public ImageService(IEventAggregator eventAggregator)
{
_eventAggregator = eventAggregator;
_eventAggregator.Subscribe(this);
_cascade = new FaceHaarCascade();
_harHaarObjectDetector = new HaarObjectDetector(_cascade, 30);
SetupDetector();
}
public void MaxSizeTest()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
50, ObjectDetectorSearchMode.Default);
Bitmap bmp = Properties.Resources.lena_color;
Rectangle[] result;
target.MaxSize = new Size(10, 60);
result = target.ProcessFrame(bmp);
Assert.AreEqual(0, result.Length);
target.MaxSize = new Size(60, 60);
result = target.ProcessFrame(bmp);
Assert.AreEqual(1, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width <= target.MaxSize.Width);
Assert.IsTrue(r.Height <= target.MaxSize.Height);
}
target.MaxSize = new Size(80, 80);
result = target.ProcessFrame(bmp);
Assert.AreEqual(2, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width <= target.MaxSize.Width);
Assert.IsTrue(r.Height <= target.MaxSize.Height);
}
}
public void ProcessFrame2()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
30, ObjectDetectorSearchMode.NoOverlap);
Bitmap bmp = Properties.Resources.lena_gray;
target.ProcessFrame(bmp);
Assert.AreEqual(1, target.DetectedObjects.Length);
Assert.AreEqual(255, target.DetectedObjects[0].X);
Assert.AreEqual(225, target.DetectedObjects[0].Y);
Assert.AreEqual(123, target.DetectedObjects[0].Width);
Assert.AreEqual(123, target.DetectedObjects[0].Height);
target = new HaarObjectDetector(cascade,
30, ObjectDetectorSearchMode.Default);
target.ProcessFrame(bmp);
Assert.AreEqual(6, target.DetectedObjects.Length);
Assert.AreEqual(255, target.DetectedObjects[0].X);
Assert.AreEqual(225, target.DetectedObjects[0].Y);
Assert.AreEqual(123, target.DetectedObjects[0].Width);
Assert.AreEqual(123, target.DetectedObjects[0].Height);
}
// Constructor
public MainForm()
{
InitializeComponent();
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade,
25, ObjectDetectorSearchMode.Single, 1.2f,
ObjectDetectorScalingMode.GreaterToSmaller);
}
public string CleanUpImage(string rawDataUrl, out string intermediateUrl)
{
intermediateUrl = null;
var cs = new FaceHaarCascade();
var detector = new HaarObjectDetector(cs, 30)
{
SearchMode = ObjectDetectorSearchMode.Average,
ScalingMode = ObjectDetectorScalingMode.SmallerToGreater,
ScalingFactor = 1.5f,
UseParallelProcessing = true,
Suppression = 2
};
string contentType;
var data = GetDataFromUrl(rawDataUrl, out contentType);
using (var ms = new MemoryStream(data))
{
var image = (Bitmap)Bitmap.FromStream(ms);
new ContrastStretch().ApplyInPlace(image);
var faces = detector.ProcessFrame(image);
if (faces.Length > 0)
{
var intermediateImage = new Bitmap(image);
new RectanglesMarker(faces, Color.Red).ApplyInPlace(intermediateImage);
var boundary = Math.Max(40, faces.Max(i => Math.Max(i.Height, i.Width)));
var x1 = Math.Max(0, faces.Min(i => i.Left) - boundary);
var y1 = Math.Max(0, faces.Min(i => i.Top) - boundary);
var x2 = Math.Min(image.Width, faces.Max(i => i.Right) + boundary);
var y2 = Math.Min(image.Height, faces.Max(i => i.Bottom) + boundary);
var newBoundingBox = new Rectangle(x1, y1, x2 - x1, y2 - y1);
