/* * 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 * (3 - clause BSD License) * * Redistribution and use in source and binary forms, with or without modification, * are permitted provided that the following conditions are met : * * *Redistributions of source code must retain the above copyright notice, * this list of conditions and the following disclaimer. * * * Redistributions 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. * * * Neither the names of the copyright holders nor the names of the contributors * may 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 copyright holders 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. */ #include "test_precomp.hpp" namespace cvtest { using namespace std; using namespace std::tr1; using namespace testing; using namespace perf; using namespace cv; using namespace cv::ximgproc; CV_ENUM(SrcTypes, CV_8UC1, CV_8UC3, CV_16UC1, CV_16UC3); typedef tuple<Size, SrcTypes> L0SmoothParams; typedef TestWithParam<L0SmoothParams> L0SmoothTest; TEST(L0SmoothTest, SplatSurfaceAccuracy) { RNG rnd(0); for (int i = 0; i < 3; i++) { Size sz(rnd.uniform(512, 1024), rnd.uniform(512, 1024)); Scalar surfaceValue; int srcCn = 3; rnd.fill(surfaceValue, RNG::UNIFORM, 0, 255); Mat src(sz, CV_MAKE_TYPE(CV_8U, srcCn), surfaceValue); double lambda = rnd.uniform(0.01, 0.05); double kappa = rnd.uniform(1.5, 5.0); Mat res; l0Smooth(src, res, lambda, kappa); // When filtering a constant image we should get the same image: double normL1 = cvtest::norm(src, res, NORM_L1)/src.total()/src.channels(); EXPECT_LE(normL1, 1.0/64); } } TEST_P(L0SmoothTest, MultiThreadReproducibility) { if (cv::getNumberOfCPUs() == 1) return; double MAX_DIF = 10.0; double MAX_MEAN_DIF = 1.0 / 8.0; int loopsCount = 2; RNG rng(0); L0SmoothParams params = GetParam(); Size size = get<0>(params); int srcType = get<1>(params); Mat src(size,srcType); if(src.depth()==CV_8U) randu(src, 0, 255); else if(src.depth()==CV_16U) randu(src, 0, 65535); else randu(src, -100000.0f, 100000.0f); for (int iter = 0; iter <= loopsCount; iter++) { double lambda = rng.uniform(0.01, 0.05); double kappa = rng.uniform(1.5, 5.0); cv::setNumThreads(cv::getNumberOfCPUs()); Mat resMultiThread; l0Smooth(src, resMultiThread, lambda, kappa); cv::setNumThreads(1); Mat resSingleThread; l0Smooth(src, resSingleThread, lambda, kappa); EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF); EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1), MAX_MEAN_DIF*src.total()*src.channels()); } } INSTANTIATE_TEST_CASE_P(FullSet, L0SmoothTest,Combine(Values(szODD, szQVGA), SrcTypes::all())); }