418 lines
		
	
	
		
			14 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			418 lines
		
	
	
		
			14 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| /*M///////////////////////////////////////////////////////////////////////////////////////
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| //
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| //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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| //
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| //  By downloading, copying, installing or using the software you agree to this license.
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| //  If you do not agree to this license, do not download, install,
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| //  copy or use the software.
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| //
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| //
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| //                           License Agreement
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| //                For Open Source Computer Vision Library
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| //
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| // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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| // Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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| // Third party copyrights are property of their respective owners.
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| //
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| // Redistribution and use in source and binary forms, with or without modification,
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| // are permitted provided that the following conditions are met:
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| //
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| //   * Redistribution's of source code must retain the above copyright notice,
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| //     this list of conditions and the following disclaimer.
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| //
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| //   * Redistribution's in binary form must reproduce the above copyright notice,
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| //     this list of conditions and the following disclaimer in the documentation
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| //     and/or other materials provided with the distribution.
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| //
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| //   * The name of the copyright holders may not be used to endorse or promote products
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| //     derived from this software without specific prior written permission.
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| //
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| // This software is provided by the copyright holders and contributors "as is" and
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| // any express or implied warranties, including, but not limited to, the implied
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| // warranties of merchantability and fitness for a particular purpose are disclaimed.
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| // In no event shall the Intel Corporation or contributors be liable for any direct,
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| // indirect, incidental, special, exemplary, or consequential damages
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| // (including, but not limited to, procurement of substitute goods or services;
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| // loss of use, data, or profits; or business interruption) however caused
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| // and on any theory of liability, whether in contract, strict liability,
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| // or tort (including negligence or otherwise) arising in any way out of
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| // the use of this software, even if advised of the possibility of such damage.
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| //
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| //M*/
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| 
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| #include "test_precomp.hpp"
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| 
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| #ifdef HAVE_TBB
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| #include "tbb/task_scheduler_init.h"
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| #endif
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| 
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| using namespace cv;
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| using namespace std;
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| 
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| class CV_solvePnPRansac_Test : public cvtest::BaseTest
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| {
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| public:
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|     CV_solvePnPRansac_Test()
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|     {
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|         eps[SOLVEPNP_ITERATIVE] = 1.0e-2;
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|         eps[SOLVEPNP_EPNP] = 1.0e-2;
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|         eps[SOLVEPNP_P3P] = 1.0e-2;
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|         eps[SOLVEPNP_DLS] = 1.0e-2;
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|         eps[SOLVEPNP_UPNP] = 1.0e-2;
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|         totalTestsCount = 10;
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|     }
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|     ~CV_solvePnPRansac_Test() {}
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| protected:
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|     void generate3DPointCloud(vector<Point3f>& points, Point3f pmin = Point3f(-1,
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|         -1, 5), Point3f pmax = Point3f(1, 1, 10))
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|     {
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|         const Point3f delta = pmax - pmin;
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|         for (size_t i = 0; i < points.size(); i++)
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|         {
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|             Point3f p(float(rand()) / RAND_MAX, float(rand()) / RAND_MAX,
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|                 float(rand()) / RAND_MAX);
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|             p.x *= delta.x;
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|             p.y *= delta.y;
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|             p.z *= delta.z;
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|             p = p + pmin;
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|             points[i] = p;
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|         }
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|     }
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| 
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|     void generateCameraMatrix(Mat& cameraMatrix, RNG& rng)
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|     {
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|         const double fcMinVal = 1e-3;
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|         const double fcMaxVal = 100;
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|         cameraMatrix.create(3, 3, CV_64FC1);
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|         cameraMatrix.setTo(Scalar(0));
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|         cameraMatrix.at<double>(0,0) = rng.uniform(fcMinVal, fcMaxVal);
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|         cameraMatrix.at<double>(1,1) = rng.uniform(fcMinVal, fcMaxVal);
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|         cameraMatrix.at<double>(0,2) = rng.uniform(fcMinVal, fcMaxVal);
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|         cameraMatrix.at<double>(1,2) = rng.uniform(fcMinVal, fcMaxVal);
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|         cameraMatrix.at<double>(2,2) = 1;
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|     }
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| 
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|     void generateDistCoeffs(Mat& distCoeffs, RNG& rng)
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|     {
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|         distCoeffs = Mat::zeros(4, 1, CV_64FC1);
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|         for (int i = 0; i < 3; i++)
