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学习OpenCV——SVM
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OpenCV開發(fā)SVM算法是基于LibSVM軟件包開發(fā)的，LibSVM是臺灣大學(xué)林智仁(Lin Chih-Jen)等開發(fā)設(shè)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(shè)置訓(xùn)練樣本集
需要兩組數(shù)據(jù)，一組是數(shù)據(jù)的類別，一組是數(shù)據(jù)的向量信息。
2）設(shè)置SVM參數(shù)
利用CvSVMParams類實現(xiàn)類內(nèi)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(shù)的類型：
CvSVM：：LINEAR 線性：u‘v
CvSVM：：POLY 多項式：(r*u'v + coef0)^degree
CvSVM：：RBF RBF函數(shù)：exp(-r|u-v|^2)
CvSVM：：SIGMOID sigmoid函數(shù)：tanh(r*u'v + coef0)
成員變量degree針對多項式核函數(shù)degree的設(shè)置，gamma針對多項式/rbf/sigmoid核函數(shù)的設(shè)置，coef0針對多項式/sigmoid核函數(shù)的設(shè)置，Cvalue為損失函數(shù)，在C-SVC、e-SVR、v-SVR中有效，nu設(shè)置v-SVC、一類SVM和v-SVR參數(shù)，p為設(shè)置e-SVR中損失函數(shù)的值，class_weightsC_SVC的權(quán)重，term_crit為SVM訓(xùn)練過程的終止條件。其中默認(rèn)值degree = 0，gamma = 1，coef0 = 0，Cvalue = 1，nu = 0，p = 0，class_weights = 0
3）訓(xùn)練SVM
調(diào)用CvSVM：：train函數(shù)建立SVM模型，第一個參數(shù)為訓(xùn)練數(shù)據(jù)，第二個參數(shù)為分類結(jié)果，最后一個參數(shù)即CvSVMParams
4）用這個SVM進(jìn)行分類
調(diào)用函數(shù)CvSVM：：predict實現(xiàn)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(diào)用函數(shù)CvSVM：：get_support_vector_count獲得支持向量的個數(shù)，CvSVM：：get_support_vector獲得對應(yīng)的索引編號的支持向量。
實現(xiàn)代碼如下：運(yùn)行步驟
[cpp]?view plaincopyprint?
?? 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);?? ?? ?? CvSVMParams?params;?? params.svm_type?=?CvSVM::C_SVC;?? params.kernel_type?=?CvSVM::LINEAR;?? params.term_crit?=?cvTermCriteria(CV_TERMCRIT_ITER,?100,?1e-6);?? ?? ?? CvSVM?SVM;?? SVM.train(trainingDataMat,?labelsMat,?Mat(),?Mat(),?params);?? ?? ?? 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;?? ????????}?? ????}?? }?? ?? ?? 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?
?? #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;??????????? ????????const?int?s?=?1000;???????????? ????????int?i,?j,?sv_num;????? ????????IplImage?*img;????? ????????CvSVM?svm?=?CvSVM();?????? ????????CvSVMParams?param;????? ????????CvTermCriteria?criteria;?? ????????CvRNG?rng?=?cvRNG(time(NULL));????? ????????CvPoint?pts[s];??????????? ????????float?data[s*2];?????????? ????????int?res[s];??????????????? ????????CvMat?data_mat,?res_mat;????? ????????CvScalar?rcolor;????? ????????const?float?*support;????? ?????????? ????????img=?cvCreateImage(cvSize(size,?size),?IPL_DEPTH_8U,?3);????? ????????cvZero(img);????? ?????????? ????????? ?????????? ????????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;????? ????????????????}????? ????????????}????? ????????}????? ?????????? ????????? ?????????? ????????cvNamedWindow("SVM",?CV_WINDOW_AUTOSIZE);????? ????????cvShowImage("SVM",?img);????? ????????cvWaitKey(0);????? ????????? ?????????? ????????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);?? ?????????? ????????? ?????????? ????????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);????? ????????????}????? ????????}????? ?????????? ????????? ?????????? ????????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);????? ????????}????? ?????????? ????????? ?????????? ????????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));????? ????????}????? ?????????? ????????? ?????????? ????????cvNamedWindow("SVM",?CV_WINDOW_AUTOSIZE);????? ????????cvShowImage("SVM",?img);????? ????????cvWaitKey(0);????? ????????cvDestroyWindow("SVM");????? ????????cvReleaseImage(&img);????? ????????return?0;????? ?????????? ????}????
實驗代碼2：用MIT人臉庫檢測，效果實在不好，檢測結(jié)果全是人臉或者全都不是人臉。原因應(yīng)該是圖像檢測沒有做好應(yīng)該用HoG等特征首先檢測，在進(jìn)行分類訓(xùn)練，不特征不明顯，肯定分類效果并不好。
[cpp]?view plaincopyprint?
?? ?? ?? ?? ?? ?? ?? #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(??)?? {?? ????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()?)?);?? ????????????}?? ????????????else?? ????????????{?? ????????????????img_path.push_back(?buf?);?? ????????????}?? ????????}?? ????}?? ????svm_data.close();?? ?? ????CvMat?*data_mat,?*res_mat;?? ????int?nImgNum?=?nLine?/?2;?????????????? ?????? ????data_mat?=?cvCreateMat(?nImgNum,?WIDTH?*?HEIGHT,?CV_32FC1?);?? ????cvSetZero(?data_mat?);?? ?????? ????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?);?? ???????cvResize(?srcImg,?sampleImg?);?? ?? ???????cvSmooth(?sampleImg,?sampleImg?);?????? ????????? ???????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.train(?data_mat,?res_mat,?NULL,?NULL,?param?);?? ?????? ????svm.save(?"SVM_DATA.xml"?);?? ?? ?? ?????? ????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
G:/program/pjSVM/face/1.png
0
G:/program/pjSVM/face/2.png
0
G:/program/pjSVM/face/3.png
0
G:/program/pjSVM/face/4.png
0
G:/program/pjSVM/face/5.png
0
G:/program/pjSVM/face/6.png
0
G:/program/pjSVM/face/7.png
0
G:/program/pjSVM/face/8.png
0
G:/program/pjSVM/face/9.png
0
G:/program/pjSVM/face/10.png
0
G:/program/pjSVM/face/11.png
0
G:/program/pjSVM/face/12.png
0
G:/program/pjSVM/face/13.png
0
G:/program/pjSVM/face/14.png
0
G:/program/pjSVM/face/15.png
1
G:/program/pjSVM/face/16.png
1
G:/program/pjSVM/face/17.png
1
G:/program/pjSVM/face/18.png
1
G:/program/pjSVM/face/19.png
1
G:/program/pjSVM/face/20.png
1
G:/program/pjSVM/face/21.png
1
G:/program/pjSVM/face/22.png
1
G:/program/pjSVM/face/23.png
1
G:/program/pjSVM/face/24.png
1
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(nèi)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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