OpenCV開發SVM算法是基于LibSVM軟件包開發的，LibSVM是臺灣大學林智仁(Lin Chih-Jen)等開發設計的一個簡單、易于使用和快速有效的SVM模式識別與回歸的軟件包。用OpenCV使用SVM算法的大概流程是

1）設置訓練樣本集

需要兩組數據，一組是數據的類別，一組是數據的向量信息。

2）設置SVM參數

利用CvSVMParams類實現類內的成員變量svm_type表示SVM類型：

CvSVM：：C_SVC? C-SVC

CvSVM：：NU_SVC v-SVC

CvSVM：：ONE_CLASS 一類SVM

CvSVM：：EPS_SVR e-SVR

CvSVM：：NU_SVR v-SVR

成員變量kernel_type表示核函數的類型：

CvSVM：：LINEAR 線性：u‘v

CvSVM：：POLY 多項式：(r*u'v + coef0)^degree

CvSVM：：RBF RBF函數：exp(-r|u-v|^2)

CvSVM：：SIGMOID sigmoid函數：tanh(r*u'v + coef0)

成員變量degree針對多項式核函數degree的設置，gamma針對多項式/rbf/sigmoid核函數的設置，coef0針對多項式/sigmoid核函數的設置，Cvalue為損失函數，在C-SVC、e-SVR、v-SVR中有效，nu設置v-SVC、一類SVM和v-SVR參數，p為設置e-SVR中損失函數的值，class_weightsC_SVC的權重，term_crit為SVM訓練過程的終止條件。其中默認值degree = 0，gamma = 1，coef0 = 0，Cvalue = 1，nu = 0，p = 0，class_weights = 0

3）訓練SVM

調用CvSVM：：train函數建立SVM模型，第一個參數為訓練數據，第二個參數為分類結果，最后一個參數即CvSVMParams

4）用這個SVM進行分類

調用函數CvSVM：：predict實現分類

5）獲得支持向量

除了分類，也可以得到SVM的支持向量，調用函數CvSVM：：get_support_vector_count獲得支持向量的個數，CvSVM：：get_support_vector獲得對應的索引編號的支持向量。

實現代碼如下：運行步驟

[cpp]?view plaincopyprint?

//?step?1:???

float?labels[4]?=?{1.0,?-1.0,?-1.0,?-1.0};??

Mat?labelsMat(3,?1,?CV_32FC1,?labels);??

??

float?trainingData[4][2]?=?{?{501,?10},?{255,?10},?{501,?255},?{10,?501}?};??

Mat?trainingDataMat(3,?2,?CV_32FC1,?trainingData);??

??

//?step?2:???

CvSVMParams?params;??

params.svm_type?=?CvSVM::C_SVC;??

params.kernel_type?=?CvSVM::LINEAR;??

params.term_crit?=?cvTermCriteria(CV_TERMCRIT_ITER,?100,?1e-6);??

??

//?step?3:???

CvSVM?SVM;??

SVM.train(trainingDataMat,?labelsMat,?Mat(),?Mat(),?params);??

??

//?step?4:???

Vec3b?green(0,?255,?0),?blue(255,?0,?0);??

for?(int?i=0;?i<image.rows;?i++)??

{??

????for?(int?j=0;?j<image.cols;?j++)??

????{??

????????Mat?sampleMat?=?(Mat_<float>(1,2)?<<?i,j);??

????????float?response?=?SVM.predict(sampleMat);??

??

????????if?(fabs(response-1.0)?<?0.0001)??

????????{??

????????????image.at<Vec3b>(j,?i)?=?green;??

????????}??

????????else?if?(fabs(response+1.0)?<?0.001)??

????????{??

????????????image.at<Vec3b>(j,?i)?=?blue;??

????????}??

????}??

}??

??

//?step?5:???

int?c?=?SVM.get_support_vector_count();??

??

for?(int?i=0;?i<c;?i++)??

{??

????const?float*?v?=?SVM.get_support_vector(i);??

}??

?

實驗代碼1：顏色分類

[cpp]?view plaincopyprint?

//利用SVM解決2維空間向量的3級分類問題??????

