def skeletons(path, max_jobs, target):
""" Skeletonize segmentation.
"""
input_key = 'volumes/segmentation/multicut'
output_key = 'skeletons'
config_dir = './configs'
tmp_folder = './tmp_skeletons'
os.makedirs(config_dir, exist_ok=True)
config = SkeletonWorkflow.get_config()
global_config = config['global']
shebang = '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster-new/bin/python'
global_config.update({'shebang': shebang})
with open(os.path.join(config_dir, 'global.config'), 'w') as f:
json.dump(global_config, f)
resolution = [40, 4, 4]
size_threshold = 2500
task = SkeletonWorkflow(tmp_folder=tmp_folder, config_dir=config_dir,
max_jobs=max_jobs, target=target,
input_path=path, input_key=input_key,
output_path=path, output_key=output_key,
resolution=resolution, size_threshold=size_threshold)
success = luigi.build([task], local_scheduler=True)
assert success
def skeletons(sample, max_jobs=8, target='local'):
input_path = '/g/kreshuk/data/cremi/realigned/sample%s_small.n5' % sample
input_prefix = 'segmentation/multicut'
output_prefix = 'skeletons/multicut'
config_dir = './config_skeletons'
tmp_folder = './tmp_skeletons_%s' % sample
try:
os.mkdir(config_dir)
except OSError:
pass
config = SkeletonWorkflow.get_config()
global_config = config['global']
shebang = '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster_env/bin/python'
global_config.update({'shebang': shebang})
with open(os.path.join(config_dir, 'global.config'), 'w') as f:
json.dump(global_config, f)
ds_config = config['skeletonize']
ds_config.update({'threads_per_job': 8})
with open(os.path.join(config_dir, 'skeletonize.config'), 'w') as f:
json.dump(ds_config, f)
task = SkeletonWorkflow(tmp_folder=tmp_folder,
max_jobs=1,
config_dir=config_dir,
target='local',
input_path=input_path,
output_path=input_path,
input_prefix=input_prefix,
output_prefix=output_prefix,
work_scale=2)
success = luigi.build([task], local_scheduler=True)
#
if success and target == 'local':
with z5py.File(input_path) as f:
#
ds = f['skeletons/multicut/s2']
ds.n_threads = 8
skels = ds[:]
#
ds = f['raw/s2']
ds.n_threads = 8
raw = ds[:]
#
ds = f['segmentation/multicut/s2']
ds.n_threads = 8
seg = ds[:]
view([raw, seg, skels], ['raw', 'seg', 'skels'])
def test_skeletons_swc(self):
config = SkeletonWorkflow.get_config()['skeletonize']
config.update({'chunk_len': 50})
with open(os.path.join(self.config_folder, 'skeletonize.config'), 'w') as f:
json.dump(config, f)
self._run_skel_wf(format_='swc', max_jobs=8)
# check output for correctness
seg, ids = self.ids_and_seg()
out_folder = os.path.join(self.output_path, self.output_prefix, 's0')
for seg_id in ids:
# read the result from file
out_file = os.path.join(out_folder, '%i.swc' % seg_id)
skel_ids, coords, parents = su.read_swc(out_file)
coords = np.array(coords, dtype='float')
# compute the expected result
mask = seg == seg_id
skel_vol = skeletonize_3d(mask)
try:
pix_graph, coords_exp, _ = csr.skeleton_to_csgraph(skel_vol)
except ValueError:
continue
# check coordinates
coords_exp = coords_exp[1:]
self.assertEqual(coords.shape, coords_exp.shape)
self.assertTrue(np.allclose(coords, coords_exp))
def setUp(self):
self._mkdir(self.tmp_folder)
self._mkdir(self.config_folder)
global_config = SkeletonWorkflow.get_config()['global']
global_config['shebang'] = self.shebang
global_config['block_shape'] = [10, 256, 256]
with open(os.path.join(self.config_folder, 'global.config'), 'w') as f:
json.dump(global_config, f)
