//:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
//::::::::::::Detection of the hand in a gray image::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
public List<Object> Detection(Image<Gray, Byte> frame)
{
List<Object> listReturn = new List<object>(2);
haar = new CascadeClassifier(@"C:\Users\America\Documents\MySystemV1\classifier\cascade.xml");
if (frame != null)
{
System.Drawing.Rectangle[] hands = haar.DetectMultiScale(frame, 1.1, 4, new System.Drawing.Size(frame.Width / 8, frame.Height / 8), new System.Drawing.Size(frame.Width / 3, frame.Height / 3));
foreach (System.Drawing.Rectangle roi in hands)
{
roi.Inflate(-5, 17); // Make the roi bigger, becuse we dont obteined the tootal length of the fingers, in some cases.
frame.Draw(roi, new Gray (255), 3);
}
if (hands.Count() == 0)
{
Rectangle[] noDetection= new Rectangle[]{};
//noDetection[0] = Rectangle.Empty;
listReturn.Add(noDetection);
}
else
{
listReturn.Add(hands);
}
}
listReturn.Add(frame);
return listReturn;
//Regresa los dos valores si el frame es diferente de null, lo cual se supone que siempre es cierto, por que eso se toma en cuenta desde data poll
}//finaliza detection()
static void Run()
{
CascadeClassifier cas = new CascadeClassifier("haarcascade_frontalface_default.xml");
/* Mat image = new Mat("lena.jpg", LoadImageType.Color); //Read the files as an 8-bit Bgr image
long detectionTime;
List<Rectangle> faces = new List<Rectangle>();
List<Rectangle> eyes = new List<Rectangle>();
//The cuda cascade classifier doesn't seem to be able to load "haarcascade_frontalface_default.xml" file in this release
//disabling CUDA module for now
bool tryUseCuda = false;
bool tryUseOpenCL = true;
DetectFace.Detect(
image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
faces, eyes,
tryUseCuda,
tryUseOpenCL,
out detectionTime);
foreach (Rectangle face in faces)
CvInvoke.Rectangle(image, face, new Bgr(Color.Red).MCvScalar, 2);
foreach (Rectangle eye in eyes)
CvInvoke.Rectangle(image, eye, new Bgr(Color.Blue).MCvScalar, 2);
//display the image
ImageViewer.Show(image, String.Format(
"Completed face and eye detection using {0} in {1} milliseconds",
(tryUseCuda && CudaInvoke.HasCuda) ? "GPU"
: (tryUseOpenCL && CvInvoke.HaveOpenCLCompatibleGpuDevice) ? "OpenCL"
: "CPU",
detectionTime));*/
}
public static void detectFaceCPU(Image<Bgr, Byte> image, String faceFileName, String eyesFileName, List<Rectangle> facesList, List<Rectangle> eyesList, out long detectionTime)
{
Stopwatch watch;
using (CascadeClassifier faceCascade = new CascadeClassifier(faceFileName))
using (CascadeClassifier eyesCascade = new CascadeClassifier(eyesFileName))
{
watch = Stopwatch.StartNew();
using (Image<Gray, Byte> grayImage = image.Convert<Gray, Byte>())
{
//grayImage._EqualizeHist();
Rectangle[] facesRegion = faceCascade.DetectMultiScale(grayImage, 1.1, 10, new Size(image.Width / 8, image.Height / 8), Size.Empty);
facesList.AddRange(facesRegion);
foreach (Rectangle f in facesRegion)
{
grayImage.ROI = f;
Rectangle[] eyesDetected = eyesCascade.DetectMultiScale(grayImage, 1.1, 10, new Size(image.Width / 8, image.Height / 8), Size.Empty);
grayImage.ROI = Rectangle.Empty;
foreach (Rectangle e in eyesDetected)
{
Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
eyesList.Add(eyeRect);
}
}
}
watch.Stop();
}
detectionTime = watch.ElapsedMilliseconds;
}
