private void ProcessFrame(object sender, EventArgs arg)
{
Mat frame = new Mat();
capture.Retrieve(frame, 0);
Mat image = frame; //Read the files as an 8-bit Bgr image
long detectionTime;
List faces = new List();
List eyes = new List();
//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.Purple).MCvScalar, 3);
Bitmap c = frame.Bitmap;
Bitmap bmp = new Bitmap(face.Size.Width, face.Size.Height);
Graphics g = Graphics.FromImage(bmp);
g.DrawImage(c, 0, 0, face, GraphicsUnit.Pixel);
}
foreach (Rectangle eye in eyes)
{
CvInvoke.Rectangle(image, eye, new Bgr(Color.Green).MCvScalar, 2);
}
imageBox1.Image = frame;
}
private static void DetectFaces()
{
while (true)
{
System.Threading.Thread.Sleep(100);
Mat frame = _cameraCapture.QueryFrame();
long detectionTime;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
DetectFace.Detect(
frame, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
faces, eyes,
out detectionTime);
foreach (Rectangle face in faces)
{
CvInvoke.Rectangle(frame, face, new Bgr(Color.Red).MCvScalar, 2);
var picture = frame.Bitmap.Clone(face, PixelFormat.DontCare);
picture.Save("C:\\Images\\" + Guid.NewGuid().ToString() + ".bmp", ImageFormat.Bmp);
Console.WriteLine("Face Identified");
}
}
}
private void buttonFaceExtract_Click(object sender, EventArgs e)
{
string dataDirectory = directoryFaceExtract.Text;
string[] files = Directory.GetFiles(dataDirectory, "*.jpg", SearchOption.AllDirectories);
foreach (var file in files)
{
Image <Bgr, byte> fullImage = new Image <Bgr, byte>(file);
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
long detectionTime;
DetectFace.Detect(fullImage, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes, out detectionTime);
foreach (Rectangle face in faces)
{
string fileNameImage = fileNameforExtract(file);
fullImage.ROI = face;
Directory.CreateDirectory("TrainedFaces");
fullImage.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC).ToBitmap().Save("TrainedFaces\\" + fileNameImage);
richTextBox1.Text += fileNameImage + "\n";
secondImageBox.Image = fullImage;
}
}
}
/// <summary>
/// 转换为 CameraResult
/// </summary>
/// <returns></returns>
private CameraResult ToCameraResult()
{
long detectionTime;
bool tryUseCuda = false;
bool tryUseOpenCL = true;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
//人脸及眼睛识别
this.imageCV = new Image <Bgr, byte>(this.bitmap);
DetectFace.Detect(
this.imageCV.Mat, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
faces, eyes,
tryUseCuda,
tryUseOpenCL,
out detectionTime);
//foreach (Rectangle face in faces)
// CvInvoke.Rectangle(this.imageCV, face, new Bgr(Color.Red).MCvScalar, 2);
//foreach (Rectangle eye in eyes)
// CvInvoke.Rectangle(this.imageCV, eye, new Bgr(Color.Blue).MCvScalar, 2);
//this.bitmap = this.imageCV.ToBitmap();
this.imageCV.Dispose();
this.BitmapStream.Position = 0;
this.bitmap.Save(this.BitmapStream, ImageFormat.Jpeg);
this.BitmapStream.Position = 0;
var length = this.BitmapStream.Read(this.BitmapBuffer, 0, (int)this.BitmapStream.Length);
var imgBase64 = Convert.ToBase64String(this.BitmapBuffer, 0, length);
return(new CameraResult {
imgBase64 = imgBase64, EyesMark = eyes.ToListMark(), FacesMark = faces.ToListMark()
});
}
private void StartConsumerLoop(CancellationToken cancellationToken)
{
//subcribe duoc nhieu keu
kafkaConsumer.Subscribe(Channels);
while (!cancellationToken.IsCancellationRequested)
{
try
{
var cr = this.kafkaConsumer.Consume(cancellationToken);
if (cr.Topic.Equals("REGISTER_FACE_RESPONSE"))
{
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine("REGISTER_FACE_RESPONSE --- " + cr.Message.Value);
Console.ForegroundColor = ConsoleColor.White;
RegisterFace registerFace = new RegisterFace();
registerFace = JsonConvert.DeserializeObject <RegisterFace>(cr.Message.Value);
HandleTaskRegister.Action(registerFace.Record, registerFace);
}
if (cr.Topic.Equals("DETECT_FACE_RESPONSE"))
{
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine("DETECT_FACE_RESPONSE --- " + cr.Message.Value);
Console.ForegroundColor = ConsoleColor.White;
DetectFace detectFace = new DetectFace();
detectFace = JsonConvert.DeserializeObject <DetectFace>(cr.Message.Value);
HandleTaskDetect.Action(detectFace.Record, detectFace);
}
if (cr.Topic.Equals("REMOVE_TOPIC_RESPONSE"))
{
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine("REMOVE_TOPIC_RESPONSE --- " + cr.Message.Value);
Console.ForegroundColor = ConsoleColor.White;
DeleteFace deleteFace = new DeleteFace();
deleteFace = JsonConvert.DeserializeObject <DeleteFace>(cr.Message.Value);
HandleTaskDelete.Action(deleteFace.Id, deleteFace);
}
}
catch (OperationCanceledException)
{
break;
}
catch (ConsumeException e)
{
// Consumer errors should generally be ignored (or logged) unless fatal.
