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Open Source Computer Vision Library
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344 lines
10 KiB
344 lines
10 KiB
/////////////////////////////////////////////////////////////////////////////////////// |
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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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// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved. |
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// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved. |
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// Copyright (C) 2010-2012, Advanced Micro Devices, 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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// @Authors |
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// |
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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 oclMaterials 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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#include <iomanip> |
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#ifdef HAVE_OPENCL |
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using namespace cv; |
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using namespace cv::ocl; |
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using namespace cvtest; |
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using namespace testing; |
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using namespace std; |
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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, MinDistance) |
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{ |
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double minDistance; |
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virtual void SetUp() |
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{ |
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minDistance = GET_PARAM(0); |
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} |
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}; |
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OCL_TEST_P(GoodFeaturesToTrack, Accuracy) |
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{ |
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cv::Mat frame = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame.empty()); |
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int maxCorners = 1000; |
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double qualityLevel = 0.01; |
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cv::ocl::GoodFeaturesToTrackDetector_OCL detector(maxCorners, qualityLevel, minDistance); |
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cv::ocl::oclMat d_pts; |
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detector(oclMat(frame), 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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detector.downloadPoints(d_pts, pts); |
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std::vector<cv::Point2f> pts_gold; |
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cv::goodFeaturesToTrack(frame, 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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OCL_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::ocl::GoodFeaturesToTrackDetector_OCL detector(maxCorners, qualityLevel, minDistance); |
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cv::ocl::oclMat src(100, 100, CV_8UC1, cv::Scalar::all(0)); |
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cv::ocl::oclMat 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(OCL_Video, GoodFeaturesToTrack, |
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testing::Values(MinDistance(0.0), MinDistance(3.0))); |
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////////////////////////////////////////////////////////////////////////// |
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PARAM_TEST_CASE(TVL1, bool) |
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{ |
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bool useRoi; |
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virtual void SetUp() |
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{ |
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useRoi = GET_PARAM(0); |
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} |
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}; |
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OCL_TEST_P(TVL1, Accuracy) |
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{ |
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cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame1.empty()); |
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cv::ocl::OpticalFlowDual_TVL1_OCL d_alg; |
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cv::Mat flowx = randomMat(frame0.size(), CV_32FC1, 0, 0, useRoi); |
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cv::Mat flowy = randomMat(frame0.size(), CV_32FC1, 0, 0, useRoi); |
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cv::ocl::oclMat d_flowx(flowx), d_flowy(flowy); |
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d_alg(oclMat(frame0), oclMat(frame1), 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(OCL_Video, TVL1, Values(false, true)); |
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///////////////////////////////////////////////////////////////////////////////////////////////// |
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// PyrLKOpticalFlow |
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PARAM_TEST_CASE(Sparse, bool, bool) |
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{ |
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bool useGray; |
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bool UseSmart; |
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virtual void SetUp() |
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{ |
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UseSmart = GET_PARAM(0); |
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useGray = GET_PARAM(1); |
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} |
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}; |
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OCL_TEST_P(Sparse, Mat) |
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{ |
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cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImage("gpu/opticalflow/rubberwhale2.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::ocl::oclMat 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::ocl::PyrLKOpticalFlow pyrLK; |
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cv::ocl::oclMat oclFrame0; |
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cv::ocl::oclMat oclFrame1; |
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cv::ocl::oclMat d_nextPts; |
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cv::ocl::oclMat d_status; |
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cv::ocl::oclMat d_err; |
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oclFrame0 = frame0; |
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oclFrame1 = frame1; |
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pyrLK.sparse(oclFrame0, oclFrame1, d_pts, d_nextPts, d_status, &d_err); |
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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<float> err(d_err.cols); |
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cv::Mat err_mat(1, d_err.cols, CV_32FC1, (void*)&err[0]); |
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d_err.download(err_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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std::vector<float> err_gold; |
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cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, err_gold); |
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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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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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cv::Point2i a = nextPts[i]; |
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cv::Point2i b = nextPts_gold[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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//float errdiff = std::abs(err[i] - err_gold[i]); |
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float errdiff = 0.0f; |
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if (!eq || errdiff > 1e-1) |
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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, 0.02f); |
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} |
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INSTANTIATE_TEST_CASE_P(OCL_Video, Sparse, Combine( |
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Values(false, true), |
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Values(false, true))); |
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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(Farneback, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow) |
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{ |
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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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pyrScale = GET_PARAM(0); |
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polyN = GET_PARAM(1); |
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flags = GET_PARAM(2); |
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useInitFlow = GET_PARAM(3); |
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} |
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}; |
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OCL_TEST_P(Farneback, Accuracy) |
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{ |
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cv::Mat frame0 = readImage("gpu/opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = readImage("gpu/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::ocl::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::ocl::oclMat d_flowx, d_flowy; |
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farn(oclMat(frame0), oclMat(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(oclMat(frame0), oclMat(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(OCL_Video, Farneback, testing::Combine( |
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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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#endif // HAVE_OPENCL
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