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Add option for relabelling consecutively #8

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Add option for relabelling consecutively #8

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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
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13 changes: 10 additions & 3 deletions cc_torch/connected_components.py
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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
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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([
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