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kdtree_flann.hpp
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#ifndef PCL_KDTREE_KDTREE_IMPL_FLANN_H_
#define PCL_KDTREE_KDTREE_IMPL_FLANN_H_
#include <flann/flann.hpp>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/console/print.h>
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist>
pcl::KdTreeFLANN<PointT, Dist>::KdTreeFLANN (bool sorted)
: pcl::KdTree<PointT> (sorted)
, flann_index_ ()
,
param_k_ (::flann::SearchParams (-1 , epsilon_))
, param_radius_ (::flann::SearchParams (-1, epsilon_, sorted))
{
if (!std::is_same<std::size_t, pcl::index_t>::value) {
const auto message = "FLANN is not optimized for current index type. Will incur "
"extra allocations and copy\n";
if (std::is_same<int, pcl::index_t>::value) {
PCL_DEBUG(message); // since this has been the default behavior till PCL 1.12
}
else {
PCL_WARN(message);
}
}
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist>
pcl::KdTreeFLANN<PointT, Dist>::KdTreeFLANN (const KdTreeFLANN<PointT, Dist> &k)
: pcl::KdTree<PointT> (false)
, flann_index_ ()
,
param_k_ (::flann::SearchParams (-1 , epsilon_))
, param_radius_ (::flann::SearchParams (-1, epsilon_, false))
{
*this = k;
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::setEpsilon (float eps)
{
epsilon_ = eps;
param_k_ = ::flann::SearchParams (-1 , epsilon_);
param_radius_ = ::flann::SearchParams (-1 , epsilon_, sorted_);
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::setSortedResults (bool sorted)
{
sorted_ = sorted;
param_k_ = ::flann::SearchParams (-1, epsilon_);
param_radius_ = ::flann::SearchParams (-1, epsilon_, sorted_);
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices)
{
cleanup (); // Perform an automatic cleanup of structures
epsilon_ = 0.0f; // default error bound value
dim_ = point_representation_->getNumberOfDimensions (); // Number of dimensions - default is 3 = xyz
input_ = cloud;
indices_ = indices;
// Allocate enough data
if (!input_)
{
PCL_ERROR ("[pcl::KdTreeFLANN::setInputCloud] Invalid input!\n");
return;
}
if (indices != nullptr)
{
convertCloudToArray (*input_, *indices_);
}
else
{
convertCloudToArray (*input_);
}
total_nr_points_ = static_cast<uindex_t> (index_mapping_.size ());
if (total_nr_points_ == 0)
{
PCL_ERROR ("[pcl::KdTreeFLANN::setInputCloud] Cannot create a KDTree with an empty input cloud!\n");
return;
}
flann_index_.reset (new FLANNIndex (::flann::Matrix<float> (cloud_.get (),
index_mapping_.size (),
dim_),
::flann::KDTreeSingleIndexParams (15))); // max 15 points/leaf
flann_index_->buildIndex ();
}
///////////////////////////////////////////////////////////////////////////////////////////
namespace pcl {
namespace detail {
// Replace using constexpr in C++17
template <class IndexT,
class A,
class B,
class C,
class D,
class F,
CompatWithFlann<IndexT> = true>
int
knn_search(A& index, B& query, C& k_indices, D& dists, unsigned int k, F& params)
{
// Wrap k_indices vector (no data allocation)
::flann::Matrix<index_t> k_indices_mat(&k_indices[0], 1, k);
return index.knnSearch(query, k_indices_mat, dists, k, params);
}
template <class IndexT,
class A,
class B,
class C,
class D,
class F,
NotCompatWithFlann<IndexT> = true>
int
knn_search(A& index, B& query, C& k_indices, D& dists, unsigned int k, F& params)
{
std::vector<std::size_t> indices(k);
k_indices.resize(k);
// Wrap indices vector (no data allocation)
::flann::Matrix<std::size_t> indices_mat(indices.data(), 1, k);
auto ret = index.knnSearch(query, indices_mat, dists, k, params);
// cast appropriately
std::transform(indices.cbegin(),
indices.cend(),
k_indices.begin(),
[](const auto& x) { return static_cast<pcl::index_t>(x); });
return ret;
}
template <class IndexT, class A, class B, class F, CompatWithFlann<IndexT> = true>
int
knn_search(A& index,
B& query,
std::vector<Indices>& k_indices,
std::vector<std::vector<float>>& dists,
unsigned int k,
F& params)
{
return index.knnSearch(query, k_indices, dists, k, params);
