opencv 人脸识别 （二）训练和识别

上一篇中我们对训练数据做了一些预处理，检测出人脸并保存在\pic\color\x文件夹下（x=1,2,3,...类别号），本文做训练和识别。为了识别，首先将人脸训练数据 转为灰度、对齐、归一化，再放入分类器（EigenFaceRecognizer）,最后用训练出的model进行predict。

-----------------------------------------

环境：vs2010+opencv 2.4.6.0

特征：eigenface

Input：一个人脸数据库，15个人，每人20个样本（左右）。

Output：人脸检测，并识别出每张检测到的人脸。

-----------------------------------------

1. 为训练数据预处理（ 转为灰度、对齐、归一化 ）

转为灰度和对齐是后面做训练时EigenFaceRecognizer的要求；

归一化是防止光照带来的影响

在上一篇的 2.2 Prehelper.cpp文件中加入函数

void resizeandtogray(char* dir,int k, vector<Mat> &images, vector<int> &labels,
vector<Mat> &testimages, vector<int> &testlabels);

[cpp] view plain copy







void resizeandtogray(char* dir,int K, vector<Mat> &images, vector<int> &labels,

vector<Mat> &testimages, vector<int> &testlabels)

{

IplImage* standard = cvLoadImage("D:\\privacy\\picture\\photo\\2.jpg",CV_LOAD_IMAGE_GRAYSCALE);

string cur_dir;

char id[5];

int i,j;

for(int i=1; i<=K; i++)

{

cur_dir = dir;

cur_dir.append("gray\\");

_itoa(i,id,10);

cur_dir.append(id);

const char* dd = cur_dir.c_str();

CStatDir statdir;

if (!statdir.SetInitDir(dd))

{

puts("Dir not exist");

return;

}

cout<<"Processing samples in Class "<<i<<endl;

vector<char*>file_vec = statdir.BeginBrowseFilenames("*.*");

for (j=0;j<file_vec.size();j++)

{

IplImage* cur_img = cvLoadImage(file_vec[j],CV_LOAD_IMAGE_GRAYSCALE);

cvResize(cur_img,standard,CV_INTER_AREA);

Mat cur_mat = cvarrToMat(standard,true),des_mat;

cv::normalize(cur_mat,des_mat,0, 255, NORM_MINMAX, CV_8UC1);

cvSaveImage(file_vec[j],cvCloneImage(&(IplImage) des_mat));

if(j!=file_vec.size())

{

images.push_back(des_mat);

labels.push_back(i);

}

else

{

testimages.push_back(des_mat);

testlabels.push_back(i);

}

}

cout<<file_vec.size()<<" images."<<endl;

}

}

并在main中调用：

[cpp] view plain copy







int main( )

{

CvCapture* capture = 0;

Mat frame, frameCopy, image;

string inputName;

int mode;

char dir[256] = "D:\\Courses\\CV\\Face_recognition\\pic\\";

//preprocess_trainingdata(dir,K); //face_detection and extract to file

vector<Mat> images,testimages;

vector<int> labels,testlabels;

resizeandtogray(dir,K,images,labels,testimages,testlabels); //togray, normalize and resize

system("pause");

return 0;

}

2. 训练

有了vector<Mat> images,testimages; vector<int> labels,testlabels; 可以开始训练了，我们采用EigenFaceRecognizer建模。

在Prehelper.cpp中加入函数

Ptr<FaceRecognizer> Recognition(vector<Mat> images, vector<int> labels,vector<Mat> testimages, vector<int> testlabels)；

[cpp] view plain copy







Ptr<FaceRecognizer> Recognition(vector<Mat> images, vector<int> labels,

vector<Mat> testimages, vector<int> testlabels)

{

Ptr<FaceRecognizer> model = createEigenFaceRecognizer(10);//10 Principal components

cout<<"train"<<endl;

model->train(images,labels);

int i,acc=0,predict_l;

for (i=0;i<testimages.size();i++)

{

predict_l = model->predict(testimages[i]);

if(predict_l != testlabels[i])

{

cout<<"An error in recognition: sample "<<i+1<<", predict "<<

predict_l<<", groundtruth "<<testlabels[i]<<endl;

imshow("error 1",testimages[i]);

waitKey();

}

else

acc++;

}

cout<<"Recognition Rate: "<<acc*1.0/testimages.size()<<endl;

return model;

}

Recognization()输出分错的样本和正确率，最后返回建模结果Ptr<FaceRecognizer> model

主函数改为：

[cpp] view plain copy







int main( )

{

CvCapture* capture = 0;

Mat frame, frameCopy, image;

string inputName;

int mode;

char dir[256] = "D:\\Courses\\CV\\Face_recognition\\pic\\";

//preprocess_trainingdata(dir,K); //face_detection and extract to file

vector<Mat> images,testimages;

vector<int> labels,testlabels;

//togray, normalize and resize; load to images,labels,testimages,testlabels

resizeandtogray(dir,K,images,labels,testimages,testlabels);

//recognition

Ptr<FaceRecognizer> model = Recognition(images,labels,testimages,testlabels);

char* dirmodel = new char [256];

strcpy(dirmodel,dir); strcat(dirmodel,"model.out");

FILE* f = fopen(dirmodel,"w");

fwrite(model,sizeof(model),1,f);

system("pause");

return 0;

}

最终结果：一个错分样本，正确率93.3%





文章所用代码打包链接：http://download.csdn.net/detail/abcjennifer/7047853

from: http://blog.csdn.net/abcjennifer/article/details/20446077