修改瓦片的偏移
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weixin_46229132
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971517c145
@ -298,8 +298,8 @@ class ImagePreprocessor:
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self.cluster()
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self.filter_isolated_points()
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self.filter_time_group_overlap()
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center_lat, center_lon = self.calculate_center_coordinates()
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# self.filter_alternate_images()
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center_lat, center_lon = self.calculate_center_coordinates()
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grid_points, translations = self.divide_grids()
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self.copy_images(grid_points)
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self.logger.info("预处理任务完成")
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@ -4,10 +4,7 @@ from datetime import timedelta
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from dataclasses import dataclass
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from typing import Dict, Tuple
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import psutil
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import matplotlib.pyplot as plt
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import pandas as pd
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from tqdm import tqdm
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from filter.cluster_filter import GPSCluster
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from filter.time_group_overlap_filter import TimeGroupOverlapFilter
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@ -20,6 +17,7 @@ from utils.visualizer import FilterVisualizer
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from post_pro.merge_tif import MergeTif
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from post_pro.merge_obj import MergeObj
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from post_pro.merge_laz import MergePly
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from post_pro.conv_obj import ConvertOBJ
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@dataclass
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@ -187,6 +185,28 @@ class ImagePreprocessor:
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self.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "3-Time Group Overlap")
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def calculate_center_coordinates(self):
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"""计算剩余点的中心经纬度坐标"""
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mean_lat = self.gps_points['lat'].mean()
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mean_lon = self.gps_points['lon'].mean()
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self.logger.info(f"区域中心坐标：纬度 {mean_lat:.6f}, 经度 {mean_lon:.6f}")
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return mean_lat, mean_lon
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def filter_alternate_images(self):
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"""按时间顺序隔一个删一个图像来降低密度"""
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previous_points = self.gps_points.copy()
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# 按时间戳排序
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self.gps_points = self.gps_points.sort_values('date')
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# 保留索引为偶数的行（即隔一个保留一个）
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self.gps_points = self.gps_points.iloc[::2].reset_index(drop=True)
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self.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "4-Alternate Images")
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self.logger.info(f"交替过滤后剩余 {len(self.gps_points)} 个点")
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def divide_grids(self) -> Tuple[Dict[tuple, pd.DataFrame], Dict[tuple, tuple]]:
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"""划分网格
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Returns:
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@ -220,7 +240,7 @@ class ImagePreprocessor:
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os.makedirs(output_dir, exist_ok=True)
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for point in tqdm(points, desc=f"复制网格 ({grid_id[0]},{grid_id[1]}) 的图像"):
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for point in points:
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src = os.path.join(self.config.image_dir, point["file"])
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dst = os.path.join(output_dir, point["file"])
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shutil.copy(src, dst)
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@ -245,7 +265,13 @@ class ImagePreprocessor:
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merger = MergeObj(self.config.output_dir)
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merger.merge_grid_obj(grid_points, translations)
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def post_process(self, successful_grid_points: Dict[tuple, pd.DataFrame], grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]):
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def convert_obj(self, grid_points: Dict[tuple, pd.DataFrame], center_lat: float, center_lon: float):
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"""转换OBJ模型"""
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self.logger.info("开始转换OBJ模型")
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converter = ConvertOBJ(self.config.output_dir, center_lat, center_lon)
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converter.convert_grid_obj(grid_points)
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def post_process(self, successful_grid_points: Dict[tuple, pd.DataFrame], grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple], center_lat: float, center_lon: float):
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"""后处理：合并或复制处理结果"""
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if len(successful_grid_points) < len(grid_points):
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self.logger.warning(
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@ -256,12 +282,14 @@ class ImagePreprocessor:
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if self.config.mode == "快拼模式":
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self.merge_tif(successful_grid_points, self.config.produce_dem)
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elif self.config.mode == "三维模式":
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self.merge_ply(successful_grid_points)
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self.merge_obj(successful_grid_points, translations)
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# self.merge_ply(successful_grid_points)
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# self.merge_obj(successful_grid_points, translations)
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self.convert_obj(successful_grid_points, center_lat, center_lon)
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else:
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self.merge_tif(successful_grid_points, self.config.produce_dem)
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self.merge_ply(successful_grid_points)
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self.merge_obj(successful_grid_points, translations)
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# self.merge_ply(successful_grid_points)
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# self.merge_obj(successful_grid_points, translations)
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self.convert_obj(successful_grid_points, center_lat, center_lon)
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def process(self):
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"""执行完整的预处理流程"""
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@ -270,15 +298,17 @@ class ImagePreprocessor:
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self.cluster()
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self.filter_isolated_points()
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self.filter_time_group_overlap()
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center_lat, center_lon = self.calculate_center_coordinates()
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# self.filter_alternate_images()
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grid_points, translations = self.divide_grids()
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# self.copy_images(grid_points)
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self.logger.info("预处理任务完成")
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# self.logger.info("预处理任务完成")
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# successful_grid_points = self.odm_monitor.process_all_grids(
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# grid_points, self.config.produce_dem)
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successful_grid_points = grid_points
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self.post_process(successful_grid_points,
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grid_points, translations)
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grid_points, translations, center_lat, center_lon)
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except Exception as e:
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self.logger.error(f"处理过程中发生错误: {str(e)}", exc_info=True)
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@ -13,17 +13,19 @@ class ConvertOBJ:
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def convert_grid_obj(self, grid_points):
