def MergeGroundTruth(prefix, model_prefix):
# read the segmentation data
segmentation = dataIO.ReadSegmentationData(prefix)
# get the multicut filename (with graph weights)
multicut_filename = 'multicut/{}-{}.graph'.format(model_prefix, prefix)
# read the gold data
gold = dataIO.ReadGoldData(prefix)
# read in the segmentation to gold mapping
mapping = seg2gold.Mapping(segmentation, gold)
# get the maximum segmentation value
max_value = np.amax(segmentation) + 1
# create union find data structure
union_find = [UnionFind.UnionFindElement(iv) for iv in range(max_value)]
# read in all of the labels
with open(multicut_filename, 'rb') as fd:
# read the number of vertices and edges
nvertices, nedges, = struct.unpack('QQ', fd.read(16))
# read in all of the edges
for ie in range(nedges):
# read in the two labels
label_one, label_two, = struct.unpack('QQ', fd.read(16))
# skip over the reduced labels and edge weight
fd.read(24)
# if the labels are the same and the mapping is non zero
if mapping[label_one] == mapping[label_two] and mapping[label_one]:
UnionFind.Union(union_find[label_one], union_find[label_two])
# create a mapping
mapping = np.zeros(max_value, dtype=np.int64)
# update the segmentation
for iv in range(max_value):
label = UnionFind.Find(union_find[iv]).label
mapping[iv] = label
merged_segmentation = seg2seg.MapLabels(segmentation, mapping)
gold_filename = 'gold/{}_gold.h5'.format(prefix)
# TODO fix this code temporary filename
truth_filename = 'multicut/{}-truth.h5'.format(prefix)
# temporary write h5 file
dataIO.WriteH5File(merged_segmentation, truth_filename, 'stack')
import time
start_time = time.time()
print ('Ground truth: ')
# create the command line
command = '~/software/PixelPred2Seg/comparestacks --stack1 {} --stackbase {} --dilate1 1 --dilatebase 1 --relabel1 --relabelbase --filtersize 100 --anisotropic'.format(truth_filename, gold_filename)
# execute the command
os.system(command)
print (time.time() - start_time)
def Agglomerate(prefix, model_prefix, threshold=0.5):
# read the segmentation data
segmentation = dataIO.ReadSegmentationData(prefix)
# get the multicut filename (with graph weights)
multicut_filename = 'multicut/{}-{}.graph'.format(model_prefix, prefix)
# get the maximum segmentation value
max_value = np.amax(segmentation) + 1
# create union find data structure
union_find = [UnionFind.UnionFindElement(iv) for iv in range(max_value)]
# read in all of the labels and merge the result
with open(multicut_filename, 'rb') as fd:
# read the number of vertices and edges
nvertices, nedges, = struct.unpack('QQ', fd.read(16))
# read in all of the edges
for ie in range(nedges):
# read in both labels
label_one, label_two, = struct.unpack('QQ', fd.read(16))
# skip over the reduced labels
fd.read(16)
# read in the edge weight
edge_weight, = struct.unpack('d', fd.read(8))
# merge label one and label two in the union find data structure
if (edge_weight > threshold):
UnionFind.Union(union_find[label_one], union_find[label_two])
# create a mapping
mapping = np.zeros(max_value, dtype=np.int64)
# update the segmentation
for iv in range(max_value):
label = UnionFind.Find(union_find[iv]).label
mapping[iv] = label
# update the labels
agglomerated_segmentation = seg2seg.MapLabels(segmentation, mapping)
gold_filename = 'gold/{}_gold.h5'.format(prefix)
# TODO fix this code temporary filename
agglomeration_filename = 'multicut/{}-agglomerate.h5'.format(prefix)
# temporary - write h5 file
dataIO.WriteH5File(agglomerated_segmentation, agglomeration_filename, 'stack')
import time
start_time = time.time()
print ('Agglomeration - {}:'.format(threshold))
# create the command line
command = '~/software/PixelPred2Seg/comparestacks --stack1 {} --stackbase {} --dilate1 1 --dilatebase 1 --relabel1 --relabelbase --filtersize 100 --anisotropic'.format(agglomeration_filename, gold_filename)
# execute the command
os.system(command)
print (time.time() - start_time)
from biologicalgraphs.evaluation import comparestacks
#from biologicalgraphs.cnns.biological import nodes, edges
from biologicalgraphs.cnns.biological.nodes import forward
#from biologicalgraphs.cnns.biological.edges import forward
from biologicalgraphs.transforms import seg2seg, seg2gold
from biologicalgraphs.skeletonization import generate_skeletons
from biologicalgraphs.algorithms import lifted_multicut
# the prefix name corresponds to the meta file in meta/{PREFIX}.meta
prefix = 'Kasthuri-test'
# read the ground truth for this data
gold = dataIO.ReadGoldData(prefix)
# read the input segmentation data
segmentation = dataIO.ReadSegmentationData(prefix)
# subset is either training, validation, or testing
subset = 'testing'
# remove the singleton slices
node_generation.RemoveSingletons(prefix, segmentation)
print("Step 1 done")
# need to update the prefix and segmentation
# removesingletons writes a new h5 file to disk
prefix = '{}-segmentation-wos'.format(prefix)
segmentation = dataIO.ReadSegmentationData(prefix)
# need to rerun seg2gold mapping since segmentation changed
seg2gold_mapping = seg2gold.Mapping(prefix, segmentation, gold)
print("Step 2 done")