Point Cloud Library (PCL)
1.3.1
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IN NO EVENT SHALL THE 00025 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00026 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00027 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00028 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00029 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00030 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00031 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00032 * POSSIBILITY OF SUCH DAMAGE. 00033 * 00034 * $Id: extract_clusters.hpp 3035 2011-11-01 04:29:18Z rusu $ 00035 * 00036 */ 00037 00038 #ifndef PCL_SEGMENTATION_IMPL_EXTRACT_CLUSTERS_H_ 00039 #define PCL_SEGMENTATION_IMPL_EXTRACT_CLUSTERS_H_ 00040 00041 #include "pcl/segmentation/extract_clusters.h" 00042 00044 template <typename PointT> void 00045 pcl::extractEuclideanClusters (const PointCloud<PointT> &cloud, 00046 const boost::shared_ptr<search::Search<PointT> > &tree, 00047 float tolerance, std::vector<PointIndices> &clusters, 00048 unsigned int min_pts_per_cluster, 00049 unsigned int max_pts_per_cluster) 00050 { 00051 if (tree->getInputCloud ()->points.size () != cloud.points.size ()) 00052 { 00053 PCL_ERROR ("[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n", (unsigned long)tree->getInputCloud ()->points.size (), (unsigned long)cloud.points.size ()); 00054 return; 00055 } 00056 // Create a bool vector of processed point indices, and initialize it to false 00057 std::vector<bool> processed (cloud.points.size (), false); 00058 00059 std::vector<int> nn_indices; 00060 std::vector<float> nn_distances; 00061 // Process all points in the indices vector 00062 for (size_t i = 0; i < cloud.points.size (); ++i) 00063 { 00064 if (processed[i]) 00065 continue; 00066 00067 std::vector<int> seed_queue; 00068 int sq_idx = 0; 00069 seed_queue.push_back (i); 00070 00071 processed[i] = true; 00072 00073 while (sq_idx < (int)seed_queue.size ()) 00074 { 00075 // Search for sq_idx 00076 if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances)) 00077 { 00078 sq_idx++; 00079 continue; 00080 } 00081 00082 for (size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx 00083 { 00084 if (processed[nn_indices[j]]) // Has this point been processed before ? 00085 continue; 00086 00087 // Perform a simple Euclidean clustering 00088 seed_queue.push_back (nn_indices[j]); 00089 processed[nn_indices[j]] = true; 00090 } 00091 00092 sq_idx++; 00093 } 00094 00095 // If this queue is satisfactory, add to the clusters 00096 if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster) 00097 { 00098 pcl::PointIndices r; 00099 r.indices.resize (seed_queue.size ()); 00100 for (size_t j = 0; j < seed_queue.size (); ++j) 00101 r.indices[j] = seed_queue[j]; 00102 00103 std::sort (r.indices.begin (), r.indices.end ()); 00104 r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ()); 00105 00106 r.header = cloud.header; 00107 clusters.push_back (r); // We could avoid a copy by working directly in the vector 00108 } 00109 } 00110 } 00111 00113 template <typename PointT> void 00114 pcl::extractEuclideanClusters (const PointCloud<PointT> &cloud, 00115 const std::vector<int> &indices, 00116 const boost::shared_ptr<search::Search<PointT> > &tree, 00117 float tolerance, std::vector<PointIndices> &clusters, 00118 unsigned int min_pts_per_cluster, 00119 unsigned int max_pts_per_cluster) 00120 { 00121 // \note If the tree was created over <cloud, indices>, we guarantee a 1-1 mapping between what the tree returns 00122 //and indices[i] 00123 if (tree->getInputCloud ()->points.size () != cloud.points.size ()) 00124 { 00125 PCL_ERROR ("[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n", (unsigned long)tree->getInputCloud ()->points.size (), (unsigned long)cloud.points.size ()); 00126 return; 00127 } 00128 if (tree->getIndices ()->size () != indices.size ()) 00129 { 00130 PCL_ERROR ("[pcl::extractEuclideanClusters] Tree built for a different set of indices (%lu) than the input set (%lu)!