Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
33700 Commits
6 Branches
126 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: 8c22b4b54b
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '8c22b4b54b'
${ noResults }
opencv/modules/calib3d/test/test_calibration_hand_eye.cpp

759 lines
30 KiB
Raw Blame History

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#include "opencv2/calib3d.hpp"
namespace opencv_test { namespace {
static void generatePose(RNG& rng, double min_theta, double max_theta,
double min_tx, double max_tx,
double min_ty, double max_ty,
double min_tz, double max_tz,
Mat& R, Mat& tvec,
bool random_sign)
{
Mat axis(3, 1, CV_64FC1);
for (int i = 0; i < 3; i++)
{
axis.at<double>(i,0) = rng.uniform(-1.0, 1.0);
}
double theta = rng.uniform(min_theta, max_theta);
if (random_sign)
{
theta *= std::copysign(1.0, rng.uniform(-1.0, 1.0));
}
Mat rvec(3, 1, CV_64FC1);
rvec.at<double>(0,0) = theta*axis.at<double>(0,0);
rvec.at<double>(1,0) = theta*axis.at<double>(1,0);
rvec.at<double>(2,0) = theta*axis.at<double>(2,0);
tvec.create(3, 1, CV_64FC1);
tvec.at<double>(0,0) = rng.uniform(min_tx, max_tx);
tvec.at<double>(1,0) = rng.uniform(min_ty, max_ty);
tvec.at<double>(2,0) = rng.uniform(min_tz, max_tz);
if (random_sign)
{
tvec.at<double>(0,0) *= std::copysign(1.0, rng.uniform(-1.0, 1.0));
tvec.at<double>(1,0) *= std::copysign(1.0, rng.uniform(-1.0, 1.0));
tvec.at<double>(2,0) *= std::copysign(1.0, rng.uniform(-1.0, 1.0));
}
cv::Rodrigues(rvec, R);
}
static Mat homogeneousInverse(const Mat& T)
{
CV_Assert( T.rows == 4 && T.cols == 4 );
Mat R = T(Rect(0, 0, 3, 3));
Mat t = T(Rect(3, 0, 1, 3));
Mat Rt = R.t();
Mat tinv = -Rt * t;
Mat Tinv = Mat::eye(4, 4, T.type());
Rt.copyTo(Tinv(Rect(0, 0, 3, 3)));
tinv.copyTo(Tinv(Rect(3, 0, 1, 3)));
return Tinv;
}
static void simulateDataEyeInHand(RNG& rng, int nPoses,
std::vector<Mat> &R_gripper2base, std::vector<Mat> &t_gripper2base,
std::vector<Mat> &R_target2cam, std::vector<Mat> &t_target2cam,
bool noise, Mat& R_cam2gripper, Mat& t_cam2gripper)
{
//to avoid generating values close to zero,
//we use positive range values and randomize the sign
const bool random_sign = true;
generatePose(rng, 10.0*CV_PI/180.0, 50.0*CV_PI/180.0,
0.05, 0.5, 0.05, 0.5, 0.05, 0.5,
R_cam2gripper, t_cam2gripper, random_sign);
Mat R_target2base, t_target2base;
generatePose(rng, 5.0*CV_PI/180.0, 85.0*CV_PI/180.0,
0.5, 3.5, 0.5, 3.5, 0.5, 3.5,
R_target2base, t_target2base, random_sign);
for (int i = 0; i < nPoses; i++)
{
Mat R_gripper2base_, t_gripper2base_;
generatePose(rng, 5.0*CV_PI/180.0, 45.0*CV_PI/180.0,
0.5, 1.5, 0.5, 1.5, 0.5, 1.5,
R_gripper2base_, t_gripper2base_, random_sign);
R_gripper2base.push_back(R_gripper2base_);
t_gripper2base.push_back(t_gripper2base_);
Mat T_cam2gripper = Mat::eye(4, 4, CV_64FC1);
R_cam2gripper.copyTo(T_cam2gripper(Rect(0, 0, 3, 3)));
t_cam2gripper.copyTo(T_cam2gripper(Rect(3, 0, 1, 3)));
Mat T_gripper2base = Mat::eye(4, 4, CV_64FC1);
R_gripper2base_.copyTo(T_gripper2base(Rect(0, 0, 3, 3)));
t_gripper2base_.copyTo(T_gripper2base(Rect(3, 0, 1, 3)));
Mat T_base2cam = homogeneousInverse(T_cam2gripper) * homogeneousInverse(T_gripper2base);
