#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;




//把影象歸一化為0-255，便於顯示
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;
    }

//轉化給定的影象為行矩陣
 

Mat asRowMatrix(const vector<Mat>& src, int rtype, double alpha = 1, double beta = 0)
    {
    //樣本數量
    size_t n = src.size();
    //如果沒有樣本，返回空矩陣
    if(n == 0)
        return Mat();
    //樣本的維數
    size_t d = src[0].total();

    Mat data(n, d, rtype);
    //拷貝資料
    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);
            }
        // 確保資料能被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);
        //轉化為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演算法保持5主成分分量
    int num_components = 5;

    //執行pca演算法
    PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, num_components);

    //copy  pca演算法結果
    Mat mean = pca.mean.clone();
    Mat eigenvalues = pca.eigenvalues.clone();
    Mat eigenvectors = pca.eigenvectors.clone();

    //均值臉
    imshow("avg", norm_0_255(mean.reshape(1, db[0].rows)));

    //五個特徵臉
    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;
    }