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// Main entry code OpenCV
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#include <opencv/cv.h>
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#include <opencv/cvaux.h>
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#include "opencv2/ml/ml.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include <windows.h>
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#include <vector>
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#include <iostream>
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#include <cstdlib>
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#include <io.h>
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#include <stdlib.h>
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#include <stdio.h>
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#define HORIZONTAL 1
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#define VERTICAL 0
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using namespace std;
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using namespace cv;
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CvANN_MLP ann;
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//中国车牌
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const char strCharacters[] = {'0','1','2','3','4','5',\
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'6','7','8','9','A','B', 'C', 'D', 'E','F', 'G', 'H', /* 没有I */\
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'J', 'K', 'L', 'M', 'N', /* 没有O */ 'P', 'Q', 'R', 'S', 'T', \
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'U','V', 'W', 'X', 'Y', 'Z'};
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const int numCharacter = 34; /* 没有I和0,10个数字与24个英文字符之和 */
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//以下都是我训练时用到的中文字符数据，并不全面，有些省份没有训练数据所以没有字符
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//有些后面加数字2的表示在训练时常看到字符的一种变形，也作为训练数据存储
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const string strChinese[] = {"zh_cuan" /* 川 */, "zh_e" /* 鄂 */, "zh_gan" /* 赣*/, \
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"zh_hei" /* 黑 */, "zh_hu" /* 沪 */, "zh_ji" /* 冀 */, \
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"zh_jl" /* 吉 */, "zh_jin" /* 津 */, "zh_jing" /* 京 */, "zh_shan" /* 陕 */, \
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"zh_liao" /* 辽 */, "zh_lu" /* 鲁 */, "zh_min" /* 闽 */, "zh_ning" /* 宁 */, \
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"zh_su" /* 苏 */, "zh_sx" /* 晋 */, "zh_wan" /* 皖 */,\
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"zh_yu" /* 豫 */, "zh_yue" /* 粤 */, "zh_zhe" /* 浙 */};
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const int numChinese = 20;
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const int numAll = 54; /* 34+20=54 */
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const int numNeurons = 20;
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const int predictSize = 10;
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//create the accumulation histograms,img is a binary image, t is 水平或垂直
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Mat ProjectedHistogram(Mat img, int t)
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{
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int sz=(t)?img.rows:img.cols;
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Mat mhist=Mat::zeros(1,sz,CV_32F);
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for(int j=0; j<sz; j++){
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Mat data=(t)?img.row(j):img.col(j);
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mhist.at<float>(j)=countNonZero(data); //统计这一行或一列中，非零元素的个数，并保存到mhist中
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}
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//Normalize histogram
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double min, max;
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minMaxLoc(mhist, &min, &max);
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if(max>0)
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mhist.convertTo(mhist,-1 , 1.0f/max, 0);//用mhist直方图中的最大值，归一化直方图
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return mhist;
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}
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Mat features(Mat in, int sizeData){
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//Histogram features
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Mat vhist=ProjectedHistogram(in,VERTICAL);
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Mat hhist=ProjectedHistogram(in,HORIZONTAL);
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//Low data feature
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Mat lowData;
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resize(in, lowData, Size(sizeData, sizeData) );
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//Last 10 is the number of moments components
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int numCols=vhist.cols+hhist.cols+lowData.cols*lowData.cols;
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Mat out=Mat::zeros(1,numCols,CV_32F);
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//Asign values to feature,ANN的样本特征为水平、垂直直方图和低分辨率图像所组成的矢量
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int j=0;
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for(int i=0; i<vhist.cols; i++)
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{
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out.at<float>(j)=vhist.at<float>(i);
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j++;
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}
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for(int i=0; i<hhist.cols; i++)
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{
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out.at<float>(j)=hhist.at<float>(i);
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j++;
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}
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for(int x=0; x<lowData.cols; x++)
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{
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for(int y=0; y<lowData.rows; y++){
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out.at<float>(j)=(float)lowData.at<unsigned char>(x,y);
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j++;
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}
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}
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//if(DEBUG)
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// cout << out << "\n===========================================\n";
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return out;
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}
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void getFiles(string path, vector<string>& files )
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{
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//文件句柄
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long hFile = 0;
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//文件信息
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struct _finddata_t fileinfo;
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string p;
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if((hFile = _findfirst(p.assign(path).append("\\*").c_str(),&fileinfo)) != -1)
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{
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do
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{
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//如果是目录,迭代之
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//如果不是,加入列表
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if((fileinfo.attrib & _A_SUBDIR))
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{
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if(strcmp(fileinfo.name,".") != 0 && strcmp(fileinfo.name,"..") != 0)
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getFiles( p.assign(path).append("\\").append(fileinfo.name), files );
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}
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else
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{
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files.push_back(p.assign(path).append("\\").append(fileinfo.name) );
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}
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}while(_findnext(hFile, &fileinfo) == 0);
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_findclose(hFile);
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}
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}
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void annTrain(Mat TrainData, Mat classes, int nNeruns)
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{
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ann.clear();
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Mat layers(1, 3, CV_32SC1);
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layers.at<int>(0) = TrainData.cols;
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layers.at<int>(1) = nNeruns;
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layers.at<int>(2) = numAll;
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ann.create(layers, CvANN_MLP::SIGMOID_SYM, 1, 1);
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//Prepare trainClases
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//Create a mat with n trained data by m classes
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Mat trainClasses;
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trainClasses.create( TrainData.rows, numAll, CV_32FC1 );
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for( int i = 0; i < trainClasses.rows; i++ )
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{
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for( int k = 0; k < trainClasses.cols; k++ )
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{
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//If class of data i is same than a k class
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if( k == classes.at<int>(i) )
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trainClasses.at<float>(i,k) = 1;
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else
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trainClasses.at<float>(i,k) = 0;
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}
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}
