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 //否则，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     //最后一个人为测试样本
 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     //获得特征值，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 }

#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 //否则，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;//最后一个人为测试样本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方向分成几块int grad_y = model->getGridY();        //将一张图片在y方向分成几块double t = model->getThreshold();    //相似度阈值    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);
}