format code

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)
+        main_cluster = clustered_points[clustered_points["cluster"] == 1]
-        stats = {
+        noise_cluster = clustered_points[clustered_points["cluster"] == -1]
-            "total_points": len(clustered_points),
-            "main_cluster_points": main_cluster_points,
-            "noise_points": sum(clustered_points["cluster"] == -1),
-        }
 
-        noise_cluster = self.get_noise_cluster(clustered_points)
+        self.logger.info(f"聚类完成：主要类别包含 {len(main_cluster)} 个点，"
-        return stats
+                         f"噪声点 {len(noise_cluster)} 个")
 
-    def get_main_cluster(self, clustered_points):
+        return main_cluster
-        return clustered_points[clustered_points["cluster"] == 1]
-
-    def get_noise_cluster(self, clustered_points):
-        return clustered_points[clustered_points["cluster"] == -1]

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"""

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,
+            self.gps_points,
-            eps=self.config.cluster_eps, min_samples=self.config.cluster_min_samples)
+            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"
         )
 
-        # 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