/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" namespace opencv_test { namespace { class CV_solvePnPRansac_Test : public cvtest::BaseTest { public: CV_solvePnPRansac_Test() { eps[SOLVEPNP_ITERATIVE] = 1.0e-2; eps[SOLVEPNP_EPNP] = 1.0e-2; eps[SOLVEPNP_P3P] = 1.0e-2; eps[SOLVEPNP_AP3P] = 1.0e-2; eps[SOLVEPNP_DLS] = 1.0e-2; eps[SOLVEPNP_UPNP] = 1.0e-2; totalTestsCount = 10; pointsCount = 500; } ~CV_solvePnPRansac_Test() {} protected: void generate3DPointCloud(vector& points, Point3f pmin = Point3f(-1, -1, 5), Point3f pmax = Point3f(1, 1, 10)) { RNG rng = cv::theRNG(); // fix the seed to use "fixed" input 3D points for (size_t i = 0; i < points.size(); i++) { float _x = rng.uniform(pmin.x, pmax.x); float _y = rng.uniform(pmin.y, pmax.y); float _z = rng.uniform(pmin.z, pmax.z); points[i] = Point3f(_x, _y, _z); } } void generateCameraMatrix(Mat& cameraMatrix, RNG& rng) { const double fcMinVal = 1e-3; const double fcMaxVal = 100; cameraMatrix.create(3, 3, CV_64FC1); cameraMatrix.setTo(Scalar(0)); cameraMatrix.at(0,0) = rng.uniform(fcMinVal, fcMaxVal); cameraMatrix.at(1,1) = rng.uniform(fcMinVal, fcMaxVal); cameraMatrix.at(0,2) = rng.uniform(fcMinVal, fcMaxVal); cameraMatrix.at(1,2) = rng.uniform(fcMinVal, fcMaxVal); cameraMatrix.at(2,2) = 1; } void generateDistCoeffs(Mat& distCoeffs, RNG& rng) { distCoeffs = Mat::zeros(4, 1, CV_64FC1); for (int i = 0; i < 3; i++) distCoeffs.at(i,0) = rng.uniform(0.0, 1.0e-6); } void generatePose(Mat& rvec, Mat& tvec, RNG& rng) { const double minVal = 1.0e-3; const double maxVal = 1.0; rvec.create(3, 1, CV_64FC1); tvec.create(3, 1, CV_64FC1); for (int i = 0; i < 3; i++) { rvec.at(i,0) = rng.uniform(minVal, maxVal); tvec.at(i,0) = rng.uniform(minVal, maxVal/10); } } virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* epsilon, double& maxError) { Mat rvec, tvec; vector inliers; Mat trueRvec, trueTvec; Mat intrinsics, distCoeffs; generateCameraMatrix(intrinsics, rng); if (method == 4) intrinsics.at(1,1) = intrinsics.at(0,0); if (mode == 0) distCoeffs = Mat::zeros(4, 1, CV_64FC1); else generateDistCoeffs(distCoeffs, rng); generatePose(trueRvec, trueTvec, rng); vector projectedPoints; projectedPoints.resize(points.size()); projectPoints(points, trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints); for (size_t i = 0; i < projectedPoints.size(); i++) { if (i % 20 == 0) { projectedPoints[i] = projectedPoints[rng.uniform(0,(int)points.size()-1)]; } } solvePnPRansac(points, projectedPoints, intrinsics, distCoeffs, rvec, tvec, false, pointsCount, 0.5f, 0.99, inliers, method); bool isTestSuccess = inliers.size() >= points.size()*0.95; double rvecDiff = cvtest::norm(rvec, trueRvec, NORM_L2), tvecDiff = cvtest::norm(tvec, trueTvec, NORM_L2); isTestSuccess = isTestSuccess && rvecDiff < epsilon[method] && tvecDiff < epsilon[method]; double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff; //cout << error << " " << inliers.size() << " " << eps[method] << endl; if (error > maxError) maxError = error; return isTestSuccess; } virtual