KNN训练模型

knn训练手写体

//#include "stdafx.h"
#include "opencv2/opencv.hpp"
#include <iostream>
using namespace std;
using namespace cv;
using namespace cv::ml;

int main()
{
    
    
    Mat img = imread("/home/cubot/robotmaster/opencv-3.4.6/samples/data/digits.png");
    Mat gray;
    cvtColor(img, gray, CV_BGR2GRAY);
    int b = 20;
    int m = gray.rows / b;   //原图为1000*2000
    int n = gray.cols / b;   //裁剪为5000个20*20的小图块
    Mat data,labels;   //特征矩阵
    for (int i = 0; i < n; i++)
    {
    
    
        int offsetCol = i*b; //列上的偏移量
        for (int j = 0; j < m; j++)
        {
    
    
            int offsetRow = j*b;  //行上的偏移量
            //截取20*20的小块
            Mat tmp;
            gray(Range(offsetRow, offsetRow + b), Range(offsetCol, offsetCol + b)).copyTo(tmp);
            data.push_back(tmp.reshape(0,1));  //序列化后放入特征矩阵
            labels.push_back((int)j / 5);  //对应的标注
        }

    }
    data.convertTo(data, CV_32F); //uchar型转换为cv_32f
    int samplesNum = data.rows;
    int trainNum = 3000;
    Mat trainData, trainLabels;
    trainData = data(Range(0, trainNum), Range::all());   //前3000个样本为训练数据
    trainLabels = labels(Range(0, trainNum), Range::all());

    //使用KNN算法
    int K = 5;
    Ptr<TrainData> tData = TrainData::create(trainData, ROW_SAMPLE, trainLabels);//降训练数据封装成一个TrainData对象，送入train函数
    Ptr<KNearest> model = KNearest::create();
    model->setDefaultK(K);
    model->setIsClassifier(true);
    model->train(tData);

    //预测分类
    double train_hr = 0, test_hr = 0;
    Mat response;
    // compute prediction error on train and test data
    for (int i = 0; i < samplesNum; i++)
    {
    
    
        Mat sample = data.row(i);
        float r = model->predict(sample);   //对所有行进行预测
        //预测结果与原结果相比，相等为1，不等为0
        r = std::abs(r - labels.at<int>(i)) <= FLT_EPSILON ? 1.f : 0.f;

        if (i < trainNum)
            train_hr += r;  //累积正确数
        else
            test_hr += r;
    }

    test_hr /= samplesNum - trainNum;
    train_hr = trainNum > 0 ? train_hr / trainNum : 1.;

    printf("accuracy: train = %.1f%%, test = %.1f%%\n",
           train_hr*100., test_hr*100.);
    waitKey(0);
    return 0;
}