349 lines
14 KiB
C++
349 lines
14 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) 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 Willow Garage, Inc. 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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*/
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#ifndef PCL_SEGMENTATION_IMPL_RANDOM_WALKER_HPP
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#define PCL_SEGMENTATION_IMPL_RANDOM_WALKER_HPP
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#include <boost/bimap.hpp>
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#include <Eigen/Sparse>
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namespace pcl
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{
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namespace segmentation
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{
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namespace detail
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{
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/** \brief Multilabel graph segmentation using random walks.
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*
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* This is an implementation of the algorithm described in "Random Walks
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* for Image Segmentation" by Leo Grady.
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*
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* See the documentation of the randomWalker() function for details.
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*
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* \author Sergey Alexandrov
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* \ingroup segmentation
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*/
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template <class Graph, class EdgeWeightMap, class VertexColorMap>
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class RandomWalker
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{
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public:
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using Color = typename boost::property_traits<VertexColorMap>::value_type;
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using Weight = typename boost::property_traits<EdgeWeightMap>::value_type;
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using GraphTraits = boost::graph_traits<Graph>;
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using EdgeDescriptor = typename GraphTraits::edge_descriptor;
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using VertexDescriptor = typename GraphTraits::vertex_descriptor;
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using EdgeIterator = typename GraphTraits::edge_iterator;
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using OutEdgeIterator = typename GraphTraits::out_edge_iterator;
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using VertexIterator = typename GraphTraits::vertex_iterator;
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using VertexIndexMap = typename boost::property_map<Graph, boost::vertex_index_t>::type;
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using VertexDegreeMap = boost::iterator_property_map<typename std::vector<Weight>::iterator, VertexIndexMap>;
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using SparseMatrix = Eigen::SparseMatrix<Weight>;
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using Matrix = Eigen::Matrix<Weight, Eigen::Dynamic, Eigen::Dynamic>;
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using Vector = Eigen::Matrix<Weight, Eigen::Dynamic, 1>;
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RandomWalker (Graph& g, EdgeWeightMap weights, VertexColorMap colors)
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: g_ (g)
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, weight_map_ (weights)
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, color_map_ (colors)
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, index_map_ (boost::get (boost::vertex_index, g_))
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, degree_storage_ (boost::num_vertices (g_), 0)
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, degree_map_ (boost::make_iterator_property_map (degree_storage_.begin (), index_map_))
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{
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}
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bool
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segment ()
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{
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computeVertexDegrees ();
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buildLinearSystem ();
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return solveLinearSystem ();
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}
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void
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computeVertexDegrees ()
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{
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using namespace boost;
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EdgeIterator ei, e_end;
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for (tie (ei, e_end) = edges (g_); ei != e_end; ++ei)
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{
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Weight w = weight_map_[*ei];
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degree_map_[source (*ei, g_)] += w;
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degree_map_[target (*ei, g_)] += w;
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}
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}
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void
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buildLinearSystem ()
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{
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using namespace boost;
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using T = Eigen::Triplet<float>;
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using Triplets = std::vector<T>;
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Triplets L_triplets;
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Triplets B_triplets;
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VertexIterator vi, v_end;
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for (tie (vi, v_end) = vertices (g_); vi != v_end; ++vi)
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{
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// If this is a labeled vertex add it to the seeds list and register its color
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if (color_map_[*vi])
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{
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seeds_.push_back (*vi);
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colors_.insert (color_map_[*vi]);
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}
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// Skip seeds and vertices with zero connectivity
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if (color_map_[*vi] || std::fabs (degree_map_[*vi]) < std::numeric_limits<Weight>::epsilon ())
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continue;
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// Create a row in L matrix for the vertex
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std::size_t current_row = insertInBimap (L_vertex_bimap, *vi);
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// Add diagonal degree entry for the vertex
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L_triplets.push_back (T (current_row, current_row, degree_map_[*vi]));
