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777 lines
21 KiB
C++
777 lines
21 KiB
C++
/*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 "precomp.hpp"
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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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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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// FGDStatModel
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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;
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std::string inputFile;
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int out_cn;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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cv::gpu::setDevice(devInfo.deviceID());
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inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
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out_cn = GET_PARAM(2);
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}
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};
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TEST_P(FGDStatModel, Update)
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{
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cv::VideoCapture cap(inputFile);
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ASSERT_TRUE(cap.isOpened());
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cv::Mat frame;
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cap >> frame;
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ASSERT_FALSE(frame.empty());
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IplImage ipl_frame = frame;
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cv::Ptr<CvBGStatModel> model(cvCreateFGDStatModel(&ipl_frame));
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cv::gpu::GpuMat d_frame(frame);
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cv::gpu::FGDStatModel d_model(out_cn);
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d_model.create(d_frame);
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cv::Mat h_background;
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cv::Mat h_foreground;
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cv::Mat h_background3;
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cv::Mat backgroundDiff;
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cv::Mat foregroundDiff;
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for (int i = 0; i < 5; ++i)
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{
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cap >> frame;
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ASSERT_FALSE(frame.empty());
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ipl_frame = frame;
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int gold_count = cvUpdateBGStatModel(&ipl_frame, model);
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d_frame.upload(frame);
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int count = d_model.update(d_frame);
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ASSERT_EQ(gold_count, count);
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cv::Mat gold_background(model->background);
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cv::Mat gold_foreground(model->foreground);
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if (out_cn == 3)
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d_model.background.download(h_background3);
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else
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{
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d_model.background.download(h_background);
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cv::cvtColor(h_background, h_background3, cv::COLOR_BGRA2BGR);
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}
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d_model.foreground.download(h_foreground);
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ASSERT_MAT_NEAR(gold_background, h_background3, 1.0);
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ASSERT_MAT_NEAR(gold_foreground, h_foreground, 0.0);
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_Video, FGDStatModel, testing::Combine(
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ALL_DEVICES,
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testing::Values(std::string("768x576.avi")),
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testing::Values(Channels(3), Channels(4))));
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//////////////////////////////////////////////////////
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// MOG
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IMPLEMENT_PARAM_CLASS(LearningRate, double)
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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, 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)
|
|
{
|
|
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());
|
|
|
|
// 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);
|
|
}
|
|
|
|
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));
|
|
|
|
//////////////////////////////////////////////////////
|
|
// VideoWriter
|
|
|
|
#ifdef WIN32
|
|
|
|
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"))));
|
|
|
|
#endif // WIN32
|
|
|
|
//////////////////////////////////////////////////////
|
|
// 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"))));
|