private Image <Gray, Byte> SkinDetect(Image <Bgr, Byte> Input, IColor min, IColor max, int a)
{
Image <Gray, byte> skin = new Image <Gray, byte>(Input.Width, Input.Height);
if (a == 1)
{
Image <Ycc, Byte> YCrCbInput = Input.Convert <Ycc, Byte>();
skin = YCrCbInput.InRange((Ycc)min, (Ycc)max);
}
else
{
Image <Hsv, Byte> HsvInput = Input.Convert <Hsv, Byte>();
skin = HsvInput.InRange((Hsv)min, (Hsv)max);
}
Image <Gray, Byte> skin2 = skin.Convert <Gray, Byte>();
imageBox3.Image = skin2;
StructuringElementEx Rect12 = new StructuringElementEx(12, 12, 6, 6, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_CROSS);
StructuringElementEx Rect6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_CROSS);
CvInvoke.cvErode(skin, skin, Rect6, 1);
CvInvoke.cvDilate(skin, skin, Rect12, 1);
return(skin);
}
public Image <Bgr, Byte> FindContourSperatly(Image <Bgr, Byte> image)//tasviri ke mikhahid contour ha dar an peyda shavand be onvane vorudi be aan dade mishavad
{
Gray cannyThreshold = new Gray(180);
Gray cannyThresholdLinking = new Gray(120);
Image <Gray, Byte> grayImage = image.Convert <Gray, Byte>();
Image <Gray, Byte> cannyImage = new Image <Gray, Byte>(grayImage.Size);
CvInvoke.cvCanny(grayImage, cannyImage, 100, 360, 3);//threshold baraye canny
Image <Bgr, Byte> BoundryImage = image.CopyBlank();
StructuringElementEx kernel = new StructuringElementEx(3, 3, 1, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
CvInvoke.cvDilate(cannyImage, cannyImage, kernel, 1);
IntPtr cont = IntPtr.Zero;
Point[] pts;
Point p = new Point(0, 0);
using (MemStorage storage = new MemStorage()) //allocate storage for contour approximation
for (Contour <Point> contours = cannyImage.FindContours(Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE,
Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_EXTERNAL); contours != null; contours = contours.HNext)
{
pts = contours.ToArray();
//********keshidan khat dor ta dore contour******************/
BoundryImage.DrawPolyline(pts, true, new Bgr(255, 0, 255), 3);
/***************joda kardane ghesmate contoure tasvir****************/
}
return(BoundryImage);
}
// Cherche les contours et bounding boxes de la peau sur une image fournie
// Cette partie a été développée par nous-même
// Elle détecte et sépare les parties de peau de l'image fournie à la classe, d'après les couleurs HSV
// On va ensuite eroder et dilater les pixels restants, afin d'obtenir une image "plus nette" et réduire le bruit et les imperfections
// On va ensuite rechercher tous les contours dans l'image obtenur et mettre le plus grand de côté afin de pouvoir le traiter plus tard
public void FindSkinContours()
{
// Converti les couleurs de l'image en HSV
Image <Hsv, Byte> hsvImg = imgOrg.Convert <Hsv, Byte>();
// Filtre les pixels de l'image afin de ne garder que ceux qui se rapprochent de la couleur de la peau
// Nous avons trouvé ces valeurs sur Internet et les avons un peu adaptées pour notre utilisation
imgSkin = hsvImg.InRange(new Hsv(0, 48, 80), new Hsv(20, 255, 255));
imgSkin = imgSkin.ThresholdBinary(new Gray(200), new Gray(255));
// On erode et dilate pour éliminer les imperfections
// Nous avons trouvé ces valeurs par "tattonement" en essayant d'avoir un contour de main le plus net possible
StructuringElementEx erodeStrctEl = new StructuringElementEx(4, 4, 2, 2, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(imgSkin, imgSkin, erodeStrctEl, 1);
StructuringElementEx dilateStrctEl = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(imgSkin, imgSkin, dilateStrctEl, 3);
// Boucle sur tous les contours trouvés dans l'image
for (Contour <Point> contours = imgSkin.FindContours(Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE, Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_TREE, contoursStorage); contours != null; contours = contours.HNext)
{
// Si un contour est plus grand que 20x20 pixels, on le traite
if (contours.BoundingRectangle.Width > 20 && contours.BoundingRectangle.Height > 20)
{
// Récupère la bounding box autour du contour
Rectangle rect = contours.BoundingRectangle;
// On agrandi la bourning box et on vérifie qu'elle ne sorte pas de l'image
rect.X = rect.X - 10;
rect.X = (rect.X < 0) ? 0 : rect.X;
rect.Y = rect.Y - 10;
rect.Y = (rect.Y < 0) ? 0 : rect.Y;
rect.Height = (rect.Height + 20);
rect.Width = (rect.Width + 20);
// Ajout de la bounding box et du contour aux listes
boundingBoxes.Add(rect);
contoursList.Add(contours);
}
}
// Si des contours ont été trouvés, on cherche le plus grand
if (boundingBoxes.Count > 0)
{
FindBiggestBoundingBox();
FindBiggestContour();
isHandDetected = true;
}
else
{
// Sinon, c'est que rien n'a été détecté
isHandDetected = false;
fingerNum = 0;
}
}
public FeatureExtractor(IPerspectiveCorrector perspectiveCorrector, int N, int M, int frameWidth, int frameHeight, Image <Bgr, byte> backgroundImage)
{
this.perspectiveCorrector = perspectiveCorrector;
this.background = backgroundImage.Resize(frameWidth, frameHeight, INTER.CV_INTER_AREA).Convert <Gray, byte>();
this.N = N;
this.M = M;
this.frameWidth = frameWidth;
this.frameHeight = frameHeight;
structuringElement = new StructuringElementEx(3, 3, 1, 1, CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
}
/// <summary>
/// WORKS OK! USE IT!
/// </summary>
/// <param name="currentFrame"></param>
/// <param name="draw"></param>
/// <returns></returns>
public static Rectangle FindBiggestBlob(this Image <Bgr, Byte> currentFrame, bool draw = false)
{
using (Image <Gray, Byte> img1 = currentFrame.Convert <Gray, Byte>())
using (Image <Gray, Byte> img2 = img1.PyrDown())
using (Image <Gray, Byte> grayImage = img2.PyrUp())
using (Image <Gray, Byte> cannyImage = new Image <Gray, Byte>(grayImage.Size))
{
CvInvoke.cvCanny(grayImage, cannyImage, 10, 60, 3);
using (StructuringElementEx kernel = new StructuringElementEx(3, 3, 1, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT))
CvInvoke.cvDilate(cannyImage, cannyImage, kernel, 1);
IntPtr cont = IntPtr.Zero;
int top = int.MaxValue, left = int.MaxValue, bottom = 0, right = 0;
//allocate storage for contour approximation
using (MemStorage storage = new MemStorage())
{
for (Contour <Point> contours = cannyImage.FindContours(CHAIN_APPROX_METHOD.CV_LINK_RUNS, RETR_TYPE.CV_RETR_LIST);
contours != null; contours = contours.HNext)
{
#if KNOW_HOW
IntPtr seq = CvInvoke.cvConvexHull2(contours, storage.Ptr, Emgu.CV.CvEnum.ORIENTATION.CV_CLOCKWISE, 0);
IntPtr defects = CvInvoke.cvConvexityDefects(contours, seq, storage);
#endif
Seq <Point> tr = contours.GetConvexHull(Emgu.CV.CvEnum.ORIENTATION.CV_CLOCKWISE);
#if KNOW_HOW
Seq <Emgu.CV.Structure.MCvConvexityDefect> te = contours.GetConvexityDefacts(storage, Emgu.CV.CvEnum.ORIENTATION.CV_CLOCKWISE);
#endif
if (draw)
{
currentFrame.Draw(tr.BoundingRectangle, new Bgr(Color.Red), 3);
}
top = Math.Min(top, tr.BoundingRectangle.Top);
left = Math.Min(left, tr.BoundingRectangle.Left);
bottom = Math.Max(bottom, tr.BoundingRectangle.Bottom);
right = Math.Max(right, tr.BoundingRectangle.Right);
}
}
Rectangle result = Rectangle.FromLTRB(left, top, right, bottom);
if (draw)
{
currentFrame.Draw(result, new Bgr(Color.Blue), 3);
}
return(result);
}
}
private Image <Gray, byte> GetSkin(Image <Bgr, byte> Img, IColor min, IColor Max)
{
Image <Ycc, byte> Img_Ycbcr = Img.Convert <Ycc, byte>();
Image <Gray, byte> skins = new Image <Gray, byte>(Img.Width, Img.Height);
skins = Img_Ycbcr.InRange((Ycc)min, (Ycc)Max);
StructuringElementEx rect_for_erode = new StructuringElementEx(12, 12, 6, 6, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(skins, skins, rect_for_erode, 1);
StructuringElementEx rect_for_Dilate = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(skins, skins, rect_for_Dilate, 2);
return(skins);
}
public override Image <Gray, byte> DetectSkin(Image <Bgr, byte> Img, IColor min, IColor max)
{
Image <Ycc, Byte> currentYCrCbFrame = Img.Convert <Ycc, Byte>();
Image <Gray, byte> skin = new Image <Gray, byte>(Img.Width, Img.Height);
skin = currentYCrCbFrame.InRange((Ycc)min, (Ycc)max);
StructuringElementEx rect_12 = new StructuringElementEx(12, 12, 6, 6, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(skin, skin, rect_12, 1);
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(skin, skin, rect_6, 2);
return(skin);
}
public override Image <Gray, byte> DetectSkin(Image <Bgr, byte> Img, IColor min, IColor max)
{
//Code adapted from here
// http://blog.csdn.net/scyscyao/archive/2010/04/09/5468577.aspx
// Look at this paper for reference (Chinese!!!!!)
