Open Source Computer Vision Library https://opencv.org/
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///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Jin Ma, jin@multicorewareinc.com
// Xiaopeng Fu, fuxiaopeng2222@163.com
// Erping Pang, pang_er_ping@163.com
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
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// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
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// and on any theory of liability, whether in contract, strict liability,
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//M*/
#include "test_precomp.hpp"
#ifdef HAVE_OPENCL
using namespace cv;
using namespace cv::ocl;
using namespace cvtest;
using namespace testing;
///////K-NEAREST NEIGHBOR//////////////////////////
static void genTrainData(cv::RNG& rng, Mat& trainData, int trainDataRow, int trainDataCol,
Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
{
cv::Size size(trainDataCol, trainDataRow);
trainData = randomMat(rng, size, CV_32FC1, 1.0, 1000.0, false);
if(nClasses != 0)
{
cv::Size size1(trainDataRow, 1);
trainLabel = randomMat(rng, size1, CV_8UC1, 0, nClasses - 1, false);
trainLabel.convertTo(trainLabel, CV_32FC1);
}
}
PARAM_TEST_CASE(KNN, int, Size, int, bool)
{
int k;
int trainDataCol;
int testDataRow;
int nClass;
bool regression;
virtual void SetUp()
{
k = GET_PARAM(0);
nClass = GET_PARAM(2);
trainDataCol = GET_PARAM(1).width;
testDataRow = GET_PARAM(1).height;
regression = GET_PARAM(3);
}
};
OCL_TEST_P(KNN, Accuracy)
{
Mat trainData, trainLabels;
const int trainDataRow = 500;
genTrainData(rng, trainData, trainDataRow, trainDataCol, trainLabels, nClass);
Mat testData, testLabels;
genTrainData(rng, testData, testDataRow, trainDataCol);
KNearestNeighbour knn_ocl;
CvKNearest knn_cpu;
Mat best_label_cpu;
oclMat best_label_ocl;
/*ocl k-Nearest_Neighbor start*/
oclMat trainData_ocl;
trainData_ocl.upload(trainData);
Mat simpleIdx;
knn_ocl.train(trainData, trainLabels, simpleIdx, regression);
oclMat testdata;
testdata.upload(testData);
knn_ocl.find_nearest(testdata, k, best_label_ocl);
/*ocl k-Nearest_Neighbor end*/
/*cpu k-Nearest_Neighbor start*/
knn_cpu.train(trainData, trainLabels, simpleIdx, regression);
knn_cpu.find_nearest(testData, k, &best_label_cpu);
/*cpu k-Nearest_Neighbor end*/
if(regression)
{
EXPECT_MAT_SIMILAR(Mat(best_label_ocl), best_label_cpu, 1e-5);
}
else
{
EXPECT_MAT_NEAR(Mat(best_label_ocl), best_label_cpu, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)),
Values(4, 3), Values(false, true)));
////////////////////////////////SVM/////////////////////////////////////////////////
PARAM_TEST_CASE(SVM_OCL, int, int, int)
{
cv::Size size;
int kernel_type;
int svm_type;
Mat src, labels, samples, labels_predict;
int K;
virtual void SetUp()
{
kernel_type = GET_PARAM(0);
svm_type = GET_PARAM(1);
K = GET_PARAM(2);
cv::Size sz = cv::Size(MWIDTH, MHEIGHT);
src.create(sz, CV_32FC1);
labels.create(1, sz.height, CV_32SC1);
int row_idx = 0;
const int max_number = sz.height / K - 1;
CV_Assert(K <= sz.height);
for(int i = 0; i < K; i++ )
{
Mat center_row_header = src.row(row_idx);
center_row_header.setTo(0);
int nchannel = center_row_header.channels();
for(int j = 0; j < nchannel; j++)
{
center_row_header.at<float>(0, i * nchannel + j) = 500.0;
}
labels.at<int>(0, row_idx) = i;
for(int j = 0; (j < max_number) ||
(i == K - 1 && j < max_number + sz.height % K); j ++)
{
