/* * 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 "perf_precomp.hpp" namespace cvtest { using std::tr1::tuple; using std::tr1::get; using namespace perf; using namespace testing; using namespace cv; using namespace cv::ximgproc; typedef tuple<bool, Size, int, int, MatType> AMPerfTestParam; typedef TestBaseWithParam<AMPerfTestParam> AdaptiveManifoldPerfTest; PERF_TEST_P( AdaptiveManifoldPerfTest, perf, Combine( Values(true, false), //adjust_outliers flag Values(sz1080p, sz720p), //size Values(1, 3, 8), //joint channels num Values(1, 3), //source channels num Values(CV_8U, CV_32F) //source and joint depth ) ) { AMPerfTestParam params = GetParam(); bool adjustOutliers = get<0>(params); Size sz = get<1>(params); int jointCnNum = get<2>(params); int srcCnNum = get<3>(params); int depth = get<4>(params); Mat joint(sz, CV_MAKE_TYPE(depth, jointCnNum)); Mat src(sz, CV_MAKE_TYPE(depth, srcCnNum)); Mat dst(sz, CV_MAKE_TYPE(depth, srcCnNum)); cv::setNumThreads(cv::getNumberOfCPUs()); declare.in(joint, src, WARMUP_RNG).out(dst).tbb_threads(cv::getNumberOfCPUs()); double sigma_s = 16; double sigma_r = 0.5; TEST_CYCLE_N(3) { Mat res; amFilter(joint, src, res, sigma_s, sigma_r, adjustOutliers); //at 5th cycle sigma_s will be five times more and tree depth will be 5 sigma_s *= 1.38; sigma_r /= 1.38; } SANITY_CHECK(dst); } }