Point Cloud Library (PCL)
1.3.1
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Copyright (c) 2011, Alexandru-Eugen Ichim 00005 * Willow Garage, Inc 00006 * All rights reserved. 00007 * 00008 * Redistribution and use in source and binary forms, with or without 00009 * modification, are permitted provided that the following conditions 00010 * are met: 00011 * 00012 * * Redistributions of source code must retain the above copyright 00013 * notice, this list of conditions and the following disclaimer. 00014 * * Redistributions in binary form must reproduce the above 00015 * copyright notice, this list of conditions and the following 00016 * disclaimer in the documentation and/or other materials provided 00017 * with the distribution. 00018 * * Neither the name of Willow Garage, Inc. nor the names of its 00019 * contributors may be used to endorse or promote products derived 00020 * from this software without specific prior written permission. 00021 * 00022 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00023 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00024 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00025 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00026 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00027 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00028 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00029 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00030 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00031 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00032 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00033 * POSSIBILITY OF SUCH DAMAGE. 00034 * 00035 * $Id: statistical_multiscale_interest_region_extraction.hpp 3022 2011-11-01 03:42:22Z rusu $ 00036 */ 00037 00038 #ifndef PCL_FEATURES_IMPL_STATISTICAL_MULTISCALE_INTEREST_REGION_EXTRACTION_H_ 00039 #define PCL_FEATURES_IMPL_STATISTICAL_MULTISCALE_INTEREST_REGION_EXTRACTION_H_ 00040 00041 #include "pcl/features/statistical_multiscale_interest_region_extraction.h" 00042 #include <pcl/kdtree/kdtree_flann.h> 00043 #include <boost/graph/adjacency_list.hpp> 00044 #include <boost/property_map/property_map.hpp> 00045 #include <boost/graph/johnson_all_pairs_shortest.hpp> 00046 00047 00049 template <typename PointT> void 00050 pcl::StatisticalMultiscaleInterestRegionExtraction<PointT>::generateCloudGraph () 00051 { 00052 // generate a K-NNG (K-nearest neighbors graph) 00053 pcl::KdTreeFLANN<PointT> kdtree; 00054 kdtree.setInputCloud (input_); 00055 00056 using namespace boost; 00057 typedef property<edge_weight_t, float> Weight; 00058 typedef adjacency_list<vecS, vecS, undirectedS, no_property, Weight> Graph; 00059 Graph cloud_graph; 00060 00061 for (size_t point_i = 0; point_i < input_->points.size (); ++point_i) 00062 { 00063 std::vector<int> k_indices (16); 00064 std::vector<float> k_distances (16); 00065 kdtree.nearestKSearch (point_i, 16, k_indices, k_distances); 00066 00067 for (size_t k_i = 0; k_i < k_indices.size (); ++k_i) 00068 add_edge (point_i, k_indices[k_i], Weight (sqrt (k_distances[k_i])), cloud_graph); 00069 } 00070 00071 const size_t E = num_edges (cloud_graph), 00072 V = num_vertices (cloud_graph); 00073 PCL_INFO ("The graph has %lu vertices and %lu edges.\n", V, E); 00074 geodesic_distances_.clear (); 00075 for (size_t i = 0; i < V; ++i) 00076 { 00077 std::vector<float> aux (V); 00078 geodesic_distances_.push_back (aux); 00079 } 00080 johnson_all_pairs_shortest_paths (cloud_graph, geodesic_distances_); 00081 00082 PCL_INFO ("Done generating the graph\n"); 00083 } 00084 00085 00087 template <typename PointT> bool 00088 pcl::StatisticalMultiscaleInterestRegionExtraction<PointT>::initCompute () 00089 { 00090 if (!PCLBase<PointT>::initCompute ()) 00091 { 00092 PCL_ERROR ("[pcl::StatisticalMultiscaleInterestRegionExtraction::initCompute] PCLBase::initCompute () failed - no input cloud was given.\n"); 00093 return false; 00094 } 00095 if (scale_values_.empty ()) 00096 { 00097 PCL_ERROR ("[pcl::StatisticalMultiscaleInterestRegionExtraction::initCompute] No scale values were given\n"); 00098 return false; 00099 } 00100 00101 return true; 00102 } 00103 00104 00106 template <typename PointT> void 00107 pcl::StatisticalMultiscaleInterestRegionExtraction<PointT>::geodesicFixedRadiusSearch (size_t &query_index, 00108 float &radius, 00109 std::vector<int> &result_indices) 00110 { 00111 for (size_t i = 0; i < geodesic_distances_[query_index].size (); ++i) 00112 if (i != query_index && geodesic_distances_[query_index][i] < radius) 00113 result_indices.push_back (i); 00114 } 00115 00116 00118 template <typename PointT> void 00119 pcl::StatisticalMultiscaleInterestRegionExtraction<PointT>::computeRegionsOfInterest (std::list<IndicesPtr> &rois) 00120 { 00121 if (!initCompute ()) 00122 { 00123 PCL_ERROR ("StatisticalMultiscaleInterestRegionExtraction: not