use trainingdata array to show the training data

SVMTest.cpp

@@ -0,0 +1,70 @@
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/ml/ml.hpp>
+
+using namespace cv;
+
+int main()
+{
+    // Data for visual representation
+    int width = 512, height = 512;
+    Mat image = Mat::zeros(height, width, CV_8UC3);
+
+    // Set up training data
+    float labels[4] = {1.0, -1.0, -1.0, -1.0};
+    Mat labelsMat(4, 1, CV_32FC1, labels);
+
+    float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
+    Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
+
+    // Set up SVM's parameters
+    CvSVMParams params;
+    params.svm_type    = CvSVM::C_SVC;
+    params.kernel_type = CvSVM::LINEAR;
+    params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
+
+    // Train the SVM
+    CvSVM SVM;
+    SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);
+
+    Vec3b green(0,255,0), blue (255,0,0);
+    // Show the decision regions given by the SVM
+    for (int i = 0; i < image.rows; ++i)
+        for (int j = 0; j < image.cols; ++j)
+        {
+            Mat sampleMat = (Mat_<float>(1,2) << j,i);
+            float response = SVM.predict(sampleMat);
+
+            if (response == 1)
+                image.at<Vec3b>(i,j)  = green;
+            else if (response == -1)
+                 image.at<Vec3b>(i,j)  = blue;
+        }
+
+    // Show the training data
+    int thickness = -1;
+    int lineType = 8;
+    for (int i = 0; i < trainingDataMat.rows; i++) {
+      const CvScalar color = (labels[i] == 1) ?
+        CV_RGB(255, 255, 255) : CV_RGB(0, 0, 0);
+      circle(image, Point(trainingData[i][0], trainingData[i][1]), 5,
+          color, thickness, lineType);
+    }
+
+    // Show support vectors
+    thickness = 2;
+    lineType  = 8;
+    int c     = SVM.get_support_vector_count();
+
+    for (int i = 0; i < c; ++i)
+    {
+        const float* v = SVM.get_support_vector(i);
+        circle( image,  Point( (int) v[0], (int) v[1]),   6,  Scalar(128, 128, 128), thickness, lineType);
+    }
+
+    imwrite("result.png", image);        // save the image
+
+    imshow("SVM Simple Example", image); // show it to the user
+    waitKey(0);
+
+}