Project1,分段切割、区域生长，sample100测试得到的结果没有边缘，但是不连续
2024-05-13 20:11:08
Project1,简化了提取索引点云，点云合并
#include <pcl/visualization/cloud_viewer.h>
#include <iostream>//标准C++库中的输入输出类相关头文件。
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>//pcd 读写类相关的头文件。
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h> //PCL中支持的点类型头文件。
#include <pcl/octree/octree.h>
#include<fstream>
#include <string>
#include <vector>
#include <LasOperator.h>
#include <liblas/liblas.hpp>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>using namespace std;void loadLasFile(string s, pcl::PointCloud<pcl::PointXYZRGB> &cloud);
void loadLasFile(string s, pcl::PointCloud<pcl::PointXYZRGB> &cloud) {/**读取las文件*/std::ifstream ifs(s, std::ios::in | std::ios::binary); // 打开las文件liblas::ReaderFactory f;liblas::Reader reader = f.CreateWithStream(ifs); // 读取las文件unsigned long int nbPoints = reader.GetHeader().GetPointRecordsCount();//获取las数据点的个数cloud.width = nbPoints; //保证与las数据点的个数一致 cloud.height = 1;cloud.is_dense = false;cloud.points.resize(cloud.width * cloud.height);int i = 0;uint16_t r1, g1, b1;int r2, g2, b2;uint32_t rgb;while (reader.ReadNextPoint()) {// 获取las数据的x，y，z信息cloud.points[i].x = (reader.GetPoint().GetX());cloud.points[i].y = (reader.GetPoint().GetY());cloud.points[i].z = (reader.GetPoint().GetZ());//获取las数据的r，g，b信息r1 = (reader.GetPoint().GetColor().GetRed());g1 = (reader.GetPoint().GetColor().GetGreen());b1 = (reader.GetPoint().GetColor().GetBlue());r2 = ceil(((float)r1 / 65536)*(float)256);g2 = ceil(((float)g1 / 65536)*(float)256);b2 = ceil(((float)b1 / 65536)*(float)256);rgb = ((int)r2) << 16 | ((int)g2) << 8 | ((int)b2);cloud.points[i].rgb = *reinterpret_cast<float*>(&rgb);i++;}
}int main() {/**定义变量*///输入点云，轨迹线，保存种子pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZ>::Ptr trail(new pcl::PointCloud<pcl::PointXYZ>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud2(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr result(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr part(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr partSeed(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr seed(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr colored(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr tempCloud(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointXYZRGB tempPnt;//变量float x1, y1, x2, y2, a1, a2, a3, b11, b22, b3;//向量1，a,b为轨迹线上2点x1 = y1 = a1 = a2 = a3 = b11 = b22 = b3 = 0;x2 = 0; y2 = 1;float r = 1; //半径2float SmoothnessThreshold = 6;//3.0 0.37float CurvatureThreshold = 0.01; //设置曲率的阈值1.0//读取输入电云cout << "读取点云" << endl;//pcl::io::loadPCDFile("sample100.pcd", *cloud);loadLasFile("sample100.las", *cloud);//读取轨迹线cout << "读取轨迹线" << endl;pcl::io::loadPCDFile("trail.pcd", *trail);cout << "原始点数: " << cloud->width << " \t" << "轨迹线点数: " << trail->width << endl;//cloud构建八叉树cout << "构建八叉树" << endl;float resolution = 128.0f;pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree(resolution);octree.setInputCloud(cloud);octree.addPointsFromInputCloud();std::vector<int>pointIdx;std::vector<int>pointIdx1;/**开始循环*/for (size_t i = 0; i < trail->points.size() + 500; i += 20) {//cloud1->points.size() cloud1->points.size()+500 500cout << "-------------------------------------------------------------" << endl;a1 = trail->points[i].x;a2 = trail->points[i].y;//cloud1->points[i+8].ya3 = trail->points[i].z;b11 = trail->points[i + 20].x;b22 = trail->points[i + 20].y;//cloud1->points[i+12].yb3 = trail->points[i + 20].z;x1 = a1 - b11;y1 = a2 - b22;float cosa = (x1*x2 + y1 * y2) / (sqrt(x1*x1 + y1 * y1) + sqrt(x2*x2 + y2 * y2));float sina = sqrt(1 - cosa * cosa);float range = r / sina;//分段Eigen::Vector3f n(-100000, min(a2, b22), -100000);//0xc0c0c0c0 0x3f3f3f3fEigen::Vector3f m(100000, max(a2, b22) - 0.001, min(a3, b3));octree.boxSearch(n, m, pointIdx);cout << i << " 这段有点数：" << pointIdx.size() << "\t";pcl::copyPointCloud(*cloud, pointIdx, *part);/*cout << "保存1.pcd成功" << endl;pcl::io::savePCDFileASCII("1.pcd", *part);*///分段切割种子点pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree1(resolution);octree1.setInputCloud(part);octree1.addPointsFromInputCloud();Eigen::Vector3f p(a1 - range, min(trail->points[i + 8].y, trail->points[i + 12].y), -100000);//min(a2, b22)Eigen::Vector3f q(a1 + range, max(trail->points[i + 8].y, trail->points[i + 12].y) - 0.000001, min(a3, b3));//max(a2, b22) - 0.001octree1.boxSearch(p, q, pointIdx1);cout << "这段种子数：" << pointIdx1.size() << endl;pcl::copyPointCloud(*part, pointIdx1, *partSeed);*seed += *partSeed;/* cout << "保存2.pcd成功" << endl;pcl::io::savePCDFileASCII("2.pcd", *partSeed);*/// 创建空的tree 设置搜索的方式或者说是结构pcl::search::Search<pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> >(new pcl::search::KdTree<pcl::PointXYZRGB>);//求法线//创建一个normal点云pcl::PointCloud <pcl::Normal>::Ptr normals(new pcl::PointCloud <pcl::Normal>);pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> normal_estimator;normal_estimator.setSearchMethod(tree);normal_estimator.setInputCloud(part);normal_estimator.setKSearch(500);normal_estimator.compute(*normals);//创造区域生长分割对象 聚类对象<点，法线>pcl::RegionGrowing<pcl::PointXYZRGB, pcl::Normal> reg; //首先建立reg寄存器(区域曾长的对象）reg.setMinClusterSize(50); //最小的聚类的点数(小于这参数的平面被忽略不计）reg.setMaxClusterSize(30000000); //最大的(一般随便设置）reg.setSearchMethod(tree); //搜索方式(采用的默认是K—d树法）reg.setNumberOfNeighbours(30); //设置搜索的邻域点的个数，周围多少个点决定这是一个平面(决定容错率，设置大时有倾斜也可接受，设置小时检测到的平面会很小）reg.setInputCloud(part); //输入点//reg.setIndices (indices);reg.setInputNormals(normals); //输入的法线reg.setSmoothnessThreshold(SmoothnessThreshold / 180.0 * M_PI);//3.0 设置平滑度(设置两个法线在多大夹角内可当做是共面的）reg.setCurvatureThreshold(CurvatureThreshold);std::vector <pcl::PointIndices> clusters;reg.extract(clusters);pcl::PointIndices cluster;reg.getSegmentFromPoint(pointIdx1[0], cluster);cout << "取得的种子是：" << pointIdx1[0] << "\t生长后点数：" << cluster.indices.size() << endl;cout << "种子点" << part->points[pointIdx1[0]].x << "\t" << part->points[pointIdx1[0]].y << "\t" << part->points[pointIdx1[0]].z << "\t" << endl;cout << "-------------------------------------------------------------" << endl;pcl::copyPointCloud(*part, cluster, *tempCloud);*result += *tempCloud;}cout << "生长后的点云保存到result.pcd，0result.pcd" << endl;pcl::io::savePCDFileASCII("0result.pcd", *result);cout << "保存成功" << endl;cout << "切割后的点云保存到seedCloud，seedCloud.pcd" << endl;pcl::io::savePCDFileASCII("0seedCloud.pcd", *seed);cout << "保存成功" << endl;return 0;
}
Project1,添加输入las文件的函数
#include <pcl/visualization/cloud_viewer.h>
#include <iostream>//标准C++库中的输入输出类相关头文件。
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>//pcd 读写类相关的头文件。
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h> //PCL中支持的点类型头文件。
#include <pcl/octree/octree.h>
#include<fstream>
#include <string>
#include <vector>
#include <LasOperator.h>
#include <liblas/liblas.hpp>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>using namespace std;void loadLasFile(string s, pcl::PointCloud<pcl::PointXYZRGB> &cloud);
