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Open Source Computer Vision Library
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309 lines
10 KiB
309 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, 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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// Erping Pang, pang_er_ping@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 "test_precomp.hpp" |
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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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///////K-NEAREST NEIGHBOR////////////////////////// |
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static void genTrainData(cv::RNG& rng, Mat& trainData, int trainDataRow, int trainDataCol, |
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Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0) |
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{ |
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cv::Size size(trainDataCol, trainDataRow); |
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trainData = randomMat(rng, size, CV_32FC1, 1.0, 1000.0, false); |
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if(nClasses != 0) |
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{ |
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cv::Size size1(trainDataRow, 1); |
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trainLabel = randomMat(rng, size1, CV_8UC1, 0, nClasses - 1, false); |
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trainLabel.convertTo(trainLabel, CV_32FC1); |
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} |
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} |
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PARAM_TEST_CASE(KNN, int, Size, int, bool) |
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{ |
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int k; |
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int trainDataCol; |
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int testDataRow; |
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int nClass; |
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bool regression; |
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virtual void SetUp() |
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{ |
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k = GET_PARAM(0); |
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nClass = GET_PARAM(2); |
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trainDataCol = GET_PARAM(1).width; |
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testDataRow = GET_PARAM(1).height; |
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regression = GET_PARAM(3); |
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} |
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}; |
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OCL_TEST_P(KNN, Accuracy) |
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{ |
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Mat trainData, trainLabels; |
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const int trainDataRow = 500; |
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genTrainData(rng, trainData, trainDataRow, trainDataCol, trainLabels, nClass); |
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Mat testData, testLabels; |
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genTrainData(rng, testData, testDataRow, trainDataCol); |
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KNearestNeighbour knn_ocl; |
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CvKNearest knn_cpu; |
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Mat best_label_cpu; |
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oclMat best_label_ocl; |
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/*ocl k-Nearest_Neighbor start*/ |
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oclMat trainData_ocl; |
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trainData_ocl.upload(trainData); |
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Mat simpleIdx; |
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knn_ocl.train(trainData, trainLabels, simpleIdx, regression); |
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oclMat testdata; |
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testdata.upload(testData); |
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knn_ocl.find_nearest(testdata, k, best_label_ocl); |
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/*ocl k-Nearest_Neighbor end*/ |
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/*cpu k-Nearest_Neighbor start*/ |
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knn_cpu.train(trainData, trainLabels, simpleIdx, regression); |
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knn_cpu.find_nearest(testData, k, &best_label_cpu); |
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/*cpu k-Nearest_Neighbor end*/ |
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if(regression) |
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{ |
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EXPECT_MAT_SIMILAR(Mat(best_label_ocl), best_label_cpu, 1e-5); |
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} |
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else |
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{ |
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EXPECT_MAT_NEAR(Mat(best_label_ocl), best_label_cpu, 0.0); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)), |
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Values(4, 3), Values(false, true))); |
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////////////////////////////////SVM///////////////////////////////////////////////// |
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PARAM_TEST_CASE(SVM_OCL, int, int, int) |
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{ |
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cv::Size size; |
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int kernel_type; |
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int svm_type; |
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Mat src, labels, samples, labels_predict; |
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int K; |
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virtual void SetUp() |
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{ |
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kernel_type = GET_PARAM(0); |
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svm_type = GET_PARAM(1); |
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K = GET_PARAM(2); |
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cv::Size sz = cv::Size(MWIDTH, MHEIGHT); |
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src.create(sz, CV_32FC1); |
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labels.create(1, sz.height, CV_32SC1); |
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int row_idx = 0; |
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const int max_number = sz.height / K - 1; |
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CV_Assert(K <= sz.height); |
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for(int i = 0; i < K; i++ ) |
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{ |
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Mat center_row_header = src.row(row_idx); |
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center_row_header.setTo(0); |
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int nchannel = center_row_header.channels(); |
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for(int j = 0; j < nchannel; j++) |
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{ |
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center_row_header.at<float>(0, i * nchannel + j) = 500.0; |
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} |
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labels.at<int>(0, row_idx) = i; |
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for(int j = 0; (j < max_number) || |
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(i == K - 1 && j < max_number + sz.height % K); j ++) |
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{ |
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Mat cur_row_header = src.row(row_idx + 1 + j); |
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center_row_header.copyTo(cur_row_header); |
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Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false); |
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cur_row_header += tmpmat; |
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labels.at<int>(0, row_idx + 1 + j) = i; |
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} |
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row_idx += 1 + max_number; |
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} |
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labels.convertTo(labels, CV_32FC1); |
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cv::Size test_size = cv::Size(MWIDTH, 100); |
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samples.create(test_size, CV_32FC1); |
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labels_predict.create(1, test_size.height, CV_32SC1); |
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const int max_number_test = test_size.height / K - 1; |
