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Point Cloud Library (PCL)
1.4.0
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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 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 * $Id: organized.h 3548 2011-12-14 23:42:10Z rusu $ 00037 * 00038 */ 00039 00040 #ifndef PCL_SEARCH_ORGANIZED_NEIGHBOR_SEARCH_H_ 00041 #define PCL_SEARCH_ORGANIZED_NEIGHBOR_SEARCH_H_ 00042 00043 #include <pcl/point_cloud.h> 00044 #include <pcl/point_types.h> 00045 #include <pcl/search/search.h> 00046 00047 #include <algorithm> 00048 #include <queue> 00049 #include <vector> 00050 00051 namespace pcl 00052 { 00053 namespace search 00054 { 00067 template<typename PointT> 00068 class OrganizedNeighbor : public pcl::search::Search<PointT> 00069 { 00070 00071 public: 00072 // public typedefs 00073 typedef pcl::PointCloud<PointT> PointCloud; 00074 typedef boost::shared_ptr<PointCloud> PointCloudPtr; 00075 00076 typedef boost::shared_ptr<const PointCloud> PointCloudConstPtr; 00077 typedef boost::shared_ptr<const std::vector<int> > IndicesConstPtr; 00078 00079 typedef boost::shared_ptr<pcl::search::OrganizedNeighbor<PointT> > Ptr; 00080 typedef boost::shared_ptr<const pcl::search::OrganizedNeighbor<PointT> > ConstPtr; 00081 00083 OrganizedNeighbor () 00084 { 00085 max_distance_ = std::numeric_limits<double>::max (); 00086 oneOverFocalLength_ = 0.0f; //Indicate if it's not initialised 00087 horizontal_window_ = 0; 00088 vertical_window_ = 0; 00089 radiusLookupTableWidth_ =-1; 00090 radiusLookupTableHeight_ =-1; 00091 exactFocalLength_ = 0; // we haven't estimated it yet or we haven't set it 00092 precision_ = 0; 00093 } 00094 00096 ~OrganizedNeighbor () {} 00097 00101 inline void 00102 setInputCloud (const PointCloudConstPtr &cloud) 00103 { 00104 if (input_ != cloud) 00105 input_ = cloud; 00106 00107 if (precision_ == 1) 00108 { 00109 if(!exactFocalLength_) 00110 { 00111 estimateFocalLengthFromInputCloud (*cloud); 00112 generateRadiusLookupTable (cloud->width, cloud->height); 00113 exactFocalLength_ = 1; 00114 } 00115 } 00116 else 00117 { 00118 oneOverFocalLength_ = 1.0f; 00119 } 00120 } 00121 00126 inline void 00127 setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices) 00128 { 00129 if (input_ != cloud) 00130 input_ = cloud; 00131 00132 indices_ = indices; 00133 00134 if (precision_ == 1) 00135 { 00136 if (!exactFocalLength_) 00137 { 00138 estimateFocalLengthFromInputCloud (*cloud); 00139 generateRadiusLookupTable (cloud->width, cloud->height); 00140 exactFocalLength_ = 1; 00141 } 00142 } 00143 else 00144 oneOverFocalLength_ = 1.0f; 00145 } 00146 00150 PointCloudConstPtr 00151 getInputCloud () { return input_; } 00152 00156 inline IndicesConstPtr const 00157 getIndices () { return (indices_); } 00158 00163 inline void 00164 setPrecision (int precision) { precision_ = precision; } 00165 00167 inline int 00168 getPrecision () { return (precision_); } 00169 00173 inline void 00174 setOneOverFocalLength (double oneOverFocalLength) 00175 { 00176 oneOverFocalLength_ = oneOverFocalLength; 00177 exactFocalLength_ = 1; 00178 } 00179 00181 inline double 00182 getOneOverFocalLength () { return (oneOverFocalLength_); } 00183 00185 inline double 00186 getMaxDistance () const { return (max_distance_); } 00187 00191 inline void 00192 setMaxDistance (double max_dist) { max_distance_ = max_dist; } 00193 00198 inline void 00199 setSearchWindow (int horizontal, int vertical) 00200 { 00201 horizontal_window_ = horizontal; 00202 vertical_window_ = vertical; 00203 } 00204 00208 void 00209 setSearchWindowAsK (int k); 00210 00212 int 00213 getHorizontalSearchWindow () const { return (horizontal_window_); } 00214 00216 int 00217 getVerticalSearchWindow () const { return (vertical_window_); } 00218 00228 inline int 00229 radiusSearch (const PointCloud& cloud, 00230 int index, 00231 double radius, 00232 std::vector<int>& k_indices, 00233 std::vector<float>& k_sqr_distances, 00234 int max_nn = std::numeric_limits<int>::max ()) 00235 { 00236 return (radiusSearch (cloud.points[index], radius, k_indices, k_sqr_distances, max_nn)); 