void run(int) { ts->set_failed_test_info(cvtest::TS::OK); vector points, points_dls; points.resize(pointsCount); generate3DPointCloud(points); RNG rng = ts->get_rng(); for (int mode = 0; mode < 2; mode++) { for (int method = 0; method < SOLVEPNP_MAX_COUNT; method++) { double maxError = 0; int successfulTestsCount = 0; for (int testIndex = 0; testIndex < totalTestsCount; testIndex++) { if (runTest(rng, mode, method, points, eps, maxError)) { successfulTestsCount++; } } if (successfulTestsCount < 0.7*totalTestsCount) { ts->printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n", method, totalTestsCount - successfulTestsCount, totalTestsCount, maxError, mode); ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } cout << "mode: " << mode << ", method: " << method << " -> " << ((double)successfulTestsCount / totalTestsCount) * 100 << "%" << " (err < " << maxError << ")" << endl; } } } double eps[SOLVEPNP_MAX_COUNT]; int totalTestsCount; int pointsCount; }; class CV_solvePnP_Test : public CV_solvePnPRansac_Test { public: CV_solvePnP_Test() { eps[SOLVEPNP_ITERATIVE] = 1.0e-6; eps[SOLVEPNP_EPNP] = 1.0e-6; eps[SOLVEPNP_P3P] = 2.0e-4; eps[SOLVEPNP_AP3P] = 1.0e-4; eps[SOLVEPNP_DLS] = 1.0e-4; eps[SOLVEPNP_UPNP] = 1.0e-4; totalTestsCount = 1000; } ~CV_solvePnP_Test() {} protected: virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* epsilon, double& maxError) { Mat rvec, tvec; Mat trueRvec, trueTvec; Mat intrinsics, distCoeffs; generateCameraMatrix(intrinsics, rng); if (method == SOLVEPNP_DLS) { intrinsics.at(1,1) = intrinsics.at(0,0); } if (mode == 0) { distCoeffs = Mat::zeros(4, 1, CV_64FC1); } else { generateDistCoeffs(distCoeffs, rng); } generatePose(trueRvec, trueTvec, rng); std::vector opoints; switch(method) { case SOLVEPNP_P3P: case SOLVEPNP_AP3P: opoints = std::vector(points.begin(), points.begin()+4); break; case SOLVEPNP_UPNP: opoints = std::vector(points.begin(), points.begin()+50); break; default: opoints = points; break; } vector projectedPoints; projectedPoints.resize(opoints.size()); projectPoints(opoints, trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints); bool isEstimateSuccess = solvePnP(opoints, projectedPoints, intrinsics, distCoeffs, rvec, tvec, false, method); if (isEstimateSuccess == false) { return isEstimateSuccess; } double rvecDiff = cvtest::norm(rvec, trueRvec, NORM_L2), tvecDiff = cvtest::norm(tvec, trueTvec, NORM_L2); bool isTestSuccess = rvecDiff < epsilon[method] && tvecDiff < epsilon[method]; double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff; if (error > maxError) { maxError = error; } return isTestSuccess; } }; class CV_solveP3P_Test : public CV_solvePnPRansac_Test { public: CV_solveP3P_Test() { eps[SOLVEPNP_P3P] = 2.0e-4; eps[SOLVEPNP_AP3P] = 1.0e-4; totalTestsCount = 1000; } ~CV_solveP3P_Test() {} protected: virtual bool runTest(RNG& rng, int mode, int method, const vector& points, const double* epsilon, double& maxError) { std::vector rvecs, tvecs; Mat trueRvec, trueTvec; Mat intrinsics, distCoeffs; generateCameraMatrix(intrinsics, rng); if (mode == 0) distCoeffs = Mat::zeros(4, 1, CV_64FC1); else generateDistCoeffs(distCoeffs, rng); generatePose(trueRvec, trueTvec, rng); std::vector opoints; opoints = std::vector(points.begin(), points.begin()+3); vector projectedPoints; projectedPoints.resize(opoints.size()); projectPoints(opoints, trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints); int num_of_solutions = solveP3P(opoints, projectedPoints, intrinsics, distCoeffs, rvecs, tvecs, method); if (num_of_solutions != (int) rvecs.size() || num_of_solutions != (int) tvecs.size() || num_of_solutions == 0) return false; bool isTestSuccess = false; double error = DBL_MAX; for (unsigned int i = 0; i < rvecs.size() && !isTestSuccess; ++i) { double rvecDiff = cvtest::norm(rvecs[i], trueRvec, NORM_L2); double tvecDiff = cvtest::norm(tvecs[i], trueTvec, NORM_L2); isTestSuccess = rvecDiff < epsilon[method] && tvecDiff < epsilon[method]; error = std::min(error, std::max(rvecDiff, tvecDiff)); } if (error > maxError) maxError = error; return isTestSuccess; } virtual void run(int) { ts->set_failed_test_info(cvtest::TS::OK); vector points; points.resize(pointsCount); generate3DPointCloud(points); const int methodsCount = 2; int methods[] = {SOLVEPNP_P3P, SOLVEPNP_AP3P}; RNG rng = ts->get_rng(); for (int mode = 0; mode < 2; mode++) { for (int method = 0; method < methodsCount; method++) { double maxError = 0; int successfulTestsCount = 0; for (int testIndex = 0; testIndex < totalTestsCount; testIndex++) { if (runTest(rng, mode, methods[method], points, eps, maxError)) successfulTestsCount++; } if (successfulTestsCount < 0.7*totalTestsCount) { ts->printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n", method, totalTestsCount - successfulTestsCount, totalTestsCount, maxError, mode); ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); } cout << "mode: " << mode << ", method: " << method << " -> " << ((double)successfulTestsCount / totalTestsCount) * 100 << "%" << " (err < " << maxError << ")" << endl; } } } }; TEST(Calib3d_SolveP3P, accuracy) { CV_solveP3P_Test test; test.safe_run();} TEST(Calib3d_SolvePnPRansac, accuracy) { CV_solvePnPRansac_Test test; test.safe_run(); } TEST(Calib3d_SolvePnP, accuracy) { CV_solvePnP_Test test; test.safe_run(); } TEST(Calib3d_SolvePnPRansac, concurrency) { int count = 7*13; Mat object(1, count, CV_32FC3); randu(object, -100, 100); Mat camera_mat(3, 3, CV_32FC1); randu(camera_mat, 0.5, 1); camera_mat.at(0, 1) = 0.f; camera_mat.at(1, 0) = 0.f; camera_mat.at(2, 0) = 0.f; camera_mat.at(2, 1) = 0.f; Mat dist_coef(1, 8, CV_32F, cv::Scalar::all(0)); vector image_vec; Mat rvec_gold(1, 3, CV_32FC1); randu(rvec_gold, 0, 1); Mat tvec_gold(1, 3, CV_32FC1); randu(tvec_gold, 0, 1); projectPoints(object, rvec_gold, tvec_gold, camera_mat, dist_coef, image_vec); Mat image(1, count, CV_32FC2, &image_vec[0]); Mat rvec1, rvec2; Mat tvec1, tvec2; int threads = getNumThreads(); { // limit concurrency to get deterministic result theRNG().state = 