Canny edge detector.
The filter searches for objects' edges by applying Canny edge detector. The implementation follows Bill Green's Canny edge detection tutorial.
The filter accepts 8 bpp grayscale images for processing.
Sample usage:
// create filter CannyEdgeDetector filter = new CannyEdgeDetector( ); // apply the filter filter.ApplyInPlace( image ); Initial image:
Result image:
/// <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);
}
static ImageProcessor()
{
imageQuantizer = new ColorImageQuantizer(new MedianCutQuantizer());
grayscale = Grayscale.CommonAlgorithms.BT709;
edgeDetector = new CannyEdgeDetector();
}
public static Bitmap DoIt(Bitmap bmp)
{
GaussianBlur blur = new GaussianBlur(10, 50);
Bitmap blurred = blur.Apply(bmp);
Bitmap ret = new Bitmap(blurred.Width, blurred.Height, blurred.PixelFormat);
for (int y = 0; y < blurred.Height; y++)
{
for (int x = 0; x < blurred.Width; x++)
{
Color blurredColor = blurred.GetPixel(x, y);
Color orinalColor = bmp.GetPixel(x, y);
Color newColor = Color.FromArgb((blurredColor.R + orinalColor.R * 2) / 3,
(blurredColor.G + orinalColor.G * 2) / 3,
(blurredColor.B + orinalColor.B * 2) / 3);
ret.SetPixel(x, y, newColor);
}
}
GammaCorrection gc = new GammaCorrection(.8);
gc.ApplyInPlace(ret);
Sharpen sharpen = new Sharpen();
sharpen.ApplyInPlace(ret);
Grayscale filter = new Grayscale(0.2125, 0.7154, 0.0721);
Bitmap gray = filter.Apply(ret);
CannyEdgeDetector canny = new CannyEdgeDetector();
gray = canny.Apply(gray);
for (int y = 0; y < gray.Height; y++)
{
for (int x=0;x < gray.Width; x++)
{
if(gray.GetPixel(x,y).R > 0)
{
Color retColor = ret.GetPixel(x, y);
Color newColor = Color.FromArgb(
(int)(retColor.R * .7),
(int)(retColor.G * .7),
(int)(retColor.B * .7));
ret.SetPixel(x, y, newColor);
}
}
}
return ret;
}
/// <summary>
/// Celem metody jest przykadrowanie otrzymanego obrazka, tak aby widok zawierał jedynie kartkę papieru, oraz zwrócenie go do metody UploadFile
/// </summary>
/// <param name="img"></param>
/// <returns></returns>
public System.Drawing.Image CropImage(System.Drawing.Image img)
{
//DocumentSkewChecker skewChecker = new DocumentSkewChecker();
//double angle = skewChecker.GetSkewAngle(image);
//RotateBilinear rotationFilter = new RotateBilinear(-angle);
//rotationFilter.FillColor = Color.White;
//Bitmap rotatedImage = rotationFilter.Apply(image);
Bitmap image = new Bitmap(img);
UnmanagedImage grayImage = null;
if (image.PixelFormat == PixelFormat.Format8bppIndexed)
{
grayImage = UnmanagedImage.FromManagedImage(image);
}
else
{
grayImage = UnmanagedImage.Create(image.Width, image.Height,
PixelFormat.Format8bppIndexed);
Grayscale.CommonAlgorithms.BT709.Apply(UnmanagedImage.FromManagedImage(image), grayImage);
}
CannyEdgeDetector edgeDetector = new CannyEdgeDetector();
UnmanagedImage edgesImage = edgeDetector.Apply(grayImage);
OtsuThreshold thresholdFilter = new OtsuThreshold();
thresholdFilter.ApplyInPlace(edgesImage);
Dilatation DilatationFilter = new Dilatation();
DilatationFilter.Apply(edgesImage);
Opening OpeningFilter = new Opening();
OpeningFilter.Apply(edgesImage);
BlobCounter blobCounter = new BlobCounter();
blobCounter.MinHeight = 32;
blobCounter.MinWidth = 32;
blobCounter.FilterBlobs = true;
blobCounter.ObjectsOrder = ObjectsOrder.Size;
blobCounter.ProcessImage(edgesImage);
Blob[] blobs = blobCounter.GetObjectsInformation();
ExtractBiggestBlob BiggestBlob = new ExtractBiggestBlob();
Bitmap biggestBlobsImage = BiggestBlob.Apply(edgesImage.ToManagedImage());
AForge.IntPoint BiggestBlogCorners = BiggestBlob.BlobPosition;
Crop cropFilter = new Crop(new Rectangle(BiggestBlogCorners.X, BiggestBlogCorners.Y, biggestBlobsImage.Width, biggestBlobsImage.Height));
Bitmap croppedImage = cropFilter.Apply(image);
return croppedImage;
}
public static Bitmap canny(Bitmap image)
{
try
{
Bitmap gris = EscalaGrises(image);
AForge.Imaging.Filters.CannyEdgeDetector filter = new AForge.Imaging.Filters.CannyEdgeDetector();
System.Drawing.Bitmap newImage = filter.Apply(gris);
return(newImage);
}
catch (ArgumentException ex)
{
MessageBox.Show(ex.ToString(), "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return(null);
}
}
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;
}
//1. bright pixel / dark pixel
//2.lowest gray level
//3.highest gray level
//4.number of peaks in the x direction.