#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;?????????//圖像的長度和寬度??????

????????const?int?s?=?1000;??????????//試驗點個數（可更改！！）??????

????????int?i,?j,?sv_num;?????

????????IplImage?*img;?????

????????CvSVM?svm?=?CvSVM();????//★★★??????

????????CvSVMParams?param;?????

????????CvTermCriteria?criteria;//停止迭代的標準??????

????????CvRNG?rng?=?cvRNG(time(NULL));?????

????????CvPoint?pts[s];?????????//定義1000個點??????

????????float?data[s*2];????????//點的坐標??????

????????int?res[s];?????????????//點的所屬類??????

????????CvMat?data_mat,?res_mat;?????

????????CvScalar?rcolor;?????

????????const?float?*support;?????

????????//?(1)圖像區域的確保和初始化??????

????????img=?cvCreateImage(cvSize(size,?size),?IPL_DEPTH_8U,?3);?????

????????cvZero(img);?????

????????//確保畫像區域，并清0(用黑色作初始化處理)。??????

?????????

????????//?(2)學習數據的生成??????

????????for?(i=?0;?i<?s;?i++)?{?????

????????????pts[i].x=?cvRandInt(&rng)?%?size;???//用隨機整數賦值??????

????????????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;?????

????????????????}?????

????????????}?????

????????}?????

????????//生成2維隨機訓練數據，并將其值放在CvPoint數據類型的數組pts[?]中。??????

?????????

????????//?(3)學習數據的顯示??????

????????cvNamedWindow("SVM",?CV_WINDOW_AUTOSIZE);?????

????????cvShowImage("SVM",?img);?????

????????cvWaitKey(0);?????

?????????

????????//?(4)學習參數的生成??????

????????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種類：CvSVM::C_SVC???

????????????Kernel的種類：CvSVM::RBF???

????????????degree：10.0（此次不使用）???

????????????gamma：8.0???

????????????coef0：1.0（此次不使用）???

????????????C：10.0???

????????????nu：0.5（此次不使用）???

????????????p：0.1（此次不使用）???

????????????然后對訓練數據正規化處理，并放在CvMat型的數組里。???

????????????????????????????????????????????????????????????????*/?????

?????????

?????????

????????//☆☆☆☆☆☆☆☆☆(5)SVM學習☆☆☆☆☆☆☆☆☆☆☆☆??????

????????svm.train(&data_mat,?&res_mat,?NULL,?NULL,?param);//☆??????

????????//☆☆利用訓練數據和確定的學習參數,進行SVM學習☆☆☆☆??????????

?????????

????????//?(6)學習結果的繪圖??????

????????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);?????

????????????}?????

????????}?????

????????//為了顯示學習結果，通過輸入圖像區域的所有像素(特征向量)并進行分類。然后對輸入像素用所屬等級的顏色繪圖。??????

?????????

????????//?(7)訓練數據的再繪制??????

????????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);?????

????????}?????

????????//將訓練數據在結果圖像上重復的繪制出來。??????

?????????

????????//?(8)支持向量的繪制??????

????????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[1]?*?size)),?5,?CV_RGB(200,?200,?200));?????

????????}?????

????????//用白色的圓圈對支持向量作標記。??????

?????????

????????//?(9)圖像的顯示???????

????????cvNamedWindow("SVM",?CV_WINDOW_AUTOSIZE);?????

????????cvShowImage("SVM",?img);?????

????????cvWaitKey(0);?????

????????cvDestroyWindow("SVM");?????

????????cvReleaseImage(&img);?????

????????return?0;?????

????????//顯示實際處理結果的圖像，直到某個鍵被按下為止。??????

????}????

實驗代碼2：用MIT人臉庫檢測，效果實在不好，檢測結果全是人臉或者全都不是人臉。原因應該是圖像檢測沒有做好應該用HoG等特征首先檢測，在進行分類訓練，不特征不明顯，肯定分類效果并不好。

[cpp]?view plaincopyprint?

//??

//?File?Name:?pjSVM.cpp??

//?Author:???easyfov(easyfov@gmail.com)??

//?Company:?Lida?Optical?and?Electronic?Co.,Ltd.??

