public static Filter MakeFilter(string name, ParamSet paramSet)
{
Filter filter = null;
switch (name)
{
case "box":
filter = BoxFilter.Create(paramSet);
break;
//case "gaussian":
// filter = CreateGaussianFilter(paramSet);
// break;
//case "mitchell":
// filter = CreateMitchellFilter(paramSet);
// break;
//case "sinc":
// filter = CreateSincFilter(paramSet);
// break;
//case "triangle":
// filter = CreateTriangleFilter(paramSet);
// break;
default:
//Error("Filter \"%s\" unknown.", name.c_str());
//exit(1);
break;
}
paramSet.ReportUnused();
return(filter);
}
public void EndIteration()
{
int width = moments[0].Width;
int height = moments[0].Height;
var filter = new BoxFilter(4);
MonochromeImage momentBuffer = new(width, height);
MonochromeImage varianceBuffer = new(width, height);
// Compute the variance factors for use in the next iteration
for (int i = 0; i < moments.Count; ++i)
{
// Estimate the pixel variances:
// First, we blur the image. Then, we subtract the blurred version from the original.
// Finally, we compute and square the difference, multiplying by the number of iterations
// to obtain a coarse estimate of the variance in a single iteration.
filter.Apply(pixelValues[i], varianceBuffer);
for (int row = 0; row < height; ++row)
{
for (int col = 0; col < width; ++col)
{
var delta = pixelValues[i].GetPixel(col, row) - varianceBuffer.GetPixel(col, row);
var mean = varianceBuffer.GetPixel(col, row);
var variance = delta * delta * curIteration;
varianceBuffer.SetPixel(col, row, variance);
}
}
filter.Apply(varianceBuffer, varianceFactors[i]);
// Also filter the second moment estimates
filter.Apply(moments[i], momentBuffer);
// Compute the ratio for all non-zero pixels
for (int row = 0; row < height; ++row)
{
for (int col = 0; col < width; ++col)
{
var variance = varianceFactors[i].GetPixel(col, row);
var moment = momentBuffer.GetPixel(col, row);
if (variance > 0)
{
varianceBuffer.SetPixel(col, row, moment / variance);
}
else
{
varianceBuffer.SetPixel(col, row, 1);
}
}
}
// Apply a wide filter to the ratio image, too
filter.Apply(varianceBuffer, varianceFactors[i]);
}
isReady = true;
}
public static ResamplingFilter Create(ResamplingFilters filter) {
ResamplingFilter resamplingFilter = null;
switch (filter) {
case ResamplingFilters.Box:
resamplingFilter = new BoxFilter();
break;
case ResamplingFilters.Triangle:
resamplingFilter = new TriangleFilter();
break;
case ResamplingFilters.Hermite:
resamplingFilter = new HermiteFilter();
break;
case ResamplingFilters.Bell:
resamplingFilter = new BellFilter();
break;
case ResamplingFilters.CubicBSpline:
resamplingFilter = new CubicBSplineFilter();
break;
case ResamplingFilters.Lanczos3:
resamplingFilter = new Lanczos3Filter();
break;
case ResamplingFilters.Mitchell:
resamplingFilter = new MitchellFilter();
break;
case ResamplingFilters.Cosine:
resamplingFilter = new CosineFilter();
break;
case ResamplingFilters.CatmullRom:
resamplingFilter = new CatmullRomFilter();
break;
case ResamplingFilters.Quadratic:
resamplingFilter = new QuadraticFilter();
break;
case ResamplingFilters.QuadraticBSpline:
resamplingFilter = new QuadraticBSplineFilter();
break;
case ResamplingFilters.CubicConvolution:
resamplingFilter = new CubicConvolutionFilter();
break;
case ResamplingFilters.Lanczos8:
resamplingFilter = new Lanczos8Filter();
break;
}
return resamplingFilter;
}
public static ResamplingFilter Create(ResamplingFilters filter)
{
ResamplingFilter resamplingFilter = null;
switch (filter)
{
case ResamplingFilters.Box:
resamplingFilter = new BoxFilter();
break;
case ResamplingFilters.Triangle:
resamplingFilter = new TriangleFilter();
break;