//5.number of peaks in the y direction.
public static double[] ExtractFeatures(Bitmap bmp,int i)
{
//Apply GrayScale
GrayscaleBT709 greyScaleFilter = new GrayscaleBT709();
Bitmap newBmp = greyScaleFilter.Apply((Bitmap)bmp.Clone());
//Count Blobs
BlobCounter blobCounter = new BlobCounter();
blobCounter.BackgroundThreshold = Color.FromArgb(255, 150, 150, 150);
blobCounter.ProcessImage(newBmp);
int blobs = (blobCounter.ObjectsCount - 1) * 30;
//Count Corner
SusanCornersDetector scd = new SusanCornersDetector();
scd.DifferenceThreshold = 70;
scd.GeometricalThreshold = 8;
int corners = scd.ProcessImage((Bitmap)newBmp.Clone()).Count();
//Apply Edge Filter
CannyEdgeDetector filter = new CannyEdgeDetector();
//newBmp = filter.Apply(newBmp);
Histogram his = new HorizontalIntensityStatistics(newBmp).Gray;
Histogram vis = new VerticalIntensityStatistics(newBmp).Gray;
HoughLineTransformation lineTransform = new HoughLineTransformation();
// apply Hough line transofrm
lineTransform.ProcessImage(filter.Apply(newBmp));
Bitmap houghLineImage = lineTransform.ToBitmap();
// get lines using relative intensity
HoughLine[] lines = lineTransform.GetLinesByRelativeIntensity(1);
int linesCount = lines.Count() * 30;
double[] features = new double[13] { blobs, corners, his.Max, his.Min, his.Mean, his.Median, his.StdDev,
vis.Max, vis.Min, vis.Mean, vis.Median, vis.StdDev,linesCount};
//double[] features = new double[3] { blobs, corners,lines};
newBmp.Save(String.Format("test{0}.bmp",i));
return features;
}
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;
}
public static FoundColorSpaces Find(Bitmap bmp)
{
FoundColorSpaces ret = new FoundColorSpaces();
ret.OriginalColorSpace = bmp;
ret.GrayColorSpace = Grayscale.CommonAlgorithms.BT709.Apply(ret.OriginalColorSpace);
CannyEdgeDetector edges = new CannyEdgeDetector();
Threshold threshold = new Threshold();
ret.Edges = threshold.Apply(edges.Apply(ret.GrayColorSpace));
ret.BinaryColorSpace = threshold.Apply(ret.GrayColorSpace);
//ret.CorrectedRGBColorSpace = new Bitmap(bmp.Width, bmp.Height, PixelFormat.Format24bppRgb);
return ret;
}
Bitmap processImageCenterline(string filename)
{
using (Bitmap SampleImage = (Bitmap)System.Drawing.Image.FromFile(filename))
{
// We must convert it to grayscale because
// the filter accepts 8 bpp grayscale images
Grayscale GF = new Grayscale(0.2125, 0.7154, 0.0721);
using (Bitmap GSampleImage = GF.Apply(SampleImage))
{
// Saving the grayscale image, so we could see it later
// Detecting image edges and saving the result
CannyEdgeDetector CED = new CannyEdgeDetector(0, 70);
//CED.ApplyInPlace(GSampleImage);
//BradleyLocalThresholding bwfilter = new BradleyLocalThresholding();
//bwfilter.ApplyInPlace(GSampleImage);
// create filter
