private static void TwoDotGrad(FileInfo file, string outputFileName)
{
double[] a = { 0, 65, 180, 255 };
double[] b = { 0, 40, 210, 255 };
var f = MathNet.Numerics.Interpolate.Linear(a, b);
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(file.FullName);
imageLoader.AddNoise(new GaussNoise(new Normal(0.0001, 0.0001)));
Bitmap image = (Bitmap)imageLoader.Image;
imageLoader.PlotHistogram("before", OutputPath, file);
int height = image.Height;
int width = image.Width;
for (var y = 0; y < height; y++)
{
for (var x = 0; x < width; x++)
{
double color = f.Interpolate(image.GetPixel(x, y).R);
Color destinationColor = Color.FromArgb(
Convert.ToByte(color.Clamp(0, 255)),
Convert.ToByte(color.Clamp(0, 255)),
Convert.ToByte(color.Clamp(0, 255)));
image.SetPixel(x, y, destinationColor);
}
}
imageLoader.Image = image;
imageLoader.Save($"{outputFileName}_gradation_{file.Extension}");
imageLoader.PlotHistogram("after", OutputPath, file);
}
}
private static void LaplacianFilter(FileInfo file, string outputFileName)
{
Console.WriteLine("***LAPLACIAN FILTER***");
if (file == null)
{
throw new ArgumentNullException(nameof(file));
}
double[] alphas = { 0, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6 };
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(file.FullName);
imageLoader.AddNoise(new GaussNoise(new Normal(0.0001, 0.0001)));
Image I = imageLoader.Image;
Console.WriteLine($"Avg luma of src: {imageLoader.CalculateAvgLuminocity()}");
imageLoader.PlotHistogram("src", OutputPath, file);
foreach (var alpha in alphas)
{
if (alpha == 0)
{
double[,] I1 = imageLoader.AddLaplacianFilterAlpha0();
//imageLoader.Save($"{outputFileName}_I1_laplacianfiltered{file.Extension}");
//Console.WriteLine($"Avg luma of I1: {imageLoader.CalculateAvgLuminocity()}");
//imageLoader.PlotHistogram("I1", OutputPath, file);
imageLoader.Add128(I1);
imageLoader.Save($"{outputFileName}_I1+128_laplacianfiltered{file.Extension}");
Console.WriteLine($"Avg luma of I1 + 128: {imageLoader.CalculateAvgLuminocity()}");
imageLoader.PlotHistogram("I1+128", OutputPath, file);
imageLoader.Image = I;
imageLoader.AddImage(I, I1);
imageLoader.Save($"{outputFileName}_I2_laplacianfiltered{file.Extension}");
Console.WriteLine($"Avg luma of I2: {imageLoader.CalculateAvgLuminocity()}");
imageLoader.PlotHistogram("I2", OutputPath, file);
}
else
{
imageLoader.AddLaplacianFilter(alpha);
imageLoader.Save($"{outputFileName}laplacianfiltered_(alpha={alpha}){file.Extension}");
Console.WriteLine($"Avg luma of alpha = {alpha}: {imageLoader.CalculateAvgLuminocity()}");
imageLoader.PlotHistogram($"alpha={alpha}", OutputPath, file);
}
imageLoader.Image = I;
}
}
}
private void PlotMedianFilter()
{
var plotMedian = new PlotModel {
Title = $"Median Filter Plot for {fileInfo.Name}"
};
plotMedian.Axes.Add(new LinearAxis {
Title = "R", Position = AxisPosition.Bottom
});
plotMedian.Axes.Add(new LinearAxis {
Title = "PSNR", Position = AxisPosition.Left
});
var probValue = new List <double>
{
0.025,
0.050,
0.125,
0.250
};
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(fileInfo.FullName);
Image image = imageLoader.Image;
foreach (var value in probValue)
{
var points = new LineSeries
{
StrokeThickness = 2,
MarkerSize = 4,
Title = $"{value * 2 * 100} %"
};
points.MarkerType = MarkerType.Circle;
for (var R = 1; R <= 5; R++)
{
imageLoader.AddNoise(new ImpulseNoise(value, value));
imageLoader.AddMedianFilter(R);
double psnr = imageLoader.CalculatePSNR(image);
points.Points.Add(new DataPoint(R, psnr));
imageLoader.Image = image;
}
plotMedian.Series.Add(points);
}
}
pngExporter.ExportToFile(plotMedian, $"{outputPath}/{fileInfo.Name}/MedianFilterPlot.png");
}
private void PlotGaussFilter()
{
var plotGauss = new PlotModel {
Title = $"Gauss Filter Plot for {fileInfo.Name}"
};
plotGauss.Axes.Add(new LinearAxis {
Title = "Sigma", Position = AxisPosition.Bottom
});
