public ACFaceRecognition()
{
// Максимальное к-во лиц на фото - 30.
MCvAvgComp[][] facesDetected = new MCvAvgComp[MAX_FACES_IN_IMAGE][];
lIntDatabaseImagesLabels = new List<int>();
lStringDatabaseImagesLabels = new List<string>();
}
private static void DetectAndDrawEyes(Image<Bgr, byte> image, Image<Gray, byte> gray, MCvAvgComp f, HaarCascade eye)
{
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 e in eyesDetected[0])
{
Rectangle eyeRect = e.rect;
eyeRect.Offset(f.rect.X, f.rect.Y);
image.Draw(eyeRect, new Bgr(Color.Red), 2);
}
}
/// <summary>
/// The get mouth.
/// </summary>
/// <param name="frame">
/// The frame.
/// </param>
/// <param name="face">
/// The face.
/// </param>
/// <returns>
/// The <see cref="MCvAvgComp"/>.
/// </returns>
private MCvAvgComp GetMouth(Image<Bgr, byte> frame, MCvAvgComp face)
{
var grayFrame = frame.Convert<Gray, byte>();
var mouthArea = new Rectangle(
face.rect.X,
face.rect.Y + (face.rect.Height / 2),
face.rect.Width,
face.rect.Height / 2);
grayFrame.ROI = mouthArea;
var mouthsDetected = this.mouthCascade.Detect(grayFrame);
var mouth = mouthsDetected[0];
foreach (var possibleMouth in mouthsDetected)
{
if ((possibleMouth.rect.Width * possibleMouth.rect.Height) > mouth.rect.Width * mouth.rect.Height)
{
mouth = possibleMouth;
}
}
return mouth;
}
/// <summary>
/// The draw mouth.
/// </summary>
/// <param name="frame">
/// The frame.
/// </param>
/// <param name="face">
/// The face.
/// </param>
/// <param name="mouth">
/// The mouth.
/// </param>
/// <param name="color">
/// The color.
/// </param>
private void DrawMouth(Image<Bgr, byte> frame, MCvAvgComp face, MCvAvgComp mouth, Color color)
{
var mouthRect = mouth.rect;
mouthRect.Offset(face.rect.X, face.rect.Y + (face.rect.Height / 2));
frame.Draw(mouthRect, new Bgr(color), 2);
}
private string recognizerall(MCvAvgComp f)
{
string[] ten = new string[5];
ten[0] = "";
if (trainingImages.ToArray().Length != 0)
{
// /Term Criteria for face recognition with numbers of trained images like max Iteration,eps > =>chinh xac
MCvTermCriteria termCrit = new MCvTermCriteria(tong, 0.6);
MCvTermCriteria termCritn = new MCvTermCriteria(tong, 0.7);
MCvTermCriteria termCritm = new MCvTermCriteria(tong, 0.7);
MCvTermCriteria termCriteL = new MCvTermCriteria(tong, 0.7);
MCvTermCriteria termCriteR = new MCvTermCriteria(tong, 0.7);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
2000,
ref termCrit);
ten[0] = recognizer.Recognize(resultface);
/*
///////////////////////////////////////////////////kiem tra nose/
if (resultnose != null)
{
EigenObjectRecognizer recognizernose = new EigenObjectRecognizer(
trainingImagenose.ToArray(),
labels.ToArray(),
1000,
ref termCritn);
ten[1] = recognizernose.Recognize(resultnose);
currentFrame.Draw("nose: "+ten[1], ref font, new Point(f.rect.X - 2, f.rect.Y - 15), new Bgr(Color.DarkBlue));
}
//////////////////////////////////////////////////////////
if (resultmouth != null)
{
EigenObjectRecognizer recognizermouth = new EigenObjectRecognizer(
trainingImagemouth.ToArray(),
labels.ToArray(),
1000,
ref termCritm);
ten[2] = recognizermouth.Recognize(resultmouth);
currentFrame.Draw("mouth: "+ten[2], ref font, new Point(f.rect.X - 2, f.rect.Y - 30), new Bgr(Color.LightGreen));
}
if (resulteyeL != null)
{
EigenObjectRecognizer recognizereyeL = new EigenObjectRecognizer(
trainingImageneyeL.ToArray(),
labels.ToArray(),
1000,
ref termCriteL);
ten[3] = recognizereyeL.Recognize(resulteyeL);
currentFrame.Draw("eyes: "+ten[3], ref font, new Point(f.rect.X - 45, f.rect.Y - 45), new Bgr(Color.LightGreen));
}
if (resulteyeR != null)
{
EigenObjectRecognizer recognizereyeR = new EigenObjectRecognizer(
trainingImageneyeR.ToArray(),
labels.ToArray(),
600,
ref termCriteR);
ten[4] = recognizereyeR.Recognize(resulteyeR);
currentFrame.Draw(ten[4], ref font, new Point(f.rect.X +65, f.rect.Y - 45), new Bgr(Color.LightGreen));
}
}
int tam = 0;
string name="";
for (int i = 1; i < 5; i++)
{
if (ten[0] == ten[i]) tam++;
if (tam > 2&&ten[0]!=null) { name = ten[0]; break; } else name = "";
}
*/
}
return ten[0];
}
private bool GetCoordination(MCvAvgComp acHum)
{
if (!CB_Parallel.Checked)
{
return BitAnalysis.StartDye(acHum.rect.X, acHum.rect.Y, acHum.rect.Width, acHum.rect.Height, imgGray); // Analyze in Haar detection
}
else
{
return BitAnalysis_P.StartDye(acHum.rect.X, acHum.rect.Y, acHum.rect.Width, acHum.rect.Height, imgGray);