Mat T_target2base = Mat::eye(4, 4, CV_64FC1);
R_target2base.copyTo(T_target2base(Rect(0, 0, 3, 3)));
t_target2base.copyTo(T_target2base(Rect(3, 0, 1, 3)));
Mat T_target2cam = T_base2cam * T_target2base;
if (noise)
{
//Add some noise for the transformation between the target and the camera
Mat R_target2cam_noise = T_target2cam(Rect(0, 0, 3, 3));
Mat rvec_target2cam_noise;
cv::Rodrigues(R_target2cam_noise, rvec_target2cam_noise);
rvec_target2cam_noise.at<double>(0,0) += rng.gaussian(0.002);
rvec_target2cam_noise.at<double>(1,0) += rng.gaussian(0.002);
rvec_target2cam_noise.at<double>(2,0) += rng.gaussian(0.002);
cv::Rodrigues(rvec_target2cam_noise, R_target2cam_noise);
Mat t_target2cam_noise = T_target2cam(Rect(3, 0, 1, 3));
t_target2cam_noise.at<double>(0,0) += rng.gaussian(0.005);
t_target2cam_noise.at<double>(1,0) += rng.gaussian(0.005);
t_target2cam_noise.at<double>(2,0) += rng.gaussian(0.005);
//Add some noise for the transformation between the gripper and the robot base
Mat R_gripper2base_noise = T_gripper2base(Rect(0, 0, 3, 3));
Mat rvec_gripper2base_noise;
cv::Rodrigues(R_gripper2base_noise, rvec_gripper2base_noise);
rvec_gripper2base_noise.at<double>(0,0) += rng.gaussian(0.001);
rvec_gripper2base_noise.at<double>(1,0) += rng.gaussian(0.001);
rvec_gripper2base_noise.at<double>(2,0) += rng.gaussian(0.001);
cv::Rodrigues(rvec_gripper2base_noise, R_gripper2base_noise);
Mat t_gripper2base_noise = T_gripper2base(Rect(3, 0, 1, 3));
t_gripper2base_noise.at<double>(0,0) += rng.gaussian(0.001);
t_gripper2base_noise.at<double>(1,0) += rng.gaussian(0.001);
t_gripper2base_noise.at<double>(2,0) += rng.gaussian(0.001);
}
//Test rvec representation
Mat rvec_target2cam;
cv::Rodrigues(T_target2cam(Rect(0, 0, 3, 3)), rvec_target2cam);
R_target2cam.push_back(rvec_target2cam);
t_target2cam.push_back(T_target2cam(Rect(3, 0, 1, 3)));
}
}
static void simulateDataEyeToHand(RNG& rng, int nPoses,
std::vector<Mat> &R_base2gripper, std::vector<Mat> &t_base2gripper,
std::vector<Mat> &R_target2cam, std::vector<Mat> &t_target2cam,
bool noise, Mat& R_cam2base, Mat& t_cam2base)
{
//to avoid generating values close to zero,
//we use positive range values and randomize the sign
const bool random_sign = true;
generatePose(rng, 10.0*CV_PI/180.0, 50.0*CV_PI/180.0,
0.5, 3.5, 0.5, 3.5, 0.5, 3.5,
R_cam2base, t_cam2base, random_sign);
Mat R_target2gripper, t_target2gripper;
generatePose(rng, 5.0*CV_PI/180.0, 85.0*CV_PI/180.0,
0.05, 0.5, 0.05, 0.5, 0.05, 0.5,
R_target2gripper, t_target2gripper, random_sign);
Mat T_target2gripper = Mat::eye(4, 4, CV_64FC1);
R_target2gripper.copyTo(T_target2gripper(Rect(0, 0, 3, 3)));
t_target2gripper.copyTo(T_target2gripper(Rect(3, 0, 1, 3)));
for (int i = 0; i < nPoses; i++)
{
Mat R_gripper2base_, t_gripper2base_;
generatePose(rng, 5.0*CV_PI/180.0, 45.0*CV_PI/180.0,
0.5, 1.5, 0.5, 1.5, 0.5, 1.5,
R_gripper2base_, t_gripper2base_, random_sign);
Mat R_base2gripper_ = R_gripper2base_.t();
Mat t_base2gripper_ = -R_base2gripper_ * t_gripper2base_;
Mat T_gripper2base = Mat::eye(4, 4, CV_64FC1);
R_gripper2base_.copyTo(T_gripper2base(Rect(0, 0, 3, 3)));
