pcl中計算程序運行時間有不少函數,其中利用控制檯的時間計算是:
首先必須包含頭文件 #include <pcl/console/time.h>
,其次,pcl::console::TicToc time; time.tic(); +程序段 + cout<<time.toc()/1000<<"s"<<endl;
就能夠以秒輸出「程序段」的運行時間。php
#include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/point_cloud.h> pcl::PointCloud<pcl::PointXYZ>::Ptr cloudPointer(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ> cloud; cloud = *cloudPointer; cloudPointer = cloud.makeShared();
#include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/common/common.h> pcl::PointCloud<pcl::PointXYZ>::Ptr cloud; cloud = pcl::PointCloud<pcl::PointXYZ>::Ptr (new pcl::PointCloud<pcl::PointXYZ>); pcl::io::loadPCDFile<pcl::PointXYZ> ("your_pcd_file.pcd", *cloud); pcl::PointXYZ minPt, maxPt; pcl::getMinMax3D (*cloud, minPt, maxPt);
#include <pcl/io/pcd_io.h> #include <pcl/common/impl/io.hpp> #include <pcl/point_types.h> #include <pcl/point_cloud.h> pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>); pcl::io::loadPCDFile<pcl::PointXYZ>("C:\office3-after21111.pcd", *cloud); pcl::PointCloud<pcl::PointXYZ>::Ptr cloudOut(new pcl::PointCloud<pcl::PointXYZ>); std::vector<int > indexs = { 1, 2, 5 }; pcl::copyPointCloud(*cloud, indexs, *cloudOut);
#include <pcl/io/pcd_io.h> #include <pcl/common/impl/io.hpp> #include <pcl/point_types.h> #include <pcl/point_cloud.h> pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>); pcl::io::loadPCDFile<pcl::PointXYZ>("C:\office3-after21111.pcd", *cloud); pcl::PointCloud<pcl::PointXYZ>::iterator index = cloud->begin(); cloud->erase(index);//刪除第一個 index = cloud->begin() + 5; cloud->erase(cloud->begin());//刪除第5個 pcl::PointXYZ point = { 1, 1, 1 }; //在索引號爲5的位置1上插入一點,原來的點後移一位 cloud->insert(cloud->begin() + 5, point); cloud->push_back(point);//從點雲最後面插入一點 std::cout << cloud->points[5].x;//輸出1
若是刪除的點太多建議用上面的方法拷貝到新點雲,再賦值給原點雲,若是要添加不少點,建議先resize,而後用循環向點雲裏的添加。redis
#include <pcl/io/pcd_io.h> #include <pcl/common/impl/io.hpp> #include <pcl/point_types.h> #include <pcl/point_cloud.h> #include <pcl/common/transforms.h> pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::io::loadPCDFile("path/.pcd",*cloud); //全局變化 //構造變化矩陣 Eigen::Matrix4f transform_1 = Eigen::Matrix4f::Identity(); float theta = M_PI/4; //旋轉的度數,這裏是45度 transform_1 (0,0) = cos (theta); //這裏是繞的Z軸旋轉 transform_1 (0,1) = -sin(theta); transform_1 (1,0) = sin (theta); transform_1 (1,1) = cos (theta); // transform_1 (0,2) = 0.3; //這樣會產生縮放效果 // transform_1 (1,2) = 0.6; // transform_1 (2,2) = 1; transform_1 (0,3) = 25; //這裏沿X軸平移 transform_1 (1,3) = 30; transform_1 (2,3) = 380; pcl::PointCloud<pcl::PointXYZ>::Ptr transform_cloud1 (new pcl::PointCloud<pcl::PointXYZ>); pcl::transformPointCloud(*cloud,*transform_cloud1,transform_1); //不言而喻 //局部 pcl::transformPointCloud(*cloud,pcl::PointIndices indices,*transform_cloud1,matrix); //第一個參數爲輸入,第二個參數爲輸入點雲中部分點集索引,第三個爲存儲對象,第四個是變換矩陣。
