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Open Source Computer Vision Library
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979 lines
26 KiB
979 lines
26 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of Intel Corporation may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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#ifdef HAVE_CUDA |
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//#define DUMP |
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////////////////////////////////////////////////////// |
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// BroxOpticalFlow |
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#define BROX_OPTICAL_FLOW_DUMP_FILE "opticalflow/brox_optical_flow.bin" |
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#define BROX_OPTICAL_FLOW_DUMP_FILE_CC20 "opticalflow/brox_optical_flow_cc20.bin" |
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struct BroxOpticalFlow : testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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virtual void SetUp() |
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{ |
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devInfo = GetParam(); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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TEST_P(BroxOpticalFlow, Regression) |
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{ |
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cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1); |
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ASSERT_FALSE(frame1.empty()); |
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cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/, |
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10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/); |
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cv::gpu::GpuMat u; |
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cv::gpu::GpuMat v; |
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brox(loadMat(frame0), loadMat(frame1), u, v); |
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#ifndef DUMP |
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std::string fname(cvtest::TS::ptr()->get_data_path()); |
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if (devInfo.majorVersion() >= 2) |
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fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20; |
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else |
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fname += BROX_OPTICAL_FLOW_DUMP_FILE; |
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std::ifstream f(fname.c_str(), std::ios_base::binary); |
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int rows, cols; |
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f.read((char*)&rows, sizeof(rows)); |
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f.read((char*)&cols, sizeof(cols)); |
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cv::Mat u_gold(rows, cols, CV_32FC1); |
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for (int i = 0; i < u_gold.rows; ++i) |
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f.read(u_gold.ptr<char>(i), u_gold.cols * sizeof(float)); |
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cv::Mat v_gold(rows, cols, CV_32FC1); |
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for (int i = 0; i < v_gold.rows; ++i) |
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f.read(v_gold.ptr<char>(i), v_gold.cols * sizeof(float)); |
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EXPECT_MAT_NEAR(u_gold, u, 0); |
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EXPECT_MAT_NEAR(v_gold, v, 0); |
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#else |
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std::string fname(cvtest::TS::ptr()->get_data_path()); |
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if (devInfo.majorVersion() >= 2) |
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fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20; |
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else |
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fname += BROX_OPTICAL_FLOW_DUMP_FILE; |
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std::ofstream f(fname.c_str(), std::ios_base::binary); |
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f.write((char*)&u.rows, sizeof(u.rows)); |
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f.write((char*)&u.cols, sizeof(u.cols)); |
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cv::Mat h_u(u); |
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cv::Mat h_v(v); |
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for (int i = 0; i < u.rows; ++i) |
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f.write(h_u.ptr<char>(i), u.cols * sizeof(float)); |
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for (int i = 0; i < v.rows; ++i) |
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f.write(h_v.ptr<char>(i), v.cols * sizeof(float)); |
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#endif |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, BroxOpticalFlow, ALL_DEVICES); |
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////////////////////////////////////////////////////// |
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// GoodFeaturesToTrack |
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IMPLEMENT_PARAM_CLASS(MinDistance, double) |
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PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, MinDistance) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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double minDistance; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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minDistance = GET_PARAM(1); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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TEST_P(GoodFeaturesToTrack, Accuracy) |
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{ |
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cv::Mat image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(image.empty()); |
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int maxCorners = 1000; |
