#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/contrib/contrib.hpp"#include <iostream>
#include <fstream>
#include <sstream>using namespace cv;
using namespace std;//把图像归一化为0-255，便于显示
Mat norm_0_255(const Mat& src){Mat dst;switch(src.channels()){case 1:cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);break;case 3:cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3);break;default:src.copyTo(dst);break;}return dst;}//转化给定的图像为行矩阵
Mat asRowMatrix(const vector<Mat>& src, int rtype, double alpha = 1, double beta = 0){//样本数量
    size_t n = src.size();//如果没有样本，返回空矩阵
    if(n == 0)return Mat();//样本的维数
    size_t d = src[0].total();Mat data(n, d, rtype);//拷贝数据
    for(int i = 0; i < n; i++){if(src[i].empty()) {string error_message = format("Image number %d was empty, please check your input data.", i);CV_Error(CV_StsBadArg, error_message);}// 确保数据能被reshape
        if(src[i].total() != d) {string error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src[i].total());CV_Error(CV_StsBadArg, error_message);}Mat xi = data.row(i);//转化为1行，n列的格式
        if(src[i].isContinuous()){src[i].reshape(1, 1).convertTo(xi, rtype, alpha, beta);} else {src[i].clone().reshape(1, 1).convertTo(xi, rtype, alpha, beta);}}return data;}int main(int argc, const char *argv[]){vector<Mat> db;string prefix = "../att_faces/";db.push_back(imread(prefix + "s1/1.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/2.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/3.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/4.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/5.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/6.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/7.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/8.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/9.pgm", IMREAD_GRAYSCALE));db.push_back(imread(prefix + "s1/10.pgm", IMREAD_GRAYSCALE));// Build a matrix with the observations in row:
    Mat data = asRowMatrix(db, CV_32FC1);// PCA算法保持5主成分分量
    int num_components = 5;//执行pca算法
    PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, num_components);//copy  pca算法结果
    Mat mean = pca.mean.clone();Mat eigenvalues = pca.eigenvalues.clone();Mat eigenvectors = pca.eigenvectors.clone();//均值脸
    imshow("avg", norm_0_255(mean.reshape(1, db[0].rows)));//五个特征脸
    imshow("pc1", norm_0_255(pca.eigenvectors.row(0)).reshape(1, db[0].rows));imshow("pc2", norm_0_255(pca.eigenvectors.row(1)).reshape(1, db[0].rows));imshow("pc3", norm_0_255(pca.eigenvectors.row(2)).reshape(1, db[0].rows));imshow("pc4", norm_0_255(pca.eigenvectors.row(3)).reshape(1, db[0].rows));imshow("pc5", norm_0_255(pca.eigenvectors.row(4)).reshape(1, db[0].rows));while(1)waitKey(0);// Success!
    return 0;}