private void frmMain_Load(object sender, EventArgs e)
{
OpenFileDialog dlgSourcePicture = new OpenFileDialog();
dlgSourcePicture.Filter = "Image Files(*.BMP;*.JPG;*.GIF;*.PNG)|*.BMP;*.JPG;*.GIF;*.PNG|All files (*.*)|*.*";
dlgSourcePicture.Multiselect = false;
if (dlgSourcePicture.ShowDialog() != System.Windows.Forms.DialogResult.OK)
Application.Exit();
m_imgSource = new Image<Bgr, Byte>((Bitmap) Image.FromFile(dlgSourcePicture.FileName)).Convert<Hsv, Byte>();
m_imgThreshold = new Image<Gray, Byte>(m_imgSource.Size);
m_frmSourceImage = new ImageViewer(m_imgSource, "Original Image");
m_frmSourceImage.ShowIcon = false;
m_frmSourceImage.MaximizeBox = false;
m_frmSourceImage.Show();
m_frmSourceImage.SetDesktopLocation(100, 0);
m_frmSourceImage.SizeChanged += m_frmSourceImage_SizeChanged;
m_frmThresholdImage = new ImageViewer(m_imgThreshold, "Threshold Image");
m_frmThresholdImage.ShowIcon = false;
m_frmThresholdImage.MaximizeBox = false;
m_frmThresholdImage.FormBorderStyle = System.Windows.Forms.FormBorderStyle.FixedDialog;
m_frmThresholdImage.Show();
m_frmThresholdImage.SetDesktopLocation(m_frmSourceImage.DesktopLocation.X + m_frmSourceImage.Size.Width + 100, m_frmSourceImage.DesktopLocation.Y);
ProduceThresholdImage();
}
protected VideoProcessor()
{
m_opencv = OpenCVController.GetSingletonInstance();
viewer = new ImageViewer();
vs = VisualData.GetSingleton();
viewer.Show();
this.ColorWriteBitmap = new WriteableBitmap(FrameWidth, FrameHeight, 96.0, 96.0,
System.Windows.Media.PixelFormats.Bgr24, null);
this.DepthWriteBitmap = new WriteableBitmap(FrameWidth, FrameHeight, 96.0, 96.0,
System.Windows.Media.PixelFormats.Bgr24, null);
this.WrtBMP_RightHandFront = new WriteableBitmap(handShapeWidth, handShapeHeight, 96.0, 96.0,
System.Windows.Media.PixelFormats.Gray8, null);
this.WrtBMP_LeftHandFront = new WriteableBitmap(handShapeWidth, handShapeHeight, 96.0, 96.0,
System.Windows.Media.PixelFormats.Gray8, null);
rightHandPosition = new System.Drawing.Point();
}
private void Form1_Load(object sender, EventArgs e)
{
int AllMarkCount = 6;
//原始影像=照片, 目標影像=Mark輸入影像
//原始影像長寬
int orgImgWidth = 640;
int orgImgHeight = 480;
//滑動窗掃描間隔
int slidingWindowStepX = 3;
int slidingWindowStepY = 3;
//滑動窗長寬(目標影像長寬)
int tarImgWidth = 50;
int tarImgHeight = 50;
Size MarkSizeByCell = new Size(5, 5); //以子區塊為單位的mark長寬
Size CellSize = new Size(tarImgWidth / MarkSizeByCell.Width, tarImgHeight / MarkSizeByCell.Height);
// ------------------
//KNN分類器訓練
// Mark類別 載入標籤影像
List <string> sampleImgPath = Directory.GetFiles(@"Image\").ToList();
List <Mark> sampleMark = new List <Mark>();
// 擷取特徵
// KNN分類器 監督學習
for (int i = 0; i < sampleImgPath.Count; i++)
{
FileInfo curFile = new FileInfo(sampleImgPath[i]);
string exName = curFile.Extension;
if (exName == ".png")
{
Image <Gray, byte> imageTmp = new Image <Gray, byte>(sampleImgPath[i]);
//threshold ----------------------