// create filter sequence
FiltersSequence filterSequence = new FiltersSequence();
// Inverting image to get white image on black background
filterSequence.Add(new Invert());
filterSequence.Add(new SISThreshold());
// Finding skeleton
filterSequence.Add(new SimpleSkeletonization());
//clean image from scratches
short[,] se = new short[,] {
{ -1, -1, -1 },
{ 0, 1, 0 },
{ -1, -1, -1 }};
filterSequence.Add(new HitAndMiss(se, HitAndMiss.Modes.Thinning));
//filterSequence.Add(new Median( ));
//filterSequence.Add(new Dilatation());
filterSequence.Add(new Invert());
// apply the filter and rfeturn value
return filterSequence.Apply(GSampleImage);
}
}
}
/// <summary>
///
/// </summary>
/// <param name="image"></param>
/// <returns></returns>
public static Image CannyEdges(this Image image)
{
CannyEdgeDetector cannyEdge = new CannyEdgeDetector();
return cannyEdge.Apply(BitmapGrayscale(image));
}
private void button2_Click(object sender, EventArgs e)
{
button2.Text = "处理中";
switch (comboBox4.SelectedIndex)
{
case 0:
{
Bitmap temp = (Bitmap)pictureBox1.Image;
OilPainting filter3 = new OilPainting(10);
// apply the filter
filter3.ApplyInPlace(temp);
this.pictureBox2.Image = ResizeBitmap(temp);
break;
}
case 1:
{
Bitmap temp = (Bitmap)pictureBox1.Image;
temp = new Grayscale(0.2125, 0.7154, 0.0721).Apply(temp);
DifferenceEdgeDetector edgeDetector = new DifferenceEdgeDetector();
temp = edgeDetector.Apply(temp);
temp = new Threshold((int)numericUpDown1.Value).Apply(temp);
//FillHoles filter2 = new FillHoles();
//filter2.MaxHoleHeight = MinHeight;
//filter2.MaxHoleWidth = MaxWidth;
//filter2.CoupledSizeFiltering = false;
// apply the filter
//temp = filter2.Apply(temp);
//HorizontalRunLengthSmoothing hrls = new HorizontalRunLengthSmoothing(40);
// apply the filter
//hrls.ApplyInPlace(temp);
/*AForge.Imaging.Filters.BlobsFiltering filter = new AForge.Imaging.Filters.BlobsFiltering();
* // 设置过滤条件（对象长、宽至少为70）
* filter.CoupledSizeFiltering = true;
* filter.MaxWidth = (int)numericUpDown3.Value;
* filter.MaxHeight = (int)numericUpDown4.Value;
* filter.MinWidth = (int)numericUpDown5.Value;
* filter.MinHeight = (int)numericUpDown6.Value;
* filter.ApplyInPlace(temp);*/
BlobCounter blobCounter = new BlobCounter();
blobCounter.MinHeight = 32;
blobCounter.MinWidth = 32;
blobCounter.FilterBlobs = true;
blobCounter.ObjectsOrder = ObjectsOrder.Size;
// 4 - find all stand alone blobs
blobCounter.ProcessImage(temp);
Blob[] blobs = blobCounter.GetObjectsInformation();
SimpleShapeChecker shapeChecker = new SimpleShapeChecker();
List <IntPoint> corners = null;
List <IntPoint> corners2 = null;
for (int i = 0, n = blobs.Length; i < n; i++)
{
List <IntPoint> edgePoints = blobCounter.GetBlobsEdgePoints(blobs[i]);
// does it look like a quadrilateral ?
