public void ListStart(Image <Gray, Byte> img)
{
var bin = img.ThresholdAdaptive(new Gray(255), Emgu.CV.CvEnum.ADAPTIVE_THRESHOLD_TYPE.CV_ADAPTIVE_THRESH_MEAN_C, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY, 11, new Gray(0));
form.imageBox1.Image = bin;
// flood fill
form.listBox1.Items.Clear();
var tmp = bin.Clone();
var mask = new Image <Gray, Byte>(tmp.Width + 2, tmp.Height + 2);
for (int i = 0; i < tmp.Width; i++)
{
for (int j = 0; j < tmp.Height; j++)
{
if (tmp.Data[j, i, 0] == 255)
{
MCvConnectedComp comp = new MCvConnectedComp();
var seed = new Point(i, j);
CvInvoke.cvFloodFill(tmp, seed, new MCvScalar(0),
new MCvScalar(0), new MCvScalar(0), out comp,
Emgu.CV.CvEnum.CONNECTIVITY.EIGHT_CONNECTED,
Emgu.CV.CvEnum.FLOODFILL_FLAG.DEFAULT, mask.Ptr);
if (comp.area < 50)
{
}
}
}
}
form.imageBox1.Image = tmp;
}
//1. 畫面上選取要追蹤的區域這裡我稱為 物件 (Object)，也就是要被追蹤的東東
//2. 計算出物件的 直方圖 (Histogram)
//3. 將物件的直方圖與輸入畫面進行 反向投影(Backproject) 計算
//4. 透過 物件追蹤演算法(Camshift) 快速地框出反向投影計算出的結果
//5. 回傳追蹤區域
//於是一直重複 3~5 的過程，我們的物件追蹤程式就大功告成了！
public ObjectTracking(Image <Bgr, Byte> image, Rectangle ROI)
{
// Initialize parameters
//A boundary box
trackbox = new MCvBox2D();
//連通物件
trackcomp = new MCvConnectedComp();
//Hue圖?
hue = new Image <Gray, byte>(image.Width, image.Height);
//*待理解
hue._EqualizeHist();
//遮罩
mask = new Image <Gray, byte>(image.Width, image.Height);
//直方圖
hist = new DenseHistogram(30, new RangeF(0, 180));
//一張普通的灰階圖
backproject = new Image <Gray, byte>(image.Width, image.Height);
// Assign Object's ROI from source image.
// Region of Interest 只針對特定區域進行處理
trackingWindow = ROI;
// Producing Object's hist
//產生物件直方圖
CalObjectHist(image);
}
public Rectangle Tracking(Image <Bgr, Byte> image)
{
MCvConnectedComp cc = new MCvConnectedComp();
UpdateHue(image);
// Calucate BackProject
backproject = hist.BackProject(new Image <Gray, Byte>[] { hue });
// Apply mask
backproject._And(mask);
// Tracking windows empty means camshift lost bounding-box last time
// here we give camshift a new start window from 0,0 (you could change it)
if (trackingWindow.IsEmpty || trackingWindow.Width == 0 || trackingWindow.Height == 0)
{
trackingWindow = new Rectangle(0, 0, 100, 100);
}
//CvInvoke.cvCamShift(backproject, trackingWindow,
// new MCvTermCriteria(10, 1), out trackcomp, out trackbox);
CvInvoke.cvMeanShift(backproject, trackingWindow,
new MCvTermCriteria(10, 1), out cc);
// // update tracking window
// trackingWindow = trackcomp.rect;
return(trackingWindow);
}
public ObjectTracking(Image <Bgr, Byte> image, Rectangle ROI)
{
// Initialize parameters
trackbox = new MCvBox2D();
trackcomp = new MCvConnectedComp();
hue = new Image <Gray, byte>(image.Width, image.Height);
hue._EqualizeHist();
mask = new Image <Gray, byte>(image.Width, image.Height);
hist = new DenseHistogram(30, new RangeF(0, 180));
backproject = new Image <Gray, byte>(image.Width, image.Height);
// Assign Object's ROI from source image.
