def test_sphere(self):
# (B, W, H, D)
volume = torch.Tensor([[[(x - 10)**2 + (y - 10)**2 + (z - 10)**2
for z in range(20)] for y in range(20)]
for x in range(20)]).unsqueeze(0)
volume = volume.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume,
isolevel=64,
return_local_coords=False)
data_filename = "test_marching_cubes_data/sphere_level64.pickle"
filename = os.path.join(DATA_DIR, data_filename)
with open(filename, "rb") as file:
verts_and_faces = pickle.load(file)
expected_verts = verts_and_faces["verts"].squeeze()
expected_faces = verts_and_faces["faces"].squeeze()
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
verts, faces = marching_cubes_naive(volume,
isolevel=64,
return_local_coords=True)
expected_verts = convert_to_local(expected_verts, 20)
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
# Check all values are in the range [-1, 1]
self.assertTrue(verts[0].ge(-1).all() and verts[0].le(1).all())
def test_case3(self):
volume_data = torch.ones(1, 2, 2, 2) # (B, W, H, D)
volume_data[0, 0, 0, 0] = 0
volume_data[0, 1, 1, 0] = 0
volume_data = volume_data.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume_data,
return_local_coords=False)
expected_verts = torch.tensor([
[0.5000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.5000],
[1.0000, 1.0000, 0.5000],
[0.5000, 1.0000, 0.0000],
[1.0000, 0.5000, 0.0000],
[0.0000, 0.5000, 0.0000],
])
expected_faces = torch.tensor([[0, 1, 5], [4, 3, 2]])
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
verts, faces = marching_cubes_naive(volume_data,
return_local_coords=True)
expected_verts = convert_to_local(expected_verts, 2)
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
def test_single_point(self):
volume_data = torch.zeros(1, 3, 3, 3) # (B, W, H, D)
volume_data[0, 1, 1, 1] = 1
volume_data = volume_data.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume_data,
return_local_coords=False)
expected_verts = torch.tensor([
[0.5, 1, 1],
[1, 1, 0.5],
[1, 0.5, 1],
[1, 1, 1.5],
[1, 1.5, 1],
[1.5, 1, 1],
])
expected_faces = torch.tensor([
[2, 0, 1],
[2, 3, 0],
[0, 4, 1],
[3, 4, 0],
[5, 2, 1],
[3, 2, 5],
[5, 1, 4],
[3, 5, 4],
])
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
verts, faces = marching_cubes_naive(volume_data,
return_local_coords=True)
expected_verts = convert_to_local(expected_verts, 3)
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
self.assertTrue(verts[0].ge(-1).all() and verts[0].le(1).all())
def test_cube_no_duplicate_verts(self):
volume_data = torch.zeros(1, 5, 5, 5) # (B, W, H, D)
volume_data[0, 1, 1, 1] = 1
volume_data[0, 1, 1, 2] = 1
volume_data[0, 2, 1, 1] = 1
volume_data[0, 2, 1, 2] = 1
volume_data[0, 1, 2, 1] = 1
volume_data[0, 1, 2, 2] = 1
volume_data[0, 2, 2, 1] = 1
volume_data[0, 2, 2, 2] = 1
volume_data = volume_data.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume_data,
1,
return_local_coords=False)
expected_verts = torch.tensor([
[1.0, 1.0, 1.0],
[1.0, 1.0, 2.0],
[1.0, 2.0, 1.0],
[1.0, 2.0, 2.0],
[2.0, 1.0, 1.0],
[2.0, 1.0, 2.0],
[2.0, 2.0, 1.0],
[2.0, 2.0, 2.0],
])
expected_faces = torch.tensor([
[1, 3, 0],
[3, 2, 0],
[5, 1, 4],
[4, 1, 0],
[4, 0, 6],
[0, 2, 6],
[5, 7, 1],
[1, 7, 3],
[7, 6, 3],
[6, 2, 3],
[5, 4, 7],
[7, 4, 6],
])
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
verts, faces = marching_cubes_naive(volume_data,
1,
return_local_coords=True)
expected_verts = convert_to_local(expected_verts, 5)
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
# Check all values are in the range [-1, 1]
self.assertTrue(verts[0].ge(-1).all() and verts[0].le(1).all())
def test_empty_volume(self): # case 0
volume_data = torch.ones(1, 2, 2, 2) # (B, W, H, D)
verts, faces = marching_cubes_naive(volume_data,
return_local_coords=False)
expected_verts = torch.tensor([])
expected_faces = torch.tensor([], dtype=torch.int64)
self.assertClose(verts, expected_verts)
self.assertClose(faces, expected_faces)
def test_sphere_surface_area(self):
if USE_SCIKIT:
from skimage.measure import marching_cubes_classic, mesh_surface_area
# (B, W, H, D)
volume = torch.Tensor([[[(x - 10)**2 + (y - 10)**2 + (z - 10)**2
for z in range(20)] for y in range(20)]
for x in range(20)]).unsqueeze(0)
volume = volume.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume, isolevel=64)
verts_sci, faces_sci = marching_cubes_classic(volume[0], level=64)
surf = mesh_surface_area(verts[0], faces[0])
