diff --git a/odm_preprocess.py b/odm_preprocess.py index 2f1c319..74b0b7e 100644 --- a/odm_preprocess.py +++ b/odm_preprocess.py @@ -250,11 +250,11 @@ class ImagePreprocessor: 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[tuple, pd.DataFrame], produce_dem: bool): + def merge_tif(self, grid_points: Dict[tuple, pd.DataFrame], mode: str, produce_dem: bool): """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, produce_dem) + merger.merge_all_tifs(grid_points, mode) def merge_ply(self, grid_points: Dict[tuple, pd.DataFrame]): """合并所有网格的PLY点云""" @@ -282,18 +282,19 @@ class ImagePreprocessor: f"将只合并成功处理的 {len(successful_grid_points)} 个网格" ) - 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_tif(successful_grid_points, self.config.produce_dem) - # self.merge_ply(successful_grid_points) - # self.merge_obj(successful_grid_points, translations) - self.convert_obj(successful_grid_points) - else: - self.merge_tif(successful_grid_points, self.config.produce_dem) + # if self.config.mode == "快拼模式": + self.merge_tif(successful_grid_points, self.config.mode, + self.config.produce_dem) + if self.config.mode == "三维模式": + # self.merge_tif(successful_grid_points, self.config.produce_dem) # self.merge_ply(successful_grid_points) # self.merge_obj(successful_grid_points, translations) self.convert_obj(successful_grid_points) + # else: + # self.merge_tif(successful_grid_points, self.config.produce_dem) + # # self.merge_ply(successful_grid_points) + # # self.merge_obj(successful_grid_points, translations) + # self.convert_obj(successful_grid_points) def process(self): """执行完整的预处理流程""" diff --git a/odm_preprocess_fast.py b/odm_preprocess_fast.py index 3939ae2..11ad0aa 100644 --- a/odm_preprocess_fast.py +++ b/odm_preprocess_fast.py @@ -17,7 +17,7 @@ from utils.visualizer import FilterVisualizer from post_pro.merge_tif import MergeTif from post_pro.merge_obj import MergeObj from post_pro.merge_laz import MergePly -from post_pro.conv_obj import ConvertOBJ +from post_pro.conv_obj2 import ConvertOBJ @dataclass @@ -44,6 +44,7 @@ class PreprocessConfig: grid_size: float = 500 # 几个pipline过程是否开启 mode: str = "快拼模式" + accuracy: str = "medium" produce_dem: bool = False @@ -223,6 +224,8 @@ class ImagePreprocessor: self.gps_points ) grid_divider.visualize_grids(self.gps_points, grids) + if len(grids) >= 20: + self.logger.warning("网格数量已超过20, 需要人工调整分区") return grid_points, translations @@ -302,6 +305,9 @@ class ImagePreprocessor: # self.copy_images(grid_points) # self.logger.info("预处理任务完成") + # successful_grid_points = self.odm_monitor.process_all_grids( + # grid_points, self.config.produce_dem, self.config.accuracy) + # successful_grid_points = self.odm_monitor.process_all_grids( # grid_points, self.config.produce_dem) successful_grid_points = grid_points diff --git a/post_pro/conv_obj2.py b/post_pro/conv_obj2.py new file mode 100644 index 0000000..d796056 --- /dev/null +++ b/post_pro/conv_obj2.py @@ -0,0 +1,262 @@ +import os +import subprocess +import json +import shutil +import logging +from pyproj import Transformer +import cv2 + + +class ConvertOBJ: + def __init__(self, output_dir: str): + self.output_dir = output_dir + # 用于存储所有grid的UTM范围 + self.ref_east = float('inf') + self.ref_north = float('inf') + # 初始化UTM到WGS84的转换器 + self.transformer = Transformer.from_crs( + "EPSG:32649", "EPSG:4326", always_xy=True) + self.logger = logging.getLogger('UAV_Preprocess.ConvertOBJ') + + def convert_grid_obj(self, grid_points): + """转换每个网格的OBJ文件为OSGB格式""" + os.makedirs(os.path.join(self.output_dir, + "osgb", "Data"), exist_ok=True) + + # 以第一个grid的UTM坐标作为参照系 + first_grid_id = list(grid_points.keys())[0] + first_grid_dir = os.path.join( + self.output_dir, + f"grid_{first_grid_id[0]}_{first_grid_id[1]}", + "project" + ) + log_file = os.path.join( + first_grid_dir, "odm_orthophoto", "odm_orthophoto_log.txt") + self.ref_east, self.ref_north = self.read_utm_offset(log_file) + + for grid_id in grid_points.keys(): + try: + self._convert_single_grid(grid_id, grid_points) + except Exception as e: + self.logger.error(f"网格 {grid_id} 转换失败: {str(e)}") + + self._create_merged_metadata() + + def _convert_single_grid(self, grid_id, grid_points): + """转换单个网格的OBJ文件""" + # 构建相关路径 + grid_name = f"grid_{grid_id[0]}_{grid_id[1]}" + project_dir = os.path.join(self.output_dir, grid_name, "project") + texturing_dir = os.path.join(project_dir, "odm_texturing") + texturing_dst_dir = os.path.join(project_dir, "odm_texturing_dst") + split_obj_dir = os.path.join(texturing_dst_dir, "split_obj") + opensfm_dir = os.path.join(project_dir, "opensfm") + log_file = os.path.join( + project_dir, "odm_orthophoto", "odm_orthophoto_log.txt") + os.makedirs(texturing_dst_dir, exist_ok=True) + + # 修改obj文件z坐标的值 + min_25d_z = self.get_min_z_from_obj(os.path.join( + project_dir, 'odm_texturing_25d', 'odm_textured_model_geo.obj')) + self.modify_z_in_obj(texturing_dir, min_25d_z) + + # 在新文件夹下，利用UTM偏移量，修改obj文件顶点坐标，纹理文件下采样 + utm_offset = self.read_utm_offset(log_file) + modified_obj = self.modify_obj_coordinates( + texturing_dir, texturing_dst_dir, utm_offset) + self.downsample_texture(texturing_dir, texturing_dst_dir) + + # 将obj文件进行切片 + self.logger.info(f"开始切片网格 {grid_id} 的OBJ文件") + os.makedirs(split_obj_dir) + cmd = ( + f"D:\software\Obj2Tiles\Obj2Tiles.exe --stage Splitting --lods 1 --divisions 3 " + f"{modified_obj} {split_obj_dir}" + ) + subprocess.run(cmd, check=True) + + # 执行格式转换，Linux下osgconv有问题，记得注释掉 + self.logger.info(f"开始转换网格 {grid_id} 的OBJ文件") + # 先获取split_obj_dir下的所有obj文件 + obj_lod_dir = os.path.join(split_obj_dir, "LOD-0") + obj_files = [f for f in os.listdir( + obj_lod_dir) if f.endswith('.obj')] + for obj_file in obj_files: + obj_path = os.path.join(obj_lod_dir, obj_file) + osgb_file = os.path.splitext(obj_file)[0] + '.osgb' + osgb_path = os.path.join(split_obj_dir, osgb_file) + # 执行 osgconv 命令 + subprocess.run(['osgconv', obj_path, osgb_path], check=True) + + # 创建OSGB目录结构，复制文件 + osgb_base_dir = os.path.join(self.output_dir, "osgb") + data_dir = os.path.join(osgb_base_dir, "Data") + for obj_file in obj_files: + obj_file_name = os.path.splitext(obj_file)[0] + tile_dirs = os.path.join(data_dir, f"{obj_file_name}") + os.makedirs(tile_dirs, exist_ok=True) + shutil.copy2(os.path.join(split_obj_dir, obj_file_name+".osgb"), tile_dirs) + + def _create_merged_metadata(self): + """创建合并后的metadata.xml文件""" + # 转换为WGS84经纬度 + center_lon, center_lat = self.transformer.transform( + self.ref_east, self.ref_north) + metadata_content = f""" + + EPSG:4326 + {center_lon},{center_lat},0 + + Visible + + """ + + metadata_file = os.path.join(self.output_dir, "osgb", "metadata.xml") + with open(metadata_file, 'w', encoding='utf-8') as f: + f.write(metadata_content) + + def read_utm_offset(self, log_file: str) -> tuple: + """读取UTM偏移量""" + try: + east_offset = None + north_offset = None + + with open(log_file, 'r') as f: + lines = f.readlines() + for i, line in enumerate(lines): + if 'utm_north_offset' in line and i + 1 < len(lines): + north_offset = float(lines[i + 1].strip()) + elif 'utm_east_offset' in line and i + 1 < len(lines): + east_offset = float(lines[i + 1].strip()) + + if east_offset is None or north_offset is None: + raise ValueError("未找到UTM偏移量") + + return east_offset, north_offset + except Exception as e: + self.logger.error(f"读取UTM偏移量时发生错误: {str(e)}") + raise + + def modify_obj_coordinates(self, texturing_dir: str, texturing_dst_dir: str, utm_offset: tuple) -> str: + """修改obj文件中的顶点坐标，使用相对坐标系""" + obj_file = os.path.join( + texturing_dir, "odm_textured_model_modified.obj") + obj_dst_file = os.path.join( + texturing_dst_dir, "odm_textured_model_geo_utm.obj") + if not os.path.exists(obj_file): + raise FileNotFoundError(f"找不到OBJ文件: {obj_file}") + shutil.copy2(os.path.join(texturing_dir, "odm_textured_model_geo.mtl"), + os.path.join(texturing_dst_dir, "odm_textured_model_geo.mtl")) + east_offset, north_offset = utm_offset + self.logger.info( + f"UTM坐标偏移：{east_offset - self.ref_east}, {north_offset - self.ref_north}") + + try: + with open(obj_file, 'r') as f_in, open(obj_dst_file, 'w') as f_out: + for line in f_in: + if line.startswith('v '): + # 处理顶点坐标行 + parts = line.strip().split() + # 使用相对于整体最小UTM坐标的偏移 + x = float(parts[1]) + (east_offset - self.ref_east) + y = float(parts[2]) + (north_offset - self.ref_north) + z = float(parts[3]) + f_out.write(f'v {x:.6f} {z:.6f} {-y:.6f}\n') + elif line.startswith('vn '): # 处理法线向量 + parts = line.split() + nx = float(parts[1]) + ny = float(parts[2]) + nz = float(parts[3]) + # 同步反转法线的 Y 轴 + new_line = f"vn {nx} {nz} {-ny}\n" + f_out.write(new_line) + else: + # 其他行直接写入 + f_out.write(line) + + return obj_dst_file + except Exception as e: + self.logger.error(f"修改obj坐标时发生错误: {str(e)}") + raise + + def downsample_texture(self, src_dir: str, dst_dir: str): + """复制并重命名纹理文件，对大于100MB的文件进行多次下采样，直到文件小于100MB + Args: + src_dir: 源纹理目录 + dst_dir: 目标纹理目录 + """ + for file in os.listdir(src_dir): + if file.lower().endswith(('.png')): + src_path = os.path.join(src_dir, file) + dst_path = os.path.join(dst_dir, file) + + # 检查文件大小（以字节为单位） + file_size = os.path.getsize(src_path) + if file_size <= 100 * 1024 * 1024: # 如果文件小于等于100MB，直接复制 + shutil.copy2(src_path, dst_path) + else: + # 文件大于100MB，进行下采样 + img = cv2.imread(src_path, cv2.IMREAD_UNCHANGED) + if_first_ds = True + while file_size > 100 * 1024 * 1024: # 大于100MB + self.logger.info(f"纹理文件 {file} 大于100MB，进行下采样") + + if if_first_ds: + # 计算新的尺寸（长宽各变为1/4） + new_size = (img.shape[1] // 4, + img.shape[0] // 4) # 逐步减小尺寸 + # 使用双三次插值进行下采样 + resized_img = cv2.resize( + img, new_size, interpolation=cv2.INTER_CUBIC) + if_first_ds = False + else: + # 计算新的尺寸（长宽各变为1/2） + new_size = (img.shape[1] // 2, + img.shape[0] // 2) # 逐步减小尺寸 + # 使用双三次插值进行下采样 + resized_img = cv2.resize( + img, new_size, interpolation=cv2.INTER_CUBIC) + + # 更新文件路径为下采样后的路径 + cv2.imwrite(dst_path, resized_img, [ + cv2.IMWRITE_PNG_COMPRESSION, 9]) + + # 更新文件大小和图像 + file_size = os.path.getsize(dst_path) + img = cv2.imread(dst_path, cv2.IMREAD_UNCHANGED) + self.logger.info( + f"下采样后文件大小: {file_size / (1024 * 1024):.2f} MB") + + def get_min_z_from_obj(self, file_path): + min_z = float('inf') # 初始值设为无穷大 + with open(file_path, 'r') as obj_file: + for line in obj_file: + # 检查每一行是否是顶点定义（以 'v ' 开头） + if line.startswith('v '): + # 获取顶点坐标 + parts = line.split() + # 将z值转换为浮动数字 + z = float(parts[3]) + # 更新最小z值 + if z < min_z: + min_z = z + return min_z + + def modify_z_in_obj(self, texturing_dir, min_25d_z): + obj_file = os.path.join(texturing_dir, 'odm_textured_model_geo.obj') + output_file = os.path.join( + texturing_dir, 'odm_textured_model_modified.obj') + with open(obj_file, 'r') as f_in, open(output_file, 'w') as f_out: + for line in f_in: + if line.startswith('v '): # 顶点坐标行 + parts = line.strip().split() + x = float(parts[1]) + y = float(parts[2]) + z = float(parts[3]) + + if z < min_25d_z: + z = min_25d_z + + f_out.write(f"v {x} {y} {z}\n") + else: + f_out.write(line) diff --git a/post_pro/merge_tif.py b/post_pro/merge_tif.py index de6081f..613e535 100644 --- a/post_pro/merge_tif.py +++ b/post_pro/merge_tif.py @@ -192,7 +192,7 @@ 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[tuple, pd.DataFrame], produce_dem: bool): + def merge_all_tifs(self, grid_points: Dict[tuple, pd.DataFrame], mode: str): """合并所有产品（正射影像、DSM和DTM）""" try: products = [ @@ -204,7 +204,7 @@ class MergeTif: }, ] - if produce_dem: + if mode == '三维模式': products.append( { 'name': 'DSM', diff --git a/utils/odm_monitor.py b/utils/odm_monitor.py index 8f33e3f..8a5cf2a 100644 --- a/utils/odm_monitor.py +++ b/utils/odm_monitor.py @@ -182,7 +182,7 @@ class ODMProcessMonitor: 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":