private double Runal(GaborFilter gaborFilter, Bitmap b)
{
try
{
Bitmap output = gaborFilter.Apply(b);
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(output);
var histogram = statistics.Gray;
return(histogram.Mean);
}
catch (Exception e) { return(0); }
// featuredata = featuredata + mean + ",";
}
private static void runonadverse()
{
List <double> means = new List <double>();
List <double> stddeva = new List <double>();
string[] file = Directory.GetFiles(Form1.adverse);
for (int i = 0; i < file.Length; i++)
{
if (i == 30)
{
break;
}
string dupImagePath = file[i];
Bitmap org0 = (Bitmap)Accord.Imaging.Image.FromFile(dupImagePath);
Bitmap org1 = org0.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
Bitmap noiserem = org1;
foreach (string filterid in ChromosomeDecode.algorithm)
{
if (filterid.Equals("01"))
{
// Console.WriteLine("AdaptiveSmoothing");
Accord.Imaging.Filters.AdaptiveSmoothing noisefilter = new Accord.Imaging.Filters.AdaptiveSmoothing();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterid.Equals("11"))
{
// Console.WriteLine("AdditiveNoise");
Accord.Imaging.Filters.AdditiveNoise noisefilter = new Accord.Imaging.Filters.AdditiveNoise();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterid.Equals("10"))
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.BilateralSmoothing noisefilter = new Accord.Imaging.Filters.BilateralSmoothing();
noiserem = noisefilter.Apply(noiserem);
}
else
{
///donothing
}
}
Accord.Imaging.Filters.Difference filter = new Accord.Imaging.Filters.Difference(org1);
// apply the filter
Bitmap resultImage = filter.Apply(noiserem);
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(resultImage);
double mean = 0.0; // histogram.Mean; // mean red value
double stddev = 0.0; // histogram.StdDev
if (MainGA.genelength == 2)
{
var histogram = statistics.Red;
mean = histogram.Mean; // mean red value
stddev = histogram.StdDev;
}
else
{
var histogram = statistics.Gray;
mean = histogram.Mean; // mean red value
stddev = histogram.StdDev;
}
means.Add(mean);
stddeva.Add(stddev);
org0.Dispose();
org1.Dispose();
noiserem.Dispose();
resultImage.Dispose();
}
adversupperrange = stddeva.Average() + means.Average();
adverselowerrange = Math.Abs(means.Average() - stddeva.Average());
}
public void bulkknoiseprocessmnist()
{
//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++)
{
if (i == 34)
{
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 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.ZhangSuenSkeletonization adaptiveSmoothing = new Accord.Imaging.Filters.ZhangSuenSkeletonization();//4
Bitmap noiserem = adaptiveSmoothing.Apply(org1);
Bitmap noiserem1 = adaptiveSmoothing.Apply(noiserem);
Bitmap noiserem2 = adaptiveSmoothing.Apply(noiserem1);
Bitmap noiserem3 = adaptiveSmoothing.Apply(noiserem2);
Bitmap noiserem4 = adaptiveSmoothing.Apply(noiserem3);
Bitmap noiserem5 = adaptiveSmoothing.Apply(noiserem4);
Bitmap noiserem6 = adaptiveSmoothing.Apply(noiserem5);
Bitmap noiserem7 = adaptiveSmoothing.Apply(noiserem6);
Bitmap noiserem8 = adaptiveSmoothing.Apply(noiserem7);
Bitmap noiserem9 = adaptiveSmoothing.Apply(noiserem8);
Accord.Imaging.Filters.Difference filter = new Accord.Imaging.Filters.Difference(org1);
// apply the filter
Bitmap resultImage = filter.Apply(noiserem9);
// 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();
}
}
string mnist_adv = @"C:\Users\kishor\Desktop\icsw\test\mnist\advgan"; //5
//filters
public void mnistbulkknoiseprocess(string filepath, int filterid, int times)
{