Console.WriteLine($"Consume error: {e.Error.Reason}");
if (e.Error.IsFatal)
{
break;
}
}
catch (Exception e)
{
Console.WriteLine($"Unexpected error: {e}");
break;
}
}
}
string Run(string fname)
{
IImage image;
//Read the files as an 8-bit Bgr image
image = new UMat(fname, ImreadModes.Color); //UMat version
//image = new Mat("lena.jpg", ImreadModes.Color); //CPU version
// listmMats.Add(image);
//
var aaa = new Image <Emgu.CV.Structure.Gray, byte>(fname);
trainingImages.Add(aaa);
Names_List_ID.Add(Names_List_ID.Count());
long detectionTime;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
// richTextBox1.AppendText(Environment.NewLine + "aaa");
DetectFace.Detect(
image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
faces, eyes,
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);
}
// richTextBox1.AppendText(Environment.NewLine + "bbb");
//display the image
using (InputArray iaImage = image.GetInputArray())
{
// recognizer.Train(image, );
var tempfile = Path.GetTempFileName() + ".jpg";
// richTextBox1.AppendText(Environment.NewLine + "ccc");
image.Save(tempfile);
// aaa.Save(tempfile);
// recognizer.Train(image, Names_List_ID.ToArray());
// listmMats.Add(iaImage);
//var a = new ImageViewer();
//a.BackgroundImage = ;
//a.ShowDialog();
//ImageViewer.Show(image, String.Format(
// // ImageViewer.Show(image, String.Format(
// "Completed face and eye detection using {0} in {1} milliseconds",
// (iaImage.Kind == InputArray.Type.CudaGpuMat && CudaInvoke.HasCuda) ? "CUDA" :
// (iaImage.IsUMat && CvInvoke.UseOpenCL) ? "OpenCL"
// : "CPU",
// detectionTime));
richTextBox1.AppendText(Environment.NewLine + "eee");
return(tempfile);
}
}
/// <summary>
/// 采集到新图片数据帧的callback函数
/// </summary>
/// <param name="sender"></param>
/// <param name="eventArgs"></param>
void CaptureNewFrame(Object sender, NewFrameEventArgs eventArgs)
{
// Wroking on switch turn on, only process once when enabled by timer outside
if (processOnNewFrame)
{
//refresh captured image
captureImage = (Bitmap)eventArgs.Frame.Clone();
//start once and capture only one image
// Always On process, send captured image to event
OnImageCaptured(new CameraEventArgs(captureImage));
//call the local face detect method
if (localFaceDetect)
{
Bitmap detectImage = (Bitmap)eventArgs.Frame.Clone();
long detectionTime;
List <Rectangle> faces = new List <Rectangle>();
DetectFace.Detect(detectImage, "haarcascade_frontalface_default.xml", faces, out detectionTime);
if (faces.Count <= 0)
{
//no face in the image
}
else
{
if (localFaceDistanceFilter)
{
for (int i = 0; i < faces.Count; i++)
{
Rectangle face = faces[i];
if ((face.Size.Height <= ThresholdSize.Height) || (face.Size.Width <= ThresholdSize.Width))
{
faces.Remove(face);
}
}
}
if (faces.Count > 0)
{
//captureImage = DetectFace.Show(captureImage, faces);
//var face = DetectFace.SeprateFace(captureImage, faces);
var face = DetectFace.PickOneBigFace(detectImage, faces);
//only raise event when there is close enough face
//OnImageCaptured(new CameraEventArgs(captureImage));
OnFaceCaptured(new CameraFaceEventArgs(face));
}
}
}
else
{
OnFaceCaptured(new CameraFaceEventArgs(captureImage));
}
//auto turn off the process to reduce processing time cost
processOnNewFrame = false;
}
}
private void ProcessFrame(object sender, EventArgs arg)
{
Mat frame = new Mat();
capture.Retrieve(frame, 0);
Mat image = frame; //Doc file theo kieu mau RBG 8-bit
long detectionTime;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyesleft = new List <Rectangle>();
List <Rectangle> eyesright = 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_lefteye_2splits.xml", "haarcascade_righteye_2splits.xml",
faces, eyesleft, eyesright,
tryUseCuda,
tryUseOpenCL,
out detectionTime);
foreach (Rectangle face in faces)
{
CvInvoke.Rectangle(image, face, new Bgr(Color.White).MCvScalar, 3);
Bitmap c = frame.Bitmap;
Bitmap bmp = new Bitmap(face.Size.Width, face.Size.Height);
Graphics g = Graphics.FromImage(bmp);