\n", (unsigned long)tree->getIndices ()->size (), (unsigned long)indices.size ()); 00131 return; 00132 } 00133 00134 // Create a bool vector of processed point indices, and initialize it to false 00135 std::vector<bool> processed (indices.size (), false); 00136 00137 std::vector<int> nn_indices; 00138 std::vector<float> nn_distances; 00139 // Process all points in the indices vector 00140 for (size_t i = 0; i < indices.size (); ++i) 00141 { 00142 if (processed[i]) 00143 continue; 00144 00145 std::vector<int> seed_queue; 00146 int sq_idx = 0; 00147 seed_queue.push_back (i); 00148 00149 processed[i] = true; 00150 00151 while (sq_idx < (int)seed_queue.size ()) 00152 { 00153 // Search for sq_idx 00154 if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances)) 00155 { 00156 sq_idx++; 00157 continue; 00158 } 00159 00160 for (size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx 00161 { 00162 if (processed[nn_indices[j]]) // Has this point been processed before ? 00163 continue; 00164 00165 // Perform a simple Euclidean clustering 00166 seed_queue.push_back (nn_indices[j]); 00167 processed[nn_indices[j]] = true; 00168 } 00169 00170 sq_idx++; 00171 } 00172 00173 // If this queue is satisfactory, add to the clusters 00174 if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster) 00175 { 00176 pcl::PointIndices r; 00177 r.indices.resize (seed_queue.size ()); 00178 for (size_t j = 0; j < seed_queue.size (); ++j) 00179 // This is the only place where indices come into play 00180 r.indices[j] = indices[seed_queue[j]]; 00181 00182 //r.indices.assign(seed_queue.begin(), seed_queue.end()); 00183 std::sort (r.indices.begin (), r.indices.end ()); 00184 r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ()); 00185 00186 r.header = cloud.header; 00187 clusters.push_back (r); // We could avoid a copy by working directly in the vector 00188 } 00189 } 00190 } 00191 00195 00196 template <typename PointT> void 00197 pcl::EuclideanClusterExtraction<PointT>::extract (std::vector<PointIndices> &clusters) 00198 { 00199 if (!initCompute () || 00200 (input_ != 0 && input_->points.empty ()) || 00201 (indices_ != 0 && indices_->empty ())) 00202 { 00203 clusters.clear (); 00204 return; 00205 } 00206 00207 // Initialize the spatial locator 00208 if (!tree_) 00209 { 00210 if (input_->isOrganized ()) 00211 tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ()); 00212 else 00213 tree_.reset (new pcl::search::KdTree<PointT> (false)); 00214 } 00215 00216 // Send the input dataset to the spatial locator 00217 tree_->setInputCloud (input_, indices_); 00218 extractEuclideanClusters (*input_, *indices_, tree_, cluster_tolerance_, clusters, min_pts_per_cluster_, max_pts_per_cluster_); 00219 00220 //tree_->setInputCloud (input_); 00221 //extractEuclideanClusters (*input_, tree_, cluster_tolerance_, clusters, min_pts_per_cluster_, max_pts_per_cluster_); 00222 00223 // Sort the clusters based on their size (largest one first) 00224 std::sort (clusters.rbegin (), clusters.rend (), comparePointClusters); 00225 00226 deinitCompute (); 00227 } 00228 00229 #define PCL_INSTANTIATE_EuclideanClusterExtraction(T) template class PCL_EXPORTS pcl::EuclideanClusterExtraction<T>; 00230 #define PCL_INSTANTIATE_extractEuclideanClusters(T) template void PCL_EXPORTS pcl::extractEuclideanClusters<T>(const pcl::PointCloud<T> &, const boost::shared_ptr<pcl::search::Search<T> > &, float , std::vector<pcl::PointIndices> &, unsigned int, unsigned int); 00231 #define PCL_INSTANTIATE_extractEuclideanClusters_indices(T) template void PCL_EXPORTS pcl::extractEuclideanClusters<T>(const pcl::PointCloud<T> &, const std::vector<int> &, const boost::shared_ptr<pcl::search::Search<T> > &, float , std::vector<pcl::PointIndices> &, unsigned int, unsigned int); 00232 00233 #endif // PCL_EXTRACT_CLUSTERS_IMPL_H_