vhacdx

import numpy as np import vhacdx import trimesh # Used to generate example mesh # 1. Create a sample mesh (e.g., a simple box) # For a more complex shape, you might load from a file: mesh = trimesh.load('your_mesh.obj') mesh = trimesh.creation.box() vertices = mesh.vertices.astype(np.float64) # VHACD expects float64 faces = mesh.faces.astype(np.int32) # VHACD expects int32 print(f"Original mesh has {len(vertices)} vertices and {len(faces)} faces.") # 2. Perform convex decomposition # Parameters like 'resolution' and 'depth' significantly impact quality and performance. # Consult the underlying VHACD C++ library documentation for detailed parameter explanations. decomposed_hulls = vhacdx.vhacd( vertices=vertices, faces=faces, resolution=100000, # Max voxels generated during voxelization (default 100,000) depth=8 # Max recursion depth for the V-HACD algorithm (default 8) ) # 3. Process the output print(f"Decomposed into {len(decomposed_hulls)} convex hulls.") # Each hull is a dictionary containing its own 'vertices' and 'faces' for i, hull in enumerate(decomposed_hulls): hull_vertices = hull['vertices'] hull_faces = hull['faces'] print(f" Hull {i}: {len(hull_vertices)} vertices, {len(hull_faces)} faces") # You could then, for example, create trimesh objects for each hull: # hull_mesh = trimesh.Trimesh(vertices=hull_vertices, faces=hull_faces) # hull_mesh.show() # To visualize each hull separately