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// Iterate over incident vertices and add entries on corresponding columns of L or B
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OutEdgeIterator ei, e_end;
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for (tie (ei, e_end) = out_edges (*vi, g_); ei != e_end; ++ei)
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{
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Weight w = weight_map_[*ei];
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VertexDescriptor tgt = target (*ei, g_);
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Color color = color_map_[tgt];
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if (color)
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{
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// This is a seed and will go to B matrix
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std::size_t column;
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if (B_color_bimap.right.count (color) == 0)
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{
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// This is the first time we encountered this color, create a new column in B
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column = insertInBimap (B_color_bimap, color);
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}
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else
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{
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column = B_color_bimap.right.at (color);
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}
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B_triplets.push_back (T (current_row, column, w));
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}
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else
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{
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// This is a non-seed and will go to L matrix,
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// but only if a row for this vertex already exists
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if (L_vertex_bimap.right.count (tgt) && L_vertex_bimap.right.at (tgt) != current_row)
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{
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L_triplets.push_back (T (current_row, L_vertex_bimap.right.at (tgt), -w));
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}
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}
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}
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}
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std::size_t num_equations = L_vertex_bimap.size ();
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std::size_t num_colors = B_color_bimap.size ();
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L.resize (num_equations, num_equations);
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B.resize (num_equations, num_colors);
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if (!L_triplets.empty ())
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L.setFromTriplets(L_triplets.begin(), L_triplets.end());
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if (!B_triplets.empty ())
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B.setFromTriplets(B_triplets.begin(), B_triplets.end());
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}
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bool solveLinearSystem()
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{
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X.resize (L.rows (), B.cols ());
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// Nothing to solve
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if (L.rows () == 0 || B.cols () == 0)
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return true;
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Eigen::SimplicialCholesky<SparseMatrix, Eigen::Lower> cg;
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cg.compute (L);
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bool succeeded = true;
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for (Eigen::Index i = 0; i < B.cols (); ++i)
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{
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Vector b = B.col (i);
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X.col (i) = cg.solve (b);
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if (cg.info () != Eigen::Success)
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succeeded = false;
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}
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assignColors ();
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return succeeded;
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}
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void
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assignColors ()
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{
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using namespace boost;
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if (X.cols ())
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for (Eigen::Index i = 0; i < X.rows (); ++i)
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{
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std::size_t max_column;
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X.row (i).maxCoeff (&max_column);
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VertexDescriptor vertex = L_vertex_bimap.left.at (i);
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Color color = B_color_bimap.left.at (max_column);
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color_map_[vertex] = color;
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}
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}
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void
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getPotentials (Matrix& potentials, std::map<Color, std::size_t>& color_to_column_map)
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{
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using namespace boost;
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potentials = Matrix::Zero (num_vertices (g_), colors_.size ());
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// Copy over rows from X
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for (Eigen::Index i = 0; i < X.rows (); ++i)
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potentials.row (L_vertex_bimap.left.at (i)).head (X.cols ()) = X.row (i);
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// In rows that correspond to seeds put ones in proper columns
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for (std::size_t i = 0; i < seeds_.size (); ++i)
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{
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VertexDescriptor v = seeds_[i];
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insertInBimap (B_color_bimap, color_map_[v]);
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potentials (seeds_[i], B_color_bimap.right.at (color_map_[seeds_[i]])) = 1;
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}
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// Fill in a map that associates colors with columns in potentials matrix
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color_to_column_map.clear ();
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for (Eigen::Index i = 0; i < potentials.cols (); ++i)
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color_to_column_map[B_color_bimap.left.at (i)] = i;
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}
