更新merge代码

README.md

@@ -19,3 +19,4 @@ conda install -c conda-forge open3d
 - command_runner中rerun需要更新
 - grid要动态分割大小
 - 任务队列
+- 目前obj转osgb的软件windows没有装上，linux成功了,后续需要做一个docker镜像

odm_preprocess.py

@@ -209,6 +209,26 @@ class ImagePreprocessor:
         )
         grid_points = grid_divider.assign_to_grids(self.gps_points, grids)
         self.logger.info(f"成功划分为 {len(grid_points)} 个网格")
+        
+        # 生成image_groups.txt文件
+        try:
+            groups_file = os.path.join(self.config.output_dir, "image_groups.txt")
+            self.logger.info(f"开始生成分组文件: {groups_file}")
+            
+            with open(groups_file, 'w') as f:
+                for grid_idx, points_lt in grid_points.items():
+                    # 使用ASCII字母作为组标识（A, B, C...）
+                    group_letter = chr(65 + grid_idx)  # 65是ASCII中'A'的编码
+                    
+                    # 为每个网格中的图像写入分组信息
+                    for point in points_lt:
+                        f.write(f"{point['file']} {group_letter}\n")
+                        
+            self.logger.info(f"分组文件生成成功: {groups_file}")
+        except Exception as e:
+            self.logger.error(f"生成分组文件时发生错误: {str(e)}", exc_info=True)
+            raise
+        
         return grid_points
 
     def copy_images(self, grid_points: Dict[int, pd.DataFrame]):
@@ -270,8 +290,8 @@ class ImagePreprocessor:
 if __name__ == "__main__":
     # 创建配置
     config = PreprocessConfig(
-        image_dir=r"E:\datasets\UAV\1815\images",
-        output_dir=r"G:\ODM_output\1815",
+        image_dir=r"E:\datasets\UAV\134\project\images",
+        output_dir=r"G:\ODM_output\134_test",
 
         cluster_eps=0.01,
         cluster_min_samples=5,
@@ -287,8 +307,8 @@ if __name__ == "__main__":
         filter_dense_distance_threshold=10,
         filter_time_threshold=timedelta(minutes=5),
 
-        grid_size=500,
-        grid_overlap=0.05,
+        grid_size=300,
+        grid_overlap=0.1,
 