trackingWindow = ROI;
// Producing Object's hist
CalObjectHist(image);
}
private void Pulse()
{
using (ColorImageFrame imageFrame = _kinectSensor.ColorStream.OpenNextFrame(200))
{
if (imageFrame == null)
{
return;
}
using (Image <Bgr, byte> image = imageFrame.ToOpenCVImage <Bgr, byte>())
using (MemStorage storage = new MemStorage()) //create storage for motion components
{
if (_forgroundDetector == null)
{
_forgroundDetector = new BGStatModel <Bgr>(image
, Emgu.CV.CvEnum.BG_STAT_TYPE.GAUSSIAN_BG_MODEL);
}
_forgroundDetector.Update(image);
//update the motion history
_motionHistory.Update(_forgroundDetector.ForgroundMask);
//get a copy of the motion mask and enhance its color
double[] minValues, maxValues;
System.Drawing.Point[] minLoc, maxLoc;
_motionHistory.Mask.MinMax(out minValues, out maxValues
, out minLoc, out maxLoc);
Image <Gray, Byte> motionMask = _motionHistory.Mask
.Mul(255.0 / maxValues[0]);
//create the motion image
Image <Bgr, Byte> motionImage = new Image <Bgr, byte>(motionMask.Size);
motionImage[0] = motionMask;
//Threshold to define a motion area
//reduce the value to detect smaller motion
double minArea = 100;
storage.Clear(); //clear the storage
Seq <MCvConnectedComp> motionComponents = _motionHistory.GetMotionComponents(storage);
bool isMotionDetected = false;
//iterate through each of the motion component
for (int c = 0; c < motionComponents.Count(); c++)
{
MCvConnectedComp comp = motionComponents[c];
//reject the components that have small area;
if (comp.area < minArea)
{
continue;
}
OnDetection();
isMotionDetected = true;
break;
}
if (isMotionDetected == false)
{
OnDetectionStopped();
this.Dispatcher.Invoke(new Action(() => rgbImage.Source = null));
StopRecording();
return;
}
this.Dispatcher.Invoke(
new Action(() => rgbImage.Source = imageFrame.ToBitmapSource())
);
Record(imageFrame);
}
}
}
public void run()
{
if (Thread.CurrentThread.Name.EndsWith("2"))
{
Thread.Sleep(10000);//MessageBox.Show("play " + Thread.CurrentThread.Name);// //
}
if (Thread.CurrentThread.Name.EndsWith("3"))
{
Thread.Sleep(23000); //MessageBox.Show("play " + Thread.CurrentThread.Name);// Thread.Sleep(5000);
}
try
{
//if (Thread.CurrentThread.Name.Equals("camera2"))
// Thread.Sleep(1000);
switch (camno)
{
case 1:
break;
case 2:
break;
case 3:
break;
default:
break;
}
//Thread Processing
while ((currImage = cap.QueryFrame()) != null)
{
//### updating the currImage every time
currImage = currImage.Resize(pictureBox1.Width, pictureBox1.Height, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
//### Background Subtraction ~ currImage is subtracted with the bgImage and the result is stored in finalBlobImg.