void loadLasFile(string s, pcl::PointCloud<pcl::PointXYZRGB> &cloud) {/**读取las文件*/std::ifstream ifs(s, std::ios::in | std::ios::binary); // 打开las文件liblas::ReaderFactory f;liblas::Reader reader = f.CreateWithStream(ifs); // 读取las文件unsigned long int nbPoints = reader.GetHeader().GetPointRecordsCount();//获取las数据点的个数cloud.width = nbPoints; //保证与las数据点的个数一致 cloud.height = 1;cloud.is_dense = false;cloud.points.resize(cloud.width * cloud.height);int i = 0;uint16_t r1, g1, b1;int r2, g2, b2;uint32_t rgb;while (reader.ReadNextPoint()) {// 获取las数据的x，y，z信息cloud.points[i].x = (reader.GetPoint().GetX());cloud.points[i].y = (reader.GetPoint().GetY());cloud.points[i].z = (reader.GetPoint().GetZ());//获取las数据的r，g，b信息r1 = (reader.GetPoint().GetColor().GetRed());g1 = (reader.GetPoint().GetColor().GetGreen());b1 = (reader.GetPoint().GetColor().GetBlue());r2 = ceil(((float)r1 / 65536)*(float)256);g2 = ceil(((float)g1 / 65536)*(float)256);b2 = ceil(((float)b1 / 65536)*(float)256);rgb = ((int)r2) << 16 | ((int)g2) << 8 | ((int)b2);cloud.points[i].rgb = *reinterpret_cast<float*>(&rgb);i++;}
}int main() {/**定义变量*///输入点云，轨迹线，保存种子pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZ>::Ptr trail(new pcl::PointCloud<pcl::PointXYZ>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud2(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr result(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr part(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr partSeed(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr seed(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr colored(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointXYZRGB tempPnt;//变量float x1, y1, x2, y2, a1, a2, a3, b11, b22, b3;//向量1，a,b为轨迹线上2点x1 = y1 = a1 = a2 = a3 = b11 = b22 = b3 = 0;x2 = 0; y2 = 1;float r = 1; //半径2float SmoothnessThreshold =6;//3.0 0.37float CurvatureThreshold = 0.01; //设置曲率的阈值1.0//读取输入电云cout << "读取点云" << endl;//pcl::io::loadPCDFile("sample100.pcd", *cloud);loadLasFile("sample10.las", *cloud);//读取轨迹线cout << "读取轨迹线" << endl;pcl::io::loadPCDFile("trail.pcd", *trail);cout << "原始点数: " << cloud->width << " \t" << "轨迹线点数: " << trail->width << endl;//cloud构建八叉树cout << "构建八叉树" << endl;float resolution = 128.0f;pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree(resolution);octree.setInputCloud(cloud);octree.addPointsFromInputCloud();std::vector<int>pointIdxRadiusSearch;std::vector<int>pointIdxRadiusSearch1;//seed.txt保存切割得到的种子索引ofstream ofs;ofs.open("seed.txt");/**开始循环*/for (size_t i = 0; i < trail->points.size() + 500; i += 20) {//cloud1->points.size() cloud1->points.size()+500 500cout << "-------------------------------------------------------------" << endl;a1 = trail->points[i].x;a2 = trail->points[i].y;//cloud1->points[i+8].ya3 = trail->points[i].z;b11 = trail->points[i + 20].x;b22 = trail->points[i + 20].y;//cloud1->points[i+12].yb3 = trail->points[i + 20].z;x1 = a1 - b11;y1 = a2 - b22;float cosa = (x1*x2 + y1 * y2) / (sqrt(x1*x1 + y1 * y1) + sqrt(x2*x2 + y2 * y2));float sina = sqrt(1 - cosa * cosa);float range = r / sina;//分段Eigen::Vector3f n(-100000, min(a2, b22), -100000);//0xc0c0c0c0 0x3f3f3f3fEigen::Vector3f m(100000, max(a2, b22) - 0.001, min(a3, b3));octree.boxSearch(n, m, pointIdxRadiusSearch);cout << i<<" 这段有点数：" << pointIdxRadiusSearch.size() << "\t";for (size_t j = 0; j < pointIdxRadiusSearch.size(); ++j) {tempPnt.x = cloud->points[pointIdxRadiusSearch[j]].x;tempPnt.y = cloud->points[pointIdxRadiusSearch[j]].y;tempPnt.z = cloud->points[pointIdxRadiusSearch[j]].z;tempPnt.rgb = cloud->points[pointIdxRadiusSearch[j]].rgb;part->push_back(tempPnt);}/*cout << "保存1.pcd成功" << endl;pcl::io::savePCDFileASCII("1.pcd", *part);*///分段切割种子点pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree1(resolution);octree1.setInputCloud(part);octree1.addPointsFromInputCloud();Eigen::Vector3f