115 lines
4.3 KiB
Python
115 lines
4.3 KiB
Python
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import os
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import matplotlib.pyplot as plt
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import pandas as pd
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import logging
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from typing import List, Optional
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class FilterVisualizer:
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"""过滤结果可视化器"""
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def __init__(self, output_dir: str):
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self.output_dir = output_dir
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self.logger = logging.getLogger('UAV_Preprocess.Visualizer')
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def visualize_filter_step(self,
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current_points: pd.DataFrame,
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previous_points: pd.DataFrame,
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step_name: str,
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save_name: Optional[str] = None):
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"""
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可视化单个过滤步骤的结果
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Args:
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current_points: 当前步骤后的点
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previous_points: 上一步骤的点
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step_name: 步骤名称
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save_name: 保存文件名，默认为step_name
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"""
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self.logger.info(f"开始生成{step_name}的可视化结果")
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# 找出被过滤掉的点
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filtered_files = set(previous_points['file']) - set(current_points['file'])
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filtered_points = previous_points[previous_points['file'].isin(filtered_files)]
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# 创建图形
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plt.figure(figsize=(20, 16))
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# 绘制保留的点
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plt.scatter(current_points['lon'], current_points['lat'],
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color='blue', label='Retained Points',
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alpha=0.6, s=50)
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# 绘制被过滤的点
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plt.scatter(filtered_points['lon'], filtered_points['lat'],
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color='red', marker='x', label='Filtered Points',
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alpha=0.6, s=100)
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# 设置图形属性
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plt.title(f"GPS Points After {step_name}", fontsize=14)
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plt.xlabel("Longitude", fontsize=12)
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plt.ylabel("Latitude", fontsize=12)
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plt.grid(True)
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# 添加统计信息
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stats_text = (f"Total Points: {len(previous_points)}\n"
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f"Filtered Points: {len(filtered_points)}\n"
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f"Remaining Points: {len(current_points)}")
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plt.figtext(0.02, 0.02, stats_text, fontsize=10,
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bbox=dict(facecolor='white', alpha=0.8))
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plt.legend()
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# 保存图形
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save_name = save_name or step_name.lower().replace(' ', '_')
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save_path = os.path.join(self.output_dir, f'filter_{save_name}.png')
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.close()
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self.logger.info(f"{step_name}过滤可视化结果已保存至 {save_path}")
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def visualize_all_steps(self,
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points_history: List[pd.DataFrame],
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step_names: List[str]):
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"""
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可视化所有过滤步骤的结果
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Args:
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points_history: 每个步骤的点数据列表
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step_names: 步骤名称列表
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"""
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if len(points_history) != len(step_names):
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raise ValueError("点数据列表和步骤名称列表长度不匹配")
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plt.figure(figsize=(20, 16))
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# 使用不同的颜色表示不同步骤
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colors = plt.cm.rainbow(np.linspace(0, 1, len(points_history)))
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# 绘制每个步骤的点
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for i, (points, color) in enumerate(zip(points_history, colors)):
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plt.scatter(points['lon'], points['lat'],
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color=color, label=f'After {step_names[i]}',
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alpha=0.6, s=50)
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# 设置图形属性
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plt.title("GPS Points Filtering Process", fontsize=14)
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plt.xlabel("Longitude", fontsize=12)
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plt.ylabel("Latitude", fontsize=12)
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plt.grid(True)
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# 添加统计<E7BB9F><E8AEA1>息
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stats_text = "Points Count:\n" + "\n".join(
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f"{step_names[i]}: {len(points)}"
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for i, points in enumerate(points_history)
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)
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plt.figtext(0.02, 0.02, stats_text, fontsize=10,
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bbox=dict(facecolor='white', alpha=0.8))
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plt.legend()
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# 保存图形
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save_path = os.path.join(self.output_dir, 'filter_all_steps.png')
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.close()
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self.logger.info(f"所有过滤步骤的可视化结果已保存至 {save_path}")
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