00237 } 00238 00248 int 00249 radiusSearch (const PointCloudConstPtr &cloud, 00250 int index, 00251 double radius, 00252 std::vector<int> &k_indices, 00253 std::vector<float> &k_sqr_distances, 00254 int max_nn = std::numeric_limits<int>::max ()); 00255 00265 int 00266 radiusSearch (int index, 00267 const double radius, 00268 std::vector<int> &k_indices, 00269 std::vector<float> &k_sqr_distances, 00270 int max_nn = std::numeric_limits<int>::max ()) const; 00271 00280 int 00281 radiusSearch (const PointT &p_q, 00282 const double radius, 00283 std::vector<int> &k_indices, 00284 std::vector<float> &k_sqr_distances, 00285 int max_nn = std::numeric_limits<int>::max ()) const; 00286 00291 double 00292 estimateFocalLengthFromInputCloud (const pcl::PointCloud<PointT> &cloud); 00293 00301 int 00302 exactNearestKSearch (const PointT &p_q, 00303 int k, 00304 std::vector<int> &k_indices, 00305 std::vector<float> &k_sqr_distances); 00306 00315 inline int 00316 exactNearestKSearch (int index, 00317 int k, 00318 std::vector<int> &k_indices, 00319 std::vector<float> &k_sqr_distances); 00320 00329 inline int 00330 exactNearestKSearch (const pcl::PointCloud<PointT> &cloud, 00331 int index, 00332 int k, 00333 std::vector<int> &k_indices, 00334 std::vector<float> &k_sqr_distances); 00335 00345 int 00346 nearestKSearch (const PointT &p_q, 00347 int k, 00348 std::vector<int> &k_indices, 00349 std::vector<float> &k_sqr_distances) 00350 { 00351 PCL_ERROR ("[pcl::search::OrganizedNeighbor::approxNearestKSearch] Method not implemented!\n"); 00352 return (0); 00353 } 00354 00364 int 00365 nearestKSearch (int index, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances); 00366 00376 int 00377 nearestKSearch (const pcl::PointCloud<PointT> &cloud, int index, int k, 00378 std::vector<int> &k_indices, std::vector<float> &k_sqr_distances); 00379 00380 protected: 00385 class RadiusSearchLoopkupEntry 00386 { 00387 public: 00389 RadiusSearchLoopkupEntry () {} 00390 00392 virtual ~RadiusSearchLoopkupEntry () {} 00393 00398 void 00399 defineShiftedSearchPoint (int x_shift, int y_shift) 00400 { 00401 x_diff_ = x_shift; 00402 y_diff_ = y_shift; 00403 00404 squared_distance_ = x_diff_ * x_diff_ + y_diff_ * y_diff_; 00405 } 00406 00408 bool 00409 operator< (const RadiusSearchLoopkupEntry& rhs) const 00410 { 00411 return (this->squared_distance_ < rhs.squared_distance_); 00412 } 00413 00414 // Public globals 00415 int x_diff_;int y_diff_;int squared_distance_; 00416 }; 00417 00422 class NearestNeighborCandidate 00423 { 00424 public: 00426 NearestNeighborCandidate () {} 00427 00429 virtual ~NearestNeighborCandidate () {} 00430 00432 bool 00433 operator< (const NearestNeighborCandidate& rhs) const 00434 { 00435 return (this->squared_distance_ < rhs.squared_distance_); 00436 } 00437 00438 // Public globals 00439 int index_; 00440 double squared_distance_; 00441 }; 00442 00447 const PointT& 00448 getPointByIndex (const unsigned int index) const; 00449 00455 void 00456 generateRadiusLookupTable (unsigned int width, unsigned int height); 00457 00463 inline void 00464 pointPlaneProjection (const PointT& point, int& xpos, int& ypos) const 00465 { 00466 xpos = (int)pcl_round (point.x / (point.z * oneOverFocalLength_)); 00467 ypos = (int)pcl_round (point.y / (point.z * oneOverFocalLength_)); 00468 } 00469 00478 void 00479 getProjectedRadiusSearchBox (const PointT& point, double squared_radius, int& minX, int& minY, 00480 int& maxX, int& maxY) const; 00481 00482 00484 virtual std::string 00485 getName () const { return ("Organized_Neighbor_Search"); } 00486 00488 int horizontal_window_; 00490 int vertical_window_; 00492 int min_pts_; 00493 00495 PointCloudConstPtr input_; 00497 IndicesConstPtr indices_; 00498 00500 double max_distance_; 00501 00503 double oneOverFocalLength_; 00504 00506 std::vector<RadiusSearchLoopkupEntry> radiusSearchLookup_; 00507 00508 int radiusLookupTableWidth_; 00509 int radiusLookupTableHeight_; 00510 00512 int precision_; 00513 00515 bool exactFocalLength_; 00516 }; 00517 } 00518 } 00519 00520 #endif 00521
1.7.6.1