347 lines
12 KiB
Python
347 lines
12 KiB
Python
import os
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import shutil
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from datetime import timedelta
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from dataclasses import dataclass
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from typing import Dict, Optional
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import matplotlib.pyplot as plt
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import pandas as pd
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from tqdm import tqdm
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from filter.cluster_filter import GPSCluster
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from filter.time_group_overlap_filter import TimeGroupOverlapFilter
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from filter.gps_filter import GPSFilter
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from utils.odm_monitor import ODMProcessMonitor
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from utils.gps_extractor import GPSExtractor
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from utils.grid_divider import GridDivider
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from utils.logger import setup_logger
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from utils.visualizer import FilterVisualizer
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from post_pro.merge_tif import MergeTif
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from tools.test_docker_run import run_docker_command
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from post_pro.merge_obj import MergeObj
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from post_pro.merge_ply import MergePly
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@dataclass
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class PreprocessConfig:
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"""预处理配置类"""
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image_dir: str
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output_dir: Optional[str] = None
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# 聚类过滤参数
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cluster_eps: float = 0.01
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cluster_min_samples: int = 5
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# 时间组重叠过滤参数
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time_group_overlap_threshold: float = 0.7
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time_group_interval: timedelta = timedelta(minutes=5)
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# 孤立点过滤参数
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filter_distance_threshold: float = 0.001 # 经纬度距离
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filter_min_neighbors: int = 6
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# 密集点过滤参数
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filter_grid_size: float = 0.001
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filter_dense_distance_threshold: float = 10 # 普通距离，单位：米
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filter_time_threshold: timedelta = timedelta(minutes=5)
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# 网格划分参数
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grid_overlap: float = 0.05
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grid_size: float = 500
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# 几个pipline过程是否开启
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fast_mode: bool = False
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class ImagePreprocessor:
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def __init__(self, config: PreprocessConfig):
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self.config = config
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self.grandpa_dir = os.path.dirname(
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os.path.dirname(self.config.image_dir))
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self.config.output_dir = os.path.join(self.grandpa_dir, 'preprocess')
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# 清理并重建输出目录
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if os.path.exists(config.output_dir):
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self._clean_output_dir()
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self._setup_output_dirs()
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# 初始化其他组件
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self.logger = setup_logger(config.output_dir)
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self.gps_points = None
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self.odm_monitor = ODMProcessMonitor(
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config.output_dir, fast_mode=config.fast_mode)
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self.visualizer = FilterVisualizer(config.output_dir)
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def _clean_output_dir(self):
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"""清理输出目录"""
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try:
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shutil.rmtree(self.config.output_dir)
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print(f"已清理输出目录: {self.config.output_dir}")
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except Exception as e:
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print(f"清理输出目录时发生错误: {str(e)}")
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raise
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def _setup_output_dirs(self):
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"""创建必要的输出目录结构"""
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try:
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# 创建主输出目录
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os.makedirs(self.config.output_dir)
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# 创建过滤图像保存目录
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os.makedirs(os.path.join(self.config.output_dir, 'filter_imgs_visual'))
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# 创建日志目录
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os.makedirs(os.path.join(self.config.output_dir, 'logs'))
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print(f"已创建输出目录结构: {self.config.output_dir}")
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except Exception as e:
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print(f"创建输出目录时发生错误: {str(e)}")
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raise
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def extract_gps(self) -> pd.DataFrame:
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"""提取GPS数据"""
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self.logger.info("开始提取GPS数据")
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extractor = GPSExtractor(self.config.image_dir)
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self.gps_points = extractor.extract_all_gps()
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self.logger.info(f"成功提取 {len(self.gps_points)} 个GPS点")
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return self.gps_points
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def cluster(self, previous_points) -> pd.DataFrame:
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"""使用DBSCAN对GPS点进行聚类，只保留最大的类"""
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self.logger.info("开始聚类")
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# 创建聚类器并执行聚类
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clusterer = GPSCluster(
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previous_points, output_dir=self.config.output_dir,
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eps=self.config.cluster_eps, min_samples=self.config.cluster_min_samples)
