#include "stdafx.h"
#include <opencv2/opencv.hpp>
#include <iostream>
#include <fstream>
#include <sstream>
#include <math.h>
#include <vector>
#include <windows.h>
#include <io.h>
#include <time.h>
using namespace cv;
using namespace std;
#define HORIZONTAL 1
#define VERTICAL 0
CvANN_MLP ann;
const char strCharacters[] = { '0','1','2','3','4','5',\
'6','7','8','9'};
const int numCharacter = 10;
const int numNeurons = 40;
const int predictSize = 10;
//交叉训练 就是拿100个样本处理测试 其余用做训练
//generateRandom(0,100,TrainingData.rows - 1,&markSample);
void generateRandom(int n,int test_num,int min,int max,vector<int>*mark_samples)
{
int range = max - min;
int index = rand() % range + min;
if (mark_samples->at(index) == 0)
{
mark_samples->at(index) = 1;
n++;
}
if (n < test_num)
generateRandom(n,test_num,min,max,mark_samples);
}
vector<string> getFiles(const string &folder,const bool all /* = true */)
{
vector<string> files;
list<string> subfolders;
subfolders.push_back(folder);
while (!subfolders.empty()) {
string current_folder(subfolders.back());
if (*(current_folder.end() - 1) != '/') { //确保地址是 “ ..../*” 结尾的
current_folder.append("/*");
}
else {
current_folder.append("*");
}
subfolders.pop_back();
struct _finddata_t file_info;
long file_handler = _findfirst(current_folder.c_str(),&file_info);
while (file_handler != -1)
{
if (all &&
(!strcmp(file_info.name,".") || !strcmp(file_info.name,".."))) {
if (_findnext(file_handler,&file_info) != 0) break;
continue;
}
if (file_info.attrib & _A_SUBDIR)
{
// it's a sub folder
if (all)
{
// will search sub folder
string folder(current_folder);
folder.pop_back(); // 就是减去最后的* string 的pop_back Delete last character Erases the last character of the string,effectively reducing its length by one.
folder.append(file_info.name);
subfolders.push_back(folder.c_str());
}
}
else
{
// it's a file
string file_path;
// current_folder.pop_back();
file_path.assign(current_folder.c_str()).pop_back();
file_path.append(file_info.name);
files.push_back(file_path);
}
if (_findnext(file_handler,&file_info) != 0) break;
} // while
_findclose(file_handler);
}
return files;
}
//AppendText("output.txt",txt);
void AppendText(string filename,string text)
{
fstream ftxt;
ftxt.open(filename,ios::out | ios::app);
if (ftxt.fail())
{
cout << "创建文件失败!" << endl;
getchar();
}
ftxt << text << endl;
ftxt.close();
}
// ！获取垂直和水平方向直方图
Mat ProjectedHistogram(Mat img,int t)
{
int sz = (t) ? img.rows : img.cols;
Mat mhist = Mat::zeros(1,sz,CV_32F);
for (int j = 0; j<sz; j++) {
Mat data = (t) ? img.row(j) : img.col(j);
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
}
//Normalize histogram
double min,max;
minMaxLoc(mhist,&min,&max);
if (max>0)
mhist.convertTo(mhist,-1,1.0f / max,0);//用mhist直方图中的最大值，归一化直方图
return mhist;
}
Mat features(Mat in,int sizeData)
{
//Histogram features
Mat vhist = ProjectedHistogram(in,VERTICAL);
Mat hhist = ProjectedHistogram(in,HORIZONTAL);
//Low data feature
Mat lowData;
resize(in,lowData,Size(sizeData,sizeData));
//Last 10 is the number of moments components
int numCols = vhist.cols + hhist.cols + lowData.cols*lowData.cols;
//int numCols = vhist.cols + hhist.cols;