switch (this.state)
{
case "BGSUB":
// Console.WriteLine("_________________");
if (!Main.tracking) //&& Main.lasttrack!=camno
{
Console.WriteLine("###########" + Thread.CurrentThread.Name + "starts BGSUB");
blobDistanceList = bgSubtraction(currImage, bgImage, ref finalBlobImg, ref blobRect);
if (blobDistanceList != null)
{
foreach (Blob blob in blobDistanceList)
{
//Console.WriteLine("Blob::" + blob.distance + " " + blob.rect);
if (blob.distance < Main.Dis) //set the tracker to cam with same object //&& Main.lasttrack!=camno
{
track_window_mean = blob.rect;
if (blob.rect.Width < 50)
{
track_window_mean.Width = 60;
}
else
{
track_window_mean.Width = blob.rect.Width;
}
if (blob.rect.Width + blob.rect.X > 319)
{
track_window_mean.X = track_window_mean.X - 10;
track_window_mean.Width = 40;
}
if (blob.rect.Height + blob.rect.Y > 239)
{
track_window_mean.Height = track_window_mean.Height - 20;
}
if (blob.rect.Y == 0)
{
track_window_mean.Y = blob.rect.Y + 15;
}
//Console.WriteLine("Blob::::::::" + blob.distance + " " + blob.rect);
Main.lasttrack = camno;
Main.tracking = true;
state = "TRACK";
Console.WriteLine("###########" + Thread.CurrentThread.Name + "starts TRACK");
break;
}
}
}
}
break;
case "TRACK":
blobDistanceList = bgSubtraction(currImage, bgImage, ref finalBlobImg, ref blobRect);
//--------------
foreach (Blob blob in blobDistanceList)
{
Console.WriteLine("Tracking Blob== " + blob.distance + " " + blob.rect);
}
Console.WriteLine("----");
//-------------
Image <Hsv, Byte> hsv = new Image <Hsv, Byte>(w, h);
hsv = currImage.Convert <Hsv, Byte>();
Console.WriteLine("1");
//extract the hue and value channels
Image <Gray, Byte>[] channels = hsv.Split(); //split into components
Image <Gray, Byte>[] imghue = new Image <Gray, byte> [1]; imghue[0] = channels[0]; //hsv, so channels[0] is hue.
Image <Gray, Byte> imgval = channels[2]; //hsv, so channels[2] is value.
Image <Gray, Byte> imgsat = channels[1]; //hsv, so channels[1] is saturation.
mask = new Image <Gray, Byte>(w, h);
Hsv hsv_lower = new Hsv(0, smin, Math.Min(vmin, vmax));
Hsv hsv_upper = new Hsv(180, 256, Math.Max(vmin, vmax));
mask = hsv.InRange(hsv_lower, hsv_upper);
Image <Gray, Byte> backproject = Main.hist.BackProject(imghue);
mask = mask.And(finalBlobImg.Dilate(2));
backproject = mask.And(backproject);
MCvConnectedComp trac_comp = new MCvConnectedComp();
//Console.WriteLine("2");
MCvTermCriteria criteria_mean = new MCvTermCriteria(100, 0.002);
pictureBox2.Image = mask.Bitmap;
//Console.WriteLine(criteria_mean.GetType);
try
{
Emgu.CV.CvInvoke.cvMeanShift(backproject, track_window_mean, criteria_mean, out trac_comp);
}
catch (CvException e)
{
Console.WriteLine(track_window_mean);
MessageBox.Show(e.ToString());
}
// Console.WriteLine("3");
currImage.Draw(trac_comp.rect, new Bgr(255, 0, 0), 2);
currImage.Draw(new Cross2DF(new PointF((trac_comp.rect.X + trac_comp.rect.Width / 2), (trac_comp.rect.Y + trac_comp.rect.Height / 2)), 20, 20), new Bgr(255, 255, 255), 2);
track_window_mean = trac_comp.rect;
//check person left the view
Image <Gray, byte> subImgBg = bgImgGy.GetSubRect(trac_comp.rect);
Image <Gray, byte> subImgFg = currImgGy.GetSubRect(trac_comp.rect);
Image <Gray, byte> imMask = subImgFg.AbsDiff(subImgBg);
Gray cnt = imMask.GetAverage();
if (cnt.Intensity < 10)
{
Main.lasttrack = camno;
Main.tracking = false;
state = "BGSUB";
Console.WriteLine("###########" + Thread.CurrentThread.Name + "switches to BGSUB");
}
//---------------------------
outimage = new Image <Gray, byte>(w, h);
for (int i = 0; i < trac_comp.rect.Height; i++)
{
for (int j = 0; j < trac_comp.rect.Width; j++)
{
//subImgFg.Data[i, j, 0] = 255;
if (imMask.Data[i, j, 0] < Main.ThSub)
{
imMask.Data[i, j, 0] = 0;
outimage.Data[i + trac_comp.rect.Y, j + trac_comp.rect.X, 0] = 0;
}
else
{
imMask.Data[i, j, 0] = 255;
outimage.Data[i + trac_comp.rect.Y, j + trac_comp.rect.X, 0] = 255;
}
}
//Console.WriteLine();
}
outimage._Erode(2);
outimage._Dilate(3);
try
{
Image <Bgr, byte> subimg = currImage.And((outimage.Convert <Bgr, Byte>())).GetSubRect(trac_comp.rect);
//subimg.GetSubRect(subrect);//.Save(Thread.CurrentThread.Name + "\\" + k++ + ".jpg");
//Calc HISTOGRAM of each blob
DenseHistogram histBlob = new DenseHistogram(hdims, hranges); //cvCreateHist(1, &hdims, CV_HIST_ARRAY, &hranges, 1);
Image <Hsv, byte> hsvBlob = subimg.Convert <Hsv, byte>();
//extract the hue and value channels
Image <Gray, Byte>[] channelsBlob = hsvBlob.Split(); //split into components
Image <Gray, Byte>[] imghueBlob = new Image <Gray, byte> [1]; imghueBlob[0] = channelsBlob[0]; //hsv, so channels[0] is hue.
