C++编程模板匹配超详细的识别手写数字实现示例

#include<iostream>
#include<fstream>
#include<opencv2/opencv.hpp>
#include<opencv2/highgui.hpp>
#include<opencv2/core.hpp>
#include<io.h>                          //api和结构体
#include<string.h>
#include<string>
using namespace std;
using namespace cv;
void ergodicTest(string filename, string name);    //遍历函数
string Image_Compression(string imgpath);          //压缩图片并返回字符串
int distance(string str1, string str2);            //对比函数不一样的位数
void compare();
int turn(char a);
void main()
{
	const char* filepath = "E:\\learn\\vsfile\\c++project\\pictureData\\train-images";     
	ergodicTest(filepath,"train_num.txt");         //处理训练集
	const char* test_path= "E:\\learn\\vsfile\\c++project\\pictureData\\test-images";
	ergodicTest(test_path, "test_num.txt");
	compare();   
}
 
void ergodicTest(string filename,string name)       //遍历并把路径存到files
{
	string firstfilename = filename + "\\*.bmp";
	struct _finddata_t fileinfo;
	intptr_t handle;            //不能用long，因为精度问题会导致访问冲突,longlong也可
	string rout = "E:\\learn\\vsfile\\c++project\\pictureData\\" + name;
	ofstream file;
	file.open(rout, ios::out);
	handle = _findfirst(firstfilename.c_str(), &fileinfo);
	if ( _findfirst(firstfilename.c_str(), &fileinfo) != -1)
	{
		do
		{
			file << fileinfo.name << ":" << Image_Compression(filename + "\\" + fileinfo.name) << endl;
		} while (!_findnext(handle, &fileinfo));
		file.close();
		_findclose(handle);
	}
}
string Image_Compression(string imgpath)   //输入图片地址返回图片二值像素字符
{
	Mat Image = imread(imgpath);               //输入的图片
	cvtColor(Image, Image, COLOR_BGR2GRAY);
	int Matrix[28][28];                        //将digitization转化为字符串类型
	for (int row = 0; row < Image.rows; row++)  //把图片的像素点传给数组
		for (int col = 0; col < Image.cols; col++)
		{
			Matrix[row][col] = Image.at<uchar>(row, col);
		}
	string img_str = "";                   //用来存储结果字符串
	int x = 0, y = 0;
	for (int k = 1; k < 50; k++)
	{
		int total = 0;
		for (int q = 0; q < 4; q++)
			for (int p = 0; p < 4; p++)
				if (Matrix[x + q][y + p] > 127) total += 1;
		y = (y + 4) % 28;
		if (total >= 6) img_str += '1';    //将28*28的图片转化为7*7即压缩
		else img_str += '0';
		if (k % 7 == 0)
		{
			x += 4;
			y = 0;
		}
	}
	return img_str;
}
 
int distance(string str1, string str2)  //比对两个字符串有多少个不一样
{
	int counts=0;
	for (int i = 0; i < str1.length(); i++)
	{
		if (str1[i] == str2[i]) continue;
		else counts++; 
	}
	return counts;
}
int turn(char a)
{
	stringstream str;
	int f = 1;
	str << a;
	str >> f;
	str.clear();
	return f;
}
 
void compare()
{
	ifstream train_data;//建立读文件流
	ifstream test_data;
	string tmp1 = "";         //从train中取数据存在tmp1
	string tmp11 = "";
	string tmp2 = "";         //从test中取
	string tmp22 = "";
	bool refused = false; //拒绝识别标志
	int tr_num = 0;       //用来存储最小值的文件名(训练集)
	int num_refused = 0;   //拒绝识别个数
	int num_false = 0;     //识别错误个数
	int num_true = 0;      //正确识别个数
	test_data.open("E:\\learn\\vsfile\\c++project\\pictureData\\test_num.txt");
	for (int p = 0; p < 200; p++)
	{
		int total = 49;      //方便比大小，设置初值为49
		getline(test_data, tmp2);
		tmp22 = tmp2;    //在切割字符串之前保留，以便后面知晓该字符串是哪个数字的
		if(tmp2.length()==57) tmp2.erase(0,8); //erase函数是用来切割字符串的，这里是切割第0位的后面8位，存剩余的其他位
		else tmp2.erase(0,9);
		train_data.open("E:\\learn\\vsfile\\c++project\\pictureData\\train_num.txt");
		for (int j = 0; j < 1000; j++)         //一个测试样本和所有训练样本对比
		{
			getline(train_data, tmp1);
			tmp11 = tmp1;
			if (tmp1.length() == 57) tmp1.erase(0, 8);
			else tmp1.erase(0, 9);
			if (distance(tmp1, tmp2) < total)  //取最相近的
			{
				refused = false;   //拒绝识别被设置为否，即识别没有被拒绝
				total = distance(tmp1, tmp2);
				tr_num = turn(tmp11[0]);          //记录数字
			}
			else if(distance(tmp1, tmp2) == total && tr_num!= turn(tmp11[0]))  //发现相同相似度，且两者归属的数字不同
			{
				refused = true;   //拒绝识别
				continue;          //循环继续
			}	
		}
		train_data.close();
 
		if (!refused)
		{
			if (tr_num == turn(tmp22[0]))
			{
				//cout << tmp2[0] << endl;
				num_true++;
				cout << "识别为：" << tr_num << endl;
			}
			else
			{
				num_false++;
				cout << "识别错误！" << endl;
			}
		}
		else
		{
			num_refused++;
			cout << "拒绝识别！" << endl;
		}
	}
	test_data.close();
	double t = num_true / 200.0, f = num_false / 200.0, r = num_refused / 200.0;
	cout << "正确率为：" << t << endl;
	cout << "错误率为：" << f << endl;
	cout << "拒绝识别率为：" << r << endl;
}