3d_track_code_template.py

import open3d as o3d
import numpy as np

# Function to process LiDAR point cloud data
def process_point_cloud(pcd):
    # Preprocess point cloud (e.g., remove ground plane, filter noise)
    # Your preprocessing steps here
    
    # Perform object detection and tracking
    # Your object detection and tracking algorithm here
    
    # Return tracked objects
    return tracked_objects

# Function to visualize LiDAR point cloud and tracked objects
def visualize_point_cloud(pcd, tracked_objects):
    # Create visualizer object
    vis = o3d.visualization.Visualizer()
    vis.create_window()
    
    # Add point cloud to the visualizer
    vis.add_geometry(pcd)
    
    # Add tracked objects to the visualizer
    for obj in tracked_objects:
        # Create bounding box around the object
        bbox = o3d.geometry.OrientedBoundingBox(center=obj['center'], 
                                                R=obj['rotation'], 
                                                extent=obj['extent'])
        
        # Create wireframe box
        mesh_box = o3d.geometry.LineSet.create_from_oriented_bounding_box(bbox)
        mesh_box.paint_uniform_color(obj['color'])
        
        # Add wireframe box to the visualizer
        vis.add_geometry(mesh_box)
    
    # Run visualizer
    vis.run()
    vis.destroy_window()

# Main function
def main():
    # Create LiDAR sensor object or connect to a real sensor
    # Your LiDAR sensor initialization code here
    
    while True:
        # Get LiDAR point cloud data
        point_cloud = get_lidar_point_cloud()
        
        # Convert point cloud to Open3D format
        pcd = o3d.geometry.PointCloud()
        pcd.points = o3d.utility.Vector3dVector(point_cloud)
        
        # Process point cloud and obtain tracked objects
        tracked_objects = process_point_cloud(pcd)
        
        # Visualize point cloud and tracked objects
        visualize_point_cloud(pcd, tracked_objects)

# Entry point
if __name__ == '__main__':
    main()