private void CameraCapture()
{
frame = new Mat(); //Matrix to save the picture
capture.Retrieve(frame, 0); //retrieve the picture to the matrinx
using (Image <Bgr, byte> image = frame.ToImage <Bgr, byte>())
{
if (frame != null)
{
Image <Gray, byte> grayFrame = frame.ToImage <Gray, byte>(); // display the image in the imageBox
faces = cascade.DetectMultiScale(grayFrame, 1.1, 2, new Size(30, 30));
Bitmap BitmapInput = grayFrame.ToBitmap();
Bitmap ExtractedFace;
Graphics FaceCanvas;
if (faces.Count() > 0)
{
foreach (var face in faces)
{
image.Draw(face, new Bgr(Color.Blue), 1); // draw rectangles in the picture
ExtractedFace = new Bitmap(face.Width, face.Height);
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(BitmapInput, 0, 0, face, GraphicsUnit.Pixel);
}
}
imageBox1.Image = image; // display the image in the imageBox
}
}
}
public Rectangle[] Detect(Image <Bgr, Byte> image, double scaleFactor, int minNeighbors, int windowSizeX, int windowSizeY)
{
Image <Gray, byte> grayImage = image.Convert <Gray, byte>();
Graphics FaceCanvas;
Bitmap BmpInput = grayImage.ToBitmap();
this.scaleFactor = scaleFactor;
this.minNeighbors = minNeighbors;
this.windowSizeX = windowSizeX;
this.windowSizeY = windowSizeY;
faces = csClassifier.DetectMultiScale(grayImage, this.scaleFactor, this.minNeighbors,
new Size(this.windowSizeX, this.windowSizeY), new Size(this.windowSizeX, this.windowSizeY));
if (faces.Length > 0)
{
this.numberOFFaces = faces.Length;
ExtractedFaces = new Bitmap[numberOFFaces];
for (int i = 0; i < numberOFFaces; i++)
{
ExtractedFaces[i] = new Bitmap(faces[i].Width, faces[i].Height);
FaceCanvas = Graphics.FromImage(ExtractedFaces[i]);
FaceCanvas.DrawImage(BmpInput, 0, 0, faces[i], GraphicsUnit.Pixel);
}
}
return(faces);
}
private void ProcessFrame(object sender, EventArgs arg)
{
Mat frame = new Mat(); //Matrix to save the picture
capture.Retrieve(frame, 0); //retrieve the picture to the matrinx
Image <Bgr, byte> image = frame.ToImage <Bgr, byte>();
FaceNo = 0;
if (frame != null)
{
Image <Gray, byte> grayFrame = frame.ToImage <Gray, byte>(); // display the image in the imageBox
faces = cascade.DetectMultiScale(grayFrame, 1.1, 2, new Size(30, 30));
Bitmap BitmapInput = grayFrame.ToBitmap();
Bitmap ExtractedFace;
Graphics FaceCanvas;
//countTable.Text = faces.Count().ToString();
if (faces.Count() > 0)
{
foreach (var face in faces)
{
image.Draw(face, new Bgr(Color.Blue), 1); // draw rectangles in the picture
ExtractedFace = new Bitmap(face.Width, face.Height);
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(BitmapInput, 0, 0, face, GraphicsUnit.Pixel);
ExtFaces.Add(ExtractedFace);
FaceNo++;
}
}
imageBox1.Image = image; // display the image in the imageBox
}
}
private void VideoCapture()
{
using (frame = FrameCapture.QueryFrame())
{
if (frame != null)
{
using (Image <Gray, byte> grayFrame = frame.ToImage <Gray, byte>())
{
faces = cascade.DetectMultiScale(grayFrame, 1.1, 2, new Size(100, 100));
Bitmap BitmapInput = grayFrame.ToBitmap();
Graphics FaceCanvas;
if (faces.Count() > 0)
{
foreach (var face in faces)
{
grayFrame.Draw(face, new Gray(), 2); // draw rectangles in the picture
ExtractedFace = new Bitmap(face.Width, face.Height);
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(BitmapInput, 0, 0, face, GraphicsUnit.Pixel);
}
imageBox1.Image = grayFrame;
}
}
}
}
}
public void DetectFaceEyeHaar()
{
try
{
int personNumber = 0;
string eyePath = Path.GetFullPath(@"../../data/haarcascade_eye.xml");