t_gripper2base_.copyTo(T_gripper2base(Rect(3, 0, 1, 3)));
Mat T_cam2base = Mat::eye(4, 4, CV_64FC1);
R_cam2base.copyTo(T_cam2base(Rect(0, 0, 3, 3)));
t_cam2base.copyTo(T_cam2base(Rect(3, 0, 1, 3)));
Mat T_target2cam = homogeneousInverse(T_cam2base) * T_gripper2base * T_target2gripper;
if (noise)
{
//Add some noise for the transformation between the target and the camera
Mat R_target2cam_noise = T_target2cam(Rect(0, 0, 3, 3));
Mat rvec_target2cam_noise;
cv::Rodrigues(R_target2cam_noise, rvec_target2cam_noise);
rvec_target2cam_noise.at<double>(0,0) += rng.gaussian(0.002);
rvec_target2cam_noise.at<double>(1,0) += rng.gaussian(0.002);
rvec_target2cam_noise.at<double>(2,0) += rng.gaussian(0.002);
cv::Rodrigues(rvec_target2cam_noise, R_target2cam_noise);
Mat t_target2cam_noise = T_target2cam(Rect(3, 0, 1, 3));
t_target2cam_noise.at<double>(0,0) += rng.gaussian(0.005);
t_target2cam_noise.at<double>(1,0) += rng.gaussian(0.005);
t_target2cam_noise.at<double>(2,0) += rng.gaussian(0.005);
//Add some noise for the transformation between the robot base and the gripper
Mat rvec_base2gripper_noise;
cv::Rodrigues(R_base2gripper_, rvec_base2gripper_noise);
rvec_base2gripper_noise.at<double>(0,0) += rng.gaussian(0.001);
rvec_base2gripper_noise.at<double>(1,0) += rng.gaussian(0.001);
rvec_base2gripper_noise.at<double>(2,0) += rng.gaussian(0.001);
cv::Rodrigues(rvec_base2gripper_noise, R_base2gripper_);
t_base2gripper_.at<double>(0,0) += rng.gaussian(0.001);
t_base2gripper_.at<double>(1,0) += rng.gaussian(0.001);
t_base2gripper_.at<double>(2,0) += rng.gaussian(0.001);
}
R_base2gripper.push_back(R_base2gripper_);
t_base2gripper.push_back(t_base2gripper_);
//Test rvec representation
Mat rvec_target2cam;
cv::Rodrigues(T_target2cam(Rect(0, 0, 3, 3)), rvec_target2cam);
R_target2cam.push_back(rvec_target2cam);
t_target2cam.push_back(T_target2cam(Rect(3, 0, 1, 3)));
}
}
static std::string getMethodName(HandEyeCalibrationMethod method)
{
std::string method_name = "";
switch (method)
{
case CALIB_HAND_EYE_TSAI:
method_name = "Tsai";
break;
case CALIB_HAND_EYE_PARK:
method_name = "Park";
break;
case CALIB_HAND_EYE_HORAUD:
method_name = "Horaud";
break;
case CALIB_HAND_EYE_ANDREFF:
method_name = "Andreff";
break;
case CALIB_HAND_EYE_DANIILIDIS:
method_name = "Daniilidis";
break;
default:
break;
}
return method_name;
}
static std::string getMethodName(RobotWorldHandEyeCalibrationMethod method)
{
std::string method_name = "";
switch (method)
{
case CALIB_ROBOT_WORLD_HAND_EYE_SHAH:
method_name = "Shah";
break;
case CALIB_ROBOT_WORLD_HAND_EYE_LI:
method_name = "Li";
break;
default:
break;
}
return method_name;
}
static void printStats(const std::string& methodName, const std::vector<double>& rvec_diff, const std::vector<double>& tvec_diff)
{
double max_rvec_diff = *std::max_element(rvec_diff.begin(), rvec_diff.end());
double mean_rvec_diff = std::accumulate(rvec_diff.begin(),
rvec_diff.end(), 0.0) / rvec_diff.size();
double sq_sum_rvec_diff = std::inner_product(rvec_diff.begin(), rvec_diff.end(),
rvec_diff.begin(), 0.0);
double std_rvec_diff = std::sqrt(sq_sum_rvec_diff / rvec_diff.size() - mean_rvec_diff * mean_rvec_diff);
