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Update lmc.py #2

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Update lmc.py #2

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25 changes: 25 additions & 0 deletions scripts/utils/lmc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,25 @@
import elf.segmentation.features as feats
import elf.segmentation.watershed as ws


# if the compute_affinity features function doesn't work because of windows issues
def affinity_feature_fallback(rag, ws, affs, offsets):
assert offsets == [[-1, 0, 0], [0, -1, 0], [0, 0, -1]]
xy_boundaries = np.maximum(affs[1], affs[2])
z_boundaries = affs[0]

# compute in plane features
features = feats.compute_boundary_mean_and_length(rag, xy_boundaries)
# compute between plane features
z_features = feats.compute_boundary_mean_and_length(rag, z_boundaries)

# over-ride between plane features in features
z_edges = feats.compute_z_edge_mask(rag, ws)
features[between_plane_edges] = z_features[z_edges]

return features


def mc_baseline(affs, fragments=None):
affs = 1 - affs
boundary_input = np.maximum(affs[1], affs[2])
Expand All @@ -16,6 +35,8 @@ def mc_baseline(affs, fragments=None):
fragments[z] = wsz
rag = feats.compute_rag(fragments)
offsets = [[-1, 0, 0], [0, -1, 0], [0, 0, -1]]
# This is only necessary if compute_affinity_features does not work due to issues on windows or mac.
# costs = affinity_feature_fallback(rag, fragments, affs, offsets)[:, 0]
costs = feats.compute_affinity_features(rag, affs, offsets)[:, 0]
edge_sizes = feats.compute_boundary_mean_and_length(rag, boundary_input)[:, 1]
costs = mc.transform_probabilities_to_costs(costs, edge_sizes=edge_sizes)
Expand All @@ -36,7 +57,11 @@ def multicut_multi(affs, offsets=[[-1, 0, 0], [0, -1, 0], [0, 0, -1]], fragments
offset += max_id
fragments[z] = wsz
rag = feats.compute_rag(fragments)

# This is only necessary if compute_affinity_features does not work due to issues on windows or mac.
# costs = affinity_feature_fallback(rag, fragments, affs, offsets)[:, 0]
costs = feats.compute_affinity_features(rag, affs, offsets)[:, 0]

edge_sizes = feats.compute_boundary_mean_and_length(rag, boundary_input)[:, 1]
costs = mc.transform_probabilities_to_costs(costs, edge_sizes=edge_sizes)
node_labels = mc.multicut_kernighan_lin(rag, costs)
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