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double qualityLevel = 0.01; |
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cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance); |
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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS)) |
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{ |
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try |
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{ |
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cv::gpu::GpuMat d_pts; |
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detector(loadMat(image), d_pts); |
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} |
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catch (const cv::Exception& e) |
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{ |
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ASSERT_EQ(CV_StsNotImplemented, e.code); |
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} |
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} |
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else |
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{ |
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cv::gpu::GpuMat d_pts; |
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detector(loadMat(image), d_pts); |
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std::vector<cv::Point2f> pts(d_pts.cols); |
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cv::Mat pts_mat(1, d_pts.cols, CV_32FC2, (void*)&pts[0]); |
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d_pts.download(pts_mat); |
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std::vector<cv::Point2f> pts_gold; |
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cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance); |
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ASSERT_EQ(pts_gold.size(), pts.size()); |
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size_t mistmatch = 0; |
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for (size_t i = 0; i < pts.size(); ++i) |
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{ |
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cv::Point2i a = pts_gold[i]; |
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cv::Point2i b = pts[i]; |
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bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1; |
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if (!eq) |
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++mistmatch; |
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} |
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double bad_ratio = static_cast<double>(mistmatch) / pts.size(); |
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ASSERT_LE(bad_ratio, 0.01); |
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} |
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} |
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TEST_P(GoodFeaturesToTrack, EmptyCorners) |
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{ |
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int maxCorners = 1000; |
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double qualityLevel = 0.01; |
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cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance); |
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cv::gpu::GpuMat src(100, 100, CV_8UC1, cv::Scalar::all(0)); |
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cv::gpu::GpuMat corners(1, maxCorners, CV_32FC2); |
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detector(src, corners); |
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ASSERT_TRUE( corners.empty() ); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, GoodFeaturesToTrack, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(MinDistance(0.0), MinDistance(3.0)))); |
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////////////////////////////////////////////////////// |
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// PyrLKOpticalFlow |
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IMPLEMENT_PARAM_CLASS(UseGray, bool) |
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PARAM_TEST_CASE(PyrLKOpticalFlow, cv::gpu::DeviceInfo, UseGray) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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bool useGray; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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useGray = GET_PARAM(1); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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TEST_P(PyrLKOpticalFlow, Sparse) |
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{ |
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cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); |
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ASSERT_FALSE(frame1.empty()); |
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cv::Mat gray_frame; |
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if (useGray) |
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gray_frame = frame0; |
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else |
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cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY); |
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std::vector<cv::Point2f> pts; |
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cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0); |
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cv::gpu::GpuMat d_pts; |
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cv::Mat pts_mat(1, (int)pts.size(), CV_32FC2, (void*)&pts[0]); |
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d_pts.upload(pts_mat); |
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cv::gpu::PyrLKOpticalFlow pyrLK; |
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cv::gpu::GpuMat d_nextPts; |
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cv::gpu::GpuMat d_status; |
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pyrLK.sparse(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status); |
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std::vector<cv::Point2f> nextPts(d_nextPts.cols); |
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cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]); |
