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#include<iostream>
#include<opencv2/opencv.hpp>
#include<opencv2/ml.hpp>
using namespace std;
using namespace cv;
using namespace cv::ml;
//**自定义结构体
struct MyNum
{
cv::Mat mat; //数字图片
cv::Rect rect;//相对整张图所在矩形
int label;//数字标签
};
int main()
{
Mat src = imread("digit.png");
if (src.empty())
{
cout << "No Image..." << endl;
system("pause");
return -1;
}
Mat gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
const int classNum = 10; //总共有0~9个数字类别
const int picNum = 20;//每个类别共20张图片
const int pic_w = 28;//图片宽
const int pic_h = 28;//图片高
//将数据集分为训练集、测试集
double totalNum = classNum * picNum;//图片总数
double per = 0.8; //百分比--修改百分比可改变训练集、测试集比重
double trainNum = totalNum * per;//训练图片数量
double testNum = totalNum * (1.0 - per);//测试图片数量
Mat Train_Data, Train_Label;//用于训练
vector<MyNum>TestData;//用于测试
for (int i = 0; i < picNum; i++)
{
for (int j = 0; j < classNum; j++)
{
//将所有图片数据都拷贝到Mat矩阵里
Mat temp;
gray(Range(j*pic_w, j*pic_w + pic_w), Range(i*pic_h, i*pic_h + pic_h)).copyTo(temp);
Train_Data.push_back(temp.reshape(0, 1)); //将temp数字图像reshape成一行数据，然后一一追加到Train_Data矩阵中
Train_Label.push_back(j);
//额外用于测试
if (i * classNum + j >= trainNum)
{
TestData.push_back({ temp,Rect(i*pic_w,j*pic_h,pic_w,pic_h),j });
}
}
}
//准备训练数据集
Train_Data.convertTo(Train_Data, CV_32FC1); //转化为CV_32FC1类型
Train_Label.convertTo(Train_Label, CV_32FC1);
Mat TrainDataMat = Train_Data(Range(0, trainNum), Range::all()); //只取trainNum行训练
Mat TrainLabelMat = Train_Label(Range(0, trainNum), Range::all());
//KNN训练
const int k = 3; //k值，取奇数，影响最终识别率
Ptr<KNearest>knn = KNearest::create(); //构造KNN模型
knn->setDefaultK(k);//设定k值
knn->setIsClassifier(true);//KNN算法可用于分类、回归。
knn->setAlgorithmType(KNearest::BRUTE_FORCE);//字符匹配算法
knn->train(TrainDataMat, ROW_SAMPLE, TrainLabelMat);//模型训练
//预测及结果显示
double count = 0.0;
Scalar color;
for (int i = 0; i < TestData.size(); i++)
{
//将测试图片转成CV_32FC1，单行形式
Mat data = TestData[i].mat.reshape(0, 1);
data.convertTo(data, CV_32FC1);
Mat sample = data(Range(0, data.rows), Range::all());
float f = knn->predict(sample); //预测
if (f == TestData[i].label)
{
color = Scalar(0, 255, 0); //如果预测正确，绘制绿色，并且结果+1
count++;
}
else
{
color = Scalar(0, 0, 255);//如果预测错误，绘制红色
}
rectangle(src, TestData[i].rect, color, 2);
}
//将绘制结果拷贝到一张新图上
Mat result(Size(src.cols, src.rows + 50), CV_8UC3, Scalar::all(255));
src.copyTo(result(Rect(0, 0, src.cols, src.rows)));
//将得分在结果图上显示
char text[10];
int score = (count / testNum) * 100;
sprintf_s(text, "%s%d%s", "Score:", score, "%");
putText(result, text, Point((result.cols / 2) - 80, result.rows - 15), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 255, 0), 2);
imshow("test", result);
imwrite("result.jpg", result);
waitKey(0);
system("pause");
return 0;
}
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