g.DrawImage(c, 0, 0, face, GraphicsUnit.Pixel);
foreach (Rectangle eyeleft in eyesleft)
{
CvInvoke.Rectangle(image, eyeleft, new Bgr(Color.Black).MCvScalar, 3);
if (eyeleft.X != 0)
{
SendKeys.SendWait("{PGUP}");
}
}
foreach (Rectangle eyeright in eyesright)
{
CvInvoke.Rectangle(image, eyeright, new Bgr(Color.Blue).MCvScalar, 3);
if (eyeright.X != 0)
{
SendKeys.SendWait("{PGDN}");
}
}
}
imageBox2.Image = frame;//thiet lap hinh anh
}
private void GetFaces()
{
long detectionTime;
IImage image = (IImage)frame;//这一步是重点
faceImage = frame.Bitmap;
DetectFace.Detect(image
, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
faces, eyes,
out detectionTime);
}
public FaceDetectionPage()
: base()
{
var button = this.GetButton();
button.Text = "Perform Face Detection";
button.Clicked += OnButtonClicked;
OnImagesLoaded += async(sender, image) =>
{
if (image == null || image[0] == null)
{
return;
}
SetMessage("Please wait...");
//SetImage(image[0]);
SetImage(null);
FileDownloadManager downloadManager = new FileDownloadManager();
String url = "https://github.com/opencv/opencv/raw/4.2.0/data/haarcascades/";
downloadManager.AddFile(url + "/haarcascade_frontalface_default.xml", "haarcascade");
downloadManager.AddFile(url + "/haarcascade_eye.xml", "haarcascade");
downloadManager.OnDownloadProgressChanged += DownloadManager_OnDownloadProgressChanged;
await downloadManager.Download();
String faceFile = downloadManager.Files[0].LocalFile;
String eyeFile = downloadManager.Files[1].LocalFile;
long time;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
using (UMat img = image[0].GetUMat(AccessType.ReadWrite))
DetectFace.Detect(img, faceFile, eyeFile, faces, eyes, out time);
//Draw the faces in red
foreach (Rectangle rect in faces)
{
CvInvoke.Rectangle(image[0], rect, new MCvScalar(0, 0, 255), 2);
}
//Draw the eyes in blue
foreach (Rectangle rect in eyes)
{
CvInvoke.Rectangle(image[0], rect, new MCvScalar(255, 0, 0), 2);
}
String computeDevice = CvInvoke.UseOpenCL ? "OpenCL: " + Ocl.Device.Default.Name : "CPU";
SetMessage(String.Format("Detected with {1} in {0} milliseconds.", time, computeDevice));
SetImage(image[0]);
};
}
private void AddBindings(string serverPath)
{
_ninjectKernel.Bind <IPictureRepository>().To <EFPictureRepository>();
// TODO Использовать относительный путь
var faceDetect = new DetectFace(serverPath + "bin\\haarcascade_frontalface_default.xml");
_ninjectKernel.Bind <DetectFace>().ToConstant(faceDetect);
_ninjectKernel.Bind <IFiltering>().To <Pixelate>()
.WithConstructorArgument("pixelsX", 7)
.WithConstructorArgument("pixelsY", 7);
}
public FaceDetectionPage()
: base()
{
var button = this.GetButton();
button.Text = "Perform Face Detection";
button.Clicked += OnButtonClicked;
OnImagesLoaded += async(sender, image) =>
{
if (image == null || image [0] == null)
{
return;
}
SetMessage("Please wait...");
SetImage(null);
Task <Tuple <Mat, long> > t = new Task <Tuple <Mat, long> >(
() =>
{
String faceFile;
String eyeFile;
bool fileOk = CheckCascadeFile("haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
out faceFile,
out eyeFile);
long time;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
using (UMat img = image[0].GetUMat(AccessType.ReadWrite))
DetectFace.Detect(img, faceFile, eyeFile, faces, eyes, out time);
foreach (Rectangle rect in faces)
{
CvInvoke.Rectangle(image[0], rect, new MCvScalar(0, 0, 255), 2);
}
foreach (Rectangle rect in eyes)
{
CvInvoke.Rectangle(image[0], rect, new MCvScalar(255, 0, 0), 2);
}
return(new Tuple <Mat, long>(image[0], time));
});
t.Start();
var result = await t;
SetImage(t.Result.Item1);
String computeDevice = CvInvoke.UseOpenCL ? "OpenCL: " + Ocl.Device.Default.Name : "CPU";
SetMessage(String.Format("Detected with {1} in {0} milliseconds.", t.Result.Item2, computeDevice));
};
}
protected override void OnCreate(Bundle bundle)
{
base.OnCreate(bundle);
OnButtonClick += delegate
{
AppPreference appPreference = new AppPreference();