if (shapeChecker.IsQuadrilateral(edgePoints, out corners))
{
// get edge points on the left and on the right side
List <IntPoint> leftEdgePoints, rightEdgePoints;
blobCounter.GetBlobsLeftAndRightEdges(blobs[i],
out leftEdgePoints, out rightEdgePoints);
listBox1.DataSource = leftEdgePoints;
listBox2.DataSource = rightEdgePoints;
}
}
//listBox1.DataSource = corners;
//listBox2.DataSource = corners2;
this.pictureBox1.Image = temp;
break;
}
case 2:
{
Bitmap bt2 = new Bitmap(@"D:\TCL条码\截图01.bmp");
Bitmap bt1 = new Bitmap(@"D:\TCL条码\截图03.bmp");
//Bitmap bt1 = new Bitmap(pictureBox2.Image);
ExhaustiveTemplateMatching tm = new ExhaustiveTemplateMatching(0.80f);
//基于一定的相似性阈值获得匹配块
TemplateMatch[] matchings = tm.ProcessImage(bt1, bt2);
BitmapData data = bt1.LockBits(
new Rectangle(0, 0, bt1.Width, bt1.Height),
ImageLockMode.ReadWrite, bt1.PixelFormat);
foreach (TemplateMatch m in matchings)
{
Drawing.Rectangle(data, m.Rectangle, Color.Red);
}
bt1.UnlockBits(data);
pictureBox2.Image = bt1;
break;
}
case 3:
{
Bitmap bt2 = new Bitmap(@"D:\TCL条码\Canny算法.png");
AForge.Imaging.Filters.BlobsFiltering filter = new AForge.Imaging.Filters.BlobsFiltering();
// 设置过滤条件（对象长、宽至少为70）
filter.CoupledSizeFiltering = true;
filter.MaxWidth = (int)numericUpDown3.Value;
filter.MaxHeight = (int)numericUpDown4.Value;
filter.MinWidth = (int)numericUpDown5.Value;
filter.MinHeight = (int)numericUpDown6.Value;
filter.ApplyInPlace(bt2);
pictureBox1.Image = bt2;
byte[] RESULT = BitmapToBytes(bt2);
break;
}
case 4:
{
Bitmap temp = (Bitmap)pictureBox1.Image;
temp = new Grayscale(0.2125, 0.7154, 0.0721).Apply(temp);
AForge.Imaging.Filters.CannyEdgeDetector filter = new AForge.Imaging.Filters.CannyEdgeDetector();
filter.ApplyInPlace(temp);
pictureBox2.Image = temp;
break;
}
}
button2.Text = "处理";
}
public CannyEdgeDetectProcessor()
: base()
{
m_filter = new CannyEdgeDetector();
}
// =========================================================
private void Edge_detectFunc(ref Bitmap frame, int par_int)
{
frame = Grayscale.CommonAlgorithms.RMY.Apply(frame); // Make gray
switch (par_int) {
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:
// can we not have references to canny in the code. gives me ptsd flashbacks
CannyEdgeDetector Nightmare = new CannyEdgeDetector();
// apply the filter
Nightmare.ApplyInPlace(frame);
break;
default:
HomogenityEdgeDetector filter = new HomogenityEdgeDetector();
filter.ApplyInPlace(frame);
break;
}
GrayscaleToRGB RGBfilter = new GrayscaleToRGB(); // back to color format
frame = RGBfilter.Apply(frame);
}
public Bitmap Detect(Bitmap bitmap)
{
Bitmap grayscaleBitmap = Grayscale.CommonAlgorithms.BT709.Apply(bitmap);
IFilter smoothingFilter = null;
switch (_smoothMode)
{
case "None": smoothingFilter = null; break;
case "Mean": smoothingFilter = new Mean(); break;
case "Median": smoothingFilter = new Median(); break;
case "Conservative": smoothingFilter = new ConservativeSmoothing(); break;
case "Adaptive": smoothingFilter = new AdaptiveSmoothing(); break;
case "Bilateral": smoothingFilter = new BilateralSmoothing(); break;
}
Bitmap smoothBitmap = smoothingFilter != null ? smoothingFilter.Apply(grayscaleBitmap) : grayscaleBitmap;
IFilter edgeFilter = null;
switch (_edgeMode)
{
case "Homogenity": edgeFilter = new HomogenityEdgeDetector(); break;
case "Difference": edgeFilter = new DifferenceEdgeDetector(); break;
case "Sobel": edgeFilter = new SobelEdgeDetector(); break;
case "Canny": edgeFilter = new CannyEdgeDetector(); break;
}