def _run_skel_wf(self, format_, max_jobs):
task = SkeletonWorkflow(tmp_folder=self.tmp_folder,
config_dir=self.config_folder,
target=self.target, max_jobs=max_jobs,
input_path=self.path, input_prefix=self.input_prefix,
output_path=self.output_path, output_prefix=self.output_prefix,
work_scale=0, skeleton_format=format_)
ret = luigi.build([task], local_scheduler=True)
self.assertTrue(ret)
f = z5py.File(self.output_path)
self.assertTrue(self.output_prefix in f)
out_key = os.path.join(self.output_prefix, 's0')
self.assertTrue(out_key in f)
def skeletonize(scale, target, max_jobs):
path = '/g/kreshuk/data/FIB25/data.n5'
input_key = 'volumes/paintera/multicut/data/s%i' % scale
output_key = 'skeletons/s%i' % scale
config_dir = './configs'
tmp_folder = './tmp_skeletons_%i' % scale
os.makedirs(config_dir, exist_ok=True)
config = SkeletonWorkflow.get_config()
global_config = config['global']
shebang = '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster-new/bin/python'
global_config.update({'shebang': shebang})
with open(os.path.join(config_dir, 'global.config'), 'w') as f:
json.dump(global_config, f)
config = config['skeletonize']
config.update({'time_limit': 600, 'mem_limit': 16})
with open(os.path.join(config_dir, 'skeletonize.config'), 'w') as f:
json.dump(config, f)
resolution = [8, 8, 8]
size_threshold = 2000
max_id = z5py.File(path)['volumes/paintera/multicut/data'].attrs['maxId']
task = SkeletonWorkflow(tmp_folder=tmp_folder,
config_dir=config_dir,
max_jobs=max_jobs,
target=target,
input_path=path,
input_key=input_key,
output_path=path,
output_key=output_key,
resolution=resolution,
size_threshold=size_threshold,
max_id=max_id)
success = luigi.build([task], local_scheduler=True)
assert success
def test_skeletons_volume(self):
config = SkeletonWorkflow.get_config()['skeletonize']
config.update({'threads_per_job': 8})
with open(os.path.join(self.config_folder, 'skeletonize.config'), 'w') as f:
json.dump(config, f)
self._run_skel_wf(format_='volume', max_jobs=1)
# check output for correctness
seg, ids = self.ids_and_seg()
out_key = os.path.join(self.output_prefix, 's0')
result = z5py.File(self.output_path)[out_key][:]
for seg_id in ids:
res = result == seg_id
mask = seg == seg_id
exp = skeletonize_3d(mask) > 0
self.assertTrue(np.allclose(res, exp))
def test_skeletons_n5(self):
config = SkeletonWorkflow.get_config()['skeletonize']
config.update({'chunk_len': 50})
with open(os.path.join(self.config_folder, 'skeletonize.config'), 'w') as f:
json.dump(config, f)
self._run_skel_wf(format_='n5', max_jobs=8)
# check output for correctness
seg, ids = self.ids_and_seg()
out_key = os.path.join(self.output_prefix, 's0')
ds = z5py.File(self.output_path)[out_key]
for seg_id in ids:
# read the result from file
coords, edges = su.read_n5(ds, seg_id)
# compute the expected result
mask = seg == seg_id
skel_vol = skeletonize_3d(mask)
try:
pix_graph, coords_exp, _ = csr.skeleton_to_csgraph(skel_vol)
except ValueError:
continue
# check coordinates
coords_exp = coords_exp[1:].astype('uint64')
self.assertEqual(coords.shape, coords_exp.shape)
self.assertTrue(np.allclose(coords, coords_exp))
# check edges
graph = csr.numba_csgraph(pix_graph)
n_points = len(coords)
edges_exp = [[u, v] for u in range(1, n_points + 1)
for v in graph.neighbors(u) if u < v]
edges_exp = np.array(edges_exp)
edges_exp -= 1
self.assertEqual(edges.shape, edges_exp.shape)
self.assertTrue(np.allclose(edges, edges_exp))