帮我写一个C++程序，我有一个候选框容器，std::vectorcv::Rect bboxes;还有这些框的中心点容器std::vectorcv::Point centers，这些我都已经计算好可以当作输入，我想使用dbscan聚类把小框融合成大框，麻烦帮我写一个程序。我没有dbscan库的支持，需要你给我提供dbscan函数的实现。

好的，我可以帮您实现一个简单的dbscan函数来聚类小框并融合成大框。以下是代码实现：

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

using namespace std;

const double eps = 20.0; // DBSCAN中的半径参数
const int minPts = 3; // DBSCAN中的最小点数参数

// 计算两个点之间的欧几里得距离
double calc_dist(cv::Point p1, cv::Point p2) {
    double dx = p1.x - p2.x;
    double dy = p1.y - p2.y;
    return sqrt(dx * dx + dy * dy);
}

// 获取以p为中心，半径为eps的圆内的所有点
vector<int> get_neighbors(int p, vector<cv::Point>& centers) {
    vector<int> neighbors;
    for (int i = 0; i < centers.size(); i++) {
        if (calc_dist(centers[i], centers[p]) <= eps) {
            neighbors.push_back(i);
        }
    }
    return neighbors;
}

// 进行dbscan聚类
vector<vector<int>> dbscan(vector<cv::Point>& centers) {
    int n = centers.size();
    vector<vector<int>> clusters;
    vector<int> visited(n, 0); // 0表示未访问，1表示已访问
    for (int i = 0; i < n; i++) {
        if (visited[i]) continue;
        visited[i] = 1;
        vector<int> neighbors = get_neighbors(i, centers);
        if (neighbors.size() < minPts) continue;
        vector<int> cluster;
        cluster.push_back(i);
        for (int j = 0; j < neighbors.size(); j++) {
            int p = neighbors[j];
            if (visited[p]) continue;
            visited[p] = 1;
            vector<int> p_neighbors = get_neighbors(p, centers);
            if (p_neighbors.size() >= minPts) {
                neighbors.insert(neighbors.end(), p_neighbors.begin(), p_neighbors.end());
            }
            if (!find(cluster.begin(), cluster.end(), p)) {
                cluster.push_back(p);
            }
        }
        clusters.push_back(cluster);
    }
    return clusters;
}

// 融合小框
vector<cv::Rect> merge_rects(vector<cv::Rect>& bboxes, vector<vector<int>>& clusters) {
    vector<cv::Rect> merged_rects;
    for (int i = 0; i < clusters.size(); i++) {
        int left = bboxes[clusters[i][0]].tl().x;
        int top = bboxes[clusters[i][0]].tl().y;
        int right = bboxes[clusters[i][0]].br().x;
        int bottom = bboxes[clusters[i][0]].br().y;
        for (int j = 1; j < clusters[i].size(); j++) {
            cv::Rect rect = bboxes[clusters[i][j]];
            left = min(left, rect.tl().x);
            top = min(top, rect.tl().y);
            right = max(right, rect.br().x);
            bottom = max(bottom, rect.br().y);
        }
        merged_rects.push_back(cv::Rect(left, top, right - left, bottom - top));
    }
    return merged_rects;
}

int main() {
    // 假设已有候选框容器bboxes和中心点容器centers
    vector<cv::Rect> bboxes;
    vector<cv::Point> centers;
    // ...
    // 进行dbscan聚类
    vector<vector<int>> clusters = dbscan(centers);
    // 融合小框
    vector<cv::Rect> merged_rects = merge_rects(bboxes, clusters);
    // 输出结果
    for (int i = 0; i < merged_rects.size(); i++) {
        cout << "merged rect " << i << ": " << merged_rects[i] << endl;
    }
    return 0;
}

代码中用到的dbscan算法是一种密度聚类算法，它的核心思想是将数据点分为核心点、边界点和噪声点三类，然后通过核心点之间的连通性不断扩展簇。在本代码中，我们通过计算每个点的邻居点来判断其是否为核心点，然后不断扩展邻居点直到簇不再增加。最终，聚类出来的点被用来计算融合后的大框。