Point Cloud Library (PCL)
1.7.0
|
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 Willow Garage, Inc. 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 */ 00037 00038 #ifndef PCL_KEYPOINT_IMPL_H_ 00039 #define PCL_KEYPOINT_IMPL_H_ 00040 00041 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////// 00042 template <typename PointInT, typename PointOutT> bool 00043 pcl::Keypoint<PointInT, PointOutT>::initCompute () 00044 { 00045 if (!PCLBase<PointInT>::initCompute ()) 00046 return (false); 00047 00048 // Initialize the spatial locator 00049 if (!tree_) 00050 { 00051 if (input_->isOrganized ()) 00052 tree_.reset (new pcl::search::OrganizedNeighbor<PointInT> ()); 00053 else 00054 tree_.reset (new pcl::search::KdTree<PointInT> (false)); 00055 } 00056 00057 // If no search surface has been defined, use the input dataset as the search surface itself 00058 if (!surface_) 00059 surface_ = input_; 00060 00061 // Send the surface dataset to the spatial locator 00062 tree_->setInputCloud (surface_); 00063 00064 // Do a fast check to see if the search parameters are well defined 00065 if (search_radius_ != 0.0) 00066 { 00067 if (k_ != 0) 00068 { 00069 PCL_ERROR ("[pcl::%s::initCompute] Both radius (%f) and K (%d) defined! Set one of them to zero first and then re-run compute ().\n", getClassName ().c_str (), search_radius_, k_); 00070 return (false); 00071 } 00072 else // Use the radiusSearch () function 00073 { 00074 search_parameter_ = search_radius_; 00075 if (surface_ == input_) // if the two surfaces are the same 00076 { 00077 // Declare the search locator definition 00078 int (KdTree::*radiusSearch)(int index, double radius, std::vector<int> &k_indices, 00079 std::vector<float> &k_distances, unsigned int max_nn) const = &KdTree::radiusSearch; 00080 search_method_ = boost::bind (radiusSearch, boost::ref (tree_), _1, _2, _3, _4, 0); 00081 } 00082 else 00083 { 00084 // Declare the search locator definition 00085 int (KdTree::*radiusSearchSurface)(const PointCloudIn &cloud, int index, double radius, std::vector<int> &k_indices, 00086 std::vector<float> &k_distances, unsigned int max_nn) const = &KdTree::radiusSearch; 00087 search_method_surface_ = boost::bind (radiusSearchSurface, boost::ref (tree_), _1, _2, _3, _4, _5, 0); 00088 } 00089 } 00090 } 00091 else 00092 { 00093 if (k_ != 0) // Use the nearestKSearch () function 00094 { 00095 search_parameter_ = k_; 00096 if (surface_ == input_) // if the two surfaces are the same 00097 { 00098 // Declare the search locator definition 00099 int (KdTree::*nearestKSearch)(int index, int k, std::vector<int> &k_indices, std::vector<float> &k_distances) const = &KdTree::nearestKSearch; 00100 search_method_ = boost::bind (nearestKSearch, boost::ref (tree_), _1, _2, _3, _4); 00101 } 00102 else 00103 { 00104 // Declare the search locator definition 00105 int (KdTree::*nearestKSearchSurface)(const PointCloudIn &cloud, int index, int k, std::vector<int> &k_indices, std::vector<float> &k_distances) const = &KdTree::nearestKSearch; 00106 search_method_surface_ = boost::bind (nearestKSearchSurface, boost::ref (tree_), _1, _2, _3, _4, _5); 00107 } 00108 } 00109 else 00110 { 00111 PCL_ERROR ("[pcl::%s::initCompute] Neither radius nor K defined! Set one of them to a positive number first and then re-run compute ().\n", getClassName ().c_str ()); 00112 return (false); 00113 } 00114 } 00115 00116 return (true); 00117 } 00118 00119 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////// 00120 template <typename PointInT, typename PointOutT> inline void 00121 pcl::Keypoint<PointInT, PointOutT>::compute (PointCloudOut &output) 00122 { 00123 if (!initCompute ()) 00124 { 00125 PCL_ERROR ("[pcl::%s::compute] initCompute failed!\n", getClassName ().c_str ()); 00126 return; 00127 } 00128 00129 // Perform the actual computation 00130 detectKeypoints (output); 00131 00132 deinitCompute (); 00133 00134 // Reset the surface 00135 if (input_ == surface_) 00136 surface_.reset (); 00137 } 00138 00139 #endif //#ifndef PCL_KEYPOINT_IMPL_H_ 00140