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d_nextPts.download(nextPts_mat); |
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std::vector<unsigned char> status(d_status.cols); |
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cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*)&status[0]); |
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d_status.download(status_mat); |
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std::vector<cv::Point2f> nextPts_gold; |
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std::vector<unsigned char> status_gold; |
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cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray()); |
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ASSERT_EQ(nextPts_gold.size(), nextPts.size()); |
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ASSERT_EQ(status_gold.size(), status.size()); |
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size_t mistmatch = 0; |
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for (size_t i = 0; i < nextPts.size(); ++i) |
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{ |
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cv::Point2i a = nextPts[i]; |
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cv::Point2i b = nextPts_gold[i]; |
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if (status[i] != status_gold[i]) |
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{ |
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++mistmatch; |
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continue; |
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} |
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if (status[i]) |
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{ |
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bool eq = std::abs(a.x - b.x) <= 1 && std::abs(a.y - b.y) <= 1; |
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if (!eq) |
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++mistmatch; |
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} |
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} |
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double bad_ratio = static_cast<double>(mistmatch) / nextPts.size(); |
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ASSERT_LE(bad_ratio, 0.01); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, PyrLKOpticalFlow, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(UseGray(true), UseGray(false)))); |
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////////////////////////////////////////////////////// |
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// FarnebackOpticalFlow |
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IMPLEMENT_PARAM_CLASS(PyrScale, double) |
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IMPLEMENT_PARAM_CLASS(PolyN, int) |
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CV_FLAGS(FarnebackOptFlowFlags, 0, cv::OPTFLOW_FARNEBACK_GAUSSIAN) |
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IMPLEMENT_PARAM_CLASS(UseInitFlow, bool) |
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PARAM_TEST_CASE(FarnebackOpticalFlow, cv::gpu::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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double pyrScale; |
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int polyN; |
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int flags; |
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bool useInitFlow; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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pyrScale = GET_PARAM(1); |
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polyN = GET_PARAM(2); |
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flags = GET_PARAM(3); |
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useInitFlow = GET_PARAM(4); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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TEST_P(FarnebackOpticalFlow, Accuracy) |
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{ |
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cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame1.empty()); |
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double polySigma = polyN <= 5 ? 1.1 : 1.5; |
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cv::gpu::FarnebackOpticalFlow calc; |
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calc.pyrScale = pyrScale; |
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calc.polyN = polyN; |
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calc.polySigma = polySigma; |
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calc.flags = flags; |
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cv::gpu::GpuMat d_flowx, d_flowy; |
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calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy); |
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cv::Mat flow; |
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if (useInitFlow) |
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{ |
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cv::Mat flowxy[] = {cv::Mat(d_flowx), cv::Mat(d_flowy)}; |
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cv::merge(flowxy, 2, flow); |
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} |
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if (useInitFlow) |
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{ |
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calc.flags |= cv::OPTFLOW_USE_INITIAL_FLOW; |
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calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy); |
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} |
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cv::calcOpticalFlowFarneback( |
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frame0, frame1, flow, calc.pyrScale, calc.numLevels, calc.winSize, |
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calc.numIters, calc.polyN, calc.polySigma, calc.flags); |
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std::vector<cv::Mat> flowxy; |
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cv::split(flow, flowxy); |
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EXPECT_MAT_SIMILAR(flowxy[0], d_flowx, 0.1); |
