decompose_no_classify_yet

decompose_V1.py

'''
in verts v
in faces s
out v_out v
out e_out s
'''

import numpy as np


def find_cylinder_endpoints(vertices, faces):
    # Convert vertices to NumPy for efficiency
    v = np.array(vertices)

    # Extract boundary edges (those shared by only one face)
    edge_count = {}
    for face in faces:
        for i in range(len(face)):
            edge = tuple(sorted((face[i], face[(i + 1) % len(face)])))
            edge_count[edge] = edge_count.get(edge, 0) + 1
    boundary_edges = [e for e, count in edge_count.items() if count == 1]

    # Group boundary edges into loops
    loops = find_edge_loops(boundary_edges)

    # Output vertex centers (approximate endpoints)
    v_out = [np.mean(v[loop], axis=0) for loop in loops]
    e_out = [(i, i + 1) for i in range(0, len(v_out), 2)]
    
    return v_out, e_out


def find_edge_loops(edges):
    # Find all edge-connected loops
    edge_map = {}
    for e1, e2 in edges:
        edge_map.setdefault(e1, []).append(e2)
        edge_map.setdefault(e2, []).append(e1)
    
    loops = []
    visited = set()
    for start in edge_map:
        if start in visited:
            continue
        loop = []
        current = start
        while current not in visited:
            visited.add(current)
            loop.append(current)
            next_candidates = [n for n in edge_map[current] if n not in visited]
            if not next_candidates:
                break
            current = next_candidates[0]
        loops.append(loop)
    return loops


for vlist, flist in zip(verts, faces):
    pts = find_cylinder_endpoints(vlist, flist)
    nverts, nedges = pts
    nnverts = [n.tolist() for n in nverts]
    v_out.append(nnverts)
    e_out.append(nedges)