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94 lines
3.3 KiB
C++
94 lines
3.3 KiB
C++
/*
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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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* (3 - clause BSD License)
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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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* *Redistributions 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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* * Redistributions 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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* * Neither the names of the copyright holders nor the names of the contributors
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* may be used to endorse or promote products derived from this software
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* 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 copyright holders 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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#include "perf_precomp.hpp"
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namespace cvtest
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{
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using std::tr1::tuple;
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using std::tr1::get;
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using namespace perf;
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using namespace testing;
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using namespace cv;
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using namespace cv::ximgproc;
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typedef tuple<bool, Size, int, int, MatType> AMPerfTestParam;
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typedef TestBaseWithParam<AMPerfTestParam> AdaptiveManifoldPerfTest;
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PERF_TEST_P( AdaptiveManifoldPerfTest, perf,
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Combine(
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Values(true, false), //adjust_outliers flag
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Values(sz1080p, sz720p), //size
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Values(1, 3, 8), //joint channels num
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Values(1, 3), //source channels num
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Values(CV_8U, CV_32F) //source and joint depth
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)
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)
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{
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AMPerfTestParam params = GetParam();
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bool adjustOutliers = get<0>(params);
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Size sz = get<1>(params);
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int jointCnNum = get<2>(params);
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int srcCnNum = get<3>(params);
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int depth = get<4>(params);
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Mat joint(sz, CV_MAKE_TYPE(depth, jointCnNum));
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Mat src(sz, CV_MAKE_TYPE(depth, srcCnNum));
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Mat dst(sz, CV_MAKE_TYPE(depth, srcCnNum));
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cv::setNumThreads(cv::getNumberOfCPUs());
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declare.in(joint, src, WARMUP_RNG).out(dst).tbb_threads(cv::getNumberOfCPUs());
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double sigma_s = 16;
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double sigma_r = 0.5;
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TEST_CYCLE_N(3)
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{
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Mat res;
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amFilter(joint, src, res, sigma_s, sigma_r, adjustOutliers);
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//at 5th cycle sigma_s will be five times more and tree depth will be 5
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sigma_s *= 1.38;
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sigma_r /= 1.38;
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}
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SANITY_CHECK_NOTHING();
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}
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}
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