 
         mode="重建模式",

odm_preprocess_fast.py

@@ -0,0 +1,319 @@
+import os
+import shutil
+from datetime import timedelta
+from dataclasses import dataclass
+from typing import Dict
+
+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 tools.test_docker_run import run_docker_command
+from post_pro.merge_obj import MergeObj
+from post_pro.merge_ply 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"
+
+
+class ImagePreprocessor:
+    def __init__(self, config: PreprocessConfig):
+        self.config = config
+
+        # # 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 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")
+        return self.gps_points
+
+    def cluster(self) -> pd.DataFrame:
+        """使用DBSCAN对GPS点进行聚类，只保留最大的类"""
+        self.logger.info("开始聚类")
+        previous_points = self.gps_points.copy()
+
+        # 创建聚类器并执行聚类
+        clusterer = GPSCluster(
+            self.gps_points, output_dir=self.config.output_dir,
+            eps=self.config.cluster_eps, min_samples=self.config.cluster_min_samples)
+
+        # 获取主要类别的点
+        self.clustered_points = clusterer.fit()
+        self.gps_points = clusterer.get_main_cluster(self.clustered_points)
+
+        # 获取统计信息并记录
+        stats = clusterer.get_cluster_stats(self.clustered_points)
+        self.logger.info(
+            f"聚类完成：主要类别包含 {stats['main_cluster_points']} 个点，"
+            f"噪声点 {stats['noise_points']} 个"
+        )
+
+        # 可视化聚类结果
+        self.visualizer.visualize_filter_step(
+            self.gps_points, previous_points, "1-Clustering")
+
+        return self.gps_points
+
+    def filter_time_group_overlap(self) -> pd.DataFrame:
+        """过滤重叠的时间组"""
+        self.logger.info("开始过滤重叠时间组")
+
+        self.logger.info("开始过滤重叠时间组")
+        previous_points = self.gps_points.copy()
+
+        filter = TimeGroupOverlapFilter(
+            self.config.image_dir,
+            self.config.output_dir,
+            overlap_threshold=self.config.time_group_overlap_threshold
+        )
+
+        deleted_files = filter.filter_overlapping_groups(
+            time_threshold=self.config.time_group_interval
+        )
+
+        # 更新GPS点数据，移除被删除的图像
+        self.gps_points = self.gps_points[~self.gps_points['file'].isin(
+            deleted_files)]
+        self.logger.info(f"重叠时间组过滤后剩余 {len(self.gps_points)} 个GPS点")
+
+        # 可视化过滤结果
+        self.visualizer.visualize_filter_step(
+            self.gps_points, previous_points, "2-Time Group Overlap")
+
+        return self.gps_points
+
+    # TODO 过滤算法还需要更新
+    def filter_points(self) -> pd.DataFrame:
+        """过滤GPS点"""
+
+        self.logger.info("开始过滤GPS点")
+        filter = GPSFilter(self.config.output_dir)
+
+        # 过滤孤立点
+        previous_points = self.gps_points.copy()
+        self.logger.info(
+            f"开始过滤孤立点(距离阈值: {self.config.filter_distance_threshold}, "
+            f"最小邻居数: {self.config.filter_min_neighbors})"
+        )
+        self.gps_points = filter.filter_isolated_points(
+            self.gps_points,
+            self.config.filter_distance_threshold,
+            self.config.filter_min_neighbors,
+        )
+        self.logger.info(f"孤立点过滤后剩余 {len(self.gps_points)} 个GPS点")
+
+        # 可视化孤立点过滤结果
+        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")
+
+        # # 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
+
+    def divide_grids(self) -> Dict[int, pd.DataFrame]:
+        """划分网格"""
+        self.logger.info(f"开始划分网格 (重叠率: {self.config.grid_overlap})")
+        grid_divider = GridDivider(
+            overlap=self.config.grid_overlap,
+            output_dir=self.config.output_dir
+        )
+        grids = grid_divider.divide_grids(
+            self.gps_points, grid_size=self.config.grid_size
+        )
+        grid_points = grid_divider.assign_to_grids(self.gps_points, grids)
+        self.logger.info(f"成功划分为 {len(grid_points)} 个网格")
+        
+        # 生成image_groups.txt文件
+        try:
+            groups_file = os.path.join(self.config.output_dir, "image_groups.txt")
+            self.logger.info(f"开始生成分组文件: {groups_file}")
+            
+            with open(groups_file, 'w') as f:
+                for grid_idx, points_lt in grid_points.items():
+                    # 使用ASCII字母作为组标识（A, B, C...）
+                    group_letter = chr(65 + grid_idx)  # 65是ASCII中'A'的编码
+                    
+                    # 为每个网格中的图像写入分组信息
+                    for point in points_lt:
+                        f.write(f"{point['file']} {group_letter}\n")
+                        
+            self.logger.info(f"分组文件生成成功: {groups_file}")
+        except Exception as e:
+            self.logger.error(f"生成分组文件时发生错误: {str(e)}", exc_info=True)
+            raise
+        
+        return grid_points
+
+    def copy_images(self, grid_points: Dict[int, pd.DataFrame]):
+        """复制图像到目标文件夹"""
+        self.logger.info("开始复制图像文件")
+        self.logger.info("开始复制图像文件")
+
+        for grid_idx, points in grid_points.items():
+            output_dir = os.path.join(
+                self.config.output_dir, f"grid_{grid_idx + 1}", "project", "images"
+            )
+
+            os.makedirs(output_dir, exist_ok=True)
+
+            for point in tqdm(points, desc=f"复制网格 {grid_idx + 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_idx + 1} 包含 {len(points)} 张图像")
+
+    def merge_tif(self, grid_points: Dict[int, pd.DataFrame]):
+        """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)
+
+    def merge_obj(self, grid_points: Dict[int, pd.DataFrame]):
+        """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)
+
+    def merge_ply(self, grid_points: Dict[int, 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_ply(grid_points)
+
+    def process(self):
+        """执行完整的预处理流程"""
+        try:
+            self.extract_gps()
+            self.cluster()
+            # self.filter_time_group_overlap()
+            self.filter_points()
+            grid_points = self.divide_grids()
+            # self.copy_images(grid_points)
+            self.logger.info("预处理任务完成")
+
+            # self.odm_monitor.process_all_grids(grid_points)
+            # self.merge_tif(grid_points)
+            self.merge_ply(grid_points)
+            self.merge_obj(grid_points)
+        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\1009\project\images",
+        output_dir=r"G:\ODM_output\1009",
+
+        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=300,
+        grid_overlap=0.1,
+
+
+        mode="重建模式",
+    )
+
+    # 创建处理器并执行
+    processor = ImagePreprocessor(config)
+    processor.process()

