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);
}
}
//var cascade = new Accord.Vision.Detection.Cascades.FaceHaarCascade();
public Bitmap ProcessFrame(Bitmap inputImage)
{
Image <Bgr, byte> imageFrame = new Image <Bgr, byte>(new Bitmap(inputImage));
// In this example, we will be creating a cascade for a Face detector:
// var cascade = HaarCascade.FromXml("filename.xml"); (OpenCV 2.0 format)
//not overlapping objects!!
//min size 50 pixels
var detector = new HaarObjectDetector(_cascade, minSize: 50,
searchMode: ObjectDetectorSearchMode.NoOverlap);
Rectangle[] rectangles = detector.ProcessFrame(inputImage);
foreach (var rectangle in rectangles)
{
imageFrame.ROI = rectangle;
var facePatch = GetBlackRectangle(rectangle);
CvInvoke.cvCopy(facePatch, imageFrame, IntPtr.Zero);
//imageFrame.Draw(rectangle, new Bgr(Color.Indigo), 3);
}
imageFrame.ROI = Rectangle.Empty;
return(imageFrame.Bitmap);
}
private void button1_Click(object sender, EventArgs e)
{
detector.SearchMode = (ObjectDetectorSearchMode)cbMode.SelectedValue;
detector.ScalingMode = (ObjectDetectorScalingMode)cbScaling.SelectedValue;
detector.ScalingFactor = 1.5f;
detector.MinSize = new Size(32, 32);
detector.UseParallelProcessing = cbParallel.Checked;
Stopwatch sw = Stopwatch.StartNew();
// prepare grayscale image
picture = Accord.Imaging.Image.Clone(new Bitmap(pictureBox1.Image), System.Drawing.Imaging.PixelFormat.Format24bppRgb);
// Process frame to detect objects
Rectangle[] objects = detector.ProcessFrame(picture);
sw.Stop();
if (objects.Length > 0)
{
RectanglesMarker marker = new RectanglesMarker(objects, Color.Red);
pictureBox1.Image = marker.Apply(picture);
}
toolStripStatusLabel1.Text = string.Format("Completed detection of {0} objects in {1}.",
objects.Length, sw.Elapsed);
}
private Bitmap FacePicDetect(Bitmap bitmap)
{
lock (this)
{
//留存原始的照片，如果正在拍照，则保存此未添加人脸识别框的照片
currentPicture = (Bitmap)bitmap.Clone();
if (detector == null)
{
//先实例化用于检测人脸的对象detector
detector = new HaarObjectDetector(new FaceHaarCascade(), 100)
{
SearchMode = ObjectDetectorSearchMode.Single, //搜索模式
ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller, //缩放模式
ScalingFactor = 1.5f, //在搜索期间重新缩放搜索窗口时要使用的重新缩放因子
UseParallelProcessing = true
}; //面部级联对象 + 搜索对象时使用的最小窗口大小
}
// 开始对检测区域进行检测并返回结果数组
Rectangle[] regions = detector.ProcessFrame(bitmap);
if (regions != null && regions.Length > 0)
{
//人脸标记
RectanglesMarker marker = new RectanglesMarker(regions, Color.Orange);
regions = null;
return(marker.Apply(bitmap));
}
regions = null;
return(bitmap);
}
}
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);
}
public static Rectangle detectFace(Bitmap bitmap)
{
var rectangle = new Rectangle();
var cascade = new FaceHaarCascade();
var detector = new HaarObjectDetector(cascade, 600);
detector.SearchMode = ObjectDetectorSearchMode.Default;
detector.ScalingFactor = 1.5F;
detector.ScalingMode = ObjectDetectorScalingMode.SmallerToGreater;
detector.UseParallelProcessing = true;
detector.Suppression = 3;
var faceObjects = detector.ProcessFrame(bitmap);
var possbleFaces = new List <Rectangle>();
foreach (var face in faceObjects)
{
if (face.Width > 100 && face.Height > 100)
{
possbleFaces.Add(face);
}
}
if (possbleFaces.Count > 0)
{
int x = possbleFaces.Sum((r) => r.X) / possbleFaces.Count;
int y = possbleFaces.Sum((r) => r.Y) / possbleFaces.Count;
int width = possbleFaces.Sum((r) => r.Width) / possbleFaces.Count;
