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.h 3035 2011-11-01 04:29:18Z rusu $ 00035 * 00036 */ 00037 00038 #ifndef PCL_EXTRACT_CLUSTERS_H_ 00039 #define PCL_EXTRACT_CLUSTERS_H_ 00040 00041 #include <pcl/pcl_base.h> 00042 00043 #include "pcl/search/pcl_search.h" 00044 00045 namespace pcl 00046 { 00048 00058 template <typename PointT> void extractEuclideanClusters (const PointCloud<PointT> &cloud, const boost::shared_ptr<search::Search<PointT> > &tree, float tolerance, std::vector<PointIndices> &clusters, unsigned int min_pts_per_cluster = 1, unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ()); 00059 00061 00072 template <typename PointT> void extractEuclideanClusters (const PointCloud<PointT> &cloud, const std::vector<int> &indices, const boost::shared_ptr<search::Search<PointT> > &tree, float tolerance, std::vector<PointIndices> &clusters, unsigned int min_pts_per_cluster = 1, unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ()); 00073 00075 00088 template <typename PointT, typename Normal> void 00089 extractEuclideanClusters ( 00090 const PointCloud<PointT> &cloud, const PointCloud<Normal> &normals, 00091 float tolerance, const boost::shared_ptr<KdTree<PointT> > &tree, 00092 std::vector<PointIndices> &clusters, double eps_angle, 00093 unsigned int min_pts_per_cluster = 1, 00094 unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ()) 00095 { 00096 if (tree->getInputCloud ()->points.size () != cloud.points.size ()) 00097 { 00098 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 ()); 00099 return; 00100 } 00101 if (cloud.points.size () != normals.points.size ()) 00102 { 00103 PCL_ERROR ("[pcl::extractEuclideanClusters] Number of points in the input point cloud (%lu) different than normals (%lu)!\n", (unsigned long)cloud.points.size (), (unsigned long)normals.points.size ()); 00104 return; 00105 } 00106 00107 // Create a bool vector of processed point indices, and initialize it to false 00108 std::vector<bool> processed (cloud.points.size (), false); 00109 00110 std::vector<int> nn_indices; 00111 std::vector<float> nn_distances; 00112 // Process all points in the indices vector 00113 for (size_t i = 0; i < cloud.points.size (); ++i) 00114 { 00115 if (processed[i]) 00116 continue; 00117 00118 std::vector<unsigned int> seed_queue; 00119 int sq_idx = 0; 00120 seed_queue.push_back (i); 00121 00122 processed[i] = true; 00123 00124 while (sq_idx < (int)seed_queue.size ()) 00125 { 00126 // Search for sq_idx 00127 if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances)) 00128 { 00129 sq_idx++; 00130 continue; 00131 } 00132 00133 for (size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx 00134 { 00135 if (processed[nn_indices[j]]) // Has this point been processed before ? 00136 continue; 00137 00138 //processed[nn_indices[j]] = true; 00139 // [-1;1] 00140 double dot_p = normals.points[i].normal[0] * normals.points[nn_indices[j]].normal[0] + 00141 normals.points[i].normal[1] * normals.points[nn_indices[j]].normal[1] + 00142 normals.points[i].normal[2] * normals.points[nn_indices[j]].normal[2]; 00143 if ( fabs (acos (dot_p)) < eps_angle ) 00144 { 00145 processed[nn_indices[j]] = true; 00146 seed_queue.push_back (nn_indices[j]); 00147 } 00148 } 00149 00150 sq_idx++; 00151 } 00152 00153 // If this queue is satisfactory, add to the clusters 00154 if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster) 00155 { 00156 pcl::PointIndices r; 00157 r.indices.resize (seed_queue.size ()); 00158 for (size_t j = 0; j < seed_queue.size (); ++j) 00159 r.indices[j] = seed_queue[j]; 00160 00161 std::sort (r.indices.begin (), r.indices.end ()); 00162 r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ()); 00163 00164 r.header = cloud.header; 00165 clusters.push_back (r); // We could avoid a copy by working directly in the vector 00166 } 00167 } 00168 } 00169 00170 00172 00186 template <typename PointT, typename Normal> 00187 void extractEuclideanClusters ( 00188 const PointCloud<PointT> &cloud, const PointCloud<Normal> &normals, 00189 const std::vector<int> &indices, const boost::shared_ptr<KdTree<PointT> > &tree, 00190 float tolerance, std::vector<PointIndices> &clusters, double eps_angle, 00191 unsigned int min_pts_per_cluster = 1, 00192 unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ()) 00193 { 00194 // \note If the tree was created over <cloud, indices>, we guarantee a 1-1 mapping between what the tree returns 00195 //and indices[i] 00196 if (tree->getInputCloud ()->points.size () != cloud.points.size ()) 00197 { 00198 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 ()); 00199 return; 00200 } 00201 if (tree->getIndices ()->size () != indices.size ()) 00202 { 00203 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 ()); 00204 return; 00205 } 00206 if (cloud.points.size () != normals.points.size ()) 00207 { 00208 PCL_ERROR ("[pcl::extractEuclideanClusters] Number of points in the input point cloud (%lu) different than normals (%lu)!