Update app.py

app.py

@@ -1,105 +1,213 @@
+
 import gradio as gr
 import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import matplotlib.image as mpimg
 import numpy as np
 import random
+import os
+import threading
+import time
+from IPython.display import Audio, display
 
-# Robot and environment variables
-pose = [250, 250, 0]
-trajectory = []
+# Global state
+pose = {"x": 0, "z": 0, "angle": 0}
+trajectory = [(0, 0)]
+obstacle_hits = []
+color_index = 0
+rgb_colors = ['red', 'green', 'blue']
+noise_enabled = True
 obstacles = []
 auto_mode = False
 
-def generate_obstacles(num_obstacles=5):
-    global obstacles
-    obstacles = []
-    for _ in range(num_obstacles):
-        x = random.randint(50, 450)
-        y = random.randint(50, 450)
-        w = random.randint(20, 50)
-        h = random.randint(20, 50)
-        obstacles.append((x, y, w, h))
+# Generate obstacles
+def generate_obstacles(count=10):
+    return [{
+        "x": random.uniform(-8, 8),
+        "z": random.uniform(-8, 8),
+        "radius": random.uniform(0.5, 1.2)
+    } for _ in range(count)]
+
+obstacles = generate_obstacles(10)
+
+# Toggle noise
+def toggle_noise():
+    global noise_enabled
+    noise_enabled = not noise_enabled
+    return "Noise: ON" if noise_enabled else "Noise: OFF"
 
+# Reset simulation
+def reset_sim(count):
+    global pose, trajectory, obstacles, obstacle_hits, color_index
+    pose = {"x": 0, "z": 0, "angle": 0}
+    trajectory = [(0, 0)]
+    obstacle_hits = []
+    color_index = 0
+    obstacles = generate_obstacles(int(count))
+    return render_env(), render_slam_map(), None, f"Simulation Reset with {count} obstacles"
+
+# Check collision
+def check_collision(x, z):
+    for obs in obstacles:
+        dist = np.sqrt((obs["x"] - x)**2 + (obs["z"] - z)**2)
+        if dist <= obs["radius"] + 0.2:
+            return True
+    return False
+
+# Movement logic
 def move_robot(direction):
     global pose, trajectory
-    dx, dy = 0, 0
-    step_size = 10
+    step = 1
+    direction = direction.upper()
+
     if direction == "W":
-        dy = -step_size
+        new_x, new_z = pose["x"], pose["z"] + step
+        pose["angle"] = 90
     elif direction == "S":
-        dy = step_size
+        new_x, new_z = pose["x"], pose["z"] - step
+        pose["angle"] = -90
     elif direction == "A":
-        dx = -step_size
+        new_x, new_z = pose["x"] - step, pose["z"]
+        pose["angle"] = 180
     elif direction == "D":
-        dx = step_size
-    pose[0] += dx
-    pose[1] += dy
-    trajectory.append(tuple(pose[:2]))
-    return render_env(), render_slam_map(), None, f"Moved {direction}"
+        new_x, new_z = pose["x"] + step, pose["z"]
+        pose["angle"] = 0
+    else:
+        return render_env(), render_slam_map(), None, "❌ Invalid Key"
 
