/// <summary>
/// 选择轮廓并计算轮廓特征
/// </summary>
private void NumListContours_ValueChanged(object sender, EventArgs e)
{
//先绘制此条轮廓
mArgs.FeatureType = ContourFeatureType.Contour;
mArgs.ContourIndex = (int)NumListContours.Value;
Params_Changed();
//再计算此条轮廓的特征
VectorOfPoint cnt = mContours[mArgs.ContourIndex];
TxtContourArea.Text = CvInvoke.ContourArea(cnt).ToString();
TxtArcLength.Text = CvInvoke.ArcLength(cnt, true).ToString();
MCvMoments moments = CvInvoke.Moments(cnt);
TxtMoments.Text = string.Format("M00: {0}\r\nM01: {1}\r\nM02: {02}\r\nM03: {3}\r\nM10: {4}\r\nM11: {5}\r\nM12: {6}\r\nM20: {7}\r\nM21: {8}\r\nM30: {9}\r\n" +
"Mu02: {10}\r\nMu03: {11}\r\nMu11: {12}\r\nMu12: {13}\r\nMu20: {14}\r\nMu21: {15}\r\nMu30: {16}", moments.M00,
moments.M01, moments.M02, moments.M03, moments.M10, moments.M11, moments.M12, moments.M20, moments.M21, moments.M30,
moments.Mu02, moments.Mu03, moments.Mu11, moments.Mu12, moments.Mu20, moments.Mu21, moments.Mu30);
TxtContourCenter.Text = string.Format("({0},{1})", moments.M10 / moments.M00, moments.M01 / moments.M00);
double[] huMoments = moments.GetHuMoment();
for (int i = 0; i < huMoments.Length; i++)
{
TxtHuMoments.Text += huMoments[i] + "\r\n";
}
}
private Image <Bgr, byte> DetectAndDrawCircles(Image <Gray, byte> binaryImage, Image <Bgr, byte> imageFrame, MCvScalar color, Color filter)
{
//// detect circles
//CircleF[] circles = CvInvoke.HoughCircles(binaryImage, Emgu.CV.CvEnum.HoughType.Gradient, 2, binaryImage.Rows / 8, 100, 50, 0, 100);
Image <Bgr, byte> temp = imageFrame.Clone();
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(binaryImage, contours, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
//for(int i = 0; i < contours.Size; i++)
if (contours.Size > 0)
{
MCvMoments moment = CvInvoke.Moments(contours[0]);
double area = moment.M00;
if (area > 400)
{
MCvPoint2D64f position = new MCvPoint2D64f(moment.M10 / area, moment.M01 / area);
Point p = new Point((int)position.X, (int)position.Y);
if (filter == Color.Blue)
{
blueCircle.Add(p);
}
CvInvoke.Circle(temp, p, 2, color, 3);
CvInvoke.DrawContours(temp, contours, 0, color, 4);
}
}
return(temp);
}
private void calculPosBaguette()
{
// retrieve the position of the object in the player hand, using the centroid of the biggest contour
// the position is normalized in camera space (from -1 to +1)
if (biggestContours != null)
{
MCvMoments moments = CvInvoke.Moments(biggestContours);
float objectPosition_x = (-1.0f + 2.0f * (float)(moments.M10 / moments.M00) / 640);
float objectPosition_y = (+1.0f - 2.0f * (float)(moments.M01 / moments.M00) / 480);
if (!float.IsNaN(objectPosition_x))
{
{
Debug.Log(objectPosition_x);
float x = 0.0f;
if (objectPosition_x < 0)
{
x = Math.Max(objectPosition_x, -0.4f);
}
else
{
x = Math.Min(objectPosition_x, 0.47f);
}
baguette.transform.position = new Vector3(-x, baguette.transform.position.y, baguette.transform.position.z);
}
}
}
}
double getEccentricity(MCvMoments mu)
{
return((Math.Sqrt(mu.m20 - mu.m02) - 4 * mu.m11 * mu.m11) / (Math.Sqrt(mu.m20 + mu.m02)));
//double bigSqrt = Math.Sqrt((mu.m20 - mu.m02) * (mu.m20 - mu.m02) + 4 * mu.m11 * mu.m11);
//return (double)(mu.m20 + mu.m02 + bigSqrt) / (mu.m20 + mu.m02 - bigSqrt);
