Inheritance: Emgu.Util.UnmanagedObject
Example #1
0
        protected void UploadButton_Click(object sender, EventArgs e)
        {
            try
            {
                if (!((FileUpload1.PostedFile.ContentType == "image/jpeg") ||
                    (FileUpload1.PostedFile.ContentType == "image/png") ||
                    (FileUpload1.PostedFile.ContentType == "image/gif") ||
                    (FileUpload1.PostedFile.ContentType == "image/bmp"))) throw new Exception("Неизвестный тип файла");

                string PhotoFolder = Request.PhysicalApplicationPath + @"\photos\";

                if (!Directory.Exists(PhotoFolder)) Directory.CreateDirectory(PhotoFolder);

                string extention = Path.GetExtension(FileUpload1.FileName);
                string uniqueName = Path.ChangeExtension(FileUpload1.FileName, DateTime.Now.Ticks.ToString());

                string upFile = Path.Combine(PhotoFolder, uniqueName + extention);
                FileUpload1.SaveAs(upFile);

                //Распознование лиц

                HaarCascade haarCascade = new HaarCascade(Request.PhysicalApplicationPath + @"\haarcascade_frontalface_alt2.xml");

                Image<Bgr, Byte> image = new Image<Bgr, Byte>(upFile);
                Image<Gray, Byte> grayImage = image.Convert<Gray, Byte>();

                Bitmap srcImage = image.ToBitmap();

                var detectedFaces = grayImage.DetectHaarCascade(haarCascade)[0];
                foreach (var face in detectedFaces)
                {
                    Image<Bgr, Byte> imFace = image.Copy(face.rect);
                    //Пикселизация (фактор подобран эмпирически)
                    //при данном факторе одиноково хорошо пикселизируются и большие и маленькие лица
                    double factor = 0.02 + (double)10 / (double)face.rect.Height;

                    imFace = imFace.Resize(factor, 0);
                    imFace = imFace.Resize(1 / factor, 0);

                    Bitmap faceBitmap = imFace.ToBitmap();

                    using (Graphics grD = Graphics.FromImage(srcImage))
                    {
                        grD.DrawImage(faceBitmap, new Point(face.rect.Left, face.rect.Top));
                    }
                }
                string uniqueName_processed = uniqueName + "_processed";

                srcImage.Save(Path.Combine(PhotoFolder, uniqueName_processed + extention));

                imgTitle.Visible = true;
                Image1.ImageUrl = "photos/" + uniqueName_processed + extention;

            }
            catch (Exception ex)
            {
                Session["ErrorMsg"] = ex.Message;
                Response.Redirect("~/error.aspx", true);
            }
        }
Example #2
0
        public EyesDetection(bool type)
        {
            this._image = null;
            this._faces = new HaarCascade(@currentDirectory + @"\Resources\haarcascade_frontalface_alt2.xml");
            this._eyes = new HaarCascade(@currentDirectory + @"\Resources\haarcascade_eye.xml");

        }
Example #3
0
 private void frmCamShift_Load(object sender, EventArgs e)
 {
     cap = new Capture(0);
     //haar = new HaarCascade("haarcascade_frontalface_alt.xml");
     haar = new HaarCascade("haarcascade.xml");
     cap.FlipHorizontal = true;
 }
        public static Image<Gray, byte> DetectAndTrimFace(int[] pixels, Size initialSize, Size outputSize, String haarcascadePath)
        {
            var inBitmap = ConvertToBitmap(pixels, initialSize.Width, initialSize.Width);

            //for testing purposes I can the picture to a folder
            //inBitmap.Save(@"E:\data\phototest\received.bmp");

            var grayframe = new Image<Gray, byte>(inBitmap);

            var haar = new HaarCascade(haarcascadePath);
            var faces = haar.Detect(grayframe,
                1.2,
                3,
                HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
                new Size(30, 30));

            if (faces.Count() != 1)
            {
                return null;
            }
            var face = faces[0];

            var returnImage = grayframe.Copy(face.rect).Resize(outputSize.Width, outputSize.Height, INTER.CV_INTER_CUBIC);

