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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 "test_precomp.hpp"
#include "opencv2/ximgproc/sparse_match_interpolator.hpp"
#include <fstream>
namespace cvtest
{
using namespace std;
using namespace std::tr1;
using namespace testing;
using namespace perf;
using namespace cv;
using namespace cv::ximgproc;
static string getDataDir()
{
return cvtest::TS::ptr()->get_data_path();
}
const float FLOW_TAG_FLOAT = 202021.25f;
Mat readOpticalFlow( const String& path )
{
// CV_Assert(sizeof(float) == 4);
//FIXME: ensure right sizes of int and float - here and in writeOpticalFlow()
Mat_<Point2f> flow;
ifstream file(path.c_str(), std::ios_base::binary);
if ( !file.good() )
return flow; // no file - return empty matrix
float tag;
file.read((char*) &tag, sizeof(float));
if ( tag != FLOW_TAG_FLOAT )
return flow;
int width, height;
file.read((char*) &width, 4);
file.read((char*) &height, 4);
flow.create(height, width);
for ( int i = 0; i < flow.rows; ++i )
{
for ( int j = 0; j < flow.cols; ++j )
{
Point2f u;
file.read((char*) &u.x, sizeof(float));
file.read((char*) &u.y, sizeof(float));
if ( !file.good() )
{
flow.release();
return flow;
}
flow(i, j) = u;
}
}
file.close();
return flow;
}
CV_ENUM(GuideTypes, CV_8UC1, CV_8UC3)
typedef tuple<Size, GuideTypes> InterpolatorParams;
typedef TestWithParam<InterpolatorParams> InterpolatorTest;
TEST(InterpolatorTest, ReferenceAccuracy)
{
double MAX_DIF = 1.0;
double MAX_MEAN_DIF = 1.0 / 256.0;
string dir = getDataDir() + "cv/sparse_match_interpolator";
Mat src = imread(getDataDir() + "cv/optflow/RubberWhale1.png",IMREAD_COLOR);
ASSERT_FALSE(src.empty());
Mat ref_flow = readOpticalFlow(dir + "/RubberWhale_reference_result.flo");
ASSERT_FALSE(ref_flow.empty());
ifstream file((dir + "/RubberWhale_sparse_matches.txt").c_str());
float from_x,from_y,to_x,to_y;
vector<Point2f> from_points;
vector<Point2f> to_points;
while(file >> from_x >> from_y >> to_x >> to_y)
{
from_points.push_back(Point2f(from_x,from_y));
to_points.push_back(Point2f(to_x,to_y));
}
cv::setNumThreads(cv::getNumberOfCPUs());
Mat res_flow;
Ptr<EdgeAwareInterpolator> interpolator = createEdgeAwareInterpolator();
interpolator->setK(128);
interpolator->setSigma(0.05f);
interpolator->setUsePostProcessing(true);
interpolator->setFGSLambda(500.0f);
interpolator->setFGSSigma(1.5f);
interpolator->interpolate(src,from_points,Mat(),to_points,res_flow);
EXPECT_LE(cv::norm(res_flow, ref_flow, NORM_INF), MAX_DIF);
EXPECT_LE(cv::norm(res_flow, ref_flow, NORM_L1) , MAX_MEAN_DIF*res_flow.total());
}
TEST_P(InterpolatorTest, MultiThreadReproducibility)
{
if (cv::getNumberOfCPUs() == 1)
return;
double MAX_DIF = 1.0;
double MAX_MEAN_DIF = 1.0 / 256.0;
int loopsCount = 2;
RNG rng(0);
InterpolatorParams params = GetParam();
Size size = get<0>(params);
int guideType = get<1>(params);
Mat from(size, guideType);
randu(from, 0, 255);
int num_matches = rng.uniform(5,SHRT_MAX-1);
vector<Point2f> from_points;
vector<Point2f> to_points;
for(int i=0;i<num_matches;i++)
{
from_points.push_back(Point2f(rng.uniform(0.01f,(float)size.width-1.01f),rng.uniform(0.01f,(float)size.height-1.01f)));
to_points.push_back(Point2f(rng.uniform(0.01f,(float)size.width-1.01f),rng.uniform(0.01f,(float)size.height-1.01f)));
}
for (int iter = 0; iter <= loopsCount; iter++)
{
int K = rng.uniform(4,512);
float sigma = rng.uniform(0.01f,0.5f);
float FGSlambda = rng.uniform(100.0f, 10000.0f);
float FGSsigma = rng.uniform(0.5f, 100.0f);
Ptr<EdgeAwareInterpolator> interpolator = createEdgeAwareInterpolator();
interpolator->setK(K);
interpolator->setSigma(sigma);
interpolator->setUsePostProcessing(true);
interpolator->setFGSLambda(FGSlambda);
interpolator->setFGSSigma(FGSsigma);
cv::setNumThreads(cv::getNumberOfCPUs());
Mat resMultiThread;
interpolator->interpolate(from,from_points,Mat(),to_points,resMultiThread);
cv::setNumThreads(1);
Mat resSingleThread;
interpolator->interpolate(from,from_points,Mat(),to_points,resSingleThread);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1) , MAX_MEAN_DIF*resMultiThread.total());
}
}
INSTANTIATE_TEST_CASE_P(FullSet,InterpolatorTest, Combine(Values(szODD,szVGA), GuideTypes::all()));
}
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