diff --git a/odm_preprocess.py b/odm_preprocess.py index a58afc1..5977ddf 100644 --- a/odm_preprocess.py +++ b/odm_preprocess.py @@ -197,7 +197,7 @@ class ImagePreprocessor: return self.gps_points - def divide_grids(self) -> Dict[int, pd.DataFrame]: + def divide_grids(self) -> Dict[tuple, 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( @@ -208,50 +208,29 @@ class ImagePreprocessor: self.gps_points, grid_size=self.config.grid_size ) grid_points = grid_divider.assign_to_grids(self.gps_points, grids) - - # 将grid_divider添加到grid_points中 - grid_points['grid_divider'] = grid_divider - - self.logger.info(f"成功划分为 {len(grid_points)-1} 个网格") # -1是因为包含了grid_divider - - # 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 - + + # -1是因为包含了grid_divider + self.logger.info(f"成功划分为 {len(grid_points)} 个网格") + return grid_points - def copy_images(self, grid_points: Dict[int, pd.DataFrame]): + def copy_images(self, grid_points: Dict[tuple, 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(): + for grid_id, points in grid_points.items(): + output_dir = os.path.join( - self.config.output_dir, f"grid_{grid_idx + 1}", "project", "images" + self.config.output_dir, f"grid_{grid_id[0]}_{grid_id[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"): + for point in tqdm(points, desc=f"复制网格 {grid_id} 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") + self.logger.info(f"网格 {grid_id} 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""" @@ -269,7 +248,7 @@ class ImagePreprocessor: """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) + merger.merge_grid_laz(grid_points) def process(self): """执行完整的预处理流程""" @@ -283,9 +262,9 @@ class ImagePreprocessor: 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_obj(grid_points) - self.merge_ply(grid_points) + # self.merge_tif(grid_points) + # self.merge_obj(grid_points) + # self.merge_ply(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 diff --git a/odm_preprocess_fast.py b/odm_preprocess_fast.py index 50035a8..8035277 100644 --- a/odm_preprocess_fast.py +++ b/odm_preprocess_fast.py @@ -197,7 +197,7 @@ class ImagePreprocessor: return self.gps_points - def divide_grids(self) -> Dict[int, pd.DataFrame]: + def divide_grids(self) -> Dict[tuple, 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( @@ -209,63 +209,43 @@ 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]): + def copy_images(self, grid_points: Dict[tuple, 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(): + for grid_id, points in grid_points.items(): output_dir = os.path.join( - self.config.output_dir, f"grid_{grid_idx + 1}", "project", "images" + self.config.output_dir, f"grid_{grid_id[0]}_{grid_id[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"): + for point in tqdm(points, desc=f"复制网格 ({grid_id[0]},{grid_id[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") + self.logger.info(f"网格 ({grid_id[0]},{grid_id[1]}) 包含 {len(points)} 张图像") - def merge_tif(self, grid_points: Dict[int, pd.DataFrame]): + def merge_tif(self, grid_points: Dict[tuple, 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]): + def merge_obj(self, grid_points: Dict[tuple, 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) + merger.merge_grid_obj(grid_points, self.config.grid_size) - def merge_ply(self, grid_points: Dict[int, pd.DataFrame]): + def merge_ply(self, grid_points: Dict[tuple, 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) + merger.merge_grid_laz(grid_points) def process(self): """执行完整的预处理流程""" @@ -280,7 +260,7 @@ class ImagePreprocessor: # self.odm_monitor.process_all_grids(grid_points) # self.merge_tif(grid_points) - self.merge_ply(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) diff --git a/post_pro/merge_laz.py b/post_pro/merge_laz.py index e963929..d934b5f 100644 --- a/post_pro/merge_laz.py +++ b/post_pro/merge_laz.py @@ -11,7 +11,7 @@ class MergePly: self.output_dir = output_dir self.logger = logging.getLogger('UAV_Preprocess.MergePly') - def merge_grid_laz(self, grid_points: Dict[int, list]): + def merge_grid_laz(self, grid_points: Dict[tuple, list]): """合并所有网格的点云""" self.logger.info("开始合并所有网格的laz点云") @@ -21,26 +21,26 @@ class MergePly: try: laz_lt = [] - for grid_idx, points in grid_points.items(): + for grid_id, points in grid_points.items(): grid_laz = os.path.join( self.output_dir, - f"grid_{grid_idx + 1}", + f"grid_{grid_id[0]}_{grid_id[1]}", "project", "odm_georeferencing", "odm_georeferenced_model.laz" ) if not os.path.exists(grid_laz): - self.logger.warning(f"参考网格的laz文件不存在: {grid_laz}") + self.logger.warning(f"网格 ({grid_id[0]},{grid_id[1]}) 的laz文件不存在: {grid_laz}") continue laz_lt.append(grid_laz) kwargs = { - 'all_inputs': " ".join(laz_lt), - 'output': os.path.join(self.output_dir, 'merged_pointcloud.laz') - } + 'all_inputs': " ".join(laz_lt), + 'output': os.path.join(self.output_dir, 'merged_pointcloud.laz') + } - subprocess.run('D:\\software\\LAStools\\bin\\lasmerge.exe -i {all_inputs} -o "{output}"'.format(**kwargs)) + subprocess.run('D:\\software\\LAStools\\bin\\lasmerge64.exe -i {all_inputs} -o "{output}"'.format(**kwargs)) 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) @@ -56,10 +56,10 @@ if __name__ == "__main__": # 构造测试用的grid_points字典 grid_points = { - 0: [], # 不再需要GPS点信息 - 1: [] + (0, 0): [], # 不再需要GPS点信息 + (0, 1): [] } # 创建MergePly实例并执行合并 merge_ply = MergePly(output_dir) - merge_ply.merge_grid_ply(grid_points) + merge_ply.merge_grid_laz(grid_points) diff --git a/post_pro/merge_obj.py b/post_pro/merge_obj.py index 72f6252..edeb48f 100644 --- a/post_pro/merge_obj.py +++ b/post_pro/merge_obj.py @@ -69,30 +69,22 @@ class MergeObj: self.logger.error(f"合并OBJ模型时发生错误: {str(e)}", exc_info=True) raise - def calculate_translation(self, grid_idx: int, grid_points: Dict[int, pd.DataFrame], grid_size: float) -> tuple: - """根据网格索引和大小计算平移量""" - # 从grid_points中获取网格划分器 - grid_divider = grid_points.get('grid_divider', None) - if grid_divider is None: - # 如果没有grid_divider，使用默认的计算方式 - row = grid_idx // 2 - col = grid_idx % 2 - else: - # 使用grid_divider获取正确的网格坐标 - row, col = grid_divider.get_grid_coordinates(grid_idx) - + def calculate_translation(self, grid_id: tuple, grid_size: float) -> tuple: + """根据网格坐标和大小计算平移量""" + # 直接使用网格的二维坐标计算平移量 + col, row = grid_id # grid_id是(width_idx, height_idx)格式 + # 计算平移量，考虑到重叠 - overlap_factor = 0.9 # 重叠因子，与grid_divider中的overlap对应 - x_translation = col * grid_size * overlap_factor - y_translation = row * grid_size * overlap_factor + x_translation = col * grid_size + y_translation = row * grid_size self.logger.info( - f"网格 {grid_idx} 的位置: 行={row}, 列={col}" + f"网格 ({col},{row}) 的平移量: x={x_translation}, y={y_translation}" ) return (x_translation, y_translation, 0) # z轴不需要平移 - def merge_grid_obj(self, grid_points: Dict[int, pd.DataFrame], grid_size: float = 500): + def merge_grid_obj(self, grid_points: Dict[tuple, pd.DataFrame], grid_size: float = 500): """合并所有网格的OBJ模型""" self.logger.info("开始合并所有网格的OBJ模型") @@ -104,20 +96,17 @@ class MergeObj: merge_count = 0 try: - for grid_idx, points in grid_points.items(): - if grid_idx == 'grid_divider': # 跳过grid_divider对象 - continue - + for grid_id, points in grid_points.items(): grid_obj = os.path.join( self.output_dir, - f"grid_{grid_idx + 1}", + f"grid_{grid_id[0]}_{grid_id[1]}", "project", "odm_texturing", "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_id[0]},{grid_id[1]}) 的OBJ文件不存在: {grid_obj}") continue if input_obj1 is None: @@ -128,7 +117,7 @@ class MergeObj: output_obj = os.path.join(self.output_dir, f"merged_model_{merge_count}.obj") # 计算当前网格的平移量 - translation = self.calculate_translation(grid_idx, grid_points, grid_size) + translation = self.calculate_translation(grid_id, grid_size) self.logger.info( f"开始合并第 {merge_count + 1} 次:\n" @@ -169,14 +158,13 @@ if __name__ == "__main__": 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({ + (0, 0): pd.DataFrame({ 'latitude': [39.9, 39.91], 'longitude': [116.3, 116.31], 'altitude': [100, 101] }), - 1: pd.DataFrame({ + (0, 1): pd.DataFrame({ 'latitude': [39.92, 39.93], 'longitude': [116.32, 116.33], 'altitude': [102, 103] diff --git a/post_pro/merge_tif.py b/post_pro/merge_tif.py index 688b5e7..ee3f048 100644 --- a/post_pro/merge_tif.py +++ b/post_pro/merge_tif.py @@ -73,7 +73,7 @@ class MergeTif: self.logger.error(f"影像拼接过程中发生错误: {str(e)}", exc_info=True) raise - def merge_grid_tif(self, grid_points: Dict[int, pd.DataFrame], product_info: dict): + def merge_grid_tif(self, grid_points: Dict[tuple, pd.DataFrame], product_info: dict): """合并指定产品的所有网格""" product_name = product_info['name'] product_path = product_info['path'] @@ -89,10 +89,10 @@ class MergeTif: merge_count = 0 try: - for grid_idx, points in grid_points.items(): + for grid_id, points in grid_points.items(): grid_tif = os.path.join( self.output_dir, - f"grid_{grid_idx + 1}", + f"grid_{grid_id[0]}_{grid_id[1]}", "project", product_path, filename @@ -100,7 +100,7 @@ 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}") + f"网格 ({grid_id[0]},{grid_id[1]}) 的{product_name}不存在: {grid_tif}") continue if input_tif1 is None: @@ -134,7 +134,7 @@ class MergeTif: 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]): + def merge_all_tifs(self, grid_points: Dict[tuple, pd.DataFrame]): """合并所有产品（正射影像、DSM和DTM）""" try: products = [ @@ -181,12 +181,12 @@ if __name__ == "__main__": # 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({ + (0, 0): pd.DataFrame({ 'latitude': [39.9, 39.91], 'longitude': [116.3, 116.31], 'altitude': [100, 101] }), - 1: pd.DataFrame({ + (0, 1): pd.DataFrame({ 'latitude': [39.92, 39.93], 'longitude': [116.32, 116.33], 'altitude': [102, 103] diff --git a/utils/grid_divider.py b/utils/grid_divider.py index 459d07d..7f603e0 100644 --- a/utils/grid_divider.py +++ b/utils/grid_divider.py @@ -37,8 +37,6 @@ class GridDivider: lon_step = (max_lon - min_lon) / self.num_grids_width grids = [] - grid_indices = {} # 存储网格的二维索引 - grid_idx = 0 for i in range(self.num_grids_height): for j in range(self.num_grids_width): @@ -47,61 +45,54 @@ class GridDivider: grid_min_lon = min_lon + j * lon_step - self.overlap * lon_step grid_max_lon = min_lon + (j + 1) * lon_step + self.overlap * lon_step + grid_id = (j, i) # 使用(width_idx, height_idx)元组作为网格标识 grids.append((grid_min_lat, grid_max_lat, grid_min_lon, grid_max_lon)) - grid_indices[grid_idx] = (i, j) # 存储每个网格的行列索引 self.logger.debug( - f"网格[{i},{j}] (索引{grid_idx}): 纬度[{grid_min_lat:.6f}, {grid_max_lat:.6f}], " + f"网格[{j},{i}]: 纬度[{grid_min_lat:.6f}, {grid_max_lat:.6f}], " f"经度[{grid_min_lon:.6f}, {grid_max_lon:.6f}]" ) - grid_idx += 1 self.logger.info( f"成功划分为 {len(grids)} 个网格 ({self.num_grids_width}x{self.num_grids_height})") - # 保存网格索引信息 - self.grid_indices = grid_indices # 添加可视化调用 self.visualize_grids(points_df, grids) return grids - def get_grid_coordinates(self, grid_idx): - """获取网格的二维坐标""" - return self.grid_indices.get(grid_idx, (0, 0)) - - def get_grid_dimensions(self): - """获取网格的维度""" - return self.num_grids_width, self.num_grids_height def assign_to_grids(self, points_df, grids): """将点分配到对应网格""" self.logger.info(f"开始将 {len(points_df)} 个点分配到网格中") - grid_points = {i: [] for i in range(len(grids))} + grid_points = {} # 使用字典存储每个网格的点 points_assigned = 0 multiple_grid_points = 0 + for i in range(self.num_grids_height): + for j in range(self.num_grids_width): + grid_points[(j, i)] = [] # 使用(width_idx, height_idx)元组 + for _, point in points_df.iterrows(): point_assigned = False - for i, (min_lat, max_lat, min_lon, max_lon) in enumerate(grids): - if min_lat <= point['lat'] <= max_lat and min_lon <= point['lon'] <= max_lon: - grid_points[i].append(point.to_dict()) - if point_assigned: - multiple_grid_points += 1 - else: - points_assigned += 1 - point_assigned = True - - self.logger.debug( - f"点 {point['file']} (纬度: {point['lat']:.6f}, 经度: {point['lon']:.6f}) " - f"被分配到网格" - ) + for i in range(self.num_grids_height): + for j in range(self.num_grids_width): + grid_idx = i * self.num_grids_width + j + min_lat, max_lat, min_lon, max_lon = grids[grid_idx] + + if min_lat <= point['lat'] <= max_lat and min_lon <= point['lon'] <= max_lon: + grid_points[(j, i)].append(point.to_dict()) + if point_assigned: + multiple_grid_points += 1 + else: + points_assigned += 1 + point_assigned = True # 记录每个网格的点数 - for grid_idx, points in grid_points.items(): - self.logger.info(f"网格 {grid_idx} 包含 {len(points)} 个点") + for grid_id, points in grid_points.items(): + self.logger.info(f"网格 {grid_id} 包含 {len(points)} 个点") self.logger.info( f"点分配完成: 总点数 {len(points_df)}, " @@ -122,15 +113,19 @@ class GridDivider: c='blue', s=10, alpha=0.6, label='GPS点') # 绘制网格 - for i, (min_lat, max_lat, min_lon, max_lon) in enumerate(grids): - plt.plot([min_lon, max_lon, max_lon, min_lon, min_lon], - [min_lat, min_lat, max_lat, max_lat, min_lat], - 'r-', alpha=0.5) - # 在网格中心添加网格编号 - center_lon = (min_lon + max_lon) / 2 - center_lat = (min_lat + max_lat) / 2 - plt.text(center_lon, center_lat, str(i), - horizontalalignment='center', verticalalignment='center') + for i in range(self.num_grids_height): + for j in range(self.num_grids_width): + grid_idx = i * self.num_grids_width + j + min_lat, max_lat, min_lon, max_lon = grids[grid_idx] + + plt.plot([min_lon, max_lon, max_lon, min_lon, min_lon], + [min_lat, min_lat, max_lat, max_lat, min_lat], + 'r-', alpha=0.5) + # 在网格中心添加网格编号 + center_lon = (min_lon + max_lon) / 2 + center_lat = (min_lat + max_lat) / 2 + plt.text(center_lon, center_lat, f"({j},{i})", # 显示(width_idx, height_idx) + horizontalalignment='center', verticalalignment='center') plt.title('网格划分与GPS点分布图') plt.xlabel('经度') diff --git a/utils/odm_monitor.py b/utils/odm_monitor.py index 9b50c1a..d03de12 100644 --- a/utils/odm_monitor.py +++ b/utils/odm_monitor.py @@ -20,9 +20,9 @@ class ODMProcessMonitor: success_markers.append('odm_texturing') return all(os.path.exists(os.path.join(grid_dir, 'project', marker)) for marker in success_markers) - def run_odm_with_monitor(self, grid_dir: str, grid_idx: int, fast_mode: bool = True) -> Tuple[bool, str]: + def run_odm_with_monitor(self, grid_dir: str, grid_id: tuple, fast_mode: bool = True) -> Tuple[bool, str]: """运行ODM命令""" - self.logger.info(f"开始处理网格 {grid_idx + 1}") + self.logger.info(f"开始处理网格 ({grid_id[0]},{grid_id[1]})") # 构建Docker命令 grid_dir = grid_dir[0].lower()+grid_dir[1:].replace('\\', '/') @@ -54,25 +54,25 @@ class ODMProcessMonitor: self.logger.error(f"==========stderr==========: {stderr}") # 检查执行结果 if self._check_success(grid_dir): - self.logger.info(f"网格 {grid_idx + 1} 处理成功") + self.logger.info(f"网格 ({grid_id[0]},{grid_id[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" + self.logger.error(f"网格 ({grid_id[0]},{grid_id[1]}) 处理失败") + return False, f"网格 ({grid_id[0]},{grid_id[1]}) 处理失败" - def process_all_grids(self, grid_points: Dict[int, pd.DataFrame]): + def process_all_grids(self, grid_points: Dict[tuple, pd.DataFrame]): """处理所有网格""" self.logger.info("开始执行网格处理") - for grid_idx in grid_points.keys(): + for grid_id in grid_points.keys(): grid_dir = os.path.join( - self.output_dir, f'grid_{grid_idx + 1}' + self.output_dir, f'grid_{grid_id[0]}_{grid_id[1]}' ) success, error_msg = self.run_odm_with_monitor( grid_dir=grid_dir, - grid_idx=grid_idx, + grid_id=grid_id, fast_mode=(self.mode == "快拼模式") ) if not success: - raise Exception(f"网格 {grid_idx + 1} 处理失败: {error_msg}") + raise Exception(f"网格 ({grid_id[0]},{grid_id[1]}) 处理失败: {error_msg}")