Upload fourth chapter

Signed-off-by: Hao Guo <[email protected]>

第四章/1 kdtree_search/source/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
+project(kdtree_search)
+find_package(PCL 1.2 REQUIRED)
+include_directories(${PCL_INCLUDE_DIRS})
+link_directories(${PCL_LIBRARY_DIRS})
+add_definitions(${PCL_DEFINITIONS})
+add_executable(kdtree_search kdtree_search.cpp)
+target_link_libraries(kdtree_search ${PCL_LIBRARIES})

第四章/1 kdtree_search/source/kdtree_search.cpp

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+#include <pcl/point_cloud.h>
+#include <pcl/kdtree/kdtree_flann.h>
+#include <iostream>
+#include <vector>
+#include <ctime>
+
+int main (int argc, char**argv)
+{
+srand (time (NULL));
+pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
+//点云生成
+cloud->width =1000;
+cloud->height =1;
+cloud->points.resize (cloud->width * cloud->height);
+for (size_t i=0; i< cloud->points.size (); ++i)
+ {
+cloud->points[i].x =1024.0f* rand () / (RAND_MAX +1.0f);
+cloud->points[i].y =1024.0f* rand () / (RAND_MAX +1.0f);
+cloud->points[i].z =1024.0f* rand () / (RAND_MAX +1.0f);
+ }
+pcl::KdTreeFLANN<pcl::PointXYZ>kdtree;
+kdtree.setInputCloud (cloud);
+pcl::PointXYZ searchPoint;
+searchPoint.x=1024.0f* rand () / (RAND_MAX +1.0f);
+searchPoint.y=1024.0f* rand () / (RAND_MAX +1.0f);
+searchPoint.z=1024.0f* rand () / (RAND_MAX +1.0f);
+// k近邻搜索
+int K =10;
+std::vector<int>pointIdxNKNSearch(K);
+std::vector<float>pointNKNSquaredDistance(K);
+std::cout<<"K nearest neighbor search at ("<<searchPoint.x
+<<" "<<searchPoint.y
+<<" "<<searchPoint.z
+<<") with K="<< K <<std::endl;
+if ( kdtree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) >0 )
+ {
+for (size_t i=0; i<pointIdxNKNSearch.size (); ++i)
+std::cout<<" "<< cloud->points[ pointIdxNKNSearch[i] ].x 
+<<" "<< cloud->points[pointIdxNKNSearch[i] ].y 
+<<" "<< cloud->points[pointIdxNKNSearch[i] ].z 
+<<" (squared distance: "<<pointNKNSquaredDistance[i] <<")"<<std::endl;
+ }
+// 在半径r内搜索近邻
+std::vector<int> pointIdxRadiusSearch;
+std::vector<float> pointRadiusSquaredDistance;
+float radius =256.0f* rand () / (RAND_MAX +1.0f);
+std::cout<<"Neighbors within radius search at ("<<searchPoint.x
+<<" "<<searchPoint.y
+<<" "<<searchPoint.z
+<<") with radius="<< radius <<std::endl;
+if ( kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) >0 )
+ {
+for (size_t i=0; i<pointIdxRadiusSearch.size (); ++i)
+std::cout<<" "<< cloud->points[ pointIdxRadiusSearch[i] ].x 
+<<" "<< cloud->points[pointIdxRadiusSearch[i] ].y 
+<<" "<< cloud->points[pointIdxRadiusSearch[i] ].z 
+<<" (squared distance: "<<pointRadiusSquaredDistance[i] <<")"<<std::endl;
+ }
+return 0;
+}

第四章/2 point_cloud_compression/source/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
+project(point_cloud_compression)
+find_package(PCL 1.2 REQUIRED)
+include_directories(${PCL_INCLUDE_DIRS})
+link_directories(${PCL_LIBRARY_DIRS})
+add_definitions(${PCL_DEFINITIONS})
+add_executable(point_cloud_compression point_cloud_compression.cpp)
+target_link_libraries(point_cloud_compression ${PCL_LIBRARIES})