}
template <class IndexT, class A, class B, class F, NotCompatWithFlann<IndexT> = true>
int
knn_search(A& index,
B& query,
std::vector<Indices>& k_indices,
std::vector<std::vector<float>>& dists,
unsigned int k,
F& params)
{
std::vector<std::vector<std::size_t>> indices;
// flann will resize accordingly
auto ret = index.knnSearch(query, indices, dists, k, params);
k_indices.resize(indices.size());
{
auto it = indices.cbegin();
auto jt = k_indices.begin();
for (; it != indices.cend(); ++it, ++jt) {
jt->resize(it->size());
std::copy(it->cbegin(), it->cend(), jt->begin());
}
}
return ret;
}
} // namespace detail
template <class FlannIndex,
class Query,
class Indices,
class Distances,
class SearchParams>
int
knn_search(const FlannIndex& index,
const Query& query,
Indices& indices,
Distances& dists,
unsigned int k,
const SearchParams& params)
{
return detail::knn_search<pcl::index_t>(index, query, indices, dists, k, params);
}
} // namespace pcl
template <typename PointT, typename Dist> int
pcl::KdTreeFLANN<PointT, Dist>::nearestKSearch (const PointT &point, unsigned int k,
Indices &k_indices,
std::vector<float> &k_distances) const
{
assert (point_representation_->isValid (point) && "Invalid (NaN, Inf) point coordinates given to nearestKSearch!");
if (k > total_nr_points_)
k = total_nr_points_;
k_indices.resize (k);
k_distances.resize (k);
if (k==0)
return 0;
std::vector<float> query (dim_);
point_representation_->vectorize (static_cast<PointT> (point), query);
// Wrap the k_distances vector (no data copy)
::flann::Matrix<float> k_distances_mat (k_distances.data(), 1, k);
knn_search(*flann_index_,
::flann::Matrix<float>(query.data(), 1, dim_),
k_indices,
k_distances_mat,
k,
param_k_);
// Do mapping to original point cloud
if (!identity_mapping_)
{
for (std::size_t i = 0; i < static_cast<std::size_t> (k); ++i)
{
auto& neighbor_index = k_indices[i];
neighbor_index = index_mapping_[neighbor_index];
}
}
return (k);
}
///////////////////////////////////////////////////////////////////////////////////////////
namespace pcl {
namespace detail {
// Replace using constexpr in C++17
template <class IndexT,
class A,
class B,
class C,
class D,
class F,
CompatWithFlann<IndexT> = true>
int
radius_search(A& index, B& query, C& k_indices, D& dists, float radius, F& params)
{
std::vector<pcl::Indices> indices(1);
int neighbors_in_radius = index.radiusSearch(query, indices, dists, radius, params);
k_indices = std::move(indices[0]);
return neighbors_in_radius;
}
template <class IndexT,
class A,
class B,
class C,
class D,
class F,
NotCompatWithFlann<IndexT> = true>
int
radius_search(A& index, B& query, C& k_indices, D& dists, float radius, F& params)
{
std::vector<std::vector<std::size_t>> indices(1);
int neighbors_in_radius = index.radiusSearch(query, indices, dists, radius, params);
k_indices.resize(indices[0].size());
// cast appropriately
std::transform(indices[0].cbegin(),
indices[0].cend(),
k_indices.begin(),
[](const auto& x) { return static_cast<pcl::index_t>(x); });
return neighbors_in_radius;
}
template <class IndexT, class A, class B, class F, CompatWithFlann<IndexT> = true>
int
radius_search(A& index,
B& query,
std::vector<Indices>& k_indices,
std::vector<std::vector<float>>& dists,
float radius,
F& params)
{
return index.radiusSearch(query, k_indices, dists, radius, params);
}
template <class IndexT, class A, class B, class F, NotCompatWithFlann<IndexT> = true>
int
radius_search(A& index,
B& query,
std::vector<Indices>& k_indices,
std::vector<std::vector<float>>& dists,
float radius,
F& params)
{
std::vector<std::vector<std::size_t>> indices;
// flann will resize accordingly
auto ret = index.radiusSearch(query, indices, dists, radius, params);
k_indices.resize(indices.size());
{
auto it = indices.cbegin();
auto jt = k_indices.begin();
for (; it != indices.cend(); ++it, ++jt) {
jt->resize(it->size());
std::copy(it->cbegin(), it->cend(), jt->begin());
}
}
return ret;
