public Mat Ball_only(Mat a)
{
Mat bwmat = new Mat();
CvInvoke.CvtColor(a, bwmat, Emgu.CV.CvEnum.ColorConversion.Bgr2Hsv);
CvInvoke.InRange(bwmat, new ScalarArray(new MCvScalar((double)Global.colors.B1, (double)Global.colors.G1, (double)Global.colors.R1)), new ScalarArray(new MCvScalar((double)Global.colors.B2, (double)Global.colors.G2, (double)Global.colors.R2)), bwmat);
CvInvoke.MedianBlur(bwmat, bwmat, 7);
CvInvoke.Dilate(bwmat, bwmat, s, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, sk);
CvInvoke.Erode(bwmat, bwmat, s, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, sk);
CvInvoke.Blur(bwmat, bwmat, new Size(10, 10), new Point(-1, -1), Emgu.CV.CvEnum.BorderType.Default);
CvInvoke.Threshold(bwmat, bwmat, 20, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
CvInvoke.Blur(bwmat, bwmat, new Size(10, 10), new Point(-1, -1), Emgu.CV.CvEnum.BorderType.Default);
CvInvoke.Threshold(bwmat, bwmat, 20, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
CvInvoke.Blur(bwmat, bwmat, new Size(10, 10), new Point(-1, -1), Emgu.CV.CvEnum.BorderType.Default);
CvInvoke.Threshold(bwmat, bwmat, 20, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
CvInvoke.Blur(bwmat, bwmat, new Size(10, 10), new Point(-1, -1), Emgu.CV.CvEnum.BorderType.Default);
CvInvoke.Threshold(bwmat, bwmat, 20, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
CvInvoke.Blur(bwmat, bwmat, new Size(10, 10), new Point(-1, -1), Emgu.CV.CvEnum.BorderType.Default);
CvInvoke.Threshold(bwmat, bwmat, 20, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
CvInvoke.Blur(bwmat, bwmat, new Size(10, 10), new Point(-1, -1), Emgu.CV.CvEnum.BorderType.Default);
CvInvoke.Threshold(bwmat, bwmat, 20, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
CvInvoke.Blur(bwmat, bwmat, new Size(10, 10), new Point(-1, -1), Emgu.CV.CvEnum.BorderType.Default);
CvInvoke.Threshold(bwmat, bwmat, 20, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
return(bwmat);
}
//阈值化处理后图像的边缘光滑处理
//size表示取均值的窗口大小，threshold表示对均值图像进行二值化的阈值
private static void imageblur(Mat roi_threshold, Mat roi_blur, Size blur_size, int srcthreshold)
{
int height = roi_threshold.Rows;
int width = roi_threshold.Cols;
//CvInvoke.Blur(roi_threshold,roi_blur,blur_size);
CvInvoke.Blur(roi_threshold, roi_blur, blur_size, new Point(-1, -1));
Image <Gray, byte> src = roi_blur.ToImage <Gray, byte>();
for (int i = 0; i < height; i++)
{
// Byte[] p = roi_blur.GetData();
for (int j = 0; j < width; j++)
{
if (src[i, j].Intensity < srcthreshold)
{
src.Data[i, j, 0] = 0;
}
else
{
src.Data[i, j, 0] = 255;
}
}
}
roi_blur = src.Mat;
//imshow("Blur", dst);
}
// Update is called once per frame
void Update()
{
biggestContour = new VectorOfPoint();
contours = new VectorOfVectorOfPoint();
biggestContourArea = 0;
Mat image, imgGray, imgHSV, imgBlur, imgMedianBlur, imgGaussianBlur;
image = _webcam.QueryFrame();
CvInvoke.Flip(image, image, FlipType.Horizontal);
imgBlur = image.Clone();
imgMedianBlur = image.Clone();
imgGaussianBlur = image.Clone();
CvInvoke.Blur(image, imgBlur, new Size(sizeBlur, sizeBlur), new Point(-1, 1));
CvInvoke.GaussianBlur(image, imgGaussianBlur, new Size(sizeBlur, sizeBlur), sizeBlur / 2.0);
CvInvoke.MedianBlur(image, imgMedianBlur, sizeBlur);
imgGray = image.Clone();
CvInvoke.CvtColor(image, imgGray, ColorConversion.Bgr2Gray);
imgHSV = image.Clone();
CvInvoke.CvtColor(image, imgHSV, ColorConversion.Rgb2Hsv);
Image <Hsv, byte> ImgHSV = imgHSV.ToImage <Hsv, byte>();
Image <Gray, byte> hsv = ImgHSV.InRange(new Hsv(60 - intensity, sMin, vMin), new Hsv(60 + intensity, sMax, vMax));
CvInvoke.Imshow("HSV", hsv);
Image <Gray, byte> dilate = hsv.Clone();
Mat structuringElement = CvInvoke.GetStructuringElement(ElementShape.Ellipse, new Size(2 * sizeStruct + 1, 2 * sizeStruct + 1), new Point(sizeStruct, sizeStruct));