// http://www.chinamca.com/UploadFile/200642991948257.pdf
Image <Ycc, Byte> currentYCrCbFrame = Img.Convert <Ycc, Byte>();
Image <Gray, Byte> skin = new Image <Gray, Byte>(Img.Width, Img.Height);
int y, cr, cb, l, x1, y1, value;
int rows = Img.Rows;
int cols = Img.Cols;
Byte[,,] YCrCbData = currentYCrCbFrame.Data;
Byte[,,] skinData = skin.Data;
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
y = YCrCbData[i, j, 0];
cr = YCrCbData[i, j, 1];
cb = YCrCbData[i, j, 2];
cb -= 109;
cr -= 152;
x1 = (819 * cr - 614 * cb) / 32 + 51;
y1 = (819 * cr + 614 * cb) / 32 + 77;
x1 = x1 * 41 / 1024;
y1 = y1 * 73 / 1024;
value = x1 * x1 + y1 * y1;
if (y < 100)
{
skinData[i, j, 0] = (value < 700) ? (byte)255 : (byte)0;
}
else
{
skinData[i, j, 0] = (value < 850) ? (byte)255 : (byte)0;
}
}
}
var rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(skin, skin, rect_6, 1);
CvInvoke.cvDilate(skin, skin, rect_6, 2);
return(skin);
}
private Math::Matrix <double> CalculateInputMatrix(IList <string> frameImageFilePaths)
{
Math::Matrix <double> X = new DenseMatrix(frameImageFilePaths.Count, featureExtractor.Length);
StructuringElementEx kernel = new StructuringElementEx(3, 3, 1, 1, CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
ParallelOptions options = new ParallelOptions();
options.MaxDegreeOfParallelism = 1;
Parallel.ForEach(Enumerable.Range(0, frameImageFilePaths.Count), options, i =>
{
Math::Vector <double> featureVector = featureExtractor.ExtractFeatures(new Image <Gray, Byte>(frameImageFilePaths[i]));
X.SetSubMatrix(i, 1, 0, featureExtractor.Length, featureVector.ToRowMatrix());
});
Console.Write(X.Row(1));
return(X);
}
public override Image <Gray, byte> DetectSkin(Image <Bgr, byte> Img, IColor min, IColor max)
{
Image <Ycc, Byte> currentYCrCbFrame = Img.Convert <Ycc, Byte>();
Image <Gray, Byte> skin = new Image <Gray, Byte>(Img.Width, Img.Height);
int y, cr, cb, l, x1, y1, value;
int rows = Img.Rows;
int cols = Img.Cols;
Byte[, ,] YCrCbData = currentYCrCbFrame.Data;
Byte[, ,] skinData = skin.Data;
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
y = YCrCbData[i, j, 0];
cr = YCrCbData[i, j, 1];
cb = YCrCbData[i, j, 2];
cb -= 109;
cr -= 152;
x1 = (819 * cr - 614 * cb) / 32 + 51;
y1 = (819 * cr + 614 * cb) / 32 + 77;
x1 = x1 * 41 / 1024;
y1 = y1 * 73 / 1024;
value = x1 * x1 + y1 * y1;
if (y < 100)
{
skinData[i, j, 0] = (value < 700) ? (byte)255 : (byte)0;
}
else
{
skinData[i, j, 0] = (value < 850) ? (byte)255 : (byte)0;
}
}
}
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(skin, skin, rect_6, 1);
CvInvoke.cvDilate(skin, skin, rect_6, 2);
return(skin);
}
///////////////////////////////////////////////////////////////////////////////////////////
void processFrameAndUpdateGUI(object sender, EventArgs arg)
{
imgOriginal = capWebcam.QueryFrame(); // get next frame from the webcam
if (imgOriginal == null) // if we did not get a frame
{ // show error via message box
MessageBox.Show("unable to read from webcam" + Environment.NewLine + Environment.NewLine +
"exiting program");
Environment.Exit(0); // and exit program
}
imgBlurredBGR = imgOriginal.SmoothGaussian(5); // blur
imgProcessed = imgBlurredBGR.InRange(new Bgr(0, 0, 175), new Bgr(100, 100, 256)); // filter on color
imgProcessed = imgProcessed.SmoothGaussian(5); // blur again
StructuringElementEx structuringElementEx = new StructuringElementEx(5, 5, 1, 1, CV_ELEMENT_SHAPE.CV_SHAPE_RECT); // declare structuring element to use in dilate and erode
CvInvoke.cvDilate(imgProcessed, imgProcessed, structuringElementEx, 1); // close image (dilate, then erode)
CvInvoke.cvErode(imgProcessed, imgProcessed, structuringElementEx, 1); // closing "closes" (i.e. fills in) foreground gaps
CircleF[] circles = imgProcessed.HoughCircles(new Gray(100), new Gray(50), 2, imgProcessed.Height / 4, 10, 400)[0]; // fill variable circles with all circles in the processed image
foreach (CircleF circle in circles) // for each circle
{
if (txtXYRadius.Text != "")
{
txtXYRadius.AppendText(Environment.NewLine); // if we are not on the first line in the text box then insert a new line char
}
txtXYRadius.AppendText("ball position = x " + circle.Center.X.ToString().PadLeft(4) + // print ball position and radius
", y = " + circle.Center.Y.ToString().PadLeft(4) + //
", radius = " + circle.Radius.ToString("###.000").PadLeft(7)); //
txtXYRadius.ScrollToCaret(); // scroll down in text box so most recent line added (at the bottom) will be shown
// draw a small green circle at the center of the detected object
CvInvoke.cvCircle(imgOriginal, new Point((int)circle.Center.X, (int)circle.Center.Y), 3, new MCvScalar(0, 255, 0), -1, LINE_TYPE.CV_AA, 0);
imgOriginal.Draw(circle, new Bgr(Color.Red), 3); // draw a red circle around the detected object
}
ibOriginal.Image = imgOriginal; // update image boxes on form
ibProcessed.Image = imgProcessed; //
}
//장애물 색상이 맞는지 판별
public int obstacle_YccColorCheck(Image <Bgr, Byte> iamge, int totalPicxel, int pos_x, int pos_y, int img_width, int img_height, int min1, int min2, int min3, int max1, int max2, int max3)
{
int pixCount = 0;
Image <Ycc, Byte> YCrCbFrame = iamge.Convert <Ycc, Byte>(); //YCrCb 변환
Image <Gray, byte> colorSetting = new Image <Gray, byte>(YCrCbFrame.Width, YCrCbFrame.Height); //Ycc범위로 뽑아낸 것을 gray로 바꿔서 수축팽창 하기 위해
Ycc YCrCb_min = new Ycc(min1, min2, min3);
Ycc YCrCb_max = new Ycc(max1, max2, max3); //blue 색 범위
colorSetting = YCrCbFrame.InRange((Ycc)YCrCb_min, (Ycc)YCrCb_max); //색 범위 설정
StructuringElementEx rect_12 = new StructuringElementEx(12, 12, 6, 6, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(colorSetting, colorSetting, rect_12, 1);
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(colorSetting, colorSetting, rect_6, 2); //수축 팽창
Image <Bgr, Byte> colorCount = colorSetting.Convert <Bgr, Byte>(); //픽셀수 세기 위해
for (int x = pos_x; x < pos_x + img_width; x++)
{
for (int y = pos_y; y < pos_y + img_height; y++)
{
if (colorCount[y, x].Equals(new Bgr(255, 255, 255)))
{
pixCount++;
if (totalPicxel / 10 <= pixCount) //일정 픽섹 이상시 색상배열 변경후 종료
{
return(1);
}
if (x > pos_x / 5 + x && y > pos_y / 5 + y) //좌표의 1/5 넘으면 없는걸로
{
return(-1);
}
}
}
}
return(-1);
}
public static double GetMinkowskiDimension(Image <Gray, byte> image)
{
Image <Gray, byte> dilated = new Image <Gray, byte>(image.Width, image.Height);
int nMaxDisk = 15;
int diskStep = 1;
int nDiskSteps = ((nMaxDisk - 2) / diskStep) + 1;
MathN::Matrix <double> X = new DenseMatrix(nDiskSteps, 2);
MathN::Vector <double> Y = new DenseVector(nDiskSteps);
X[0, 0] = 1;
X[0, 1] = Math.Log(image.CountNonzero()[0]);
int row = 1;
for (int diskSize = 1 + diskStep; diskSize < nMaxDisk; diskSize += diskStep)
{
StructuringElementEx kernel = new StructuringElementEx(diskSize, diskSize, (diskSize - 1) / 2, (diskSize - 1) / 2, CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
CvInvoke.cvDilate(image, dilated, kernel, 1);
X[row, 0] = 1;
X[row, 1] = Math.Log(diskSize);
Y[row] = Math.Log(dilated.CountNonzero()[0]);
if (Double.IsNegativeInfinity(Y[row]))
{
Y[row] = 0;
}
++row;
}
MathN::Vector <double> W = X.QR().Solve(Y);
double slope = W[1];
return(2 - slope);
}
void IdleProcess(object sender, EventArgs e)
{
Emgu.CV.Image <Bgr, Byte> CameraImage = camera.QueryFrame();
CameraImage = new Image <Bgr, Byte>(CropImage(ImageRect));
if (iscroping)
{
CameraImage.Draw(new Rectangle(cropx, cropy, cropwidth, cropheight), new Bgr(Color.Green), 3);
}
camimagebox.Image = CameraImage.Bitmap;
ProcessedImage = CameraImage.InRange(new Bgr(Bmin.Value, Gmin.Value, Rmin.Value), new Bgr(Bmax.Value, Gmax.Value, Rmax.Value));
ProcessedImage.SmoothGaussian(9);
StructuringElementEx rect_12 = new StructuringElementEx(12, 12, 6, 6, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(ProcessedImage, ProcessedImage, rect_12, 1);
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(ProcessedImage, ProcessedImage, rect_6, 2);
ExtractShapes(ProcessedImage, CameraImage);
processedimagebox.Image = ProcessedImage.Bitmap;
/* if (portconnected)
* {
* if (tosend == 1)
* serialPort1.Write("To send Cuboid");
* else if (tosend == 2)
* serialPort1.Write("To Send Cylinder");
* }
*/
label1.Text = rectangle_x + "& Y:" + rectangle_y;
}
public bool detectAlone(Image <Gray, byte> img, Image <Gray, byte> prev, Rectangle eyeROI, Rectangle prevEyeROI, ref Boolean isShook)
{
if (eyeROI.Width == 0 || prevEyeROI.Width == 0)
{
return(false);
}
// when the difference between two eyeROI is huge, then it is probably caused by shaking
int axisGap = 10;
if (System.Math.Abs(eyeROI.X - prevEyeROI.X) > axisGap || System.Math.Abs(eyeROI.Y - prevEyeROI.Y) > axisGap)
{
isShook = true;
return(false);
}
int widthGap = 10;
if (System.Math.Abs(eyeROI.Width - prevEyeROI.Height) > widthGap)
{
isShook = true;
return(false);
}
// Enlarge the ROI to make the algorithm more stable
float widthFactor = 1.2F;
int newX = (int)(eyeROI.X - (widthFactor - 1) * eyeROI.Width / 2);
int newY = (int)(eyeROI.Y - (widthFactor - 1) * eyeROI.Height / 2);
int newWidth = (int)(widthFactor * eyeROI.Width);
int newHeight = (int)(widthFactor * eyeROI.Height);
// Boundary judgment
if (newX < 0)
{
newX = 0;
}
if (newY < 0)
{
newY = 0;
}
if (newX + newWidth > img.Width)
{
newX = img.Width - newWidth;
}
if (newY + newHeight > img.Height)
{
newY = img.Height - newHeight;
}
Rectangle newROI = new Rectangle(new Point(newX, newY), new Size(newWidth, newHeight));
Image <Gray, byte> diff = new Image <Gray, byte>(img.Width, img.Height);
CvInvoke.cvSetImageROI(img, newROI);
CvInvoke.cvSetImageROI(prev, newROI);
CvInvoke.cvSetImageROI(diff, newROI);
CvInvoke.cvSub(img, prev, diff, IntPtr.Zero);
CvInvoke.cvThreshold(diff, diff, 100, 255, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY);
StructuringElementEx element = new StructuringElementEx(5, 5, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
CvInvoke.cvMorphologyEx(diff, diff, IntPtr.Zero, element, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_CLOSE, 1);
CvInvoke.cvMorphologyEx(diff, diff, IntPtr.Zero, element, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_OPEN, 3);
IntPtr comp = new IntPtr();//存放检测到的图像块的首地址
MemStorage storage = new MemStorage();
int nc = CvInvoke.cvFindContours(diff, storage, ref comp, StructSize.MCvContour,
Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_CCOMP, Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE, new Point(0, 0));
// save eye region and previous eye region for latter use
Image <Gray, byte> eye = img.Copy();
Image <Gray, byte> prevEye = prev.Copy();
CvInvoke.cvResetImageROI(img);
CvInvoke.cvResetImageROI(prev);
CvInvoke.cvResetImageROI(diff);
if (nc != 1)
{
return(false);
}
Rectangle rect = CvInvoke.cvBoundingRect(comp, 1);
rect.X = rect.X + newROI.X;
rect.Y = rect.Y + newROI.Y;
if (rect.X < eyeROI.X || rect.Y < eyeROI.Y)
{
return(false);
}
if (rect.X + rect.Width > eyeROI.X + eyeROI.Width || rect.Y + rect.Height > eyeROI.Y + eyeROI.Height)
{
return(false);
}
/// reduce the effct of shaking
byte pixelThreshold = 100;
int sumThresholdLow = 100;
int sumThresholdUp = 600;
int sumPixelEye = 0;
for (int i = 0; i < eye.Width; i++)
{
for (int j = 0; j < eye.Height; j++)
{
if (eye.Data[i, j, 0] < pixelThreshold)
{
sumPixelEye++;
}
}
}
int sumPixelPrevEye = 0;
for (int i = 0; i < prevEye.Width; i++)
{
for (int j = 0; j < prevEye.Height; j++)
{
if (prevEye.Data[i, j, 0] < pixelThreshold)
{
sumPixelPrevEye++;
}
}
}
if (sumPixelEye > sumThresholdLow && sumPixelEye < sumThresholdUp && sumPixelPrevEye > sumThresholdLow && sumPixelPrevEye < sumThresholdUp)
{
isShook = true;
return(false);
}
///
return(true);
}
void CountingFingersAndUpdateGUI(object Sender, EventArgs agr)
{
int Num_Fingers = 0;
Double FirstResult = 0;
Double SecondResult = 0;
//querying image
Orignal_img = WebCam.QueryFrame().Resize(621, 446, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
if (Orignal_img == null)
{
return;
}
//Applying YCrCb filter
//Image<Ycc, Byte> currentYCrCbFrame = imgOrignal.Convert<Ycc, byte>();
//Image<Gray, byte> GloveColor = new Image<Gray, byte>(imgOrignal.Width, imgOrignal.Height);
//skin = currentYCrCbFrame.InRange(new Ycc(0,139,0), new Ycc(193, 255, 193));
gloveDetector = new YCrCbGloveDetector();
Image <Gray, Byte> GloveColor = gloveDetector.DetectGlove(Orignal_img, YCrCb_min, YCrCb_max);
OrignalCopy.Image = GloveColor.Resize(190, 167, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
iborignal.Image = Orignal_img;
StructuringElementEx Shape_rect_12 = new StructuringElementEx(10, 10, 5, 5, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//Eroding the source image using the specified structuring element
CvInvoke.cvErode(GloveColor, GloveColor, Shape_rect_12, 1);
StructuringElementEx Shape_rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//dilating the source image using the specified structuring element
CvInvoke.cvDilate(GloveColor, GloveColor, Shape_rect_6, 1);
GloveColor = GloveColor.Flip(FLIP.HORIZONTAL); //by uncmommenting this can stop flipping the image
GloveColor = GloveColor.SmoothGaussian(9); //smoothing the filterd , eroded and dilated image.
