从列表中过滤掉附近的点

import numpy as np
from sklearn.neighbors import NearestNeighbors

def filter_nearby_points(points, radius):
    """
    :param points: 一个包含点坐标的numpy数组，形状为(N, 2)
    :param radius: 过滤的半径距离
    :return: 过滤后的点列表
    """
    # 使用K-Nearest Neighbors算法找到每个点的K个最近邻居
    knn = NearestNeighbors(n_neighbors=2)
    knn.fit(points)
    distances, indices = knn.kneighbors(points, return_distance=True)

    # 过滤掉距离小于给定半径的点
    filtered_points = []
    for i in range(len(points)):
        if distances[i][1] > radius:
            filtered_points.append(points[i])

    return np.array(filtered_points)