Ronny大神曾在OpenCV进阶之路：神经网络识别车牌字符写过有关用神经网络识别车牌字符的文章，但无奈仅仅给出了样本，没有详细的代码，在这里补足这个遗憾。

opencv环境请自行配置。因为用到了MFC类库查找文件夹下的文件夹，请选择使用共享的MFC类库。

下面是代码：#include <afx.h> const char*mlpmodel="ann.xml"; //中国车牌 const char strCharacters[] = {'0','1','2','3','4','5',\'6','7','8','9','A','B', 'C', 'D', 'E','F', 'G', 'H', /* 没有I */\'J', 'K', 'L', 'M', 'N', /* 没有O */ 'P', 'Q', 'R', 'S', 'T', \'U','V', 'W', 'X', 'Y', 'Z'}; void calcGradientFeat(const Mat& imgSrc, vector<float>& feat)  {     float sumMatValue(const Mat& image); // 计算图像中像素灰度值总和         Mat image;     cvtColor(imgSrc,image,CV_BGR2GRAY);     resize(image,image,Size(8,16));         // 计算x方向和y方向上的滤波     float mask[3][3] = { { 1, 2, 1 }, { 0, 0, 0 }, { -1, -2, -1 } };    Mat y_mask = Mat(3, 3, CV_32F, mask) / 8;     Mat x_mask = y_mask.t(); // 转置     Mat sobelX, sobelY;    filter2D(image, sobelX, CV_32F, x_mask);     filter2D(image, sobelY, CV_32F, y_mask);    sobelX = abs(sobelX);     sobelY = abs(sobelY);    float totleValueX = sumMatValue(sobelX);     float totleValueY = sumMatValue(sobelY);    // 将图像划分为4*2共8个格子。计算每一个格子里灰度值总和的百分比     for (int i = 0; i < image.rows; i = i + 4)     {         for (int j = 0; j < image.cols; j = j + 4)         {             Mat subImageX = sobelX(Rect(j, i, 4, 4));             feat.push_back(sumMatValue(subImageX) / totleValueX);             Mat subImageY= sobelY(Rect(j, i, 4, 4));             feat.push_back(sumMatValue(subImageY) / totleValueY);         }     }  }   float sumMatValue(const Mat& image)   {       float sumValue = 0;       int r = image.rows;       int c = image.cols;       if (image.isContinuous())       {           c = r*c;           r = 1;          }       for (int i = 0; i < r; i++)       {           const uchar* linePtr = image.ptr<uchar>(i);           for (int j = 0; j < c; j++)           {               sumValue += linePtr[j];           }       }       return sumValue;   } void getFileFromDir(const char *directorypath,vector<string> &vfiles) {CFileFind finder;string ext2find=directorypath;ext2find+="\\*.png";bool bResult=finder.FindFile(ext2find.c_str());if(!bResult){return ;}while (bResult){bResult = finder.FindNextFile();if (finder.IsDots()||finder.IsDirectory()) continue;string str(finder.GetFilePath().GetBuffer(finder.GetFilePath().GetLength()));vfiles.push_back(str); } } void readSample(const char *directorypath,Mat &samples,Mat &labels) {cout<<"start reading characters:"<<endl; for(int i=0;i<sizeof(strCharacters);i++){ // cout<<"reading:"<<strCharacters[i]<<endl;string subdir=directorypath;subdir=subdir+"\\"+strCharacters[i];vector<string> vfiles;getFileFromDir(subdir.c_str(),vfiles);int stotal=0;for(vector<string>::iterator it=vfiles.begin();it!=vfiles.end();it++){   Mat img=imread(*it);Mat m(img.rows,img.cols,CV_32FC1);for(int j=0;j<img.rows;j++)for(int k=0;k<img.cols;k++)m.at<float>(j,k)=img.at<uchar>(j,k);resize(m,m,Size(4,8));m=m.reshape(1,1);normalize(m,m);samples.push_back(m);Mat fl=Mat::zeros(1,34,CV_32FC1);fl.at<float>(0,i)=1;labels.push_back(fl);}}cout<<"good,reading characters finished!"<<endl; } void MLPTrain(Mat &train,Mat &trainLabel) {CvANN_MLP NeuralNetworks;std::vector<int> LayerSizes;LayerSizes.push_back(train.cols);    // input layerLayerSizes.push_back(train.cols+trainLabel.cols);// hidden layer has neuronsLayerSizes.push_back(trainLabel.cols);   // output layer// Activate functionint ActivateFunc = CvANN_MLP::SIGMOID_SYM;double Alpha = 1;double Beta = 1;// create the networkNeuralNetworks.create(cv::Mat(LayerSizes), ActivateFunc, Alpha, Beta);// Training ParamsCvANN_MLP_TrainParams TrainParams;TrainParams.train_method = CvANN_MLP_TrainParams::BACKPROP;TrainParams.bp_dw_scale = 0.0001;TrainParams.bp_moment_scale = 0;// iteration numberCvTermCriteria TermCrlt;TermCrlt.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;TermCrlt.epsilon = 0.0001f;TermCrlt.max_iter = 1000;TrainParams.term_crit = TermCrlt;// Training the networkscout<<"starting mlp training"<<endl;NeuralNetworks.train(train,trainLabel, cv::Mat(), cv::Mat(), TrainParams);NeuralNetworks.save(mlpmodel);cout<<"mlp train finished"<<endl; } void MLPTest(Mat &test,Mat &testLabel) {CvANN_MLP NeuralNetworks;NeuralNetworks.load(mlpmodel);int total=0;int right=0,error=0;while((bool)(total<test.rows)){Mat m=test.row(total);Mat nearest(1, 10, CV_32FC1, Scalar(0));NeuralNetworks.predict(m, nearest);//char label=testLabel.at<char>(total);Point maxLoc;minMaxLoc(nearest, NULL, NULL, NULL, &maxLoc);char ret=maxLoc.x;//char label=testLabel.at<float>(total,0);char label=0;for(int i=0;i<34;i++){if(testLabel.at<float>(total,i)==1){label=i;break;}}if(ret==label)right++;elseerror++;total++;}cout<<"precision"<<right*1.0/total<<endl; } int main() {Mat Sameples,Labels;readSample("charSamples",Sameples,Labels); // MLPTrain(Sameples,Labels);MLPTest(Sameples,Labels);return 0; }

转载于:https://www.cnblogs.com/bhlsheji/p/5396918.html

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