307 lines
12 KiB
C++
307 lines
12 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2010-2011, Willow Garage, Inc.
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* Copyright (c) 2012-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* $Id$
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*
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*/
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#pragma once
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#include <pcl/features/fpfh.h>
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#include <pcl/common/point_tests.h> // for pcl::isFinite
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#include <pcl/features/pfh_tools.h>
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#include <set> // for std::set
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> bool
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pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::computePairFeatures (
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const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
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{
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pcl::computePairFeatures (cloud[p_idx].getVector4fMap (), normals[p_idx].getNormalVector4fMap (),
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cloud[q_idx].getVector4fMap (), normals[q_idx].getNormalVector4fMap (),
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f1, f2, f3, f4);
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return (true);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::computePointSPFHSignature (
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const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
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pcl::index_t p_idx, int row, const pcl::Indices &indices,
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Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3)
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{
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Eigen::Vector4f pfh_tuple;
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// Get the number of bins from the histograms size
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// @TODO: use arrays
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int nr_bins_f1 = static_cast<int> (hist_f1.cols ());
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int nr_bins_f2 = static_cast<int> (hist_f2.cols ());
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int nr_bins_f3 = static_cast<int> (hist_f3.cols ());
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// Factorization constant
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float hist_incr = 100.0f / static_cast<float>(indices.size () - 1);
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// Iterate over all the points in the neighborhood
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for (const auto &index : indices)
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{
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// Avoid unnecessary returns
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if (p_idx == index)
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continue;
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// Compute the pair P to NNi
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if (!computePairFeatures (cloud, normals, p_idx, index, pfh_tuple[0], pfh_tuple[1], pfh_tuple[2], pfh_tuple[3]))
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continue;
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// Normalize the f1, f2, f3 features and push them in the histogram
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int h_index = static_cast<int> (std::floor (nr_bins_f1 * ((pfh_tuple[0] + M_PI) * d_pi_)));
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if (h_index < 0) h_index = 0;
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if (h_index >= nr_bins_f1) h_index = nr_bins_f1 - 1;
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hist_f1 (row, h_index) += hist_incr;
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h_index = static_cast<int> (std::floor (nr_bins_f2 * ((pfh_tuple[1] + 1.0) * 0.5)));
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if (h_index < 0) h_index = 0;
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if (h_index >= nr_bins_f2) h_index = nr_bins_f2 - 1;
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hist_f2 (row, h_index) += hist_incr;
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h_index = static_cast<int> (std::floor (nr_bins_f3 * ((pfh_tuple[2] + 1.0) * 0.5)));
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if (h_index < 0) h_index = 0;
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if (h_index >= nr_bins_f3) h_index = nr_bins_f3 - 1;
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hist_f3 (row, h_index) += hist_incr;
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::weightPointSPFHSignature (
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const Eigen::MatrixXf &hist_f1, const Eigen::MatrixXf &hist_f2, const Eigen::MatrixXf &hist_f3,
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const pcl::Indices &indices, const std::vector<float> &dists, Eigen::VectorXf &fpfh_histogram)
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{
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assert (indices.size () == dists.size ());
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// @TODO: use arrays
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double sum_f1 = 0.0, sum_f2 = 0.0, sum_f3 = 0.0;
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float weight = 0.0, val_f1, val_f2, val_f3;
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// Get the number of bins from the histograms size
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const auto nr_bins_f1 = hist_f1.cols ();
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const auto nr_bins_f2 = hist_f2.cols ();
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const auto nr_bins_f3 = hist_f3.cols ();
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const auto nr_bins_f12 = nr_bins_f1 + nr_bins_f2;
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// Clear the histogram
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fpfh_histogram.setZero (nr_bins_f1 + nr_bins_f2 + nr_bins_f3);
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// Use the entire patch
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for (std::size_t idx = 0; idx < indices.size (); ++idx)
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{
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// Minus the query point itself
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if (dists[idx] == 0)
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continue;
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// Standard weighting function used
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weight = 1.0f / dists[idx];
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// Weight the SPFH of the query point with the SPFH of its neighbors
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for (Eigen::MatrixXf::Index f1_i = 0; f1_i < nr_bins_f1; ++f1_i)
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{
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val_f1 = hist_f1 (indices[idx], f1_i) * weight;
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sum_f1 += val_f1;
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fpfh_histogram[f1_i] += val_f1;
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}
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for (Eigen::MatrixXf::Index f2_i = 0; f2_i < nr_bins_f2; ++f2_i)
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{
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val_f2 = hist_f2 (indices[idx], f2_i) * weight;
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sum_f2 += val_f2;
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fpfh_histogram[f2_i + nr_bins_f1] += val_f2;
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}
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for (Eigen::MatrixXf::Index f3_i = 0; f3_i < nr_bins_f3; ++f3_i)
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{
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val_f3 = hist_f3 (indices[idx], f3_i) * weight;
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sum_f3 += val_f3;
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fpfh_histogram[f3_i + nr_bins_f12] += val_f3;
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}
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}
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if (sum_f1 != 0)
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sum_f1 = 100.0 / sum_f1; // histogram values sum up to 100
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if (sum_f2 != 0)
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sum_f2 = 100.0 / sum_f2; // histogram values sum up to 100
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if (sum_f3 != 0)
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sum_f3 = 100.0 / sum_f3; // histogram values sum up to 100
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// Adjust final FPFH values
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const auto denormalize_with = [](auto factor)
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{
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return [=](const auto& data) { return data * factor; };
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};
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auto last = fpfh_histogram.data ();
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last = std::transform(last, last + nr_bins_f1, last, denormalize_with (sum_f1));
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last = std::transform(last, last + nr_bins_f2, last, denormalize_with (sum_f2));
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std::transform(last, last + nr_bins_f3, last, denormalize_with (sum_f3));
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::computeSPFHSignatures (std::vector<int> &spfh_hist_lookup,
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Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3)
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{
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// Allocate enough space to hold the NN search results
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// \note This resize is irrelevant for a radiusSearch ().