post_pro/merge_obj.py

@@ -71,12 +71,13 @@ class MergeObj:
                     self.output_dir,
                     f"grid_{grid_idx + 1}",
                     "project",
-                    "odm_texturing",
+                    "odm_texturing_25d",
                     "odm_textured_model_geo.obj"
                 )
 
                 if not os.path.exists(grid_obj):
-                    self.logger.warning(f"网格 {grid_idx + 1} 的OBJ文件不存在: {grid_obj}")
+                    self.logger.warning(
+                        f"网格 {grid_idx + 1} 的OBJ文件不存在: {grid_obj}")
                     continue
 
                 if input_obj1 is None:
@@ -84,7 +85,8 @@ class MergeObj:
                     self.logger.info(f"设置第一个输入OBJ: {input_obj1}")
                 else:
                     input_obj2 = grid_obj
-                    output_obj = os.path.join(self.output_dir, "merged_model.obj")
+                    output_obj = os.path.join(
+                        self.output_dir, "merged_model.obj")
 
                     self.logger.info(
                         f"开始合并第 {merge_count + 1} 次:\n"
@@ -107,3 +109,34 @@ class MergeObj:
         except Exception as e:
             self.logger.error(f"OBJ模型合并过程中发生错误: {str(e)}", exc_info=True)
             raise
+
+
+if __name__ == "__main__":
+    import sys
+    sys.path.append(os.path.dirname(
+        os.path.dirname(os.path.abspath(__file__))))
+    from utils.logger import setup_logger
+    import pandas as pd
+
+    # 设置输出目录和日志
+    output_dir = r"G:\ODM_output\1009"
+    setup_logger(output_dir)
+
+    # 构造测试用的grid_points字典
+    # 假设我们有两个网格，每个网格包含一些GPS点的DataFrame
+    grid_points = {
+        0: pd.DataFrame({
+            'latitude': [39.9, 39.91],
+            'longitude': [116.3, 116.31],
+            'altitude': [100, 101]
+        }),
+        1: pd.DataFrame({
+            'latitude': [39.92, 39.93],
+            'longitude': [116.32, 116.33],
+            'altitude': [102, 103]
+        })
+    }
+
+    # 创建MergeObj实例并执行合并
+    merge_obj = MergeObj(output_dir)
+    merge_obj.merge_grid_obj(grid_points)

post_pro/merge_ply.py

@@ -1,7 +1,7 @@
 import os
 import logging
 import numpy as np
-from typing import Dict
+from typing import Dict, Tuple
 import pandas as pd
 import open3d as o3d
 
@@ -11,8 +11,51 @@ class MergePly:
         self.output_dir = output_dir
         self.logger = logging.getLogger('UAV_Preprocess.MergePly')
         
