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pfh.hpp
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2011, Willow Garage, Inc.
* Copyright (c) 2012-, Open Perception, Inc.
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#pragma once
#include <pcl/features/pfh.h>
#include <pcl/features/pfh_tools.h> // for computePairFeatures
#include <pcl/common/point_tests.h> // for pcl::isFinite
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> bool
pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computePairFeatures (
const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
{
pcl::computePairFeatures (cloud[p_idx].getVector4fMap (), normals[p_idx].getNormalVector4fMap (),
cloud[q_idx].getVector4fMap (), normals[q_idx].getNormalVector4fMap (),
f1, f2, f3, f4);
return (true);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computePointPFHSignature (
const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram)
{
int h_index, h_p;
// Clear the resultant point histogram
pfh_histogram.setZero ();
// Factorization constant
float hist_incr = 100.0f / static_cast<float> (indices.size () * (indices.size () - 1) / 2);
std::pair<int, int> key;
bool key_found = false;
// Iterate over all the points in the neighborhood
for (std::size_t i_idx = 0; i_idx < indices.size (); ++i_idx)
{
for (std::size_t j_idx = 0; j_idx < i_idx; ++j_idx)
{
// If the 3D points are invalid, don't bother estimating, just continue
if (!isFinite (cloud[indices[i_idx]]) || !isFinite (cloud[indices[j_idx]]))
continue;
if (use_cache_)
{
// In order to create the key, always use the smaller index as the first key pair member
int p1, p2;
// if (indices[i_idx] >= indices[j_idx])
// {
p1 = indices[i_idx];
p2 = indices[j_idx];
// }
// else
// {
// p1 = indices[j_idx];
// p2 = indices[i_idx];
// }
key = std::pair<int, int> (p1, p2);
// Check to see if we already estimated this pair in the global hashmap
auto fm_it = feature_map_.find (key);
if (fm_it != feature_map_.end ())
{
pfh_tuple_ = fm_it->second;
key_found = true;
}
else
{
// Compute the pair NNi to NNj
if (!computePairFeatures (cloud, normals, indices[i_idx], indices[j_idx],
pfh_tuple_[0], pfh_tuple_[1], pfh_tuple_[2], pfh_tuple_[3]))
continue;
key_found = false;
}
}
else
if (!computePairFeatures (cloud, normals, indices[i_idx], indices[j_idx],
pfh_tuple_[0], pfh_tuple_[1], pfh_tuple_[2], pfh_tuple_[3]))
continue;
// Normalize the f1, f2, f3 features and push them in the histogram
f_index_[0] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[0] + M_PI) * d_pi_)));
if (f_index_[0] < 0) f_index_[0] = 0;
if (f_index_[0] >= nr_split) f_index_[0] = nr_split - 1;
f_index_[1] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[1] + 1.0) * 0.5)));
if (f_index_[1] < 0) f_index_[1] = 0;
if (f_index_[1] >= nr_split) f_index_[1] = nr_split - 1;
f_index_[2] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[2] + 1.0) * 0.5)));
if (f_index_[2] < 0) f_index_[2] = 0;
if (f_index_[2] >= nr_split) f_index_[2] = nr_split - 1;
// Copy into the histogram
h_index = 0;
h_p = 1;
for (const int &d : f_index_)
{
h_index += h_p * d;
h_p *= nr_split;
}
pfh_histogram[h_index] += hist_incr;
if (use_cache_ && !key_found)
{
// Save the value in the hashmap
feature_map_[key] = pfh_tuple_;
// Use a maximum cache so that we don't go overboard on RAM usage
key_list_.push (key);
// Check to see if we need to remove an element due to exceeding max_size
if (key_list_.size () > max_cache_size_)
{
// Remove the oldest element.
feature_map_.erase (key_list_.front ());
key_list_.pop ();
}
}
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
// Clear the feature map
feature_map_.clear ();
std::queue<std::pair<int, int> > empty;
std::swap (key_list_, empty);
pfh_histogram_.setZero (nr_subdiv_ * nr_subdiv_ * nr_subdiv_);
// Allocate enough space to hold the results
// \note This resize is irrelevant for a radiusSearch ().
pcl::Indices nn_indices (k_);
std::vector<float> nn_dists (k_);
output.is_dense = true;
// Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
if (input_->is_dense)
{
// Iterating over the entire index vector
for (std::size_t idx = 0; idx < indices_->size (); ++idx)
{
if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
{
for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// Estimate the PFH signature at each patch
computePointPFHSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfh_histogram_);
// Copy into the resultant cloud
for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
output[idx].histogram[d] = pfh_histogram_[d];
}
}
else
{
// Iterating over the entire index vector
for (std::size_t idx = 0; idx < indices_->size (); ++idx)
{
if (!isFinite ((*input_)[(*indices_)[idx]]) ||
this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
{
for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
output.is_dense = false;
continue;
}
// Estimate the PFH signature at each patch
computePointPFHSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfh_histogram_);
// Copy into the resultant cloud
for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
output[idx].histogram[d] = pfh_histogram_[d];
}
}
}
#define PCL_INSTANTIATE_PFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PFHEstimation<T,NT,OutT>;
pcl::PFHEstimation::computePointPFHSignature
void computePointPFHSignature(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram)
Estimate the PFH (Point Feature Histograms) individual signatures of the three angular (f1,...
Definition pfh.hpp:61
pcl::PFHEstimation::computePairFeatures
bool computePairFeatures(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
Definition pfh.hpp:49
pcl::PFHEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the Point Feature Histograms (PFH) descriptors at a set of points given by <setInputCloud ()...
Definition pfh.hpp:167
pcl::PFHEstimation::PointCloudOut
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition pfh.h:95
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::computePairFeatures
PCL_EXPORTS bool computePairFeatures(const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
pcl::isFinite
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition point_tests.h:55
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
M_PI
#define M_PI
Definition pcl_macros.h:201