\n", (unsigned long)cloud.points.size (), (unsigned long)normals.points.size ()); 00209 return; 00210 } 00211 // Create a bool vector of processed point indices, and initialize it to false 00212 std::vector<bool> processed (indices.size (), false); 00213 00214 std::vector<int> nn_indices; 00215 std::vector<float> nn_distances; 00216 // Process all points in the indices vector 00217 for (size_t i = 0; i < indices.size (); ++i) 00218 { 00219 if (processed[i]) 00220 continue; 00221 00222 std::vector<int> seed_queue; 00223 int sq_idx = 0; 00224 seed_queue.push_back (i); 00225 00226 processed[i] = true; 00227 00228 while (sq_idx < (int)seed_queue.size ()) 00229 { 00230 // Search for sq_idx 00231 if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances)) 00232 { 00233 sq_idx++; 00234 continue; 00235 } 00236 00237 for (size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx 00238 { 00239 if (processed[nn_indices[j]]) // Has this point been processed before ? 00240 continue; 00241 00242 //processed[nn_indices[j]] = true; 00243 // [-1;1] 00244 double dot_p = 00245 normals.points[indices[i]].normal[0] * normals.points[indices[nn_indices[j]]].normal[0] + 00246 normals.points[indices[i]].normal[1] * normals.points[indices[nn_indices[j]]].normal[1] + 00247 normals.points[indices[i]].normal[2] * normals.points[indices[nn_indices[j]]].normal[2]; 00248 if ( fabs (acos (dot_p)) < eps_angle ) 00249 { 00250 processed[nn_indices[j]] = true; 00251 seed_queue.push_back (nn_indices[j]); 00252 } 00253 } 00254 00255 sq_idx++; 00256 } 00257 00258 // If this queue is satisfactory, add to the clusters 00259 if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster) 00260 { 00261 pcl::PointIndices r; 00262 r.indices.resize (seed_queue.size ()); 00263 for (size_t j = 0; j < seed_queue.size (); ++j) 00264 r.indices[j] = indices[seed_queue[j]]; 00265 00266 std::sort (r.indices.begin (), r.indices.end ()); 00267 r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ()); 00268 00269 r.header = cloud.header; 00270 clusters.push_back (r); 00271 } 00272 } 00273 } 00274 00278 00282 template <typename PointT> 00283 class EuclideanClusterExtraction: public PCLBase<PointT> 00284 { 00285 typedef PCLBase<PointT> BasePCLBase; 00286 00287 public: 00288 typedef pcl::PointCloud<PointT> PointCloud; 00289 typedef typename PointCloud::Ptr PointCloudPtr; 00290 typedef typename PointCloud::ConstPtr PointCloudConstPtr; 00291 00292 typedef typename pcl::search::Search<PointT> KdTree; 00293 typedef typename pcl::search::Search<PointT>::Ptr KdTreePtr; 00294 00295 typedef PointIndices::Ptr PointIndicesPtr; 00296 typedef PointIndices::ConstPtr PointIndicesConstPtr; 00297 00299 00300 EuclideanClusterExtraction () : tree_ (), min_pts_per_cluster_ (1), 00301 max_pts_per_cluster_ (std::numeric_limits<int>::max ()) 00302 {}; 00303 00307 inline void setSearchMethod (const KdTreePtr &tree) { tree_ = tree; } 00308 00310 inline KdTreePtr getSearchMethod () { return (tree_); } 00311 00315 inline void setClusterTolerance (double tolerance) { cluster_tolerance_ = tolerance; } 00316 00318 inline double getClusterTolerance () { return (cluster_tolerance_); } 00319 00323 inline void setMinClusterSize (int min_cluster_size) { min_pts_per_cluster_ = min_cluster_size; } 00324 00326 inline int getMinClusterSize () { return (min_pts_per_cluster_); } 00327 00331 inline void setMaxClusterSize (int max_cluster_size) { max_pts_per_cluster_ = max_cluster_size; } 00332 00334 inline int getMaxClusterSize () { return (max_pts_per_cluster_); } 00335 00339 void extract (std::vector<PointIndices> &clusters); 00340 00341 protected: 00342 // Members derived from the base class 00343 using BasePCLBase::input_; 00344 using BasePCLBase::indices_; 00345 using BasePCLBase::initCompute; 00346 using BasePCLBase::deinitCompute; 00347 00349 KdTreePtr tree_; 00350 00352 double cluster_tolerance_; 00353 00355 int min_pts_per_cluster_; 00356 00358 int max_pts_per_cluster_; 00359 00361 virtual std::string getClassName () const { return ("EuclideanClusterExtraction"); } 00362 00363 }; 00364 00368 inline bool 00369 comparePointClusters (const pcl::PointIndices &a, const pcl::PointIndices &b) 00370 { 00371 return (a.indices.size () < b.indices.size ()); 00372 } 00373 } 00374 00375 #endif //#ifndef PCL_EXTRACT_CLUSTERS_H_