opencv 人脸识别 训练和识别
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来自:http://blog.csdn.net/abcjennifer/article/details/20446077
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+ 2.4.6.0
特征:eigenface
Input:一个人脸,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
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
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
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%
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