CvInvoke.Dilate(hsv, dilate, structuringElement, new Point(-1, -1), nbOfIter, BorderType.Constant, new MCvScalar(0));
CvInvoke.Erode(dilate, dilate, structuringElement, new Point(-1, -1), nbOfIter, BorderType.Constant, new MCvScalar(0));
Mat hierarchy = new Mat();
CvInvoke.FindContours(dilate, contours, hierarchy, RetrType.List, ChainApproxMethod.ChainApproxNone);
for (int i = 0; i < contours.Size; i++)
{
if (CvInvoke.ContourArea(contours[i]) > biggestContourArea)
{
biggestContour = contours[i];
biggestContourIndex = i;
biggestContourArea = CvInvoke.ContourArea(contours[i]);
}
}
var moments = CvInvoke.Moments(biggestContour);
int cx = (int)(moments.M10 / moments.M00);
int cy = (int)(moments.M01 / moments.M00);
Point centroid = new Point(cx, cy);
CvInvoke.Circle(image, centroid, 2, new MCvScalar(0, 0, 255), 2);
CvInvoke.DrawContours(image, contours, biggestContourIndex, new MCvScalar(0, 0, 255), 5);
CvInvoke.Imshow("DILATATION", dilate);
CvInvoke.Imshow("Webcam View", image);
CvInvoke.WaitKey(24);
}
private ImageData Operation(ImageForm_Service service, List <int> args)
{
if (args == null)
{
return(null);
}
if (args.Count < 4)
{
return(null);
}
Size k = new Size(args[0], args[1]);
Point anchor = new Point(args[2], args[3]);
try
{
Image <Bgra, byte> image = new Image <Bgra, byte>(service.data.LastData().Bitmap);
//Image<Gray, byte> gray = image.Convert<Gray, byte>();
//Image<Gray, byte> blur = new Image<Gray, byte>(gray.Width, gray.Height, new Gray(0));
//CvInvoke.Blur(gray, blur, k, anchor);
Image <Bgra, byte> blur = new Image <Bgra, byte>(image.Width, image.Height);
CvInvoke.Blur(image, blur, k, anchor);
return(new ImageData(blur.Bitmap, service.data.LastData().ID));
}
catch
{
return(null);
}
}
public static RotatedRect[] detectBarcodes(Bitmap inputImage)
{
RotatedRect[] toReturn = new RotatedRect[2];
Image <Gray, Byte> grayImage = new Image <Bgr, byte>(inputImage).Convert <Gray, Byte>();
CvInvoke.Threshold(grayImage, grayImage, 120, 1000, Emgu.CV.CvEnum.ThresholdType.BinaryInv);
CvInvoke.Blur(grayImage, grayImage, new Size(27, 9), new Point(-1, -1));
Mat kernel = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(21, 7), new Point(-1, -1));
CvInvoke.MorphologyEx(grayImage, grayImage, Emgu.CV.CvEnum.MorphOp.Close, kernel, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar(1));
CvInvoke.Erode(grayImage, grayImage, null, new Point(-1, -1), 40, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar(1));
CvInvoke.Dilate(grayImage, grayImage, null, new Point(-1, -1), 40, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar(1));
grayImage.ToBitmap().Save("backscatter.png");
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(grayImage, contours, null, Emgu.CV.CvEnum.RetrType.List, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
Graphics boxGraphics = Graphics.FromImage(inputImage);
toReturn[0] = CvInvoke.MinAreaRect(contours[0]);
toReturn[1] = CvInvoke.MinAreaRect(contours[1]);
Debug.WriteLine("Barcode Size: " + contours.Size);
return(toReturn);
}
//图片边缘光滑处理
//size表示取均值的窗口大小，threshold表示对均值图像进行二值化的阈值
private static void imageblur(Mat roi_threshold, Mat roi_blur, Size blur_size, int srcthreshold)
{
int height = roi_threshold.Rows;
int width = roi_threshold.Cols;
//CvInvoke.Blur(roi_threshold,roi_blur,blur_size);
CvInvoke.Blur(roi_threshold, roi_blur, blur_size, new Point(-1, -1));
for (int i = 0; i < height; i++)
{
Byte[] p = roi_blur.GetData();
for (int j = 0; j < width; j++)
{
if (p[j] < srcthreshold)
{
p[j] = 0;
}
else
{
p[j] = 255;
}
}
}
//imshow("Blur", dst);
}
/// <summary>
/// Tries to find the line in the image and returns a group of lines at the edges of the detected line.
