private void edgesToolStripMenuItem_Click(object sender, EventArgs e) { CannyEdgeDetector gb = new CannyEdgeDetector(); newImg = gb.Apply(newImg); pictureBox4.Image = newImg; }
private void timerSampleMaker_Tick(object sender, EventArgs e) { if (_sourceImage != null) { try { // Loading some file Bitmap _sample = (Bitmap)AForge.Imaging.Image.Clone(_sourceImage); // We must convert it to grayscale because // the filter accepts 8 bpp grayscale images Grayscale GF = new Grayscale(0.2125, 0.7154, 0.0721); Bitmap GSampleImage = GF.Apply(_sample); // Detecting image edges and saving the result CannyEdgeDetector CED = new CannyEdgeDetector(0, 70); CED.ApplyInPlace(GSampleImage); Bitmap _temp = new Bitmap(320, 240); _temp = GSampleImage; pictureBoxSource.Image = _temp; ProcessByImage(_temp); } catch (Exception ex) { MessageBox.Show(ex.Message); } } }
private void edgeToolStripMenuItem_Click(object sender, EventArgs e) { CannyEdgeDetector edgeObj = new CannyEdgeDetector(); edgeImage = new Bitmap(edgeObj.Apply(grayImage)); pictureBox3.Image = edgeImage; }
private void CannyEdgeDetectionToolStripMenuItem1_Click(object sender, EventArgs e) { CannyEdgeDetector ed = new CannyEdgeDetector(); Bitmap s1 = ed.Apply(grayImage); pictureBox3.Image = s1; }
private void OnDetect(object sender, RoutedEventArgs e) { DebugView.Children.Clear(); BitmapSource src = ImageContent.Source as BitmapSource; if (src != null) { float min = float.Parse(Min.Text); float max = float.Parse(Max.Text); int minEdgeLength = int.Parse(EdgeLength.Text); DetectButton.IsEnabled = false; CannyEdgeDetector canny = new CannyEdgeDetector(src, max, min, minEdgeLength); canny.EdgeDetected += OnEdgeDetected; Task.Run(new Action(() => { canny.DetectEdges(); canny.EdgeDetected -= OnEdgeDetected; Dispatcher.BeginInvoke(new Action(() => { DetectButton.IsEnabled = true; })); })); //ShowImage(canny.ToImage<int>(canny.FilteredImage), "Filtered"); //ShowImage(canny.ToImage<float>(canny.DerivativeX), "DerivativeX"); //ShowImage(canny.ToImage<float>(canny.NonMax), "NonMax"); //ShowImage(canny.ToImage<float>(canny.Gradient), "Gradient"); //ShowImage(canny.ToImage<int>(canny.HorizontalEdges), "HorizontalEdges"); //ShowImage(canny.ToImage<int>(canny.VerticalEdges), "VerticalEdges"); //ShowImage(canny.ToImage<int>(canny.EdgeMap), "EdgeMap"); } }
void CannyToolStripMenuItemClick(object sender, EventArgs e) { //jika gambar kosong/null maka akan mengembalikan nilai kosong/null if (gambar == null) { return; } //clone variable gambar ke variable gambar2 dan menginisiasi class Rectangle //dan menerapkan format pixel 24bppRgb gambar2 = gambar.Clone(new Rectangle(0, 0, gambar.Width, gambar.Height), System.Drawing.Imaging.PixelFormat.Format24bppRgb); //convert ke grayscale terlebih dahulu lalu aplikasikan filter Canny. //inisiasi filter grayscale disertai argumen Grayscale grayscaleF = new Grayscale(0.2125, 0.7154, 0.0721); //aplikasikan filter grayscale ke gambar2 gambar2 = grayscaleF.Apply(gambar2); //inisiasi filter Canny CannyEdgeDetector cannyEdgeDetector = new CannyEdgeDetector(); //aplikasikan filter pada gambar2 cannyEdgeDetector.ApplyInPlace(gambar2); //tampilkan pada picturebox2 pictureBox2.Image = gambar2; }
