public void Evaluate(int SpreadMax)
{
if (this.FOutLayer[0] == null) { this.FOutLayer[0] = new DX11Resource<DX11Layer>(); }
if (this.FEnabled[0])
{
this.FLayerIn.Sync();
}
if (rectangles.Length != SpreadMax)
{
rectangles = new System.Drawing.Rectangle[SpreadMax];
}
for (int i = 0; i < SpreadMax; i++)
{
int px ,py,sx,sy;
px = (int)FInPosition[i].X;
py = (int)FInPosition[i].Y;
sx = (int)FInSize[i].X;
sy = (int)FInSize[i].Y;
rectangles[i] = new System.Drawing.Rectangle(px, py, sx, sy);
}
}
private void CompositionTarget_Rendering(object sender, EventArgs e)
{
_status.Fill = _rd;
#region Recognition
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
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);
foreach (var f in _rects)
{
result = currentFrame.Copy(f).Convert<Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
_status.Fill = new SolidColorBrush(Colors.Green);
currentFrame.Draw(f, new Bgr(System.Drawing.Color.Red), 2);
//if we have already trained
if (CommonData.TrainingImages.Count > 0)
{
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
recognizer = new EigenObjectRecognizer(
CommonData.TrainingImages.ToArray(),
CommonData.Names.ToArray(),
3000,
ref termCrit);
string name = recognizer.Recognize(result);
currentFrame.Draw(name, ref font, new System.Drawing.Point(f.X - 2, f.Y - 2),
new Bgr(System.Drawing.Color.LightGreen));
}
//finally draw the source
_imgCamera.Source = ImageHelper.ToBitmapSource(currentFrame);
}
#endregion
}
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);
}