20121010; setNumThreads(1); solvePnPRansac(object, image, camera_mat, dist_coef, rvec1, tvec1); } { setNumThreads(threads); Mat rvec; Mat tvec; // parallel executions for(int i = 0; i < 10; ++i) { cv::theRNG().state = 20121010; solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec); } } { // single thread again theRNG().state = 20121010; setNumThreads(1); solvePnPRansac(object, image, camera_mat, dist_coef, rvec2, tvec2); } double rnorm = cvtest::norm(rvec1, rvec2, NORM_INF); double tnorm = cvtest::norm(tvec1, tvec2, NORM_INF); EXPECT_LT(rnorm, 1e-6); EXPECT_LT(tnorm, 1e-6); } TEST(Calib3d_SolvePnPRansac, input_type) { const int numPoints = 10; Matx33d intrinsics(5.4794130238156129e+002, 0., 2.9835545700043139e+002, 0., 5.4817724002728005e+002, 2.3062194051986233e+002, 0., 0., 1.); std::vector points3d; std::vector points2d; for (int i = 0; i < numPoints; i+=2) { points3d.push_back(cv::Point3i(5+i, 3, 2)); points3d.push_back(cv::Point3i(5+i, 3+i, 2+i)); points2d.push_back(cv::Point2i(0, i)); points2d.push_back(cv::Point2i(-i, i)); } Mat R1, t1, R2, t2, R3, t3, R4, t4; EXPECT_TRUE(solvePnPRansac(points3d, points2d, intrinsics, cv::Mat(), R1, t1)); Mat points3dMat(points3d); Mat points2dMat(points2d); EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R2, t2)); points3dMat = points3dMat.reshape(3, 1); points2dMat = points2dMat.reshape(2, 1); EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R3, t3)); points3dMat = points3dMat.reshape(1, numPoints); points2dMat = points2dMat.reshape(1, numPoints); EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R4, t4)); EXPECT_LE(cvtest::norm(R1, R2, NORM_INF), 1e-6); EXPECT_LE(cvtest::norm(t1, t2, NORM_INF), 1e-6); EXPECT_LE(cvtest::norm(R1, R3, NORM_INF), 1e-6); EXPECT_LE(cvtest::norm(t1, t3, NORM_INF), 1e-6); EXPECT_LE(cvtest::norm(R1, R4, NORM_INF), 1e-6); EXPECT_LE(cvtest::norm(t1, t4, NORM_INF), 1e-6); } TEST(Calib3d_SolvePnP, double_support) { Matx33d intrinsics(5.4794130238156129e+002, 0., 2.9835545700043139e+002, 0., 5.4817724002728005e+002, 2.3062194051986233e+002, 0., 0., 1.); std::vector points3d; std::vector points2d; std::vector points3dF; std::vector points2dF; for (int i = 0; i < 10 ; i+=2) { points3d.push_back(cv::Point3d(5+i, 3, 2)); points3dF.push_back(cv::Point3d(5+i, 3, 2)); points3d.push_back(cv::Point3d(5+i, 3+i, 2+i)); points3dF.push_back(cv::Point3d(5+i, 3+i, 2+i)); points2d.push_back(cv::Point2d(0, i)); points2dF.push_back(cv::Point2d(0, i)); points2d.push_back(cv::Point2d(-i, i)); points2dF.push_back(cv::Point2d(-i, i)); } Mat R,t, RF, tF; vector inliers; solvePnPRansac(points3dF, points2dF, intrinsics, cv::Mat(), RF, tF, true, 100, 8.f, 0.999, inliers, cv::SOLVEPNP_P3P); solvePnPRansac(points3d, points2d, intrinsics, cv::Mat(), R, t, true, 100, 8.f, 0.999, inliers, cv::SOLVEPNP_P3P); EXPECT_LE(cvtest::norm(R, Mat_(RF), NORM_INF), 1e-3); EXPECT_LE(cvtest::norm(t, Mat_(tF), NORM_INF), 1e-3); } TEST(Calib3d_SolvePnP, translation) { Mat cameraIntrinsic = Mat::eye(3,3, CV_32FC1); vector