+    if check_collision(new_x, new_z):
+        # Play collision1.mp3 directly if in Jupyter/Colab
+        if os.path.exists("collision1.mp3"):
+            try:
+                display(Audio("collision1.mp3", autoplay=True))
+            except:
+                pass
+        return render_env(), render_slam_map(), None, "🚫 Collision detected!"
+
+    pose["x"], pose["z"] = new_x, new_z
+    if noise_enabled:
+        noisy_x = pose["x"] + random.uniform(-0.1, 0.1)
+        noisy_z = pose["z"] + random.uniform(-0.1, 0.1)
+        trajectory.append((noisy_x, noisy_z))
+    else:
+        trajectory.append((pose["x"], pose["z"]))
+
+    return render_env(), render_slam_map(), None, "Moved " + direction
+
+# Environment view
 def render_env():
-    fig, ax = plt.subplots(figsize=(5, 5))
-    ax.set_xlim(0, 500)
-    ax.set_ylim(0, 500)
-    ax.set_aspect('equal')
-    # Draw robot
-    ax.plot(pose[0], pose[1], 'bo', label="Robot")
-    if trajectory:
-        ax.plot(*zip(*trajectory), 'b--', label="Trajectory")
-    # Draw obstacles
-    for (x, y, w, h) in obstacles:
-        ax.add_patch(plt.Rectangle((x, y), w, h, color='red'))
-    ax.legend()
-    plt.close()
+    global obstacle_hits
+    fig, ax = plt.subplots()
+    ax.set_xlim(-10, 10)
+    ax.set_ylim(-10, 10)
+    ax.set_title("SLAM Environment View")
+
+    try:
+        bg = mpimg.imread("map.png")
+        ax.imshow(bg, extent=(-10, 10, -10, 10), alpha=0.2)
+    except:
+        pass
+
+    for obs in obstacles:
+        circ = plt.Circle((obs["x"], obs["z"]), obs["radius"], color="gray", alpha=0.6)
+        ax.add_patch(circ)
+
+    ax.plot(pose["x"], pose["z"], 'ro', markersize=8)
+
+    angles = np.linspace(0, 2*np.pi, 24)
+    for ang in angles:
+        for r in np.linspace(0, 3, 30):
+            scan_x = pose["x"] + r * np.cos(ang)
+            scan_z = pose["z"] + r * np.sin(ang)
+            if check_collision(scan_x, scan_z):
+                ax.plot([pose["x"], scan_x], [pose["z"], scan_z], 'g-', linewidth=0.5)
+                obstacle_hits.append((scan_x, scan_z))
+                break
+
+    plt.close(fig)
     return fig
 
+# SLAM map
 def render_slam_map():
-    fig, ax = plt.subplots(figsize=(5, 5))
-    ax.set_xlim(0, 500)
-    ax.set_ylim(0, 500)
-    ax.set_aspect('equal')
-    ax.plot(pose[0], pose[1], 'go', label="SLAM Robot")
-    ax.legend()
-    plt.close()
+    global color_index
+    fig, ax = plt.subplots()
+    ax.set_title("SLAM Trajectory Map")
+    x_vals = [x for x, z in trajectory]
+    z_vals = [z for x, z in trajectory]
+    ax.plot(x_vals, z_vals, 'bo-', markersize=3)
+    ax.grid(True)
+
+    if obstacle_hits:
+        current_color = rgb_colors[color_index % 3]
+        for hit in obstacle_hits[-20:]:
+            ax.plot(hit[0], hit[1], 'o', color=current_color, markersize=6)
+        color_index += 1
+
+    plt.close(fig)
     return fig
 
-def toggle_auto(auto_state):
-    return not auto_state, "Auto mode ON" if not auto_state else "Auto mode OFF"
+# Text command
+def handle_text_input(direction):
+    return move_robot(direction.strip().upper())
 
-def auto_step(auto_state):
-    if auto_state:
-        direction = random.choice(["W", "A", "S", "D"])
-        return move_robot(direction)
-    return render_env(), render_slam_map(), None, "Auto mode is OFF"
+# Auto movement thread
+def auto_movement(update_callback):
+    global auto_mode
+    directions = ['W', 'A', 'S', 'D']
+    while auto_mode:
+        direction = random.choice(directions)
+        env, slam, _, msg = move_robot(direction)
+        update_callback(env, slam, None, f"[AUTO] {msg}")
+        time.sleep(1)
 
-# Build Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## SLAM Robot Demo with Auto Mode")
-    env_plot = gr.Plot()
-    slam_plot = gr.Plot()
-    collision_audio = gr.Audio(type="filepath", label="Collision")
-    status_text = gr.Textbox(label="Status")
+def toggle_auto_mode(env_plot, slam_plot, collision_audio, status_text):
+    global auto_mode
+    auto_mode = not auto_mode
 
