import os 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 matplotlib.pyplot as plt import pandas as pd from tqdm import tqdm from filter.cluster_filter import GPSCluster from filter.time_group_overlap_filter import TimeGroupOverlapFilter from filter.gps_filter import GPSFilter from utils.odm_monitor import ODMProcessMonitor from utils.gps_extractor import GPSExtractor from utils.grid_divider import GridDivider from utils.logger import setup_logger from utils.visualizer import FilterVisualizer from post_pro.merge_tif import MergeTif from tools.test_docker_run import run_docker_command from post_pro.merge_obj import MergeObj from post_pro.merge_laz import MergePly @dataclass class PreprocessConfig: """预处理配置类""" image_dir: str output_dir: str # 聚类过滤参数 cluster_eps: float = 0.01 cluster_min_samples: int = 5 # 时间组重叠过滤参数 time_group_overlap_threshold: float = 0.7 time_group_interval: timedelta = timedelta(minutes=5) # 孤立点过滤参数 filter_distance_threshold: float = 0.001 # 经纬度距离 filter_min_neighbors: int = 6 # 密集点过滤参数 filter_grid_size: float = 0.001 filter_dense_distance_threshold: float = 10 # 普通距离，单位：米 filter_time_threshold: timedelta = timedelta(minutes=5) # 网格划分参数 grid_overlap: float = 0.05 grid_size: float = 500 # 几个pipline过程是否开启 mode: str = "快拼模式" produce_dem: bool = False class ImagePreprocessor: def __init__(self, config: PreprocessConfig): self.config = config # 检查磁盘空间 self._check_disk_space() # 清理并重建输出目录 if os.path.exists(config.output_dir): self._clean_output_dir() self._setup_output_dirs() # 初始化其他组件 self.logger = setup_logger(config.output_dir) self.gps_points = None self.odm_monitor = ODMProcessMonitor( config.output_dir, mode=config.mode) self.visualizer = FilterVisualizer(config.output_dir) def _clean_output_dir(self): """清理输出目录""" try: shutil.rmtree(self.config.output_dir) print(f"已清理输出目录: {self.config.output_dir}") except Exception as e: print(f"清理输出目录时发生错误: {str(e)}") raise def _setup_output_dirs(self): """创建必要的输出目录结构""" try: # 创建主输出目录 os.makedirs(self.config.output_dir) # 创建过滤图像保存目录 os.makedirs(os.path.join(self.config.output_dir, 'filter_imgs')) # 创建日志目录 os.makedirs(os.path.join(self.config.output_dir, 'logs')) print(f"已创建输出目录结构: {self.config.output_dir}") except Exception as e: print(f"创建输出目录时发生错误: {str(e)}") raise def _get_directory_size(self, path): """获取目录的总大小（字节）""" total_size = 0 for dirpath, dirnames, filenames in os.walk(path): for filename in filenames: file_path = os.path.join(dirpath, filename) try: total_size += os.path.getsize(file_path) except (OSError, FileNotFoundError): continue return total_size def _check_disk_space(self): """检查磁盘空间是否足够""" # 获取输入目录大小 input_size = self._get_directory_size(self.config.image_dir) # 获取输出目录所在磁盘的剩余空间 output_drive = os.path.splitdrive( os.path.abspath(self.config.output_dir))[0] if not output_drive: # 处理Linux/Unix路径 output_drive = '/' disk_usage = psutil.disk_usage(output_drive) free_space = disk_usage.free # 计算所需空间（输入大小的1.5倍） required_space = input_size * 1.5 if free_space < required_space: error_msg = ( f"磁盘空间不足!