273 lines
10 KiB
C++
273 lines
10 KiB
C++
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/*
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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) 2009, 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: gfpfh.hpp 2218 2011-08-25 20:27:15Z rusu $
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*
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*/
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#ifndef PCL_FEATURES_IMPL_GFPFH_H_
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#define PCL_FEATURES_IMPL_GFPFH_H_
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#include <pcl/features/gfpfh.h>
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#include <pcl/octree/octree_search.h>
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#include <Eigen/Core> // for Vector3f
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#include <algorithm>
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#include <fstream>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT> void
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pcl::GFPFHEstimation<PointInT, PointNT, PointOutT>::compute (PointCloudOut &output)
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{
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if (!Feature<PointInT, PointOutT>::initCompute ())
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{
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output.width = output.height = 0;
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output.clear ();
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return;
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}
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// Copy the header
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output.header = input_->header;
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// Resize the output dataset
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// Important! We should only allocate precisely how many elements we will need, otherwise
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// we risk at pre-allocating too much memory which could lead to bad_alloc
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// (see http://dev.pointclouds.org/issues/657)
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output.width = output.height = 1;
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output.is_dense = input_->is_dense;
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output.resize (1);
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// Perform the actual feature computation
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computeFeature (output);
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Feature<PointInT, PointOutT>::deinitCompute ();
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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::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
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{
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pcl::octree::OctreePointCloudSearch<PointInT> octree (octree_leaf_size_);
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octree.setInputCloud (input_);
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octree.addPointsFromInputCloud ();
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typename pcl::PointCloud<PointInT>::VectorType occupied_cells;
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octree.getOccupiedVoxelCenters (occupied_cells);
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// Determine the voxels crosses along the line segments
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// formed by every pair of occupied cells.
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std::vector< std::vector<int> > line_histograms;
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for (std::size_t i = 0; i < occupied_cells.size (); ++i)
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{
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Eigen::Vector3f origin = occupied_cells[i].getVector3fMap ();
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for (std::size_t j = i+1; j < occupied_cells.size (); ++j)
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{
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typename pcl::PointCloud<PointInT>::VectorType intersected_cells;
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Eigen::Vector3f end = occupied_cells[j].getVector3fMap ();
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octree.getApproxIntersectedVoxelCentersBySegment (origin, end, intersected_cells, 0.5f);
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// Intersected cells are ordered from closest to furthest w.r.t. the origin.
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std::vector<int> histogram;
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for (std::size_t k = 0; k < intersected_cells.size (); ++k)
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{
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pcl::Indices indices;
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octree.voxelSearch (intersected_cells[k], indices);
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int label = emptyLabel ();
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if (!indices.empty ())
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{
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label = getDominantLabel (indices);
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}
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histogram.push_back (label);
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}
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line_histograms.push_back(histogram);
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}
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}
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std::vector< std::vector<int> > transition_histograms;
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computeTransitionHistograms (line_histograms, transition_histograms);
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std::vector<float> distances;
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computeDistancesToMean (transition_histograms, distances);
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std::vector<float> gfpfh_histogram;
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computeDistanceHistogram (distances, gfpfh_histogram);
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output.clear ();
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output.width = 1;
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output.height = 1;
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output.resize (1);
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std::copy (gfpfh_histogram.cbegin (), gfpfh_histogram.cend (), output[0].histogram);
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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::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeTransitionHistograms (const std::vector< std::vector<int> >& label_histograms,
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std::vector< std::vector<int> >& transition_histograms)
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{
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transition_histograms.resize (label_histograms.size ());
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for (std::size_t i = 0; i < label_histograms.size (); ++i)
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{
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transition_histograms[i].resize ((getNumberOfClasses () + 2) * (getNumberOfClasses () + 1) / 2, 0);
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std::vector< std::vector <int> > transitions (getNumberOfClasses () + 1);
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for (auto &transition : transitions)
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{
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transition.resize (getNumberOfClasses () + 1, 0);
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}
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for (std::size_t k = 1; k < label_histograms[i].size (); ++k)
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{
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std::uint32_t first_class = label_histograms[i][k-1];
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std::uint32_t second_class = label_histograms[i][k];
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// Order has no influence.
