【opencv】神经网络识别字母+数字

继承自本人博客：

http://blog.csdn.net/qq_15947787/article/details/51385861

原文只是识别数字0-9，简单修改后可以识别24个字母（除了I，O）与数字。

把0与O看成一类，1与I看成一类

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 MatMat 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;}