-    def merge_two_plys(self, ply1_path: str, ply2_path: str, output_path: str):
-        """合并两个PLY文件"""
+    def read_corners_file(self, grid_idx: int) -> Tuple[float, float]:
+        """读取角点文件并计算中心点坐标
+        角点文件格式：xmin ymin xmax ymax
+        """
+        corners_file = os.path.join(
+            self.output_dir,
+            f"grid_{grid_idx + 1}",
+            "project",
+            "odm_orthophoto",
+            "odm_orthophoto_corners.txt"
+        )
+        
+        try:
+            if not os.path.exists(corners_file):
+                raise FileNotFoundError(f"角点文件不存在: {corners_file}")
+                
+            # 读取角点文件
+            with open(corners_file, 'r') as f:
+                line = f.readline().strip()
+                if not line:
+                    raise ValueError(f"角点文件为空: {corners_file}")
+                
+                # 解析四个角点值：xmin ymin xmax ymax
+                xmin, ymin, xmax, ymax = map(float, line.split())
+                
+                # 计算中心点坐标
+                center_x = (xmin + xmax) / 2
+                center_y = (ymin + ymax) / 2
+                
+                self.logger.info(
+                    f"网格 {grid_idx + 1} 边界坐标: \n"
+                    f"xmin={xmin:.2f}, ymin={ymin:.2f}\n"
+                    f"xmax={xmax:.2f}, ymax={ymax:.2f}\n"
+                    f"中心点: x={center_x:.2f}, y={center_y:.2f}"
+                )
+                return center_x, center_y
+                
+        except Exception as e:
+            self.logger.error(f"读取角点文件时发生错误: {str(e)}", exc_info=True)
+            raise
+
+    def merge_two_plys(self, ply1_path: str, ply2_path: str, output_path: str,
+                      center1: Tuple[float, float],
+                      center2: Tuple[float, float]):
+        """合并两个PLY文件，使用中心点坐标进行对齐"""
         try:
             self.logger.info("开始合并PLY点云")
             self.logger.info(f"输入点云1: {ply1_path}")
@@ -30,15 +73,15 @@ class MergePly:
             if pcd1 is None or pcd2 is None:
                 raise ValueError("无法读取点云文件")
 
-            # 获取点云中心
-            center1 = pcd1.get_center()
-            center2 = pcd2.get_center()
+            # 计算平移向量（直接使用中心点坐标差）
+            translation = np.array([
+                center2[0] - center1[0],  # x方向的平移
+                center2[1] - center1[1],  # y方向的平移
+                0.0  # z方向不平移
+            ])
             
-            # 计算平移向量
-            translation_vector = center2 - center1
-
-            # 对齐点云
-            pcd2.translate(translation_vector)
+            # 对第二个点云进行平移
+            pcd2.translate(translation*100)
 
             # 合并点云
             combined_pcd = pcd1 + pcd2
@@ -53,56 +96,93 @@ class MergePly:
             self.logger.error(f"合并PLY点云时发生错误: {str(e)}", exc_info=True)
             raise
 
-    def merge_grid_ply(self, grid_points: Dict[int, pd.DataFrame]):
-        """合并所有网格的PLY点云"""
+    def merge_grid_ply(self, grid_points: Dict[int, list]):
+        """合并所有网格的PLY点云，以第一个网格为参考点"""
         self.logger.info("开始合并所有网格的PLY点云")
 
         if len(grid_points) < 2:
             self.logger.info("只有一个网格，无需合并")
             return
 
-        input_ply1, input_ply2 = None, None
-        merge_count = 0
-
         try:
-            for grid_idx, points in grid_points.items():
-                grid_ply = os.path.join(
+            # 获取网格索引列表并排序
+            grid_indices = sorted(grid_points.keys())
+            
+            # 读取第一个网格作为参考网格
+            ref_idx = grid_indices[0]
+            ref_ply = os.path.join(
+                self.output_dir,
+                f"grid_{ref_idx + 1}",
+                "project",
+                "odm_filterpoints",
+                "point_cloud.ply"
+            )
+            
+            if not os.path.exists(ref_ply):
+                raise FileNotFoundError(f"参考网格的PLY文件不存在: {ref_ply}")
+            
+            # 获取参考网格的中心点坐标
+            ref_center = self.read_corners_file(ref_idx)
+            self.logger.info(f"参考网格(grid_{ref_idx + 1})中心点: x={ref_center[0]:.2f}, y={ref_center[1]:.2f}")
+            
+            # 将参考点云复制到输出位置作为初始合并结果
+            output_ply = os.path.join(self.output_dir, "merged_pointcloud.ply")
+            import shutil
+            shutil.copy2(ref_ply, output_ply)
+            
+            # 依次处理其他网格
+            for grid_idx in grid_indices[1:]:
+                current_ply = os.path.join(
                     self.output_dir,
                     f"grid_{grid_idx + 1}",
                     "project",
-                    "odm_georeferencing",
-                    "odm_georeferenced_model.ply"
+                    "odm_filterpoints",
+                    "point_cloud.ply"
                 )
 
