718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
760 lines
30 KiB
C++
760 lines
30 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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#include "opencv2/calib3d.hpp"
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namespace opencv_test { namespace {
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static void generatePose(RNG& rng, double min_theta, double max_theta,
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double min_tx, double max_tx,
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double min_ty, double max_ty,
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double min_tz, double max_tz,
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Mat& R, Mat& tvec,
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bool random_sign)
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{
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Mat axis(3, 1, CV_64FC1);
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for (int i = 0; i < 3; i++)
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{
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axis.at<double>(i,0) = rng.uniform(-1.0, 1.0);
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}
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double theta = rng.uniform(min_theta, max_theta);
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if (random_sign)
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{
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theta *= std::copysign(1.0, rng.uniform(-1.0, 1.0));
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}
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Mat rvec(3, 1, CV_64FC1);
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rvec.at<double>(0,0) = theta*axis.at<double>(0,0);
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rvec.at<double>(1,0) = theta*axis.at<double>(1,0);
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rvec.at<double>(2,0) = theta*axis.at<double>(2,0);
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tvec.create(3, 1, CV_64FC1);
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tvec.at<double>(0,0) = rng.uniform(min_tx, max_tx);
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tvec.at<double>(1,0) = rng.uniform(min_ty, max_ty);
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tvec.at<double>(2,0) = rng.uniform(min_tz, max_tz);
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if (random_sign)
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{
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tvec.at<double>(0,0) *= std::copysign(1.0, rng.uniform(-1.0, 1.0));
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tvec.at<double>(1,0) *= std::copysign(1.0, rng.uniform(-1.0, 1.0));
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tvec.at<double>(2,0) *= std::copysign(1.0, rng.uniform(-1.0, 1.0));
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}
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cv::Rodrigues(rvec, R);
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}
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static Mat homogeneousInverse(const Mat& T)
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{
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CV_Assert( T.rows == 4 && T.cols == 4 );
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Mat R = T(Rect(0, 0, 3, 3));
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Mat t = T(Rect(3, 0, 1, 3));
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Mat Rt = R.t();
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Mat tinv = -Rt * t;
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Mat Tinv = Mat::eye(4, 4, T.type());
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Rt.copyTo(Tinv(Rect(0, 0, 3, 3)));
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tinv.copyTo(Tinv(Rect(3, 0, 1, 3)));
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return Tinv;
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}
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static void simulateDataEyeInHand(RNG& rng, int nPoses,
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std::vector<Mat> &R_gripper2base, std::vector<Mat> &t_gripper2base,
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std::vector<Mat> &R_target2cam, std::vector<Mat> &t_target2cam,
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bool noise, Mat& R_cam2gripper, Mat& t_cam2gripper)
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{
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//to avoid generating values close to zero,
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//we use positive range values and randomize the sign
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const bool random_sign = true;
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generatePose(rng, 10.0*CV_PI/180.0, 50.0*CV_PI/180.0,
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0.05, 0.5, 0.05, 0.5, 0.05, 0.5,
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R_cam2gripper, t_cam2gripper, random_sign);
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Mat R_target2base, t_target2base;
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generatePose(rng, 5.0*CV_PI/180.0, 85.0*CV_PI/180.0,
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0.5, 3.5, 0.5, 3.5, 0.5, 3.5,
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R_target2base, t_target2base, random_sign);
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for (int i = 0; i < nPoses; i++)
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{
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Mat R_gripper2base_, t_gripper2base_;
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generatePose(rng, 5.0*CV_PI/180.0, 45.0*CV_PI/180.0,
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0.5, 1.5, 0.5, 1.5, 0.5, 1.5,
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R_gripper2base_, t_gripper2base_, random_sign);
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R_gripper2base.push_back(R_gripper2base_);
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t_gripper2base.push_back(t_gripper2base_);
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Mat T_cam2gripper = Mat::eye(4, 4, CV_64FC1);
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R_cam2gripper.copyTo(T_cam2gripper(Rect(0, 0, 3, 3)));
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t_cam2gripper.copyTo(T_cam2gripper(Rect(3, 0, 1, 3)));
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Mat T_gripper2base = Mat::eye(4, 4, CV_64FC1);
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R_gripper2base_.copyTo(T_gripper2base(Rect(0, 0, 3, 3)));
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t_gripper2base_.copyTo(T_gripper2base(Rect(3, 0, 1, 3)));
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Mat T_base2cam = homogeneousInverse(T_cam2gripper) * homogeneousInverse(T_gripper2base);
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Mat T_target2base = Mat::eye(4, 4, CV_64FC1);
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R_target2base.copyTo(T_target2base(Rect(0, 0, 3, 3)));