public static void Detect(Image<Bgr, Byte> image, String faceFileName, List<Rectangle> recFaces, List<Image<Bgr, Byte>> imgFaces, out long detectionTime)
{
Stopwatch watch;
{
//Read the HaarCascade objects
using (CascadeClassifier faceClassifier = new CascadeClassifier(faceFileName))
{
watch = Stopwatch.StartNew();
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>()) //Convert it to Grayscale
{
//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
Rectangle[] facesDetected = faceClassifier.DetectMultiScale(
gray,
1.1,
10,
new Size(20, 20),
Size.Empty);
recFaces.AddRange(facesDetected);
//Now for each rectangle, get the sub face image from the coordinates and store it for display later
foreach (Rectangle rec in facesDetected)
imgFaces.Add(image.GetSubRect(rec));
}
watch.Stop();
}
}
detectionTime = watch.ElapsedMilliseconds;
}
public RecognitionOnPrem()
{
InitializeComponent();
Loaded += (s, e) =>
{
if (grabber == null)
{
_faceClassifier = new CascadeClassifier("haarcascade_frontalface_default.xml");
//count number of trained faces
ContTrain = CommonData.TrainingImages.Count;
grabber = new Capture();
grabber.QueryFrame();
}
else
{
grabber.Start();
}
};
Unloaded += (s, e) => {
grabber.Stop();
};
CompositionTarget.Rendering += CompositionTarget_Rendering;
}
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);
}
}
}
public FaceDetectionAlgorithm()
{
string path = new Uri(Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().GetName().CodeBase) + "\\Files\\Cascades").LocalPath+"\\";
_faceHaarCascade = new CascadeClassifier(path + "haarcascade_frontalface_alt.xml");
_eyeHaarCascade = new CascadeClassifier(path + "haarcascade_eye.xml");
}
private void Process(object sender, EventArgs arg)
{
classify = new CascadeClassifier("haarcascade_frontalface_default.xml");
classify1 = new CascadeClassifier("haarcascade_eye.xml");
Image<Bgr, Byte> img = cap.QueryFrame().ToImage<Bgr, Byte>();
if (img != null)
{
Image<Gray,byte> grayscaleframe = img.Convert<Gray, byte>();
var faces = classify.DetectMultiScale(grayscaleframe, 1.1,3,Size.Empty,Size.Empty);
var eyes = classify1.DetectMultiScale(grayscaleframe, 1.1, 10,Size.Empty,Size.Empty);
foreach(var face in faces)
{
img.Draw(face, new Bgr(Color.Blue));
}
foreach(var eye in eyes)
{
img.Draw(eye, new Bgr(Color.Red), 2);
}
}
imgCap.Image = img;
}
//Процедура обработки изображения и поика циферблата
private void button1_Click(object sender, EventArgs e)
{
Image<Bgr, Byte> frame = (Image<Bgr, Byte>)VideoImage.Image; //Полученный кадр
using (CascadeClassifier dial = new CascadeClassifier(faceFileName)) //Каскад
using (Image<Gray, Byte> gray = frame.Convert<Gray, Byte>()) //Хаар работает с ЧБ изображением
{
//Детектируем
Rectangle[] facesDetected2 = dial.DetectMultiScale(
gray, //Исходное изображение
1.1, //Коэффициент увеличения изображения
6, //Группировка предварительно обнаруженных событий. Чем их меньше, тем больше ложных тревог
new Size(5, 5), //Минимальный размер циферблата
Size.Empty); //Максимальный размер циферблата
//Выводим всё найденное
foreach (Rectangle f in facesDetected2)
{
frame.Draw(f, new Bgr(Color.Blue), 2);
VideoImage.Image = frame;
//frame.ROI = f;
frame.Save("out.bmp");
Bitmap bmp = new Bitmap("out.bmp");
BitmapToBlackWhite2(bmp).Save("out_black.bmp");
LabelConnectedComponents(gray, 0).Save("label.bmp");
// BinaryImage.Image =