using (Image <Bgr, Byte> image = PickImage("lena.jpg"))
{
ISharedPreferences preference = PreferenceManager.GetDefaultSharedPreferences(ApplicationContext);
String appVersion = PackageManager.GetPackageInfo(PackageName, Android.Content.PM.PackageInfoFlags.Activities).VersionName;
if (!preference.Contains("cascade-data-version") || !preference.GetString("cascade-data-version", null).Equals(appVersion) ||
!(preference.Contains("cascade-eye-data-path") || preference.Contains("cascade-face-data-path")))
{
AndroidFileAsset.OverwriteMethod overwriteMethod = AndroidFileAsset.OverwriteMethod.AlwaysOverwrite;
FileInfo eyeFile = AndroidFileAsset.WritePermanantFileAsset(this, "haarcascade_eye.xml", "cascade", overwriteMethod);
FileInfo faceFile = AndroidFileAsset.WritePermanantFileAsset(this, "haarcascade_frontalface_default.xml", "cascade", overwriteMethod);
//save tesseract data path
ISharedPreferencesEditor editor = preference.Edit();
editor.PutString("cascade-data-version", appVersion);
editor.PutString("cascade-eye-data-path", eyeFile.FullName);
editor.PutString("cascade-face-data-path", faceFile.FullName);
editor.Commit();
}
string eyeXml = preference.GetString("cascade-eye-data-path", null);
string faceXml = preference.GetString("cascade-face-data-path", null);
long time;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
bool tryUseOpenCL = appPreference.UseOpenCL;
DetectFace.Detect(image.Mat, faceXml, eyeXml, faces, eyes, false, tryUseOpenCL, out time);
SetMessage(String.Format("Detected with {1} in {0} milliseconds.", time, CvInvoke.UseOpenCL ? "OpenCL" : "CPU"));
foreach (Rectangle rect in faces)
{
image.Draw(rect, new Bgr(System.Drawing.Color.Red), 2);
}
foreach (Rectangle rect in eyes)
{
image.Draw(rect, new Bgr(System.Drawing.Color.Blue), 2);
}
SetImageBitmap(image.ToBitmap());
}
};
}
private void ProcessFrame(object sender, EventArgs arg)
{
if (faceRecog.Checked == true)
{
long recpoints;
Image <Bgr, Byte> img = new Image <Bgr, byte>(secondImageBox.Image.Bitmap);
using (Image <Gray, Byte> modelImage = img.Convert <Gray, Byte>())
using (Image <Gray, Byte> observedImage = _capture.RetrieveBgrFrame().Convert <Gray, Byte>())
{
Image <Bgr, byte> result = SurfRecognizer.Draw(modelImage, observedImage, out recpoints);
captureImageBox.Image = observedImage;
if (recpoints > 10)
{
MCvFont f = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_COMPLEX, 1.0, 1.0);
//Draw "Hello, world." on the image using the specific font
result.Draw("Person Recognited, Welcome", ref f, new Point(40, 40), new Bgr(0, 255, 0));
ImageViewer.Show(result, String.Format(" {0} Points Recognited", recpoints));
}
}
}
///////////////////////////////////////////////////////////////////////
if (faceRecog.Checked == false)
{
Image <Bgr, Byte> detectedface;
Image <Bgr, Byte> frame = _capture.RetrieveBgrFrame();
Image <Bgr, Byte> image = frame.Resize(400, 300, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);//Read the files as an 8-bit Bgr image
long detectionTime;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
DetectFace.Detect(image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes, out detectionTime);
foreach (Rectangle face in faces)
{
image.Draw(face, new Bgr(Color.Red), 2);
image.ROI = face;
detectedface = image;
if (eqHisChecked.Checked == false)
{
secondImageBox.Image = detectedface.Convert <Gray, Byte>().Resize(2, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
CvInvoke.cvResetImageROI(image);
}
foreach (Rectangle eye in eyes)
{
image.Draw(eye, new Bgr(Color.Blue), 2);
}
captureImageBox.Image = image;
}
}
bool HaveFace(Image <Bgr, Byte> fname)
{
long detectionTime;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
DetectFace.Detect(
fname, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
faces, eyes,
out detectionTime);
if (faces.Count == 1 && eyes.Count == 2)
{
return(true);
}
return(false);
}
List <Rectangle> DF(Image <Bgr, byte> image)
{
long detectionTime;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
