138 lines
5.4 KiB
Python
138 lines
5.4 KiB
Python
import os
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import matplotlib.pyplot as plt
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from datetime import timedelta
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import logging
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import numpy as np
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from utils.gps_extractor import GPSExtractor
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from utils.logger import setup_logger
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class GPSTimeVisualizer:
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"""按时间组可视化GPS点"""
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def __init__(self, image_dir: str, output_dir: str):
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self.image_dir = image_dir
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self.output_dir = output_dir
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self.logger = logging.getLogger('UAV_Preprocess.GPSVisualizer')
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def _group_by_time(self, points_df, time_threshold=timedelta(minutes=5)):
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"""按时间间隔对点进行分组"""
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if 'date' not in points_df.columns:
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self.logger.error("数据中缺少date列")
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return [points_df]
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# 将date为空的行单独作为一组
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null_date_group = points_df[points_df['date'].isna()]
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valid_date_points = points_df[points_df['date'].notna()]
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if not null_date_group.empty:
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self.logger.info(f"发现 {len(null_date_group)} 个无时间戳的点，将作为单独分组")
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if valid_date_points.empty:
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self.logger.warning("没有有效的时间戳数据")
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return [null_date_group] if not null_date_group.empty else []
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# 按时间排序
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valid_date_points = valid_date_points.sort_values('date')
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# 计算时间差
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time_diffs = valid_date_points['date'].diff()
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# 找到时间差超过阈值的位置
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time_groups = []
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current_group_start = 0
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for idx, time_diff in enumerate(time_diffs):
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if time_diff and time_diff > time_threshold:
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# 添加当前组
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current_group = valid_date_points.iloc[current_group_start:idx]
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time_groups.append(current_group)
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current_group_start = idx
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# 添加最后一组
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last_group = valid_date_points.iloc[current_group_start:]
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if not last_group.empty:
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time_groups.append(last_group)
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# 如果有空时间戳的点，将其作为最后一组
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if not null_date_group.empty:
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time_groups.append(null_date_group)
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return time_groups
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def visualize_time_groups(self, time_threshold=timedelta(minutes=5)):
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"""在同一张图上显示所有时间组，用不同颜色区分"""
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# 提取GPS数据
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extractor = GPSExtractor(self.image_dir)
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gps_points = extractor.extract_all_gps()
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# 按时间分组
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time_groups = self._group_by_time(gps_points, time_threshold)
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# 创建图形
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plt.figure(figsize=(15, 10))
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# 生成不同的颜色
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colors = plt.cm.rainbow(np.linspace(0, 1, len(time_groups)))
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# 为每个时间组绘制点和轨迹
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for idx, (group, color) in enumerate(zip(time_groups, colors)):
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if not group['date'].isna().any():
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# 有时间戳的组
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sorted_group = group.sort_values('date')
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# 绘制轨迹线
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plt.plot(sorted_group['lon'], sorted_group['lat'],
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color=color, linestyle='-', linewidth=1.5, alpha=0.6,
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label=f'Flight Path {idx + 1}')
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# 绘制GPS点
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plt.scatter(sorted_group['lon'], sorted_group['lat'],
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color=color, marker='o', s=30, alpha=0.6)
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# 标记起点和终点
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plt.scatter(sorted_group['lon'].iloc[0], sorted_group['lat'].iloc[0],
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color=color, marker='^', s=100,
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label=f'Start {idx + 1} ({sorted_group["date"].min().strftime("%H:%M:%S")})')
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plt.scatter(sorted_group['lon'].iloc[-1], sorted_group['lat'].iloc[-1],
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color=color, marker='s', s=100,
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label=f'End {idx + 1} ({sorted_group["date"].max().strftime("%H:%M:%S")})')
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else:
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# 无时间戳的组
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plt.scatter(group['lon'], group['lat'],
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color=color, marker='x', s=50, alpha=0.6,
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label='No Timestamp Points')
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plt.title("GPS Points by Time Groups", 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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# 调整图例位置和大小
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plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=10)
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# 调整布局以适应图例
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plt.tight_layout()
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# 保存图片
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plt.savefig(os.path.join(self.output_dir, 'gps_time_groups_combined.png'),
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dpi=300, bbox_inches='tight')
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plt.close()
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self.logger.info(f"已生成包含 {len(time_groups)} 个时间组的组合可视化图形")
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if __name__ == '__main__':
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# 设置数据集路径
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DATASET = r'F:\error_data\20241108134711\3D'
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output_dir = r'E:\studio2\ODM_pro\test'
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os.makedirs(output_dir, exist_ok=True)
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# 设置日志
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setup_logger(os.path.dirname(output_dir))
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# 创建可视化器并生成图形
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visualizer = GPSTimeVisualizer(DATASET, output_dir)
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visualizer.visualize_time_groups(time_threshold=timedelta(minutes=5)) |