Method performClassification

3rd-party/cove/libvectorizer/Vectorizer.cpp:308–378

! Runs classfication. The progress observer is called during work if set. * \param[in] progressObserver Pointer to class implementing ProgressObserver. * In case it is null pointer no calls take place. */

 * In case it is null pointer no calls take place.
 */
bool Vectorizer::performClassification(ProgressObserver* progressObserver)
{
	// invalidate previous images
	bwImage = classifiedImage = QImage();
	std::unique_ptr<KohonenPatternGetter> pg;

	// if no initial colors have been set, select random ones
	if (sourceImageColors.empty()) setInitColors({});

	std::vector<OrganizableElement*> classes(sourceImageColors.size());
	for (std::size_t i = 0; i < sourceImageColors.size(); i++)
		classes[i] = sourceImageColors[i].get();

	KohonenMap km;
	km.setClasses(classes);

	switch (learnMethod)
	{
	case KOHONEN_CLASSIC:
	{
		switch (patternStrategy)
		{
		case PATTERN_RANDOM:
			pg = std::make_unique<RandomPatternGetter>(sourceImage, mc.get());
			break;
		default:
			qWarning("UNIMPLEMENTED pattern getter");
		}

		std::unique_ptr<KohonenAlphaGetter> ag;
		switch (alphaStrategy)
		{
		case ALPHA_CLASSIC:
			ag = std::make_unique<ClassicAlphaGetter>(initAlpha, q, E, minAlpha,
													  progressObserver);
			break;
		default:
			qWarning("UNIMPLEMENTED alpha getter");
		}

		km.performLearning(*ag, *pg);
	}
	break;
	case KOHONEN_BATCH:
	{
		auto bg = std::make_unique<SequentialPatternGetter>(
			sourceImage, mc.get(), progressObserver);
		quality = km.performBatchLearning(*bg);
		classifiedImage = *bg->getClassifiedImage();
	}
	break;
	default:
		qWarning("UNIMPLEMENTED classification method");
	}

	bool cancel = progressObserver && progressObserver->isInterruptionRequested();
	if (cancel)
	{