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C++点云区域生长利用PCL库
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描述
利用PCL库进行点云区域生长
代码
代码中的部分参数，还是要根据你的点云数据的实际情况，进行更改的。
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举例子，代码中有这样两句话
pass.setFilterFieldName ("z"); pass.setFilterLimits (-1000, 1000);按照相机的z轴方向过滤点，由于我的点单位是mm，所以是-1米到1米，如果你的点云单位是米，上面的参数很显然应该是-1和1
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完整的main.cpp
#include <iostream>
//点云需要的头文件
#include <pcl/point_types.h>
#include <pcl/io/ply_io.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/passthrough.h>
#include <pcl/features/normal_3d.h>
void drawPointCloud(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, std::string titleName)
{
pcl::visualization::PCLVisualizer viewer (titleName);
int v (0);
viewer.createViewPort (0.0, 0.0, 1.0, 1.0, v);
viewer.addCoordinateSystem(0.5);
float bckgr_gray_level = 0.0; // Black
float txt_gray_lvl = 1.0 - bckgr_gray_level;
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud_in_color_h (cloud, (int) 255 * txt_gray_lvl, (int) 255 * txt_gray_lvl, (int) 255 * txt_gray_lvl);
viewer.addPointCloud (cloud, cloud_in_color_h, "cloud_in_v1", v);
viewer.addText (titleName, 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_1", v);
viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v);
viewer.setCameraPosition (-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
viewer.setSize (1280, 1024);
while (!viewer.wasStopped())
{
viewer.spinOnce();
}
}
void regionGrowing(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_input)
{
std::cout<<"[regionGrowing] input pointcloud size: "<<cloud_input->size() << std::endl;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downsampled(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_processed(new pcl::PointCloud<pcl::PointXYZ>);
//Use a voxelSampler to downsample
pcl::VoxelGrid<pcl::PointXYZ> voxelSampler;
voxelSampler.setInputCloud(cloud_input);
voxelSampler.setLeafSize(0.1f, 0.1f, 0.1f);
voxelSampler.filter(*cloud_downsampled);
//Use a filter to reduce noise
pcl::StatisticalOutlierRemoval<pcl::PointXYZ> statFilter;
statFilter.setInputCloud(cloud_downsampled);
statFilter.setMeanK(10);
statFilter.setStddevMulThresh(0.2);
statFilter.filter(*cloud_processed);
// Create the normal estimation class, and pass the input dataset to it
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setInputCloud(cloud_processed);
// Create an empty kdtree representation, and pass it to the normal estimation object.
// Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());
ne.setSearchMethod(tree);
// Output datasets
pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
// setRadiusSearch and setKSearch are two methods of searching, both are useful, we just use setKSearch
// ne.setRadiusSearch(0.01); // Use all neighbors in a sphere of radius 1cm
ne.setKSearch(10);
// Compute the features
ne.compute(*normals);
pcl::IndicesPtr indices (new std::vector <int>);
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (cloud_processed);
pass.setFilterFieldName ("z");
pass.setFilterLimits (-1000, 1000);
pass.filter (*indices);
//聚类对象
pcl::RegionGrowing<pcl::PointXYZ, pcl::Normal> reg;
reg.setMinClusterSize (5000); //最小聚类的点数 50
reg.setMaxClusterSize (1000000); //最大聚类的点数 1000000
reg.setSearchMethod (tree); //搜索方式
reg.setNumberOfNeighbours (30); //设置搜索的邻域点的个数 30
reg.setInputCloud (cloud_processed); //输入点云
//reg.setIndices (indices);
reg.setInputNormals (normals); //输入的法线
reg.setSmoothnessThreshold (50.0 / 180.0 * M_PI); //设置平滑度 3
reg.setCurvatureThreshold (1.0); //设置曲率的阈值
std::vector <pcl::PointIndices> clusters;
reg.extract (clusters);
//输出点云簇的个数
std::cout << "Number of clusters is equal to " << clusters.size () << std::endl;
std::cout << "First cluster has " << clusters[0].indices.size () << " points." << endl;
//输出每个点云簇的点数
for (int i = 0 ; i<clusters.size() ; i++){
