Threshold binarization with error carry.
The filter is similar to Threshold filter in the way, that it also uses threshold value for image binarization. Unlike regular threshold filter, this filter uses cumulative pixel value in comparing with threshold value. If cumulative pixel value is below threshold value, then image pixel becomes black. If cumulative pixel value is equal or higher than threshold value, then image pixel becomes white and cumulative pixel value is decreased by 255. In the beginning of each image line the cumulative value is reset to 0.
The filter accepts 8 bpp grayscale images for processing.
Sample usage:
// create filter Threshold filter = new Threshold( 100 ); // apply the filter filter.ApplyInPlace( image ); Initial image:
Result image:
public static Bitmap ApplyFiliter(ImageFiliter imgFilter, Bitmap bmp, byte Value, byte Value2)
{
Bitmap newImage = null;
//ContrastCorrection filter2 = new ContrastCorrection(1.0);
//newImage = filter2.Apply(bmp);
if (imgFilter != ImageFiliter.None)
{
IFilter filter3 = Grayscale.CommonAlgorithms.Y;
newImage = filter3.Apply(bmp);
if (imgFilter == ImageFiliter.Threshold)
{
IFilter filter = null;
if (Value == 0) filter = new Threshold();
else filter = new Threshold(Value);
newImage = filter.Apply(newImage);
//IterativeThreshold filter = new IterativeThreshold(Value2, Value);
//// apply the filter
// newImage = filter.Apply(newImage);
}
if (imgFilter == ImageFiliter.ThresholdWithCarry)
{
IFilter filter = new ThresholdWithCarry();
newImage = filter.Apply(newImage);
}
else if (imgFilter == ImageFiliter.OrderedDithering)
{
IFilter filter = new OrderedDithering();
newImage = filter.Apply(newImage);
}
else if (imgFilter == ImageFiliter.BayerDithering)
{
IFilter filter = new BayerDithering();
newImage = filter.Apply(newImage);
}
else if (imgFilter == ImageFiliter.FloydSteinbergDithering)
{
IFilter filter = new FloydSteinbergDithering();
newImage = filter.Apply(newImage);
}
else if (imgFilter == ImageFiliter.BurkesDithering)
{
IFilter filter = new BurkesDithering();
newImage = filter.Apply(newImage);
}
else if (imgFilter == ImageFiliter.JarvisJudiceNinkeDithering)
{
IFilter filter = new JarvisJudiceNinkeDithering();
newImage = filter.Apply(newImage);
}
else if (imgFilter == ImageFiliter.SierraDithering)
{
IFilter filter = new SierraDithering();
newImage = filter.Apply(newImage);
}
else if (imgFilter == ImageFiliter.StuckiDithering)
{
IFilter filter = new StuckiDithering();
newImage = filter.Apply(newImage);
}
else if (imgFilter == ImageFiliter.Convolution)
{
// create filter
//OtsuThreshold filter = new OtsuThreshold();
//// apply the filter
//filter.ApplyInPlace(newImage);
//// create filter
//IterativeThreshold filter = new IterativeThreshold(0);
//// apply the filter
//newImage = filter.Apply(newImage);
int[,] kernel = {
{ -2, -1, 0 },
{ -1, 1, 1 },
{ 0, 1, 2 }
};
// create filter
Convolution filter = new Convolution(kernel);
// apply the filter
filter.ApplyInPlace(newImage);
}
newImage = BitmapTo1Bpp(newImage);
}
else newImage = BitmapTo1Bpp(bmp);
//轉換成 1bit bps
return newImage;
}
