#include <iostream>

#include <fstream>

#include <opencv2/core/core.hpp>

#include <opencv2/highgui/highgui.hpp>

#include <opencv2/imgproc/imgproc.hpp>

#include <opencv2/objdetect/objdetect.hpp>

#include <opencv2/ml/ml.hpp>

using namespace std;

using namespace cv;

using namespace cv::ml;

int main(int argc,char* argv[])

{

Mat img = imread("./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);

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;

}