UAV/post_pro/merge_obj.py

import os
import logging
import numpy as np
from typing import Dict
import pandas as pd


class MergeObj:
    def __init__(self, output_dir: str):
        self.output_dir = output_dir
        self.logger = logging.getLogger('UAV_Preprocess.MergeObj')

    def read_obj(self, file_path):
        """读取.obj文件，返回顶点列表和面列表"""
        vertices = []
        faces = []
        
        with open(file_path, 'r') as file:
            for line in file:
                parts = line.split()
                if len(parts) == 0:
                    continue
                if parts[0] == 'v':  # 顶点
                    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])])
        
        return vertices, faces

    def write_obj(self, file_path, vertices, faces):
        """将修改后的顶点和面列表写入到.obj文件"""
        with open(file_path, 'w') as file:
            for vertex in vertices:
                file.write(f"v {vertex[0]} {vertex[1]} {vertex[2]}\n")
            
            for face in faces:
                file.write(f"f {face[0]} {face[1]} {face[2]}\n")

    def translate_vertices(self, vertices, translation):
        """平移顶点"""
        return [[v[0] + translation[0], v[1] + translation[1], v[2] + translation[2]] for v in vertices]

    def merge_two_objs(self, obj1_path: str, obj2_path: str, output_path: str, translation):
        """合并两个OBJ文件"""
        try:
            self.logger.info(f"开始合并OBJ模型:\n输入1: {obj1_path}\n输入2: {obj2_path}")
            
            # 检查输入文件是否存在
            if not os.path.exists(obj1_path) or not os.path.exists(obj2_path):
                raise FileNotFoundError("输入模型文件不存在")

            # 读取两个obj文件
            vertices1, faces1 = self.read_obj(obj1_path)
            vertices2, faces2 = self.read_obj(obj2_path)
            
            # 平移第二个模型的顶点
            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]
            
            # 写入合并后的obj文件
            self.write_obj(output_path, all_vertices, all_faces)
            
            self.logger.info(f"模型合并成功，已保存至: {output_path}")

        except Exception as e:
            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)
            
        # 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
        
        self.logger.info(
            f"网格 {grid_idx} 的位置: 行={row}, 列={col}"
        )
        
        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):
        """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")

        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:
            for grid_idx, points in grid_points.items():
                if grid_idx == 'grid_divider':  # 跳过grid_divider对象
                    continue
                    
                grid_obj = os.path.join(
                    self.output_dir,
                    f"grid_{grid_idx + 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}")
                    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")

                    # 计算当前网格的平移量
                    translation = self.calculate_translation(grid_idx, grid_points, grid_size)
                    
                    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}"
                    )

                    self.merge_two_objs(input_obj1, input_obj2, output_obj, translation)
                    merge_count += 1

                    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)

            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}"
            )

        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


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)