case ResamplingFilters.Hermite:
resamplingFilter = new HermiteFilter();
break;
case ResamplingFilters.Bell:
resamplingFilter = new BellFilter();
break;
case ResamplingFilters.CubicBSpline:
resamplingFilter = new CubicBSplineFilter();
break;
case ResamplingFilters.Lanczos3:
resamplingFilter = new Lanczos3Filter();
break;
case ResamplingFilters.Mitchell:
resamplingFilter = new MitchellFilter();
break;
case ResamplingFilters.Cosine:
resamplingFilter = new CosineFilter();
break;
case ResamplingFilters.CatmullRom:
resamplingFilter = new CatmullRomFilter();
break;
case ResamplingFilters.Quadratic:
resamplingFilter = new QuadraticFilter();
break;
case ResamplingFilters.QuadraticBSpline:
resamplingFilter = new QuadraticBSplineFilter();
break;
case ResamplingFilters.CubicConvolution:
resamplingFilter = new CubicConvolutionFilter();
break;
case ResamplingFilters.Lanczos8:
resamplingFilter = new Lanczos8Filter();
break;
}
return(resamplingFilter);
}
using System;
using System.Drawing;
using System.IO;
using ImageFilter.Filters;
using ImageFilter.Noises;
using ImageFilter.Extensions;
using OxyPlot;
using OxyPlot.WindowsForms;
using OxyPlot.Axes;
using OxyPlot.Series;
using System.Collections.Generic;
namespace ImageFilter
{
public class ImageLoader : IDisposable
{
#region Fields
private bool isDisposed;
#endregion
#region Propeties
public Image Image { get; set; }
public string ImagePath { get; private set; }
#endregion
#region Methods
public double CalculatePSNR(Image image)
{
var img1 = (Bitmap) Image;
var img2 = (Bitmap) image;
int width = image.Width;
int height = image.Height;
double psnrY = 0;
using (var img1BMP = new ConcurrentBitmap(img1))
{
using (var img2BMP = new ConcurrentBitmap(img2))
{
// For each line
for (var y = 0; y < height; y++)
{
// For each pixel
for (var x = 0; x < width; x++)
{
Color img1Color = img1BMP.GetPixel(x, y);
// Assumes that img2 is not in Y component
Color tmpColor = img2BMP.GetPixel(x, y);
var I = (int) (tmpColor.R * 0.299
+ tmpColor.G * 0.587
+ tmpColor.B * 0.114);
Color img2Color = Color.FromArgb(I, I, I);
psnrY += Math.Pow(img1Color.R - img2Color.R, 2);
}
}
}
}
psnrY = 10 * Math.Log10(width * height * Math.Pow(Math.Pow(2, 8) - 1, 2) / psnrY);
/*Console.WriteLine($"Y: {psnrY}");*/
return psnrY;
}
public ImageLoader Load(string filePath)
{
var fileInfo = new FileInfo(filePath);
if (!fileInfo.Exists)
{
throw new FileNotFoundException(filePath);
}
ImagePath = filePath;
// Open a file stream
using (var fileStream = new FileStream(filePath, FileMode.Open, FileAccess.Read))
{
var stream = new MemoryStream();
fileStream.CopyTo(stream);
stream.Position = 0;
Image = Image.FromStream(stream);
}
return this;
}
public ImageLoader Save(string filePath)
{
var dirInfo = new DirectoryInfo(Path.GetDirectoryName(filePath));
if (!dirInfo.Exists)
{
dirInfo.Create();
}
Image.Save(filePath);
return this;
}
public ImageLoader AddImage(Image I, double[,] img)
{
Bitmap one = (Bitmap) I;
//Bitmap two = (Bitmap) img;
Bitmap res = new Bitmap(one.Width, one.Height);
int height = Image.Height;
int width = Image.Width;
for (int y = 0; y < height; y++)
{
for (var x = 0; x < width; x++)
{
double blue;
double green;
double red = green = blue = 0;
Color colorOne = one.GetPixel(x, y);
int colorTwo = (int) img[y, x];//two.GetPixel(x, y);
red = colorOne.R + colorTwo;
green = colorOne.G + colorTwo;
blue = colorOne.B + colorTwo;
Color destinationColor = Color.FromArgb(
Convert.ToByte(red.Clamp(0, 255)),
Convert.ToByte(green.Clamp(0, 255)),
Convert.ToByte(blue.Clamp(0, 255)));
res.SetPixel(x, y, destinationColor);
}
}
Image = res;
return this;
}
public ImageLoader Add128(double[,] I)
{
