public static RecognizerResult Recognize(string unknownImg)
{
RecognizerResult rcgnResult = new RecognizerResult();
if (recognizer == null)
{
return(null);
}
// Recognize the image
Image <Gray, Byte> testImage = new Image <Gray, Byte>(unknownImg);
float[] distances = recognizer.GetEigenDistances(testImage);
string[] labels = recognizer.Labels;
int num = labels.Count();
for (int i = 0; i < num; i++)
{
rcgnResult.Distances.Add(labels[i], distances[i]);
}
EigenObjectRecognizer.RecognitionResult result = recognizer.Recognize(testImage);
if (result != null)
{
rcgnResult.Distance = result.Distance;
rcgnResult.Label = result.Label;
}
rcgnResult.FileName = Path.GetFullPath(unknownImg);
return(rcgnResult);
}
private void FrameProcedure(object sender, EventArgs e)
{
Users.Add("");
//Set Frame/Size
Frame = camera.QueryFrame().Resize(640, 480, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
grayFace = Frame.Convert <Gray, Byte>();
MCvAvgComp[][] faceDedectedNow = grayFace.DetectHaarCascade(faceDetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new System.Drawing.Size(20, 20));
foreach (MCvAvgComp f in faceDedectedNow[0])
{
result = Frame.Copy(f.rect).Convert <Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
Frame.Draw(f.rect, new Bgr(Color.Transparent), 2);
if (trainingImages.ToArray().Length != 0)
{
MCvTermCriteria termCriteria = new MCvTermCriteria(Count, 0.01);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), labels.ToArray(), 1500, ref termCriteria);
name = recognizer.Recognize(result);
Frame.Draw(name, ref font, new Point(f.rect.X - 4, f.rect.Y - 4), new Bgr(Color.OrangeRed));
}
Users.Add("");
}
cameraBox.Image = Frame;
names = "";
Users.Clear();
}
void FrameGrabber(object sender, EventArgs e)
{
NamePersons.Add("");
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
gray = currentFrame.Convert <Gray, Byte>();
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
foreach (MCvAvgComp f in facesDetected[0])
{
t = t + 1;
result = currentFrame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
currentFrame.Draw(f.rect, new Bgr(Color.Red), 2);
if (trainingImages.ToArray().Length != 0)
{
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
5000,
ref termCrit);
name = recognizer.Recognize(result);
currentFrame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));
}
NamePersons[t - 1] = name;
NamePersons.Add("");
/*
* gray.ROI = f.rect;
* MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
* eye,
* 1.1,
* 10,
* Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
* new Size(20, 20));
* gray.ROI = Rectangle.Empty;
*
* foreach (MCvAvgComp ey in eyesDetected[0])
* {
* Rectangle eyeRect = ey.rect;
* eyeRect.Offset(f.rect.X, f.rect.Y);
* currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 2);
* }
*/
}
t = 0;
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
imageBoxFrameGrabber.Image = currentFrame;
NamePersons.Clear();
}