pcl經常使用小知識

時間計算

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

如何實現相似pcl::PointCloud::Ptr和pcl::PointCloud的兩個類相互轉換?

#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();

如何查找點雲的x,y,z的極值?

#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);

若是知道須要保存點的索引,如何從原點雲中拷貝點到新點雲?

#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);

如何從點雲裏刪除和添加點?

#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

如何對點雲進行全局或局部變換

#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。

連接兩個點雲字段(兩點雲大小必須相同)

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);

如何從點雲中刪除無效點

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;
    }

將xyzrgb格式轉換爲xyz格式的點雲

#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);

}

flann kdtree 查詢k近鄰

//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;
        }

關於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

計算點的索引

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

計算質心

Eigen::Vector4f centroid;  //質心
     pcl::compute3DCentroid(*cloud_smoothed,centroid); //估計質心的座標

從網格提取頂點(將網格轉化爲點)

#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

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