// Usage identical to RandomTreeImage class
void RandomForestImage::train(const std::vector<LabeledRGBDImage>& trainLabelImages, size_t numLabels,
bool trainTreesSequentially) {
if (trainLabelImages.empty()) {
throw std::runtime_error("no training images");
}
const size_t treeCount = ensemble.size();
const int numThreads = configuration.getNumThreads();
tbb::task_scheduler_init init(numThreads);
CURFIL_INFO("learning image tree ensemble. " << treeCount << " trees with " << numThreads << " threads");
for (size_t treeNr = 0; treeNr < treeCount; ++treeNr) {
ensemble[treeNr] = boost::make_shared<RandomTreeImage>(treeNr, configuration);
}
RandomSource randomSource(configuration.getRandomSeed());
const int SEED = randomSource.uniformSampler(0xFFFF).getNext();
auto train =
[&](boost::shared_ptr<RandomTreeImage>& tree) {
utils::Timer timer;
auto seed = SEED + tree->getId();
RandomSource randomSource(seed);
std::vector<LabeledRGBDImage> sampledTrainLabelImages = trainLabelImages;
if (configuration.getMaxImages() > 0 && static_cast<int>(trainLabelImages.size()) > configuration.getMaxImages()) {
ReservoirSampler<LabeledRGBDImage> reservoirSampler(configuration.getMaxImages());
Sampler sampler = randomSource.uniformSampler(0, 10 * trainLabelImages.size());
for (auto& image : trainLabelImages) {
reservoirSampler.sample(sampler, image);
}
CURFIL_INFO("tree " << tree->getId() << ": sampled " << reservoirSampler.getReservoir().size()
<< " out of " << trainLabelImages.size() << " images");
sampledTrainLabelImages = reservoirSampler.getReservoir();
}
tree->train(sampledTrainLabelImages, randomSource, configuration.getSamplesPerImage() / treeCount, numLabels);
CURFIL_INFO("finished tree " << tree->getId() << " with random seed " << seed << " in " << timer.format(3));
};
if (!trainTreesSequentially && numThreads > 1) {
tbb::parallel_for_each(ensemble.begin(), ensemble.end(), train);
} else {
std::for_each(ensemble.begin(), ensemble.end(), train);
}
}
void HairyBrush::fromDabWithDensity(KisFixedPaintDeviceSP dab, qreal density)
{
int width = dab->bounds().width();
int height = dab->bounds().height();
int centerX = width * 0.5;
int centerY = height * 0.5;
// make mask
Bristle * bristle = 0;
qreal alpha;
quint8 * dabPointer = dab->data();
quint8 pixelSize = dab->pixelSize();
const KoColorSpace * cs = dab->colorSpace();
KoColor bristleColor(cs);
KisRandomSource randomSource(0);
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
alpha = cs->opacityF(dabPointer);
if (alpha != 0.0) {
if (density == 1.0 || randomSource.generateNormalized() <= density) {
memcpy(bristleColor.data(), dabPointer, pixelSize);
bristle = new Bristle(x - centerX, y - centerY, alpha); // using value from image as length of bristle
bristle->setColor(bristleColor);
m_bristles.append(bristle);
}
}
dabPointer += pixelSize;
}
}
}
