public void bulkknoiseprocess()
{
// string[] file = Directory.GetFiles(@"C:\Users\kishor\Desktop\DS160\New folder (4)\a", "*.png");
// string[] file = Directory.GetFiles(@"C:\Users\kishor\Desktop\DS160\GAN\tensorflow-adversarial-master\tensorflow-adversarial-master\example\img\fgsm", "*.png");
// string[] file = Directory.GetFiles(@"C:\Users\kishor\Desktop\DS160\GAN\tensorflow-adversarial-master\tensorflow-adversarial-master\example\img\jsma\New folder", "*.png");
// string[] file = Directory.GetFiles(@"C:\Users\kishor\Desktop\DS160\GAN\tensorflow-adversarial-master\tensorflow-adversarial-master\example\img\deepfool", "*.png");
string[] file = Directory.GetFiles(@"C:\Users\kishor\Desktop\DS160\GAN\tensorflow-adversarial-master\tensorflow-adversarial-master\example\img\cw2", "*.png");
// string[] file = Directory.GetFiles(@"C:\Users\kishor\Desktop\DS160\GAN\tensorflow-adversarial-master\tensorflow-adversarial-master\example\img\ADV GAN", "*.png");
// System.Console.Write("\n" + descriptors.Count+" v ");
for (int i = 0; i < file.Length; i++)
{
string dupImagePath = file[i];
Bitmap org1 = (Bitmap)Accord.Imaging.Image.FromFile(dupImagePath);
Bitmap org2 = org1.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb); // 1,2,3
org1.Dispose();
org1 = ToGrayscale(org2);
// Bitmap org2 = org1.Clone(System.Drawing.Imaging.PixelFormat.Format8bppIndexed); // 4
// Accord.Imaging.Filters.GrayscaleBT709 Grayscalea = new Accord.Imaging.Filters.GrayscaleBT709();//4
// org1=Grayscalea.Apply(org2);
// Accord.Imaging.Filters.AdaptiveSmoothing adaptiveSmoothing = new Accord.Imaging.Filters.AdaptiveSmoothing(); //1
// Accord.Imaging.Filters.AdditiveNoise adaptiveSmoothing = new Accord.Imaging.Filters.AdditiveNoise(); //2
// Accord.Imaging.Filters.BilateralSmoothing adaptiveSmoothing = new Accord.Imaging.Filters.BilateralSmoothing();/3
Accord.Imaging.Filters.SimpleSkeletonization adaptiveSmoothing = new Accord.Imaging.Filters.SimpleSkeletonization();//4
Bitmap noiserem = adaptiveSmoothing.Apply(org1);
Accord.Imaging.Filters.Difference filter = new Accord.Imaging.Filters.Difference(org1);
// apply the filter
Bitmap resultImage = filter.Apply(noiserem);
// resultImage.Save(i + ".png");
// HistogramsOfOrientedGradients hog = new HistogramsOfOrientedGradients(numberOfBins: 9, blockSize: 3, cellSize: 6);
// Use it to extract descriptors from the Lena image:
// List<double[]> descriptors = hog.ProcessImage(resultImage);
// var a = hog.Histograms;
Accord.Imaging.ImageStatistics statistics =
new Accord.Imaging.ImageStatistics(resultImage);
// get the red histogram
var histogram = statistics.Gray;
// get the values
double mean = histogram.Mean; // mean red value
double stddev = histogram.StdDev; // standard deviation of red values
// int median = histogram.Median; // median red value
// int min = histogram.Min; // min red value
// int max = histogram.Max; // max value
// get 90% range around the median
// var range = histogram.GetRange(0.9);
Console.WriteLine("" + mean + "," + stddev);
org2.Dispose();
noiserem.Dispose();
resultImage.Dispose();
}
}
/// <summary>
/// ///////////////////////////
/// cfiar
/// </summary>
public void Cfiarbulkknoiseprocess(string filepath, int filterid, int times)
{
string[] file = Directory.GetFiles(filepath);
for (int i = 0; i < file.Length; i++)
{
if (i == 32)
{
break;
}
string dupImagePath = file[i];
Bitmap org1 = (Bitmap)Accord.Imaging.Image.FromFile(dupImagePath);
Bitmap org2 = org1.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb); // 1,2,3
org1.Dispose();
org1 = ToGrayscale(org2);
Bitmap noiserem = null;
if (filterid == 1)
{
Accord.Imaging.Filters.AdaptiveSmoothing noisefilter = new Accord.Imaging.Filters.AdaptiveSmoothing();
noiserem = noisefilter.Apply(org1);
}
else if (filterid == 2)
{
Accord.Imaging.Filters.AdditiveNoise noisefilter = new Accord.Imaging.Filters.AdditiveNoise();
noiserem = noisefilter.Apply(org1);
}
else if (filterid == 3)
{
Accord.Imaging.Filters.BilateralSmoothing noisefilter = new Accord.Imaging.Filters.BilateralSmoothing();
noiserem = noisefilter.Apply(org1);
}
else if (filterid == 4)
{
Accord.Imaging.Filters.SimpleSkeletonization noisefilter = new Accord.Imaging.Filters.SimpleSkeletonization();
noiserem = noisefilter.Apply(org1);
}
Accord.Imaging.Filters.Difference filter = new Accord.Imaging.Filters.Difference(org1);
// apply the filter
Bitmap resultImage = filter.Apply(noiserem);
// resultImage.Save(i + ".png");
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(resultImage);
// get the red histogram
var histogram = statistics.Gray;
// get the values
double mean = histogram.Mean; // mean red value
double stddev = histogram.StdDev; // standard deviation of red values
// int median = histogram.Median; // median red value
// int min = histogram.Min; // min red value
// int max = histogram.Max; // max value
// get 90% range around the median
// var range = histogram.GetRange(0.9);
Console.WriteLine("" + mean + "," + stddev);
rt.Text = rt.Text + "\n" + "" + mean + "," + stddev;
org2.Dispose();
noiserem.Dispose();
resultImage.Dispose();
}
}
