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https://github.com/opencv/opencv.git
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404 lines
12 KiB
C++
404 lines
12 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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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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using namespace cvtest;
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//////////////////////////////////////////////////////
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// BroxOpticalFlow
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//#define BROX_DUMP
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struct BroxOpticalFlow : testing::TestWithParam<cv::cuda::DeviceInfo>
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{
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cv::cuda::DeviceInfo devInfo;
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virtual void SetUp()
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{
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devInfo = GetParam();
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_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::Ptr<cv::cuda::BroxOpticalFlow> brox =
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cv::cuda::BroxOpticalFlow::create(0.197 /*alpha*/, 50.0 /*gamma*/, 0.8 /*scale_factor*/,
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10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
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cv::cuda::GpuMat flow;
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brox->calc(loadMat(frame0), loadMat(frame1), flow);
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cv::cuda::GpuMat flows[2];
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cv::cuda::split(flow, flows);
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cv::cuda::GpuMat u = flows[0];
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cv::cuda::GpuMat v = flows[1];
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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 += "opticalflow/brox_optical_flow_cc20.bin";
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else
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fname += "opticalflow/brox_optical_flow.bin";
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#ifndef BROX_DUMP
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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_SIMILAR(u_gold, u, 1e-3);
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EXPECT_MAT_SIMILAR(v_gold, v, 1e-3);
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#else
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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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CUDA_TEST_P(BroxOpticalFlow, OpticalFlowNan)
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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::Mat r_frame0, r_frame1;
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cv::resize(frame0, r_frame0, cv::Size(1380,1000));
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cv::resize(frame1, r_frame1, cv::Size(1380,1000));
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cv::Ptr<cv::cuda::BroxOpticalFlow> brox =
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cv::cuda::BroxOpticalFlow::create(0.197 /*alpha*/, 50.0 /*gamma*/, 0.8 /*scale_factor*/,
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10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);
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cv::cuda::GpuMat flow;
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brox->calc(loadMat(frame0), loadMat(frame1), flow);
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cv::cuda::GpuMat flows[2];
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cv::cuda::split(flow, flows);
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cv::cuda::GpuMat u = flows[0];
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cv::cuda::GpuMat v = flows[1];
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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(CUDA_OptFlow, BroxOpticalFlow, ALL_DEVICES);
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//////////////////////////////////////////////////////
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// PyrLKOpticalFlow
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namespace
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{
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IMPLEMENT_PARAM_CLASS(Chan, int)
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IMPLEMENT_PARAM_CLASS(DataType, int)
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}
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PARAM_TEST_CASE(PyrLKOpticalFlow, cv::cuda::DeviceInfo, Chan, DataType)
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{
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cv::cuda::DeviceInfo devInfo;
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int channels;
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int dataType;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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channels = GET_PARAM(1);
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dataType = GET_PARAM(2);
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_TEST_P(PyrLKOpticalFlow, Sparse)
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{
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cv::Mat frame0 = readImage("opticalflow/frame0.png", channels == 1 ? 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", channels == 1 ? 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 (channels == 1)
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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::cuda::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::Ptr<cv::cuda::SparsePyrLKOpticalFlow> pyrLK =
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cv::cuda::SparsePyrLKOpticalFlow::create();
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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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cv::cuda::GpuMat d_nextPts;
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cv::cuda::GpuMat d_status;
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cv::Mat converted0, converted1;
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if(channels == 4)
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{
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cv::cvtColor(frame0, frame0, cv::COLOR_BGR2BGRA);
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cv::cvtColor(frame1, frame1, cv::COLOR_BGR2BGRA);
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}
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frame0.convertTo(converted0, dataType);
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frame1.convertTo(converted1, dataType);
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pyrLK->calc(loadMat(converted0), loadMat(converted1), 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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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(CUDA_OptFlow, PyrLKOpticalFlow, testing::Combine(
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ALL_DEVICES,
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testing::Values(Chan(1), Chan(3), Chan(4)),
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testing::Values(DataType(CV_8U), DataType(CV_16U), DataType(CV_32S), DataType(CV_32F))));
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//////////////////////////////////////////////////////
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// FarnebackOpticalFlow
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namespace
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{
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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, OPTFLOW_FARNEBACK_GAUSSIAN)
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IMPLEMENT_PARAM_CLASS(UseInitFlow, bool)
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}
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PARAM_TEST_CASE(FarnebackOpticalFlow, cv::cuda::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
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{
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cv::cuda::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::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_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::Ptr<cv::cuda::FarnebackOpticalFlow> farn =
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cv::cuda::FarnebackOpticalFlow::create();
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farn->setPyrScale(pyrScale);
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farn->setPolyN(polyN);
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farn->setPolySigma(polySigma);
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farn->setFlags(flags);
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cv::cuda::GpuMat d_flow;
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farn->calc(loadMat(frame0), loadMat(frame1), d_flow);
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cv::Mat flow;
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if (useInitFlow)
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{
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d_flow.download(flow);
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farn->setFlags(farn->getFlags() | cv::OPTFLOW_USE_INITIAL_FLOW);
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farn->calc(loadMat(frame0), loadMat(frame1), d_flow);
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}
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cv::calcOpticalFlowFarneback(
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frame0, frame1, flow, farn->getPyrScale(), farn->getNumLevels(), farn->getWinSize(),
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farn->getNumIters(), farn->getPolyN(), farn->getPolySigma(), farn->getFlags());
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EXPECT_MAT_SIMILAR(flow, d_flow, 0.1);
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}
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INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, 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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//////////////////////////////////////////////////////
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// OpticalFlowDual_TVL1
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namespace
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{
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IMPLEMENT_PARAM_CLASS(Gamma, double)
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}
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PARAM_TEST_CASE(OpticalFlowDual_TVL1, cv::cuda::DeviceInfo, Gamma)
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{
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cv::cuda::DeviceInfo devInfo;
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double gamma;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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gamma = GET_PARAM(1);
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_TEST_P(OpticalFlowDual_TVL1, 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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cv::Ptr<cv::cuda::OpticalFlowDual_TVL1> d_alg =
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cv::cuda::OpticalFlowDual_TVL1::create();
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d_alg->setNumIterations(10);
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d_alg->setGamma(gamma);
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cv::cuda::GpuMat d_flow;
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d_alg->calc(loadMat(frame0), loadMat(frame1), d_flow);
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cv::Ptr<cv::DualTVL1OpticalFlow> alg = cv::createOptFlow_DualTVL1();
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alg->setMedianFiltering(1);
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alg->setInnerIterations(1);
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alg->setOuterIterations(d_alg->getNumIterations());
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alg->setGamma(gamma);
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cv::Mat flow;
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alg->calc(frame0, frame1, flow);
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EXPECT_MAT_SIMILAR(flow, d_flow, 4e-3);
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}
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INSTANTIATE_TEST_CASE_P(CUDA_OptFlow, OpticalFlowDual_TVL1, testing::Combine(
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ALL_DEVICES,
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testing::Values(Gamma(0.0), Gamma(1.0))));
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#endif // HAVE_CUDA
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