/* * 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())); }