249 lines
10 KiB
Python
249 lines
10 KiB
Python
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import os
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import math
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from itertools import combinations
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import numpy as np
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from scipy.spatial import KDTree
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import logging
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import pandas as pd
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from datetime import datetime, timedelta
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class GPSFilter:
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"""过滤密集点及孤立点"""
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def __init__(self, output_dir):
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self.logger = logging.getLogger('UAV_Preprocess.GPSFilter')
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@staticmethod
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def _haversine(lat1, lon1, lat2, lon2):
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"""计算两点之间的地理距离（单位：米）"""
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R = 6371000 # 地球平均半径，单位：米
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phi1, phi2 = math.radians(lat1), math.radians(lat2)
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delta_phi = math.radians(lat2 - lat1)
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delta_lambda = math.radians(lon2 - lon1)
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a = math.sin(delta_phi / 2) ** 2 + math.cos(phi1) * \
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math.cos(phi2) * math.sin(delta_lambda / 2) ** 2
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c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
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return R * c
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@staticmethod
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def _assign_to_grid(lat, lon, grid_size, min_lat, min_lon):
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"""根据经纬度和网格大小，将点分配到网格"""
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grid_x = int((lat - min_lat) // grid_size)
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grid_y = int((lon - min_lon) // grid_size)
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return grid_x, grid_y
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def _get_distances(self, points_df, grid_size):
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"""读取图片 GPS 坐标，计算点对之间的距离并排序"""
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# 确定经纬度范围
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min_lat, max_lat = points_df['lat'].min(), points_df['lat'].max()
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min_lon, max_lon = points_df['lon'].min(), points_df['lon'].max()
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self.logger.info(
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f"经纬度范围：纬度[{min_lat:.6f}, {max_lat:.6f}]，纬度范围[{max_lat-min_lat:.6f}]，"
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f"经度[{min_lon:.6f}, {max_lon:.6f}]，经度范围[{max_lon-min_lon:.6f}]")
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# 分配到网格
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grid_map = {}
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for _, row in points_df.iterrows():
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grid = self._assign_to_grid(
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row['lat'], row['lon'], grid_size, min_lat, min_lon)
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if grid not in grid_map:
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grid_map[grid] = []
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grid_map[grid].append((row['file'], row['lat'], row['lon']))
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self.logger.info(f"图像点已分配到 {len(grid_map)} 个网格中")
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# 在每个网格中计算两两距离并排序
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sorted_distances = {}
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for grid, images in grid_map.items():
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distances = []
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for (img1, lat1, lon1), (img2, lat2, lon2) in combinations(images, 2):
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dist = self._haversine(lat1, lon1, lat2, lon2)
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distances.append((img1, img2, dist))
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distances.sort(key=lambda x: x[2]) # 按距离升序排序
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sorted_distances[grid] = distances
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self.logger.debug(f"网格 {grid} 中计算了 {len(distances)} 个距离对")
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return sorted_distances
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def _group_by_time(self, points_df: pd.DataFrame, time_threshold: timedelta) -> list:
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"""根据拍摄时间分组图片
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如果相邻两张图片的拍摄时间差超过5分钟，则进行切分
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Args:
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points_df: 包含图片信息的DataFrame，必须包含'file'和'date'列
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time_threshold: 时间间隔阈值，默认5分钟
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Returns:
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list: 每个元素为时间组内的点数据
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"""
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if 'date' not in points_df.columns:
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self.logger.error("数据中缺少date列")
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return [points_df]
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# 将date为空的行单独作为一组
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null_date_group = points_df[points_df['date'].isna()]
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valid_date_points = points_df[points_df['date'].notna()]
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if not null_date_group.empty:
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self.logger.info(f"发现 {len(null_date_group)} 个无时间戳的点，将作为单独分组")
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if valid_date_points.empty:
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self.logger.warning("没有有效的时间戳数据")
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return [null_date_group] if not null_date_group.empty else []
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# 按时间排序
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valid_date_points = valid_date_points.sort_values('date')
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self.logger.info(
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f"有效时间范围: {valid_date_points['date'].min()} 到 {valid_date_points['date'].max()}")
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# 计算时间差
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time_diffs = valid_date_points['date'].diff()
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# 找到时间差超过阈值的位置
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time_groups = []
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current_group_start = 0
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for idx, time_diff in enumerate(time_diffs):
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if time_diff and time_diff > time_threshold:
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# 添加当前组
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current_group = valid_date_points.iloc[current_group_start:idx]
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time_groups.append(current_group)
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# 记录断点信息
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break_time = valid_date_points.iloc[idx]['date']
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group_start_time = current_group.iloc[0]['date']
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group_end_time = current_group.iloc[-1]['date']
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self.logger.info(
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f"时间组 {len(time_groups)}: {len(current_group)} 个点, "
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f"时间范围 [{group_start_time} - {group_end_time}]"
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)
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self.logger.info(