int height = possbleFaces.Sum((r) => r.Width) / possbleFaces.Count;
rectangle = new Rectangle(x, y, width, height);
}
return(rectangle);
}
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 void Init(PictureBox pb)
{
FilterInfoCollection VideoCaptuerDevices = new FilterInfoCollection(FilterCategory.VideoInputDevice);
VideoCaptureDevice FinalVideoSource = new VideoCaptureDevice(VideoCaptuerDevices[0].MonikerString);
FinalVideoSource.NewFrame += new NewFrameEventHandler((sender, eventArgs) =>
{
Bitmap image = (Bitmap)eventArgs.Frame.Clone();
FaceHaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector detector = new HaarObjectDetector(cascade, 80);
detector.UseParallelProcessing = true;
Rectangle[] faces = detector.ProcessFrame(image);
Graphics g = Graphics.FromImage(image);
foreach (var face in faces)
{
Pen p = new Pen(Color.Red, 10f);
g.DrawRectangle(p, face);
}
g.Dispose();
pb.Image = image;
});
FinalVideoSource.DesiredFrameRate = 1;
FinalVideoSource.DesiredFrameSize = new Size(1, 500);
FinalVideoSource.Start();
}
private void button1_Click(object sender, EventArgs e)
{
detector.SearchMode = (ObjectDetectorSearchMode)cbMode.SelectedValue;
detector.ScalingMode = (ObjectDetectorScalingMode)cbScaling.SelectedValue;
detector.ScalingFactor = 1.5f;
detector.UseParallelProcessing = cbParallel.Checked;
detector.Suppression = 2;
Stopwatch sw = Stopwatch.StartNew();
// Process frame to detect objects
Rectangle[] objects = detector.ProcessFrame(picture);
sw.Stop();
if (objects.Length > 0)
{
RectanglesMarker marker = new RectanglesMarker(objects, Color.Fuchsia);
pictureBox1.Image = marker.Apply(picture);
}
toolStripStatusLabel1.Text = string.Format("Completed detection of {0} objects in {1}.",
objects.Length, sw.Elapsed);
}
static void Main(string[] args)
{
////ApplyBiTonal
//Bitmap b = (Bitmap)Image.FromFile("test.png");
//Processor.ApplyBiTonal(ref b, (byte.MaxValue * 3) / 2, System.Drawing.Color.Red, System.Drawing.Color.White);
//b.Save("result.png");
//WebClient wc = new WebClient();
//byte[] bytes = wc.DownloadData("https://lpportalvhds5rnqyvf495z0.blob.core.windows.net/imagger/408fcdc5-8e91-4dc9-ada8-87287c8cf77f-orinal.jpeg");
//MemoryStream ms = new MemoryStream(bytes);
//Bitmap imageBitmap = new Bitmap(ms);
//Processor.ApplyBrightness(ref imageBitmap, 100);
////Processor.ApplyBiTonal(ref imageBitmap, (byte.MaxValue * 3) / 2, System.Drawing.Color.Red, System.Drawing.Color.White);
//imageBitmap.Save("result.png");
//WebClient wc = new WebClient();
//byte[] bytes = wc.DownloadData("https://lpportalvhds5rnqyvf495z0.blob.core.windows.net/imagger/157bed84-5f10-4f43-a5e5-7f0d146e3452-orinal.jpeg");
//MemoryStream ms = new MemoryStream(bytes);
//Bitmap imageBitmap = new Bitmap(ms);
//Processor.ApplyRandomJitter(ref imageBitmap, 20); //, System.Drawing.Color.Red, System.Drawing.Color.White);
//imageBitmap.Save("result.png");
//Bitmap br = (Bitmap)Image.FromFile("test.png");
//Processor.ApplyBrightness(ref br, 100);
//br.Save("result-br.png");
#region Detect faces
WebClient wc = new WebClient();
byte[] bytes = wc.DownloadData("https://scontent-fra3-1.xx.fbcdn.net/v/t1.0-1/c0.46.200.200/1724176_10151691353752537_1722497807_n.jpg?oh=19d89af364674bd704cd38613135b4e1&oe=583097F3");
MemoryStream ms = new MemoryStream(bytes);
Bitmap picture = new Bitmap(ms);
HaarObjectDetector detector;
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade, 30);
