opencv官方样例学习笔记——introduction_to_svm

来源：互联网发布：瑜伽教学软件编辑：程序博客网时间：2024/06/05 14:11

Andrew moore的ppthttp://www.autonlab.org/tutorials/svm15.pdf这个ppt介绍svm说的挺好的。

svm是（support vector machine）简称，我现还是初学状态，了解到了以下几个概念：

1）线性与非线性

有若干黑点和白点，用一根直线把他们分开，就是线性（不精于数学，若理解有误欢迎指正）

不能用直线分开，就是非线性：

opencv3.0官方svm线性的例子比较简单，附上我的一些理解：

#include <opencv2/core.hpp>#include <opencv2/imgproc.hpp>#include "opencv2/imgcodecs.hpp"#include <opencv2/highgui.hpp>#include <opencv2/ml.hpp>using namespace cv;using namespace cv::ml;int main(int, char**){    // Data for visual representation    int width = 512, height = 512;    Mat image = Mat::zeros(height, width, CV_8UC3);    // Set up training data    //! [setup1]    int labels[4] = {1, -1, -1, -1};//标记{501,10}是黑点，{255,10}/{501,255}/{10,501}是白点    float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };//可以自己多加点    //! [setup1]    //! [setup2]    Mat trainingDataMat(4, 2, CV_32FC1, trainingData);//这里CV_32FC1必须，对应float，我试过trainingData数据类型int，会出错的。    Mat labelsMat(4, 1, CV_32SC1, labels);    //! [setup2]    // Train the SVM    //! [init]    Ptr<SVM> svm = SVM::create();//创建向量机    svm->setType(SVM::C_SVC);//向量机类型    svm->setKernel(SVM::LINEAR);//线性    svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));//暂时不理解，先背下来。。    //! [init]    //! [train]    svm->train(trainingDataMat, ROW_SAMPLE, labelsMat);    //! [train]    // Show the decision regions given by the SVM    //! [show]    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) << j,i);//是j,i，试过i,j会出错。            float response = svm->predict(sampleMat);            if (response == 1)                image.at<Vec3b>(i,j)  = green;            else if (response == -1)                image.at<Vec3b>(i,j)  = blue;        }    //! [show]    // Show the training data//标记数据点，不是核心程序    //! [show_data]    int thickness = -1;    int lineType = 8;    circle(image, Point(501,  10), 5, Scalar(  0,   0,   0), thickness, lineType );    circle(image, Point(255,  10), 5, Scalar(255, 255, 255), thickness, lineType );    circle(image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType );    circle(image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType );    //! [show_data]    // Show support vectors//得到支持向量，显示左上方一个灰点，我调了数据点的数量和位置，好像都没有影像它，不太理解。。    //! [show_vectors]    thickness = 2;    lineType  = 8;    Mat sv = svm->getSupportVectors();    for (int i = 0; i < sv.rows; ++i)    {        const float* v = sv.ptr<float>(i);        circle(image,  Point( (int) v[0], (int) v[1]),   6,  Scalar(128, 128, 128), thickness, lineType);    }    //! [show_vectors]    imwrite("result.png", image);        // save the image    imshow("SVM Simple Example", image); // show it to the user    waitKey(0);}

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