-                if not os.path.exists(grid_ply):
-                    self.logger.warning(f"网格 {grid_idx + 1} 的PLY文件不存在: {grid_ply}")
+                if not os.path.exists(current_ply):
+                    self.logger.warning(f"网格 {grid_idx + 1} 的PLY文件不存在: {current_ply}")
                     continue
 
-                if input_ply1 is None:
-                    input_ply1 = grid_ply
-                    self.logger.info(f"设置第一个输入PLY: {input_ply1}")
-                else:
-                    input_ply2 = grid_ply
-                    output_ply = os.path.join(self.output_dir, "merged_pointcloud.ply")
+                # 读取当前网格的中心点坐标
+                current_center = self.read_corners_file(grid_idx)
                 
                 self.logger.info(
-                        f"开始合并第 {merge_count + 1} 次:\n"
-                        f"输入1: {input_ply1}\n"
-                        f"输入2: {input_ply2}\n"
-                        f"输出: {output_ply}"
+                    f"处理网格 {grid_idx + 1}:\n"
+                    f"合并点云: {current_ply}\n"
+                    f"当前网格中心点: x={current_center[0]:.2f}, y={current_center[1]:.2f}"
                 )
 
-                    self.merge_two_plys(input_ply1, input_ply2, output_ply)
-                    merge_count += 1
-
-                    input_ply1 = output_ply
-                    input_ply2 = None
-
-            self.logger.info(
-                f"PLY点云合并完成，共执行 {merge_count} 次合并，"
-                f"最终输出文件: {input_ply1}"
+                # 合并点云，始终使用第一个网格的中心点作为参考点
+                self.merge_two_plys(
+                    output_ply,      # 当前合并结果
+                    current_ply,     # 要合并的新点云
+                    output_ply,      # 覆盖原有的合并结果
+                    ref_center,      # 参考网格中心点（始终不变）
+                    current_center   # 当前网格中心点
                 )
 
+            self.logger.info(f"PLY点云合并完成，最终输出文件: {output_ply}")
+
         except Exception as e:
             self.logger.error(f"PLY点云合并过程中发生错误: {str(e)}", exc_info=True)
             raise
+
+
+if __name__ == "__main__":
+    from utils.logger import setup_logger
+    import open3d as o3d
+
+    # 设置输出目录和日志
+    output_dir = r"G:\ODM_output\1009"
+    setup_logger(output_dir)
+
+    # 构造测试用的grid_points字典
+    grid_points = {
+        0: [],  # 不再需要GPS点信息
+        1: []
+    }
+
+    # 创建MergePly实例并执行合并
+    merge_ply = MergePly(output_dir)
+    merge_ply.merge_grid_ply(grid_points)

post_pro/merge_tif.py

@@ -3,8 +3,6 @@ import logging
 import os
 from typing import Dict
 import pandas as pd
-import sys
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 
 class MergeTif:
@@ -53,7 +51,8 @@ class MergeTif:
             )
 
             self.logger.info("开始执行影像拼接...")
-            result = gdal.Warp(output_tif, [input_tif1, input_tif2], options=warp_options)
+            result = gdal.Warp(
+                output_tif, [input_tif1, input_tif2], options=warp_options)
 
             if result is None:
                 error_msg = "影像拼接失败"
@@ -100,7 +99,8 @@ class MergeTif:
                 )
 
