/*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" #ifdef HAVE_CUDA ////////////////////////////////////////////////////// // FGDStatModel namespace cv { template<> void Ptr::delete_obj() { cvReleaseBGStatModel(&obj); } } PARAM_TEST_CASE(FGDStatModel, cv::gpu::DeviceInfo, std::string, Channels) { cv::gpu::DeviceInfo devInfo; std::string inputFile; int out_cn; virtual void SetUp() { devInfo = GET_PARAM(0); cv::gpu::setDevice(devInfo.deviceID()); inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1); out_cn = GET_PARAM(2); } }; GPU_TEST_P(FGDStatModel, Update) { cv::VideoCapture cap(inputFile); ASSERT_TRUE(cap.isOpened()); cv::Mat frame; cap >> frame; ASSERT_FALSE(frame.empty()); IplImage ipl_frame = frame; cv::Ptr model(cvCreateFGDStatModel(&ipl_frame)); cv::gpu::GpuMat d_frame(frame); cv::gpu::FGDStatModel d_model(out_cn); d_model.create(d_frame); cv::Mat h_background; cv::Mat h_foreground; cv::Mat h_background3; cv::Mat backgroundDiff; cv::Mat foregroundDiff; for (int i = 0; i < 5; ++i) { cap >> frame; ASSERT_FALSE(frame.empty()); ipl_frame = frame; int gold_count = cvUpdateBGStatModel(&ipl_frame, model); d_frame.upload(frame); int count = d_model.update(d_frame); ASSERT_EQ(gold_count, count); cv::Mat gold_background(model->background); cv::Mat gold_foreground(model->foreground); if (out_cn == 3) d_model.background.download(h_background3); else { d_model.background.download(h_background); cv::cvtColor(h_background, h_background3, cv::COLOR_BGRA2BGR); } d_model.foreground.download(h_foreground); ASSERT_MAT_NEAR(gold_background, h_background3, 1.0); ASSERT_MAT_NEAR(gold_foreground, h_foreground, 0.0); } } INSTANTIATE_TEST_CASE_P(GPU_Video, FGDStatModel, testing::Combine( ALL_DEVICES, testing::Values(std::string("768x576.avi")), testing::Values(Channels(3), Channels(4)))); ////////////////////////////////////////////////////// // MOG namespace { IMPLEMENT_PARAM_CLASS(UseGray, bool) IMPLEMENT_PARAM_CLASS(LearningRate, double) } PARAM_TEST_CASE(MOG, cv::gpu::DeviceInfo, std::string, UseGray, LearningRate, UseRoi) { cv::gpu::DeviceInfo devInfo; std::string inputFile; bool useGray; double learningRate; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); cv::gpu::setDevice(devInfo.deviceID()); inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1); useGray = GET_PARAM(2); learningRate = GET_PARAM(3); useRoi = GET_PARAM(4); } }; GPU_TEST_P(MOG, Update) { cv::VideoCapture cap(inputFile); ASSERT_TRUE(cap.isOpened()); cv::Mat frame; cap >> frame; ASSERT_FALSE(frame.empty()); cv::gpu::MOG_GPU mog; cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi); cv::BackgroundSubtractorMOG mog_gold; cv::Mat foreground_gold; for (int i = 0; i < 10; ++i) { cap >> frame; ASSERT_FALSE(frame.empty()); if (useGray) { cv::Mat temp; cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY); cv::swap(temp, frame); } mog(loadMat(frame, useRoi), foreground, (float)learningRate); mog_gold(frame, foreground_gold, learningRate); ASSERT_MAT_NEAR(foreground_gold, foreground, 0.0); } } INSTANTIATE_TEST_CASE_P(GPU_Video, MOG, testing::Combine( ALL_DEVICES, testing::Values(std::string("768x576.avi")), testing::Values(UseGray(true), UseGray(false)), testing::Values(LearningRate(0.0), LearningRate(0.01)), WHOLE_SUBMAT)); ////////////////////////////////////////////////////// // MOG2 namespace { IMPLEMENT_PARAM_CLASS(DetectShadow, bool) } PARAM_TEST_CASE(MOG2, cv::gpu::DeviceInfo, std::string, UseGray, DetectShadow, UseRoi) { cv::gpu::DeviceInfo devInfo; std::string inputFile; bool useGray; bool detectShadow; bool useRoi; virtual void SetUp() { devInfo = GET_PARAM(0); cv::gpu::setDevice(devInfo.deviceID()); inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1); useGray = GET_PARAM(2); detectShadow = GET_PARAM(3); useRoi = GET_PARAM(4); } }; GPU_TEST_P(MOG2, Update) { cv::VideoCapture cap(inputFile); ASSERT_TRUE(cap.isOpened()); cv::Mat frame; cap >> frame; ASSERT_FALSE(frame.empty()); cv::gpu::MOG2_GPU mog2; mog2.bShadowDetection = detectShadow; cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi); cv::BackgroundSubtractorMOG2 mog2_gold; mog2_gold.set("detectShadows", detectShadow); cv::Mat foreground_gold; for (int i = 0; i < 10; ++i) { cap >> frame; ASSERT_FALSE(frame.empty()); if (useGray) { cv::Mat temp; cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY); cv::swap(temp, frame); } mog2(loadMat(frame, useRoi), foreground); mog2_gold(frame, foreground_gold); if (detectShadow) { ASSERT_MAT_SIMILAR(foreground_gold, foreground, 1e-2); } else { ASSERT_MAT_NEAR(foreground_gold, foreground, 0); } } } GPU_TEST_P(MOG2, getBackgroundImage) { if (useGray) return; cv::VideoCapture cap(inputFile); ASSERT_TRUE(cap.isOpened()); cv::Mat frame; cv::gpu::MOG2_GPU mog2; mog2.bShadowDetection = detectShadow; cv::gpu::GpuMat foreground; cv::BackgroundSubtractorMOG2 mog2_gold; mog2_gold.set("detectShadows", detectShadow); cv::Mat foreground_gold; for (int i = 0; i < 10; ++i) { cap >> frame; ASSERT_FALSE(frame.empty()); mog2(loadMat(frame, useRoi), foreground); mog2_gold(frame, foreground_gold); } cv::gpu::GpuMat background = createMat(frame.size(), frame.type(), useRoi); mog2.getBackgroundImage(background); cv::Mat background_gold; mog2_gold.getBackgroundImage(background_gold); ASSERT_MAT_NEAR(background_gold, background, 0); } INSTANTIATE_TEST_CASE_P(GPU_Video, MOG2, testing::Combine( ALL_DEVICES, testing::Values(std::string("768x576.avi")), testing::Values(UseGray(true), UseGray(false)), testing::Values(DetectShadow(true), DetectShadow(false)), WHOLE_SUBMAT)); ////////////////////////////////////////////////////// // VIBE PARAM_TEST_CASE(VIBE, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi) { }; GPU_TEST_P(VIBE, Accuracy) { const cv::gpu::DeviceInfo devInfo = GET_PARAM(0); cv::gpu::setDevice(devInfo.deviceID()); const cv::Size size = GET_PARAM(1); const int type = GET_PARAM(2); const bool useRoi = GET_PARAM(3); const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255)); cv::Mat frame = randomMat(size, type, 0.0, 100); cv::gpu::GpuMat d_frame = loadMat(frame, useRoi); cv::gpu::VIBE_GPU vibe; cv::gpu::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi); vibe.initialize(d_frame); for (int i = 0; i < 20; ++i) vibe(d_frame, d_fgmask); frame = randomMat(size, type, 160, 255); d_frame = loadMat(frame, useRoi); vibe(d_frame, d_fgmask); // now fgmask should be entirely foreground ASSERT_MAT_NEAR(fullfg, d_fgmask, 0); } INSTANTIATE_TEST_CASE_P(GPU_Video, VIBE, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4)), WHOLE_SUBMAT)); ////////////////////////////////////////////////////// // GMG PARAM_TEST_CASE(GMG, cv::gpu::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi) { }; GPU_TEST_P(GMG, Accuracy) { const cv::gpu::DeviceInfo devInfo = GET_PARAM(0); cv::gpu::setDevice(devInfo.deviceID()); const cv::Size size = GET_PARAM(1); const int depth = GET_PARAM(2); const int channels = GET_PARAM(3); const bool useRoi = GET_PARAM(4); const int type = CV_MAKE_TYPE(depth, channels); const cv::Mat zeros(size, CV_8UC1, cv::Scalar::all(0)); const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255)); cv::Mat frame = randomMat(size, type, 0, 100); cv::gpu::GpuMat d_frame = loadMat(frame, useRoi); cv::gpu::GMG_GPU gmg; gmg.numInitializationFrames = 5; gmg.smoothingRadius = 0; gmg.initialize(d_frame.size(), 0, 255); cv::gpu::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi); for (int i = 0; i < gmg.numInitializationFrames; ++i) { gmg(d_frame, d_fgmask); // fgmask should be entirely background during training ASSERT_MAT_NEAR(zeros, d_fgmask, 0); } frame = randomMat(size, type, 160, 255); d_frame = loadMat(frame, useRoi); gmg(d_frame, d_fgmask); // now fgmask should be entirely foreground ASSERT_MAT_NEAR(fullfg, d_fgmask, 0); } INSTANTIATE_TEST_CASE_P(GPU_Video, GMG, testing::Combine( ALL_DEVICES, DIFFERENT_SIZES, testing::Values(MatType(CV_8U), MatType(CV_16U), MatType(CV_32F)), testing::Values(Channels(1), Channels(3), Channels(4)), WHOLE_SUBMAT)); #endif // HAVE_CUDA