基于pcl库使用kruskal最小生成树算法将点云ply文件进行处理后连接文件内的所有点并可视化的c++代码

以下是使用PCL库实现Kruskal最小生成树算法并可视化点云PLY文件的C++代码示例：

#include <iostream>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/io/ply_io.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/moment_of_inertia_estimation.h>
#include <pcl/common/centroid.h>
#include <pcl/common/common.h>

// Define a structure to represent an edge
struct Edge
{
    int src, dest;
    float weight;
};

// Define a structure to represent a subset for union-find
struct Subset
{
    int parent;
    int rank;
};

// Find set of an element i (uses path compression technique)
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 of x and y (uses union by rank)
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++;
    }
}

// Comparison function used to sort edges in non-decreasing order by weight
bool compareEdges(const Edge &a, const Edge &b)
{
    return a.weight < b.weight;
}

// Function to construct MST using Kruskal's algorithm
void KruskalMST(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, pcl::PointCloud<pcl::PointXYZ>::Ptr output)
{
    int numVertices = cloud->size();
    int numEdges = numVertices * (numVertices - 1) / 2;

    // Create a vector to store all edges in the graph
    std::vector<Edge> edges(numEdges);

    int edgeIndex = 0;
    for (int i = 0; i < numVertices; i++)
    {
        for (int j = i + 1; j < numVertices; j++)
        {
            Edge e;
            e.src = i;
            e.dest = j;
            e.weight = pcl::euclideanDistance(cloud->points[i], cloud->points[j]);
            edges[edgeIndex++] = e;
        }
    }

    // Sort edges in non-decreasing order by weight
    std::sort(edges.begin(), edges.end(), compareEdges);

    // Allocate memory for creating V subsets
    Subset *subsets = new Subset[numVertices * sizeof(Subset)];

    // Create V subsets with single elements
    for (int v = 0; v < numVertices; ++v)
    {
        subsets[v].parent = v;
        subsets[v].rank = 0;
    }

    // Initialize MST to store edges of minimum spanning tree
    std::vector<Edge> mst(numVertices - 1);

    // Keep track of edges included in MST
    int mstIndex = 0;

    // Index variable used to pick next edge
    int edgeCount = 0;

    // Loop until all edges are included in MST or maximum spanning tree is formed
    while (mstIndex < numVertices - 1)
    {
        // Pick the smallest edge
        Edge nextEdge = edges[edgeCount++];

        int x = find(subsets, nextEdge.src);
        int y = find(subsets, nextEdge.dest);

        // If including this edge doesn't cause cycle, include it in MST
        if (x != y)
        {
            mst[mstIndex++] = nextEdge;
            Union(subsets, x, y);
        }
    }

    // Free allocated memory
    delete[] subsets;

    // Create output point cloud
    output->resize(numVertices);
    for (int i = 0; i < numVertices; i++)
    {
        output->points[i] = cloud->points[i];
    }

    // Add edges of MST to output point cloud
    for (int i = 0; i < mstIndex; i++)
    {
        int src = mst[i].src;
        int dest = mst[i].dest;
        output->points[src].x = cloud->points[src].x;
        output->points[src].y = cloud->points[src].y;
        output->points[src].z = cloud->points[src].z;
        output->points[dest].x = cloud->points[dest].x;
        output->points[dest].y = cloud->points[dest].y;
        output->points[dest].z = cloud->points[dest].z;
    }
}

int main()
{
    // Load input point cloud from PLY file
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr output(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PLYReader reader;
    reader.read("input.ply", *cloud);

    // Apply Kruskal's algorithm to construct MST
    KruskalMST(cloud, output);

    // Visualize the original point cloud and the MST
    pcl::visualization::CloudViewer viewer("Point Cloud Viewer");
    viewer.showCloud(cloud, "Original Point Cloud");
    viewer.showCloud(output, "Minimum Spanning Tree");

    while (!viewer.wasStopped())
    {
    }

    return 0;
}

请注意，此示例仅处理具有PointXYZ类型的点云PLY文件。如果您的PLY文件中包含其他类型的点云数据，请相应地修改代码。此外，您需要将PLY文件的名称更改为实际的文件名