Point Cloud Library (PCL)
1.7.0
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2010-2011, Willow Garage, Inc. 00006 * 00007 * All rights reserved. 00008 * 00009 * Redistribution and use in source and binary forms, with or without 00010 * modification, are permitted provided that the following conditions 00011 * are met: 00012 * 00013 * * Redistributions of source code must retain the above copyright 00014 * notice, this list of conditions and the following disclaimer. 00015 * * Redistributions in binary form must reproduce the above 00016 * copyright notice, this list of conditions and the following 00017 * disclaimer in the documentation and/or other materials provided 00018 * with the distribution. 00019 * * Neither the name of the copyright holder(s) nor the names of its 00020 * contributors may be used to endorse or promote products derived 00021 * from this software without specific prior written permission. 00022 * 00023 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00024 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00025 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00026 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00027 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00028 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00029 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00030 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00031 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00032 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00033 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00034 * POSSIBILITY OF SUCH DAMAGE. 00035 * 00036 * $id $ 00037 */ 00038 00039 #ifndef PCL_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_ 00040 #define PCL_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_ 00041 00042 #include <pcl/segmentation/seeded_hue_segmentation.h> 00043 00044 ////////////////////////////////////////////////////////////////////////////////////////////// 00045 void 00046 pcl::seededHueSegmentation (const PointCloud<PointXYZRGB> &cloud, 00047 const boost::shared_ptr<search::Search<PointXYZRGB> > &tree, 00048 float tolerance, 00049 PointIndices &indices_in, 00050 PointIndices &indices_out, 00051 float delta_hue) 00052 { 00053 if (tree->getInputCloud ()->points.size () != cloud.points.size ()) 00054 { 00055 PCL_ERROR ("[pcl::seededHueSegmentation] Tree built for a different point cloud dataset (%zu) than the input cloud (%zu)!\n", tree->getInputCloud ()->points.size (), cloud.points.size ()); 00056 return; 00057 } 00058 // Create a bool vector of processed point indices, and initialize it to false 00059 std::vector<bool> processed (cloud.points.size (), false); 00060 00061 std::vector<int> nn_indices; 00062 std::vector<float> nn_distances; 00063 00064 // Process all points in the indices vector 00065 for (size_t k = 0; k < indices_in.indices.size (); ++k) 00066 { 00067 int i = indices_in.indices[k]; 00068 if (processed[i]) 00069 continue; 00070 00071 processed[i] = true; 00072 00073 std::vector<int> seed_queue; 00074 int sq_idx = 0; 00075 seed_queue.push_back (i); 00076 00077 PointXYZRGB p; 00078 p = cloud.points[i]; 00079 PointXYZHSV h; 00080 PointXYZRGBtoXYZHSV(p, h); 00081 00082 while (sq_idx < static_cast<int> (seed_queue.size ())) 00083 { 00084 int ret = tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances, std::numeric_limits<int>::max()); 00085 if(ret == -1) 00086 PCL_ERROR("[pcl::seededHueSegmentation] radiusSearch returned error code -1"); 00087 // Search for sq_idx 00088 if (!ret) 00089 { 00090 sq_idx++; 00091 continue; 00092 } 00093 00094 for (size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx 00095 { 00096 if (processed[nn_indices[j]]) // Has this point been processed before ? 