def label_iterator(self,
key=None,
labellist=None,
background=None,
area=None):
"""
:param key:
:param labellist:
:param background:
:param area: supply area in the format area=np.s_[numpy indexing], i.e. area=np.s_[:,:,:] for a full 3d image
Note that this affects only the determination of which labels are iterated over, when labellist is supplied
this parameter has no effect
:return:
"""
if labellist is None:
labellist = self.unique(keys=key, return_only=True).yield_an_item()
if area is not None:
labellist = lib.unique(self[key][area])
if background is not None:
labellist = filter(lambda x: x != 0, labellist)
for lbl in labellist:
yield lbl
def label_iterator(self, key=None, labellist=None, background=None, area=None):
"""
:param key:
:param labellist:
:param background:
:param area: supply area in the format area=np.s_[numpy indexing], i.e. area=np.s_[:,:,:] for a full 3d image
Note that this affects only the determination of which labels are iterated over, when labellist is supplied
this parameter has no effect
:return:
"""
if labellist is None:
labellist = self.unique(keys=key, return_only=True)[key]
if area is not None:
labellist = lib.unique(self[key][area])
if background is not None:
labellist = filter(lambda x: x != 0, labellist)
for lbl in labellist:
yield lbl
def merge_adjacent_objects(hfp):
params = hfp.get_params()
thisparams = params['merge_adjacent_objects']
numberbysize = thisparams['numberbysize']
numberbyrandom = thisparams['numberbyrandom']
targetnames = params['largeobjmnames']
# Get only the relevant labels
labels = lib.unique(hfp['largeobj'])
hfp.logging('labels = {}', labels)
# Seed the randomize function
random.seed(thisparams['seed'])
hfp.astype(np.uint32, keys='largeobj')
(grag, rag) = graphs.gridRegionAdjacencyGraph(hfp['largeobj'], ignoreLabel=0)
edge_ids = rag.edgeIds()
# hfp.logging('Edge ids: {}', edge_ids)
# Type 1:
# Select edges by size (smallest edges)
hfp.logging('Number of edgeLengths = {}', len(rag.edgeLengths()))
edgelen_ids = dict(zip(edge_ids, rag.edgeLengths()))
# ifp.logging('edgelen_ids = {}', edgelen_ids)
sorted_edgelens = np.sort(rag.edgeLengths())
#
smallest_merge_lens = sorted_edgelens[0:numberbysize]
hfp.logging('Lengths selected for merging: {}', smallest_merge_lens)
#
smallest_merge_ids = []
for x in smallest_merge_lens:
edge_id = edgelen_ids.keys()[edgelen_ids.values().index(x)]
smallest_merge_ids.append(edge_id)
edgelen_ids.pop(edge_id)
#
edge_ids = edgelen_ids.keys()
hfp.logging('Edge IDs selected for merging due to size: {}', smallest_merge_ids)
# Type 2:
# Randomly choose edges
random_merge_ids = random.sample(edge_ids, numberbyrandom)
hfp.logging('Edge IDs randomly selected for merging: {}', random_merge_ids)
# Now get the label ids
smallest_merge_labelids_u = [rag.uId(rag.edgeFromId(x)) for x in smallest_merge_ids]
smallest_merge_labelids_v = [rag.vId(rag.edgeFromId(x)) for x in smallest_merge_ids]
smallest_merge_labelids = list(zip(smallest_merge_labelids_u, smallest_merge_labelids_v))
random_merge_labelids_u = [rag.uId(rag.edgeFromId(x)) for x in random_merge_ids]
random_merge_labelids_v = [rag.vId(rag.edgeFromId(x)) for x in random_merge_ids]
random_merge_labelids = list(zip(random_merge_labelids_u, random_merge_labelids_v))
hfp.logging('Label IDs selected for merging by size: {}', smallest_merge_labelids)
hfp.logging('Label IDs randomly selected for merging: {}', random_merge_labelids)
# Concatenate
all_merge_labelids = smallest_merge_labelids + random_merge_labelids
# Sort
hfp.logging('all_merge_labelids = {}', all_merge_labelids)
all_merge_labelids = [sorted(x) for x in all_merge_labelids]
all_merge_labelids = sorted(all_merge_labelids)
hfp.logging('all_merge_labelids = {}', all_merge_labelids)
# Store this for later use
hfp[targetnames[1]] = smallest_merge_labelids
hfp[targetnames[2]] = random_merge_labelids