Mat out = Mat::zeros(1,numCols,CV_32F);
//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
int j = 0;
for (int i = 0; i<vhist.cols; i++)
{
out.at<float>(j) = vhist.at<float>(i);
j++;
}
for (int i = 0; i<hhist.cols; i++)
{
out.at<float>(j) = hhist.at<float>(i);
j++;
}
for (int x = 0; x<lowData.cols; x++)
{
for (int y = 0; y<lowData.rows; y++) {
out.at<float>(j) = (float)lowData.at<unsigned char>(x,y);
j++;
}
}
//if(DEBUG)
// cout << out << "\n===========================================\n";
return out;
}
void annTrain(Mat TrainData,Mat classes,int nNeruns)
{
ann.clear();
Mat layers(1,3,CV_32SC1);
layers.at<int>(0) = TrainData.cols;
layers.at<int>(1) = nNeruns;
layers.at<int>(2) = numCharacter;
ann.create(layers,CvANN_MLP::SIGMOID_SYM,1,1);
//Prepare trainClases
//Create a mat with n trained data by m classes
Mat trainClasses;
trainClasses.create(TrainData.rows,numCharacter,CV_32FC1);
for (int i = 0; i < trainClasses.rows; i++)
{
for (int k = 0; k < trainClasses.cols; k++)
{
//If class of data i is same than a k class
if (k == classes.at<int>(i))
trainClasses.at<float>(i,k) = 1;
else
trainClasses.at<float>(i,k) = 0;
}
}
Mat weights(1,TrainData.rows,CV_32FC1,Scalar::all(1));
//Learn classifier
// ann.train( TrainData,trainClasses,weights );
//Setup the BPNetwork
// Set up BPNetwork's parameters
CvANN_MLP_TrainParams params;
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = 0.1;
params.bp_moment_scale = 0.1;
//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.;
ann.train(TrainData,Mat(),params);
//Myadd
//ann.save("MyAnn.xml"); //这里训练好了就可以直接保存了
}
int recog(Mat features)
{
int result = -1;
Mat Predict_result(1,CV_32FC1);
ann.predict(features,Predict_result);
Point maxLoc;
double maxVal;
minMaxLoc(Predict_result,&maxVal,&maxLoc);
return maxLoc.x;
}
float ANN_test(Mat samples_set,Mat sample_labels)
{
int correctNum = 0;
float accurate = 0;
for (int i = 0; i < samples_set.rows; i++)
{
int result = recog(samples_set.row(i));
if (result == sample_labels.at<int>(i))
correctNum++;
}
accurate = (float)correctNum / samples_set.rows;
return accurate;
}
int saveTrainData()
{
cout << "Begin saveTrainData" << endl;
Mat classes;
Mat trainingDataf5;
Mat trainingDataf10;
Mat trainingDataf15;
Mat trainingDataf20;
vector<int> trainingLabels;
string path = "charSamples";
for (int i = 0; i < numCharacter; i++)
{
cout << "Character: " << strCharacters[i] << "\n";
stringstream ss(stringstream::in | stringstream::out);
ss << path << "/" << strCharacters[i];
auto files = getFiles(ss.str(),1);
int size = files.size();
for (int j = 0; j < size; j++)
{
cout << files[j].c_str() << endl;
Mat img = imread(files[j].c_str(),0);
Mat f5 = features(img,5);
Mat f10 = features(img,10);
Mat f15 = features(img,15);
Mat f20 = features(img,20);
trainingDataf5.push_back(f5);
trainingDataf10.push_back(f10);
trainingDataf15.push_back(f15);
trainingDataf20.push_back(f20);
trainingLabels.push_back(i); //每一幅字符图片所对应的字符类别索引下标
}
}
trainingDataf5.convertTo(trainingDataf5,CV_32FC1);
trainingDataf10.convertTo(trainingDataf10,CV_32FC1);
trainingDataf15.convertTo(trainingDataf15,CV_32FC1);
