可视化模块修改
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weixin_46229132
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5c382e1810
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@ -15,8 +15,11 @@ class DirectoryManager:
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def clean_output_dir(self):
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"""清理输出目录"""
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try:
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shutil.rmtree(self.config.output_dir)
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print(f"已清理输出目录: {self.config.output_dir}")
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if os.path.exists(self.config.output_dir):
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shutil.rmtree(self.config.output_dir)
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print(f"已清理输出目录: {self.config.output_dir}")
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else:
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pass
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except Exception as e:
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print(f"清理输出目录时发生错误: {str(e)}")
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raise
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@ -65,7 +68,7 @@ class DirectoryManager:
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free_space = disk_usage.free
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# 计算所需空间（输入大小的10倍）
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required_space = input_size * 10
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required_space = input_size * 8
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if free_space < required_space:
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error_msg = (
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@ -2,34 +2,35 @@ import logging
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import os
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from datetime import datetime
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def setup_logger(output_dir):
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# 创建logs目录
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log_dir = os.path.join(output_dir, 'logs')
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# 创建日志文件名（包含时间戳）
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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log_file = os.path.join(log_dir, f'preprocess_{timestamp}.log')
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# 配置日志格式
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formatter = logging.Formatter(
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'%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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# 配置文件处理器
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file_handler = logging.FileHandler(log_file, encoding='utf-8')
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file_handler.setFormatter(formatter)
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# 配置控制台处理器
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console_handler = logging.StreamHandler()
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console_handler.setFormatter(formatter)
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# 获取根日志记录器
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logger = logging.getLogger('UAV_Preprocess')
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logger.setLevel(logging.INFO)
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# 添加处理器
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logger.addHandler(file_handler)
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logger.addHandler(console_handler)
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return logger
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return logger
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@ -85,21 +85,20 @@ class ODMProcessMonitor:
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self.logger.error("容器运行失败的详细错误日志：")
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for line in error_msg:
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self.logger.error(line)
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container.remove()
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time.sleep(5)
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else:
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# 获取所有日志
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logs = container.logs().decode("utf-8").splitlines()
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# 输出最后 50 行日志
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self.logger.info("容器运行完成，以下是最后 50 行日志：")
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for line in logs[-50:]:
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self.logger.info(line)
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success = True
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error_msg = ""
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container.remove()
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break
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# 删除容器
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container.remove()
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time.sleep(5)
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return success, error_msg
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@ -8,11 +8,11 @@ from pyproj import Transformer
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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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"""
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初始化可视化器
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Args:
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output_dir: 输出目录路径
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"""
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@ -24,28 +24,28 @@ class FilterVisualizer:
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"EPSG:32649", # UTM49N
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always_xy=True
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)
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def _convert_to_utm(self, lon: pd.Series, lat: pd.Series) -> tuple:
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"""
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将经纬度坐标转换为UTM坐标
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Args:
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lon: 经度序列
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lat: 纬度序列
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Returns:
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tuple: (x坐标, y坐标)
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"""
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return self.transformer.transform(lon, lat)
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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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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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@ -53,39 +53,43 @@ class FilterVisualizer:
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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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filtered_files = set(
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previous_points['file']) - set(current_points['file'])
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filtered_points = previous_points[previous_points['file'].isin(
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filtered_files)]
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# 转换坐标到UTM
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current_x, current_y = self._convert_to_utm(current_points['lon'], current_points['lat'])
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filtered_x, filtered_y = self._convert_to_utm(filtered_points['lon'], filtered_points['lat'])
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current_x, current_y = self._convert_to_utm(
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current_points['lon'], current_points['lat'])
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filtered_x, filtered_y = self._convert_to_utm(
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filtered_points['lon'], filtered_points['lat'])
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# 创建图形
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plt.rcParams['font.sans-serif']=['SimHei']#黑体
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 黑体
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plt.rcParams['axes.unicode_minus'] = False
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plt.figure()
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plt.figure(figsize=(20, 20))
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# 绘制保留的点
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plt.scatter(current_x, current_y,
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color='blue', label='保留的点',
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alpha=0.6, s=50)
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color='blue', label='保留的点',
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alpha=0.6, s=5)
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# 绘制被过滤的点
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if not filtered_points.empty:
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plt.scatter(filtered_x, filtered_y,
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color='red', marker='x', label='过滤的点',
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alpha=0.6, s=100)
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color='red', marker='x', label='过滤的点')
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# 设置图形属性
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plt.title(f"{step_name}后的GPS点\n"
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f"(过滤: {len(filtered_points)}, 保留: {len(current_points)})",
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fontsize=14)
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f"(过滤: {len(filtered_points)}, 保留: {len(current_points)})",
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fontsize=14)
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plt.xlabel("东向坐标 (米)", fontsize=12)
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plt.ylabel("北向坐标 (米)", fontsize=12)
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plt.grid(True)
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plt.axis('equal')
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# 添加统计信息
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stats_text = (
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f"原始点数: {len(previous_points)}\n"
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@ -94,20 +98,21 @@ class FilterVisualizer:
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f"过滤率: {len(filtered_points)/len(previous_points)*100:.1f}%"
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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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bbox=dict(facecolor='white', alpha=0.8))
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# 添加图例
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plt.legend(loc='upper right', fontsize=10)
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# 调整布局
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plt.tight_layout()
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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, 'filter_imgs', f'filter_{save_name}.png')
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save_path = os.path.join(
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self.output_dir, 'filter_imgs', 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(
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f"{step_name}过滤可视化结果已保存至 {save_path}\n"
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f"过滤掉 {len(filtered_points)} 个点，"
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@ -120,11 +125,11 @@ if __name__ == '__main__':
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# 测试代码
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import numpy as np
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from datetime import datetime
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# 创建测试数据
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np.random.seed(42)
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n_points = 1000
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# 生成随机点
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test_data = pd.DataFrame({
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'lon': np.random.uniform(120, 121, n_points),
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@ -132,16 +137,16 @@ if __name__ == '__main__':
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'file': [f'img_{i}.jpg' for i in range(n_points)],
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'date': [datetime.now() for _ in range(n_points)]
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})
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# 随机选择点作为过滤后的结果
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filtered_data = test_data.sample(n=800)
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# 测试可视化
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visualizer = FilterVisualizer('test_output')
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os.makedirs('test_output', exist_ok=True)
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visualizer.visualize_filter_step(
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filtered_data,
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test_data,
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"Test Filter"
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)
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)
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