Orignal_img = Orignal_img.Flip(FLIP.HORIZONTAL); //**by uncmommenting this can stop flip the image
//extracting contours,applying convexty defect allgoritm to find the count of fingers
Contour <Point> Detectedcontours = GloveColor.FindContours();
Contour <Point> LargestContour = null;
//extracting the biggest contour.
while (Detectedcontours != null)
{
FirstResult = Detectedcontours.Area;
if (FirstResult > SecondResult)
{
SecondResult = FirstResult;
LargestContour = Detectedcontours;
}
Detectedcontours = Detectedcontours.HNext;
}
//applying convexty defect allgoritm to find the count of fingers
if (LargestContour != null)
{
Num_Fingers = 0; //number of Finger
LargestContour = LargestContour.ApproxPoly((0.000025));
Orignal_img.Draw(LargestContour, new Bgr(Color.LimeGreen), 2);
Hull = LargestContour.GetConvexHull(ORIENTATION.CV_CLOCKWISE);
Found_defects = LargestContour.GetConvexityDefacts(storage, ORIENTATION.CV_CLOCKWISE);
Orignal_img.DrawPolyline(Hull.ToArray(), true, new Bgr(0, 0, 256), 3);
Orignal_img.Draw(new CircleF(new PointF(OutLine_box.center.X, OutLine_box.center.Y), 5), new Bgr(200, 125, 75), 3);
OutLine_box = LargestContour.GetMinAreaRect();
filteredHull = new Seq <Point>(storage);
for (int i = 0; i < Hull.Total; i++)
{
if (Math.Sqrt(Math.Pow(Hull[i].X - Hull[i + 1].X, 2) + Math.Pow(Hull[i].Y - Hull[i + 1].Y, 2)) > OutLine_box.size.Width / 10)
{
filteredHull.Push(Hull[i]);
}
}
ArrayOfDefects = Found_defects.ToArray();
for (int i = 0; i < Found_defects.Total; i++)
{
PointF PointOfStart = new PointF((float)ArrayOfDefects[i].StartPoint.X,
(float)ArrayOfDefects[i].StartPoint.Y);
PointF PointOfdepth = new PointF((float)ArrayOfDefects[i].DepthPoint.X,
(float)ArrayOfDefects[i].DepthPoint.Y);
PointF PointOfend = new PointF((float)ArrayOfDefects[i].EndPoint.X,
(float)ArrayOfDefects[i].EndPoint.Y);
CircleF CircleOfStart = new CircleF(PointOfStart, 10f);
CircleF CircleOfDepth = new CircleF(PointOfdepth, 10f);
CircleF CircleOfEnd = new CircleF(PointOfend, 10f);
if ((CircleOfStart.Center.Y < OutLine_box.center.Y || CircleOfDepth.Center.Y < OutLine_box.center.Y) &&
(CircleOfStart.Center.Y < CircleOfDepth.Center.Y) &&
(Math.Sqrt(Math.Pow(CircleOfStart.Center.X - CircleOfDepth.Center.X, 2) +
Math.Pow(CircleOfStart.Center.Y - CircleOfDepth.Center.Y, 2)) >
OutLine_box.size.Height / 6.5))
{
Num_Fingers++;
}
Orignal_img.Draw(CircleOfStart, new Bgr(Color.SkyBlue), 2);
Orignal_img.Draw(CircleOfDepth, new Bgr(Color.Yellow), 2);
}
label2.Text = Num_Fingers.ToString(); // updating finger count
}//End of convexty defect allgoritm
// Finding the center of contour
MCvMoments handMovements = new MCvMoments(); // a new MCvMoments object
try
{
handMovements = LargestContour.GetMoments(); // Moments of biggestContour
}
catch (NullReferenceException except)
{
//label2.Text = except.Message;
return;
}
CvInvoke.cvMoments(LargestContour, ref handMovements, 0);
double m_00 = CvInvoke.cvGetSpatialMoment(ref handMovements, 0, 0);
double m_10 = CvInvoke.cvGetSpatialMoment(ref handMovements, 1, 0);
double m_01 = CvInvoke.cvGetSpatialMoment(ref handMovements, 0, 1);
int current_X = Convert.ToInt32(m_10 / m_00) / 10; // X location of centre of contour
int current_Y = Convert.ToInt32(m_01 / m_00) / 10; // Y location of center of contour
// transfer control to webcam only if button has already been clicked
if (PressedButtonState)
{
// move cursor to center of contour only if Finger count is 1 or 0 (finger_num<=1)
// i.e. palm is closed
if (Num_Fingers <= 1)
{
Cursor.Position = new Point(current_X * 20, current_Y * 20);
}
// Leave the cursor where it was and Do mouse click, if finger count >= 4
if (Num_Fingers == 5)
{
DoMouseRightClick(); // mouse left button click funtion
}
if (Num_Fingers == 4)
{
DoMouseDoubleClick(); // mouse left button click funtion
}
}
iborignal.Image = Orignal_img; // display orignal image
}
public void ProcessFrameAndUpdateGUI(object sender, EventArgs e)
{
int fingerCount = 0;
Double contourArea = 0;
Double maxArea = 0;
imgFrame = WebCamFeed.QueryFrame();
sensitivity = sensitivitySlider.Value; //Defines scope for navigation ; changed from the SETTINGS page.
if (imgFrame == null)
{
return;
}
if (gloveMode.Checked) // Changes 'minRange' & 'maxRange' for Contrasting Backgrounds
{
gloveColor.Enabled = true;
}
else
{
gloveColor.Enabled = false;
}
Image <Ycc, Byte> currentYCrCbFrame = imgFrame.Convert <Ycc, byte>();
//Converts the original frame to a YCrCb frame for filtering.
Image <Gray, byte> skin = new Image <Gray, byte>(imgFrame.Width, imgFrame.Height);
//Stores Gray image of areas of interest
skin = currentYCrCbFrame.InRange(minRange, maxRange);
//Extracts areas within range for skin color
//Noise Filtering
StructuringElementEx rect_12 = new StructuringElementEx(10, 10, 5, 5, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(skin, skin, rect_12, 1);
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(skin, skin, rect_6, 2);
skin = skin.Flip(FLIP.HORIZONTAL);
skin = skin.SmoothGaussian(9);
imgFrame = imgFrame.Flip(FLIP.HORIZONTAL);
Contour <Point> contours = skin.FindContours();
Contour <Point> biggestContour = null;
while (contours != null)
{
contourArea = contours.Area;
if (contourArea > maxArea)
{
maxArea = contourArea;
biggestContour = contours;
}
contours = contours.HNext; //checks for the next contour
}
#region Convexity Defects Algorithm
if (biggestContour != null)
{
fingerCount = 0;
biggestContour = biggestContour.ApproxPoly(0.00025);
imgFrame.Draw(biggestContour, new Bgr(Color.LimeGreen), 2); //For debugging
Hull = biggestContour.GetConvexHull(ORIENTATION.CV_CLOCKWISE);
defects = biggestContour.GetConvexityDefacts(storage, ORIENTATION.CV_CLOCKWISE);
imgFrame.DrawPolyline(Hull.ToArray(), true, new Bgr(0, 0, 256), 2); //For debugging
box = biggestContour.GetMinAreaRect();
defectArray = defects.ToArray();
for (int i = 0; i < defects.Total; i++)
{
PointF startPoint = new PointF((float)defectArray[i].StartPoint.X,
(float)defectArray[i].StartPoint.Y);
PointF depthPoint = new PointF((float)defectArray[i].DepthPoint.X,
(float)defectArray[i].DepthPoint.Y);
PointF endPoint = new PointF((float)defectArray[i].EndPoint.X,
(float)defectArray[i].EndPoint.Y);
CircleF startCircle = new CircleF(startPoint, 5f);
CircleF depthCircle = new CircleF(depthPoint, 5f);
CircleF endCircle = new CircleF(endPoint, 5f);
if ((startCircle.Center.Y < box.center.Y || depthCircle.Center.Y < box.center.Y) &&
(startCircle.Center.Y < depthCircle.Center.Y) &&
(Math.Sqrt(Math.Pow(startCircle.Center.X - depthCircle.Center.X, 2) +
Math.Pow(startCircle.Center.Y - depthCircle.Center.Y, 2)) >
box.size.Height / 6.5))
{
fingerCount++;
}
}
}
#endregion
MCvMoments moment = new MCvMoments();
try
{
moment = biggestContour.GetMoments();
}
catch (NullReferenceException except)
{
AirNav.BalloonTipTitle = "AirNav";
AirNav.BalloonTipText = "Contour Not Found: " + except.Message;
AirNav.ShowBalloonTip(1000);
return;
}
try
{
CvInvoke.cvMoments(biggestContour, ref moment, 0);
}
catch (Exception)
{
throw;
}
double m00 = CvInvoke.cvGetSpatialMoment(ref moment, 0, 0);
double m10 = CvInvoke.cvGetSpatialMoment(ref moment, 1, 0);
double m01 = CvInvoke.cvGetSpatialMoment(ref moment, 0, 1);
int currentX = Convert.ToInt32(m10 / m00) / 10;
int currentY = Convert.ToInt32(m01 / m00) / 10;
// Current coordinates of the center of the palm
if (showCoordinates)
{
coordinatesTextBox.Text = "X: " + currentX.ToString() + " Y: " + currentY.ToString();
}
if (fingerCount >= 4 && !started && startPressed)
{
started = true;
}
if (started)
{
Navigate(fingerCount, currentX, currentY);
}
display.SetZoomScale(0.5, new Point(100, 100));
if (displayToggle)
{
display.Image = skin;
}
else
{
display.Image = imgFrame;
}
}
public double MatchIteration(Matrix X, Matrix Y, Matrix V1, Matrix V2, Matrix t1, Matrix t2)
{
timer.Clear();
double timeused = 0;
timer.Restart();
var tk = t1.Clone(); // tk=t1;
int w = 4;
int ndum = (int)(nsamp * ndum_frac);
nsamp1 = nsamp2 = nsamp;
#region demo2迭代
Matrix Xk = X.Clone();
int N1 = V1.Rows, N2 = V2.Columns;
if (display_flag)
{
Draw(X, Y, V1, V2, @"D:\Play Data\Iteration\原图.bmp", "原始图像和采样");
//DrawGradient(X, Y, t1, t2, N2, N1, @"D:\Play Data\Iteration\原图梯度.bmp", "原始图像切向量");
}
int k = 0;
var out_vec_1 = Utils.InitArray <bool>(nsamp1, false); //out_vec_1=zeros(1,nsamp1);
var out_vec_2 = Utils.InitArray <bool>(nsamp2, false); //out_vec_2=zeros(1,nsamp2);
double ori_weight = 0.1;
double tan_eps = 1.0;
bool affine_start_flag = true;
bool polarity_flag = true;
double matchcost = double.MaxValue;
double sc_cost = 0, aff_cost = 0, E = 0;
Matrix cx = null, cy = null; // cx, cy是插值线性方程组的解的两列
Matrix axt = null, wxt = null, ayt = null, wyt = null, d2 = null, U = null,
X2 = null, Y2 = null, X2b = null, X3b = null, Y3 = null;
double mean_dist_1 = 0, mean_dist_2 = 0;
var min1 = new[] { new { Val = 0.0, Idx = 0 } };
var min2 = min1;
#region 用于打网格的坐标
Matrix coordX = null, coordY = null;
int coordMargin = (int)(N1 * coordMarginRate);
MatrixUtils.CreateGrid(N1 + coordMargin * 2, N2 + coordMargin * 2, out coordX, out coordY);
coordX = coordX.Each(v => v - coordMargin * 2);
coordY = coordY.Each(v => v - coordMargin * 2);
//int MM = N1 * N2 / 25; // M=length(x);
#endregion
timeused += timer.StopAndSay("初始化");
while (k < n_iter)
{
Debug("Iter={0}", k);
#region 计算两个形状上下文
timer.Restart();
// [BH1,mean_dist_1]=sc_compute(Xk',zeros(1,nsamp),mean_dist_global,nbins_theta,nbins_r,r_inner,r_outer,out_vec_1);
var BH1 = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_1, out_vec_1);
//var BH1 = ComputeSC(Xk.Transpose(), t1.Transpose(), mean_dist_global, out mean_dist_1, out_vec_1);
// [BH2,mean_dist_2]=sc_compute(Y',zeros(1,nsamp),mean_dist_global,nbins_theta,nbins_r,r_inner,r_outer,out_vec_2);
var BH2 = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_2, out_vec_2);
//var BH2 = ComputeSC(Y.Transpose(), t2.Transpose(), mean_dist_global, out mean_dist_2, out_vec_2);
timeused += timer.StopAndSay("计算两个形状上下文");
Debug("Mean_dist_1:{0:F4}", mean_dist_1);
Debug("Mean_dist_2:{0:F4}", mean_dist_2);
#endregion
#region 计算lambda_o和beta_k
double lambda_o;
if (affine_start_flag)
{
if (k == 0)
{
lambda_o = 1000;
}
else
{
lambda_o = beta_init * Math.Pow(r, k - 1); // lambda_o=beta_init*r^(k-2);
}
}
else
{
lambda_o = beta_init * Math.Pow(r, k); // lambda_o=beta_init*r^(k-1);
}
double beta_k = mean_dist_2 * mean_dist_2 * lambda_o;
#endregion
#region 计算代价矩阵
timer.Restart();
var costmat_shape = HistCost(BH1, BH2); // costmat_shape = hist_cost_2(BH1, BH2);
// theta_diff=repmat(tk,1,nsamp)-repmat(t2',nsamp,1);
var theta_diff = tk.RepMat(1, nsamp) - t2.Transpose().RepMat(nsamp, 1);
