text: #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[]) {
      // Check for valid command line arguments, print usage
      // if no arguments were given.
      if (argc != 2) {
          cout << "usage: " << argv[0] << " <csv.ext>" << endl;
          exit(1);
      }
      // Get the path to your CSV.
      string fn_csv = string(argv[1]);
      // 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;
  }