plotGauss.Axes.Add(new LinearAxis {
Title = "PSNR", Position = AxisPosition.Left
});
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(fileInfo.FullName);
Image image = imageLoader.Image;
Image noise = imageLoader.AddNoise(new GaussNoise(new Normal(0, 0.25))).Image;
for (var R = 1; R <= 8; R++)
{
var points = new LineSeries
{
StrokeThickness = 2,
MarkerSize = 4,
Title = $"R = {R}"
};
points.MarkerType = MarkerType.Circle;
for (var sigma = 0.5; sigma <= 3; sigma += 0.5)
{
imageLoader.Image = noise;
imageLoader.AddGaussFilter(R, sigma);
double psnr = imageLoader.CalculatePSNR(image);
points.Points.Add(new DataPoint(sigma, psnr));
imageLoader.Image = image;
}
plotGauss.Series.Add(points);
}
}
pngExporter.ExportToFile(plotGauss, $"{outputPath}/{fileInfo.Name}/GaussFilterPlot.png");
}
private void PlotImpulseNoise()
{
var plotGauss = new PlotModel {
Title = $"Impulse Noise Plot for {fileInfo.Name}"
};
plotGauss.Axes.Add(new LinearAxis {
Title = "p2", Position = AxisPosition.Bottom
});
plotGauss.Axes.Add(new LinearAxis {
Title = "PSNR", Position = AxisPosition.Left
});
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(fileInfo.FullName);
Image image = imageLoader.Image;
for (var p1 = 0.1; p1 <= 0.5; p1 += 0.1)
{
var impulsePoints = new LineSeries
{
StrokeThickness = 2,
MarkerSize = 4,
Title = $"p1 = {p1}"
};
impulsePoints.MarkerType = MarkerType.Circle;
for (var p2 = 0.0; p2 <= 0.5; p2 += 0.1)
{
imageLoader.AddNoise(new ImpulseNoise(p1, p2));
double psnr = imageLoader.CalculatePSNR(image);
impulsePoints.Points.Add(new DataPoint(p2, psnr));
imageLoader.Image = image;
}
plotGauss.Series.Add(impulsePoints);
}
}
pngExporter.ExportToFile(plotGauss, $"{outputPath}/{fileInfo.Name}/ImpulseNoisePlot.png");
}
private void PlotAdditiveNoise()
{
var plotGauss = new PlotModel {
Title = $"Additive Noise Plot for {fileInfo.Name}"
};
plotGauss.Axes.Add(new LinearAxis {
Title = "Standard Deviation", Position = AxisPosition.Bottom
});
plotGauss.Axes.Add(new LinearAxis {
Title = "PSNR", Position = AxisPosition.Left
});
var gaussPoints = new LineSeries
{
StrokeThickness = 2,
MarkerSize = 4,
Color = OxyColors.Red
};
gaussPoints.MarkerType = MarkerType.Circle;
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(fileInfo.FullName);
Image image = imageLoader.Image;
for (var stddev = 0.0; stddev < 240; stddev += 10)
{
imageLoader.AddNoise(new GaussNoise(new Normal(0, stddev)));
double psnr = imageLoader.CalculatePSNR(image);
gaussPoints.Points.Add(new DataPoint(stddev, psnr));
imageLoader.Image = image;
}
}
plotGauss.Series.Add(gaussPoints);
pngExporter.ExportToFile(plotGauss, $"{outputPath}/{fileInfo.Name}/AdditiveNoisePlot.png");
}
void PlotGaussR()
{
var plotGauss = new PlotModel {
Title = $"Gauss R with sigma 2 for {fileInfo.Name}"
};
plotGauss.Axes.Add(new LinearAxis {
Title = "R", Position = AxisPosition.Bottom
});
plotGauss.Axes.Add(new LinearAxis {
Title = "PSNR", Position = AxisPosition.Left
});
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(fileInfo.FullName);
Image image = imageLoader.Image;
var points = new LineSeries
{
StrokeThickness = 2,
MarkerSize = 4,
Color = OxyColors.Red
};
for (var R = 1; R <= 8; R++)
{
//imageLoader.Image = noise;
imageLoader.AddNoise(new GaussNoise(new Normal(0, 35))); // 100 dispersy
imageLoader.AddGaussFilter(R, 2);
double psnr = imageLoader.CalculatePSNR(image);
points.Points.Add(new DataPoint(R, psnr));
imageLoader.Image = image;
}
plotGauss.Series.Add(points);
}
pngExporter.ExportToFile(plotGauss, $"{outputPath}/{fileInfo.Name}/GaussFilterPlotR.png");
}
private static void BoxFilter(FileInfo file, string outputFileName)
{
Console.WriteLine("***BOX FILTER***");
if (file == null)
{
throw new ArgumentNullException(nameof(file));
}
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(file.FullName);
Image image = imageLoader.Image;
