济南网站建设选聚搜网络一x,旅游目的地网站建设,包装盒在线设计网站,设计一个网站开发方案凹凸型分割算法适用于颜色类似、棱角分明的物体场景分割。LCCP方法不依赖点云颜色#xff0c;只使用空间信息和法线信息。
算法流程#xff1a;
1、基于超体聚类的过分割#xff1b;
2、在超体聚类的基础上再聚类。
算法思路#xff1a;
1、基于CC和SC判断凹凸性…凹凸型分割算法适用于颜色类似、棱角分明的物体场景分割。LCCP方法不依赖点云颜色只使用空间信息和法线信息。
算法流程
1、基于超体聚类的过分割
2、在超体聚类的基础上再聚类。
算法思路
1、基于CC和SC判断凹凸性CC是利用相邻两片中心连线向量与法向量的夹角来判断两片是凹还是凸SC判别阈值与两体素的夹角。 若α1α2则为凹反之则为凸。
2、在标记完各个小区域凹凸关系后则采用区域增长算法将小区域聚类成较大物体。
3、滤除多余噪点即可获得点云分割结果。 示例代码
//超体聚类LCCP
//#include stdafx.h#include stdlib.h
#include cmath
#include limits.h
#include boost/format.hpp
#include fstream #include pcl/console/parse.h
#include pcl/io/pcd_io.h
#include pcl/visualization/pcl_visualizer.h
#include pcl/visualization/point_cloud_color_handlers.h
#include pcl/visualization/cloud_viewer.h#include pcl/filters/passthrough.h
#include pcl/segmentation/supervoxel_clustering.h #include pcl/segmentation/lccp_segmentation.h #define Random(x) (rand() % x)typedef pcl::PointXYZRGBA PointT;
typedef pcl::LCCPSegmentationPointT::SupervoxelAdjacencyList SuperVoxelAdjacencyList;int main(int argc, char ** argv)
{//输入点云 pcl::PointCloudPointT::Ptr input_cloud_ptr(new pcl::PointCloudPointT);pcl::PCLPointCloud2 input_pointcloud2;if (pcl::io::loadPCDFile(E:\\PercipioVision\\depth2pointcloud\\testdata\\test-Cloud1.pcd, input_pointcloud2)){PCL_ERROR(ERROR: Could not read input point cloud );return (3);}pcl::fromPCLPointCloud2(input_pointcloud2, *input_cloud_ptr);PCL_INFO(Done making cloud\n);//粒子距离体素大小空间八叉树的分辨率类kinect或xtion获取的数据0.008左右合适float voxel_resolution 2.0f;//晶核距离种子的分辨率一般可设置为体素分辨率的50倍以上float seed_resolution 100.0f;//颜色容差针对分割场景如果分割场景中各个物体之间的颜色特征差异明显可设置较大float color_importance 0.1f;//设置较大且其他影响较小时基本按照空间分辨率来决定体素分割float spatial_importance 1.0f;//针对分割场景如果分割场景中各个物体连通处的法线特征差异明显可设置较大//但在实际使用中需要针对数据的结构适当考虑发现估计的准确性等因素float normal_importance 4.0f;bool use_single_cam_transform false;bool use_supervoxel_refinement false;unsigned int k_factor 0;//voxel_resolution is the resolution (in meters) of voxels used、seed_resolution is the average size (in meters) of resulting supervoxels pcl::SupervoxelClusteringPointT super(voxel_resolution, seed_resolution);super.setUseSingleCameraTransform(use_single_cam_transform);super.setInputCloud(input_cloud_ptr);//Set the importance of color for supervoxels. super.setColorImportance(color_importance);//Set the importance of spatial distance for supervoxels.super.setSpatialImportance(spatial_importance);//Set the importance of scalar normal product for supervoxels. super.setNormalImportance(normal_importance);std::mapuint32_t, pcl::SupervoxelPointT::Ptr supervoxel_clusters;PCL_INFO(Extracting supervoxels\n);super.extract(supervoxel_clusters);PCL_INFO(Getting supervoxel adjacency\n);std::multimapuint32_t, uint32_t supervoxel_adjacency;super.getSupervoxelAdjacency(supervoxel_adjacency);pcl::PointCloudpcl::PointNormal::Ptr sv_centroid_normal_cloud pcl::SupervoxelClusteringPointT::makeSupervoxelNormalCloud(supervoxel_clusters);//LCCP分割float concavity_tolerance_threshold 20;float smoothness_threshold 0.2;uint32_t min_segment_size 0;bool use_extended_convexity false;bool use_sanity_criterion false;PCL_INFO(Starting Segmentation\n);pcl::LCCPSegmentationPointT lccp;//设置CC判断的依据lccp.setConcavityToleranceThreshold(concavity_tolerance_threshold);//设置是否使用阶梯检测这个条件会检测两个超体素之间是否是一个step。//如果两个超体素之间的面到面距离expected_distance smoothness_threshold_*voxel_resolution_则这个两个超体素被判定为unsmooth并被标记为凹。lccp.setSmoothnessCheck(true, voxel_resolution, seed_resolution, smoothness_threshold);//设置CC判断中公共距离被判定为凸的个数lccp.setKFactor(k_factor);//输入超体分割后的点云lccp.setInputSupervoxels(supervoxel_clusters, supervoxel_adjacency);lccp.setMinSegmentSize(min_segment_size);lccp.segment();PCL_INFO(Interpolation voxel cloud - input cloud and relabeling\n);pcl::PointCloudpcl::PointXYZL::Ptr sv_labeled_cloud super.getLabeledCloud();pcl::PointCloudpcl::PointXYZL::Ptr lccp_labeled_cloud sv_labeled_cloud-makeShared();lccp.relabelCloud(*lccp_labeled_cloud);SuperVoxelAdjacencyList sv_adjacency_list;lccp.getSVAdjacencyList(sv_adjacency_list);// 根据label值提取点云int j 0;pcl::PointCloudpcl::PointXYZL::Ptr ColoredCloud2(new pcl::PointCloudpcl::PointXYZL);ColoredCloud2-height 1;ColoredCloud2-width lccp_labeled_cloud-size();ColoredCloud2-resize(lccp_labeled_cloud-size());for (int i 0; i lccp_labeled_cloud-size(); i) {if (lccp_labeled_cloud-points[i].label 3) {ColoredCloud2-points[j].x lccp_labeled_cloud-points[i].x;ColoredCloud2-points[j].y lccp_labeled_cloud-points[i].y;ColoredCloud2-points[j].z lccp_labeled_cloud-points[i].z;ColoredCloud2-points[j].label lccp_labeled_cloud-points[i].label;j;}}pcl::io::savePCDFileASCII(E:\\PercipioVision\\depth2pointcloud\\testdata\\3.pcd, *ColoredCloud2);// Configure Visualizer//pcl::visualization::PCLVisualizer viewer pcl::visualization::PCLVisualizer(3D Viewer, false);//viewer.addPointCloud(lccp_labeled_cloud, Segmented point cloud);pcl::io::savePCDFileASCII(E:\\PercipioVision\\depth2pointcloud\\testdata\\分割后合并.pcd, *lccp_labeled_cloud);return 0;
}
