ODM_pro/odm_preprocess.py

import os
import shutil
from datetime import timedelta
from dataclasses import dataclass
from typing import Dict

import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm

from filter.cluster_filter import GPSCluster
from utils.command_runner import CommandRunner
from utils.gps_extractor import GPSExtractor
from filter.gps_filter import GPSFilter
from utils.grid_divider import GridDivider
from utils.logger import setup_logger
from filter.time_group_overlap_filter import TimeGroupOverlapFilter


@dataclass
class PreprocessConfig:
    """预处理配置类"""

    image_dir: str
    output_dir: str
    # 聚类过滤参数
    cluster_eps: float = 0.01
    cluster_min_samples: int = 5
    # 时间组重叠过滤参数
    time_group_overlap_threshold: float = 0.7
    time_group_interval: timedelta = timedelta(minutes=5)
    enable_time_group_filter: bool = True
    # 孤立点过滤参数
    filter_distance_threshold: float = 0.001  # 经纬度距离
    filter_min_neighbors: int = 6
    # 密集点过滤参数
    filter_grid_size: float = 0.001
    filter_dense_distance_threshold: float = 10  # 普通距离，单位：米
    filter_time_threshold: timedelta = timedelta(minutes=5)
    # 网格划分参数
    grid_overlap: float = 0.05
    grid_size: float = 500
    # 几个pipline过程是否开启
    enable_filter: bool = True
    enable_grid_division: bool = True
    enable_visualization: bool = True
    enable_copy_images: bool = True
    mode: str = "快拼模式"


class ImagePreprocessor:
    def __init__(self, config: PreprocessConfig):
        self.config = config
        self.logger = setup_logger(config.output_dir)
        self.gps_points = []
        self.command_runner = CommandRunner(
            config.output_dir, mode=config.mode)

    def extract_gps(self) -> pd.DataFrame:
        """提取GPS数据"""
        self.logger.info("开始提取GPS数据")
        extractor = GPSExtractor(self.config.image_dir)
        self.gps_points = extractor.extract_all_gps()
        self.logger.info(f"成功提取 {len(self.gps_points)} 个GPS点")
        return self.gps_points

    def cluster(self) -> pd.DataFrame:
        """使用DBSCAN对GPS点进行聚类，只保留最大的类"""
        self.logger.info("开始聚类")
        # 创建聚类器并执行聚类
        clusterer = GPSCluster(
            self.gps_points, output_dir=self.config.output_dir,
            eps=self.config.cluster_eps, min_samples=self.config.cluster_min_samples)
        # 获取主要类别的点
        self.clustered_points = clusterer.fit()
        self.gps_points = clusterer.get_main_cluster(self.clustered_points)
        # 获取统计信息并记录
        stats = clusterer.get_cluster_stats(self.clustered_points)
        self.logger.info(
            f"聚类完成：主要类别包含 {stats['main_cluster_points']} 个点，"
            f"噪声点 {stats['noise_points']} 个"
        )

    def filter_time_group_overlap(self) -> pd.DataFrame:
        """过滤重叠的时间组"""
        if not self.config.enable_time_group_filter:
            return self.gps_points

        self.logger.info("开始过滤重叠时间组")
        filter = TimeGroupOverlapFilter(
            self.config.image_dir,
            self.config.output_dir,
            overlap_threshold=self.config.time_group_overlap_threshold
        )

        deleted_files = filter.filter_overlapping_groups(
            time_threshold=self.config.time_group_interval
        )

        # 更新GPS点数据，移除被删除的图像
        self.gps_points = self.gps_points[~self.gps_points['file'].isin(
            deleted_files)]
        self.logger.info(f"重叠时间组过滤后剩余 {len(self.gps_points)} 个GPS点")

        return self.gps_points

    # TODO 过滤算法还需要更新
    def filter_points(self) -> pd.DataFrame:
        """过滤GPS点"""
        if not self.config.enable_filter:
            return self.gps_points

        self.logger.info("开始过滤GPS点")
        filter = GPSFilter(self.config.output_dir)

        self.logger.info(
            f"开始过滤孤立点(距离阈值: {self.config.filter_distance_threshold}, 最小邻居数: {self.config.filter_min_neighbors})"
        )
        self.gps_points = filter.filter_isolated_points(
            self.gps_points,
            self.config.filter_distance_threshold,
            self.config.filter_min_neighbors,
        )
        self.logger.info(f"孤立点过滤后剩余 {len(self.gps_points)} 个GPS点")

