public class Utils
  {
    public static NDarray Normalize(string path)
    {
      var colorMode = Settings.Channels == 3 ? "rgb" : "grayscale";
      var img = ImageUtil.LoadImg(path, color_mode: colorMode, target_size: (Settings.ImgWidth, Settings.ImgHeight));
      return ImageUtil.ImageToArray(img) / 255;
    }

  }

public class DataSet
  {
    private string _pathToFolder;
    private string[] _extList;
    private List<int> _labels;
    private List<NDarray> _objs;
    private double _validationSplit;
    public int NumberClasses { get; set; }
    public NDarray TrainX { get; set; }
    public NDarray ValidationX { get; set; }
    public NDarray TrainY { get; set; }
    public NDarray ValidationY { get; set; }

    public DataSet(string pathToFolder, string[] extList, int numberClasses, double validationSplit)
    {
      _pathToFolder = pathToFolder;
      _extList = extList;
      NumberClasses = numberClasses;
      _labels = new List<int>();
      _objs = new List<NDarray>();
      _validationSplit = validationSplit;
    }

    public void LoadDataSet()
    {
      // Process the list of files found in the directory.
      string[] fileEntries = Directory.GetFiles(_pathToFolder);
      foreach (string fileName in fileEntries)
        if (IsRequiredExtFile(fileName))
          ProcessFile(fileName);

      MapToClassRange();
      GetTrainValidationData();
    }

    private bool IsRequiredExtFile(string fileName)
    {
      foreach (var ext in _extList)
      {
        if (fileName.Contains("." + ext))
        {
          return true;
        }
      }

      return false;
    }

    private void MapToClassRange()
    {
      HashSet<int> uniqueLabels = _labels.ToHashSet();
      var uniqueLabelList = uniqueLabels.ToList();
      uniqueLabelList.Sort();

      _labels = _labels.Select(x => uniqueLabelList.IndexOf(x)).ToList();
    }

    private NDarray OneHotEncoding(List<int> labels)
    {
      var npLabels = np.array(labels.ToArray()).reshape(-1);
      return Util.ToCategorical(npLabels, num_classes: NumberClasses);
    }

    private void ProcessFile(string path)
    {
      _objs.Add(Utils.Normalize(path));
      ProcessLabel(Path.GetFileName(path));
    }

    private void ProcessLabel(string filename)
    {
      _labels.Add(int.Parse(ExtractClassFromFileName(filename)));
    }

    private string ExtractClassFromFileName(string filename)
    {
      return filename.Split('_')[0].Replace("class", "");
    }

    private void GetTrainValidationData()
    {
      var listIndices = Enumerable.Range(0, _labels.Count).ToList();
      var toValidate = _objs.Count * _validationSplit;
      var random = new Random();
      var xValResult = new List<NDarray>();
      var yValResult = new List<int>();
      var xTrainResult = new List<NDarray>();
      var yTrainResult = new List<int>();

      // Split validation data
      for (var i = 0; i < toValidate; i++)
      {
        var randomIndex = random.Next(0, listIndices.Count);
        var indexVal = listIndices[randomIndex];
        xValResult.Add(_objs[indexVal]);
        yValResult.Add(_labels[indexVal]);
        listIndices.RemoveAt(randomIndex);
      }

      // Split rest (training data)
      listIndices.ForEach(indexVal => 
      { 
        xTrainResult.Add(_objs[indexVal]);
        yTrainResult.Add(_labels[indexVal]);
      });

      TrainY = OneHotEncoding(yTrainResult);
      ValidationY = OneHotEncoding(yValResult);
      TrainX = np.array(xTrainResult);
      ValidationX = np.array(xValResult);
    }
}

var numberClasses = 60;
var fileExt = new string[] { ".png" };
var dataSetFilePath = @"C:/Users/arnal/Downloads/coin_dataset";
var dataSet = new PreProcessing.DataSet(dataSetFilePath, fileExt, numberClasses, 0.2);
dataSet.LoadDataSet();