trainingDataf20.convertTo(trainingDataf20,CV_32FC1);
Mat(trainingLabels).copyTo(classes);
FileStorage fs("train/features_data.xml",FileStorage::WRITE);
fs << "TrainingDataF5" << trainingDataf5;
fs << "TrainingDataF10" << trainingDataf10;
fs << "TrainingDataF15" << trainingDataf15;
fs << "TrainingDataF20" << trainingDataf20;
fs << "classes" << classes;
fs.release();
cout << "End saveTrainData" << endl;
return 0;
}
void ANN_Cross_Train_and_Test(int Imagsize,int Layers )
{
String training;
Mat TrainingData;
Mat Classes;
FileStorage fs;
fs.open("train/features_data.xml",FileStorage::READ);
cout << "Begin to ANN_Cross_Train_and_Test " << endl;
char *txt = new char[50];
sprintf(txt,"交叉训练，特征维度%d,网络层数%d",40 + Imagsize * Imagsize,Layers);
AppendText("output.txt",txt);
cout << txt << endl;
stringstream ss(stringstream::in | stringstream::out);
ss << "TrainingDataF" << Imagsize;
training = ss.str();
fs[training] >> TrainingData;
fs["classes"] >> Classes;
fs.release();
float result = 0.0;
srand(time(NULL));
vector<int> markSample(TrainingData.rows,0);
//void generateRandom(int n,vector<int>*mark_samples)
//随机的令markSample中的100个值为1
generateRandom(0,&markSample);
Mat train_set,train_labels;
Mat sample_set,sample_labels;
for (int i = 0; i < TrainingData.rows; i++)
{
if (markSample[i] == 1)
{
sample_set.push_back(TrainingData.row(i));
sample_labels.push_back(Classes.row(i));
}
else
{
train_set.push_back(TrainingData.row(i));
train_labels.push_back(Classes.row(i));
}
}
annTrain(train_set,train_labels,Layers);
result = ANN_test(sample_set,sample_labels);
sprintf(txt,"正确率%f\n",result);
cout << txt << endl;
AppendText("output.txt",txt);
cout << "End the ANN_Cross_Train_and_Test" << endl;
cout << endl;
}
void ANN_test_Main()
{
int DigitSize[4] = { 5,10,15,20};
int LayerNum[14] = { 10,20,30,40,50,60,70,80,90,120,150,200,500 };
for (int i = 0; i < 4; i++)
{
for (int j = 0; j < 14; j++)
{
ANN_Cross_Train_and_Test(DigitSize[i],LayerNum[j]);
}
}
}
void ANN_saveModel(int _predictsize,int _neurons)
{
FileStorage fs;
fs.open("train/features_data.xml",FileStorage::READ);
Mat TrainingData;
Mat Classes;
string training;
if (1)
{
stringstream ss(stringstream::in | stringstream::out);
ss << "TrainingDataF" << _predictsize;
training = ss.str();
}
fs[training] >> TrainingData;
fs["classes"] >> Classes;
//train the Ann
cout << "Begin to saveModelChar predictSize:" << _predictsize
<< " neurons:" << _neurons << endl;
annTrain(TrainingData,Classes,_neurons);
cout << "End the saveModelChar" << endl;
string model_name = "train/ann10_40.xml";
//if(1)
//{
// stringstream ss(stringstream::in | stringstream::out);
// ss << "ann_prd" << _predictsize << "_neu"<< _neurons << ".xml";
// model_name = ss.str();
//}
FileStorage fsTo(model_name,cv::FileStorage::WRITE);
ann.write(*fsTo,"ann");
}
int main()
{
cout << "To be begin." << endl;
saveTrainData();
//ANN_saveModel(10,40);
ANN_test_Main();
cout << "To be end." << endl;
int end;
cin >> end;
return 0;
}
这里是神经网络训练部分，采用的是交叉训练。下图是交叉训练的一些结果：
可以看出，特征维度并不是越多越好，隐含层也不是越多越好，这个没有具体的定论，只有通过交叉训练的结果来看，然后在后面的识别中选择正确率高的特征维度和隐含层数，还要兼顾时间效率。然后保存xml文件以后就可以直接使用这个xml文件来识别。具体的识别见我上一篇博客。
哦，忘记加了，训练的图片，都是20*20的图片。下面是路径：