Bitmap edgeBitmap = edgeFilter != null ? edgeFilter.Apply(smoothBitmap) : smoothBitmap;
IFilter threshholdFilter = new Threshold(_threshold);
Bitmap thresholdBitmap = _threshold == 0 ? edgeBitmap : threshholdFilter.Apply(edgeBitmap);
BlobCounter blobCounter = new BlobCounter();
blobCounter.FilterBlobs = true;
blobCounter.MinHeight = _minHeight;
blobCounter.MinWidth = _minWidth;
blobCounter.ProcessImage(thresholdBitmap);
Blob[] blobs = blobCounter.GetObjectsInformation();
Bitmap outputBitmap = new Bitmap(thresholdBitmap.Width, thresholdBitmap.Height, PixelFormat.Format24bppRgb);
Graphics bitmapGraphics = Graphics.FromImage(outputBitmap);
Bitmap inputBitmap = null;
switch (_drawMode)
{
case "Original": inputBitmap = bitmap; break;
case "Grayscale": inputBitmap = grayscaleBitmap; break;
case "Smooth": inputBitmap = smoothBitmap; break;
case "Edge": inputBitmap = edgeBitmap; break;
case "Threshold": inputBitmap = thresholdBitmap; break;
}
if (inputBitmap != null)
bitmapGraphics.DrawImage(inputBitmap, 0, 0);
Pen nonConvexPen = new Pen(Color.Red, 2);
Pen nonRectPen = new Pen(Color.Orange, 2);
Pen cardPen = new Pen(Color.Blue, 2);
SimpleShapeChecker shapeChecker = new SimpleShapeChecker();
List<IntPoint> cardPositions = new List<IntPoint>();
for (int i = 0; i < blobs.Length; i++)
{
List<IntPoint> edgePoints = blobCounter.GetBlobsEdgePoints(blobs[i]);
List<IntPoint> corners;
if (shapeChecker.IsConvexPolygon(edgePoints, out corners))
{
PolygonSubType subType = shapeChecker.CheckPolygonSubType(corners);
if ((subType == PolygonSubType.Parallelogram || subType == PolygonSubType.Rectangle) && corners.Count == 4)
{
// Check if its sideways, if so rearrange the corners so it's vertical.
RearrangeCorners(corners);
// Prevent detecting the same card twice by comparing distance against other detected cards.
bool sameCard = false;
foreach (IntPoint point in cardPositions)
{
if (corners[0].DistanceTo(point) < _minDistance)
{
sameCard = true;
break;
}
}
if (sameCard)
continue;
// Hack to prevent it from detecting smaller sections of the card instead of the whole card.
if (GetArea(corners) < _minArea)
continue;
cardPositions.Add(corners[0]);
bitmapGraphics.DrawPolygon(cardPen, ToPointsArray(corners));
}
else
{
foreach (IntPoint point in edgePoints.Take(300))
{
bitmapGraphics.DrawEllipse(nonRectPen, point.X, point.Y, 1, 1);
}
}
}
else
{
foreach (IntPoint point in edgePoints.Take(300))
{
bitmapGraphics.DrawEllipse(nonConvexPen, point.X, point.Y, 1, 1);
}
}
}
bitmapGraphics.Dispose();
nonConvexPen.Dispose();
nonRectPen.Dispose();
cardPen.Dispose();
return outputBitmap;
}
/// <summary>
/// <para>Pulls the image</para>
/// <para>Runs the ocr on it</para>
/// <para>fills in the blanks</para>
/// <para>submits the page</para>
/// </summary>
/// <param name="challenge"></param>
/// <param name="cancellationToken"></param>
/// <param name="answer"></param>
/// <returns></returns>
private bool SolveCaptcha( Uri challenge, CancellationToken cancellationToken, out String answer ) {
answer = null;
var tesseractEngine = this.TesseractEngine;
if ( null == tesseractEngine ) {
return false;
}
var captchaData = this.PullCaptchaData( challenge );
if ( captchaData.ImageUri == null ) {
captchaData.Status = CaptchaStatus.NoImageFoundToBeSolved;
this.UpdateCaptchaData( captchaData );
return false;
}
Console.WriteLine( Resources.Uber_SolveCaptcha_Attempting_OCR_on__0_, captchaData.ImageUri.AbsolutePath );
captchaData.Status = CaptchaStatus.SolvingImage;
this.UpdateCaptchaData( captchaData );
var folder = new Folder( Path.GetTempPath() );