//gray.ThresholdAdaptive(new Gray(255), ADAPTIVE_THRESHOLD_TYPE.CV_ADAPTIVE_THRESH_MEAN_C, THRESH.CV_THRESH_OTSU, 5, new Gray(0.03)).Save("another.bmp");
}
}
}
public AIRecognition()
{
InitializeComponent();
_faceClassifier = new CascadeClassifier("haarcascade_frontalface_default.xml");
Loaded += (s, e) =>
{
_vmodel.Pictures.Clear();
_vmodel.PersonRecognized = 0;
this.DataContext = _vmodel;
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;
}
public BlinkForm()
{
InitializeComponent();
faceClassifier = new CascadeClassifier(Application.StartupPath + "\\Classifier\\haarcascade_frontalface_default.xml");
eyePairClassifier = new CascadeClassifier(Application.StartupPath + "\\Classifier\\haarcascade_mcs_eyepair_big.xml");
eyeClassifier = new CascadeClassifier(Application.StartupPath + "\\Classifier\\haarcascade_eye_tree_eyeglasses.xml");
Detect();
}
public TrainData()
{
InitializeComponent();
fr1 = new EigenFaceRecognizer(80, double.PositiveInfinity);//The recognitoion object
fr2 = new FisherFaceRecognizer(-1, 3100);//The recognitoion object
fr3 = new LBPHFaceRecognizer(1, 8, 8, 8, 100);//50
cascade = new CascadeClassifier("haarcascade_frontalface_default.xml"); //this file contains the training
}
public Classifier()
{
_internalClassifier = new CascadeClassifier(CascadPath);
ScaleFactor = 1.1;
MinNeighbours = 6;
MinSize = new Size(10, 10);
MaxSize = Size.Empty;
}
public Rectangle[] FindEyes(string eyeFileName, Image<Gray, Byte> imageFace)
{
using (CascadeClassifier eye = new CascadeClassifier(eyeFileName))
using (Mat eyeRegionMat = new Mat())
{
Rectangle[] eyeRegion = eye.DetectMultiScale(imageFace, eyescale, eyeneighbors, new Size(eyeminsize, eyeminsize), new Size(eyemaxsize, eyemaxsize));
return eyeRegion;
}
}
public SDMHand(PictureBox picturePreview) : this()
{
_picturePreview = picturePreview;
_cascadeClassifier = new CascadeClassifier(@".\HaarCascade\face.xml");
_rectangles = new List<Rectangle>(SDMConfig.BufferSize);
_nui = new NUI(CommandBox);
Show();
}
// Use this for initialization
void Start()
{
Texture2D lenaTexture = Resources.Load<Texture2D>("lena");
//updateTextureWithString("load lena ok");
Image<Bgr, Byte> img = TextureConvert.Texture2dToImage<Bgr, byte>(lenaTexture);
//updateTextureWithString("convert to image ok");
//String fileName = "haarcascade_frontalface_default";
//String fileName = "lbpcascade_frontalface";
String fileName = "haarcascade_frontalface_alt2";
String filePath = Path.Combine(Application.persistentDataPath, fileName + ".xml");
//if (!File.Exists(filePath))
{
//updateTextureWithString("start move cascade xml");
TextAsset cascadeModel = Resources.Load<TextAsset>(fileName);
#if UNITY_METRO
UnityEngine.Windows.File.WriteAllBytes(filePath, cascadeModel.bytes);
#else
File.WriteAllBytes(filePath, cascadeModel.bytes);
#endif
//updateTextureWithString("File size: " + new FileInfo(filePath).Length);
}
using (CascadeClassifier classifier = new CascadeClassifier(filePath))
using (Image<Gray, Byte> gray = img.Convert<Gray, byte>())
{
//updateTextureWithString("classifier create ok");
Rectangle[] faces = null;
try
{
faces = classifier.DetectMultiScale(gray);
//updateTextureWithString("face detected");
foreach (Rectangle face in faces)
{
CvInvoke.Rectangle(img, face, new MCvScalar(0, 255, 0));
}
}
catch (Exception e)
{
//updateTextureWithString(e.Message);
return;
}
