private void buttonStart_Click(object sender, EventArgs e)
{
ImageFactory imageFactory = new ImageFactory();
imageFactory.Load(pictureBox1.BackgroundImage);
IEdgeFilter edgeFilter = new SobelEdgeFilter();
imageFactory.DetectEdges(edgeFilter, true);
pictureBox1.BackgroundImage = imageFactory.Image;
}
/// <summary>
/// Performs the DetectEdges effect from the ImageProcessor library.
/// </summary>
/// <param name="image">The image with which to apply the effect.</param>
/// <param name="filter">The IEdgeFilter object that will detect the edges.</param>
/// <param name="greyscale">Whether or not the resulting image will be greyscale.</param>
/// <returns>A new image with the DetectEdges effect applied.</returns>
public Image DetectEdges(Image image, IEdgeFilter filter, bool greyscale = true)
{
using var factory = new ImageFactory();
factory.Load(image);
factory.DetectEdges(filter, greyscale);
var result = factory.Image;
return(new Bitmap(result));
}
public bool processImages()
{
collectImmages(startDir);
IEdgeFilter filter = new KirschEdgeFilter();
int threshold_value = 150; //0-255
for (int i = 0; i < filePaths.Length; i++)
{
string aimDirThis = aimDir + "/res" + i + ".png";
string aimDirThisPre = aimDir + "/respre" + i + ".png";
Image original = Image.FromFile((filePaths[i]));
Bitmap resized = new Bitmap(original, new Size(aimWidth, aimHeight));
Bitmap greyscale = MakeGrayscale3(resized);
MemoryStream outStream = new MemoryStream();
imageProcessor.Load(greyscale);
imageProcessor.DetectEdges(filter, false);
imageProcessor.Save(aimDir + "/temp" + "/res" + i + ".png");
imageProcessor.Save(outStream);
//Als nächstes eine Binärisierung mit Threshhold auf dem Kanten Bild
//Dann alles im greyscale Bild nur Pixel behalten, die im Binär = 1
Image <Gray, Byte> img = new Image <Gray, Byte>(aimDir + "/temp" + "/res" + i + ".png");
img = img.ThresholdBinary(new Gray(threshold_value), new Gray(255)).Dilate(1).Erode(1);
var labels = new Mat();
var stats = new Mat();
var centroids = new Mat();
var nLabels = CvInvoke.ConnectedComponentsWithStats(img, labels, stats, centroids);
int biggestIndex = 0;
int biggestArea = 0;
int[] statsData = new int[stats.Rows * stats.Cols];
stats.CopyTo(statsData); // Inhalt der 2-D Matrix in 1D Array umwandeln
//Suche größter weißer Bereich
for (int j = 5; j < statsData.Length; j = j + 5) //erste Component ist meistens das Schwarze, kann deswegen ignoriert werden
{
var area = statsData[j + 4];
if (area > biggestArea)
{
biggestArea = area;
biggestIndex = j;
}
}
/*
* var x = statsData[i * stats.Cols + 0];
* var y = statsData[i * stats.Cols + 1];
* var width = statsData[i * stats.Cols + 2];
* var height = statsData[i * stats.Cols + 3];
* var area = statsData[i * stats.Cols + 4];
*/
//Bitmap source = greyscale;
Bitmap edges = new Bitmap(outStream);
int componentX = statsData[biggestIndex + 0];
int componentY = statsData[biggestIndex + 1];
int componentWidth = statsData[biggestIndex + 2];
int componentHeight = statsData[biggestIndex + 3];
Bitmap CroppedColor = cropping(componentX, componentY, componentWidth, componentHeight, resized, true);
Bitmap CroppedImage = cropping(componentX, componentY, componentWidth, componentHeight, greyscale, false);
string color = determineColor(CroppedColor);
CroppedImage.Save(aimDir + "/res" + i + color + ".png", ImageFormat.Png);
//CroppedColor.Save(aimDir + "/resC" + i + color + ".png", ImageFormat.Png);
edges = null;
CroppedImage = null;
img = null;
}
return(true);
}
public override void Apply(ImageFactory fs)
{
fs = fs.DetectEdges(filter, greyscale);
}