string facePath = Path.GetFullPath(@"../../data/haarcascade_frontalface_default.xml");
CascadeClassifier classifierEye = new CascadeClassifier(eyePath);
CascadeClassifier classifierFace = new CascadeClassifier(facePath);
var imgGray = imgInput.Convert <Gray, byte>().Clone();
double scale = Convert.ToDouble(textBoxScale.Text);
int neig = Convert.ToInt32(numericNeig.Value);
int border = Convert.ToInt32(numericBorder.Value);
double r, g, b;
r = Convert.ToDouble(panelBorderColor.BackColor.R);
g = Convert.ToDouble(panelBorderColor.BackColor.G);
b = Convert.ToDouble(panelBorderColor.BackColor.B);
Rectangle[] faces = classifierFace.DetectMultiScale(imgGray, scale, neig);
if (faces.Length > 0)
{
Bitmap bmpInput = imgGray.ToBitmap();
Graphics FaceCanvas;
ExtFaces = new Bitmap[faces.Length];
Bitmap ExtractedFace; // empty
faceNo = 0;
foreach (var face in faces)
{
personNumber += 1;
imgInput.Draw(face, new Bgr(0, 0, 255), border);
ExtractedFace = new Bitmap(face.Width, face.Height);
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(bmpInput, 0, 0, face, GraphicsUnit.Pixel);
ExtFaces[faceNo] = ExtractedFace;
faceNo++;
imgGray.ROI = face;
Rectangle[] eyes = classifierEye.DetectMultiScale(imgGray, scale, neig);
foreach (var eye in eyes)
{
var e = eye;
e.X += face.X;
e.Y += face.Y;
imgInput.Draw(e, new Bgr(0, 255, 0), border);
}
}
pictureBox.Image = imgInput.Bitmap;
labelPersonNumber.Text = Convert.ToString(personNumber);
}
}
catch (Exception ex)
{
throw new Exception(ex.Message);
}
}
public void SpawnGuest(int fixFloor = 99)
{
int currentFloor = Random.Range(0, HotelController.Instance.hotel.floors.Count);
int targetFloor = currentFloor;
float maxWaitingTime = Random.Range(50f, 120f);
// maxWaitingTime = Random.Range(1, 2f);
while (targetFloor == currentFloor)
{
targetFloor = Random.Range(0, HotelController.Instance.hotel.floors.Count);
}
if (fixFloor != 99)
{
currentFloor = fixFloor;
targetFloor = 1;
}
Guest guest = new Guest(currentFloor, targetFloor, maxWaitingTime);
GameObject guestGO = SimplePool.Spawn(guestPrefab, Vector3.zero, Quaternion.identity);
guestGO.transform.SetParent(worldTransform);
guestGO.transform.position = new Vector3(4.5f, (float)currentFloor, 0f);
guestGO.transform.localScale = new Vector3(0.8f, 0.8f, 0.8f);
guestGO.transform.eulerAngles = new Vector3(0f, 90f, 0f);
guestGO.GetComponent <FaceCanvas>().hud.gameObject.SetActive(true);
guestGO.SetActive(true);
guests.Add(guest);
guestGameObjects.Add(guestGO);
guestToGameObjectMap.Add(guest, guestGO);
gameObjectToGuestMap.Add(guestGO, guest);
guestGO.transform.DOMove(new Vector3(Random.Range(-1.8f, 1.2f), guestGO.transform.position.y, Random.Range(-0.5f, 1.5f)), Random.Range(2f, 3f))
.OnComplete(() => GuestReadyForLift(guest, guestGO));
guestGO.name = "Guest";
FaceCanvas hud = guestGO.GetComponent <FaceCanvas>();
hud.targetFloorText.text = HotelController.Instance.GetFloor(targetFloor).shortName;
guest.hud = hud;
UpdatePatienceBar(guest);
guestCount[currentFloor]++;
UpdateLeftPanel(currentFloor);
spawnGap = Mathf.Max(minSpawnGap, spawnGap - spawnReduce);
}
//face detection function using viola-jones method
private void detectFaces()
{
if (ImageFrame != null) // confirm that image is valid
{
//convert the image to gray scale
Image <Gray, byte> grayframe = ImageFrame.Convert <Gray, byte>();
windowsSize = int.Parse(textBoxDetScale.Text);
ScaleIncreaseRate = Double.Parse(comboBoxScaleRate.Text);
minNeighbors = int.Parse(comboBoxMinNeigh.Text);
