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https://github.com/opencv/opencv.git
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b4a4a05bdc
Additional notes with this commit: 1. Add cornerHarris_dxdy and cornerMinEigenVal_dxdy to get the interim dx and dy output of Sobel operator; 2. Add minMax_buf to allow user to reuse buffers in minMax; 3. Fix an error when either min or max pointer fed into minMax is NULL; 4. Corner sorter temporarily uses C++ STL's quick sort. A parallel selection sort in OpneCL is contained in the implementation but disabled due to poor performance at the moment. 5. Accuracy test for ocl gfft.
278 lines
8.2 KiB
C++
278 lines
8.2 KiB
C++
///////////////////////////////////////////////////////////////////////////////////////
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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 "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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extern string workdir;
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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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TEST_P(GoodFeaturesToTrack, Accuracy)
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{
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cv::Mat frame = readImage(workdir + "../gpu/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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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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TEST_P(TVL1, Accuracy)
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{
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cv::Mat frame0 = readImage(workdir + "../gpu/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(frame0.empty());
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cv::Mat frame1 = readImage(workdir + "../gpu/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::RNG &rng = TS::ptr()->get_rng();
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cv::Mat flowx = randomMat(rng, frame0.size(), CV_32FC1, 0, 0, useRoi);
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cv::Mat flowy = randomMat(rng, 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(true, false));
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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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TEST_P(Sparse, Mat)
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{
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cv::Mat frame0 = readImage(workdir + "../gpu/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(workdir + "../gpu/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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#endif // HAVE_OPENCL
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