            //cleanup managed resources
            haar.Dispose();
            grayframe.Dispose();

            return returnImage;
        }
Example #5
0
        public MainForm()
        {
            InitializeComponent();

            face = new HaarCascade("haarcascade_frontalface_default.xml");
             //   grabber = new Capture();
              //  grabber.QueryFrame();
              //  Application.Idle += new EventHandler(FrameGrabber);

            this.WindowState = FormWindowState.Maximized;

            _videoPacketDecoderWorker = new VideoPacketDecoderWorker(PixelFormat.BGR24, true, OnVideoPacketDecoded);
            _videoPacketDecoderWorker.Start();

            _droneClient = new DroneClient("192.168.1.1");
            _droneClient.NavigationPacketAcquired += OnNavigationPacketAcquired;
            _droneClient.VideoPacketAcquired += OnVideoPacketAcquired;
            _droneClient.NavigationDataAcquired += data => _navigationData = data;

            tmrStateUpdate.Enabled = true;
            tmrVideoUpdate.Enabled = true;

            _playerForms = new List<PlayerForm>();

            _videoPacketDecoderWorker.UnhandledException += UnhandledException;

            _droneClient.Start();
        }
Example #6
0
        private void Window_Loaded(object sender, RoutedEventArgs e)
        {
            var sourceImage = new Bitmap("C:\\Steve_Wozniak.jpg");

            string haarcascade = "haarcascade_frontalface_default.xml";

            using (HaarCascade face = new HaarCascade(haarcascade))
            {
                var image = new Image<Rgb, Byte>(sourceImage);

                using (var gray = image.Convert<Gray, Byte>())
                {
                    var detectedFaces = face.Detect(
                                            gray,
                                            1.1,
                                            10,
                                            Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
                                            new System.Drawing.Size(20, 20));

                    var firstFace = detectedFaces[0];
                    System.Drawing.Bitmap bmpImage = image.Bitmap;
                    System.Drawing.Bitmap bmpCrop = bmpImage.Clone(firstFace.rect,
                                                                    bmpImage.PixelFormat);

                    var cropedImage = new Image<Rgb, Byte>(bmpCrop);

                    MainImage.Source = ToBitmapSource(sourceImage);
                    DetectedFaceImage.Source = ToBitmapSource(cropedImage.Bitmap);
                }
            }
        }
Example #7
0
        public static Image<Bgr, byte> DetectAndDrawFaces(Image<Bgr, byte> image, HaarCascade face, HaarCascade eye)
        {
            Image<Gray, Byte> gray = image.Convert<Gray, Byte>(); //Convert it to Grayscale

            gray._EqualizeHist();

            //Detect the faces  from the gray scale image and store the locations as rectangle
            //The first dimensional is the channel
            //The second dimension is the index of the rectangle in the specific channel
            MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
               face,
               1.1,
               10,
               Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
               new Size(20, 20));

            foreach (MCvAvgComp f in facesDetected[0])
            {
                //draw the face detected in the 0th (gray) channel with blue color
                image.Draw(f.rect, new Bgr(Color.Blue), 2);

                //Set the region of interest on the faces
                if (eye != null)
                    DetectAndDrawEyes(image, gray, f, eye);
            }
            return image;
        }
Example #8
0
        public Image<Bgr, Byte> FaceDetection(Image Image)
        {
            face = new HaarCascade("haarcascade_frontalface_default.xml");
            Utility UTl = new Utility();

            //Get the current frame form capture device
            Image<Bgr, Byte> currentFrame = UTl.ImageToBgrByte(Image);

            //Convert it to Grayscale
            gray = currentFrame.Convert<Gray, Byte>();

            //Face Detector
            MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(face,1.2,10,Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,new Size(20, 20));

            //Action for element detected
            try
            {
                MCvAvgComp f = facesDetected[0][0];

            result = currentFrame.Copy(f.rect).Convert<Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
            //draw the face detected in the 0th (gray) channel with blue color
            currentFrame.Draw(f.rect, new Bgr(Color.White), 2);
            }
            catch (Exception ex)
            {
                MessageBox.Show("Camera Error: Empty frames arrived" + ex.Message.ToString(), "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);