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pcl::Indices nn_indices (k_);
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std::vector<float> nn_dists (k_);
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std::set<int> spfh_indices;
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spfh_hist_lookup.resize (surface_->size ());
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// Build a list of (unique) indices for which we will need to compute SPFH signatures
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// (We need an SPFH signature for every point that is a neighbor of any point in input_[indices_])
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if (surface_ != input_ ||
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indices_->size () != surface_->size ())
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{
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for (const auto& p_idx: *indices_)
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{
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if (this->searchForNeighbors (p_idx, search_parameter_, nn_indices, nn_dists) == 0)
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continue;
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spfh_indices.insert (nn_indices.begin (), nn_indices.end ());
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}
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}
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else
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{
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// Special case: When a feature must be computed at every point, there is no need for a neighborhood search
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for (std::size_t idx = 0; idx < indices_->size (); ++idx)
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spfh_indices.insert (static_cast<int> (idx));
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}
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// Initialize the arrays that will store the SPFH signatures
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std::size_t data_size = spfh_indices.size ();
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hist_f1.setZero (data_size, nr_bins_f1_);
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hist_f2.setZero (data_size, nr_bins_f2_);
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hist_f3.setZero (data_size, nr_bins_f3_);
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// Compute SPFH signatures for every point that needs them
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std::size_t i = 0;
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for (const auto& p_idx: spfh_indices)
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{
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// Find the neighborhood around p_idx
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if (this->searchForNeighbors (*surface_, p_idx, search_parameter_, nn_indices, nn_dists) == 0)
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continue;
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// Estimate the SPFH signature around p_idx
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computePointSPFHSignature (*surface_, *normals_, p_idx, i, nn_indices, hist_f1, hist_f2, hist_f3);
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// Populate a lookup table for converting a point index to its corresponding row in the spfh_hist_* matrices
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spfh_hist_lookup[p_idx] = i;
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i++;
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> void
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pcl::FPFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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// Allocate enough space to hold the NN search results
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// \note This resize is irrelevant for a radiusSearch ().
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pcl::Indices nn_indices (k_);
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std::vector<float> nn_dists (k_);
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std::vector<int> spfh_hist_lookup;
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computeSPFHSignatures (spfh_hist_lookup, hist_f1_, hist_f2_, hist_f3_);
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output.is_dense = true;
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// Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
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if (input_->is_dense)
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{
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// Iterate over the entire index vector
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for (std::size_t idx = 0; idx < indices_->size (); ++idx)
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{
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if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
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{
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for (Eigen::Index d = 0; d < fpfh_histogram_.size (); ++d)
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output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
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output.is_dense = false;
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continue;
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}
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// ... and remap the nn_indices values so that they represent row indices in the spfh_hist_* matrices
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// instead of indices into surface_->points
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for (auto &nn_index : nn_indices)
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nn_index = spfh_hist_lookup[nn_index];
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// Compute the FPFH signature (i.e. compute a weighted combination of local SPFH signatures) ...
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weightPointSPFHSignature (hist_f1_, hist_f2_, hist_f3_, nn_indices, nn_dists, fpfh_histogram_);
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// ...and copy it into the output cloud
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std::copy_n(fpfh_histogram_.data (), fpfh_histogram_.size (), output[idx].histogram);
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}
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}
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else
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{
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// Iterate over the entire index vector
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for (std::size_t idx = 0; idx < indices_->size (); ++idx)
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{
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if (!isFinite ((*input_)[(*indices_)[idx]]) ||
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this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
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{
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for (Eigen::Index d = 0; d < fpfh_histogram_.size (); ++d)
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output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
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output.is_dense = false;
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continue;
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}
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// ... and remap the nn_indices values so that they represent row indices in the spfh_hist_* matrices
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// instead of indices into surface_->points
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for (auto &nn_index : nn_indices)
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nn_index = spfh_hist_lookup[nn_index];
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// Compute the FPFH signature (i.e. compute a weighted combination of local SPFH signatures) ...
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weightPointSPFHSignature (hist_f1_, hist_f2_, hist_f3_, nn_indices, nn_dists, fpfh_histogram_);
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// ...and copy it into the output cloud
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std::copy_n(fpfh_histogram_.data (), fpfh_histogram_.size (), output[idx].histogram);
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}
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}
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}
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#define PCL_INSTANTIATE_FPFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::FPFHEstimation<T,NT,OutT>;
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