Image <Gray, Byte> maskBlob = hsvBlob.InRange(hsv_lower, hsv_upper);
histBlob.Calculate(imghueBlob, false, maskBlob);
double distance = CvInvoke.cvCompareHist(Main.hist.Ptr, histBlob.Ptr, Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_BHATTACHARYYA);
//if (distance < 0.15)
//{
// // Main.hist = histBlob;
// Console.WriteLine(Thread.CurrentThread.Name + " ===== " + distance);
//}
}
catch (CvException cve)
{
MessageBox.Show(cve.StackTrace);
}
//---------------------------
//pictureBox2.Image = mask.Bitmap;
//pictureBox3.Image = mask.And(finalBlobImg).Bitmap;
break;
}
pictureBox1.Image = currImage.Bitmap;
Thread.Sleep(20);
}
Console.WriteLine("###########" + Thread.CurrentThread.Name + " exited");
}catch (CvException e)
{}
}
public static extern int cvMeanShift(
IntPtr probImage,
Rectangle window,
MCvTermCriteria criteria,
out MCvConnectedComp comp);
public static extern int cvCamShift(
IntPtr probImage,
Rectangle window,
MCvTermCriteria criteria,
out MCvConnectedComp comp,
out MCvBox2D box);
private SkeletonPoint FindHand(short[] depthData, Rectangle rect)
{
CvInvoke.cvZero(HandImage.Ptr);
var handImageData = HandImage.Data;
var handMaskData = HandMask.Data;
var playerMaskData = playerMask.Data;
var maxDepth = 0;
var minDepth = Int32.MaxValue;
for (int y = rect.Top; y < rect.Top + rect.Height && y < height; y++)
{
for (int x = rect.Left; x < rect.Left + rect.Width && x < width; x++)
{
if (y > 0 && x > 0 && handMaskData[y, x, 0] > 0 &&
playerMaskData[y, x, 0] > 0)
{
var depth = DepthUtil.RawToDepth(depthData[y * width + x]);
maxDepth = Math.Max(maxDepth, depth);
if (depth < minDepth)
{
minDepth = depth;
}
}
}
}
var scale = (float)255 / (maxDepth - minDepth);
for (int y = rect.Top; y < rect.Top + rect.Height && y < height; y++)
{
for (int x = rect.Left; x < rect.Left + rect.Width && x < width; x++)
{
if (y > 0 && x > 0 && playerMaskData[y, x, 0] > 0 &&
handMaskData[y, x, 0] > 0)
{
var depth = DepthUtil.RawToDepth(depthData[y * width + x]);
handImageData[y, x, 0] = (byte)((maxDepth - depth) * scale);
}
}
}
var connectedComp = new MCvConnectedComp();
var shiftedBox = new MCvBox2D();
CvInvoke.cvCamShift(HandImage.Ptr, rect, new MCvTermCriteria(0.0), out connectedComp,
out shiftedBox);
PrevHand = new windows.Point(HandBox.center.X, HandBox.center.Y);
HandBox = shiftedBox;
var newRect = shiftedBox.MinAreaRect();
var aveDepth = 0.0;
var count = 0;
for (int y = newRect.Top; y < newRect.Top + newRect.Height && y < height; y++)
{
for (int x = newRect.Left; x < newRect.Left + newRect.Width && x < width; x++)
{
if (x > 0 && y > 0 && playerMaskData[y, x, 0] > 0 &&
handMaskData[y, x, 0] > 0)
{
var depth = DepthUtil.RawToDepth(depthData[y * width + x]);
aveDepth += depth;
