153 lines
4.8 KiB
Python
153 lines
4.8 KiB
Python
import os
|
||
import matplotlib.pyplot as plt
|
||
import pandas as pd
|
||
import logging
|
||
from typing import Optional
|
||
from pyproj import Transformer
|
||
|
||
|
||
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')
|
||
# 创建坐标转换器
|
||
self.transformer = Transformer.from_crs(
|
||
"EPSG:4326", # WGS84经纬度坐标系
|
||
"EPSG:32649", # UTM49N
|
||
always_xy=True
|
||
)
|
||
|
||
def _convert_to_utm(self, lon: pd.Series, lat: pd.Series) -> tuple:
|
||
"""
|
||
将经纬度坐标转换为UTM坐标
|
||
|
||
Args:
|
||
lon: 经度序列
|
||
lat: 纬度序列
|
||
|
||
Returns:
|
||
tuple: (x坐标, y坐标)
|
||
"""
|
||
return self.transformer.transform(lon, lat)
|
||
|
||
def visualize_filter_step(self,
|
||
current_points: pd.DataFrame,
|
||
previous_points: pd.DataFrame,
|
||
step_name: str,
|
||
save_name: Optional[str] = None):
|
||
"""
|
||
可视化单个过滤步骤的结果
|
||
|
||
Args:
|
||
current_points: 当前步骤后的点
|
||
previous_points: 上一步骤的点
|
||
step_name: 步骤名称
|
||
save_name: 保存文件名，默认为step_name
|
||
"""
|
||
self.logger.info(f"开始生成{step_name}的可视化结果")
|
||
|
||
# 找出被过滤掉的点
|
||
filtered_files = set(
|
||
previous_points['file']) - set(current_points['file'])
|
||
filtered_points = previous_points[previous_points['file'].isin(
|
||
filtered_files)]
|
||
|
||
# 转换坐标到UTM
|
||
current_x, current_y = self._convert_to_utm(
|
||
current_points['lon'], current_points['lat'])
|
||
filtered_x, filtered_y = self._convert_to_utm(
|
||
filtered_points['lon'], filtered_points['lat'])
|
||
|
||
# 创建图形
|
||
plt.rcParams['font.sans-serif'] = ['SimHei'] # 黑体
|
||
plt.rcParams['axes.unicode_minus'] = False
|
||
plt.figure(figsize=(20, 20))
|
||
|
||
# 绘制保留的点
|
||
plt.scatter(current_x, current_y,
|
||
color='blue', label='保留的点',
|
||
alpha=0.6, s=5)
|
||
|
||
# 绘制被过滤的点
|
||
if not filtered_points.empty:
|
||
plt.scatter(filtered_x, filtered_y,
|
||
color='red', marker='x', label='过滤的点')
|
||
|
||
# 设置图形属性
|
||
plt.title(f"{step_name}后的GPS点\n"
|
||
f"(过滤: {len(filtered_points)}, 保留: {len(current_points)})",
|
||
fontsize=14)
|
||
plt.xlabel("东向坐标 (米)", fontsize=12)
|
||
plt.ylabel("北向坐标 (米)", fontsize=12)
|
||
plt.grid(True)
|
||
plt.axis('equal')
|
||
|
||
# 添加统计信息
|
||
stats_text = (
|
||
f"原始点数: {len(previous_points)}\n"
|
||
f"过滤点数: {len(filtered_points)}\n"
|
||
f"保留点数: {len(current_points)}\n"
|
||
f"过滤率: {len(filtered_points)/len(previous_points)*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', 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(current_points)} 个点，"
|
||
f"过滤率 {len(filtered_points)/len(previous_points)*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"
|
||
)
|