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Kruskal最小生成树算法处理点云PLY文件并进行可视化的C++代码示例：

#include <iostream>
#include <fstream>
#include <vector>
#include <algorithm>

struct Edge {
    int src, dest;
    double weight;
};

struct Point {
    double x, y, z;
};

struct Cluster {
    std::vector<int> points;
};

class DisjointSet {
private:
    std::vector<int> parent, rank;

public:
    DisjointSet(int n) {
        parent.resize(n);
        rank.resize(n);
        for (int i = 0; i < n; i++) {
            parent[i] = i;
            rank[i] = 0;
        }
    }

    int find(int i) {
        if (i != parent[i])
            parent[i] = find(parent[i]);
        return parent[i];
    }

    void unionSets(int x, int y) {
        int xRoot = find(x);
        int yRoot = find(y);

        if (rank[xRoot] < rank[yRoot])
            parent[xRoot] = yRoot;
        else if (rank[xRoot] > rank[yRoot])
            parent[yRoot] = xRoot;
        else {
            parent[yRoot] = xRoot;
            rank[xRoot]++;
        }
    }
};

bool compareEdges(const Edge& a, const Edge& b) {
    return a.weight < b.weight;
}

std::vector<Edge> kruskalMST(std::vector<Point>& points, std::vector<Edge>& edges) {
    std::vector<Edge> result;
    DisjointSet ds(points.size());

    std::sort(edges.begin(), edges.end(), compareEdges);

    for (const auto& edge : edges) {
        int srcRoot = ds.find(edge.src);
        int destRoot = ds.find(edge.dest);

        if (srcRoot != destRoot) {
            result.push_back(edge);
            ds.unionSets(srcRoot, destRoot);
        }
    }

    return result;
}

void visualizePointCloud(std::vector<Point>& points, std::vector<Edge>& mst) {
    std::ofstream output("output.ply");
    output << "ply\n";
    output << "format ascii 1.0\n";
    output << "element vertex " << points.size() << "\n";
    output << "property float x\n";
    output << "property float y\n";
    output << "property float z\n";
    output << "element edge " << mst.size() << "\n";
    output << "property int vertex1\n";
    output << "property int vertex2\n";
    output << "end_header\n";

    for (const auto& point : points) {
        output << point.x << " " << point.y << " " << point.z << "\n";
    }

    for (const auto& edge : mst) {
        output << edge.src << " " << edge.dest << "\n";
    }

    output.close();
}

int main() {
    std::ifstream input("input.ply");
    std::vector<Point> points;
    std::vector<Edge> edges;

    std::string line;
    int numPoints, numFaces;
    bool isData = false;

    while (std::getline(input, line)) {
        if (isData) {
            std::istringstream iss(line);
            Point point;
            iss >> point.x >> point.y >> point.z;
            points.push_back(point);
        }
        if (line.find("element vertex") != std::string::npos) {
            std::istringstream iss(line);
            std::string vertexStr;
            iss >> vertexStr >> numPoints;
        }
        if (line.find("element face") != std::string::npos) {
            std::istringstream iss(line);
            std::string faceStr;
            iss >> faceStr >> numFaces;
        }
        if (line.find("end_header") != std::string::npos) {
            isData = true;
        }
    }

    for (int i = 0; i < numPoints; i++) {
        for (int j = i + 1; j < numPoints; j++) {
            Edge edge;
            edge.src = i;
            edge.dest = j;
            edge.weight = std::sqrt(std::pow(points[i].x - points[j].x, 2) + std::pow(points[i].y - points[j].y, 2) + std::pow(points[i].z - points[j].z, 2));
            edges.push_back(edge);
        }
    }

    std::vector<Edge> mst = kruskalMST(points, edges);
    visualizePointCloud(points, mst);

    return 0;
}

请注意，此代码假设PLY文件的点云数据是以ASCII格式存储的，并且在每个点的行中按顺序指定x、y和z坐标。如果PLY文件使用不同的格式，请相应地调整代码。此外，代码还假设输入文件名为“input.ply”，并将最小生成树的可视化结果写入名为“output.ply”的输出文件中。您可以根据需要对文件名和路径进行更改