            }
            return currentFrame;
        }
Example #9
0
 private void Form1_Load(object sender, EventArgs e)
 {
     capturar = new Capture(0);
     haarCascade1 = new HaarCascade("haarcascade_frontalface_default.xml");
     timer1.Interval = 40;
     timer1.Enabled=true;
 }
        public Form1()
        {
            InitializeComponent();
            
            face = new HaarCascade("haarcascade_frontalface_default.xml");
            calcface = new HaarCascade("haarcascade_frontalface_default.xml");
            eyeWithGlass = new CascadeClassifier("haarcascade_eye_tree_eyeglasses.xml");
            nose = new CascadeClassifier("haarcascade_mcs_nose.xml");
            mouth = new CascadeClassifier("haarcascade_mcs_mouth.xml");

            mydb = new DBConn();

            recogNameResult = new List<string>();
            recogDistanceResult = new List<double>();
            minEye = new Size(10, 10);
            maxEye = new Size(225, 225);
            minNose = new Size(10, 10);
            maxNose = new Size(225, 225);
            minMouth = new Size(10, 10);
            maxMouth = new Size(225, 225);
            font = new MCvFont(FONT.CV_FONT_HERSHEY_TRIPLEX, 0.5d, 0.5d);    

            //log record
            DateTime now = DateTime.Now;
            logName = now.ToString();
            logName = logName.Replace("/", "").Replace(":", "").Replace(" ", "");      
        }
        public void ProcessFaces()
        {
            this._stopwatch.Reset();
               this._stopwatch.Start();
               // get the current frame from our webcam
               Image<Bgr, Byte> frame = _capture.QueryFrame();
               Image<Gray, Byte> gray;
               //Read the HaarCascade objects
               using (HaarCascade face = new HaarCascade(FACEFILENAME)) {
                    //Convert it to Grayscale
                    gray = frame.Convert<Gray, Byte>();
                    //normalizes brightness and increases contrast of the image
                    gray._EqualizeHist();
                    //Detect the faces  from the gray scale image and store the locations as rectangle
                    //The first dimensional is the channel
                    //The second dimension is the index of the rectangle in the specific channel
                    MCvAvgComp[] facesDetected = face.Detect(
                         gray,
                         1.1,
                         10,
                         Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
                         new System.Drawing.Size(20, 20));
                    if (facesDetected != null && facesDetected.Length > 0) {
                         this.notifyIcon.ShowBalloonTip(5000, "Alert!", "Shoulder surfer detected!",ToolTipIcon.Warning);
                         Console.WriteLine("Shoulder surfer detected!");
                    }

               }
               this._stopwatch.Stop();
               Console.WriteLine("Elapsed {0}s", this._stopwatch.Elapsed.TotalSeconds);
        }
Example #12
0
        public App(Boolean cameraTurn)
        {
            InitializeComponent();
            images = slider.Images();
            pictureBox2.Image = images[0];
            if (!cameraTurn)
            {
                cameraState = false;
                pictureBox1.Hide();
            }
            else
            {
                pictureBox2.Hide();
            }

            timer = new DispatcherTimer();
            timer.Tick += new EventHandler(timer1_Tick);
            timer.Interval = new TimeSpan(0, 0, 0, 0, 1);
            timer.Start();

            timer2.Interval = 1000;
            timer2.Start();

            //picturebox jest rozciągany na cały screen
            pictureBox1.Dock = DockStyle.Fill;
            // a następnie zawartosc jest rozciagana na całego pictureboxa
            fullScreen.EnterFullScreen(this);

            //pictureBox1.Hide();
            capture = new Capture();
            haarCascade = new HaarCascade(@"haarcascade_frontalface_alt_tree.xml");
        }
        public FrmPrincipal()
        {
            InitializeComponent();
            //Proses mendekteksi wajah
            face = new HaarCascade("haarcascade_frontalface_default.xml");

            try
            {
                //Prose label untuk setiap gambar
                string Labelsinfo = File.ReadAllText(Application.StartupPath + "/TrainedFaces/TrainedLabels.txt");
                string[] Labels = Labelsinfo.Split('%');
                NumLabels = Convert.ToInt16(Labels[0]);
                ContTrain = NumLabels;
                string LoadFaces;

                for (int tf = 1; tf < NumLabels+1; tf++)
                {
                    LoadFaces = "face" + tf + ".bmp";
                    trainingImages.Add(new Image<Gray, byte>(Application.StartupPath + "/TrainedFaces/" + LoadFaces));
                    labels.Add(Labels[tf]);
                }