/// </summary>
/// <param name="img"></param>
/// <returns></returns>
private LineSegment2D[] filterLines(Image <Bgr, Byte> img)
{
if (!Directory.Exists(_path))
{
System.IO.Directory.CreateDirectory(_path);
}
Image <Hsv, Byte> hsvImage = img.Convert <Hsv, Byte>();
Image <Gray, Byte>[] channels = hsvImage.Split();
Image <Gray, byte> grayImg = channels[2].InRange(new Gray(0), new Gray(30));
//Decrease noise in the images
CvInvoke.Dilate(grayImg, grayImg, null, new Point(), 1, BorderType.Default, default(MCvScalar));
CvInvoke.Erode(grayImg, grayImg, null, new Point(), 2, BorderType.Default, default(MCvScalar));
CvInvoke.Blur(grayImg, grayImg, new Size(20, 20), new Point());
LineSegment2D[] lines = grayImg.HoughLines(0, 0, 5, 5, 10, 7, 1)[0];
//Show lines in image for logging and testing purposes
img = grayImg.Convert <Bgr, Byte>();
foreach (LineSegment2D line in lines)
{
img.Draw(line, new Bgr(Color.Green), 3);
}
img.Save(_path + "OutputImage" + _count + ".png");
return(lines);
}
Mat preprocess(Mat momel)
{
Mat squirrel = new Mat();
CvInvoke.CvtColor(momel, squirrel, ColorConversion.Bgr2Bgra);
CvInvoke.Blur(squirrel, squirrel, new Size(3, 3), new Point(-1, 1));
return(squirrel);
}
private void buttonBlurOpenCV_Click(object sender, EventArgs e)
{
if (blurFactor < 3)
{
blurFactor = 3;
}
CvInvoke.Blur(imageOriginal, imageConverted, new Size(blurFactor, blurFactor), new Point(0, 0), BorderType.Default);
imageBoxShapen.Image = imageConverted;
}
//convert image in black and white
Image <Gray, byte> Convert(Vector3 seuilb, Vector3 seuilh)
{
CvInvoke.CvtColor(imageMat, imageHSV, ColorConversion.Bgr2Hsv);
CvInvoke.Blur(imageHSV, imageHSV, new Size(4, 4), new Point(1, 1));
seuilbasHsv = new Hsv(seuilb.x, seuilb.y, seuilb.z);
seuilhautHsv = new Hsv(seuilh.x, seuilh.y, seuilh.z);
Image <Hsv, byte> imgConverti = imageHSV.ToImage <Hsv, byte>();
Image <Gray, byte> imgseuil = imgConverti.InRange(seuilbasHsv, seuilhautHsv);
return(imgseuil);
}
private void MeanToolStripMenuItem_Click(object sender, EventArgs e)
{
if (!src.IsEmpty)
{
FilterSize fs = new FilterSize();
fs.ShowDialog();
dst = new Mat();
dst = src.Clone();
CvInvoke.Blur(src, dst, new Size(par, par), new Point(-1, -1));
imageBox1.Image = dst;
}
}
private void button3_Click(object sender, EventArgs e)
{
Image <Bgr, byte> dst = src.CopyBlank();
CvInvoke.Blur(src, dst, new Size(g_nBlurValue, g_nBlurValue), new Point(-1, -1));
//第一个参数，InputArray类型的src，输入图像，即源图像，填Mat类的对象即可。该函数对通道是独立处理的，且可以处理任意通道数的图片，但需要注意，待处理的图片深度应该为CV_8U, CV_16U, CV_16S, CV_32F 以及 CV_64F之一。
//第二个参数，OutputArray类型的dst，即目标图像，需要和源图片有一样的尺寸和类型。比如可以用Mat::Clone，以源图片为模板，来初始化得到如假包换的目标图。
//第三个参数，Size类型的ksize内核的大小。一般这样写Size(w, h)来表示内核的大小(其中，w 为像素宽度， h为像素高度)。Size（3,3）就表示3x3的核大小，Size（5,5）就表示5x5的核大小
//第四个参数，Point类型的anchor，表示锚点（即被平滑的那个点），注意他有默认值Point(-1, -1)。如果这个点坐标是负值的话，就表示取核的中心为锚点，所以默认值Point(-1, -1)表示这个锚点在核的中心。
//第五个参数，int类型的borderType，用于推断图像外部像素的某种边界模式。有默认值BORDER_DEFAULT，我们一般不去管它。
imageBox2.Image = dst;
}
private void ResetBackground(IImage background)
{
Log.Info("(Re)initializing background");
var blurredBackground = new Image <Gray, byte>(background.Size);
