public void Train()
{
videoGrab.Pause();
var images = trainDataDAL.GetImages().ToList();
labelMap = new LabelMap(trainDataDAL.GetLabelMap());
var faceEmbeddings = images
.Select(img => (labelMap.Map[img.Label], detectionModule.GetFaceEmbedding(img.Image)))
.Where(tuple => tuple.Item2 != null)
.ToList();
if (faceEmbeddings != null && faceEmbeddings.Any())
{
recognitionModule.Train(faceEmbeddings, trainedModel, "PersonalModels//First//");
}
var faceEmbeddingsSecond = images
.Select(img => (labelMap.Map[img.Label], detectionModule.GetFaceEmbeddingSecond(img.Image)))
.Where(tuple => tuple.Item2 != null)
.ToList();
if (faceEmbeddings != null && faceEmbeddings.Any())
{
recognitionModule.Train(faceEmbeddingsSecond, trainedSecondModel, "PersonalModels//Second//");
}
_hasTrainedModel = false; //force reloading the new model before start recognition
videoGrab.Start();
}
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}");
}
}