chore(dev): update environment and tooling configuration

mini-nav/habitat/test.py

@@ -1,57 +1,159 @@
-import habitat_sim
-import numpy as np
-import plotly.express as px
+import marimo
 
-# 配置场景
-scene_path = "data/scene_datasets/habitat-test-scenes/skokloster-castle.glb"
-
-sim_cfg = habitat_sim.SimulatorConfiguration()
-sim_cfg.scene_id = scene_path
-sim_cfg.enable_physics = False
-
-# 配置 agent
-agent_cfg = habitat_sim.agent.AgentConfiguration()
-rgb_sensor_spec = habitat_sim.CameraSensorSpec()
-rgb_sensor_spec.uuid = "color_sensor"
-rgb_sensor_spec.sensor_type = habitat_sim.SensorType.COLOR
-rgb_sensor_spec.resolution = [256, 256]
-rgb_sensor_spec.position = [0.0, 1.5, 0.0]
-
-agent_cfg.sensor_specifications = [rgb_sensor_spec]
-
-# 创建 simulator 实例
-cfg = habitat_sim.Configuration(sim_cfg, [agent_cfg])
-sim = habitat_sim.Simulator(cfg)
-
-# 初始化 agent
-agent = sim.initialize_agent(0)
-
-# 设置 agent 初始位置
-agent_state = habitat_sim.AgentState()
-agent_state.position = np.array([0.0, 0.0, 0.0])
-agent.set_state(agent_state)
-
-state = agent.get_state()
-print("位置:", state.position)
-print("旋转四元数:", state.rotation)
-
-observations = sim.get_sensor_observations()
-rgb_image = observations["color_sensor"]  # numpy array
-print("RGB shape:", rgb_image.shape)
+__generated_with = "0.21.1"
+app = marimo.App()
 
 
-# 假设 rgb_image 已经被定义（例如一个 numpy array 或 PIL Image）
-fig = px.imshow(rgb_image)
+@app.cell
+def _():
+    import habitat_sim
+    from habitat_sim import ShortestPath
+    import numpy as np
+    import plotly.express as px
+    from matplotlib import pyplot as plt
 
-# 隐藏坐标轴（等同于 plt.axis("off")）
-fig.update_xaxes(visible=False)
-fig.update_yaxes(visible=False)
+    return ShortestPath, habitat_sim, np, plt
 
-# 去除多余的边距，使图片填满画布
-fig.update_layout(
-    margin=dict(l=0, r=0, t=0, b=0),
-    coloraxis_showscale=False,  # 如果是灰度图或带colorbar，这行可以隐藏色条
-)
 
-# 输出成 PNG 图片
-fig.write_html("outputs/output.html")
+@app.cell
+def _(habitat_sim):
+    # 配置场景
+    scene_path = "data/scene_datasets/habitat-test-scenes/skokloster-castle.glb"
+
+    sim_cfg = habitat_sim.SimulatorConfiguration()
+    sim_cfg.scene_id = scene_path
+    sim_cfg.enable_physics = False
+    return (sim_cfg,)
+
+
+@app.cell
+def _(habitat_sim, sim_cfg):
+    # 配置 agent
+    agent_cfg = habitat_sim.agent.AgentConfiguration()
+    rgb_sensor_spec = habitat_sim.CameraSensorSpec()
+    rgb_sensor_spec.uuid = "color_sensor"
+    rgb_sensor_spec.sensor_type = habitat_sim.SensorType.COLOR
+    rgb_sensor_spec.resolution = [256, 256]
+    rgb_sensor_spec.position = [0.0, 1.5, 0.0]
+
+    agent_cfg.sensor_specifications = [rgb_sensor_spec]
+
+    # 创建 simulator 实例
+    cfg = habitat_sim.Configuration(sim_cfg, [agent_cfg])
+    sim = habitat_sim.Simulator(cfg)
+    return (sim,)
+
+
+@app.cell
+def _(habitat_sim, plt, sim):
+    # 初始化 agent
+    agent = sim.initialize_agent(0)
+
+    # 设置 agent 初始位置：使用可导航点，而不是写死 [0,0,0]
+    agent_state = habitat_sim.AgentState()
+    agent_state.position = sim.pathfinder.get_random_navigable_point()
+    agent.set_state(agent_state)
+
+    state = agent.get_state()
+    print("位置:", state.position)
+    print("旋转四元数:", state.rotation)
+
+    observations = sim.get_sensor_observations()
+    rgb_image = observations["color_sensor"]
+    print("RGB shape:", rgb_image.shape)
+    print("RGB min/max:", rgb_image[..., :3].min(), rgb_image[..., :3].max())
+
+
+    plt.imshow(rgb_image)
+    plt.axis("off")
+    plt.show()
+    return (agent,)
+
+
+@app.cell
+def _(agent, plt, sim):
+    action_names = list(agent.agent_config.action_space.keys())
+    print("可用动作:", action_names)
+
+    # 进行一步移动
+    sim.step("move_forward")
+
+    import random
+    6
+    for i in range(5):
+        action = random.choice(action_names)
+        obs = sim.step(action)
+        rgb = obs["color_sensor"]
+        plt.imshow(rgb)
+        plt.axis("off")
+        plt.show()
+    return
+
+
+@app.cell
+def _(sim):
+    pathfinder = sim.pathfinder
+    print("NavMesh是否加载:", pathfinder.is_loaded)
+    print("可导航面积:", pathfinder.navigable_area)
+    print("场景边界:", pathfinder.get_bounds())
+
+    # 获取随机可导航点
+    rand_point = pathfinder.get_random_navigable_point()
+    print("随机可导航点:", rand_point)
+    return (pathfinder,)
+
+
+@app.cell
+def _(ShortestPath, pathfinder):
+    start = pathfinder.get_random_navigable_point()
+    end = pathfinder.get_random_navigable_point()
+
+    path = ShortestPath()
+    path.requested_start = start
+    path.requested_end = end
+
+    found = pathfinder.find_path(path)
+    print("是否找到路径:", found)
+    print("路径点:", path.points)
+    print("路径长度:", path.geodesic_distance)
+    return path, start
+
+
+@app.cell
+def _(path, pathfinder, plt, sim, start):
+    from habitat.utils.visualizations import maps
+
+    top_down_map = maps.get_topdown_map(
+        pathfinder, height=start[1], meters_per_pixel=0.05
+    )
+
+    # 绘制路径
+    trajectory = [
+        maps.to_grid(p[2], p[0], top_down_map.shape, pathfinder=sim.pathfinder)
+        for p in path.points
+    ]
+    maps.draw_path(top_down_map, trajectory)
+
+    plt.imshow(top_down_map)
+    plt.axis("off")
+    plt.show()
+    return
+
+
+@app.cell
+def _(habitat_sim, np):
+    # 高级控制：连续动作和滑动碰撞
+
+    vel_control = habitat_sim.physics.VelocityControl()
+    vel_control.controlling_lin_vel = True
+    vel_control.lin_vel_is_local = True
+    vel_control.controlling_ang_vel = True
+
+    # 设置线速度和角速度
+    vel_control.linear_velocity = np.array([0, 0, -1.0])  # forward
+    vel_control.angular_velocity = np.array([0, 0.1, 0])  # rotation
+    return
+
+
+if __name__ == "__main__":
+    app.run()