private static void FinalizeFrame(Tomogram tom)
{
int numberOfBackgroundClasses = tom.DataClasses.Where(n => n != 0).Count();
float[] classKey = new float[numberOfBackgroundClasses];
for (int c = 0; c < classKey.Length; c++)
{
float v = (float)RandomNormal.Next(tom.Random, 85, 15);
classKey[c] = v * tom.MRCScaler;
}
for (int y = 0, i = 0; y < tom.Height; y++)
{
for (int x = 0; x < tom.Width; x++, i++)
{
int classNumber = tom.DataClasses[i];
if (classNumber > 0 && classNumber <= tom.BackgroundDensity)
{
tom.Data[i] = classKey[classNumber];
}
}
}
GaussianBlur blur = GaussianBlur.BuildBlur(2.0f, 4);
tom.Data = blur.BlurData(tom.Data, tom.Width, tom.Height);
for (int y = 0, i = 0; y < tom.Height; y++)
{
for (int x = 0; x < tom.Width; x++, i++)
{
int classNumber = tom.DataClasses[i];
if (classNumber == -1)
{
float v = tom.Random.Next(50, 60);
tom.Data[i] = v * tom.MRCScaler;
}
}
}
tom.Data = blur.BlurData(tom.Data, tom.Width, tom.Height);
}
private static void FinalizeFrame(Tomogram tom, Random rand, MRCFile file,
string serializedSamplerPath)
{
float minValue = file.MinPixelValue;
float maxValue = file.MaxPixelValue;
tom.MRCScaler = 255.0f / (maxValue - minValue);
tom.MinimumTomogramValue = minValue;
int[] classes = tom.DataClasses.Where(n => n != -1).Distinct().ToArray();
Dictionary <int, float> classValues = new Dictionary <int, float>();
for (int c = 0; c < classes.Length; c++)
{
MRCFrame frame = file.Frames[145];// rand.Next(0, file.Frames.Count - 1)];
int randomX = rand.Next(511, 611);
int randomY = rand.Next(292, 392);
int randomIndex = randomY * frame.Width + randomX;
float value = frame.Data[randomIndex];
classValues.Add(classes[c], value);
}
for (int y = 0, i = 0; y < tom.Height; y++)
{
for (int x = 0; x < tom.Width; x++, i++)
{
int classNumber = tom.DataClasses[i];
if (classNumber >= 0)
{
tom.Data[i] = classValues[classNumber];
}
}
}
List <float> distribution = null;
BinaryFormatter bf = new BinaryFormatter();
using (FileStream fin = File.OpenRead(serializedSamplerPath))
{
distribution = bf.Deserialize(fin) as List <float>;
}
int[] mask = new int[tom.Width * tom.Height];
for (int y = 0, i = 0; y < tom.Height; y++)
{
for (int x = 0; x < tom.Width; x++, i++)
{
int classNumber = tom.DataClasses[i];
if (classNumber == -1)
{
tom.Data[i] = distribution[rand.Next(0, distribution.Count - 1)];
mask[i] = 0;
}
else
{
mask[i] = 1;
}
}
}
// tom.Data = blur.BlurData(tom.Data, tom.Width, tom.Height);
GaussianBlur blur = GaussianBlur.BuildBlur(.65f, tom.BlurRadius);
tom.Data = blur.SelectivelyBlurData(tom.Data, mask, tom.Width, tom.Height, 1f, rand);
float[] finalData = new float[tom.FinalHeight * tom.FinalWidth];
int[] finalDataClasses = new int[tom.FinalHeight * tom.FinalWidth];
for (int y = 0, dstIndex = 0; y < tom.FinalHeight; y++)
{
for (int x = 0; x < tom.FinalHeight; x++, dstIndex++)
{
int srcIndex = (y + tom.BlurRadius) * tom.Width + (x + tom.BlurRadius);
finalData[dstIndex] = tom.Data[srcIndex];
finalDataClasses[dstIndex] = tom.DataClasses[srcIndex];
}
}
// hack. clean this up.
tom.Data = finalData;
tom.DataClasses = finalDataClasses;
tom.Width = tom.FinalWidth;
tom.Height = tom.FinalHeight;
}