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|             distCoeffs.at<double>(i,0) = rng.uniform(0.0, 1.0e-6);
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|     }
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| 
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|     void generatePose(Mat& rvec, Mat& tvec, RNG& rng)
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|     {
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|         const double minVal = 1.0e-3;
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|         const double maxVal = 1.0;
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|         rvec.create(3, 1, CV_64FC1);
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|         tvec.create(3, 1, CV_64FC1);
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|         for (int i = 0; i < 3; i++)
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|         {
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|             rvec.at<double>(i,0) = rng.uniform(minVal, maxVal);
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|             tvec.at<double>(i,0) = rng.uniform(minVal, maxVal/10);
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|         }
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|     }
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| 
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|     virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* epsilon, double& maxError)
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|     {
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|         Mat rvec, tvec;
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|         vector<int> inliers;
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|         Mat trueRvec, trueTvec;
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|         Mat intrinsics, distCoeffs;
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|         generateCameraMatrix(intrinsics, rng);
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|         if (method == 4) intrinsics.at<double>(1,1) = intrinsics.at<double>(0,0);
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|         if (mode == 0)
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|             distCoeffs = Mat::zeros(4, 1, CV_64FC1);
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|         else
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|             generateDistCoeffs(distCoeffs, rng);
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|         generatePose(trueRvec, trueTvec, rng);
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| 
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|         vector<Point2f> projectedPoints;
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|         projectedPoints.resize(points.size());
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|         projectPoints(Mat(points), trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints);
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|         for (size_t i = 0; i < projectedPoints.size(); i++)
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|         {
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|             if (i % 20 == 0)
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|             {
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|                 projectedPoints[i] = projectedPoints[rng.uniform(0,(int)points.size()-1)];
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|             }
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|         }
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| 
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|         solvePnPRansac(points, projectedPoints, intrinsics, distCoeffs, rvec, tvec,
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|             false, 500, 0.5f, 0.99, inliers, method);
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| 
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|         bool isTestSuccess = inliers.size() >= points.size()*0.95;
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| 
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|         double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec);
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|         isTestSuccess = isTestSuccess && rvecDiff < epsilon[method] && tvecDiff < epsilon[method];
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|         double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff;
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|         //cout << error << " " << inliers.size() << " " << eps[method] << endl;
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|         if (error > maxError)
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|             maxError = error;
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| 
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|         return isTestSuccess;
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|     }
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| 
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|     void run(int)
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|     {
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|         ts->set_failed_test_info(cvtest::TS::OK);
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| 
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|         vector<Point3f> points, points_dls;
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|         const int pointsCount = 500;
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|         points.resize(pointsCount);
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|         generate3DPointCloud(points);
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| 
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|         const int methodsCount = 5;
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|         RNG rng = ts->get_rng();
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| 
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| 
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|         for (int mode = 0; mode < 2; mode++)
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|         {
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|             for (int method = 0; method < methodsCount; method++)
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|             {
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|                 double maxError = 0;
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|                 int successfulTestsCount = 0;
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|                 for (int testIndex = 0; testIndex < totalTestsCount; testIndex++)
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|                 {
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|                     if (runTest(rng, mode, method, points, eps, maxError))
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|                         successfulTestsCount++;
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|                 }
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|                 //cout <<  maxError << " " << successfulTestsCount << endl;
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|                 if (successfulTestsCount < 0.7*totalTestsCount)
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|                 {
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|                     ts->printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n",
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|                         method, totalTestsCount - successfulTestsCount, totalTestsCount, maxError, mode);
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|                     ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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|                 }
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|                 cout << "mode: " << mode << ", method: " << method << " -> "
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|                      << ((double)successfulTestsCount / totalTestsCount) * 100 << "%"
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|                      << " (err < " << maxError << ")" << endl;
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|             }
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|         }
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|     }
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|     double eps[5];
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|     int totalTestsCount;
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| };
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| 
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| class CV_solvePnP_Test : public CV_solvePnPRansac_Test
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| {
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| public:
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|     CV_solvePnP_Test()
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|     {
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|         eps[SOLVEPNP_ITERATIVE] = 1.0e-6;
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|         eps[SOLVEPNP_EPNP] = 1.0e-6;