DetectFace.Detect(image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes, out detectionTime);
return(faces);
//foreach (Rectangle eye in eyes)
// image.Draw(eye, new Bgr(Color.Blue), 2);
//display the image
//ImageViewer.Show(image, String.Format(
// "Completed face and eye detection using {0} in {1} milliseconds",
// GpuInvoke.HasCuda ? "GPU" : "CPU",
// detectionTime));
}
public override async Task <DetectPersonResult> ExecuteAsync(DetectPersonContext request)
{
var retResult = new DetectPersonResult();
SelectPictureResult pictureResult;
if (request.UseCamera)
{
var pictureRequest = new TakePictureRequest {
CameraOption = request.CameraOption, MaxPixelDimension = 500
};
pictureResult = await TakePicture.ExecuteAsync(pictureRequest);
}
else
{
var choosePictureRequest = new ChoosePictureRequest {
MaxPixelDimension = 500
};
pictureResult = await ChoosePicture.ExecuteAsync(choosePictureRequest);
}
retResult.Notification.AddRange(retResult.Notification);
if (retResult.IsValid() && pictureResult.TaskResult == TaskResult.Success)
{
var faceContext = new DetectFaceContext
{
FaceImage = pictureResult.Image,
DetectFaceAttributes = request.DetectFaceAttributes,
DetectFaceId = request.DetectFaceId,
DetectFaceLandmarks = request.DetectFaceLandmarks
};
var recogResult = await DetectFace.ExecuteAsync(faceContext);
retResult.Notification.AddRange(recogResult.Notification);
if (retResult.IsValid())
{
retResult.FaceData = recogResult.Face;
}
}
return(retResult);
}
public override void ViewDidLoad()
{
base.ViewDidLoad();
ButtonText = "Detect Face & Eyes";
OnButtonClick += delegate
{
long processingTime;
using (CascadeClassifier faceCascadeClassifier = new CascadeClassifier("haarcascade_frontalface_default.xml"))
using (CascadeClassifier eyeCascadeClassifier = new CascadeClassifier("haarcascade_eye.xml"))
using (Image <Bgr, Byte> image = new Image <Bgr, Byte>("lena.jpg"))
{
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
DetectFace.Detect(
image.Mat,
faceCascadeClassifier,
eyeCascadeClassifier,
faces,
eyes,
out processingTime
);
foreach (Rectangle face in faces)
{
image.Draw(face, new Bgr(Color.Red), 1);
}
foreach (Rectangle eye in eyes)
{
image.Draw(eye, new Bgr(Color.Blue), 1);
}
Size frameSize = FrameSize;
using (Image <Bgr, Byte> resized = image.Resize(frameSize.Width, frameSize.Height, Emgu.CV.CvEnum.Inter.Nearest, true))
{
SetImage(resized);
}
}
MessageText = String.Format(
"Processing Time: {0} milliseconds.",
processingTime
);
};
}
private void addDatabaseButton_Click(object sender, EventArgs e)
{
//Take time for save filename
string fileName = textBox1.Text + "_" + DateTime.Now.Day.ToString() + "-" + DateTime.Now.Month.ToString() + "-" + DateTime.Now.Year.ToString()
+ "-" + DateTime.Now.Hour.ToString() + "-" + DateTime.Now.Minute.ToString() + "-" + DateTime.Now.Second.ToString() + ".jpg";
//First The faces in the Image is detected
Image <Bgr, Byte> image = _capture.RetrieveBgrFrame().Resize(400, 300, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
long detectionTime;
DetectFace.Detect(image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes, out detectionTime);
foreach (Rectangle face in faces)
{
image.ROI = face;
}
Directory.CreateDirectory("TrainedFaces");
image.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC).ToBitmap().Save("TrainedFaces\\" + fileName);
}
private async void OnButtonClicked(Object sender, EventArgs args)
{
Mat[] images = await LoadImages(new string[] { "lena.jpg" });
if (images == null || images[0] == null)
{
return;
}
SetMessage("Please wait...");
//SetImage(image[0]);
SetImage(null);
await InitDetector();
long time;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
using (UMat img = images[0].GetUMat(AccessType.ReadWrite))
DetectFace.Detect(img, _faceCascadeClassifier, _eyeCascadeClassifier, faces, eyes, out time);
//Draw the faces in red
foreach (Rectangle rect in faces)
{
CvInvoke.Rectangle(images[0], rect, new MCvScalar(0, 0, 255), 2);
