177 lines
6.7 KiB
C++
177 lines
6.7 KiB
C++
/*
|
|
* 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.
|
|
*
|
|
* All rights reserved.
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions
|
|
* are met:
|
|
*
|
|
* * Redistributions of source code must retain the above copyright
|
|
* notice, this list of conditions and the following disclaimer.
|
|
* * Redistributions in binary form must reproduce the above
|
|
* copyright notice, this list of conditions and the following
|
|
* disclaimer in the documentation and/or other materials provided
|
|
* with the distribution.
|
|
* * Neither the name of the copyright holder(s) nor the names of its
|
|
* contributors may be used to endorse or promote products derived
|
|
* from this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
|
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
|
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
|
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
|
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
|
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
|
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
|
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
|
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
|
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
|
* POSSIBILITY OF SUCH DAMAGE.
|
|
*
|
|
* $Id$
|
|
*
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include <pcl/features/fpfh_omp.h>
|
|
|
|
#include <pcl/common/point_tests.h> // for pcl::isFinite
|
|
|
|
#include <numeric>
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename PointInT, typename PointNT, typename PointOutT> void
|
|
pcl::FPFHEstimationOMP<PointInT, PointNT, PointOutT>::setNumberOfThreads (unsigned int nr_threads)
|
|
{
|
|
if (nr_threads == 0)
|
|
#ifdef _OPENMP
|
|
threads_ = omp_get_num_procs();
|
|
#else
|
|
threads_ = 1;
|
|
#endif
|
|
else
|
|
threads_ = nr_threads;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename PointInT, typename PointNT, typename PointOutT> void
|
|
pcl::FPFHEstimationOMP<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
|
|
{
|
|
std::vector<int> spfh_indices_vec;
|
|
std::vector<int> spfh_hist_lookup (surface_->size ());
|
|
|
|
// Build a list of (unique) indices for which we will need to compute SPFH signatures
|
|
// (We need an SPFH signature for every point that is a neighbor of any point in input_[indices_])
|
|
if (surface_ != input_ ||
|
|
indices_->size () != surface_->size ())
|
|
{
|
|
pcl::Indices nn_indices (k_); // \note These resizes are irrelevant for a radiusSearch ().
|
|
std::vector<float> nn_dists (k_);
|
|
|
|
std::set<int> spfh_indices_set;
|
|
for (std::size_t idx = 0; idx < indices_->size (); ++idx)
|
|
{
|
|
int p_idx = (*indices_)[idx];
|
|
if (!isFinite ((*input_)[p_idx]) ||
|
|
this->searchForNeighbors (p_idx, search_parameter_, nn_indices, nn_dists) == 0)
|
|
continue;
|
|
|
|
spfh_indices_set.insert (nn_indices.begin (), nn_indices.end ());
|
|
}
|
|
spfh_indices_vec.resize (spfh_indices_set.size ());
|
|
std::copy (spfh_indices_set.cbegin (), spfh_indices_set.cend (), spfh_indices_vec.begin ());
|
|
}
|
|
else
|
|
{
|
|
// Special case: When a feature must be computed at every point, there is no need for a neighborhood search
|
|
spfh_indices_vec.resize (indices_->size ());
|
|
std::iota(spfh_indices_vec.begin (), spfh_indices_vec.end (),
|
|
static_cast<decltype(spfh_indices_vec)::value_type>(0));
|
|
}
|
|
|
|
// Initialize the arrays that will store the SPFH signatures
|
|
const auto data_size = spfh_indices_vec.size ();
|
|
hist_f1_.setZero (data_size, nr_bins_f1_);
|
|
hist_f2_.setZero (data_size, nr_bins_f2_);
|
|
hist_f3_.setZero (data_size, nr_bins_f3_);
|
|
|
|
pcl::Indices nn_indices (k_); // \note These resizes are irrelevant for a radiusSearch ().
|
|
std::vector<float> nn_dists (k_);
|
|
|
|
// Compute SPFH signatures for every point that needs them
|
|
|
|
#pragma omp parallel for \
|
|
default(none) \
|
|
shared(spfh_hist_lookup, spfh_indices_vec) \
|
|
firstprivate(nn_indices, nn_dists) \
|
|
num_threads(threads_)
|
|
for (std::ptrdiff_t i = 0; i < static_cast<std::ptrdiff_t> (spfh_indices_vec.size ()); ++i)
|
|
{
|
|
// Get the next point index
|
|
int p_idx = spfh_indices_vec[i];
|
|
|
|
// Find the neighborhood around p_idx
|
|
if (!isFinite ((*surface_)[p_idx]) ||
|
|
this->searchForNeighbors (*surface_, p_idx, search_parameter_, nn_indices, nn_dists) == 0)
|
|
continue;
|
|
|
|
// Estimate the SPFH signature around p_idx
|
|
this->computePointSPFHSignature (*surface_, *normals_, p_idx, i, nn_indices, hist_f1_, hist_f2_, hist_f3_);
|
|
|
|
// Populate a lookup table for converting a point index to its corresponding row in the spfh_hist_* matrices
|
|
spfh_hist_lookup[p_idx] = i;
|
|
}
|
|
|
|
// Initialize the array that will store the FPFH signature
|
|
int nr_bins = nr_bins_f1_ + nr_bins_f2_ + nr_bins_f3_;
|
|
|
|
nn_indices.clear();
|
|
nn_dists.clear();
|
|
|
|
// Iterate over the entire index vector
|
|
#pragma omp parallel for \
|
|
default(none) \
|
|
shared(nr_bins, output, spfh_hist_lookup) \
|
|
firstprivate(nn_dists, nn_indices) \
|
|
num_threads(threads_)
|
|
for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
|
|
{
|
|
// Find the indices of point idx's neighbors...
|
|
if (!isFinite ((*input_)[(*indices_)[idx]]) ||
|
|
this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
|
|
{
|
|
for (int d = 0; d < nr_bins; ++d)
|
|
output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
|
|
|
|
output.is_dense = false;
|
|
continue;
|
|
}
|
|
|
|
|
|
// ... and remap the nn_indices values so that they represent row indices in the spfh_hist_* matrices
|
|
// instead of indices into surface_->points
|
|
for (auto &nn_index : nn_indices)
|
|
nn_index = spfh_hist_lookup[nn_index];
|
|
|
|
// Compute the FPFH signature (i.e. compute a weighted combination of local SPFH signatures) ...
|
|
Eigen::VectorXf fpfh_histogram = Eigen::VectorXf::Zero (nr_bins);
|
|
weightPointSPFHSignature (hist_f1_, hist_f2_, hist_f3_, nn_indices, nn_dists, fpfh_histogram);
|
|
|
|
// ...and copy it into the output cloud
|
|
for (int d = 0; d < nr_bins; ++d)
|
|
output[idx].histogram[d] = fpfh_histogram[d];
|
|
}
|
|
|
|
}
|
|
|
|
#define PCL_INSTANTIATE_FPFHEstimationOMP(T,NT,OutT) template class PCL_EXPORTS pcl::FPFHEstimationOMP<T,NT,OutT>;
|
|
|