detector.SearchMode = ObjectDetectorSearchMode.NoOverlap;
detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
detector.ScalingFactor = 1.5f;
detector.UseParallelProcessing = true;
Rectangle[] objects = detector.ProcessFrame(picture);
// RandomJitter.ApplyRandomJitterOnRectangulars(ref picture, 20, objects);
// RandomJitter.ApplyRandomJitterOnRectangulars2(ref picture, 20, objects);
RandomJitter.ApplyAddNoise(ref picture, objects);
picture.Save("result.png");
// var p = RandomJitter.ApplyColorMatrix(picture, objects);
//p.Save("result.png");
#endregion
}
public static bool HaveFace(Bitmap imagePassport)
{
Rectangle cloneRect = new Rectangle((int)(imagePassport.Width * 0.05), (int)(imagePassport.Height / 2.0 + imagePassport.Width * 0.129), (int)(imagePassport.Width * 0.28), (int)(imagePassport.Width * 0.357));
Bitmap cloneBitmap = imagePassport.Clone(cloneRect, imagePassport.PixelFormat);
HaarObjectDetector faceDetector = new HaarObjectDetector(new FaceHaarCascade(), minSize: 70, searchMode: ObjectDetectorSearchMode.Average);
// распознаём лица
IEnumerable <Rectangle> face = faceDetector.ProcessFrame(cloneBitmap);
return(face.Count() > 0);
}
private void btnTrainFace_Click(object sender, EventArgs e)
{
try
{
detector = new HaarObjectDetector(cascade, 30);
detector.SearchMode = ObjectDetectorSearchMode.Single;//.NoOverlap;//.Default; //.Average;
detector.ScalingFactor = 1.5f;
detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
detector.UseParallelProcessing = true;
detector.Suppression = 3;
Bitmap picture = new Bitmap(pictureBox1.Image);
Image <Bgr, byte> Frame = new Image <Bgr, byte>(picture);
Stopwatch sw = Stopwatch.StartNew();
Rectangle[] faceObjects = detector.ProcessFrame(picture);
sw.Stop();
if (faceObjects.Length > 0)
{
RectanglesMarker marker = new RectanglesMarker(faceObjects, Color.Fuchsia);
pictureBox1.Image = marker.Apply(picture);
//Graphics g = Graphics.FromImage(pictureBox1.Image);
foreach (var face in faceObjects)
{
//g.DrawRectangle(Pens.DeepSkyBlue, face);
Frame.Draw(face, new Bgr(Color.Red), 3);
Bitmap c = Frame.ToBitmap();
Bitmap bmp = new Bitmap(face.Width, face.Height);
Graphics gg = Graphics.FromImage(bmp);
gg.DrawImage(c, 0, 0, face, GraphicsUnit.Pixel);
pictureBox2.Image = bmp;
//bmp.Save("myface(accord) " + DateTime.Now.Second.ToString());
gg.Dispose();
}
//g.Dispose();
//label1.Text = "Completed operation!! " + faceObjects.Length.ToString() + " Face detected";
MessageBox.Show("Train Face operation successful!!! " + faceObjects.Length.ToString() + " Face detected", "Train face", MessageBoxButtons.OK, MessageBoxIcon.Information);
txtFaceId.Text = genFaceId();
txtUsername.Text = "User" + txtFaceId.Text;
btnUpload.Enabled = false;
btnTrainFace.Enabled = false;
btnSave.Enabled = true;
}
else
{
MessageBox.Show("Image cannot be trained!!! No face detected in the current image", "Fail to Train face", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
catch (Exception er)
{
MessageBox.Show(er.Message, "Face Detection and Recognition Failure", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
/// <summary>
/// Processes the image.
/// </summary>
/// <param name="factory">
/// The current instance of the <see cref="T:ImageProcessor.ImageFactory"/> class containing
/// the image to process.
/// </param>
/// <returns>
/// The processed image from the current instance of the <see cref="T:ImageProcessor.ImageFactory"/> class.
