public ImageDescription filter(ImageDescription inputImage)
{
ImageDescription outputImage = new ImageDescription();
outputImage.sizeX = newSizeX;
outputImage.sizeY = newSizeY;
foreach (ColorChannelEnum colorChannel in colorChannelsToFilter)
{
byte[,] channel = inputImage.getColorChannel(colorChannel);
ImageDescription temp = new ImageDescription();
temp.sizeX = inputImage.sizeX;
temp.sizeY = inputImage.sizeY;
temp.grayscale = true;
temp.setColorChannel(ColorChannelEnum.Gray, channel);
Bitmap tempBitmap = ImageDescriptionUtil.convertToBitmap(temp);
Bitmap output = ImageDescriptionUtil.resizeImage(tempBitmap, newSizeX, newSizeY);
temp = ImageDescriptionUtil.fromBitmap(output);
temp.computeGrayscale();
outputImage.setColorChannel(colorChannel, temp.gray);
}
if (colorChannelsToFilter.Count == 1 && colorChannelsToFilter.Contains(ColorChannelEnum.Gray))
{
outputImage.grayscale = true;
}
return(outputImage);
}
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 static void saveToPath(ImageDescription imageDescription, string filePath, string fileExtension)
{
Image imageToSave = ImageDescriptionUtil.convertToBitmap(imageDescription);
string fullFileName = Path.ChangeExtension(filePath, fileExtension);
switch (fileExtension)
{
case ".png": imageToSave.Save(fullFileName, ImageFormat.Png); break;
case ".jpg": imageToSave.Save(fullFileName, ImageFormat.Jpeg); break;
case ".bmp": imageToSave.Save(fullFileName, ImageFormat.Bmp); break;
case ".gif": imageToSave.Save(fullFileName, ImageFormat.Gif); break;
case ".ico": imageToSave.Save(fullFileName, ImageFormat.Icon); break;
case ".emf": imageToSave.Save(fullFileName, ImageFormat.Emf); break;
case ".exif": imageToSave.Save(fullFileName, ImageFormat.Exif); break;
case ".tiff": imageToSave.Save(fullFileName, ImageFormat.Tiff); break;
case ".wmf": imageToSave.Save(fullFileName, ImageFormat.Wmf); break;
default: imageToSave.Save(fullFileName, ImageFormat.Png); break;
}
}
public ImageDescription test(ImageDescription inputImage)
{
setInputImageToContexts(inputImage);
ImageDescription outputImage = ImageDescriptionUtil.createGrayscaleImageWithSameSize(inputImage);
computeIndexes();
//for (int positionY = 0; positionY < inputImage.sizeY; positionY++)
//{
// for (int positionX = 0; positionX < inputImage.sizeX; positionX++)
// {
int numberOfIndexes = currentInputImage.sizeX * currentInputImage.sizeY;
//for (int computedIndex = 0; computedIndex < numberOfIndexes; computedIndex++)
Parallel.For(0, numberOfIndexes, new ParallelOptions { MaxDegreeOfParallelism = GeneralConfiguration.maximumNumberOfThreads }, (computedIndex) =>
{
int positionX = computedIndex % currentInputImage.sizeX;
int positionY = computedIndex / currentInputImage.sizeX;
float probability;
float feebackProbability;
float outputProbability;
computeProbabilityForPosition(positionX, positionY, computedIndex, out probability, out feebackProbability, out outputProbability);
outputImage.gray[positionY, positionX] = (byte)(outputProbability * 255 + 0.5f);
}
);
// }
//}
return outputImage;
}
public virtual ImageDescription filter(ImageDescription inputImage)
{
inputImage.computeGrayscale();
int imageSizeX = inputImage.sizeX;
int imageSizeY = inputImage.sizeY;