double max_tvec_diff = *std::max_element(tvec_diff.begin(), tvec_diff.end());
double mean_tvec_diff = std::accumulate(tvec_diff.begin(),
tvec_diff.end(), 0.0) / tvec_diff.size();
double sq_sum_tvec_diff = std::inner_product(tvec_diff.begin(), tvec_diff.end(),
tvec_diff.begin(), 0.0);
double std_tvec_diff = std::sqrt(sq_sum_tvec_diff / tvec_diff.size() - mean_tvec_diff * mean_tvec_diff);
std::cout << "Method " << methodName << ":\n"
<< "Max rvec error: " << max_rvec_diff << ", Mean rvec error: " << mean_rvec_diff
<< ", Std rvec error: " << std_rvec_diff << "\n"
<< "Max tvec error: " << max_tvec_diff << ", Mean tvec error: " << mean_tvec_diff
<< ", Std tvec error: " << std_tvec_diff << std::endl;
}
static void loadDataset(std::vector<Mat>& R_target2cam, std::vector<Mat>& t_target2cam,
std::vector<Mat>& R_base2gripper, std::vector<Mat>& t_base2gripper)
{
const std::string camera_poses_filename = findDataFile("cv/robot_world_hand_eye_calibration/cali.txt");
const std::string end_effector_poses = findDataFile("cv/robot_world_hand_eye_calibration/robot_cali.txt");
// Parse camera poses, the pose of the chessboard in the camera frame
{
std::ifstream file(camera_poses_filename);
ASSERT_TRUE(file.is_open());
int ndata = 0;
file >> ndata;
R_target2cam.reserve(ndata);
t_target2cam.reserve(ndata);
std::string image_name;
Matx33d cameraMatrix;
Matx33d R;
Matx31d t;
Matx16d distCoeffs;
Matx13d distCoeffs2;
while (file >> image_name >>
cameraMatrix(0,0) >> cameraMatrix(0,1) >> cameraMatrix(0,2) >>
cameraMatrix(1,0) >> cameraMatrix(1,1) >> cameraMatrix(1,2) >>
cameraMatrix(2,0) >> cameraMatrix(2,1) >> cameraMatrix(2,2) >>
R(0,0) >> R(0,1) >> R(0,2) >>
R(1,0) >> R(1,1) >> R(1,2) >>
R(2,0) >> R(2,1) >> R(2,2) >>
t(0) >> t(1) >> t(2) >>
distCoeffs(0) >> distCoeffs(1) >> distCoeffs(2) >> distCoeffs(3) >> distCoeffs(4) >>
distCoeffs2(0) >> distCoeffs2(1) >> distCoeffs2(2)) {
R_target2cam.push_back(Mat(R));
t_target2cam.push_back(Mat(t));
}
}
// Parse robot poses, the pose of the robot base in the robot hand frame
{
std::ifstream file(end_effector_poses);
ASSERT_TRUE(file.is_open());
int ndata = 0;
file >> ndata;
R_base2gripper.reserve(ndata);
t_base2gripper.reserve(ndata);
Matx33d R;
Matx31d t;
Matx14d last_row;
while (file >>
R(0,0) >> R(0,1) >> R(0,2) >> t(0) >>
R(1,0) >> R(1,1) >> R(1,2) >> t(1) >>
R(2,0) >> R(2,1) >> R(2,2) >> t(2) >>
last_row(0) >> last_row(1) >> last_row(2) >> last_row(3)) {
R_base2gripper.push_back(Mat(R));
t_base2gripper.push_back(Mat(t));
}
}
}
static void loadResults(Matx33d& wRb, Matx31d& wtb, Matx33d& cRg, Matx31d& ctg)
{
const std::string transformations_filename = findDataFile("cv/robot_world_hand_eye_calibration/rwhe_AA_RPI/transformations.txt");
std::ifstream file(transformations_filename);
ASSERT_TRUE(file.is_open());
std::string str;
//Parse X
file >> str;
Matx44d wTb;
for (int i = 0; i < 4; i++)
{
for (int j = 0; j < 4; j++)
{
file >> wTb(i,j);
}
}
//Parse Z
file >> str;
int cam_num = 0;
//Parse camera number
file >> cam_num;
Matx44d cTg;
for (int i = 0; i < 4; i++)
{
for (int j = 0; j < 4; j++)
{
file >> cTg(i,j);
