public double PredictPersonConfidenceFirst(Mat mat, int personId)
{
if (!_hasTrainedModel)
{
EnsureTrained();
}
var faceEmb = detectionModule.GetFaceEmbedding(mat);
if (faceEmb == null)
{
return(0);
}
var recognitionPersonModule = new FaceRecognitionModule();
var personalModelFileFilePath = String.Format("PersonalModels//First//{0}.trained", personId);
if (File.Exists(personalModelFileFilePath))
{
Console.WriteLine("[INFO] Second Model exists, loading");
recognitionPersonModule.Load(personalModelFileFilePath);
}
else
{
Console.WriteLine("[INFO] Model doesn't exist, started training");
Train();
}
var prediction = recognitionPersonModule.Predict(faceEmb);
return(prediction.Distance);
}
public void EnsureTrained()
{
if (File.Exists(trainedModel))
{
Console.WriteLine("[INFO] Model exists, loading");
recognitionModule = new FaceRecognitionModule(); // prevent crashes during repeatable load
recognitionModule.Load(trainedModel);
}
else
{
Console.WriteLine("[INFO] Model doesn't exist, started training");
Train();
}
if (File.Exists(trainedSecondModel))
{
Console.WriteLine("[INFO] Second Model exists, loading");
recognitionModuleSecond.Load(trainedSecondModel);
}
else
{
Console.WriteLine("[INFO] Model doesn't exist, started training");
Train();
}
_hasTrainedModel = true;
}
public Surveillance(IVideoGrab videoGrab, ITrainDataDAL trainDataDAL, double confidence = 0.5)
{
this.videoGrab = videoGrab;
this.trainDataDAL = trainDataDAL;
this.confidence = confidence;
faceEyeDetector = new FaceEyeDetector("Models\\haarcascade_frontalface_default.xml", "Models\\haarcascade_eye.xml");
recognitionModule = new FaceRecognitionModule();
recognitionModuleSecond = new FaceRecognitionModule();
detectionModule = new DetectionModule(faceEmbeddingsModel, faceEmbeddingsSecondModel, confidence);
door = new DoorManager();
labelMap = new LabelMap(trainDataDAL.GetLabelMap());
videoGrab.ImageGrabbed += OnImageGrabbed;
PersonDetected += OnPersonDetected;
}
private static void RunOptions(FaceRecognitionParams facePars)
{
var images = GetImages(facePars.DataSet).ToList();
var detectionModule = new DetectionModule(facePars);
var faces = detectionModule
.GetFaces(images)
.Select(f => (GetPersonName(f.Item1), f.Item2))
.ToList();
var labelMap = new LabelMap(faces.Select(f => f.Item1).Distinct());
var labeledFaces = faces.Select(f => (labelMap.Map[f.Item1], f.Item2));
var testImages = GetImages(facePars.TestSet).ToList();
var recognitionModule = new FaceRecognitionModule();
recognitionModule.Train(labeledFaces.ToList(), facePars.Embeddings);
foreach (var(name, bytes) in testImages)
{
var testImg = detectionModule.ProcessImage(bytes);
var prediction = recognitionModule.Predict(testImg);
Console.WriteLine($"Img name : {name} Prediction: {labelMap.ReverseMap[prediction.Label]}, Dist : {prediction.Distance}");
}
}
