#include "iostream"\n#include "pcl/point_types.h"\n#include "pcl/io/ply_io.h"\n#include "pcl/visualization/pcl_visualizer.h"\n#include "pcl/common/centroid.h"\n#include "pcl/features/normal_3d.h"\n#include "pcl/visualization/cloud_viewer.h"\n#include "pcl/segmentation/extract_clusters.h"\n#include "vector"\n#include "unordered_map"\n#include "vtkAutoInit.h"\n\nVTK_MODULE_INIT(vtkRenderingOpenGL); // VTK was built with vtkRenderingOpenGL2\n\nusing namespace pcl; typedef pcl::PointXYZ PointT; typedef pcl::PointCloud PointCloudT; // Structure to represent an edge struct Edge { int src, tgt; float weight; }; // Structure to represent a subset for union-find struct Subset { int parent, rank; }; // Class to represent a connected, undirected and weighted graph class Graph { public: int V, E; std::vector edges; Graph(int v, int e) { V = v; E = e; } // Add an edge to the graph void addEdge(int src, int tgt, float weight) { Edge edge; edge.src = src; edge.tgt = tgt; edge.weight = weight; edges.push_back(edge); } // Find set of an element i int find(Subset subsets[], int i) { if (subsets[i].parent != i) subsets[i].parent = find(subsets, subsets[i].parent); return subsets[i].parent; } // Union of two sets x and y void Union(Subset subsets[], int x, int y) { int xroot = find(subsets, x); int yroot = find(subsets, y); if (subsets[xroot].rank < subsets[yroot].rank) subsets[xroot].parent = yroot; else if (subsets[xroot].rank > subsets[yroot].rank) subsets[yroot].parent = xroot; else { subsets[yroot].parent = xroot; subsets[xroot].rank++; } } // Kruskal's algorithm to find MST void KruskalMST(PointCloudT::Ptr cloud, std::vector& result) { // Sort all the edges in non-decreasing order of their weight std::sort(edges.begin(), edges.end(), [](const Edge& a, const Edge& b) { return a.weight < b.weight; }); // Allocate memory for creating V subsets Subset* subsets = new Subset[V]; for (int v = 0; v < V; ++v) { subsets[v].parent = v; subsets[v].rank = 0; } int i = 0; // Index used to pick the next smallest edge int e = 0; // Index used to pick the next edge to include in MST // Number of edges to be taken is equal to V-1 while (e < V - 1 && i < E) { Edge next_edge = edges[i++]; int x = find(subsets, next_edge.src); int y = find(subsets, next_edge.tgt); // If including this edge doesn't cause a cycle, include it in result and increment the index of result for next edge if (next_edge.weight < 0.043) { if (x != y) { result.push_back(next_edge); Union(subsets, x, y); ++e; } } } // Visualize the resulting minimum spanning tree pcl::visualization::PCLVisualizer viewer("Minimum Spanning Tree"); viewer.setBackgroundColor(0, 0, 0); // Add the original point cloud to the viewer pcl::visualization::PointCloudColorHandlerCustompcl::PointXYZ single_color(cloud, 255, 255, 255); viewer.addPointCloudpcl::PointXYZ(cloud, single_color, "original_cloud"); // 将最小生成树的边添加到视图器中 for (const auto& edge : result) { const auto& src_point = cloud->points[edge.src]; const auto& tgt_point = cloud->points[edge.tgt]; std::stringstream ss; ss << "edge_" << edge.src << "_" << edge.tgt; viewer.addLinepcl::PointXYZ(src_point, tgt_point, ss.str()); } // Connect two disconnected edges pcl::PointXYZ point1 = cloud->points[14]; pcl::PointXYZ point2 = cloud->points[34]; pcl::PointXYZ point3 = cloud->points[68]; pcl::PointXYZ point4 = cloud->points[62]; viewer.addLine<pcl::PointXYZ, pcl::PointXYZ>(point1, point2, 0, 255, 0, "line1"); viewer.addLine<pcl::PointXYZ, pcl::PointXYZ>(point3, point4, 0, 255, 0, "line2"); // Count the number of points in the minimum spanning tree int numPoints = 0; for (const auto& edge : result) { const auto& src_point = cloud->points[edge.src]; const auto& tgt_point = cloud->points[edge.tgt]; numPoints += 2; } std::cout << "Number of points in the minimum spanning tree: " << numPoints << std::endl; while (!viewer.wasStopped()) { viewer.spinOnce(); } } }; double euclideanDistance(PointXYZ p1, PointXYZ p2) { double dx = p2.x - p1.x; double dy = p2.y - p1.y; double dz = p2.z - p1.z; return std::sqrt(dx * dx + dy * dy + dz * dz); } int main() { // Load the input point cloud from PLY file pcl::PointCloudpcl::PointXYZ::Ptr cloud(new pcl::PointCloudpcl::PointXYZ); pcl::io::loadPLYFilepcl::PointXYZ("D:\DIANYUNWENJIANJIA\newOUSHIJULEI_ply.ply", *cloud); // Compute the centroid of the point cloud Eigen::Vector4f centroid; pcl::compute3DCentroid(*cloud, centroid); // Compute the normals of the point cloud pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne; pcl::PointCloudpcl::Normal::Ptr cloud_normals(new pcl::PointCloudpcl::Normal); pcl::search::KdTreepcl::PointXYZ::Ptr tree(new pcl::search::KdTreepcl::PointXYZ); ne.setInputCloud(cloud); ne.setSearchMethod(tree); ne.setKSearch(40); ne.compute(*cloud_normals); // Create a graph with V vertices and E edges int V = cloud->size(); int E = V * (V - 1) / 2; Graph graph(V, E); // Calculate the edge weights based on Euclidean distance between points for (int i = 0; i < V - 1; ++i) { const auto& src_point = cloud->points[i]; for (int j = i + 1; j < V; ++j) { const auto& tgt_point = cloud->points[j]; float distance = euclideanDistance(src_point, tgt_point); graph.addEdge(i, j, distance); } } // Perform Kruskal's algorithm to find the minimum spanning tree std::vector result; graph.KruskalMST(cloud, result); // Create a new point cloud object to save the result of the minimum spanning tree pcl::PointCloudpcl::PointXYZ::Ptr new_cloud(new pcl::PointCloudpcl::PointXYZ); new_cloud->width = cloud->width; new_cloud->height = cloud->height; new_cloud->points.resize(cloud->points.size()); // Add the vertices of the minimum spanning tree to the new point cloud object for (const auto& edge : result) { const auto& src_point = cloud->points[edge.src]; const auto& tgt_point = cloud->points[edge.tgt]; new_cloud->points[edge.src] = src_point; new_cloud->points[edge.tgt] = tgt_point; } // Save the new point cloud as a PLY file pcl::io::savePLYFile("D:\DIANYUNWENJIANJIA\newKRUSKAL_ply.ply", *new_cloud, true); return 0; }