hfp[targetnames[3]] = all_merge_labelids
# Create change hash list
change_hash = IPL(data=dict(zip(np.unique(all_merge_labelids), [[x,] for x in np.unique(all_merge_labelids)])))
for i in xrange(0, 3):
prev_change_hash = IPL(data=change_hash)
for x in all_merge_labelids:
hfp.logging('Adding {} and {}', *x)
change_hash[x[0]] += change_hash[x[1]]
change_hash[x[0]] = list(np.unique(change_hash[x[0]]))
change_hash[x[1]] += change_hash[x[0]]
change_hash[x[1]] = list(np.unique(change_hash[x[1]]))
# This removes the redundancy from the hash
def reduce(hash):
br = False
for k, v in hash.iteritems():
for x in v:
if x != k:
if x in hash.keys():
del hash[x]
reduce(hash)
br = True
break
else:
br = False
if br:
break
reduce(change_hash)
# And now we have a perfect change list which we just need to iterate over and change the labels in the image
hfp.logging('change_hash after change:')
hfp.logging(change_hash)
hfp[targetnames[4]] = change_hash
# Create the merged image
# hfp.deepcopy_entry('largeobj', targetnames[0])
hfp.rename_entry('largeobj', targetnames[0])
for k, v in change_hash.iteritems():
for x in v:
if x != k:
hfp.logging('Setting {} to {}!', x, k)
hfp.filter_values(x, type='eq', setto=k, keys=targetnames[0])
def merge_adjacent_objects(
ipl, key, numberbysize=0, numberbyrandom=0, seed=None,
targetnames=('largeobjm', 'mergeids_small', 'mergeids_random', 'change_hash'),
algorithm='standard'
):
"""
:param ipl:
ipl.get_params():
merge_adjacent_objects
seed
numberbysize
numberbyrandom
largeobjmnames
- 'largeobj_merged'
- 'mergeids_small'
- 'mergeids_random'
- 'mergeids_all'
- 'change_hash'
:param key: the source key for calculation
"""
# This removes the redundancy from the hash
def reduce_hash(hash):
br = False
for k, v in hash.iteritems():
for x in v:
if x != k:
if x in hash.keys():
del hash[x]
reduce_hash(hash)
br = True
break
else:
br = False
if br:
break
if algorithm == 'pairs':
# Get only the relevant labels
labels = lib.unique(ipl[key])
ipl.logging('labels = {}', labels)
# Seed the randomize function
if seed:
random.seed(seed)
else:
random.seed()
ipl.astype(np.uint32, keys=key)
(grag, rag) = graphs.gridRegionAdjacencyGraph(ipl[key], ignoreLabel=0)
edge_ids = rag.edgeIds()
merge_ids = []
used_ids = ()
# Calculate the merges such that only pairs are found
for i in xrange(0, numberbyrandom):
c = 0
finished = False
while not finished:
edge_id = random.choice(edge_ids)
uid = rag.uId(rag.edgeFromId(edge_id))
vid = rag.vId(rag.edgeFromId(edge_id))
c += 1
if c > 50:
ipl.logging('merge_adjacent_objects: Warning: Not finding any more pairs!')
finished = True
elif uid not in used_ids and vid not in used_ids:
finished = True
used_ids += (uid, vid)
merge_ids.append((uid, vid))
ipl.logging('Label IDs randomly selected for merging: {}', merge_ids)
# Sort
merge_ids = [sorted(x) for x in merge_ids]
merge_ids = sorted(merge_ids)
ipl.logging('merge_ids = {}', merge_ids)
# Store this for later use
ipl[targetnames[2]] = merge_ids
ipl[targetnames[3]] = merge_ids
# # Create change hash list
# change_hash = IPL(data=dict(zip(np.unique(merge_ids), [[x, ] for x in np.unique(merge_ids)])))
# for i in xrange(0, 3):
# prev_change_hash = IPL(data=change_hash)
# for x in merge_ids:
# ipl.logging('Adding {} and {}', *x)
# change_hash[x[0]] += change_hash[x[1]]
# change_hash[x[0]] = list(np.unique(change_hash[x[0]]))
# change_hash[x[1]] += change_hash[x[0]]
# change_hash[x[1]] = list(np.unique(change_hash[x[1]]))
#
# reduce_hash(change_hash)
# # Change the list in the hash to np-arrays for better storage in h5 files
# for k, v in change_hash.iteritems():
# change_hash[k] = np.array(v)
# # And now we have a perfect change list which we just need to iterate over and change the labels in the image
# ipl.logging('change_hash after change:')
us = [x[0] for x in merge_ids]
change_hash = IPL(data=dict(zip(us, merge_ids)))
