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。
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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。
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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);
- 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：
- 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)；
- 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：
- 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