Emgu.CV.CvInvoke.cvThreshold(imageTmp.Ptr, imageTmp.Ptr, -1, 255d, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY | Emgu.CV.CvEnum.THRESH.CV_THRESH_OTSU);
Mark markTmp = new Mark(imageTmp, MarkSizeByCell, CellSize);
sampleMark.Add(markTmp);
sampleMark[i].GetFeatures();
int length = AllMarkCount;
for (int j = 0; j < length; j++)
{
if (curFile.Name.Contains("training0" + (j + 1).ToString()))
{
sampleMark[i].MarkIndex = j + 1;
break;
}
}
new ImageViewer(sampleMark[i].iptImg, "[" + sampleMark[i].MarkIndex.ToString() + "]FrameMean" + sampleMark[i].FrameMean.ToString() + ",StdDivSum" + sampleMark[i].StdDivSum.ToString()).Show();
}
}
// KNN分類器
// ----------------
//攝影機載入影像
List <string> orgImgPath = Directory.GetFiles(@"D:\Phisten\GoogleCloud\圖訊識別\圖訊testdata\").ToList();
int imgCoung = orgImgPath.Count;
//imgCoung = imgCoung > 5 ? 5 : imgCoung;
//imgCoung = 1;
for (int imgIdx = 0; imgIdx < imgCoung; imgIdx++)
{
string imgPath = orgImgPath[imgIdx];
Image <Rgb, byte> orgImg = new Image <Rgb, byte>(imgPath);
//正規化
Image <Gray, byte> norImg;
norImg = orgImg.Convert <Gray, byte>();
//norImg = norImg.ConvertScale<byte>(3d, -100d);
// 影像長寬
// 亮度
//SlidingWindow擷取輸入影像
List <IImage> iptImgList = new List <IImage>();
List <Rectangle> iptImgRectList = new List <Rectangle>();
Rectangle tmpRect = new Rectangle(0, 0, 50, 50);
norImg.ROI = tmpRect;
int StepWidthLimit = orgImgWidth - tarImgWidth - (orgImgWidth - tarImgWidth) % slidingWindowStepX;
int StepHeightLimit = orgImgHeight - tarImgHeight - (orgImgHeight - tarImgHeight) % slidingWindowStepY;
for (int i = 0; i < StepWidthLimit; i += slidingWindowStepX)
{
tmpRect.Y = 0;
for (int j = 0; j < StepHeightLimit; j += slidingWindowStepY)
{
Image <Gray, byte> curMarkImg = norImg.CopyBlank();
//threshold ----------------------
int greyThreshValue = (int)Emgu.CV.CvInvoke.cvThreshold(norImg.Ptr, curMarkImg.Ptr, -1, 255d, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY | Emgu.CV.CvEnum.THRESH.CV_THRESH_OTSU);
iptImgList.Add(curMarkImg);
//iptImgList.Add(norImg.Copy());
iptImgRectList.Add(norImg.ROI);
tmpRect.Offset(0, slidingWindowStepY);
norImg.ROI = tmpRect;
}
tmpRect.Offset(slidingWindowStepX, 0);
norImg.ROI = tmpRect;
}
List <Mark> markList = new List <Mark>();
for (int imgIndex = 0; imgIndex < iptImgList.Count; imgIndex++)
{
//擷取特徵
Mark curMark = new Mark(iptImgList[imgIndex] as Image <Gray, byte>, MarkSizeByCell, CellSize);
curMark.GetFeatures();
//特徵匹配
double KNNdistanceThreshold = 256d;
// 過濾外框平均值過高
// 過濾標準差總和過高
if (curMark.FrameFilter(96) && curMark.StdDivSumFilter(1024d))
{
// 最近鄰分類
SortedList <double, int> distanceSList = new SortedList <double, int>();
for (int i = 0, length = sampleMark.Count; i < length; i++)
{
double curDis = sampleMark[i].Distance(curMark);
if (curDis < KNNdistanceThreshold)
{
distanceSList.Add(curDis, sampleMark[i].MarkIndex);
}
}
//合格的Mark
//int markIndex = Phisten.Classifier.KNN(1, distanceSList);.