}
/// <summary>
/// 获取机器人法兰中心移动坐标和旋转角度，camera=0代表右侧相机，camera=1代表前方向机
/// </summary>
/// <param name="bitmap">产品图像</param>
/// <param name="cameraID">相机编号</param>
public ImageInfo GetProductParamters(Bitmap bitmap, int cameraID, int spongeH)
{
ImageInfo imageInfo = new ImageInfo();
List <VectorOfPoint> imageContours = GetContours(bitmap, cameraID, spongeH);
VectorOfPoint productContour = imageContours.Max();
MCvMoments cvMoments = CvInvoke.Moments(productContour, false);
int gravityX = Convert.ToInt32(cvMoments.M10 / cvMoments.M00);
int gravityY = Convert.ToInt32(cvMoments.M01 / cvMoments.M00);
Trace.WriteLine("用矩计算质心：X = " + gravityX.ToString() + "\tY = " + gravityY.ToString());
if (productContour != null)
{
var minRect = CvInvoke.MinAreaRect(productContour); //最小外接矩形
PointF[] pt = CvInvoke.BoxPoints(minRect); //最小外接矩形四个角点
PointF po = minRect.Center; //最小外接矩形中心
Trace.WriteLine("当前外接矩形中心：" + po.ToString());
imageInfo.CenterOfImg = po;
//长轴,短轴,倾角计算:
//AxisLong = Math.Sqrt(Math.Pow(pt[1].X - pt[0].X, 2) + Math.Pow(pt[1].Y - pt[0].Y, 2));
imageInfo.AxisLong = minRect.Size.Width > minRect.Size.Height ? minRect.Size.Width : minRect.Size.Height;
//AxisShort = Math.Sqrt(Math.Pow(pt[2].X - pt[1].X, 2) + Math.Pow(pt[2].Y - pt[1].Y, 2));
imageInfo.AxisShort = minRect.Size.Height <= minRect.Size.Width ? minRect.Size.Height : minRect.Size.Width;;
imageInfo.ImageCorner = pt;
imageInfo.RotatedAngle = Math.Abs(minRect.Angle) > 45?minRect.Angle + 90:minRect.Angle;
Matrix <double> imgCenter = new Matrix <double>(3, 1)
{
private void button3_Click(object sender, EventArgs e)
{
Mat result = new Mat(new Size(scr.Width - temp.Width + 1, scr.Height - temp.Height + 1), Emgu.CV.CvEnum.DepthType.Cv8U, 1);
//创建mat 存储输出匹配结果。
CvInvoke.MatchTemplate(scr, temp, result, Emgu.CV.CvEnum.TemplateMatchingType.SqdiffNormed); //采用系数匹配法，匹配值越大越接近准确图像。
CvInvoke.Normalize(result, result, 255, 0, Emgu.CV.CvEnum.NormType.MinMax); //把数据进行以最大值255 最小值0 进行归一化。
result = result.ToImage <Gray, byte>().Mat; //result 类型转成Byte 类型。
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint(); //创建VectorOfVectorOfPoint 类型保存轮廓。
int threshold = 180; //设置阈值。
Mat data = new Mat(); //创建data 存储阈值后的图像。
while (true)
{
CvInvoke.Threshold(result, data, threshold, 255, Emgu.CV.CvEnum.ThresholdType.BinaryInv); //阈值操作。
CvInvoke.FindContours(data, contours, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple); // 存储轮廓。
if (contours.Size <= 1) //判断匹配个数是否小于等于10
{
break;
}
threshold -= 2; //阈值降低
}
for (int i = 0; i < contours.Size; i++) //遍历每个连通域。
{
VectorOfPoint contour = contours[i];
MCvMoments moment = CvInvoke.Moments(contour); //获得连通域的矩
Point p = new Point((int)(moment.M10 / moment.M00), (int)(moment.M01 / moment.M00)); // 获得连通域的中心
CvInvoke.Rectangle(scr, new Rectangle(p, temp.Size), new MCvScalar(0, 0, 255), 4); //绘制匹配区域。
}
imageBox1.Image = scr;
}
void Moments(Image <Gray, byte> img, ref Double[] MomentSp, ref Double[] MomentCen, ref Double[] MomentNor)
{
Image <Gray, byte> imgA = new Image <Gray, byte>(img.Size);
imgA = img.ThresholdBinary(new Gray(60), new Gray(255));
Contour <Point> contourA = imgA.FindContours();
MCvMoments momentsA = contourA.GetMoments();
for (int xOrder = 0; xOrder <= 3; xOrder++)