completely initialized\n"); 00124 return; 00125 } 00126 00127 generateCloudGraph (); 00128 00129 computeF (); 00130 00131 extractExtrema (rois); 00132 } 00133 00134 00136 template <typename PointT> void 00137 pcl::StatisticalMultiscaleInterestRegionExtraction<PointT>::computeF () 00138 { 00139 PCL_INFO ("Calculating statistical information\n"); 00140 00141 // declare and initialize data structure 00142 F_scales_.resize (scale_values_.size ()); 00143 std::vector<float> point_density (input_->points.size ()), 00144 F (input_->points.size ()); 00145 std::vector<std::vector<float> > phi (input_->points.size ()); 00146 std::vector<float> phi_row (input_->points.size ()); 00147 00148 for (size_t scale_i = 0; scale_i < scale_values_.size (); ++scale_i) 00149 { 00150 float scale_squared = scale_values_[scale_i] * scale_values_[scale_i]; 00151 00152 // calculate point density for each point x_i 00153 for (size_t point_i = 0; point_i < input_->points.size (); ++point_i) 00154 { 00155 float point_density_i = 0.0; 00156 for (size_t point_j = 0; point_j < input_->points.size (); ++point_j) 00157 { 00158 float d_g = geodesic_distances_[point_i][point_j]; 00159 float phi_i_j = 1.0 / sqrt(2.0 * M_PI * scale_squared) * exp( (-1) * d_g*d_g / (2.0*scale_squared)); 00160 00161 point_density_i += phi_i_j; 00162 phi_row[point_j] = phi_i_j; 00163 } 00164 point_density[point_i] = point_density_i; 00165 phi[point_i] = phi_row; 00166 } 00167 00168 // compute weights for each pair (x_i, x_j), evaluate the operator A_hat 00169 for (size_t point_i = 0; point_i < input_->points.size (); ++point_i) 00170 { 00171 float A_hat_normalization = 0.0; 00172 PointT A_hat; A_hat.x = A_hat.y = A_hat.z = 0.0; 00173 for (size_t point_j = 0; point_j < input_->points.size (); ++point_j) 00174 { 00175 float phi_hat_i_j = phi[point_i][point_j] / (point_density[point_i] * point_density[point_j]); 00176 A_hat_normalization += phi_hat_i_j; 00177 00178 PointT aux = input_->points[point_j]; 00179 aux.x *= phi_hat_i_j; aux.y *= phi_hat_i_j; aux.z *= phi_hat_i_j; 00180 00181 A_hat.x += aux.x; A_hat.y += aux.y; A_hat.z += aux.z; 00182 } 00183 A_hat.x /= A_hat_normalization; A_hat.y /= A_hat_normalization; A_hat.z /= A_hat_normalization; 00184 00185 // compute the invariant F 00186 float aux = 2.0 / scale_values_[scale_i] * euclideanDistance<PointT, PointT> (A_hat, input_->points[point_i]); 00187 F[point_i] = aux * exp (-aux); 00188 } 00189 00190 F_scales_[scale_i] = F; 00191 } 00192 } 00193 00194 00196 template <typename PointT> void 00197 pcl::StatisticalMultiscaleInterestRegionExtraction<PointT>::extractExtrema (std::list<IndicesPtr> &rois) 00198 { 00199 std::vector<std::vector<bool> > is_min (scale_values_.size ()), 00200 is_max (scale_values_.size ()); 00201 00202 // for each point, check if it is a local extrema on each scale 00203 for (size_t scale_i = 0; scale_i < scale_values_.size (); ++scale_i) 00204 { 00205 std::vector<bool> is_min_scale (input_->points.size ()), 00206 is_max_scale (input_->points.size ()); 00207 for (size_t point_i = 0; point_i < input_->points.size (); ++point_i) 00208 { 00209 std::vector<int> nn_indices; 00210 geodesicFixedRadiusSearch (point_i, scale_values_[scale_i], nn_indices); 00211 bool is_max_point = true, is_min_point = true; 00212 for (std::vector<int>::iterator nn_it = nn_indices.begin (); nn_it != nn_indices.end (); ++nn_it) 00213 if (F_scales_[scale_i][point_i] < F_scales_[scale_i][*nn_it]) 00214 is_max_point = false; 00215 else 00216 is_min_point = false; 00217 00218 is_min_scale[point_i] = is_min_point; 00219 is_max_scale[point_i] = is_max_point; 00220 } 00221 00222 is_min[scale_i] = is_min_scale; 00223 is_max[scale_i] = is_max_scale; 00224 } 00225 00226 // look for points that are min/max over three consecutive scales 00227 for (size_t scale_i = 1; scale_i < scale_values_.size () - 1; ++scale_i) 00228 { 00229 for (size_t point_i = 0; point_i < input_->points.size (); ++point_i) 00230 if ((is_min[scale_i - 1][point_i] && is_min[scale_i][point_i] && is_min[scale_i + 1][point_i]) || 00231 (is_max[scale_i - 1][point_i] && is_max[scale_i][point_i] && is_max[scale_i + 1][point_i])) 00232 { 00233 // add the point to the result vector 00234 IndicesPtr region (new std::vector<int> ()); 00235 region->push_back (point_i); 00236 00237 // and also add its scale-sized geodesic neighborhood 00238 std::vector<int> nn_indices; 00239 geodesicFixedRadiusSearch (point_i, scale_values_[scale_i], nn_indices); 00240 region->insert (region->end (), nn_indices.begin (), nn_indices.end ()); 00241 rois.push_back (region); 00242 } 00243 } 00244 } 00245 00246 00247 #define PCL_INSTANTIATE_StatisticalMultiscaleInterestRegionExtraction(T) template class PCL_EXPORTS pcl::StatisticalMultiscaleInterestRegionExtraction<T>; 00248 00249 #endif /* PCL_FEATURES_IMPL_STATISTICAL_MULTISCALE_INTEREST_REGION_EXTRACTION_H_ */ 00250