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EXPECT_MAT_SIMILAR(flowxy[1], d_flowy, 0.1); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, FarnebackOpticalFlow, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)), |
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testing::Values(PolyN(5), PolyN(7)), |
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testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)), |
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testing::Values(UseInitFlow(false), UseInitFlow(true)))); |
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struct OpticalFlowNan : public BroxOpticalFlow {}; |
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TEST_P(OpticalFlowNan, Regression) |
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{ |
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cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat r_frame0, r_frame1; |
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cv::resize(frame0, r_frame0, cv::Size(1380,1000)); |
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cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1); |
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ASSERT_FALSE(frame1.empty()); |
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cv::resize(frame1, r_frame1, cv::Size(1380,1000)); |
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cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/, |
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5 /*inner_iterations*/, 150 /*outer_iterations*/, 10 /*solver_iterations*/); |
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cv::gpu::GpuMat u; |
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cv::gpu::GpuMat v; |
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brox(loadMat(r_frame0), loadMat(r_frame1), u, v); |
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cv::Mat h_u, h_v; |
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u.download(h_u); |
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v.download(h_v); |
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EXPECT_TRUE(cv::checkRange(h_u)); |
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EXPECT_TRUE(cv::checkRange(h_v)); |
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}; |
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INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowNan, ALL_DEVICES); |
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////////////////////////////////////////////////////// |
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// OpticalFlowDual_TVL1 |
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PARAM_TEST_CASE(OpticalFlowDual_TVL1, cv::gpu::DeviceInfo, UseRoi) |
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{ |
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}; |
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TEST_P(OpticalFlowDual_TVL1, Accuracy) |
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{ |
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cv::gpu::DeviceInfo devInfo = GET_PARAM(0); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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const bool useRoi = GET_PARAM(1); |
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cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame1.empty()); |
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cv::gpu::OpticalFlowDual_TVL1_GPU d_alg; |
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cv::gpu::GpuMat d_flowx = createMat(frame0.size(), CV_32FC1, useRoi); |
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cv::gpu::GpuMat d_flowy = createMat(frame0.size(), CV_32FC1, useRoi); |
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d_alg(loadMat(frame0, useRoi), loadMat(frame1, useRoi), d_flowx, d_flowy); |
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cv::OpticalFlowDual_TVL1 alg; |
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cv::Mat flow; |
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alg(frame0, frame1, flow); |
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cv::Mat gold[2]; |
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cv::split(flow, gold); |
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EXPECT_MAT_SIMILAR(gold[0], d_flowx, 3e-3); |
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EXPECT_MAT_SIMILAR(gold[1], d_flowy, 3e-3); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowDual_TVL1, testing::Combine( |
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ALL_DEVICES, |
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WHOLE_SUBMAT)); |
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////////////////////////////////////////////////////// |
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// OpticalFlowBM |
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void calcOpticalFlowBM(const cv::Mat& prev, const cv::Mat& curr, |
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cv::Size bSize, cv::Size shiftSize, cv::Size maxRange, int usePrevious, |
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cv::Mat& velx, cv::Mat& vely) |
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{ |
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cv::Size sz((curr.cols - bSize.width + shiftSize.width)/shiftSize.width, (curr.rows - bSize.height + shiftSize.height)/shiftSize.height); |
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velx.create(sz, CV_32FC1); |
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vely.create(sz, CV_32FC1); |
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CvMat cvprev = prev; |
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CvMat cvcurr = curr; |
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CvMat cvvelx = velx; |
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CvMat cvvely = vely; |
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cvCalcOpticalFlowBM(&cvprev, &cvcurr, bSize, shiftSize, maxRange, usePrevious, &cvvelx, &cvvely); |
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} |
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struct OpticalFlowBM : testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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}; |
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TEST_P(OpticalFlowBM, Accuracy) |
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{ |
