PCL库Kruskal最小生成树处理点云PLY文件C++代码示例

"以下是基于PCL库使用Kruskal最小生成树处理点云PLY文件的C++代码示例：\n\ncpp\n#include <iostream>\n#include <pcl/io/ply_io.h>\n#include <pcl/point_types.h>\n#include <pcl/visualization/cloud_viewer.h>\n#include <pcl/common/common.h>\n#include <pcl/common/centroid.h>\n#include <pcl/common/geometry.h>\n#include <pcl/common/transforms.h>\n\nstruct Edge {\n int src, dest;\n float weight;\n};\n\nstruct Subset {\n int parent;\n int rank;\n};\n\nclass Graph {\nprivate:\n pcl::PointCloud<pcl::PointXYZ>::Ptr cloud;\n std::vector<Edge> edges;\n\npublic:\n Graph(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud) {\n this->cloud = cloud;\n }\n\n void addEdge(int src, int dest) {\n float weight = pcl::geometry::distance(cloud->points[src], cloud->points[dest]);\n edges.push_back({src, dest, weight});\n }\n\n int find(Subset subsets[], int i) {\n if (subsets[i].parent != i)\n subsets[i].parent = find(subsets, subsets[i].parent);\n\n return subsets[i].parent;\n }\n\n void Union(Subset subsets[], int x, int y) {\n int xroot = find(subsets, x);\n int yroot = find(subsets, y);\n\n if (subsets[xroot].rank < subsets[yroot].rank)\n subsets[xroot].parent = yroot;\n else if (subsets[xroot].rank > subsets[yroot].rank)\n subsets[yroot].parent = xroot;\n else {\n subsets[yroot].parent = xroot;\n subsets[xroot].rank++;\n }\n }\n\n void kruskalMST() {\n std::vector<Edge> result;\n\n int num_vertices = cloud->size();\n int i = 0, e = 0;\n\n std::sort(edges.begin(), edges.end(), [](const Edge& a, const Edge& b) {\n return a.weight < b.weight;\n });\n\n Subset* subsets = new Subset[num_vertices * sizeof(Subset)];\n\n for (int v = 0; v < num_vertices; v++) {\n subsets[v].parent = v;\n subsets[v].rank = 0;\n }\n\n while (e < num_vertices - 1 && i < edges.size()) {\n Edge next_edge = edges[i++];\n\n int x = find(subsets, next_edge.src);\n int y = find(subsets, next_edge.dest);\n\n if (x != y) {\n result.push_back(next_edge);\n Union(subsets, x, y);\n e++;\n }\n }\n\n pcl::PointCloud<pcl::PointXYZ>::Ptr result_cloud(new pcl::PointCloud<pcl::PointXYZ>);\n result_cloud->resize(result.size() * 2);\n\n for (int i = 0; i < result.size(); i++) {\n int src = result[i].src;\n int dest = result[i].dest;\n\n result_cloud->points[2 * i] = cloud->points[src];\n result_cloud->points[2 * i + 1] = cloud->points[dest];\n }\n\n pcl::visualization::CloudViewer viewer("Minimum Spanning Tree");\n viewer.showCloud(result_cloud);\n while (!viewer.wasStopped()) {\n }\n }\n};\n\nint main() {\n pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);\n pcl::io::loadPLYFile<pcl::PointXYZ>("input.ply", *cloud);\n\n Graph graph(cloud);\n\n // Connect all points in the cloud\n for (int i = 0; i < cloud->size(); i++) {\n for (int j = i + 1; j < cloud->size(); j++) {\n graph.addEdge(i, j);\n }\n }\n\n graph.kruskalMST();\n\n return 0;\n}\n\n\n请注意，此代码假设输入PLY文件中的点云是无序的，并且使用Kruskal算法创建最小生成树。\n