ipl.logging('change_hash: {}', change_hash)
ipl[targetnames[4]] = change_hash
elif algorithm == 'standard':
# Get only the relevant labels
labels = lib.unique(ipl[key])
ipl.logging('labels = {}', labels)
# Seed the randomize function
random.seed(seed)
ipl.astype(np.uint32, keys=key)
(grag, rag) = graphs.gridRegionAdjacencyGraph(ipl[key], ignoreLabel=0)
edge_ids = rag.edgeIds()
# ipl.logging('Edge ids: {}', edge_ids)
# Type 1:
# Select edges by size (smallest edges)
ipl.logging('Number of edgeLengths = {}', len(rag.edgeLengths()))
edgelen_ids = dict(zip(edge_ids, rag.edgeLengths()))
# ifp.logging('edgelen_ids = {}', edgelen_ids)
sorted_edgelens = np.sort(rag.edgeLengths())
#
smallest_merge_lens = sorted_edgelens[0:numberbysize]
ipl.logging('Lengths selected for merging: {}', smallest_merge_lens)
#
smallest_merge_ids = []
for x in smallest_merge_lens:
edge_id = edgelen_ids.keys()[edgelen_ids.values().index(x)]
smallest_merge_ids.append(edge_id)
edgelen_ids.pop(edge_id)
#
edge_ids = edgelen_ids.keys()
ipl.logging('Edge IDs selected for merging due to size: {}', smallest_merge_ids)
# Type 2:
# Randomly choose edges
random_merge_ids = random.sample(edge_ids, numberbyrandom)
ipl.logging('Edge IDs randomly selected for merging: {}', random_merge_ids)
# Now get the label ids
smallest_merge_labelids_u = [rag.uId(rag.edgeFromId(x)) for x in smallest_merge_ids]
smallest_merge_labelids_v = [rag.vId(rag.edgeFromId(x)) for x in smallest_merge_ids]
smallest_merge_labelids = list(zip(smallest_merge_labelids_u, smallest_merge_labelids_v))
random_merge_labelids_u = [rag.uId(rag.edgeFromId(x)) for x in random_merge_ids]
random_merge_labelids_v = [rag.vId(rag.edgeFromId(x)) for x in random_merge_ids]
random_merge_labelids = list(zip(random_merge_labelids_u, random_merge_labelids_v))
ipl.logging('Label IDs selected for merging by size: {}', smallest_merge_labelids)
ipl.logging('Label IDs randomly selected for merging: {}', random_merge_labelids)
# Concatenate
all_merge_labelids = smallest_merge_labelids + random_merge_labelids
# Sort
ipl.logging('all_merge_labelids = {}', all_merge_labelids)
all_merge_labelids = [sorted(x) for x in all_merge_labelids]
all_merge_labelids = sorted(all_merge_labelids)
ipl.logging('all_merge_labelids = {}', all_merge_labelids)
# Store this for later use
ipl[targetnames[1]] = smallest_merge_labelids
ipl[targetnames[2]] = random_merge_labelids
ipl[targetnames[3]] = all_merge_labelids
# Create change hash list
change_hash = IPL(data=dict(zip(np.unique(all_merge_labelids), [[x,] for x in np.unique(all_merge_labelids)])))
for i in xrange(0, 3):
prev_change_hash = IPL(data=change_hash)
for x in all_merge_labelids:
ipl.logging('Adding {} and {}', *x)
change_hash[x[0]] += change_hash[x[1]]
change_hash[x[0]] = list(np.unique(change_hash[x[0]]))
change_hash[x[1]] += change_hash[x[0]]
change_hash[x[1]] = list(np.unique(change_hash[x[1]]))
# This removes the redundancy from the hash
reduce_hash(change_hash)
# Change the list in the hash to np-arrays for better storage in h5 files
for k, v in change_hash.iteritems():
change_hash[k] = np.array(v)
# And now we have a perfect change list which we just need to iterate over and change the labels in the image
ipl.logging('change_hash after change:')
ipl.logging(change_hash)
ipl[targetnames[4]] = change_hash
# Create the merged image
# ipl.deepcopy_entry('largeobj', targetnames[0])
ipl.rename_entry(key, targetnames[0])
for k, v in change_hash.iteritems():
for x in v:
if x != k:
ipl.logging('Setting {} to {}!', x, k)
ipl.filter_values(x, type='eq', setto=k, keys=targetnames[0])
return ipl
def merge_adjacent_objects(ipl, key, thisparams):
"""
:param ipl:
ipl.get_params():
merge_adjacent_objects
seed
numberbysize
numberbyrandom
largeobjmnames
- 'largeobj_merged'
- 'mergeids_small'
- 'mergeids_random'
- 'mergeids_all'
- 'change_hash'