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cv::gpu::DeviceInfo devInfo = GetParam(); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame1.empty()); |
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cv::Size block_size(16, 16); |
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cv::Size shift_size(1, 1); |
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cv::Size max_range(16, 16); |
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cv::gpu::GpuMat d_velx, d_vely, buf; |
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cv::gpu::calcOpticalFlowBM(loadMat(frame0), loadMat(frame1), |
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block_size, shift_size, max_range, false, |
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d_velx, d_vely, buf); |
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cv::Mat velx, vely; |
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calcOpticalFlowBM(frame0, frame1, block_size, shift_size, max_range, false, velx, vely); |
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EXPECT_MAT_NEAR(velx, d_velx, 0); |
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EXPECT_MAT_NEAR(vely, d_vely, 0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowBM, ALL_DEVICES); |
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////////////////////////////////////////////////////// |
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// FGDStatModel |
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|
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namespace cv |
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{ |
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template<> void Ptr<CvBGStatModel>::delete_obj() |
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{ |
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cvReleaseBGStatModel(&obj); |
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} |
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} |
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PARAM_TEST_CASE(FGDStatModel, cv::gpu::DeviceInfo, std::string, Channels) |
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{ |
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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); |
|
} |
|
}; |
|
|
|
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<CvBGStatModel> 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 |
|
|
|
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); |
|
} |
|
}; |
|
|
|
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 |
|
|
|
PARAM_TEST_CASE(MOG2, cv::gpu::DeviceInfo, std::string, UseGray, UseRoi) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
std::string inputFile; |
|
bool useGray; |
|
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); |
|
|
|
useRoi = GET_PARAM(3); |
|
} |
|
}; |
|
|
|
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; |
|
cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi); |
|
|
|
cv::BackgroundSubtractorMOG2 mog2_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); |
|
} |
|
|
|
mog2(loadMat(frame, useRoi), foreground); |
|
|
|
mog2_gold(frame, foreground_gold); |
|
|
|
double norm = cv::norm(foreground_gold, cv::Mat(foreground), cv::NORM_L1); |
|
|
|
norm /= foreground_gold.size().area(); |
|
|
|
ASSERT_LE(norm, 0.09); |
|
} |
|
} |
|
|
|
TEST_P(MOG2, getBackgroundImage) |
|
{ |
|
if (useGray) |
|
return; |
|
|
|
cv::VideoCapture cap(inputFile); |
|
ASSERT_TRUE(cap.isOpened()); |
|
|
|
cv::Mat frame; |
|
|
|
cv::gpu::MOG2_GPU mog2; |
|
cv::gpu::GpuMat foreground; |
|
|
|
cv::BackgroundSubtractorMOG2 mog2_gold; |
|
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)), |
|
WHOLE_SUBMAT)); |
|
|
|
////////////////////////////////////////////////////// |
|
// VIBE |
|
|
|
PARAM_TEST_CASE(VIBE, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi) |
|
{ |
|
}; |
|
|
|
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) |
|
{ |
|
}; |
|
|
|
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)); |
|
|
|
#ifdef WIN32 |
|
|
|
////////////////////////////////////////////////////// |
|
// VideoWriter |
|
|
|
PARAM_TEST_CASE(VideoWriter, cv::gpu::DeviceInfo, std::string) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
std::string inputFile; |
|
|
|
std::string outputFile; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
inputFile = GET_PARAM(1); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile; |
|
outputFile = cv::tempfile(".avi"); |
|
} |
|
}; |
|
|
|
TEST_P(VideoWriter, Regression) |
|
{ |
|
const double FPS = 25.0; |
|
|
|
cv::VideoCapture reader(inputFile); |
|
ASSERT_TRUE( reader.isOpened() ); |
|
|
|
cv::gpu::VideoWriter_GPU d_writer; |
|
|
|
cv::Mat frame; |
|
cv::gpu::GpuMat d_frame; |
|
|
|
for (int i = 0; i < 10; ++i) |
|
{ |
|
reader >> frame; |
|
ASSERT_FALSE(frame.empty()); |
|
|
|
d_frame.upload(frame); |
|
|
|
if (!d_writer.isOpened()) |
|
d_writer.open(outputFile, frame.size(), FPS); |
|
|
|
d_writer.write(d_frame); |
|
} |
|
|
|
reader.release(); |
|
d_writer.close(); |
|
|
|
reader.open(outputFile); |
|
ASSERT_TRUE( reader.isOpened() ); |
|
|
|
for (int i = 0; i < 5; ++i) |
|
{ |
|
reader >> frame; |
|
ASSERT_FALSE( frame.empty() ); |
|
} |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_Video, VideoWriter, testing::Combine( |
|
ALL_DEVICES, |
|
testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi")))); |
|
|
|
////////////////////////////////////////////////////// |
|
// VideoReader |
|
|
|
PARAM_TEST_CASE(VideoReader, cv::gpu::DeviceInfo, std::string) |
|
{ |
|
cv::gpu::DeviceInfo devInfo; |
|
std::string inputFile; |
|
|
|
virtual void SetUp() |
|
{ |
|
devInfo = GET_PARAM(0); |
|
inputFile = GET_PARAM(1); |
|
|
|
cv::gpu::setDevice(devInfo.deviceID()); |
|
|
|
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile; |
|
} |
|
}; |
|
|
|
TEST_P(VideoReader, Regression) |
|
{ |
|
cv::gpu::VideoReader_GPU reader(inputFile); |
|
ASSERT_TRUE( reader.isOpened() ); |
|
|
|
cv::gpu::GpuMat frame; |
|
|
|
for (int i = 0; i < 10; ++i) |
|
{ |
|
ASSERT_TRUE( reader.read(frame) ); |
|
ASSERT_FALSE( frame.empty() ); |
|
} |
|
|
|
reader.close(); |
|
ASSERT_FALSE( reader.isOpened() ); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_Video, VideoReader, testing::Combine( |
|
ALL_DEVICES, |
|
testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi")))); |
|
|
|
#endif // WIN32 |
|
|
|
#endif // HAVE_CUDA
|
|
|