[generate traindatabase.xml]

#include "opencv2\core\core.hpp"
#include "opencv2\contrib\contrib.hpp"
#include "opencv2\highgui\highgui.hpp"

#include <iostream>
#include <fstream>
#include <sstream>

using namespace cv;
using namespace std;

static Mat norm_0_255(InputArray _src) {
    Mat src = _src.getMat();
    // Create and return normalized image;
    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;
}
static void read_csv(const string& filename, vector<Mat> &images, vector<int> & labels, char separator = ';') {
    std::ifstream file(filename.c_str(), ifstream::in);
    if (!file) {
        string error_message = "No valid input file was given, please check the given filename.";
        CV_Error(CV_StsBadArg, error_message);
    }
    string line, path, classlabel;
    while (getline(file,line)) {
        stringstream liness(line);
        getline(liness, path, separator);
        getline(liness, classlabel);
        if (!path.empty() && !classlabel.empty()) {
            Mat t = imread(path,0);
            if ( t.rows < 1) {
                continue;
            }
            else {
                cout << path << endl;
                images.push_back(t);
                labels.push_back(atoi(classlabel.c_str()));
            }
        }
    }
    //namedWindow( "Display window",WINDOW_AUTOSIZE);
    //imshow("Display window", images[10]);
    //waitKey(0);
}
int main(int argc, const char *argv[]) {
    if (argc < 2 ) {
        cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
        exit(1);
    }
    string output_folder = ".";
    if (argc == 3) {
        output_folder = string(argv[2]);
    }
    string fn_csv = string(argv[1]);
    vector<Mat> images;
    vector<int> labels;
    try {
        read_csv(fn_csv, images, labels);
    } catch (cv::Exception& e) {
        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
        // nothing more we can do
        exit(1);
    }
    /*
    * stop here !!!
    */
    if(images.size() <= 1) {
        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
        CV_Error(CV_StsError, error_message);
    }
    int height = images[0].rows;
    Mat testSample = images[images.size() -1];
    int testLabel = labels[labels.size() -1];
    images.pop_back();
    labels.pop_back();

    //namedWindow( "Display window",WINDOW_AUTOSIZE);
    //imshow("Display window", testSample);
    //waitKey(0);

    int num_components = 10;
    double threshold = 10.0;

    Ptr<FaceRecognizer> model = createEigenFaceRecognizer(num_components,threshold);
    model->train(images, labels);
    model->save("traindatabase.xml");

    //int predictedLa
    Mat eigenvalues = model->getMat("eigenvalues");

    //cout << "eigenvalues = " << endl;
    //cout << eigenvalues << endl;

    Mat W = model->getMat("eigenvectors");
    Mat mean = model->getMat("mean");

    //namedWindow( "Display window",WINDOW_AUTOSIZE);
    //imshow("Display window", norm_0_255(mean.reshape(1,images[0].rows)));
    //waitKey(0);
    for (int i = 0; i < min(10,W.cols); i++) {
        Mat ev = W.col(i).clone();
        Mat grayscale = norm_0_255(ev.reshape(1,height));
        //Mat cgrayscale;
        //applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
        if (argc == 2) {
            imshow(format("eigenface_%d", i), grayscale);
        } else {
            imwrite(format("%s/eigenface_%d.png", output_folder.c_str(), i), norm_0_255(grayscale));
        }
    }
    Mat evs = Mat(W, Range::all(), Range(0,num_components));

    /*
    * Stop here !!!
    */
    for (int num_components = min(W.cols,10); num_components < min (W.cols,300); num_components+=15) {
        Mat evs = Mat(W, Range::all(), Range(0,num_components));
        Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
        cout << "projection" << endl;
        cout << projection << endl;
        Mat reconstrction = subspaceReconstruct(evs, mean, projection);

        // Normalize the result
        reconstrction = norm_0_255(reconstrction.reshape(1,images[0].rows));

    }
    if(argc == 2) {
        waitKey(0);
    }
    getchar();
    return 0;
}