Matrix costmat_theta;
if (polarity_flag)
{
// costmat_theta=0.5*(1-cos(theta_diff));
//costmat_theta = 0.5 * (Ones(costmat_shape.Rows, costmat_shape.Columns) - theta_diff.Each(v => Math.Cos(v)));
costmat_theta = theta_diff.Each(v => 0.5 * (1 - Math.Cos(v)));
}
else
{
// costmat_theta=0.5*(1-cos(2*theta_diff));
//costmat_theta = 0.5 * (Ones(costmat_shape.Rows, costmat_shape.Columns) - theta_diff.Each(v => Math.Cos(2 * v)));
costmat_theta = theta_diff.Each(v => 0.5 * (1 - Math.Cos(2 * v)));
}
// costmat=(1-ori_weight)*costmat_shape+ori_weight*costmat_theta;
var costmat = (1 - ori_weight) * costmat_shape + ori_weight * costmat_theta;
int nptsd = nsamp + ndum; // nptsd=nsamp+ndum;
var costmat2 = new DenseMatrix(nptsd, nptsd, eps_dum); // costmat2=eps_dum*ones(nptsd,nptsd);
costmat2.SetSubMatrix(0, nsamp, 0, nsamp, costmat); // costmat2(1:nsamp,1:nsamp)=costmat;
timeused += timer.StopAndSay("计算代价矩阵");
#endregion
#region 匈牙利算法
timer.Restart();
var costmat_int = new int[nptsd, nptsd];
for (int i = 0; i < nptsd; ++i)
{
for (int j = 0; j < nptsd; ++j)
{
costmat_int[i, j] = (int)(costmat2[i, j] * 10000);
}
}
var km = new KM(nptsd, costmat_int);
km.Match(false);
matchcost = km.MatchResult / 10000.0;
int[] cvec = km.MatchPair; // cvec=hungarian(costmat2);
timeused += timer.StopAndSay("匈牙利算法");
#endregion
#region 计算野点标记向量，重排匹配点
timer.Restart();
int[] cvec2 = cvec.Select((v, i) => new { Val = v, Idx = i })
.OrderBy(v => v.Val)
.Select(v => v.Idx)
.ToArray(); // [a,cvec2]=sort(cvec);
out_vec_1 = cvec2.Take(nsamp1).Select(v => v > nsamp2).ToArray(); // out_vec_1=cvec2(1:nsamp1)>nsamp2;
out_vec_2 = cvec.Take(nsamp2).Select(v => v > nsamp1).ToArray(); // out_vec_2=cvec(1:nsamp2)>nsamp1;
//X2 = NaNs(nptsd, 2); // X2=NaN*ones(nptsd,2);
//X2.SetSubMatrix(0, nsamp1, 0, X2.Columns, Xk); // X2(1:nsamp1,:)=Xk;
//X2 = X2.SortRowsBy(cvec); // X2=X2(cvec,:);
X2b = NaNs(nptsd, 2); // X2b=NaN*ones(nptsd,2);
X2b.SetSubMatrix(0, nsamp1, 0, X2b.Columns, X); // X2b(1:nsamp1,:)=X;
X2b = X2b.SortRowsBy(cvec); // X2b=X2b(cvec,:);
Y2 = NaNs(nptsd, 2); // Y2=NaN*ones(nptsd,2);
Y2.SetSubMatrix(0, nsamp2, 0, Y2.Columns, Y); // Y2(1:nsamp2,:)=Y;
var ind_good = X2b.GetColumn(1).Take(nsamp).FindIdxBy(v => !double.IsNaN(v)); // ind_good=find(~isnan(X2b(1:nsamp,1)));
int n_good = ind_good.Length; // n_good=length(ind_good);
X3b = X2b.FilterRowsBy(ind_good); // X3b=X2b(ind_good,:);
Y3 = Y2.FilterRowsBy(ind_good); // Y3=Y2(ind_good,:);
timeused += timer.StopAndSay("计算野点标记向量，重排匹配点");
#endregion
#region figure 2
if (display_flag)
{
//figure(2)
//plot(X2(:,1),X2(:,2),'b+',Y2(:,1),Y2(:,2),'ro')
//hold on
//h=plot([X2(:,1) Y2(:,1)]',[X2(:,2) Y2(:,2)]','k-');
//if display_flag
//% set(h,'linewidth',1)
//quiver(Xk(:,1),Xk(:,2),cos(tk),sin(tk),0.5,'b') // 画箭头
//quiver(Y(:,1),Y(:,2),cos(t2),sin(t2),0.5,'r')
DrawGradient(Xk, Y, tk, t2, N2, N1,
String.Format(@"D:\Play Data\Iteration\梯度\{0}.bmp", k),
String.Format("Iter={0}梯度方向\n匹配点数{1}", k, n_good));
//end
//hold off
//axis('ij')
//title([int2str(n_good) ' correspondences (warped X)'])
//axis([1 N2 1 N1])
//drawnow
}
#endregion
#region figure 3 显示未变形图的匹配关系
if (display_flag)
{
//% show the correspondences between the untransformed images
//figure(3)
//plot(X(:,1),X(:,2),'b+',Y(:,1),Y(:,2),'ro')
//ind=cvec(ind_good);
//hold on
//plot([X2b(:,1) Y2(:,1)]',[X2b(:,2) Y2(:,2)]','k-')
//hold off
//axis('ij')
//title([int2str(n_good) ' correspondences (unwarped X)'])
//axis([1 N2 1 N1])
//drawnow
Draw(X, Y, cvec, null, N2, N1, String.Format(@"D:\Play Data\Iteration\匹配\{0}.bmp", k),
String.Format("Iter={0}\n匹配代价{1},匹配数{2}", k, matchcost, n_good));
}
#endregion
#region 求解变换矩阵
timer.Restart();
Bookstein(X3b, Y3, beta_k, ref cx, ref cy, ref E); // [cx,cy,E]=bookstein(X3b,Y3,beta_k);
timeused += timer.StopAndSay("求解变换矩阵");
#endregion
#region 通过解出来的变换对点和梯度进行变换
timer.Restart();
// % calculate affine cost
var A = MatrixUtils.RankHorizon(
cx.GetSubMatrix(n_good + 1, 2, 0, 1), cy.GetSubMatrix(n_good + 1, 2, 0, 1)
); //A=[cx(n_good+2:n_good+3,:) cy(n_good+2:n_good+3,:)];
var s = new Svd(A, true).S(); // s=svd(A);
aff_cost = Math.Log(s[0] / s[1]); // aff_cost=log(s(1)/s(2));
// % calculate shape context cost
min1 = costmat.GetColumns().Select(col => {
int minwhere = 0;
for (int i = 1; i < col.Count; ++i)
{
if (col[i] < col[minwhere])
{
minwhere = i;
}
}
return(new { Val = col[minwhere], Idx = minwhere });
}).ToArray();// [a1,b1]=min(costmat,[],1);
min2 = costmat.GetRows().Select(row => {
int minwhere = 0;
for (int i = 1; i < row.Count; ++i)
{
if (row[i] < row[minwhere])
{
minwhere = i;
}
}
return(new { Val = row[minwhere], Idx = minwhere });
}).ToArray(); // [a2,b2]=min(costmat,[],2);}
sc_cost = Math.Max(min1.Average(a => a.Val), min2.Average(a => a.Val)); // sc_cost=max(mean(a1),mean(a2));
// % warp each coordinate
axt = cx.GetSubMatrix(n_good, 3, 0, 1).Transpose(); // axt是cx中的最后三个，即a的x分量
wxt = cx.GetSubMatrix(0, n_good, 0, 1).Transpose(); // wxt是cs中的前n个，即w的x分量
ayt = cy.GetSubMatrix(n_good, 3, 0, 1).Transpose();
wyt = cy.GetSubMatrix(0, n_good, 0, 1).Transpose();
d2 = Dist2(X3b, X).Each(v => v > 0 ? v : 0); // d2=max(dist2(X3b,X),0);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon))); // U=d2.*log(d2+eps);
Debug("MatchCost:{0:F4}\taff_cost:{1:F4}\tsc_cost:{2:F4}\tE:{3:F8}", matchcost, aff_cost, sc_cost, E);
var Z = Transformation(X, U, axt, wxt, ayt, wyt);
//% apply the warp to the tangent vectors to get the new angles
var Xtan = X + tan_eps * MatrixUtils.RankHorizon(t1.Each(Math.Cos), t1.Each(Math.Sin)); // Xtan=X+tan_eps*[cos(t1) sin(t1)];
d2 = Dist2(X3b, Xtan).Each(v => v > 0 ? v : 0); // d2=max(dist2(X3b,Xtan),0);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon))); // U=d2.*log(d2+eps);
//Transformation(Xtan, U, axt, wxt, ayt, wyt, out fx, out fy);
//var Ztan = MatrixUtils.RankVertical(fx, fy).Transpose(); // Ztan=[fx; fy]';
var Ztan = Transformation(Xtan, U, axt, wxt, ayt, wyt);
for (int i = 0; i < nsamp; ++i)
{
tk[i, 0] = Math.Atan2(Ztan[i, 1] - Z[i, 1], Ztan[i, 0] - Z[i, 0]);
}//tk=atan2(Ztan(:,2)-Z(:,2),Ztan(:,1)-Z(:,1));
timeused += timer.StopAndSay("通过解出来的变换对点和梯度进行变换");
#endregion
#region figure 4 显示变形后的点集
if (display_flag)
{
//figure(4)
//plot(Z(:,1),Z(:,2),'b+',Y(:,1),Y(:,2),'ro');
//axis('ij')
//title(['k=' int2str(k) ', \lambda_o=' num2str(lambda_o) ', I_f=' num2str(E) ', aff.cost=' num2str(aff_cost) ', SC cost=' num2str(sc_cost)])
//axis([1 N2 1 N1])
//% show warped coordinate grid
//fx_aff=cx(n_good+1:n_good+3)'*[ones(1,M); x'; y'];
//d2=dist2(X3b,[x y]);
//fx_wrp=cx(1:n_good)'*(d2.*log(d2+eps));
//fx=fx_aff+fx_wrp;
//fy_aff=cy(n_good+1:n_good+3)'*[ones(1,M); x'; y'];
//fy_wrp=cy(1:n_good)'*(d2.*log(d2+eps));
//fy=fy_aff+fy_wrp;
//hold on
//plot(fx,fy,'k.','markersize',1)
//hold off
//drawnow
d2 = Dist2(X3b, MatrixUtils.RankHorizon(coordX, coordY));//d2=dist2(X3b,[x y]);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon)));
//Transformation(MatrixUtils.RankHorizon(coordX, coordY), U, axt, wxt, ayt, wyt, out fx, out fy);
//var coordsT = MatrixUtils.RankVertical(fx, fy).Transpose();
var coordsT = Transformation(MatrixUtils.RankHorizon(coordX, coordY), U, axt, wxt, ayt, wyt);
Draw(Z, Y, null, coordsT, N2, N1, String.Format(@"D:\Play Data\Iteration\变换\{0}.bmp", k),
String.Format("Iter={0}\nλo={1:F4},If={2:F4},aff_cost{3:F4},sc_cost{4:F4}", k, lambda_o, E, aff_cost, sc_cost));
}
#endregion
Xk = Z.Clone();
++k;
}
#endregion
#region 迭代完成后的代价计算
#region 我来尝试计算Dsc
// Xk是Q变换后的结果，而Y是模板图形P
/*
* Dsc = Avg_each_p_in_P(argmin(q in Q, C(p, T(q))) + Avg_each_q_in_Q(argmin(p in P, C(p, T(q)))
* */
double mean_dist_final_1;
var scQ = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_final_1, out_vec_1);
//var scQ = ComputeSC(Xk.Transpose(), Zeros(1, nsamp), mean_dist_1, out mean_dist_final_1, out_vec_1);
//var scQ = ComputeSC(Xk.Transpose(), t1.Transpose(), mean_dist_1, out mean_dist_final_1, out_vec_1);
double mean_dist_final_2;
var scP = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_global, out mean_dist_final_2, out_vec_2);
//var scP = ComputeSC(Y.Transpose(), Zeros(1, nsamp), mean_dist_2, out mean_dist_final_2, out_vec_2);
//var scP = ComputeSC(Y.Transpose(), t2.Transpose(), mean_dist_2, out mean_dist_final_2, out_vec_2);
var costmat_final = HistCost(scQ, scP);
double distance_sc = costmat_final.GetRows().Select(row => row.Min()).Average()
+ costmat_final.GetColumns().Select(col => col.Min()).Average();
Debug("distance_sc:{0}", distance_sc);
#endregion
#region 图像变换和插值
timer.Restart();
//[x,y]=meshgrid(1:N2,1:N1);
//x=x(:);y=y(:);
Matrix x = null, y = null;
MatrixUtils.CreateGrid(N1, N2, out x, out y);
//int M = N1 * N2; // M=length(x);
d2 = Dist2(X3b, MatrixUtils.RankHorizon(x, y));//d2=dist2(X3b,[x y]);
U = d2.PointMultiply(d2.Each(v => Math.Log(v + Epsilon)));
//Transformation(MatrixUtils.RankHorizon(x, y), U, axt, wxt, ayt, wyt, out fx, out fy);
var fxy = Transformation(MatrixUtils.RankHorizon(x, y), U, axt, wxt, ayt, wyt);
//disp('computing warped image...')
//V1w=griddata(reshape(fx,N1,N2),reshape(fy,N1,N2),V1,reshape(x,N1,N2),reshape(y,N1,N2));
Matrix V1w = Interpolation(
fxy.GetSubMatrix(0, fxy.Rows, 0, 1).Reshape(N1, N2),
fxy.GetSubMatrix(0, fxy.Rows, 1, 1).Reshape(N1, N2),
V1
);
#region 这个山寨插值方法会造成图像裂缝，用闭运算来尝试修补
Image <Gray, Byte> img = new Image <Gray, byte>(N2, N1);
for (int i = 0; i < N2; ++i)
{
for (int j = 0; j < N1; ++j)
{
img[i, j] = new Gray(V1w[i, j] * 255);
}
}
var see = new StructuringElementEx(new int[, ] {
{ 1, 1, 1 }, { 1, 1, 1 }, { 1, 1, 1 }
}, 1, 1);
//img = img.MorphologyEx(see, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_CLOSE, 1);
img = img.Dilate(1).Erode(1);
for (int i = 0; i < N2; ++i)
{
for (int j = 0; j < N1; ++j)
{
V1w[i, j] = img[i, j].Intensity / 255;
}
}
img.Dispose();
#endregion
timeused += timer.StopAndSay("图像变换和插值");
#endregion
//fz=find(isnan(V1w));
//V1w(fz)=0;
var ssd = (V2 - V1w).Each(v => v * v); //ssd=(V2-V1w).^2; %%%%%%SSD在这里
var ssd_global = ssd.SumAll(); //ssd_global=sum(ssd(:));
Debug("ssd_global:{0}", ssd_global);
#region figure 5
if (display_flag)
{
// figure(5)
// subplot(2,2,1)
// im(V1)
// subplot(2,2,2)
// im(V2)
// subplot(2,2,4)
// im(V1w)
// title('V1 after warping')
// subplot(2,2,3)
// im(V2-V1w)
// h=title(['SSD=' num2str(ssd_global)]);
// colormap(cmap)
}
#endregion
#region 窗口SSD比较
timer.Restart();
//%%%
//%%% windowed SSD comparison
//%%%
var wd = 2 * w + 1; //wd=2*w+1;
var win_fun = MatrixUtils.GaussianKernal(wd); //win_fun=gaussker(wd);
//% extract sets of blocks around each coordinate
//% first do 1st shape; need to use transformed coords.
