基于Qt和Opencv3实现机器学习：利用贝叶斯分类（C++版）

原创已于 2024-10-17 21:51:34 修改

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#qt #c++ #机器学习 #分类

于 2023-05-15 10:01:15 首次发布

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第一步：在pro文件里面设置路径

INCLUDEPATH += /usr/local/include \  
                /usr/local/include/opencv \  
                /usr/local/include/opencv2  
  
LIBS += /usr/local/lib/libopencv_highgui.so \  
        /usr/local/lib/libopencv_core.so    \  
        /usr/local/lib/libopencv_imgproc.so \  
        /usr/local/lib/libopencv_imgcodecs.so \  
        /usr/local/lib/libopencv_ml.so

第二步：建立cpp文件

#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, 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
    Ptr<NormalBayesClassifier> model=NormalBayesClassifier::create();
 
    // 设置训练数据
    Ptr<TrainData> tData =TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
 
    //训练分类器
    model->train(tData);
 
    Vec3b green(0, 255, 0), blue(255, 0, 0);
    // Show the decision regions given by the SVM
    for (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-1
 
        if (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);
        else
            circle(image, pt, 5, c2, thickness, lineType);
 
    }
 
    imshow("normal Bayessian classifier Simple Example", image); // show it to the user
    waitKey(0);
 
}

第三步：运行程序，出现的结果