| public DetectMultiScale ( OpenCvSharp.Mat image, double scaleFactor = 1.1, int minNeighbors = 3, HaarDetectionType flags, Size minSize = null, Size maxSize = null ) : Rect[] | ||
| image | OpenCvSharp.Mat | Matrix of the type CV_8U containing an image where objects are detected. |
| scaleFactor | double | Parameter specifying how much the image size is reduced at each image scale. |
| minNeighbors | int | Parameter specifying how many neighbors each candidate rectangle should have to retain it. |
| flags | HaarDetectionType | Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. /// It is not used for a new cascade. |
| minSize | Size | Minimum possible object size. Objects smaller than that are ignored. |
| maxSize | Size | Maximum possible object size. Objects larger than that are ignored. |
| return | Rect[] | |
/// <summary>
///
/// </summary>
/// <param name="cascade"></param>
/// <returns></returns>
private Mat DetectFace(CascadeClassifier cascade)
{
Mat result;
using (var src = new Mat(FilePath.Image.Yalta, ImreadModes.Color))
using (var gray = new Mat())
{
result = src.Clone();
Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);
// Detect faces
Rect[] faces = cascade.DetectMultiScale(
gray, 1.08, 2, HaarDetectionType.ScaleImage, new Size(30, 30));
// Render all detected faces
foreach (Rect face in faces)
{
var center = new Point
{
X = (int)(face.X + face.Width * 0.5),
Y = (int)(face.Y + face.Height * 0.5)
};
var axes = new Size
{
Width = (int)(face.Width * 0.5),
Height = (int)(face.Height * 0.5)
};
Cv2.Ellipse(result, center, axes, 0, 0, 360, new Scalar(255, 0, 255), 4);
}
}
return result;
}
private int OpenCVFaceDetector(string path)
{
// uses openCv Library
OpenCvSharp.CascadeClassifier faceClassifier = new OpenCvSharp.CascadeClassifier(@"./haarcascade/haarcascade_frontalface_alt.xml");
OpenCvSharp.Mat result;
Rect[] faces = new Rect[0];
using (var src = new OpenCvSharp.Mat(path, OpenCvSharp.ImreadModes.Color))
using (var gray = new OpenCvSharp.Mat())
{
result = src.Clone();
Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);
// Detect faces
faces = faceClassifier.DetectMultiScale(gray, 1.08, 2, OpenCvSharp.HaarDetectionType.ScaleImage);
List <System.Drawing.Rectangle> rfaces = new List <System.Drawing.Rectangle>();
foreach (Rect face in faces)
{
System.Drawing.Rectangle r = new System.Drawing.Rectangle(face.X, face.Y, face.Width, face.Height);
this.GetLandmarks(gray, face, rfaces);
rfaces.Add(r);
}
DrawOnImage?.Invoke(rfaces.ToArray(), new System.Drawing.Size(result.Width, result.Height));
}
result.Dispose();
return(faces.Length);
}
public byte[] Mark(Mat srcImage)
{
try
{
var grayImage = new Mat();
Cv2.CvtColor(srcImage, grayImage, ColorConversionCodes.BGRA2GRAY);
Cv2.EqualizeHist(grayImage, grayImage);
var cascade = new CascadeClassifier($@"{AppDomain.CurrentDomain.BaseDirectory}/Services/Data/haarcascade_frontalface_alt.xml");
var faces = cascade.DetectMultiScale(
grayImage,
1.1,
3, HaarDetectionType.DoRoughSearch | HaarDetectionType.ScaleImage
);
if (faces.Length < 1)