private void EdgeDetectImg(ref Bitmap frame) { frame = Grayscale.CommonAlgorithms.RMY.Apply(frame); // Make gray switch (EdgeDetectValue) { case 1: SobelEdgeDetector SobelFilter = new SobelEdgeDetector(); SobelFilter.ApplyInPlace(frame); break; case 2: DifferenceEdgeDetector DifferenceFilter = new DifferenceEdgeDetector(); DifferenceFilter.ApplyInPlace(frame); break; case 3: HomogenityEdgeDetector HomogenityFilter = new HomogenityEdgeDetector(); HomogenityFilter.ApplyInPlace(frame); break; case 4: CannyEdgeDetector Cannyfilter = new CannyEdgeDetector(); // apply the MirrFilter Cannyfilter.ApplyInPlace(frame); break; default: HomogenityEdgeDetector filter = new HomogenityEdgeDetector(); filter.ApplyInPlace(frame); break; } GrayscaleToRGB RGBfilter = new GrayscaleToRGB(); // back to color format frame = RGBfilter.Apply(frame); }
private void cannyEdgeToolStripMenuItem_Click(object sender, EventArgs e) { Bitmap grayImage = Grayscale.CommonAlgorithms.BT709.Apply(fd); CannyEdgeDetector canny = new CannyEdgeDetector(0, 0, 1.4); Bitmap edgeImage = canny.Apply(grayImage); pictureBox6.Image = edgeImage; // Console.WriteLine(edgeImage.Width); int i, j, x, y, u, v; double con1 = 0; Color blackC = Color.FromArgb(Color.Black.ToArgb()); u = edgeImage.Width / 6; v = edgeImage.Height / 6; //Console.WriteLine(u); //Console.WriteLine(v); for (i = 0; i < 6; i++) { for (j = 0; j < 6; j++) { int count = 0; for (x = i * u; x < ((i * u) + u); x++) { for (y = j * v; y < ((j * v) + v); y++) { Color c = (edgeImage.GetPixel(x, y)); //Console.WriteLine(c); if (c != blackC) { count++; con1++; } } } featureVector.Add(count); //Console.WriteLine(count); } } //Calculate Normalization and add the value to the featureNormVector List <double> featureNormVector = new List <double>(); //Total Pixel Count //Console.WriteLine(con1); int z; //Normalization for (z = 0; z < featureVector.Count; z++) { double normalizedValue = featureVector[z] / con1; Console.WriteLine(normalizedValue); featureNormVector.Add(normalizedValue); } }
public override Bitmap ApplyFilter(Bitmap img) { CannyEdgeDetector ced = new CannyEdgeDetector(255, 0); this.imgEdgeDetected = ced.Apply(img); return(this.imgEdgeDetected); }
/// <summary> /// Classify user's image /// </summary> /// <param name="sender"></param> /// <param name="e"></param> private void buttonClassify_Click(object sender, EventArgs e) { //Create bitmap for recognition users's image Bitmap image = (Bitmap)(pictureBox1.Image); //Create Canny detector for contours CannyEdgeDetector filterCanny = new CannyEdgeDetector(); //Detect contour on image filterCanny.ApplyInPlace(image); //Transform image in feature vector double[] featureVector = (bagOfContourFragments as ITransform <Bitmap, double[]>).Transform(image); //SVM decide from which class this image string animal = GetAnimalClass(this.multiSVM.Decide(featureVector)); //display this information for user label2.Text = "This is: " + animal + "?"; //Show buttons to analyse correct detect labelCorrect.Visible = true; buttonCorrectYes.Visible = true; buttonCorrectNo.Visible = true; }
/// <summary> /// Applies Canny filter on specified image. (uses AForge implementation) /// </summary> /// <param name="im">image</param> /// <param name="lowThreshold">Low threshold value used for hysteresis</param> /// <param name="highThreshold">High threshold value used for hysteresis</param> /// <param name="sigma">Gaussian sigma</param> /// <param name="gaussianSize">Gaussian filter size</param> /// <returns>Processed image with Canny filter</returns> public static Gray <byte>[,] Canny(this Gray <byte>[,] im, byte lowThreshold = 20, byte highThreshold = 100, double sigma = 1.4, int gaussianSize = 5) { CannyEdgeDetector canny = new CannyEdgeDetector(lowThreshold, highThreshold, sigma); canny.GaussianSize = gaussianSize; return(im.ApplyFilter(canny)); }