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row_idx = 0; |
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for(int i = 0; i < K; i++ ) |
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{ |
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Mat center_row_header = samples.row(row_idx); |
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center_row_header.setTo(0); |
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int nchannel = center_row_header.channels(); |
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for(int j = 0; j < nchannel; j++) |
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{ |
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center_row_header.at<float>(0, i * nchannel + j) = 500.0; |
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} |
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labels_predict.at<int>(0, row_idx) = i; |
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for(int j = 0; (j < max_number_test) || |
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(i == K - 1 && j < max_number_test + test_size.height % K); j ++) |
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{ |
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Mat cur_row_header = samples.row(row_idx + 1 + j); |
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center_row_header.copyTo(cur_row_header); |
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Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false); |
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cur_row_header += tmpmat; |
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labels_predict.at<int>(0, row_idx + 1 + j) = i; |
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} |
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row_idx += 1 + max_number_test; |
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} |
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labels_predict.convertTo(labels_predict, CV_32FC1); |
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} |
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}; |
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OCL_TEST_P(SVM_OCL, Accuracy) |
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{ |
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CvSVMParams params; |
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params.degree = 0.4; |
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params.gamma = 1; |
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params.coef0 = 1; |
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params.C = 1; |
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params.nu = 0.5; |
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params.p = 1; |
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params.svm_type = svm_type; |
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params.kernel_type = kernel_type; |
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params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.001); |
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CvSVM SVM; |
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SVM.train(src, labels, Mat(), Mat(), params); |
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cv::ocl::CvSVM_OCL SVM_OCL; |
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SVM_OCL.train(src, labels, Mat(), Mat(), params); |
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int c = SVM.get_support_vector_count(); |
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int c1 = SVM_OCL.get_support_vector_count(); |
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Mat sv(c, MHEIGHT, CV_32FC1); |
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Mat sv_ocl(c1, MHEIGHT, CV_32FC1); |
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for(int i = 0; i < c; i++) |
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{ |
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const float* v = SVM.get_support_vector(i); |
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for(int j = 0; j < MHEIGHT; j++) |
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{ |
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sv.at<float>(i, j) = v[j]; |
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} |
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} |
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for(int i = 0; i < c1; i++) |
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{ |
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const float* v_ocl = SVM_OCL.get_support_vector(i); |
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for(int j = 0; j < MHEIGHT; j++) |
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{ |
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sv_ocl.at<float>(i, j) = v_ocl[j]; |
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} |
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} |
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cv::BFMatcher matcher(cv::NORM_L2); |
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std::vector<cv::DMatch> matches; |
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matcher.match(sv, sv_ocl, matches); |
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int count = 0; |
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for(std::vector<cv::DMatch>::iterator itr = matches.begin(); itr != matches.end(); itr++) |
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{ |
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if((*itr).distance < 0.1) |
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{ |
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count ++; |
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} |
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} |
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if(c != 0) |
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{ |
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float matchedRatio = (float)count / c; |
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EXPECT_GT(matchedRatio, 0.95); |
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} |
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if(c != 0) |
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{ |
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CvMat *result = cvCreateMat(1, samples.rows, CV_32FC1); |
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CvMat test_samples = samples; |
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CvMat *result_ocl = cvCreateMat(1, samples.rows, CV_32FC1); |
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SVM.predict(&test_samples, result); |
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SVM_OCL.predict(&test_samples, result_ocl); |
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int true_resp = 0, true_resp_ocl = 0; |
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for (int i = 0; i < samples.rows; i++) |
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{ |
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if (result->data.fl[i] == labels_predict.at<float>(0, i)) |
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{ |
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true_resp++; |
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} |
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} |
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float matchedRatio = (float)true_resp / samples.rows; |
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for (int i = 0; i < samples.rows; i++) |
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{ |
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if (result_ocl->data.fl[i] == labels_predict.at<float>(0, i)) |
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{ |
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true_resp_ocl++; |
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} |
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} |
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float matchedRatio_ocl = (float)true_resp_ocl / samples.rows; |
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if(matchedRatio != 0 && true_resp_ocl < true_resp) |
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{ |
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EXPECT_NEAR(matchedRatio_ocl, matchedRatio, 0.03); |
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} |
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} |
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} |
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// TODO FIXIT: CvSVM::EPS_SVR case is crashed inside CPU implementation |
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// Anonymous enums are not supported well so cast them to 'int' |
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INSTANTIATE_TEST_CASE_P(OCL_ML, SVM_OCL, testing::Combine( |
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Values((int)CvSVM::LINEAR, (int)CvSVM::POLY, (int)CvSVM::RBF, (int)CvSVM::SIGMOID), |
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Values((int)CvSVM::C_SVC, (int)CvSVM::NU_SVC, (int)CvSVM::ONE_CLASS, (int)CvSVM::NU_SVR), |
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Values(2, 3, 4) |
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)); |
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#endif // HAVE_OPENCL
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