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319 lines
11 KiB
C++
319 lines
11 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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#include "test_precomp.hpp"
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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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using namespace cv;
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using namespace std;
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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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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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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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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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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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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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solvePnPRansac(points, projectedPoints, intrinsics, distCoeffs, rvec, tvec,
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false, 500, 0.5, 0.99, inliers, method);
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bool isTestSuccess = inliers.size() >= points.size()*0.95;
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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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return isTestSuccess;
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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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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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const int methodsCount = 5;
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RNG rng = ts->get_rng();
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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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}
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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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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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~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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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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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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solvePnP(opoints, projectedPoints, intrinsics, distCoeffs, rvec, tvec,
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false, method);
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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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double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff;
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if (error > maxError)
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maxError = error;
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return isTestSuccess;
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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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#ifdef HAVE_TBB
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TEST(DISABLED_Calib3d_SolvePnPRansac, concurrency)
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{
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int count = 7*13;
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Mat object(1, count, CV_32FC3);
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randu(object, -100, 100);
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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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Mat dist_coef(1, 8, CV_32F, cv::Scalar::all(0));
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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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Mat image(1, count, CV_32FC2, &image_vec[0]);
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Mat rvec1, rvec2;
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Mat tvec1, tvec2;
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{
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// limit concurrency to get deterministic result
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cv::theRNG().state = 20121010;
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tbb::task_scheduler_init one_thread(1);
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solvePnPRansac(object, image, camera_mat, dist_coef, rvec1, tvec1);
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}
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if(1)
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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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// single thread again
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cv::theRNG().state = 20121010;
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tbb::task_scheduler_init one_thread(1);
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solvePnPRansac(object, image, camera_mat, dist_coef, rvec2, tvec2);
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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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EXPECT_LT(rnorm, 1e-6);
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EXPECT_LT(tnorm, 1e-6);
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}
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#endif
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