/// </returns>
public Image ProcessImage(ImageFactory factory)
{
Bitmap newImage = null;
Bitmap grey = null;
Image image = factory.Image;
try
{
HaarCascade cascade = this.DynamicParameter;
grey = new Bitmap(image.Width, image.Height);
grey.SetResolution(image.HorizontalResolution, image.VerticalResolution);
grey = MatrixFilters.GreyScale.TransformImage(image, grey);
HaarObjectDetector detector = new HaarObjectDetector(cascade)
{
SearchMode = ObjectDetectorSearchMode.NoOverlap,
ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller,
ScalingFactor = 1.5f
};
// Process frame to detect objects
Rectangle[] rectangles = detector.ProcessFrame(grey);
grey.Dispose();
newImage = new Bitmap(image);
newImage.SetResolution(image.HorizontalResolution, image.VerticalResolution);
using (Graphics graphics = Graphics.FromImage(newImage))
{
using (Pen blackPen = new Pen(Color.White))
{
blackPen.Width = 4;
graphics.DrawRectangles(blackPen, rectangles);
}
}
image.Dispose();
image = newImage;
}
catch (Exception ex)
{
if (grey != null)
{
grey.Dispose();
}
if (newImage != null)
{
newImage.Dispose();
}
throw new ImageProcessingException("Error processing image with " + this.GetType().Name, ex);
}
return(image);
}
private Bitmap FaceFind(Bitmap bmpBitmap)
{
HaarObjectDetector faceDetector = new HaarObjectDetector(new FaceHaarCascade(), minSize: 300, searchMode: ObjectDetectorSearchMode.Single);
RectanglesMarker faceMarker = new RectanglesMarker(Color.Red)
{
Rectangles = faceDetector.ProcessFrame(bmpBitmap)
};
faceMarker.ApplyInPlace(bmpBitmap);
return(bmpBitmap);
}
/// <summary>
/// Process an image and obtain haar features
/// </summary>
/// <param name="input">Image to have face detection performed on.</param>
/// <returns>Rectangle[] which represents all detected faces</returns>
private Rectangle[] ProcessImage(double[,] input)
{
try {
Task <Rectangle[]> getFaces = Task.Run(() => _faceDetector.ProcessFrame(input));
return(getFaces.Result);
}
catch (Exception e) {
// ignored
}
return(null);
}
internal List <Rectangle> ExtractFaces(Bitmap picture, FaceDetectorParameters faceDetectorParameters)
{
_detector.MinSize = new Size(faceDetectorParameters.MinimumSize, faceDetectorParameters.MinimumSize);
_detector.ScalingFactor = faceDetectorParameters.ScalingFactor;
_detector.ScalingMode = faceDetectorParameters.ScalingMode;
_detector.SearchMode = faceDetectorParameters.SearchMode;
_detector.UseParallelProcessing = faceDetectorParameters.UseParallelProcessing;
_detector.MaxSize = new Size(600, 600);
_detector.Suppression = faceDetectorParameters.Suppression;
return(_detector.ProcessFrame(picture, (x) => { Logging.Log(x); })
.ToList());
}
static void TestHaar()
{
if (Environment.Is64BitProcess)
{
throw new Exception("Run in 32-bits");
}
// Let's test the detector using a sample video from
// the collection of test videos in the framework:
TestVideos ds = new TestVideos();
string fileName = ds["crowd.mp4"];
// In this example, we will be creating a cascade for a Face detector:
var cascade = new Accord.Vision.Detection.Cascades.FaceHaarCascade();
// Now, create a new Haar object detector with the cascade:
var detector = new HaarObjectDetector(cascade, minSize: 25,
searchMode: ObjectDetectorSearchMode.Average,
scalingMode: ObjectDetectorScalingMode.SmallerToGreater,
scaleFactor: 1.1f)
{
Suppression = 5 // This should make sure we only report regions as faces if
// they have been detected at least 5 times within different cascade scales.
