/*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. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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" #ifdef HAVE_CUDA using namespace cvtest; namespace { ////////////////////////////////////////////////////// // BroxOpticalFlow //#define BROX_DUMP struct BroxOpticalFlow : testing::TestWithParam { cv::cuda::DeviceInfo devInfo; virtual void SetUp() { devInfo = GetParam(); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(BroxOpticalFlow, Regression) { cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1); ASSERT_FALSE(frame0.empty()); cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1); ASSERT_FALSE(frame1.empty()); cv::Ptr brox = cv::cuda::BroxOpticalFlow::create(0.197 /*alpha*/, 50.0 /*gamma*/, 0.8 /*scale_factor*/, 10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/); cv::cuda::GpuMat flow; brox->calc(loadMat(frame0), loadMat(frame1), flow); cv::cuda::GpuMat flows[2]; cv::cuda::split(flow, flows); cv::cuda::GpuMat u = flows[0]; cv::cuda::GpuMat v = flows[1]; std::string fname(cvtest::TS::ptr()->get_data_path()); if (devInfo.majorVersion() >= 2) fname += "opticalflow/brox_optical_flow_cc20.bin"; else fname += "opticalflow/brox_optical_flow.bin"; #ifndef BROX_DUMP std::ifstream f(fname.c_str(), std::ios_base::binary); int rows, cols; f.read((char*) &rows, sizeof(rows)); f.read((char*) &cols, sizeof(cols)); cv::Mat u_gold(rows, cols, CV_32FC1); for (int i = 0; i < u_gold.rows; ++i) f.read(u_gold.ptr(i), u_gold.cols * sizeof(float)); cv::Mat v_gold(rows, cols, CV_32FC1); for (int i = 0; i < v_gold.rows; ++i) f.read(v_gold.ptr(i), v_gold.cols * sizeof(float)); EXPECT_MAT_SIMILAR(u_gold, u, 1e-3); EXPECT_MAT_SIMILAR(v_gold, v, 1e-3); #else std::ofstream f(fname.c_str(), std::ios_base::binary); f.write((char*) &u.rows, sizeof(u.rows)); f.write((char*) &u.cols, sizeof(u.cols)); cv::Mat h_u(u); cv::Mat h_v(v); for (int i = 0; i < u.rows; ++i) f.write(h_u.ptr(i), u.cols * sizeof(float)); for (int i = 0; i < v.rows; ++i) f.write(h_v.ptr(i), v.cols * sizeof(float)); #endif } CUDA_TEST_P(BroxOpticalFlow, OpticalFlowNan) { cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1); ASSERT_FALSE(frame0.empty()); cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1); ASSERT_FALSE(frame1.empty()); cv::Mat r_frame0, r_frame1; cv::resize(frame0, r_frame0, cv::Size(1380,1000)); cv::resize(frame1, r_frame1, cv::Size(1380,1000)); cv::Ptr brox = cv::cuda::BroxOpticalFlow::create(0.197 /*alpha*/, 50.0 /*gamma*/, 0.8 /*scale_factor*/, 10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/); cv::cuda::GpuMat flow; brox->calc(loadMat(frame0), loadMat(frame1), flow); cv::cuda::GpuMat flows[2]; cv::cuda::split(flow, flows); cv::cuda::GpuMat u = flows[0]; cv::cuda::GpuMat v = flows[1]; cv::Mat h_u, h_v; u.download(h_u); v.download(h_v); EXPECT_TRUE(cv::checkRange(h_u)); EXPECT_TRUE(cv::checkRange(h_v)); }; INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, BroxOpticalFlow, ALL_DEVICES); ////////////////////////////////////////////////////// // PyrLKOpticalFlow namespace { IMPLEMENT_PARAM_CLASS(Chan, int) IMPLEMENT_PARAM_CLASS(DataType, int) } PARAM_TEST_CASE(PyrLKOpticalFlow, cv::cuda::DeviceInfo, Chan, DataType) { cv::cuda::DeviceInfo devInfo; int channels; int dataType; virtual void SetUp() { devInfo = GET_PARAM(0); channels = GET_PARAM(1); dataType = GET_PARAM(2); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(PyrLKOpticalFlow, Sparse) { cv::Mat frame0 = readImage("opticalflow/frame0.png", channels == 1 ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); ASSERT_FALSE(frame0.empty()); cv::Mat frame1 = readImage("opticalflow/frame1.png", channels == 1 ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); ASSERT_FALSE(frame1.empty()); cv::Mat gray_frame; if (channels == 1) gray_frame = frame0; else cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY); std::vector pts; cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0); cv::cuda::GpuMat d_pts; cv::Mat pts_mat(1, (int) pts.size(), CV_32FC2, (void*) &pts[0]); d_pts.upload(pts_mat); cv::Ptr pyrLK = cv::cuda::SparsePyrLKOpticalFlow::create(); std::vector nextPts_gold; std::vector status_gold; cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray()); cv::cuda::GpuMat d_nextPts; cv::cuda::GpuMat d_status; cv::Mat converted0, converted1; if(channels == 