if (distanceSList.Count > 0)// && distanceSList.Keys[0] < KNNdistanceThreshold)
{
curMark.MarkIndex = distanceSList.Values[0];
curMark.MarkIndexDistance = distanceSList.Keys[0];
Rectangle markRect = iptImgRectList[imgIndex];
curMark.MarkRectangle = markRect;
bool IsNewMark = true;
//重疊過濾
for (int i = 0, length = markList.Count; i < length; i++)
{
if (markRect.IntersectsWith(markList[i].MarkRectangle)) //若區域重疊則不新增mark
{
if (distanceSList.Keys[0] < markList[i].MarkIndexDistance) //且若curMark距離較近
{
//則替換Mark
markList[i] = curMark;
}
IsNewMark = false;
break;
}
}
if (IsNewMark)
{
//否則新增Mark
markList.Add(curMark);
}
}
}
}
Image <Rgb, byte> outputImg = orgImg.Convert <Gray, byte>().Convert <Rgb, byte>();
outputImg = outputImg.SmoothGaussian(3);
MCvFont pen1 = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_SIMPLEX, 0.5d, 0.5d);
//繪製分類結果
for (int i = 0; i < markList.Count; i++)
{
string fileName1 = i.ToString() + ".jpg";
//markList[i].iptImg.Save(@"opt\" + fileName1);
outputImg.Draw(markList[i].MarkRectangle, new Rgb(255, 0, 0), 1);
outputImg.Draw("[" + markList[i].MarkIndex + "]" + Math.Round(markList[i].MarkIndexDistance), ref pen1, markList[i].MarkRectangle.Location, new Rgb(50, 0, 200));
}
//輸出影像
this.Width = 0;
this.Height = 0;
ImageViewer imgViewer = new Emgu.CV.UI.ImageViewer(outputImg);
imgViewer.Show();
}
}
private void btnDetect_Click(object sender, EventArgs e)
{
Image<Bgr, Byte> frame = currentFrame;
List<Rectangle> faceList = new List<Rectangle>();
Rectangle eyeLeft = new Rectangle(), eyeRight = new Rectangle();
long detectionTime;
DetectFace.detectFaceGPU(frame, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faceList, out eyeLeft, out eyeRight, out detectionTime);
foreach (var item in faceList)
{
frame.Draw(item, new Bgr(Color.Green), 1);
frame.Draw(eyeLeft, new Bgr(Color.Red), 1);
frame.Draw(eyeRight, new Bgr(Color.Yellow), 1);
#region źrenice
//frame2.ROI = item;
//double cannyThreshold = 180.0;
//double circleAccumulatorThreshold = 120;
//Image<Gray, Byte> frameGray = frame2.Convert<Gray, Byte>();
//CircleF[] circles = frameGray.HoughCircles(
// new Gray(cannyThreshold),
// new Gray(circleAccumulatorThreshold),
// 2.0, //Resolution of the accumulator used to detect centers of the circles
// 20.0, //min distance
// 5, //min radius
// 0 //max radius
// )[0]; //Get the circles from the first channel
//foreach (var item2 in circles)
//{
// frame2.Draw(new CircleF(item2.Center, item2.Radius), new Bgr(Color.White), 1);
//}
#endregion
}
//Image<Bgr, Byte> eyeLeftCopy = currentFrame.Clone();
//Image<Gray, Byte> frameGray = frame.Convert<Gray, Byte>();
//eyeLeftCopy.ROI = eyeLeft;
#region test
// eyeLeftCopy.Resize(eyeLeftCopy.Width * 4, eyeLeftCopy.Height * 4, Emgu.CV.CvEnum.INTER.CV_INTER_LANCZOS4);
//Image<Bgr, Byte> img;
//OpenFileDialog OF = new OpenFileDialog();
//if (OF.ShowDialog() == System.Windows.Forms.DialogResult.OK)
//{
// img = new Image<Bgr, byte>(OF.FileName);
// //crop image so we remove eyebrow
// //30-85
// Image<Gray, Byte> img_crop = img.Convert<Gray, Byte>().Copy(new Rectangle(10, 30, img.Width - 10, 55));
// //smooth
// img_crop.PyrDown().PyrUp();
// //apply inverse suppression
// img_crop = img_crop.ThresholdBinaryInv(new Gray(20), new Gray(255));
// //lets see what we have
// CvInvoke.cvShowImage("Threshold Image", img_crop);
// /*So lets fiind the iris*/
// //thios doesn't matter really as we have threshold the image to a binary type
// double cannyThreshold = 180.0;
// //use this to calibarte size
// double circleAccumulatorThreshold = 20;
// CircleF[] circles = img_crop.HoughCircles(
// new Gray(cannyThreshold),
// new Gray(circleAccumulatorThreshold),