}
}
public DetectorResult(MCvAvgComp[] results, int factor, Image<Bgr, byte> image)
{
this.results = results;
this.factor = factor;
this.Image = image;
}
public DetectorResult(MCvAvgComp[] results, int factor)
{
this.results = results;
this.factor = factor;
}
public DetectorResult(MCvAvgComp[] results)
{
this.results = results;
}
/// <summary>
/// This method returns the 2 ROIs that are more "aligned", i.e. with the
/// lowest difference in the Y coordinate
/// It calculates the distances between all the ROIs and selects the appropriate pair
/// </summary>
/// <param name="ROIs"></param>
/// <returns></returns>
private MCvAvgComp[] AlignedROIs(MCvAvgComp[] ROIs)
{
MCvAvgComp[] alignedROIs = new MCvAvgComp[2];
int N = ROIs.Length;
Matrix<int> distancesY = new Matrix<int>(N, N);
distancesY = distancesY.Add(100000);
double minimum;
double maximum;
Point minimumLocation;
Point maximumLocation;
for (int i = 0; i < N; i++)
{
for (int j = i + 1; j < N; j++)
{
// If both rectangles do not intersect, we add their distance to the matrix
// MT: Min distance of 2 x roi.width
if (ROIs[j].rect.IntersectsWith(ROIs[i].rect) == false && Math.Abs(ROIs[j].rect.X - ROIs[i].rect.X) > ROIs[j].rect.Width*2.5)
distancesY[i, j] = Math.Abs(ROIs[j].rect.Y - ROIs[i].rect.Y);
}
}
distancesY.MinMax(out minimum, out maximum, out minimumLocation, out maximumLocation);
alignedROIs[0] = ROIs[minimumLocation.X];
alignedROIs[1] = ROIs[minimumLocation.Y];
return alignedROIs;
}
private MCvAvgComp DrawEyes(MCvAvgComp f)
{
// Our Region of interest where find eyes will start with a sample estimation using face metric
Int32 StartSearchEyes = f.rect.Top + (f.rect.Height * 3 / 11);
Point startingPointSearchEyes = new Point(f.rect.X, StartSearchEyes);
Point endingPointSearchEyes = new Point((f.rect.X + f.rect.Width), StartSearchEyes);
Size searchEyesAreaSize = new Size(f.rect.Width, (f.rect.Height * 2 / 9));
Point lowerEyesPointOptimized = new Point(f.rect.X, StartSearchEyes + searchEyesAreaSize.Height);
Size eyeAreaSize = new Size(f.rect.Width / 2, (f.rect.Height * 2 / 9));
Point startingLeftEyePointOptimized = new Point(f.rect.X + f.rect.Width / 2, StartSearchEyes);
Rectangle possibleROI_eyes = new Rectangle(startingPointSearchEyes, searchEyesAreaSize);
Rectangle possibleROI_rightEye = new Rectangle(startingPointSearchEyes, eyeAreaSize);
Rectangle possibleROI_leftEye = new Rectangle(startingLeftEyePointOptimized, eyeAreaSize);
#region Drawing Utilities
// Let's draw our search area, first the upper line
currentFrame.Draw(new LineSegment2D(startingPointSearchEyes, endingPointSearchEyes), new Bgr(Color.White), 3);
// draw the bottom line
currentFrame.Draw(new LineSegment2D(lowerEyesPointOptimized, new Point((lowerEyesPointOptimized.X + f.rect.Width), (StartSearchEyes + searchEyesAreaSize.Height))), new Bgr(Color.White), 3);
// draw the eyes search vertical line
currentFrame.Draw(new LineSegment2D(startingLeftEyePointOptimized, new Point(startingLeftEyePointOptimized.X, (StartSearchEyes + searchEyesAreaSize.Height))), new Bgr(Color.White), 3);
// MCvFont font = new MCvFont(FONT.CV_FONT_HERSHEY_TRIPLEX, 0.6d, 0.6d);
// frame.Draw("Search Eyes Area", ref font, new Point((startingLeftEyePointOptimized.X - 80), (StartSearchEyes + searchEyesAreaSize.Height + 15)), new Bgr(Color.Yellow));
// frame.Draw("Right Eye Area", ref font, new Point(startingPointSearchEyes.X, startingPointSearchEyes.Y - 10), new Bgr(Color.Aqua));
// frame.Draw("Left Eye Area", ref font, new Point(startingLeftEyePointOptimized.X + searchEyesAreaSize.Height / 2, startingPointSearchEyes.Y - 10), new Bgr(Color.Yellow));
#endregion
return f;
}
private IEnumerable<Image<Gray, byte>> TrainFaces(MCvAvgComp[][] facesDetected)
{
foreach (MCvAvgComp f in facesDetected[0])
{
TrainedFace = currentFrame.Copy(f.rect).Convert<Gray, byte>();
yield return TrainedFace;
}
}
private void addToFlow(Image<Bgr, byte> result, MCvAvgComp f, string name)
{
PictureBox pbox = new PictureBox();
pbox.Tag = new object[] { new int[] { f.rect.X, f.rect.Y,f.rect.Width, f.rect.Height}, name };
pbox.Click += new EventHandler(pbox_Click);
pbox.ContextMenuStrip = contextMenuStrip1;
pbox.SizeMode = PictureBoxSizeMode.StretchImage;
pbox.Height = 50;
pbox.Width = 50;
pbox.Cursor = Cursors.Hand;
pbox.Image = result.Copy(f.rect).Bitmap;
flowLayoutPanel1.Controls.Add(pbox);
}