surf_sci = mesh_surface_area(verts_sci, faces_sci)
self.assertClose(surf, surf_sci)
def test_double_ellipsoid_surface_area(self):
if USE_SCIKIT:
import numpy as np
from skimage.draw import ellipsoid
from skimage.measure import marching_cubes_classic, mesh_surface_area
ellip_base = ellipsoid(6, 10, 16, levelset=True)
ellip_double = np.concatenate(
(ellip_base[:-1, ...], ellip_base[2:, ...]), axis=0)
volume = torch.Tensor(ellip_double).unsqueeze(0)
volume = volume.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume, isolevel=0)
verts_sci, faces_sci = marching_cubes_classic(volume[0], level=0)
surf = mesh_surface_area(verts[0], faces[0])
surf_sci = mesh_surface_area(verts_sci, faces_sci)
self.assertClose(surf, surf_sci)
def test_double_ellipsoid(self):
if USE_SCIKIT:
import numpy as np
from skimage.draw import ellipsoid
ellip_base = ellipsoid(6, 10, 16, levelset=True)
ellip_double = np.concatenate(
(ellip_base[:-1, ...], ellip_base[2:, ...]), axis=0)
volume = torch.Tensor(ellip_double).unsqueeze(0)
volume = volume.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume, isolevel=0.001)
data_filename = "test_marching_cubes_data/double_ellipsoid.pickle"
filename = os.path.join(DATA_DIR, data_filename)
with open(filename, "rb") as file:
verts_and_faces = pickle.load(file)
expected_verts = verts_and_faces["verts"]
expected_faces = verts_and_faces["faces"]
self.assertClose(verts[0], expected_verts[0])
self.assertClose(faces[0], expected_faces[0])
def test_cube_surface_area(self):
if USE_SCIKIT:
from skimage.measure import marching_cubes_classic, mesh_surface_area
volume_data = torch.zeros(1, 5, 5, 5)
volume_data[0, 1, 1, 1] = 1
volume_data[0, 1, 1, 2] = 1
volume_data[0, 2, 1, 1] = 1
volume_data[0, 2, 1, 2] = 1
volume_data[0, 1, 2, 1] = 1
volume_data[0, 1, 2, 2] = 1
volume_data[0, 2, 2, 1] = 1
volume_data[0, 2, 2, 2] = 1
volume_data = volume_data.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume_data,
return_local_coords=False)
verts_sci, faces_sci = marching_cubes_classic(volume_data[0])
surf = mesh_surface_area(verts[0], faces[0])
surf_sci = mesh_surface_area(verts_sci, faces_sci)
self.assertClose(surf, surf_sci)
def convert():
marching_cubes_naive(volume_data, return_local_coords=False)
torch.cuda.synchronize()
def test_cube(self):
volume_data = torch.zeros(1, 5, 5, 5) # (B, W, H, D)
volume_data[0, 1, 1, 1] = 1
volume_data[0, 1, 1, 2] = 1
volume_data[0, 2, 1, 1] = 1
volume_data[0, 2, 1, 2] = 1
volume_data[0, 1, 2, 1] = 1
volume_data[0, 1, 2, 2] = 1
volume_data[0, 2, 2, 1] = 1
volume_data[0, 2, 2, 2] = 1
volume_data = volume_data.permute(0, 3, 2, 1) # (B, D, H, W)
verts, faces = marching_cubes_naive(volume_data,
0.9,
return_local_coords=False)
expected_verts = torch.tensor([
[0.9000, 1.0000, 1.0000],
[1.0000, 1.0000, 0.9000],
[1.0000, 0.9000, 1.0000],
[0.9000, 1.0000, 2.0000],
[1.0000, 0.9000, 2.0000],
[1.0000, 1.0000, 2.1000],
[0.9000, 2.0000, 1.0000],
[1.0000, 2.0000, 0.9000],
[0.9000, 2.0000, 2.0000],
[1.0000, 2.0000, 2.1000],
[1.0000, 2.1000, 1.0000],
[1.0000, 2.1000, 2.0000],
[2.0000, 1.0000, 0.9000],
[2.0000, 0.9000, 1.0000],
[2.0000, 0.9000, 2.0000],
[2.0000, 1.0000, 2.1000],
[2.0000, 2.0000, 0.9000],
[2.0000, 2.0000, 2.1000],
[2.0000, 2.1000, 1.0000],
[2.0000, 2.1000, 2.0000],
[2.1000, 1.0000, 1.0000],
[2.1000, 1.0000, 2.0000],
[2.1000, 2.0000, 1.0000],
[2.1000, 2.0000, 2.0000],
])
expected_faces = torch.tensor([
[2, 0, 1],
[2, 4, 3],
[0, 2, 3],
[4, 5, 3],
[0, 6, 7],
[1, 0, 7],
[3, 8, 0],
[8, 6, 0],
[5, 9, 8],
[3, 5, 8],
[6, 10, 7],
[11, 10, 6],
[8, 11, 6],
[9, 11, 8],
[13, 2, 1],
[12, 13, 1],
[14, 4, 13],
[13, 4, 2],
[4, 14, 15],
[5, 4, 15],
[12, 1, 16],
[1, 7, 16],
[15, 17, 5],
[5, 17, 9],
[16, 7, 10],
[18, 16, 10],
[19, 18, 11],
[18, 10, 11],
[9, 17, 19],
[11, 9, 19],
[20, 13, 12],
[20, 21, 14],
[13, 20, 14],
[15, 14, 21],
[22, 20, 12],
[16, 22, 12],
[21, 20, 23],
[23, 20, 22],
[17, 15, 21],
[23, 17, 21],
[22, 16, 18],
[23, 22, 18],
[19, 23, 18],
[17, 23, 19],
])
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
verts, faces = marching_cubes_naive(volume_data,
0.9,
return_local_coords=True)
expected_verts = convert_to_local(expected_verts, 5)
self.assertClose(verts[0], expected_verts)
self.assertClose(faces[0], expected_faces)
# Check all values are in the range [-1, 1]
self.assertTrue(verts[0].ge(-1).all() and verts[0].le(1).all())