//updateTextureWithString(String.Format("{0} face found on image of {1} x {2}", faces.Length, img.Width, img.Height));
}
Texture2D texture = TextureConvert.ImageToTexture2D(img, FlipType.Vertical);
this.GetComponent<GUITexture>().texture = texture;
this.GetComponent<GUITexture>().pixelInset = new Rect(-img.Width / 2, -img.Height / 2, img.Width, img.Height);
}
private void EmguFaceDetector(string path)
{
if (capture == null)
{
capture = new Emgu.CV.VideoCapture(0);
}
capture.ImageGrabbed += Capture_ImageGrabbed;
capture.Start();
emguFaceClassifier = new Emgu.CV.CascadeClassifier(@"./haarcascade/haarcascade_frontalface_alt.xml");
}
public void pump(ref AdvertDetails _advertDetails)
{
CascadeClassifier cc = new CascadeClassifier(VWclassifier);
bool carExitst = _advertDetails.CarFound?true:false;
Image<Gray, byte> image;
if(carExitst)
image = _advertDetails.Image.GetSubRect(_advertDetails.Rect).Convert<Gray, byte>();
else
image = _advertDetails.Image.Convert<Gray, byte>();
Rectangle[] logosFound = cc.DetectMultiScale(image, 1.05, 1, new Size(20,20), new Size(40,40));
}
public MainWindow()
{
InitializeComponent();
capture = new Capture();
haarCascade = new CascadeClassifier("../../haarcascade_frontalface_alt.xml");
timer = new DispatcherTimer();
timer.Tick += new EventHandler(timer_Tick);
timer.Interval = new TimeSpan(0, 0, 0, 0, 33);
timer.Start();
}
public JanelaTreinarDetector()
{
InitializeComponent();
CvInvoke.UseOpenCL = false;
//Parent = _Parent;
//Face = Parent.Face;
Face = new CascadeClassifier("haarcascade_frontalface_default.xml");//Our face detection method
ENC_Parameters.Param[0] = ENC;
Image_Encoder_JPG = GetEncoder(ImageFormat.Jpeg);
initialise_capture();
}
// Use this for initialization
void Start()
{
Texture2D lenaTexture = Resources.Load<Texture2D>("lena");
UMat img = new UMat();
TextureConvert.Texture2dToOutputArray(lenaTexture, img);
CvInvoke.Flip(img, img, FlipType.Vertical);
//String fileName = "haarcascade_frontalface_default";
//String fileName = "lbpcascade_frontalface";
String fileName = "haarcascade_frontalface_alt2";
String filePath = Path.Combine(Application.persistentDataPath, fileName + ".xml");
//if (!File.Exists(filePath))
{
TextAsset cascadeModel = Resources.Load<TextAsset>(fileName);
#if UNITY_METRO
UnityEngine.Windows.File.WriteAllBytes(filePath, cascadeModel.bytes);
#else
File.WriteAllBytes(filePath, cascadeModel.bytes);
#endif
}
using (CascadeClassifier classifier = new CascadeClassifier(filePath))
using (UMat gray = new UMat())
{
CvInvoke.CvtColor(img, gray, ColorConversion.Bgr2Gray);
Rectangle[] faces = null;
try
{
faces = classifier.DetectMultiScale(gray);
foreach (Rectangle face in faces)
{
CvInvoke.Rectangle(img, face, new MCvScalar(0, 255, 0));
}
}
catch (Exception e)
{
Debug.Log(e.Message);
return;
}
}
Texture2D texture = TextureConvert.InputArrayToTexture2D(img, FlipType.Vertical);
this.GetComponent<GUITexture>().texture = texture;
Size s = img.Size;
this.GetComponent<GUITexture>().pixelInset = new Rect(-s.Width / 2, -s.Height / 2, s.Width, s.Height);
}
public FaceDetectorDevice()
{
//Read the HaarCascade objects
CvInvoke.UseOpenCL = false;
faceClassifier = new CascadeClassifier(@"Resources\haarcascades\frontalface_alt.xml");
eyeClassifier = new CascadeClassifier(@"Resources\haarcascades\eye.xml");
mouthClassifier = new CascadeClassifier(@"Resources\haarcascades\mouth.xml");
