88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
from sklearn.cluster import DBSCAN
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from sklearn.preprocessing import StandardScaler
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import os
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class GPSCluster:
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def __init__(self, gps_points, output_dir: str, eps=0.01, min_samples=5):
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"""
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初始化GPS聚类器
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参数:
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eps: DBSCAN的邻域半径参数
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min_samples: DBSCAN的最小样本数参数
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"""
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self.eps = eps
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self.min_samples = min_samples
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self.dbscan = DBSCAN(eps=eps, min_samples=min_samples)
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self.scaler = StandardScaler()
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self.gps_points = gps_points
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self.log_file = os.path.join(output_dir, 'del_imgs.txt')
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def fit(self):
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"""
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对GPS点进行聚类，只保留最大的类
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参数:
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gps_points: 包含'lat'和'lon'列的DataFrame
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返回:
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带有聚类标签的DataFrame，其中最大类标记为1，其他点标记为-1
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"""
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# 提取经纬度数据
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X = self.gps_points[["lon", "lat"]].values
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# # 数据标准化
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# X_scaled = self.scaler.fit_transform(X)
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# 执行DBSCAN聚类
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labels = self.dbscan.fit_predict(X)
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# 找出最大类的标签（排除噪声点-1）
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unique_labels = [l for l in set(labels) if l != -1]
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if unique_labels: # 如果有聚类
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label_counts = [(l, sum(labels == l)) for l in unique_labels]
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largest_label = max(label_counts, key=lambda x: x[1])[0]
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# 将最大类标记为1，其他都标记为-1
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new_labels = (labels == largest_label).astype(int)
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new_labels[new_labels == 0] = -1
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else: # 如果没有聚类，全部标记为-1
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new_labels = labels
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# 将聚类结果添加到原始数据中
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result_df = self.gps_points.copy()
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result_df["cluster"] = new_labels
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return result_df
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def get_cluster_stats(self, clustered_points):
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"""
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获取聚类统计信息
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参数:
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clustered_points: 带有聚类标签的DataFrame
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返回:
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聚类统计信息的字典
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"""
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main_cluster_points = sum(clustered_points["cluster"] == 1)
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stats = {
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"total_points": len(clustered_points),
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"main_cluster_points": main_cluster_points,
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"noise_points": sum(clustered_points["cluster"] == -1),
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}
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noise_cluster = self.get_noise_cluster(clustered_points)
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with open(self.log_file, 'a', encoding='utf-8') as f:
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for i, (_, row) in enumerate(noise_cluster.iterrows()):
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f.write(row['file']+'\n')
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f.write('\n')
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return stats
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def get_main_cluster(self, clustered_points):
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return clustered_points[clustered_points["cluster"] == 1]
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def get_noise_cluster(self, clustered_points):
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return clustered_points[clustered_points["cluster"] == -1]
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