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