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226 lines
7.6 KiB
C++
226 lines
7.6 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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namespace opencv_test { namespace {
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class CV_Affine3D_EstTest : public cvtest::BaseTest
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{
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public:
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CV_Affine3D_EstTest();
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~CV_Affine3D_EstTest();
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protected:
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void run(int);
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bool test4Points();
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bool testNPoints();
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};
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CV_Affine3D_EstTest::CV_Affine3D_EstTest()
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{
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}
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CV_Affine3D_EstTest::~CV_Affine3D_EstTest() {}
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float rngIn(float from, float to) { return from + (to-from) * (float)theRNG(); }
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struct WrapAff
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{
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const double *F;
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WrapAff(const Mat& aff) : F(aff.ptr<double>()) {}
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Point3f operator()(const Point3f& p)
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{
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return Point3f( (float)(p.x * F[0] + p.y * F[1] + p.z * F[2] + F[3]),
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(float)(p.x * F[4] + p.y * F[5] + p.z * F[6] + F[7]),
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(float)(p.x * F[8] + p.y * F[9] + p.z * F[10] + F[11]) );
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}
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};
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bool CV_Affine3D_EstTest::test4Points()
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{
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Mat aff(3, 4, CV_64F);
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cv::randu(aff, Scalar(1), Scalar(3));
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// setting points that are no in the same line
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Mat fpts(1, 4, CV_32FC3);
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Mat tpts(1, 4, CV_32FC3);
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fpts.ptr<Point3f>()[0] = Point3f( rngIn(1,2), rngIn(1,2), rngIn(5, 6) );
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fpts.ptr<Point3f>()[1] = Point3f( rngIn(3,4), rngIn(3,4), rngIn(5, 6) );
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fpts.ptr<Point3f>()[2] = Point3f( rngIn(1,2), rngIn(3,4), rngIn(5, 6) );
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fpts.ptr<Point3f>()[3] = Point3f( rngIn(3,4), rngIn(1,2), rngIn(5, 6) );
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std::transform(fpts.ptr<Point3f>(), fpts.ptr<Point3f>() + 4, tpts.ptr<Point3f>(), WrapAff(aff));
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Mat aff_est;
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vector<uchar> outliers;
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estimateAffine3D(fpts, tpts, aff_est, outliers);
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const double thres = 1e-3;
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if (cvtest::norm(aff_est, aff, NORM_INF) > thres)
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{
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//cout << cvtest::norm(aff_est, aff, NORM_INF) << endl;
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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return false;
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}
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return true;
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}
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struct Noise
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{
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float l;
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Noise(float level) : l(level) {}
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Point3f operator()(const Point3f& p)
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{
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RNG& rng = theRNG();
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return Point3f( p.x + l * (float)rng, p.y + l * (float)rng, p.z + l * (float)rng);
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}
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};
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bool CV_Affine3D_EstTest::testNPoints()
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{
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Mat aff(3, 4, CV_64F);
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cv::randu(aff, Scalar(-2), Scalar(2));
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// setting points that are no in the same line
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const int n = 100;
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const int m = 3*n/5;
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const Point3f shift_outl = Point3f(15, 15, 15);
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const float noise_level = 20.f;
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Mat fpts(1, n, CV_32FC3);
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Mat tpts(1, n, CV_32FC3);
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randu(fpts, Scalar::all(0), Scalar::all(100));
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std::transform(fpts.ptr<Point3f>(), fpts.ptr<Point3f>() + n, tpts.ptr<Point3f>(), WrapAff(aff));
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/* adding noise*/
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std::transform(tpts.ptr<Point3f>() + m, tpts.ptr<Point3f>() + n, tpts.ptr<Point3f>() + m,
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[=] (const Point3f& pt) -> Point3f { return Noise(noise_level)(pt + shift_outl); });
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Mat aff_est;
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vector<uchar> outl;
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int res = estimateAffine3D(fpts, tpts, aff_est, outl);
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if (!res)
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{
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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return false;
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}
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const double thres = 1e-4;
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if (cvtest::norm(aff_est, aff, NORM_INF) > thres)
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{
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cout << "aff est: " << aff_est << endl;
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cout << "aff ref: " << aff << endl;
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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return false;
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}
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bool outl_good = std::count(outl.begin(), outl.end(), 1) == m &&
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m == std::accumulate(outl.begin(), outl.begin() + m, 0);
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if (!outl_good)
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{
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ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
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return false;
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}
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return true;
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}
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void CV_Affine3D_EstTest::run( int /* start_from */)
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{
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cvtest::DefaultRngAuto dra;
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if (!test4Points())
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return;
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if (!testNPoints())
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return;
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ts->set_failed_test_info(cvtest::TS::OK);
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}
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TEST(Calib3d_EstimateAffine3D, accuracy) { CV_Affine3D_EstTest test; test.safe_run(); }
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TEST(Calib3d_EstimateAffine3D, regression_16007)
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{
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std::vector<cv::Point3f> m1, m2;
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m1.push_back(Point3f(1.0f, 0.0f, 0.0f)); m2.push_back(Point3f(1.0f, 1.0f, 0.0f));
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m1.push_back(Point3f(1.0f, 0.0f, 1.0f)); m2.push_back(Point3f(1.0f, 1.0f, 1.0f));
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m1.push_back(Point3f(0.5f, 0.0f, 0.5f)); m2.push_back(Point3f(0.5f, 1.0f, 0.5f));
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m1.push_back(Point3f(2.5f, 0.0f, 2.5f)); m2.push_back(Point3f(2.5f, 1.0f, 2.5f));
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m1.push_back(Point3f(2.0f, 0.0f, 1.0f)); m2.push_back(Point3f(2.0f, 1.0f, 1.0f));
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cv::Mat m3D, inl;
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int res = cv::estimateAffine3D(m1, m2, m3D, inl);
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EXPECT_EQ(1, res);
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}
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TEST(Calib3d_EstimateAffine3D, umeyama_3_pt)
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{
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std::vector<cv::Vec3d> points = {{{0.80549149, 0.8225781, 0.79949521},
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{0.28906756, 0.57158557, 0.9864789},
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{0.58266182, 0.65474983, 0.25078834}}};
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cv::Mat R = (cv::Mat_<double>(3,3) << 0.9689135, -0.0232753, 0.2463025,
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0.0236362, 0.9997195, 0.0014915,
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-0.2462682, 0.0043765, 0.9691918);
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cv::Vec3d t(1., 2., 3.);
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cv::Affine3d transform(R, t);
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std::vector<cv::Vec3d> transformed_points(points.size());
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std::transform(points.begin(), points.end(), transformed_points.begin(), [transform](const cv::Vec3d v){return transform * v;});
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double scale;
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cv::Mat trafo_est = estimateAffine3D(points, transformed_points, &scale);
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Mat R_est(trafo_est(Rect(0, 0, 3, 3)));
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EXPECT_LE(cvtest::norm(R_est, R, NORM_INF), 1e-6);
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Vec3d t_est = trafo_est.col(3);
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EXPECT_LE(cvtest::norm(t_est, t, NORM_INF), 1e-6);
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EXPECT_NEAR(scale, 1.0, 1e-6);
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}
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}} // namespace
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