{
for (int yOrder = 0; yOrder <= 3; yOrder++)
{
if (xOrder + yOrder <= 3)
{
MomentSp[3 * xOrder + yOrder] = momentsA.GetSpatialMoment(xOrder, yOrder);
MomentCen[3 * xOrder + yOrder] = momentsA.GetCentralMoment(xOrder, yOrder);
MomentNor[3 * xOrder + yOrder] = momentsA.GetNormalizedCentralMoment(xOrder, yOrder);
}
}
}
/* for (int m = 0; m < 12; m++)
* {
* richTextBox4.AppendText("SP:"+MomentSp[m]+" CEN:"+MomentCen[m]+" Nor: "+MomentNor[m]+"\n");
* }*/
}
void Hu(Image <Gray, byte> img, ref Double[] MomentHu, ref Double[] MomentR)
{
double r1 = 0, r2 = 0, r3 = 0, r4 = 0, r5 = 0, r6 = 0, r7 = 0, r8 = 0, r9 = 0, r10 = 0;
Image <Gray, byte> imgA = new Image <Gray, byte>(img.Size);
imgA = img.ThresholdBinary(new Gray(60), new Gray(255));
Contour <Point> contourA = imgA.FindContours();
MCvMoments momentsA = contourA.GetMoments();
MCvHuMoments HuMomentsA = momentsA.GetHuMoment();
MomentHu[0] = HuMomentsA.hu1;
MomentHu[1] = HuMomentsA.hu2;
MomentHu[2] = HuMomentsA.hu3;
MomentHu[3] = HuMomentsA.hu4;
MomentHu[4] = HuMomentsA.hu5;
MomentHu[5] = HuMomentsA.hu6;
Rmoment(HuMomentsA.hu1, HuMomentsA.hu2, HuMomentsA.hu3, HuMomentsA.hu4, HuMomentsA.hu5, HuMomentsA.hu6, ref r1, ref r2, ref r3, ref r4, ref r5, ref r6, ref r7, ref r8, ref r9, ref r10);
MomentR[0] = r1;
MomentR[1] = r2;
MomentR[2] = r3;
MomentR[3] = r4;
MomentR[4] = r5;
MomentR[5] = r6;
MomentR[6] = r7;
MomentR[7] = r8;
MomentR[8] = r9;
MomentR[9] = r10;
}
public static List <NeuronBodyMask> GenerateNeuronBodyMasks(List <VectorOfPoint> input)
{
List <NeuronBodyMask> result = new List <NeuronBodyMask>();
NeuronBodyMask tmp_mask = new NeuronBodyMask();
Rectangle R = new Rectangle();
Image <Gray, Byte> maskImg = new Image <Gray, Byte>(1, 1, new Gray(0));
Point C = new Point();
VectorOfVectorOfPoint tmp_vvop = new VectorOfVectorOfPoint();
MCvMoments moments = new MCvMoments();
for (int i = 0; i < input.Count; i++)
{
R = CvInvoke.BoundingRectangle(input[i]);
maskImg = new Image <Gray, byte>(184, 140, new Gray(0));
tmp_vvop = new VectorOfVectorOfPoint();
tmp_vvop.Push(input[i]);
CvInvoke.DrawContours(maskImg, tmp_vvop, -1, new MCvScalar(255), -1, LineType.EightConnected);
moments = CvInvoke.Moments(input[i]);
C = new Point((int)(moments.M10 / moments.M00), (int)(moments.M01 / moments.M00));
//maskImg.Draw(new Point[] { C }, new Gray(100), 1);
tmp_mask = new NeuronBodyMask(R, maskImg, C);
result.Add(tmp_mask);
}
return(result);
}
/// <summary>
/// Get the moments for this point sequence
/// </summary>
/// <returns>the moments for this point sequence</returns>
public MCvMoments GetMoments()
{
MCvMoments moment = new MCvMoments();
CvInvoke.cvMoments(Ptr, ref moment, 0);
return(moment);
}
static ContourProperties FishContour(Mat image_raw, Mat background)
{
bool fishcont_found = false;
Size frsize = new Size(image_raw.Width, image_raw.Height);
Mat image = new Mat(frsize, Emgu.CV.CvEnum.DepthType.Cv8U, 1);
ContourProperties contprops = new ContourProperties();
ThresholdType ttype = 0;
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
Mat hierarchy = new Mat();
CvInvoke.AbsDiff(image_raw, background, image);
// This should be 30 as the LB. Switched to 20 to see if i could pick up paramecia.
