public List <ImageDescription> computeImageForLayers(ImageDescription inputImage, int numberOfLayersToCompute)
{
List <ImageDescription> computedImages = new List <ImageDescription>(numberOfLayersToCompute);
for (int i = 0; i < numberOfLayersToCompute; i++)
{
EdgeDetectionAlgorithm algorithm = layers[i].algorithm;
int layerResizeFactor = layers[i].resizeFactor;
ImageDescription newInputImage = 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;
}
else
{
newInputImage = resizeColor.filter(inputImage);
}
if (i > 0)
{
ImageDescription resizedComputed = resizeGrayscale.filter(computedImages[i - 1]);
newInputImage.setColorChannel(ColorChannelEnum.Layer, resizedComputed.gray);
}
ImageDescription layerOutputImage = algorithm.test(newInputImage);
computedImages.Add(layerOutputImage);
}
return(computedImages);
}
public void trainWithBaseAlgorithm(EdgeDetectionAlgorithm algorithm, EdgeDetectionAlgorithm baseAlgorithm, int resizeFactor)
{
DateTime trainingStart = DateTime.Now;
float totalLoss = 0;
List <String> fileList = new List <string>(benchmark.getTrainingFilesPathList());
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);
ImageDescription computedImage = baseAlgorithm.test(inputImage);
ResizeFilter resizeColor = new ResizeFilter(inputImage.sizeX / resizeFactor, inputImage.sizeY / resizeFactor, ImageDescriptionUtil.colorChannels);
ImageDescription newInputImage = resizeColor.filter(inputImage);
ImageDescription inputImageGroundTruth = ImageFileHandler.loadFromPath(benchmark.getTrainingFileGroundTruth(trainingFileName));
inputImageGroundTruth.computeGrayscale();
ResizeFilter resizeGrayscale = new ResizeFilter(inputImage.sizeX / resizeFactor, inputImage.sizeY / resizeFactor, ImageDescriptionUtil.grayscaleChannel);
ImageDescription newInputImageGroundTruth = resizeGrayscale.filter(inputImageGroundTruth);
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"));
}
private static void testAlgorithmOnFile(string algorithmToTest, string filename)
{
Console.WriteLine("Started loading " + algorithmToTest);
EdgeDetectionAlgorithm edgeDetectionAlgorithm = EdgeDetectionAlgorithmUtil.loadAlgorithmFromCompressedFile(algorithmToTest);
Console.WriteLine("Loaded algorithm. Testing.");
ImageDescription inputImage = ImageFileHandler.loadFromPath(filename);
ImageDescription outputImage = edgeDetectionAlgorithm.test(inputImage);
ImageFileHandler.saveToPath(outputImage, "test", ".png");
Console.WriteLine("Saved");
}
public void test(EdgeDetectionAlgorithm algorithm)
{
DateTime testingStart = DateTime.Now;
List <String> fileList = benchmark.getTestFilesPathList();
int index = 1;
string outputDirectory = null;
foreach (string testFileName in fileList)
{
DateTime start = DateTime.Now;
outputDirectory = Path.GetDirectoryName(benchmark.getTestFileOutputPathWithoutExtension(testFileName));
if (!Directory.Exists(outputDirectory))
{
Directory.CreateDirectory(outputDirectory);
}
Console.WriteLine(index + "/" + fileList.Count + " Testing file: " + Path.GetFileName(testFileName));
ImageDescription inputImage = ImageFileHandler.loadFromPath(testFileName);
ImageDescription outputImage = algorithm.test(inputImage);
ImageFileHandler.saveToPath(outputImage, benchmark.getTestFileOutputPathWithoutExtension(testFileName), outputFileExtension);
double timeElapsed = (DateTime.Now - start).TotalSeconds;
double timeElapsedSoFar = (DateTime.Now - testingStart).TotalSeconds;
double estimatedTime = (timeElapsedSoFar / index) * (fileList.Count - index);
Console.WriteLine(timeElapsed.ToString("0.00") + "s Time elapsed: "
+ timeElapsedSoFar.ToString("0.00") + "s ETA: " + estimatedTime.ToString("0.00") + "s");
index++;
}
double totalTimeElapsed = (DateTime.Now - testingStart).TotalSeconds;
Console.WriteLine("Testing took " + totalTimeElapsed.ToString("0.00") + " sec.");
if (testOnTrainingFiles)
{
Console.WriteLine("Testing on training files");
testingStart = DateTime.Now;
index = 0;
// we have the outputDirectory from test, else, relative to the exe
outputDirectory = Path.Combine(outputDirectory, trainingFilesTestOutput);
if (!Directory.Exists(outputDirectory))
{
Directory.CreateDirectory(outputDirectory);
}
fileList = new List <string>(benchmark.getTrainingFilesPathList());
foreach (string trainingFileName in fileList)
{
DateTime start = DateTime.Now;
string outputPath = Path.Combine(outputDirectory, Path.GetFileNameWithoutExtension(trainingFileName));
Console.WriteLine(index + "/" + fileList.Count + " Testing file: " + Path.GetFileName(trainingFileName));
ImageDescription inputImage = ImageFileHandler.loadFromPath(trainingFileName);
ImageDescription outputImage = algorithm.test(inputImage);
ImageFileHandler.saveToPath(outputImage, outputPath, outputFileExtension);
index++;
double timeElapsed = (DateTime.Now - start).TotalSeconds;
Console.WriteLine(timeElapsed.ToString("0.00") + " seconds");
}
totalTimeElapsed = (DateTime.Now - testingStart).TotalSeconds;
Console.WriteLine("Testing on training files took " + totalTimeElapsed.ToString("0.00") + " sec.");
}
}