opencv的机器学习检测在ML库中具有很大的相似性。简单来说都可以分成两步：
1、训练/得到分类器。
2、使用分类器进行数据分类。
实现这两步所需要的也只是两个关键函数：
1、train()
2、predict()
以下我们就14组数据进行训练，每组数据有两个参数，来得到分类器，然后再对一组有两个参数的数据进行预测。

预测参数：

下面开始进行编码拟合：

朴素贝叶斯分类

#include "opencv2/ml/ml.hpp"

using namespace std;
using namespace cv;
const int NUM = 14;   //训练样本的个数
const int D = 2;     //维度
//14个维数为4的训练样本集
double inputArr[NUM][D] =
{ 21,3,
24,3,
30,4,
15,3,
13,3,
19,4,
18,3,
12,3,
13,2,
26,4,
17,3,
16,3,
14,3,
16,3,
};

//一个测试样本的特征向量  
double testArr[] = { 20, 3};

int main()
{
    Mat trainData(NUM, D, CV_32FC1);//构建训练样本的特征向量  
    for (int i = 0; i<NUM; i++)  {
        for (int j = 0; j<D; j++)  {
            trainData.at<float>(i, j) = inputArr[i][j + 1];
        }
    }
    Mat trainResponse = (Mat_<float>(NUM, 1) <<
        39,36,53,31,22,25,33,26,50,25,24,57,33,23);//构建训练样本的类别标签  

    CvNormalBayesClassifier Mybayes;
    bool trainFlag = Mybayes.train(trainData, trainResponse);//进行贝叶斯分类器训练  
    if (trainFlag)  {
        Mybayes.save("normalBayes.txt");
    }
    else  {
        system("pause");
        exit(-1);
    }
    CvNormalBayesClassifier Tbayes;
    Tbayes.load("normalBayes.txt");

    Mat testSample(1, D, CV_32FC1);//构建测试样本  
    for (int i = 0; i<D; i++)  {
        testSample.at<float>(0, i) = testArr[i];
    }
    float flag = Tbayes.predict(testSample);//进行测试  
    cout << "flag = " << flag << endl;
    system("pause");
    return 0;
}

得到的结果为：

朴素贝叶斯分类器

决策树

#include "opencv2/core/core_c.h"
#include "opencv2/ml/ml.hpp"
#include<stdlib.h>
#include <iostream>

int main()
{
    //init data
    float fdata[14][2] = { 
        21, 3,
        24, 3,
        30, 4,
        15, 3,
        13, 3,
        19, 4,
        18, 3,
        12, 3,
        13, 2,
        26, 4,
        17, 3,
        16, 3,
        14, 3,
        16, 3,
    };
    cv::Mat data(14, 2, CV_32F, fdata);
    float fresponses[14] = { 39, 36, 53, 31, 22, 25, 33, 26, 50, 25, 24, 57, 33, 23 };
    cv::Mat responses(14, 1, CV_32F, fresponses);
    CvDTree *tree;
    CvDTreeParams params(10,2, 0,true,15, 0, true,true, NULL);
    tree = new CvDTree;
    tree->train(data, CV_ROW_SAMPLE, responses, cv::Mat(),
        cv::Mat(), cv::Mat(), cv::Mat(),
        params);

    double testArr[] = { 20, 3 };
    cv::Mat sample(1, 2, CV_32F, testArr);
    double flag = tree->predict(sample, cv::Mat())->value;
    std::cout << "flag=" << flag << std::endl;
    system("pause");
    return 0;
}

得到的结果是：

决策树

随机森林

#include "opencv2/core/core.hpp"  
#include "opencv2/highgui/highgui.hpp"  
#include "opencv2/imgproc/imgproc.hpp"  
#include "opencv2/ml/ml.hpp"  

#include<stdlib.h>
#include <iostream>  
using namespace cv;
using namespace std;

int main(int argc, char** argv)
{
    double trainingData[14][2] = { 21, 3,
        24, 3,
        30, 4,
        15, 3,
        13, 3,
        19, 4,
        18, 3,
        12, 3,
        13, 2,
        26, 4,
        17, 3,
        16, 3,
        14, 3,
        16, 3 };
    CvMat trainingDataCvMat = cvMat(14, 2, CV_32FC1, trainingData);

    float responses[14] = { 39, 36, 53, 31, 22, 25, 33, 26, 50, 25, 24, 57, 33, 23 };
    CvMat responsesCvMat = cvMat(14, 1, CV_32FC1, responses);

    CvRTParams params = CvRTParams(10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER);

    CvERTrees etrees;
    etrees.train(&trainingDataCvMat, CV_ROW_SAMPLE, &responsesCvMat,
        NULL, NULL, NULL, NULL, params);

    double sampleData[2] = { 20, 3 };
    Mat sampleMat(2, 1, CV_32FC1, sampleData);
    float flag = etrees.predict(sampleMat);
    cout << "flag=" << flag << endl;
    system("pause");
    return 0;
}

得到的结果是：

随机森林

boosting

boosting原理

所以boosting只支持二级分类，我们为了和上述其他几种情况作对比，将不小于26的response值设置为1，不大于25的response值设置为0，来进行分类训练，观察得到的结果。

response的新旧值对照表

#include "opencv2/core/core.hpp"  
#include "opencv2/highgui/highgui.hpp"  
#include "opencv2/imgproc/imgproc.hpp"  
#include "opencv2/ml/ml.hpp"  
#include <iostream>  
#include<stdlib.h>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
    float trainingData[14][2] = { 21, 3,
        24, 3,
        30, 4,
        15, 3,
        13, 3,
        19, 4,
        18, 3,
        12, 3,
        13, 2,
        26, 4,
        17, 3,
        16, 3,
        14, 3,
        16, 3 };
    Mat trainingDataMat(14, 2, CV_32FC1, trainingData);
    float responses[14] = { 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0 };
    Mat responsesMat(14, 1, CV_32FC1, responses);
    float prior[] = {1,1};
    CvBoostParams params(CvBoost::REAL, // boost_type    
        10, // weak_count    
        0.95, // weight_trim_rate    
        15, // max_depth    
        false, // use_surrogates    
        prior
        );

    CvBoost boost;
    boost.train(trainingDataMat,
        CV_ROW_SAMPLE,
        responsesMat,
        Mat(),
        Mat(),
        Mat(),
        Mat(),
        params
        );
    float myData[2] = { 20, 3 };
    Mat myDataMat(2, 1, CV_32FC1, myData);
    double flag = boost.predict(myDataMat);
    cout << "flag=" << flag << endl;
    system("pause");
    return 0;
}

boosting

可以认为boosting计算出的值为26。