}
//Draw the eyes in blue
foreach (Rectangle rect in eyes)
{
CvInvoke.Rectangle(images[0], rect, new MCvScalar(255, 0, 0), 2);
}
String computeDevice = CvInvoke.UseOpenCL ? "OpenCL: " + Ocl.Device.Default.Name : "CPU";
SetMessage(String.Format("Detected with {1} in {0} milliseconds.", time, computeDevice));
SetImage(images[0]);
}
public static Mat detectAndDisplayFunc(Mat frame)
{
var faces = new List <Rectangle>();
var eyes = new List <Rectangle>();
DetectFace.Detect(
frame, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml",
faces, eyes,
out var detectionTime);
//paint faces
foreach (Rectangle face in faces)
{
CvInvoke.Rectangle(frame, face, new Bgr(Color.Red).MCvScalar, 2);
}
//paint eyes
foreach (Rectangle eye in eyes)
{
CvInvoke.Rectangle(frame, eye, new Bgr(Color.Blue).MCvScalar, 2);
}
return(frame);
}
private void ProcessFrame(object sender, EventArgs arg)
{
if (_capture != null && _capture.Ptr != IntPtr.Zero)
{
Mat frame = _capture.QueryFrame();
long detectionTime;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
DetectFace.Detect(frame, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes, out detectionTime);
foreach (Rectangle face in faces)
{
Rectangle recFrame = new Rectangle(face.X, face.Y, face.Width, face.Height);
CvInvoke.Rectangle(frame, recFrame, new Bgr(Color.Red).MCvScalar, 2);
CvInvoke.Rectangle(frame, new Rectangle(recFrame.X, recFrame.Y - 20, recFrame.Width / 2, 20), new Bgr(Color.Red).MCvScalar, -1);
CvInvoke.PutText(frame, "Xmal", new Point(recFrame.X + 3, recFrame.Y - 3), FontFace.HersheyComplexSmall, 1.0, new MCvScalar(255.0, 255.0, 255.0), 1);
}
pbImg.Image = frame;
}
}
private void ProcessFrame2(object sender, EventArgs arg)
{
if (comboBoxCapture.Text == "Camera")
{
image = _capture.RetrieveBgrFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
if (comboBoxCapture.Text == "Single Image")
{
OpenFileDialog Openfile = new OpenFileDialog();
if (Openfile.ShowDialog() == DialogResult.OK)
{
image = new Image <Bgr, byte>(Openfile.FileName);
}
}
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
long detectionTime;
DetectFace.Detect(image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes, out detectionTime);
foreach (Rectangle face in faces)
{
//Image ROI selected as each face in image
if (workCorruptedImages.Checked == true)
{
image.ROI = face;
}
if (faceRecog.Checked == true)
{
//now program apply selected algorithm if recognition has started
//For SURF Algorithm
if (comboBoxAlgorithm.Text == "SURF Feature Extractor")
{
string dataDirectory = Directory.GetCurrentDirectory() + "\\TrainedFaces";
string[] files = Directory.GetFiles(dataDirectory, "*.jpg", SearchOption.AllDirectories);
foreach (var file in files)
{
richTextBox1.Text += file.ToString();
long recpoints;
Image <Bgr, Byte> sampleImage = new Image <Bgr, Byte>(file);
secondImageBox.Image = sampleImage;
using (Image <Gray, Byte> modelImage = sampleImage.Convert <Gray, Byte>())
using (Image <Gray, Byte> observedImage = image.Convert <Gray, Byte>())
{
Image <Bgr, byte> result = SurfRecognizer.Draw(modelImage, observedImage, out recpoints);
//captureImageBox.Image = observedImage;
if (recpoints > 10)
{
MCvFont f = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_COMPLEX, 1.0, 1.0);
result.Draw("Person Recognited, Welcome", ref f, new Point(40, 40), new Bgr(0, 255, 0));
ImageViewer.Show(result, String.Format(" {0} Points Recognited", recpoints));
}
}
}
}
//For EigenFaces
else if (comboBoxAlgorithm.Text == "EigenFaces")
{
CvInvoke.cvResetImageROI(image);
//image._EqualizeHist();
if (eqHisChecked.Checked == true)
{
image._EqualizeHist();
}
var result = eigenFaceRecognizer.Predict(image.Convert <Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC));
if (result.Label != -1)
{
image.Draw(eigenlabels[result.Label].ToString(), ref font, new Point(face.X - 2, face.Y - 2), new Bgr(Color.LightGreen));
label6.Text = result.Distance.ToString();
}
}
//For FisherFaces
