Blobs filtering by size.
The filter performs filtering of blobs by their size in the specified source image - all blobs, which are smaller or bigger then specified limits, are removed from the image.
The filter accepts 8 bpp grayscale images and 24/32 color images for processing.
Sample usage:
// create filter BlobsFiltering filter = new BlobsFiltering( ); // configure filter filter.CoupledSizeFiltering = true; filter.MinWidth = 70; filter.MinHeight = 70; // apply the filter filter.ApplyInPlace( image ); Initial image:
Result image:
private string reconhecerCaptcha(Image img)
{
Bitmap imagem = new Bitmap(img);
imagem = imagem.Clone(new Rectangle(0, 0, img.Width, img.Height), System.Drawing.Imaging.PixelFormat.Format24bppRgb);
Erosion erosion = new Erosion();
Dilatation dilatation = new Dilatation();
Invert inverter = new Invert();
ColorFiltering cor = new ColorFiltering();
cor.Blue = new AForge.IntRange(200, 255);
cor.Red = new AForge.IntRange(200, 255);
cor.Green = new AForge.IntRange(200, 255);
Opening open = new Opening();
BlobsFiltering bc = new BlobsFiltering();
Closing close = new Closing();
GaussianSharpen gs = new GaussianSharpen();
ContrastCorrection cc = new ContrastCorrection();
bc.MinHeight = 10;
FiltersSequence seq = new FiltersSequence(gs, inverter, open, inverter, bc, inverter, open, cc, cor, bc, inverter);
pictureBox.Image = seq.Apply(imagem);
string reconhecido = OCR((Bitmap)pictureBox.Image);
return reconhecido;
}
private void button3_Click(object sender, EventArgs e)
{
Bitmap b = new Bitmap(pictureBox2.Image);
b = new BlobsFiltering(2, 2, b.Width, b.Height).Apply(b);
b = new BlobsFiltering(1, 2, b.Width, b.Height).Apply(b);
b = new BlobsFiltering(1, 1, b.Width, b.Height).Apply(b);
//b = new BlobsFiltering(10,10, b.Width, b.Height).Apply(b);
this.pictureBox2.Image = b;
}
/// <summary>
/// Detects and recognizes cards from source image
/// </summary>
/// <param name="source">Source image to be scanned</param>
/// <returns>Recognized Cards</returns>
public CardCollection Recognize(Bitmap source)
{
CardCollection collection = new CardCollection(); //Collection that will hold cards
Bitmap temp = source.Clone() as Bitmap; //Clone image to keep original image
FiltersSequence seq = new FiltersSequence();
seq.Add(Grayscale.CommonAlgorithms.BT709); //First add grayScaling filter
seq.Add(new OtsuThreshold()); //Then add binarization(thresholding) filter
temp = seq.Apply(source); // Apply filters on source image
//Extract blobs from image whose size width and height larger than 150
BlobCounter extractor = new BlobCounter();
extractor.FilterBlobs = true;
extractor.MinWidth = extractor.MinHeight = 150;
extractor.MaxWidth = extractor.MaxHeight = 350;
extractor.ProcessImage(temp);
//Will be used transform(extract) cards on source image
QuadrilateralTransformation quadTransformer = new QuadrilateralTransformation();
//Will be used resize(scaling) cards
ResizeBilinear resizer = new ResizeBilinear(CardWidth, CardHeight);
foreach (Blob blob in extractor.GetObjectsInformation())
{
//Get Edge points of card
List<IntPoint> edgePoints = extractor.GetBlobsEdgePoints(blob);
//Calculate/Find corners of card on source image from edge points
List<IntPoint> corners = PointsCloud.FindQuadrilateralCorners(edgePoints);
quadTransformer.SourceQuadrilateral = corners; //Set corners for transforming card
quadTransformer.AutomaticSizeCalculaton = true;
Bitmap cardImg = quadTransformer.Apply(source); //Extract(transform) card image
if (cardImg.Width > cardImg.Height) //If card is positioned horizontally
cardImg.RotateFlip(RotateFlipType.Rotate90FlipNone); //Rotate
cardImg = resizer.Apply(cardImg); //Normalize card size
Card card = new Card(cardImg, corners.ToArray()); //Create Card Object
char color = ScanColor(card.GetTopRightPart()); //Scan color
bool faceCard = IsFaceCard(cardImg); //Determine type of card(face or not)
if (!faceCard)
{
card.Suit = ScanSuit(cardImg, color); //Scan Suit of non-face card
card.Rank = ScanRank(cardImg); //Scan Rank of non-face card
}
else
{
Bitmap topRight = card.GetTopRightPart();
seq.Clear();
seq.Add(Grayscale.CommonAlgorithms.BT709);
seq.Add(new BradleyLocalThresholding());
topRight = seq.Apply(topRight);
BlobsFiltering bFilter = new BlobsFiltering(5, 5, 150, 150);
bFilter.ApplyInPlace(topRight); //Filter blobs that can not be a suit
card.Suit = ScanFaceSuit(topRight, color); //Scan suit of face card
card.Rank = ScanFaceRank(topRight); //Scan rank of face card
}
collection.Add(card); //Add card to collection
}
return collection;
}
private void InitFilters()
{
L_brownFilter = new ColorFiltering();
D_brownFilter = new ColorFiltering();
L_brownFilter.Red = new IntRange(125, 140);
L_brownFilter.Green = new IntRange(95, 110);
L_brownFilter.Blue = new IntRange(110, 130);
D_brownFilter.Red = new IntRange(55, 85);
D_brownFilter.Green = new IntRange(45, 75);
D_brownFilter.Blue = new IntRange(45, 75);
blobFilter = new BlobsFiltering();
blobFilter.CoupledSizeFiltering = true;
blobFilter.MinWidth = 70;
blobFilter.MinHeight = 70;
diffFilter = new Difference();
diffFilter.OverlayImage = back;
thresholdFilter = new Threshold(40);
erosionFilter = new Erosion();
edgeFilter = new Edges();
openFilter = new Opening();
pixelFilter = new Pixellate();
morphFilter = new Morph();
morphFilter.SourcePercent = 0.9;
towardsFilter = new MoveTowards();
towardsFilter.StepSize = 10;
blobCounter = new BlobCounter();
blobGrabber = new ExtractBiggestBlob();
}
protected override void UpdateFilter()
{
filter = new BlobsFiltering(MinWidth, MinHeight, MaxWidth, MaxHeight, CoupledFiltering);
}
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";
}