}
} // namespace detail
template <class FlannIndex,
class Query,
class Indices,
class Distances,
class SearchParams>
int
radius_search(const FlannIndex& index,
const Query& query,
Indices& indices,
Distances& dists,
float radius,
const SearchParams& params)
{
return detail::radius_search<pcl::index_t>(
index, query, indices, dists, radius, params);
}
} // namespace pcl
template <typename PointT, typename Dist> int
pcl::KdTreeFLANN<PointT, Dist>::radiusSearch (const PointT &point, double radius, Indices &k_indices,
std::vector<float> &k_sqr_dists, unsigned int max_nn) const
{
assert (point_representation_->isValid (point) && "Invalid (NaN, Inf) point coordinates given to radiusSearch!");
std::vector<float> query (dim_);
point_representation_->vectorize (static_cast<PointT> (point), query);
// Has max_nn been set properly?
if (max_nn == 0 || max_nn > total_nr_points_)
max_nn = total_nr_points_;
std::vector<std::vector<float> > dists(1);
::flann::SearchParams params (param_radius_);
if (max_nn == total_nr_points_)
params.max_neighbors = -1; // return all neighbors in radius
else
params.max_neighbors = max_nn;
auto query_mat = ::flann::Matrix<float>(query.data(), 1, dim_);
int neighbors_in_radius = radius_search(*flann_index_,
query_mat,
k_indices,
dists,
static_cast<float>(radius * radius),
params);
k_sqr_dists = dists[0];
// Do mapping to original point cloud
if (!identity_mapping_)
{
for (int i = 0; i < neighbors_in_radius; ++i)
{
auto& neighbor_index = k_indices[i];
neighbor_index = index_mapping_[neighbor_index];
}
}
return (neighbors_in_radius);
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::cleanup ()
{
// Data array cleanup
index_mapping_.clear ();
if (indices_)
indices_.reset ();
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::convertCloudToArray (const PointCloud &cloud)
{
// No point in doing anything if the array is empty
if (cloud.empty ())
{
cloud_.reset ();
return;
}
const auto original_no_of_points = cloud.size ();
cloud_.reset (new float[original_no_of_points * dim_], std::default_delete<float[]> ());
float* cloud_ptr = cloud_.get ();
index_mapping_.reserve (original_no_of_points);
identity_mapping_ = true;
for (std::size_t cloud_index = 0; cloud_index < original_no_of_points; ++cloud_index)
{
// Check if the point is invalid
if (!point_representation_->isValid (cloud[cloud_index]))
{
identity_mapping_ = false;
continue;
}
index_mapping_.push_back (cloud_index);
point_representation_->vectorize (cloud[cloud_index], cloud_ptr);
cloud_ptr += dim_;
}
}
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::convertCloudToArray (const PointCloud &cloud, const Indices &indices)
{
// No point in doing anything if the array is empty
if (cloud.empty ())
{
cloud_.reset ();
return;
}
int original_no_of_points = static_cast<int> (indices.size ());
cloud_.reset (new float[original_no_of_points * dim_], std::default_delete<float[]> ());
float* cloud_ptr = cloud_.get ();
index_mapping_.reserve (original_no_of_points);
// its a subcloud -> false
// true only identity:
// - indices size equals cloud size
// - indices only contain values between 0 and cloud.size - 1
// - no index is multiple times in the list
// => index is complete
// But we can not guarantee that => identity_mapping_ = false
identity_mapping_ = false;
for (const auto &index : indices)
{
// Check if the point is invalid
if (!point_representation_->isValid (cloud[index]))
continue;
// map from 0 - N -> indices [0] - indices [N]
index_mapping_.push_back (index); // If the returned index should be for the indices vector
point_representation_->vectorize (cloud[index], cloud_ptr);
cloud_ptr += dim_;
}
}
#define PCL_INSTANTIATE_KdTreeFLANN(T) template class PCL_EXPORTS pcl::KdTreeFLANN<T>;
#endif //#ifndef _PCL_KDTREE_KDTREE_IMPL_FLANN_H_
flann::Index
Definition kdtree_flann.h:52
flann::Matrix
Definition flann_search.h:50
pcl::KdTreeFLANN
KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures.