:param key: the source key for calculation
"""
numberbysize = thisparams['numberbysize']
numberbyrandom = thisparams['numberbyrandom']
targetnames = params['largeobjmnames']
# Get only the relevant labels
labels = lib.unique(ipl[key])
ipl.logging('labels = {}', labels)
# Seed the randomize function
random.seed(thisparams['seed'])
ipl.astype(np.uint32, keys=key)
(grag, rag) = graphs.gridRegionAdjacencyGraph(ipl[key], ignoreLabel=0)
edge_ids = rag.edgeIds()
# ipl.logging('Edge ids: {}', edge_ids)
# Type 1:
# Select edges by size (smallest edges)
ipl.logging('Number of edgeLengths = {}', len(rag.edgeLengths()))
edgelen_ids = dict(zip(edge_ids, rag.edgeLengths()))
# ifp.logging('edgelen_ids = {}', edgelen_ids)
sorted_edgelens = np.sort(rag.edgeLengths())
#
smallest_merge_lens = sorted_edgelens[0:numberbysize]
ipl.logging('Lengths selected for merging: {}', smallest_merge_lens)
#
smallest_merge_ids = []
for x in smallest_merge_lens:
edge_id = edgelen_ids.keys()[edgelen_ids.values().index(x)]
smallest_merge_ids.append(edge_id)
edgelen_ids.pop(edge_id)
#
edge_ids = edgelen_ids.keys()
ipl.logging('Edge IDs selected for merging due to size: {}',
smallest_merge_ids)
# Type 2:
# Randomly choose edges
random_merge_ids = random.sample(edge_ids, numberbyrandom)
ipl.logging('Edge IDs randomly selected for merging: {}', random_merge_ids)
# Now get the label ids
smallest_merge_labelids_u = [
rag.uId(rag.edgeFromId(x)) for x in smallest_merge_ids
]
smallest_merge_labelids_v = [
rag.vId(rag.edgeFromId(x)) for x in smallest_merge_ids
]
smallest_merge_labelids = list(
zip(smallest_merge_labelids_u, smallest_merge_labelids_v))
random_merge_labelids_u = [
rag.uId(rag.edgeFromId(x)) for x in random_merge_ids
]
random_merge_labelids_v = [
rag.vId(rag.edgeFromId(x)) for x in random_merge_ids
]
random_merge_labelids = list(
zip(random_merge_labelids_u, random_merge_labelids_v))
ipl.logging('Label IDs selected for merging by size: {}',
smallest_merge_labelids)
ipl.logging('Label IDs randomly selected for merging: {}',
random_merge_labelids)
# Concatenate
all_merge_labelids = smallest_merge_labelids + random_merge_labelids
# Sort
ipl.logging('all_merge_labelids = {}', all_merge_labelids)
all_merge_labelids = [sorted(x) for x in all_merge_labelids]
all_merge_labelids = sorted(all_merge_labelids)
ipl.logging('all_merge_labelids = {}', all_merge_labelids)
# Store this for later use
ipl[targetnames[1]] = smallest_merge_labelids
ipl[targetnames[2]] = random_merge_labelids
ipl[targetnames[3]] = all_merge_labelids
# Create change hash list
change_hash = IPL(data=dict(
zip(np.unique(all_merge_labelids), [[
x,
] for x in np.unique(all_merge_labelids)])))
for i in xrange(0, 3):
prev_change_hash = IPL(data=change_hash)
for x in all_merge_labelids:
ipl.logging('Adding {} and {}', *x)
change_hash[x[0]] += change_hash[x[1]]
change_hash[x[0]] = list(np.unique(change_hash[x[0]]))
change_hash[x[1]] += change_hash[x[0]]
change_hash[x[1]] = list(np.unique(change_hash[x[1]]))
# This removes the redundancy from the hash
def reduce(hash):
br = False
for k, v in hash.iteritems():
for x in v:
if x != k:
if x in hash.keys():
del hash[x]
reduce(hash)
br = True
break
else:
br = False
if br:
break
reduce(change_hash)
# Change the list in the hash to np-arrays for better storage in h5 files
for k, v in change_hash.iteritems():
change_hash[k] = np.array(v)
# And now we have a perfect change list which we just need to iterate over and change the labels in the image
ipl.logging('change_hash after change:')
ipl.logging(change_hash)
ipl[targetnames[4]] = change_hash
# Create the merged image
# ipl.deepcopy_entry('largeobj', targetnames[0])
ipl.rename_entry(key, targetnames[0])
for k, v in change_hash.iteritems():
for x in v:
if x != k:
ipl.logging('Setting {} to {}!', x, k)
ipl.filter_values(x, type='eq', setto=k, keys=targetnames[0])
return ipl
