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Open Source Computer Vision Library
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357 lines
12 KiB
357 lines
12 KiB
/*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) 2010-2012, Multicoreware, Inc., 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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// Fangfang Bai, fangfang@multicorewareinc.com |
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// Jin Ma, jin@multicorewareinc.com |
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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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///////////// PyrLKOpticalFlow //////////////////////// |
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PERFTEST(PyrLKOpticalFlow) |
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{ |
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std::string images1[] = {"rubberwhale1.png", "basketball1.png"}; |
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std::string images2[] = {"rubberwhale2.png", "basketball2.png"}; |
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for (size_t i = 0; i < sizeof(images1) / sizeof(std::string); i++) |
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{ |
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Mat frame0 = imread(abspath(images1[i]), i == 0 ? IMREAD_COLOR : IMREAD_GRAYSCALE); |
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if (frame0.empty()) |
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{ |
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std::string errstr = "can't open " + images1[i]; |
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throw runtime_error(errstr); |
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} |
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Mat frame1 = imread(abspath(images2[i]), i == 0 ? IMREAD_COLOR : IMREAD_GRAYSCALE); |
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if (frame1.empty()) |
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{ |
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std::string errstr = "can't open " + images2[i]; |
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throw runtime_error(errstr); |
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} |
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Mat gray_frame; |
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if (i == 0) |
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{ |
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cvtColor(frame0, gray_frame, COLOR_BGR2GRAY); |
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} |
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for (int points = Min_Size; points <= Max_Size; points *= Multiple) |
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{ |
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if (i == 0) |
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SUBTEST << frame0.cols << "x" << frame0.rows << "; color; " << points << " points"; |
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else |
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SUBTEST << frame0.cols << "x" << frame0.rows << "; gray; " << points << " points"; |
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Mat ocl_nextPts; |
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Mat ocl_status; |
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vector<Point2f> pts; |
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goodFeaturesToTrack(i == 0 ? gray_frame : frame0, pts, points, 0.01, 0.0); |
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vector<Point2f> nextPts; |
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vector<unsigned char> status; |
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vector<float> err; |
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calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts, status, err); |
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CPU_ON; |
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calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts, status, err); |
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CPU_OFF; |
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ocl::PyrLKOpticalFlow d_pyrLK; |
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ocl::oclMat d_frame0(frame0); |
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ocl::oclMat d_frame1(frame1); |
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ocl::oclMat d_pts; |
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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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ocl::oclMat d_nextPts; |
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ocl::oclMat d_status; |
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ocl::oclMat d_err; |
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WARMUP_ON; |
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d_pyrLK.sparse(d_frame0, d_frame1, d_pts, d_nextPts, d_status, &d_err); |
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WARMUP_OFF; |
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GPU_ON; |
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d_pyrLK.sparse(d_frame0, d_frame1, d_pts, d_nextPts, d_status, &d_err); |
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GPU_OFF; |
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GPU_FULL_ON; |
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d_frame0.upload(frame0); |
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d_frame1.upload(frame1); |
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d_pts.upload(pts_mat); |
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d_pyrLK.sparse(d_frame0, d_frame1, d_pts, d_nextPts, d_status, &d_err); |
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if (!d_nextPts.empty()) |
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d_nextPts.download(ocl_nextPts); |
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if (!d_status.empty()) |
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d_status.download(ocl_status); |
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GPU_FULL_OFF; |
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size_t mismatch = 0; |
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for (int i = 0; i < (int)nextPts.size(); ++i) |
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{ |
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if(status[i] != ocl_status.at<unsigned char>(0, i)){ |
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mismatch++; |
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continue; |
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} |
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if(status[i]){ |
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Point2f gpu_rst = ocl_nextPts.at<Point2f>(0, i); |
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Point2f cpu_rst = nextPts[i]; |
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if(fabs(gpu_rst.x - cpu_rst.x) >= 1. || fabs(gpu_rst.y - cpu_rst.y) >= 1.) |
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mismatch++; |
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} |
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} |
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double ratio = (double)mismatch / (double)nextPts.size(); |
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if(ratio < .02) |
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TestSystem::instance().setAccurate(1, ratio); |
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else |
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TestSystem::instance().setAccurate(0, ratio); |
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} |
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} |
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} |
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PERFTEST(tvl1flow) |
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{ |
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cv::Mat frame0 = imread("rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
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assert(!frame0.empty()); |
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cv::Mat frame1 = imread("rubberwhale2.png", cv::IMREAD_GRAYSCALE); |
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assert(!frame1.empty()); |
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cv::ocl::OpticalFlowDual_TVL1_OCL d_alg; |
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cv::ocl::oclMat d_flowx(frame0.size(), CV_32FC1); |
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cv::ocl::oclMat d_flowy(frame1.size(), CV_32FC1); |
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cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1(); |
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cv::Mat flow; |
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SUBTEST << frame0.cols << 'x' << frame0.rows << "; rubberwhale1.png; "<<frame1.cols<<'x'<<frame1.rows<<"; rubberwhale2.png"; |
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alg->calc(frame0, frame1, flow); |
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CPU_ON; |
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alg->calc(frame0, frame1, flow); |
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CPU_OFF; |
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cv::Mat gold[2]; |
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cv::split(flow, gold); |
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cv::ocl::oclMat d0(frame0.size(), CV_32FC1); |
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d0.upload(frame0); |
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cv::ocl::oclMat d1(frame1.size(), CV_32FC1); |
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d1.upload(frame1); |
