#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 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()) {
            images.push_back(imread(path, 0));
            labels.push_back(atoi(classlabel.c_str()));
            }
        }
    }

int main(int argc, const char *argv[]) 
    {

// Get the path to your CSV.
    string fn_csv = string("facerec_at_t.txt");
// These vectors hold the images and corresponding labels.
    vector<Mat> images;
    vector<int> labels;
// Read in the data. This can fail if no valid
// input filename is given.
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);
        }
// Quit if there are not enough images for this demo.
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);
        }
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size:
int height = images[0].rows;
// The following lines simply get the last images from
// your dataset and remove it from the vector. This is
// done, so that the training data (which we learn the
// cv::FaceRecognizer on) and the test data we test
// the model with, do not overlap.
    Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
    images.pop_back();
    labels.pop_back();
// The following lines create an LBPH model for
// face recognition and train it with the images and
// labels read from the given CSV file.
//
// The LBPHFaceRecognizer uses Extended Local Binary Patterns
// (it's probably configurable with other operators at a later
// point), and has the following default values
//
//      radius = 1
//      neighbors = 8
//      grid_x = 8
//      grid_y = 8
//
// So if you want a LBPH FaceRecognizer using a radius of
// 2 and 16 neighbors, call the factory method with:
//
//      cv::createLBPHFaceRecognizer(2, 16);
//
// And if you want a threshold (e.g. 123.0) call it with its default values:
//
//      cv::createLBPHFaceRecognizer(1,8,8,8,123.0)
//
    Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
    model->train(images, labels);
// The following line predicts the label of a given
// test image:
int predictedLabel = model->predict(testSample);
//
// To get the confidence of a prediction call the model with:
//
//      int predictedLabel = -1;
//      double confidence = 0.0;
//      model->predict(testSample, predictedLabel, confidence);
//
    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
    cout << result_message << endl;
// Sometimes you'll need to get/set internal model data,
// which isn't exposed by the public cv::FaceRecognizer.
// Since each cv::FaceRecognizer is derived from a
// cv::Algorithm, you can query the data.
//
// First we'll use it to set the threshold of the FaceRecognizer
// to 0.0 without retraining the model. This can be useful if
// you are evaluating the model:
//
    model->set("threshold", 0.0);
// Now the threshold of this model is set to 0.0. A prediction
// now returns -1, as it's impossible to have a distance below
// it
    predictedLabel = model->predict(testSample);
    cout << "Predicted class = " << predictedLabel << endl;
// Show some informations about the model, as there's no cool
// Model data to display as in Eigenfaces/Fisherfaces.
// Due to efficiency reasons the LBP images are not stored
// within the model:
    cout << "Model Information:" << endl;
    string model_info = format("\tLBPH(radius=%i, neighbors=%i, grid_x=%i, grid_y=%i, threshold=%.2f)",
        model->getInt("radius"),
        model->getInt("neighbors"),
        model->getInt("grid_x"),
        model->getInt("grid_y"),
        model->getDouble("threshold"));
    cout << model_info << endl;
// We could get the histograms for example:
    vector<Mat> histograms = model->getMatVector("histograms");
// But should I really visualize it? Probably the length is interesting:
    cout << "Size of the histograms: " << histograms[0].total() << endl;
return 0;
    }