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合并obj算法更新

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This commit is contained in:
龙澳 2024-12-31 22:29:24 +08:00
parent b3d7c37399
commit c8eaf997a2
4 changed files with 242 additions and 83 deletions
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18
odm_preprocess.py
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@ -2,7 +2,7 @@ import os
import shutil
from datetime import timedelta
from dataclasses import dataclass
from typing import Dict
from typing import Dict, Tuple
import matplotlib.pyplot as plt
import pandas as pd
@ -197,14 +197,14 @@ class ImagePreprocessor:
return self.gps_points
def divide_grids(self) -> Dict[tuple, pd.DataFrame]:
def divide_grids(self) -> Tuple[Dict[tuple, pd.DataFrame], Dict[tuple, tuple]]:
"""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(
grids, translations = grid_divider.divide_grids(
self.gps_points, grid_size=self.config.grid_size
)
grid_points = grid_divider.assign_to_grids(self.gps_points, grids)
@ -212,7 +212,7 @@ class ImagePreprocessor:
# -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
return grid_points, translations
def copy_images(self, grid_points: Dict[tuple, pd.DataFrame]):
"""复制图像到目标文件夹"""
@ -238,11 +238,11 @@ class ImagePreprocessor:
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], translations: Dict[tuple, tuple]):
"""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, translations)
def merge_ply(self, grid_points: Dict[int, pd.DataFrame]):
"""合并所有网格的PLY点云"""
@ -257,13 +257,13 @@ class ImagePreprocessor:
self.cluster()
# self.filter_time_group_overlap()
self.filter_points()
grid_points = self.divide_grids()
grid_points, translations = 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.odm_monitor.process_all_grids(grid_points)
# self.merge_tif(grid_points)
# self.merge_obj(grid_points)
# self.merge_obj(grid_points, translations)
# 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)

23
odm_preprocess_fast.py
View File

@ -2,7 +2,7 @@ import os
import shutil
from datetime import timedelta
from dataclasses import dataclass
from typing import Dict
from typing import Dict, Tuple
import matplotlib.pyplot as plt
import pandas as pd
@ -197,20 +197,25 @@ class ImagePreprocessor:
return self.gps_points
def divide_grids(self) -> Dict[tuple, pd.DataFrame]:
"""划分网格"""
def divide_grids(self) -> Tuple[Dict[tuple, pd.DataFrame], Dict[tuple, tuple]]:
"""划分网格
Returns:
tuple: (grid_points, translations)
- grid_points: 网格点数据字典
- translations: 网格平移量字典
"""
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(
grids, translations = 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")
return grid_points
return grid_points, translations
def copy_images(self, grid_points: Dict[tuple, pd.DataFrame]):
"""复制图像到目标文件夹"""
@ -235,11 +240,11 @@ class ImagePreprocessor:
merger = MergeTif(self.config.output_dir)
merger.merge_all_tifs(grid_points)
def merge_obj(self, grid_points: Dict[tuple, pd.DataFrame]):
def merge_obj(self, grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]):
"""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, self.config.grid_size)
merger.merge_grid_obj(grid_points, translations)
def merge_ply(self, grid_points: Dict[tuple, pd.DataFrame]):
"""合并所有网格的PLY点云"""
@ -254,14 +259,14 @@ class ImagePreprocessor:
self.cluster()
# self.filter_time_group_overlap()
self.filter_points()
grid_points = self.divide_grids()
grid_points, translations = 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)
self.merge_obj(grid_points, translations)
except Exception as e:
self.logger.error(f"处理过程中发生错误: {str(e)}", exc_info=True)
raise