imageLoader.AddNoise(new GaussNoise(new Normal(0, 0.25)));
imageLoader.Save($"{outputFileName}noise{file.Extension}");
Console.WriteLine($"psnr-noise: {imageLoader.CalculatePSNR(image):F2}");
imageLoader.AddBoxFilter(5);
imageLoader.Save($"{outputFileName}boxfiltered{file.Extension}");
Console.WriteLine($"psnr-boxfilter: {imageLoader.CalculatePSNR(image):F2}");
}
}
private static void MedianFilter(FileInfo file, string outputFileName)
{
Console.WriteLine("***MEDIAN FILTER***");
if (file == null)
{
throw new ArgumentNullException(nameof(file));
}
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(file.FullName);
Image image = imageLoader.Image;
imageLoader.AddNoise(new ImpulseNoise(0.125, 0.125));
imageLoader.Save($"{outputFileName}impulsenoise{file.Extension}");
Console.WriteLine($"psnr-noise: {imageLoader.CalculatePSNR(image):F2}");
imageLoader.AddMedianFilter(2);
imageLoader.Save($"{outputFileName}medianfiltered{file.Extension}");
Console.WriteLine($"psnr-medianfilter: {imageLoader.CalculatePSNR(image):F2}");
}
}
private static void SobelFilter(FileInfo file, string outputFileName)
{
Console.WriteLine("***SOBEL FILTER***");
if (file == null)
{
throw new ArgumentNullException(nameof(file));
}
using (var imageLoader = new ImageLoader())
{
imageLoader.Load(file.FullName);
imageLoader.AddNoise(new GaussNoise(new Normal(0.0001, 0.0001)));
Image I = imageLoader.Image;
//Image Gh = imageLoader.AddSobelFilter("H").Image;
//imageLoader.Save($"{outputFileName}_GH_sobel{file.Extension}");
//imageLoader.Image = I;
//Image Gv = imageLoader.AddSobelFilter("V").Image;
//imageLoader.Save($"{outputFileName}_GV_sobel{file.Extension}");
double[,] Gv = imageLoader.GetSobelFilterDouble("V");
double[,] Gh = imageLoader.GetSobelFilterDouble("H");
Image ColorMap = Filters.SobelFilter.GetColorMap(Gv, Gh);
imageLoader.Image = ColorMap;
imageLoader.Save($"{outputFileName}_sobel_ColorMap_{file.Extension}");
for (int thr = 15; thr < 200; thr += 10)
{
Image BinaryCard = Filters.SobelFilter.GetBinaryCard(thr, Gv, Gh);
imageLoader.Image = BinaryCard;
imageLoader.Save($"{outputFileName}_sobel_BinaryCard_(thr={thr}){file.Extension}");
}
}
}
private static void Noise(FileInfo file)
{
Console.WriteLine("***ADDING NOISES***");
if (file == null)
{
throw new ArgumentNullException(nameof(file));
}
var gaussPoints = new LineSeries
{
StrokeThickness = 2,
MarkerSize = 4,
Color = OxyColors.Red
};
var impulsePoints = new LineSeries
{
StrokeThickness = 2,
MarkerSize = 4,
Color = OxyColors.Blue
};
var psnrGauss = new List <double>();
var psnrImpulse = new List <double>();
using (var imageLoader = new ImageLoader())
{
var stddevValues = new List <double>
{
0.025,
0.05,
0.10,
0.25,
0.5
};
var probValuse = new List <double>
{
0.025,
0.05,
0.1,
0.25,
0.5
};
imageLoader.Load(file.FullName);
Image image = imageLoader.Image;
foreach (double stddev in stddevValues)
{
imageLoader.AddNoise(new GaussNoise(new Normal(0, stddev)));
double psnr = imageLoader.CalculatePSNR(image);
psnrGauss.Add(psnr);
gaussPoints.Points.Add(new DataPoint(stddev, psnr));
imageLoader.Image = image;
}
foreach (double prob in probValuse)
{
imageLoader.AddNoise(new ImpulseNoise(prob, prob));
double psnr = imageLoader.CalculatePSNR(image);
psnrImpulse.Add(psnr);
impulsePoints.Points.Add(new DataPoint(prob, psnr));
imageLoader.Image = image;
}
}
Console.WriteLine("Gauss noise: ");
foreach (double psnr in psnrGauss)
{
Console.Write($"{psnr:F2}; ");
}
Console.WriteLine();
Console.WriteLine("Impulse noise: ");
foreach (double psnr in psnrImpulse)
{
Console.Write($"{psnr:F2}; ");
}
Console.WriteLine();
var pngExporter = new PngExporter {
Width = 1280, Height = 720, Background = OxyColors.White
};
var plotGauss = new PlotModel {
Title = "Gauss noise plot"
};
plotGauss.Series.Add(gaussPoints);
plotGauss.Series.Add(impulsePoints);
pngExporter.ExportToFile(plotGauss, $"{OutputPath}/noise_psnr.png");
}
