UAV_odm_merge/utils/visualizer.py

import os
import matplotlib.pyplot as plt
import pandas as pd
import logging
from typing import Optional


class FilterVisualizer:
    """过滤结果可视化器"""

    def __init__(self, output_dir: str):
        """
        初始化可视化器

        Args:
            output_dir: 输出目录路径
        """
        self.output_dir = output_dir
        self.logger = logging.getLogger('UAV_Preprocess.Visualizer')

    def visualize_filter_step(self,
                              retained_points: pd.DataFrame,
                              filtered_points: pd.DataFrame,
                              step_name: str,
                              save_name: Optional[str] = None):
        """
        可视化单个过滤步骤的结果

        Args:
            retained_points: 留下的点
            filtered_points: 过滤掉的点
            step_name: 步骤名称
            save_name: 保存文件名，默认为step_name
        """
        total_points_len = len(retained_points) + len(filtered_points)
        self.logger.info(f"开始生成{step_name}的可视化结果")

        # 创建图形
        plt.figure(figsize=(20, 16))

        # 绘制保留的点
        plt.scatter(retained_points['lon'], retained_points['lat'],
                    color='blue', label='Retained Points',
                    alpha=0.6, s=50)

        # 绘制被过滤的点
        if not filtered_points.empty:
            plt.scatter(filtered_points['lon'], filtered_points['lat'],
                        color='red', marker='x', label='Filtered Points',
                        alpha=0.6, s=100)

        # 设置图形属性
        plt.title(f"GPS Points After {step_name}\n"
                  f"(Filtered: {len(filtered_points)}, Retained: {len(retained_points)})",
                  fontsize=14)
        plt.xlabel("Longitude", fontsize=12)
        plt.ylabel("Latitude", fontsize=12)
        plt.grid(True)

        # 添加统计信息
        stats_text = (
            f"Original Points: {total_points_len}\n"
            f"Filtered Points: {len(filtered_points)}\n"
            f"Remaining Points: {len(retained_points)}\n"
            f"Filter Rate: {len(filtered_points)/total_points_len*100:.1f}%"
        )
        plt.figtext(0.02, 0.02, stats_text, fontsize=10,
                    bbox=dict(facecolor='white', alpha=0.8))

        # 添加图例
        plt.legend(loc='upper right', fontsize=10)

        # 调整布局
        plt.tight_layout()

        # 保存图形
        save_name = save_name or step_name.lower().replace(' ', '_')
        save_path = os.path.join(
            self.output_dir, 'filter_imgs_visual', f'filter_{save_name}.png')
        plt.savefig(save_path, dpi=300, bbox_inches='tight')
        plt.close()

        self.logger.info(
            f"{step_name}过滤可视化结果已保存至 {save_path}\n"
            f"过滤掉 {len(filtered_points)} 个点，"
            f"保留 {len(retained_points)} 个点，"
            f"过滤率 {len(filtered_points)/total_points_len*100:.1f}%"
        )


if __name__ == '__main__':
    # 测试代码
    import numpy as np
    from datetime import datetime

    # 创建测试数据
    np.random.seed(42)
    n_points = 1000

    # 生成随机点
    test_data = pd.DataFrame({
        'lon': np.random.uniform(120, 121, n_points),
        'lat': np.random.uniform(30, 31, n_points),
        'file': [f'img_{i}.jpg' for i in range(n_points)],
        'date': [datetime.now() for _ in range(n_points)]
    })

    # 随机选择点作为过滤后的结果
    filtered_data = test_data.sample(n=800)

    # 测试可视化
    visualizer = FilterVisualizer('test_output')
    os.makedirs('test_output', exist_ok=True)

    visualizer.visualize_filter_step(
        filtered_data,
        test_data,
        "Test Filter"
    )