//http://apps.hi.baidu.com/share/detail/32719017??

//??

??

#include?<cv.h>??

#include?<highgui.h>??

#include?<ml.h>??

??

#include?<iostream>??

#include?<fstream>??

#include?<string>??

#include?<vector>??

using?namespace?std;??

??

#define?WIDTH?20??

#define?HEIGHT?20??

??

int?main(?/*int?argc,?char**?argv*/?)??

{??

????vector<string>?img_path;??

????vector<int>?img_catg;??

????int?nLine?=?0;??

????string?buf;??

????ifstream?svm_data(?"E:/SVM_DATA.txt"?);??

??

????while(?svm_data?)??

????{??

????????if(?getline(?svm_data,?buf?)?)??

????????{??

????????????nLine?++;??

????????????if(?nLine?%?2?==?0?)??

????????????{??

?????????????????img_catg.push_back(?atoi(?buf.c_str()?)?);//atoi將字符串轉換成整型，標志（0,1）??

????????????}??

????????????else??

????????????{??

????????????????img_path.push_back(?buf?);//圖像路徑??

????????????}??

????????}??

????}??

????svm_data.close();//關閉文件??

??

????CvMat?*data_mat,?*res_mat;??

????int?nImgNum?=?nLine?/?2;????????????//讀入樣本數量??

????樣本矩陣，nImgNum：橫坐標是樣本數量，?WIDTH?*?HEIGHT：樣本特征向量，即圖像大小??

????data_mat?=?cvCreateMat(?nImgNum,?WIDTH?*?HEIGHT,?CV_32FC1?);??

????cvSetZero(?data_mat?);??

????//類型矩陣,存儲每個樣本的類型標志??

????res_mat?=?cvCreateMat(?nImgNum,?1,?CV_32FC1?);??

????cvSetZero(?res_mat?);??

??

????IplImage?*srcImg,?*sampleImg;??

????float?b;??

????DWORD?n;??

??

????for(?string::size_type?i?=?0;?i?!=?img_path.size();?i++?)??

????{??

???????srcImg?=?cvLoadImage(?img_path[i].c_str(),?CV_LOAD_IMAGE_GRAYSCALE?);??

???????if(?srcImg?==?NULL?)??

???????{??

????????????cout<<"?can?not?load?the?image:?"<<img_path[i].c_str()<<endl;??

????????????continue;??

???????}??

??

???????cout<<"?processing?"<<img_path[i].c_str()<<endl;??

??

???????sampleImg?=?cvCreateImage(?cvSize(?WIDTH,?HEIGHT?),?IPL_DEPTH_8U,?1?);//樣本大小（WIDTH,?HEIGHT）??

???????cvResize(?srcImg,?sampleImg?);//改變圖像大小??

??

???????cvSmooth(?sampleImg,?sampleImg?);????//降噪??

???????//生成訓練數據??

???????n?=?0;??

????????for(?int?ii?=?0;?ii?<?sampleImg->height;?ii++?)??

????????{??

????????????for(?int?jj?=?0;?jj?<?sampleImg->width;?jj++,?n++?)??

????????????{??

?????????????????b?=?(float)((int)((uchar)(?sampleImg->imageData?+?sampleImg->widthStep?*?ii?+?jj?))?/?255.0?);??

?????????????????cvmSet(?data_mat,?(int)i,?n,?b?);??

????????????}??

????????}??

????????cvmSet(?res_mat,?i,?0,?img_catg[i]?);??

????????cout<<"?end?processing?"<<img_path[i].c_str()<<"?"<<img_catg[i]<<endl;??

????}??

??

??

????CvSVM?svm?=?CvSVM();??

????CvSVMParams?param;??

????CvTermCriteria?criteria;??

????criteria?=?cvTermCriteria(?CV_TERMCRIT_EPS,?1000,?FLT_EPSILON?);??

????param?=?CvSVMParams(?CvSVM::C_SVC,?CvSVM::RBF,?10.0,?0.09,?1.0,?10.0,?0.5,?1.0,?NULL,?criteria?);??

?????/*???

????????????SVM種類：CvSVM::C_SVC???