            }
            catch(Exception e)
            {

                MessageBox.Show(" Database Masih Kosong !!! ", "Maaf", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
            }
        }
        public FormManualTrain(Form1 frm1)
        {
            InitializeComponent();
            browseImage = new OpenFileDialog();

            _form1 = frm1;

            eigenRecog = new Classifier_Train();
            face = new HaarCascade("haarcascade_frontalface_default.xml");
            eyeWithGlass = new CascadeClassifier("haarcascade_eye_tree_eyeglasses.xml");
            mydb = new DBConn();
            minEye = new Size(10, 10);
            maxEye = new Size(225, 225);
            font = new MCvFont(FONT.CV_FONT_HERSHEY_TRIPLEX, 0.5d, 0.5d);
            if (File.ReadAllText("setting.txt") != null)
            {
                folderPath = File.ReadAllText("setting.txt");
            }
            else
            {
                FolderBrowserDialog b = new FolderBrowserDialog();
                b.Description = "Please select your installation path";
                DialogResult r = b.ShowDialog();
                if (r == DialogResult.OK) // Test result.
                {
                    folderPath = b.SelectedPath;
                    Console.WriteLine(folderPath);
                    File.WriteAllText(@"setting.txt", folderPath);
                    MessageBox.Show("Path is at " + folderPath);
                }
            }
        }
Example #15
0
        public Admin()
        {
            InitializeComponent();

            face = new HaarCascade("haarcascade_frontalface_default.xml");

            Loaded += (s, e) =>
            {
                this.DataContext = CommonData.PicturesVM;
                if (grabber == null)
                {
                    CommonData.LoadSavedData();
                    //check how many faces we already have
                    _countFaces = CommonData.PicturesVM.Pictures.Count;

                    grabber = new Capture();
                    grabber.QueryFrame();
                    grabber.Start();
                }
                else
                {
                    grabber.Start();
                }

            };
            Unloaded += (s, e) =>
            {
                grabber.Stop();
            };

            CompositionTarget.Rendering += CompositionTarget_Rendering;
        }
		private Bitmap DetectFace(Bitmap faceImage)
		{
			var image = new Image<Bgr, byte>(faceImage);
			var gray = image.Convert<Gray, Byte>();
			var haarCascadeFilePath = _httpContext.Server.MapPath("haarcascade_frontalface_default.xml");
			var face = new HaarCascade(haarCascadeFilePath);
			MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(face, 1.1, 10, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
			Image<Gray, byte> result = null;

			foreach (MCvAvgComp f in facesDetected[0])
			{
				//draw the face detected in the 0th (gray) channel with blue color
				image.Draw(f.rect, new Bgr(Color.Blue), 2);
				result = image.Copy(f.rect).Convert<Gray, byte>();
				break;
			}

			if (result != null)
			{
				result = result.Resize(150, 150, INTER.CV_INTER_CUBIC);

				return result.Bitmap;
			}

			return null;
		}
Example #17
0
        public FrmPrincipal()
        {
            InitializeComponent();
            //Load haarcascades for face detection
            face = new HaarCascade("haarcascade_frontalface_default.xml");
            //eye = new HaarCascade("haarcascade_eye.xml");
            try
            {
                //Load of previus trainned faces and labels for each image
                string Labelsinfo = File.ReadAllText(Application.StartupPath + "/TrainedFaces/TrainedLabels.txt");
                string[] Labels = Labelsinfo.Split('%');
                NumLabels = Convert.ToInt16(Labels[0]);
                ContTrain = NumLabels;
                string LoadFaces;

                for (int tf = 1; tf < NumLabels + 1; tf++)
                {
                    LoadFaces = "face" + tf + ".bmp";
                    trainingImages.Add(new Image<Gray, byte>(Application.StartupPath + "/TrainedFaces/" + LoadFaces));
                    labels.Add(Labels[tf]);
                }

