def edge_decisions(overseg_vol, groundtruth_vol, asdict=True):
"""
Given an oversegmentation and a reference segmentation,
return a dict of {(sp1, sp2) : bool} indicating whether or
not edge (sp1,sp2) is ON in the reference segmentation.
If asdict=False, return separate ndarrays for edge_ids
and boolean decisions instead of combined dict.
"""
sp_edges_per_axis = []
for axis in range(overseg_vol.ndim):
edge_mask = edge_mask_for_axis(overseg_vol, axis)
sp_edges = edge_ids_for_axis(overseg_vol, edge_mask, axis)
del edge_mask
sp_edges_per_axis.append(sp_edges)
unique_sp_edges = unique_edge_labels(sp_edges_per_axis)[["sp1",
"sp2"]].values
sp_to_gt_mapping = label_vol_mapping(overseg_vol, groundtruth_vol)
decisions = sp_to_gt_mapping[unique_sp_edges[0]] != sp_to_gt_mapping[
unique_sp_edges[1]]
if asdict:
return dict(zip(map(tuple, unique_sp_edges), decisions))
return unique_sp_edges, decisions
def test_label_vol_mapping():
# 1 2
# 3 4
vol1 = np.zeros((20,20), dtype=np.uint8)
vol1[ 0:10, 0:10] = 1
vol1[ 0:10, 10:20] = 2
vol1[10:20, 0:10] = 3
vol1[10:20, 10:20] = 4
# 2 3
# 4 5
vol2 = vol1.copy() + 1
assert (label_vol_mapping(vol1, vol2) == [0,2,3,4,5]).all()
assert (label_vol_mapping(vol1[3:], vol2[:-3]) == [0,2,3,4,5]).all()
assert (label_vol_mapping(vol1[6:], vol2[:-6]) == [0,2,3,2,3]).all()
# 7 7
# 4 5
vol2[( vol2 == 2 ).nonzero()] = 7
vol2[( vol2 == 3 ).nonzero()] = 7
assert (label_vol_mapping(vol1, vol2) == [0,7,7,4,5]).all()
def edge_decisions( overseg_vol, groundtruth_vol, asdict=True ):
"""
Given an oversegmentation and a reference segmentation,
return a dict of {(sp1, sp2) : bool} indicating whether or
not edge (sp1,sp2) is ON in the reference segmentation.
If asdict=False, return separate ndarrays for edge_ids
and boolean decisions instead of combined dict.
"""
sp_edges_per_axis = []
for axis in range(overseg_vol.ndim):
edge_mask = edge_mask_for_axis(overseg_vol, axis)
sp_edges = edge_ids_for_axis(overseg_vol, edge_mask, axis)
del edge_mask
sp_edges_per_axis.append( sp_edges )
unique_sp_edges = unique_edge_labels(sp_edges_per_axis)[['sp1', 'sp2']].values
sp_to_gt_mapping = label_vol_mapping(overseg_vol, groundtruth_vol)
decisions = sp_to_gt_mapping[unique_sp_edges[0]] != sp_to_gt_mapping[unique_sp_edges[1]]
if asdict:
return dict( izip(imap(tuple, unique_sp_edges), decisions) )
return unique_sp_edges, decisions
return segmentation_img
if __name__ == "__main__":
# 1 2
# 3 4
vol1 = np.zeros((20,20), dtype=np.uint8)
vol1[ 0:10, 0:10] = 1
vol1[ 0:10, 10:20] = 2
vol1[10:20, 0:10] = 3
vol1[10:20, 10:20] = 4
# 2 3
# 4 5
vol2 = vol1.copy() + 1
assert (label_vol_mapping(vol1, vol2) == [0,2,3,4,5]).all()
assert (label_vol_mapping(vol1[3:], vol2[:-3]) == [0,2,3,4,5]).all()
assert (label_vol_mapping(vol1[6:], vol2[:-6]) == [0,2,3,2,3]).all()
# 7 7
# 4 5
vol2[( vol2 == 2 ).nonzero()] = 7
vol2[( vol2 == 3 ).nonzero()] = 7
assert (label_vol_mapping(vol1, vol2) == [0,7,7,4,5]).all()
decision_dict = edge_decisions(vol1, vol2, asdict=True)
edge_ids, decisions = edge_decisions(vol1, vol2, asdict=False)
relabel_volume_from_edge_decisions( vol1, edge_ids, decisions )
print "DONE"
