结果后处理修改

main.py

@@ -0,0 +1,50 @@
+import argparse
+from datetime import timedelta
+from odm_preprocess import PreprocessConfig, ImagePreprocessor
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='ODM预处理工具')
+    
+    # 必需参数
+    parser.add_argument('--image_dir', required=True, help='输入图片目录路径')
+    parser.add_argument('--output_dir', required=True, help='输出目录路径')
+    
+    # 可选参数
+    parser.add_argument('--mode', default='重建模式', choices=['快拼模式', '三维模式', '重建模式'], help='处理模式')
+    parser.add_argument('--grid_size', type=float, default=1000, help='网格大小(米)')
+    parser.add_argument('--grid_overlap', type=float, default=0.05, help='网格重叠率')
+    parser.add_argument('--produce_dem', action='store_true', help='是否生成DEM')
+    
+    args = parser.parse_args()
+    return args
+
+def main():
+    args = parse_args()
+    
+    # 创建配置
+    config = PreprocessConfig(
+        image_dir=args.image_dir,
+        output_dir=args.output_dir,
+        mode=args.mode,
+        grid_size=args.grid_size,
+        grid_overlap=args.grid_overlap,
+        produce_dem=args.produce_dem,
+        
+        # 其他参数使用默认值
+        cluster_eps=0.01,
+        cluster_min_samples=5,
+        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),
+    )
+    
+    # 创建处理器并执行
+    processor = ImagePreprocessor(config)
+    processor.process()
+
+if __name__ == '__main__':
+    main()

odm_preprocess.py

@@ -247,17 +247,68 @@ class ImagePreprocessor:
         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) == 1:
-            self.logger.info(f"只有一个网格{successful_grid_points.keys()}，无需合并")
+            # 获取唯一成功的网格ID
-            self.logger.info(f"生产结果请在{successful_grid_points.keys()[0]}目录下查看")
+            grid_id = list(successful_grid_points.keys())[0]
+            self.logger.info(f"只有一个网格 {grid_id} 成功处理，直接复制结果")
+
+            # 构建源目录路径（成功网格的输出目录）
+            src_dir = os.path.join(
+                self.config.output_dir, f"grid_{grid_id[0]}_{grid_id[1]}", "project")
+
+            # 复制正射影像
+            if self.config.mode == "快拼模式":
+                shutil.copy2(os.path.join(src_dir, "odm_orthophoto", "odm_orthophoto.original.tif"),
+                             os.path.join(self.config.output_dir,
+                                          "orthophoto.tif")
+                             )
+            elif self.config.mode == "三维模式":
+                shutil.copy2(os.path.join(src_dir, "odm_georeferencing", "odm_georeferenced_model.laz"),
+                             os.path.join(self.config.output_dir,
+                                          "pointcloud.laz")
+                             )
+                shutil.copytree(os.path.join(src_dir, "odm_texturing"),
+                           os.path.join(self.config.output_dir, "texturing"))
+            elif self.config.mode == "重建模式":
+                shutil.copy2(os.path.join(src_dir, "odm_orthophoto", "odm_orthophoto.tif"),
+                             os.path.join(self.config.output_dir,
+                                          "orthophoto.tif")
+                             )
+                shutil.copy2(os.path.join(src_dir, "odm_georeferencing", "odm_georeferenced_model.laz"),
+                             os.path.join(self.config.output_dir,
+                                          "odm_georeferenced_model.laz")
+                             )
+                shutil.copytree(os.path.join(src_dir, "odm_texturing"),
+                           os.path.join(self.config.output_dir, "texturing"))
+            if self.config.produce_dem:
+                shutil.copy2(
+                    os.path.join(src_dir, "odm_dem", "dsm.tif"),
+                    os.path.join(self.config.output_dir, "dsm.tif")
+                )
+                shutil.copy2(
+                    os.path.join(src_dir, "odm_dem", "dtm.tif"),
+                    os.path.join(self.config.output_dir, "dtm.tif")
+                )
+
+            self.logger.info("单网格结果复制完成")
+
             return
+
+        # 如果有多个网格，执行合并操作
         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":
+        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)
 
@@ -286,8 +337,8 @@ class ImagePreprocessor:
 if __name__ == "__main__":
     # 创建配置
     config = PreprocessConfig(
-        image_dir=r"G:\test_data\error1_L",
+        image_dir=r"E:\datasets\UAV\134\project\images",
-        output_dir=r"G:\ODM_output\error1_L",
+        output_dir=r"G:\ODM_output\134",
 
         cluster_eps=0.01,
         cluster_min_samples=5,
@@ -307,7 +358,7 @@ if __name__ == "__main__":
         grid_overlap=0.05,
 
