def make_new_fragment_segment_assignment():
path1 = '/g/kreshuk/data/arendt/platyneris_v1/data.n5'
k1 = 'volumes/paintera/proofread_cells_multiset/data/s0'
path2 = './data.n5'
k2 = 'volumes/segmentation2/s0'
tmp_folder = './tmp_subdivision_labels'
config_folder = os.path.join(tmp_folder, 'configs')
set_default_qos('high')
write_default_global_config(config_folder)
target = 'slurm'
max_jobs = 200
assignments = node_labels(path1, k1, path2, k2, 'new-frag-seg', tmp_folder,
target, max_jobs)
assert assignments.ndim == 1
assignments = np.concatenate([
np.arange(len(assignments), dtype='uint64')[:, None], assignments[:,
None]
],
axis=1)
with z5py.File(path2) as f:
f.create_dataset('node_labels/fragment-segment-assignment2',
data=assignments,
compression='gzip',
chunks=assignments.shape)
def cell_segmentation_workflow(path,
aff_path,
mask_path,
mask_key,
region_path,
region_key,
use_curated_affs,
use_lmc,
tmp_folder,
target,
max_jobs,
roi_begin=None,
roi_end=None):
# number of jobs and threads for target
assert target in ('slurm', 'local')
if target == 'local':
max_jobs_mc = 1
max_threads = 16
else:
max_jobs_mc = 15
max_threads = 8
config_dir = os.path.join(tmp_folder, 'configs')
write_default_global_config(config_dir, roi_begin, roi_end)
run_watershed(path, aff_path, use_curated_affs, mask_path, mask_key,
tmp_folder, target, max_jobs)
if use_lmc:
run_lmc(path, aff_path, use_curated_affs, region_path, region_key,
tmp_folder, target, max_threads, max_jobs, max_jobs_mc)
else:
run_mc(path, aff_path, use_curated_affs, tmp_folder, target,
max_threads, max_jobs, max_jobs_mc)
# postprocessing:
# 1.) compute sizes for size threshold
run_morphology(path, use_curated_affs, use_lmc, tmp_folder, target,
max_jobs)
identifier = 'result'
# we unmerge only if we also use lmc, because this takes nuclei into account
if use_lmc:
# 2.) unmerge cells with more than one assigned nucleus
unmerge_nuclei(path, use_curated_affs, tmp_folder, target, max_jobs,
max_threads)
identifier = 'filtered_unmerge'
# 3.) filter sizes with graph watershed
filter_size(path, use_curated_affs, use_lmc, identifier, target,
tmp_folder, max_jobs, max_threads)
def downscale(path, in_key, out_key, tmp_folder, max_jobs, target, n_scales=5):
task = DownscalingWorkflow
config_folder = os.path.join(tmp_folder, 'configs')
write_default_global_config(config_folder)
configs = task.get_config()
config = configs['downscaling']
config.update({
'mem_limit': 8,
'time_limit': 120,
'library_kwargs': {
'order': 0
}
})
with open(os.path.join(config_folder, 'downscaling.config'), 'w') as f:
json.dump(config, f)
scale_factors = [[2, 2, 2]] * n_scales
halos = [[0, 0, 0]] * n_scales
t = task(tmp_folder=tmp_folder,
config_dir=config_folder,
target=target,
max_jobs=max_jobs,
input_path=path,
input_key=in_key,
output_key_prefix=out_key,
scale_factors=scale_factors,
halos=halos,
metadata_format='paintera')
ret = luigi.build([t], local_scheduler=True)
if not ret:
raise RuntimeError("Downscaling the segmentation failed")
with z5py.File(path, 'r') as f:
ds = f[in_key]
max_id = ds.attrs['maxId']
for scale in range(n_scales + 1):
scale_key = '%s/s%i' % (out_key, scale)
add_max_id(path, scale_key, max_id=max_id)
def compute_baseline_tables(version, target, max_jobs):
path = BASELINE_ROOT
folder = os.path.join(ROOT, version, 'images', 'local')
for name in BASELINE_NAMES:
key = 'volumes/cells/%s/filtered_size' % name
out_path = '%s.csv' % name
if os.path.exists(out_path):
continue
tmp_folder = './tmp_regions_%s' % name
config_folder = os.path.join(tmp_folder, 'configs')
write_default_global_config(config_folder)
label_ids = get_label_ids(path, key)
region_attributes(path,
out_path,
folder,
label_ids,
tmp_folder,
target=target,
max_jobs=max_jobs,
key_seg=key)
def make_proofreading_project(project_folder, tmp_folder, assignments,
block_labels, block_roi, target, max_jobs):
if len(block_labels) == 0:
return
# don't do anything if we have a paintera project already
if os.path.exists(os.path.join(project_folder, 'attributes.json')):
return
os.makedirs(project_folder, exist_ok=True)
