import sys
import h5py
import numpy
segh5 = sys.argv[1]
predh5 = sys.argv[2]
classifier = sys.argv[3]
threshold = float(sys.argv[4])
from neuroproof import Agglomeration
# open as uint32 and float respectively
seg = numpy.array(h5py.File(segh5)['stack'], numpy.uint32)
pred = numpy.array(h5py.File(predh5)['volume/predictions'], numpy.float32)
pred = pred.transpose((2,1,0,3))
pred = pred.copy()
res = Agglomeration.agglomerate(seg, pred, classifier, threshold)
# should be 232 unique labels (including 0) in the resulting segmentation
if len(numpy.unique(res)) != 232:
exit(1)
print "SUCCESS"
def neuroproof_agglomerate(grayscale, predictions, supervoxels, classifier, threshold = 0.20, mitochannel = 2):
"""Main agglomeration function
Args:
grayscale = 3D uing8 (z,y,x) -- Not used.
predictions = 4D float32 numpy label array (z, y, x, ch)
supervoxels = 3D uint32 numpy label array (z,y,x)
classifier = file location or DVID (assume to be xml unless .h5 is explict in name)
threshold = threshold (default = 0.20)
mitochannel = prediction channel for mito (default 2) (empty means no mito mode)
Returns:
segmentation = 3D numpy label array (z,y,x)
"""
print("neuroproof_agglomerate(): Starting with label data: dtype={}, shape={}".format(str(supervoxels.dtype), supervoxels.shape))
import numpy
# return immediately if no segmentation
if len(numpy.unique(supervoxels)) <= 1:
return supervoxels
#from neuroproof import Classifier, Agglomeration
from neuroproof import Agglomeration
import os
# verify channels
assert predictions.ndim == 4
z,y,x,nch = predictions.shape
if nch > 2:
# make sure mito is in the second channel
predictions[[[[2, mitochannel]]]] = predictions[[[[mitochannel, mitochannel]]]]
pathname = str(classifier["path"])
tempfilehold = None
tclassfile = ""
# write classifier to temporary file if stored on DVID
if "dvid-server" in classifier:
# allow user to specify any server and version for the data
dvidserver = classifier["dvid-server"]
uuid = classifier["uuid"]
# extract file and store into temporary location
node_service = retrieve_node_service(str(dvidserver), str(uuid))
name_key = pathname.split('/')
classfile = node_service.get(name_key[0], name_key[1])
# create temp file
import tempfile
tempfilehold = tempfile.NamedTemporaryFile(delete=False)
# open file and write data
with open(tempfilehold.name, 'w') as fout:
fout.write(classfile)
# move temporary file to have the same extension as provided file
if pathname.endswith('.h5'):
tclassfile = tempfilehold.name + ".h5"
else:
tclassfile = tempfilehold.name + ".xml"
os.rename(tempfilehold.name, tclassfile)
else:
# just read from directory
tclassfile = pathname
# load classifier from file
#classifier = loadClassifier(tclassfile)
# run agglomeration (supervoxels must be 32 uint and predicitons must be float32)
segmentation = Agglomeration.agglomerate(supervoxels.astype(numpy.uint32), predictions.astype(numpy.float32), tclassfile, threshold)
if tempfilehold is not None:
os.remove(tclassfile)
return segmentation