CvInvoke.Blur(background, blurredBackground, new Size(10, 10), new Point(-1, -1));
blurredBackground.Save($@"C:\Thermobox\background{++_backgroundIndex}.jpg");
_backgroundMean = CvInvoke.Mean(blurredBackground).V0;
_lastBackgroundReset = _timeProvider.Now;
_noBoundingBox = null;
_resetBackground = null;
_foundNothingCount = 0;
}
private void button7_Click(object sender, EventArgs e)
{
if (sourceImg == null)
{
MessageBox.Show("请选择图片!");
return;
}
Image <Bgr, Byte> blurImg = new Image <Bgr, byte>(imageBox1.Image.Bitmap);
//blurImg = blurImg.SmoothBlur(blurImg.Width, blurImg.Height);
Point anchor = new Point(-1, -1);
int size = trackBar2.Value * 2 + 3;
CvInvoke.Blur(blurImg, blurImg, new Size(size, size), anchor);
imageBox3.Image = blurImg;
label3.Text = "均值滤波";
}
private void FrmBlurAverage_PassValuesEvent(object sender, FunctionArgs.BlurAverageArgs e)
{
Size ksize = new Size(e.KernelSize, e.KernelSize);
Point anchor = new Point(-1, -1);
//ToDo: 添加BorderType的选项（问题点：BoderType.Constant）
BorderType borderType = BorderType.Default;
switch (e.BlurType)
{
case FilterType.Average:
CvInvoke.Blur(mCurrentImage, mTempImage, ksize, anchor, borderType);
break;
case FilterType.Box:
CvInvoke.BoxFilter(mCurrentImage, mTempImage, DepthType.Default, ksize, anchor, e.Normalize, borderType);
break;
case FilterType.Gaussian:
//ToDo: 添加SigmaX的选项
CvInvoke.GaussianBlur(mCurrentImage, mTempImage, ksize, e.SigmaX, 0, borderType);
break;
case FilterType.Median:
CvInvoke.MedianBlur(mCurrentImage, mTempImage, e.KernelSize);
break;
case FilterType.Bilateral:
//ToDo: 双边滤波的选项
//CvInvoke.BilateralFilter(mCurrentImage,mTempImage)
break;
default:
break;
}
//没有启用预览，恢复当前的图
if (!e.PreviewEnabled)
{
mFrmMainImage.SetImageSource(mCurrentImage);
}
else
{
mFrmMainImage.SetImageSource(mTempImage);
}
}
private void button2_Click(object sender, EventArgs e)
{
double Thershold1 = Convert.ToDouble(numericUpDown1.Value);
double Thershold2 = Convert.ToDouble(numericUpDown2.Value);
Image <Gray, byte> dst_gray = src.Convert <Gray, byte>();
// 先使用 3x3内核来降噪
CvInvoke.Blur(dst_gray, dst_gray, new Size(3, 3), new Point(1, 1));
CvInvoke.Canny(dst_gray, dst_gray, Thershold1, Thershold2);
//第一个参数，InputArray类型的image，输入图像，即源图像，填Mat类的对象即可，且需为单通道8位图像。
//第二个参数，OutputArray类型的edges，输出的边缘图，需要和源图片有一样的尺寸和类型。
//第三个参数，double类型的threshold1，第一个滞后性阈值。
//第四个参数，double类型的threshold2，第二个滞后性阈值。
//第五个参数，int类型的apertureSize，表示应用Sobel算子的孔径大小，其有默认值3。
//第六个参数，bool类型的L2gradient，一个计算图像梯度幅值的标识，有默认值false。
imageBox2.Image = dst_gray;
}
public static byte[] Canny(IntPtr buffer, int width, int height, bool smooth = false)
{
unsafe
{
Mat source = new Mat(height, width, DepthType.Cv8U, 1, buffer, width);
Mat blurred = new Mat(height, width, DepthType.Cv8U, 1);
if (smooth)
{
CvInvoke.Blur(source, blurred, new Size(3, 3), new Point(-1, -1));
}
Mat cannyEdges = new Mat(height, width, DepthType.Cv8U, 1);
double cannyThreshold = 180.0;
double cannyThresholdLinking = 60.0;
CvInvoke.Canny(smooth ? blurred : source, cannyEdges, cannyThreshold, cannyThresholdLinking);
return(cannyEdges.GetData());
}
}
