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Merge pull request #20636 from thezane:recoverPoseFromDifferentCameras

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Recover pose from different cameras (version 2)

* add recoverPose for two different cameras

* Address review comments from original PR

* Address new review comments

* Rename private api

Co-authored-by: tompollok <tom.pollok@gmail.com>
Co-authored-by: Zane <zane.huang@mail.utoronto.ca>
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thezane 3 years ago committed by GitHub
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  1. 70
      modules/calib3d/include/opencv2/calib3d.hpp
  2. 70
      modules/calib3d/src/five-point.cpp
  3. 48
      modules/calib3d/test/test_cameracalibration.cpp

70
modules/calib3d/include/opencv2/calib3d.hpp
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@ -2842,6 +2842,76 @@ unit length.
*/
CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t );
/** @brief Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of
inliers that pass the check.
@param points1 Array of N 2D points from the first image. The point coordinates should be
floating-point (single or double precision).
@param points2 Array of the second image points of the same size and format as points1 .
@param cameraMatrix1 Input/output camera matrix for the first camera, the same as in
@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
@param distCoeffs1 Input/output vector of distortion coefficients, the same as in
@ref calibrateCamera.
@param cameraMatrix2 Input/output camera matrix for the first camera, the same as in
@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
@param distCoeffs2 Input/output vector of distortion coefficients, the same as in
@ref calibrateCamera.
@param E The output essential matrix.
@param 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.
@param 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.
@param method Method for computing an essential matrix.
- @ref RANSAC for the RANSAC algorithm.
- @ref LMEDS for the LMedS algorithm.
@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
confidence (probability) that the estimated matrix is correct.
@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
line in pixels, beyond which the point is considered an outlier and is not used for computing the
final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
point localization, image resolution, and the image noise.
@param 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 @cite 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.:
@code
// 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] = ...;
}
// Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
// Output: Essential matrix, relative rotation and relative translation.
Mat E, R, t, mask;
recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);
@endcode
*/
CV_EXPORTS_W int recoverPose( InputArray points1, InputArray points2,
InputArray cameraMatrix1, InputArray distCoeffs1,
InputArray cameraMatrix2, InputArray distCoeffs2,
OutputArray E, OutputArray R, OutputArray t,
int method = cv::RANSAC, double prob = 0.999, double threshold = 1.0,
InputOutputArray mask = noArray());
/** @brief 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.