private void reapplyToolStripMenuItem6_Click(object sender, EventArgs e) { //GrayscaleBT709 grayObject = new GrayscaleBT709(); //pictureBox2.Image = grayObject.Apply((Bitmap)pictureBox2.Image); CannyEdgeDetector filter = new CannyEdgeDetector(); pictureBox2.Image = filter.Apply((Bitmap)pictureBox2.Image); }
//metoda zrwacająca wynik filtru Cannyego public static Bitmap Canny(Bitmap bitmap) { Bitmap temporary = ImageSupporter.ColorToGrayscale(bitmap);; CannyEdgeDetector cannyEdgeDetector = new CannyEdgeDetector(); temporary = cannyEdgeDetector.Apply((Bitmap)temporary.Clone()); return(ImageSupporter.GrayScaleToColor(temporary)); }
public void ApplyCannyFilter(byte low, byte high, double sigma) { var f = new SobelEdgeDetector(); var filter = new CannyEdgeDetector(low, high, sigma); _bitmap = filter.Apply(_bitmap); OnPropertyChanged("ShowBitmap"); }
Bitmap CannyEdge(Bitmap bmp) { Bitmap tmp = AForge.Imaging.Image.Clone(bmp, bmp.PixelFormat); //higherThreshold default = 100 lower default = 20 CannyEdgeDetector obj_canny = new CannyEdgeDetector(150, 10); Bitmap cannyEdges = obj_canny.Apply(tmp.PixelFormat != PixelFormat.Format8bppIndexed ? Grayscale.CommonAlgorithms.BT709.Apply(tmp) : tmp); return(cannyEdges); }
public void cannyImageConvert() { //Apply canny edge detection on workingImage Grayscale grayscaleFilter = new Grayscale(.21, .07, .72); CannyEdgeDetector cannyFilter = new CannyEdgeDetector(); workingImage = grayscaleFilter.Apply(workingImage); workingImage = cannyFilter.Apply(workingImage); cannyImagePanel.Image = workingImage; }
private void AutoCrop(AttachmentDialogImageItem img) { CannyEdgeDetector edgeDetector = new CannyEdgeDetector(img.Bitmap, 20, 80, 30); edgeDetector.DetectEdges(); Rect bounds = edgeDetector.EdgeBounds; AddResizer(img, bounds); SetDirty(); }
private static Image <TColor, TDepth> Canny <TColor, TDepth>(this Image <TColor, TDepth> im, byte lowThreshold, byte highThreshold, double sigma, int gaussianSize = 5) where TColor : IColor where TDepth : struct { CannyEdgeDetector canny = new CannyEdgeDetector(lowThreshold, highThreshold, sigma); canny.GaussianSize = gaussianSize; return(im.ApplyFilter(canny)); }
public static Bitmap AccordCanny(Bitmap src) { Bitmap ret = (Bitmap)Convert(src); //Bitmap ret2 = (Bitmap)ret.Clone(); byte avg = GetAVG(ret); //ret.UnlockBits(null); CannyEdgeDetector Canny = new CannyEdgeDetector((byte)(avg / 3 - 20), (byte)(2 * (avg / 3) + 20), 1); return(Canny.Apply(ret)); }
/// <summary> /// Show edges on image /// </summary> /// <param name="image"></param> /// <returns></returns> public static Image CannyEdges(this Bitmap image) { if (image.PixelFormat != PixelFormat.Format8bppIndexed) { throw new NotSupportedException("Blob extractor can be applied to binary 8bpp images only"); } CannyEdgeDetector cannyEdge = new CannyEdgeDetector(); return(cannyEdge.Apply(image)); }
