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/*
* 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_NOTHING();
}
}
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