public static void LoadSavedData()
{
if (File.Exists(TARGET_PATH + LABELS_FILE) && PicturesVM.Pictures.Count <= 0)
{
ImageFiles = Directory.GetFiles(TARGET_PATH, "*."+ IMAGE_EXT);
Names = File.ReadAllText(TARGET_PATH + LABELS_FILE).Split('%')
.TakeWhile(x => !string.IsNullOrEmpty(x))
.ToList();
int countLabels = 0;
foreach (var f in ImageFiles)
{
#region filling VModel
PictureModel pm = new PictureModel();
pm.ImgSource = new WriteableBitmap(new BitmapImage(new Uri(f)));
pm.Name = Names[countLabels];
pm.ID = countLabels;
PicturesVM.Pictures.Add(pm);
#endregion
#region Filling Training Images
TrainingImages.Add(new Image<Gray, byte>(f));
#endregion
countLabels++;
}
}
}
private async void CompositionTarget_Rendering(object sender, EventArgs e)
{
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
if (_rects == null || _rects.Length == 0)
{
gray = currentFrame.Convert<Gray, Byte>();
var size = new System.Drawing.Size(20, 20);
var window = new System.Drawing.Size(grabber.Width, grabber.Height);
_rects = _faceClassifier.DetectMultiScale(gray, 1.2, 10, size, window);
_vmodel.PersonRecognized = _rects.Length;
if (_rects.Length > 0)
{
/*
1) save the current rendered faces
2) upload the current frame to detect
3) verify with trained images
*/
string snapshot = CommonData.TARGET_SNAPSHOT_PAHT + DateTime.Now.ToString().Replace('/', '_').Replace(':', '_') + "." + CommonData.IMAGE_EXT;
currentFrame.Save(snapshot);
_progressRec.IsIndeterminate = false;
_progressRec.IsEnabled = true;
var fr = await UploadAndDetectFaces(snapshot);
//detect all faces
foreach (var trainedFile in CommonData.ImageFiles)
{
var fileStream = File.OpenRead(trainedFile);
var f = await faceServiceClient.DetectAsync(fileStream);
_trainedFacesAI.AddRange(f.ToList());
}
int i = 0;
//verify reading from all db
foreach (var face in _detectedFaceFromAI)
{
foreach (var secondFace in _trainedFacesAI)
{
var res = await faceServiceClient.VerifyAsync(face.FaceId, secondFace.FaceId);
PictureModel _model = new PictureModel();
_model.ImgSource = new WriteableBitmap(new BitmapImage(new Uri(CommonData.ImageFiles[i])));
_model.AIID = face.FaceId.ToString();
if (res.IsIdentical)
{
_model.Name = CommonData.Names[i];
_model.ID = i;
_model.IsVerified = true;
_model.Confidence = res.Confidence;
}
else
{
_model.Name = "Unkonwn";
_model.IsVerified = false;
_model.Confidence = res.Confidence;
}
var c = _vmodel.Pictures.Where(x => x.AIID == _model.AIID).Count();
if (!(c > 0))//adds only if is not already added
{
_vmodel.Pictures.Add(_model);
}
i++;
}
}
_progressRec.IsIndeterminate = false;
_progressRec.IsEnabled = false;
}
}
_imgSource.Source = ImageHelper.ToBitmapSource(currentFrame);
}
private void RegisterFaces()
{
//iterates till detects a face in the image
while (facesDetected == null || facesDetected[0].Length == 0)
{
gray = grabber.QueryGrayFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//Face Detector
facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new System.Drawing.Size(20, 20));
foreach (var f in TrainFaces(facesDetected))
{
var t = f.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
t.Save(CommonData.TARGET_PATH + "face" + _countFaces + "." + CommonData.IMAGE_EXT);
//File.AppendAllText(Environment.CurrentDirectory + "/TrainedFaces/TrainedLabels.txt", "names");
PictureModel _model = new PictureModel();
_model.ImgSource = ImageHelper.ToBitmapSource(t).ToWriteableBitmap();
_model.ID = _countFaces;
CommonData.PicturesVM.Pictures.Add(_model);
_countFaces++;
}
//Debug.WriteLine("d " + ++count);
Thread.Sleep(100);//give it some time to elaborate if the CPU is full
}
}