Exemple #1
0
        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);
        }
Exemple #3
0
    // 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);
    }
Exemple #4
0
        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);
            }
        }
Exemple #5
0
        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);
        }
Exemple #6
0
        //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);
        }
Exemple #7
0
        /// <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);
        }
Exemple #8
0
        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;
 }
Exemple #10
0
    //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);
    }
Exemple #11
0
 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;
     }
 }
Exemple #12
0
        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;
        }
Exemple #13
0
        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";
        }
Exemple #15
0
        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);
            }
        }
Exemple #16
0
        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;
        }
Exemple #17
0
        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());
            }
        }