Mat cur_row_header = src.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false);
cur_row_header += tmpmat;
labels.at<int>(0, row_idx + 1 + j) = i;
}
row_idx += 1 + max_number;
}
labels.convertTo(labels, CV_32FC1);
cv::Size test_size = cv::Size(MWIDTH, 100);
samples.create(test_size, CV_32FC1);
labels_predict.create(1, test_size.height, CV_32SC1);
const int max_number_test = test_size.height / K - 1;
row_idx = 0;
for(int i = 0; i < K; i++ )
{
Mat center_row_header = samples.row(row_idx);
center_row_header.setTo(0);
int nchannel = center_row_header.channels();
for(int j = 0; j < nchannel; j++)
{
center_row_header.at<float>(0, i * nchannel + j) = 500.0;
}
labels_predict.at<int>(0, row_idx) = i;
for(int j = 0; (j < max_number_test) ||
(i == K - 1 && j < max_number_test + test_size.height % K); j ++)
{
Mat cur_row_header = samples.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(cur_row_header.size(), cur_row_header.type(), 1, 100, false);
cur_row_header += tmpmat;
labels_predict.at<int>(0, row_idx + 1 + j) = i;
}
row_idx += 1 + max_number_test;
}
labels_predict.convertTo(labels_predict, CV_32FC1);
}
};
OCL_TEST_P(SVM_OCL, Accuracy)
{
CvSVMParams params;
params.degree = 0.4;
params.gamma = 1;
params.coef0 = 1;
params.C = 1;
params.nu = 0.5;
params.p = 1;
params.svm_type = svm_type;
params.kernel_type = kernel_type;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.001);
CvSVM SVM;
SVM.train(src, labels, Mat(), Mat(), params);
cv::ocl::CvSVM_OCL SVM_OCL;
SVM_OCL.train(src, labels, Mat(), Mat(), params);
int c = SVM.get_support_vector_count();
int c1 = SVM_OCL.get_support_vector_count();
Mat sv(c, MHEIGHT, CV_32FC1);
Mat sv_ocl(c1, MHEIGHT, CV_32FC1);
for(int i = 0; i < c; i++)
{
const float* v = SVM.get_support_vector(i);
for(int j = 0; j < MHEIGHT; j++)
{
sv.at<float>(i, j) = v[j];
}
}
for(int i = 0; i < c1; i++)
{
const float* v_ocl = SVM_OCL.get_support_vector(i);
for(int j = 0; j < MHEIGHT; j++)
{
sv_ocl.at<float>(i, j) = v_ocl[j];
}
}
cv::BFMatcher matcher(cv::NORM_L2);
std::vector<cv::DMatch> matches;
matcher.match(sv, sv_ocl, matches);
int count = 0;
for(std::vector<cv::DMatch>::iterator itr = matches.begin(); itr != matches.end(); itr++)
{
if((*itr).distance < 0.1)
{
count ++;
}
}
if(c != 0)
{
float matchedRatio = (float)count / c;
EXPECT_GT(matchedRatio, 0.95);
}
if(c != 0)
{
CvMat *result = cvCreateMat(1, samples.rows, CV_32FC1);
CvMat test_samples = samples;
CvMat *result_ocl = cvCreateMat(1, samples.rows, CV_32FC1);
SVM.predict(&test_samples, result);
SVM_OCL.predict(&test_samples, result_ocl);
int true_resp = 0, true_resp_ocl = 0;
for (int i = 0; i < samples.rows; i++)
{
if (result->data.fl[i] == labels_predict.at<float>(0, i))
{
true_resp++;
}
}
float matchedRatio = (float)true_resp / samples.rows;
for (int i = 0; i < samples.rows; i++)
{
if (result_ocl->data.fl[i] == labels_predict.at<float>(0, i))
{
true_resp_ocl++;
}
}
float matchedRatio_ocl = (float)true_resp_ocl / samples.rows;
if(matchedRatio != 0 && true_resp_ocl < true_resp)
{
EXPECT_NEAR(matchedRatio_ocl, matchedRatio, 0.03);
}
}
}
// TODO FIXIT: CvSVM::EPS_SVR case is crashed inside CPU implementation
// Anonymous enums are not supported well so cast them to 'int'
INSTANTIATE_TEST_CASE_P(OCL_ML, SVM_OCL, testing::Combine(
Values((int)CvSVM::LINEAR, (int)CvSVM::POLY, (int)CvSVM::RBF, (int)CvSVM::SIGMOID),
Values((int)CvSVM::C_SVC, (int)CvSVM::NU_SVC, (int)CvSVM::ONE_CLASS, (int)CvSVM::NU_SVR),
Values(2, 3, 4)
));
#endif // HAVE_OPENCL