            }
            catch (Exception e)
            {
                //MessageBox.Show(e.ToString());
                MessageBox.Show("Nothing in binary database, please add at least a face(Simply train the prototype with the Add Face Button).", "Triained faces load", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
            }
        }
        public FrmPrincipal()
        {
            InitializeComponent();
            //Load haarcascades for face detection
            face = new HaarCascade("haarcascade_frontalface_default.xml");
            try
            {
                //Load of previus trainned faces and labels for each image
                var Labelsinfo = File.ReadAllText(Application.StartupPath + "/TrainedFaces/TrainedLabels.txt");
                var Labels = Labelsinfo.Split('%');
                NumLabels = Convert.ToInt16(Labels[0]);
                ContTrain = NumLabels;
                string LoadFaces;

                for (var tf = 1; tf < NumLabels + 1; tf++)
                {
                    LoadFaces = "face" + tf + ".bmp";
                    trainingImages.Add(new Image<Gray, byte>(Application.StartupPath + "/TrainedFaces/" + LoadFaces));
                    labels.Add(Labels[tf]);
                }
            }
            catch (Exception)
            {
                //MessageBox.Show(e.ToString());
                MessageBox.Show(@"Nu este nimic in baza de data, adauga cel putin o fata.", @"Triained faces load",
                    MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
            }
        }
Example #19
0
        public DaftarPeg()
        {
            InitializeComponent();
            face = new HaarCascade("haarcascade_frontalface_default.xml");
            eye = new HaarCascade("haarcascade_eye.xml");

            try
            {
                //Load of previus trainned faces and labels for each image
                string Labelsinfo = File.ReadAllText(Application.StartupPath + "/TrainedFaces2/TrainedLabelsAll.txt");
                string[] Labels = Labelsinfo.Split('%');
                NumLabels = Convert.ToInt16(Labels[0]);
                ContTrain = NumLabels;
                string LoadFaces;

                for (int tf = 1; tf < NumLabels + 1; tf++)
                {
                    LoadFaces = "face_1" + tf + ".bmp";
                    trainingImages.Add(new Image<Gray, byte>(Application.StartupPath + "/TrainedFaces2/" + LoadFaces));
                    labels.Add(Labels[tf]);
                }

            }
            catch (Exception e)
            {
                MessageBox.Show(e.ToString());
                //MessageBox.Show("Persiapan untuk memasukkan Data Wajah  " + NIP_Enroll + ".", "Load Face Training", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
            }
        }
Example #20
0
 public Form1()
 {
     InitializeComponent();
     face = new HaarCascade("haarcascade_frontalface_default.xml");
     this.MouseWheel+=(a,b)=>{
         if(textBox1.Focused)
         {
             double d = double.Parse(textBox1.Text);
             d += b.Delta>0?0.01:-0.01;
             textBox1.Text = d.ToString();
         }
         if (textBox2.Focused)
         {
             int d = int.Parse(textBox2.Text);
             d += b.Delta > 0 ? 1 : -1;
             textBox2.Text = d.ToString();
         }
         if (textBox3.Focused)
         {
             int d = int.Parse(textBox3.Text);
             d += b.Delta > 0 ? 10 : -10;
             textBox3.Text = d.ToString();
         }
     };
 }
Example #21
0
 public Form1()
 {
     InitializeComponent();
     //Cargar la detección de Rostros Metodo de Viola-Jones
     face = new HaarCascade("haarcascade_frontalface_default.xml");
     eye = new HaarCascade("haarcascade_eye.xml");
 }
 private void btnBrowseHAAR_Click(object sender, EventArgs e)
 {
     DialogResult result = openFileDialog1.ShowDialog();
     if (result != DialogResult.OK) return;
     txtCascadeFile.Text = openFileDialog1.FileName;
     face = new HaarCascade(txtCascadeFile.Text);
 }
Example #23
0
 public FaceDetection()
 {
     this._image = null;
     this._gray = null;
     this._egray = null;
     this._processedImages = null;
     this._faces = new HaarCascade(@currentDirectory + @"\Resources\haarcascade_frontalface_alt_tree.xml");
 }
 //indicates whether frame grabbing from the cameras has been successfull
 //private bool sucessfull;
 public LiveStreamFaceDetectingThread(Size frame_size)
     : base()
 {
     haarcascade       = new HaarCascade(FRONTAL_FACE_HAARCASCADE_FILE_PATH);
     this.frame_width  = frame_size.Width;
     this.frame_height = frame_size.Height;
     WORK_DONE         = false;
 }
Example #25
0
     private void Form1_Load(object sender, EventArgs e)
     {
         // passing 0 gets zeroth webcam
         _cap = new Capture(0);
         // adjust path to find your xml
         _haar = new HaarCascade(
 "haarcascade_frontalface_alt2.xml");
     }
Example #26
0
        private void FrmPrincipal_Load(object sender, EventArgs e)
        {
            //Encender la camara

