private void CorrectLevel(int MinLevel, int MaxLevel)
{
LevelsLinear filter = new LevelsLinear();
filter.Input = new IntRange(MinLevel, MaxLevel);
filter.ApplyInPlace(_recogimg);
}
public void WriteResults(Bitmap frame, TimeAggregator timeAggregator)
{
// Reduce brightness of incoming image to approximately 20% before we write on it.
LevelsLinear filter = new LevelsLinear();
filter.OutRed = new IntRange(0, 50);
filter.OutGreen = new IntRange(0, 50);
filter.OutBlue = new IntRange(0, 50);
filter.ApplyInPlace(frame);
var maxRows = Math.Min(20, timeAggregator.Times.Count()) + 3;
frame.WriteLine($"Average (drop high & low): {timeAggregator.AverageDropHighLow.ToSecondsString()}", 0, maxRows, resultsDefaultBrush);
frame.WriteLine($"Mean: {timeAggregator.Mean.ToSecondsString()}", 1, maxRows, resultsDefaultBrush);
int currentIndex = 0;
int minIndex = timeAggregator.Times.MinIndex();
int maxIndex = timeAggregator.Times.MaxIndex();
foreach (var time in timeAggregator.Times)
{
var brush = currentIndex == minIndex ? resultsBestTimeBrush :
currentIndex == maxIndex ? resultsWorstTimeBrush : resultsDefaultBrush;
frame.WriteLine($"{currentIndex + 1}. {time.ToSecondsString()}", 3 + currentIndex, maxRows, brush);
++currentIndex;
}
}
public LevelsLinearFilter()
{
levelsLinear = new LevelsLinear();
levelsLinear.InRed = new IntRange(30, 230);
levelsLinear.InGreen = new IntRange(50, 240);
levelsLinear.InBlue = new IntRange(10, 210);
}
private static Bitmap CreateGrayscale(Bitmap image)
{
const int size = 512;
float scale = Math.Min(size / (float)image.Width, size / (float)image.Height);
ResizeBicubic resize = new ResizeBicubic((int)(image.Width*scale), (int)(image.Height*scale));
ImageStatistics stat = new ImageStatistics( image );
LevelsLinear levelsLinear = new LevelsLinear
{
Output = new IntRange(0, 255),
InRed = stat.Red.GetRange(.95),
InBlue = stat.Blue.GetRange(.95),
InGreen = stat.Green.GetRange(.95)
};
SaturationCorrection sc = new SaturationCorrection(-1);
Bitmap square = new Bitmap(size, size);
using(Bitmap resized = resize.Apply(sc.Apply(levelsLinear.Apply(image)).MakeGrayscale()))
using (Graphics g = Graphics.FromImage(square))
{
g.DrawImage(resized, new Point((size-resized.Width) / 2 ,0));
}
return square;
}
// On Filters->Levels Linear Correction
private void rgbLinearFiltersItem_Click(object sender, System.EventArgs e)
{
LevelsLinear filter = new LevelsLinear( );
filter.InRed = new IntRange(30, 230);
filter.InGreen = new IntRange(50, 240);
filter.InBlue = new IntRange(10, 210);
ApplyFilter(filter);
rgbLinearFiltersItem.Checked = true;
}
private void ProcessSingleImage(Bitmap source)
{
ImageStatistics stats = new ImageStatistics(source);
LevelsLinear levelsLinear = new LevelsLinear {
InRed = stats.Red.GetRange(0.87),
InGreen = stats.Green.GetRange(0.87),
InBlue = stats.Blue.GetRange(0.87)
};
levelsLinear.ApplyInPlace(source);
}
/*TEMP
* // On Filters->Difference edge detector
* void differenceEdgesFiltersItem_Click(Base control)
* {
* // save original image
* Bitmap originalImage = sourceImage;
