opencv3.0机器学习之SVM使用(二类线性可分)

xiaoxiao2021-02-28  28

之前找到一些代码都是2.X版本的,很多都不能运行,我大致修改了一下,全部能跑动了,2.x版本都注释了,下面替换为新的,可以对照修改。 /******************************* ** 作者: 周小小 ** 描述: struct CV_EXPORTS_W_MAP CvSVMParams { CvSVMParams(); CvSVMParams( int svm_type, //SVM类型 int kernel_type,//核函数类型 double degree,//核函数中的参数degree,针对多项式核函数; double coef0,//核函数中的参数,针对多项式/SIGMOID核函数; double Cvalue,//SVM类型(C_SVC/ EPS_SVR/ NU_SVR)的参数C。 double p, CvMat* class_weights, CvTermCriteria term_crit ); CV_PROP_RW int svm_type; CV_PROP_RW int kernel_type; CV_PROP_RW double degree; // for poly CV_PROP_RW double gamma; // for poly/rbf/sigmoid CV_PROP_RW double coef0; // for poly/sigmoid CV_PROP_RW double C; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR CV_PROP_RW double nu; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR CV_PROP_RW double p; // for CV_SVM_EPS_SVR CvMat* class_weights; // for CV_SVM_C_SVC CV_PROP_RW CvTermCriteria term_crit; // termination criteria }; SVM_params.c:SVM最优问题参数,设置C-SVC,EPS_SVR和NU_SVR的参数; SVM_params.nu:SVM最优问题参数,设置NU_SVC, ONE_CLASS 和NU_SVR的参数; SVM_params.p:SVM最优问题参数,设置EPS_SVR 中损失函数p的值. *******************************/ #include "opencv2/opencv.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/highgui.hpp" #include "opencv2/ml.hpp" //using namespace cv; //using namespace cv::ml; int main(int argc, char** argv) { // visual representation int width = 512; int height = 512; cv::Mat image = cv::Mat::zeros(height, width, CV_8UC3); // training data int labels[4] = { 1, -1, 1, -1 };//样本数据 float trainingData[4][2] = { { 501, 10 }, { 255, 10 }, { 501, 255 }, { 10, 501 } };//Mat结构特征数据 cv::Mat trainingDataMat(4, 2, CV_32FC1, trainingData);//Mat结构标签 cv::Mat labelsMat(4, 1, CV_32SC1, labels); //样本标签 // initial SVM初始化 cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create(); svm->setType(cv::ml::SVM::Types::C_SVC);//类型 svm->setKernel(cv::ml::SVM::KernelTypes::LINEAR);//核函数类型 svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, 100, 1e-6));//算法终止条件 // train operation svm->train(trainingDataMat, cv::ml::SampleTypes::ROW_SAMPLE, labelsMat); // prediction cv::Vec3b green(0, 255, 0); cv::Vec3b blue(255, 0, 0); for (int i = 0; i < image.rows; i++) { for (int j = 0; j < image.cols; j++) { cv::Mat sampleMat = (cv::Mat_<float>(1, 2) << j, i); float respose = svm->predict(sampleMat); if (respose == 1) image.at<cv::Vec3b>(i, j) = green; else if (respose == -1) image.at<cv::Vec3b>(i, j) = blue; } } int thickness = -1; int lineType = cv::LineTypes::LINE_8; //给点上色 cv::circle(image, cv::Point(501, 10), 5, cv::Scalar(0, 0, 0), thickness, lineType); cv::circle(image, cv::Point(255, 10), 5, cv::Scalar(255, 255, 255), thickness, lineType); cv::circle(image, cv::Point(501, 255), 5, cv::Scalar(0, 0, 0), thickness, lineType); cv::circle(image, cv::Point(10, 501), 5, cv::Scalar(255, 255, 255), thickness, lineType); thickness = 2; lineType = cv::LineTypes::LINE_8; cv::Mat sv = svm->getSupportVectors(); for (int i = 0; i < sv.rows; i++) { const float* v = sv.ptr<float>(i); cv::circle(image, cv::Point((int)v[0], (int)v[1]), 6, cv::Scalar(128, 128, 128), thickness, lineType); } cv::imshow("SVM Simple Example", image); cv::waitKey(0); return 0; }
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