crvec; crvec.push_back(0.f); crvec.push_back(0.f); crvec.push_back(0.f); vector ctvec; ctvec.push_back(100.f); ctvec.push_back(100.f); ctvec.push_back(0.f); vector p3d; p3d.push_back(Point3f(0,0,0)); p3d.push_back(Point3f(0,0,10)); p3d.push_back(Point3f(0,10,10)); p3d.push_back(Point3f(10,10,10)); p3d.push_back(Point3f(2,5,5)); vector p2d; projectPoints(p3d, crvec, ctvec, cameraIntrinsic, noArray(), p2d); Mat rvec; Mat tvec; rvec =(Mat_(3,1) << 0, 0, 0); tvec = (Mat_(3,1) << 100, 100, 0); solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, true); EXPECT_TRUE(checkRange(rvec)); EXPECT_TRUE(checkRange(tvec)); rvec =(Mat_(3,1) << 0, 0, 0); tvec = (Mat_(3,1) << 100, 100, 0); solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, true); EXPECT_TRUE(checkRange(rvec)); EXPECT_TRUE(checkRange(tvec)); solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, false); EXPECT_TRUE(checkRange(rvec)); EXPECT_TRUE(checkRange(tvec)); } TEST(Calib3d_SolvePnP, iterativeInitialGuess3pts) { { Matx33d intrinsics(605.4, 0.0, 317.35, 0.0, 601.2, 242.63, 0.0, 0.0, 1.0); double L = 0.1; vector p3d; p3d.push_back(Point3d(-L, -L, 0.0)); p3d.push_back(Point3d(L, -L, 0.0)); p3d.push_back(Point3d(L, L, 0.0)); Mat rvec_ground_truth = (Mat_(3,1) << 0.3, -0.2, 0.75); Mat tvec_ground_truth = (Mat_(3,1) << 0.15, -0.2, 1.5); vector p2d; projectPoints(p3d, rvec_ground_truth, tvec_ground_truth, intrinsics, noArray(), p2d); Mat rvec_est = (Mat_(3,1) << 0.2, -0.1, 0.6); Mat tvec_est = (Mat_(3,1) << 0.05, -0.05, 1.0); solvePnP(p3d, p2d, intrinsics, noArray(), rvec_est, tvec_est, true, SOLVEPNP_ITERATIVE); std::cout << "rvec_ground_truth: " << rvec_ground_truth.t() << std::endl; std::cout << "rvec_est: " << rvec_est.t() << std::endl; std::cout << "tvec_ground_truth: " << tvec_ground_truth.t() << std::endl; std::cout << "tvec_est: " << tvec_est.t() << std::endl; EXPECT_LE(cvtest::norm(rvec_ground_truth, rvec_est, NORM_INF), 1e-6); EXPECT_LE(cvtest::norm(tvec_ground_truth, tvec_est, NORM_INF), 1e-6); } { Matx33f intrinsics(605.4f, 0.0f, 317.35f, 0.0f, 601.2f, 242.63f, 0.0f, 0.0f, 1.0f); float L = 0.1f; vector p3d; p3d.push_back(Point3f(-L, -L, 0.0f)); p3d.push_back(Point3f(L, -L, 0.0f)); p3d.push_back(Point3f(L, L, 0.0f)); Mat rvec_ground_truth = (Mat_(3,1) << -0.75f, 0.4f, 0.34f); Mat tvec_ground_truth = (Mat_(3,1) << -0.15f, 0.35f, 1.58f); vector p2d; projectPoints(p3d, rvec_ground_truth, tvec_ground_truth, intrinsics, noArray(), p2d); Mat rvec_est = (Mat_(3,1) << -0.5f, 0.2f, 0.2f); Mat tvec_est = (Mat_(3,1) << 0.0f, 0.2f, 1.0f); solvePnP(p3d, p2d, intrinsics, noArray(), rvec_est, tvec_est, true, SOLVEPNP_ITERATIVE); std::cout << "rvec_ground_truth: " << rvec_ground_truth.t() << std::endl; std::cout << "rvec_est: " << rvec_est.t() << std::endl; std::cout << "tvec_ground_truth: " << tvec_ground_truth.t() << std::endl; std::cout << "tvec_est: " << tvec_est.t() << std::endl; EXPECT_LE(cvtest::norm(rvec_ground_truth, rvec_est, NORM_INF), 1e-6); EXPECT_LE(cvtest::norm(tvec_ground_truth, tvec_est, NORM_INF), 1e-6); } } }} // namespace