|
public ProcessFrame ( |
||
| frame | ||
| return | System.Drawing.Rectangle[] | |
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);
}
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 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();
}
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);
}
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);
}
/// <summary>
/// Image processor and the heart of sensor
/// </summary>
/// <param name="sender">object - owner</param>
/// <param name="eventArgs">NewFrameEventArgs - args, contains frame from camera</param>
private void processFrame(object sender, NewFrameEventArgs eventArgs)
{
Rectangle rect = eventArgs.Frame.Bounds();
PixelFormat pf = eventArgs.Frame.PixelFormat;
Bitmap frame = eventArgs.Frame.Clone(rect, pf);
scaleX = frame.Width / processWidth;
scaleY = frame.Height / processHeight;
Bitmap frameFace = eventArgs.Frame.Clone(rect, pf);
if (OnMotionDetected != null)
{
var dataMotion = frame.GetDirectAccess();
var frameUI = dataMotion.GetUnmanaged();
if (motion.ProcessFrame(frameUI) > 0.15)
{
updateMotion(true);
}
else
{
updateMotion(false);
}
frame.UnlockBits(dataMotion);
}
if (OnFaceDetected != null)
{
var dataFace = frameFace.GetDirectAccess();
var faceUI = dataFace.GetUnmanaged();
var downsample = faceUI.ResizeTo(processWidth, processHeight);
var faceDetections = detectorFace.ProcessFrame(downsample);
var faceDetections2 = detectorFaceProfile.ProcessFrame(downsample);
if (isPreview)
{
if (faceDetections.Length > 0)
{
marker = new Accord.Imaging.Filters.RectanglesMarker(faceDetections.Scale(scaleX, scaleY));
marker.MarkerColor = Color.Yellow;
frame = marker.Apply(frame);
}
if (faceDetections2.Length > 0)
{
marker = new Accord.Imaging.Filters.RectanglesMarker(faceDetections2.Scale(scaleX, scaleY));
marker.MarkerColor = Color.Yellow;
frame = marker.Apply(frame);
}
}
frameFace.UnlockBits(dataFace);
if (detectorFace.DetectedObjects != null && detectorFace.DetectedObjects.Length > 0)
{
var faces = detectorFace.DetectedObjects.ToFaces((int)scaleX, (int)scaleY);
for (int i = 0; i < faces.Length; i++)
{
var cutter = new AForge.Imaging.Filters.Crop(faces[i].Bounds);
faces[i].FaceImage = cutter.Apply(frameFace);
if (searchForFaceDirection)
{
detectorNose.ProcessFrame(faces[i].FaceImage);
if (detectorNose.DetectedObjects.Length > 0)
{
faces[i].Direction = FaceDirection.Frontal;
}
}
var eyeDetections = detectorEye.ProcessFrame(faces[i].FaceImage);
if (eyeDetections.Length > 0)
{
if (eyeDetections.Length >= 1)
{
faces[i].Direction = FaceDirection.Frontal;
}
Eye[] eyes = new Eye[eyeDetections.Length];
for (int ie = 0; ie < eyes.Length; ie++)
{
eyes[ie] = new Eye();
eyes[ie].Left = faces[i].Left + eyeDetections[ie].X;
eyes[ie].Top = faces[i].Top + eyeDetections[ie].Y;
eyes[ie].Width = eyeDetections[ie].Width;
eyes[ie].Height = eyeDetections[ie].Height;
var cutter2 = new AForge.Imaging.Filters.Crop(eyes[ie].Bounds);
eyes[ie].EyeImage = cutter.Apply(frameFace);
}
if (isPreview)
{
marker = new Accord.Imaging.Filters.RectanglesMarker(eyes.toRects());
marker.MarkerColor = Color.Orange;
frame = marker.Apply(frame);
}
updateEyeDetected(eyes);
}
}
updateFaceDetected(faces);
}
else if (detectorFaceProfile.DetectedObjects != null && detectorFaceProfile.DetectedObjects.Length > 0)
{
var faces = detectorFaceProfile.DetectedObjects.ToFaces((int)scaleX, (int)scaleY);
for (int i = 0; i < faces.Length; i++)
{
var cutter = new AForge.Imaging.Filters.Crop(faces[i].Bounds);
faces[i].FaceImage = cutter.Apply(frameFace);
if (searchForFaceDirection)
{
detectorEarLeft.ProcessFrame(faces[i].FaceImage);
if (detectorEarLeft.DetectedObjects.Length > 0)
{
faces[i].Direction = FaceDirection.TurnedRight;
}
else
{
detectorEarRight.ProcessFrame(faces[i].FaceImage);
if (detectorEarRight.DetectedObjects.Length > 0)
{
faces[i].Direction = FaceDirection.TurnedLeft;
}
else
{
faces[i].Direction = FaceDirection.NoInfo;
}
}
}
}
updateFaceDetected(faces);
}
}
updateFrameReceived(frame);
}