}
}
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
wRb(i,j) = wTb(i,j);
cRg(i,j) = cTg(i,j);
}
wtb(i) = wTb(i,3);
ctg(i) = cTg(i,3);
}
}
class CV_CalibrateHandEyeTest : public cvtest::BaseTest
{
public:
CV_CalibrateHandEyeTest(bool eyeToHand) : eyeToHandConfig(eyeToHand) {
eps_rvec[CALIB_HAND_EYE_TSAI] = 1.0e-8;
eps_rvec[CALIB_HAND_EYE_PARK] = 1.0e-8;
eps_rvec[CALIB_HAND_EYE_HORAUD] = 1.0e-8;
eps_rvec[CALIB_HAND_EYE_ANDREFF] = 1.0e-8;
eps_rvec[CALIB_HAND_EYE_DANIILIDIS] = 1.0e-8;
eps_tvec[CALIB_HAND_EYE_TSAI] = 1.0e-8;
eps_tvec[CALIB_HAND_EYE_PARK] = 1.0e-8;
eps_tvec[CALIB_HAND_EYE_HORAUD] = 1.0e-8;
eps_tvec[CALIB_HAND_EYE_ANDREFF] = 1.0e-8;
eps_tvec[CALIB_HAND_EYE_DANIILIDIS] = 1.0e-8;
eps_rvec_noise[CALIB_HAND_EYE_TSAI] = 2.0e-2;
eps_rvec_noise[CALIB_HAND_EYE_PARK] = 2.0e-2;
eps_rvec_noise[CALIB_HAND_EYE_HORAUD] = 2.0e-2;
eps_rvec_noise[CALIB_HAND_EYE_ANDREFF] = 1.0e-2;
eps_rvec_noise[CALIB_HAND_EYE_DANIILIDIS] = 1.0e-2;
eps_tvec_noise[CALIB_HAND_EYE_TSAI] = 7.0e-2;
eps_tvec_noise[CALIB_HAND_EYE_PARK] = 7.0e-2;
eps_tvec_noise[CALIB_HAND_EYE_HORAUD] = 7.0e-2;
if (eyeToHandConfig)
{
eps_tvec_noise[CALIB_HAND_EYE_ANDREFF] = 7.0e-2;
}
else
{
eps_tvec_noise[CALIB_HAND_EYE_ANDREFF] = 5.0e-2;
}
eps_tvec_noise[CALIB_HAND_EYE_DANIILIDIS] = 5.0e-2;
}
protected:
virtual void run(int);
bool eyeToHandConfig;
double eps_rvec[5];
double eps_tvec[5];
double eps_rvec_noise[5];
double eps_tvec_noise[5];
};
void CV_CalibrateHandEyeTest::run(int)
{
ts->set_failed_test_info(cvtest::TS::OK);
RNG& rng = cv::theRNG();
std::vector<std::vector<double> > vec_rvec_diff(5);
std::vector<std::vector<double> > vec_tvec_diff(5);
std::vector<std::vector<double> > vec_rvec_diff_noise(5);
std::vector<std::vector<double> > vec_tvec_diff_noise(5);
std::vector<HandEyeCalibrationMethod> methods;
methods.push_back(CALIB_HAND_EYE_TSAI);
methods.push_back(CALIB_HAND_EYE_PARK);
methods.push_back(CALIB_HAND_EYE_HORAUD);
methods.push_back(CALIB_HAND_EYE_ANDREFF);
methods.push_back(CALIB_HAND_EYE_DANIILIDIS);
const int nTests = 100;
for (int i = 0; i < nTests; i++)
{
const int nPoses = 10;
if (eyeToHandConfig)
{
{
//No noise
std::vector<Mat> R_base2gripper, t_base2gripper;
std::vector<Mat> R_target2cam, t_target2cam;
Mat R_cam2base_true, t_cam2base_true;
const bool noise = false;
simulateDataEyeToHand(rng, nPoses, R_base2gripper, t_base2gripper, R_target2cam, t_target2cam, noise,
R_cam2base_true, t_cam2base_true);
for (size_t idx = 0; idx < methods.size(); idx++)
{
Mat rvec_cam2base_true;
cv::Rodrigues(R_cam2base_true, rvec_cam2base_true);
Mat R_cam2base_est, t_cam2base_est;
calibrateHandEye(R_base2gripper, t_base2gripper, R_target2cam, t_target2cam, R_cam2base_est, t_cam2base_est, methods[idx]);
Mat rvec_cam2base_est;
cv::Rodrigues(R_cam2base_est, rvec_cam2base_est);
double rvecDiff = cvtest::norm(rvec_cam2base_true, rvec_cam2base_est, NORM_L2);
double tvecDiff = cvtest::norm(t_cam2base_true, t_cam2base_est, NORM_L2);
vec_rvec_diff[idx].push_back(rvecDiff);
vec_tvec_diff[idx].push_back(tvecDiff);
const double epsilon_rvec = eps_rvec[idx];
const double epsilon_tvec = eps_tvec[idx];