noseClassifier = new CascadeClassifier(@"Resources\haarcascades\nose.xml");
detectionNotifyTimer = new Timer();
detectionNotifyTimer.Elapsed += DetectionNotifyTimerOnElapsed;
DetectionMode = DetectionModes.Disabled;
DetectionPeriod = TimeSpan.FromMilliseconds(500);
}
public override void OnStart()
{
PackageHost.WriteInfo("Package starting - IsRunning: {0} - IsConnected: {1}", PackageHost.IsRunning, PackageHost.IsConnected);
string startupPath = System.Reflection.Assembly.GetExecutingAssembly().CodeBase;
IDBAccess dataStore = new DBAccess("facesDB.db");
recoEngine = new RecognizerEngine(
Path.Combine(Environment.CurrentDirectory, "data/facesDB.db"),
Path.Combine(Environment.CurrentDirectory, "data/RecognizerEngineData.YAML")); //"/data/facesDB.db", startupPath + "/data/RecognizerEngineData.YAML");
cap = new Capture();
//cap.QueryFrame().Save(Path.Combine(Environment.CurrentDirectory, "test.bmp"));
Task.Factory.StartNew(() =>
{
while (PackageHost.IsRunning)
{
Rectangle[] faces;
//string bla = System.Reflection.Assembly.GetExecutingAssembly(). CodeBase + "/haarcascade_frontalface_default.xml";
cascadeClassifier = new CascadeClassifier( Path.Combine(Environment.CurrentDirectory, "haarcascade_frontalface_default.xml"));
using (var imageFrame = cap.QueryFrame().ToImage<Bgr, Byte>())
{
if (imageFrame != null)
{
var grayframe = imageFrame.Convert<Gray, byte>();
faces = cascadeClassifier.DetectMultiScale(grayframe, 1.2, 10, Size.Empty); //the actual face detection happens here
PackageHost.PushStateObject<Rectangle[]>("faces", faces);
foreach (var face in faces)
{
int nameID = recoEngine.RecognizeUser(imageFrame.GetSubRect(face).Convert<Gray, byte>());
if (nameID == 0)
{
PackageHost.WriteWarn("unknown face");
PackageHost.PushStateObject<String>("Face", "Unknown");
}
else
{
string name = dataStore.GetUsername(nameID);
PackageHost.WriteInfo("face recognized : {0}", name);
PackageHost.PushStateObject<String>("Face", name);
}
}
}
}
Thread.Sleep(5000);//PackageHost.GetSettingValue<int>("RefreshRate")
}
});
}
/// <summary>
/// Implements the car detector filter and adds the details to the advertDetails object
/// </summary>
/// <param name="_advertDetails">The advertDetails object where information about the advert is stored</param>
public virtual void pump(ref AdvertDetails _advertDetails)
{
Rectangle rect = new Rectangle();
String view = "Unknown";
Image<Gray, Byte> image = _advertDetails.Image.Convert<Gray, byte>();
CascadeClassifier classifier = new CascadeClassifier(frontClassifier);
Rectangle[] rectangleList = new Rectangle[0];
classifier = new CascadeClassifier(sideClassifier);
Rectangle[] temp = classifier.DetectMultiScale(image, scaleFac, numNeighbours, side_minSize, maxSize);
if (temp.Length > rectangleList.Length)
{
rectangleList = temp;
view = "Side";
}
if(view != "Side")
{
temp = classifier.DetectMultiScale(image, scaleFac, numNeighbours, fb_minSize, maxSize);;
if(temp.Length > rectangleList.Length)
{
rectangleList = temp;
view = "Front";
}
classifier = new CascadeClassifier(backClassifier);
temp = classifier.DetectMultiScale(image, scaleFac, numNeighbours, fb_minSize, maxSize);
if (temp.Length > rectangleList.Length)
{
rectangleList = temp;
view = "Back";
}
}
if (rectangleList.Length > 0)
{
rect = getLargest(rectangleList);
_advertDetails.Rect = rect;
_advertDetails.CarFound = true;
