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OpenCV的支持向量机SVM的程序
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步驟：
1，生成隨機(jī)的點(diǎn)，并按一定的空間分布將其歸類(lèi)
2，創(chuàng)建SVM并利用隨機(jī)點(diǎn)樣本進(jìn)行訓(xùn)練
3，將整個(gè)空間按SVM分類(lèi)結(jié)果進(jìn)行劃分，并顯示支持向量
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[cpp]?view plaincopy
#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);?? ?? ?????? ????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;??? ????????????}??? ????????}??? ?? ????}?? ?? ?????? ????imshow("dst",?img);?? ????waitKey(0);?? ?? ?????? ????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);?? ?? ?????? ????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);?? }??
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學(xué)習(xí)樣本：
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分類(lèi)：
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支持向量：
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