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：OPENCV2.2 + VS2008
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
[cpp]
#include "stdafx.h"
#include <opencv2/opencv.hpp>
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("dst", img);
waitKey(0);
/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("dst", 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);
}
学习样本：
分类：
支持向量：
