Light pytorch-style 3D augmentation library
Some transform code is borrowed from SpatioTemporalSegmentation
The dependancy is numpy
.
Example
import numpy as np
import transforms as t
# Load some 3D point cloud file
coords, feats = read_ply('example.ply')
ROTATE_AXIS = 'z'
# Create multiple random augmentation transforms and combine with Compose
transform = t.Compose([
t.RandomApply([
t.ElasticDistortion([(0.2, 0.4), (0.8, 1.6)])
], 0.95),
t.RandomApply([
t.RandomTranslate([(-0.2, 0.2), (-0.2, 0.2), (0, 0)])
], 0.95),
t.Random360Rotate(ROTATE_AXIS, around_center=True),
t.RandomApply([
t.RandomRotateEachAxis([(-np.pi / 64, np.pi / 64), (-np.pi / 64, np.pi / 64), (0, 0)])
], 0.95),
t.RandomApply([
t.RandomScale(0.9, 1.1)
], 0.95),
])
# Create dummy label
N, _ = coords.shape
labels = np.zeros((N, ), dtype=np.int32)
coords, feats, labels = transform(coords, feats, labels)