opencv3实现简单的数字图像识别（KNN）

正在用opencv3做一个数字图像识别的小项目，要用到KNN，但是不熟悉它的接口，因此，借鉴了大佬的博客，基本照搬了代码，代码如下：

大佬的链接如下：http://www.cnblogs.com/denny402/p/5033898.html

// knnrecognizenum.cpp:使用knn识别手写数字
//
#include "stdafx.h"
#include<iostream>
#include<opencv2\ml\ml.hpp>
#include<highgui\highgui.hpp>
using namespace std;
using namespace cv;
using namespace cv::ml;

int main()
{
Mat img = imread("digits.png", 0);
int boot = 20;
int m = img.rows / boot;   int n = img.cols / boot;
Mat data, labels; //data和labels分别存放

//截取数据的时候要按列截取
for (int i = 0; i < n; i++)
{
int  colNum = i * boot;
for (int j = 0; j < m; j++)
{
int rowNum = j * boot;
Mat tmp;
img(Range(rowNum, rowNum + boot), Range(colNum, colNum + boot)).copyTo(tmp);
data.push_back(tmp.reshape(0, 1));         //将图像转成一维数组插入到data矩阵中
labels.push_back((int)j / 5);             //将图像对应的标注插入到labels矩阵中
}
}
data.convertTo(data, CV_32F);
int sampleNum = data.rows;
int trainNum = 3000;

Mat trainData, trainLabel;
trainData = data(Range(0, trainNum), Range::all());
trainLabel = labels(Range(0, trainNum), Range::all());

//使用KNN算法
int k = 5;
Ptr<TrainData>   tData = TrainData::create(trainData,ROW_SAMPLE, trainLabel); //ROW_SAMPLE表示一行一个样本
Ptr<KNearest> model = KNearest::create();
model->setDefaultK(k); model->setIsClassifier(true);
model->train(tData);

//预测分类
/*  Mat sample = data.row(500);
float res = model->predict(sample);
cout << "预测结果是："<< res << endl;*/ //预测一个的代码

double train_hr=0, test_hr=0;
Mat response;
for (int i = 0; i < sampleNum; i++)
{
Mat sample = data.row(i);
float r = model->predict(sample);
r = abs(r - labels.at<int>(i));
if (r <= FLT_EPSILON)// FLT_EPSILON表示最小的float浮点数，小于它，就是等于0
r = 1.f;
else
r = 0.f;
if (i < trainNum)
train_hr=train_hr+r;
else
test_hr=test_hr + r;
}
//cout << train_hr << "   " << test_hr << endl;
cout << "KNN模型在训练集上的准确率为" << train_hr / trainNum * 100 << "%，在测试集上的准确率为" << test_hr / (data.rows-trainNum)*100<<"%"<<endl;
system("pause");
return 0;
}