opencv/modules/ocl/perf/perf_ml.cpp
Jin Ma 1bfe39f485 Added knearest neighbor of OpenCL version.
It includes the accuracy/performance test and the implementation of KNN.
2013-09-22 10:23:54 +08:00

109 lines
3.9 KiB
C++

/*M///////////////////////////////////////////////////////////////////////////////////////
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// @Authors
// Jin Ma, jin@multicorewareinc.com
// Xiaopeng Fu, fuxiaopeng2222@163.com
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#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);
}