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::io::loadPCDFile("/home/yxg/pcl/pcd/mid.pcd",*cloud); pcl::NormalEstimation<pcl::PointXYZ,pcl::Normal> ne; ne.setInputCloud(cloud); pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>()); ne.setSearchMethod(tree); pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>()); ne.setKSearch(8); //ne.setRadisuSearch(0.3); ne.compute(*cloud_normals); pcl::PointCloud<pcl::PointNormal>::Ptr cloud_with_nomal (new pcl::PointCloud<pcl::PointNormal>); pcl::concatenateFields(*cloud,*cloud_normals,*cloud_with_nomal);
pcl中的無效點是指:點的某一座標值爲nan.算法
#include <pcl/point_cloud.h> #include <pcl/point_types.h> #include <pcl/filters/filter.h> #include <pcl/io/pcd_io.h> using namespace std; typedef pcl::PointXYZRGBA point; typedef pcl::PointCloud<point> CloudType; int main (int argc,char **argv) { CloudType::Ptr cloud (new CloudType); CloudType::Ptr output (new CloudType); pcl::io::loadPCDFile(argv[1],*cloud); cout<<"size is:"<<cloud->size()<<endl; vector<int> indices; pcl::removeNaNFromPointCloud(*cloud,*output,indices); cout<<"output size:"<<output->size()<<endl; pcl::io::savePCDFile("out.pcd",*output); return 0; }
#include <pcl/io/pcd_io.h> #include <ctime> #include <Eigen/Core> #include <pcl/point_types.h> #include <pcl/point_cloud.h> using namespace std; typedef pcl::PointXYZ point; typedef pcl::PointXYZRGBA pointcolor; int main(int argc,char **argv) { pcl::PointCloud<pointcolor>::Ptr input (new pcl::PointCloud<pointcolor>); pcl::io::loadPCDFile(argv[1],*input); pcl::PointCloud<point>::Ptr output (new pcl::PointCloud<point>); int M = input->points.size(); cout<<"input size is:"<<M<<endl; for (int i = 0;i <M;i++) { point p; p.x = input->points[i].x; p.y = input->points[i].y; p.z = input->points[i].z; output->points.push_back(p); } output->width = 1; output->height = M; cout<< "size is"<<output->size()<<endl; pcl::io::savePCDFile("output.pcd",*output); }
//平均密度計算 pcl::KdTreeFLANN<pcl::PointXYZ> kdtree; //建立一個快速k近鄰查詢,查詢的時候若該點在點雲中,則第一個近鄰點是其自己 kdtree.setInputCloud(cloud); int k =2; float everagedistance =0; for (int i =0; i < cloud->size()/2;i++) { vector<int> nnh ; vector<float> squaredistance; // pcl::PointXYZ p; // p = cloud->points[i]; kdtree.nearestKSearch(cloud->points[i],k,nnh,squaredistance); everagedistance += sqrt(squaredistance[1]); // cout<<everagedistance<<endl; } everagedistance = everagedistance/(cloud->size()/2); cout<<"everage distance is : "<<everagedistance<<endl;
