C++ DataPoints::setColFrom方法代码示例

void DataPointsFiltersImpl<T>::MaxDensityDataPointsFilter::inPlaceFilter(
	DataPoints& cloud)
{
	typedef typename DataPoints::View View;
	typedef typename DataPoints::ConstView ConstView;

	// Force densities to be computed
	if (!cloud.descriptorExists("densities"))
	{
		throw InvalidField("MaxDensityDataPointsFilter: Error, no densities found in descriptors.");
	}

	const int nbPointsIn = cloud.features.cols();
	View densities = cloud.getDescriptorViewByName("densities");
	const T lastDensity = densities.maxCoeff();
	const int nbSaturatedPts = (densities.cwise() == lastDensity).count();

	// fill cloud values
	int j = 0;
	for (int i = 0; i < nbPointsIn; i++)
	{
		const T density(densities(0,i));
		if (density > maxDensity)
		{
			const float r = (float)std::rand()/(float)RAND_MAX;
			float acceptRatio = maxDensity/density;

			// Handle saturation value of density
			if (density == lastDensity)
			{
				acceptRatio = acceptRatio * (1-nbSaturatedPts/nbPointsIn);
			}

			if (r < acceptRatio)
			{
				cloud.setColFrom(j, cloud, i);
				j++;
			}
		}
		else
		{
			cloud.setColFrom(j, cloud, i);
			j++;
		}
	}

	cloud.conservativeResize(j);
}

void DataPointsFiltersImpl<T>::MaxQuantileOnAxisDataPointsFilter::inPlaceFilter(
	DataPoints& cloud)
{
	if (int(dim) >= cloud.features.rows())
		throw InvalidParameter((boost::format("MaxQuantileOnAxisDataPointsFilter: Error, filtering on dimension number %1%, larger than feature dimensionality %2%") % dim % cloud.features.rows()).str());

	const int nbPointsIn = cloud.features.cols();
	const int nbPointsOut = nbPointsIn * ratio;

	// build array
	vector<T> values;
	values.reserve(cloud.features.cols());
	for (int x = 0; x < cloud.features.cols(); ++x)
		values.push_back(cloud.features(dim, x));

	// get quartiles value
	nth_element(values.begin(), values.begin() + (values.size() * ratio), values.end());
	const T limit = values[nbPointsOut];

	// copy towards beginning the elements we keep
	int j = 0;
	for (int i = 0; i < nbPointsIn; i++)
	{
		if (cloud.features(dim, i) < limit)
		{
			assert(j <= i);
			cloud.setColFrom(j, cloud, i);
			j++;
		}
	}
	assert(j <= nbPointsOut);

	cloud.conservativeResize(j);

}

void DataPointsFiltersImpl<T>::BoundingBoxDataPointsFilter::inPlaceFilter(
	DataPoints& cloud)
{
	const int nbPointsIn = cloud.features.cols();
	const int nbRows = cloud.features.rows();

	int j = 0;
	for (int i = 0; i < nbPointsIn; i++)
	{
		bool keepPt = false;
		const Vector point = cloud.features.col(i);

		// FIXME: improve performance by using Eigen array operations
		const bool x_in = (point(0) > xMin && point(0) < xMax);
		const bool y_in = (point(1) > yMin && point(1) < yMax);
		const bool z_in = (point(2) > zMin && point(2) < zMax) || nbRows == 3;
		const bool in_box = x_in && y_in && z_in;

		if(removeInside)
			keepPt = !in_box;
		else
			keepPt = in_box;

		if(keepPt)
		{
			cloud.setColFrom(j, cloud, i);
			j++;
		}
	}

	cloud.conservativeResize(j);
}

void DataPointsFiltersImpl<T>::RandomSamplingDataPointsFilter::inPlaceFilter(
	DataPoints& cloud)
{
	const int nbPointsIn = cloud.features.cols();

	int j = 0;
	for (int i = 0; i < nbPointsIn; i++)
	{
		const float r = (float)std::rand()/(float)RAND_MAX;
		if (r < prob)
		{
			cloud.setColFrom(j, cloud, i);
			j++;
		}
	}

	cloud.conservativeResize(j);
}

void RemoveNaNDataPointsFilter<T>::inPlaceFilter(
	DataPoints& cloud)
{
	const int nbPointsIn = cloud.features.cols();

	int j = 0;
	for (int i = 0; i < nbPointsIn; ++i)
	{
		const BOOST_AUTO(colArray, cloud.features.col(i).array());
		const BOOST_AUTO(hasNaN, !(colArray == colArray).all());
		if (!hasNaN)
		{
			cloud.setColFrom(j, cloud, i);
			++j;
		}
	}

	cloud.conservativeResize(j);
}

void DataPointsFiltersImpl<T>::FixStepSamplingDataPointsFilter::inPlaceFilter(
	DataPoints& cloud)
{
	const int iStep(step);
	const int nbPointsIn = cloud.features.cols();
	const int phase(rand() % iStep);

	int j = 0;
	for (int i = phase; i < nbPointsIn; i += iStep)
	{
		cloud.setColFrom(j, cloud, i);
		j++;
	}

	cloud.conservativeResize(j);

	const double deltaStep(startStep * stepMult - startStep);
	step *= stepMult;
	if (deltaStep < 0 && step < endStep)
		step = endStep;
	if (deltaStep > 0 && step > endStep)
		step = endStep;

}