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template <typename T> static inline std::size_t
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insertInBimap (boost::bimap<std::size_t, T>& bimap, T value)
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{
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if (bimap.right.count (value) != 0)
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{
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return bimap.right.at (value);
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}
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std::size_t s = bimap.size ();
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bimap.insert (typename boost::bimap<std::size_t, T>::value_type (s, value));
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return s;
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}
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Graph& g_;
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EdgeWeightMap weight_map_;
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VertexColorMap color_map_;
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VertexIndexMap index_map_;
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std::vector<VertexDescriptor> seeds_;
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std::set<Color> colors_;
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std::vector<Weight> degree_storage_;
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VertexDegreeMap degree_map_;
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SparseMatrix L;
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SparseMatrix B;
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Matrix X;
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// Map vertex identifiers to the rows/columns of L and vice versa
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boost::bimap<std::size_t, VertexDescriptor> L_vertex_bimap;
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// Map colors to the columns of B and vice versa
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boost::bimap<std::size_t, Color> B_color_bimap;
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};
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}
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template <class Graph> bool
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randomWalker (Graph& graph)
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{
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return randomWalker (graph,
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boost::get (boost::edge_weight, graph),
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boost::get (boost::vertex_color, graph));
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}
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template <class Graph, class EdgeWeightMap, class VertexColorMap> bool
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randomWalker (Graph& graph,
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EdgeWeightMap weights,
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VertexColorMap colors)
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{
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using namespace boost;
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using EdgeDescriptor = typename graph_traits<Graph>::edge_descriptor;
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using VertexDescriptor = typename graph_traits<Graph>::vertex_descriptor;
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BOOST_CONCEPT_ASSERT ((VertexListGraphConcept<Graph>)); // to have vertices(), num_vertices()
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BOOST_CONCEPT_ASSERT ((EdgeListGraphConcept<Graph>)); // to have edges()
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BOOST_CONCEPT_ASSERT ((IncidenceGraphConcept<Graph>)); // to have source(), target() and out_edges()
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BOOST_CONCEPT_ASSERT ((ReadablePropertyMapConcept<EdgeWeightMap, EdgeDescriptor>)); // read weight-values from edges
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BOOST_CONCEPT_ASSERT ((ReadWritePropertyMapConcept<VertexColorMap, VertexDescriptor>)); // read and write color-values from vertices
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::pcl::segmentation::detail::RandomWalker
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<
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Graph,
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EdgeWeightMap,
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VertexColorMap
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>
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rw (graph, weights, colors);
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return rw.segment ();
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}
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template <class Graph, class EdgeWeightMap, class VertexColorMap> bool
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randomWalker (Graph& graph,
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EdgeWeightMap weights,
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VertexColorMap colors,
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Eigen::Matrix<typename boost::property_traits<EdgeWeightMap>::value_type, Eigen::Dynamic, Eigen::Dynamic>& potentials,
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std::map<typename boost::property_traits<VertexColorMap>::value_type, std::size_t>& colors_to_columns_map)
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{
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using namespace boost;
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using EdgeDescriptor = typename graph_traits<Graph>::edge_descriptor;
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using VertexDescriptor = typename graph_traits<Graph>::vertex_descriptor;
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BOOST_CONCEPT_ASSERT ((VertexListGraphConcept<Graph>)); // to have vertices(), num_vertices()
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BOOST_CONCEPT_ASSERT ((EdgeListGraphConcept<Graph>)); // to have edges()
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BOOST_CONCEPT_ASSERT ((IncidenceGraphConcept<Graph>)); // to have source(), target() and out_edges()
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BOOST_CONCEPT_ASSERT ((ReadablePropertyMapConcept<EdgeWeightMap, EdgeDescriptor>)); // read weight-values from edges
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BOOST_CONCEPT_ASSERT ((ReadWritePropertyMapConcept<VertexColorMap, VertexDescriptor>)); // read and write color-values from vertices
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::pcl::segmentation::detail::RandomWalker
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<
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Graph,
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EdgeWeightMap,
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VertexColorMap
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>
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rw (graph, weights, colors);
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bool result = rw.segment ();
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rw.getPotentials (potentials, colors_to_columns_map);
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return result;
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}
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}
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}
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#endif /* PCL_SEGMENTATION_IMPL_RANDOM_WALKER_HPP */
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