p(a1 - range, min(trail->points[i + 8].y, trail->points[i + 12].y), -100000);//min(a2, b22)Eigen::Vector3f q(a1 + range, max(trail->points[i + 8].y, trail->points[i + 12].y)-0.000001, min(a3, b3));//max(a2, b22) - 0.001octree1.boxSearch(p, q, pointIdxRadiusSearch1);cout << "这段种子数：" << pointIdxRadiusSearch1.size() << endl;for (size_t j = 0; j < pointIdxRadiusSearch1.size(); ++j) {tempPnt.x = part->points[pointIdxRadiusSearch1[j]].x;tempPnt.y = part->points[pointIdxRadiusSearch1[j]].y;tempPnt.z = part->points[pointIdxRadiusSearch1[j]].z;tempPnt.rgb = part->points[pointIdxRadiusSearch1[j]].rgb;seed->push_back(tempPnt);}/* cout << "保存2.pcd成功" << endl;pcl::io::savePCDFileASCII("2.pcd", *partSeed);*/// 创建空的tree 设置搜索的方式或者说是结构pcl::search::Search<pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> >(new pcl::search::KdTree<pcl::PointXYZRGB>);//求法线//创建一个normal点云pcl::PointCloud <pcl::Normal>::Ptr normals(new pcl::PointCloud <pcl::Normal>);pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> normal_estimator;normal_estimator.setSearchMethod(tree);normal_estimator.setInputCloud(part);normal_estimator.setKSearch(500);normal_estimator.compute(*normals);//创造区域生长分割对象 聚类对象<点，法线>pcl::RegionGrowing<pcl::PointXYZRGB, pcl::Normal> reg; //首先建立reg寄存器(区域曾长的对象）reg.setMinClusterSize(50); //最小的聚类的点数(小于这参数的平面被忽略不计）reg.setMaxClusterSize(30000000); //最大的(一般随便设置）reg.setSearchMethod(tree); //搜索方式(采用的默认是K—d树法）reg.setNumberOfNeighbours(30); //设置搜索的邻域点的个数，周围多少个点决定这是一个平面(决定容错率，设置大时有倾斜也可接受，设置小时检测到的平面会很小）reg.setInputCloud(part); //输入点//reg.setIndices (indices);reg.setInputNormals(normals); //输入的法线reg.setSmoothnessThreshold(SmoothnessThreshold / 180.0 * M_PI);//3.0 设置平滑度(设置两个法线在多大夹角内可当做是共面的）reg.setCurvatureThreshold(CurvatureThreshold);std::vector <pcl::PointIndices> clusters;reg.extract(clusters);pcl::PointIndices cluster;//for (int j = 0;j < pointIdxRadiusSearch1.size();++j) {// reg.getSegmentFromPoint(pointIdxRadiusSearch1[0], cluster);// for (int j = 0; j < cluster.indices.size(); ++j) {// tempPnt.x = part->points[cluster.indices[j]].x;// tempPnt.y = part->points[cluster.indices[j]].y;// tempPnt.z = part->points[cluster.indices[j]].z;// tempPnt.rgb = part->points[cluster.indices[j]].rgb;// cloud3->push_back(tempPnt);// }//}reg.getSegmentFromPoint(pointIdxRadiusSearch1[0], cluster);cout << "取得的种子是：" << pointIdxRadiusSearch1[0] << "\t生长后点数：" << cluster.indices.size() << endl;cout << "种子点" << part->points[pointIdxRadiusSearch1[0]].x << "\t" << part->points[pointIdxRadiusSearch1[0]].y << "\t" << part->points[pointIdxRadiusSearch1[0]].z << "\t" << endl;cout << "-------------------------------------------------------------" << endl;for (int j = 0; j < cluster.indices.size(); ++j) {tempPnt.x = part->points[cluster.indices[j]].x;tempPnt.y = part->points[cluster.indices[j]].y;tempPnt.z = part->points[cluster.indices[j]].z;tempPnt.rgb = part->points[cluster.indices[j]].rgb;result->push_back(tempPnt);}part->clear();partSeed->clear();}ofs.close();cout << "生长后的点云保存到result.pcd，0result.pcd" << endl;pcl::io::savePCDFileASCII("0result.pcd", *result);cout << "保存成功" << endl;cout << "切割后的点云保存到seedCloud，seedCloud.pcd" << endl;pcl::io::savePCDFileASCII("0seedCloud.pcd", *seed);cout << "保存成功" << endl;return 0;
}Project1,pcd输入文件，修改点云变量名
#include <pcl/visualization/cloud_viewer.h>
#include <iostream>//标准C++库中的输入输出类相关头文件。
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>//pcd 读写类相关的头文件。
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h> //PCL中支持的点类型头文件。