                 if not os.path.exists(grid_tif):
-                    self.logger.warning(f"网格 {grid_idx + 1} 的{product_name}不存在: {grid_tif}")
+                    self.logger.warning(
+                        f"网格 {grid_idx + 1} 的{product_name}不存在: {grid_tif}")
                     continue
 
                 if input_tif1 is None:
@@ -108,7 +108,8 @@ class MergeTif:
                     self.logger.info(f"设置第一个输入{product_name}: {input_tif1}")
                 else:
                     input_tif2 = grid_tif
-                    output_tif = os.path.join(self.output_dir, f"merged_{product_info['output']}")
+                    output_tif = os.path.join(
+                        self.output_dir, f"merged_{product_info['output']}")
 
                     self.logger.info(
                         f"开始合并{product_name}第 {merge_count + 1} 次:\n"
@@ -129,7 +130,8 @@ class MergeTif:
             )
 
         except Exception as e:
-            self.logger.error(f"{product_name}合并过程中发生错误: {str(e)}", exc_info=True)
+            self.logger.error(
+                f"{product_name}合并过程中发生错误: {str(e)}", exc_info=True)
             raise
 
     def merge_all_tifs(self, grid_points: Dict[int, pd.DataFrame]):
@@ -157,7 +159,7 @@ class MergeTif:
             ]
 
             for product in products:
-                self.merge_grid_product(grid_points, product)
+                self.merge_grid_tif(grid_points, product)
 
             self.logger.info("所有产品合并完成")
         except Exception as e:
@@ -166,17 +168,31 @@ class MergeTif:
 
 
 if __name__ == "__main__":
+    import sys
+    sys.path.append(os.path.dirname(
+        os.path.dirname(os.path.abspath(__file__))))
     from utils.logger import setup_logger
+    import pandas as pd
     
-    # 定义影像路径
-    input_tif1 = r"G:\ODM_output\20241024100834\output\grid_1\project\odm_orthophoto\odm_orthophoto.tif"
-    input_tif2 = r"G:\ODM_output\20241024100834\output\grid_2\project\odm_orthophoto\odm_orthophoto.tif"
-    output_tif = r"G:\ODM_output\20241024100834\output\merged_orthophoto.tif"
-
-    # 设置日志
-    output_dir = r"E:\studio2\ODM_pro\test"
+    # 设置输出目录和日志
+    output_dir = r"G:\ODM_output\1009"
     setup_logger(output_dir)
 
-    # 执行拼接
+    # 构造测试用的grid_points字典
+    # 假设我们有两个网格，每个网格包含一些GPS点的DataFrame
+    grid_points = {
+        0: pd.DataFrame({
+            'latitude': [39.9, 39.91],
+            'longitude': [116.3, 116.31],
+            'altitude': [100, 101]
+        }),
+        1: pd.DataFrame({
+            'latitude': [39.92, 39.93],
+            'longitude': [116.32, 116.33],
+            'altitude': [102, 103]
+        })
+    }
+
+    # 创建MergeTif实例并执行合并
     merge_tif = MergeTif(output_dir)
-    merge_tif.merge_two_tifs(input_tif1, input_tif2, output_tif)
+    merge_tif.merge_all_tifs(grid_points)

utils/odm_monitor.py

@@ -50,12 +50,14 @@ class ODMProcessMonitor:
         stdout, stderr = result.stdout.decode(
             'utf-8'), result.stderr.decode('utf-8')
 
+        self.logger.info(f"==========stdout==========: {stdout}")
+        self.logger.error(f"==========stderr==========: {stderr}")
         # 检查执行结果
         if self._check_success(grid_dir):
-            self.logger.info(stdout, stderr)
             self.logger.info(f"网格 {grid_idx + 1} 处理成功")
             return True, ""
-
+        else:
+            self.logger.error(f"网格 {grid_idx + 1} 处理失败")
             return False, f"网格 {grid_idx + 1} 处理失败"
 
     def process_all_grids(self, grid_points: Dict[int, pd.DataFrame]):