private bool FaceDetect(string sandboxPath, string cascadeXmlPath)
{
//using (IplImage img = new IplImage(sandboxPath, LoadMode.Color))
try {
//using (IplImage img = Cv.LoadImage(sandboxPath, LoadMode.Color))
using (IplImage img = Cv.LoadImage(sandboxPath, LoadMode.GrayScale))
{
using (IplImage gray = Cv.CreateImage(new CvSize(img.Width, img.Height), BitDepth.U8, 1))
{
//Cv.CvtColor(img, gray, ColorConversion.BgrToGray);
Cv.Resize(gray, img, Interpolation.Linear);
Cv.EqualizeHist(img, img);
//EqualizeHist = 히스토그램 평활화. 그레이 이미지를 특출나게 어둡거나 밝은 부분을 적당히 펴줘서 전체 값이 일정하게 되도록 해줌
}
using (CvHaarClassifierCascade cascade = CvHaarClassifierCascade.FromFile(cascadeXmlPath))
//haarcascade_frontalface_alt2.xml얼굴 검출에 대한 기계학습 자료가 담겨져 있음.
//이자료와 비교해서 비슷하면 얼굴로 인식하게 되어 있음
using (CvMemStorage storage = new CvMemStorage())
{
storage.Clear();
//얼굴의 검출
CvSeq <CvAvgComp> faces = Cv.HaarDetectObjects(img, cascade, storage, 1.1, 2, 0, new CvSize(30, 30));
//검출된 얼굴을 facesdp 저장. faces.Total에 총 검풀된 얼굴수가 들어가 있음
Console.WriteLine("-----");
for (int i = 0; i < faces.Total; i++)
{
CvAvgComp comp = (CvAvgComp)Cv.GetSeqElem(faces, i);
Console.WriteLine(comp.Rect.TopLeft + " " + comp.Rect.Width + " " + comp.Rect.Height);
}
return(faces.Total > 0);
}
}
}
catch (Exception e)
{
System.Console.WriteLine(e);
return(false);
}
}
private List <ObjRect> DetectFeaturesInFace(IplImage img, CvMemStorage storage, CvAvgComp face)
{
List <ObjRect> eyes = new List <ObjRect>();
storage.Clear();
//Take the top 4/8ths of the face as the region of interest
CvRect r = face.Rect;
r.Height = Cv.Round((double)r.Height / 2);
img.SetROI(r);
//Look for pairs there
CvAvgComp[] pairs = Cv.HaarDetectObjects(img, Cascades["EyePair22"], storage, 1.0850, 2, 0, new CvSize(r.Width < 50 ? 11 : 22, r.Width < 50 ? 3 : 5)).ToArrayAndDispose();
//Array.Sort<CvAvgComp>(pairs, CompareByNeighbors);
//Look for individual eyes if no pairs were found
if (pairs.Length == 0)
{
//Drop 1/2.75th off the top, leaving us with a full-width rectangle starting at 1/5.5th and stopping at 1/2th of face height.
int aFifth = Cv.Round((double)r.Height * 2 / 5.5);
r.Y += aFifth;
r.Height -= aFifth;
eyes.AddRange(DetectEyesInRegion(img, storage, r));
}
//If there are pairs, evalutate them all. Finding eyes within multiple pairs is unlikely
for (var i = 0; i < pairs.Length; i++)
{
CvAvgComp pair = pairs[i]; //Adjust for ROI
pair.Rect.X += r.X;
pair.Rect.Y += r.Y;
eyes.AddRange(DetectFeaturesInPair(img, storage, pair));
}
if (eyes.Count > 0)
{
eyes.Add(new ObjRect(face.Rect.ToRectangleF(), FeatureType.Face));
}
return(eyes);
}
private List <ObjRect> DetectFeaturesInPair(IplImage img, CvMemStorage storage, CvAvgComp eyePair)
{
List <ObjRect> eyes = new List <ObjRect>();
CvRect pair = eyePair.Rect;
//Inflate 100% vertically, centering
pair.Top -= pair.Height / 2;
pair.Height *= 2;
if (pair.Top < 0)
{
pair.Height += pair.Top; pair.Top = 0;
}
if (pair.Height >= img.Height)
{
pair.Height = img.Height;
}
if (pair.Bottom >= img.Height)
{
pair.Top = img.Height - pair.Height;
}
//Inflate 20% on each side, centering
pair.Left -= pair.Width / 5;
pair.Width += pair.Width / 5 * 2;
pair.Left = Math.Max(0, pair.Left);
pair.Width = Math.Min(img.Width - pair.Left, pair.Width);
eyes.AddRange(DetectEyesInRegion(img, storage, pair));
if (eyes.Count > 0)
{
eyes.Add(new ObjRect(eyePair.Rect.ToRectangleF(), FeatureType.EyePair));
}
return(eyes);
}
protected int CompareByNeighbors(CvAvgComp a, CvAvgComp b)
{
return(b.Neighbors.CompareTo(a.Neighbors));
}