CvInvoke.Threshold(image, image, 10, 255, ttype);
// UNCOMMENT IF YOU WANT TO SHOW THRESHOLDED IMAGE
String camerawindow = "Camera Window";
CvInvoke.NamedWindow(camerawindow);
CvInvoke.Imshow(camerawindow, image);
CvInvoke.WaitKey(1);
CvInvoke.FindContours(image, contours, hierarchy, RetrType.External, ChainApproxMethod.ChainApproxNone);
int fish_contour_index = 0;
Rectangle bounding_rect = new Rectangle();
for (int ind = 0; ind < contours.Size; ind++)
{
bounding_rect = CvInvoke.BoundingRectangle(contours[ind]);
if (bounding_rect.Width > bounding_rect.Height)
{
contprops.height = bounding_rect.Width;
}
else
{
contprops.height = bounding_rect.Height;
}
if (contprops.height < 50 && contprops.height > 25)
{
fish_contour_index = ind;
fishcont_found = true;
break;
}
}
if (fishcont_found)
{
var contourCenter = new Point();
var contourCOM = new Point();
MCvMoments com = new MCvMoments();
com = CvInvoke.Moments(contours[fish_contour_index]);
contourCOM.X = (int)(com.M10 / com.M00);
contourCOM.Y = (int) (com.M01 / com.M00);
contourCenter.X = (int)(bounding_rect.X + (float)bounding_rect.Width / (float)2);
contourCenter.Y = (int)(bounding_rect.Y + (float)bounding_rect.Height / (float)2);
contprops.center = contourCenter;
contprops.com = contourCOM;
}
else
{
Console.WriteLine(contprops.com);
Console.WriteLine(contprops.height);
Console.WriteLine("no contours");
}
return contprops;
}
/// <summary>
/// Suskaičiuojamos kamuoliuko koordinatės
/// </summary>
/// <param name="img"></param>
/// <returns></returns>
public Point GetBallCoordinates(Mat img)
{
MCvMoments moments = CvInvoke.Moments(ProcessFrame(img));
double dM01 = moments.M01;
double dM10 = moments.M10;
double dArea = moments.M00;
int posX = (int)(dM10 / dArea);
int posY = (int)(dM01 / dArea);
return(new Point(posX, posY));
}
void DrawLine(VectorOfPoint biggestContour, Image <Hsv, Byte> image)
{
//CENTROID
Hsv couleurPoint = new Hsv(179, 100, 50);
MCvMoments moments = CvInvoke.Moments(biggestContour);
int cx = (int)(moments.M10 / moments.M00);
int cy = (int)(moments.M01 / moments.M00);
Point[] centroid = { new Point(cx, cy), new Point(cx, cy) };
image.Draw(centroid, couleurPoint, 10);
}
private void ProcessFrame(object sender, EventArgs e)
{
if (_capture != null && _capture.Ptr != IntPtr.Zero)
{
_capture.Retrieve(frame, 0);
gpuFrame.Upload(frame);
cudaBgMOG2.Apply(gpuFrame, gpuSub);
CudaInvoke.Threshold(gpuSub, gpuSub, 12, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
gpuSub.Download(outSub);
CvInvoke.FindContours(outSub, contours, hiererachy, Emgu.CV.CvEnum.RetrType.List, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxNone);
for (int i = 0; i < contours.Size; i++)
{
if (CvInvoke.ContourArea(contours[i]) > 50)
{
contoursGood.Push(contours[i]);
}
}
grayImage = new Image <Gray, byte>(frame.Width, frame.Height, new Gray(0));
grayImage.SetZero();
CvInvoke.DrawContours(grayImage, contoursGood, -1, new MCvScalar(255, 255, 255), -1);
CvInvoke.Dilate(grayImage, grayImage, element, new Point(-1, -1), 6, Emgu.CV.CvEnum.BorderType.Constant, new MCvScalar(255, 255, 255));
contoursGood.Clear();
CvInvoke.FindContours(grayImage, contours, hiererachy, Emgu.CV.CvEnum.RetrType.List, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxNone);
List <Point> points = new List <Point>();
for (int i = 0; i < contours.Size; i++)
{
MCvMoments moments = CvInvoke.Moments(contours[i], false);
Point WeightedCentroid = new Point((int)(moments.M10 / moments.M00), (int)(moments.M01 / moments.M00));
points.Add(WeightedCentroid);
}
blobList.AssignToBlobs(points);
blobList.Draw(frame);
blobList.Draw(mask);
blobList.Update();
CvInvoke.DrawContours(frame, contours, -1, new MCvScalar(0, 0, 255));
imageBox1.Image = frame;
imageBox2.Image = mask;
grayImage.Dispose();
indexFrame++;
}
}