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"""转换每个网格的OBJ文件为OSGB格式"""
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os.makedirs(os.path.join(self.output_dir, "osgb"), exist_ok=True)
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os.makedirs(os.path.join(self.output_dir, "osgb", "Data"), exist_ok=True)
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tile_infos = []
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for grid_id in grid_points.keys():
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try:
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self._convert_single_grid(grid_id)
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tile_info = self._convert_single_grid(grid_id, grid_points)
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tile_infos.append(tile_info)
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except Exception as e:
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self.logger.error(f"网格 {grid_id} 转换失败: {str(e)}")
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# 在所有网格处理完成后创建总的metadata.xml
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self._create_merged_metadata()
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self._create_merged_metadata(tile_infos)
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def _convert_single_grid(self, grid_id):
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def _convert_single_grid(self, grid_id, grid_points):
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"""转换单个网格的OBJ文件"""
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# 1. 构建相关路径
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grid_name = f"grid_{grid_id[0]}_{grid_id[1]}"
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@ -40,9 +42,24 @@ class ConvertOBJ:
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self.logger.info(f"开始转换网格 {grid_id} 的OBJ文件")
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output_osgb = os.path.join(texturing_dir, "Tile.osgb")
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# 计算当前网格相对于中心点的偏移
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grid_data = grid_points[grid_id]
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lats = [point['lat'] for point in grid_data]
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lons = [point['lon'] for point in grid_data]
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min_lat = min(lats)
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min_lon = min(lons)
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# 计算偏移量（米）
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offset_x = self._calculate_distance(self.center_lat, self.center_lon, self.center_lat, min_lon)
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offset_y = self._calculate_distance(self.center_lat, self.center_lon, min_lat, self.center_lon)
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# 修改转换命令，使用正确的参数格式
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cmd = (
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f"osgconv --compressed --smooth --fix-transparency -o 0,1,0-0,0,-1 "
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f"{obj_file} {output_osgb}"
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f"osgconv {obj_file} {output_osgb} "
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f"--compressed --smooth --fix-transparency "
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f"-t {offset_x},{offset_y},0 " # 使用 -t 参数进行平移
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f"-o 0,1,0-0,0,-1"
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)
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try:
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@ -65,16 +82,78 @@ class ConvertOBJ:
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target_osgb = os.path.join(tile_dir, f"Tile_{grid_id[0]}_{grid_id[1]}.osgb")
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shutil.copy2(output_osgb, target_osgb)
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self.logger.info(f"网格 {grid_id} 转换完成")
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# 计算当前网格的边界框
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grid_data = grid_points[grid_id]
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# 假设grid_data是一个列表，每个元素都是包含lat和lon的字典
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lats = [point['lat'] for point in grid_data]
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lons = [point['lon'] for point in grid_data]
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def _create_merged_metadata(self):
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min_lat = min(lats)
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max_lat = max(lats)
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min_lon = min(lons)
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max_lon = max(lons)
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# 计算相对于中心点的偏移
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offset_x = self._calculate_distance(self.center_lat, self.center_lon, self.center_lat, min_lon)
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offset_y = self._calculate_distance(self.center_lat, self.center_lon, min_lat, self.center_lon)
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tile_info = {
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'id': f"{grid_id[0]}_{grid_id[1]}",
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'bounds': {
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'min_lat': min_lat,
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'max_lat': max_lat,
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'min_lon': min_lon,
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'max_lon': max_lon
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},
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'offset': (offset_x, offset_y)
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}
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return tile_info
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def _calculate_distance(self, lat1, lon1, lat2, lon2):
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"""计算两点间的距离（米）"""
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from math import sin, cos, sqrt, atan2, radians
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R = 6371000 # 地球半径（米）
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lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
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dlat = lat2 - lat1
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dlon = lon2 - lon1
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a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
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c = 2 * atan2(sqrt(a), sqrt(1-a))
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return R * c
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def _create_merged_metadata(self, tile_infos):
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"""创建合并后的metadata.xml文件"""
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metadata_content = f"""<?xml version="1.0" encoding="utf-8"?>
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<ModelMetadata version="1">
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<!--Spatial Reference System-->
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<SRS>EPSG:4326</SRS>
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<!--Origin in Spatial Reference System-->
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<SRSOrigin>{self.center_lon},{self.center_lat},0.000000</SRSOrigin>
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<TileStructure>
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<RootNode>
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<BoundingBox>
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<MinLat>{min([t['bounds']['min_lat'] for t in tile_infos])}</MinLat>
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<MaxLat>{max([t['bounds']['max_lat'] for t in tile_infos])}</MaxLat>
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<MinLon>{min([t['bounds']['min_lon'] for t in tile_infos])}</MinLon>
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<MaxLon>{max([t['bounds']['max_lon'] for t in tile_infos])}</MaxLon>
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</BoundingBox>
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<Tiles>"""
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for tile in tile_infos:
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metadata_content += f"""
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<Tile id="{tile['id']}">
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<Offset>{tile['offset'][0]},{tile['offset'][1]},0</Offset>
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<BoundingBox>
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<MinLat>{tile['bounds']['min_lat']}</MinLat>
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<MaxLat>{tile['bounds']['max_lat']}</MaxLat>
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<MinLon>{tile['bounds']['min_lon']}</MinLon>
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<MaxLon>{tile['bounds']['max_lon']}</MaxLon>
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</BoundingBox>
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</Tile>"""
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metadata_content += """
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</Tiles>
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</RootNode>
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</TileStructure>
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<Texture>
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<ColorSource>Visible</ColorSource>
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</Texture>
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