//@Override
public Image process(Image imageIn)
{
Image clone = imageIn.clone();
imageIn = gradientFx.process(imageIn);
ImageBlender blender = new ImageBlender();
blender.Mode = BlendMode.ColorBurn;
return saturationFx.process(blender.Blend(clone, imageIn));
//return imageIn;// saturationFx.process(imageIn);
}
//@Override
public Image process(Image imageIn)
{
Image clone = imageIn.clone();
imageIn = gradientFx.process(imageIn);
ImageBlender blender = new ImageBlender();
blender.Mode = BlendMode.Subractive;
return(saturationFx.process(blender.Blend(clone, imageIn)));
//return imageIn;// saturationFx.process(imageIn);
}
//@Override
public CustomImage process(CustomImage imageIn)
{
ParamEdgeDetectFilter pde = new ParamEdgeDetectFilter();
pde.K00 = 1;
pde.K01 = 2;
pde.K02 = 1;
pde.Threshold = 0.25f;
pde.DoGrayConversion = false;
ImageBlender ib = new ImageBlender();
ib.Mode = (int)BlendMode.Multiply;
return ib.Blend(imageIn.clone(), pde.process(imageIn));
}
//@Override
public Image process(Image imageIn)
{
GradientMapFilter gmf = new GradientMapFilter(Gradient.BlackSepia());
gmf.ContrastFactor = 0.15f;
ImageBlender ib = new ImageBlender();
ib.Mixture = 0.7f;
ib.Mode = BlendMode.Overlay;
imageIn = ib.Blend(imageIn.clone(), gmf.process(imageIn));
VignetteFilter vigette = new VignetteFilter();
vigette.Size = 0.7f;
return vigette.process(imageIn);
}
//@Override
public Image process(Image imageIn)
{
ParamEdgeDetectFilter pde = new ParamEdgeDetectFilter();
pde.K00 = 1;
pde.K01 = 2;
pde.K02 = 1;
pde.Threshold = 0.25f;
pde.DoGrayConversion = false;
pde.DoInversion = false;
ImageBlender ib = new ImageBlender();
ib.Mode = (int)BlendMode.LinearLight;
ib.Mixture = 2.5f;
return ib.Blend(imageIn.clone(), pde.process(imageIn));
}
//@Override
public Image process(Image imageIn)
{
ParamEdgeDetectFilter pde = new ParamEdgeDetectFilter();
pde.K00 = 1;
pde.K01 = 2;
pde.K02 = 1;
pde.Threshold = 0.25f;
pde.DoGrayConversion = false;
ImageBlender ib = new ImageBlender();
ib.Mode = (int)BlendMode.Multiply;
return(ib.Blend(imageIn.clone(), pde.process(imageIn)));
}
//@Override
public Image process(Image imageIn)
{
int r, g, b;
int width = imageIn.getWidth();
int height = imageIn.getHeight();
int ratio = width > height ? height * 32768 / width : width * 32768 / height;
// Calculate center, min and max
int cx = width >> 1;
int cy = height >> 1;
int max = cx * cx + cy * cy;
int min = (int)(max * (1 - Size));
int diff = max - min;
Image clone = imageIn.clone();
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
// Calculate distance to center and adapt aspect ratio
int dx = cx - x;
int dy = cy - y;
if (width > height)
{
dx = (dx * ratio) >> 15;
}
else
{
dy = (dy * ratio) >> 15;
}
int distSq = dx * dx + dy * dy;
r = (int)((((float)distSq / diff) * R));
g = (int)((((float)distSq / diff) * G));
b = (int)((((float)distSq / diff) * B));
r = (byte)(r > R ? R : (r < 0 ? 0 : r));
g = (byte)(g > G ? G : (g < 0 ? 0 : g));
b = (byte)(b > B ? B : (b < 0 ? 0 : b));
imageIn.setPixelColor(x, y, r, g, b);
}
}
ImageBlender blender = new ImageBlender();
blender.Mode = BlendMode.Additive;
return(blender.Blend(clone, imageIn));
}
//@Override
public Image process(Image imageIn)
{
GradientMapFilter gmf = new GradientMapFilter(Gradient.BlackSepia());
gmf.ContrastFactor = 0.15f;
ImageBlender ib = new ImageBlender();
ib.Mixture = 0.7f;
ib.Mode = BlendMode.Overlay;
imageIn = ib.Blend(imageIn.clone(), gmf.process(imageIn));
VignetteFilter vigette = new VignetteFilter();
vigette.Size = 0.7f;
return(vigette.process(imageIn));
}
//@Override
public Image process(Image imageIn)
{