};
// Now, let's open the video using FFMPEG:
var video = new VideoFileReader();
video.Open(fileName);
Stopwatch sw = Stopwatch.StartNew();
// Now, for each frame of the video
for (int frameIndex = 0; frameIndex < video.FrameCount; frameIndex++)
{
// Read the current frame into the bitmap data
Bitmap bmp = video.ReadVideoFrame(frameIndex);
// Feed the frame to the tracker
Rectangle[] faces = detector.ProcessFrame(bmp);
Console.WriteLine(faces.Length);
Console.WriteLine(bmp.Flags);
}
sw.Stop();
Console.WriteLine(sw.Elapsed);
video.Close();
}
public void MinSizeTest()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
50, ObjectDetectorSearchMode.Default);
Bitmap bmp = Accord.Imaging.Image.Clone(Resources.lena_color);
Rectangle[] result;
target.MinSize = new Size(10, 60);
result = target.ProcessFrame(bmp);
Assert.AreEqual(3, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width >= target.MinSize.Width);
Assert.IsTrue(r.Height >= target.MinSize.Height);
}
target.MinSize = new Size(85, 85);
result = target.ProcessFrame(bmp);
Assert.AreEqual(2, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width >= target.MinSize.Width);
Assert.IsTrue(r.Height >= target.MinSize.Height);
}
target.MinSize = new Size(1, 1);
result = target.ProcessFrame(bmp);
Assert.AreEqual(4, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width >= target.MinSize.Width);
Assert.IsTrue(r.Height >= target.MinSize.Height);
}
}
private Bitmap Detecting(Bitmap _bitmap)
{
UnmanagedImage im = UnmanagedImage.FromManagedImage(_bitmap);
float xscale = im.Width / 160f;
float yscale = im.Height / 120f;
ResizeNearestNeighbor resize = new ResizeNearestNeighbor(160, 120);
UnmanagedImage downsample = resize.Apply(im);
Rectangle[] regions = detector.ProcessFrame(downsample);
if (regions.Length > 0)
{
tracker.Reset();
// Reduce the face size to avoid tracking background
Rectangle window = new Rectangle(
(int)((regions[0].X + regions[0].Width / 2f) * xscale),
(int)((regions[0].Y + regions[0].Height / 2f) * yscale),
1, 1);
window.Inflate(
(int)(0.2f * regions[0].Width * xscale),
(int)(0.4f * regions[0].Height * yscale));
this.FaceTracked = window;
// Initialize tracker
tracker.SearchWindow = window;
tracker.ProcessFrame(im);
marker = new RectanglesMarker(window);
marker.ApplyInPlace(im);
// (Bitmap) Helpers.BitmapHelper.ByteArrayToImage(Helpers.RijndaelHelper.EncryptBytes(Helpers.BitmapHelper.ImageToByte(im.ToManagedImage()), "fzafa", "afzd"))
this.isTracking = true;
return(im.ToManagedImage());
}
else
{
this.isDetecting = false;
return(_bitmap);
}
}
private void timer1_Tick(object sender, EventArgs e)
{
Mat frame = new Mat();
frame = cap.QueryFrame();
Image <Bgr, byte> frameImage = frame.ToImage <Bgr, byte>();
Image <Gray, byte> grayFrameImage = frameImage.Convert <Gray, byte>();
if (frame != null)
{
Rectangle[] faces = detector.ProcessFrame(grayFrameImage.ToBitmap());
RectanglesMarker marker = new RectanglesMarker(faces, Color.Fuchsia);
imageViewer.Image = new Image <Bgr, byte>(marker.Apply(frame.Bitmap));
}
}
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);
}
public static List <Rectangle> DetectFaces(Bitmap image)
{
List <Rectangle> xfaces = new List <Rectangle>();
HaarObjectDetector detector;
detector = new HaarObjectDetector(new FaceHaarCascade(), 20, ObjectDetectorSearchMode.Average, 1.1f, ObjectDetectorScalingMode.SmallerToGreater);
detector.UseParallelProcessing = true;
detector.Suppression = 2;
var grayImage = Grayscale.CommonAlgorithms.BT709.Apply(image);
HistogramEqualization filter = new HistogramEqualization();
filter.ApplyInPlace(grayImage);
Rectangle[] faces = detector.ProcessFrame(grayImage);
xfaces.AddRange(faces);
return(xfaces);
}
public Bitmap Detect()
{
_detector.ScalingFactor = 1.5f;
_detector.UseParallelProcessing = true;
