Repository for OpenCV's extra modules
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/*
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* 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
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* In no event shall copyright holders or contributors be liable for any direct,
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*/
#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()));
}