count++;
}
}
}
aveDepth /= count;
var shiftedCenterX = Math.Max(0, shiftedBox.center.X);
shiftedCenterX = Math.Min(shiftedCenterX, width);
var shiftedCenterY = Math.Max(0, shiftedBox.center.Y);
shiftedCenterY = Math.Min(shiftedCenterY, height);
return(mapper.MapDepthPointToSkeletonPoint((int)shiftedCenterX,
(int)shiftedCenterY, (int)aveDepth));
}
public void run()
{
//to read each frame and display in picturebox
if (Thread.CurrentThread.Name.Equals("camera2"))
{
Thread.Sleep(25000);
}
while ((currImage = cap.QueryFrame()) != null)
{
//### updating the currImage every time
currImage = currImage.Resize(pictureBox1.Width, pictureBox1.Height, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
//### Background Subtraction ~ currImage is subtracted with the bgImage and the result is stored in finalBlobImg.
bgSubtraction(currImage, bgImage, ref finalBlobImg, ref blobRect);
if (Main.state1.Equals(Thread.CurrentThread.Name))
{
Image <Hsv, Byte> hsv = new Image <Hsv, Byte>(w, h);
hsv = currImage.Convert <Hsv, Byte>();
//extract the hue and value channels
Image <Gray, Byte>[] channels = hsv.Split(); //split into components
Image <Gray, Byte>[] imghue = new Image <Gray, byte> [1]; imghue[0] = channels[0]; //hsv, so channels[0] is hue.
Image <Gray, Byte> imgval = channels[2]; //hsv, so channels[2] is value.
Image <Gray, Byte> imgsat = channels[1]; //hsv, so channels[1] is saturation.
/**
* Check if the pixels in hsv fall within a particular range.
* H: 0 to 180
* S: smin to 256
* V: vmin to vmax
* Store the result in variable: mask
*/
mask = new Image <Gray, Byte>(w, h);
Hsv hsv_lower = new Hsv(0, smin, Math.Min(vmin, vmax));
Hsv hsv_upper = new Hsv(180, 256, Math.Max(vmin, vmax));
mask = hsv.InRange(hsv_lower, hsv_upper);
Image <Gray, Byte> backproject = Main.hist.BackProject(imghue);
mask = mask.And(finalBlobImg);
backproject = mask.And(backproject);
MCvConnectedComp trac_comp = new MCvConnectedComp();
MCvTermCriteria criteria_mean = new MCvTermCriteria(100, 0.025);
//Console.WriteLine(criteria_mean.GetType);
Emgu.CV.CvInvoke.cvMeanShift(backproject, track_window_mean, criteria_mean, out trac_comp);
currImage.Draw(trac_comp.rect, new Bgr(255, 0, 0), 2);
track_window_mean = trac_comp.rect;
//check person left the view
Image <Gray, byte> subImgBg = bgImgGy.GetSubRect(trac_comp.rect);
Image <Gray, byte> subImgFg = currImgGy.GetSubRect(trac_comp.rect);
Image <Gray, byte> imMask = subImgFg.AbsDiff(subImgBg);
Gray cnt = imMask.GetAverage();
pictureBox2.Image = mask.Bitmap;
pictureBox3.Image = mask.And(finalBlobImg).Bitmap;
}
// pictureBox3.Image = finalBlobImg.Bitmap;
pictureBox1.Image = currImage.Bitmap;
//mean
/* Update tracking window for camshift */
Thread.Sleep(10);
}
}