def PrintResults(prefix, vertex_ones, vertex_twos, edge_weights,
maintained_edges, algorithm):
# get the ground truth and print out the results
seg2gold_mapping = seg2gold.Mapping(prefix)
# get the number of edges
nedges = edge_weights.shape[0]
# see how multicut has changed the results
labels = []
cnn_results = []
multicut_results = []
# go through each edge
for ie in range(nedges):
vertex_one = vertex_ones[ie]
vertex_two = vertex_twos[ie]
# skip if there is no ground truth
if seg2gold_mapping[vertex_one] < 1 or seg2gold_mapping[vertex_two] < 1:
continue
# over 0.5 on edge weight means the edge should collapse
cnn_results.append(edge_weights[ie] > 0.5)
# since this edge has ground truth add to list
# subtract one here since a maintained edge is one that should not be merged
multicut_results.append(1 - maintained_edges[ie])
if seg2gold_mapping[vertex_one] == seg2gold_mapping[vertex_two]:
labels.append(True)
else:
labels.append(False)
print 'CNN Results:'
PrecisionAndRecall(np.array(labels), np.array(cnn_results))
print 'Multicut Results'
PrecisionAndRecall(np.array(labels), np.array(multicut_results))
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)
# 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")
# generate locations for segments that are too small
node_generation.GenerateNodes(prefix, segmentation, subset, seg2gold_mapping)
print("Step 3 done")
# run inference for node network
node_model_prefix = '/n/home03/tanav/axonem/biologicalgraphs/neuronseg/architectures/nodes-400nm-3x20x60x60-Kasthuri/nodes'
forward.Forward(prefix,
node_model_prefix,
segmentation,
subset,
seg2gold_mapping,
evaluate=True)