//Detect faces from the gray-scale image
var faces = grayframe.DetectHaarCascade(haar_face, ScaleIncreaseRate, minNeighbors, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(windowsSize, windowsSize))[0];
int TotalFaces = faces.Length;
textBox1.Text = TotalFaces.ToString();
if (faces.Length > 0)
{
Bitmap BmpInput = grayframe.ToBitmap();
Bitmap ExtractedFace; //empty
Graphics FaceCanvas;
ExtFaces = new Bitmap[faces.Length];
faceNo = 0;
foreach (var face in faces)
{
ImageFrame.Draw(face.rect, new Bgr(Color.Red), 3);
//set the size of the empty box(ExtractedFace) which will later contain the detected face
ExtractedFace = new Bitmap(face.rect.Width, face.rect.Height);
//set empty image as FaceCanvas, for painting
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(BmpInput, 0, 0, face.rect, GraphicsUnit.Pixel);
ExtFaces[faceNo] = ExtractedFace;
faceNo++;
}
pbExtractedFaces.Image = ExtFaces[0];
//Display the detected faces in imagebox
CamImageBox.Image = ImageFrame;
btnNext.Enabled = true;
btnPrev.Enabled = true;
}
}
}
////////////////////////////////////////////////////////////////////////////////////////
// FUNCTIONS USED TO DETECT FACES IN INPUT IMAGE
////////////////////////////////////////////////////////////////////////////////////////
private void DetectFaces()
{
Image <Gray, byte> grayframe = ImageFrame.Convert <Gray, byte>();
//Assign user-defined Values to parameter variables:
MinNeighbors = int.Parse(comboBoxMinNeigh.Text); // the 3rd parameter
WindowsSize = int.Parse(textBoxWinSiz.Text); // the 5th parameter
ScaleIncreaseRate = Double.Parse(comboBoxScIncRte.Text); //the 2nd parameter
//detect faces from the gray-scale image and store into an array of type 'var',i.e 'MCvAvgComp[]'
var faces = grayframe.DetectHaarCascade(haar, ScaleIncreaseRate, MinNeighbors,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(WindowsSize, WindowsSize))[0];
if (faces.Length > 0)
{
MessageBox.Show("Total Faces Detected: " + faces.Length.ToString());
Bitmap BmpInput = grayframe.ToBitmap();
Bitmap ExtractedFace; //empty
Graphics FaceCanvas;
ExtFaces = new Bitmap[faces.Length];
faceNo = 0;
//draw a green rectangle on each detected face in image
foreach (var face in faces)
{
ImageFrame.Draw(face.rect, new Bgr(Color.Green), 3);
//set the size of the empty box(ExtractedFace) which will later contain the detected face
ExtractedFace = new Bitmap(face.rect.Width, face.rect.Height);
//set empty image as FaceCanvas, for painting
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(BmpInput, 0, 0, face.rect, GraphicsUnit.Pixel);
ExtFaces[faceNo] = ExtractedFace;
faceNo++;
}
pbExtractedFaces.Image = ExtFaces[0];
//Display the detected faces in imagebox
CamImageBox.Image = ImageFrame;
btnNext.Enabled = true;
btnPrev.Enabled = true;
}
}
private void CameraCapture()
{
Mat frame = new Mat(); //Matrix to save the picture
Camera.Retrieve(frame, 0); //retrieve the picture to the matrinx
Image <Bgr, byte> image = frame.ToImage <Bgr, byte>();
if (frame != null)
{
Image <Gray, byte> grayframe = frame.ToImage <Gray, byte>(); // display the image in the imageBox
faces = cascade.DetectMultiScale(grayframe, 1.1, 2, new Size(30, 30));
Bitmap BitmapInput = grayframe.ToBitmap();
Bitmap ExtractedFace;
Graphics FaceCanvas;
ExtFaces = new List <Bitmap>();
if (faces.Count() > 0)
{
foreach (var face in faces)
{
image.Draw(face, new Bgr(Color.Green), 1); // draw rectangles in the picture
ExtractedFace = new Bitmap(face.Width, face.Height);
