prepare_data.py

# Copyright 2016-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import glob, plyfile, numpy as np, multiprocessing as mp, torch

# Map relevant classes to {0,1,...,19}, and ignored classes to -100
remapper=np.ones(150)*(-100)
for i,x in enumerate([1,2,3,4,5,6,7,8,9,10,11,12,14,16,24,28,33,34,36,39]):
    remapper[x]=i

files=sorted(glob.glob('*/*_vh_clean_2.ply'))
files2=sorted(glob.glob('*/*_vh_clean_2.labels.ply'))
assert len(files) == len(files2)

def f(fn):
    fn2 = fn[:-3]+'labels.ply'
    a=plyfile.PlyData().read(fn)
    v=np.array([list(x) for x in a.elements[0]])
    coords=np.ascontiguousarray(v[:,:3]-v[:,:3].mean(0))
    colors=np.ascontiguousarray(v[:,3:6])/127.5-1
    a=plyfile.PlyData().read(fn2)
    w=remapper[np.array(a.elements[0]['label'])]
    torch.save((coords,colors,w),fn[:-4]+'.pth')
    print(fn, fn2)

p = mp.Pool(processes=mp.cpu_count())
p.map(f,files)
p.close()
p.join()