{
return(null);
}
var face = faces.FirstOrDefault();
var image = new Image();
var file = image.ConvertToByte(srcImage);
return(Crop(file, face.X, face.Y, face.Width, face.Height));
}
catch (Exception e)
{
return(null);
}
}
public static List <OpenCvSharp.Rect> DetectFaces(OpenCvSharp.Mat image)
{
List <OpenCvSharp.Rect> faces = new List <OpenCvSharp.Rect>();
var facesCascade = HttpContext.Current.Server.MapPath("~/face.xml");
using (OpenCvSharp.CascadeClassifier face = new OpenCvSharp.CascadeClassifier(facesCascade))
{
using (OpenCvSharp.Mat ugray = new OpenCvSharp.Mat())
{
Cv2.CvtColor(image, ugray, ColorConversionCodes.BGRA2GRAY);
Cv2.EqualizeHist(ugray, ugray);
var facesDetected = face.DetectMultiScale(
image: ugray,
scaleFactor: 1.1,
minNeighbors: 10,
flags: HaarDetectionType.DoRoughSearch | HaarDetectionType.ScaleImage,
minSize: new OpenCvSharp.Size(20, 20));
faces.AddRange(facesDetected);
}
}
return(faces);
}
private void Init()
{
new Thread(() =>
{
var eye_casc = new cv.CascadeClassifier("eye.xml");
var left_eye_casc = new cv.CascadeClassifier("left_eye.xml");
var right_eye_casc = new cv.CascadeClassifier("right_eye.xml");
var face_casc = new cv.CascadeClassifier("fface_default.xml");
cap = new cv.VideoCapture(0);
while (true)
{
if (released)
{
break;
}
var img = new cv.Mat();
cap.Read(img);
var gray = img.CvtColor(cv.ColorConversionCodes.BGR2GRAY);
var gaus = gray.AdaptiveThreshold(255, cv.AdaptiveThresholdTypes.GaussianC, cv.ThresholdTypes.Binary, 115, 1);
img = gaus;
var faces = face_casc.DetectMultiScale(gray, 1.3, 5);
RenderTargetBitmap eyes_lay = null;
foreach (var face in faces)
{
var rect = new cv.Rect(face.Location, face.Size);
//img.Rectangle(rect, new cv.Scalar(255, 0, 0));
var sub_ing = gray[rect];
var sub_ing_rgb = img[rect];
//left eye
var eyes = eye_casc.DetectMultiScale(sub_ing, 1.3, 2);
int count = 0;
foreach (var eye in eyes)
{
count++;
if (count > 2)
{
count = 0;
break;
}
var rect_eye = new cv.Rect(eye.Location, eye.Size);
if (eye.X + eye.Width < face.Width / 2)
{
//sub_ing_rgb.Rectangle(rect_eye, new cv.Scalar(0, 255, 0));
Dispatcher.Invoke(() =>
{
eyes_lay = DrawImg(cv.Extensions.BitmapSourceConverter.ToBitmapSource(img), eye.X + face.X, eye.Y + face.Y, eye.Width, eye.Height, eye_l, scale_w, scale_h);
});
}
}
//left eye
count = 0;
foreach (var eye in eyes)
{
count++;
if (count > 2)
{
break;
}
var rect_eye = new cv.Rect(eye.Location, eye.Size);
if (eye.X + eye.Width > face.Width / 2)
{
Dispatcher.Invoke(() =>
{
if (eyes_lay != null)
{
eyes_lay = DrawImg(eyes_lay, eye.X + face.X, eye.Y + face.Y, eye.Width, eye.Height, eye_r, scale_w, scale_h);
}
else
{
eyes_lay = DrawImg(cv.Extensions.BitmapSourceConverter.ToBitmapSource(img), eye.X + face.X, eye.Y + face.Y, eye.Width, eye.Height, eye_r, scale_w, scale_h);
}
});
}
}
}
Dispatcher.Invoke(() =>
{
if (eyes_lay != null)
{
OutImg.Source = eyes_lay;
}
else
{
OutImg.Source = cv.Extensions.BitmapSourceConverter.ToBitmapSource(img);
}
});
//Thread.Sleep(100);
GC.Collect();
}
})
{
IsBackground = true
}.Start();
}