byte[,] inputGray = inputImage.gray;
byte[,] outputGray = new byte[imageSizeY, imageSizeX];
// 1. Gauss
float[,] gaussConvolutionMatrix = FilterBankUtil.generateNormalizedGaussConvolutionMatrix(sigma, 5);
float[,] gaussResult = ImageDescriptionUtil.mirroredMarginConvolution(inputGray, gaussConvolutionMatrix);
// 2. Gradient
float[,] dx = ImageDescriptionUtil.mirroredMarginConvolution(gaussResult, FilterBankUtil.normalizedSobelX);
float[,] dy = ImageDescriptionUtil.mirroredMarginConvolution(gaussResult, FilterBankUtil.normalizedSobelY);
// 3. Gradient Amplitude
float[,] amplitudeResult = new float[imageSizeY, imageSizeX];
for (int i = 0; i < imageSizeY; i++)
{
for (int j = 0; j < imageSizeX; j++)
{
amplitudeResult[i, j] = (float)Math.Sqrt(dx[i, j] * dx[i, j] + dy[i, j] * dy[i, j]);
}
}
for (var i = 0; i < imageSizeY; i++)
{
for (var j = 0; j < imageSizeX; j++)
{
if (amplitudeResult[i, j] < 255)
{
outputGray[i, j] = (byte)(amplitudeResult[i, j] + 0.5f);
}
else
{
outputGray[i, j] = 255;
}
}
}
ImageDescription outputImage = new ImageDescription();
outputImage.sizeX = imageSizeX;
outputImage.sizeY = imageSizeY;
foreach (ColorChannelEnum colorChannel in Enum.GetValues(typeof(ColorChannelEnum)))
{
outputImage.setColorChannel(colorChannel, inputImage.getColorChannel(colorChannel));
}
outputImage.setColorChannel(ColorChannelEnum.Sobel, outputGray);
return(outputImage);
}
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);
}
public ImageDescription filter(ImageDescription inputImage)
{
ImageDescription outputImage = new ImageDescription();
outputImage.sizeX = inputImage.sizeX;
outputImage.sizeY = inputImage.sizeY;
if (colorChannelsToFilter.Count == 1 && colorChannelsToFilter.Contains(ColorChannelEnum.Gray))
{
outputImage.grayscale = true;
}
foreach (ColorChannelEnum channelEnum in colorChannelsToFilter)
{
byte[,] inputChannel = inputImage.getColorChannel(channelEnum);
byte[,] outputChannel = new byte[outputImage.sizeY, outputImage.sizeX];
outputImage.setColorChannel(channelEnum, outputChannel);
for (int y = 0; y < inputImage.sizeY; y++)
{
for (int x = 0; x < inputImage.sizeX; x++)
{
int index = 0;
for (int i = -halfSize; i <= halfSize; i++)
{
for (int j = -halfSize; j <= halfSize; j++)
{
medianValueArray[index++] = inputChannel[ImageDescriptionUtil.outsideMirroredPosition(y + i, inputImage.sizeY), ImageDescriptionUtil.outsideMirroredPosition(x + j, inputImage.sizeX)];
}
}
Array.Sort(medianValueArray);
outputChannel[y, x] = medianValueArray[medianPosition];
}
}
}
return(outputImage);
}
public static ImageDescription loadFromPath(string filePath)
{
Bitmap bitmap = new Bitmap(filePath);
return(ImageDescriptionUtil.fromBitmap(bitmap));
}
public virtual ImageDescription filter(ImageDescription inputImage)
{
inputImage.computeGrayscale();
int imageSizeX = inputImage.sizeX;
int imageSizeY = inputImage.sizeY;
byte[,] inputGray = inputImage.gray;
byte[,] outputGray = new byte[imageSizeY, imageSizeX];
float[,] gaussConvolutionMatrix = FilterBankUtil.generateNormalizedGaussConvolutionMatrix(sigma, 7);
float[,] gaussResult = ImageDescriptionUtil.mirroredMarginConvolution(inputGray, gaussConvolutionMatrix);
List <float[, ]> templates = FilterBankUtil.normalizedKirschTemplates;