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|         eps[SOLVEPNP_P3P] = 1.0e-4;
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|         eps[SOLVEPNP_DLS] = 1.0e-4;
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|         eps[SOLVEPNP_UPNP] = 1.0e-4;
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|         totalTestsCount = 1000;
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|     }
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| 
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|     ~CV_solvePnP_Test() {}
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| protected:
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|     virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* epsilon, double& maxError)
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|     {
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|         Mat rvec, tvec;
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|         Mat trueRvec, trueTvec;
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|         Mat intrinsics, distCoeffs;
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|         generateCameraMatrix(intrinsics, rng);
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|         if (method == 4) intrinsics.at<double>(1,1) = intrinsics.at<double>(0,0);
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|         if (mode == 0)
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|             distCoeffs = Mat::zeros(4, 1, CV_64FC1);
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|         else
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|             generateDistCoeffs(distCoeffs, rng);
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|         generatePose(trueRvec, trueTvec, rng);
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| 
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|         std::vector<Point3f> opoints;
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|         if (method == 2)
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|         {
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|             opoints = std::vector<Point3f>(points.begin(), points.begin()+4);
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|         }
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|         else if(method == 3)
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|         {
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|             opoints = std::vector<Point3f>(points.begin(), points.begin()+50);
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|         }
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|         else
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|             opoints = points;
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| 
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|         vector<Point2f> projectedPoints;
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|         projectedPoints.resize(opoints.size());
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|         projectPoints(Mat(opoints), trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints);
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| 
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|         solvePnP(opoints, projectedPoints, intrinsics, distCoeffs, rvec, tvec,
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|             false, method);
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| 
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|         double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec);
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|         bool isTestSuccess = rvecDiff < epsilon[method] && tvecDiff < epsilon[method];
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| 
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|         double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff;
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|         if (error > maxError)
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|             maxError = error;
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| 
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|         return isTestSuccess;
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|     }
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| };
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| 
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| TEST(Calib3d_SolvePnPRansac, accuracy) { CV_solvePnPRansac_Test test; test.safe_run(); }
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| TEST(Calib3d_SolvePnP, accuracy) { CV_solvePnP_Test test; test.safe_run(); }
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| 
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| TEST(Calib3d_SolvePnPRansac, concurrency)
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| {
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|     int count = 7*13;
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| 
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|     Mat object(1, count, CV_32FC3);
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|     randu(object, -100, 100);
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| 
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|     Mat camera_mat(3, 3, CV_32FC1);
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|     randu(camera_mat, 0.5, 1);
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|     camera_mat.at<float>(0, 1) = 0.f;
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|     camera_mat.at<float>(1, 0) = 0.f;
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|     camera_mat.at<float>(2, 0) = 0.f;
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|     camera_mat.at<float>(2, 1) = 0.f;
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| 
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|     Mat dist_coef(1, 8, CV_32F, cv::Scalar::all(0));
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| 
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|     vector<cv::Point2f> image_vec;
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|     Mat rvec_gold(1, 3, CV_32FC1);
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|     randu(rvec_gold, 0, 1);
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|     Mat tvec_gold(1, 3, CV_32FC1);
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|     randu(tvec_gold, 0, 1);
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|     projectPoints(object, rvec_gold, tvec_gold, camera_mat, dist_coef, image_vec);
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| 
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|     Mat image(1, count, CV_32FC2, &image_vec[0]);
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| 
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|     Mat rvec1, rvec2;
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|     Mat tvec1, tvec2;
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| 
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|     {
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|         // limit concurrency to get deterministic result
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|         theRNG().state = 20121010;
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|         setNumThreads(1);
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|         solvePnPRansac(object, image, camera_mat, dist_coef, rvec1, tvec1);
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|     }
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| 
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|     {
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|         Mat rvec;
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|         Mat tvec;
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|         // parallel executions
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|         for(int i = 0; i < 10; ++i)
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|         {
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|             cv::theRNG().state = 20121010;
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|             solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
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|         }
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|     }
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| 
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|     {
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|         // single thread again
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|         theRNG().state = 20121010;
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|         setNumThreads(1);
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|         solvePnPRansac(object, image, camera_mat, dist_coef, rvec2, tvec2);
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|     }
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| 
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|     double rnorm = cvtest::norm(rvec1, rvec2, NORM_INF);
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|     double tnorm = cvtest::norm(tvec1, tvec2, NORM_INF);
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| 
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|     EXPECT_LT(rnorm, 1e-6);
