private Image <Gray, byte> HaarDetection(string xmlFile, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE haarDetectionTipe, string path)
{
var bitmap = new Bitmap("TrainedFaces/downloaded/" + path);
var image = new Image <Gray, Byte>(bitmap);
//Face Detector
MCvAvgComp[][] faces = image.DetectHaarCascade(
new HaarCascade(xmlFile),
1.1,
10,
haarDetectionTipe,
new Size(40, 40)
);
Image <Gray, byte> newFace = null;
//Action for each element detected
foreach (MCvAvgComp f in faces[0])
{
newFace = image.Copy(f.rect).Convert <Gray, byte>().Resize(200, 200, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
break;
}
return(newFace);
}
public static Rectangle[] Detect(Image <Bgr, Byte> image, string cascadeFile,
double scaleFactor = 1.3, int minNeighbors = 10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE detectionType = Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
int minSize = 20, int maxSize = 0)
{
string cascadeFilePath = CascadeManager.GetCascade(cascadeFile);
Size minimumSize;
if (minSize == 0)
{
minimumSize = Size.Empty;
}
else
{
minimumSize = new Size(minSize, minSize);
}
Size maximumSize;
if (maxSize == 0)
{
maximumSize = Size.Empty;
}
else
{
maximumSize = new Size(maxSize, maxSize);
}
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier cascade = new GpuCascadeClassifier(cascadeFilePath))
using (GpuImage <Bgr, Byte> gpuImage = new GpuImage <Bgr, byte>(image))
using (GpuImage <Gray, Byte> gpuGray = gpuImage.Convert <Gray, Byte>())
{
return(cascade.DetectMultiScale(gpuGray, scaleFactor, minNeighbors, minimumSize));
}
}
else
{
using (HaarCascade cascade = new HaarCascade(cascadeFilePath))
using (Image <Gray, Byte> gray = image.Convert <Gray, Byte>())
{
gray._EqualizeHist();
MCvAvgComp[] detected = cascade.Detect(gray,
scaleFactor, minNeighbors,
detectionType,
minimumSize, maximumSize);
return((from x in detected
select x.rect).ToArray());
}
}
}
private Image <Gray, byte> HaarDetectionAlt2(Emgu.CV.CvEnum.HAAR_DETECTION_TYPE haarDetectionTipe, string path)
{
return(HaarDetection("haarcascade_frontalface_alt2.xml", haarDetectionTipe, path));
}