Definition kdtree_flann.h:132
pcl::KdTreeFLANN::setEpsilon
void setEpsilon(float eps) override
Set the search epsilon precision (error bound) for nearest neighbors searches.
Definition kdtree_flann.hpp:82
pcl::KdTreeFLANN::IndicesConstPtr
shared_ptr< const Indices > IndicesConstPtr
Definition kdtree_flann.h:146
pcl::KdTreeFLANN::nearestKSearch
int nearestKSearch(const PointT &point, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for k-nearest neighbors for the given query point.
Definition kdtree_flann.hpp:235
pcl::KdTreeFLANN::radiusSearch
int radiusSearch(const PointT &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
Search for all the nearest neighbors of the query point in a given radius.
Definition kdtree_flann.hpp:373
pcl::KdTreeFLANN::PointCloudConstPtr
typename KdTree< PointT >::PointCloudConstPtr PointCloudConstPtr
Definition kdtree_flann.h:143
pcl::KdTreeFLANN::setSortedResults
void setSortedResults(bool sorted)
Definition kdtree_flann.hpp:91
pcl::KdTreeFLANN::KdTreeFLANN
KdTreeFLANN(bool sorted=true)
Default Constructor for KdTreeFLANN.
Definition kdtree_flann.hpp:49
pcl::KdTreeFLANN::setInputCloud
void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
Provide a pointer to the input dataset.
Definition kdtree_flann.hpp:100
pcl::KdTree
KdTree represents the base spatial locator class for kd-tree implementations.
Definition kdtree.h:56
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PointCloud::empty
bool empty() const
Definition point_cloud.h:446
pcl::PointCloud::size
std::size_t size() const
Definition point_cloud.h:443
pcl::B
@ B
Definition norms.h:54
flann
Definition kdtree_flann.h:50
pcl::detail::knn_search
int knn_search(A &index, B &query, C &k_indices, D &dists, unsigned int k, F &params)
Definition kdtree_flann.hpp:150
pcl::detail::radius_search
int radius_search(A &index, B &query, C &k_indices, D &dists, float radius, F &params)
Definition kdtree_flann.hpp:288
pcl
Definition convolution.h:46
pcl::uindex_t
detail::int_type_t< detail::index_type_size, false > uindex_t
Type used for an unsigned index in PCL.
Definition types.h:120
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl::radius_search
int radius_search(const FlannIndex &index, const Query &query, Indices &indices, Distances &dists, float radius, const SearchParams &params)
Compatibility template function to allow use of various types of indices with FLANN.
Definition kdtree_flann.hpp:360
pcl::knn_search
int knn_search(const FlannIndex &index, const Query &query, Indices &indices, Distances &dists, unsigned int k, const SearchParams &params)
Compatibility template function to allow use of various types of indices with FLANN.
Definition kdtree_flann.hpp:223
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition point_types.hpp:594