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if (second_class < first_class)
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std::swap (first_class, second_class);
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transitions[first_class][second_class] += 1;
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}
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// Build a one-dimension histogram out of it.
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std::size_t flat_index = 0;
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for (std::size_t m = 0; m < transitions.size (); ++m)
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for (std::size_t n = m; n < transitions[m].size (); ++n)
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{
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transition_histograms[i][flat_index] = transitions[m][n];
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++flat_index;
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}
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assert (flat_index == transition_histograms[i].size ());
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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::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeDistancesToMean (const std::vector< std::vector<int> >& transition_histograms,
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std::vector<float>& distances)
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{
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distances.resize (transition_histograms.size ());
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std::vector<float> mean_histogram;
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computeMeanHistogram (transition_histograms, mean_histogram);
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for (std::size_t i = 0; i < transition_histograms.size (); ++i)
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{
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float d = computeHIKDistance (transition_histograms[i], mean_histogram);
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distances[i] = d;
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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::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeDistanceHistogram (const std::vector<float>& distances,
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std::vector<float>& histogram)
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{
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std::vector<float>::const_iterator min_it, max_it;
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std::tie (min_it, max_it) = std::minmax_element (distances.cbegin (), distances.cend ());
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assert (min_it != distances.cend ());
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assert (max_it != distances.cend ());
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const float min_value = *min_it;
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const float max_value = *max_it;
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histogram.resize (descriptorSize (), 0);
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const float range = max_value - min_value;
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using binSizeT = decltype(descriptorSize());
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const binSizeT max_bin = descriptorSize () - 1;
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for (const float &distance : distances)
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{
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const auto raw_bin = descriptorSize () * (distance - min_value) / range;
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const auto bin = std::min<binSizeT> (max_bin, static_cast<binSizeT> (std::floor (raw_bin)));
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histogram[bin] += 1;
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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::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeMeanHistogram (const std::vector< std::vector<int> >& histograms,
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std::vector<float>& mean_histogram)
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{
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assert (histograms.size () > 0);
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mean_histogram.resize (histograms[0].size (), 0);
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for (const auto &histogram : histograms)
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for (std::size_t j = 0; j < histogram.size (); ++j)
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mean_histogram[j] += static_cast<float> (histogram[j]);
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for (float &i : mean_histogram)
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i /= static_cast<float> (histograms.size ());
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> float
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pcl::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeHIKDistance (const std::vector<int>& histogram,
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const std::vector<float>& mean_histogram)
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{
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assert (histogram.size () == mean_histogram.size ());
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float norm = 0.f;
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for (std::size_t i = 0; i < histogram.size (); ++i)
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norm += std::min (static_cast<float> (histogram[i]), mean_histogram[i]);
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norm /= static_cast<float> (histogram.size ());
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return (norm);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointInT, typename PointNT, typename PointOutT> std::uint32_t
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pcl::GFPFHEstimation<PointInT, PointNT, PointOutT>::getDominantLabel (const pcl::Indices& indices)
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{
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std::vector<std::uint32_t> counts (getNumberOfClasses () + 1, 0);
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for (const auto &nn_index : indices)
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{
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std::uint32_t label = (*labels_)[nn_index].label;
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counts[label] += 1;
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}
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const auto max_it = std::max_element (counts.cbegin (), counts.cend ());
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if (max_it == counts.end ())
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return (emptyLabel ());
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return std::distance(counts.cbegin (), max_it);
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}
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#define PCL_INSTANTIATE_GFPFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::GFPFHEstimation<T,NT,OutT>;
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#endif // PCL_FEATURES_IMPL_GFPFH_H_
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