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f"在时间 {break_time} 处发现断点，时间差为 {time_diff}")
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current_group_start = idx
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# 添加最后一组
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last_group = valid_date_points.iloc[current_group_start:]
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if not last_group.empty:
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time_groups.append(last_group)
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self.logger.info(
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f"时间组 {len(time_groups)}: {len(last_group)} 个点, "
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f"时间范围 [{last_group.iloc[0]['date']} - {last_group.iloc[-1]['date']}]"
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)
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# 如果有空时间戳的点，将其作为最后一组
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if not null_date_group.empty:
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time_groups.append(null_date_group)
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self.logger.info(f"添加无时间戳组: {len(null_date_group)} 个点")
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self.logger.info(f"共分为 {len(time_groups)} 个时间组")
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return time_groups
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def filter_dense_points(self, points_df, grid_size=0.001, distance_threshold=13, time_threshold=timedelta(minutes=5)):
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"""
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过滤密集点，先按时间分组，再在每个时间组内过滤。
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空时间戳的点不进行过滤。
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Args:
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points_df: 点数据
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grid_size: 网格大小
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distance_threshold: 距离阈值（米）
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time_interval: 时间间隔（秒）
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"""
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self.logger.info(f"开始按时间分组过滤密集点 (网格大小: {grid_size}, "
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f"距离阈值: {distance_threshold}米, 分组时间间隔: {time_threshold}秒)")
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# 按时间分组
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time_groups = self._group_by_time(points_df, time_threshold)
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# 存储所有要删除的图片
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all_to_del_imgs = []
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# 对每个时间组进行密集点过滤
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for group_idx, group_points in enumerate(time_groups):
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# 检查是否为空时间戳组（最后一组）
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if group_idx == len(time_groups) - 1 and group_points['date'].isna().any():
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self.logger.info(f"跳过无时间戳组 (包含 {len(group_points)} 个点)")
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continue
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self.logger.info(
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f"处理时间组 {group_idx + 1} (包含 {len(group_points)} 个点)")
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# 计算该组内的点间距离
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sorted_distances = self._get_distances(group_points, grid_size)
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group_to_del_imgs = []
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# 在每个网格中过滤密集点
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for grid, distances in sorted_distances.items():
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grid_del_count = 0
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while distances:
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candidate_img1, candidate_img2, dist = distances[0]
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if dist < distance_threshold:
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distances.pop(0)
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# 获取候选图片的其他最短距离
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candidate_img1_dist = None
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candidate_img2_dist = None
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for distance in distances:
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if candidate_img1 in distance:
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candidate_img1_dist = distance[2]
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break
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for distance in distances:
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if candidate_img2 in distance:
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candidate_img2_dist = distance[2]
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break
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# 选择要删除的点
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if candidate_img1_dist and candidate_img2_dist:
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to_del_img = candidate_img1 if candidate_img1_dist < candidate_img2_dist else candidate_img2
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group_to_del_imgs.append(to_del_img)
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grid_del_count += 1
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self.logger.debug(
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f"时间组 {group_idx + 1} 网格 {grid} 删除密集点: {to_del_img} (距离: {dist:.2f}米)")
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distances = [
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d for d in distances if to_del_img not in d]
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else:
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break
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if grid_del_count > 0:
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self.logger.info(
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f"时间组 {group_idx + 1} 网格 {grid} 删除了 {grid_del_count} 个密集点")
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all_to_del_imgs.extend(group_to_del_imgs)
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self.logger.info(
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f"时间组 {group_idx + 1} 共删除 {len(group_to_del_imgs)} 个密集点")
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# 过滤数据
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filtered_df = points_df[~points_df['file'].isin(all_to_del_imgs)]
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self.logger.info(
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f"密集点过滤完成，共删除 {len(all_to_del_imgs)} 个点，剩余 {len(filtered_df)} 个点")
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return filtered_df
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def filter_isolated_points(self, points_df, threshold_distance=0.001, min_neighbors=6):
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"""过滤孤立点"""
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self.logger.info(
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f"开始过滤孤立点 (距离阈值: {threshold_distance}, 最小邻居数: {min_neighbors})")
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coords = points_df[['lat', 'lon']].values
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kdtree = KDTree(coords)
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neighbors_count = [len(kdtree.query_ball_point(
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coord, threshold_distance)) for coord in coords]
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isolated_points = []
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for i, (_, row) in enumerate(points_df.iterrows()):
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if neighbors_count[i] < min_neighbors:
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isolated_points.append(row['file'])
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self.logger.debug(
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f"删除孤立点: {row['file']} (邻居数: {neighbors_count[i]})")
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filtered_df = points_df[~points_df['file'].isin(isolated_points)]
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self.logger.info(
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f"孤立点过滤完成，共删除 {len(isolated_points)} 个点，剩余 {len(filtered_df)} 个点")
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return filtered_df
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