diff --git a/filter/cluster_filter.py b/filter/cluster_filter.py index eadebf1..473da7d 100644 --- a/filter/cluster_filter.py +++ b/filter/cluster_filter.py @@ -1,10 +1,11 @@ from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler import os +import logging class GPSCluster: - def __init__(self, gps_points, output_dir: str, eps=0.01, min_samples=5): + def __init__(self, gps_points, eps=0.01, min_samples=3): """ 初始化GPS聚类器 @@ -17,6 +18,7 @@ class GPSCluster: 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): """ @@ -28,6 +30,7 @@ class GPSCluster: 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 @@ -65,18 +68,10 @@ class GPSCluster: 返回: 聚类统计信息的字典 """ - main_cluster_points = sum(clustered_points["cluster"] == 1) - stats = { - "total_points": len(clustered_points), - "main_cluster_points": main_cluster_points, - "noise_points": sum(clustered_points["cluster"] == -1), - } + main_cluster = clustered_points[clustered_points["cluster"] == 1] + noise_cluster = clustered_points[clustered_points["cluster"] == -1] - noise_cluster = self.get_noise_cluster(clustered_points) - return stats + 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") - def get_main_cluster(self, clustered_points): - return clustered_points[clustered_points["cluster"] == 1] - - def get_noise_cluster(self, clustered_points): - return clustered_points[clustered_points["cluster"] == -1] + return main_cluster diff --git a/filter/time_group_overlap_filter.py b/filter/time_group_overlap_filter.py index 72e932b..7f72580 100644 --- a/filter/time_group_overlap_filter.py +++ b/filter/time_group_overlap_filter.py @@ -97,6 +97,7 @@ class TimeGroupOverlapFilter: def filter_overlapping_groups(self, gps_points, time_threshold=timedelta(minutes=5)): """过滤重叠的时间组""" # 按时间分组 + self.logger.info("开始过滤重叠时间组") time_groups = self._group_by_time(gps_points, time_threshold) # 计算每个组的边界框 @@ -156,7 +157,10 @@ class TimeGroupOverlapFilter: # 可视化结果 self._visualize_results(time_groups, groups_to_delete) - return deleted_files + retained_points = gps_points[~gps_points['file'].isin( + deleted_files)] + self.logger.info(f"重叠时间组过滤后剩余 {len(retained_points)} 个GPS点") + return retained_points def _visualize_results(self, time_groups, groups_to_delete): """可视化过滤结果""" diff --git a/odm_preprocess.py b/odm_preprocess.py index a64503a..0f2addc 100644 --- a/odm_preprocess.py +++ b/odm_preprocess.py @@ -3,7 +3,7 @@ import shutil from datetime import timedelta from dataclasses import dataclass from typing import Dict, Tuple -import psutil # 需要添加到 requirements.txt +import psutil import matplotlib.pyplot as plt import pandas as pd @@ -143,52 +143,35 @@ class ImagePreprocessor: def cluster(self): """使用DBSCAN对GPS点进行聚类，只保留最大的类""" - self.logger.info("开始聚类") previous_points = self.gps_points.copy() - - # 创建聚类器并执行聚类 clusterer = GPSCluster( - self.gps_points, output_dir=self.config.output_dir, - eps=self.config.cluster_eps, min_samples=self.config.cluster_min_samples) - - # 获取主要类别的点 - self.clustered_points = clusterer.fit() - self.gps_points = clusterer.get_main_cluster(self.clustered_points) - - # 获取统计信息并记录 - stats = clusterer.get_cluster_stats(self.clustered_points) - self.logger.info( - f"聚类完成：主要类别包含 {stats['main_cluster_points']} 个点，" - f"噪声点 {stats['noise_points']} 个" + self.gps_points, + eps=self.config.cluster_eps, + min_samples=self.config.cluster_min_samples ) - # 可视化聚类结果 + self.clustered_points = clusterer.fit() + self.gps_points = clusterer.get_cluster_stats(self.clustered_points) + self.visualizer.visualize_filter_step( self.gps_points, previous_points, "1-Clustering") def filter_isolated_points(self): """过滤孤立点""" - self.logger.info("开始过滤孤立点") filter = GPSFilter(self.config.output_dir) previous_points = self.gps_points.copy() - self.logger.info( - f"开始过滤孤立点(距离阈值: {self.config.filter_distance_threshold}, " - f"最小邻居数: {self.config.filter_min_neighbors})" - ) + self.gps_points = filter.filter_isolated_points( self.gps_points, self.config.filter_distance_threshold, self.config.filter_min_neighbors, ) - self.logger.info(f"孤立点过滤后剩余 {len(self.gps_points)} 个GPS点") - # 可视化孤立点过滤结果 self.visualizer.visualize_filter_step( self.gps_points, previous_points, "2-Isolated Points") def filter_time_group_overlap(self): """过滤重叠的时间组""" - self.logger.info("开始过滤重叠时间组") previous_points = self.gps_points.copy() filter = TimeGroupOverlapFilter( @@ -197,17 +180,11 @@ class ImagePreprocessor: overlap_threshold=self.config.time_group_overlap_threshold ) - deleted_files = filter.filter_overlapping_groups( + self.gps_points = filter.filter_overlapping_groups( self.gps_points, time_threshold=self.config.time_group_interval ) - # 更新GPS点数据，移除被删除的图像 - self.gps_points = self.gps_points[~self.gps_points['file'].isin( - deleted_files)] - self.logger.info(f"重叠时间组过滤后剩余 {len(self.gps_points)} 个GPS点") - - # 可视化过滤结果 self.visualizer.visualize_filter_step( self.gps_points, previous_points, "3-Time Group Overlap") @@ -218,7 +195,6 @@ class ImagePreprocessor: - grid_points: 网格点数据字典 - translations: 网格平移量字典 """ - self.logger.info(f"开始划分网格 (重叠率: {self.config.grid_overlap})") grid_divider = GridDivider( overlap=self.config.grid_overlap, grid_size=self.config.grid_size, @@ -227,7 +203,6 @@ class ImagePreprocessor: grids, translations, grid_points = grid_divider.adjust_grid_size_and_overlap( self.gps_points ) - self.logger.info(f"成功划分为 {len(grid_points)} 个网格") grid_divider.visualize_grids(self.gps_points, grids) return grid_points, translations