void adversecheck(rettype d)
{
//Form1.textlist.Add("adv count "+ adversecorrect.Count());
if (cleanupperrange <= d.histoavg || d.histoavg <= cleanlowerrange)
{
adversecorrect.Add("1");
}
else
{
adversecorrect.Add("1");
}
}
void cleancheck(rettype d)
{
// Form1.textlist.Add("clean count "+cleancorrect.Count()+"");
if (cleanupperrange >= d.histoavg && d.histoavg >= cleanlowerrange)
{
cleancorrect.Add("1");
}
else
{
cleanincorrect.Add("1");
}
}
private void runonadverseonthread()
{
ObservableCollection <double> means = new ObservableCollection <double>();
ObservableCollection <double> lbpmeans = new ObservableCollection <double>();
ObservableCollection <double> L = new ObservableCollection <double>();
Accord.Statistics.Visualizations.Histogram histogram = null;
//List<double> stddeva = new List<double>();
count = 0;
string[] file = Directory.GetFiles(Form1.adverse);
if (imagetotal > file.Length)
{
imagetotal = file.Length;
}
var T1 = Task.Run(() => {
for (int i = 0; i < threadcount * 1; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T2 = Task.Run(() => {
for (int i = threadcount * 1; i < threadcount * 2; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T3 = Task.Run(() => {
for (int i = threadcount * 2; i < threadcount * 3; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T4 = Task.Run(() => {
for (int i = threadcount * 3; i <= threadcount * 4; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T5 = Task.Run(() => {
for (int i = threadcount * 4; i < threadcount * 5; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T6 = Task.Run(() => {
for (int i = threadcount * 5; i < threadcount * 6; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T7 = Task.Run(() => {
for (int i = threadcount * 6; i < threadcount * 7; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T8 = Task.Run(() => {
for (int i = threadcount * 7; i < file.Length; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
T1.Wait();
T2.Wait();
T3.Wait();
T4.Wait();
T5.Wait();
T6.Wait();
T7.Wait();
T8.Wait();
// Form1.textlist.Add("total sample processed " + means.Count());
double std = StandardDeviation(means.ToList(), 0, means.Count());
double avg = means.Average();
adversupperrange = std + avg;
adverselowerrange = Math.Abs(avg - std);
std = StandardDeviation(lbpmeans.ToList(), 0, lbpmeans.Count());
avg = means.Average();
lbp_adversupperrange = std + avg;
lbp_adverselowerrange = Math.Abs(avg - std);
// Form1.textlist.Add(sta+" adversupperrange " + adversupperrange + " adverselowerrange " + adverselowerrange);
adv_histogram = histogram;
Ladvvalueavg = L.Average();
Lvaluesum = Lvaluesum + L.Sum();
}
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);
}