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(BitmapInput, 0, 0, face, GraphicsUnit.Pixel);
ExtFaces.Add(ExtractedFace);
Image <Gray, byte> ExtracreFaceAsImage = new Image <Gray, byte>(ExtractedFace).Resize(100, 100, Inter.Cubic);
/*FaceRecognizer.PredictionResult result = new FaceRecognizer.PredictionResult();
* result = fr1.Predict(ExtracreFaceAsImage);
*
* if (attendanceResult.ContainsKey(result.Label))
* attendanceResult.Add(result.Label, 0);
* else
* attendanceResult.Add(result.Label, ++attendanceResult[result.Label]);
*
* result = fr3.Predict(ExtracreFaceAsImage);//receiving the result
* if (attendanceResult.ContainsKey(result.Label))
* attendanceResult.Add(result.Label, 0);
* else
* attendanceResult.Add(result.Label, ++attendanceResult[result.Label]);
* /*result = fr2.Predict(ExtracreFaceAsImage);//receiving the result
* attendanceResult.Add(result.Label, ++attendanceResult[result.Label]);*/
}
}
imageBox1.Image = image;
}
}
//----------------------------------------------------------------------------//
//<<<<<<<<------FUNCTION USED TO DETECT AND EXTRACT FACES--------------->>>>>>>>
//----------------------------------------------------------------------------//
private void DetectFaces()
{
Image <Gray, byte> grayframe = TestImage.Convert <Gray, byte>();
//Assign user-defined Values to parameter variables:
MinNeighbors = int.Parse(comboBoxMinNeigh.Text); // the 3rd parameter
WindowsSize = int.Parse(textBoxWinSiz.Text); // the 5th parameter
ScaleIncreaseRate = Double.Parse(comboBoxScIncRte.Text); //the 2nd parameter
//detect faces from the gray-scale image and store into an array of type 'var',i.e 'MCvAvgComp[]'
var faces = grayframe.DetectHaarCascade(haar, ScaleIncreaseRate, MinNeighbors,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(WindowsSize, WindowsSize))[0];
if (faces.Length > 0)
{
MessageBox.Show("Total Faces Detected: " + faces.Length.ToString());
Bitmap BmpInput = grayframe.ToBitmap();
Bitmap ExtractedFace; // an empty "box"/"image" to hold the extracted face.
Graphics FaceCanvas;
//Set The Face Number
//FaceCollection = Directory.GetFiles(@"Face Collection\", "*.bmp");
//int FaceNo = FaceCollection.Length;
//A Bitmap Array to hold the extracted faces
EXfaces = new Bitmap[faces.Length];
int i = 0;
//draw a green rectangle on each detected face in image
foreach (var face in faces)
{
//locate the detected face & mark with a rectangle
TestImage.Draw(face.rect, new Bgr(Color.Green), 3);
//set the size of the empty box(ExtractedFace) which will later contain the detected face
ExtractedFace = new Bitmap(face.rect.Width, face.rect.Height);
//set empty image as FaceCanvas, for painting
FaceCanvas = Graphics.FromImage(ExtractedFace);
//graphics draws the located face on the faceCancas, thus filling the empty ExtractedFace
//image with exact pixels of the face to be extracted from input image
FaceCanvas.DrawImage(BmpInput, 0, 0, face.rect, GraphicsUnit.Pixel);
//save this extracted face to hard disk
//ExtractedFace.Save(@"Face Collection\" + "Face_" + (FaceNo++) + ".bmp");//save images in folder
//Save this extracted face to array
EXfaces[i] = ExtractedFace;
i++;
}
//Display the detected faces in imagebox
CamImageBox.Image = TestImage;
MessageBox.Show(faces.Length.ToString() + " Face(s) Extracted sucessfully!");
pbCollectedFaces.Image = EXfaces[0];
btnAddtoTS.Enabled = true;
txtBoxFaceName.Enabled = true;
if (faces.Length > 1)
{
btnNext.Enabled = true;
btnPrev.Enabled = true;
}
else
{