public Form1()
{
InitializeComponent();
detector = new AdaptiveSkinDetector(1, AdaptiveSkinDetector.MorphingMethod.NONE);
hsv_min = new Hsv(0, 45, 0);
hsv_max = new Hsv(20, 255, 255);
YCrCb_min = new Ycc(0, 131, 80);
YCrCb_max = new Ycc(255, 185, 135);
float flot = 15;
bgs = new BackgroundSubtractorMOG2(30, flot, false);
mv = new MCvMoments();
}
private void detectarCentro()
{
matrizImagen = new int[img.Height, img.Width];
/*if (img!=null)
* {
* for(int i = 0;i<img.Height;i++)
* {
* for(int j=0;j<img.Width;j++)
* {
* if (imgout[i, j].Intensity == 0)
* {
* matrizImagen[i, j] = 0;
* }
*
*
* }
* }
* }*/
Emgu.CV.Util.VectorOfVectorOfPoint contours = new Emgu.CV.Util.VectorOfVectorOfPoint();
Mat mat = new Mat();
CvInvoke.FindContours(imgout, contours, mat, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
for (int x = 0; x < contours.Size; x++)
{
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
imgout[i, j] = new Gray(0);
}
}
var area = CvInvoke.ContourArea(contours[x]);
if (area > (int)numericSenCount.Value)
{
CvInvoke.DrawContours(imgout, contours, x, new MCvScalar(255, 255, 255));
MCvMoments moments = CvInvoke.Moments(imgout.Mat, false);
Point centroide = new Point((int)(moments.M10 / moments.M00), (int)(moments.M01 / moments.M00));
papel = pictureBox1.CreateGraphics();
pluma.Width = 5;
pluma.Color = Color.DarkRed;
papel.DrawRectangle(pluma, (int)(moments.M10 / moments.M00), (int)(moments.M01 / moments.M00), 5, 5);
}
}
detectarBorde();
//pictureBox2.Image = imgout.Bitmap;
}
private void contours_Click(object sender, EventArgs e)
{
Image <Bgr, byte> image_contour = new Image <Bgr, byte>(imagetest.Width, imagetest.Height, new Bgr(0, 0, 0));
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(imagetest, contours, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxNone);
//筛选出面积不为0的轮廓并画出
VectorOfVectorOfPoint use_contours = new VectorOfVectorOfPoint();
for (int i = 0; i < contours.Size; i++)
{
//获取独立的连通轮廓
VectorOfPoint contour = contours[i];
//计算连通轮廓的面积
double area = CvInvoke.ContourArea(contour);
//进行面积筛选
if (area > 20)
{
//添加筛选后的连通轮廓
use_contours.Push(contour);
}
}
CvInvoke.DrawContours(image_contour, use_contours, -1, new MCvScalar(0, 255, 0), 1);
int ksize = use_contours.Size;
double[] m00 = new double[ksize];
double[] m01 = new double[ksize];
double[] m10 = new double[ksize];
Point[] gravity = new Point[ksize];//用于存储轮廓中心点坐标
MCvMoments[] moments = new MCvMoments[ksize];
for (int i = 0; i < ksize; i++)
{
VectorOfPoint contour = use_contours[i];
//计算当前轮廓的矩
moments[i] = CvInvoke.Moments(contour, true);
m00[i] = moments[i].M00;
m01[i] = moments[i].M01;
m10[i] = moments[i].M10;
int x = Convert.ToInt32(m10[i] / m00[i]);//计算当前轮廓中心点坐标
int y = Convert.ToInt32(m01[i] / m00[i]);
gravity[i] = new Point(x, y);
//image_contour.Draw(new CircleF(gravity[i], 2), new Bgr(0, 255, 0), 2);
image_contour.Draw(i.ToString(), new Point(gravity[i].X - 10, gravity[i].Y + 30), Emgu.CV.CvEnum.FontFace.HersheyComplexSmall, 1, new Bgr(0, 0, 255));
image_contour.Draw((gravity[i].X).ToString() + "," + (gravity[i].Y).ToString(), gravity[i], Emgu.CV.CvEnum.FontFace.HersheySimplex, 1, new Bgr(0, 0, 255));
}
imageBox1.Image = image_contour;
}
protected PointF GetPositionFromBinary(Emgu.CV.Image <Emgu.CV.Structure.Gray, Byte> binaryImage, bool drawPositionInfo)
{
int maxNumberOfObects = 50;
int minObjectArea = 30 * 30;
int maxObjectArea = (int)(binaryImage.Size.Width * binaryImage.Size.Height / 1.05);
ratPosition = new IncorrectPosition();
//Point positionNotFound = new Point(50000, 50000);
//var positionNotFound = new PointF(float.NaN, float.NaN);
Emgu.CV.Image <Emgu.CV.Structure.Gray, Byte> temporaryBinaryImage = binaryImage.Clone();
Emgu.CV.Util.VectorOfVectorOfPoint contours = new Emgu.CV.Util.VectorOfVectorOfPoint();