var win_list_1 = Zeros(nsamp, wd * wd);//win_list_1=zeros(nsamp,wd^2);
for (int qq = 0; qq < nsamp; ++qq)
{
int row_qq = (int)Xk[qq, 1], // row_qq=round(Xk(qq,2));
col_qq = (int)Xk[qq, 0]; // col_qq=round(Xk(qq,1));
row_qq = Math.Max(w + 1, Math.Min(N1 - w - 1, row_qq)); // row_qq=max(w+1,min(N1-w,row_qq));
col_qq = Math.Max(w + 1, Math.Min(N2 - w - 1, col_qq)); // col_qq=max(w+1,min(N2-w,col_qq));
// tmp=V1w(row_qq-w:row_qq+w,col_qq-w:col_qq+w);
var tmp = V1w.GetSubMatrix(row_qq - w, w * 2 + 1, col_qq - w, w * 2 + 1);
tmp = win_fun.PointMultiply(tmp);// tmp=win_fun.*tmp;
// win_list_1(qq,:)=tmp(:)';
win_list_1.SetSubMatrix(qq, 1, 0, win_list_1.Columns, tmp.Reshape(1, tmp.Rows * tmp.Columns));
}
//% now do 2nd shape
var win_list_2 = Zeros(nsamp, wd * wd);//win_list_2=zeros(nsamp,wd^2);
for (int qq = 0; qq < nsamp; ++qq)
{
int row_qq = (int)Y[qq, 1], // row_qq = round(Y(qq, 2));
col_qq = (int)Y[qq, 0]; // col_qq = round(Y(qq, 1));
row_qq = Math.Max(w + 1, Math.Min(N1 - w - 1, row_qq)); // row_qq=max(w+1,min(N1-w,row_qq));
col_qq = Math.Max(w + 1, Math.Min(N2 - w - 1, col_qq)); // col_qq=max(w+1,min(N2-w,col_qq));
// tmp=V2(row_qq-w:row_qq+w,col_qq-w:col_qq+w)
var tmp = V2.GetSubMatrix(row_qq - w, w * 2 + 1, col_qq - w, w * 2 + 1);
tmp = win_fun.PointMultiply(tmp); // tmp=win_fun.*tmp;
win_list_2.SetSubMatrix(qq, 1, 0, win_list_2.Columns, tmp.Reshape(1, tmp.Rows * tmp.Columns)); // win_list_2(qq,:)=tmp(:)';
}
var ssd_all = Dist2(win_list_1, win_list_2).Each(Math.Sqrt);//ssd_all=sqrt(dist2(win_list_1,win_list_2));
timeused += timer.StopAndSay("窗口SSD比较");
#endregion
#region 最后的KNN，不知道干嘛用
timer.Restart();
//%%%%%%% KNN在此
//% loop over nearest neighbors in both directions, project in
//% both directions, take maximum
double cost_1 = 0, cost_2 = 0;
//List<double> cost_1 = new List<double>(), cost_2 = new List<double>();
for (int qq = 0; qq < nsamp; ++qq)
{
cost_1 += ssd_all[qq, min2[qq].Idx]; // cost_1=cost_1+ssd_all(qq,b2(qq));
cost_2 += ssd_all[min1[qq].Idx, qq]; // cost_2=cost_2+ssd_all(b1(qq),qq);
//cost_1.Add(ssd_all[qq, min2[qq].Idx]);
//cost_2.Add(ssd_all[min1[qq].Idx, qq]);
}
var ssd_local = (1.0 / nsamp) * Math.Max(cost_1, cost_2);
var ssd_local_avg = (1.0 / nsamp) * 0.5 * (cost_1 + cost_2);
//var ssd_local = (1.0 / nsamp) * Math.Max(cost_1.Average(), cost_2.Average());//ssd_local=(1/nsamp)*max(mean(cost_1),mean(cost_2));
//var ssd_local_avg = (1.0 / nsamp) * 0.5 * (cost_1.Average() + cost_2.Average());//ssd_local_avg=(1/nsamp)*0.5*(mean(cost_1)+mean(cost_2));
Debug("ssd_local:{0}", ssd_local);
Debug("ssd_local_avg:{0}", ssd_local_avg);
timeused += timer.StopAndSay("计算ssd_local");
#region 最后的组合图
//if display_flag
//% set(h,'string',['local SSD=' num2str(ssd_local) ', avg. local SSD=' num2str(ssd_local_avg)])
// set(h,'string',['local SSD=' num2str(ssd_local)])
//end
if (display_flag)
{
Draw(V1, @"D:\Play Data\Iteration\结果\0-V1.bmp", "V1");
Draw(V2, @"D:\Play Data\Iteration\结果\1-V2.bmp", "V2");
Draw(V1w, @"D:\Play Data\Iteration\结果\2-V1w.bmp", "V1 after warping");
Draw(V2 - V1w, @"D:\Play Data\Iteration\结果\3-V2-V1w.bmp",
String.Format("local SSD={0:F8}", ssd_local_avg));
}
#endregion
#endregion
#endregion
var distance = 1.6 * ssd_local_avg + distance_sc + 0.3 * E; // 不知道最后的距离公式到底是什么
Debug("distance={0:F8}", distance);
return(distance);
}
void ProcessFramAndUpdateGUI(object Sender, EventArgs agr)
{
int Finger_num = 0;
Double Result1 = 0;
Double Result2 = 0;
//querying image
imgOrignal = CapWebCam.QueryFrame();
if (imgOrignal == null)
{
return;
}
//Applying YCrCb filter
Image <Ycc, Byte> currentYCrCbFrame = imgOrignal.Convert <Ycc, byte>();
Image <Gray, byte> skin = new Image <Gray, byte>(imgOrignal.Width, imgOrignal.Height);
skin = currentYCrCbFrame.InRange(new Ycc(0, 131, 80), new Ycc(255, 185, 135));
StructuringElementEx rect_12 = new StructuringElementEx(10, 10, 5, 5, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//Eroding the source image using the specified structuring element
CvInvoke.cvErode(skin, skin, rect_12, 1);
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//dilating the source image using the specified structuring element
CvInvoke.cvDilate(skin, skin, rect_6, 2);
skin = skin.Flip(FLIP.HORIZONTAL);
//smoothing the filterd , eroded and dilated image.
skin = skin.SmoothGaussian(9);
imgOrignal = imgOrignal.Flip(FLIP.HORIZONTAL);
//extracting contours.
Contour <Point> contours = skin.FindContours();
Contour <Point> biggestContour = null;
//extracting the biggest contour.
while (contours != null)
{
Result1 = contours.Area;
if (Result1 > Result2)
{
Result2 = Result1;
biggestContour = contours;
}
contours = contours.HNext;
}
//applying convexty defect allgoritm to find the count of fingers
if (biggestContour != null)
{
Finger_num = 0;
biggestContour = biggestContour.ApproxPoly((0.00025));
imgOrignal.Draw(biggestContour, new Bgr(Color.LimeGreen), 2);
Hull = biggestContour.GetConvexHull(ORIENTATION.CV_CLOCKWISE);
defects = biggestContour.GetConvexityDefacts(storage, ORIENTATION.CV_CLOCKWISE);
imgOrignal.DrawPolyline(Hull.ToArray(), true, new Bgr(0, 0, 256), 2);
box = biggestContour.GetMinAreaRect();
defectArray = defects.ToArray();
for (int i = 0; i < defects.Total; i++)
{
PointF startPoint = new PointF((float)defectArray[i].StartPoint.X,
(float)defectArray[i].StartPoint.Y);
PointF depthPoint = new PointF((float)defectArray[i].DepthPoint.X,
(float)defectArray[i].DepthPoint.Y);
PointF endPoint = new PointF((float)defectArray[i].EndPoint.X,
(float)defectArray[i].EndPoint.Y);
CircleF startCircle = new CircleF(startPoint, 5f);
CircleF depthCircle = new CircleF(depthPoint, 5f);
CircleF endCircle = new CircleF(endPoint, 5f);
if ((startCircle.Center.Y < box.center.Y || depthCircle.Center.Y < box.center.Y) &&
(startCircle.Center.Y < depthCircle.Center.Y) &&
(Math.Sqrt(Math.Pow(startCircle.Center.X - depthCircle.Center.X, 2) +
Math.Pow(startCircle.Center.Y - depthCircle.Center.Y, 2)) >
box.size.Height / 6.5))
{
Finger_num++;
}
}
label2.Text = Finger_num.ToString(); // updating finger count
}
// Finding the center of contour
MCvMoments moment = new MCvMoments(); // a new MCvMoments object
try
{
moment = biggestContour.GetMoments(); // Moments of biggestContour
}
catch (NullReferenceException except)
{
//label3.Text = except.Message;
return;
}
CvInvoke.cvMoments(biggestContour, ref moment, 0);
double m_00 = CvInvoke.cvGetSpatialMoment(ref moment, 0, 0);
double m_10 = CvInvoke.cvGetSpatialMoment(ref moment, 1, 0);
double m_01 = CvInvoke.cvGetSpatialMoment(ref moment, 0, 1);
int current_X = Convert.ToInt32(m_10 / m_00) / 10; // X location of centre of contour
int current_Y = Convert.ToInt32(m_01 / m_00) / 10; // Y location of center of contour
// transfer control to webcam only if button has already been clicked
if (button_pressed)
{
// move cursor to center of contour only if Finger count is 1 or 0
// i.e. palm is closed
if (Finger_num == 0 || Finger_num == 1)
{
Cursor.Position = new Point(current_X * 20, current_Y * 20);
}
// Leave the cursor where it was and Do mouse click, if finger count >= 4
if (Finger_num >= 4)
{
DoMouseClick(); // function clicks mouse left button
}
}
iborignal.Image = imgOrignal; // display orignal image
}
//각각의 색상을 트레킹해서 rect을 만들어줌
public void color_traking(int index, int min1, int min2, int min3, int max1, int max2, int max3, Image <Bgr, Byte> iamge, Rectangle[] rect)
{
int pixCount = 0, small_pixCount = 0;
Image <Ycc, Byte> YCrCbFrame = iamge.Convert <Ycc, Byte>(); //YCrCb 변환
Image <Gray, byte> colorSetting = new Image <Gray, byte>(YCrCbFrame.Width, YCrCbFrame.Height);
Ycc YCrCb_min = new Ycc(min1, min2, min3);
Ycc YCrCb_max = new Ycc(max1, max2, max3); //blue 색 범위
colorSetting = YCrCbFrame.InRange((Ycc)YCrCb_min, (Ycc)YCrCb_max); //색 범위 설정
StructuringElementEx rect_12 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(colorSetting, colorSetting, rect_12, 1);
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(colorSetting, colorSetting, rect_6, 2); //수축 팽창
Image <Bgr, Byte> colorCount = colorSetting.Convert <Bgr, Byte>(); //픽셀수 세기 위해
//작은 원 찾기
//YCrCb_min = new Ycc(0, 0, 0);
//YCrCb_max = new Ycc(255, 146, 100); //큰원yellow 색 범위
YCrCb_min = new Ycc(0, 0, 0);
//YCrCb_max = new Ycc(255, 150, 114); //작은 원 yellow 색 범위
YCrCb_max = new Ycc(255, 150, 120); //작은 원 yellow 색 범위
colorSetting = YCrCbFrame.InRange((Ycc)YCrCb_min, (Ycc)YCrCb_max); //색 범위 설정
rect_12 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(colorSetting, colorSetting, rect_12, 1);
rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(colorSetting, colorSetting, rect_6, 2); //수축 팽창
Image <Bgr, Byte> small_colorCount = colorSetting.Convert <Bgr, Byte>(); //픽셀수 세기 위해
int x_p = 0, y_p = 0; // 큰 원 픽셀수 저장
int small_x = 0, small_y = 0; // 작은원 픽셀수 저장
//이미지가 범위를 벗어날경우 처리
if (color_ROI[index].X < 0)
{
color_ROI[index].X = 0;
}
if (color_ROI[index].Y < 0)
{
color_ROI[index].Y = 0;
}
if (color_ROI[index].X + img_width > iamge.Width)
{
color_ROI[index].X = iamge.Width - img_width;
}
if (color_ROI[index].Y + img_height > iamge.Height)
{
color_ROI[index].Y = iamge.Height - img_height;
}
//픽셀수 셈
for (int x = color_ROI[index].X; x < color_ROI[index].X + img_width; x++)
{
for (int y = color_ROI[index].Y; y < color_ROI[index].Y + img_height; y++)
{
if (!colorCount[y, x].Equals(new Bgr(0, 0, 0)))
{
pixCount++;
x_p += x;
y_p += y;
}
}
}
//픽셀 개수에 따라
if (pixCount >= 10) //개수가 0이 아닐때 ROI 변경해줌
{
int big_center_x = x_p / pixCount;
int big_center_y = y_p / pixCount; //큰 원 중심좌표
//사라진것을 판별하기 위해.. 원랜 마이너스값으로 좌표가 계산되어 일부러 음수좌표는 0으로 만들고 사라졌을때 좌표를 -1로 만듬
int tmp_x = big_center_x - glo.TemplateWidth / 2;
int tmp_y = big_center_y - glo.TemplateHeight / 2;
int tmp_width = glo.TemplateWidth;
int tmp_height = glo.TemplateHeight;
if (tmp_x < 0)
{
tmp_width += tmp_x;
tmp_x = 0;
}
if (tmp_y < 0)
{
tmp_height += tmp_y;
tmp_y = 0;
}
if (tmp_x + img_width > glo.rect_width)
{
tmp_width = img_width - (tmp_x + img_width - glo.rect_width);
}
if (tmp_y + img_height > glo.rect_height)
{
tmp_height = img_height - (tmp_y + img_height - glo.rect_height);
}
int x_end = tmp_x + img_width;
int y_end = tmp_y + img_height;
if (x_end > glo.rect_width)
{
x_end = glo.rect_width;
}
if (y_end > glo.rect_height)
{
y_end = glo.rect_height;
}
for (int x = tmp_x; x < x_end; x++)
{
for (int y = tmp_y; y < y_end; y++)
{
if (!small_colorCount[y, x].Equals(new Bgr(0, 0, 0)))
{
small_pixCount++;
small_x += x;
small_y += y;
}
}
}
rect[index] = new Rectangle(tmp_x, tmp_y, tmp_width, tmp_height); //사각형의 왼쪽 위의 좌표
color_ROI[index].X = big_center_x - glo.TemplateWidth / 2;
color_ROI[index].Y = big_center_y - glo.TemplateHeight / 2;
if (small_pixCount != 0)
{
int small_center_x = small_x / small_pixCount;
int small_center_y = small_y / small_pixCount;
int C = big_center_x - small_center_x;
