask4 = (Q.row(2) < dist) & mask4;
mask1 = mask1.t();
mask2 = mask2.t();
mask3 = mask3.t();
mask4 = mask4.t();
// If _mask is given, then use it to filter outliers.
if (!_mask.empty())
{
Mat mask = _mask.getMat();
CV_Assert(mask.size() == mask1.size());
bitwise_and(mask, mask1, mask1);
bitwise_and(mask, mask2, mask2);
bitwise_and(mask, mask3, mask3);
bitwise_and(mask, mask4, mask4);
}
if (_mask.empty() && _mask.needed())
{
_mask.create(mask1.size(), CV_8U);
}
CV_Assert(_R.needed() && _t.needed());
_R.create(3, 3, R1.type());
_t.create(3, 1, t.type());
int good1 = countNonZero(mask1);
int good2 = countNonZero(mask2);
int good3 = countNonZero(mask3);
int good4 = countNonZero(mask4);
if (good1 >= good2 && good1 >= good3 && good1 >= good4)
{
R1.copyTo(_R);
t.copyTo(_t);
if (_mask.needed()) mask1.copyTo(_mask);
return good1;
}
else if (good2 >= good1 && good2 >= good3 && good2 >= good4)
{
R2.copyTo(_R);
t.copyTo(_t);
if (_mask.needed()) mask2.copyTo(_mask);
return good2;
}
else if (good3 >= good1 && good3 >= good2 && good3 >= good4)
{
t = -t;
R1.copyTo(_R);
t.copyTo(_t);
if (_mask.needed()) mask3.copyTo(_mask);
return good3;
}
else
{
t = -t;
R2.copyTo(_R);
t.copyTo(_t);
if (_mask.needed()) mask4.copyTo(_mask);
return good4;
}
}
int recoverPose( InputArray E, InputArray _points1, InputArray _points2, OutputArray _R,
OutputArray _t, double focal, Point2d pp, InputOutputArray _mask)
{
Mat cameraMatrix = (Mat_<double>(3,3) << focal, 0, pp.x, 0, focal, pp.y, 0, 0, 1);
return cv::recoverPose(E, _points1, _points2, cameraMatrix, _R, _t, _mask);
}
}