//Maybe a better accuracy test would be to compare the mean and std errors with some thresholds?
if (rvecDiff > epsilon_rvec || tvecDiff > epsilon_tvec)
{
ts->printf(cvtest::TS::LOG, "Invalid accuracy (no noise) for method: %s, rvecDiff: %f, epsilon_rvec: %f, tvecDiff: %f, epsilon_tvec: %f\n",
getMethodName(methods[idx]).c_str(), rvecDiff, epsilon_rvec, tvecDiff, epsilon_tvec);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
}
{
//Gaussian noise on transformations between calibration target frame and camera frame and between robot base and gripper frames
std::vector<Mat> R_base2gripper, t_base2gripper;
std::vector<Mat> R_target2cam, t_target2cam;
Mat R_cam2base_true, t_cam2base_true;
const bool noise = true;
simulateDataEyeToHand(rng, nPoses, R_base2gripper, t_base2gripper, R_target2cam, t_target2cam, noise,
R_cam2base_true, t_cam2base_true);
for (size_t idx = 0; idx < methods.size(); idx++)
{
Mat rvec_cam2base_true;
cv::Rodrigues(R_cam2base_true, rvec_cam2base_true);
Mat R_cam2base_est, t_cam2base_est;
calibrateHandEye(R_base2gripper, t_base2gripper, R_target2cam, t_target2cam, R_cam2base_est, t_cam2base_est, methods[idx]);
Mat rvec_cam2base_est;
cv::Rodrigues(R_cam2base_est, rvec_cam2base_est);
double rvecDiff = cvtest::norm(rvec_cam2base_true, rvec_cam2base_est, NORM_L2);
double tvecDiff = cvtest::norm(t_cam2base_true, t_cam2base_est, NORM_L2);
vec_rvec_diff_noise[idx].push_back(rvecDiff);
vec_tvec_diff_noise[idx].push_back(tvecDiff);
const double epsilon_rvec = eps_rvec_noise[idx];
const double epsilon_tvec = eps_tvec_noise[idx];
//Maybe a better accuracy test would be to compare the mean and std errors with some thresholds?
if (rvecDiff > epsilon_rvec || tvecDiff > epsilon_tvec)
{
ts->printf(cvtest::TS::LOG, "Invalid accuracy (noise) for method: %s, rvecDiff: %f, epsilon_rvec: %f, tvecDiff: %f, epsilon_tvec: %f\n",
getMethodName(methods[idx]).c_str(), rvecDiff, epsilon_rvec, tvecDiff, epsilon_tvec);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
}
}
else
{
{
//No noise
std::vector<Mat> R_gripper2base, t_gripper2base;
std::vector<Mat> R_target2cam, t_target2cam;
Mat R_cam2gripper_true, t_cam2gripper_true;
const bool noise = false;
simulateDataEyeInHand(rng, nPoses, R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, noise,
R_cam2gripper_true, t_cam2gripper_true);
for (size_t idx = 0; idx < methods.size(); idx++)
{
Mat rvec_cam2gripper_true;
cv::Rodrigues(R_cam2gripper_true, rvec_cam2gripper_true);
Mat R_cam2gripper_est, t_cam2gripper_est;
calibrateHandEye(R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, R_cam2gripper_est, t_cam2gripper_est, methods[idx]);
Mat rvec_cam2gripper_est;
cv::Rodrigues(R_cam2gripper_est, rvec_cam2gripper_est);
double rvecDiff = cvtest::norm(rvec_cam2gripper_true, rvec_cam2gripper_est, NORM_L2);
double tvecDiff = cvtest::norm(t_cam2gripper_true, t_cam2gripper_est, NORM_L2);
vec_rvec_diff[idx].push_back(rvecDiff);
vec_tvec_diff[idx].push_back(tvecDiff);
const double epsilon_rvec = eps_rvec[idx];
const double epsilon_tvec = eps_tvec[idx];
//Maybe a better accuracy test would be to compare the mean and std errors with some thresholds?
