def test_load_pointcloud_bad_order(self):
"""
Ply file with a strange property order
"""
file = "\n".join([
"ply",
"format ascii 1.0",
"element vertex 1",
"property uchar green",
"property float x",
"property float z",
"property uchar red",
"property float y",
"property uchar blue",
"end_header",
"1 2 3 4 5 6",
])
io = IO()
pointcloud_gpu = io.load_pointcloud(StringIO(file), device="cuda:0")
self.assertEqual(pointcloud_gpu.device, torch.device("cuda:0"))
pointcloud = pointcloud_gpu.to(torch.device("cpu"))
expected_points = torch.tensor([[[2, 5, 3]]], dtype=torch.float32)
expected_features = torch.tensor([[[4, 1, 6]]], dtype=torch.float32)
self.assertClose(pointcloud.points_padded(), expected_points)
self.assertClose(pointcloud.features_padded(), expected_features)
def test_save_load_with_normals(self):
points = torch.tensor([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0]],
dtype=torch.float32)
normals = torch.tensor([[0, 1, 0], [1, 0, 0], [1, 4, 1], [1, 0, 0]],
dtype=torch.float32)
features = torch.rand_like(points)
for do_features, do_normals in itertools.product([True, False],
[True, False]):
cloud = Pointclouds(
points=[points],
features=[features] if do_features else None,
normals=[normals] if do_normals else None,
)
device = torch.device("cuda:0")
io = IO()
with NamedTemporaryFile(mode="w", suffix=".ply") as f:
io.save_pointcloud(cloud.cuda(), f.name)
f.flush()
cloud2 = io.load_pointcloud(f.name, device=device)
self.assertEqual(cloud2.device, device)
cloud2 = cloud2.cpu()
self.assertClose(cloud2.points_padded(), cloud.points_padded())
if do_normals:
self.assertClose(cloud2.normals_padded(),
cloud.normals_padded())
else:
self.assertIsNone(cloud.normals_padded())
self.assertIsNone(cloud2.normals_padded())
if do_features:
self.assertClose(cloud2.features_packed(), features)
else:
self.assertIsNone(cloud2.features_packed())
def test_save_pointcloud(self):
header = "\n".join([
"ply",
"format binary_little_endian 1.0",
"element vertex 8",
"property float x",
"property float y",
"property float z",
"property float red",
"property float green",
"property float blue",
"end_header",
"",
]).encode("ascii")
data = struct.pack("<" + "f" * 48, *range(48))
points = torch.FloatTensor([0, 1, 2]) + 6 * torch.arange(8)[:, None]
features = torch.FloatTensor([3, 4, 5]) + 6 * torch.arange(8)[:, None]
pointcloud = Pointclouds(points=[points], features=[features])
io = IO()
with NamedTemporaryFile(mode="rb", suffix=".ply") as f:
io.save_pointcloud(data=pointcloud, path=f.name)
f.flush()
f.seek(0)
actual_data = f.read()
reloaded_pointcloud = io.load_pointcloud(f.name)
self.assertEqual(header + data, actual_data)
self.assertClose(reloaded_pointcloud.points_list()[0], points)
self.assertClose(reloaded_pointcloud.features_list()[0], features)
with NamedTemporaryFile(mode="r", suffix=".ply") as f:
io.save_pointcloud(data=pointcloud, path=f.name, binary=False)
reloaded_pointcloud2 = io.load_pointcloud(f.name)
self.assertEqual(f.readline(), "ply\n")
self.assertEqual(f.readline(), "format ascii 1.0\n")
self.assertClose(reloaded_pointcloud2.points_list()[0], points)
self.assertClose(reloaded_pointcloud2.features_list()[0], features)
def test_load_cloudcompare_pointcloud(self):
"""
Test loading a pointcloud styled like some cloudcompare output.
cloudcompare is an open source 3D point cloud processing software.
"""
header = "\n".join([
"ply",
"format binary_little_endian 1.0",
"obj_info Not a key-value pair!",
"element vertex 8",
"property double x",
"property double y",
"property double z",
"property uchar red",
"property uchar green",
"property uchar blue",
"property float my_Favorite",
"end_header",
"",
]).encode("ascii")
data = struct.pack("<" + "dddBBBf" * 8, *range(56))
io = IO()
with NamedTemporaryFile(mode="wb", suffix=".ply") as f:
f.write(header)
f.write(data)
f.flush()
pointcloud = io.load_pointcloud(f.name)
self.assertClose(
pointcloud.points_padded()[0],
torch.FloatTensor([0, 1, 2]) + 7 * torch.arange(8)[:, None],
)
self.assertClose(
pointcloud.features_padded()[0],
torch.FloatTensor([3, 4, 5]) + 7 * torch.arange(8)[:, None],
)
def test_save_pointcloud(self):
header = "\n".join([
"ply",
"format binary_little_endian 1.0",
"element vertex 8",
"property float x",
"property float y",
"property float z",
"property float red",
"property float green",
"property float blue",
"end_header",
"",
]).encode("ascii")
data = struct.pack("<" + "f" * 48, *range(48))
points = torch.FloatTensor([0, 1, 2]) + 6 * torch.arange(8)[:, None]
features_large = torch.FloatTensor([3, 4, 5
]) + 6 * torch.arange(8)[:, None]
features = features_large / 255.0
pointcloud_largefeatures = Pointclouds(points=[points],
features=[features_large])
pointcloud = Pointclouds(points=[points], features=[features])
io = IO()
with NamedTemporaryFile(mode="rb", suffix=".ply") as f:
io.save_pointcloud(data=pointcloud_largefeatures, path=f.name)
f.flush()
f.seek(0)
actual_data = f.read()
reloaded_pointcloud = io.load_pointcloud(f.name)
self.assertEqual(header + data, actual_data)
self.assertClose(reloaded_pointcloud.points_list()[0], points)
self.assertClose(reloaded_pointcloud.features_list()[0],
features_large)
# Test the load-save cycle leaves file completely unchanged
with NamedTemporaryFile(mode="rb", suffix=".ply") as f:
io.save_pointcloud(
data=reloaded_pointcloud,
path=f.name,
)
f.flush()
f.seek(0)
data2 = f.read()
self.assertEqual(data2, actual_data)
with NamedTemporaryFile(mode="r", suffix=".ply") as f:
io.save_pointcloud(data=pointcloud,
path=f.name,
binary=False,
decimal_places=9)
reloaded_pointcloud2 = io.load_pointcloud(f.name)
self.assertEqual(f.readline(), "ply\n")
self.assertEqual(f.readline(), "format ascii 1.0\n")
self.assertClose(reloaded_pointcloud2.points_list()[0], points)
self.assertClose(reloaded_pointcloud2.features_list()[0], features)
for binary in [True, False]:
with NamedTemporaryFile(mode="rb", suffix=".ply") as f:
io.save_pointcloud(data=pointcloud,
path=f.name,
colors_as_uint8=True,
binary=binary)
f.flush()
f.seek(0)
actual_data = f.read()
reloaded_pointcloud3 = io.load_pointcloud(f.name)
self.assertClose(reloaded_pointcloud3.features_list()[0], features)
self.assertIn(b"property uchar green", actual_data)
# Test the load-save cycle leaves file completely unchanged
with NamedTemporaryFile(mode="rb", suffix=".ply") as f:
io.save_pointcloud(
data=reloaded_pointcloud3,
path=f.name,
binary=binary,
colors_as_uint8=True,
)
f.flush()
f.seek(0)
data2 = f.read()
self.assertEqual(data2, actual_data)