Generation

time complexityThu, 16 Mar 2023

# def clustering_dbscan(pcd, clustering_output): """ Performs clustering on a 3D point cloud using the DBSCAN algorithm and visualizes the results. Here is a brief explanation of each step in the function: 1. Convert the point cloud to a numpy array 2. Define the parameters for DBSCAN 3. Perform clustering using DBSCAN 4. Create a color map for the clusters 5. Assign colors to each point based on its cluster label 6. Create a new point cloud with the cluster labels as colors 7. Visualize the results 8. Save the resulting clustered point cloud to a file Args: pcd (open3d.geometry.PointCloud): A 3D point cloud. clustering_output (str): The filename to save the resulting clustered point cloud. Returns: None """ # Convert the point cloud to a numpy array points = np.asarray(pcd.points) # Define the parameters for DBSCAN eps = 0.3 # radius of neighborhood min_samples = 10 # minimum number of points to form a cluster # Perform clustering using DBSCAN dbscan = DBSCAN(eps=eps, min_samples=min_samples) labels = dbscan.fit_predict(points) # Print the number of clusters found num_clusters = len(set(labels)) - (1 if -1 in labels else 0) print(f"Found {num_clusters} clusters") # Create a color map for the clusters colors = np.random.uniform(0, 1, size=(num_clusters, 3)) # Assign colors to each point based on its cluster label point_colors = np.zeros((points.shape[0], 3)) for i, label in enumerate(labels): if label == -1: point_colors[i] = [0.5, 0.5, 0.5] # Grey for noise points else: point_colors[i] = colors[label] # Use the color corresponding to the cluster label # Create a new point cloud with the cluster labels as colors pcd_clustered = o3d.geometry.PointCloud(pcd) pcd_clustered.colors = o3d.utility.Vector3dVector(point_colors) # Visualize the results o3d.visualization.draw_geometries([pcd_clustered]) o3d.io.write_point_cloud(clustering_output, pcd_clustered, write_ascii=False, compressed=False, print_progress=False)

``O(n^2)``