            capturar = new Capture(0);
            timer1.Interval = 40;
            timer1.Enabled = true;
            haar = new HaarCascade("haarcascade_frontalface_default.xml");
        }
		public FaceTrackingInstance(ImageRGB image, HaarCascade cascade)
		{
			FSource = image;
			FHaarCascade = cascade;
			FTrackingThread = new Thread(fnFindFacesThread);
			FTrackingThread.Start();

			IsRunning = true;
		}
Example #28
0
 private void Window_Loaded(object sender, RoutedEventArgs e)
 {
     capture = new Capture();
     haarCascade = new HaarCascade(@"haarcascade_frontalface_alt_tree.xml");
     timer = new DispatcherTimer();
     timer.Tick += new EventHandler(timer_Tick);
     timer.Interval = new TimeSpan(0, 0, 0, 0, 1);
     timer.Start();
 }
 public ReviewFaceDetectingThread(Size frame_size)
     : base()
 {
     haarcascade                        = new HaarCascade(FRONTAL_FACE_HAARCASCADE_FILE_PATH);
     its_time_to_pick_perpetrator_faces = false;
     this.frame_size = frame_size;
     WORK_DONE = false;
     counter                            = 0;
     previous_id                        = 0;
 }
        protected virtual MCvAvgComp[] GetFacesVector(Image<Gray, byte> grayImage)
        {
            var haarCascade = new HaarCascade(this.HaarCascadePath);

            return haarCascade.Detect(grayImage,
                this.ScanFactor, //the image where the object are to be detected from
                this.Neighbours, //factor by witch the window is scaled in subsequent scans
                HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, //min number of neighbour rectangles
                Size.Empty,
                Size.Empty);
        }
 public FrmFaceDetection()
 {
     try
     {
         InitializeComponent();
         webcamVideo = new Capture();
         //faceDetection = new HaarCascade(Application.StartupPath + "\\haarcascade_frontalface_default.xml");
         faceDetection = new Emgu.CV.HaarCascade("haarcascade_frontalface_default.xml");
         cascade       = new FaceHaarCascade();
     }
     catch (Exception er)
     {
         MessageBox.Show(er.Message, "Face Detection and Recognition Failure", MessageBoxButtons.OK, MessageBoxIcon.Error);
     }
 }
Example #32
0
        public static EmguCVFaceDetector Get(Bitmap image, string full_name_of_xml)
        {
            Image <Bgr, Byte>  image1    = new Image <Bgr, Byte>(image);
            Image <Gray, Byte> grayImage = image1.Convert <Gray, Byte>();

            Emgu.CV.HaarCascade faceCascade;
            if (full_name_of_xml == "\0")
            {
                faceCascade = new Emgu.CV.HaarCascade("haarcascade_frontalface_alt.xml");
            }
            else
            {
                faceCascade = new Emgu.CV.HaarCascade(full_name_of_xml);
            }
            return(new EmguCVFaceDetector(grayImage, image1, faceCascade));
        }
Example #33
0
 public EmguCVFaceDetector(Image <Gray, Byte> grayimage1, Image <Bgr, Byte> image1, Emgu.CV.HaarCascade faceCascade1)
 {
     grayimage   = grayimage1;
     image       = image1;
     faceCascade = faceCascade1;
 }