private void FaceMonitoring(object sender, EventArgs e)
{
try
{
// Get the current frame form capture device
CurrentFrame = Grabber.QueryFrame().Resize(320, 240, INTER.CV_INTER_CUBIC);
//Video.Serve(CurrentFrame.Bytes);
//System.Diagnostics.Debug.Print(Convert.ToBase64String(CurrentFrame.Bytes));
// Convert it to Grayscale
Gray = CurrentFrame.Convert <Gray, byte>();
// Face Detector
MCvAvgComp[][] facesDetected = Gray.DetectHaarCascade(Face, 1.2, 10, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
if (facesDetected == null)
{
return;
}
// Action for each element detected
foreach (MCvAvgComp face in facesDetected[0])
{
Result = CurrentFrame.Copy(face.rect).Convert <Gray, byte>().Resize(100, 100, INTER.CV_INTER_CUBIC);
// Draw the face detected in the 0th (gray) channel with blue color
CurrentFrame.Draw(face.rect, new Bgr(Color.Red), 2);
// Check if there is trained images to find a match
if (TrainingImages.ToArray().Length != 0)
{
var eigenDistanceThreshold = 3000;
var eps = 0.001;
// TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(TrainedFacesCounter, eps);
// Eigen face recognizer
FaceRecognitionEngine recognizer = new FaceRecognitionEngine(TrainingImages.ToArray(), LabelList.ToArray(), eigenDistanceThreshold, ref termCrit);
var imgLabel = recognizer.Recognize(Result);
LogStats($"{imgLabel} - Recognized");
// Draw the label for each face detected and recognized
// CurrentFrame.Draw(imgLabel, ref Font, new Point(face.rect.X - 2, face.rect.Y - 2), new Bgr(Color.White));
MethodInvoker inv = delegate { lblLabelName.Text = imgLabel.Replace("_", " ").ToUpper(); };
Invoke(inv);
}
}
// Show the face procesed and recognized
imageBoxFrameGrabber.Image = CurrentFrame;
}
catch
{
}
}
public List <Eye> DetectEye()
{
MCvAvgComp[][] objects = Gray.DetectHaarCascade(_eyeCascade);
List <Eye> res = new List <Eye>();
foreach (MCvAvgComp o in objects[0])
{
_image.ROI = o.rect;
res.Add(new Eye(_image.Copy(), o.rect));
}
_image.ROI = Rectangle.Empty;
return(res);
}
private void SavePicture(object sender, EventArgs e)
{
try
{
TrainedFacesCounter++;
// Get a gray frame from capture device
Gray = Grabber.QueryGrayFrame().Resize(320, 240, INTER.CV_INTER_CUBIC);
// Face Detector
var scaleFactor = 1.2;
var minNeighbors = 10;
var detectionType = HAAR_DETECTION_TYPE.DO_CANNY_PRUNING;
MCvAvgComp[][] facesDetected = Gray.DetectHaarCascade(Face, scaleFactor, minNeighbors, detectionType, new Size(20, 20));
// Action for each element detected
foreach (MCvAvgComp f in facesDetected[0])
{
TrainedFace = CurrentFrame.Copy(f.rect).Convert <Gray, byte>();
break;
}
LogIt($"Handle frame - scaleFactor:{scaleFactor} minNeighbors:{minNeighbors} detectionType:{detectionType.ToString()}");
string labelName = textBox1.Text.Trim().Replace(" ", "_").ToLower();
// Resize face detected image in order to force to compare the same size with the
// Test image with cubic interpolation type method
TrainedFace = Result.Resize(100, 100, INTER.CV_INTER_CUBIC);
// Show face added in gray scale
imageBox1.Image = TrainedFace;
var imgPath = $"/TrainedFaces/{labelName}@face{DateTime.Now.ToString("yyyymmddhhmmssmm")}.bmp";
TrainedFace.Save(Application.StartupPath + imgPath);
}
catch { }
}