#include <pcl/octree/octree.h>
#include<fstream>
#include <string>
#include <vector>
#include <LasOperator.h>
#include <liblas/liblas.hpp>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>using namespace std;int main() {/**定义变量*///输入点云，轨迹线，保存种子pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZ>::Ptr trail(new pcl::PointCloud<pcl::PointXYZ>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud2(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr result(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr part(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr partSeed(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr seed(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr colored(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointXYZRGB tempPnt;//变量float x1, y1, x2, y2, a1, a2, a3, b11, b22, b3;//向量1，a,b为轨迹线上2点x1 = y1 = a1 = a2 = a3 = b11 = b22 = b3 = 0;x2 = 0; y2 = 1;float r = 1; //半径2float SmoothnessThreshold =6;//3.0 0.37float CurvatureThreshold = 0.01; //设置曲率的阈值1.0//读取输入电云cout << "读取点云" << endl;pcl::io::loadPCDFile("o.pcd", *cloud);//读取轨迹线cout << "读取轨迹线" << endl;pcl::io::loadPCDFile("trail.pcd", *trail);cout << "原始点数: " << cloud->width << " \t" << "轨迹线点数: " << trail->width << endl;//cloud构建八叉树cout << "构建八叉树" << endl;float resolution = 128.0f;pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree(resolution);octree.setInputCloud(cloud);octree.addPointsFromInputCloud();std::vector<int>pointIdxRadiusSearch;std::vector<int>pointIdxRadiusSearch1;//seed.txt保存切割得到的种子索引ofstream ofs;ofs.open("seed.txt");/**开始循环*/for (size_t i = 0; i < trail->points.size() + 500; i += 20) {//cloud1->points.size() cloud1->points.size()+500 500cout << "-------------------------------------------------------------" << endl;a1 = trail->points[i].x;a2 = trail->points[i].y;//cloud1->points[i+8].ya3 = trail->points[i].z;b11 = trail->points[i + 20].x;b22 = trail->points[i + 20].y;//cloud1->points[i+12].yb3 = trail->points[i + 20].z;x1 = a1 - b11;y1 = a2 - b22;float cosa = (x1*x2 + y1 * y2) / (sqrt(x1*x1 + y1 * y1) + sqrt(x2*x2 + y2 * y2));float sina = sqrt(1 - cosa * cosa);float range = r / sina;//分段Eigen::Vector3f n(-100000, min(a2, b22), -100000);//0xc0c0c0c0 0x3f3f3f3fEigen::Vector3f m(100000, max(a2, b22) - 0.001, min(a3, b3));octree.boxSearch(n, m, pointIdxRadiusSearch);cout << i<<" 这段有点数：" << pointIdxRadiusSearch.size() << "\t";for (size_t j = 0; j < pointIdxRadiusSearch.size(); ++j) {tempPnt.x = cloud->points[pointIdxRadiusSearch[j]].x;tempPnt.y = cloud->points[pointIdxRadiusSearch[j]].y;tempPnt.z = cloud->points[pointIdxRadiusSearch[j]].z;tempPnt.rgb = cloud->points[pointIdxRadiusSearch[j]].rgb;part->push_back(tempPnt);}/*cout << "保存1.pcd成功" << endl;pcl::io::savePCDFileASCII("1.pcd", *part);*///分段切割种子点pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree1(resolution);octree1.setInputCloud(part);octree1.addPointsFromInputCloud();Eigen::Vector3f p(a1 - range, min(trail->points[i + 8].y, trail->points[i + 12].y), -100000);//min(a2, b22)Eigen::Vector3f q(a1 + range, max(trail->points[i + 8].y, trail->points[i + 12].y)-0.000001, min(a3, b3));//max(a2, b22) - 0.001octree1.boxSearch(p, q, pointIdxRadiusSearch1);cout << "这段种子数：" << pointIdxRadiusSearch1.size() << endl;for (size_t j = 0; j < pointIdxRadiusSearch1.size(); ++j) {tempPnt.x = part->points[pointIdxRadiusSearch1[j]].x;tempPnt.y = part->points[pointIdxRadiusSearch1[j]].y;tempPnt.z = part->points[pointIdxRadiusSearch1[j]].z;tempPnt.rgb = part->points[pointIdxRadiusSearch1[j]].rgb;seed->push_back(tempPnt);}/* cout << "保存2.pcd成功" << endl;pcl::io::savePCDFileASCII("2.pcd", *partSeed);*/// 创建空的tree 设置搜索的方式或者说是结构pcl::search::Search<pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> >(new pcl::search::KdTree<pcl::PointXYZRGB>);//求法线//创建一个normal点云pcl::PointCloud <pcl::Normal>::Ptr normals(new pcl::PointCloud <pcl::Normal>);pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> normal_estimator;normal_estimator.setSearchMethod(tree);normal_estimator.setInputCloud(part);normal_estimator.setKSearch(500);normal_estimator.compute(*normals);//创造区域生长分割对象 聚类对象<点，法线>pcl::RegionGrowing<pcl::PointXYZRGB, pcl::Normal> reg; //首先建立reg寄存器(区域曾长的对象）reg.setMinClusterSize(50); //最小的聚类的点数(小于这参数的平面被忽略不计）reg.setMaxClusterSize(30000000); //最大的(一般随便设置）reg.setSearchMethod(tree); //搜索方式(采用的默认是K—d树法）reg.setNumberOfNeighbours(30); //设置搜索的邻域点的个数，周围多少个点决定这是一个平面(决定容错率，设置大时有倾斜也可接受，设置小时检测到的平面会很小）reg.setInputCloud(part); //输入点//reg.setIndices (indices);reg.setInputNormals(normals); //输入的法线reg.setSmoothnessThreshold(SmoothnessThreshold / 180.0 * M_PI);//3.0 设置平滑度(设置两个法线在多大夹角内可当做是共面的）reg.setCurvatureThreshold(CurvatureThreshold);std::vector <pcl::PointIndices> clusters;reg.extract(clusters);pcl::PointIndices cluster;//for (int j = 0;j < pointIdxRadiusSearch1.size();++j) {// reg.getSegmentFromPoint(pointIdxRadiusSearch1[0], cluster);// for (int j = 0; j < cluster.indices.size(); ++j) {// tempPnt.x = part->points[cluster.indices[j]].x;// tempPnt.y = part->points[cluster.indices[j]].y;// tempPnt.z = part->points[cluster.indices[j]].z;// tempPnt.rgb = part->points[cluster.indices[j]].rgb;// cloud3->push_back(tempPnt);// }//}reg.getSegmentFromPoint(pointIdxRadiusSearch1[0], cluster);cout << "取得的种子是：" << pointIdxRadiusSearch1[0] << "\t生长后点数：" << cluster.indices.size() << endl;cout << "种子点" << part->points[pointIdxRadiusSearch1[0]].x << "\t" << part->points[pointIdxRadiusSearch1[0]].y << "\t" << part->points[pointIdxRadiusSearch1[0]].z << "\t" << endl;cout << "-------------------------------------------------------------" << endl;for (int j = 0; j < cluster.indices.size(); ++j) {tempPnt.x = part->points[cluster.indices[j]].x;tempPnt.y = part->points[cluster.indices[j]].y;tempPnt.z = part->points[cluster.indices[j]].z;tempPnt.rgb = part->points[cluster.indices[j]].rgb;result->push_back(tempPnt);}part->clear();partSeed->clear();}ofs.close();cout << "生长后的点云保存到cloud3，0result.pcd" << endl;pcl::io::savePCDFileASCII("0result.pcd", *result);cout << "保存成功" << endl;cout << "切割后的点云保存到seedCloud，seedCloud.pcd" << endl;pcl::io::savePCDFileASCII("0seedCloud.pcd", *seed);cout << "保存成功" << endl;return 0;
}Project1,分段切割、区域生长，sample100测试得到的结果没有边缘，但是不连续
#include <pcl/visualization/cloud_viewer.h>
#include <iostream>//标准C++库中的输入输出类相关头文件。
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>//pcd 读写类相关的头文件。
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h> //PCL中支持的点类型头文件。
#include <pcl/octree/octree.h>
#include<fstream>
#include <string>
#include <vector>
#include <LasOperator.h>
#include <liblas/liblas.hpp>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>using namespace std;int main() {/**定义变量*///输入点云，轨迹线，保存种子pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZ>::Ptr cloud1(new pcl::PointCloud<pcl::PointXYZ>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud2(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud3(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr part(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr partSeed(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr seedCloud(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointCloud<pcl::PointXYZRGB>::Ptr