public int RecognizeUser(Image <Gray, byte> userImage)
{
/* Stream stream = new MemoryStream();
* stream.Write(userImage, 0, userImage.Length);
* var faceImage = new Image<Gray, byte>(new Bitmap(stream));*/
try
{
_faceRecognizer.Load(_recognizerFilePath);
var result = _faceRecognizer.Predict(userImage.Resize(200, 200, Inter.Cubic, false));
System.Diagnostics.Debug.WriteLine(result.Label + " - " + result.Distance);
return(result.Label);//result.Distance < 100 ? result.Label : -1;
}
catch (Exception ex)
{
if (ex.Message != "OpenCV: Unsupported file storage format")
{
MessageBox.Show("Mensagem de erro desconhecida:" + ex.Message);
}
else
{
MessageBox.Show("A imagem fornecida não possui um formato válido.");
}
}
return(-1);
}
/// <summary>
/// Recognise a Grayscale Image using the trained Eigen Recogniser
/// </summary>
/// <param name="Input_image"></param>
/// <returns></returns>
public string Recognise(Image <Gray, byte> Input_image, int Eigen_Thresh = -1)
{
if (_IsTrained)
{
FaceRecognizer.PredictionResult ER = recognizer.Predict(Input_image);
if (ER.Label == -1)
{
Eigen_label = "Unknown";
Eigen_Distance = 0;
return(Eigen_label);
}
else
{
Eigen_label = Names_List[ER.Label];
Eigen_Distance = (float)ER.Distance;
if (Eigen_Thresh > -1)
{
Eigen_threshold = Eigen_Thresh;
}
//Only use the post threshold rule if we are using an Eigen Recognizer
//since Fisher and LBHP threshold set during the constructor will work correctly
switch (Recognizer_Type)
{
case ("EMGU.CV.EigenFaceRecognizer"):
if (Eigen_Distance > Eigen_threshold)
{
return(Eigen_label);
}
else
{
return("Unknown");
}
case ("EMGU.CV.LBPHFaceRecognizer"):
case ("EMGU.CV.FisherFaceRecognizer"):
default:
return(Eigen_label); //the threshold set in training controls unknowns
}
}
}
else
{
return("");
}
}
/// <summary>
/// Recognise a Grayscale Image using the trained Eigen Recogniser
/// </summary>
/// <param name="Input_image"></param>
/// <returns></returns>
public string Recognise(Image <Gray, byte> Input_image, int Eigen_Thresh = -1)
{
if (_IsTrained)
{
FaceRecognizer.PredictionResult ER = recognizer.Predict(Input_image);
if (ER.Label == -1)
{
Eigen_label = "Unknown";
Eigen_Distance = 0;
return(Eigen_label);
}
else
{
Eigen_label = Names_List[ER.Label];
Eigen_Distance = (float)ER.Distance;
if (Eigen_Thresh > -1)
{
Eigen_threshold = Eigen_Thresh;
}
//Only use the post threshold rule if we are using an Eigen Recognizer
//since Fisher and LBHP threshold set during the constructor will work correctly
if (Eigen_Distance > Eigen_threshold)
{
return(Eigen_label);
}
else
{
return("Unknown");
}
}
}
else
{
return("");
}
}
