(转载请删除括号里的内容)
时间计算
pcl中计算程序运行时间有很多函数，其中利用控制台的时间计算
首先必须包含头文件 #include <pcl/console/time.h>
|
1
2
3
4
5
6
|
#include <pcl/console/time.h>
pcl::console::TicToc time;
time.tic();
cout<<time.toc()/1000<<"s"<<endl;
|
pcl::PointCloud::Ptr和pcl::PointCloud的两个类相互转换
|
1
2
3
4
5
6
7
8
|
#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();
|
查找点云的x，y，z的极值
|
1
2
3
4
5
6
7
|
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/common/common.h><br>
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile<pcl::PointXYZ> ("your_pcd_file.pcd", *cloud);
pcl::PointXYZ minPt, maxPt;
pcl::getMinMax3D (*cloud, minPt, maxPt);
|
如果知道需要保存点的索引，如何从原点云中拷贝点到新点云？
|
1
2
3
4
5
6
7
8
9
10
|
#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);
|
取已知索引之外的点云
|
1
2
3
4
5
6
7
8
9
10
|
pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
inliers->indices = pointIdxRadiusSearchMap;
std::vector<int> pointIdxRadiusSearchMap;
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud(_laser3d_map);
extract.setIndices(inliers);
extract.setNegative(true);
extract.filter(*map_3d_2);
|
如何从点云里删除和添加点？
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
|
#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);
index = cloud->begin() + 5;
cloud->erase(cloud->begin());
pcl::PointXYZ point = { 1, 1, 1 };
cloud->insert(cloud->begin() + 5, point);
cloud->push_back(point);
std::cout << cloud->points[5].x;
|
如果删除的点太多建议用上面的方法拷贝到新点云，再赋值给原点云，如果要添加很多点，建议先resize，然后用循环向点云里的添加。
如何对点云进行全局或局部变换
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
|
#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);
Eigen::Matrix4f transform_1 = Eigen::Matrix4f::Identity();
float theta = M_PI/4;
transform_1 (0,0) = cos (theta);
transform_1 (0,1) = -sin(theta);
transform_1 (1,0) = sin (theta);
transform_1 (1,1) = cos (theta);
transform_1 (0,3) = 25;
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);
|
链接两个点云字段（两点云大小必须相同）
|
1
2
3
4
5
6
7
8
9
10
11
12
13
|
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.compute(*cloud_normals);
pcl::PointCloud<pcl::PointNormal>::Ptr cloud_with_nomal (new pcl::PointCloud<pcl::PointNormal>);
pcl::concatenateFields(*cloud,*cloud_normals,*cloud_with_nomal);
|
删除无效点
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
|
#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;
}
|
xyzrgb格式转换为xyz格式的点云
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
|
#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 查询k近邻
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
|
pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;
kdtree.setInputCloud(cloud);
int k =2;
float everagedistance =0;
for (int i =0; i < cloud->size()/2;i++)
{
vector<int> nnh ;
vector<float> squaredistance;
kdtree.nearestKSearch(cloud->points[i],k,nnh,squaredistance);
everagedistance += sqrt(squaredistance[1]);
}
everagedistance = everagedistance/(cloud->size()/2);
cout<<"everage distance is : "<<everagedistance<<endl;
|
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
|
#include <pcl/kdtree/kdtree_flann.h>
pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;
kdtree.setInputCloud (in_cloud);
pcl::PointXYZ searchPoint;
searchPoint.x = 1;
searchPoint.y = 2;
searchPoint.z = 3;
int k = 10;
std::vector<int> pointIdxNKNSearch(k);
std::vector<float>pointNKNSquareDistance(k);
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;
}
float radius = 40.0f;
std::vector<int> pointIdxRadiusSearch;
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;
}
|
关于ply文件
后缀命名为.ply格式文件，常用的点云数据文件。ply文件不仅可以存储点数据，而且可以存储网格数据. 用emacs打开一个ply文件，观察表头，如果表头element face的值为0,则表示该文件为点云文件，如果element face的值为某一正整数N，则表示该文件为网格文件，且包含N个网格.所以利用pcl读取 ply 文件，不能一味用pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PintT>)来读取。在读取ply文件时候，首先要分清该文件是点云还是网格类文件。如果是点云文件，则按照一般的点云类去读取即可，官网例子,就是这样。如果ply文件是网格类，则需要
|
1
2
3
|
pcl::PolygonMesh mesh;
pcl::io::loadPLYFile(argv[1],mesh);
pcl::io::savePLYFile("result.ply", mesh);
|
读取。（官网例子之所以能成功，是因为它对模型进行了细分处理，使得网格变成了点）
计算点的索引
例如sift算法中，pcl无法直接提供索引（主要原因是sift点是通过计算出来的，在某些不同参数下，sift点可能并非源数据中的点，而是某些点的近似），若要获取索引，则可利用以下函数：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
|
void getIndices (pointcloud::Ptr cloudin, pointcloud keypoints, pcl::PointIndices::Ptr indices)
{
pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;
kdtree.setInputCloud(cloudin);
std::vector<float>pointNKNSquareDistance;
std::vector<int> pointIdxNKNSearch;
for (size_t i =0; i < keypoints.size();i++)
{
kdtree.nearestKSearch(keypoints.points[i],1,pointIdxNKNSearch,pointNKNSquareDistance);
indices->indices.push_back(pointIdxNKNSearch[0]);
}
}
|
其思想就是：将原始数据插入到flann的kdtree中，寻找keypoints的最近邻，如果距离等于0,则说明是同一点，提取索引即可.
计算质心
|
1
2
|
Eigen::Vector4f centroid;
pcl::compute3DCentroid(*cloud_smoothed,centroid);
|
从网格提取顶点（将网格转化为点）
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
|
#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所属头文件；
#include <pcl/io/vtk_io.h>
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h>
using namespace pcl;
<br>int main(int argc,char **argv)
{
pcl::PolygonMesh 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;
}
|
以上代码可以从.obj或.ply面片格式转化为点云类型。
---------------------
作者：采男孩的小蘑菇
来源：CNBLOGS
原文：https://www.cnblogs.com/flyinggod/p/9478000.html
版权声明：本文为作者原创文章，转载请附上博文链接！
内容解析By：CSDN,CNBLOG博客文章一键转载插件
标签：PointCloud,PCL,博客园,括号,pcl,new,include,Ptr,cloud
来源： https://www.cnblogs.com/excellentliu/p/15724972.html