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t_target2base.copyTo(T_target2base(Rect(3, 0, 1, 3)));
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Mat T_target2cam = T_base2cam * T_target2base;
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if (noise)
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{
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//Add some noise for the transformation between the target and the camera
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Mat R_target2cam_noise = T_target2cam(Rect(0, 0, 3, 3));
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Mat rvec_target2cam_noise;
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cv::Rodrigues(R_target2cam_noise, rvec_target2cam_noise);
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rvec_target2cam_noise.at<double>(0,0) += rng.gaussian(0.002);
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rvec_target2cam_noise.at<double>(1,0) += rng.gaussian(0.002);
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rvec_target2cam_noise.at<double>(2,0) += rng.gaussian(0.002);
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cv::Rodrigues(rvec_target2cam_noise, R_target2cam_noise);
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Mat t_target2cam_noise = T_target2cam(Rect(3, 0, 1, 3));
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t_target2cam_noise.at<double>(0,0) += rng.gaussian(0.005);
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t_target2cam_noise.at<double>(1,0) += rng.gaussian(0.005);
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t_target2cam_noise.at<double>(2,0) += rng.gaussian(0.005);
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//Add some noise for the transformation between the gripper and the robot base
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Mat R_gripper2base_noise = T_gripper2base(Rect(0, 0, 3, 3));
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Mat rvec_gripper2base_noise;
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cv::Rodrigues(R_gripper2base_noise, rvec_gripper2base_noise);
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rvec_gripper2base_noise.at<double>(0,0) += rng.gaussian(0.001);
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rvec_gripper2base_noise.at<double>(1,0) += rng.gaussian(0.001);
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rvec_gripper2base_noise.at<double>(2,0) += rng.gaussian(0.001);
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cv::Rodrigues(rvec_gripper2base_noise, R_gripper2base_noise);
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Mat t_gripper2base_noise = T_gripper2base(Rect(3, 0, 1, 3));
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t_gripper2base_noise.at<double>(0,0) += rng.gaussian(0.001);
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t_gripper2base_noise.at<double>(1,0) += rng.gaussian(0.001);
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t_gripper2base_noise.at<double>(2,0) += rng.gaussian(0.001);
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}
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//Test rvec representation
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Mat rvec_target2cam;
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cv::Rodrigues(T_target2cam(Rect(0, 0, 3, 3)), rvec_target2cam);
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R_target2cam.push_back(rvec_target2cam);
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t_target2cam.push_back(T_target2cam(Rect(3, 0, 1, 3)));
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}
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}
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static void simulateDataEyeToHand(RNG& rng, int nPoses,
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std::vector<Mat> &R_base2gripper, std::vector<Mat> &t_base2gripper,
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std::vector<Mat> &R_target2cam, std::vector<Mat> &t_target2cam,
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bool noise, Mat& R_cam2base, Mat& t_cam2base)
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{
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//to avoid generating values close to zero,
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//we use positive range values and randomize the sign
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const bool random_sign = true;
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generatePose(rng, 10.0*CV_PI/180.0, 50.0*CV_PI/180.0,
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0.5, 3.5, 0.5, 3.5, 0.5, 3.5,
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R_cam2base, t_cam2base, random_sign);
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Mat R_target2gripper, t_target2gripper;
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generatePose(rng, 5.0*CV_PI/180.0, 85.0*CV_PI/180.0,
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0.05, 0.5, 0.05, 0.5, 0.05, 0.5,
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R_target2gripper, t_target2gripper, random_sign);
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Mat T_target2gripper = Mat::eye(4, 4, CV_64FC1);
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R_target2gripper.copyTo(T_target2gripper(Rect(0, 0, 3, 3)));
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t_target2gripper.copyTo(T_target2gripper(Rect(3, 0, 1, 3)));
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for (int i = 0; i < nPoses; i++)
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{
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Mat R_gripper2base_, t_gripper2base_;
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generatePose(rng, 5.0*CV_PI/180.0, 45.0*CV_PI/180.0,
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0.5, 1.5, 0.5, 1.5, 0.5, 1.5,
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R_gripper2base_, t_gripper2base_, random_sign);
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Mat R_base2gripper_ = R_gripper2base_.t();
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Mat t_base2gripper_ = -R_base2gripper_ * t_gripper2base_;
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Mat T_gripper2base = Mat::eye(4, 4, CV_64FC1);
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R_gripper2base_.copyTo(T_gripper2base(Rect(0, 0, 3, 3)));
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t_gripper2base_.copyTo(T_gripper2base(Rect(3, 0, 1, 3)));
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Mat T_cam2base = Mat::eye(4, 4, CV_64FC1);
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R_cam2base.copyTo(T_cam2base(Rect(0, 0, 3, 3)));
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t_cam2base.copyTo(T_cam2base(Rect(3, 0, 1, 3)));
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Mat T_target2cam = homogeneousInverse(T_cam2base) * T_gripper2base * T_target2gripper;
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if (noise)
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{
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//Add some noise for the transformation between the target and the camera