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|     EXPECT_LT(tnorm, 1e-6);
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| }
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| 
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| TEST(Calib3d_SolvePnPRansac, input_type)
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| {
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|     const int numPoints = 10;
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|     Matx33d intrinsics(5.4794130238156129e+002, 0., 2.9835545700043139e+002, 0.,
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|         5.4817724002728005e+002, 2.3062194051986233e+002, 0., 0., 1.);
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| 
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|     std::vector<cv::Point3f> points3d;
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|     std::vector<cv::Point2f> points2d;
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|     for (int i = 0; i < numPoints; i++)
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|     {
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|         points3d.push_back(cv::Point3i(i, 0, 0));
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|         points2d.push_back(cv::Point2i(i, 0));
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|     }
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|     Mat R1, t1, R2, t2, R3, t3, R4, t4;
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| 
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|     EXPECT_TRUE(solvePnPRansac(points3d, points2d, intrinsics, cv::Mat(), R1, t1));
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| 
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|     Mat points3dMat(points3d);
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|     Mat points2dMat(points2d);
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|     EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R2, t2));
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| 
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|     points3dMat = points3dMat.reshape(3, 1);
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|     points2dMat = points2dMat.reshape(2, 1);
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|     EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R3, t3));
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| 
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|     points3dMat = points3dMat.reshape(1, numPoints);
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|     points2dMat = points2dMat.reshape(1, numPoints);
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|     EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R4, t4));
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| 
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|     EXPECT_LE(norm(R1, R2, NORM_INF), 1e-6);
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|     EXPECT_LE(norm(t1, t2, NORM_INF), 1e-6);
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|     EXPECT_LE(norm(R1, R3, NORM_INF), 1e-6);
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|     EXPECT_LE(norm(t1, t3, NORM_INF), 1e-6);
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|     EXPECT_LE(norm(R1, R4, NORM_INF), 1e-6);
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|     EXPECT_LE(norm(t1, t4, NORM_INF), 1e-6);
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| }
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| 
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| TEST(Calib3d_SolvePnP, double_support)
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| {
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|     Matx33d intrinsics(5.4794130238156129e+002, 0., 2.9835545700043139e+002, 0.,
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|                        5.4817724002728005e+002, 2.3062194051986233e+002, 0., 0., 1.);
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|     std::vector<cv::Point3d> points3d;
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|     std::vector<cv::Point2d> points2d;
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|     std::vector<cv::Point3f> points3dF;
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|     std::vector<cv::Point2f> points2dF;
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|     for (int i = 0; i < 10 ; i++)
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|     {
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|         points3d.push_back(cv::Point3d(i,0,0));
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|         points3dF.push_back(cv::Point3d(i,0,0));
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|         points2d.push_back(cv::Point2d(i,0));
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|         points2dF.push_back(cv::Point2d(i,0));
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|     }
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|     Mat R,t, RF, tF;
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|     vector<int> inliers;
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| 
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|     solvePnPRansac(points3dF, points2dF, intrinsics, cv::Mat(), RF, tF, true, 100, 8.f, 0.999, inliers, cv::SOLVEPNP_P3P);
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|     solvePnPRansac(points3d, points2d, intrinsics, cv::Mat(), R, t, true, 100, 8.f, 0.999, inliers, cv::SOLVEPNP_P3P);
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| 
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|     EXPECT_LE(norm(R, Mat_<double>(RF), NORM_INF), 1e-3);
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|     EXPECT_LE(norm(t, Mat_<double>(tF), NORM_INF), 1e-3);
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| }
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| 
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| TEST(Calib3d_SolvePnP, translation)
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| {
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|     Mat cameraIntrinsic = Mat::eye(3,3, CV_32FC1);
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|     vector<float> crvec;
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|     crvec.push_back(0.f);
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|     crvec.push_back(0.f);
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|     crvec.push_back(0.f);
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|     vector<float> ctvec;
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|     ctvec.push_back(100.f);
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|     ctvec.push_back(100.f);
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|     ctvec.push_back(0.f);
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|     vector<Point3f> p3d;
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|     p3d.push_back(Point3f(0,0,0));
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|     p3d.push_back(Point3f(0,0,10));
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|     p3d.push_back(Point3f(0,10,10));
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|     p3d.push_back(Point3f(10,10,10));
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|     p3d.push_back(Point3f(2,5,5));
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| 
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|     vector<Point2f> p2d;
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|     projectPoints(p3d, crvec, ctvec, cameraIntrinsic, noArray(), p2d);
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|     Mat rvec;
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|     Mat tvec;
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|     rvec =(Mat_<float>(3,1) << 0, 0, 0);
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|     tvec = (Mat_<float>(3,1) << 100, 100, 0);
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| 
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|     solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, true);
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|     EXPECT_TRUE(checkRange(rvec));
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|     EXPECT_TRUE(checkRange(tvec));
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| 
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|     rvec =(Mat_<double>(3,1) << 0, 0, 0);
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|     tvec = (Mat_<double>(3,1) << 100, 100, 0);
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|     solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, true);
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|     EXPECT_TRUE(checkRange(rvec));
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|     EXPECT_TRUE(checkRange(tvec));
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| 
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|     solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, false);
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|     EXPECT_TRUE(checkRange(rvec));
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|     EXPECT_TRUE(checkRange(tvec));
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| }
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