int D = big_center_y - small_center_y;
double E = Math.Atan2(D, C);
double result = E * (180 / 3.14192);
if (result < 0)
{
result = 360 + result;
}
// glo.mapObstacleLock.EnterWriteLock(); //critical section start
double ref_angle = 45;
double margin = 13;
if (ref_angle * 2 - margin <= result && result <= ref_angle * 2 + margin)
{
glo.direction[index] = 0;
}
else if (ref_angle * 3 - margin <= result && result <= ref_angle * 3 + margin)
{
glo.direction[index] = 1;
}
else if (ref_angle * 4 - margin <= result && result <= ref_angle * 4 + margin)
{
glo.direction[index] = 2;
}
else if (ref_angle * 5 - margin <= result && result <= ref_angle * 5 + margin)
{
glo.direction[index] = 3;
}
else if (ref_angle * 6 - margin <= result && result <= ref_angle * 6 + margin)
{
glo.direction[index] = 4;
}
else if (ref_angle * 7 - margin <= result && result <= ref_angle * 7 + margin)
{
glo.direction[index] = 5;
}
else if (ref_angle * 0 <= result && result <= ref_angle * 0 + margin || ref_angle * 8 - margin <= result && result <= ref_angle * 8)
{
glo.direction[index] = 6;
}
else if (ref_angle * 1 - margin <= result && result <= ref_angle * 1 + margin)
{
glo.direction[index] = 7;
}
else
{
glo.direction[index] = -1;
}
/*
* if (index == 0)
* {
* if (glo.direction[index] != -1)
* {
* Console.WriteLine("result = " + result);
* Console.WriteLine("globals.direction[i] = " + glo.direction[index]);
* }
*
* }
*/
//glo.mapObstacleLock.ExitWriteLock(); //critical section end
/*
* if (index == 0)
* {
* if (glo.direction[index] == -1)
* Console.WriteLine("i = " + index + " direction[index] = " + glo.direction[index] + "알수 없는 각도" + " result = " + result);
* else
* Console.WriteLine("i = " + index + " direction[index] = " + glo.direction[index] + " result = " + result);
* }
*/
// if (index == 3)
// Console.WriteLine("");
/*
* if (glo.direction[index] == -1)
* Console.WriteLine("i = " + index + " direction[index] = " + glo.direction[index] + " result = " + result);
*/
}
else
{
rect[index] = new Rectangle(0, 0, 0, 0); //사라졌을때 좌표를 0,0 길이 0, 0로 만듬
color[index] = false;
change_check[index] = true;
color_count--;
color_ROI[index].X = 0;
color_ROI[index].Y = 0;
Console.WriteLine("노랑 사라짐");
}
}
else
{
rect[index] = new Rectangle(0, 0, 0, 0); //사라졌을때 좌표를 0,0 길이 0, 0로 만듬
color[index] = false;
change_check[index] = true;
color_count--;
color_ROI[index].X = 0;
color_ROI[index].Y = 0;
Console.WriteLine("차량 사라짐");
}
}
private void timer1_Tick(object sender, EventArgs e)
{
using (Image <Bgr, byte> nextFrame = cap.QueryFrame())
{
if (nextFrame != null)
{
Image <Gray, byte> grayframe = nextFrame.Convert <Gray, byte>();
Image <Bgr, Byte> img = nextFrame;
//convert to grayscale
Image <Gray, Byte> gray = img.Convert <Gray, Byte>();
//convert to binary image using the threshold
gray = gray.ThresholdBinary(new Gray(128), new Gray(128));
// copy pixels from the original image where pixels in
// mask image is nonzero
Image <Bgr, Byte> newimg = img.Copy(gray);
// display result
pictureBox2.Image = newimg.ToBitmap();
pictureBox1.Image = nextFrame.ToBitmap();
/////////////////////
//Image<Gray, Byte> grayscale = nextFrame.Convert<Gray, Byte>();
//grayscale = grayscale.Canny(new Gray(0), new Gray(255)).Not(); //invert with Not()
//img = nextFrame.And(grayscale.Convert<Bgr, Byte>(), grayscale); //And function in action
//pictureBox3.Image = img.ToBitmap();
///////////////////////////////
Image <Bgr, Byte> ori = nextFrame;
Image <Gray, Byte> grayscale = ori.Convert <Gray, Byte>();
Image <Gray, Byte> thresh = grayscale.ThresholdToZero(new Gray(128));//(new Gray(50), new Gray(255));
StructuringElementEx ex = new StructuringElementEx(8, 8, 1, 1, CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
thresh._MorphologyEx(ex, CV_MORPH_OP.CV_MOP_OPEN, 1);
pictureBox3.Image = thresh.ToBitmap();
}
}
}
//Ycc 색상 모델로 색상 정보 추출함
public void YccColorCheck(int index, int min1, int min2, int min3, int max1, int max2, int max3)
{
int pixCount = 0;
Image <Ycc, Byte> YCrCbFrame = colorCheckImage.Convert <Ycc, Byte>(); //YCrCb 변환
Image <Gray, byte> colorSetting = new Image <Gray, byte>(YCrCbFrame.Width, YCrCbFrame.Height); //Ycc범위로 뽑아낸 것을 gray로 바꿔서 수축팽창 하기 위해
Ycc YCrCb_min = new Ycc(min1, min2, min3);
Ycc YCrCb_max = new Ycc(max1, max2, max3); // 색 범위
colorSetting = YCrCbFrame.InRange((Ycc)YCrCb_min, (Ycc)YCrCb_max); //색 범위 설정
StructuringElementEx rect_12 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvErode(colorSetting, colorSetting, rect_12, 1);
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(colorSetting, colorSetting, rect_6, 2); //수축 팽창
Image <Bgr, Byte> colorCount = colorSetting.Convert <Bgr, Byte>(); //픽셀수 세기 위해
//이미지가 범위를 벗어날경우 처리
if (pos_x < 0)
{
pos_x = 0;
}
if (pos_y < 0)
{
pos_y = 0;
}
if (pos_x + img_width > colorCheckImage.Width)
{
pos_x = colorCheckImage.Width - img_width;
}
if (pos_y + img_height > colorCheckImage.Height)
{
pos_y = colorCheckImage.Height - img_height;
}
for (int x = pos_x; x < pos_x + img_width; x++)
{
for (int y = pos_y; y < pos_y + img_height; y++)
{
if (!colorCount[y, x].Equals(new Bgr(0, 0, 0)))
{
pixCount++;
if (totalPicxel / 3 <= pixCount) //일정 픽섹 이상시 색상배열 변경후 종료
{
color[index] = true;
color_ROI[index].X = x;
color_ROI[index].Y = y;
color_count++;
change_check[index] = false;
int margin = 0;
ugvList.Add(new UGV("A" + index, glo.TemplateWidth - margin, glo.TemplateHeight - margin, x + 30, y + 30, colorStr[index]));
return;
}
}
}
}
}
public FingerprintFeatureVector extractFeatureVector(EmguGrayImageInputData input)
{
var input_img = input.Image.Clone();
Image <Gray, byte> grayImg = input_img.Convert <Gray, byte>();
Image <Gray, byte> binImg = new Image <Gray, byte>(grayImg.Size);
// OTSU threshold
CvInvoke.cvThreshold(grayImg, binImg, 100, 255, Emgu.CV.CvEnum.THRESH.CV_THRESH_OTSU | Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY);
// INVERT
binImg = binImg.Not();
// SKELETONIZATION
Image <Gray, byte> skel = new Image <Gray, byte>(binImg.Size);
for (int y = 0; y < skel.Height; y++)
{
for (int x = 0; x < skel.Width; x++)
{
skel.Data[y, x, 0] = 0;
}
}
Image <Gray, byte> img = skel.Copy();
for (int y = 0; y < skel.Height; y++)
{
for (int x = 0; x < skel.Width; x++)
{
img.Data[y, x, 0] = binImg.Data[y, x, 0];
}
}
StructuringElementEx element;
element = new StructuringElementEx(3, 3, 1, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_CROSS);
Image <Gray, byte> temp;
bool done = false;
do
{
temp = img.MorphologyEx(element, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_OPEN, 1);
temp = temp.Not();
temp = temp.And(img);
skel = skel.Or(temp);
img = img.Erode(1);
double[] min, max;
Point[] pmin, pmax;
img.MinMax(out min, out max, out pmin, out pmax);
done = (max[0] == 0);
} while (!done);
// RIDGE COUNT
FingerprintFeatureVector ridgeCounts = new FingerprintFeatureVector();
ridgeCounts.Data = new int[6];
for (int i = 1; i <= 5; i++)
{
int ridge_count = this.getRidgeCount(i, skel);
ridgeCounts.Data[i] = ridge_count;
}
return(ridgeCounts);
}
private void ptnProcess_Click(object sender, EventArgs e)
{
vehicleType = textBoxvehicleType.Text;
if (vehicleType == "")
{
MessageBox.Show("Please Enter Vehicle Type");
}
else
{
var filePath = @"data.csv";
using (var wr = new StreamWriter(filePath, true, System.Text.Encoding.UTF8))
{
var sb = new System.Text.StringBuilder();
sb.Append(vehicleType);
sb.Append(",");
frameGrayOriginal = frame.Convert <Gray, Byte>().PyrDown().PyrUp();
Image <Gray, Byte> frameGray = frame.Convert <Gray, Byte>().PyrDown().PyrUp();
Image <Gray, Byte> BGGray = BG.Convert <Gray, Byte>().PyrDown().PyrUp();
CvInvoke.cvAbsDiff(BGGray, frameGray, frameGray);
OriginalImageBox.Image = frameGray;
CvInvoke.cvSmooth(frameGray, frameGray, SMOOTH_TYPE.CV_GAUSSIAN, 15, 15, 3, 1);
frameGray = frameGray.ThresholdBinary(new Gray(60), new Gray(255));
//OriginalImageBox.Image = frameGrayOriginal;
StructuringElementEx rect_6 = new StructuringElementEx(8, 8, 5, 5, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//dilating the source image using the specified structuring element
CvInvoke.cvDilate(frameGray, frameGray, rect_6, 6);
StructuringElementEx rect_12 = new StructuringElementEx(4, 4, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//Eroding the source image using the specified structuring element
CvInvoke.cvErode(frameGray, frameGray, rect_12, 4);
resultImage = frameGray.And(frameGrayOriginal);
FindLargestObject();
TextBox.AppendText("Height : " + r.Height.ToString() + "\n");
sb.Append(r.Height);
sb.Append(",");
TextBox.AppendText("Width : " + r.Width.ToString() + "\n");
sb.Append(r.Width);
sb.Append(",");
TextBox.AppendText("Ratio : " + ((float)r.Width / (float)r.Height).ToString() + "\n");
sb.Append((float)r.Width / (float)r.Height);
sb.Append(",");
Image <Gray, Byte> cannyEdges = resultImage.Canny(150, 80);
StructuringElementEx rect_2 = new StructuringElementEx(4, 4, 1, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
CvInvoke.cvDilate(cannyEdges, cannyEdges, rect_2, 1);
//OriginalImageBox.Image = cannyEdges;
CvInvoke.cvErode(cannyEdges, cannyEdges, rect_2, 1);
//OriginalImageBox.Image = cannyEdges;
Image <Gray, float> cornerStrength = new Image <Gray, float>(cannyEdges.Size);
CvInvoke.cvCornerHarris(
cannyEdges, //source image
cornerStrength, //result image
3, //neighborhood size
3, //aperture size
0.2); //Harris parameter
//threshold the corner strengths
cornerStrength._ThresholdBinary(new Gray(0.01), new Gray(255));
int contCorners = 0;
for (int x = 0; x < cornerStrength.Width; x++)
{
for (int y = 0; y < cornerStrength.Height; y++)
{
Gray imagenP = cornerStrength[y, x];
if (imagenP.Intensity == 255)
{
contCorners++;
}
}
}
sb.Append(contCorners);
sb.Append(",");
// ProcessImageBox.Image = cornerStrength;
LineSegment2D[] lines = cannyEdges.HoughLinesBinary(
3, //Distance resolution in pixel-related units
Math.PI / 2, //Angle resolution measured in radians.
150, //threshold
80, //min Line width
10 //gap between lines
)[0]; //Get the lines from the first channel
foreach (LineSegment2D line in lines)
{
frame.Draw(line, new Bgr(Color.Red), 2);
count++;
}
//TextBox.AppendText("Horizontal Lines : " + count.ToString() + "\n");
sb.Append(count);
sb.Append(",");
count = 0;
lines = cannyEdges.HoughLinesBinary(
1, //Distance resolution in pixel-related units
Math.PI, //Angle resolution measured in radians.
80, //threshold
70, //min Line width
5 //gap between lines
)[0]; //Get the lines from the first channel
foreach (LineSegment2D line in lines)
{
frame.Draw(line, new Bgr(Color.White), 2);
count++;
}
// TextBox.AppendText("vertical Lines : " + count.ToString() + "\n");
sb.Append(count);
sb.Append(",");
count = 0;
lines = cannyEdges.HoughLinesBinary(
3, //Distance resolution in pixel-related units
Math.PI / 4, //Angle resolution measured in radians.