        self.logger.info(
            f"开始过滤密集点(网格大小: {self.config.filter_grid_size}, 距离阈值: {self.config.filter_dense_distance_threshold})"
        )
        self.gps_points = filter.filter_dense_points(
            self.gps_points,
            grid_size=self.config.filter_grid_size,
            distance_threshold=self.config.filter_dense_distance_threshold,
            time_threshold=self.config.filter_time_threshold,
        )
        self.logger.info(f"密集点过滤后剩余 {len(self.gps_points)} 个GPS点")
        return self.gps_points

    def divide_grids(self) -> Dict[int, pd.DataFrame]:
        """划分网格"""
        if not self.config.enable_grid_division:
            return {0: self.gps_points}  # 不划分网格时，所有点放在一个网格中

        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)
        grids = 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

    def copy_images(self, grid_points: Dict[int, pd.DataFrame]):
        """复制图像到目标文件夹"""
        if not self.config.enable_copy_images:
            return

        self.logger.info("开始复制图像文件")

        for grid_idx, points in grid_points.items():
            if self.config.enable_grid_division:
                output_dir = os.path.join(
                    self.config.output_dir, f"grid_{grid_idx + 1}", "project", "images"
                )
            else:
                output_dir = os.path.join(
                    self.config.output_dir, "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"):
                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")

    def visualize_results(self):
        """可视化处理结果"""
        if not self.config.enable_visualization:
            return

        self.logger.info("开始生成可视化结果")
        extractor = GPSExtractor(self.config.image_dir)
        original_points_df = extractor.extract_all_gps()

        # 读取被过滤的图片列表
        with open(
            os.path.join(self.config.output_dir, "del_imgs.txt"), "r", encoding="utf-8"
        ) as file:
            filtered_files = [line.strip() for line in file if line.strip()]

        # 创建一个新的图形
        plt.figure(figsize=(20, 16))

        # 绘制所有原始点
        plt.scatter(
            original_points_df["lon"],
            original_points_df["lat"],
            color="blue",
            label="Original Points",
            alpha=0.6,
        )

        # 绘制被过滤的点
        filtered_points_df = original_points_df[
            original_points_df["file"].isin(filtered_files)
        ]
        plt.scatter(
            filtered_points_df["lon"],
            filtered_points_df["lat"],
            color="red",
            marker="x",
            label="Filtered Points",
            alpha=0.6,
        )

        # 设置图形属性
        plt.title("GPS Coordinates of Images", fontsize=14)
        plt.xlabel("Longitude", fontsize=12)
        plt.ylabel("Latitude", fontsize=12)
        plt.grid(True)
        plt.legend()

        # 保存图形
        plt.savefig(os.path.join(self.config.output_dir, "filter_GPS.png"))
        plt.close()
        self.logger.info("预处理结果图已保存")

    def process(self):
        """执行完整的预处理流程"""
        try:
            self.extract_gps()
            self.cluster()
            self.filter_time_group_overlap()
            # self.filter_points()
            # grid_points = self.divide_grids()
            # self.copy_images(grid_points)
            # self.visualize_results()
            # self.logger.info("预处理任务完成")
            # self.command_runner.run_grid_commands(
            #     grid_points,
            #     self.config.enable_grid_division,
            # )
            # TODO 拼图
        except Exception as e:
            self.logger.error(f"处理过程中发生错误: {str(e)}", exc_info=True)
            raise


if __name__ == "__main__":
    # 创建配置
    config = PreprocessConfig(
        image_dir=r"F:\error_data\20241016140912\code\images",
        output_dir=r"G:\output",

        cluster_eps=0.01,
        cluster_min_samples=5,

        # 添加时间组重叠过滤参数
        time_group_overlap_threshold=0.7,
        time_group_interval=timedelta(minutes=5),
        enable_time_group_filter=True,

        filter_distance_threshold=0.001,
        filter_min_neighbors=6,

        filter_grid_size=0.001,
        filter_dense_distance_threshold=10,
        filter_time_threshold=timedelta(minutes=5),

        grid_overlap=0.03,
        grid_size=1000,

        enable_filter=True,
        enable_grid_division=True,
        enable_visualization=True,
        enable_copy_images=True,

        mode="快拼模式",
    )

    # 创建处理器并执行
    processor = ImagePreprocessor(config)
    processor.process()