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Mat R_target2cam_noise = T_target2cam(Rect(0, 0, 3, 3));
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Mat rvec_target2cam_noise;
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cv::Rodrigues(R_target2cam_noise, rvec_target2cam_noise);
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rvec_target2cam_noise.at<double>(0,0) += rng.gaussian(0.002);
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rvec_target2cam_noise.at<double>(1,0) += rng.gaussian(0.002);
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rvec_target2cam_noise.at<double>(2,0) += rng.gaussian(0.002);
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cv::Rodrigues(rvec_target2cam_noise, R_target2cam_noise);
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Mat t_target2cam_noise = T_target2cam(Rect(3, 0, 1, 3));
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t_target2cam_noise.at<double>(0,0) += rng.gaussian(0.005);
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t_target2cam_noise.at<double>(1,0) += rng.gaussian(0.005);
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t_target2cam_noise.at<double>(2,0) += rng.gaussian(0.005);
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//Add some noise for the transformation between the robot base and the gripper
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Mat rvec_base2gripper_noise;
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cv::Rodrigues(R_base2gripper_, rvec_base2gripper_noise);
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rvec_base2gripper_noise.at<double>(0,0) += rng.gaussian(0.001);
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rvec_base2gripper_noise.at<double>(1,0) += rng.gaussian(0.001);
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rvec_base2gripper_noise.at<double>(2,0) += rng.gaussian(0.001);
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cv::Rodrigues(rvec_base2gripper_noise, R_base2gripper_);
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t_base2gripper_.at<double>(0,0) += rng.gaussian(0.001);
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t_base2gripper_.at<double>(1,0) += rng.gaussian(0.001);
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t_base2gripper_.at<double>(2,0) += rng.gaussian(0.001);
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}
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R_base2gripper.push_back(R_base2gripper_);
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t_base2gripper.push_back(t_base2gripper_);
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//Test rvec representation
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Mat rvec_target2cam;
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cv::Rodrigues(T_target2cam(Rect(0, 0, 3, 3)), rvec_target2cam);
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R_target2cam.push_back(rvec_target2cam);
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t_target2cam.push_back(T_target2cam(Rect(3, 0, 1, 3)));
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}
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}
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static std::string getMethodName(HandEyeCalibrationMethod method)
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{
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std::string method_name = "";
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switch (method)
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{
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case CALIB_HAND_EYE_TSAI:
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method_name = "Tsai";
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break;
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case CALIB_HAND_EYE_PARK:
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method_name = "Park";
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break;
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case CALIB_HAND_EYE_HORAUD:
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method_name = "Horaud";
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break;
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case CALIB_HAND_EYE_ANDREFF:
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method_name = "Andreff";
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break;
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case CALIB_HAND_EYE_DANIILIDIS:
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method_name = "Daniilidis";
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break;
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default:
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break;
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}
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return method_name;
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}
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static std::string getMethodName(RobotWorldHandEyeCalibrationMethod method)
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{
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std::string method_name = "";
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switch (method)
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{
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case CALIB_ROBOT_WORLD_HAND_EYE_SHAH:
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method_name = "Shah";
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break;
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case CALIB_ROBOT_WORLD_HAND_EYE_LI:
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method_name = "Li";
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break;
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default:
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break;
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}
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return method_name;
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}
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static void printStats(const std::string& methodName, const std::vector<double>& rvec_diff, const std::vector<double>& tvec_diff)
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{
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double max_rvec_diff = *std::max_element(rvec_diff.begin(), rvec_diff.end());
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double mean_rvec_diff = std::accumulate(rvec_diff.begin(),
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rvec_diff.end(), 0.0) / rvec_diff.size();
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double sq_sum_rvec_diff = std::inner_product(rvec_diff.begin(), rvec_diff.end(),
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rvec_diff.begin(), 0.0);
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double std_rvec_diff = std::sqrt(sq_sum_rvec_diff / rvec_diff.size() - mean_rvec_diff * mean_rvec_diff);
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double max_tvec_diff = *std::max_element(tvec_diff.begin(), tvec_diff.end());
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double mean_tvec_diff = std::accumulate(tvec_diff.begin(),
