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>::ShadowDataPointsFilter::inPlaceFilter(
DataPoints& cloud)
{
// Check if normals are present
if (!cloud.descriptorExists("normals"))
{
throw InvalidField("ShadowDataPointsFilter, Error: cannot find normals in descriptors");
}
const int dim = cloud.features.rows();
const BOOST_AUTO(normals, cloud.getDescriptorViewByName("normals"));
int j = 0;
for(int i=0; i < cloud.features.cols(); i++)
{
const Vector normal = normals.col(i).normalized();
const Vector point = cloud.features.block(0, i, dim-1, 1).normalized();
const T value = anyabs(normal.dot(point));
if(value > eps) // test to keep the points
{
cloud.features.col(j) = cloud.features.col(i);
cloud.descriptors.col(j) = cloud.descriptors.col(i);
j++;
}
}
cloud.conservativeResize(j);
}
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>::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>::MaxDistDataPointsFilter::inPlaceFilter(
DataPoints& cloud)
{
if (dim >= cloud.features.rows() - 1)
{
throw InvalidParameter(
(boost::format("MaxDistDataPointsFilter: Error, filtering on dimension number %1%, larger than authorized axis id %2%") % dim % (cloud.features.rows() - 2)).str());
}
const int nbPointsIn = cloud.features.cols();
const int nbRows = cloud.features.rows();
int j = 0;
if(dim == -1) // Euclidean distance
{
for (int i = 0; i < nbPointsIn; i++)
{
const T absMaxDist = anyabs(maxDist);
if (cloud.features.col(i).head(nbRows-1).norm() < absMaxDist)
{
cloud.setColFrom(j, cloud, i);
j++;
}
}
}
else // Single-axis distance
{
for (int i = 0; i < nbPointsIn; i++)
{
if ((cloud.features(dim, i)) < maxDist)
{
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;
}
void CovarianceSamplingDataPointsFilter<T>::inPlaceFilter(DataPoints& cloud)
{
const std::size_t featDim(cloud.features.rows());
assert(featDim == 4); //3D pts only
//Check number of points
const std::size_t nbPoints = cloud.getNbPoints();
if(nbSample >= nbPoints)
return;
//Check if there is normals info
if (!cloud.descriptorExists("normals"))
throw InvalidField("OrientNormalsDataPointsFilter: Error, cannot find normals in descriptors.");
const auto& normals = cloud.getDescriptorViewByName("normals");
std::vector<std::size_t> keepIndexes;
keepIndexes.resize(nbSample);
///---- Part A, as we compare the cloud with himself, the overlap is 100%, so we keep all points
//A.1 and A.2 - Compute candidates
std::vector<std::size_t> candidates ;
candidates.resize(nbPoints);
for (std::size_t i = 0; i < nbPoints; ++i) candidates[i] = i;
const std::size_t nbCandidates = candidates.size();
//Compute centroid
Vector3 center;
for(std::size_t i = 0; i < featDim - 1; ++i) center(i) = T(0.);
for (std::size_t i = 0; i < nbCandidates; ++i)
for (std::size_t f = 0; f <= 3; ++f)
center(f) += cloud.features(f,candidates[i]);
for(std::size_t i = 0; i <= 3; ++i) center(i) /= T(nbCandidates);
//Compute torque normalization
T Lnorm = 1.0;
if(normalizationMethod == TorqueNormMethod::L1)
{
Lnorm = 1.0;
}
else if(normalizationMethod == TorqueNormMethod::Lavg)
{
Lnorm = 0.0;
for (std::size_t i = 0; i < nbCandidates; ++i)
Lnorm += (cloud.features.col(candidates[i]).head(3) - center).norm();
Lnorm /= nbCandidates;
}
else if(normalizationMethod == TorqueNormMethod::Lmax)
{
const Vector minValues = cloud.features.rowwise().minCoeff();
const Vector maxValues = cloud.features.rowwise().maxCoeff();
const Vector3 radii = maxValues.head(3) - minValues.head(3);
Lnorm = radii.maxCoeff() / 2.; //radii.mean() / 2.;
}
//A.3 - Compute 6x6 covariance matrix + EigenVectors
