opencv学习之路（40）、人脸识别算法——EigenFace、FisherFace、LBPH
1、人脸识别算法之特征脸方法（Eigenface）
一、原理介绍及数据收集
特征脸方法主要是基于PCA降维实现。算法
详细介绍和主要思想能够参考数据库
http://blog.csdn.net/u010006643/article/details/46417127app
上述博客的人脸数据库打不开了，你们能够去下面这个博客下载ORL人脸数据库测试
http://blog.csdn.net/xdzzju/article/details/50445160spa
下载后，ORL人脸数据库有40我的，每人10张照片。.net
二、流程
三、相关图示
四、代码
1 #include <opencv2/opencv.hpp> 2 #include <opencv2/face.hpp> 3 4 using namespace cv; 5 using namespace cv::face; 6 using namespace std; 7 8 //对原图归一化 9 Mat normal(Mat src, Mat dst) { 10 if (src.channels() == 1)//若原图单通道 11 normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1); 12 else //不然，原图三通道 13 normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3); 14 return dst; 15 } 16 17 void main() { 18 //读取文件，转换为数据流 19 string filename = string("at.txt"); 20 ifstream file(filename.c_str(), ifstream::in); 21 if (!file) 22 cout << "error" << endl; 23 24 string line, path, classlabel; 25 vector<Mat>image; 26 vector<int>labels; 27 char separator = ';'; 28 while (getline(file,line)) 29 { 30 stringstream liness(line); 31 getline(liness, path, separator); 32 getline(liness, classlabel); 33 if (!path.empty()&&!classlabel.empty()) 34 { 35 //cout << "path:" << path<< endl; 36 image.push_back(imread(path, 0)); 37 labels.push_back(atoi(classlabel.c_str())); 38 } 39 } 40 41 if (image.size() < 1 || labels.size() < 1) 42 cout << "invalid image path..." << endl; 43 44 int height = image[0].rows; 45 int width = image[0].cols; 46 //cout << "height:" << height << ",width:" << width<<endl; 47 48 //最后一我的为测试样本 49 Mat testSample = image[image.size() - 1]; 50 int testLabel = labels[labels.size() - 1]; 51 image.pop_back(); 52 labels.pop_back(); 53 54 //训练 55 Ptr<BasicFaceRecognizer>model = createEigenFaceRecognizer(); 56 model->train(image, labels); 57 58 //识别 59 int predictLabel = model->predict(testSample); 60 cout << "actual label:" << testLabel << ",predict label:" << predictLabel << endl; 61 62 //得到特征值，特征向量，均值 平均脸 63 Mat eigenvalues = model->getEigenValues(); 64 Mat eigenvectors = model->getEigenVectors(); 65 Mat mean = model->getMean(); 66 Mat meanFace = mean.reshape(1,height); 67 Mat dst; 68 dst= normal(meanFace,dst); 69 imshow("Mean Face", dst); 70 71 //特征脸 72 for (int i = 0; i < min(10,eigenvectors.cols); i++) 73 { 74 Mat ev = eigenvectors.col(i).clone(); 75 Mat eigenFace = ev.reshape(1, height); 76 Mat grayscale; 77 grayscale = normal(eigenFace, grayscale); 78 Mat colorface; 79 applyColorMap(grayscale, colorface, COLORMAP_BONE); 80 char* winTitle = new char[128]; 81 sprintf(winTitle, "eigenface_%d", i); 82 imshow(winTitle, colorface); 83 } 84 85 //重建人脸 86 for (int num = min(10, eigenvectors.cols); num < min(300, eigenvectors.cols); num+=15) 87 { 88 Mat evs = Mat(eigenvectors, Range::all(), Range(0, num)); 89 Mat projection = LDA::subspaceProject(evs, mean, image[0].reshape(1, 1)); 90 Mat reconstruction= LDA::subspaceReconstruct(evs, mean, projection); 91 92 Mat result = reconstruction.reshape(1, height); 93 reconstruction = normal(result, reconstruction); 94 char* winTitle = new char[128]; 95 sprintf(winTitle, "recon_face_%d", num); 96 imshow(winTitle, reconstruction); 97 } 98 99 waitKey(0); 100 }
2、FisherFace（LDA线性判别分析）
一、理论介绍
http://blog.csdn.net/feirose/article/details/395529973d
二、流程
三、PCA和LDA的对比
四、代码（与特征脸代码几乎一致）
此处只列出修改部分code
55行模型训练 Ptr<BasicFaceRecognizer>model = createFisherFaceRecognizer();
72行显示特征脸 for (int i = 0; i < min(16,eigenvectors.cols); i++)
Mat ev = eigenvectors.col(i).clone();
86行重建人脸 for (int num = 0; num < min(16, eigenvectors.cols); num++)
3、LBPH
一、原理介绍
你们能够参考http://blog.csdn.net/xiaomaishiwoa/article/details/46640377orm
2、流程
三、代码
#include <opencv2/opencv.hpp> #include <opencv2/face.hpp> using namespace cv; using namespace cv::face; using namespace std; //对原图归一化 Mat normal(Mat src, Mat dst) { if (src.channels() == 1)//若原图单通道 normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1); else //不然，原图三通道 normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3); return dst; } void main() { //读取文件，转换为数据流 string filename = string("at.txt"); ifstream file(filename.c_str(), ifstream::in); if (!file) cout << "error" << endl; string line, path, classlabel; vector<Mat>image; vector<int>labels; char separator = ';'; while (getline(file,line)) { stringstream liness(line); getline(liness, path, separator); getline(liness, classlabel); if (!path.empty()&&!classlabel.empty()) { //cout << "path:" << path<< endl; image.push_back(imread(path, 0)); labels.push_back(atoi(classlabel.c_str())); } } if (image.size() < 1 || labels.size() < 1) cout << "invalid image path..." << endl; int height = image[0].rows; int width = image[0].cols; //cout << "height:" << height << ",width:" << width<<endl; //最后一我的为测试样本 Mat testSample = image[image.size() - 1]; int testLabel = labels[labels.size() - 1]; image.pop_back(); labels.pop_back(); //训练 Ptr<LBPHFaceRecognizer>model = createLBPHFaceRecognizer(); model->train(image, labels); //识别 int predictLabel = model->predict(testSample); cout << "actual label:" << testLabel << ",predict label:" << predictLabel << endl; //打印参数 int radius = model->getRadius(); //中心像素点到周围像素点的距离 int neibs = model->getNeighbors(); //周围像素点的个数 int grad_x = model->getGridX(); //将一张图片在x方向分红几块 int grad_y = model->getGridY(); //将一张图片在y方向分红几块 double t = model->getThreshold(); //类似度阈值 cout << "radius:" << radius << endl; cout << "neibs:" << neibs << endl; cout << "grad_x:" << grad_x << endl; cout << "grad_y:" << grad_y << endl; cout << "threshold:" << t<<endl; waitKey(0); }