#include "stdafx.h"

#include "opencv2\opencv.hpp"

#include <iostream>

using namespace std;

using namespace cv;

using namespace cv::ml;

int main()

{

    Mat img = imread("E:/opencv/opencv/sources/samples/data/digits.png");

    Mat gray;

    cvtColor(img, gray, CV_BGR2GRAY);

    int b = ;

    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 = ; i < n; i++)

    {

        int offsetCol = i*b; //列上的偏移量

        for (int j = ; 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(,));  //序列化后放入特征矩阵

            labels.push_back((int)j / );  //对应的标注

        }

    }

    data.convertTo(data, CV_32F); //uchar型转换为cv_32f

    int samplesNum = data.rows;

    int trainNum = ;

    Mat trainData, trainLabels;

    trainData = data(Range(, trainNum), Range::all());   //前3000个样本为训练数据

    trainLabels = labels(Range(, trainNum), Range::all());

    //使用KNN算法

    int K = ;

    Ptr<TrainData> tData = TrainData::create(trainData, ROW_SAMPLE, trainLabels);

    Ptr<KNearest> model = KNearest::create();

    model->setDefaultK(K);

    model->setIsClassifier(true);

    model->train(tData);

    //预测分类

    double train_hr = , test_hr = ;

    Mat response;

    // compute prediction error on train and test data

    for (int i = ; 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 ? .f : .f;          

        if (i < trainNum)

            train_hr += r;  //累积正确数

        else

            test_hr += r;

    }

    test_hr /= samplesNum - trainNum;

    train_hr = trainNum >  ? train_hr / trainNum : .;

    printf("accuracy: train = %.1f%%, test = %.1f%%\n",

        train_hr*., test_hr*.);

    waitKey();

    return ;

}