189
post_pro/merge_obj.py
View File

@ -3,6 +3,7 @@ import logging
import numpy as np
from typing import Dict
import pandas as pd
import shutil
class MergeObj:
@ -21,9 +22,11 @@ class MergeObj:
if len(parts) == 0:
continue
if parts[0] == 'v': # 顶点
vertices.append([float(parts[1]), float(parts[2]), float(parts[3])])
vertices.append(
[float(parts[1]), float(parts[2]), float(parts[3])])
elif parts[0] == 'f': # 面
faces.append([int(parts[1].split('/')[0]), int(parts[2].split('/')[0]), int(parts[3].split('/')[0])])
faces.append([int(parts[1].split(
'/')[0]), int(parts[2].split('/')[0]), int(parts[3].split('/')[0])])
return vertices, faces
@ -54,11 +57,14 @@ class MergeObj:
vertices2, faces2 = self.read_obj(obj2_path)
# 平移第二个模型的顶点
vertices2_translated = self.translate_vertices(vertices2, translation)
vertices2_translated = self.translate_vertices(
vertices2, translation)
# 合并顶点和面
all_vertices = vertices1 + vertices2_translated
all_faces = faces1 + [[f[0] + len(vertices1), f[1] + len(vertices1), f[2] + len(vertices1)] for f in faces2]
all_faces = faces1 + \
[[f[0] + len(vertices1), f[1] + len(vertices1),
f[2] + len(vertices1)] for f in faces2]
# 写入合并后的obj文件
self.write_obj(output_path, all_vertices, all_faces)
@ -69,76 +75,161 @@ 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_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
def read_mtl(self, file_path):
"""读取.mtl文件内容"""
with open(file_path, 'r') as file:
return file.read()
# 计算平移量，考虑到重叠
x_translation = col * grid_size
y_translation = row * grid_size
def copy_texture_files(self, src_dir: str, dst_dir: str, grid_id: tuple):
"""复制并重命名纹理文件
Args:
src_dir: 源纹理文件目录
dst_dir: 目标纹理文件目录
grid_id: 网格ID，用于重命名
"""
# 确保目标目录存在
os.makedirs(dst_dir, exist_ok=True)
self.logger.info(
f"网格 ({col},{row}) 的平移量: x={x_translation}, y={y_translation}"
)
# 复制所有png文件并重命名
for file in os.listdir(src_dir):
if file.endswith('.png'):
src_file = os.path.join(src_dir, file)
# 在文件名前添加网格ID前缀
new_name = f"grid_{grid_id[0]}_{grid_id[1]}_{file}"
dst_file = os.path.join(dst_dir, new_name)
shutil.copy2(src_file, dst_file)
self.logger.debug(f"复制纹理文件: {file} -> {new_name}")
return (x_translation, y_translation, 0) # z轴不需要平移
return dst_dir
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"""
def update_mtl_content(self, mtl_content: str, grid_id: tuple) -> str:
"""更新MTL文件内容，修改纹理文件路径
Args:
mtl_content: 原MTL文件内容
grid_id: 网格ID，用于重命名纹理文件
Returns:
更新后的MTL文件内容
"""
updated_content = []
for line in mtl_content.split('\n'):
if line.startswith('map_Kd'): # 纹理文件路径行
# 获取原始文件名
original_file = line.split()[-1]
# 添加网格ID前缀
new_file = f"grid_{grid_id[0]}_{grid_id[1]}_{os.path.basename(original_file)}"
# 更新行内容
line = f"map_Kd {new_file}"
updated_content.append(line)
return '\n'.join(updated_content)
def merge_grid_obj(self, grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]):
"""合并所有网格的OBJ模型和纹理"""
self.logger.info("开始合并所有网格的OBJ模型")
if len(grid_points) < 2:
self.logger.info("只有一个网格，无需合并")
return
input_obj1, input_obj2 = None, None
merge_count = 0
try:
# 创建输出目录
output_model_dir = os.path.join(self.output_dir, "merged_model")
os.makedirs(output_model_dir, exist_ok=True)
# 获取所有有效的网格OBJ文件
grid_objs = {}