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# 获取主要类别的点
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self.clustered_points = clusterer.fit()
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# 获取统计信息并记录
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stats, retained_points, removed_points = clusterer.get_cluster_stats(
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self.clustered_points)
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self.logger.info(
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f"聚类完成：主要类别包含 {stats['main_cluster_points']} 个点，"
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f"噪声点 {stats['noise_points']} 个"
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)
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# 可视化聚类结果
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self.visualizer.visualize_filter_step(
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retained_points, removed_points, "1-Clustering")
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# 移动被过滤的图片
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self.move_images(removed_points, "cluster")
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return retained_points
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def filter_time_group_overlap(self, previous_points) -> pd.DataFrame:
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"""过滤重叠的时间组"""
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self.logger.info("开始过滤重叠时间组")
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self.logger.info("开始过滤重叠时间组")
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filter = TimeGroupOverlapFilter(
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self.config.image_dir,
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self.config.output_dir,
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overlap_threshold=self.config.time_group_overlap_threshold
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)
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deleted_files = filter.filter_overlapping_groups(
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time_threshold=self.config.time_group_interval
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)
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# 更新GPS点数据，移除被删除的图像
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retained_points = previous_points[~previous_points['file'].isin(
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deleted_files)]
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removed_points = previous_points[previous_points['file'].isin(
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deleted_files)]
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self.logger.info(f"重叠时间组过滤后剩余 {len(retained_points)} 个GPS点")
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# 可视化过滤结果
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self.visualizer.visualize_filter_step(
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retained_points, removed_points, "2-Time Group Overlap")
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# 移动被过滤的图片
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self.move_images(removed_points, "time_group_overlap")
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return retained_points
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# TODO 过滤算法还需要更新
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def filter_points(self, previous_points) -> pd.DataFrame:
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"""过滤GPS点"""
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self.logger.info("开始过滤GPS点")
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filter = GPSFilter(self.config.output_dir)
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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: {self.config.filter_distance_threshold}, "
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f"最小邻居数: {self.config.filter_min_neighbors})"
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)
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retained_points, removed_points = filter.filter_isolated_points(
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previous_points,
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self.config.filter_distance_threshold,
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self.config.filter_min_neighbors,
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)
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self.logger.info(f"孤立点过滤后剩余 {len(retained_points)} 个GPS点")
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# 可视化孤立点过滤结果
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self.visualizer.visualize_filter_step(
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retained_points, removed_points, "3-Isolated Points")
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# 移动被过滤的图片
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self.move_images(removed_points, "isolated_points")
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# # 过滤密集点
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# previous_points = self.gps_points.copy()
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# self.logger.info(
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# f"开始过滤密集点(网格大小: {self.config.filter_grid_size}, "
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# f"距离阈值: {self.config.filter_dense_distance_threshold})"
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# )
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# self.gps_points = filter.filter_dense_points(
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# self.gps_points,
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# grid_size=self.config.filter_grid_size,
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# distance_threshold=self.config.filter_dense_distance_threshold,
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# time_threshold=self.config.filter_time_threshold,
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# )
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# self.logger.info(f"密集点过滤后剩余 {len(self.gps_points)} 个GPS点")
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# # 可视化密集点过滤结果
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# self.visualizer.visualize_filter_step(
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# self.gps_points, previous_points, "4-Dense Points")
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return retained_points
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def divide_grids(self) -> Dict[int, pd.DataFrame]:
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"""划分网格"""
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self.logger.info(f"开始划分网格 (重叠率: {self.config.grid_overlap})")
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grid_divider = GridDivider(
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overlap=self.config.grid_overlap,
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output_dir=self.config.output_dir
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)
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grids = grid_divider.divide_grids(
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self.gps_points, grid_size=self.config.grid_size
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)
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grid_points = grid_divider.assign_to_grids(self.gps_points, grids)
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self.logger.info(f"成功划分为 {len(grid_points)} 个网格")
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# 生成image_groups.txt文件
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try:
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groups_file = os.path.join(
