629 lines
19 KiB
C++
629 lines
19 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::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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GPU_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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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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GPU_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::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, BroxOpticalFlow, ALL_DEVICES);
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//////////////////////////////////////////////////////
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// GoodFeaturesToTrack
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namespace
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{
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IMPLEMENT_PARAM_CLASS(MinDistance, double)
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}
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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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GPU_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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cv::gpu::GpuMat d_pts;
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detector(loadMat(image), d_pts);
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ASSERT_FALSE(d_pts.empty());
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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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GPU_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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namespace
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{
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IMPLEMENT_PARAM_CLASS(UseGray, bool)
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}
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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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GPU_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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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::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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GPU_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 farn;
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farn.pyrScale = pyrScale;
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farn.polyN = polyN;
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farn.polySigma = polySigma;
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farn.flags = flags;
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cv::gpu::GpuMat d_flowx, d_flowy;
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farn(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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farn.flags |= cv::OPTFLOW_USE_INITIAL_FLOW;
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farn(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, farn.pyrScale, farn.numLevels, farn.winSize,
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farn.numIters, farn.polyN, farn.polySigma, farn.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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//////////////////////////////////////////////////////
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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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cv::gpu::DeviceInfo devInfo;
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bool useRoi;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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useRoi = GET_PARAM(1);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_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::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::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
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cv::Mat flow;
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alg->calc(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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namespace
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{
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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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}
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struct OpticalFlowBM : testing::TestWithParam<cv::gpu::DeviceInfo>
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{
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};
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GPU_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::resize(frame0, frame0, cv::Size(), 0.5, 0.5);
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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::resize(frame1, frame1, cv::Size(), 0.5, 0.5);
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|
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cv::Size block_size(8, 8);
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cv::Size shift_size(1, 1);
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cv::Size max_range(8, 8);
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|
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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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|
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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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|
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EXPECT_MAT_NEAR(velx, d_velx, 1e-6);
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EXPECT_MAT_NEAR(vely, d_vely, 1e-6);
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}
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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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|
//////////////////////////////////////////////////////
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|
// FastOpticalFlowBM
|
|
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|
namespace
|
|
{
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void FastOpticalFlowBM_gold(const cv::Mat_<uchar>& I0, const cv::Mat_<uchar>& I1, cv::Mat_<float>& velx, cv::Mat_<float>& vely, int search_window, int block_window)
|
|
{
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velx.create(I0.size());
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|
vely.create(I0.size());
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|
|
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int search_radius = search_window / 2;
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|
int block_radius = block_window / 2;
|
|
|
|
for (int y = 0; y < I0.rows; ++y)
|
|
{
|
|
for (int x = 0; x < I0.cols; ++x)
|
|
{
|
|
int bestDist = std::numeric_limits<int>::max();
|
|
int bestDx = 0;
|
|
int bestDy = 0;
|
|
|
|
for (int dy = -search_radius; dy <= search_radius; ++dy)
|
|
{
|
|
for (int dx = -search_radius; dx <= search_radius; ++dx)
|
|
{
|
|
int dist = 0;
|
|
|
|
for (int by = -block_radius; by <= block_radius; ++by)
|
|
{
|
|
for (int bx = -block_radius; bx <= block_radius; ++bx)
|
|
{
|
|
int I0_val = I0(cv::borderInterpolate(y + by, I0.rows, cv::BORDER_DEFAULT), cv::borderInterpolate(x + bx, I0.cols, cv::BORDER_DEFAULT));
|
|
int I1_val = I1(cv::borderInterpolate(y + dy + by, I0.rows, cv::BORDER_DEFAULT), cv::borderInterpolate(x + dx + bx, I0.cols, cv::BORDER_DEFAULT));
|
|
|
|
dist += std::abs(I0_val - I1_val);
|
|
}
|
|
}
|
|
|
|
if (dist < bestDist)
|
|
{
|
|
bestDist = dist;
|
|
bestDx = dx;
|
|
bestDy = dy;
|
|
}
|
|
}
|
|
}
|
|
|
|
velx(y, x) = (float) bestDx;
|
|
vely(y, x) = (float) bestDy;
|
|
}
|
|
}
|
|
}
|
|
|
|
double calc_rmse(const cv::Mat_<float>& flow1, const cv::Mat_<float>& flow2)
|
|
{
|
|
double sum = 0.0;
|
|
|
|
for (int y = 0; y < flow1.rows; ++y)
|
|
{
|
|
for (int x = 0; x < flow1.cols; ++x)
|
|
{
|
|
double diff = flow1(y, x) - flow2(y, x);
|
|
sum += diff * diff;
|
|
}
|
|
}
|
|
|
|
return std::sqrt(sum / flow1.size().area());
|
|
}
|
|
}
|
|
|
|
struct FastOpticalFlowBM : testing::TestWithParam<cv::gpu::DeviceInfo>
|
|
{
|
|
};
|
|
|
|
GPU_TEST_P(FastOpticalFlowBM, Accuracy)
|
|
{
|
|
const double MAX_RMSE = 0.6;
|
|
|
|
int search_window = 15;
|
|
int block_window = 5;
|
|
|
|
cv::gpu::DeviceInfo devInfo = GetParam();
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
|
|
cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
|
|
ASSERT_FALSE(frame0.empty());
|
|
|
|
cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
|
|
ASSERT_FALSE(frame1.empty());
|
|
|
|
cv::Size smallSize(320, 240);
|
|
cv::Mat frame0_small;
|
|
cv::Mat frame1_small;
|
|
|
|
cv::resize(frame0, frame0_small, smallSize);
|
|
cv::resize(frame1, frame1_small, smallSize);
|
|
|
|
cv::gpu::GpuMat d_flowx;
|
|
cv::gpu::GpuMat d_flowy;
|
|
cv::gpu::FastOpticalFlowBM fastBM;
|
|
|
|
fastBM(loadMat(frame0_small), loadMat(frame1_small), d_flowx, d_flowy, search_window, block_window);
|
|
|
|
cv::Mat_<float> flowx;
|
|
cv::Mat_<float> flowy;
|
|
FastOpticalFlowBM_gold(frame0_small, frame1_small, flowx, flowy, search_window, block_window);
|
|
|
|
double err;
|
|
|
|
err = calc_rmse(flowx, cv::Mat(d_flowx));
|
|
EXPECT_LE(err, MAX_RMSE);
|
|
|
|
err = calc_rmse(flowy, cv::Mat(d_flowy));
|
|
EXPECT_LE(err, MAX_RMSE);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_Video, FastOpticalFlowBM, ALL_DEVICES);
|
|
|
|
#endif // HAVE_CUDA
|