int r, g, b;
int width = imageIn.getWidth();
int height = imageIn.getHeight();
int ratio = width > height ? height * 32768 / width : width * 32768 / height;
// Calculate center, min and max
int cx = width >> 1;
int cy = height >> 1;
int max = cx * cx + cy * cy;
int min = (int)(max * (1 - Size));
int diff = max - min;
Image clone = imageIn.clone();
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
// Calculate distance to center and adapt aspect ratio
int dx = cx - x;
int dy = cy - y;
if (width > height)
dx = (dx * ratio) >> 15;
else
dy = (dy * ratio) >> 15;
int distSq = dx * dx + dy * dy;
r = (int)((((float)distSq / diff) * R));
g = (int)((((float)distSq / diff) * G));
b = (int)((((float)distSq / diff) * B));
r = (byte)(r > R ? R : (r < 0 ? 0 : r));
g = (byte)(g > G ? G : (g < 0 ? 0 : g));
b = (byte)(b > B ? B : (b < 0 ? 0 : b));
imageIn.setPixelColor(x, y, r, g, b);
}
}
ImageBlender blender = new ImageBlender();
blender.Mode = BlendMode.Additive;
return blender.Blend(clone, imageIn);
}
public void trainNestedAlgorithm(ContextualMemoryNestedAlgorithm nestedAlgorithm)
{
BlackAndWhiteConverter blackAndWhiteConverter = new BlackAndWhiteConverter(1);
//BlackAndWhiteConverter blackAndWhiteConverter = new BlackAndWhiteConverter(63);
List <String> fileList = new List <string>(benchmark.getTrainingFilesPathList());
List <ContextualMemoryNestedAlgorithmLayer> layers = nestedAlgorithm.getLayers();
for (int layerIndex = 0; layerIndex < layers.Count; layerIndex++)
{
ContextualMemoryNestedAlgorithmLayer layer = layers[layerIndex];
layer.initialize();
Console.WriteLine("Layer: " + (layerIndex + 1) + "/" + layers.Count);
EdgeDetectionAlgorithm algorithm = layer.algorithm;
DateTime trainingStart = DateTime.Now;
float totalLoss = 0;
int totalNumberOfFiles = numberOfTrainingSetPasses * fileList.Count;
int totalIndex = 0;
for (int pass = 0; pass < numberOfTrainingSetPasses; pass++)
{
ListUtils.Shuffle(fileList);
int index = 1;
float totalPassLoss = 0;
DateTime trainingPassStart = DateTime.Now;
foreach (string trainingFileName in fileList)
{
DateTime start = DateTime.Now;
Console.WriteLine("Pass: "******"/" + numberOfTrainingSetPasses + ", " + index + "/" + fileList.Count + " Training file: " + Path.GetFileName(trainingFileName));
ImageDescription inputImage = ImageFileHandler.loadFromPath(trainingFileName);
int layerResizeFactor = layer.resizeFactor;
ImageDescription computedImage = null;
if (layerIndex > 0)
{
List <ImageDescription> computedImages = nestedAlgorithm.computeImageForLayers(inputImage, layerIndex);
computedImage = computedImages[layerIndex - 1];
}
ImageDescription inputImageGroundTruth = ImageFileHandler.loadFromPath(benchmark.getTrainingFileGroundTruth(trainingFileName));
inputImageGroundTruth = blackAndWhiteConverter.filter(inputImageGroundTruth);
ImageDescription newInputImage = null;
ImageDescription newInputImageGroundTruth = null;
ResizeFilter resizeGrayscale = new ResizeFilter(inputImage.sizeX / layerResizeFactor, inputImage.sizeY / layerResizeFactor, ImageDescriptionUtil.grayscaleChannel);
ResizeFilter resizeColor = new ResizeFilter(inputImage.sizeX / layerResizeFactor, inputImage.sizeY / layerResizeFactor, ImageDescriptionUtil.colorChannels);
if (layerResizeFactor == 1)
{
newInputImage = inputImage;
newInputImageGroundTruth = inputImageGroundTruth;
}
else
{
newInputImage = resizeColor.filter(inputImage);
newInputImageGroundTruth = resizeGrayscale.filter(inputImageGroundTruth);
}
if (layerIndex > 0)
{
ImageDescription resizedComputed = resizeGrayscale.filter(computedImage);
newInputImage.setColorChannel(ColorChannelEnum.Layer, resizedComputed.gray);
