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//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;}