 
-        mode="重建模式",
+        mode="快拼模式",
         produce_dem=False,
     )
 

post_pro/merge_laz.py

@@ -35,7 +35,7 @@ class MergePly:
 
             kwargs = {
                 'all_inputs': " ".join(laz_files),
-                'output': os.path.join(self.output_dir, 'merged_pointcloud.laz')
+                'output': os.path.join(self.output_dir, 'pointcloud.laz')
             }
 
             subprocess.run('D:\\software\\LAStools\\bin\\lasmerge64.exe -i {all_inputs} -o "{output}"'.format(**kwargs))

post_pro/merge_obj.py

@@ -175,7 +175,7 @@ class MergeObj:
                 all_face_materials.append(material_map2.get(material))
 
             # 写入合并后的obj文件，使用与MTL文件相同的名称
-            mtl_filename = "merged_model.mtl"  # 使用固定的MTL文件名
+            mtl_filename = "textured_model.mtl"  # 使用固定的MTL文件名
             self.write_obj(output_path, all_vertices, all_tex_coords, all_normals,
                            all_faces, all_face_materials, mtl_filename)
             self.logger.info(f"模型合并成功，已保存至: {output_path}")
@@ -273,7 +273,7 @@ class MergeObj:
 
         try:
             # 创建输出目录
-            output_model_dir = os.path.join(self.output_dir, "merged_model")
+            output_model_dir = os.path.join(self.output_dir, "texturing")
             os.makedirs(output_model_dir, exist_ok=True)
 
             # 获取所有有效的网格文件
@@ -323,7 +323,7 @@ class MergeObj:
                 all_materials.update(updated_materials)
 
             # 写入合并后的MTL文件
-            final_mtl = os.path.join(output_model_dir, "merged_model.mtl")
+            final_mtl = os.path.join(output_model_dir, "textured_model.mtl")
             with open(final_mtl, 'w') as f:
                 for mat_name, content in all_materials.items():
                     f.write(f"newmtl {mat_name}\n")
@@ -353,7 +353,7 @@ class MergeObj:
                 merged_obj = temp_output
 
             # 最终结果
-            final_obj = os.path.join(output_model_dir, "merged_model.obj")
+            final_obj = os.path.join(output_model_dir, "textured_model.obj")
             try:
                 if os.path.exists(final_obj):
                     os.remove(final_obj)
@@ -377,8 +377,8 @@ class MergeObj:
 
             self.logger.info(
                 f"模型合并完成，输出目录: {output_model_dir}\n"
-                f"- OBJ文件: merged_model.obj\n"
+                f"- OBJ文件: textured_model.obj\n"
-                f"- MTL文件: merged_model.mtl\n"
+                f"- MTL文件: textured_model.mtl\n"
                 f"- 纹理文件: {len(os.listdir(output_model_dir)) - 2}个"
             )
 

utils/odm_monitor.py

@@ -94,11 +94,11 @@ class ODMProcessMonitor:
             self.logger.error(f"检查正射影像时发生错误: {str(e)}")
             return False
 
-    def run_odm_with_monitor(self, grid_dir: str, grid_id: tuple, fast_mode: bool = True, produce_dem: bool = False) -> Tuple[bool, str]:
+    def run_odm_with_monitor(self, grid_dir: str, grid_id: tuple, produce_dem: bool = False) -> Tuple[bool, str]:
         """运行ODM命令"""
-        if produce_dem and fast_mode:
+        # if produce_dem and self.mode == "快拼模式":
-            self.logger.error("快拼模式下无法生成DEM，请调整生产参数")
+        #     self.logger.error("快拼模式下无法生成DEM，请调整生产参数")
-            return False, "快拼模式下无法生成DEM，请调整生产参数"
+        #     return False, "快拼模式下无法生成DEM，请调整生产参数"
 
         self.logger.info(f"开始处理网格 ({grid_id[0]},{grid_id[1]})")
         success = False
@@ -136,11 +136,15 @@ class ODMProcessMonitor:
                         f"--dtm "
                     )
 
-                if fast_mode:
+                if self.mode == "快拼模式":
                     docker_command += (
-                        f"--fast-orthophoto "
+                        #f"--fast-orthophoto "
                         f"--skip-3dmodel "
                     )
+                elif self.mode == "三维模式":
+                    docker_command += (
+                        f"--skip-orthophoto "
+                    )
 
                 docker_command += "--rerun-all"
                 self.logger.info(docker_command)
@@ -224,7 +228,6 @@ class ODMProcessMonitor:
                 success, error_msg = self.run_odm_with_monitor(
                     grid_dir=grid_dir,
                     grid_id=grid_id,
-                    fast_mode=(self.mode == "快拼模式"),
                     produce_dem=produce_dem
                 )