70
modules/calib3d/src/five-point.cpp
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@ -401,6 +401,29 @@ protected:
}
};
// Find essential matrix given undistorted points and two cameras.
static Mat findEssentialMat_( InputArray _points1, InputArray _points2,
InputArray cameraMatrix1, InputArray cameraMatrix2,
int method, double prob, double threshold, OutputArray _mask)
{
// Scale the points back. We use "arithmetic mean" between the supplied two camera matrices.
// Thanks to such 2-stage procedure RANSAC threshold still makes sense, because the undistorted
// and rescaled points have a similar value range to the original ones.
Mat _pointsTransformed1, _pointsTransformed2;
Mat cm1 = cameraMatrix1.getMat(), cm2 = cameraMatrix2.getMat(), cm0;
Mat(cm1 + cm2).convertTo(cm0, CV_64F, 0.5);
CV_Assert(cm0.rows == 3 && cm0.cols == 3);
CV_Assert(std::abs(cm0.at<double>(2, 0)) < 1e-3 &&
std::abs(cm0.at<double>(2, 1)) < 1e-3 &&
std::abs(cm0.at<double>(2, 2) - 1.) < 1e-3);
Mat affine = cm0.rowRange(0, 2);
transform(_points1, _pointsTransformed1, affine);
transform(_points2, _pointsTransformed2, affine);
return findEssentialMat(_pointsTransformed1, _pointsTransformed2, cm0, method, prob, threshold, _mask);
}
}
// Input should be a vector of n 2D points or a Nx2 matrix
@ -489,25 +512,10 @@ cv::Mat cv::findEssentialMat( InputArray _points1, InputArray _points2,
CV_INSTRUMENT_REGION();
// Undistort image points, bring them to 3x3 identity "camera matrix"
Mat _pointsUntistorted1, _pointsUntistorted2;
undistortPoints(_points1, _pointsUntistorted1, cameraMatrix1, distCoeffs1);
undistortPoints(_points2, _pointsUntistorted2, cameraMatrix2, distCoeffs2);
// Scale the points back. We use "arithmetic mean" between the supplied two camera matrices.
// Thanks to such 2-stage procedure RANSAC threshold still makes sense, because the undistorted
// and rescaled points have a similar value range to the original ones.
Mat cm1 = cameraMatrix1.getMat(), cm2 = cameraMatrix2.getMat(), cm0;
Mat(cm1 + cm2).convertTo(cm0, CV_64F, 0.5);
CV_Assert(cm0.rows == 3 && cm0.cols == 3);
CV_Assert(std::abs(cm0.at<double>(2, 0)) < 1e-3 &&
std::abs(cm0.at<double>(2, 1)) < 1e-3 &&
std::abs(cm0.at<double>(2, 2) - 1.) < 1e-3);
Mat affine = cm0.rowRange(0, 2);
transform(_pointsUntistorted1, _pointsUntistorted1, affine);
transform(_pointsUntistorted2, _pointsUntistorted2, affine);
return findEssentialMat(_pointsUntistorted1, _pointsUntistorted2, cm0, method, prob, threshold, _mask);
Mat _pointsUndistorted1, _pointsUndistorted2;
undistortPoints(_points1, _pointsUndistorted1, cameraMatrix1, distCoeffs1);
undistortPoints(_points2, _pointsUndistorted2, cameraMatrix2, distCoeffs2);
return findEssentialMat_(_pointsUndistorted1, _pointsUndistorted2, cameraMatrix1, cameraMatrix2, method, prob, threshold, _mask);
}
cv::Mat cv::findEssentialMat( InputArray points1, InputArray points2,
@ -524,6 +532,30 @@ cv::Mat cv::findEssentialMat( InputArray points1, InputArray points2,
}
int cv::recoverPose( InputArray _points1, InputArray _points2,
InputArray cameraMatrix1, InputArray distCoeffs1,
InputArray cameraMatrix2, InputArray distCoeffs2,
OutputArray E, OutputArray R, OutputArray t,
int method, double prob, double threshold,
InputOutputArray _mask)
{
CV_INSTRUMENT_REGION();
// Undistort image points, bring them to 3x3 identity "camera matrix"
Mat _pointsUndistorted1, _pointsUndistorted2;
undistortPoints(_points1, _pointsUndistorted1, cameraMatrix1, distCoeffs1);
undistortPoints(_points2, _pointsUndistorted2, cameraMatrix2, distCoeffs2);
// Get essential matrix.
Mat _E = findEssentialMat_(_pointsUndistorted1, _pointsUndistorted2, cameraMatrix1, cameraMatrix2,
method, prob, threshold, _mask);
CV_Assert(_E.cols == 3 && _E.rows == 3);
E.create(3, 3, _E.type());
_E.copyTo(E);
return recoverPose(_E, _pointsUndistorted1, _pointsUndistorted2, Mat::eye(3,3, CV_64F), R, t, _mask);
}
int cv::recoverPose( InputArray E, InputArray _points1, InputArray _points2,
InputArray _cameraMatrix, OutputArray _R, OutputArray _t, double distanceThresh,
InputOutputArray _mask, OutputArray triangulatedPoints)

