public ImageDescription blendImages(List <ImageDescription> images)
{
int newSizeX, newSizeY;
List <ImageDescription> imagesToBlend;
ImageDescriptionUtil.makeAllImagesSameSize(images, out newSizeX, out newSizeY, out imagesToBlend);
ImageDescription output = new ImageDescription();
output.sizeX = newSizeX;
output.sizeY = newSizeY;
output.grayscale = true;
float blendFactor = 1.0f / images.Count();
byte[,] outputGray = new byte[newSizeY, newSizeX];
for (int i = 0; i < newSizeY; i++)
{
for (int j = 0; j < newSizeX; j++)
{
float sum = 0;
for (int imageIndex = 0; imageIndex < images.Count; imageIndex++)
{
sum += imagesToBlend[imageIndex].gray[i, j];
}
sum *= blendFactor;
outputGray[i, j] = (byte)(sum + 0.5f);
}
}
output.gray = outputGray;
return(output);
}
public float train(List <ImageDescription> inputImages, ImageDescription inputImageGroundTruth)
{
int newSizeX, newSizeY;
List <ImageDescription> imagesToBlend;
ImageDescriptionUtil.makeAllImagesSameSize(inputImages, out newSizeX, out newSizeY, out imagesToBlend);
float entropyLoss = 0;
for (int i = 0; i < newSizeY; i++)
{
for (int j = 0; j < newSizeX; j++)
{
float stretchedProbability = computePerPixelStretchedProbability(imagesToBlend, i, j);
float probability = LogisticHelper.squash(stretchedProbability);
if (probability < LogisticHelper.probabilityMinValue)
{
probability = LogisticHelper.probabilityMinValue;
}
else
{
if (probability > LogisticHelper.probabilityMaxValue)
{
probability = LogisticHelper.probabilityMaxValue;
}
}
float groundTruthProbability = groundTruthProbabilityCache[inputImageGroundTruth.gray[i, j]];
entropyLoss += LogisticHelper.computeEntropyLoss(probability, groundTruthProbability);
#if useEntropyLoss
float loss = groundTruthProbability - probability;
#else
float loss = (groundTruthProbability - probability) * probability * (1 - probability);
#endif
for (int imageIndex = 0; imageIndex < imagesToBlend.Count; imageIndex++)
{
byte pixelValue = imagesToBlend[imageIndex].gray[i, j];
weights[imageIndex] += learningConstant * stretchedPixelValueCache[pixelValue] * loss;
}
}
}
return(entropyLoss);
}
public ImageDescription blendImages(List <ImageDescription> images)
{
int newSizeX, newSizeY;
List <ImageDescription> imagesToBlend;
ImageDescriptionUtil.makeAllImagesSameSize(images, out newSizeX, out newSizeY, out imagesToBlend);
ImageDescription output = new ImageDescription();
output.sizeX = newSizeX;
output.sizeY = newSizeY;
output.grayscale = true;
byte[,] outputGray = new byte[newSizeY, newSizeX];
for (int i = 0; i < newSizeY; i++)
{
for (int j = 0; j < newSizeX; j++)
{
float stretchedProbability = computePerPixelStretchedProbability(imagesToBlend, i, j);
float probability = LogisticHelper.squash(stretchedProbability);
if (probability < LogisticHelper.probabilityMinValue)
{
probability = LogisticHelper.probabilityMinValue;
}
else
{
if (probability > LogisticHelper.probabilityMaxValue)
{
probability = LogisticHelper.probabilityMaxValue;
}
}
outputGray[i, j] = (byte)(probability * 255.0f + 0.5f);
}
}
output.gray = outputGray;
return(output);
}
