更新nvidia docker命令

README.md

@@ -10,7 +10,6 @@ pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
 
 ## TODO
 
-- 过滤算法需要更新
-- 合并obj影像需要更新
 - command_runner中rerun需要更新
+- grid要动态分割大小
 - 任务队列

odm_preprocess.py

@@ -177,23 +177,23 @@ class ImagePreprocessor:
         self.visualizer.visualize_filter_step(
             self.gps_points, previous_points, "3-Isolated Points")
 
-        # 过滤密集点
-        previous_points = self.gps_points.copy()
-        self.logger.info(
-            f"开始过滤密集点(网格大小: {self.config.filter_grid_size}, "
-            f"距离阈值: {self.config.filter_dense_distance_threshold})"
-        )
-        self.gps_points = filter.filter_dense_points(
-            self.gps_points,
-            grid_size=self.config.filter_grid_size,
-            distance_threshold=self.config.filter_dense_distance_threshold,
-            time_threshold=self.config.filter_time_threshold,
-        )
-        self.logger.info(f"密集点过滤后剩余 {len(self.gps_points)} 个GPS点")
+        # # 过滤密集点
+        # previous_points = self.gps_points.copy()
+        # self.logger.info(
+        #     f"开始过滤密集点(网格大小: {self.config.filter_grid_size}, "
+        #     f"距离阈值: {self.config.filter_dense_distance_threshold})"
+        # )
+        # self.gps_points = filter.filter_dense_points(
+        #     self.gps_points,
+        #     grid_size=self.config.filter_grid_size,
+        #     distance_threshold=self.config.filter_dense_distance_threshold,
+        #     time_threshold=self.config.filter_time_threshold,
+        # )
+        # self.logger.info(f"密集点过滤后剩余 {len(self.gps_points)} 个GPS点")
 
-        # 可视化密集点过滤结果
-        self.visualizer.visualize_filter_step(
-            self.gps_points, previous_points, "4-Dense Points")
+        # # 可视化密集点过滤结果
+        # self.visualizer.visualize_filter_step(
+        #     self.gps_points, previous_points, "4-Dense Points")
 
         return self.gps_points
 
@@ -253,7 +253,7 @@ class ImagePreprocessor:
             self.extract_gps()
             self.cluster()
             # self.filter_time_group_overlap()
-            # self.filter_points()
+            self.filter_points()
             grid_points = self.divide_grids()
             self.copy_images(grid_points)
             self.logger.info("预处理任务完成")
@@ -270,8 +270,8 @@ class ImagePreprocessor:
 if __name__ == "__main__":
     # 创建配置
     config = PreprocessConfig(
-        image_dir=r"E:\datasets\UAV\1009\project\images",
-        output_dir=r"G:\ODM_output\1009",
+        image_dir=r"E:\datasets\UAV\1815\images",
+        output_dir=r"G:\ODM_output\1815",
 
         cluster_eps=0.01,
         cluster_min_samples=5,
@@ -287,8 +287,8 @@ if __name__ == "__main__":
         filter_dense_distance_threshold=10,
         filter_time_threshold=timedelta(minutes=5),
 
-        grid_size=300,
-        grid_overlap=0.03,
+        grid_size=500,
+        grid_overlap=0.05,
 
 
         mode="重建模式",

utils/odm_monitor.py

@@ -27,7 +27,7 @@ class ODMProcessMonitor:
         # 构建Docker命令
         grid_dir = grid_dir[0].lower()+grid_dir[1:].replace('\\', '/')
         docker_command = (
-            f"docker run -ti --rm "
+            f"docker run --gpus all -ti --rm "
             f"-v {grid_dir}:/datasets "
             f"opendronemap/odm "
             f"--project-path /datasets project "