List <float[, ]> results = new List <float[, ]>(templates.Count);
foreach (float[,] template in templates)
{
results.Add(ImageDescriptionUtil.mirroredMarginConvolution(gaussResult, template));
}
float[,] amplitudeResult = new float[imageSizeY, imageSizeX];
int[,] anglesResult = new int[imageSizeY, imageSizeX];
for (int i = 0; i < imageSizeY; i++)
{
for (int j = 0; j < imageSizeX; j++)
{
int direction = 0;
float maxValue = 0;
for (int templateIndex = 0; templateIndex < templates.Count; templateIndex++)
{
float value = results[templateIndex][i, j];
if (value > maxValue)
{
maxValue = value;
direction = templateIndex;
}
}
amplitudeResult[i, j] = maxValue;
anglesResult[i, j] = direction;
}
}
if (!applyNms)
{
for (var i = 0; i < imageSizeY; i++)
{
for (var j = 0; j < imageSizeX; j++)
{
if (amplitudeResult[i, j] < 255)
{
outputGray[i, j] = (byte)(amplitudeResult[i, j] + 0.5f);
}
else
{
outputGray[i, j] = 255;
}
}
}
}
else
{
float[,] nmsResult = new float[imageSizeY, imageSizeX];
for (int i = 0; i < imageSizeY; i++)
{
for (int j = 0; j < imageSizeX; j++)
{
int angle = anglesResult[i, j];
if (angle == 2 || angle == 6)
{
if ((i == 0 || amplitudeResult[i, j] >= amplitudeResult[i - 1, j]) &&
(i == imageSizeY - 1 || amplitudeResult[i, j] > amplitudeResult[i + 1, j]))
{
nmsResult[i, j] = amplitudeResult[i, j];
}
}
else
{
if (angle == 1 || angle == 5)
{
if ((i == 0 || j == imageSizeX - 1 || amplitudeResult[i, j] >= amplitudeResult[i - 1, j + 1]) &&
(i == imageSizeY - 1 || j == 0 || amplitudeResult[i, j] > amplitudeResult[i + 1, j - 1]))
{
nmsResult[i, j] = amplitudeResult[i, j];
}
}
else
{
if (angle == 3 || angle == 7)
{
if ((i == 0 || j == 0 || amplitudeResult[i, j] >= amplitudeResult[i - 1, j - 1]) &&
(i == imageSizeY - 1 || j == imageSizeX - 1 || amplitudeResult[i, j] > amplitudeResult[i + 1, j + 1]))
{
nmsResult[i, j] = amplitudeResult[i, j];
}
}
else
{
if ((j == 0 || amplitudeResult[i, j] >= amplitudeResult[i, j - 1]) &&
(j == imageSizeX - 1 || amplitudeResult[i, j] > amplitudeResult[i, j + 1]))
{
nmsResult[i, j] = amplitudeResult[i, j];
}
}
}
}
}
}
float[,] hysteresisResult = new float[imageSizeY, imageSizeX];
bool[,] retainedPositions = applyHysteresisThreshold(nmsResult, imageSizeX, imageSizeY);
for (var i = 0; i < imageSizeY; i++)
{
for (var j = 0; j < imageSizeX; j++)
{
if (retainedPositions[i, j])
{
hysteresisResult[i, j] = nmsResult[i, j];
}
}
}
for (var i = 0; i < imageSizeY; i++)
{
for (var j = 0; j < imageSizeX; j++)
{
if (hysteresisResult[i, j] < 255)
{
outputGray[i, j] = (byte)(hysteresisResult[i, j] + 0.5f);
}
else
{
outputGray[i, j] = 255;
}
}
}
}
ImageDescription outputImage = new ImageDescription();
outputImage.sizeX = imageSizeX;
outputImage.sizeY = imageSizeY;
foreach (ColorChannelEnum colorChannel in Enum.GetValues(typeof(ColorChannelEnum)))
{
outputImage.setColorChannel(colorChannel, inputImage.getColorChannel(colorChannel));
}
outputImage.setColorChannel(ColorChannelEnum.Kirsch, outputGray);
return(outputImage);
}
public virtual ImageDescription filter(ImageDescription inputImage)
{
return(ImageDescriptionUtil.mirroredMarginConvolution(inputImage, colorChannelsToFilter, convolutionMatrix));
}