Emgu.CV.Mat hierarchy = new Mat();
CvInvoke.FindContours(temporaryBinaryImage, contours, hierarchy, RetrType.Ccomp, ChainApproxMethod.ChainApproxSimple);
if (contours.Size > 0)
{
int numberOfObjects = contours.Size;
//var h = hierarchy.GetInputOutputArray();
if (numberOfObjects < maxNumberOfObects)
{
float referenceArea = 0;
//index = hierarchy.GetData(new int[] { index, 0 })[0]
for (int index = 0; index < numberOfObjects; index++)
{
IInputArray contour = contours.GetInputArray().GetMat(index);
MCvMoments moment = CvInvoke.Moments(contour);
float area = (float)moment.M00;
if (area > minObjectArea && area < maxObjectArea && area > referenceArea)
{
ratPosition = new CorrectPosition(
x: (float)moment.M10 / area,
y: (float)moment.M01 / area);
referenceArea = area;
}
}
}
}
if (drawPositionInfo)
{
DrawPositionInfo();
}
var positionToSend = new PointF
(
x: -2 * (ratPosition.Value.X - binaryImage.Size.Width / 2) / binaryImage.Size.Width,
y: -2 * (ratPosition.Value.Y - binaryImage.Size.Height / 2) / binaryImage.Size.Height
);
return(positionToSend);
}
private void frameProcessor(object sender, EventArgs e)
{
// Retreive frame from webcam
if (fromWebcam.Retrieve(currentVideoFrame, 0))
{
// Resize should preserve aspect ratio
resizedCurrentVideoFrame = keepAspectRatioResize(currentVideoFrame, new Size(fromCamPictureBox.Width, fromCamPictureBox.Height));
//CvInvoke.Blur(resizedCurrentVideoFrame, blurOriginalCurrentVideoFrame, new Size(3, 3), new Point(-1, -1));
CvInvoke.MedianBlur(resizedCurrentVideoFrame, blurOriginalCurrentVideoFrame, 5);
CvInvoke.CvtColor(blurOriginalCurrentVideoFrame, hsvVideoFrame, ColorConversion.Bgr2Hsv);
Mat[] hsvImageChannels = hsvVideoFrame.Split();
// Iterate over defined color range
for (int colorRange = 0; colorRange < interestedColors.Count; colorRange++)
{
Mat segmentationResultMat = new Mat();
CvInvoke.InRange(hsvImageChannels[0], new ScalarArray(interestedColors[colorRange].Min), new ScalarArray(interestedColors[colorRange].Max), segmentationResultMat);
VectorOfVectorOfPoint currentColorContours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(segmentationResultMat, currentColorContours, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
int currentColorBlob = 0;
double sumContour = 0;
for (int contourNo = 0; contourNo < currentColorContours.Size; contourNo++)
{
double currentContourArea = CvInvoke.ContourArea(currentColorContours[contourNo]);
if (currentContourArea > resizedCurrentVideoFrame.Width * resizedCurrentVideoFrame.Height * 0.0003 &&
currentContourArea < resizedCurrentVideoFrame.Width * resizedCurrentVideoFrame.Height * 0.5)
{
sumContour += currentContourArea;
Rectangle contourBBox = CvInvoke.BoundingRectangle(currentColorContours[contourNo]);
CvInvoke.DrawContours(resizedCurrentVideoFrame, currentColorContours, contourNo, new MCvScalar(0, 255, 0), 2);
MCvMoments contourMoment = CvInvoke.Moments(currentColorContours[contourNo]);
Point weightedCentroid = new Point((int)(contourMoment.M10 / contourMoment.M00), (int)(contourMoment.M01 / contourMoment.M00));
CvInvoke.PutText(resizedCurrentVideoFrame, (colorRange + 1).ToString(), weightedCentroid, FontFace.HersheyComplexSmall, 4.0, new Bgr(0, 0, 255).MCvScalar);
currentColorBlob++;
}
}
// Use timer to update UI blob count
blobCount[colorRange] = (sumContour / (currentVideoFrame.Rows * currentVideoFrame.Cols));
}
fromCamPictureBox.Image = resizedCurrentVideoFrame.Bitmap;
}
else
{
//No Frame Retreive, reopen camera => Something may be happen (VirtualBox bug)
System.Console.WriteLine("No Frame");
}
}
//public static Contour<Point> CopyWithTransform(this Contour<Point> sourceContour, Func<Point, Point> transform)
//{
// MemStorage stor = new MemStorage();
// Contour<Point> cpContour = new Contour<Point>(stor);
// List<Point> lPointsToPush = new List<Point>();