colored(new pcl::PointCloud<pcl::PointXYZRGB>);pcl::PointXYZRGB tempPnt;//变量float x1, y1, x2, y2, a1, a2, a3, b11, b22, b3;//向量1，a,b为轨迹线上2点x1 = y1 = a1 = a2 = a3 = b11 = b22 = b3 = 0;x2 = 0; y2 = 1;float r = 1; //半径2float SmoothnessThreshold =6;//3.0 0.37float CurvatureThreshold = 0.01; //设置曲率的阈值1.0/**读取las文件，打开点云，初始化octree*/std::ifstream ifs("sample10.las", std::ios::in | std::ios::binary); // 打开las文件liblas::ReaderFactory f;liblas::Reader reader = f.CreateWithStream(ifs); // 读取las文件unsigned long int nbPoints = reader.GetHeader().GetPointRecordsCount();//获取las数据点的个数cloud->width = nbPoints; //保证与las数据点的个数一致 cloud->height = 1;cloud->is_dense = false;cloud->points.resize(cloud->width * cloud->height);part->width = 0;part->height = 1;part->is_dense = false;part->points.resize(part->width * part->height);partSeed->width = 0;partSeed->height = 1;partSeed->is_dense = false;partSeed->points.resize(partSeed->width * partSeed->height);int i = 0;uint16_t r1, g1, b1;int r2, g2, b2;uint32_t rgb;while (reader.ReadNextPoint()) {// 获取las数据的x，y，z信息cloud->points[i].x = (reader.GetPoint().GetX());cloud->points[i].y = (reader.GetPoint().GetY());cloud->points[i].z = (reader.GetPoint().GetZ());//获取las数据的r，g，b信息r1 = (reader.GetPoint().GetColor().GetRed());g1 = (reader.GetPoint().GetColor().GetGreen());b1 = (reader.GetPoint().GetColor().GetBlue());r2 = ceil(((float)r1 / 65536)*(float)256);g2 = ceil(((float)g1 / 65536)*(float)256);b2 = ceil(((float)b1 / 65536)*(float)256);rgb = ((int)r2) << 16 | ((int)g2) << 8 | ((int)b2);cloud->points[i].rgb = *reinterpret_cast<float*>(&rgb);i++;}float resolution = 128.0f;pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree(resolution);octree.setInputCloud(cloud);octree.addPointsFromInputCloud();std::vector<int>pointIdxRadiusSearch;std::vector<int>pointIdxRadiusSearch1;cout << "原始点云: " << cloud->width << " \t" << cloud->points.size() << endl;//读取轨迹线pcl::io::loadPCDFile("trail.pcd", *cloud1);cout << "轨迹线: " << cloud1->width << " \t" << cloud1->points.size() << endl;//seed.txt保存切割得到的种子索引ofstream ofs;ofs.open("seed.txt");//cluster保存区域生长得到的分段//pcl::PointIndices tempCluster;/**开始循环*/for (size_t i = 0; i < cloud1->points.size() + 500; i += 20) {//cloud1->points.size() cloud1->points.size()+500 500//a1 = cloud1->points[i].x;a2 = cloud1->points[i].y;//cloud1->points[i+8].ya3 = cloud1->points[i].z;b11 = cloud1->points[i + 20].x;b22 = cloud1->points[i + 20].y;//cloud1->points[i+12].yb3 = cloud1->points[i + 20].z;x1 = a1 - b11;y1 = a2 - b22;float cosa = (x1*x2 + y1 * y2) / (sqrt(x1*x1 + y1 * y1) + sqrt(x2*x2 + y2 * y2));float sina = sqrt(1 - cosa * cosa);float range = r / sina;//分段Eigen::Vector3f n(-100000, min(a2, b22), -100000);//0xc0c0c0c0 0x3f3f3f3fEigen::Vector3f m(100000, max(a2, b22) - 0.001, min(a3, b3));octree.boxSearch(n, m, pointIdxRadiusSearch);cout << "这段有点数：" << pointIdxRadiusSearch.size() << endl;for (size_t j = 0; j < pointIdxRadiusSearch.size(); ++j) {tempPnt.x = cloud->points[pointIdxRadiusSearch[j]].x;tempPnt.y = cloud->points[pointIdxRadiusSearch[j]].y;tempPnt.z = cloud->points[pointIdxRadiusSearch[j]].z;tempPnt.rgb = cloud->points[pointIdxRadiusSearch[j]].rgb;part->push_back(tempPnt);}/*cout << "保存1.pcd成功" << endl;pcl::io::savePCDFileASCII("1.pcd", *part);*///切割种子点pcl::octree::OctreePointCloudSearch<pcl::PointXYZRGB> octree1(resolution);octree1.setInputCloud(part);octree1.addPointsFromInputCloud();Eigen::Vector3f p(a1 - range, min(cloud1->points[i + 8].y, cloud1->points[i + 12].y), -100000);//min(a2, b22)Eigen::Vector3f q(a1 + range, max(cloud1->points[i + 