private static void Main(string[] args)
{
Mat src = new Mat("data/tsukuba_left.png", LoadMode.GrayScale);
Mat dst20 = new Mat();
Mat dst40 = new Mat();
Mat dst44 = new Mat();
using (CLAHE clahe = Cv2.CreateCLAHE())
{
clahe.ClipLimit = 20;
clahe.Apply(src, dst20);
clahe.ClipLimit = 40;
clahe.Apply(src, dst40);
clahe.TilesGridSize = new Size(4, 4);
clahe.Apply(src, dst44);
}
Window.ShowImages(src, dst20, dst40, dst44);
/*var img1 = new IplImage("data/lenna.png", LoadMode.Color);
* var img2 = new IplImage("data/match2.png", LoadMode.Color);
* Surf(img1, img2);*/
//Mat[] mats = StitchingPreprocess(400, 400, 10);
//Stitching(mats);
//Track();
//Run();
}
private static void Clahe()
{
Mat src = new Mat("data/tsukuba_left.png", ImreadModes.GrayScale);
Mat dst20 = new Mat();
Mat dst40 = new Mat();
Mat dst44 = new Mat();
using (CLAHE clahe = Cv2.CreateCLAHE())
{
clahe.ClipLimit = 20;
clahe.Apply(src, dst20);
clahe.ClipLimit = 40;
clahe.Apply(src, dst40);
clahe.TilesGridSize = new Size(4, 4);
clahe.Apply(src, dst44);
}
Window.ShowImages(src, dst20, dst40, dst44);
}
public void Run()
{
Mat src = new Mat(FilePath.Image.TsukubaLeft, ImreadModes.Grayscale);
Mat dst1 = new Mat();
Mat dst2 = new Mat();
Mat dst3 = new Mat();
using (CLAHE clahe = Cv2.CreateCLAHE())
{
clahe.ClipLimit = 20;
clahe.Apply(src, dst1);
clahe.ClipLimit = 40;
clahe.Apply(src, dst2);
clahe.TilesGridSize = new Size(4, 4);
clahe.Apply(src, dst3);
}
Window.ShowImages(
new[] { src, dst1, dst2, dst3 },
new[] { "src", "dst clip20", "dst clip40", "dst tile4x4" });
}
//5. 图像增强方法2（输入图像为灰度图像）(通过clahe函数进行自适应直方图均衡化对其进行图像增强)
public static Mat ImageEnhancementMethod2(Mat inMat, Mat outMat)
{
//Mat mat5 = new Mat();
using (CLAHE clahe = Cv2.CreateCLAHE())
{
//clahe.ClipLimit = 20;
clahe.TilesGridSize = new Size(4, 4);
clahe.Apply(inMat, outMat);
//clahe.ClipLimit = 40;
//clahe.Apply(src, dst2);
//clahe.TilesGridSize = new Size(4, 4);
//clahe.Apply(src, dst3);
}
return(outMat);
}
public static Mat EscalaGrisesEqualizada(Mat imagen)
{
/* bgr a grayscale */
Mat imagenGris = new Mat();
Cv2.CvtColor(imagen, imagenGris, ColorConversionCodes.BGR2GRAY);
/* ecualización por histograma */
Mat imagenGrisEqualizada = new Mat();
CLAHE ecualizadorHistograma = Cv2.CreateCLAHE(5, new Size(3, 3));
ecualizadorHistograma.Apply(imagenGris, imagenGrisEqualizada);
/* liberar memoria */
imagenGris.Release();
return(imagenGrisEqualizada);
}
public static void NormalizeRGB(this Mat self, Mat output, double clip)
{
if (self.Channel != 3)
{
throw new NotSupportedException("Channel sould be RGB");
}
ConvertColor(self, ColorConversionCodes.BGR2Lab);
Mat[] spl = Split(self);
CLAHE c = CLAHE.Create(clip, new OpenCvSharp.Size(8, 8));
c.Apply(spl[0], spl[0]);
Merge(self, spl);
ConvertColor(self, ColorConversionCodes.Lab2BGR);
}
public void cuda_CLAHE()
{
Mat src = Image("lenna.png", ImreadModes.Grayscale);
Size size = src.Size();
Cuda.CLAHE clahe = Cuda.CLAHE.create(20.0);
CLAHE clahe_gold = CLAHE.Create(20.0);
using (GpuMat g_src = new GpuMat(size, src.Type()))
using (GpuMat dst = new GpuMat()) {
g_src.Upload(src);
clahe.Apply(g_src, dst);
Mat dst_gold = new Mat();
clahe_gold.Apply(src, dst_gold);
ImageEquals(dst_gold, dst, 1.0);
ShowImagesWhenDebugMode(src, dst);
}
}
private Mat Recipe(string path, double value, string option)
{
if (path != null)
{
Mat orgMat = new Mat(path);
Mat previewMat = new Mat();
#region //Algorithm
Mat matrix = new Mat();
switch (option)
{
case "Contrast":
Cv2.AddWeighted(orgMat, value, orgMat, 0, 0, previewMat);
break;
//AddWeighted 함수를 이용해서 gamma 인자를 통해 가중치의 합에 추가적인 덧셈을 한꺼번에 수행 할 수 있다.