Document document;
folder.TryGetTempDocument( document: out document, extension: "png" );
this.PictureBoxChallenge.Image.Save( document.FullPathWithFileName, ImageFormat.Png );
var aforgeImage = AForge.Imaging.Image.FromFile( document.FullPathWithFileName );
var smoothing = new ConservativeSmoothing();
var cannyEdgeDetector = new CannyEdgeDetector();
cannyEdgeDetector.Apply( aforgeImage );
aforgeImage.Save( document.FullPathWithFileName, ImageFormat.Png );
this.PictureBoxChallenge.ImageLocation = document.FullPathWithFileName;
this.PictureBoxChallenge.Load();
this.Throttle( Seconds.Ten );
using ( var img = Pix.LoadFromFile( document.FullPathWithFileName ).Deskew() ) {
using ( var page = tesseractEngine.Process( img, PageSegMode.SingleLine ) ) {
answer = page.GetText();
var paragraph = new Paragraph( answer );
answer = new Sentence( paragraph.ToStrings( " " ) ).ToStrings( " " );
FluentTimers.Create( Minutes.One, () => document.Delete() ).AndStart();
if ( !String.IsNullOrWhiteSpace( answer ) ) {
captchaData.Status = CaptchaStatus.SolvedChallenge;
this.UpdateCaptchaData( captchaData );
return true;
}
return false;
}
}
}
// =========================================================
private void Edge_detectFunc(ref Bitmap frame, int par_int, double par_d, int par_R, int par_G, int par_B)
{
frame = Grayscale.CommonAlgorithms.RMY.Apply(frame); // Make gray
switch (par_int)
{
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 videoNewFrame(object sender, NewFrameEventArgs args)
{
Bitmap temp = args.Frame.Clone() as Bitmap;
switch (comboBox3.SelectedIndex)
{
case 0:
break;
case 1:
temp = new Grayscale(0.2125, 0.7154, 0.0721).Apply(temp);
break;
case 2:
temp = new Grayscale(0.2125, 0.7154, 0.0721).Apply(temp);
temp = new Threshold((int)numericUpDown1.Value).Apply(temp);
break;
case 3:
temp = new Grayscale(0.2125, 0.7154, 0.0721).Apply(temp);
AForge.Imaging.Filters.CannyEdgeDetector filter = new AForge.Imaging.Filters.CannyEdgeDetector();
filter.ApplyInPlace(temp);
break;
case 4:
for (int i = 0; i < (int)numericUpDown2.Value; i++)
{
temp = new Dilatation().Apply(temp);
}
break;
case 5:
for (int i = 0; i < (int)numericUpDown2.Value; i++)
{
temp = new Erosion().Apply(temp);
}
break;
case 6:
temp = new Grayscale(0.2125, 0.7154, 0.0721).Apply(temp);
AForge.Imaging.Filters.BradleyLocalThresholding filter1 = new AForge.Imaging.Filters.BradleyLocalThresholding();
filter1.ApplyInPlace(temp);
break;
}
switch (comboBox2.SelectedIndex)
{
case 0:
{
BarcodeReader reader = new BarcodeReader();
reader.Options.CharacterSet = "UTF-8";
Result result = reader.Decode(temp);
if (result != null)
{
if (wait == false)
{
MessageBox.Show(result.ToString());
wait = true;
}
}
else
{
wait = false;
}
break;
}
case 1:
{
/*Bitmap pImg = MakeGrayscale3((Bitmap)temp);
* using (ZBar.ImageScanner scanner = new ZBar.ImageScanner())
* {
* scanner.SetConfiguration(ZBar.SymbolType.None, ZBar.Config.Enable, 0);
* scanner.SetConfiguration(ZBar.SymbolType.CODE39, ZBar.Config.Enable, 1);
* scanner.SetConfiguration(ZBar.SymbolType.CODE128, ZBar.Config.Enable, 1);
*
* List<ZBar.Symbol> symbols = new List<ZBar.Symbol>();
* symbols = scanner.Scan((System.Drawing.Image)pImg);
* if (symbols != null && symbols.Count > 0)
* {
* string result = string.Empty;
* symbols.ForEach(s => result += s.Data);
* if (wait == false)
* {
* MessageBox.Show(result);
* wait = true;
* }
* }
* else
* wait = false;
* }*/
break;
}
case 2:
{
break;
}
case 3:
break;
}
this.pictureBox1.Image = ResizeBitmap(temp);
}