第四章/2 point_cloud_compression/source/point_cloud_compression.cpp

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+#include <pcl/point_cloud.h>
+#include <pcl/point_types.h>
+#include <pcl/io/openni_grabber.h>
+#include <pcl/visualization/cloud_viewer.h>
+#include <pcl/compression/octree_pointcloud_compression.h>
+#include <stdio.h>
+#include <sstream>
+#include <stdlib.h>
+
+#ifdef WIN32
+# define sleep(x) Sleep((x)*1000)
+#endif
+
+class SimpleOpenNIViewer
+{
+public:
+SimpleOpenNIViewer () :
+viewer (" Point Cloud Compression Example")
+ {
+ }
+
+void
+cloud_cb_ (const pcl::PointCloud<pcl::PointXYZRGBA>::ConstPtr& cloud)
+ {
+if (!viewer.wasStopped ())
+ {
+// 存储压缩点云的字节流
+std::stringstream compressedData;
+// 输出点云
+pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloudOut (new pcl::PointCloud<pcl::PointXYZRGBA> ());
+// 压缩点云
+PointCloudEncoder->encodePointCloud (cloud, compressedData);
+// 解压缩点云
+PointCloudDecoder->decodePointCloud (compressedData, cloudOut);
+//可视化解压缩点云
+viewer.showCloud (cloudOut);
+ }
+ }
+
+void
+run ()
+ {
+bool showStatistics=true;
+// 压缩选项详见 /io/include/pcl/compression/compression_profiles.h
+pcl::octree::compression_Profiles_e compressionProfile=pcl::octree::MED_RES_ONLINE_COMPRESSION_WITH_COLOR;
+// 初始化压缩与解压缩对象
+PointCloudEncoder=new pcl::octree::PointCloudCompression<pcl::PointXYZRGBA> (compressionProfile, showStatistics);
+PointCloudDecoder=new pcl::octree::PointCloudCompression<pcl::PointXYZRGBA> ();
+//创建从 OpenNI获取点云的对象
+pcl::Grabber* interface =new pcl::OpenNIGrabber ();
+//建立回调函数
+ boost::function<void
+ (const pcl::PointCloud<pcl::PointXYZRGBA>::ConstPtr&)> f = boost::bind (&SimpleOpenNIViewer::cloud_cb_, this, _1);
+// 建立回调函数与回调信号之间联系
+boost::signals2::connection c = interface->registerCallback (f);
+// 开始接收点云数据流
+interface->start ();
+while (!viewer.wasStopped ())
+ {
+sleep (1);
+ }
+interface->stop ();
+// 删除点云压缩与解压缩对象实例
+delete (PointCloudEncoder);
+delete (PointCloudDecoder);
+ }
+pcl::visualization::CloudViewer viewer;
+pcl::octree::PointCloudCompression<pcl::PointXYZRGBA>*PointCloudEncoder;
+pcl::octree::PointCloudCompression<pcl::PointXYZRGBA>*PointCloudDecoder;
+};
+
+int
+main (int argc, char**argv)
+{
+SimpleOpenNIViewer v;
+v.run ();
+return (0);
+} 

第四章/3 octree_search/source/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
+project(octree_search)
+find_package(PCL 1.2 REQUIRED)
+include_directories(${PCL_INCLUDE_DIRS})
+link_directories(${PCL_LIBRARY_DIRS})
+add_definitions(${PCL_DEFINITIONS})
+add_executable(octree_search octree_search.cpp)
+target_link_libraries(octree_search ${PCL_LIBRARIES})

第四章/3 octree_search/source/octree_search.cpp

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+#include <pcl/point_cloud.h>
+#include <pcl/octree/octree.h>
+#include <iostream>
+#include <vector>
+#include <ctime>
+
+int
+main (int argc, char**argv)
+{
+srand ((unsigned int) time (NULL));
+pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
+// 创建点云数据
+cloud->width =1000;
+cloud->height =1;
+cloud->points.resize (cloud->width * cloud->height);
+for (size_t i=0; i< cloud->points.size (); ++i)
+ {
+cloud->points[i].x =1024.0f* rand () / (RAND_MAX +1.0f);
+cloud->points[i].y =1024.0f* rand () / (RAND_MAX +1.0f);
+cloud->points[i].z =1024.0f* rand () / (RAND_MAX +1.0f);
+ }
+float resolution =128.0f;
+pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree(resolution);
+octree.setInputCloud (cloud);
+octree.addPointsFromInputCloud ();
+pcl::PointXYZ searchPoint;
+searchPoint.x=1024.0f* rand () / (RAND_MAX +1.0f);
+searchPoint.y=1024.0f* rand () / (RAND_MAX +1.0f);
+searchPoint.z=1024.0f* rand () / (RAND_MAX +1.0f);
+// 体素内近邻搜索
+std::vector<int>pointIdxVec;
+if (octree.voxelSearch (searchPoint, pointIdxVec))
+ {
+std::cout<<"Neighbors within voxel search at ("<<searchPoint.x
+<<" "<<searchPoint.y
+<<" "<<searchPoint.z<<")"
+<<std::endl;
+for (size_t i=0; i<pointIdxVec.size (); ++i)
+std::cout<<" "<< cloud->points[pointIdxVec[i]].x 
+<<" "<< cloud->points[pointIdxVec[i]].y 
+<<" "<< cloud->points[pointIdxVec[i]].z <<std::endl;
+ }
+//K近邻搜索
+int K =10;
+std::vector<int>pointIdxNKNSearch;
+std::vector<float>pointNKNSquaredDistance;
+std::cout<<"K nearest neighbor search at ("<<searchPoint.x
+<<" "<<searchPoint.y
+<<" "<<searchPoint.z
+<<") with K="<< K <<std::endl;
+if (octree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) >0)
+ {
+for (size_t i=0; i<pointIdxNKNSearch.size (); ++i)
+std::cout<<" "<< cloud->points[ pointIdxNKNSearch[i] ].x 
+<<" "<< cloud->points[pointIdxNKNSearch[i] ].y 
+<<" "<< cloud->points[pointIdxNKNSearch[i] ].z 
+<<" (squared distance: "<<pointNKNSquaredDistance[i] <<")"<<std::endl;
+ }
+//半径内近邻搜索
+std::vector<int>pointIdxRadiusSearch;
+std::vector<float>pointRadiusSquaredDistance;
+float radius =256.0f* rand () / (RAND_MAX +1.0f);
+std::cout<<"Neighbors within radius search at ("<<searchPoint.x
+<<" "<<searchPoint.y
+<<" "<<searchPoint.z
+<<") with radius="<< radius <<std::endl;
+if (octree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) >0)
+ {
+for (size_t i=0; i<pointIdxRadiusSearch.size (); ++i)
+std::cout<<" "<< cloud->points[ pointIdxRadiusSearch[i] ].x 
+<<" "<< cloud->points[pointIdxRadiusSearch[i] ].y 
+<<" "<< cloud->points[pointIdxRadiusSearch[i] ].z 
+<<" (squared distance: "<<pointRadiusSquaredDistance[i] <<")"<<std::endl;
+ }
+}

