opencv图像识别英文字符，神经网络识别字母

小君 2023-01-30 17:16:35 962

附件从原文下载即可。

//opencv2.4.9 vs2012 64位
#include <windows.h>
#include <iostream>
#include <fstream>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
char* WcharToChar(const wchar_t* wp)
{
char *m_char;
int len= WideCharToMultiByte(CP_ACP 0 wp wcslen(wp) NULL 0 NULL NULL);
m_char=new char[len 1];
WideCharToMultiByte(CP_ACP 0 wp wcslen(wp) m_char len NULL NULL);
m_char[len]='\0';
return m_char;
}
wchar_t* CharToWchar(const char* c)
{
wchar_t *m_wchar;
int len = MultiByteToWideChar(CP_ACP 0 c strlen(c) NULL 0);
m_wchar=new wchar_t[len 1];
MultiByteToWideChar(CP_ACP 0 c strlen(c) m_wchar len);
m_wchar[len]='\0';
return m_wchar;
}
wchar_t* StringToWchar(const string& s)
{
const char* p=s.c_str();
return CharToWchar(p);
}
int main()
{
const string fileform = "*.png";
const string perfileReadPath = "charSamples";
const int sample_mun_perclass = 20;//训练字符每类数量
const int class_mun = 10 26;//训练字符类数 0-9 A-Z 除了I、O
const int image_cols = 8;
const int image_rows = 16;
string fileReadName
fileReadPath;
char temp[256];
float trainingData[class_mun*sample_mun_perclass][image_rows*image_cols] = {{0}};//每一行一个训练样本
float labels[class_mun*sample_mun_perclass][class_mun]={{0}};//训练样本标签
for(int i = 0; i <= class_mun - 1; i )//不同类
{
//读取每个类文件夹下所有图像
int j = 0;//每一类读取图像个数计数
if (i <= 9)//0-9
{
sprintf(temp "%d" i);
//printf("%d\n" i);
}
else//A-Z
{
sprintf(temp "%c" i 55);
//printf("%c\n" i 55);
}
fileReadPath = perfileReadPath "/" temp "/" fileform;
cout<<"文件夹"<<temp<<endl;
HANDLE hFile;
LPCTSTR lpFileName = StringToWchar(fileReadPath);//指定搜索目录和文件类型，如搜索d盘的音频文件可以是"D:\\*.mp3"
WIN32_FIND_DATA pNextInfo; //搜索得到的文件信息将储存在pNextInfo中;
hFile = FindFirstFile(lpFileName &pNextInfo);//请注意是 &pNextInfo 不是 pNextInfo;
if(hFile == INVALID_HANDLE_VALUE)
{
continue;//搜索失败
}
//do-while循环读取
do
{
if(pNextInfo.cFileName[0] == '.')//过滤.和..
continue;
j ;//读取一张图
//wcout<<pNextInfo.cFileName<<endl;
//printf("%s\n" WcharToChar(pNextInfo.cFileName));
//对读入的图片进行处理
Mat srcImage = imread( perfileReadPath "/" temp "/" WcharToChar(pNextInfo.cFileName) CV_LOAD_IMAGE_GRAYSCALE);
Mat resizeImage;
Mat trainImage;
Mat result;
resize(srcImage resizeImage Size(image_cols image_rows) (0 0) (0 0) CV_INTER_AREA);//使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现
threshold(resizeImage trainImage 0 255 CV_THRESH_BINARY|CV_THRESH_OTSU);
for(int k = 0; k<image_rows*image_cols; k)
{
trainingData[i*sample_mun_perclass (j-1)][k] = (float)trainImage.data[k];
//trainingData[i*sample_mun_perclass (j-1)][k] = (float)trainImage.at<unsigned char>((int)k/8 (int)k%8);//(float)train_image.data[k];
//cout<<trainingData[i*sample_mun_perclass (j-1)][k] <<" "<< (float)trainImage.at<unsigned char>(k/8 k%8)<<endl;