// 2.0, //Resolution of the accumulator used to detect centers of the circles
// 20.0, //min distance
// 5, //min radius
// 0 //max radius
// )[0]; //Get the circles from the first channel
// //lets store it for pupil detection
// CircleF Iris = new CircleF();
// foreach (CircleF circle in circles)
// {
// //lets do some post-processing of the data so we only look at circles that an be the write size and not to large
// if (circle.Radius < 18)
// {
// //show it on the image by setting ROI to account for crop offset
// img.ROI = new Rectangle(10, 30, img.Width - 10, 55);
// img.Draw(circle, new Bgr(Color.Red), 2);
// //reset ROI
// img.ROI = new Rectangle();
// //store it
// Iris = circle;
// }
// }
// /*Lets try and find the pupil*/
// using (Image<Gray, Byte> smaller_crop_img = img_crop.Copy(new Rectangle((int)Iris.Center.X - (int)Iris.Radius, (int)Iris.Center.Y - (int)Iris.Radius, (int)Iris.Radius * 2, (int)Iris.Radius * 2)))
// {
// //lets see what we have
// CvInvoke.cvShowImage("Iris Image", smaller_crop_img);
// //reset our threshold
// circleAccumulatorThreshold = 5;
// //overwite the circles detected
// circles = smaller_crop_img.HoughCircles(
// new Gray(cannyThreshold),
// new Gray(circleAccumulatorThreshold),
// 2.0, //Resolution of the accumulator used to detect centers of the circles
// 20.0, //min distance
// 5, //min radius
// 0 //max radius
// )[0]; //Get the circles from the first channel
// //draw it on the image
// foreach (CircleF circle in circles)
// {
// //Include some post-processing of the data so we only look at circles
// //that can be the centre example if (circle.Center.X > Iris.Center.X + 5 && circle.Center.X < Iris.Center.X - 5 )
// //show it on the image don't forget to account for offset of crop (10,30, img.Width - 10, 55);
// img.ROI = new Rectangle(((int)Iris.Center.X - (int)Iris.Radius) + 10, ((int)Iris.Center.Y - (int)Iris.Radius) + 30, (int)Iris.Radius * 2, (int)Iris.Radius * 2);
// img.Draw(circle, new Bgr(Color.Blue), 2);
// //reset ROI
// img.ROI = new Rectangle();
// }
// }
// //diplay Image
// ImageViewer test = new ImageViewer(img);
// test.Text = "test";
// test.Show();
//}
#endregion
ImageViewer okno2 = new ImageViewer(frame, detectionTime.ToString());
okno2.Show();
#region zabawa z warstwami
//Image<Bgr, Byte> redImg = new Image<Bgr, Byte>(new Image<Gray, Byte>[]
//{
// new Image<Gray, Byte>(currentFrame.Width, currentFrame.Height), //Blue channel, set to zero
// new Image<Gray, Byte>(currentFrame.Width, currentFrame.Height), //Green channel, set to zero
// currentFrame[2]
//}); //Red channel from img
//Image<Bgr, Byte> blueImg = new Image<Bgr, Byte>(new Image<Gray, Byte>[]
//{
// currentFrame[0],//Blue channel, set to zero
// new Image<Gray, Byte>(currentFrame.Width, currentFrame.Height), //Green channel, set to zero
// new Image<Gray, Byte>(currentFrame.Width, currentFrame.Height) //Red channel from img
//});
//Image<Bgr, Byte> greenImg = new Image<Bgr, Byte>(new Image<Gray, Byte>[]
//{
// new Image<Gray, Byte>(currentFrame.Width, currentFrame.Height), //Blue channel, set to zero
// currentFrame[1],//Green channel, set to zero
// new Image<Gray, Byte>(currentFrame.Width, currentFrame.Height) //Red channel from img
//});
//Image<Bgr, Byte> mix = new Image<Bgr, byte>(new Image<Gray, Byte>[]
//{
// currentFrame[0],
// new Image<Gray, Byte>(currentFrame.Width, currentFrame.Height),
// currentFrame[2]
//});
//ImageViewer red = new ImageViewer(redImg);
//red.Show();
//ImageViewer blue = new ImageViewer(blueImg);
//blue.Show();
//ImageViewer green = new ImageViewer(greenImg);
//green.Show();
//ImageViewer mixer = new ImageViewer(mix);
//mixer.Show();
#endregion
}