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Mat weights( 1, TrainData.rows, CV_32FC1, Scalar::all(1) );
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//Learn classifier
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ann.train( TrainData, trainClasses, weights );
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}
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int saveTrainData()
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{
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cout << "Begin saveTrainData" << endl;
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Mat classes;
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Mat trainingDataf5;
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Mat trainingDataf10;
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Mat trainingDataf15;
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Mat trainingDataf20;
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vector<int> trainingLabels;
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string path = "train/data/chars_recognise_ann/chars2/chars2";
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for(int i = 0; i < numCharacter; i++)
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{
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cout << "Character: "<< strCharacters[i] << "\n";
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stringstream ss(stringstream::in | stringstream::out);
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ss << path << "/" << strCharacters[i];
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vector<string> files;
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getFiles(ss.str(), files);
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int size = files.size();
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for (int j = 0; j < size; j++)
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{
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cout << files[j].c_str() << endl;
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Mat img = imread(files[j].c_str(), 0);
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Mat f5=features(img, 5);
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Mat f10=features(img, 10);
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Mat f15=features(img, 15);
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Mat f20=features(img, 20);
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trainingDataf5.push_back(f5);
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trainingDataf10.push_back(f10);
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trainingDataf15.push_back(f15);
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trainingDataf20.push_back(f20);
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trainingLabels.push_back(i); //每一幅字符图片所对应的字符类别索引下标
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}
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}
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path = "train/data/chars_recognise_ann/charsChinese/charsChinese";
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for (int i = 0; i < numChinese; i++)
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{
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cout << "Character: "<< strChinese[i] << "\n";
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stringstream ss(stringstream::in | stringstream::out);
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ss << path << "/" << strChinese[i];
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vector<string> files;
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getFiles(ss.str(), files);
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int size = files.size();
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for (int j = 0; j < size; j++)
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{
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cout << files[j].c_str() << endl;
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Mat img = imread(files[j].c_str(), 0);
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Mat f5=features(img, 5);
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Mat f10=features(img, 10);
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Mat f15=features(img, 15);
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Mat f20=features(img, 20);
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trainingDataf5.push_back(f5);
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trainingDataf10.push_back(f10);
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trainingDataf15.push_back(f15);
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trainingDataf20.push_back(f20);
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trainingLabels.push_back(i + numCharacter);
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}
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}
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trainingDataf5.convertTo(trainingDataf5, CV_32FC1);
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trainingDataf10.convertTo(trainingDataf10, CV_32FC1);
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trainingDataf15.convertTo(trainingDataf15, CV_32FC1);
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trainingDataf20.convertTo(trainingDataf20, CV_32FC1);
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Mat(trainingLabels).copyTo(classes);
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FileStorage fs("train/ann_data.xml", FileStorage::WRITE);
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fs << "TrainingDataF5" << trainingDataf5;
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fs << "TrainingDataF10" << trainingDataf10;
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fs << "TrainingDataF15" << trainingDataf15;
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fs << "TrainingDataF20" << trainingDataf20;
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fs << "classes" << classes;
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fs.release();
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cout << "End saveTrainData" << endl;
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return 0;
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}
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void saveModel(int _predictsize, int _neurons)
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{
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FileStorage fs;
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fs.open("train/ann_data.xml", FileStorage::READ);
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Mat TrainingData;
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Mat Classes;
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string training;
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if(1)
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{
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stringstream ss(stringstream::in | stringstream::out);
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ss << "TrainingDataF" << _predictsize;
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training = ss.str();
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}
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fs[training] >> TrainingData;
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fs["classes"] >> Classes;
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//train the Ann
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cout << "Begin to saveModelChar predictSize:" << _predictsize
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<< " neurons:" << _neurons << endl;
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double start = GetTickCount();
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annTrain(TrainingData, Classes, _neurons);
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double end = GetTickCount();
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cout << "GetTickCount:" << (end-start)/1000 << endl;
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cout << "End the saveModelChar" << endl;
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string model_name = "train/ann.xml";
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//if(1)
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//{
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// stringstream ss(stringstream::in | stringstream::out);
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// ss << "ann_prd" << _predictsize << "_neu"<< _neurons << ".xml";
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// model_name = ss.str();
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//}
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FileStorage fsTo(model_name, cv::FileStorage::WRITE);
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ann.write(*fsTo, "ann");
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}
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int main()
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{
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cout << "To be begin." << endl;
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saveTrainData();
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//可根据需要训练不同的predictSize或者neurons的ANN模型
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//for (int i = 2; i <= 2; i ++)
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//{
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// int size = i * 5;
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// for (int j = 5; j <= 10; j++)
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// {
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// int neurons = j * 10;
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// saveModel(size, neurons);
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// }
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//}
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//这里演示只训练model文件夹下的ann.xml，此模型是一个predictSize=10,neurons=40的ANN模型。
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//根据机器的不同，训练时间不一样，但一般需要10分钟左右，所以慢慢等一会吧。
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saveModel(10, 40);
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cout << "To be end." << endl;
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int end;
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cin >> end;
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return 0;
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}
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