//computes weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma)
//http://suyeongpark.me/archives/tag/opencv/page/2
case "Brightness":
Cv2.Add(orgMat, value, previewMat);
break;
//Add 함수를 이용해서 영상의 덧셈을 수행 한다.
//Add 연산에서는 자동으로 포화 연산을 수행한다.
//http://suyeongpark.me/archives/tag/opencv/page/2
case "Blur":
Cv2.GaussianBlur(orgMat, previewMat, new OpenCvSharp.Size(9, 9), value, 1, BorderTypes.Default); //GaussianBlur
break;
//영상이나 이미지를 흐림 효과를 주어 번지게 하기 위해 사용합니다. 해당 픽셀의 주변값들과 비교하고 계산하여 픽셀들의 색상 값을 재조정합니다.
//각 필세마다 주변의 픽셀들의 값을 비교하고 계산하여 픽섹들의 값을 재조정 하게 됩니다. 단순 블러의 경우 파란 사격형 안에 평균값으로
//붉은색 값을 재종하게 되고, 모든 픽셀들에 대하여 적용을 하게 된다.
//https://076923.github.io/posts/C-opencv-13/
case "Rotation":
matrix = Cv2.GetRotationMatrix2D(new Point2f(orgMat.Width / 2, orgMat.Height / 2), value, 1.0); // 2x3 회전 행렬 생성 함수 GetRotationMatrix2D
Cv2.WarpAffine(orgMat, previewMat, matrix, new OpenCvSharp.Size(orgMat.Width, orgMat.Height), InterpolationFlags.Linear, BorderTypes.Replicate);
break;
//WarpAffine(원본 배열, 결과 배열, 행렬, 결과 배열의 크기) 결과 배열의 크기를 설정하는 이유는 회전 후, 원본 배열의 이미지 크기와 다를 수 있기 때문이다.
//Interpolation.Linear은 영상이나 이미지 보간을 위해 보편적으로 사용되는 보간법이다.
//BoderTypes.Replicate 여백을 검은색으로 채우면서 회전이 되더라도 zeropadding 된다.
//https://076923.github.io/posts/C-opencv-6/
case "Rotation90":
matrix = Cv2.GetRotationMatrix2D(new Point2f(orgMat.Width / 2, orgMat.Height / 2), 90, 1.0);
Cv2.WarpAffine(orgMat, previewMat, matrix, new OpenCvSharp.Size(orgMat.Width, orgMat.Height), InterpolationFlags.Linear, BorderTypes.Reflect);
break;
//WarpAffine(원본 배열, 결과 배열, 행렬, 결과 배열의 크기) 결과 배열의 크기를 설정하는 이유는 회전 후, 원본 배열의 이미지 크기와 다를 수 있기 때문이다.
//Interpolation.Linear은 영상이나 이미지 보간을 위해 보편적으로 사용되는 보간법이다.
//BoderTypes.Replicate 여백을 검은색으로 채우면서 회전이 되더라도 zeropadding 된다.
//https://076923.github.io/posts/C-opencv-6/
case "Horizontal Flip":
Cv2.Flip(orgMat, previewMat, FlipMode.Y);
break;
//Flip(원본 이미지, 결과 이미지, 대칭 축 색상 공간을 변환), 대칭 축(FlipMode)를 사용하여 대칭 진행
//https://076923.github.io/posts/C-opencv-5/
case "Vertical Flip":
Cv2.Flip(orgMat, previewMat, FlipMode.X);
break;
//Flip(원본 이미지, 결과 이미지, 대칭 축 색상 공간을 변환), 대칭 축(FlipMode)를 사용하여 대칭 진행
//https://076923.github.io/posts/C-opencv-5/
case "Noise":
matrix = new Mat(orgMat.Size(), MatType.CV_8UC3);
Cv2.Randn(matrix, Scalar.All(0), Scalar.All(value));
Cv2.AddWeighted(orgMat, 1, matrix, 1, 0, previewMat);
break;
//Randn 정규 분포를 나타내는 이미지를 랜덤하게 생성하는 방법
//AddWeighted 두 이미지를 가중치를 설정하여 합치면서 진행
//
case "Zoom In":
//#1. Center만 확대
double width_param = (int)(0.8 * orgMat.Width); // 0.8이 배율 orgMat.Width이 원본이미지의 사이즈 // 나중에 0.8만 80%형식으로 바꿔서 파라미터로 빼야됨
double height_param = (int)(0.8 * orgMat.Height); // 0.8이 배율 orgMat.Height 원본이미지의 사이즈 //
int startX = orgMat.Width - (int)width_param; // 이미지를 Crop해올 좌상단 위치 지정하는값 // 원본사이즈 - 배율로 감소한 사이즈
int startY = orgMat.Height - (int)height_param; //
Mat tempMat = new Mat(orgMat, new OpenCvSharp.Rect(startX, startY, (int)width_param - (int)(0.2 * orgMat.Width), (int)height_param - (int)(0.2 * orgMat.Height))); //중간과정 mat이고 Rect안에 x,y,width,height 값 지정해주는거
//예외처리 범위 밖으로 벗어나는경우 shift시키거나 , 제로페딩을 시키거나
//예외처리
Cv2.Resize(tempMat, previewMat, new OpenCvSharp.Size(orgMat.Width, orgMat.Height), (double)((double)orgMat.Width / (double)(width_param - (int)(0.2 * orgMat.Width))),
(double)((double)orgMat.Height / ((double)(height_param - (int)(0.2 * orgMat.Height)))), InterpolationFlags.Cubic);
// (double) ( (double)orgMat.Width / (double)width_param)
// 형변환 원본이미지 형변환 / 타겟이미지 배율 == 타겟이미지가 원본이미지 대비 몇배인가? 의 수식임
// (double) ( (double)orgMat.Height / (double)height_param)
break;
case "Sharpen":
float filterBase = -1f;
float filterCenter = filterBase * -9;
float[] data = new float[9] {
filterBase, filterBase, filterBase,
filterBase, filterCenter, filterBase,
filterBase, filterBase, filterBase
};
Mat kernel = new Mat(3, 3, MatType.CV_32F, data);
Cv2.Filter2D(orgMat, previewMat, orgMat.Type(), kernel);
break;
//
//
//
// Contrast Limited Adapative Histogram Equalization
case "CLAHE":
CLAHE test = Cv2.CreateCLAHE();
test.SetClipLimit(10.0f);
if (value < 1)
{
value = 1;
}
test.SetTilesGridSize(new OpenCvSharp.Size(value, value));
Mat normalized = new Mat();
Mat temp = new Mat();
Cv2.CvtColor(orgMat, orgMat, ColorConversionCodes.RGB2HSV);
var splitedMat = orgMat.Split();
test.Apply(splitedMat[2], splitedMat[2]);
Cv2.Merge(splitedMat, previewMat);
Cv2.CvtColor(previewMat, previewMat, ColorConversionCodes.HSV2RGB);
break;
//
//
//
default:
break;
}
matrix.Dispose(); //이미지의 메모리 할당을 해제 합니다.
orgMat.Dispose(); //이미지의 메모리 할당을 해제 합니다.
return(previewMat);
#endregion
}
return(null);
}
public static double?GetRotationAngle(string inputFileFolderPath, string inputFileName, bool showMessageBoxes, ImageBox iboxRaw, ImageBox iboxProcessed, out double msecElapsed)
{
double?rotationAngle = null;
msecElapsed = 0;
// Hough algo does a bad job detecting horizontal lines. So we rotate the image by a set amount before running the Hough.
double houghRotationOffsetAngle = 25.0;
try
{
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
if (iboxProcessed != null)
{
iboxProcessed.Image = null;
iboxProcessed.Refresh();
}
Mat rotated = new Mat();
Mat src = new Mat(inputFileFolderPath + inputFileName, ImreadModes.Grayscale);
if (showMessageBoxes && iboxRaw != null)
{
iboxRaw.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(src);
}
// Not needed if we read as grayscale to start with.