_advertDetails.View = view;
_advertDetails.CarRating = 1;
}
else
{
_advertDetails.CarFound = false;
_advertDetails.Error = "No car found.";
}
}
public static void Detect(Image<Bgr, Byte> image, String faceFileName, String eyeFileName, List<System.Drawing.Rectangle> faces, List<System.Drawing.Rectangle> eyes)
{
using (CascadeClassifier face = new CascadeClassifier(faceFileName))
//Read the eyeFileName objects
using (CascadeClassifier eye = new CascadeClassifier(eyeFileName))
{
var watch = Stopwatch.StartNew();
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>()) //Convert it to Grayscale
{
//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
System.Drawing.Rectangle[] facesDetected = face.DetectMultiScale(
gray,
1.1,
10,
new System.Drawing.Size(20, 20),
System.Drawing.Size.Empty);
faces.AddRange(facesDetected);
foreach (System.Drawing.Rectangle f in facesDetected)
{
//Set the region of interest on the faces
gray.ROI = f;
System.Drawing.Rectangle[] eyesDetected = eye.DetectMultiScale(
gray,
1.1,
10,
new System.Drawing.Size(20, 20),
System.Drawing.Size.Empty);
gray.ROI = System.Drawing.Rectangle.Empty;
foreach (System.Drawing.Rectangle e in eyesDetected)
{
System.Drawing.Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
eyes.Add(eyeRect);
}
}
}
watch.Stop();
}
}
float disNo_Mo; //nose-mouth
public ImproveRecognize()
{
mineye = new Size(10,10);
maxeye = new Size(50, 50);
minnose = new Size(10, 10);
maxnose = new Size(50, 50);
minmouth = new Size(10, 10);
maxmouth = new Size(50, 100);
eyewithglass = new CascadeClassifier("haarcascade_eye_tree_eyeglasses.xml");
nose = new CascadeClassifier("haarcascade_mcs_nose.xml");
mouth = new CascadeClassifier("haarcascade_mcs_mouth.xml");
coord = new Point[4];
}
public static void detectFace(Image<Bgr, Byte> image, String faceFileName, List<Rectangle> facesList, out long detectionTime)
{
Stopwatch watch;
using (CascadeClassifier faceCascade = new CascadeClassifier(faceFileName))
{
watch = Stopwatch.StartNew();
using (Image<Gray, Byte> grayImage = image.Convert<Gray, Byte>())
{
Rectangle[] facesRegion = faceCascade.DetectMultiScale(grayImage, 1.1, 10, new Size(24, 24), Size.Empty);
facesList.AddRange(facesRegion);
}
watch.Stop();
}
detectionTime = watch.ElapsedMilliseconds;
}
public static void Detect(Mat image, String faceFileName,
String eyeFileName, List<Rectangle> faces, List<Rectangle> eyes)
{
//Read the HaarCascade objects
using (CascadeClassifier face = new CascadeClassifier(faceFileName))
using (CascadeClassifier eye = new CascadeClassifier(eyeFileName))
{
Size size = new Size(image.Rows, image.Cols);
using (UMat ugray = new UMat(size, DepthType.Cv8U, 1))
{
//CvInvoke.CvtColor(image, ugray, ColorConversion.Bgr2Gray);
//normalizes brightness and increases contrast of the image
CvInvoke.EqualizeHist(image, ugray);
//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
Rectangle[] facesDetected = face.DetectMultiScale(
ugray,
1.1,
10,
new Size(20, 20));
faces.AddRange(facesDetected);
foreach (Rectangle f in facesDetected)
{
//Get the region of interest on the faces
using (UMat faceRegion = new UMat(ugray, f))
{
Rectangle[] eyesDetected = eye.DetectMultiScale(
faceRegion,
1.1,
10,
new Size(20, 20));
foreach (Rectangle e in eyesDetected)
{
Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
eyes.Add(eyeRect);
}
}
}
}
}
}
//what this function does ?
