From 58159698784dbe6e49acc048c064e1aa70d1ba0d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=BE=99=E6=BE=B3?= <islong.ao@outlook.com>
Date: Tue, 14 Jan 2025 10:35:43 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BF=AEbug?=
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---
 odm_preprocess_fast.py | 318 +++++++++++++++++++++++++++++++++++++++++
 post_pro/merge_tif.py  |   4 +-
 2 files changed, 320 insertions(+), 2 deletions(-)
 create mode 100644 odm_preprocess_fast.py

diff --git a/odm_preprocess_fast.py b/odm_preprocess_fast.py
new file mode 100644
index 0000000..7228537
--- /dev/null
+++ b/odm_preprocess_fast.py
@@ -0,0 +1,318 @@
+import os
+import shutil
+from datetime import timedelta
+from dataclasses import dataclass
+from typing import Dict, Tuple
+import psutil
+
+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 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 = '/home'
+
+        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 * 12
+
+        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"""
+        previous_points = self.gps_points.copy()
+        clusterer = GPSCluster(
+            self.gps_points,
+            eps=self.config.cluster_eps,
+            min_samples=self.config.cluster_min_samples
+        )
+
+        self.clustered_points = clusterer.fit()
+        self.gps_points = clusterer.get_cluster_stats(self.clustered_points)
+
+        self.visualizer.visualize_filter_step(
+            self.gps_points, previous_points, "1-Clustering")
+
+    def filter_isolated_points(self):
+        """过滤孤立点"""
+        filter = GPSFilter(self.config.output_dir)
+        previous_points = self.gps_points.copy()
+
+        self.gps_points = filter.filter_isolated_points(
+            self.gps_points,
+            self.config.filter_distance_threshold,
+            self.config.filter_min_neighbors,
+        )
+
+        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"""
+        previous_points = self.gps_points.copy()
+
+        filter = TimeGroupOverlapFilter(
+            self.config.image_dir,
+            self.config.output_dir,
+            overlap_threshold=self.config.time_group_overlap_threshold
+        )
+
+        self.gps_points = filter.filter_overlapping_groups(
+            self.gps_points,
+            time_threshold=self.config.time_group_interval
+        )
+
+        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
+        """
+        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
+        )
+        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]):
+        """后处理：合并或复制处理结果"""
+        if 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"
+            )
+
+        if self.config.mode == "快拼模式":
+            self.merge_tif(successful_grid_points, self.config.produce_dem)
+        elif self.config.mode == "三维模式":
+            self.merge_ply(successful_grid_points)
+            self.merge_obj(successful_grid_points, translations)
+        else:
+            self.merge_tif(successful_grid_points, self.config.produce_dem)
+            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)
+            successful_grid_points = grid_points
+            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\134\project\images",
+        output_dir=r"G:\ODM_output\134",
+
+        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=800,
+        grid_overlap=0.05,
+
+
+        mode="快拼模式",
+        produce_dem=False,
+    )
+
+    # 创建处理器并执行
+    processor = ImagePreprocessor(config)
+    processor.process()
diff --git a/post_pro/merge_tif.py b/post_pro/merge_tif.py
index 866eae6..e1e8b79 100644
--- a/post_pro/merge_tif.py
+++ b/post_pro/merge_tif.py
@@ -129,13 +129,13 @@ class MergeTif:
                 file_size_mb = os.path.getsize(
                     grid_tif) / (1024 * 1024)  # 转换为MB
                 if file_size_mb > 600:
-                    file_name = file_name.replace(".original", "")
+                    filename = filename.replace(".original", "")
                     grid_tif = os.path.join(
                         self.output_dir,
                         f"grid_{grid_id[0]}_{grid_id[1]}",
                         "project",
                         product_path,
-                        file_name
+                        filename
                     )
 
                 if input_tif1 is None: