svm一般都是兩分類的問題，有時候我們需要多分類的時候 以下代碼就派上用場了。

話不多說，直接上代碼。

// svm_test.cpp : 定義控制臺應用程序的入口點。 //#include "stdafx.h" #include <iostream> #include <fstream> #include <vector> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/ml/ml.hpp>using namespace cv; using namespace std;#define skyface_API extern __declspec(dllexport) skyface_API int sex_detect(vector<float> &feats, const char* modpth);Mat traindata(string path, int num) {vector<vector<float>> data(num, vector<float>(2048, 0));ifstream ifs;ifs.open(path);for (int i = 0; i < num; i++){for (int j = 0; j < 2048; j++){ifs >> data[i][j];}}ifs.close();Mat class_n_data(data.size(), data.at(0).size(), CV_32FC1);for (int i = 0; i < data.size(); i++)for (int j = 0; j < data.at(0).size(); j++)class_n_data.at<float>(i, j) = data.at(i).at(j);return class_n_data; }Mat get_traindata3(Mat class1, Mat class2, Mat class3) {Mat traindata(class1.rows + class2.rows + class3.rows , 2048, CV_32FC1);Mat tmp = traindata.rowRange(0, class1.rows);class1.copyTo(tmp);tmp = traindata.rowRange(class1.rows, class1.rows + class2.rows);class2.copyTo(tmp);tmp = traindata.rowRange(class1.rows + class2.rows, class1.rows + class2.rows + class3.rows);class3.copyTo(tmp);cout << "獲取到訓練數(shù)據(jù)！" << endl;return traindata; }Mat get_labels3(Mat class1, Mat class2, Mat class3) {Mat labels(class1.rows + class2.rows + class3.rows , 1, CV_32FC1);labels.rowRange(0, class1.rows).setTo(1);labels.rowRange(class1.rows, class1.rows + class2.rows).setTo(2);labels.rowRange(class1.rows + class2.rows, class1.rows + class2.rows + class3.rows).setTo(3);return labels; }void trainSVM(Mat traindata, Mat labels, string modelpth) {//------------------------ 2. Set up the support vector machines parameters --------------------CvSVMParams params;params.svm_type = SVM::C_SVC;params.C = 0.1;params.kernel_type = SVM::LINEAR;params.term_crit = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);//------------------------ 3. Train the svm ----------------------------------------------------cout << "Starting training process" << endl;CvSVM svm;svm.train(traindata, labels, Mat(), Mat(), params);cout << "Finished training process" << endl;svm.save("../data/model_AGE.txt"); }int sex_detect(vector<float> &feats, const char* modpth) {CvSVM SVM;SVM.load(modpth);int i;float* testdata = new float[2048];for (int i = 0; i < 2048; i++){testdata[i] = feats[i];}Mat test = Mat(1, 2048, CV_32FC1, testdata);float result = SVM.predict(test);delete[] testdata;return result; } int main() {//int labels[3]=[class1,class2,class3];Mat class1 = traindata("../data/feats_left.txt",40);Mat class2 = traindata("../data/feats_right.txt",36);Mat class3 = traindata("../data/feats_pos.txt",48);//Mat traindata = get_traindata(class1, class2);//Mat labels = get_labels(class1, class2);Mat traindata = get_traindata3(class1, class2, class3);Mat labels = get_labels3(class1, class2, class3);trainSVM(traindata, labels, "*");CvSVM SVM;SVM.load("../data/model_AGE.txt");ifstream ifs;float testdata[2048];ifs.open("../data/feats_test.txt");for (int i = 0; i < 2048; i++){ifs >> testdata[i];}Mat test = Mat(1, 2048, CV_32FC1, testdata);float result = SVM.predict(test);if (result == 1)cout << "左偏30度" << endl;else if (result == 2)cout<< "右偏30度" <<endl;else if (result == 3)cout<< "正臉" <<endl;ifs.close();system("pause"); }

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