// open camera, take a capture
// when an image is grabbed (captured) add it to the capture object
// then, process frame :
// create a matrix object to save the picture
// create Matrix to save the pictures in
// put what camera captured into the matrix object : Mat
// convert frame obj to an image obj
// if frames object contain images :
// loop at all images . detect faces and draw blue rectangles on detected faces
// Add the all extracted faces to : extfaces (arraylist) && count no of faces (INT counter)
// DisplayThe recording by the camera in the imagebox
public VideoCapturing()
{
InitializeComponent();
cascade = new CascadeClassifier("haarcascade_frontalface_default.xml"); //this file contains the training
CvInvoke.UseOpenCL = false;
try
{
capture = new Capture();
ExtFaces = new List<Bitmap>();
capture.ImageGrabbed += ProcessFrame;
}
catch (NullReferenceException excpt)
{
MessageBox.Show(excpt.Message);
}
}
private void EmguFaceDetector()
{
Emgu.CV.CascadeClassifier emguFaceClassifier = null;
if (File.Exists(this.ImagePath))
{
if (File.Exists(@"./haarcascade/haarcascade_frontalface_alt.xml"))
{
emguFaceClassifier = new Emgu.CV.CascadeClassifier(@"./haarcascade/haarcascade_frontalface_alt.xml");
Emgu.CV.Mat src = CvInvoke.Imread(this.ImagePath, 0);
Emgu.CV.Mat gray = new Emgu.CV.Mat();
CvInvoke.CvtColor(src, gray, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray);
var faces = emguFaceClassifier.DetectMultiScale(gray, 1.1, 2, new System.Drawing.Size(30, 30));
int facecnt = faces.Length;
}
}
}
public Form1()
{
InitializeComponent();
recognizer = new LBPHFaceRecognizer(1, 8, 8, 9, 65);
classifier = new CascadeClassifier(haarcascade);
GPU_classifier = new GpuCascadeClassifier(haarcascade_cuda);
font = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_TRIPLEX, 0.5, 0.5);
if (File.Exists(@"traningdata.xml"))
{
recognizer.Load(@"traningdata.xml");
}
else
{
foreach (var file in Directory.GetFiles(Application.StartupPath + @"\Traning Faces\"))
{
try { temp = new Image<Gray, Byte>(file); }
catch { continue; }
temp._EqualizeHist();
var detectedFaces = classifier.DetectMultiScale(temp, 1.1, 15, new Size(24, 24), Size.Empty);
if (detectedFaces.Length == 0)
{
continue;
}
temp.ROI = detectedFaces[0];
temp = temp.Copy();
temp = temp.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
imagesList.Add(temp);
imagesLabels.Add(Path.GetFileNameWithoutExtension(file));
}
for (int i = 0; i < imagesList.Count; i++)
{
imagesLabels_indices.Add(i);
}
try { recognizer.Train(imagesList.ToArray(), imagesLabels_indices.ToArray()); }
catch (Exception ex)
{
MessageBox.Show(ex.Message);
Environment.Exit(0);
}
}
}
public FormTrain(Form1 frm1)
{
InitializeComponent();
_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);
captureT = new Capture();
Application.Idle += new EventHandler(runningCamera);
}
public static List <Rectangle> DetectFaces(Emgu.CV.Mat image)
{
List <Rectangle> faces = new List <Rectangle>();
var facesCascade = HttpContext.Current.Server.MapPath("~/face.xml");
using (Emgu.CV.CascadeClassifier face = new Emgu.CV.CascadeClassifier(facesCascade))
