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