for grid_id, points in grid_points.items():
grid_obj = os.path.join(
grid_base_dir = os.path.join(
self.output_dir,
f"grid_{grid_id[0]}_{grid_id[1]}",
"project",
"odm_texturing",
"odm_textured_model_geo.obj"
"odm_texturing"
)
grid_obj = os.path.join(grid_base_dir, "odm_textured_model_geo.obj")
grid_mtl = os.path.join(grid_base_dir, "odm_textured_model_geo.mtl")
if not os.path.exists(grid_obj):
self.logger.warning(f"网格 ({grid_id[0]},{grid_id[1]}) 的OBJ文件不存在: {grid_obj}")
if not os.path.exists(grid_obj) or not os.path.exists(grid_mtl):
self.logger.warning(
f"网格 ({grid_id[0]},{grid_id[1]}) 的OBJ或MTL文件不存在")
continue
if input_obj1 is None:
input_obj1 = grid_obj
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, f"merged_model_{merge_count}.obj")
grid_objs[grid_id] = {
'obj': grid_obj,
'mtl': grid_mtl,
'base_dir': grid_base_dir
}
# 计算当前网格的平移量
translation = self.calculate_translation(grid_id, grid_size)
if not grid_objs:
self.logger.error("没有找到有效的OBJ文件")
return
self.logger.info(
f"开始合并第 {merge_count + 1} 次:\n"
f"平移量: {translation}\n"
f"输出: {output_obj}"
)
# 使用第一个网格作为参考
reference_id = list(grid_objs.keys())[0]
merged_obj = grid_objs[reference_id]['obj']
self.merge_two_objs(input_obj1, input_obj2, output_obj, translation)
merge_count += 1
# 复制参考网格的纹理文件
self.copy_texture_files(
grid_objs[reference_id]['base_dir'],
output_model_dir,
reference_id
)
input_obj1 = output_obj
input_obj2 = None
# 最后的结果重命名为merged_model.obj
final_output = os.path.join(self.output_dir, "merged_model.obj")
if os.path.exists(input_obj1) and input_obj1 != final_output:
os.rename(input_obj1, final_output)
# 复制并更新参考网格的MTL文件
ref_mtl_content = self.read_mtl(grid_objs[reference_id]['mtl'])
updated_mtl = self.update_mtl_content(ref_mtl_content, reference_id)
self.logger.info(
f"OBJ模型合并完成，共执行 {merge_count} 次合并，"
f"最终输出文件: {final_output}"
f"使用网格 ({reference_id[0]},{reference_id[1]}) 作为参考网格")
# 依次合并其他网格
for grid_id, grid_files in list(grid_objs.items())[1:]:
# 复制当前网格的纹理文件
self.copy_texture_files(
grid_files['base_dir'],
output_model_dir,
grid_id
)
# 更新当前网格的MTL内容
current_mtl = self.read_mtl(grid_files['mtl'])
updated_mtl += '\n' + self.update_mtl_content(current_mtl, grid_id)
# 获取平移量
translation = translations[grid_id]
translation = (translation[0], translation[1], 0) # 添加z轴的0平移
output_obj = os.path.join(
output_model_dir,
f"merged_model_{reference_id[0]}_{reference_id[1]}_{grid_id[0]}_{grid_id[1]}.obj"
)
self.logger.info(
f"合并网格 ({grid_id[0]},{grid_id[1]}):\n"
f"平移量: x={translation[0]:.2f}m, y={translation[1]:.2f}m\n"
f"输出: {output_obj}"
)
self.merge_two_objs(merged_obj, grid_files['obj'],
output_obj, translation)
merged_obj = output_obj
# 最后的结果
final_obj = os.path.join(output_model_dir, "merged_model.obj")
final_mtl = os.path.join(output_model_dir, "merged_model.mtl")
# 保存最终的OBJ和MTL文件
if os.path.exists(merged_obj) and merged_obj != final_obj:
shutil.copy2(merged_obj, final_obj)
os.remove(merged_obj)
# 保存合并后的MTL文件
with open(final_mtl, 'w') as f:
f.write(updated_mtl)
self.logger.info(
f"模型合并完成，输出目录: {output_model_dir}\n"
f"- OBJ文件: merged_model.obj\n"
f"- MTL文件: merged_model.mtl\n"
f"- 纹理文件: {len(os.listdir(output_model_dir)) - 2}个PNG文件"
)
except Exception as e:

