【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
#include
#include
#include

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"<

        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<
            //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
            {
                trainingData[i*sample_mun_perclass+(j-1)][k] = (float)trainImage.data[k];
                //trainingData[i*sample_mun_perclass+(j-1)][k] = (float)trainImage.at((int)k/8,(int)k%8);//(float)train_image.data[k];
                //cout<(k/8,k%8)<
            }

        }while (FindNextFile(hFile,&pNextInfo) && j//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！"<

    // 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:"<
    ofstream outfile("out.txt");
    outfile<
    //cout<
    cout<<"labelsMat——OK！"<

    //训练代码

    cout<<"training start...."<
    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...."<
    bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);

    bp.save("../bpcharModel.xml"); //save classifier
    cout<<"training finish...bpModel1.xml saved "<


    //测试神经网络
    cout<<"测试："<
    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
    {
        sampleMat.at<float>(0,i) = (float)test_temp.at(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："<"    相似度:"<"%"<
    imshow("test_image",test_image);
    waitKey(0);

    return 0;
}