new RectanglesMarker(new [] { newBoundingBox }, Color.Blue).ApplyInPlace(intermediateImage);
using (var ms2 = new MemoryStream())
{
intermediateImage.Save(ms2, ImageFormat.Jpeg);
intermediateUrl = string.Concat("data:image/jpeg;base64,", Convert.ToBase64String(ms2.ToArray()));
}
// perform no cropping of the image - post the original
}
// save off at JPG/100
var finalImage = ImageHelper.GetBytes(s => image.Save(s, ImageHelper.JPEGEncoder(), ImageHelper.Quality(100)));
var newDataUrl = string.Concat("data:image/jpeg;base64,", Convert.ToBase64String(finalImage));
return newDataUrl;
}
}
public MainForm()
{
InitializeComponent();
cbMode.DataSource = Enum.GetValues(typeof(ObjectDetectorSearchMode));
cbScaling.DataSource = Enum.GetValues(typeof(ObjectDetectorScalingMode));
cbMode.SelectedItem = ObjectDetectorSearchMode.Single;
cbScaling.SelectedItem = ObjectDetectorScalingMode.GreaterToSmaller;
toolStripStatusLabel1.Text = "Please select the detector options and click Detect to begin.";
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade, 30);
}
public void ProcessFrame()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
50, ObjectDetectorSearchMode.NoOverlap);
Bitmap bmp = Properties.Resources.lena_color;
target.ProcessFrame(bmp);
Assert.AreEqual(1, target.DetectedObjects.Length);
Assert.AreEqual(126, target.DetectedObjects[0].X);
Assert.AreEqual(112, target.DetectedObjects[0].Y);
Assert.AreEqual(59, target.DetectedObjects[0].Width);
Assert.AreEqual(59, target.DetectedObjects[0].Height);
}
static void Main(string[] args)
{
// 画像の取得
var img = new Bitmap(@"src.jpg");
// グレースケール化
//var gray = new AForge.Imaging.Filters.Grayscale(0.2125, 0.7154, 0.0721).Apply(img);
// カスケード識別器の読み込み
var cascadeFace = Accord.Vision.Detection.Cascades.FaceHaarCascade.FromXml(@"haarcascade_frontalface_default.xml");
// Haar-Like特徴量による物体検出を行うクラスの生成
var detectorFace = new Accord.Vision.Detection.HaarObjectDetector(cascadeFace);
// 読み込んだ画像から顔の位置を検出（顔の位置はRectangle[]で返される）
var faces = detectorFace.ProcessFrame(img);
// 画像に検出された顔の位置を書き込みPictureBoxに表示
var markerFaces = new Accord.Imaging.Filters.RectanglesMarker(faces, Color.Yellow);
img = markerFaces.Apply(img);
// 保存
//Bitmap img2 = markerFaces.ToBitmap();
img.Save(@"dst.jpg");
img.Dispose();
}
static void Main(string[] args)
{
//http://www.codeproject.com/Tips/561129/Face-Detection-with-Lines-of-Code-VB-NET
//describing Viola Jones here : http://makematics.com/research/viola-jones/
//choosing scaling factor : http://www.mathworks.com/help/vision/ref/vision.cascadeobjectdetector-class.html#btc108o
string fileName = "9_r.jpg";
var image = new Bitmap("C:/temp/FaceDetection/"+fileName);
var cascade = new FaceHaarCascade();
var detector = new HaarObjectDetector(cascade,30);
detector.SearchMode = ObjectDetectorSearchMode.Average;
detector.Suppression = 3;
detector.MaxSize =new Size(image.Width,image.Height);
int scalingValue = image.Width > image.Height ? image.Width : image.Height;
detector.ScalingFactor = scalingValue / (scalingValue-0.5f);
detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
detector.UseParallelProcessing = true;
detector.Suppression = 1;
var sw = new Stopwatch();
sw.Start();
Rectangle[] faceObjects = detector.ProcessFrame(image);
