static void GUI()
{
Logger.Enabled = true;
new GUIProgram(
tomatoDir,
"TrainingSet.txt",
loader => TomatoSetHelper.SplitExact(300, 300, loader),
new Size(800, 600)
).Start();
}
protected override void OnStart(IDictionary <SetType, List <Tomato> > imageSets)
{
//traning phase
var trainSet = TomatoSetHelper.Expand(imageSets[SetType.TRANING]);
classifier.Train(trainSet.Item1, trainSet.Item2, trainSet.Item3, trainSet.Item4);
//load test images
var testImages = new ImageLoaderHelper(base.ComputePath(this.indexName));
//the classifier predicts the class label for each image in the test set.
var lines = ExportClassificationResult(classifier, testImages.Images);
File.WriteAllLines(base.ComputePath("submission" + DateTime.Now.ToFileTime() + ".txt"), lines.ToArray());
log.LogNewLine("evaluation done, output in submission<timestamp>.txt");
}
static void EvalPerfomance()
{
var classifier = standardClassifier();
Logger.Enabled = true;
var program = new ConsolePerformanceEvaluator(
tomatoDir,
"TrainingSet.txt",
loader => TomatoSetHelper.SplitExact(300, 300, loader),
new List <TomatoClassifier>(new TomatoClassifier[] { classifier })
);
program.Start();
}
static void ExportFeatureOnCsv()
{
var extractor = new LBPExtractor(false);
Logger.Enabled = true;
var program = new ExtractCSVProgram(
tomatoDir,
"TrainingSet.txt",
loader => TomatoSetHelper.SplitExact(100, 100, loader),
extractor
);
program.Start();
}
protected override void OnStart(IDictionary <SetType, List <Tomato> > imageSets)
{
var mappedTrain = TomatoSetHelper.Expand(imageSets[SetType.TRANING]);
var mappedVal = TomatoSetHelper.Expand(imageSets[SetType.VALIDATION]);
log.LogNewLine("Extract feature on train...");
var train = extractor.ExtractDescriptors(mappedTrain.Item1, mappedTrain.Item2, mappedTrain.Item3, mappedTrain.Item4);
log.LogNewLine("");
log.LogNewLine("Extract feature on validation...");
var validation = extractor.ExtractDescriptors(mappedVal.Item1, mappedVal.Item2, mappedVal.Item3, mappedVal.Item4);
WriteCsvOn(train, "Train.csv");
WriteCsvOn(validation, "Validation.csv");
}
static void CompetiotionOutput()
{
var classifier = standardClassifier();
Logger.Enabled = true;
var program = new ConsoleCompetitionOutput(
tomatoDir,
"TrainingSet.txt",
loader => TomatoSetHelper.SplitExact(300, 300, loader),
classifier,
"TestSet.txt"
);
program.Start();
}
protected override void OnStart(IDictionary <SetType, List <Tomato> > imageSets)
{
var train = TomatoSetHelper.Expand(imageSets[SetType.TRANING]);
var i = 0;
foreach (TomatoClassifier classifier in classifiers)
{
log.LogNewLine("Classifier : " + i++);
log.LogNewLine("___________________");
stopWatch.Start();
classifier.Train(train.Item1, train.Item2, train.Item3, train.Item4);
stopWatch.Stop();
log.LogNewLine("train time : " + stopWatch.Elapsed.TotalSeconds + " seconds");
EvalPerformance(classifier, imageSets[SetType.VALIDATION]);
log.LogNewLine("___________________");
}
Console.ReadLine();
}
private void ExtractFeature(TomatoDescriptorExtractor extractor, List <Tomato> tomatoes)
{
var adapted = TomatoSetHelper.Expand(tomatoes);
extractor.ExtractDescriptors(adapted.Item1, adapted.Item2, adapted.Item3, adapted.Item4);
}
private void Train(TomatoClassifier classifier, List <Tomato> tomatoes)
{
var adapted = TomatoSetHelper.Expand(tomatoes);
classifier.Train(adapted.Item1, adapted.Item2, adapted.Item3, adapted.Item4);
}