public override void computeIndexes(ImageDescription inputImage, int positionX, int positionY, int computedIndex, int[,] computedIndexes)
{
byte[] byteValues = new byte[longestRay + 1];
int[] contextHashesForRay = new int[longestRay + 1];
#if useQuantizedDerivative
byte[] byteDerivativeValues = new byte[longestRay + 1];
int[] contextDerivativeHashesForRay = new int[longestRay + 1];
#endif
int currentIndex = 0;
foreach (ColorChannelEnum selectedColorChannel in selectedColorChannels)
{
byte[,] colorChannel = inputImage.getColorChannel(selectedColorChannel);
for (int ray = 0; ray < numberOfRays; ray++)
{
RelativePixelInformation[] relativePixelInformation = relativePixelInformationsForRay[ray];
for (int i = 0; i < relativePixelInformation.Length; i++)
{
byteValues[i] = ImageDescriptionUtil.getPixelMirrored(colorChannel, positionX + relativePixelInformation[i].deltaX, positionY + relativePixelInformation[i].deltaY);
#if useQuantizedDerivative
byteDerivativeValues[i] = byteValues[i];
#endif
}
#if useQuantizedDerivative
if (trueColorChannels.Contains(selectedColorChannel))
{
for (int i = relativePixelInformation.Length - 1; i > 0; i--)
{
byteDerivativeValues[i] = (byte)((byteDerivativeValues[i] - byteDerivativeValues[i - 1] + 256) & 255);
}
}
#endif
NumberUtils.fnvOneAtATimeHash(byteValues, relativePixelInformation.Length, contextHashesForRay);
//NumberUtils.fnvOneAtATimeWithMaskHash(byteValues, byteMaskValues, relativePixelInformation.Length, contextHashesForRay);
//NumberUtils.jenkinsOneAtATimeHash(byteValues, relativePixelInformation.Length, contextHashesForRay);
#if useQuantizedDerivative
if (trueColorChannels.Contains(selectedColorChannel))
{
NumberUtils.fnvOneAtATimeWithMaskHash(byteDerivativeValues, byteMaskValues, relativePixelInformation.Length, contextDerivativeHashesForRay);
}
#endif
for (int i = 0; i < contextLenghtsForRay[ray].Length; i++)
{
int contextLength = contextLenghtsForRay[ray][i];
int contextIndex;
#if useQuantizedDerivative
int contextDerivativeIndex;
#endif
if (contextLength == 1)
{
contextIndex = byteValues[0];
#if useQuantizedDerivative
contextDerivativeIndex = byteDerivativeValues[0];
#endif
}
else
{
if (contextLength == 2)
{
contextIndex = (byteValues[0] << 8) + byteValues[1];
#if useQuantizedDerivative
contextDerivativeIndex = (byteDerivativeValues[0] << 8) + byteDerivativeValues[1];
#endif
}
else
{
if (contextLength == 3 && maxTableSizeBits >= 24)
{
contextIndex = (byteValues[0] << 16) + (byteValues[1] << 8) + byteValues[2];
#if useQuantizedDerivative
contextDerivativeIndex = (byteDerivativeValues[0] << 16) + (byteDerivativeValues[1] << 8) + byteDerivativeValues[2];
#endif
}
else
{
contextIndex = contextHashesForRay[contextLength - 1];
#if useQuantizedDerivative
contextDerivativeIndex = contextDerivativeHashesForRay[contextLength - 1];
#endif
}
}
}
computedIndexes[computedIndex, currentIndex] = contextIndex;
currentIndex += 1;
#if useQuantizedDerivative
if (trueColorChannels.Contains(selectedColorChannel))
{
computedIndexes[computedIndex, currentIndex] = contextDerivativeIndex;
currentIndex += 1;
}
#endif
}
}
}
}
public virtual ImageDescription filter(ImageDescription inputImage)
{
inputImage.computeGrayscale();
int imageSizeX = inputImage.sizeX;