00097 continue; 00098 00099 PointXYZRGB p_l; 00100 p_l = cloud.points[nn_indices[j]]; 00101 PointXYZHSV h_l; 00102 PointXYZRGBtoXYZHSV(p_l, h_l); 00103 00104 if (fabs(h_l.h - h.h) < delta_hue) 00105 { 00106 seed_queue.push_back (nn_indices[j]); 00107 processed[nn_indices[j]] = true; 00108 } 00109 } 00110 00111 sq_idx++; 00112 } 00113 // Copy the seed queue into the output indices 00114 for (size_t l = 0; l < seed_queue.size (); ++l) 00115 indices_out.indices.push_back(seed_queue[l]); 00116 } 00117 // This is purely esthetical, can be removed for speed purposes 00118 std::sort (indices_out.indices.begin (), indices_out.indices.end ()); 00119 } 00120 ////////////////////////////////////////////////////////////////////////////////////////////// 00121 void 00122 pcl::seededHueSegmentation (const PointCloud<PointXYZRGB> &cloud, 00123 const boost::shared_ptr<search::Search<PointXYZRGBL> > &tree, 00124 float tolerance, 00125 PointIndices &indices_in, 00126 PointIndices &indices_out, 00127 float delta_hue) 00128 { 00129 if (tree->getInputCloud ()->points.size () != cloud.points.size ()) 00130 { 00131 PCL_ERROR ("[pcl::seededHueSegmentation] Tree built for a different point cloud dataset (%zu) than the input cloud (%zu)!\n", tree->getInputCloud ()->points.size (), cloud.points.size ()); 00132 return; 00133 } 00134 // Create a bool vector of processed point indices, and initialize it to false 00135 std::vector<bool> processed (cloud.points.size (), false); 00136 00137 std::vector<int> nn_indices; 00138 std::vector<float> nn_distances; 00139 00140 // Process all points in the indices vector 00141 for (size_t k = 0; k < indices_in.indices.size (); ++k) 00142 { 00143 int i = indices_in.indices[k]; 00144 if (processed[i]) 00145 continue; 00146 00147 processed[i] = true; 00148 00149 std::vector<int> seed_queue; 00150 int sq_idx = 0; 00151 seed_queue.push_back (i); 00152 00153 PointXYZRGB p; 00154 p = cloud.points[i]; 00155 PointXYZHSV h; 00156 PointXYZRGBtoXYZHSV(p, h); 00157 00158 while (sq_idx < static_cast<int> (seed_queue.size ())) 00159 { 00160 int ret = tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances, std::numeric_limits<int>::max()); 00161 if(ret == -1) 00162 PCL_ERROR("[pcl::seededHueSegmentation] radiusSearch returned error code -1"); 00163 // Search for sq_idx 00164 if (!ret) 00165 { 00166 sq_idx++; 00167 continue; 00168 } 00169 for (size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx 00170 { 00171 if (processed[nn_indices[j]]) // Has this point been processed before ? 00172 continue; 00173 00174 PointXYZRGB p_l; 00175 p_l = cloud.points[nn_indices[j]]; 00176 PointXYZHSV h_l; 00177 PointXYZRGBtoXYZHSV(p_l, h_l); 00178 00179 if (fabs(h_l.h - h.h) < delta_hue) 00180 { 00181 seed_queue.push_back (nn_indices[j]); 00182 processed[nn_indices[j]] = true; 00183 } 00184 } 00185 00186 sq_idx++; 00187 } 00188 // Copy the seed queue into the output indices 00189 for (size_t l = 0; l < seed_queue.size (); ++l) 00190 indices_out.indices.push_back(seed_queue[l]); 00191 } 00192 // This is purely esthetical, can be removed for speed purposes 00193 std::sort (indices_out.indices.begin (), indices_out.indices.end ()); 00194 } 00195 ////////////////////////////////////////////////////////////////////////////////////////////// 00196 ////////////////////////////////////////////////////////////////////////////////////////////// 00197 00198 void 00199 pcl::SeededHueSegmentation::segment (PointIndices &indices_in, PointIndices &indices_out) 00200 { 00201 if (!initCompute () || 00202 (input_ != 0 && input_->points.empty ()) || 00203 (indices_ != 0 && indices_->empty ())) 00204 { 00205 indices_out.indices.clear (); 00206 return; 00207 } 00208 00209 // Initialize the spatial locator 00210 if (!tree_) 00211 { 00212 if (input_->isOrganized ()) 00213 tree_.reset (new pcl::search::OrganizedNeighbor<PointXYZRGB> ()); 00214 else 00215 tree_.reset (new pcl::search::KdTree<PointXYZRGB> (false)); 00216 } 00217 00218 // Send the input dataset to the spatial locator 00219 tree_->setInputCloud (input_); 00220 seededHueSegmentation (*input_, tree_, static_cast<float> (cluster_tolerance_), indices_in, indices_out, delta_hue_); 00221 deinitCompute (); 00222 } 00223 00224 #endif // PCL_EXTRACT_CLUSTERS_IMPL_H_