∙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 plaincopyprint?1.void resizeandtogray(char* dir,int K, vector<Mat> &images, vector<int> &labels,2. vector<Mat> &testimages, vector<int> &testlabels)3.{4. IplImage* standard = cvLoadImage("D:\\privacy\\picture\\photo\\2.jpg",CV_LOAD_IMAGE_GRAYSCALE);5. string cur_dir;6.char id[5];7.int i,j;8. for(int i=1; i<=K; i++)9. {10. cur_dir = dir;11. cur_dir.append("gray\\");12. _itoa(i,id,10);13. cur_dir.append(id);14. const char* dd = cur_dir.c_str();15. CStatDir statdir;16. if (!statdir.SetInitDir(dd))17. {18. puts("Dir not exist");19. return;20. }21. cout<<"Processing samples in Class "<<i<<endl;22. vector<char*>file_vec = statdir.BeginBrowseFilenames("*.*");23. for (j=0;j<file_vec.size();j++)24. {25. IplImage* cur_img = cvLoadImage(file_vec[j],CV_LOAD_IMAGE_GRAYSCALE);26. cvResize(cur_img,standard,CV_INTER_AREA);27. Mat cur_mat = cvarrToMat(standard,true),des_mat;28. cv::normalize(cur_mat,des_mat,0, 255, NORM_MINMAX, CV_8UC1);29. cvSaveImage(file_vec[j],cvCloneImage(&(IplImage) des_mat));30. if(j!=file_vec.size())31. {32. images.push_back(des_mat);33. labels.push_back(i);34. }35. else36. {37. testimages.push_back(des_mat);38. testlabels.push_back(i);39. }40. }41. cout<<file_vec.size()<<" images."<<endl;42. }43.}并在main中调用：[cpp]view plaincopyprint?1.int main( )2.{3. CvCapture* capture = 0;4. Mat frame, frameCopy, image;5. string inputName;6.int mode;7.8.char dir[256] = "D:\\Courses\\CV\\Face_recognition\\pic\\";9. //preprocess_trainingdata(dir,K); //face_detection and extract to file10. vector<Mat> images,testimages;11. vector<int> labels,testlabels;12. resizeandtogray(dir,K,images,labels,testimages,testlabels); //togray,normalize and resize13.14. system("pause");15. return 0;16.}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 plaincopyprint?1.Ptr<FaceRecognizer> Recognition(vector<Mat> images, vector<int> labels,2. vector<Mat> testimages, vector<int> testlabels)3.{4. Ptr<FaceRecognizer> model = createEigenFaceRecognizer(10);//10 Principal components5. cout<<"train"<<endl;6. model->train(images,labels);7.int i,acc=0,predict_l;8. for (i=0;i<testimages.size();i++)9. {10. predict_l = model->predict(testimages[i]);11. if(predict_l != testlabels[i])12. {13. cout<<"An error in recognition: sample "<<i+1<<", predict "<<14. predict_l<<", groundtruth "<<testlabels[i]<<endl;15. imshow("error 1",testimages[i]);16. waitKey();17. }18. else19. acc++;20. }21. cout<<"Recognition Rate: "<<acc*1.0/testimages.size()<<endl;22. return model;23.}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 plaincopyprint?1.int main( )2.{3. CvCapture* capture = 0;4. Mat frame, frameCopy, image;5. string inputName;6.int mode;7.8.char dir[256] = "D:\\Courses\\CV\\Face_recognition\\pic\\";9. //preprocess_trainingdata(dir,K); //face_detection and extract to file10. vector<Mat> images,testimages;11. vector<int> labels,testlabels;12. //togray, normalize and resize; load to images,labels,testimages,testlabels13. resizeandtogray(dir,K,images,labels,testimages,testlabels);14. //recognition15. Ptr<FaceRecognizer> model = Recognition(images,labels,testimages,testlabels);16.char* dirmodel = new char [256];17. strcpy(dirmodel,dir); strcat(dirmodel,"model.out");18.FILE* f = fopen(dirmodel,"w");19. fwrite(model,sizeof(model),1,f);20. system("pause");21. return 0;22.}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：/detail/abcjennifer/7047853关于Computer Vision更多的学习资料将继续更新，敬请关注本博客和新浪微博RachelZhang。