代码如下：

import numpy as np
import cv2
import time
import os


#聚类算法
def cluster(points, radius=100,nums=250):
    """
    points: pointcloud
    radius: max cluster range
    """
    print("................", len(points))
    items = []
    while len(points)>1:
        item = np.array([points[0]])
        base = points[0]
        points = np.delete(points, 0, 0)
        distance = (points[:,0]-base[0])**2+(points[:,1]-base[1])**2#获得距离
        infected_points = np.where(distance <= radius**2)#与base距离小于radius**2的点的坐标
        item = np.append(item, points[infected_points], axis=0)
        border_points = points[infected_points]
        points = np.delete(points, infected_points, 0)
        while len(border_points) > 0:
            border_base = border_points[0]
            border_points = np.delete(border_points, 0, 0)
            border_distance = (points[:,0]-border_base[0])**2+(points[:,1]-border_base[1])**2
            border_infected_points = np.where(border_distance <= radius**2)
            #print("/",border_infected_points)
            item = np.append(item, points[border_infected_points], axis=0)
            if len(border_infected_points)>0:
                for k in border_infected_points:
                    if points[k] not in border_points:
                        border_points=np.append(border_points,points[k], axis=0)
                #border_points = points[border_infected_points]
            points = np.delete(points, border_infected_points, 0)
        if len(item) >= nums:
            items.append(item)
    return items



if __name__ == '__main__':

    start=time.time()
    img_paths = r"D:\AI\project\eye_hand_biaoding\railways\dbscan\img\000003.jpg"
    img = cv2.imread(img_paths,0)
    clusters=cluster(points, radius=100,nums=250)

        代码中的第一个函数是聚类算法，输入的参数是灰度图片，聚类的半径，和成为类的最少点的个数。建议使用该算法时尽量减小数据量，如果图片的像素很大，可以截取成几个部分进行聚类。