第四章/4 octree_change_detection/source/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
+project(octree_change_detection)
+find_package(PCL 1.2 REQUIRED)
+include_directories(${PCL_INCLUDE_DIRS})
+link_directories(${PCL_LIBRARY_DIRS})
+add_definitions(${PCL_DEFINITIONS})
+add_executable (octree_change_detection octree_change_detection.cpp)
+target_link_libraries (octree_change_detection ${PCL_LIBRARIES})

第四章/4 octree_change_detection/source/octree_change_detection.cpp

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+#include <pcl/point_cloud.h>
+#include <pcl/octree/octree.h>
+#include <iostream>
+#include <vector>
+#include <ctime>
+
+int
+main (intargc, char**argv)
+{
+srand ((unsignedint) time (NULL));
+// 八叉树分辨率 即体素的大小
+float resolution =32.0f;
+// 初始化空间变化检测对象
+pcl::octree::OctreePointCloudChangeDetector<pcl::PointXYZ>octree (resolution);
+pcl::PointCloud<pcl::PointXYZ>::PtrcloudA (newpcl::PointCloud<pcl::PointXYZ> );
+//为cloudA创建点云
+cloudA->width =128;
+cloudA->height =1;
+cloudA->points.resize (cloudA->width *cloudA->height);
+for (size_ti=0; i<cloudA->points.size (); ++i)
+ {
+cloudA->points[i].x =64.0f* rand () / (RAND_MAX +1.0f);
+cloudA->points[i].y =64.0f* rand () / (RAND_MAX +1.0f);
+cloudA->points[i].z =64.0f* rand () / (RAND_MAX +1.0f);
+ }
+//添加点云到八叉树，建立八叉树
+octree.setInputCloud (cloudA);
+octree.addPointsFromInputCloud ();
+// 交换八叉树缓存，但是cloudA对应的八叉树仍在内存中
+octree.switchBuffers ();
+pcl::PointCloud<pcl::PointXYZ>::PtrcloudB (new pcl::PointCloud<pcl::PointXYZ> );
+// 为cloudB创建点云
+cloudB->width =128;
+cloudB->height =1;
+cloudB->points.resize (cloudB->width *cloudB->height);
+for (size_ti=0; i<cloudB->points.size (); ++i)
+ {
+cloudB->points[i].x =64.0f* rand () / (RAND_MAX +1.0f);
+cloudB->points[i].y =64.0f* rand () / (RAND_MAX +1.0f);
+cloudB->points[i].z =64.0f* rand () / (RAND_MAX +1.0f);
+ }
+//添加 cloudB到八叉树
+octree.setInputCloud (cloudB);
+octree.addPointsFromInputCloud ();
+std::vector<int>newPointIdxVector;
+//获取前一cloudA对应的八叉树在cloudB对应八叉树中没有的体素
+octree.getPointIndicesFromNewVoxels (newPointIdxVector);
+//打印输出点
+std::cout<<"Output from getPointIndicesFromNewVoxels:"<<std::endl;
+for (size_ti=0; i<newPointIdxVector.size (); ++i)
+std::cout<<i<<"# Index:"<<newPointIdxVector[i]
+<<" Point:"<<cloudB->points[newPointIdxVector[i]].x <<" "
+<<cloudB->points[newPointIdxVector[i]].y <<" "
+<<cloudB->points[newPointIdxVector[i]].z <<std::endl;
+}