/// <summary>
/// for other params, <see>AbstractProgram</see>
/// </summary>
/// <param name="classifier">The classifier to train and to evalaute</param>
/// <param name="testIndexName">index files that link each image in the test set</param>
public ConsoleCompetitionOutput(string tomatoDirPath,
string indexFileName,
SplitStrategy split,
TomatoClassifier classifier,
string testIndexName) : base(tomatoDirPath, indexFileName, split)
{
this.indexName = testIndexName;
this.classifier = classifier;
}
/// <summary>
///
/// </summary>
/// <param name="tomatoSets">
/// The tomato set divided into training and validation.
/// In the training set, the classificator learns the svm params.
/// The classifier predicts label on the validation set.
/// </param>
public Controller(
IDictionary <SetType, List <Tomato> > tomatoSets)
{
this.images = new List <VisualEvent>();
imageExtractor = new LBPExtractor(true);
imageExtractor.VisualEventManager += OnImageComputed;
classifier = new TomatoClassifier(new LBPExtractor(false), SVMClassifier.Linear(0.43f));
this.tomatoSets = tomatoSets;
}
/*
* for each prediction, this method create a line following this schema:
* {image_name} {label} {score}
*/
private List <string> ExportClassificationResult(TomatoClassifier classifier, List <Tomato> testTomatoes)
{
var classifiedResult = new List <string>();
log.LogNewLine("compute label on test images..");
foreach (Tomato tomato in testTomatoes)
{
var score = 0.0;
var res = classifier.Classify(tomato.ColoredImage, tomato.GrayImage1, tomato.GrayImage2, out score);
classifiedResult.Add(tomato.Name + " " + (int)res + " " + score);
}
return(classifiedResult);
}
/*
* this method eval the classifier performance in the validation set.
* the metrics used are:
* - precision;
* - recall;
* - accuracy;
* - confusion matrix.
*/
private void EvalPerformance(TomatoClassifier classifier, List <Tomato> tomatoes)
{
var falsePositive = 0.0;
var falseNegative = 0.0;
var truePositive = 0.0;
var trueNegative = 0.0;
log.LogNewLine("eval...");
stopWatch.Restart();
foreach (Tomato tomato in tomatoes)
{
log.Log(".");
double score = 0;
var predicted = classifier.Classify(tomato.ColoredImage, tomato.GrayImage1, tomato.GrayImage2, out score);
if (predicted == (int)tomato.Type)
{
var updated = predicted == (int)(TomatoType.BAD) ? trueNegative++ : truePositive++;
}
else
{
var updated = predicted == (int)(TomatoType.BAD) ? falseNegative++ : falsePositive++;
}
}
stopWatch.Stop();
log.LogNewLine("");
log.LogNewLine("predict " + tomatoes.Count / stopWatch.Elapsed.Seconds + " tomatoes per second");
log.LogNewLine("ends..");
log.LogNewLine("precision : " + truePositive / (truePositive + falsePositive));
log.LogNewLine("recall : " + truePositive / (truePositive + falseNegative));
log.LogNewLine("accuracy : " + (truePositive + trueNegative) / tomatoes.Count);
log.LogNewLine("-- CONFUSION MATRIX --");
log.LogNewLine("BAD\t" + trueNegative + "\t" + falseNegative);
log.LogNewLine("OK \t" + falsePositive + "\t" + truePositive);
log.LogNewLine(" \tBAD\tOK");
log.LogNewLine("...");
}
private void Train(TomatoClassifier classifier, List <Tomato> tomatoes)
{
var adapted = TomatoSetHelper.Expand(tomatoes);
classifier.Train(adapted.Item1, adapted.Item2, adapted.Item3, adapted.Item4);
}
