ocl: tests: RNG usage refactoring

This commit is contained in:
Alexander Alekhin
2013-10-02 20:18:48 +04:00
parent 8224f9843e
commit de0f310e81
19 changed files with 316 additions and 486 deletions

View File

@@ -50,10 +50,9 @@ using namespace cv::ocl;
using namespace cvtest;
using namespace testing;
///////K-NEAREST NEIGHBOR//////////////////////////
static void genTrainData(Mat& trainData, int trainDataRow, int trainDataCol,
static void genTrainData(cv::RNG& rng, Mat& trainData, int trainDataRow, int trainDataCol,
Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
{
cv::RNG &rng = TS::ptr()->get_rng();
cv::Size size(trainDataCol, trainDataRow);
trainData = randomMat(rng, size, CV_32FC1, 1.0, 1000.0, false);
if(nClasses != 0)
@@ -85,10 +84,10 @@ TEST_P(KNN, Accuracy)
{
Mat trainData, trainLabels;
const int trainDataRow = 500;
genTrainData(trainData, trainDataRow, trainDataCol, trainLabels, nClass);
genTrainData(rng, trainData, trainDataRow, trainDataCol, trainLabels, nClass);
Mat testData, testLabels;
genTrainData(testData, testDataRow, trainDataCol);
genTrainData(rng, testData, testDataRow, trainDataCol);
KNearestNeighbour knn_ocl;
CvKNearest knn_cpu;
@@ -130,7 +129,6 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
int svm_type;
Mat src, labels, samples, labels_predict;
int K;
cv::RNG rng ;
virtual void SetUp()
{
@@ -138,7 +136,6 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
kernel_type = GET_PARAM(0);
svm_type = GET_PARAM(1);
K = GET_PARAM(2);
rng = TS::ptr()->get_rng();
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
src.create(size, CV_32FC1);
labels.create(1, size.height, CV_32SC1);
@@ -160,7 +157,7 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
{
Mat cur_row_header = src.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), 1, 100, false);
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;
}
@@ -187,7 +184,7 @@ PARAM_TEST_CASE(SVM_OCL, int, int, int)
{
Mat cur_row_header = samples.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), 1, 100, false);
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;
}