auto computeCovariance = [Lnorm, nbCandidates, &cloud, ¢er, &normals, &candidates](Matrix66 & cov) -> void {
//Compute F matrix, see Eq. (4)
Eigen::Matrix<T, 6, Eigen::Dynamic> F(6, nbCandidates);
for(std::size_t i = 0; i < nbCandidates; ++i)
{
const Vector3 p = cloud.features.col(candidates[i]).head(3) - center; // pi-c
const Vector3 ni = normals.col(candidates[i]).head(3);
//compute (1 / L) * (pi - c) x ni
F.template block<3, 1>(0, i) = (1. / Lnorm) * p.cross(ni);
//set ni part
F.template block<3, 1>(3, i) = ni;
}
// Compute the covariance matrix Cov = FF'
cov = F * F.transpose();
};
Matrix66 covariance;
computeCovariance(covariance);
Eigen::EigenSolver<Matrix66> solver(covariance);
const Matrix66 eigenVe = solver.eigenvectors().real();
const Vector6 eigenVa = solver.eigenvalues().real();
///---- Part B
//B.1 - Compute the v-6 for each candidate
std::vector<Vector6, Eigen::aligned_allocator<Vector6>> v; // v[i] = [(pi-c) x ni ; ni ]'
v.resize(nbCandidates);
for(std::size_t i = 0; i < nbCandidates; ++i)
{
const Vector3 p = cloud.features.col(candidates[i]).head(3) - center; // pi-c
const Vector3 ni = normals.col(candidates[i]).head(3);
v[i].template block<3, 1>(0, 0) = (1. / Lnorm) * p.cross(ni);
v[i].template block<3, 1>(3, 0) = ni;
}
//B.2 - Compute the 6 sorted list based on dot product (vi . Xk) = magnitude, with Xk the kth-EigenVector
std::vector<std::list<std::pair<int, T>>> L; // contain list of pair (index, magnitude) contribution to the eigens vectors
L.resize(6);
//sort by decreasing magnitude
auto comp = [](const std::pair<int, T>& p1, const std::pair<int, T>& p2) -> bool {
return p1.second > p2.second;
};
for(std::size_t k = 0; k < 6; ++k)
{
for(std::size_t i = 0; i < nbCandidates; ++i )
{
L[k].push_back(std::make_pair(i, std::fabs( v[i].dot(eigenVe.template block<6,1>(0, k)) )));
}
L[k].sort(comp);
}
std::vector<T> t(6, T(0.)); //contains the sums of squared magnitudes
std::vector<bool> sampledPoints(nbCandidates, false); //maintain flag to avoid resampling the same point in an other list
///Add point iteratively till we got the desired number of point
for(std::size_t i = 0; i < nbSample; ++i)
{
//B.3 - Equally constrained all eigen vectors
// magnitude contribute to t_i where i is the indice of th least contrained eigen vector
//Find least constrained vector
std::size_t k = 0;
for (std::size_t i = 0; i < 6; ++i)
{
if (t[k] > t[i])
k = i;
}
// Add the point from the top of the list corresponding to the dimension to the set of samples
while(sampledPoints[L[k].front().first])
L[k].pop_front(); //remove already sampled point
//Get index to keep
const std::size_t idToKeep = static_cast<std::size_t>(L[k].front().first);
L[k].pop_front();
sampledPoints[idToKeep] = true; //set flag to avoid resampling
//B.4 - Update the running total
for (std::size_t k = 0; k < 6; ++k)
{
const T magnitude = v[idToKeep].dot(eigenVe.template block<6, 1>(0, k));
t[k] += (magnitude * magnitude);
}
keepIndexes[i] = candidates[idToKeep];
}
//TODO: evaluate performances between this solution and sorting the indexes
// We build map of (old index to new index), in case we sample pts at the begining of the pointcloud
std::unordered_map<std::size_t, std::size_t> mapidx;
std::size_t idx = 0;
///(4) Sample the point cloud
for(std::size_t id : keepIndexes)
{
//retrieve index from lookup table if sampling in already switched element
if(id<idx)
id = mapidx[id];
//Switch columns id and idx
cloud.swapCols(idx, id);
//Maintain new index position
mapidx[idx] = id;
//Update index
++idx;
}
cloud.conservativeResize(nbSample);
}