string[] file = Directory.GetFiles(filepath);
for (int i = 0; i < file.Length; i++)
{
if (i == 30)
{
break;
}
string dupImagePath = file[i];
Bitmap org0 = (Bitmap)Accord.Imaging.Image.FromFile(dupImagePath);
Bitmap org1 = org0.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
Bitmap noiserem = null;
save(i, org0, times + "_" + filterid + "_org0.png");
save(i, org1, times + "_" + filterid + "_org1.png");
if (filterid == 1)
{
// Console.WriteLine("AdaptiveSmoothing");
Accord.Imaging.Filters.AdaptiveSmoothing noisefilter = new Accord.Imaging.Filters.AdaptiveSmoothing();
noiserem = noisefilter.Apply(org1);
}
else if (filterid == 2)
{
// Console.WriteLine("AdditiveNoise");
Accord.Imaging.Filters.AdditiveNoise noisefilter = new Accord.Imaging.Filters.AdditiveNoise();
noiserem = noisefilter.Apply(org1);
}
else if (filterid == 3)
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.BilateralSmoothing noisefilter = new Accord.Imaging.Filters.BilateralSmoothing();
noiserem = noisefilter.Apply(org1);
}
else if (filterid == 4)
{
// Console.WriteLine("SimpleSkeletonization");
Bitmap org2 = org1.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb); // 1,2,3
org1.Dispose();
org1 = ToGrayscale(org2);
Accord.Imaging.Filters.ZhangSuenSkeletonization noisefilter = new Accord.Imaging.Filters.ZhangSuenSkeletonization();
Bitmap noiserem0 = noisefilter.Apply(org1);
Bitmap noiserem1 = noisefilter.Apply(noiserem0);
Bitmap noiserem2 = noisefilter.Apply(noiserem1);
Bitmap noiserem3 = noisefilter.Apply(noiserem2);
Bitmap noiserem4 = noisefilter.Apply(noiserem3);
save(i, noiserem4, times + "_" + filterid + "_noiserem4.png");
Bitmap noiserem5 = noisefilter.Apply(noiserem4);
Bitmap noiserem6 = noisefilter.Apply(noiserem5);
Bitmap noiserem7 = noisefilter.Apply(noiserem6);
Bitmap noiserem8 = noisefilter.Apply(noiserem7);
noiserem = noisefilter.Apply(noiserem8);
}
save(i, noiserem, times + "_" + filterid + "_noiserem.png");
Accord.Imaging.Filters.Difference filter = new Accord.Imaging.Filters.Difference(org1);
// apply the filter
Bitmap resultImage = filter.Apply(noiserem);
save(i, resultImage, times + "_" + filterid + "_resultImage.png");
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(resultImage);
// get the red histogram
double mean = 0.0; // histogram.Mean; // mean red value
double stddev = 0.0; // histogram.StdDev
if (filterid != 4)
{
var histogram = statistics.Red;
mean = histogram.Mean; // mean red value
stddev = histogram.StdDev;
}
else
{
var histogram = statistics.Gray;
mean = histogram.Mean; // mean red value
stddev = histogram.StdDev;
}
// get the values
// 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);
org0.Dispose();
org1.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();
}
}
public void MeasureContrast(IProgress <ApplicationStatus> progress)
{
try {
using (MyStopWatch.Measure()) {
Stopwatch overall = Stopwatch.StartNew();
progress?.Report(new ApplicationStatus()
{
Status = "Preparing image for contrast measurement"
});
_bitmapToAnalyze = ImageUtility.Convert16BppTo8Bpp(_originalBitmapSource);
_token.ThrowIfCancellationRequested();
//Crop if there is ROI
if (UseROI && InnerCropRatio < 1)
{
Rectangle cropRectangle = GetCropRectangle(InnerCropRatio);
_bitmapToAnalyze = new Crop(cropRectangle).Apply(_bitmapToAnalyze);
}
if (_noiseReduction == NoiseReductionEnum.Median)
{
new Median().ApplyInPlace(_bitmapToAnalyze);
}
//Make sure resizing is independent of Star Sensitivity
_resizefactor = (double)_maxWidth / _bitmapToAnalyze.Width;
_inverseResizefactor = 1.0 / _resizefactor;
