先不做docker并行处理

utils/odm_monitor.py

@@ -6,8 +6,6 @@ from typing import Dict, Tuple
 import pandas as pd
 import numpy as np
 from osgeo import gdal
-import threading
-from concurrent.futures import ThreadPoolExecutor, as_completed
 
 
 class NotOverlapError(Exception):
@@ -18,11 +16,10 @@ class NotOverlapError(Exception):
 class ODMProcessMonitor:
     """ODM处理监控器"""
 
-    def __init__(self, output_dir: str, mode: str = "快拼模式", max_parallel: int = 2):
+    def __init__(self, output_dir: str, mode: str = "快拼模式"):
         self.output_dir = output_dir
         self.logger = logging.getLogger('UAV_Preprocess.ODMMonitor')
         self.mode = mode
-        self.max_parallel = max_parallel  # 最大并行数
 
     def _check_success(self, grid_dir: str) -> bool:
         """检查ODM是否执行成功
@@ -148,19 +145,11 @@ class ODMProcessMonitor:
                 # 构建Docker命令
                 grid_dir = grid_dir[0].lower()+grid_dir[1:].replace('\\', '/')
                 docker_command = (
-                    f"docker run "
-                    # 资源限制
-                    f"--gpus all "  # 指定使用的GPU
-                    f"--memory=32g "         # 限制内存使用
-                    f"--memory-swap=36g "    # 限制包含swap的总内存
-                    f"--cpus=10.0 "          # 限制CPU使用数量
-                    # 其他参数
-                    f"-ti --rm "
-                    f"--name odm_grid_{grid_id[0]}_{grid_id[1]} "
+                    f"docker run --gpus all -ti --rm "
                     f"-v {grid_dir}:/datasets "
                     f"opendronemap/odm:gpu "
                     f"--project-path /datasets project "
-                    f"--max-concurrency 10 "
+                    f"--max-concurrency 15 "
                     f"--force-gps "
                 )
 
@@ -249,34 +238,34 @@ class ODMProcessMonitor:
         return success, error_msg
 
     def process_all_grids(self, grid_points: Dict[tuple, pd.DataFrame], produce_dem: bool) -> Dict[tuple, pd.DataFrame]:
-        """并行处理所有网格"""
+        """处理所有网格
+
+        Returns:
+            Dict[tuple, pd.DataFrame]: 成功处理的网格点数据字典
+        """
         self.logger.info("开始执行网格处理")
         successful_grid_points = {}
         failed_grids = []
 
-        # 使用线程池进行并行处理
-        with ThreadPoolExecutor(max_workers=self.max_parallel) as executor:
-            # 创建所有任务
-            future_to_grid = {
-                executor.submit(
-                    self.run_odm_with_monitor,
-                    os.path.join(self.output_dir, f'grid_{grid_id[0]}_{grid_id[1]}'),
-                    grid_id,
-                    produce_dem
-                ): (grid_id, points) for grid_id, points in grid_points.items()
-            }
+        for grid_id, points in grid_points.items():
+            grid_dir = os.path.join(
+                self.output_dir, f'grid_{grid_id[0]}_{grid_id[1]}'
+            )
 
-            # 等待所有任务完成并收集结果
-            for future in as_completed(future_to_grid):
-                grid_id, points = future_to_grid[future]
             try:
-                    success, error_msg = future.result()
+                success, error_msg = self.run_odm_with_monitor(
+                    grid_dir=grid_dir,
+                    grid_id=grid_id,
+                    produce_dem=produce_dem
+                )
+
                 if success:
                     successful_grid_points[grid_id] = points
                 else:
                     self.logger.error(
                         f"网格 ({grid_id[0]},{grid_id[1]}) 处理失败: {error_msg}")
                     failed_grids.append((grid_id, error_msg))
+
             except Exception as e:
                 error_msg = str(e)
                 self.logger.error(