* // get grayscale image
* sourceImage = Grayscale.CommonAlgorithms.RMY.Apply(sourceImage);
* // apply edge filter
* ApplyFilter(new DifferenceEdgeDetector());
* // delete grayscale image and restore original
* sourceImage.Dispose();
* sourceImage = originalImage;
*
* differenceEdgesFiltersItem.IsChecked = true;
* }
*
*
* // On Filters->Homogenity edge detector
* void homogenityEdgesFiltersItem_Click(Base control)
* {
* // save original image
* Bitmap originalImage = sourceImage;
* // get grayscale image
* sourceImage = Grayscale.CommonAlgorithms.RMY.Apply(sourceImage);
* // apply edge filter
* ApplyFilter(new HomogenityEdgeDetector());
* // delete grayscale image and restore original
* sourceImage.Dispose();
* sourceImage = originalImage;
*
* homogenityEdgesFiltersItem.IsChecked = true;
* }
*
*
* // On Filters->Sobel edge detector
* void sobelEdgesFiltersItem_Click(Base control)
* {
* // save original image
* Bitmap originalImage = sourceImage;
* // get grayscale image
* sourceImage = Grayscale.CommonAlgorithms.RMY.Apply(sourceImage);
* // apply edge filter
* ApplyFilter(new SobelEdgeDetector());
* // delete grayscale image and restore original
* sourceImage.Dispose();
* sourceImage = originalImage;
*
* sobelEdgesFiltersItem.IsChecked = true;
* }*/
// On Filters->Levels Linear Correction
void rgbLinearFiltersItem_Click(Base control)
{
LevelsLinear filter = new LevelsLinear();
filter.InRed = new IntRange(30, 230);
filter.InGreen = new IntRange(50, 240);
filter.InBlue = new IntRange(10, 210);
ApplyFilter(filter);
rgbLinearFiltersItem.IsChecked = true;
}
private void levelsLinearCorrectionToolStripMenuItem_Click(object sender, EventArgs e)
{
UncheckAllMenuItems();
LevelsLinear filter = new LevelsLinear();
filter.InRed = new IntRange(30, 230);
filter.InGreen = new IntRange(50, 240);
filter.InBlue = new IntRange(10, 210);
ApplyFilter(filter);
levelsLinearCorrectionToolStripMenuItem.Checked = true;
}
private void sbColorRemap_ValueChanged(object sender, EventArgs e)
{
Bitmap img = new Bitmap(image);
LevelsLinear filter = new LevelsLinear();
// set ranges
filter.InRed = new IntRange(sbColorRemap.Value, 300);
filter.InGreen = new IntRange(sbColorRemap.Value, 300);
filter.InBlue = new IntRange(sbColorRemap.Value, 300);
// apply the filter
filter.ApplyInPlace(img);
pictureBox2.Image = img;
}
private Bitmap ProcessSingleImage(Bitmap _src)
{
ImageStatistics stats = new ImageStatistics(_src);
LevelsLinear levelsLinear = new LevelsLinear();
levelsLinear.InRed = stats.Red.GetRange(0.87);
levelsLinear.InGreen = stats.Green.GetRange(0.87);
levelsLinear.InBlue = stats.Blue.GetRange(0.87);
levelsLinear.ApplyInPlace(_src);
return(_src);
}
// On Filters->Levels Linear Correction
//水平线性矫正
private void rgbLinearFiltersItem_Click(object sender, System.EventArgs e)
{
LevelsLinear filter = new LevelsLinear();
/*
* 该滤波器通过将指定信道的输入范围映射到输出范围来执行对RGB信道的线性校正。
* 它类似于颜色映射，但是重新映射是线性的。
*/
filter.InRed = new IntRange(30, 230);
filter.InGreen = new IntRange(50, 240);
filter.InBlue = new IntRange(10, 210);
ApplyFilter(filter);
rgbLinearFiltersItem.Checked = true;
}
public static Image ApplyRangeLevels(int minimum, int maximum, Image inputImage)
{
// create filter
LevelsLinear filter = new LevelsLinear()
{ /* set ranges*/
InRed = new AForge.IntRange(minimum, maximum),
InGreen = new AForge.IntRange(minimum, maximum),
InBlue = new AForge.IntRange(minimum, maximum)
};
// apply the filter
using (Bitmap filteredImage = (Bitmap)inputImage.Clone())
{
filter.ApplyInPlace(filteredImage);
return((Bitmap)filteredImage.Clone());
}
}
public PreProcess(UISettings ui, FileData file)
{
try {
ExtendFileData(file);
BitmapOriginal = (ui.WorkDirectory + "\\" + FileName).FileTo24bbpRgb(ResizeRatio: ui.ResizeValue, FrameCrop: ui.CropValue, ImageZoom: 1, RotateDegree: ui.RotateDegree);
UnmanagedMarkup = UnmanagedImage.FromManagedImage(BitmapOriginal);
ImageStatistics stats = null;
Threshold AFbinary = new Threshold(1);
Grayscale AFgray = new Grayscale(0.1, 0.7, 0.2);
if (ui.ExcludeColorSwitch && ui.ExcludeColorRadius > 0)
{
System.Windows.Media.Color excolor = (System.Windows.Media.Color)System.Windows.Media.ColorConverter.ConvertFromString(ui.ExcludeColorHex);
EuclideanColorFiltering AFexcolor = new EuclideanColorFiltering(new RGB(excolor.R, excolor.G, excolor.B), (short)ui.ExcludeColorRadius);
UnmanagedExclude = AFbinary.Apply(AFgray.Apply(AFexcolor.Apply(UnmanagedMarkup)));
}
else
{
UnmanagedExclude = UnmanagedImage.Create(UnmanagedMarkup.Width, UnmanagedMarkup.Height, PixelFormat.Format8bppIndexed);
}
if (ui.WhiteBalanceSwitch || ui.BlackBalanceSwitch) // need to apply auto white/black balance
{
Invert AFinvert = new Invert();
stats = new ImageStatistics(UnmanagedMarkup, AFinvert.Apply(UnmanagedExclude));
int lowend = (ui.BlackBalanceSwitch) ? (int)Math.Round(0.333d * (stats.RedWithoutBlack.Center2QuantileValue(ui.BlackBalance) + stats.GreenWithoutBlack.Center2QuantileValue(ui.BlackBalance) + stats.BlueWithoutBlack.Center2QuantileValue(ui.BlackBalance))):0;
LevelsLinear levelsLinear = new LevelsLinear {
InRed = new IntRange(lowend, (ui.WhiteBalanceSwitch) ? stats.RedWithoutBlack.Center2QuantileValue(ui.WhiteBalance) : 255),
InGreen = new IntRange(lowend, (ui.WhiteBalanceSwitch) ? stats.GreenWithoutBlack.Center2QuantileValue(ui.WhiteBalance) : 255),
InBlue = new IntRange(lowend, (ui.WhiteBalanceSwitch) ? stats.BlueWithoutBlack.Center2QuantileValue(ui.WhiteBalance) : 255),
};
//LevelsLinear levelsLinear = new LevelsLinear {
// InRed=new IntRange((ui.BlackBalanceSwitch)?stats.RedWithoutBlack.Center2QuantileValue(ui.BlackBalance):0, (ui.WhiteBalanceSwitch)?stats.RedWithoutBlack.Center2QuantileValue(ui.WhiteBalance):255),
// InGreen=new IntRange((ui.BlackBalanceSwitch)?stats.GreenWithoutBlack.Center2QuantileValue(ui.BlackBalance):0, (ui.WhiteBalanceSwitch)?stats.GreenWithoutBlack.Center2QuantileValue(ui.WhiteBalance):255),
// InBlue=new IntRange((ui.BlackBalanceSwitch)?stats.BlueWithoutBlack.Center2QuantileValue(ui.BlackBalance):0, (ui.WhiteBalanceSwitch)?stats.BlueWithoutBlack.Center2QuantileValue(ui.WhiteBalance):255),
//};
levelsLinear.ApplyInPlace(UnmanagedMarkup);
}
if (ui.GaussianBlurSwitch && ui.GaussianBlur != 0) // Gaussian Blur and Darken
{
GaussianBlur AFgblur = new GaussianBlur(11.0, Math.Max(ui.GaussianBlur, 0) * 2 + 1); // Gaussian Blur sigma = 8.0 kernel size = 7
Intersect AFintersect = new Intersect(AFgblur.Apply(UnmanagedMarkup));
UnmanagedMarkup = AFintersect.Apply(UnmanagedMarkup);
}
UnmanagedGray = AFgray.Apply(UnmanagedMarkup); // directly turn into gray
} catch { throw new Exception("Error Occured During PreProcessing"); }
}
private void RunLevels()
{
BitmapType = mFilter.BitmapTypes.None;
Effect = new LevelsLinear();
Effect.InRed = new Accord.IntRange((int)RedIn.T0, (int)RedIn.T1);
Effect.OutRed = new Accord.IntRange((int)RedOut.T0, (int)RedOut.T1);
Effect.InGreen = new Accord.IntRange((int)GreenIn.T0, (int)GreenIn.T1);
Effect.OutGreen = new Accord.IntRange((int)GreenOut.T0, (int)GreenOut.T1);
Effect.InBlue = new Accord.IntRange((int)BlueIn.T0, (int)BlueIn.T1);
Effect.OutBlue = new Accord.IntRange((int)BlueOut.T0, (int)BlueOut.T1);
Effect.InGray = new Accord.IntRange((int)GrayIn.T0, (int)GrayIn.T1);
Effect.OutGray = new Accord.IntRange((int)GrayOut.T0, (int)GrayOut.T1);
filter = Effect;
}
private Bitmap ProcessSingleImage(Bitmap _src)
{
Bitmap img = (_src.PixelFormat == PixelFormat.Format24bppRgb) ? _src : CloneTo24bpp(_src);
ImageStatistics stats = new ImageStatistics(img);
LevelsLinear levelsLinear = new LevelsLinear();
levelsLinear.InRed = stats.Red.GetRange(0.87);
levelsLinear.InGreen = stats.Green.GetRange(0.87);
levelsLinear.InBlue = stats.Blue.GetRange(0.87);
levelsLinear.ApplyInPlace(img);
if (_src.PixelFormat != PixelFormat.Format24bppRgb)
{
Graphics g = Graphics.FromImage(_src);
g.DrawImageUnscaled(img, 0, 0);
img.Dispose();
}
return(_src);
}
private Bitmap AddFilter(Bitmap bitmap)
{
switch (filterNumber)
{
case Filters.BrightnessCorrection:
return(ApplyFilter(bitmap, new BrightnessCorrection()));
case Filters.ColorFiltering:
return(ApplyFilter(bitmap, new ColorFiltering(new IntRange(25, 230), new IntRange(25, 230), new IntRange(25, 230))));
case Filters.ContrastCorrection:
return(ApplyFilter(bitmap, new ContrastCorrection()));
case Filters.DifferenceEdgeDetector:
bitmap = Grayscale.CommonAlgorithms.RMY.Apply(bitmap);
return(ApplyFilter(bitmap, new DifferenceEdgeDetector()));
case Filters.FloydSteinbergDithering:
bitmap = Grayscale.CommonAlgorithms.RMY.Apply(bitmap);
return(ApplyFilter(bitmap, new FloydSteinbergDithering()));
case Filters.Grayscale:
return(ApplyFilter(bitmap, Grayscale.CommonAlgorithms.BT709));
case Filters.HomogenityEdgeDetector:
bitmap = Grayscale.CommonAlgorithms.RMY.Apply(bitmap);
return(ApplyFilter(bitmap, new HomogenityEdgeDetector()));
case Filters.HSLFiltering:
return(ApplyFilter(bitmap, new HSLFiltering(new IntRange(330, 30), new Range(0, 1), new Range(0, 1))));
case Filters.HueModifier:
return(ApplyFilter(bitmap, new HueModifier(50)));
case Filters.Jitter:
return(ApplyFilter(bitmap, new Jitter()));
case Filters.LevelsLinear:
LevelsLinear lfilter = new LevelsLinear();
lfilter.InRed = new IntRange(30, 230);
lfilter.InGreen = new IntRange(50, 240);
lfilter.InBlue = new IntRange(10, 210);
return(ApplyFilter(bitmap, lfilter));
case Filters.OrderedDithering:
bitmap = Grayscale.CommonAlgorithms.RMY.Apply(bitmap);
return(ApplyFilter(bitmap, new OrderedDithering()));
case Filters.RotateChannels:
return(ApplyFilter(bitmap, new RotateChannels()));
case Filters.SaturationCorrection:
return(ApplyFilter(bitmap, new SaturationCorrection(0.15f)));
case Filters.Sepia:
return(ApplyFilter(bitmap, new Sepia()));
case Filters.SobelEdgeDetector:
bitmap = Grayscale.CommonAlgorithms.RMY.Apply(bitmap);
return(ApplyFilter(bitmap, new SobelEdgeDetector()));
case Filters.Threshold:
bitmap = Grayscale.CommonAlgorithms.RMY.Apply(bitmap);
return(ApplyFilter(bitmap, new Threshold()));
case Filters.YCbCrFiltering:
return(ApplyFilter(bitmap, new YCbCrFiltering(new Range(0.2f, 0.9f), new Range(-0.3f, 0.3f), new Range(-0.3f, 0.3f))));
case Filters.YCbCrLinear:
YCbCrLinear filter = new YCbCrLinear();
filter.InCb = new Range(-0.3f, 0.3f);
return(ApplyFilter(bitmap, filter));
default:
return(bitmap);
}
}
public MinMaxLinearStretch()
{
levelsLinear = new LevelsLinear();
}
public Bitmap ProcessImage(ImageProcessorSettings settings)
{
Bitmap output = Image.Clone() as Bitmap;
if (settings.RemoveBorder > 0)
{
int x = settings.RemoveBorder;
output = new Crop(new Rectangle(x, x, Image.Width - (2 * x), Image.Height - (2 * x))).Apply(Image);
}
if (settings.RemovePixelNoise > 0)
{
for (int i = 0; i < settings.RemovePixelNoise; i++)
{
// Use a copy for iteration
Bitmap bitmap = new Bitmap(output);
for (int j = 1; j < (output.Height - 1); j++)
{
for (int k = 1; k < (output.Width - 1); k++)
{
if (output.GetPixel(k, j).GetBrightness() < 0.5)
{
// Top left
float tl = output.GetPixel(k - 1, j).GetBrightness();
// Top right
float tr = output.GetPixel(k + 1, j).GetBrightness();
// Bottom left
float bl = output.GetPixel(k, j - 1).GetBrightness();
// Bottom right
float br = output.GetPixel(k, j + 1).GetBrightness();
// Test the edges
if (tl > 0.5 && tr > 0.5)
{
bitmap.SetPixel(k, j, Color.White);
}
if (bl > 0.5 && br > 0.5)
{
bitmap.SetPixel(k, j, Color.White);
}
}
}
}
output = bitmap;
}
}
if (settings.LinearizeColors)
{
ImageStatistics statistics = new ImageStatistics(output);
output = new LevelsLinear {
InRed = new Range(statistics.Red.Min, statistics.Red.Max),
InGreen = new Range(statistics.Green.Min, statistics.Green.Max),
InBlue = new Range(statistics.Blue.Min, statistics.Blue.Max)
}.Apply(output);
}
if (settings.InvertColors)
{
output = new Invert().Apply(output);
}
if (settings.ImageBlur)
{
output = new Blur().Apply(output);
}
if (settings.BrightnessThreshold > 0)
{
// Brightness threshold is in percent..
decimal threshold = (decimal)settings.BrightnessThreshold / 100M;
output = new Threshold(Convert.ToByte((decimal)(threshold * 255M)), 255).Apply(output);
}
return(output);
}
/// <summary>
/// Process the filter on the specified image.
/// </summary>
///
/// <param name="image">Source image data.</param>
/// <param name="rect">Image rectangle for processing by the filter.</param>
///
protected override unsafe void ProcessFilter( UnmanagedImage image, Rectangle rect )
{
int pixelSize = Image.GetPixelFormatSize( image.PixelFormat ) / 8;
int startX = rect.Left;
int startY = rect.Top;
int stopX = startX + rect.Width;
int stopY = startY + rect.Height;
int stride = image.Stride;
int offset = stride - rect.Width * pixelSize;
// levels linear correction filter is going to be used on STEP 2
LevelsLinear levelsLinear = new LevelsLinear( );
// STEP 1 - search for min and max pixel values
byte* ptr = (byte*) image.ImageData.ToPointer( );
// check image format
if ( image.PixelFormat == PixelFormat.Format8bppIndexed )
{
// allign pointer to the first pixel to process
ptr += ( startY * stride + startX );
byte min = 255;
byte max = 0;
for ( int y = startY; y < stopY; y++ )
{
for ( int x = startX; x < stopX; x++, ptr++ )
{
byte value = *ptr;
if ( value < min )
min = value;
if ( value > max )
max = value;
}
ptr += offset;
}
levelsLinear.InGray = new IntRange( min, max );
}
else
{
// allign pointer to the first pixel to process
ptr += ( startY * stride + startX * pixelSize );
byte minR = 255, minG = 255, minB = 255;
byte maxR = 0, maxG = 0, maxB = 0;
for ( int y = startY; y < stopY; y++ )
{
for ( int x = startX; x < stopX; x++, ptr += pixelSize )
{
// red
byte value = ptr[RGB.R];
if ( value < minR )
minR = value;
if ( value > maxR )
maxR = value;
// green
value = ptr[RGB.G];
if ( value < minG )
minG = value;
if ( value > maxG )
maxG = value;
// blue
value = ptr[RGB.B];
if ( value < minB )
minB = value;
if ( value > maxB )
maxB = value;
}
ptr += offset;
}
levelsLinear.InRed = new IntRange( minR, maxR );
levelsLinear.InGreen = new IntRange( minG, maxG );
levelsLinear.InBlue = new IntRange( minB, maxB );
}
// STEP 2 - run levels linear correction
levelsLinear.ApplyInPlace( image, rect );
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
Bitmap img = null;
DA.GetData(0, ref img);
Interval L = new Interval(0, 1);
DA.GetData("Luminance", ref L);
Interval S = new Interval(0, 1);
DA.GetData("Saturation", ref S);
Interval Y = new Interval(0, 255);
DA.GetData("Luma", ref Y);
Interval Cb = new Interval(-1, 1);
DA.GetData("Chroma_Blue", ref Cb);
Interval Cr = new Interval(-1, 1);
DA.GetData("Chroma_Red", ref Cr);
Interval R = new Interval(0, 255);
DA.GetData("Red", ref R);
Interval G = new Interval(0, 255);
DA.GetData("Green", ref G);
Interval B = new Interval(0, 255);
DA.GetData("Blue", ref B);
Bitmap filteredImage = img;
Interval bInterval = new Interval(0, 1);
Interval Interval255 = new Interval(0, 255);
Interval IntervalMinusOne = new Interval(-1, 1);
//////////////////////////////////////////////////////////////////////////
HSLLinear myHSLfilter = new HSLLinear();
if (bInterval.IncludesInterval(L))
{
myHSLfilter.InLuminance = new AForge.Range(Convert.ToSingle(L.Min), Convert.ToSingle(L.Max));
}
if (bInterval.IncludesInterval(S))
{
myHSLfilter.InSaturation = new AForge.Range(Convert.ToSingle(S.Min), Convert.ToSingle(S.Max));
}
filteredImage = myHSLfilter.Apply(img);
//////////////////////////////////////////////////////////////////////////
YCbCrLinear myYCbCrfilter = new YCbCrLinear();
if (Interval255.IncludesInterval(Y))
{