int imageSizeY = inputImage.sizeY;
byte[,] inputGray = inputImage.gray;
byte[,] outputGray = new byte[imageSizeY, imageSizeX];
// 1. Gauss
float[,] gaussConvolutionMatrix = FilterBankUtil.generateNormalizedGaussConvolutionMatrix(sigma, 5);
float[,] gaussResult = ImageDescriptionUtil.mirroredMarginConvolution(inputGray, gaussConvolutionMatrix);
// 2. Gradient
float[,] dx = ImageDescriptionUtil.mirroredMarginConvolution(gaussResult, FilterBankUtil.normalizedSobelX);
float[,] dy = ImageDescriptionUtil.mirroredMarginConvolution(gaussResult, FilterBankUtil.normalizedSobelY);
// 3. Gradient Amplitude
float[,] amplitudeResult = new float[imageSizeY, imageSizeX];
for (int i = 0; i < imageSizeY; i++)
{
for (int j = 0; j < imageSizeX; j++)
{
amplitudeResult[i, j] = (float)Math.Sqrt(dx[i, j] * dx[i, j] + dy[i, j] * dy[i, j]);
}
}
// 4. Angle of gradient
float[,] anglesResult = new float[imageSizeY, imageSizeX];
for (int i = 0; i < imageSizeY; i++)
{
for (int j = 0; j < imageSizeX; j++)
{
anglesResult[i, j] = (float)Math.Atan2(dx[i, j], dy[i, j]);
}
}
// 5. Non maximal suppresion
float[,] nmsResult = new float[imageSizeY, imageSizeX];
for (int i = 1; i < imageSizeY - 1; i++)
{
for (int j = 1; j < imageSizeX - 1; j++)
{
float angle = anglesResult[i, j];
if ((angle <= (5 * Math.PI) / 8 && angle > (3 * Math.PI) / 8) || (angle > -(5 * Math.PI) / 8 && angle <= -(3 * Math.PI) / 8))
{
if (amplitudeResult[i, j] > amplitudeResult[i - 1, j] && amplitudeResult[i, j] > amplitudeResult[i + 1, j])
{
nmsResult[i, j] = amplitudeResult[i, j];
}
}
else
{
if (angle <= (3 * Math.PI) / 8 && angle > Math.PI / 8 || angle > -(7 * Math.PI) / 8 && angle <= -(5 * Math.PI) / 8)
{
if (amplitudeResult[i, j] > amplitudeResult[i - 1, j + 1] && amplitudeResult[i, j] > amplitudeResult[i + 1, j - 1])
{
nmsResult[i, j] = amplitudeResult[i, j];
}
}
else
{
if (angle <= (7 * Math.PI / 8) && angle > (5 * Math.PI / 8) || angle > -(3 * Math.PI) / 8 && angle < -(Math.PI / 8))
{
if (amplitudeResult[i, j] > amplitudeResult[i - 1, j - 1] && amplitudeResult[i, j] > amplitudeResult[i + 1, j + 1])
{
nmsResult[i, j] = amplitudeResult[i, j];
}
}
else
{
if (amplitudeResult[i, j] > amplitudeResult[i, j - 1] && amplitudeResult[i, j] > amplitudeResult[i, j + 1])
{
nmsResult[i, j] = amplitudeResult[i, j];
}
}
}
}
}
}
// 6. Hysteresis thresolding
float[,] hysteresisResult = new float[imageSizeY, imageSizeX];
bool[,] retainedPositions = applyHysteresisThreshold(nmsResult, imageSizeX, imageSizeY);
for (var i = 0; i < imageSizeY; i++)
{
for (var j = 0; j < imageSizeX; j++)
{
if (retainedPositions[i, j])
{
hysteresisResult[i, j] = nmsResult[i, j];
}
}
}
for (var i = 0; i < imageSizeY; i++)
{
for (var j = 0; j < imageSizeX; j++)
{
if (hysteresisResult[i, j] < 255)
{
outputGray[i, j] = (byte)(hysteresisResult[i, j] + 0.5f);
}
else
{
outputGray[i, j] = 255;
}
}
}
ImageDescription outputImage = new ImageDescription();
outputImage.sizeX = imageSizeX;
outputImage.sizeY = imageSizeY;
foreach (ColorChannelEnum colorChannel in Enum.GetValues(typeof(ColorChannelEnum)))
{
outputImage.setColorChannel(colorChannel, inputImage.getColorChannel(colorChannel));
}
outputImage.setColorChannel(ColorChannelEnum.Canny, outputGray);
return(outputImage);
}