#include <pcl/kdtree/kdtree_flann.h> pcl::KdTreeFLANN<pcl::PointXYZ> kdtree; //建立KDtree kdtree.setInputCloud (in_cloud); pcl::PointXYZ searchPoint; //建立目標點,(搜索該點的近鄰) searchPoint.x = 1; searchPoint.y = 2; searchPoint.z = 3; //查詢近鄰點的個數 int k = 10; //近鄰點的個數 std::vector<int> pointIdxNKNSearch(k); //存儲近鄰點集的索引 std::vector<float>pointNKNSquareDistance(k); //近鄰點集的距離 if (kdtree.nearestKSearch(searchPoint,k,pointIdxNKNSearch,pointNKNSquareDistance)>0) { for (size_t i = 0; i < pointIdxNKNSearch.size (); ++i) std::cout << " " << in_cloud->points[ pointIdxNKNSearch[i] ].x << " " << in_cloud->points[ pointIdxNKNSearch[i] ].y << " " <<in_cloud->points[ pointIdxNKNSearch[i] ].z << " (squared distance: " <<pointNKNSquareDistance[i] << ")" << std::endl; } //半徑爲r的近鄰點 float radius = 40.0f; //實際上是求的40*40距離範圍內的點 std::vector<int> pointIdxRadiusSearch; //存儲的對應的平方距離 std::vector<float> a; if ( kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, a) > 0 ) { for (size_t i = 0; i < pointIdxRadiusSearch.size (); ++i) std::cout << " " << in_cloud->points[ pointIdxRadiusSearch[i] ].x << " " <<in_cloud->points[ pointIdxRadiusSearch[i] ].y << " " << in_cloud->points[ pointIdxRadiusSearch[i] ].z << " (squared distance: " <<a[i] << ")" << std::endl; }
ply
文件後綴命名爲.ply
格式文件,經常使用的點雲數據文件。ply
文件不只能夠存儲點
數據,並且能夠存儲網格
數據. 用emacs打開一個ply
文件,觀察表頭,若是表頭element face
的值爲0,ze則表示該文件爲點雲文件,若是element face
的值爲某一正整數N,則表示該文件爲網格文件,且包含N個網格.
因此利用pcl讀取 ply 文件,不能一味用pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PintT>)
來讀取。
在讀取ply
文件時候,首先要分清該文件是點雲仍是網格類文件。若是是點雲文件,則按照通常的點雲類去讀取便可,官網例子,就是這樣。
若是ply
文件是網格類,則須要函數
pcl::PolygonMesh mesh; pcl::io::loadPLYFile(argv[1],mesh); pcl::io::savePLYFile("result.ply", mesh);
讀取。(官網例子之因此能成功,是由於它對模型進行了細分處理,使得網格變成了點)spa
例如sift算法中,pcl沒法直接提供索引(主要緣由是sift點是經過計算出來的,在某些不一樣參數下,sift點可能並不是源數據中的點,而是某些點的近似),若要獲取索引,則可利用如下函數:rest
void getIndices (pointcloud::Ptr cloudin, pointcloud keypoints, pcl::PointIndices::Ptr indices) { pcl::KdTreeFLANN<pcl::PointXYZ> kdtree; kdtree.setInputCloud(cloudin); std::vector<float>pointNKNSquareDistance; //近鄰點集的距離 std::vector<int> pointIdxNKNSearch; for (size_t i =0; i < keypoints.size();i++) { kdtree.nearestKSearch(keypoints.points[i],1,pointIdxNKNSearch,pointNKNSquareDistance); // cout<<"the distance is:"<<pointNKNSquareDistance[0]<<endl; // cout<<"the indieces is:"<<pointIdxNKNSearch[0]<<endl; indices->indices.push_back(pointIdxNKNSearch[0]); } }
其思想就是:將原始數據插入到flann的kdtree中,尋找keypoints的最近鄰,若是距離等於0,則說明是同一點,提取索引便可.code
Eigen::Vector4f centroid; //質心 pcl::compute3DCentroid(*cloud_smoothed,centroid); //估計質心的座標
#include <pcl/io/io.h> #include <pcl/io/pcd_io.h> #include <pcl/io/obj_io.h> #include <pcl/PolygonMesh.h> #include <pcl/point_cloud.h> #include <pcl/io/vtk_lib_io.h>//loadPolygonFileOBJ所屬頭文件; #include <pcl/io/vtk_io.h> #include <pcl/io/ply_io.h> #include <pcl/point_types.h> using namespace pcl; int main(int argc,char **argv) { pcl::PolygonMesh mesh; // pcl::io::loadPolygonFileOBJ(argv[1], mesh); pcl::io::loadPLYFile(argv[1],mesh); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>); pcl::fromPCLPointCloud2(mesh.cloud, *cloud); pcl::io::savePCDFileASCII("result.pcd", *cloud); return 0; }
以上代碼能夠從.obj或.ply面片格式轉化爲點雲類型。orm