PCL 点云最小生成树可视化：鼠标点击显示节点索引

#include #include <pcl/point_types.h> #include <pcl/io/ply_io.h> #include <pcl/visualization/pcl_visualizer.h> #include <pcl/common/centroid.h> #include <pcl/features/normal_3d.h> #include <pcl/visualization/cloud_viewer.h> #include <pcl/segmentation/extract_clusters.h> #include #include <unordered_map> #include "vtkAutoInit.h"

VTK_MODULE_INIT(vtkRenderingOpenGL); // VTK was built with vtkRenderingOpenGL2

using 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<Edge> result;  // This will store the resultant MST

    // 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 (x != y && next_edge.weight < 0.045)
        {
            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::PointCloudColorHandlerCustom<pcl::PointXYZ> single_color(cloud, 255, 255, 255);
    viewer.addPointCloud<pcl::PointXYZ>(cloud, single_color, "original_cloud");

    // 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;

    // Function to handle mouse events
    void pointPickingEvent(const pcl::visualization::PointPickingEvent& event, void* viewer_void)
    {
        int index = event.getPointIndex();
        std::cout << "Picked point index: " << index << std::endl;
    }

    // Register the point picking callback function
    viewer.registerPointPickingCallback(pointPickingEvent, (void*)&viewer);

    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::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>);
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::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
graph.KruskalMST(cloud);
// 创建一个新的点云对象来保存最小生成树的结果
pcl::PointCloud<pcl::PointXYZ>::Ptr new_cloud(new pcl::PointCloud<pcl::PointXYZ>);
std::vector<Edge> result;
new_cloud->width = cloud->width;
new_cloud->height = cloud->height;
new_cloud->points.resize(cloud->points.size());

// 将最小生成树的顶点添加到新的点云对象
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;
}

// 将新的点云保存为PLY文件
pcl::io::savePLYFile("D:\\DIANYUNWENJIANJIA\\newKRUSKAL_ply.ply", *new_cloud, true);
return 0;