{
using (UMat ugray = new UMat())
{
CvInvoke.CvtColor(image, ugray, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray);
CvInvoke.EqualizeHist(ugray, ugray);
Rectangle[] facesDetected = face.DetectMultiScale(
ugray,
1.1,
10,
new System.Drawing.Size(20, 20));
faces.AddRange(facesDetected);
}
}
return(faces);
}
public Face_Detection_Form()
{
InitializeComponent();
_faceDetected = new Emgu.CV.CascadeClassifier("Cascade_Class_Face.xml");
try
{
string labelsInfo = File.ReadAllText(Application.StartupPath + "\\faces\\faces.txt");
string[] labels = labelsInfo.Split(',');
Numlabels = Convert.ToInt16(labels[0]);
Count = Numlabels;
string Faceload;
for (int i = 1; i < Numlabels + 1; i++)
{
Faceload = "Face" + i + ".bmp";
trainningImages.Add(new Image <Gray, byte>(Application.StartupPath + "\\faces\\faces.txt"));
_labelsObject.Add(labels[i]);
}
}
catch (Exception ex)
{
MessageBox.Show("Can not read data from Database!");
}
}
public async Task <HttpResponseMessage> Index()
{
if (!Request.Content.IsMimeMultipartContent())
{
throw new HttpResponseException(HttpStatusCode.UnsupportedMediaType);
}
Emgu.CV.CascadeClassifier cc = new Emgu.CV.CascadeClassifier(System.Web.Hosting.HostingEnvironment.MapPath("/haarcascade_frontalface_alt_tree.xml"));
var provider = new MultipartMemoryStreamProvider();
await Request.Content.ReadAsMultipartAsync(provider);
foreach (var file in provider.Contents)
{
var filename = file.Headers.ContentDisposition.FileName.Trim('\"');
var buffer = await file.ReadAsByteArrayAsync();
using (MemoryStream mStream = new MemoryStream(buffer, 0, buffer.Length))
{
mStream.Position = 0;
//Do whatever you want with filename and its binary data.
using (Bitmap bmp = new Bitmap(mStream))
{
using (Emgu.CV.Image <Emgu.CV.Structure.Bgr, Int32> img = new Emgu.CV.Image <Emgu.CV.Structure.Bgr, Int32>(bmp))
{
if (img != null)
{
var grayframe = img.Convert <Emgu.CV.Structure.Gray, byte>();
var faces = cc.DetectMultiScale(grayframe);//, 1.1, 10, Size.Empty);
int faceCount = 0;
foreach (var face in faces)
{
// only returns the first face found
faceCount++;
using (Bitmap faceBmp = new Bitmap(face.Right - face.Left, face.Bottom - face.Top))
{
Graphics g = Graphics.FromImage(faceBmp);
g.DrawImage(bmp, new Rectangle(0, 0, faceBmp.Width, faceBmp.Height), face.Left, face.Top, faceBmp.Width, faceBmp.Height, GraphicsUnit.Pixel);
MemoryStream outStream = new MemoryStream();
faceBmp.Save(outStream, System.Drawing.Imaging.ImageFormat.Jpeg);
var result = new HttpResponseMessage(HttpStatusCode.OK)
{
Content = new ByteArrayContent(outStream.ToArray()),
};
result.Content.Headers.ContentDisposition =
new System.Net.Http.Headers.ContentDispositionHeaderValue("attachment")
{
FileName = "face.jpg"
};
result.Content.Headers.ContentType = new System.Net.Http.Headers.MediaTypeHeaderValue("image/jpeg");
result.Content.Headers.ContentLength = outStream.Length;
return(result);
}
}
}
}
}
}
}
throw new HttpResponseException(HttpStatusCode.InternalServerError);
}
public FrameGrabber()
{
Detector = new CascadeClassifier("haarcascade_profileface.xml");
}