if (rvecDiff > epsilon_rvec || tvecDiff > epsilon_tvec)
{
ts->printf(cvtest::TS::LOG, "Invalid accuracy (no noise) for method: %s, rvecDiff: %f, epsilon_rvec: %f, tvecDiff: %f, epsilon_tvec: %f\n",
getMethodName(methods[idx]).c_str(), rvecDiff, epsilon_rvec, tvecDiff, epsilon_tvec);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
}
{
//Gaussian noise on transformations between calibration target frame and camera frame and between gripper and robot base frames
std::vector<Mat> R_gripper2base, t_gripper2base;
std::vector<Mat> R_target2cam, t_target2cam;
Mat R_cam2gripper_true, t_cam2gripper_true;
const bool noise = true;
simulateDataEyeInHand(rng, nPoses, R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, noise,
R_cam2gripper_true, t_cam2gripper_true);
for (size_t idx = 0; idx < methods.size(); idx++)
{
Mat rvec_cam2gripper_true;
cv::Rodrigues(R_cam2gripper_true, rvec_cam2gripper_true);
Mat R_cam2gripper_est, t_cam2gripper_est;
calibrateHandEye(R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, R_cam2gripper_est, t_cam2gripper_est, methods[idx]);
Mat rvec_cam2gripper_est;
cv::Rodrigues(R_cam2gripper_est, rvec_cam2gripper_est);
double rvecDiff = cvtest::norm(rvec_cam2gripper_true, rvec_cam2gripper_est, NORM_L2);
double tvecDiff = cvtest::norm(t_cam2gripper_true, t_cam2gripper_est, NORM_L2);
vec_rvec_diff_noise[idx].push_back(rvecDiff);
vec_tvec_diff_noise[idx].push_back(tvecDiff);
const double epsilon_rvec = eps_rvec_noise[idx];
const double epsilon_tvec = eps_tvec_noise[idx];
//Maybe a better accuracy test would be to compare the mean and std errors with some thresholds?
if (rvecDiff > epsilon_rvec || tvecDiff > epsilon_tvec)
{
ts->printf(cvtest::TS::LOG, "Invalid accuracy (noise) for method: %s, rvecDiff: %f, epsilon_rvec: %f, tvecDiff: %f, epsilon_tvec: %f\n",
getMethodName(methods[idx]).c_str(), rvecDiff, epsilon_rvec, tvecDiff, epsilon_tvec);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
}
}
}
for (size_t idx = 0; idx < methods.size(); idx++)
{
std::cout << std::endl;
printStats(getMethodName(methods[idx]), vec_rvec_diff[idx], vec_tvec_diff[idx]);
printStats("(noise) " + getMethodName(methods[idx]), vec_rvec_diff_noise[idx], vec_tvec_diff_noise[idx]);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////
TEST(Calib3d_CalibrateHandEye, regression_eye_in_hand)
{
//Eye-in-Hand configuration (camera mounted on the robot end-effector observing a static calibration pattern)
const bool eyeToHand = false;
CV_CalibrateHandEyeTest test(eyeToHand);
test.safe_run();
}
TEST(Calib3d_CalibrateHandEye, regression_eye_to_hand)
{
//Eye-to-Hand configuration (static camera observing a calibration pattern mounted on the robot end-effector)
const bool eyeToHand = true;
CV_CalibrateHandEyeTest test(eyeToHand);
test.safe_run();
}
TEST(Calib3d_CalibrateHandEye, regression_17986)
{
std::vector<Mat> R_target2cam, t_target2cam;