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os.path.dirname(self.config.image_dir), "image_groups.txt"
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)
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self.logger.info(f"开始生成分组文件: {groups_file}")
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with open(groups_file, 'w') as f:
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for grid_idx, points_lt in grid_points.items():
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# 使用ASCII字母作为组标识（A, B, C...）
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group_letter = chr(65 + grid_idx) # 65是ASCII中'A'的编码
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# 为每个网格中的图像写入分组信息
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for point in points_lt:
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f.write(f"{point['file']} {group_letter}\n")
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self.logger.info(f"分组文件生成成功: {groups_file}")
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except Exception as e:
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self.logger.error(f"生成分组文件时发生错误: {str(e)}", exc_info=True)
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raise
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def move_images(self, removed_points: pd.DataFrame, step_name: str):
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"""
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将被过滤掉的图片移动到ret文件夹中
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Args:
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removed_points: 被过滤掉的GPS点对应的数据
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step_name: 过滤步骤名称，用于创建子文件夹
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"""
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if removed_points.empty:
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return
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# 创建ret目录和对应步骤的子目录
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ret_dir = os.path.join(self.grandpa_dir, 'ret')
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os.makedirs(ret_dir, exist_ok=True)
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self.logger.info(f"开始移动{step_name}步骤中被过滤的图片")
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# 移动每张被过滤的图片
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for _, point in removed_points.iterrows():
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src_path = os.path.join(self.config.image_dir, point['file'])
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dst_path = os.path.join(ret_dir, point['file'])
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try:
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shutil.move(src_path, dst_path)
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except Exception as e:
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self.logger.warning(f"移动图片 {point['file']} 时发生错误: {str(e)}")
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self.logger.info(f"完成移动 {len(removed_points)} 张被{step_name}过滤的图片")
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def restore_filtered_images(self):
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"""将ret文件夹中的图片恢复到原始图片目录"""
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try:
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# 获取ret文件夹路径
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ret_dir = os.path.join(self.grandpa_dir, 'ret')
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if not os.path.exists(ret_dir):
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self.logger.info("没有找到ret文件夹，跳过恢复步骤")
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return
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self.logger.info("开始恢复被过滤的图片")
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# 获取ret文件夹中的所有图片
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filtered_images = os.listdir(ret_dir)
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# 将图片移回原始目录
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for img in filtered_images:
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src_path = os.path.join(ret_dir, img)
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dst_path = os.path.join(self.config.image_dir, img)
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try:
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shutil.move(src_path, dst_path)
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except Exception as e:
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self.logger.warning(f"恢复图片 {img} 时发生错误: {str(e)}")
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self.logger.info(f"成功恢复 {len(filtered_images)} 张图片")
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except Exception as e:
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self.logger.error(f"恢复图片过程中发生错误: {str(e)}", exc_info=True)
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raise
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def process(self):
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"""执行完整的预处理流程"""
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try:
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self.extract_gps()
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self.gps_points = self.cluster(self.gps_points)
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# self.gps_points = self.filter_time_group_overlap(self.gps_points)
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self.gps_points = self.filter_points(self.gps_points)
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self.divide_grids()
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self.logger.info("预处理任务完成")
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self.odm_monitor.run_odm_with_monitor(
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self.grandpa_dir, self.config.fast_mode)
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self.restore_filtered_images()
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except Exception as e:
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self.logger.error(f"处理过程中发生错误: {str(e)}", exc_info=True)
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raise
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if __name__ == "__main__":
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# 创建配置
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config = PreprocessConfig(
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image_dir=r"G:\error_data\20241104140457\project\images",
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cluster_eps=0.01,
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cluster_min_samples=5,
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# 添加时间组重叠过滤参数
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time_group_overlap_threshold=0.7,
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time_group_interval=timedelta(minutes=5),
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filter_distance_threshold=0.001,
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filter_min_neighbors=6,
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filter_grid_size=0.001,
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filter_dense_distance_threshold=10,
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filter_time_threshold=timedelta(minutes=5),
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grid_size=1000,
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grid_overlap=0.05,
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fast_mode=False,
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)
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# 创建处理器并执行
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processor = ImagePreprocessor(config)
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processor.process()
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