else if (comboBoxAlgorithm.Text == "FisherFaces")
{
CvInvoke.cvResetImageROI(image);
if (eqHisChecked.Checked == true)
{
image._EqualizeHist();
}
var result = fisherFaceRecognizer.Predict(image.Convert <Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC));
if (result.Label != -1)
{
image.Draw(fisherlabels[result.Label].ToString(), ref font, new Point(face.X - 2, face.Y - 2), new Bgr(Color.LightGreen));
label6.Text = result.Distance.ToString();
}
}
//For LBPH
else if (comboBoxAlgorithm.Text == "LBPHFaces")
{
if (eqHisChecked.Checked == true)
{
image._EqualizeHist();
}
var result = lbphFaceRecognizer.Predict(image.Convert <Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC));
if (result.Label != -1)
{
CvInvoke.cvResetImageROI(image);
image.Draw(lbphlabels[result.Label].ToString(), ref font, new Point(face.X - 2, face.Y - 2), new Bgr(Color.LightGreen));
label6.Text = result.Distance.ToString();
label7.Text = lbphlabels[result.Label].ToString();
}
}
}
CvInvoke.cvResetImageROI(image);
image.Draw(face, new Bgr(Color.Red), 2);
}
captureImageBox.Image = image;
}
protected override void OnCreate(Bundle bundle)
{
base.OnCreate(bundle);
OnImagePicked += (sender, image) =>
{
if (image == null)
{
return;
}
AppPreference appPreference = new AppPreference();
CvInvoke.UseOpenCL = appPreference.UseOpenCL;
String oclDeviceName = appPreference.OpenClDeviceName;
if (!String.IsNullOrEmpty(oclDeviceName))
{
CvInvoke.OclSetDefaultDevice(oclDeviceName);
}
ISharedPreferences preference = PreferenceManager.GetDefaultSharedPreferences(ApplicationContext);
String appVersion = PackageManager.GetPackageInfo(PackageName, Android.Content.PM.PackageInfoFlags.Activities).VersionName;
if (!preference.Contains("cascade-data-version") || !preference.GetString("cascade-data-version", null).Equals(appVersion) ||
!(preference.Contains("cascade-eye-data-path") || preference.Contains("cascade-face-data-path")))
{
AndroidFileAsset.OverwriteMethod overwriteMethod = AndroidFileAsset.OverwriteMethod.AlwaysOverwrite;
FileInfo eyeFile = AndroidFileAsset.WritePermanantFileAsset(this, "haarcascade_eye.xml", "cascade", overwriteMethod);
FileInfo faceFile = AndroidFileAsset.WritePermanantFileAsset(this, "haarcascade_frontalface_default.xml", "cascade", overwriteMethod);
//save tesseract data path
ISharedPreferencesEditor editor = preference.Edit();
editor.PutString("cascade-data-version", appVersion);
editor.PutString("cascade-eye-data-path", eyeFile.FullName);
editor.PutString("cascade-face-data-path", faceFile.FullName);
editor.Commit();
}
string eyeXml = preference.GetString("cascade-eye-data-path", null);
string faceXml = preference.GetString("cascade-face-data-path", null);
long time;
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
DetectFace.Detect(image, faceXml, eyeXml, faces, eyes, out time);
String computeDevice = CvInvoke.UseOpenCL ? "OpenCL: " + Emgu.CV.Ocl.Device.Default.Name : "CPU";
SetMessage(String.Format("Detected with {1} in {0} milliseconds.", time, computeDevice));
foreach (Rectangle rect in faces)
{
CvInvoke.Rectangle(image, rect, new Bgr(System.Drawing.Color.Red).MCvScalar, 2);
}
foreach (Rectangle rect in eyes)
{
CvInvoke.Rectangle(image, rect, new Bgr(System.Drawing.Color.Blue).MCvScalar, 2);
}
SetImageBitmap(image.ToBitmap());
image.Dispose();
if (CvInvoke.UseOpenCL)
{
CvInvoke.OclFinish();
}
};
OnButtonClick += (sender, args) =>
{
PickImage("lena.jpg");
};
}
public HomeController(DetectFace detectFace, IFiltering fiters, IPictureRepository repository)
{
_detectFace = detectFace;
_filters = fiters;
_repository = repository;
}
/// <summary>
/// Метод разпознования объектов
/// </summary>
/// <param name="filenameForRecognize"></param>
/// <param name="cascade"></param>
private void Recognize(string filenameForRecognize, string cascade)
{
string filename = string.Format("{0}/{1}", App._APRSDir, cascade);
long time;
List <Rectangle> tubes = new List <Rectangle>();
