Fix new blank line at EOF.

diff --git a/modules/ocl/test/test_ml.cpp b/modules/ocl/test/test_ml.cpp
index 834fc4e..af86d35 100644 (file)
--- a/modules/ocl/test/test_ml.cpp
+++ b/modules/ocl/test/test_ml.cpp
@@ -121,4 +121,180 @@ TEST_P(KNN, Accuracy)
 }
 INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)),
     Values(4, 3), Values(false, true)));
-#endif // HAVE_OPENCL
\ No newline at end of file
+
+////////////////////////////////SVM/////////////////////////////////////////////////
+PARAM_TEST_CASE(SVM_OCL, int, int, int)
+{
+    cv::Size size;
+    int kernel_type;
+    int svm_type;
+    Mat src, labels, samples, labels_predict;
+    int K;
+    cv::RNG rng ;
+
+    virtual void SetUp()
+    {
+
+        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);
+        int row_idx = 0;
+        const int max_number = size.height / K - 1;
+        CV_Assert(K <= size.height);
+        for(int i = 0; i < K; i++ )
+        {
+            Mat center_row_header = src.row(row_idx);
+            center_row_header.setTo(0);
+            int nchannel = center_row_header.channels();
+            for(int j = 0; j < nchannel; j++)
+            {
+                center_row_header.at<float>(0, i * nchannel + j) = 500.0;
+            }
+            labels.at<int>(0, row_idx) = i;
+            for(int j = 0; (j < max_number) ||
+                    (i == K - 1 && j < max_number + size.height % K); j ++)
+            {
+                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);
+                cur_row_header += tmpmat;
+                labels.at<int>(0, row_idx + 1 + j) = i;
+            }
+            row_idx += 1 + max_number;
+        }
+        labels.convertTo(labels, CV_32FC1);
+        cv::Size test_size = cv::Size(MWIDTH, 100);
+        samples.create(test_size, CV_32FC1);
+        labels_predict.create(1, test_size.height, CV_32SC1);
+        const int max_number_test = test_size.height / K - 1;
+        row_idx = 0;
+        for(int i = 0; i < K; i++ )
+        {
+            Mat center_row_header = samples.row(row_idx);
+            center_row_header.setTo(0);
+            int nchannel = center_row_header.channels();
+            for(int j = 0; j < nchannel; j++)
+            {
+                center_row_header.at<float>(0, i * nchannel + j) = 500.0;
+            }
+            labels_predict.at<int>(0, row_idx) = i;
+            for(int j = 0; (j < max_number_test) ||
+                    (i == K - 1 && j < max_number_test + test_size.height % K); j ++)
+            {
+                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);
+                cur_row_header += tmpmat;
+                labels_predict.at<int>(0, row_idx + 1 + j) = i;
+            }
+            row_idx += 1 + max_number_test;
+        }
+        labels_predict.convertTo(labels_predict, CV_32FC1);
+    }
+};
+TEST_P(SVM_OCL, Accuracy)
+{
+    CvSVMParams params;
+    params.degree = 0.4;
+    params.gamma = 1;
+    params.coef0 = 1;
+    params.C = 1;
+    params.nu = 0.5;
+    params.p = 1;
+    params.svm_type = svm_type;
+    params.kernel_type = kernel_type;
+
+    params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.001);
+
+    CvSVM SVM;
+    SVM.train(src, labels, Mat(), Mat(), params);
+
+    cv::ocl::CvSVM_OCL SVM_OCL;
+    SVM_OCL.train(src, labels, Mat(), Mat(), params);
+
+    int c = SVM.get_support_vector_count();
+    int c1 = SVM_OCL.get_support_vector_count();
+
+    Mat sv(c, MHEIGHT, CV_32FC1);
+    Mat sv_ocl(c1, MHEIGHT, CV_32FC1);
+    for(int i = 0; i < c; i++)
+    {
+        const float* v = SVM.get_support_vector(i);
+
+        for(int j = 0; j < MHEIGHT; j++)
+        {
+            sv.at<float>(i, j) = v[j];
+        }
+    }
+    for(int i = 0; i < c1; i++)
+    {
+        const float* v_ocl = SVM_OCL.get_support_vector(i);
+
+        for(int j = 0; j < MHEIGHT; j++)
+        {
+            sv_ocl.at<float>(i, j) = v_ocl[j];
+        }
+    }
+    cv::BFMatcher matcher(cv::NORM_L2);
+    std::vector<cv::DMatch> matches;
+    matcher.match(sv, sv_ocl, matches);
+    int count = 0;
+
+    for(std::vector<cv::DMatch>::iterator itr = matches.begin(); itr != matches.end(); itr++)
+    {
+        if((*itr).distance < 0.1)
+        {
+            count ++;
+        }
+    }
+    if(c != 0)
+    {
+        float matchedRatio = (float)count / c;
+        EXPECT_GT(matchedRatio, 0.95);
+    }
+    if(c != 0)
+    {
+        CvMat *result = cvCreateMat(1, samples.rows, CV_32FC1);
+        CvMat test_samples = samples;
+
+        CvMat *result_ocl = cvCreateMat(1, samples.rows, CV_32FC1);
+
+        SVM.predict(&test_samples, result);
+
+        SVM_OCL.predict(&test_samples, result_ocl);
+
+        int true_resp = 0, true_resp_ocl = 0;
+        for (int i = 0; i < samples.rows; i++)
+        {
+            if (result->data.fl[i] == labels_predict.at<float>(0, i))
+            {
+                true_resp++;
+            }
+        }
+        float matchedRatio = (float)true_resp / samples.rows;
+
+        for (int i = 0; i < samples.rows; i++)
+        {
+            if (result_ocl->data.fl[i] == labels_predict.at<float>(0, i))
+            {
+                true_resp_ocl++;
+            }
+        }
+        float matchedRatio_ocl = (float)true_resp_ocl / samples.rows;
+
+        if(matchedRatio != 0 && true_resp_ocl < true_resp)
+        {
+            EXPECT_NEAR(matchedRatio_ocl, matchedRatio, 0.03);
+        }
+    }
+}
+INSTANTIATE_TEST_CASE_P(OCL_ML, SVM_OCL, testing::Combine(
+                            Values(CvSVM::LINEAR, CvSVM::POLY, CvSVM::RBF, CvSVM::SIGMOID),
+                            Values(CvSVM::C_SVC, CvSVM::NU_SVC, CvSVM::ONE_CLASS, CvSVM::EPS_SVR, CvSVM::NU_SVR),
+                            Values(2, 3, 4)
+                        ));
+#endif // HAVE_OPENCL