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 static void loadAndTestModel(string inputModelFilename, string relativeOutputPath)
{
EdgeDetectionAlgorithm algorithm = EdgeDetectionAlgorithmUtil.loadAlgorithmFromCompressedFile(inputModelFilename);
BerkeleyEdgeDetectionBenchmark benchmark = new BerkeleyEdgeDetectionBenchmark(localBenchmarkPath, Path.Combine(localBenchmarkPath, relativeOutputPath));
EdgeDetectionProcessor processor = new EdgeDetectionProcessor(benchmark, 0, true);
Console.WriteLine("Testing started.");
processor.test(algorithm);
Console.WriteLine("Testing finished.");
Console.WriteLine("Validation started.");
processor.validate();
Console.WriteLine("Validation finished.");
}
public static void trainAndSaveModel(string outputModelFilename, string relativeOutputPath,
int numberOfRays = 16, int rayLength = 10, int memoryBits = 24, int numberOfPasses = 1, bool useRotations = false)
{
ISet <ColorChannelEnum> colorChannels = new HashSet <ColorChannelEnum> {
ColorChannelEnum.Red, ColorChannelEnum.Green, ColorChannelEnum.Blue
};
ISet <ColorChannelEnum> colorChannelsAndComputed = new HashSet <ColorChannelEnum> {
ColorChannelEnum.Red, ColorChannelEnum.Green, ColorChannelEnum.Blue, ColorChannelEnum.Canny, ColorChannelEnum.Kirsch
};
ImageFilterChain filterChain = new ImageFilterChain();
filterChain.addFilter(new CannyAppenderFilter());
filterChain.addFilter(new KirschAppenderFilter());
filterChain.addFilter(new GaussFilterExcludeComputed(5, 1.4f, colorChannels));
//filterChain.addFilter(new MedianFilter(1, colorChannels));
ContextualMemoryEdgeDetectionAlgorithm algorithm = new ContextualMemoryEdgeDetectionAlgorithm(colorChannelsAndComputed, rayLength, memoryBits, numberOfRays);
//ContextualMemoryEdgeDetectionAlgorithmNoCollisions algorithm = new ContextualMemoryEdgeDetectionAlgorithmNoCollisions(colorChannelsAndComputed, rayLength, memoryBits, numberOfRays);
//ContextualMemoryEdgeDetectionAlgorithmNoCollisionsBiasReplace algorithm =
// new ContextualMemoryEdgeDetectionAlgorithmNoCollisionsBiasReplace(colorChannelsAndComputed, rayLength, memoryBits, numberOfRays);
algorithm.inputImageFilterChain = filterChain;
BerkeleyEdgeDetectionBenchmark benchmark = new BerkeleyEdgeDetectionBenchmark(localBenchmarkPath, Path.Combine(localBenchmarkPath, relativeOutputPath), useRotations, false);
EdgeDetectionProcessor processor = new EdgeDetectionProcessor(benchmark, numberOfPasses, true);
Console.WriteLine("Training started.");
processor.train(algorithm);
Console.WriteLine("Training finished.");
EdgeDetectionAlgorithmUtil.saveToCompressedFile(algorithm, outputModelFilename);
EdgeDetectionAlgorithm algorithmToTest = EdgeDetectionAlgorithmUtil.loadAlgorithmFromCompressedFile(outputModelFilename);
Console.WriteLine("Testing started.");
processor.test(algorithmToTest);
Console.WriteLine("Testing finished.");
Console.WriteLine("Validation started.");
processor.validate();
Console.WriteLine("Validation finished.");
}
private static void trainTestAndSaveNestedAlgorithm()
{
string relativeImageOutputFolder = "cm m20 collisions 4layers thr1 1 2 4 8 canny kirsh nms newderivative (gauss) square l16 squaremix";
string outputModelFilename = relativeImageOutputFolder + ".alg";
int numberOfPasses = 1;
int? trainingSetSizeLimit = null;
bool useRotations = false;
bool testOnTrainingFiles = false;
ISet <ColorChannelEnum> colorChannels = new HashSet <ColorChannelEnum> {
ColorChannelEnum.Red, ColorChannelEnum.Green, ColorChannelEnum.Blue, ColorChannelEnum.Canny, ColorChannelEnum.Kirsch
};
ISet <ColorChannelEnum> colorAndComputedChannels = new HashSet <ColorChannelEnum> {
ColorChannelEnum.Red, ColorChannelEnum.Green, ColorChannelEnum.Blue, ColorChannelEnum.Canny, ColorChannelEnum.Kirsch, ColorChannelEnum.Layer
};
ContextualMemoryNestedAlgorithm nestedAlgorithm = new ContextualMemoryNestedAlgorithm();
ContextualMemoryNestedAlgorithmLayer layer0 = new ContextualMemoryNestedAlgorithmLayer();
layer0.colorChannels = colorChannels;
layer0.longestContextLength = 16;
layer0.numberOfRays = 16;
layer0.tableSizeBits = 20;
layer0.resizeFactor = 1;
layer0.outputResults = true;
nestedAlgorithm.addLayer(layer0);
ContextualMemoryNestedAlgorithmLayer layer1 = new ContextualMemoryNestedAlgorithmLayer();
layer1.colorChannels = colorAndComputedChannels;
layer1.longestContextLength = 16;
layer1.numberOfRays = 16;
layer1.tableSizeBits = 20;
layer1.resizeFactor = 2;
layer1.outputResults = true;
nestedAlgorithm.addLayer(layer1);
ContextualMemoryNestedAlgorithmLayer layer2 = new ContextualMemoryNestedAlgorithmLayer();
layer2.colorChannels = colorAndComputedChannels;
layer2.longestContextLength = 16;
layer2.numberOfRays = 16;
layer2.tableSizeBits = 20;
layer2.resizeFactor = 4;
layer2.outputResults = true;
nestedAlgorithm.addLayer(layer2);
ContextualMemoryNestedAlgorithmLayer layer3 = new ContextualMemoryNestedAlgorithmLayer();
layer3.colorChannels = colorAndComputedChannels;
layer3.longestContextLength = 16;
layer3.numberOfRays = 16;
layer3.tableSizeBits = 19;
layer3.resizeFactor = 8;
layer3.outputResults = true;
nestedAlgorithm.addLayer(layer3);
//nestedAlgorithm.setImageBlender(new SimpleGrayscaleImageBlender());
nestedAlgorithm.setImageBlender(new LogisticMixGrayscaleImageBlender(nestedAlgorithm.getLayers().Count));
BerkeleyEdgeDetectionBenchmark benchmark = new BerkeleyEdgeDetectionBenchmark(localBenchmarkPath, Path.Combine(localBenchmarkPath, relativeImageOutputFolder), useRotations, false, trainingSetSizeLimit);
EdgeDetectionProcessor processor = new EdgeDetectionProcessor(benchmark, numberOfPasses, testOnTrainingFiles);
Console.WriteLine("Training started.");
processor.trainNestedAlgorithm(nestedAlgorithm);
Console.WriteLine("Training finished.");
EdgeDetectionAlgorithmUtil.saveToCompressedFile(nestedAlgorithm, outputModelFilename);
nestedAlgorithm = null;
EdgeDetectionAlgorithm algorithm = EdgeDetectionAlgorithmUtil.loadAlgorithmFromCompressedFile(outputModelFilename);
Console.WriteLine("Testing started.");
processor.testNestedAlgorithm((ContextualMemoryNestedAlgorithm)algorithm);
Console.WriteLine("Testing finished.");
}