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216 lines
7.5 KiB
C++
216 lines
7.5 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 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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// Jin Ma, jin@multicorewareinc.com
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// Xiaopeng Fu, fuxiaopeng2222@163.com
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors as is and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "perf_precomp.hpp"
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using namespace perf;
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using namespace std;
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using namespace cv::ocl;
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using namespace cv;
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using std::tr1::tuple;
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using std::tr1::get;
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////////////////////////////////// K-NEAREST NEIGHBOR ////////////////////////////////////
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static void genData(Mat& trainData, Size size, Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
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{
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trainData.create(size, CV_32FC1);
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randu(trainData, 1.0, 100.0);
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if (nClasses != 0)
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{
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trainLabel.create(size.height, 1, CV_8UC1);
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randu(trainLabel, 0, nClasses - 1);
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trainLabel.convertTo(trainLabel, CV_32FC1);
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}
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}
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typedef tuple<int> KNNParamType;
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typedef TestBaseWithParam<KNNParamType> KNNFixture;
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PERF_TEST_P(KNNFixture, KNN,
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testing::Values(1000, 2000, 4000))
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{
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KNNParamType params = GetParam();
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const int rows = get<0>(params);
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int columns = 100;
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int k = rows/250;
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Mat trainData, trainLabels;
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Size size(columns, rows);
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genData(trainData, size, trainLabels, 3);
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Mat testData;
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genData(testData, size);
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Mat best_label;
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if (RUN_PLAIN_IMPL)
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{
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TEST_CYCLE()
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{
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CvKNearest knn_cpu;
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knn_cpu.train(trainData, trainLabels);
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knn_cpu.find_nearest(testData, k, &best_label);
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}
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}
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else if (RUN_OCL_IMPL)
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{
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cv::ocl::oclMat best_label_ocl;
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cv::ocl::oclMat testdata;
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testdata.upload(testData);
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OCL_TEST_CYCLE()
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{
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cv::ocl::KNearestNeighbour knn_ocl;
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knn_ocl.train(trainData, trainLabels);
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knn_ocl.find_nearest(testdata, k, best_label_ocl);
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}
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best_label_ocl.download(best_label);
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}
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else
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OCL_PERF_ELSE
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SANITY_CHECK(best_label);
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}
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typedef TestBaseWithParam<tuple<int> > SVMFixture;
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// code is based on: samples\cpp\tutorial_code\ml\non_linear_svms\non_linear_svms.cpp
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PERF_TEST_P(SVMFixture, DISABLED_SVM,
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testing::Values(50, 100))
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{
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const int NTRAINING_SAMPLES = get<0>(GetParam()); // Number of training samples per class
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#define FRAC_LINEAR_SEP 0.9f // Fraction of samples which compose the linear separable part
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const int WIDTH = 512, HEIGHT = 512;
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Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);
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Mat labels (2*NTRAINING_SAMPLES, 1, CV_32FC1);
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RNG rng(100); // Random value generation class
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// Set up the linearly separable part of the training data
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int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
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// Generate random points for the class 1
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Mat trainClass = trainData.rowRange(0, nLinearSamples);
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// The x coordinate of the points is in [0, 0.4)
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Mat c = trainClass.colRange(0, 1);
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rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4 * WIDTH));
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2);
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rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
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// Generate random points for the class 2
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trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);
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// The x coordinate of the points is in [0.6, 1]
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c = trainClass.colRange(0 , 1);
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rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2);
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rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
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//------------------ Set up the non-linearly separable part of the training data ---------------
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// Generate random points for the classes 1 and 2
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trainClass = trainData.rowRange( nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
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// The x coordinate of the points is in [0.4, 0.6)
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c = trainClass.colRange(0,1);
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rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2);
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rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
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//------------------------- Set up the labels for the classes ---------------------------------
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labels.rowRange( 0, NTRAINING_SAMPLES).setTo(1); // Class 1
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labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2); // Class 2
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//------------------------ Set up the support vector machines parameters --------------------
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CvSVMParams params;
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params.svm_type = SVM::C_SVC;
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params.C = 0.1;
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params.kernel_type = SVM::LINEAR;
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params.term_crit = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);
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Mat dst = Mat::zeros(HEIGHT, WIDTH, CV_8UC1);
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Mat samples(WIDTH*HEIGHT, 2, CV_32FC1);
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int k = 0;
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for (int i = 0; i < HEIGHT; ++i)
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{
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for (int j = 0; j < WIDTH; ++j)
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{
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samples.at<float>(k, 0) = (float)i;
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samples.at<float>(k, 0) = (float)j;
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k++;
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}
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}
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Mat results(WIDTH*HEIGHT, 1, CV_32FC1);
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CvMat samples_ = samples;
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CvMat results_ = results;
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if (RUN_PLAIN_IMPL)
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{
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CvSVM svm;
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svm.train(trainData, labels, Mat(), Mat(), params);
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TEST_CYCLE()
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{
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svm.predict(&samples_, &results_);
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}
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}
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else if (RUN_OCL_IMPL)
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{
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CvSVM_OCL svm;
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svm.train(trainData, labels, Mat(), Mat(), params);
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OCL_TEST_CYCLE()
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{
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svm.predict(&samples_, &results_);
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}
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}
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else
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OCL_PERF_ELSE
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SANITY_CHECK_NOTHING();
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}
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