}
float loss = algorithm.train(newInputImage, newInputImageGroundTruth);
totalLoss += loss;
totalPassLoss += loss;
index++;
totalIndex++;
double timeElapsed = (DateTime.Now - start).TotalSeconds;
double timeElapsedSoFar = (DateTime.Now - trainingStart).TotalSeconds;
double estimatedTime = (timeElapsedSoFar / totalIndex) * (totalNumberOfFiles - totalIndex);
Console.WriteLine("Loss: " + loss.ToString("0.00") + " Time: " + timeElapsed.ToString("0.00") + "s Time elapsed: "
+ timeElapsedSoFar.ToString("0.00") + "s ETA: " + estimatedTime.ToString("0.00") + "s");
}
double tariningPassTimeElapsed = (DateTime.Now - trainingPassStart).TotalSeconds;
Console.WriteLine("Pass took " + tariningPassTimeElapsed.ToString("0.00") + " sec. Pass loss: " + totalPassLoss.ToString("0.00")
+ " Avg loss: " + (totalPassLoss / (fileList.Count)).ToString("0.00"));
}
double totalTimeElapsed = (DateTime.Now - trainingStart).TotalSeconds;
Console.WriteLine("Training took " + totalTimeElapsed.ToString("0.00") + " sec. Total loss: " + totalLoss.ToString("0.00")
+ " Avg loss: " + (totalLoss / (totalNumberOfFiles)).ToString("0.00"));
}
Console.WriteLine("Training blender");
DateTime blenderTrainingStart = DateTime.Now;
float blenderTotalLoss = 0;
int blenderTotalNumberOfFiles = /* numberOfTrainingSetPasses * */ fileList.Count;
int blenderTotalIndex = 0;
ImageBlender blender = nestedAlgorithm.getImageBlender();
//for (int pass = 0; pass < numberOfTrainingSetPasses; pass++)
{
ListUtils.Shuffle(fileList);
int index = 1;
float totalPassLoss = 0;
DateTime trainingPassStart = DateTime.Now;
foreach (string trainingFileName in fileList)
{
DateTime start = DateTime.Now;
//Console.Write("Pass: "******"/" + numberOfTrainingSetPasses + ", ");
Console.WriteLine(index + "/" + fileList.Count + " Training file: " + Path.GetFileName(trainingFileName));
ImageDescription inputImage = ImageFileHandler.loadFromPath(trainingFileName);
List <ImageDescription> computedImages = nestedAlgorithm.computeImageForLayers(inputImage, layers.Count);
ImageDescription inputImageGroundTruth = ImageFileHandler.loadFromPath(benchmark.getTrainingFileGroundTruth(trainingFileName));
inputImageGroundTruth = blackAndWhiteConverter.filter(inputImageGroundTruth);
float blenderLoss = blender.train(computedImages, inputImageGroundTruth);
blenderTotalLoss += blenderLoss;
totalPassLoss += blenderLoss;
index++;
blenderTotalIndex++;
double timeElapsed = (DateTime.Now - start).TotalSeconds;
double timeElapsedSoFar = (DateTime.Now - blenderTrainingStart).TotalSeconds;
double estimatedTime = (timeElapsedSoFar / blenderTotalIndex) * (blenderTotalNumberOfFiles - blenderTotalIndex);
Console.WriteLine("Loss: " + blenderLoss.ToString("0.00") + " Time: " + timeElapsed.ToString("0.00") + "s Time elapsed: "
+ timeElapsedSoFar.ToString("0.00") + "s ETA: " + estimatedTime.ToString("0.00") + "s");
}
//double tariningPassTimeElapsed = (DateTime.Now - trainingPassStart).TotalSeconds;
//Console.WriteLine("Pass took " + tariningPassTimeElapsed.ToString("0.00") + " sec. Pass loss: " + totalPassLoss.ToString("0.00")
//+ " Avg loss: " + (totalPassLoss / (fileList.Count)).ToString("0.00"));
}
double blenderTotalTimeElapsed = (DateTime.Now - blenderTrainingStart).TotalSeconds;
Console.WriteLine("Training took " + blenderTotalTimeElapsed.ToString("0.00") + " sec. Total loss: " + blenderTotalLoss.ToString("0.00")
+ " Avg loss: " + (blenderTotalLoss / (blenderTotalNumberOfFiles)).ToString("0.00"));
}
public void setImageBlender(ImageBlender imageBlender)
{
this.imageBlender = imageBlender;
}