8].y, cloud1->points[i + 12].y)-0.000001, min(a3, b3));//max(a2, b22) - 0.001octree1.boxSearch(p, q, pointIdxRadiusSearch1);cout << i<<"\t"<<"这段种子数：" << pointIdxRadiusSearch1.size() << endl;for (size_t j = 0; j < pointIdxRadiusSearch1.size(); ++j) {tempPnt.x = part->points[pointIdxRadiusSearch1[j]].x;tempPnt.y = part->points[pointIdxRadiusSearch1[j]].y;tempPnt.z = part->points[pointIdxRadiusSearch1[j]].z;tempPnt.rgb = part->points[pointIdxRadiusSearch1[j]].rgb;seedCloud->push_back(tempPnt);}//cout << "能到这里吗" << endl;/* cout << "保存2.pcd成功" << endl;pcl::io::savePCDFileASCII("2.pcd", *partSeed);*/// 创建空的tree 设置搜索的方式或者说是结构pcl::search::Search<pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> >(new pcl::search::KdTree<pcl::PointXYZRGB>);//求法线//创建一个normal点云pcl::PointCloud <pcl::Normal>::Ptr normals(new pcl::PointCloud <pcl::Normal>);pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> normal_estimator;normal_estimator.setSearchMethod(tree);normal_estimator.setInputCloud(part);normal_estimator.setKSearch(500);normal_estimator.compute(*normals);//创造区域生长分割对象 聚类对象<点，法线>pcl::RegionGrowing<pcl::PointXYZRGB, pcl::Normal> reg; //首先建立reg寄存器(区域曾长的对象）reg.setMinClusterSize(50); //最小的聚类的点数(小于这参数的平面被忽略不计）reg.setMaxClusterSize(30000000); //最大的(一般随便设置）reg.setSearchMethod(tree); //搜索方式(采用的默认是K—d树法）reg.setNumberOfNeighbours(30); //设置搜索的邻域点的个数，周围多少个点决定这是一个平面(决定容错率，设置大时有倾斜也可接受，设置小时检测到的平面会很小）reg.setInputCloud(part); //输入点//reg.setIndices (indices);reg.setInputNormals(normals); //输入的法线reg.setSmoothnessThreshold(SmoothnessThreshold / 180.0 * M_PI);//3.0 设置平滑度(设置两个法线在多大夹角内可当做是共面的）reg.setCurvatureThreshold(CurvatureThreshold);std::vector <pcl::PointIndices> clusters;reg.extract(clusters);pcl::PointIndices cluster;for (int j = 0;j < pointIdxRadiusSearch1.size();++j) {reg.getSegmentFromPoint(pointIdxRadiusSearch1[0], cluster);for (int j = 0; j < cluster.indices.size(); ++j) {tempPnt.x = part->points[cluster.indices[j]].x;tempPnt.y = part->points[cluster.indices[j]].y;tempPnt.z = part->points[cluster.indices[j]].z;tempPnt.rgb = part->points[cluster.indices[j]].rgb;cloud3->push_back(tempPnt);}}/*reg.getSegmentFromPoint(pointIdxRadiusSearch1[0], cluster);cout << "取得的种子是：" << pointIdxRadiusSearch1[0] << "\t生长后点数：" << cluster.indices.size() << endl;cout << "-------------------------------------------------------------" << endl;cout << "种子点" << part->points[pointIdxRadiusSearch1[0]].x << "\t" << part->points[pointIdxRadiusSearch1[0]].y << "\t" << part->points[pointIdxRadiusSearch1[0]].z << "\t" << endl;*/for (int j = 0; j < cluster.indices.size(); ++j) {tempPnt.x = part->points[cluster.indices[j]].x;tempPnt.y = part->points[cluster.indices[j]].y;tempPnt.z = part->points[cluster.indices[j]].z;tempPnt.rgb = part->points[cluster.indices[j]].rgb;cloud3->push_back(tempPnt);}//cluster.indices.clear();pointIdxRadiusSearch.clear();pointIdxRadiusSearch1.clear();part->clear();partSeed->clear();}ofs.close();cout << "生长后的点云保存到cloud3，regionGrow.pcd" << endl;//cloud3->width = cluster.indices.size(); //保证与las数据点的个数一致 cloud3->height = 1;cloud3->is_dense = false;//cloud3->points.resize(cloud3->width * cloud3->height);pcl::io::savePCDFileASCII("0regionGrow.pcd", *cloud3);cout << "保存成功" << endl;cout << "切割后的点云保存到seedCloud，seedCloud.pcd" << endl;//cloud3->width = cluster.indices.size(); //保证与las数据点的个数一致 seedCloud->height = 1;seedCloud->is_dense = false;//cloud3->points.resize(cloud3->width * cloud3->height);pcl::io::savePCDFileASCII("0seedCloud.pcd", *seedCloud);cout << "保存成功" << endl;return 0;
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