106 lines
3.5 KiB
Python
106 lines
3.5 KiB
Python
import sys
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import os
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# 添加路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(current_dir)
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env_dir = os.path.join(project_root, "Env")
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sys.path.insert(0, project_root)
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sys.path.insert(0, env_dir)
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# 现在可以导入了
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from scenario_env import MultiAgentScenarioEnv
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from metadrive.engine.asset_loader import AssetLoader
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.animation import FuncAnimation
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class DummyPolicy:
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"""
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占位策略,用于数据检查时初始化环境
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不需要实际执行动作,只是为了满足环境初始化要求
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"""
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def act(self, *args, **kwargs):
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# 返回零动作 [throttle, steering]
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return np.array([0.0, 0.0])
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def visualize_expert_trajectory(env, scenario_idx=0):
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"""
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可视化专家轨迹的俯视图动画
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"""
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env.reset()
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expert_trajs = env.expert_trajectories
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if len(expert_trajs) == 0:
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print("当前场景无专家轨迹")
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return
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# 设置绘图
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fig, ax = plt.subplots(figsize=(12, 12))
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# 获取所有轨迹的最大时间长度
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max_timestep = max(traj["end_timestep"] for traj in expert_trajs.values())
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min_timestep = min(traj["start_timestep"] for traj in expert_trajs.values())
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# 绘制完整轨迹(淡色)
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colors = plt.cm.tab10(np.linspace(0, 1, len(expert_trajs)))
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for idx, (obj_id, traj) in enumerate(expert_trajs.items()):
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positions = traj["positions"][:, :2]
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ax.plot(positions[:, 0], positions[:, 1],
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color=colors[idx], alpha=0.3, linewidth=1,
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label=f'Vehicle {obj_id[:6]}')
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# 初始化当前位置标记
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scatter = ax.scatter([], [], s=200, c='red', marker='o', edgecolors='black', linewidths=2)
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time_text = ax.text(0.02, 0.95, '', transform=ax.transAxes, fontsize=14)
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ax.set_xlabel('X (m)')
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ax.set_ylabel('Y (m)')
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ax.set_title(f'Expert Trajectory Visualization - Scenario {scenario_idx}')
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ax.legend(loc='upper right', fontsize=8)
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ax.grid(True, alpha=0.3)
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ax.axis('equal')
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def update(frame):
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current_time = min_timestep + frame
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# 收集当前时间所有车辆的位置
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current_positions = []
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for traj in expert_trajs.values():
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if traj["start_timestep"] <= current_time <= traj["end_timestep"]:
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idx = current_time - traj["start_timestep"]
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pos = traj["positions"][idx, :2]
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current_positions.append(pos)
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if len(current_positions) > 0:
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current_positions = np.array(current_positions)
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scatter.set_offsets(current_positions)
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time_text.set_text(f'Time: {frame * 0.1:.1f}s (Frame {frame})')
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return scatter, time_text
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anim = FuncAnimation(fig, update, frames=max_timestep-min_timestep+1,
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interval=100, blit=True, repeat=True)
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plt.tight_layout()
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plt.show()
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return anim
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if __name__ == "__main__":
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WAYMO_DATA_DIR = r"/home/huangfukk/mdsn"
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data_dir = AssetLoader.file_path(WAYMO_DATA_DIR, "exp_filtered", unix_style=False)
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env = MultiAgentScenarioEnv(
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config={
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"data_directory": data_dir,
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"is_multi_agent": True,
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"num_controlled_agents": 3,
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"use_render": False,
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},
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agent2policy=DummyPolicy()
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)
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# 可视化第一个场景
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anim = visualize_expert_trajectory(env, scenario_idx=0)
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