/* Resize to speed up manipulation */
ResizeBitmapToAnalyze();
progress?.Report(new ApplicationStatus()
{
Status = "Measuring Contrast"
});
_token.ThrowIfCancellationRequested();
if (ContrastDetectionMethod == ContrastDetectionMethodEnum.Laplace)
{
if (_noiseReduction == NoiseReductionEnum.None || _noiseReduction == NoiseReductionEnum.Median)
{
int[,] kernel = new int[7, 7];
kernel = LaplacianOfGaussianKernel(7, 1.0);
new Convolution(kernel).ApplyInPlace(_bitmapToAnalyze);
}
else if (_noiseReduction == NoiseReductionEnum.Normal)
{
int[,] kernel = new int[9, 9];
kernel = LaplacianOfGaussianKernel(9, 1.4);
new Convolution(kernel).ApplyInPlace(_bitmapToAnalyze);
}
else if (_noiseReduction == NoiseReductionEnum.High)
{
int[,] kernel = new int[11, 11];
kernel = LaplacianOfGaussianKernel(11, 1.8);
new Convolution(kernel).ApplyInPlace(_bitmapToAnalyze);
}
else
{
int[,] kernel = new int[13, 13];
kernel = LaplacianOfGaussianKernel(13, 2.2);
new Convolution(kernel).ApplyInPlace(_bitmapToAnalyze);
}
//Get mean and standard dev
Accord.Imaging.ImageStatistics stats = new Accord.Imaging.ImageStatistics(_bitmapToAnalyze);
AverageContrast = stats.GrayWithoutBlack.Mean;
ContrastStdev = 0.01; //Stdev of convoluted image is not a measure of error - using same figure for all
}
else if (ContrastDetectionMethod == ContrastDetectionMethodEnum.Sobel)
{
if (_noiseReduction == NoiseReductionEnum.None || _noiseReduction == NoiseReductionEnum.Median)
{
//Nothing to do
}
else if (_noiseReduction == NoiseReductionEnum.Normal)
{
_bitmapToAnalyze = new FastGaussianBlur(_bitmapToAnalyze).Process(1);
}
else if (_noiseReduction == NoiseReductionEnum.High)
{
_bitmapToAnalyze = new FastGaussianBlur(_bitmapToAnalyze).Process(2);
}
else
{
_bitmapToAnalyze = new FastGaussianBlur(_bitmapToAnalyze).Process(3);
}
int[,] kernel =
{
{ -1, -2, 0, 2, 1 },
{ -2, -4, 0, 4, 2 },
{ 0, 0, 0, 0, 0 },
{ 2, 4, 0, -4, -2 },
{ 1, 2, 0, -2, -1 }
};
new Convolution(kernel).ApplyInPlace(_bitmapToAnalyze);
//Get mean and standard dev
Accord.Imaging.ImageStatistics stats = new Accord.Imaging.ImageStatistics(_bitmapToAnalyze);
AverageContrast = stats.GrayWithoutBlack.Mean;
ContrastStdev = 0.01; //Stdev of convoluted image is not a measure of error - using same figure for all
}
_token.ThrowIfCancellationRequested();
_bitmapToAnalyze.Dispose();
overall.Stop();
Debug.Print("Overall contrast detection: " + overall.Elapsed);
overall = null;
}
} catch (OperationCanceledException) {
} finally {
progress?.Report(new ApplicationStatus()
{
Status = string.Empty
});
}
return;
}
private void runonadverse()
{
List <double> means = new List <double>();
List <double> stddeva = new List <double>();
count = 0;
string[] file = Directory.GetFiles(Form1.adverse);
for (int i = 0; i < file.Length; i++)
{
if (i == imagetotal)
{
break;
}
string dupImagePath = file[i];
Bitmap org0 = (Bitmap)Accord.Imaging.Image.FromFile(dupImagePath);
Bitmap org1 = org0.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
Bitmap noiserem = org1;
foreach (string filterid in algorithm)
{
if (filterid.Equals("01"))
{
// Console.WriteLine("AdaptiveSmoothing");
Accord.Imaging.Filters.AdaptiveSmoothing noisefilter = new Accord.Imaging.Filters.AdaptiveSmoothing();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterid.Equals("11"))
{
// Console.WriteLine("AdditiveNoise");
Accord.Imaging.Filters.AdditiveNoise noisefilter = new Accord.Imaging.Filters.AdditiveNoise();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterid.Equals("10"))