Rectangle[] objects = _detector.ProcessFrame(image);
if (objects.Length > 0)
{
RectanglesMarker marker = new RectanglesMarker(objects, Color.Yellow);
image = marker.Apply(image);
}
return(image);
}
private void button1_Click(object sender, EventArgs e)
{
try
{
detector = new HaarObjectDetector(cascade, 30);
detector.SearchMode = ObjectDetectorSearchMode.Single; //.Default;//.NoOverlap;//
detector.ScalingFactor = 2.5f;
detector.ScalingMode = ObjectDetectorScalingMode.SmallerToGreater; //.GreaterToSmaller;
detector.UseParallelProcessing = true;
detector.Suppression = 3;
Bitmap picture = new Bitmap(pictureBox2.Image);
Image <Bgr, byte> Frame = new Image <Bgr, byte>(picture);
Stopwatch sw = Stopwatch.StartNew();
Rectangle[] faceObjects = detector.ProcessFrame(picture);
sw.Stop();
if (faceObjects.Length > 0)
{
RectanglesMarker marker = new RectanglesMarker(faceObjects, Color.Fuchsia);
pictureBox2.Image = marker.Apply(picture);
}
label1.Text = "Operation Completed!!! " + faceObjects.Length.ToString() + " Face detected";
Graphics g = Graphics.FromImage(pictureBox2.Image);
foreach (var face in faceObjects)
{
g.DrawRectangle(Pens.DeepSkyBlue, face);
Frame.Draw(face, new Bgr(Color.Red), 3);
Bitmap c = Frame.ToBitmap();
Bitmap bmp = new Bitmap(face.Width, face.Height);
Graphics gg = Graphics.FromImage(bmp);
gg.DrawImage(c, 0, 0, face, GraphicsUnit.Pixel);
//pictureBox2.Image = bmp;
bmp.Save("myface(accord) " + DateTime.Now.Second.ToString());
MessageBox.Show("Face Detected!!!, Face Save as:" + "myface(accord) " + DateTime.Now.Second.ToString(), "Face Detection Successfully", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
//g.Dispose()
}
catch (Exception er)
{
MessageBox.Show(er.Message, "Face Detection Failure", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public FaceDetectionResult Process(Bitmap picture)
{
try
{
// Process frame to detect objects
Rectangle[] objects = detector.ProcessFrame(picture);
if (objects.Length == 0)
{
return(FaceDetectionResult.FaceNotFound);
}
return(objects.Length > 1 ? FaceDetectionResult.MultipleFacesFound : FaceDetectionResult.OneFaceFound);
}
catch (Exception e)
{
return(FaceDetectionResult.Error);
}
}
/// <summary>
/// Called when videoPlayer receives a new frame.
/// </summary>
/// <param name="sender"></param>
/// <param name="image"></param>
private void videoPlayer_NewFrame(object sender, ref Bitmap image)
{
// grayscale the image
var grayscale = new GrayscaleBT709();
var frame = grayscale.Apply(image);
// only detect once every 5 frames
if (frameCounter++ % 5 == 0)
{
// scan the image and grab the first face
var faces = faceDetector.ProcessFrame(frame);
if (faces.Count() > 0)
{
// copy the face
faceRect = faces.First();
var face = image.Clone(faces.First(), PixelFormat.DontCare);
// search the face for the nose
var noses = noseDetector.ProcessFrame(face);
if (noses.Count() > 0)
{
// record new nose location
noseRect = noses.First();
noseRect.Offset(faceRect.Location);
}
}
}
// draw the mustache
using (Graphics g = Graphics.FromImage(image))
{
// we want a mustache three times as wide as the nose
int mustacheWidth = 2 * noseRect.Width;
int mustacheHeight = mustacheWidth * mustache.Height / mustache.Width;
// center the mustache on the bottom of the nose
int x1 = noseRect.X - (mustacheWidth / 4);
int x2 = noseRect.X + noseRect.Width + (mustacheWidth / 4);
int y1 = noseRect.Y + noseRect.Height - (mustacheHeight / 2);
int y2 = noseRect.Y + noseRect.Height + (mustacheHeight / 2);
// draw the mustache
g.DrawImage(mustache, x1, y1, x2 - x1, y2 - y1);
}
}
public void ProcessFrame()
{
#region doc_example
// In order to use a HaarObjectDetector, first we have to tell it
// which type of objects we would like to detect. And in a Haar detector,
// different object classifiers are specified in terms of a HaarCascade.
