Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add option for relabelling consecutively #8

Open
wants to merge 5 commits into
base: main
Choose a base branch
from
Open

Add option for relabelling consecutively #8

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
8c7eba3
Add relabel argument
juliusbierk Jun 8, 2023
6628c5e
add test for relabel argument
juliusbierk Jun 8, 2023
80e7e10
fix test problem
juliusbierk Jun 8, 2023
a2ea9bb
Update connected_components.py
juliusbierk Jun 8, 2023
a0221a5
Update connected_components.py
juliusbierk Jun 8, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 10 additions & 3 deletions cc_torch/connected_components.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,19 +2,26 @@
from cc_torch import _C


def connected_components_labeling(x):
def connected_components_labeling(x, relabel=False):
"""
Connected Components Labeling by Block Union Find(BUF) algorithm.

Args:
x (cuda.ByteTensor): must be uint8, cuda and even num shapes
relabel (bool): whether to return labels in range [0, max_label]

Return:
label (cuda.IntTensor)
"""
if x.ndim == 2:
return _C.cc_2d(x)
ret = _C.cc_2d(x)
elif x.ndim == 3:
return _C.cc_3d(x)
ret = _C.cc_3d(x)
else:
raise ValueError("x must be [H, W] or [D, H, W] shapes")

if relabel:
vs, idxs = torch.unique(ret, return_inverse=True, sorted=True)
ret = torch.arange(len(vs), device=vs.device)[idxs]

return ret
28 changes: 28 additions & 0 deletions tests/test_cc.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,34 @@ def test_2d(self):

output = cc_torch.connected_components_labeling(img_2d)
self.assertTrue((output == expected_output).all())

def test_relabel(self):
img_2d = torch.tensor([
1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0], dtype=torch.uint8).reshape(12, 8).cuda()

expected_output = torch.tensor(
[[1, 1, 0, 1, 1, 1, 1, 1],
[0, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 1, 0, 0, 2],
[0, 0, 0, 1, 0, 0, 2, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 3, 0, 0],
[0, 0, 0, 0, 0, 3, 0, 0],
[0, 4, 0, 3, 3, 3, 3, 3],
[0, 4, 0, 0, 3, 3, 3, 0],
[0, 4, 0, 0, 3, 3, 3, 0]], dtype=torch.int32).cuda()

output = cc_torch.connected_components_labeling(img_2d, relabel=True)
self.assertTrue((output == expected_output).all())

def test_3d(self):
img_2d = torch.tensor([
Expand Down