btnNext.Enabled = false;
btnPrev.Enabled = false;
}
}
else
{
MessageBox.Show("NO faces Detected!");
}
}
private void DetectFaces()
{
Image <Gray, byte> grayframe = ImageFrame.Convert <Gray, byte>();
//detect faces from the gray-scale image and store into an array of type 'var',i.e 'MCvAvgComp[]'
var faces = grayframe.DetectHaarCascade(
haar,
1.2,
2,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(25, 25))[0];
Bitmap BmpInput = grayframe.ToBitmap();
Bitmap ExtractedFace; //empty
Graphics FaceCanvas; //empty
ExtFaces = new Bitmap[faces.Length];
FaceRecc = new Bitmap[faces.Length];
//draw a green rectangle on each detected face in image
foreach (var face in faces)
{
ImageFrame.Draw(face.rect, new Bgr(Color.Green), 3);
//set the size of the empty box(ExtractedFace) for image to store
ExtractedFace = new Bitmap(face.rect.Width, face.rect.Height);
//set empty image as face canvas
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(BmpInput, 0, 0, face.rect, GraphicsUnit.Pixel);
try
{
ExtFaces[faceNo] = ExtractedFace;
FaceRecc[faceNo] = ExtractedFace;
faceNo++;
}
catch (System.IndexOutOfRangeException ew)
{
MessageBox.Show("Invalid Input");
}
}
try
{
faceNo = 0;
pbDetectedFaces.Image = ExtFaces[faceNo];
//Display the detected faces in imagebox
InputImageBox.Image = ImageFrame;
Next.Enabled = true;
Previous.Enabled = true;
DFName.Enabled = true;
DFRollNo.Enabled = true;
btnAddToTrainingSet.Enabled = true;
btnRecognize.Enabled = true;
}
catch (Exception e)
{
MessageBox.Show("No faces found. Please provide valid input");
}
}
private void VideoCapture()
{
using (frame = VideoPlayer.QueryFrame())
{
if (frame != null)
{
using (Image <Gray, byte> grayFrame = frame.ToImage <Gray, byte>()) // display the image in the imageBox
{
faces = cascade.DetectMultiScale(grayFrame, 1.1, 2, new Size(100, 100));
Bitmap BitmapInput = grayFrame.ToBitmap();
Bitmap ExtractedFace;
Graphics FaceCanvas;
String s = "";
if (faces.Count() > 0)
{
int i = 0;
foreach (var face in faces)
{
grayFrame.Draw(face, new Gray(), 2); // draw rectangles in the picture
ExtractedFace = new Bitmap(face.Width, face.Height);
FaceCanvas = Graphics.FromImage(ExtractedFace);
FaceCanvas.DrawImage(BitmapInput, 0, 0, face, GraphicsUnit.Pixel);
Image <Gray, byte> ExtracreFaceAsImage = new Image <Gray, byte>(ExtractedFace).Resize(200, 200, Inter.Cubic);
ExtracreFaceAsImage._EqualizeHist();
FaceRecognizer.PredictionResult result = new FaceRecognizer.PredictionResult();
result = fr1.Predict(ExtracreFaceAsImage);
if (result.Distance <= 8000)
{
//s += i + " " + result.Label + " (" + result.Distance + ") ";
if (!attendanceResult.ContainsKey(result.Label))
{
attendanceResult.Add(result.Label, 0);
}
else
{
attendanceResult[result.Label]++;
}
}
result = fr2.Predict(ExtracreFaceAsImage);//receiving the result
if (result.Distance <= 3100)
{
//s += i + " " + result.Label + " (" + result.Distance + ") ";
if (!attendanceResult.ContainsKey(result.Label))
{
attendanceResult.Add(result.Label, 0);
}
else
{
attendanceResult[result.Label]++;
}
}
result = fr3.Predict(ExtracreFaceAsImage);//receiving the result
if (result.Distance <= 100)
{
//s += i + " " + result.Label + " (" + result.Distance + ") ";
if (!attendanceResult.ContainsKey(result.Label))
{
attendanceResult.Add(result.Label, 0);
}
else
{
attendanceResult[result.Label]++;
}
}
//ExtracreFaceAsImage.Save("E:\\Result\\extfaces\\" + i + ".bmp");
s += "\n";
i++;
}
imageBox1.Image = grayFrame.Resize(800, 600, Inter.Cubic);
}
}
}
}
}