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WARMUP_ON; |
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d_alg(d0, d1, d_flowx, d_flowy); |
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WARMUP_OFF; |
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/* |
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double diff1 = 0.0, diff2 = 0.0; |
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if(ExceptedMatSimilar(gold[0], cv::Mat(d_flowx), 3e-3, diff1) == 1 |
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&&ExceptedMatSimilar(gold[1], cv::Mat(d_flowy), 3e-3, diff2) == 1) |
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TestSystem::instance().setAccurate(1); |
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else |
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TestSystem::instance().setAccurate(0); |
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TestSystem::instance().setDiff(diff1); |
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TestSystem::instance().setDiff(diff2); |
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*/ |
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GPU_ON; |
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d_alg(d0, d1, d_flowx, d_flowy); |
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d_alg.collectGarbage(); |
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GPU_OFF; |
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cv::Mat flowx, flowy; |
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GPU_FULL_ON; |
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d0.upload(frame0); |
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d1.upload(frame1); |
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d_alg(d0, d1, d_flowx, d_flowy); |
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d_alg.collectGarbage(); |
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d_flowx.download(flowx); |
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d_flowy.download(flowy); |
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GPU_FULL_OFF; |
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TestSystem::instance().ExceptedMatSimilar(gold[0], flowx, 3e-3); |
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TestSystem::instance().ExceptedMatSimilar(gold[1], flowy, 3e-3); |
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} |
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///////////// FarnebackOpticalFlow //////////////////////// |
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PERFTEST(FarnebackOpticalFlow) |
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{ |
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cv::Mat frame0 = imread("rubberwhale1.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame0.empty()); |
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cv::Mat frame1 = imread("rubberwhale2.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(frame1.empty()); |
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cv::ocl::oclMat d_frame0(frame0), d_frame1(frame1); |
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int polyNs[2] = { 5, 7 }; |
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double polySigmas[2] = { 1.1, 1.5 }; |
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int farneFlags[2] = { 0, cv::OPTFLOW_FARNEBACK_GAUSSIAN }; |
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bool UseInitFlows[2] = { false, true }; |
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double pyrScale = 0.5; |
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string farneFlagStrs[2] = { "BoxFilter", "GaussianBlur" }; |
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string useInitFlowStrs[2] = { "", "UseInitFlow" }; |
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for ( int i = 0; i < 2; ++i) |
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{ |
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int polyN = polyNs[i]; |
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double polySigma = polySigmas[i]; |
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for ( int j = 0; j < 2; ++j) |
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{ |
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int flags = farneFlags[j]; |
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for ( int k = 0; k < 2; ++k) |
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{ |
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bool useInitFlow = UseInitFlows[k]; |
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SUBTEST << "polyN(" << polyN << "); " << farneFlagStrs[j] << "; " << useInitFlowStrs[k]; |
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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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cv::Mat flow, flowBuf, flowxBuf, flowyBuf; |
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WARMUP_ON; |
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farn(d_frame0, d_frame1, d_flowx, d_flowy); |
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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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flow.copyTo(flowBuf); |
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flowxy[0].copyTo(flowxBuf); |
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flowxy[1].copyTo(flowyBuf); |
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farn.flags |= cv::OPTFLOW_USE_INITIAL_FLOW; |
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farn(d_frame0, d_frame1, d_flowx, d_flowy); |
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} |
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WARMUP_OFF; |
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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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double diff0 = 0.0; |
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TestSystem::instance().setAccurate(ExceptedMatSimilar(flowxy[0], cv::Mat(d_flowx), 0.1, diff0)); |
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TestSystem::instance().setDiff(diff0); |
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double diff1 = 0.0; |
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TestSystem::instance().setAccurate(ExceptedMatSimilar(flowxy[1], cv::Mat(d_flowy), 0.1, diff1)); |
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TestSystem::instance().setDiff(diff1); |
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if (useInitFlow) |
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{ |
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cv::Mat flowx, flowy; |
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farn.flags = (flags | cv::OPTFLOW_USE_INITIAL_FLOW); |
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CPU_ON; |
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cv::calcOpticalFlowFarneback( |
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frame0, frame1, flowBuf, farn.pyrScale, farn.numLevels, farn.winSize, |
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farn.numIters, farn.polyN, farn.polySigma, farn.flags); |
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CPU_OFF; |
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GPU_ON; |
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farn(d_frame0, d_frame1, d_flowx, d_flowy); |
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GPU_OFF; |
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GPU_FULL_ON; |
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d_frame0.upload(frame0); |
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d_frame1.upload(frame1); |
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d_flowx.upload(flowxBuf); |
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d_flowy.upload(flowyBuf); |
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farn(d_frame0, d_frame1, d_flowx, d_flowy); |
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d_flowx.download(flowx); |
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d_flowy.download(flowy); |
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GPU_FULL_OFF; |
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} |
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else |
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{ |
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cv::Mat flow, flowx, flowy; |
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cv::ocl::oclMat d_flowx, d_flowy; |
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farn.flags = flags; |
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CPU_ON; |
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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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CPU_OFF; |
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GPU_ON; |
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farn(d_frame0, d_frame1, d_flowx, d_flowy); |
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GPU_OFF; |
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GPU_FULL_ON; |
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d_frame0.upload(frame0); |
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d_frame1.upload(frame1); |
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farn(d_frame0, d_frame1, d_flowx, d_flowy); |
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d_flowx.download(flowx); |
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d_flowy.download(flowy); |
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GPU_FULL_OFF; |
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} |
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} |
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} |
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} |
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} |
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