OpenCV的支持向量机SVM的程序

转自http://blog.csdn.net/firefight/article/details/6400060

为了学习OPENCV SVM分类器, 参考网上的"利用SVM解决2维空间向量的分类问题"实现并改为C++代码，仅供参考

环境：OPENCV2.4.9 + VS2013

步骤：
1，生成随机的点，并按一定的空间分布将其归类
2，创建SVM并利用随机点样本进行训练
3，将整个空间按SVM分类结果进行划分，并显示支持向量

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 #include "iostream" #include <opencv2/opencv.hpp> #include <opencv2/ml/ml.hpp> using namespace cv; using namespace std;

 void drawCross(Mat &img, Point center, Scalar color)
 {
     int col = center.x > 2 ? center.x : 2;
     int row = center.y> 2 ? center.y : 2;

     line(img, Point(col -2, row - 2), Point(col + 2, row + 2), color);
     line(img, Point(col + 2, row - 2), Point(col - 2, row + 2), color);
 }

 int newSvmTest(int rows, int cols, int testCount)
 {
     if(testCount > rows * cols)
         return 0;

     Mat img = Mat::zeros(rows, cols, CV_8UC3);
     Mat testPoint = Mat::zeros(rows, cols, CV_8UC1);
     Mat data = Mat::zeros(testCount, 2, CV_32FC1);
     Mat res = Mat::zeros(testCount, 1, CV_32SC1);

     //Create random test points
     for (int i= 0; i< testCount; i++)
     {
         int row = rand() % rows;
         int col = rand() % cols;

         if(testPoint.at<unsigned char>(row, col) == 0)
         {
             testPoint.at<unsigned char>(row, col) = 1;
             data.at<float>(i, 0) = float (col) / cols;
             data.at<float>(i, 1) = float (row) / rows;
         }
         else
         {
             i--;
             continue;
         }

         if (row > ( 50 * cos(col * CV_PI/ 100) + 200) )
         {
             drawCross(img, Point(col, row), CV_RGB(255, 0, 0));
             res.at<unsigned int>(i, 0) = 1;
         }
         else
         {
             if (col > 200)
             {
                 drawCross(img, Point(col, row), CV_RGB(0, 255, 0));
                 res.at<unsigned int>(i, 0) = 2;
             }
             else
             {
                 drawCross(img, Point(col, row), CV_RGB(0, 0, 255));
                 res.at<unsigned int>(i, 0) = 3;
             }
         }

     }

     //Show test points
     imshow("随机数", img);


     /////////////START SVM TRAINNING//////////////////
     CvSVM svm = CvSVM();
     CvSVMParams param;
     CvTermCriteria criteria;

     criteria= cvTermCriteria(CV_TERMCRIT_EPS, 1000, FLT_EPSILON);
     param= CvSVMParams (CvSVM::C_SVC, CvSVM::RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, NULL, criteria);

     svm.train(data, res, Mat(), Mat(), param);

     for (int i= 0; i< rows; i++)
     {
         for (int j= 0; j< cols; j++)
         {
             Mat m = Mat::zeros(1, 2, CV_32FC1);
             m.at<float>(0,0) = float (j) / cols;
             m.at<float>(0,1) = float (i) / rows;

             float ret = 0.0;
             ret = svm.predict(m);
             Scalar rcolor;

             switch ((int) ret)
             {
                 case 1: rcolor= CV_RGB(100, 0, 0); break;
                 case 2: rcolor= CV_RGB(0, 100, 0); break;
                 case 3: rcolor= CV_RGB(0, 0, 100); break;
             }

             line(img, Point(j,i), Point(j,i), rcolor);
         }
     }

     imshow("分区效果", img);
     //waitKey(0);

     //Show support vectors
     int sv_num= svm.get_support_vector_count();
     for (int i= 0; i< sv_num; i++)
     {
         const float* support = svm.get_support_vector(i);
         circle(img, Point((int) (support[0] * cols), (int) (support[1] * rows)), 5, CV_RGB(200, 200, 200));
     }

     imshow("dst", img);
     waitKey(0);

     return 0;
 }

 int main(int argc, char** argv)
 {
     return newSvmTest(400, 600, 100);
 }