71
utils/grid_divider.py
View File

@ -16,7 +16,12 @@ class GridDivider:
self.num_grids_height = 0
def divide_grids(self, points_df, grid_size=500):
"""计算边界框并划分网格"""
"""计算边界框并划分网格
Returns:
tuple: (grids, translations)
- grids: 网格边界列表
- translations: 网格平移量字典
"""
self.logger.info("开始划分网格")
min_lat, max_lat = points_df['lat'].min(), points_df['lat'].max()
@ -37,7 +42,9 @@ class GridDivider:
lon_step = (max_lon - min_lon) / self.num_grids_width
grids = []
grid_translations = {} # 存储每个网格相对于第一个网格的平移量
# 先创建所有网格
for i in range(self.num_grids_height):
for j in range(self.num_grids_width):
grid_min_lat = min_lat + i * lat_step - self.overlap * lat_step
@ -46,21 +53,36 @@ class GridDivider:
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_bounds = (grid_min_lat, grid_max_lat, grid_min_lon, grid_max_lon)
grids.append(grid_bounds)
self.logger.debug(
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}]"
)
# 计算每个网格相对于第一个网格的平移量
reference_grid = grids[0]
for i in range(self.num_grids_height):
for j in range(self.num_grids_width):
grid_id = (j, i)
grid_idx = i * self.num_grids_width + j
if grid_idx == 0: # 参考网格
grid_translations[grid_id] = (0, 0)
else:
translation = self.calculate_grid_translation(reference_grid, grids[grid_idx])
grid_translations[grid_id] = translation
self.logger.debug(
f"网格[{j},{i}]相对于参考网格的平移量: x={translation[0]:.2f}m, y={translation[1]:.2f}m"
)
self.logger.info(
f"成功划分为 {len(grids)} 个网格 ({self.num_grids_width}x{self.num_grids_height})")
# 添加可视化调用
self.visualize_grids(points_df, grids)
return grids
return grids, grid_translations
def assign_to_grids(self, points_df, grids):
@ -141,3 +163,44 @@ class GridDivider:
self.logger.info(f"网格划分可视化图已保存至: {save_path}")
plt.close()
def get_grid_center(self, grid_bounds) -> tuple:
"""计算网格中心点的经纬度
Args:
grid_bounds: (min_lat, max_lat, min_lon, max_lon)
Returns:
(center_lat, center_lon)
"""
min_lat, max_lat, min_lon, max_lon = grid_bounds
return ((min_lat + max_lat) / 2, (min_lon + max_lon) / 2)
def calculate_grid_translation(self, reference_grid: tuple, target_grid: tuple) -> tuple:
"""计算目标网格相对于参考网格的平移距离（米）
Args:
reference_grid: 参考网格的边界 (min_lat, max_lat, min_lon, max_lon)
target_grid: 目标网格的边界 (min_lat, max_lat, min_lon, max_lon)
Returns:
(x_translation, y_translation): 在米制单位下的平移量
"""
ref_center = self.get_grid_center(reference_grid)
target_center = self.get_grid_center(target_grid)
# 计算经度方向的距离（x轴）
x_distance = geodesic(
(ref_center[0], ref_center[1]),
(ref_center[0], target_center[1])
).meters
# 如果目标在参考点西边，距离为负
if target_center[1] < ref_center[1]:
x_distance = -x_distance
# 计算纬度方向的距离（y轴）
y_distance = geodesic(
(ref_center[0], ref_center[1]),
(target_center[0], ref_center[1])
).meters
# 如果目标在参考点南边，距离为负
if target_center[0] < ref_center[0]:
y_distance = -y_distance
return (x_distance, y_distance)