def get_features(paths, featureimages, featurelist, max_paths_per_label, ipl=None):
newfeats = IPL()
keylist = range(0, max_paths_per_label)
keylist = [str(x) for x in keylist]
# Iterate over all paths, yielding a list of one path per label object until no paths are left
for i, keys, vals in paths.simultaneous_iterator(
max_count_per_item=max_paths_per_label,
keylist=keylist):
if ipl is not None:
ipl.logging('Working in iteration = {}', i)
ipl.logging('Keys: {}', keys)
if not keys:
continue
# Create a working image
image = np.zeros(np.array(featureimages.yield_an_item()).shape, dtype=np.uint32)
# And fill it with one path per label object
c = 1
for curk, curv in (dict(zip(keys, vals))).iteritems():
curv = np.array(curv)
curv = lib.swapaxes(curv, 0, 1)
lib.positions2value(image, curv, c)
c += 1
# TODO: If this loop iterated over the parameter list it would be more broadly applicable
for d, k, v, kl in featureimages.data_iterator():
if type(v) is not IPL:
# Extract the region features of the working image
# TODO: Extract feature 'Count' manually due to anisotropy
newnewfeats = IPL(
data=vigra.analysis.extractRegionFeatures(
np.array(v).astype(np.float32),
image, ignoreLabel=0,
features=featurelist
)
)
# Append to the recently computed list of features
for nk, nv in newnewfeats.iteritems():
nv = nv[1:]
if newfeats.inkeys(kl+[nk]):
try:
newfeats[kl + [nk]] = np.concatenate((newfeats[kl + [nk]], nv))
except ValueError:
pass
else:
newfeats[kl + [nk]] = nv
return newfeats