Bitmap image = (Bitmap) Image;
Bitmap res = new Bitmap(Image.Width, Image.Height);
int height = Image.Height;
int width = Image.Width;
for (int y = 0; y < height; y++)
{
for (var x = 0; x < width; x++)
{
double blue;
double green;
double red = green = blue = 0;
red = I[y, x];// 128;
blue = I[y, x];// + 128;
green = I[y, x];//
red += 128;
blue += 128;
green += 128;
Color destinationColor = Color.FromArgb(
Convert.ToByte(red.Clamp(0, 255)),
Convert.ToByte(green.Clamp(0, 255)),
Convert.ToByte(blue.Clamp(0, 255)));
res.SetPixel(x, y, destinationColor);
}
}
Image = res;
return this;
}
public ImageLoader AddNoise(INoise noise)
{
Image = noise.ProcessPicture(this);
return this;
}
public ImageLoader AddBoxFilter(int size)
{
var boxFilter = new BoxFilter(size);
Image = boxFilter.ProcessPicture(this);
return this;
}
public ImageLoader AddGaussFilter(int size, double sigma)
{
var boxFilter = new GaussFilter(size, sigma);
Image = boxFilter.ProcessPicture(this);
return this;
}
public ImageLoader AddMedianFilter(int size)
{
var medianFilter = new MedianFilter(size);
Image = medianFilter.ProcessPicture(this);
return this;
}
public ImageLoader AddLaplacianFilter(double alpha)
{
var laplacianFilter = new LaplacianFilter(alpha);
Image = laplacianFilter.ProcessPicture(this);
return this;
}
public double[,] AddLaplacianFilterAlpha0()
{
var laplacianFilter = new LaplacianFilter(0);
double[,] res = laplacianFilter.ProcessPictureAlpha0(this);
return res;
}
public ImageLoader AddSobelFilter(string direction)
{
var sobelFilter = new SobelFilter(direction == "V" ? 1 : 0);
Image = sobelFilter.ProcessPicture(this);
return this;
}
public double[,] GetSobelFilterDouble(string direction)
{
var sobelFilter = new SobelFilter(direction == "V" ? 1 : 0);
return sobelFilter.Get(this);
}
public double CalculateAvgLuminocity()
{
double luminocity = 0;
Bitmap image = (Bitmap)Image;
int height = image.Height;
int width = image.Width;
for (int y = 0; y < height; y++)
{
for (var x = 0; x < width; x++)
{
luminocity += image.GetPixel(x, y).R;
}
}
return luminocity / (double) (height * width);
}
private SortedDictionary<int, int> GetFrequency()
{
Bitmap image = (Bitmap)Image;
int height = image.Height;
int width = image.Width;
var result = new SortedDictionary<int, int>();
for (int y = 0; y < height; y++)
{
for (var x = 0; x < width; x++)
{
int Y = image.GetPixel(x, y).R;
if (result.ContainsKey(Y))
{
result[Y] = result[Y] + 1;
} else
{
result.Add(Y, 1);
}
}
}
return result;
}
public void PlotHistogram(string name, string outputPath, FileInfo fileInfo)
{
PngExporter pngExporter = new PngExporter { Width = 1280, Height = 720, Background = OxyColors.White };
var freq = GetFrequency();
var x = freq.Values;
var y = freq.Keys;
var plotGauss = new PlotModel { Title = $"Histogram for {name}" };
plotGauss.Axes.Add(new LinearAxis { Title = "x", Position = AxisPosition.Bottom });
plotGauss.Axes.Add(new LinearAxis { Title = "y", Position = AxisPosition.Left });
var barSeries = new LinearBarSeries
{
};
foreach (var item in freq)
{
barSeries.Points.Add(new DataPoint(item.Key, item.Value));
}
plotGauss.Series.Add(barSeries);
pngExporter.ExportToFile(plotGauss, $"{outputPath}/{fileInfo.Name}/histograms/{name}{fileInfo.Extension}");
}
#region Dispose
~ImageLoader()
{
Dispose(false);
}
public void Dispose()
{
Dispose(true);
// Already cleaned up in dispose method, supress GC
GC.SuppressFinalize(this);
}
private void Dispose(bool disposing)
{
if (isDisposed)
{
return;
}
if (disposing)
{
if (Image != null)
{
Image.Dispose();
Image = null;
}
}
isDisposed = true;
}
#endregion
#endregion
}
}
public BoxFilterTests()
{
this.boxFilter = new BoxFilter();
}
