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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// 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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
// * The name of Intel Corporation may not 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 the Intel Corporation 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.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
typedef tuple<Size> OFParams;
typedef TestWithParam<OFParams> DenseOpticalFlow_DIS;
typedef TestWithParam<OFParams> DenseOpticalFlow_VariationalRefinement;
TEST_P(DenseOpticalFlow_DIS, MultithreadReproducibility)
{
double MAX_DIF = 0.01;
double MAX_MEAN_DIF = 0.001;
int loopsCount = 2;
RNG rng(0);
OFParams params = GetParam();
Size size = get<0>(params);
int nThreads = cv::getNumThreads();
if (nThreads == 1)
throw SkipTestException("Single thread environment");
for (int iter = 0; iter <= loopsCount; iter++)
{
Mat frame1(size, CV_8U);
randu(frame1, 0, 255);
Mat frame2(size, CV_8U);
randu(frame2, 0, 255);
Ptr<DISOpticalFlow> algo = createOptFlow_DIS();
int psz = rng.uniform(4, 16);
int pstr = rng.uniform(1, psz - 1);
int grad_iter = rng.uniform(1, 64);
int var_iter = rng.uniform(0, 10);
bool use_mean_normalization = !!rng.uniform(0, 2);
bool use_spatial_propagation = !!rng.uniform(0, 2);
algo->setFinestScale(0);
algo->setPatchSize(psz);
algo->setPatchStride(pstr);
algo->setGradientDescentIterations(grad_iter);
algo->setVariationalRefinementIterations(var_iter);
algo->setUseMeanNormalization(use_mean_normalization);
algo->setUseSpatialPropagation(use_spatial_propagation);
cv::setNumThreads(nThreads);
Mat resMultiThread;
algo->calc(frame1, frame2, resMultiThread);
cv::setNumThreads(1);
Mat resSingleThread;
algo->calc(frame1, frame2, resSingleThread);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1), MAX_MEAN_DIF * frame1.total());
// resulting flow should be within the frame bounds:
double min_val, max_val;
minMaxLoc(resMultiThread, &min_val, &max_val);
EXPECT_LE(abs(min_val), sqrt( static_cast<double>(size.height * size.height + size.width * size.width)) );
EXPECT_LE(abs(max_val), sqrt( static_cast<double>(size.height * size.height + size.width * size.width)) );
}
}
INSTANTIATE_TEST_CASE_P(FullSet, DenseOpticalFlow_DIS, Values(szODD, szQVGA));
TEST_P(DenseOpticalFlow_VariationalRefinement, MultithreadReproducibility)
{
double MAX_DIF = 0.01;
double MAX_MEAN_DIF = 0.001;
float input_flow_rad = 5.0;
int loopsCount = 2;
RNG rng(0);
OFParams params = GetParam();
Size size = get<0>(params);
int nThreads = cv::getNumThreads();
if (nThreads == 1)
throw SkipTestException("Single thread environment");
for (int iter = 0; iter <= loopsCount; iter++)
{
Mat frame1(size, CV_8U);
randu(frame1, 0, 255);
Mat frame2(size, CV_8U);
randu(frame2, 0, 255);
Mat flow(size, CV_32FC2);
randu(flow, -input_flow_rad, input_flow_rad);
Ptr<VariationalRefinement> var = createVariationalFlowRefinement();
var->setAlpha(rng.uniform(1.0f, 100.0f));
var->setGamma(rng.uniform(0.1f, 10.0f));
var->setDelta(rng.uniform(0.1f, 10.0f));
var->setSorIterations(rng.uniform(1, 20));
var->setFixedPointIterations(rng.uniform(1, 20));
var->setOmega(rng.uniform(1.01f, 1.99f));
cv::setNumThreads(nThreads);
Mat resMultiThread;
flow.copyTo(resMultiThread);
var->calc(frame1, frame2, resMultiThread);
cv::setNumThreads(1);
Mat resSingleThread;
flow.copyTo(resSingleThread);
var->calc(frame1, frame2, resSingleThread);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1), MAX_MEAN_DIF * frame1.total());
// resulting flow should be within the frame bounds:
double min_val, max_val;
minMaxLoc(resMultiThread, &min_val, &max_val);
EXPECT_LE(abs(min_val), sqrt( static_cast<double>(size.height * size.height + size.width * size.width)) );
EXPECT_LE(abs(max_val), sqrt( static_cast<double>(size.height * size.height + size.width * size.width)) );
}
}
INSTANTIATE_TEST_CASE_P(FullSet, DenseOpticalFlow_VariationalRefinement, Values(szODD, szQVGA));
}} // namespace