config_dir = os.path.join(tmp_folder, 'configs')
roi_begin, roi_end = block_roi
write_default_global_config(config_dir, roi_begin, roi_end)
with open(os.path.join(config_dir, 'global.config'), 'r') as f:
block_shape = json.load(f)['block_shape']
data_path = os.path.join(project_folder, 'data.n5')
f = z5py.File(data_path)
f.require_group('volumes')
# make a link to the raw data
raw_out_key = 'volumes/raw'
if raw_out_key not in f:
print("Make raw symlink")
raw_in = os.path.join(RAW_PATH, RAW_KEY)
raw_out = os.path.join(data_path, raw_out_key)
os.symlink(raw_in, raw_out)
# get the relevant fragment segment assignments for this block
print("Get assignment mask")
assignment_mask = np.isin(assignments[:, 1], block_labels)
assert assignment_mask.sum() > 0
block_assignments = assignments[assignment_mask]
assert block_assignments.shape[0] == assignment_mask.sum()
assert block_assignments.shape[1] == 2
print("Sub assignments have the shape:", block_assignments.shape)
# copy the relevant part of the fragment segment assignment
print("Copy the assignments")
g_out = f.require_group('volumes/paintera')
save_assignments = block_assignments.T
ds_ass = g_out.require_dataset('fragment-segment-assignment',
shape=save_assignments.shape,
chunks=save_assignments.shape,
compression='gzip',
dtype='uint64')
ds_ass[:] = save_assignments
# copy the relevant parts of the watersheds
print("Copy the watersheds")
ws_ids = block_assignments[:, 0]
copy_watersheds(PAINTERA_PATH, os.path.join(PAINTERA_KEY,
'data/s0'), data_path,
'volumes/watershed', ws_ids, tmp_folder, target, max_jobs)
# make the paintera data
res = [0.025, 0.01, 0.01]
restrict_sets = [-1, -1, 5, 4, 4, 3, 3, 1]
print("Make new paintera data")
set_default_roi(roi_begin, roi_end)
set_default_block_shape(block_shape)
convert_to_paintera_format(data_path,
raw_out_key,
'volumes/watershed',
'volumes/paintera',
label_scale=1,
resolution=res,
tmp_folder=tmp_folder,
target=target,
max_jobs=max_jobs,
max_threads=16,
convert_to_label_multisets=True,
restrict_sets=restrict_sets)
def export_selected_projects(projects, rois_to_blocks, target, max_jobs):
""" Export only selected projects and fill in the rest with the
old global paintera project. This means we need to keep ids consistent
between projects.
"""
project_folders = [
os.path.join(PROJECT_ROOT, 'project%02i' % project_id)
for project_id in projects
]
assert all(os.path.exists(pfolder) for pfolder in project_folders)
tmp_folder = './tmp_export'
tmp_path = os.path.join(tmp_folder, 'data.n5')
#
# load the original paintera data
#
# copy the watershed segmentation
ws_in_key = os.path.join(PAINTERA_KEY, 'data', 's0')
ws_out_key = 'volumes/watershed'
copy_watersheds(PAINTERA_PATH, ws_in_key, tmp_path, ws_out_key, None,
tmp_folder, target, max_jobs)
with z5py.File(tmp_path, 'r') as f:
max_id = f[ws_out_key].attrs['maxId']
# load the fragment segment assignments
ass_key = os.path.join(PAINTERA_KEY, 'fragment-segment-assignment')
with z5py.File(PAINTERA_PATH, 'r') as f:
assignments = f[ass_key][:].T
#
# load corrections from the projects and insert them
#
for project_folder in project_folders:
proj_id = int(project_folder[-2:])
tmp_project = os.path.join(tmp_folder, 'tmp_proj%i' % proj_id)
project_path = os.path.join(project_folder, 'data.n5')
project_in_root = 'volumes/paintera'
project_in_key = os.path.join(project_in_root, 'data', 's0')
# set the bounding box for this project
config_dir = os.path.join(tmp_project, 'configs')
rb, re = rois_to_blocks[proj_id]
set_default_block_shape([50, 512, 512])
write_default_global_config(config_dir, rb, re)
# copy this watersheds, offsetting everything with the current max id
copy_watersheds(project_path,
project_in_key,
tmp_path,
ws_out_key,
None,
tmp_project,
target,
max_jobs,
offset=max_id,
insert_mode=True)
# update the fragment segment assignment
project_ass_key = os.path.join(project_in_root,
'fragment-segment-assignment')
with z5py.File(project_path, 'r') as f:
this_assignments = f[project_ass_key][:].T
# offset the assignments
this_assignments += max_id