// foreach (Point pt in sourceContour) lPointsToPush.Add(transform(pt));
// cpContour.PushMulti(lPointsToPush.ToArray(), Emgu.CV.CvEnum.BACK_OR_FRONT.BACK);
// Contour<Point> copyContourTransformed = new Contour<Point>(0, stor);
// return copyContourTransformed;
//}
//public static Contour<Point> Copy(this Contour<Point> sourceContour)
//{
// Contour<Point> copyContour = new Contour<Point>(new MemStorage());
// copyContour.PushMulti(sourceContour.ToArray(), Emgu.CV.CvEnum.BACK_OR_FRONT.BACK);
// return copyContour;
//}
public static PointD MassCenter(this Contour <Point> theContour)
{
//Rectangle rectContourBounding = CvInvoke.BoundingRectangle(theContour);
Rectangle rectContourBounding = theContour.BoundingRectangle; // CvInvoke.BoundingRectangle(theContour);
Image <Gray, byte> tmpImg = new Image <Gray, byte>(rectContourBounding.Right,
rectContourBounding.Bottom);
//tmpImg.Draw(theContour.ToArray(), white, -1);
tmpImg.Draw(theContour, white, -1);
MCvMoments moments = tmpImg.GetMoments(true);
double cx = moments.m10 / moments.m00;
double cy = moments.m01 / moments.m00;
return(new PointD(cx, cy));
}
private double area_check(IImage bw)
{
double area;
MCvMoments moments = new MCvMoments();
CvInvoke.cvMoments(bw.Ptr, ref moments, 1);
double moment10 = CvInvoke.cvGetSpatialMoment(ref moments, 1, 0);
double moment01 = CvInvoke.cvGetSpatialMoment(ref moments, 0, 1);
area = CvInvoke.cvGetCentralMoment(ref moments, 0, 0);
corX = (int)(moment10 / area);
corY = (int)(moment01 / area);
return(area);
}
void Hu(Image <Gray, byte> img, ref Double[] MomentHu)
{
Image <Gray, byte> imgA = new Image <Gray, byte>(img.Size);
imgA = img.ThresholdBinary(new Gray(60), new Gray(255));
Contour <Point> contourA = imgA.FindContours();
MCvMoments momentsA = contourA.GetMoments();
MCvHuMoments HuMomentsA = momentsA.GetHuMoment();
MomentHu[0] = HuMomentsA.hu1;
MomentHu[1] = HuMomentsA.hu2;
MomentHu[2] = HuMomentsA.hu3;
MomentHu[3] = HuMomentsA.hu4;
MomentHu[4] = HuMomentsA.hu5;
MomentHu[5] = HuMomentsA.hu6;
}
public void GetHuMoments(Image <Gray, Byte> img, double[] m_hu)
{
MCvMoments moments = new MCvMoments();
MCvHuMoments hu = new MCvHuMoments();
CvInvoke.cvMoments(img, ref moments, 0);
CvInvoke.cvGetHuMoments(ref moments, ref hu);
m_hu[0] = hu.hu1;
m_hu[1] = hu.hu2;
m_hu[2] = hu.hu3;
m_hu[3] = hu.hu4;
m_hu[4] = hu.hu5;
m_hu[5] = hu.hu6;
m_hu[6] = hu.hu7;
}
// get the Hu
public static bool IsGetShapeFeature(string ImgPath, ref MCvHuMoments huMoments)
{
IntPtr img = IntPtr.Zero;
MCvMoments moments = new MCvMoments();
if ((img = CvInvoke.cvLoadImage(ImgPath, LOAD_IMAGE_TYPE.CV_LOAD_IMAGE_GRAYSCALE)) != IntPtr.Zero)
{
CvInvoke.cvMoments(img, ref moments, 0);
CvInvoke.cvGetHuMoments(ref moments, ref huMoments);
CvInvoke.cvReleaseImage(ref img);
return(true);
}
else
{
return(false);
}
}
//finds the centre of the rectangle in question - only called if a rectangle has been found i.e. pts0-4 exist
private Point getCentre(VectorOfPoint contour)
{
////find the centre of the vector distance between pts 2 and 0, and 1 and 3
//get the moments of the contour
MCvMoments contourMoments = CvInvoke.Moments(contour);
/*The moments returned are used to find the centre (x,y) co-ords
* x = m10/m00
* y = m01/m00
*/
int centre_x = Convert.ToInt32(contourMoments.M10 / contourMoments.M00);
int centre_y = Convert.ToInt32(contourMoments.M01 / contourMoments.M00);
Point centre = new Point(centre_x, centre_y);
return(centre);
}
private void btnDetectObjects_Click(object sender, EventArgs e)
{
var tmpImage = image.Copy();
Image <Gray, byte> grayImage = tmpImage.InRange(new Bgr(color), new Bgr(color));
VectorOfVectorOfPoint shapes = new VectorOfVectorOfPoint();
CvInvoke.FindContours(grayImage, shapes, new Mat(), RetrType.External, ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < shapes.Size; i++)