List <Rectangle> old = new List <Rectangle>();
List <double> areaList = new List <double>();
CircleF circle = new CircleF();
int counter = 0;
int i = 1; //counter tubes
MCvFont font = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_COMPLEX_SMALL, 1.0, 1.0);
var ArchFile = filenameForRecognize.LoadAndResizeBitmap(1024, 1024);
//Haar cascade
var FileForRecognize = new Image <Bgr, byte>(ArchFile);
DetectFace.Detect(FileForRecognize, filename, filename, tubes, old, out time);
double AVGarea = 0.00;
foreach (Rectangle tube in tubes)
{
var area = (3.14) * tube.Width * tube.Height;
areaList.Add(area);
}
try
{
AVGarea = areaList.Average();
}
catch (Exception nullObjDetect)
{
Toast.MakeText(this, "Нет найденых объектов!!!", ToastLength.Short).Show();
}
foreach (var tube in tubes.OrderBy(s => s.X).ThenBy(u => u.Y))
{
System.Drawing.Point point = new System.Drawing.Point(tube.X + tube.Width / 3, tube.Y + tube.Height / 2);
circle.Center = new System.Drawing.PointF(tube.X + tube.Width / 2, tube.Y + tube.Height / 2);
circle.Radius = tube.Width / 2;
var area = (3.14) * tube.Width * tube.Height;
if (area / AVGarea * 100 <= 40) // меньше или равно 20 % от среднего по детектируемым объектам - не выводить в детектор
{
continue;
}
counter = i++;
if (FileForRecognize.Width <= 1024 && FileForRecognize.Height <= 768)
{
FileForRecognize.Draw(circle, new Bgr(System.Drawing.Color.Yellow), 1);
FileForRecognize.Draw(string.Format("{0}", counter), ref font, point, new Bgr(System.Drawing.Color.Red));
}
else
{
FileForRecognize.Draw(circle, new Bgr(System.Drawing.Color.Yellow), 7);
FileForRecognize.Draw(string.Format("{0}", counter), ref font, point, new Bgr(System.Drawing.Color.Red));
}
}
//Toast.MakeText(this, "Количество: " + counter + " Затрачено времени: " + time, ToastLength.Long).Show();
AlertDialog.Builder alert = new AlertDialog.Builder(this);
alert.SetTitle("Подтверждение");
alert.SetMessage(string.Format("Распознано объектов: {0} , " +
" Время распознавания: {1}", counter.ToString(), time.ToString()));
alert.SetPositiveButton("Подтверждение", (senderAlert, args) => { Toast.MakeText(this, "Подтверждено!", ToastLength.Short).Show(); });
RunOnUiThread(() => { alert.Show(); });
imageView.SetImageBitmap(FileForRecognize.ToBitmap());
GC.Collect();
}
public IActionResult Analysis()
{
string fileName = TempData["file"].ToString();
string webFile = "~/CameraPhotos" + $@"/{fileName}";
string filePath = Path.Combine(Environment.WebRootPath, "CameraPhotos") + $@"\{fileName}";
FaceAttributesViewModel faceAttributes = null;
IList <FaceAttributesViewModel> listOfFaces = new List <FaceAttributesViewModel>();
try
{
DetectFace detector = new DetectFace(filePath, Config);
var detectorTask = detector.Run();
var csFaceList = detectorTask.Result;
if (csFaceList.Count() > 0)
{
foreach (var face in csFaceList)
{
faceAttributes = new FaceAttributesViewModel
{
Age = face.FaceAttributes.Age,
Gender = face.FaceAttributes.Gender.ToString().ToLower() == "male" ? "Hombre" : "Mujer",
Makeup = face.FaceAttributes.Makeup.LipMakeup || face.FaceAttributes.Makeup.EyeMakeup ? "Si" : "No",
FacialHair = ((face.FaceAttributes.FacialHair.Beard > 0) ||
(face.FaceAttributes.FacialHair.Moustache > 0) ||
(face.FaceAttributes.FacialHair.Sideburns > 0)) ? "Si" : "No",
Glasses = face.FaceAttributes.Glasses.ToString(),
Smile = face.FaceAttributes.Smile > 0.2 ? "Si" : "No",
Image = webFile,
Hair = face.FaceAttributes.Hair.HairColor[0].Color.ToString()
};
listOfFaces.Add(faceAttributes);
}
}
else
{
faceAttributes = new FaceAttributesViewModel
{
Image = webFile,
Age = -1
};
listOfFaces.Add(faceAttributes);
}
}
catch (Exception)
{
throw;
}
////Data for testing the view if you don't want to call CS
//faceAttributes = new FaceAttributesViewModel
//{
// Age = 43,
// Gender = "Hombre",
// Makeup = "No",
// FacialHair = "No",
// Glasses = "Si",
// Smile = "No",
// Hair = "Brown",
// Image = webFile
//};
//listOfFaces.Add(faceAttributes);
return(View(listOfFaces));
}