std::cout << "ID = "<<i << " cluster has " << clusters[i].indices.size() << " points." << endl;
}
//输出第一个点云簇的前10个点的序号
int counter = 0;
std::cout<<"ID = 0 cluster first 10 points id are : ";
while (counter < clusters[0].indices.size ())
{
std::cout <<clusters[0].indices[counter] << ", ";
counter++;
if (counter == 10)
{
break;
}
}
std::cout << std::endl;
// 用不同颜色划分各个点云簇
pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud ();
pcl::visualization::CloudViewer viewer("Cluster viewer");
viewer.showCloud(colored_cloud);
while (!viewer.wasStopped())
{
boost::this_thread::sleep(boost::posix_time::microseconds(100000));
}
// 把想要的某一点云簇画出来
int need = 1;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_objectonly(new pcl::PointCloud<pcl::PointXYZ>);
pcl::copyPointCloud(*cloud_processed, clusters[need].indices, *cloud_objectonly);
pcl::visualization::PCLVisualizer viewer_part ("Final1 with Visualization");
int v1 (0);
int v2 (1);
viewer_part.createViewPort (0.0, 0.0, 0.5, 1.0, v1);
viewer_part.createViewPort (0.5, 0.0, 1.0, 1.0, v2);
viewer_part.addCoordinateSystem(0.5);
float bckgr_gray_level = 0.0; // Black
float txt_gray_lvl = 1.0 - bckgr_gray_level;
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud_in_color_h (cloud_processed, (int) 255 * txt_gray_lvl, (int) 255 * txt_gray_lvl, (int) 255 * txt_gray_lvl);
viewer_part.addPointCloud (cloud_processed, cloud_in_color_h, "cloud_in_v1", v1);
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud_tr_color_h (cloud_objectonly, 20, 180, 20);
viewer_part.addPointCloud (cloud_objectonly, cloud_tr_color_h, "cloud_tr_v1", v2);
viewer_part.addText ("The Fucking Original Point Cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_1", v1);
viewer_part.addText ("The Fucking Processed Point Cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_2", v2);
viewer_part.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);
viewer_part.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v2);
viewer_part.setCameraPosition (-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
viewer_part.setSize (1280, 1024);
while (!viewer_part.wasStopped())
{
viewer_part.spinOnce();
}
// 画出点云和法线
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer_normal(new pcl::visualization::PCLVisualizer("3D Viewer"));
viewer_normal->setBackgroundColor(0, 0, 0);
viewer_normal->addPointCloud<pcl::PointXYZRGB>(colored_cloud, "sample cloud");
viewer_normal->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");
viewer_normal->addPointCloudNormals<pcl::PointXYZRGB, pcl::Normal>(colored_cloud, normals, 10, 0.2, "normals");
viewer_normal->addCoordinateSystem(1.0);
viewer_normal->initCameraParameters();
viewer_normal->setCameraPosition (-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
while (!viewer_normal->wasStopped())
{
viewer_normal->spinOnce(100);
boost::this_thread::sleep(boost::posix_time::microseconds(100000));
}
}
pcl::PointCloud<pcl::PointXYZ>::Ptr loadPointCloud(std::string path)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPLYFile<pcl::PointXYZ>(path, *cloud) == -1)
{
PCL_ERROR("Couldnot read file.\n");
return 0;
}
std::cout<<"pointcloud size: "<<cloud->width<<" * "<<cloud->height << std::endl;
return cloud;
}
int main(int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud;
cloud = loadPointCloud("../standard.ply");
regionGrowing(cloud);
return 1;
}
附赠的CMakeLists.txt
cmake_minimum_required(VERSION 2.8.7)
project(test)
find_package(PCL 1.5 REQUIRED)
include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fexceptions -frtti -pthread -O3 -march=core2")
set(ROOT "${CMAKE_CURRENT_SOURCE_DIR}/")
include_directories(
${ROOT}
${ROOT}/include
)
file(GLOB SOURCES
"*.cpp"
)
link_directories(
${ROOT}/lib
)
add_executable(test ${SOURCES})
target_link_libraries(test ${PCL_LIBRARIES})
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