}
}while (FindNextFile(hFile &pNextInfo) && j<sample_mun_perclass);//如果设置读入的图片数量，则以设置的为准，如果图片不够，则读取文件夹下所有图片
}
// Set up training data Mat
Mat trainingDataMat(class_mun*sample_mun_perclass image_rows*image_cols CV_32FC1 trainingData);
cout<<"trainingDataMat——OK！"<<endl;
// Set up label data
for(int i = 0;i <= class_mun-1; i)
{
for(int j = 0;j <= sample_mun_perclass - 1; j)
{
for(int k = 0;k < class_mun; k)
{
if(k == i)
if (k == 18)
{
labels[i*sample_mun_perclass j][1] = 1;
}
else if(k == 24)
{
labels[i*sample_mun_perclass j][0] = 1;
}
else
{
labels[i*sample_mun_perclass j][k] = 1;
}
else
labels[i*sample_mun_perclass j][k] = 0;
}
}
}
Mat labelsMat(class_mun*sample_mun_perclass class_mun CV_32FC1 labels);
cout<<"labelsMat:"<<endl;
ofstream outfile("out.txt");
outfile<<labelsMat;
//cout<<labelsMat<<endl;
cout<<"labelsMat——OK！"<<endl;
//训练代码
cout<<"training start...."<<endl;
CvANN_MLP bp;
// Set up BPNetwork's parameters
CvANN_MLP_Trainparams params;
params.train_method=CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale=0.001;
params.bp_moment_scale=0.1;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS 10000 0.0001); //设置结束条件
//params.train_method=CvANN_MLP_TrainParams::RPROP;
//params.rp_dw0 = 0.1;
//params.rp_dw_plus = 1.2;
//params.rp_dw_minus = 0.5;
//params.rp_dw_min = FLT_EPSILON;
//params.rp_dw_max = 50.;
//Setup the BPNetwork
Mat layerSizes=(Mat_<int>(1 5) << image_rows*image_cols 128 128 128 class_mun);
bp.create(layerSizes CvANN_MLP::SIGMOID_SYM 1.0 1.0);//CvANN_MLP::SIGMOID_SYM
//CvANN_MLP::GAUSSIAN
//CvANN_MLP::IDENTITY
cout<<"training...."<<endl;
bp.train(trainingDataMat labelsMat Mat() Mat() params);
bp.save("../bpcharModel.xml"); //save classifier
cout<<"training finish...bpModel1.xml saved "<<endl;
//测试神经网络
cout<<"测试："<<endl;
Mat test_image = imread("test4.png" CV_LOAD_IMAGE_GRAYSCALE);
Mat test_temp;
resize(test_image test_temp Size(image_cols image_rows) (0 0) (0 0) CV_INTER_AREA);//使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现
threshold(test_temp test_temp 0 255 CV_THRESH_BINARY|CV_THRESH_OTSU);
Mat_<float>sampleMat(1 image_rows*image_cols);
for(int i = 0; i<image_rows*image_cols; i)
{
sampleMat.at<float>(0 i) = (float)test_temp.at<uchar>(i/8 i%8);
}
Mat responseMat;
bp.predict(sampleMat responseMat);
Point maxLoc;
double maxVal = 0;
minMaxLoc(responseMat NULL &maxVal NULL &maxLoc);
if (maxLoc.x <= 9)//0-9
{
sprintf(temp "%d" maxLoc.x);
//printf("%d\n" i);
}
else//A-Z
{
sprintf(temp "%c" maxLoc.x 55);
//printf("%c\n" i 55);
}
cout<<"识别结果："<<temp<<" 相似度:"<<maxVal*100<<"%"<<endl;
imshow("test_image" test_image);
waitKey(0);
return 0;
}

opencv图像识别英文字符，神经网络识别字母(1)

网站首页

返回栏目

opencv图像识别英文字符，神经网络识别字母

猜您喜欢：

相关文章