myYCbCrfilter.InCb = new AForge.Range(Convert.ToSingle(Y.Min), Convert.ToSingle(Y.Max));
}
if (IntervalMinusOne.IncludesInterval(Cb))
{
myYCbCrfilter.InCb = new AForge.Range(Convert.ToSingle(Cb.Min), Convert.ToSingle(Cb.Max));
}
if (IntervalMinusOne.IncludesInterval(Cr))
{
myYCbCrfilter.InCr = new AForge.Range(Convert.ToSingle(Cr.Min), Convert.ToSingle(Cr.Max));
}
filteredImage = myYCbCrfilter.Apply(filteredImage);
//////////////////////////////////////////////////////////////////////////
LevelsLinear myRGBfilter = new LevelsLinear();
if (Interval255.IncludesInterval(R))
{
myRGBfilter.InRed = new AForge.IntRange((int)(R.Min), (int)(R.Max));
}
if (Interval255.IncludesInterval(G))
{
myRGBfilter.InGreen = new AForge.IntRange((int)(G.Min), (int)(G.Max));
}
if (Interval255.IncludesInterval(B))
{
myRGBfilter.InBlue = new AForge.IntRange((int)(B.Min), (int)(B.Max));
}
filteredImage = myRGBfilter.Apply(filteredImage);
DA.SetData(0, filteredImage);
}
public IList <IFilter> GetFilterList(ImageProcessingSettings imageProcessingSettings)
{
var filterList = new List <IFilter>();
// Brightness
if (imageProcessingSettings.BrightnessAdjustment.Value != 0)
{
BrightnessCorrection filter = new BrightnessCorrection(imageProcessingSettings.BrightnessAdjustment.Value);
filterList.Add(filter);
}
// Contrast
if (imageProcessingSettings.ContrastAdjustment.Value != 0)
{
ContrastCorrection filter = new ContrastCorrection(imageProcessingSettings.ContrastAdjustment.Value);
filterList.Add(filter);
}
// Saturation
if (imageProcessingSettings.SaturationAdjustment.Value != 0)
{
// Only use 60% of the available correction range
float adjustedValue = ((float)imageProcessingSettings.SaturationAdjustment.Value / 100) * 60;
float value = (float)adjustedValue / 100;
SaturationCorrection filter = new SaturationCorrection(value);
filterList.Add(filter);
}
// Sharpness
if (imageProcessingSettings.SharpnessAdjustment.Value != 0)
{
double sigma = 1.5;
GaussianSharpen filter = new GaussianSharpen(sigma, imageProcessingSettings.SharpnessAdjustment.Value);
filterList.Add(filter);
}
// Red, Green, Blue
if (imageProcessingSettings.RedAdjustment.Value != 0 ||
imageProcessingSettings.GreenAdjustment.Value != 0 ||
imageProcessingSettings.BlueAdjustment.Value != 0)
{
LevelsLinear filter = new LevelsLinear();
if (imageProcessingSettings.RedAdjustment.Value != 0)
{
float val = ((float)imageProcessingSettings.RedAdjustment.Value / 100) * 255;
if (imageProcessingSettings.RedAdjustment.Value > 0)
{
var finalVal = 255 - (int)val;
filter.InRed = new IntRange(0, finalVal);
}
else
{
val = val * -1;
filter.InRed = new IntRange((int)val, 255);
}
}
if (imageProcessingSettings.GreenAdjustment.Value != 0)
{
float val = ((float)imageProcessingSettings.GreenAdjustment.Value / 100) * 255;
if (imageProcessingSettings.GreenAdjustment.Value > 0)
{
var finalVal = 255 - (int)val;
filter.InGreen = new IntRange(0, finalVal);
}
else
{
val = val * -1;
filter.InGreen = new IntRange((int)val, 255);
}
}
if (imageProcessingSettings.BlueAdjustment.Value != 0)
{
float val = ((float)imageProcessingSettings.BlueAdjustment.Value / 100) * 255;
if (imageProcessingSettings.BlueAdjustment.Value > 0)
{
var finalVal = 255 - (int)val;
filter.InBlue = new IntRange(0, finalVal);
}
else
{
val = val * -1;
filter.InBlue = new IntRange((int)val, 255);
}
}
filterList.Add(filter);
}
return(filterList);
}