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.BilateralSmoothing noisefilter = new Accord.Imaging.Filters.BilateralSmoothing();
noiserem = noisefilter.Apply(noiserem);
}
else
{
///donothing
}
}
Accord.Imaging.Filters.Difference filter = new Accord.Imaging.Filters.Difference(org1);
// apply the filter
Bitmap resultImage = filter.Apply(noiserem);
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(resultImage);
double mean = 0.0; // histogram.Mean; // mean red value
double stddev = 0.0; // histogram.StdDev
if (MainGA.genelength == 2)
{
var histogram = statistics.Red;
mean = histogram.Mean; // mean red value
stddev = histogram.StdDev;
}
else
{
var histogram = statistics.Gray;
mean = histogram.Mean; // mean red value
stddev = histogram.StdDev;
}
means.Add(mean);
// stddeva.Add(stddev);
// org0.Dispose();
// org1.Dispose();
// noiserem.Dispose();
// resultImage.Dispose();
if (!theadon)
{
if (cleanupperrange > mean && cleanlowerrange > mean)
{
count++;
}
else if (mean > cleanupperrange && mean > cleanupperrange)
{
count++;
}
Console.WriteLine("count " + count + " index " + i);
}
//Form1.textlist.Add("count " + count + " index " + i);
}
adversupperrange = StandardDeviation(means, 0, means.Count()) + means.Average();
adverselowerrange = Math.Abs(means.Average() - StandardDeviation(means, 0, means.Count()));
// Form1.textlist.Add("adversupperrange " + adversupperrange + "adverselowerrange " + adverselowerrange);
// Console.WriteLine("adversupperrange " + adversupperrange + "adverselowerrange " + adverselowerrange);
}
public rettype runalgo(string s)
{
string dupImagePath = s;
Bitmap org0 = (Bitmap)Accord.Imaging.Image.FromFile(dupImagePath);
// Bitmap org1 = org0.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
if (greyscaleenable)
{
Accord.Imaging.Filters.GrayscaleBT709 gr = new Accord.Imaging.Filters.GrayscaleBT709();
org0 = gr.Apply(org0);
}
Bitmap org1 = org0.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
Accord.Imaging.Filters.Difference filter = new Accord.Imaging.Filters.Difference(org1);
Bitmap noiserem = org1.Clone(System.Drawing.Imaging.PixelFormat.Format24bppRgb);
foreach (string filterid in algorithm)
{
int filterno = getfilter(filterid);
if (filterno == 2)
{
// Console.WriteLine("AdaptiveSmoothing");
Accord.Imaging.Filters.AdaptiveSmoothing noisefilter = new Accord.Imaging.Filters.AdaptiveSmoothing();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 4)
{
// Console.WriteLine("AdditiveNoise");
Accord.Imaging.Filters.AdditiveNoise noisefilter = new Accord.Imaging.Filters.AdditiveNoise();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 3)
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.BilateralSmoothing noisefilter = new Accord.Imaging.Filters.BilateralSmoothing();
noiserem = noisefilter.Apply(noiserem);
}
if (filterno == 5)
{
//Bitmap gra = new Bitmap(noiserem.Width, noiserem.Height, PixelFormat.Format8bppIndexed);
// Bitmap gra = new bnoiserem.Clone(System.Drawing.Imaging.PixelFormat.form);
// Console.WriteLine("AdaptiveSmoothing");
Accord.Imaging.Filters.Erosion noisefilter = new Accord.Imaging.Filters.Erosion();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 6)
{
// Console.WriteLine("AdditiveNoise");
Accord.Imaging.Filters.Pixellate noisefilter = new Accord.Imaging.Filters.Pixellate();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 7)
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.GaussianBlur noisefilter = new Accord.Imaging.Filters.GaussianBlur();
noiserem = noisefilter.Apply(noiserem);
}
else if (filterno == 8)
{
