private void SaveFace(object sender, RoutedEventArgs e)
{
if (SelectedFace == null)
{
MessageBox.Show("Выберите лицо для регистрации", "Ошибка регистрации", MessageBoxButton.OK, MessageBoxImage.Warning);
return;
}
FaceRecognizer = new FisherFaceRecognizer(0, 3500);
var imageList = new VectorOfMat();
var labelList = new VectorOfInt();
imageList.Push(SelectedFace.CVImage.Resize(100, 100, Inter.Cubic).Mat);
var samples = Directory.GetFiles("Face Samples");
foreach (var sample in samples)
{
imageList.Push(new Image <Gray, byte>(sample).Resize(100, 100, Inter.Cubic).Mat);
}
labelList.Push(Enumerable.Range(1, samples.Length + 1).ToArray());
FaceRecognizer.Train(imageList, labelList);
DialogResult = true;
Close();
}
public bool TrainRecognizer()
{
var allFaces = _dataStoreAccess.CallFaces("ALL_USERS");
if (allFaces != null)
{
var faceImages = new Image <Gray, byte> [allFaces.Count];
var faceLabels = new int[allFaces.Count];
for (int i = 0; i < allFaces.Count; i++)
{
Stream stream = new MemoryStream();
stream.Write(allFaces[i].Image, 0, allFaces[i].Image.Length);
var faceImage = new Image <Gray, byte>(new Bitmap(stream));
faceImages[i] = faceImage.Resize(200, 200, Inter.Cubic);
faceLabels[i] = allFaces[i].UserId;
}
_faceRecognizer.Train(faceImages, faceLabels);
_faceRecognizer.Save(_recognizerFilePath);
}
return(true);
}
/// <summary>
/// Loads the traing data given a (string) folder location
/// </summary>
/// <param name="Folder_location"></param>
/// <returns></returns>
private bool LoadTrainingData(string Folder_location)
{
if (File.Exists(Folder_location + "\\TrainedLabels.xml"))
{
try
{
//message_bar.Text = "";
Names_List.Clear();
Names_List_ID.Clear();
trainingImages.Clear();
FileStream filestream = File.OpenRead(Folder_location + "\\TrainedLabels.xml");
long filelength = filestream.Length;
byte[] xmlBytes = new byte[filelength];
filestream.Read(xmlBytes, 0, (int)filelength);
filestream.Close();
MemoryStream xmlStream = new MemoryStream(xmlBytes);
using (XmlReader xmlreader = XmlTextReader.Create(xmlStream))
{
while (xmlreader.Read())
{
if (xmlreader.IsStartElement())
{
switch (xmlreader.Name)
{
case "NAME":
if (xmlreader.Read())
{
Names_List_ID.Add(Names_List.Count); //0, 1, 2, 3....
Names_List.Add(xmlreader.Value.Trim());
NumLabels += 1;
}
break;
case "FILE":
if (xmlreader.Read())
{
//PROBLEM HERE IF TRAININGG MOVED
trainingImages.Add(new Image <Gray, byte>(Application.StartupPath + "\\TrainedFaces\\" + xmlreader.Value.Trim()));
}
break;
}
}
}
}
ContTrain = NumLabels;
if (trainingImages.ToArray().Length != 0)
{
//Eigen face recognizer
//Parameters:
// num_components – The number of components (read: Eigenfaces) kept for this Prinicpal
// Component Analysis. As a hint: There’s no rule how many components (read: Eigenfaces)
// should be kept for good reconstruction capabilities. It is based on your input data,
// so experiment with the number. Keeping 80 components should almost always be sufficient.
//
// threshold – The threshold applied in the prediciton. This still has issues as it work inversly to LBH and Fisher Methods.
// if you use 0.0 recognizer.Predict will always return -1 or unknown if you use 5000 for example unknow won't be reconised.
// As in previous versions I ignore the built in threhold methods and allow a match to be found i.e. double.PositiveInfinity
// and then use the eigen distance threshold that is return to elliminate unknowns.
//
//NOTE: The following causes the confusion, sinc two rules are used.
//--------------------------------------------------------------------------------------------------------------------------------------
//Eigen Uses
// 0 - X = unknown
// > X = Recognised
//
//Fisher and LBPH Use
// 0 - X = Recognised
// > X = Unknown
//
// Where X = Threshold value
switch (Recognizer_Type)
{
case ("EMGU.CV.LBPHFaceRecognizer"):
recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100); //50
break;
case ("EMGU.CV.FisherFaceRecognizer"):
recognizer = new FisherFaceRecognizer(0, 3500); //4000
break;
case ("EMGU.CV.EigenFaceRecognizer"):
default:
recognizer = new EigenFaceRecognizer(80, double.PositiveInfinity);
break;
}
recognizer.Train(trainingImages.ToArray(), Names_List_ID.ToArray());
// Recognizer_Type = recognizer.GetType();
// string v = recognizer.ToString(); //EMGU.CV.FisherFaceRecognizer || EMGU.CV.EigenFaceRecognizer || EMGU.CV.LBPHFaceRecognizer
return(true);
}
else
{
return(false);
}
}
catch (Exception ex)
{
Error = ex.ToString();
return(false);
}
}
else
{
return(false);
}
}