// The framework comes with some built-in cascades for common body
// parts, such as Face and Nose. However, it is also possible to
// load a cascade from cascade XML definitions in OpenCV 2.0 format.
// In this example, we will be creating a cascade for a Face detector:
var cascade = new Accord.Vision.Detection.Cascades.FaceHaarCascade();
// Note: In the case we would like to load it from XML, we could use:
// var cascade = HaarCascade.FromXml("filename.xml");
// Now, create a new Haar object detector with the cascade:
var detector = new HaarObjectDetector(cascade, minSize: 50,
searchMode: ObjectDetectorSearchMode.NoOverlap);
// Note that we have specified that we do not want overlapping objects,
// and that the minimum object an object can have is 50 pixels. Now, we
// can use the detector to classify a new image. For instance, consider
// the famous Lena picture:
Bitmap bmp = Accord.Imaging.Image.Clone(Resources.lena_color);
// We have to call ProcessFrame to detect all rectangles containing the
// object we are interested in (which in this case, is the face of Lena):
Rectangle[] rectangles = detector.ProcessFrame(bmp);
// The answer will be a single rectangle of dimensions
//
// {X = 126 Y = 112 Width = 59 Height = 59}
//
// which indeed contains the only face in the picture.
#endregion
Assert.AreEqual(1, detector.DetectedObjects.Length);
Assert.AreEqual(126, detector.DetectedObjects[0].X);
Assert.AreEqual(112, detector.DetectedObjects[0].Y);
Assert.AreEqual(59, detector.DetectedObjects[0].Width);
Assert.AreEqual(59, detector.DetectedObjects[0].Height);
}
private void video_NewFrame(object sender, NewFrameEventArgs eventArgs)
{
// get new frame
Bitmap bitmap = eventArgs.Frame;
// process the frame
var faces = Detector.ProcessFrame(bitmap);
//if (faces.Length > 0)
// Debug.WriteLine("Total Objects Detected: " + faces.Length);
if (faces.Length > 0)
{
FaceRect = faces[0];
}
FollowFace(FaceRect);
}
public static string DetectFace(string path)
{
GetPicture(path);
// Process frame to detect objects
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector detector = new HaarObjectDetector(cascade, 30);
detector.SearchMode = (ObjectDetectorSearchMode)cbMode;
detector.ScalingMode = (ObjectDetectorScalingMode)cbScaling;
detector.ScalingFactor = 1.5f;
Stopwatch sw = Stopwatch.StartNew();
Rectangle[] objects = detector.ProcessFrame(picture);
sw.Stop();
if (objects.Length > 0)
{
Console.WriteLine("here");
RectanglesMarker marker = new RectanglesMarker(objects, Color.Fuchsia);
picture = marker.Apply(picture);
}
if (picture != null)
{
Console.WriteLine("trying to print picture");
/*ImageCodecInfo myici = ImageCodecInfo.GetImageEncoders();
* int numCodecs = myici.GetLength(0);
* Encoder myEncoder = Encoder.Quality;
* EncoderParameters param = new EncoderParameters(1);
* EncoderParameter param1 = new EncoderParameter(myEncoder, 25L);
*
* picture.Save(@"output.jpg", myici, param);
*/
picture.Save("file.png", ImageFormat.Png);;
}
return(path);
}
public static void Main(string[] args)
{
var picture = Resource.lena_color;
HaarCascade cascade = new FaceHaarCascade();
var detector = new HaarObjectDetector(cascade, 30);
//detector.SearchMode = ObjectDetectorSearchMode.Average;
//detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
//detector.ScalingFactor = 1.5f;
//detector.UseParallelProcessing = false;
//detector.Suppression = 2;
Rectangle[] objects = detector.ProcessFrame(picture);
if (objects.Length > 0)
{
RectanglesMarker marker = new RectanglesMarker(objects, Color.Fuchsia);
var markedup = marker.Apply(picture);
markedup.Save($"{basedir}{sep}{sep}markedup.jpg");
}
}