dbscan点云聚类算法c++

DBSCAN是一种基于密度的聚类算法，它可以对点云数据进行聚类。下面是DBSCAN点云聚类算法的C++实现：

#include <iostream>
#include <vector>
#include <cmath>

using namespace std;

struct Point {
    double x, y;
    int clusterid;
};

void dbscan(vector<Point>& points, double eps, int minPts) {
    int clusterid = 1;
    for (int i = 0; i < points.size(); i++) {
        Point& p = points[i];
        if (p.clusterid != -1) continue; // 已分类
        vector<Point*> neighbors;
        for (int j = 0; j < points.size(); j++) {
            if (i == j) continue;
            Point& q = points[j];
            double dist = sqrt(pow(p.x - q.x, 2) + pow(p.y - q.y, 2));
            if (dist <= eps) neighbors.push_back(&q);
        }
        if (neighbors.size() < minPts) { 
            p.clusterid = 0; // 标记为噪声点
            continue;
        }
        p.clusterid = clusterid;
        for (int j = 0; j < neighbors.size(); j++) {
            Point& q = *neighbors[j];
            if (q.clusterid == 0) q.clusterid = clusterid;
            if (q.clusterid != -1) continue;
            q.clusterid = clusterid;
            vector<Point*> subneighbors;
            for (int k = 0; k < points.size(); k++) {
                if (k == i || k == j) continue;
                Point& r = points[k];
                double dist = sqrt(pow(q.x - r.x, 2) + pow(q.y - r.y, 2));
                if (dist <= eps) subneighbors.push_back(&r);
            }
            if (subneighbors.size() >= minPts) {
                neighbors.insert(neighbors.end(), subneighbors.begin(), subneighbors.end());
            }
        }
        clusterid++;
    }
}

int main() {
    vector<Point> points = {
        {1, 2, -1}, {2, 3, -1}, {2, 4, -1}, {3, 3, -1},
        {7, 6, -1}, {7, 7, -1}, {8, 7, -1}, {8, 8, -1}
    };
    double eps = 2;
    int minPts = 2;
    dbscan(points, eps, minPts);

    for (int i = 0; i < points.size(); i++) {
        Point& p = points[i];
        cout << "(" << p.x << "," << p.y << ")";
        if (p.clusterid == 0) cout << " noise\n";
        else cout << " cluster " << p.clusterid << endl;
    }
    return 0;
}

这里假设点云数据已经以结构体Point的形式存储在vector中，包括x、y坐标和聚类id。dbscan函数实现了DBSCAN算法的核心部分，其中eps和minPts是算法的两个重要参数，分别表示邻域半径和最小密度阈值。最后，程序输出每个点的坐标和聚类id，其中聚类id为0表示该点为噪声点。