{
double shapePerimiter = CvInvoke.ArcLength(shapes[i], true);
if (shapePerimiter >= size)
{
VectorOfPoint approx = new VectorOfPoint();
CvInvoke.ApproxPolyDP(shapes[i], approx, shapePerimiter * 0.03, true);
MCvMoments moments = CvInvoke.Moments(shapes[i]);
Point shapeCenter = new Point((int)(moments.M10 / moments.M00), (int)(moments.M01 / moments.M00));
MCvScalar textColor = new MCvScalar(0, 0, 0);
MCvScalar borderColor = new MCvScalar(0, 0, 0);
if (shape.Equals("Pravougaonik") && approx.Size == 4)
{
CvInvoke.DrawContours(tmpImage, shapes, i, borderColor, 3);
CvInvoke.PutText(tmpImage, "Pravougaonik", shapeCenter, FontFace.HersheyTriplex, 0.6, textColor, 2);
}
else if (shape.Equals("Trougao") && approx.Size == 3)
{
CvInvoke.DrawContours(tmpImage, shapes, i, borderColor, 3);
CvInvoke.PutText(tmpImage, "Trougao", shapeCenter, FontFace.HersheyTriplex, 0.6, textColor, 2);
}
else if (shape.Equals("Krug") && approx.Size > 4)
{
CvInvoke.DrawContours(tmpImage, shapes, i, borderColor, 3);
CvInvoke.PutText(tmpImage, "Krug", shapeCenter, FontFace.HersheyTriplex, 0.6, textColor, 2);
}
}
}
pictureBox.Image = GetBitmapWithFiltersApplied(tmpImage.Bitmap);
}
void Moments(Image <Gray, byte> img, ref Double[] MomentSp, ref Double[] MomentCen, ref Double[] MomentNor)
{
Image <Gray, byte> imgA = new Image <Gray, byte>(img.Size);
imgA = img.ThresholdBinary(new Gray(60), new Gray(255));
Contour <Point> contourA = imgA.FindContours();
MCvMoments momentsA = contourA.GetMoments();
for (int xOrder = 0; xOrder <= 3; xOrder++)
{
for (int yOrder = 0; yOrder <= 3; yOrder++)
{
if (xOrder + yOrder <= 3)
{
MomentSp[3 * xOrder + yOrder] = momentsA.GetSpatialMoment(xOrder, yOrder);
MomentCen[3 * xOrder + yOrder] = momentsA.GetCentralMoment(xOrder, yOrder);
MomentNor[3 * xOrder + yOrder] = momentsA.GetNormalizedCentralMoment(xOrder, yOrder);
}
}
}
}
private void mechanika()
{
image_PB4.Data = image_PB2.Data;
Image <Gray, byte> image_mech = image_PB4.Convert <Gray, byte>();
MCvMoments m = CvInvoke.Moments(image_mech, true);
srodek_ciezkosci.X = (int)(m.M10 / m.M00);
srodek_ciezkosci.Y = (int)(m.M01 / m.M00);
System.Diagnostics.Debug.WriteLine("srodek_ciezkosci " + srodek_ciezkosci.ToString());
System.Diagnostics.Debug.WriteLine("srodek_ciezkosci " + srodek_ciezkosci.X);
System.Diagnostics.Debug.WriteLine("srodek_ciezkosci " + srodek_ciezkosci.Y);
if (srodek_ciezkosci.X > 0 && srodek_ciezkosci.Y > 0)
{
srodek = true;
}
else
{
srodek = false;
}
}
public Point FindLaser(Image <Gray, Byte> src)
{
double moment10, moment01; // Moments used in locating laser point
double area; // Central moment used in locating laser point
Point laser = new Point(); // Laser pointer position
MCvMoments moments = new MCvMoments(); // Moment object used for laser pointer location
// Find initial moments
CvInvoke.cvMoments(src, ref moments, 1);
// Find precise moments
moment10 = CvInvoke.cvGetSpatialMoment(ref moments, 1, 0);
moment01 = CvInvoke.cvGetSpatialMoment(ref moments, 0, 1);
area = CvInvoke.cvGetCentralMoment(ref moments, 0, 0);
// Calculate laser point position
laser.X = (int)(moment10 / area);
laser.Y = (int)(moment01 / area);
// Return laser pointer position
return(laser);
}
public static double CircularRatioVA(VectorOfPoint contour)
{
MCvMoments moments = CvInvoke.Moments(contour);
int xCenter = (int)(moments.M10 / moments.M00);
int yCenter = (int)(moments.M01 / moments.M00);
Point centroid = new Point(xCenter, yCenter);
List <double> listOfDist = new List <double>();
for (int i = 0; i < contour.Size; i++)
{
double distanceNow = Geometry.Distance(contour[i], centroid);
listOfDist.Add(distanceNow);
}
double mean = Mean(listOfDist);
double sigma = Sigma(listOfDist, mean);
return(sigma / mean);
}