/// <summary> /// Отобразить контуры для входного изображения /// </summary> /// <param name="filename">Входной файл</param> /// <param name="resultFilename">Результирующий файл, содержащий контуры</param> public static void DrawEdges(string filename, string resultFilename) { var canny = new CannyEdgeDetector((byte)80, (byte)180); var inputBmp = new Bitmap(filename); Bitmap grayScaled = ToGrayscale(inputBmp); using (Bitmap edgedImage = canny.Apply(grayScaled)) { edgedImage.Save(resultFilename); } }
private Bitmap GetEdgedImage(WriteableBitmap writeableBitmap) { var edgeFilter = new CannyEdgeDetector(255, 0); var smoothFilter = new Median(); var grayFilter = new Grayscale(0.2125, 0.7154, 0.0721); var bitmap = (Bitmap)writeableBitmap; bitmap = grayFilter.Apply(bitmap); smoothFilter.ApplyInPlace(bitmap); edgeFilter.ApplyInPlace(bitmap); return(bitmap); }
private void edgeDetect() { Bitmap news = new Bitmap(imageGot); GrayscaleBT709 gs = new GrayscaleBT709(); imageGot = gs.Apply(imageGot); CannyEdgeDetector cn = new CannyEdgeDetector(); cn.LowThreshold = 0; cn.HighThreshold = 0; cn.GaussianSigma = 1.4; imageGot = cn.Apply(imageGot); pictureBox1.Image = imageGot; }
private void button4_Click(object sender, EventArgs e) { Grayscale grayfilter = new Grayscale(0.2125, 0.7154, 0.0721); Bitmap edgeImg = grayfilter.Apply(img1); OtsuThreshold filter = new OtsuThreshold(); filter.ApplyInPlace(edgeImg); CannyEdgeDetector filter2 = new CannyEdgeDetector(); img2 = filter2.Apply(edgeImg); pictureBox2.Image = img2; }
public static List <Rectangle> FindRectangles(Bitmap source) { Bitmap canny = Grayscale.CommonAlgorithms.RMY.Apply(source); CannyEdgeDetector edgeDetector = new CannyEdgeDetector(5, 20); edgeDetector.ApplyInPlace(canny); BlobCounter blobCounter = new BlobCounter { FilterBlobs = true, MinWidth = 5, MinHeight = 5 }; blobCounter.ProcessImage(canny); Blob[] blobs = blobCounter.GetObjectsInformation(); List <Rectangle> rectangles = new List <Rectangle>(); SimpleShapeChecker shapeChecker = new SimpleShapeChecker(); for (int i = 0; i < blobs.Length; i++) { List <IntPoint> edgePoints = blobCounter.GetBlobsEdgePoints(blobs[i]); if (shapeChecker.IsConvexPolygon(edgePoints, out List <IntPoint> corners)) { if (blobs[i].Rectangle.Width > canny.Width / 5.3 && blobs[i].Rectangle.Width <canny.Width / 4.41 && canny.Height / 10.3> blobs[i].Rectangle.Height && blobs[i].Rectangle.Height > canny.Height / 20.5) { rectangles.Add(blobs[i].Rectangle); } } } // order by descending area rectangles.Sort((a, b) => { if (a.Width * a.Height > b.Width * b.Height) { return(1); } else if (a.Width * a.Height < b.Width * b.Height) { return(-1); } return(0); }); return(rectangles); }
// Image Preprocessing private Bitmap ImagePreprocessing(Bitmap image, int threshold) { // Grayscale image = Grayscale.CommonAlgorithms.RMY.Apply(image); // Edge Detection (using CannyEdgeDetector) image = new CannyEdgeDetector().Apply(image); // Noise Removal int startX, startY, endX, endY; startX = image.Width; startY = image.Height; endX = 0; endY = 0; for (int i = 0; i < image.Width; i++) { for (int j = 0; j < image.Height; j++) { if (image.GetPixel(i, j).R > threshold) { if (i < startX) { startX = i; } if (j < startY) { startY = j; } if (i > endX) { endX = i; } if (j > endY) { endY = j; } } } } image = image.Clone(new Rectangle(startX, startY, endX - startX, endY - startY), PixelFormat.Format8bppIndexed); // Resize image = new ResizeBilinear(32, 32).Apply(image); return(image); }