????????????Kernel的種類：CvSVM::RBF???

????????????degree：10.0（此次不使用）???

????????????gamma：8.0???

????????????coef0：1.0（此次不使用）???

????????????C：10.0???

????????????nu：0.5（此次不使用）???

????????????p：0.1（此次不使用）???

????????????然后對訓練數據正規化處理，并放在CvMat型的數組里。???

????????????????????????????????????????????????????????????????*/?????

????//☆☆☆☆☆☆☆☆☆(5)SVM學習☆☆☆☆☆☆☆☆☆☆☆☆??????

????svm.train(?data_mat,?res_mat,?NULL,?NULL,?param?);??

????//☆☆利用訓練數據和確定的學習參數,進行SVM學習☆☆☆☆??

????svm.save(?"SVM_DATA.xml"?);??

??

??

????//檢測樣本??

????IplImage?*tst,?*tst_tmp;??

????vector<string>?img_tst_path;??

????ifstream?img_tst(?"E:/SVM_TEST.txt"?);??

????while(?img_tst?)??

????{??

????????if(?getline(?img_tst,?buf?)?)??

????????{??

????????????img_tst_path.push_back(?buf?);??

????????}??

????}??

????img_tst.close();??

??

????CvMat?*tst_mat?=?cvCreateMat(?1,?WIDTH*HEIGHT,?CV_32FC1?);??

????char?line[512];??

????ofstream?predict_txt(?"SVM_PREDICT.txt"?);??

????for(?string::size_type?j?=?0;?j?!=?img_tst_path.size();?j++?)??

????{??

????????tst?=?cvLoadImage(?img_tst_path[j].c_str(),?CV_LOAD_IMAGE_GRAYSCALE?);??

????????if(?tst?==?NULL?)??

????????{??

?????????????cout<<"?can?not?load?the?image:?"<<img_tst_path[j].c_str()<<endl;??

???????????????continue;??

???}??

???tst_tmp?=?cvCreateImage(?cvSize(?WIDTH,?HEIGHT?),?IPL_DEPTH_8U,?1?);??

???cvResize(?tst,?tst_tmp?);??

???cvSmooth(?tst_tmp,?tst_tmp?);??

???n?=?0;??

???for(int?ii?=?0;?ii?<?tst_tmp->height;?ii++?)??

???{??

?????for(int?jj?=?0;?jj?<?tst_tmp->width;?jj++,?n++?)??

?????{??

?????????b?=?(float)(((int)((uchar)tst_tmp->imageData+tst_tmp->widthStep*ii+jj))/255.0);??

?????????cvmSet(?tst_mat,?0,?n,?(double)b?);??

?????}??

???}??

??

???int?ret?=?svm.predict(?tst_mat?);??

???sprintf(?line,?"%s?%d\r\n",?img_tst_path[j].c_str(),?ret?);??

???predict_txt<<line;??

}??

predict_txt.close();??

??

cvReleaseImage(?&srcImg?);??

cvReleaseImage(?&sampleImg?);??

cvReleaseImage(?&tst?);??

cvReleaseImage(?&tst_tmp?);??

cvReleaseMat(?&data_mat?);??

cvReleaseMat(?&res_mat?);??

??

return?0;??

}??

其中

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G:/program/pjSVM/face/25.png
1
G:/program/pjSVM/face/26.png
1
G:/program/pjSVM/face/27.png
1
G:/program/pjSVM/face/28.png
1
G:/program/pjSVM/face/29.png
1
G:/program/pjSVM/face/30.png

1

SVM_TEST.txt中內容如下：

G:/program/pjSVM/try_face/5.png
G:/program/pjSVM/try_face/9.png
G:/program/pjSVM/try_face/11.png
G:/program/pjSVM/try_face/15.png
G:/program/pjSVM/try_face/2.png
G:/program/pjSVM/try_face/30.png
G:/program/pjSVM/try_face/17.png
G:/program/pjSVM/try_face/21.png
G:/program/pjSVM/try_face/24.png
G:/program/pjSVM/try_face/27.png

PS：txt操作簡單方式：http://blog.csdn.net/lytwell/article/details/6029503

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