48
modules/calib3d/test/test_cameracalibration.cpp
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@ -2142,7 +2142,17 @@ TEST(CV_RecoverPoseTest, regression_15341)
// camera matrix with both focal lengths = 1, and principal point = (0, 0)
const Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
const Mat zeroDistCoeffs = Mat::zeros(1, 5, CV_64F);
// camera matrix with focal lengths 0.5 and 0.6 respectively and principal point = (100, 200)
double cameraMatrix2Data[] = { 0.5, 0, 100,
0, 0.6, 200,
0, 0, 1 };
const Mat cameraMatrix2( 3, 3, CV_64F, cameraMatrix2Data );
// zero and nonzero distortion coefficients
double nonZeroDistCoeffsData[] = { 0.01, 0.0001, 0, 0, 1e-04, 0.2, 0.02, 0.0002 }; // k1, k2, p1, p2, k3, k4, k5, k6
vector<Mat> distCoeffsList = {Mat::zeros(1, 5, CV_64F), Mat{1, 8, CV_64F, nonZeroDistCoeffsData}};
const auto &zeroDistCoeffs = distCoeffsList[0];
int Inliers = 0;
@ -2158,14 +2168,26 @@ TEST(CV_RecoverPoseTest, regression_15341)
// Estimation of fundamental matrix using the RANSAC algorithm
Mat E, E2, R, t;
// Check pose when camera matrices are different.
for (const auto &distCoeffs: distCoeffsList)
{
E = findEssentialMat(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, RANSAC, 0.999, 1.0, mask);
recoverPose(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, E2, R, t, RANSAC, 0.999, 1.0, mask);
EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
"Two big difference between the same essential matrices computed using different functions with different cameras, testcase " << testcase;
EXPECT_EQ(0, (int)mask[13]) << "Detecting outliers in function failed with different cameras, testcase " << testcase;
}
// Check pose when camera matrices are the same.
E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
E2 = findEssentialMat(points1, points2, cameraMatrix, zeroDistCoeffs, cameraMatrix, zeroDistCoeffs, RANSAC, 0.999, 1.0, mask);
EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
"Two big difference between the same essential matrices computed using different functions, testcase " << testcase;
EXPECT_EQ(0, (int)mask[13]) << "Detecting outliers in function findEssentialMat failed, testcase " << testcase;
"Two big difference between the same essential matrices computed using different functions with same cameras, testcase " << testcase;
EXPECT_EQ(0, (int)mask[13]) << "Detecting outliers in function findEssentialMat failed with same cameras, testcase " << testcase;
points2[12] = Point2f(0.0f, 0.0f); // provoke another outlier detection for recover Pose
Inliers = recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
EXPECT_EQ(0, (int)mask[12]) << "Detecting outliers in function failed, testcase " << testcase;
EXPECT_EQ(0, (int)mask[12]) << "Detecting outliers in function failed with same cameras, testcase " << testcase;
}
else // testcase with mat input data
{
@ -2185,14 +2207,26 @@ TEST(CV_RecoverPoseTest, regression_15341)
// Estimation of fundamental matrix using the RANSAC algorithm
Mat E, E2, R, t;
// Check pose when camera matrices are different.
for (const auto &distCoeffs: distCoeffsList)
{
E = findEssentialMat(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, RANSAC, 0.999, 1.0, mask);
recoverPose(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, E2, R, t, RANSAC, 0.999, 1.0, mask);
EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
"Two big difference between the same essential matrices computed using different functions with different cameras, testcase " << testcase;
EXPECT_EQ(0, (int)mask.at<unsigned char>(13)) << "Detecting outliers in function failed with different cameras, testcase " << testcase;
}
// Check pose when camera matrices are the same.
E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
E2 = findEssentialMat(points1, points2, cameraMatrix, zeroDistCoeffs, cameraMatrix, zeroDistCoeffs, RANSAC, 0.999, 1.0, mask);
EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
"Two big difference between the same essential matrices computed using different functions, testcase " << testcase;
EXPECT_EQ(0, (int)mask.at<unsigned char>(13)) << "Detecting outliers in function findEssentialMat failed, testcase " << testcase;
"Two big difference between the same essential matrices computed using different functions with same cameras, testcase " << testcase;
EXPECT_EQ(0, (int)mask.at<unsigned char>(13)) << "Detecting outliers in function findEssentialMat failed with same cameras, testcase " << testcase;
points2.at<Point2f>(12) = Point2f(0.0f, 0.0f); // provoke an outlier detection
Inliers = recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
EXPECT_EQ(0, (int)mask.at<unsigned char>(12)) << "Detecting outliers in function failed, testcase " << testcase;
EXPECT_EQ(0, (int)mask.at<unsigned char>(12)) << "Detecting outliers in function failed with same cameras, testcase " << testcase;
}
EXPECT_EQ(Inliers, point_count - invalid_point_count) <<
"Number of inliers differs from expected number of inliers, testcase " << testcase;

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