基于opencv的SVM实现

最近在研究关于图像的分类，其中要用到SVM，便在网上找了些代码，自己进行了一些修改，其中参考了部分其他人的博客。

程序代码如下：

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <CTYPE.H>

#define NTRAINING_SAMPLES 100 // Number of training samples per class
#define FRAC_LINEAR_SEP 0.9f // Fraction of samples which compose the linear separable part

using namespace cv;
using namespace std;

int main(int argc, char* argv[])
{
int size = 400; // height and widht of image
const int s = 1000; // number of data
int i, j,sv_num;
IplImage* img;

CvSVM svm ;
CvSVMParams param;
CvTermCriteria criteria; // 停止迭代标准
CvRNG rng = cvRNG();
CvPoint pts[s]; // 定义1000个点
float data[s*2]; // 点的坐标
int res[s]; // 点的类别

CvMat data_mat, res_mat;
CvScalar rcolor;

const float* support;

// 图像区域的初始化
img = cvCreateImage(cvSize(size,size),IPL_DEPTH_8U,3);
cvZero(img);

// 学习数据的生成
for (i=0; i<s;++i)
{
pts[i].x = cvRandInt(&rng)%size;
pts[i].y = cvRandInt(&rng)%size;

if (pts[i].y>50*cos(pts[i].x*CV_PI/100)+200)
{
cvLine(img,cvPoint(pts[i].x-2,pts[i].y-2),cvPoint(pts[i].x+2,pts[i].y+2),CV_RGB(255,0,0));
cvLine(img,cvPoint(pts[i].x+2,pts[i].y-2),cvPoint(pts[i].x-2,pts[i].y+2),CV_RGB(255,0,0));
res[i]=1;
}
else
{
if (pts[i].x>200)
{
cvLine(img,cvPoint(pts[i].x-2,pts[i].y-2),cvPoint(pts[i].x+2,pts[i].y+2),CV_RGB(0,255,0));
cvLine(img,cvPoint(pts[i].x+2,pts[i].y-2),cvPoint(pts[i].x-2,pts[i].y+2),CV_RGB(0,255,0));
res[i]=2;
}
else
{
cvLine(img,cvPoint(pts[i].x-2,pts[i].y-2),cvPoint(pts[i].x+2,pts[i].y+2),CV_RGB(0,0,255));
cvLine(img,cvPoint(pts[i].x+2,pts[i].y-2),cvPoint(pts[i].x-2,pts[i].y+2),CV_RGB(0,0,255));
res[i]=3;
}
}
}

// 学习数据的现实
cvNamedWindow("SVM训练样本空间及分类",CV_WINDOW_AUTOSIZE);
cvShowImage("SVM训练样本空间及分类",img);
cvWaitKey(0);

// 学习参数的生成
for (i=0;i<s;++i)
{
data[i*2] = float(pts[i].x)/size;
data[i*2+1] = float(pts[i].y)/size;
}

cvInitMatHeader(&data_mat,s,2,CV_32FC1,data);
cvInitMatHeader(&res_mat,s,1,CV_32SC1,res);
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_mat,&res_mat,NULL,NULL,param);

// 学习结果绘图
for (i=0;i<size;i++)
{
for (j=0;j<size;j++)
{
CvMat m;
float ret = 0.0;
float a[] = {float(j)/size,float(i)/size};
cvInitMatHeader(&m,1,2,CV_32FC1,a);
ret = svm.predict(&m);

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;
}
cvSet2D(img,i,j,rcolor);
}
}

// 为了显示学习结果，通过对输入图像区域的所有像素（特征向量）进行分类，然后对输入的像素用所属颜色等级的颜色绘图
for(i=0;i<s;++i)
{
CvScalar rcolor;
switch(res[i])
{
case 1:
rcolor = CV_RGB(255,0,0);
break;
case 2:
rcolor = CV_RGB(0,255,0);
break;
case 3:
rcolor = CV_RGB(0,0,255);
break;
}
cvLine(img,cvPoint(pts[i].x-2,pts[i].y-2),cvPoint(pts[i].x+2,pts[i].y+2),rcolor);
cvLine(img,cvPoint(pts[i].x+2,pts[i].y-2),cvPoint(pts[i].x-2,pts[i].y+2),rcolor);
}

// 支持向量的绘制
sv_num = svm.get_support_vector_count();
for (i=0; i<sv_num;++i)
{
support = svm.get_support_vector(i);
cvCircle(img,cvPoint((int)(support[0]*size),(int)(support[i]*size)),5,CV_RGB(200,200,200));
}

cvNamedWindow("SVM",CV_WINDOW_AUTOSIZE);
cvShowImage("SVM分类结果及支持向量",img);
cvWaitKey(0);
cvDestroyWindow("SVM");
cvReleaseImage(&img);
return 0;
}

效果图：