// Dataset contains transformation from base to gripper frame since it contains data for AX = ZB calibration problem
std::vector<Mat> R_base2gripper, t_base2gripper;
loadDataset(R_target2cam, t_target2cam, R_base2gripper, t_base2gripper);
std::vector<HandEyeCalibrationMethod> methods = {CALIB_HAND_EYE_TSAI,
CALIB_HAND_EYE_PARK,
CALIB_HAND_EYE_HORAUD,
CALIB_HAND_EYE_ANDREFF,
CALIB_HAND_EYE_DANIILIDIS};
for (auto method : methods) {
SCOPED_TRACE(cv::format("method=%s", getMethodName(method).c_str()));
Matx33d R_cam2base_est;
Matx31d t_cam2base_est;
calibrateHandEye(R_base2gripper, t_base2gripper, R_target2cam, t_target2cam, R_cam2base_est, t_cam2base_est, method);
EXPECT_TRUE(checkRange(R_cam2base_est));
EXPECT_TRUE(checkRange(t_cam2base_est));
}
}
TEST(Calib3d_CalibrateRobotWorldHandEye, regression)
{
std::vector<Mat> R_world2cam, t_worldt2cam;
std::vector<Mat> R_base2gripper, t_base2gripper;
loadDataset(R_world2cam, t_worldt2cam, R_base2gripper, t_base2gripper);
std::vector<Mat> rvec_R_world2cam;
rvec_R_world2cam.reserve(R_world2cam.size());
for (size_t i = 0; i < R_world2cam.size(); i++)
{
Mat rvec;
cv::Rodrigues(R_world2cam[i], rvec);
rvec_R_world2cam.push_back(rvec);
}
std::vector<RobotWorldHandEyeCalibrationMethod> methods = {CALIB_ROBOT_WORLD_HAND_EYE_SHAH,
CALIB_ROBOT_WORLD_HAND_EYE_LI};
Matx33d wRb, cRg;
Matx31d wtb, ctg;
loadResults(wRb, wtb, cRg, ctg);
for (auto method : methods) {
SCOPED_TRACE(cv::format("method=%s", getMethodName(method).c_str()));
Matx33d wRb_est, cRg_est;
Matx31d wtb_est, ctg_est;
calibrateRobotWorldHandEye(rvec_R_world2cam, t_worldt2cam, R_base2gripper, t_base2gripper,
wRb_est, wtb_est, cRg_est, ctg_est, method);
EXPECT_TRUE(checkRange(wRb_est));
EXPECT_TRUE(checkRange(wtb_est));
EXPECT_TRUE(checkRange(cRg_est));
EXPECT_TRUE(checkRange(ctg_est));
//Arbitrary thresholds
const double rotation_threshold = 1.0; //1deg
const double translation_threshold = 50.0; //5cm
//X
//rotation error
Matx33d wRw_est = wRb * wRb_est.t();
Matx31d rvec_wRw_est;
cv::Rodrigues(wRw_est, rvec_wRw_est);
double X_rotation_error = cv::norm(rvec_wRw_est)*180/CV_PI;
//translation error
double X_t_error = cv::norm(wtb_est - wtb);
SCOPED_TRACE(cv::format("X rotation error=%f", X_rotation_error));
SCOPED_TRACE(cv::format("X translation error=%f", X_t_error));
EXPECT_TRUE(X_rotation_error < rotation_threshold);
EXPECT_TRUE(X_t_error < translation_threshold);
//Z
//rotation error
Matx33d cRc_est = cRg * cRg_est.t();
Matx31d rvec_cMc_est;
cv::Rodrigues(cRc_est, rvec_cMc_est);
double Z_rotation_error = cv::norm(rvec_cMc_est)*180/CV_PI;
//translation error
double Z_t_error = cv::norm(ctg_est - ctg);
SCOPED_TRACE(cv::format("Z rotation error=%f", Z_rotation_error));
SCOPED_TRACE(cv::format("Z translation error=%f", Z_t_error));
EXPECT_TRUE(Z_rotation_error < rotation_threshold);
EXPECT_TRUE(Z_t_error < translation_threshold);
}
}
}} // namespace