svm.zip_SVM算法opencv_opencv_opencv SVM_opencv svm代码_svm opencv

#include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/ml/ml.hpp> using namespace cv; int main() { // 用于保存可视化数据的矩阵 int width = 512, height = 512; Mat image = Mat::zeros(height, width, CV_8UC3); // 创建一些训练样本 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); // 设置SVM参数 CvSVMParams params; params.svm_type = CvSVM::C_SVC; params.kernel_type = CvSVM::LINEAR; params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6); // 对SVM进行训练 CvSVM SVM; SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params); Vec3b green(0, 255, 0), blue(255, 0, 0); // 将SVM断定的分划区域绘制出来 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 (response == 1) image.at<Vec3b>(j, i) = green; else if (response == -1) image.at<Vec3b>(j, i) = blue; } // 绘制训练数据点 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); // 绘制支持向量 thickness = 2; lineType = 8; int c = SVM.get_support_vector_count(); for (int i = 0; i < c; ++i) { const float* v = SVM.get_support_vector(i); circle(image, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thickness, lineType); } imwrite("result.png", image); imshow("简单SVM分类", image); waitKey(0); }