def test_points_range_filter():
pcd_range = [0.0, 0.0, 0.0, 3.0, 3.0, 3.0]
points_range_filter = PointsRangeFilter(pcd_range)
points = np.fromfile('tests/data/scannet/points/scene0000_00.bin',
np.float32).reshape(-1, 6)
ins_mask = np.fromfile('tests/data/scannet/instance_mask/scene0000_00.bin',
np.long)
sem_mask = np.fromfile('tests/data/scannet/semantic_mask/scene0000_00.bin',
np.long)
points = DepthPoints(points.copy(),
points_dim=6,
attribute_dims=dict(color=[3, 4, 5]))
input_dict = dict(points=points.clone(),
pts_instance_mask=ins_mask.copy(),
pts_semantic_mask=sem_mask.copy())
results = points_range_filter(input_dict)
shuffle_pts = results['points']
shuffle_ins_mask = results['pts_instance_mask']
shuffle_sem_mask = results['pts_semantic_mask']
select_idx = np.array(
[5, 11, 22, 26, 27, 33, 46, 47, 56, 63, 74, 78, 79, 91])
expected_pts = points.tensor.numpy()[select_idx]
expected_ins_mask = ins_mask[select_idx]
expected_sem_mask = sem_mask[select_idx]
assert np.allclose(shuffle_pts.tensor.numpy(), expected_pts)
assert np.all(shuffle_ins_mask == expected_ins_mask)
assert np.all(shuffle_sem_mask == expected_sem_mask)
repr_str = repr(points_range_filter)
expected_repr_str = f'PointsRangeFilter(point_cloud_range={pcd_range})'
assert repr_str == expected_repr_str
def test_point_shuffle():
np.random.seed(0)
torch.manual_seed(0)
point_shuffle = PointShuffle()
points = np.fromfile('tests/data/scannet/points/scene0000_00.bin',
np.float32).reshape(-1, 6)
ins_mask = np.fromfile('tests/data/scannet/instance_mask/scene0000_00.bin',
np.long)
sem_mask = np.fromfile('tests/data/scannet/semantic_mask/scene0000_00.bin',
np.long)
points = DepthPoints(points.copy(),
points_dim=6,
attribute_dims=dict(color=[3, 4, 5]))
input_dict = dict(points=points.clone(),
pts_instance_mask=ins_mask.copy(),
pts_semantic_mask=sem_mask.copy())
results = point_shuffle(input_dict)
shuffle_pts = results['points']
shuffle_ins_mask = results['pts_instance_mask']
shuffle_sem_mask = results['pts_semantic_mask']
shuffle_idx = np.array([
44, 19, 93, 90, 71, 69, 37, 95, 53, 91, 81, 42, 80, 85, 74, 56, 76, 63,
82, 40, 26, 92, 57, 10, 16, 66, 89, 41, 97, 8, 31, 24, 35, 30, 65, 7,
98, 23, 20, 29, 78, 61, 94, 15, 4, 52, 59, 5, 54, 46, 3, 28, 2, 70, 6,
60, 49, 68, 55, 72, 79, 77, 45, 1, 32, 34, 11, 0, 22, 12, 87, 50, 25,
47, 36, 96, 9, 83, 62, 84, 18, 17, 75, 67, 13, 48, 39, 21, 64, 88, 38,
27, 14, 73, 33, 58, 86, 43, 99, 51
])
expected_pts = points.tensor.numpy()[shuffle_idx]
expected_ins_mask = ins_mask[shuffle_idx]
expected_sem_mask = sem_mask[shuffle_idx]
assert np.allclose(shuffle_pts.tensor.numpy(), expected_pts)
assert np.all(shuffle_ins_mask == expected_ins_mask)
assert np.all(shuffle_sem_mask == expected_sem_mask)
repr_str = repr(point_shuffle)
expected_repr_str = 'PointShuffle'
assert repr_str == expected_repr_str