-    auto_state = gr.State(False)
+    if auto_mode:
+        def update_ui(e, s, _, t):
+            env_plot.update(value=e)
+            slam_plot.update(value=s)
+            status_text.update(value=t)
 
-    with gr.Row():
-        gr.Button("⬆️").click(fn=move_robot, inputs=gr.Textbox(value="W", visible=False), outputs=[env_plot, slam_plot, collision_audio, status_text])
-        gr.Button("⬇️").click(fn=move_robot, inputs=gr.Textbox(value="S", visible=False), outputs=[env_plot, slam_plot, collision_audio, status_text])
-        gr.Button("⬅️").click(fn=move_robot, inputs=gr.Textbox(value="A", visible=False), outputs=[env_plot, slam_plot, collision_audio, status_text])
-        gr.Button("➡️").click(fn=move_robot, inputs=gr.Textbox(value="D", visible=False), outputs=[env_plot, slam_plot, collision_audio, status_text])
-        auto_button = gr.Button("Toggle Auto Mode")
+        thread = threading.Thread(target=auto_movement, args=(update_ui,))
+        thread.daemon = True
+        thread.start()
+        return "🟢 Auto Mode: ON"
+    else:
+        return "⚪ Auto Mode: OFF"
+
+# UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 SLAM Simulation with Auto Mode + Collision Sound (No Play Button)")
 
-    # Auto mode toggle updates state
-    auto_button.click(fn=toggle_auto, inputs=auto_state, outputs=[auto_state, status_text])
+    obstacle_slider = gr.Slider(1, 20, value=10, step=1, label="Number of Obstacles")
+    direction_input = gr.Textbox(label="Type W / A / S / D and press Enter", placeholder="e.g., W")
+    status_text = gr.Textbox(label="Status")
+    collision_audio = gr.Audio(label="(Hidden) Collision Sound", interactive=False, visible=False)
 
-    # Manual auto-step button (simulates periodic move)
-    auto_step_btn = gr.Button("🔁 Auto Step")
-    auto_step_btn.click(fn=auto_step, inputs=auto_state, outputs=[env_plot, slam_plot, collision_audio, status_text])
+    with gr.Row():
+        with gr.Column():
+            env_plot = gr.Plot(label="Robot View")
+        with gr.Column():
+            slam_plot = gr.Plot(label="SLAM Map")
 
-    # Initial load state
-    demo.load(fn=lambda: (render_env(), render_slam_map(), None, "✅ Ready"),
-              outputs=[env_plot, slam_plot, collision_audio, status_text])
+    with gr.Row():
+        w = gr.Button("⬆️ W")
+        a = gr.Button("⬅️ A")
+        s = gr.Button("⬇️ S")
+        d = gr.Button("➡️ D")
+        reset = gr.Button("🔄 Reset")
+        toggle = gr.Button("🔀 Toggle Noise")
+        auto = gr.Button("🤖 Toggle Auto")
 
-    # Initialize obstacles
-    generate_obstacles()
+    w.click(fn=move_robot, inputs=gr.State("W"), outputs=[env_plot, slam_plot, collision_audio, status_text])
+    a.click(fn=move_robot, inputs=gr.State("A"), outputs=[env_plot, slam_plot, collision_audio, status_text])
+    s.click(fn=move_robot, inputs=gr.State("S"), outputs=[env_plot, slam_plot, collision_audio, status_text])
+    d.click(fn=move_robot, inputs=gr.State("D"), outputs=[env_plot, slam_plot, collision_audio, status_text])
+    reset.click(fn=reset_sim, inputs=[obstacle_slider], outputs=[env_plot, slam_plot, collision_audio, status_text])
+    toggle.click(fn=lambda: (None, None, None, toggle_noise()), outputs=[env_plot, slam_plot, collision_audio, status_text])
+    auto.click(fn=toggle_auto_mode, inputs=[env_plot, slam_plot, collision_audio, status_text], outputs=auto)
+    direction_input.submit(fn=handle_text_input, inputs=direction_input, outputs=[env_plot, slam_plot, collision_audio, status_text])
 
 demo.launch()