var p = new Pen(Color.Aqua,10);
var graphicRect = Graphics.FromImage(image);
foreach (var face in faceObjects)
{
graphicRect.DrawRectangle(p, face);
}
graphicRect.Dispose();
image.Save("C:/temp/FaceDetection/Results/Average_3/"+fileName);
sw.Stop();
}
static void Main(string[] args)
{
// 画像の取得
var img = new Bitmap(@"src.jpg");
// グレースケール化
//var gray = new AForge.Imaging.Filters.Grayscale(0.2125, 0.7154, 0.0721).Apply(img);
// カスケード識別器の読み込み
var cascadeFace = Accord.Vision.Detection.Cascades.FaceHaarCascade.FromXml(@"haarcascade_frontalface_default.xml");
// Haar-Like特徴量による物体検出を行うクラスの生成
var detectorFace = new Accord.Vision.Detection.HaarObjectDetector(cascadeFace);
// 読み込んだ画像から顔の位置を検出（顔の位置はRectangle[]で返される）
var faces = detectorFace.ProcessFrame(img);
// 画像に検出された顔の位置を書き込みPictureBoxに表示
var markerFaces = new Accord.Imaging.Filters.RectanglesMarker(faces, Color.Yellow);
img = markerFaces.Apply(img);
// 保存
//Bitmap img2 = markerFaces.ToBitmap();
img.Save(@"dst.jpg");
img.Dispose();
}
private async void MainWindow_Loaded(object sender, RoutedEventArgs e)
{
// Wait for the client to get setup
await Task.Delay(5000);
// Setup Socket Client
Client = new TcpClient();
Client.Connect(IPAddress.Parse("10.10.10.100"), 1911);
ClientStream = Client.GetStream();
// enumerate video devices
videoDevices = new FilterInfoCollection(FilterCategory.VideoInputDevice);
// create video source
videoSource = new VideoCaptureDevice(videoDevices[1].MonikerString);
videoSource.SnapshotResolution = videoSource.VideoCapabilities[7];
// set NewFrame event handler
videoSource.NewFrame += new NewFrameEventHandler(video_NewFrame);
// Setup Face Detection
Cascade = new FaceHaarCascade();
Detector = new HaarObjectDetector(Cascade, 30);
Detector.SearchMode = ObjectDetectorSearchMode.Average;
//Detector.ScalingFactor = 1f;
Detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
Detector.UseParallelProcessing = true;
Detector.Suppression = 3;
// Setup Tracking Data
CameraVector.X = 90;
CameraVector.Y = 90;
//ClientStream.Write(Encoding.UTF8.GetBytes("090,090"), 0, 7);
//await Task.Delay(3000);
// start the video source
videoSource.Start();
}
/// <summary>
/// Initialize sensor
/// </summary>
private void init()
{
try
{
if (isLocalCamera)
{
var videoDevices = new FilterInfoCollection(FilterCategory.VideoInputDevice);
video = new VideoCaptureDevice(videoDevices[localCameraIndex].MonikerString);
}
else
{
video = new MJPEGStream(this.cameraUrl);
((MJPEGStream)video).Login = this.cameraLogin;
((MJPEGStream)video).Password = this.cameraPassword;
}
video.NewFrame += new NewFrameEventHandler(processFrame);
video.VideoSourceError += new VideoSourceErrorEventHandler(processFrameError);
motionMarker = new MotionAreaHighlighting();
motion = new MotionDetector(
new SimpleBackgroundModelingDetector(),
motionMarker
);
detectorFace = Detector.Face();
detectorEye = Detector.Eye();
detectorNose = Detector.Nose();
detectorEarLeft = Detector.EarLeft();
detectorEarRight = Detector.EarRight();
detectorFaceProfile = Detector.FaceProfile();
}
catch (Exception ex)
{
updateError(ex.Message);
}
}
/// <summary>
/// Initializes a new instance of the <see cref="HeadController"/> class.