//Mat src8UC1 = new Mat();
//src.ConvertTo(src8UC1, MatType.CV_8UC1);
// I'm not sure why we do the gauss - It seems like everyone does it, it's cheap, so we do it. ~Ed
Mat gauss = new Mat();
Cv2.GaussianBlur(src, gauss, new OpenCvSharp.Size(3, 3), 2, 2);
LogEvent("gauss", showMessageBoxes, gauss, iboxProcessed);
// An attempt to get the contrast across the image to be somewhat uniform.
CLAHE claheFilter = Cv2.CreateCLAHE(4, new OpenCvSharp.Size(10, 10));
Mat clahe = new Mat();
claheFilter.Apply(gauss, clahe);
LogEvent("clahe", showMessageBoxes, clahe, iboxProcessed);
// An attempt to get the contrast across the image to be somewhat uniform.
Mat hist = new Mat();
Cv2.EqualizeHist(gauss, hist);
LogEvent("hist", showMessageBoxes, hist, iboxProcessed);
// Grab a template from some middle part of the image. Eventually, the size and location of this
// template will be specified. It is very possible we'll have to grab multiple templates, as the
// location of the template may impact the accuracy of the rotation.
// e.g. - if the template is an image of a damaged device (which may happen at any location), the calculated
// rotation may be wrong. Testing is required.
// The locations where the template matches will create an image with lines that are offset from 0/90 degrees.
// This is because we can assume that the devices are orthogonal to one another, even if the image itself is
// offset rotationally.
Rect r1 = new Rect(new OpenCvSharp.Point(1000, 1000), new OpenCvSharp.Size(500, 300));
var roi = new Mat(clahe, r1);
Mat template = new Mat(new OpenCvSharp.Size(500, 300), MatType.CV_8UC1);
roi.CopyTo(template);
LogEvent("template", showMessageBoxes, template, iboxProcessed);
Mat templateMatch = new Mat();
Cv2.MatchTemplate(clahe, template, templateMatch, TemplateMatchModes.CCoeffNormed);
LogEvent("templatematch", showMessageBoxes, templateMatch, iboxProcessed);
Mat normalized = new Mat();
normalized = templateMatch.Normalize(0, 255, NormTypes.MinMax);
normalized.ConvertTo(normalized, MatType.CV_8UC1);
LogEvent("normalized template match", showMessageBoxes, normalized, iboxProcessed);
// This winnows down the number of matches.
Mat thresh = new Mat();
Cv2.Threshold(normalized, thresh, 200, 255, ThresholdTypes.Binary);
LogEvent("threshold template match", showMessageBoxes, thresh, iboxProcessed);
// rotate the image because hough doesn't work very well to find horizontal lines.
Mat rotatedThresh = new Mat();
Cv2E.RotateDegrees(thresh, rotatedThresh, houghRotationOffsetAngle);
LogEvent("rotatedThresh", showMessageBoxes, rotatedThresh, iboxProcessed);
Mat erode = new Mat();
Cv2.Erode(rotatedThresh, erode, new Mat());
LogEvent("erode", showMessageBoxes, erode, iboxProcessed);
LineSegmentPoint[] segHoughP = Cv2.HoughLinesP(rotatedThresh, 1, Math.PI / 1800, 2, 10, 600);
Mat imageOutP = new Mat(src.Size(), MatType.CV_8UC3);
// We're limiting the rotation correction to +/- 10 degrees. So we only care about hough lines that fall within 80 to 100 or 170 to 190
List <double> anglesNear90 = new List <double>();
List <double> anglesNear0 = new List <double>();
foreach (LineSegmentPoint s in segHoughP)
{
try
{
// Add lines to the image, if we're going to look at it.
if (showMessageBoxes)
{
imageOutP.Line(s.P1, s.P2, Scalar.White, 1, LineTypes.AntiAlias, 0);
}
var radian = Math.Atan2((s.P1.Y - s.P2.Y), (s.P1.X - s.P2.X));
var angle = ((radian * (180 / Math.PI) + 360) % 360);
// We rotated the image because the hough algo does a bad job with small horizontal lines. So we take that rotation back out here.
angle += houghRotationOffsetAngle;
angle -= 180;
if (angle > 80 && angle < 100)
{
anglesNear90.Add(angle);
if (showMessageBoxes)
{
imageOutP.Line(s.P1, s.P2, Scalar.Red, 1, LineTypes.AntiAlias, 0);
}
}
if (angle > -10 && angle < 10)
{
anglesNear0.Add(angle);
if (showMessageBoxes)
{
imageOutP.Line(s.P1, s.P2, Scalar.Orange, 1, LineTypes.AntiAlias, 0);
}
}
}
catch (Exception ex)
{
// there's always some infinity risk with atan, yes? Maybe. I don't want to fail on horizontal or vertical line edge cases.
