UAV/filter/cluster_filter.py

from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
import os
import logging


class GPSCluster:
    def __init__(self, gps_points, eps=0.01, min_samples=3):
        """
        初始化GPS聚类器

        参数:
            eps: DBSCAN的邻域半径参数
            min_samples: DBSCAN的最小样本数参数
        """
        self.eps = eps
        self.min_samples = min_samples
        self.dbscan = DBSCAN(eps=eps, min_samples=min_samples)
        self.scaler = StandardScaler()
        self.gps_points = gps_points
        self.logger = logging.getLogger('UAV_Preprocess.GPSCluster')

    def fit(self):
        """
        对GPS点进行聚类，只保留最大的类

        参数:
            gps_points: 包含'lat'和'lon'列的DataFrame

        返回:
            带有聚类标签的DataFrame，其中最大类标记为1，其他点标记为-1
        """
        self.logger.info("开始聚类")
        # 提取经纬度数据
        X = self.gps_points[["lon", "lat"]].values

        # # 数据标准化
        # X_scaled = self.scaler.fit_transform(X)

        # 执行DBSCAN聚类
        labels = self.dbscan.fit_predict(X)

        # 找出最大类的标签（排除噪声点-1）
        unique_labels = [l for l in set(labels) if l != -1]
        if unique_labels:  # 如果有聚类
            label_counts = [(l, sum(labels == l)) for l in unique_labels]
            largest_label = max(label_counts, key=lambda x: x[1])[0]

            # 将最大类标记为1，其他都标记为-1
            new_labels = (labels == largest_label).astype(int)
            new_labels[new_labels == 0] = -1
        else:  # 如果没有聚类，全部标记为-1
            new_labels = labels

        # 将聚类结果添加到原始数据中
        result_df = self.gps_points.copy()
        result_df["cluster"] = new_labels

        return result_df

    def get_cluster_stats(self, clustered_points):
        """
        获取聚类统计信息

        参数:
            clustered_points: 带有聚类标签的DataFrame

        返回:
            聚类统计信息的字典
        """
        main_cluster = clustered_points[clustered_points["cluster"] == 1]
        noise_cluster = clustered_points[clustered_points["cluster"] == -1]

        self.logger.info(f"聚类完成：主要类别包含 {len(main_cluster)} 个点，"
                         f"噪声点 {len(noise_cluster)} 个")

        return main_cluster