345 lines
		
	
	
		
			10 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			345 lines
		
	
	
		
			10 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 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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| 
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| #include "test_precomp.hpp"
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| #include <iomanip>
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| 
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| #ifdef HAVE_OPENCL
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| 
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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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| //////////////////////////////////////////////////////
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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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| 
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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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| 
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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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| 
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|     int maxCorners = 1000;
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|     double qualityLevel = 0.01;
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| 
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|     cv::ocl::GoodFeaturesToTrackDetector_OCL detector(maxCorners, qualityLevel, minDistance);
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| 
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|     cv::ocl::oclMat d_pts;
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|     detector(oclMat(frame), d_pts);
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| 
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|     ASSERT_FALSE(d_pts.empty());
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| 
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|     std::vector<cv::Point2f> pts(d_pts.cols);
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| 
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|     detector.downloadPoints(d_pts, pts);
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| 
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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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| 
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|     ASSERT_EQ(pts_gold.size(), pts.size());
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| 
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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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| 
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|         bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;
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| 
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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) / pts.size();
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| 
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|     ASSERT_LE(bad_ratio, 0.01);
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| }
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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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| 
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|     cv::ocl::GoodFeaturesToTrackDetector_OCL detector(maxCorners, qualityLevel, minDistance);
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| 
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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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| 
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|     detector(src, corners);
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| 
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|     ASSERT_TRUE(corners.empty());
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| }
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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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| //////////////////////////////////////////////////////////////////////////
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| PARAM_TEST_CASE(TVL1, bool)
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| {
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|     bool useRoi;
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| 
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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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| };
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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| /////////////////////////////////////////////////////////////////////////////////////////////////
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| // PyrLKOpticalFlow
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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|     cv::ocl::PyrLKOpticalFlow pyrLK;
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| 
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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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| 
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|     oclFrame0 = frame0;
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|     oclFrame1 = frame1;
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| 
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|     pyrLK.sparse(oclFrame0, oclFrame1, d_pts, d_nextPts, d_status, &d_err);
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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|     double bad_ratio = static_cast<double>(mistmatch) / (nextPts.size());
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| 
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|     ASSERT_LE(bad_ratio, 0.02f);
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| 
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| }
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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|     double polySigma = polyN <= 5 ? 1.1 : 1.5;
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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|     std::vector<cv::Mat> flowxy;
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|     cv::split(flow, flowxy);
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| 
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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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| 
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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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| 
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| #endif // HAVE_OPENCL
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