From ef1a84373a9abcc8be9a7d530003a6793688e19a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=BE=99=E6=BE=B3?= Date: Tue, 31 Dec 2024 14:23:45 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0merge=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 1 + odm_preprocess.py | 28 +++- odm_preprocess_fast.py | 319 +++++++++++++++++++++++++++++++++++++++++ post_pro/merge_obj.py | 41 +++++- post_pro/merge_ply.py | 172 ++++++++++++++++------ post_pro/merge_tif.py | 76 ++++++---- utils/odm_monitor.py | 8 +- 7 files changed, 558 insertions(+), 87 deletions(-) create mode 100644 odm_preprocess_fast.py diff --git a/README.md b/README.md index 32b0a61..257cece 100644 --- a/README.md +++ b/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 \ No newline at end of file diff --git a/odm_preprocess.py b/odm_preprocess.py index a07da57..6034e5f 100644 --- a/odm_preprocess.py +++ b/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", diff --git a/odm_preprocess_fast.py b/odm_preprocess_fast.py new file mode 100644 index 0000000..067b706 --- /dev/null +++ b/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() diff --git a/post_pro/merge_obj.py b/post_pro/merge_obj.py index 1fa46a6..08f38f1 100644 --- a/post_pro/merge_obj.py +++ b/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" @@ -106,4 +108,35 @@ 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 \ No newline at end of file + 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) diff --git a/post_pro/merge_ply.py b/post_pro/merge_ply.py index 570860b..688bc91 100644 --- a/post_pro/merge_ply.py +++ b/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 @@ -10,9 +10,52 @@ class MergePly: def __init__(self, output_dir: str): self.output_dir = output_dir self.logger = logging.getLogger('UAV_Preprocess.MergePly') + + 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): - """合并两个PLY文件""" + 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 - translation_vector = center2 - center1 - - # 对齐点云 - pcd2.translate(translation_vector) + # 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 + ]) + + # 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"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.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}" - ) + # 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.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}" - ) + 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 \ No newline at end of file + 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) diff --git a/post_pro/merge_tif.py b/post_pro/merge_tif.py index 2ab1a9e..688b5e7 100644 --- a/post_pro/merge_tif.py +++ b/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" @@ -79,16 +78,16 @@ class MergeTif: product_name = product_info['name'] product_path = product_info['path'] filename = product_info['filename'] - + self.logger.info(f"开始合并{product_name}") - + if len(grid_points) < 2: self.logger.info("只有一个网格，无需合并") return - + input_tif1, input_tif2 = None, None merge_count = 0 - + try: for grid_idx, points in grid_points.items(): grid_tif = os.path.join( @@ -98,38 +97,41 @@ class MergeTif: product_path, filename ) - + 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: input_tif1 = grid_tif 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" f"输入1: {input_tif1}\n" f"输入2: {input_tif2}\n" f"输出: {output_tif}" ) - + self.merge_two_tifs(input_tif1, input_tif2, output_tif) merge_count += 1 - + input_tif1 = output_tif input_tif2 = None - + self.logger.info( f"{product_name}合并完成，共执行 {merge_count} 次合并，" f"最终输出文件: {input_tif1}" ) - + 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]): @@ -155,10 +157,10 @@ class MergeTif: 'output': 'dtm.tif' } ] - + 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: self.logger.error(f"产品合并过程中发生错误: {str(e)}", exc_info=True) @@ -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 - - # 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" + import pandas as pd + + # 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) diff --git a/utils/odm_monitor.py b/utils/odm_monitor.py index 6ea397c..9b50c1a 100644 --- a/utils/odm_monitor.py +++ b/utils/odm_monitor.py @@ -50,13 +50,15 @@ 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, "" - - return False, f"网格 {grid_idx + 1} 处理失败" + 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]): """处理所有网格"""