mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 06:10:02 +08:00
1bfe39f485
It includes the accuracy/performance test and the implementation of KNN.
109 lines
3.9 KiB
C++
109 lines
3.9 KiB
C++
/*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 oclMaterials 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);
|
|
} |