150, //threshold
100, //min Line width
8 //gap between lines
)[0]; //Get the lines from the first channel
foreach (LineSegment2D line in lines)
{
frame.Draw(line, new Bgr(Color.LightPink), 2);
count++;
}
// TextBox.AppendText("30 Lines : " + count.ToString() + "\n");
sb.Append(count);
sb.Append(",");
count = 0;
lines = cannyEdges.HoughLinesBinary(
3, //Distance resolution in pixel-related units
Math.PI / 3, //Angle resolution measured in radians.
50, //threshold
100, //min Line width
8 //gap between lines
)[0]; //Get the lines from the first channel
foreach (LineSegment2D line in lines)
{
frame.Draw(line, new Bgr(Color.LightPink), 2);
count++;
}
// TextBox.AppendText("60 Lines : " + count.ToString() + "\n");
sb.Append(count);
sb.Append(",");
count = 0;
lines = cannyEdges.HoughLinesBinary(
3, //Distance resolution in pixel-related units
Math.PI / 6, //Angle resolution measured in radians.
50, //threshold
100, //min Line width
8 //gap between lines
)[0]; //Get the lines from the first channel
foreach (LineSegment2D line in lines)
{
frame.Draw(line, new Bgr(Color.LightPink), 2);
count++;
}
// TextBox.AppendText("45 Lines : " + count.ToString() + "\n");
sb.Append(count);
sb.Append(",");
List <MCvBox2D> boxList1 = new List <MCvBox2D>();
List <MCvBox2D> boxList2 = new List <MCvBox2D>();
List <MCvBox2D> boxList3 = new List <MCvBox2D>();
List <MCvBox2D> boxList4 = new List <MCvBox2D>();
List <MCvBox2D> boxList5 = new List <MCvBox2D>();
List <MCvBox2D> boxList6 = new List <MCvBox2D>();
List <Triangle2DF> triangleList1 = new List <Triangle2DF>();
List <Triangle2DF> triangleList2 = new List <Triangle2DF>();
List <Triangle2DF> triangleList3 = new List <Triangle2DF>();
List <Triangle2DF> triangleList4 = new List <Triangle2DF>();
List <Triangle2DF> triangleList5 = new List <Triangle2DF>();
List <Contour <Point> > pentagonList1 = new List <Contour <Point> >();
List <Contour <Point> > pentagonList2 = new List <Contour <Point> >();
List <Contour <Point> > pentagonList3 = new List <Contour <Point> >();
List <Contour <Point> > pentagonList4 = new List <Contour <Point> >();
List <Contour <Point> > pentagonList5 = new List <Contour <Point> >();
List <Contour <Point> > pentagonList6 = new List <Contour <Point> >();
using (MemStorage storage = new MemStorage())
for (Contour <Point> contours = cannyEdges.FindContours(); contours != null; contours = contours.HNext)
{
Contour <Point> currentContour = contours.ApproxPoly(contours.Perimeter * 0.05, storage);
if (currentContour.Total == 3) //The contour has 3 vertices, it is a triangle
{
if (contours.Area > 30 && contours.Area <= 100)
{
Point[] pts_1 = currentContour.ToArray();
triangleList1.Add(new Triangle2DF(pts_1[0], pts_1[1], pts_1[2]));
}
else if (contours.Area <= 200)
{
Point[] pts_2 = currentContour.ToArray();
triangleList2.Add(new Triangle2DF(pts_2[0], pts_2[1], pts_2[2]));
}
else if (contours.Area <= 300)
{
Point[] pts_3 = currentContour.ToArray();
triangleList3.Add(new Triangle2DF(pts_3[0], pts_3[1], pts_3[2]));
}
else if (contours.Area <= 400)
{
Point[] pts_4 = currentContour.ToArray();
triangleList4.Add(new Triangle2DF(pts_4[0], pts_4[1], pts_4[2]));
}
else
{
Point[] pts_5 = currentContour.ToArray();
triangleList5.Add(new Triangle2DF(pts_5[0], pts_5[1], pts_5[2]));
}
}
else if (currentContour.Total == 4)
{
if (contours.Area > 30 && contours.Area < 50)
{
bool isRectangle = true;
Point[] pts1 = currentContour.ToArray();
LineSegment2D[] edges1 = PointCollection.PolyLine(pts1, true);
for (int i = 0; i < edges1.Length; i++)
{
double angle = Math.Abs(edges1[(i + 1) % edges1.Length].GetExteriorAngleDegree(edges1[i]));
if (angle < 88 || angle > 92)
{
isRectangle = false;
break;
}
if (isRectangle)
{
boxList1.Add(currentContour.GetMinAreaRect());
}
}
}
else if (contours.Area <= 100)
{
bool isRectangle = true;
Point[] pts2 = currentContour.ToArray();
LineSegment2D[] edges2 = PointCollection.PolyLine(pts2, true);
for (int i = 0; i < edges2.Length; i++)
{
double angle = Math.Abs(edges2[(i + 1) % edges2.Length].GetExteriorAngleDegree(edges2[i]));
if (angle < 88 || angle > 92)
{
isRectangle = false;
break;
}
if (isRectangle)
{
boxList2.Add(currentContour.GetMinAreaRect());
}
}
}
else if (contours.Area <= 200)
{
bool isRectangle = true;
Point[] pts3 = currentContour.ToArray();
LineSegment2D[] edges3 = PointCollection.PolyLine(pts3, true);
//using edges i found coordinates.
for (int i = 0; i < edges3.Length; i++)
{
double angle = Math.Abs(edges3[(i + 1) % edges3.Length].GetExteriorAngleDegree(edges3[i]));
if (angle < 88 || angle > 92)
{
isRectangle = false;
break;
}
if (isRectangle)
{
boxList3.Add(currentContour.GetMinAreaRect());
}
}
}
else if (contours.Area <= 300)
{
bool isRectangle = true;
Point[] pts4 = currentContour.ToArray();
LineSegment2D[] edges4 = PointCollection.PolyLine(pts4, true);
//using edges i found coordinates.
for (int i = 0; i < edges4.Length; i++)
{
double angle = Math.Abs(edges4[(i + 1) % edges4.Length].GetExteriorAngleDegree(edges4[i]));
if (angle < 88 || angle > 92)
{
isRectangle = false;
break;
}
if (isRectangle)
{
boxList4.Add(currentContour.GetMinAreaRect());
}
}
}
else if (contours.Area <= 400)
{
bool isRectangle = true;
Point[] pts5 = currentContour.ToArray();
LineSegment2D[] edges5 = PointCollection.PolyLine(pts5, true);
//using edges i found coordinates.
for (int i = 0; i < edges5.Length; i++)
{
double angle = Math.Abs(edges5[(i + 1) % edges5.Length].GetExteriorAngleDegree(edges5[i]));
if (angle < 88 || angle > 92)
{
isRectangle = false;
break;
}
if (isRectangle)
{
boxList5.Add(currentContour.GetMinAreaRect());
}
}
}
else
{
bool isRectangle = true;
Point[] pts6 = currentContour.ToArray();
LineSegment2D[] edges6 = PointCollection.PolyLine(pts6, true);
//using edges i found coordinates.
for (int i = 0; i < edges6.Length; i++)
{
double angle = Math.Abs(edges6[(i + 1) % edges6.Length].GetExteriorAngleDegree(edges6[i]));
if (angle < 88 || angle > 92)
{
isRectangle = false;
break;
}
if (isRectangle)
{
boxList6.Add(currentContour.GetMinAreaRect());
}
}
}
}
else if (currentContour.Total == 5)
{
if (contours.Area > 20 && contours.Area <= 50)
{
pentagonList1.Add(currentContour); // Add to list of pentagons
Point[] ptPoints1 = contours.ToArray(); // Get contour points
frame.Draw(contours, new Bgr(Color.Purple), 2);
int dotX = (ptPoints1[0].X + ptPoints1[1].X + ptPoints1[2].X + ptPoints1[3].X + ptPoints1[4].X) / 5;
int dotY = (ptPoints1[0].Y + ptPoints1[1].Y + ptPoints1[2].Y + ptPoints1[3].Y + ptPoints1[4].Y) / 5;
CircleF dot = new CircleF(new PointF(dotX, dotY), 3);
frame.Draw(dot, new Bgr(Color.Purple), -1);
}
else if (contours.Area <= 100)
{
pentagonList2.Add(currentContour);
Point[] ptPoints2 = contours.ToArray(); // Get contour points
frame.Draw(contours, new Bgr(Color.Purple), 2);
int dotX = (ptPoints2[0].X + ptPoints2[1].X + ptPoints2[2].X + ptPoints2[3].X + ptPoints2[4].X) / 5;
int dotY = (ptPoints2[0].Y + ptPoints2[1].Y + ptPoints2[2].Y + ptPoints2[3].Y + ptPoints2[4].Y) / 5;
CircleF dot = new CircleF(new PointF(dotX, dotY), 3);
frame.Draw(dot, new Bgr(Color.Purple), -1);
}
else if (contours.Area <= 200)
{
pentagonList3.Add(currentContour);
Point[] ptPoints3 = contours.ToArray(); // Get contour points
frame.Draw(contours, new Bgr(Color.Purple), 2);
int dotX = (ptPoints3[0].X + ptPoints3[1].X + ptPoints3[2].X + ptPoints3[3].X + ptPoints3[4].X) / 5;
int dotY = (ptPoints3[0].Y + ptPoints3[1].Y + ptPoints3[2].Y + ptPoints3[3].Y + ptPoints3[4].Y) / 5;
CircleF dot = new CircleF(new PointF(dotX, dotY), 3);
frame.Draw(dot, new Bgr(Color.Purple), -1);
}
else if (contours.Area <= 300)
{
pentagonList4.Add(currentContour);
Point[] ptPoints4 = contours.ToArray(); // Get contour points
frame.Draw(contours, new Bgr(Color.Purple), 2);
int dotX = (ptPoints4[0].X + ptPoints4[1].X + ptPoints4[2].X + ptPoints4[3].X + ptPoints4[4].X) / 5;
int dotY = (ptPoints4[0].Y + ptPoints4[1].Y + ptPoints4[2].Y + ptPoints4[3].Y + ptPoints4[4].Y) / 5;
CircleF dot = new CircleF(new PointF(dotX, dotY), 3);
frame.Draw(dot, new Bgr(Color.Purple), -1);
}
else if (contours.Area <= 400)
{
pentagonList5.Add(currentContour);
Point[] ptPoints5 = contours.ToArray(); // Get contour points
frame.Draw(contours, new Bgr(Color.Purple), 2);
int dotX = (ptPoints5[0].X + ptPoints5[1].X + ptPoints5[2].X + ptPoints5[3].X + ptPoints5[4].X) / 5;
int dotY = (ptPoints5[0].Y + ptPoints5[1].Y + ptPoints5[2].Y + ptPoints5[3].Y + ptPoints5[4].Y) / 5;
CircleF dot = new CircleF(new PointF(dotX, dotY), 3);
frame.Draw(dot, new Bgr(Color.Purple), -1);
}
else
{
pentagonList6.Add(currentContour);
Point[] ptPoints6 = contours.ToArray(); // Get contour points
frame.Draw(contours, new Bgr(Color.Purple), 2);
int dotX = (ptPoints6[0].X + ptPoints6[1].X + ptPoints6[2].X + ptPoints6[3].X + ptPoints6[4].X) / 5;
int dotY = (ptPoints6[0].Y + ptPoints6[1].Y + ptPoints6[2].Y + ptPoints6[3].Y + ptPoints6[4].Y) / 5;
CircleF dot = new CircleF(new PointF(dotX, dotY), 3);
frame.Draw(dot, new Bgr(Color.Purple), -1);
}
}
}
count = 0;
foreach (Contour <Point> pentagon in pentagonList1)
{
frame.Draw(pentagon, new Bgr(Color.Purple), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Contour <Point> pentagon in pentagonList2)
{
frame.Draw(pentagon, new Bgr(Color.Purple), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Contour <Point> pentagon in pentagonList3)
{
frame.Draw(pentagon, new Bgr(Color.Purple), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Contour <Point> pentagon in pentagonList4)
{
frame.Draw(pentagon, new Bgr(Color.Purple), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Contour <Point> pentagon in pentagonList5)
{
frame.Draw(pentagon, new Bgr(Color.Purple), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Contour <Point> pentagon in pentagonList6)
{
frame.Draw(pentagon, new Bgr(Color.Purple), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (MCvBox2D box in boxList1)
{
frame.Draw(box, new Bgr(Color.Purple), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (MCvBox2D box in boxList2)
{
frame.Draw(box, new Bgr(Color.LightBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (MCvBox2D box in boxList3)
{
frame.Draw(box, new Bgr(Color.LightBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (MCvBox2D box in boxList4)
{
frame.Draw(box, new Bgr(Color.LightBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (MCvBox2D box in boxList5)
{
frame.Draw(box, new Bgr(Color.LightBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Triangle2DF triangle in triangleList1)
{
frame.Draw(triangle, new Bgr(Color.DarkBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Triangle2DF triangle in triangleList2)
{
frame.Draw(triangle, new Bgr(Color.DarkBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Triangle2DF triangle in triangleList3)
{
frame.Draw(triangle, new Bgr(Color.DarkBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Triangle2DF triangle in triangleList4)
{
frame.Draw(triangle, new Bgr(Color.DarkBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
foreach (Triangle2DF triangle in triangleList5)
{
frame.Draw(triangle, new Bgr(Color.DarkBlue), 2);
count++;
}
sb.Append(count);
sb.Append(",");
count = 0;
Gray cannyThreshold = new Gray(100);
Gray circleAccumulatorThreshold = new Gray(70);
CircleF[] circles = frameGrayOriginal.HoughCircles(
cannyThreshold,
circleAccumulatorThreshold,
2.0, //Resolution of the accumulator used to detect centers of the circles
160.0, //min distance
20, //min radius
50 //max radius
)[0]; //Get the circles from the first channel
float rad = 0;
foreach (CircleF circle in circles)
{
//frame.Draw(circle, new Bgr(Color.Brown), 2);
TextBox.AppendText("Radius : " + circle.Radius.ToString() + "\n");
rad = rad + circle.Radius;
}
sb.Append(circles[0].Radius);
sb.Append(",");
sb.Append(circles[1].Radius);
sb.Append(",");
double dist = Math.Sqrt(Math.Pow(circles[0].Center.X - circles[1].Center.X, 2) + Math.Pow(circles[0].Center.Y - circles[1].Center.Y, 2));
sb.Append(dist);
sb.Append(",");
//TextBox.AppendText("circles : " + circles.Length.ToString() + "\n");
sb.Append(circles.Length);
sb.Append(",");
//TextBox.AppendText("Avg radius : " + (rad / circles.Length).ToString() + "\n");
sb.Append(rad / circles.Length);
ProcessImageBox.Image = frame;
wr.WriteLine(sb.ToString());
}
}
}
void ProcessFramAndUpdateGUI(object Sender, EventArgs agr)
{
string[] filePaths = Directory.GetFiles(filepath);
int Finger_num = 0;
Double Result1 = 0;
Double Result2 = 0;
imgOrignal = CapWebCam.QueryFrame();
if (imgOrignal == null)
{
return;
}
//Applying YCrCb filter
Image <Ycc, Byte> currentYCrCbFrame = imgOrignal.Convert <Ycc, byte>();
Image <Gray, byte> skin = new Image <Gray, byte>(imgOrignal.Width, imgOrignal.Height);
skin = currentYCrCbFrame.InRange(new Ycc(0, 131, 80), new Ycc(255, 185, 135));
StructuringElementEx rect_12 = new StructuringElementEx(10, 10, 5, 5, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//Eroding the source image using the specified structuring element
CvInvoke.cvErode(skin, skin, rect_12, 1);
StructuringElementEx rect_6 = new StructuringElementEx(6, 6, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT);
//dilating the source image using the specified structuring element
CvInvoke.cvDilate(skin, skin, rect_6, 2);
skin = skin.Flip(FLIP.HORIZONTAL);
//smoothing the filterd , eroded and dilated image.
