在opencv3中实现机器学习之：利用正态贝叶斯分类

opencv3.0版本中，实现正态贝叶斯分类器（Normal Bayes Classifier）分类实例

#include "stdafx.h"
#include "opencv2/opencv.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);

}

如果将数据换成是图片的像素值 ，则可实现图片的分类。