4) { cv::cvtColor(frame0, frame0, cv::COLOR_BGR2BGRA); cv::cvtColor(frame1, frame1, cv::COLOR_BGR2BGRA); } frame0.convertTo(converted0, dataType); frame1.convertTo(converted1, dataType); pyrLK->calc(loadMat(converted0), loadMat(converted1), d_pts, d_nextPts, d_status); std::vector nextPts(d_nextPts.cols); cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]); d_nextPts.download(nextPts_mat); std::vector status(d_status.cols); cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*)&status[0]); d_status.download(status_mat); ASSERT_EQ(nextPts_gold.size(), nextPts.size()); ASSERT_EQ(status_gold.size(), status.size()); size_t mistmatch = 0; for (size_t i = 0; i < nextPts.size(); ++i) { cv::Point2i a = nextPts[i]; cv::Point2i b = nextPts_gold[i]; if (status[i] != status_gold[i]) { ++mistmatch; continue; } if (status[i]) { bool eq = std::abs(a.x - b.x) <= 1 && std::abs(a.y - b.y) <= 1; if (!eq) ++mistmatch; } } double bad_ratio = static_cast(mistmatch) / nextPts.size(); ASSERT_LE(bad_ratio, 0.01); } INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, PyrLKOpticalFlow, testing::Combine( ALL_DEVICES, testing::Values(Chan(1), Chan(3), Chan(4)), testing::Values(DataType(CV_8U), DataType(CV_16U), DataType(CV_32S), DataType(CV_32F)))); ////////////////////////////////////////////////////// // FarnebackOpticalFlow namespace { IMPLEMENT_PARAM_CLASS(PyrScale, double) IMPLEMENT_PARAM_CLASS(PolyN, int) CV_FLAGS(FarnebackOptFlowFlags, 0, OPTFLOW_FARNEBACK_GAUSSIAN) IMPLEMENT_PARAM_CLASS(UseInitFlow, bool) } PARAM_TEST_CASE(FarnebackOpticalFlow, cv::cuda::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow) { cv::cuda::DeviceInfo devInfo; double pyrScale; int polyN; int flags; bool useInitFlow; virtual void SetUp() { devInfo = GET_PARAM(0); pyrScale = GET_PARAM(1); polyN = GET_PARAM(2); flags = GET_PARAM(3); useInitFlow = GET_PARAM(4); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(FarnebackOpticalFlow, Accuracy) { cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); ASSERT_FALSE(frame0.empty()); cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE); ASSERT_FALSE(frame1.empty()); double polySigma = polyN <= 5 ? 1.1 : 1.5; cv::Ptr farn = cv::cuda::FarnebackOpticalFlow::create(); farn->setPyrScale(pyrScale); farn->setPolyN(polyN); farn->setPolySigma(polySigma); farn->setFlags(flags); cv::cuda::GpuMat d_flow; farn->calc(loadMat(frame0), loadMat(frame1), d_flow); cv::Mat flow; if (useInitFlow) { d_flow.download(flow); farn->setFlags(farn->getFlags() | cv::OPTFLOW_USE_INITIAL_FLOW); farn->calc(loadMat(frame0), loadMat(frame1), d_flow); } cv::calcOpticalFlowFarneback( frame0, frame1, flow, farn->getPyrScale(), farn->getNumLevels(), farn->getWinSize(), farn->getNumIters(), farn->getPolyN(), farn->getPolySigma(), farn->getFlags()); EXPECT_MAT_SIMILAR(flow, d_flow, 0.1); } INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, FarnebackOpticalFlow, testing::Combine( ALL_DEVICES, testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)), testing::Values(PolyN(5), PolyN(7)), testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)), testing::Values(UseInitFlow(false), UseInitFlow(true)))); ////////////////////////////////////////////////////// // OpticalFlowDual_TVL1 namespace { IMPLEMENT_PARAM_CLASS(Gamma, double) } PARAM_TEST_CASE(OpticalFlowDual_TVL1, cv::cuda::DeviceInfo, Gamma) { cv::cuda::DeviceInfo devInfo; double gamma; virtual void SetUp() { devInfo = GET_PARAM(0); gamma = GET_PARAM(1); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(OpticalFlowDual_TVL1, Accuracy) { cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); ASSERT_FALSE(frame0.empty()); cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE); ASSERT_FALSE(frame1.empty()); cv::Ptr d_alg = cv::cuda::OpticalFlowDual_TVL1::create(); d_alg->setNumIterations(10); d_alg->setGamma(gamma); cv::cuda::GpuMat d_flow; d_alg->calc(loadMat(frame0), loadMat(frame1), d_flow); cv::Ptr alg = cv::createOptFlow_DualTVL1(); alg->setMedianFiltering(1); alg->setInnerIterations(1); alg->setOuterIterations(d_alg->getNumIterations()); alg->setGamma(gamma); cv::Mat flow; alg->calc(frame0, frame1, flow); EXPECT_MAT_SIMILAR(flow, d_flow, 4e-3); } INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, OpticalFlowDual_TVL1, testing::Combine( ALL_DEVICES, testing::Values(Gamma(0.0), Gamma(1.0)))); } // namespace #endif // HAVE_CUDA