skin = skin.SmoothGaussian(9);
imgOrignal = imgOrignal.Flip(FLIP.HORIZONTAL);
//extracting contours.
Contour <Point> contours = skin.FindContours();
Contour <Point> biggestContour = null;
//extracting the biggest contour.
while (contours != null)
{
Result1 = contours.Area;
if (Result1 > Result2)
{
Result2 = Result1;
biggestContour = contours;
}
contours = contours.HNext;
}
//applying convexty defect allgoritm to find the count of fingers
if (biggestContour != null)
{
Finger_num = 0;
biggestContour = biggestContour.ApproxPoly((0.00025));
imgOrignal.Draw(biggestContour, new Bgr(Color.LimeGreen), 2);
Hull = biggestContour.GetConvexHull(ORIENTATION.CV_CLOCKWISE);
defects = biggestContour.GetConvexityDefacts(storage, ORIENTATION.CV_CLOCKWISE);
imgOrignal.DrawPolyline(Hull.ToArray(), true, new Bgr(0, 0, 256), 2);
box = biggestContour.GetMinAreaRect();
defectArray = defects.ToArray();
for (int i = 0; i < defects.Total; i++)
{
PointF startPoint = new PointF((float)defectArray[i].StartPoint.X,
(float)defectArray[i].StartPoint.Y);
PointF depthPoint = new PointF((float)defectArray[i].DepthPoint.X,
(float)defectArray[i].DepthPoint.Y);
PointF endPoint = new PointF((float)defectArray[i].EndPoint.X,
(float)defectArray[i].EndPoint.Y);
CircleF startCircle = new CircleF(startPoint, 5f);
CircleF depthCircle = new CircleF(depthPoint, 5f);
CircleF endCircle = new CircleF(endPoint, 5f);
if ((startCircle.Center.Y < box.center.Y || depthCircle.Center.Y < box.center.Y) &&
(startCircle.Center.Y < depthCircle.Center.Y) &&
(Math.Sqrt(Math.Pow(startCircle.Center.X - depthCircle.Center.X, 2) +
Math.Pow(startCircle.Center.Y - depthCircle.Center.Y, 2)) >
box.size.Height / 6.5))
{
Finger_num++;
}
}
label2.Text = Finger_num.ToString(); // updating finger count
}
// Finding the center of contour
MCvMoments moment = new MCvMoments(); // a new MCvMoments object
try
{
moment = biggestContour.GetMoments(); // Moments of biggestContour
}
catch (NullReferenceException except)
{
//label3.Text = except.Message;
return;
}
CvInvoke.cvMoments(biggestContour, ref moment, 0);
double m_00 = CvInvoke.cvGetSpatialMoment(ref moment, 0, 0);
double m_10 = CvInvoke.cvGetSpatialMoment(ref moment, 1, 0);
double m_01 = CvInvoke.cvGetSpatialMoment(ref moment, 0, 1);
int current_X = Convert.ToInt32(m_10 / m_00) / 10; // X location of centre of contour
int current_Y = Convert.ToInt32(m_01 / m_00) / 10; // Y location of center of contour
// transfer control to webcam only if button has already been clicked
if (button_pressed)
{
if (Finger_num == 0 || Finger_num == 1)
{
Cursor.Position = new Point(current_X * 20, current_Y * 20);
}
if (Finger_num >= 4)
{
}
}
iborignal.Image = imgOrignal;
Image <Bgr, Byte> currentFrame;
Image <Gray, Byte> sourceImage = null;
for (int tu = 0; tu < filePaths.Length; tu++)
{
if (filePaths[tu].Contains(".bmp"))
{
getfilename(System.IO.Path.GetFileName(filePaths[tu]));
sourceImage = CapWebCam.QueryFrame().Convert <Gray, Byte>();
Image <Gray, Byte> templateImage = new Image <Gray, Byte>(filePaths[tu]);
Image <Gray, float> result = sourceImage.MatchTemplate(templateImage, Emgu.CV.CvEnum.TM_TYPE.CV_TM_CCOEFF_NORMED);
// iborignal.Image = result;
double[] min, max;
Point[] pointMin, pointMax;
templateImage.MinMax(out min, out max, out pointMin, out pointMax);
float[, ,] matches = result.Data;
for (int x = 0; x < matches.GetLength(0); x++)
{
for (int y = 0; y < matches.GetLength(1); y++)
{
double matchScore = matches[x, y, 0];
Console.WriteLine(matchScore);
label1.Text = Convert.ToString(matchScore).ToString();
// label4.Text = filePaths[tu];
if (matchScore > (0.40))
{
CodeClass db = new CodeClass();
db.ConnectToDatabase();
DataTable dt = db.GetTable("Select * from tbldata where FilePath='" + filePaths[tu] + "'");
if (dt.Rows.Count > 0)
{
richTextBox1.Text = dt.Rows[0][0].ToString();
richTextBox2.Text = dt.Rows[0][1].ToString();
Console.WriteLine(matchScore);
reader = new SpeechSynthesizer();
reader.SpeakAsync(richTextBox2.Text);
Rectangle rect = new Rectangle(new Point(x, y), new Size(1, 1));
imgOrignal.Draw(rect, new Bgr(Color.Blue), 1);
}
}
else
{
richTextBox1.Text = "";
richTextBox2.Text = "";
}
}
}
}
}
}
public bool detectOverall(Image <Gray, byte> img, Image <Gray, byte> prev)
{
Rectangle imgROI = new Rectangle(0, 0, 0, 0);
if (img.IsROISet)
{
imgROI = img.ROI;
CvInvoke.cvResetImageROI(img);
}
Rectangle prevROI = new Rectangle(0, 0, 0, 0);
if (prev.IsROISet)
{
prevROI = prev.ROI;
CvInvoke.cvResetImageROI(prev);
}
// step 1: compute diff = img - prev
Image <Gray, byte> diff = new Image <Gray, byte>(img.Width, img.Height);
CvInvoke.cvSub(img, prev, diff, IntPtr.Zero);
if (img.IsROISet)
{
CvInvoke.cvSetImageROI(img, imgROI);
}
if (prev.IsROISet)
{
CvInvoke.cvSetImageROI(prev, prevROI);
}
// step 2: threshold
CvInvoke.cvThreshold(diff, diff, 100, 255, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY);
// step 3: do close and open operations
StructuringElementEx element = new StructuringElementEx(5, 5, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
CvInvoke.cvMorphologyEx(diff, diff, IntPtr.Zero, element, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_CLOSE, 1);
CvInvoke.cvMorphologyEx(diff, diff, IntPtr.Zero, element, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_OPEN, 3);
// step 4: compute connection regions
IntPtr comp = new IntPtr();
MemStorage storage = new MemStorage();
int nc = CvInvoke.cvFindContours(diff, storage, ref comp, StructSize.MCvContour,
Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_CCOMP, Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE, new Point(0, 0));
// only when the number of connection regions equals 2, a blink is detected.
if (nc != 2)
{
return(false);
}
Seq <Point> compSeq = new Seq <Point>(comp, null);
Rectangle r1 = CvInvoke.cvBoundingRect(compSeq, 1);
Rectangle r2 = CvInvoke.cvBoundingRect(compSeq.HNext, 1);
// Eliminate interference
if (Math.Abs(r1.Y - r2.Y) > 20 || Math.Abs(r1.Width - r2.Width) > 20 || Math.Abs(r1.Height - r2.Height) > 20)
{
return(false);
}
// Eliminate the possibility of noses pair
if (Math.Abs(r1.X - r2.X) < 200)
{
return(false);
}
return(true);
}
public bool detectShook(Image <Gray, byte> img, Image <Gray, byte> prev)
{
// if the ROI of current image is setted up, then save the ROI and reset it
Rectangle imgROI = new Rectangle(0, 0, 0, 0);
if (img.IsROISet)
{
imgROI = img.ROI;
CvInvoke.cvResetImageROI(img);
}
// if the ROI of previous image is setted up, then save the ROI and reset it
Rectangle prevROI = new Rectangle(0, 0, 0, 0);
if (prev.IsROISet)
{
prevROI = prev.ROI;
CvInvoke.cvResetImageROI(prev);
}
Image <Gray, byte> diff = new Image <Gray, byte>(img.Width, img.Height);
CvInvoke.cvSub(img, prev, diff, IntPtr.Zero);
// if the ROI of current iamge and previous image are resetted up, then set them again to keep unchangged by the method
if (img.IsROISet)
{
CvInvoke.cvSetImageROI(img, imgROI);
}
if (prev.IsROISet)
{
CvInvoke.cvSetImageROI(prev, prevROI);
}
CvInvoke.cvThreshold(diff, diff, 50, 255, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY);
StructuringElementEx element = new StructuringElementEx(5, 5, 3, 3, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_ELLIPSE);
//CvInvoke.cvMorphologyEx(diff, diff, IntPtr.Zero, element, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_CLOSE, 1);
CvInvoke.cvMorphologyEx(diff, diff, IntPtr.Zero, element, Emgu.CV.CvEnum.CV_MORPH_OP.CV_MOP_OPEN, 7);
IntPtr comp = new IntPtr();
MemStorage storage = new MemStorage();
int nc = CvInvoke.cvFindContours(diff, storage, ref comp, StructSize.MCvContour,
Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_CCOMP, Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE, new Point(0, 0));
// when the numbers of connection regions are two huge, then a shaking is detected
if (nc > 8)
{
return(true);
}
// also, when one of the connected regions has too big width or too big height, then a shaking is detected
Seq <Point> compSeq = new Seq <Point>(comp, null);
while (compSeq != null && comp.ToInt32() != 0)
{
Rectangle r = CvInvoke.cvBoundingRect(compSeq, 1);
if (r.Width >= 200 || r.Height >= 120 || Math.Abs(r.Width - r.Height) >= 100)
{
return(true);
}
compSeq = compSeq.HNext;
}
return(false);
}
private void imagezone()
{
int i = 0;
byte j = 0;
try
{
capture = new Capture();
}
catch (NullReferenceException excpt)
{
MessageBox.Show(excpt.Message);
}
Thread.Sleep(2000);
while (true)
{
Image <Bgr, Byte> image = capture.QueryFrame(); //line 1
Image <Hsv, Byte> imgHsv = image.Convert <Hsv, Byte>();
// ImageFrame = ImageFrame.Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
for (i = 0; i < 3; i++)
{
Image <Gray, byte> imgthres = new Image <Gray, byte>(imgHsv.Size);
if (i == 0)
{
red(imgHsv, out imgthres);
}
else if (i == 1)
{
blue(imgHsv, out imgthres);
}
else if (i == 2)
{
green(imgHsv, out imgthres);
}
//erode and dilate
StructuringElementEx element = new StructuringElementEx(5, 5, 1, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_CROSS);
CvInvoke.cvErode(imgthres, imgthres, element, 1);
element = new StructuringElementEx(5, 5, 1, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_CROSS);
CvInvoke.cvDilate(imgthres, imgthres, element, 1);
//
//contour
using (MemStorage storage = new MemStorage()) //allocate storage for contour approximation
for (Contour <Point> contours = imgthres.FindContours(); contours != null; contours = contours.HNext)
{
if (contours.Area > 400) //only consider contours with area greater than 250
{
j++;
image.Draw(contours, new Bgr(Color.Gold), 4);
}
}
img_color[i] = j;
j = 0;
}
cam.Image = image;
}
}