\n" f"输入目录大小: {input_size / (1024**3):.2f} GB\n" f"所需空间: {required_space / (1024**3):.2f} GB\n" f"可用空间: {free_space / (1024**3):.2f} GB\n" f"在驱动器 {output_drive}" ) raise RuntimeError(error_msg) def extract_gps(self) -> pd.DataFrame: """提取GPS数据""" self.logger.info("开始提取GPS数据") extractor = GPSExtractor(self.config.image_dir) self.gps_points = extractor.extract_all_gps() self.logger.info(f"成功提取 {len(self.gps_points)} 个GPS点") 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" ) # 可视化聚类结果 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( self.config.image_dir, self.config.output_dir, overlap_threshold=self.config.time_group_overlap_threshold ) deleted_files = 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") def divide_grids(self) -> Tuple[Dict[tuple, pd.DataFrame], Dict[tuple, tuple]]: """划分网格 Returns: tuple: (grid_points, translations) - 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, output_dir=self.config.output_dir ) 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 def copy_images(self, grid_points: Dict[tuple, pd.DataFrame]): """复制图像到目标文件夹""" self.logger.info("开始复制图像文件") for grid_id, points in grid_points.items(): output_dir = os.path.join( self.config.output_dir, f"grid_{grid_id[0]}_{grid_id[1]}", "project", "images" ) os.makedirs(output_dir, exist_ok=True) for point in tqdm(points, desc=f"复制网格 ({grid_id[0]},{grid_id[1]}) 的图像"): src = os.path.join(self.config.image_dir, point["file"]) dst = os.path.join(output_dir, point["file"]) shutil.copy(src, dst) self.logger.info( f"网格 ({grid_id[0]},{grid_id[1]}) 包含 {len(points)} 张图像") def merge_tif(self, grid_points: Dict[tuple, pd.DataFrame], produce_dem: bool): """合并所有网格的影像产品""" self.logger.info("开始合并所有影像产品") merger = MergeTif(self.config.output_dir) merger.merge_all_tifs(grid_points, produce_dem) def merge_ply(self, grid_points: Dict[tuple, pd.DataFrame]): """合并所有网格的PLY点云""" self.logger.info("开始合并PLY点云") merger = MergePly(self.config.output_dir) merger.merge_grid_laz(grid_points) def merge_obj(self, grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]): """合并所有网格的OBJ模型""" self.logger.info("开始合并OBJ模型") merger = MergeObj(self.config.output_dir) merger.merge_grid_obj(grid_points, translations) def post_process(self, successful_grid_points: Dict[tuple, pd.DataFrame], grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]): if len(successful_grid_points) == 1: self.logger.info(f"只有一个网格{successful_grid_points.keys()}，无需合并") self.logger.info(f"生产结果请在{successful_grid_points.keys()[0]}目录下查看") return elif len(successful_grid_points) < len(grid_points): self.logger.warning( f"有 {len(grid_points) - len(successful_grid_points)} 个网格处理失败，" f"将只合并成功处理的 {len(successful_grid_points)} 个网格" ) self.merge_tif(successful_grid_points, self.config.produce_dem) if self.config.mode != "快拼模式": self.merge_ply(successful_grid_points) self.merge_obj(successful_grid_points, translations) def process(self): """执行完整的预处理流程""" try: self.extract_gps() self.cluster() self.filter_isolated_points() self.filter_time_group_overlap() grid_points, translations = self.divide_grids() self.copy_images(grid_points) self.logger.info("预处理任务完成") successful_grid_points = self.odm_monitor.process_all_grids( grid_points, self.config.produce_dem) self.post_process(successful_grid_points, grid_points, translations) except Exception as e: self.logger.error(f"处理过程中发生错误: {str(e)}", exc_info=True) raise if __name__ == "__main__": # 创建配置 config = PreprocessConfig( image_dir=r"E:\datasets\UAV\1619\project\images", output_dir=r"G:\ODM_output\1619", cluster_eps=0.01, cluster_min_samples=5, # 添加时间组重叠过滤参数 time_group_overlap_threshold=0.7, time_group_interval=timedelta(minutes=5), filter_distance_threshold=0.001, filter_min_neighbors=6, filter_grid_size=0.001, filter_dense_distance_threshold=10, filter_time_threshold=timedelta(minutes=5), grid_size=1000, grid_overlap=0.05, mode="重建模式", produce_dem=False, ) # 创建处理器并执行 processor = ImagePreprocessor(config) processor.process()