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0r = 1.f;elser = 0.f;if (i < trainNum)train_hr=train_hr+r;elsetest_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;}