317 lines
11 KiB
Python
317 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 psutil
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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 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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produce_dem: 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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# 检查磁盘空间
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self._check_disk_space()
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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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# TODO 给出警告！
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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 _get_directory_size(self, path):
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"""获取目录的总大小（字节）"""
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total_size = 0
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for dirpath, dirnames, filenames in os.walk(path):
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for filename in filenames:
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file_path = os.path.join(dirpath, filename)
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try:
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total_size += os.path.getsize(file_path)
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except (OSError, FileNotFoundError):
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continue
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return total_size
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def _check_disk_space(self):
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"""检查磁盘空间是否足够"""
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# 获取输入目录大小
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input_size = self._get_directory_size(self.config.image_dir)
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# 获取输出目录所在磁盘的剩余空间
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output_drive = os.path.splitdrive(
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os.path.abspath(self.config.output_dir))[0]
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if not output_drive: # 处理Linux/Unix路径
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output_drive = '/'
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disk_usage = psutil.disk_usage(output_drive)
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free_space = disk_usage.free
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# 计算所需空间（输入大小的1.5倍）
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required_space = input_size * 12
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if free_space < required_space:
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error_msg = (
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f"磁盘空间不足!\n"
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f"输入目录大小: {input_size / (1024**3):.2f} GB\n"
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f"所需空间: {required_space / (1024**3):.2f} GB\n"
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f"可用空间: {free_space / (1024**3):.2f} GB\n"
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f"在驱动器 {output_drive}"
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)
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raise RuntimeError(error_msg)
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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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def cluster(self):
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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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previous_points = self.gps_points.copy()
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clusterer = GPSCluster(
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self.gps_points,
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eps=self.config.cluster_eps,
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min_samples=self.config.cluster_min_samples
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)
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self.clustered_points = clusterer.fit()
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self.gps_points = clusterer.get_cluster_stats(self.clustered_points)
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self.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "1-Clustering")
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def filter_isolated_points(self):
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"""过滤孤立点"""
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filter = GPSFilter(self.config.output_dir)
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previous_points = self.gps_points.copy()
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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.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "2-Isolated Points")
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def filter_time_group_overlap(self):
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"""过滤重叠的时间组"""
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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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self.gps_points = filter.filter_overlapping_groups(
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self.gps_points,
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time_threshold=self.config.time_group_interval
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)
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self.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "3-Time Group Overlap")
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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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grid_divider = GridDivider(
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overlap=self.config.grid_overlap,
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grid_size=self.config.grid_size,
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output_dir=self.config.output_dir
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)
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grids, translations, grid_points = grid_divider.adjust_grid_size_and_overlap(
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self.gps_points
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)
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grid_divider.visualize_grids(self.gps_points, grids)
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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(
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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], produce_dem: bool):
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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, produce_dem)
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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 post_process(self, successful_grid_points: Dict[tuple, pd.DataFrame], grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]):
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if len(successful_grid_points) == 1:
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self.logger.info(f"只有一个网格{successful_grid_points.keys()}，无需合并")
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self.logger.info(f"生产结果请在{successful_grid_points.keys()[0]}目录下查看")
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return
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elif len(successful_grid_points) < len(grid_points):
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self.logger.warning(
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f"有 {len(grid_points) - len(successful_grid_points)} 个网格处理失败，"
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f"将只合并成功处理的 {len(successful_grid_points)} 个网格"
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)
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self.merge_tif(successful_grid_points, self.config.produce_dem)
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if self.config.mode != "快拼模式":
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self.merge_ply(successful_grid_points)
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self.merge_obj(successful_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_isolated_points()
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self.filter_time_group_overlap()
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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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successful_grid_points = self.odm_monitor.process_all_grids(
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grid_points, self.config.produce_dem)
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self.post_process(successful_grid_points,
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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\1619\project\images",
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output_dir=r"G:\ODM_output\1619",
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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=800,
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grid_overlap=0.05,
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mode="重建模式",
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produce_dem=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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