贝叶斯分类器

来源：互联网发布：点卡充值软件编辑：程序博客网时间：2024/06/05 15:36
代码如下：
#include <iostream>    #include <opencv2/opencv.hpp>  #include <opencv2/core/core.hpp>    #include <opencv2/highgui/highgui.hpp>   #include <opencv2/imgproc/imgproc.hpp>   #include<opencv2/ml/ml.hpp>using namespace cv;using namespace std;int main(int, char**){int width = 512, height = 512;Mat image = Mat::zeros(height, width, CV_8UC3);  //创建窗口可视化// 设置训练数据int labels[10] = { 1, -1, 1, 1, -1, 1, -1, 1, -1, -1 };Mat labelsMat(10, 1, CV_32SC1, labels);float trainingData[10][2] = { { 501, 150 }, { 255, 10 }, { 501, 255 }, { 10, 501 }, { 25, 80 },{ 150, 300 }, { 77, 200 }, { 300, 300 }, { 45, 250 }, { 200, 200 } };Mat trainingDataMat(10, 2, CV_32FC1, trainingData);// 创建贝叶斯分类器NormalBayesClassifier model = NormalBayesClassifier();//训练分类器model.train(trainingDataMat, labelsMat, Mat(), Mat());Vec3b green(0, 255, 0), blue(255, 0, 0);// Show the decision regions given by the SVMfor (int i = 0; i < image.rows; ++i)for (int j = 0; j < image.cols; ++j){Mat sampleMat = (Mat_<float>(1, 2) << j, i);  //生成测试数据float response = model.predict(sampleMat);  //进行预测，返回1或-1if (response == 1)image.at<Vec3b>(i, j) = green;else if (response == -1)image.at<Vec3b>(i, j) = blue;}// 显示训练数据int thickness = -1;int lineType = 8;Scalar c1 = Scalar::all(0); //标记为1的显示成黑点Scalar c2 = Scalar::all(255); //标记成-1的显示成白点//绘图时，先宽后高，对应先列后行for (int i = 0; i < labelsMat.rows; i++){const float* v = trainingDataMat.ptr<float>(i); //取出每行的头指针Point pt = Point((int)v[0], (int)v[1]);if (labels[i] == 1)circle(image, pt, 5, c1, thickness, lineType);elsecircle(image, pt, 5, c2, thickness, lineType);}imshow("normal Bayessian classifier Simple Example", image); // show it to the userwaitKey(0);}
效果图：
阅读全文
0 0