/// </summary>
///
public HeadController()
{
// Setup tracker
tracker = new Camshift();
tracker.Mode = CamshiftMode.RGB;
tracker.Conservative = true;
tracker.AspectRatio = 1.5f;
// Setup detector
detector = new HaarObjectDetector(new FaceHaarCascade());
detector.MinSize = new Size(25, 25);
detector.SearchMode = ObjectDetectorSearchMode.Single;
detector.ScalingFactor = 1.2f;
detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
xaxisRange = new IntRange(0, 320);
yaxisRange = new IntRange(0, 240);
scaleRange = new DoubleRange(0, Math.Sqrt(320 * 240));
angleRange = new DoubleRange(0, 2 * Math.PI);
}
/// <summary>
/// Creates Face Haar detector
/// </summary>
/// <returns>Returns asynchronous void Task</returns>
private async Task InitializeDetectorsAsync() {
await Task.Run(() => {
this.log.Info("Initializing face detector...");
// InitializeAsync face detector for multiple objects
this.detector = new HaarObjectDetector(
new FaceHaarCascade(),
this.DetectingAreaMinDefault, ObjectDetectorSearchMode.NoOverlap,
this.DetectingAreaStepDefault, ObjectDetectorScalingMode.GreaterToSmaller) {
UseParallelProcessing = true
};
});
}
private void Detect(Bitmap image,ObjectDetectorSearchMode searchMode, int supperession,ObjectDetectorScalingMode scalingMode,string targetPath,bool parallelProcessing)
{
//http://www.codeproject.com/Tips/561129/Face-Detection-with-Lines-of-Code-VB-NET
//describing Viola Jones here : http://makematics.com/research/viola-jones/
//choosing scaling factor : http://www.mathworks.com/help/vision/ref/vision.cascadeobjectdetector-class.html#btc108o
var detector = new HaarObjectDetector(new FaceHaarCascade(), 30);
detector.SearchMode = searchMode;
if (searchMode == ObjectDetectorSearchMode.Average)
detector.Suppression = supperession;
detector.MaxSize = new Size(image.Width, image.Height);
detector.ScalingMode = scalingMode;
detector.UseParallelProcessing = parallelProcessing;
int scalingValue = image.Width > image.Height ? image.Width : image.Height;
detector.ScalingFactor = scalingValue / (scalingValue - 0.5f);
Rectangle[] faceObjects = detector.ProcessFrame(image);
var p = new Pen(Color.Aqua, 10);
var graphicRect = Graphics.FromImage(image);
foreach (var face in faceObjects)
{
graphicRect.DrawRectangle(p, face);
}
graphicRect.Dispose();
image.Save(targetPath);
}
public void ProcessFrame3()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
15, ObjectDetectorSearchMode.NoOverlap);
Bitmap bmp = Properties.Resources.three;
target.ProcessFrame(bmp);
Assert.AreEqual(3, target.DetectedObjects.Length);
Assert.AreEqual(180, target.DetectedObjects[0].X);
Assert.AreEqual(275, target.DetectedObjects[0].Y);
Assert.AreEqual(41, target.DetectedObjects[0].Width);
Assert.AreEqual(41, target.DetectedObjects[0].Height);
Assert.AreEqual(168, target.DetectedObjects[1].X);
Assert.AreEqual(144, target.DetectedObjects[1].Y);
Assert.AreEqual(49, target.DetectedObjects[1].Width);
Assert.AreEqual(49, target.DetectedObjects[1].Height);
Assert.AreEqual(392, target.DetectedObjects[2].X);
Assert.AreEqual(133, target.DetectedObjects[2].Y);
Assert.AreEqual(59, target.DetectedObjects[2].Width);
Assert.AreEqual(59, target.DetectedObjects[2].Height);
target = new HaarObjectDetector(cascade,
15, ObjectDetectorSearchMode.Single);
target.ProcessFrame(bmp);
Assert.AreEqual(1, target.DetectedObjects.Length);
}
/// <summary>
/// Initializes a new instance of the <see cref="FaceController"/> class.
/// </summary>
///
public FaceController()
{
// Setup tracker
tracker = new MatchingTracker();
tracker.Extract = false;
// Setup detector
detector = new HaarObjectDetector(new NoseHaarCascade());
detector.ScalingFactor = 1.1f;
detector.SearchMode = ObjectDetectorSearchMode.Single;
detector.ScalingMode = ObjectDetectorScalingMode.SmallerToGreater;
detector.MinSize = new Size(2, 5);
//detector.MaxSize = new Size(15, 18);
}