}
}
double meanAngleNear0 = 0;
if (anglesNear0.Count > 0)
{
meanAngleNear0 = anglesNear0.Mean();
}
double meanAngleNear90 = 90;
if (anglesNear90.Count > 0)
{
meanAngleNear90 = anglesNear90.Mean();
}
// Use both the vertical and horizontal to calculate the image angle with a weighted average. It might be more accurate to use median instead of mean here.
rotationAngle = ((meanAngleNear0) * anglesNear0.Count + (meanAngleNear90 - 90) * anglesNear90.Count) / (anglesNear0.Count + anglesNear90.Count);
LogEvent("hough lines", showMessageBoxes, imageOutP, iboxProcessed);
stopWatch.Stop();
// Get the elapsed time as a TimeSpan value. Less than 400msec in debug mode via IDE.
TimeSpan ts = stopWatch.Elapsed;
msecElapsed = ts.TotalMilliseconds;
}
catch (Exception ex)
{
}
return(rotationAngle);
}
public static void MatchTranspTemplate(string sFilePath, string sTemplateFilePath, bool showMessageBoxes, ImageBox rawImageBox, ImageBox processedImageBox)
{
Mat src = new Mat(sFilePath, ImreadModes.Grayscale);
rawImageBox.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(src);
// I'm not sure why we do the gauss - It seems like everyone does it, it's cheap, so we do it. ~Ed
LogEvent("gauss", showMessageBoxes);
Mat gauss = new Mat();
Cv2.GaussianBlur(src, gauss, new OpenCvSharp.Size(3, 3), 2, 2);
processedImageBox.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(gauss);
// An attempt to get the contrast across the image to be somewhat uniform.
LogEvent("clahe", showMessageBoxes);
CLAHE claheFilter = Cv2.CreateCLAHE(4, new OpenCvSharp.Size(10, 10));
Mat clahe = new Mat();
claheFilter.Apply(gauss, clahe);
processedImageBox.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(clahe);
// Grab a template from some middle part of the image. Eventually, the size and location of this
// template will be specified. It is very possible we'll have to grab multiple templates, as the
// location of the template may impact the accuracy of the rotation.
// e.g. - if the template is an image of a damaged device (which may happen at any location), the calculated
// rotation may be wrong. Testing is required.
// The locations where the template matches will create an image with lines that are offset from 0/90 degrees.
// This is because we can assume that the devices are orthogonal to one another, even if the image itself is
// offset rotationally.
//Rect r1 = new Rect(new OpenCvSharp.Point(1000, 1000), new OpenCvSharp.Size(500, 300));
//var roi = new Mat(clahe, r1);
//Mat template = new Mat(new OpenCvSharp.Size(500, 300), MatType.CV_8UC1);
//roi.CopyTo(template);
Mat template = new Mat(sTemplateFilePath, ImreadModes.Grayscale);
processedImageBox.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(template);
//LogEvent("preotsu", showMessageBoxes);
//Mat preotsu = new Mat();
//Cv2.Threshold(clahe, preotsu, 240, 255, ThresholdTypes.Binary);
//processedImageBox.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(preotsu);
Mat templateMatch = new Mat();
// # According to http://www.devsplanet.com/question/35658323, we can only use cv2.TM_SQDIFF or cv2.TM_CCORR_NORMED for a transparent template
Cv2.MatchTemplate(clahe, template, templateMatch, TemplateMatchModes.CCorrNormed);
//LogEvent("template match", showMessageBoxes);
// can't do this - image is 32, needs to be 8uc1 or similar to convert to bitmap.
//processedImageBox.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(templateMatch);
Mat converted = new Mat();
converted = templateMatch.Normalize(0, 255, NormTypes.MinMax);
converted.ConvertTo(converted, MatType.CV_8UC1);
LogEvent("template match converted", showMessageBoxes);
processedImageBox.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(converted);
// This winnows down the number of matches.
