C++編程模板匹配超詳細的識別手寫數字實現示例

#include<iostream>
#include<fstream>
#include<opencv2/opencv.hpp>
#include<opencv2/highgui.hpp>
#include<opencv2/core.hpp>
#include<io.h>                          //api和結構體
#include<string.h>
#include<string>
using namespace std;
using namespace cv;
void ergodicTest(string filename, string name);    //遍歷函數
string Image_Compression(string imgpath);          //壓縮圖片並返回字符串
int distance(string str1, string str2);            //對比函數不一樣的位數
void compare();
int turn(char a);
void main()
{
	const char* filepath = "E:\\learn\\vsfile\\c++project\\pictureData\\train-images";     
	ergodicTest(filepath,"train_num.txt");         //處理訓練集
	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)       //遍歷並把路徑存到files
{
	string firstfilename = filename + "\\*.bmp";
	struct _finddata_t fileinfo;
	intptr_t handle;            //不能用long，因為精度問題會導致訪問沖突,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)   //輸入圖片地址返回圖片二值像素字符
{
	Mat Image = imread(imgpath);               //輸入的圖片
	cvtColor(Image, Image, COLOR_BGR2GRAY);
	int Matrix[28][28];                        //將digitization轉化為字符串類型
	for (int row = 0; row < Image.rows; row++)  //把圖片的像素點傳給數組
		for (int col = 0; col < Image.cols; col++)
		{
			Matrix[row][col] = Image.at<uchar>(row, col);
		}
	string img_str = "";                   //用來存儲結果字符串
	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';    //將28*28的圖片轉化為7*7即壓縮
		else img_str += '0';
		if (k % 7 == 0)
		{
			x += 4;
			y = 0;
		}
	}
	return img_str;
}
 
int distance(string str1, string str2)  //比對兩個字符串有多少個不一樣
{
	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;//建立讀文件流
	ifstream test_data;
	string tmp1 = "";         //從train中取數據存在tmp1
	string tmp11 = "";
	string tmp2 = "";         //從test中取
	string tmp22 = "";
	bool refused = false; //拒絕識別標志
	int tr_num = 0;       //用來存儲最小值的文件名(訓練集)
	int num_refused = 0;   //拒絕識別個數
	int num_false = 0;     //識別錯誤個數
	int num_true = 0;      //正確識別個數
	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，設置初值為49
		getline(test_data, tmp2);
		tmp22 = tmp2;    //在切割字符串之前保留，以便後面知曉該字符串是哪個數字的
		if(tmp2.length()==57) tmp2.erase(0,8); //erase函數是用來切割字符串的，這裡是切割第0位的後面8位，存剩餘的其他位
		else tmp2.erase(0,9);
		train_data.open("E:\\learn\\vsfile\\c++project\\pictureData\\train_num.txt");
		for (int j = 0; j < 1000; j++)         //一個測試樣本和所有訓練樣本對比
		{
			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;   //拒絕識別被設置為否，即識別沒有被拒絕
				total = distance(tmp1, tmp2);
				tr_num = turn(tmp11[0]);          //記錄數字
			}
			else if(distance(tmp1, tmp2) == total && tr_num!= turn(tmp11[0]))  //發現相同相似度，且兩者歸屬的數字不同
			{
				refused = true;   //拒絕識別
				continue;          //循環繼續
			}	
		}
		train_data.close();
 
		if (!refused)
		{
			if (tr_num == turn(tmp22[0]))
			{
				//cout << tmp2[0] << endl;
				num_true++;
				cout << "識別為：" << tr_num << endl;
			}
			else
			{
				num_false++;
				cout << "識別錯誤！" << endl;
			}
		}
		else
		{
			num_refused++;
			cout << "拒絕識別！" << endl;
		}
	}
	test_data.close();
	double t = num_true / 200.0, f = num_false / 200.0, r = num_refused / 200.0;
	cout << "正確率為：" << t << endl;
	cout << "錯誤率為：" << f << endl;
	cout << "拒絕識別率為：" << r << endl;
}