308 lines
11 KiB
Python
308 lines
11 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, Tuple
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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_laz 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: str
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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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mode: str = "快拼模式"
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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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# 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, mode=config.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'))
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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) -> 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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previous_points = self.gps_points.copy()
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# 创建聚类器并执行聚类
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clusterer = GPSCluster(
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self.gps_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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self.gps_points = clusterer.get_main_cluster(self.clustered_points)
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# 获取统计信息并记录
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stats = clusterer.get_cluster_stats(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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self.gps_points, previous_points, "1-Clustering")
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return self.gps_points
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def filter_time_group_overlap(self) -> 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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previous_points = self.gps_points.copy()
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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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self.gps_points = self.gps_points[~self.gps_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(self.gps_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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self.gps_points, previous_points, "2-Time Group Overlap")
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return self.gps_points
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# TODO 过滤算法还需要更新
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def filter_points(self) -> 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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previous_points = self.gps_points.copy()
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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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self.gps_points = filter.filter_isolated_points(
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self.gps_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(self.gps_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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self.gps_points, previous_points, "3-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 self.gps_points
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def divide_grids(self) -> Tuple[Dict[tuple, pd.DataFrame], Dict[tuple, tuple]]:
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"""划分网格
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Returns:
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tuple: (grid_points, translations)
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- grid_points: 网格点数据字典
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- translations: 网格平移量字典
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"""
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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, translations = 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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return grid_points, translations
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def copy_images(self, grid_points: Dict[tuple, pd.DataFrame]):
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"""复制图像到目标文件夹"""
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self.logger.info("开始复制图像文件")
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for grid_id, points in grid_points.items():
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output_dir = os.path.join(
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self.config.output_dir,
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f"grid_{grid_id[0]}_{grid_id[1]}",
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"project",
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"images"
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)
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os.makedirs(output_dir, exist_ok=True)
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for point in tqdm(points, desc=f"复制网格 ({grid_id[0]},{grid_id[1]}) 的图像"):
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src = os.path.join(self.config.image_dir, point["file"])
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dst = os.path.join(output_dir, point["file"])
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shutil.copy(src, dst)
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self.logger.info(f"网格 ({grid_id[0]},{grid_id[1]}) 包含 {len(points)} 张图像")
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def merge_tif(self, grid_points: Dict[tuple, pd.DataFrame]):
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"""合并所有网格的影像产品"""
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self.logger.info("开始合并所有影像产品")
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merger = MergeTif(self.config.output_dir)
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merger.merge_all_tifs(grid_points)
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def merge_ply(self, grid_points: Dict[tuple, pd.DataFrame]):
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"""合并所有网格的PLY点云"""
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self.logger.info("开始合并PLY点云")
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merger = MergePly(self.config.output_dir)
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merger.merge_grid_laz(grid_points)
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def merge_obj(self, grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]):
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"""合并所有网格的OBJ模型"""
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self.logger.info("开始合并OBJ模型")
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merger = MergeObj(self.config.output_dir)
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merger.merge_grid_obj(grid_points, translations)
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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.cluster()
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# self.filter_time_group_overlap()
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self.filter_points()
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grid_points, translations = self.divide_grids()
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self.copy_images(grid_points)
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self.logger.info("预处理任务完成")
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self.odm_monitor.process_all_grids(grid_points)
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self.merge_tif(grid_points)
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self.merge_ply(grid_points)
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self.merge_obj(grid_points, translations)
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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"E:\datasets\UAV\1815\project\images",
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output_dir=r"G:\ODM_output\1815",
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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=500,
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grid_overlap=0.1,
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mode="重建模式",
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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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