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tvec_diff.end(), 0.0) / tvec_diff.size();
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double sq_sum_tvec_diff = std::inner_product(tvec_diff.begin(), tvec_diff.end(),
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tvec_diff.begin(), 0.0);
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double std_tvec_diff = std::sqrt(sq_sum_tvec_diff / tvec_diff.size() - mean_tvec_diff * mean_tvec_diff);
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std::cout << "Method " << methodName << ":\n"
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<< "Max rvec error: " << max_rvec_diff << ", Mean rvec error: " << mean_rvec_diff
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<< ", Std rvec error: " << std_rvec_diff << "\n"
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<< "Max tvec error: " << max_tvec_diff << ", Mean tvec error: " << mean_tvec_diff
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<< ", Std tvec error: " << std_tvec_diff << std::endl;
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}
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static void loadDataset(std::vector<Mat>& R_target2cam, std::vector<Mat>& t_target2cam,
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std::vector<Mat>& R_base2gripper, std::vector<Mat>& t_base2gripper)
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{
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const std::string camera_poses_filename = findDataFile("cv/robot_world_hand_eye_calibration/cali.txt");
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const std::string end_effector_poses = findDataFile("cv/robot_world_hand_eye_calibration/robot_cali.txt");
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// Parse camera poses, the pose of the chessboard in the camera frame
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{
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std::ifstream file(camera_poses_filename);
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ASSERT_TRUE(file.is_open());
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int ndata = 0;
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file >> ndata;
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R_target2cam.reserve(ndata);
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t_target2cam.reserve(ndata);
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std::string image_name;
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Matx33d cameraMatrix;
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Matx33d R;
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Matx31d t;
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Matx16d distCoeffs;
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Matx13d distCoeffs2;
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while (file >> image_name >>
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cameraMatrix(0,0) >> cameraMatrix(0,1) >> cameraMatrix(0,2) >>
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cameraMatrix(1,0) >> cameraMatrix(1,1) >> cameraMatrix(1,2) >>
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cameraMatrix(2,0) >> cameraMatrix(2,1) >> cameraMatrix(2,2) >>
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R(0,0) >> R(0,1) >> R(0,2) >>
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R(1,0) >> R(1,1) >> R(1,2) >>
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R(2,0) >> R(2,1) >> R(2,2) >>
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t(0) >> t(1) >> t(2) >>
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distCoeffs(0) >> distCoeffs(1) >> distCoeffs(2) >> distCoeffs(3) >> distCoeffs(4) >>
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distCoeffs2(0) >> distCoeffs2(1) >> distCoeffs2(2)) {
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R_target2cam.push_back(Mat(R));
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t_target2cam.push_back(Mat(t));
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}
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}
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// Parse robot poses, the pose of the robot base in the robot hand frame
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{
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std::ifstream file(end_effector_poses);
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ASSERT_TRUE(file.is_open());
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int ndata = 0;
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file >> ndata;
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R_base2gripper.reserve(ndata);
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t_base2gripper.reserve(ndata);
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Matx33d R;
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Matx31d t;
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Matx14d last_row;
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while (file >>
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R(0,0) >> R(0,1) >> R(0,2) >> t(0) >>
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R(1,0) >> R(1,1) >> R(1,2) >> t(1) >>
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R(2,0) >> R(2,1) >> R(2,2) >> t(2) >>
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last_row(0) >> last_row(1) >> last_row(2) >> last_row(3)) {
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R_base2gripper.push_back(Mat(R));
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t_base2gripper.push_back(Mat(t));
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}
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}
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}
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static void loadResults(Matx33d& wRb, Matx31d& wtb, Matx33d& cRg, Matx31d& ctg)
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{
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const std::string transformations_filename = findDataFile("cv/robot_world_hand_eye_calibration/rwhe_AA_RPI/transformations.txt");
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std::ifstream file(transformations_filename);
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ASSERT_TRUE(file.is_open());
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std::string str;
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//Parse X
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file >> str;
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Matx44d wTb;
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for (int i = 0; i < 4; i++)
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{
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for (int j = 0; j < 4; j++)
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{
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|
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] = 5.0e-2;
|
|
eps_tvec_noise[CALIB_HAND_EYE_PARK] = 5.0e-2;
|
|
eps_tvec_noise[CALIB_HAND_EYE_HORAUD] = 5.0e-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 = ts->get_rng();
|
|
|
|
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
|