LogEvent("thresh", showMessageBoxes);
Mat thresh = new Mat();
Cv2.Threshold(converted, thresh, 240, 255, ThresholdTypes.Binary);
processedImageBox.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(thresh);
string matchFilename = string.Format("Matches_{0}.jpg", DateTime.Now.ToString("yyyyMMdd_hhmmss"));
thresh.SaveImage(@"..\..\..\ExampleFiles\Templates\" + matchFilename);
}
/// <summary>
/// Analyze video frame
/// </summary>
private void AnalyzeVideo()
{
var newImage = new Mat();
var newFrame = new CFrame();
newFrame = cframe;
newImage = newFrame.Frame;
newImage = newImage.CvtColor(ColorConversionCodes.BGR2GRAY);
Utilities.debugmessage("Clahe: " + Cv2.Mean(newImage)[0]);
clhLight.SetClipLimit(2);
clhLight.Apply(newImage, newImage);
var st = new Stopwatch();
Utilities.debugmessage("Clahe: " + Cv2.Mean(newImage)[0]);
st.Start();
List <YoloItem> items = yoloWrapper.Detect(newImage.Resize(new OpenCvSharp.Size(w, h)).ToBytes()).ToList();
var coeffW = ((float)newImage.Width / w);
var coeffH = ((float)newImage.Height / h);
foreach (var itm in items)
{
if (itm.Confidence < 0.66)
{
break;
}
if (itm.Type == "dcoup")
{
// Logging, Tracking
TimeSpan curTime = TimeSpan.FromMilliseconds(newFrame.frameNum * frameTime);
string[] _toAdd =
{
newFrame.frameNum.ToString(),
(itm.X * coeffW).ToString(),
(itm.Y * coeffH).ToString(),
((itm.X * coeffW) + (itm.Width * coeffH)).ToString(),
((itm.Y * coeffH) + (itm.Height * coeffW)).ToString(),
curTime.ToString(@"hh\:mm\:ss")
};
Log(_toAdd);
//if (myConnection.State == System.Data.ConnectionState.Open)
//{
// try
// {
// dataBaseLog(_toAdd);
// }
// catch { }
//}
ListViewItem item1 = new ListViewItem(newFrame.frameNum.ToString(), 0);
item1.SubItems.Add((itm.X * coeffW).ToString());
item1.SubItems.Add((itm.Y * coeffH).ToString());
item1.SubItems.Add((itm.Width * coeffW + (itm.X * coeffW)).ToString());
item1.SubItems.Add((itm.Height * coeffH + (itm.Y * coeffH)).ToString());
item1.SubItems.Add(curTime.ToString(@"hh\:mm\:ss"));
window.listView1.BeginInvoke(new Action(() =>
{
window.listView1.Items.AddRange(new ListViewItem[] { item1 });
}));
// Tracking algorithm
// Speed limit ~80 KM/h (if lenght between coups is 12-15 meters)
if (((newFrame.frameNum * frameTime) > (checkTime + 650)) && Math.Abs((float)masTrackDcoup * coeffW - (float)itm.X * coeffW) < 30)
{
checkTime = (newFrame.frameNum * frameTime);
masTrackDcoup = itm.X;
}
else if (((newFrame.frameNum * frameTime) > (checkTime + 650)))
{
CoupCount++;
checkTime = (newFrame.frameNum * frameTime);
masTrackDcoup = itm.X;
}
else if (CoupCount == 0)
{
CoupCount++;
checkTime = (newFrame.frameNum * frameTime);
masTrackDcoup = itm.X;
}
else
{
checkTime = (newFrame.frameNum * frameTime);
masTrackDcoup = itm.X;
}
}
}
st.Stop();
window.toolStripTimer.Text = "Elapsed time: " + st.ElapsedMilliseconds + " ms";
window.toolStripCounter.Text = "Count: " + CoupCount;
// Drawing new frame in picBox
window.picBox.BeginInvoke(new Action(() =>
{
window.picBox.ImageIpl = newImage.Resize(new OpenCvSharp.Size(window.picBox.Width, window.picBox.Height));
window.picBox.setRect(items);
}));
if (cframe.frameNum + 1 > frameCnt)
{
PLAY_FLAG = false;
window.Invoke(new Action(() =>
{
window.pauseButton.Enabled = false;
window.btn_Detect.Enabled = true;
}));
}
analyzeStarted = false;
}