void FinalFrame_NewFrame(object sender, NewFrameEventArgs eventArgs) { video = (Bitmap)eventArgs.Frame.Clone(); Bitmap video2 = (Bitmap)eventArgs.Frame.Clone(); if (mode == 1) { Grayscale gray = new Grayscale(0.2125, 0.7154, 0.0721); Bitmap video3 = gray.Apply(video2); CannyEdgeDetector canny = new CannyEdgeDetector(0, 70); canny.ApplyInPlace(video3); pictureBox2.Image = video3; } pictureBox1.Image = video; }
/// <summary> /// Called when videoPlayer receives a new frame. /// </summary> /// <param name="sender"></param> /// <param name="image"></param> private void videoPlayer_NewFrame(object sender, ref Bitmap image) { // convert image to grayscale var gray = new GrayscaleBT709(); var frame = gray.Apply(image); // threshold image to only keep light pixels var threshold = new Threshold(125); threshold.ApplyInPlace(frame); // blank out everything but the road var horizonY = (int)(image.Height * 0.65); var fill = new CanvasFill(new Rectangle(0, 0, image.Width, horizonY), Color.Red); fill.ApplyInPlace(frame); // detect edges var edgeDetector = new CannyEdgeDetector(); edgeDetector.ApplyInPlace(frame); // do a hough line transformation, which will search for straight lines in the frame var transform = new HoughLineTransformation(); transform.ProcessImage(frame); var rawLines = transform.GetMostIntensiveLines(50); // only keep non-horizontal lines that cross the horizon at the vanishing point var lines = from l in rawLines let range = new Range(-75, -65) where range.IsInside(l.Radius) && (l.Theta <= 85 || l.Theta >= 95) select l; // show the edge detection view in the bottom left box edgeBox.Image = frame; // show the lane detection view in the bottom right box var laneImg = new Bitmap(image.Width, image.Height); Utility.DrawHoughLines(lines, laneImg, Color.White, 1); laneBox.Image = laneImg; // draw the lanes on the main camera image too Utility.DrawHoughLines(lines, image, Color.LightGreen, 2); }
private WriteableBitmap FindPlate(IEnumerable <Rect> rects, WriteableBitmap image) { WriteableBitmap bestCandidate = null; foreach (var rect in rects) { var croppedImage = image.Crop(rect); var edgeFilter = new CannyEdgeDetector(); var smoothFilter = new Median(); var grayFilter = new Grayscale(0.2125, 0.7154, 0.0721); var blobCounter = new BlobCounter(); var cutTop = croppedImage.PixelHeight * 0.3; croppedImage = croppedImage.Crop(new Rect(0, cutTop, croppedImage.PixelWidth, croppedImage.PixelHeight)); var bitmap = (Bitmap)croppedImage; var grayImage = grayFilter.Apply(bitmap); bitmap = smoothFilter.Apply(grayImage); edgeFilter.ApplyInPlace(bitmap); blobCounter.ProcessImage(bitmap); var blobs = blobCounter.GetObjectsInformation(); var possibleChars = new List <Rectangle>(); foreach (var blob in blobs) { var objRectangle = blob.Rectangle; var ratio = (double)objRectangle.Height / (double)objRectangle.Width; if (ratio >= 1.16d && ratio <= 6.3d) { possibleChars.Add(objRectangle); } } if (possibleChars.Count == 0) { continue; } bestCandidate = croppedImage; } return(bestCandidate); }
public mEdgeCanny(int LowVal, int HighVal, int SampleSize) { Size = SampleSize; LowValue = LowVal; HighValue = HighVal; BitmapType = mFilter.BitmapTypes.GrayscaleBT709; Effect = new CannyEdgeDetector(); Effect.LowThreshold = (byte)LowValue; Effect.HighThreshold = (byte)HighValue; Effect.GaussianSize = Size; filter = Effect; }