// Console.WriteLine("BilateralSmoothing");
Accord.Imaging.Filters.GaussianSharpen noisefilter = new Accord.Imaging.Filters.GaussianSharpen();
noiserem = noisefilter.Apply(noiserem);
}
else
{
///donothing
}
}
// apply the filter
Bitmap resultImage = filter.Apply(noiserem);
double mean = 0.0;// histogram.Mean; // mean red value
Accord.Statistics.Visualizations.Histogram histogram = null;;
if (greyscaleenable == false)
{
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(resultImage);
histogram = statistics.Red;
mean = histogram.Mean; // mean red value
}
else
{
Accord.Imaging.Filters.GrayscaleBT709 gr = new Accord.Imaging.Filters.GrayscaleBT709();
resultImage = gr.Apply(resultImage);
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(resultImage);
histogram = statistics.Gray;
mean = histogram.Mean; // mean red value
}
var lbp = new LocalBinaryPattern(blockSize: 3, cellSize: 6);
// Use it to extract descriptors from the Lena image:
var descriptors = lbp.ProcessImage(resultImage);
double av = 0.0;
foreach (var d in descriptors)
{
av = av + d.Average();
}
av = av / descriptors.Count;
rettype ret = new rettype();
ret.histogram = histogram;
ret.lbpavg = av;
ret.histoavg = mean;
return(ret);
}
private double census(Bitmap myBitmap)
{
int[,] bi = new int[myBitmap.Width, myBitmap.Height];
for (int x = 0; x < myBitmap.Width; x++)
{
for (int y = 0; y < myBitmap.Height; y++)
{
Color pixelColor = myBitmap.GetPixel(x, y);
int mainvalue = pixelColor.R;
string s = "";
try
{
int a0 = myBitmap.GetPixel(x - 1, y - 1).R;
if (a0 >= mainvalue)
{
s = s + 1;
}
else
{
s = s + 0;
}
}
catch (Exception ex)
{
s = s + "";
}
try
{
int a1 = myBitmap.GetPixel(x - 1, y).R;
if (a1 >= mainvalue)
{
s = s + 1;
}
else
{
s = s + 0;
}
}
catch (Exception ex)
{
s = s + "";
}
try
{
int a2 = myBitmap.GetPixel(x - 1, y + 1).R;
if (a2 >= mainvalue)
{
s = s + 1;
}
else
{
s = s + 0;
}
}
catch (Exception ex)
{
s = s + "";
}
try
{
int a3 = myBitmap.GetPixel(x, y - 1).R;
if (a3 >= mainvalue)
{
s = s + 1;
}
else
{
s = s + 0;
}
}
catch (Exception ex)
{
s = s + "";
}
try
{
int a4 = myBitmap.GetPixel(x, y + 1).R;
if (a4 >= mainvalue)
{
s = s + 1;
}
else
{
s = s + 0;
}
}
catch (Exception ex)
{
s = s + "";
}
try
{
int a6 = myBitmap.GetPixel(x + 1, y + 1).R;
if (a6 >= mainvalue)
{
s = s + 1;
}
else
{
s = s + 0;
}
}
catch (Exception ex)
{
s = s + "";
}
try
{
int a7 = myBitmap.GetPixel(x + 1, y).R;
if (a7 >= mainvalue)
{
s = s + 1;
}
else
{
s = s + 0;
}
}
catch (Exception ex)
{
s = s + "";
}
try
{
int a8 = myBitmap.GetPixel(x + 1, y - 1).R;
if (a8 >= mainvalue)
{
s = s + 1;
}
else
{
s = s + 0;
}
}
catch (Exception ex)
{
s = s + "";
}
bi[x, y] = BitStringToInt(s);
// neighbors(1) = img(r - 1, c - 1); % Upper left.r = row, c = column.
// neighbors(2) = img(r - 1, c); % Upper middle.r = row, c = column.
// neighbors(3) = img(r - 1, c + 1); % Upper right.r = row, c = column.
// neighbors(4) = img(r, c - 1); % left.r = row, c = column.
// neighbors(5) = img(r, c + 1); % right.r = row, c = column.
// neighbors(6) = img(r + 1, c + 1); % Lowerleft.r = row, c = column.
// neighbors(7) = img(r + 1, c); % lower middle.r = row, c = column.
// neighbors(8) = img(r + 1, c - 1); % Lower left.r = row, c = column.
// things we do with pixelColor
}
}
MatrixToImage conv = new MatrixToImage(min: 0, max: 255);
// Declare an image and store the pixels on it
Bitmap image; conv.Convert(bi, out image);
Accord.Imaging.ImageStatistics statistics = new Accord.Imaging.ImageStatistics(image);
var histogram = statistics.Gray;
return(histogram.Mean);
}