opencv用SVM进行多类分类

来源：互联网发布：base64 python实现编辑：程序博客网时间：2024/05/16 13:05

最近遇到一个多分类的问题，在网上查了些有关SVM的资料，这篇日志是来自：http://wenku.baidu.com/view/81c3e210f18583d0496459f0.html 自己写代码实现了下，感觉很好使，作为一个学习的例子，放在自己的博客里，供以后查阅使用。

#include "stdafx.h"#include "cv.h"#include "highgui.h"#include "ml.h"#include <TIME.H>#include <CTYPE.H>#include <IOSTREAM>using namespace std;int main(int argc, char* argv[]){int size = 400; // height and widht of imageconst int s = 1000; // number of dataint i, j,sv_num;IplImage* img;CvSVM svm = CvSVM();CvSVMParams param;CvTermCriteria criteria; // 停止迭代标准CvRNG rng = cvRNG(time(NULL));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",CV_WINDOW_AUTOSIZE);cvShowImage("SVM",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 type:CvSVM::C_SVC  Kernel:CvSVM::RBF degree:10.0  gamma:8.0 coef0:1.0svm.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",img);cvWaitKey(0);cvDestroyWindow("SVM");cvReleaseImage(&img);return 0;}