assignments = np.concatenate([assignments, this_assignments], axis=0)
# update the max id
max_id = int(assignments.max())
# write the new segmentation
seg_out_key = 'volumes/segmentation2/s0'
serialize_segmentation(tmp_path, ws_out_key, TMP_PATH, seg_out_key,
assignments, tmp_folder, target, max_jobs)
def make_root_seg(tmp_folder, target, max_jobs):
in_path = SEG_PATH
in_key = SEG_KEY + '/s0'
ws_path = PAINTERA_PATH
ws_key = PAINTERA_KEY + "/data/s0"
out_path = TMP_PATH
out_key = 'volumes/segmentation'
assignment_out_key = 'node_labels/fragment_segment_assignment'
config_dir = os.path.join(tmp_folder, 'configs')
write_default_global_config(config_dir)
tmp_path = os.path.join(tmp_folder, 'data.n5')
# get the current fragment segment assignment
assignments = node_labels(ws_path,
ws_key,
in_path,
in_key,
'rootseg',
tmp_folder,
target=target,
max_jobs=max_jobs,
max_overlap=True,
ignore_label=None)
# find the unique ids of the watersheds
unique_key = 'uniques'
find_uniques(ws_path, ws_key, tmp_path, unique_key, tmp_folder, config_dir,
max_jobs, target)
with z5py.File(tmp_path, 'r') as f:
ds = f[unique_key]
ws_ids = ds[:]
# convert to paintera fragment segment assignments
id_offset = int(ws_ids.max()) + 1
# print("Max ws id:", id_offset)
# print("Ws len :", ws_ids.shape)
# print("Ass len :", assignments.shape)
# print(ws_ids[-10:])
assignments = assignments[ws_ids]
assignments = vigra.analysis.relabelConsecutive(assignments,
start_label=id_offset,
keep_zeros=True)[0]
assert len(assignments) == len(ws_ids), "%i, %i" % (len(assignments),
len(ws_ids))
paintera_assignments = np.concatenate(
[ws_ids[:, None], assignments[:, None]], axis=1).T
assignment_tmp_key = 'tmp_assignments'
with z5py.File(tmp_path) as f:
ds = f.require_dataset(assignment_tmp_key,
shape=paintera_assignments.shape,
compression='gzip',
chunks=paintera_assignments.shape,
dtype='uint64')
ds[:] = paintera_assignments
# make and serialize new assignments
print("Serializing assignments ...")
serialize_assignments(tmp_folder,
tmp_path,
assignment_tmp_key,
tmp_path,
unique_key,
out_path,
assignment_out_key,
locked_segments=None,
relabel_output=False,
map_to_background=None)
# write the new segmentation
print("Serializing new segmentation ...")
serialize_merged_segmentation(ws_path, ws_key, out_path, out_key, out_path,
assignment_out_key, tmp_folder, max_jobs,
target)
def compute_connected_components(ws_path,
ws_key,
seg_path,
seg_key,
out_path,
node_label_key,
cc_key,
tmp_folder,
target,
max_jobs,
graph_key='graph',
ignore_label=True):
config_folder = os.path.join(tmp_folder, 'configs')
write_default_global_config(config_folder)
#
# compute the graph
#
task = GraphWorkflow
configs = task.get_config()
conf = configs['initial_sub_graphs']
conf.update({'ignore_label': ignore_label})
with open(os.path.join(config_folder, 'inital_sub_graphs.config'),
'w') as f:
json.dump(conf, f)
n_threads = 8
task_names = ['merge_sub_graphs', 'map_edge_ids']
for tt in task_names:
conf = configs['map_edge_ids']
conf.update({'threads_per_job': n_threads, 'mem_limit': 128})
with open(os.path.join(config_folder, '%s.config' % tt), 'w') as f:
json.dump(conf, f)
t = task(tmp_folder=tmp_folder,
max_jobs=max_jobs,
config_dir=config_folder,
target=target,
input_path=ws_path,
input_key=ws_key,
graph_path=out_path,
output_key=graph_key)
ret = luigi.build([t], local_scheduler=True)
assert ret, "Graph computation failed"
#
# compute the node labels
#
task = NodeLabelWorkflow
# configs = task.get_config()
t = task(tmp_folder=tmp_folder,
max_jobs=max_jobs,
target=target,
config_dir=config_folder,
ws_path=ws_path,
ws_key=ws_key,
input_path=seg_path,
input_key=seg_key,
output_path=out_path,
output_key=node_label_key,
ignore_label=0 if ignore_label else None)
ret = luigi.build([t], local_scheduler=True)
assert ret, "Node label computation failed"
with z5py.File(out_path, 'r') as f:
node_labels = f[node_label_key][:]
#
# load the graph and check for connected components
#
ccs = _cc_nifty(out_path, graph_key, node_labels, ignore_label)
return node_labels, ccs
