def make_relabel_scripts(path, n_jobs, block_shape, tmp_dir):
from cluster_tools.relabel import make_batch_jobs
make_batch_jobs(path,
'segmentations/watershed_glia2',
os.path.join(tmp_dir, 'tmp_files', 'tmp_relabel'),
block_shape,
n_jobs,
EXECUTABLE,
use_bsub=True,
eta=[20, 10, 20])
def make_relabel_scripts(path, n_jobs, block_shape, tmp_dir):
sys.path.append('../../..')
from cluster_tools.relabel import make_batch_jobs
make_batch_jobs(path,
'segmentations/watershed2',
os.path.join(tmp_dir, 'tmp_files', 'tmp_relabel'),
block_shape,
n_jobs,
EXECUTABLE,
use_bsub=True,
eta=[5, 5, 5])
def make_ws_scripts(path, n_jobs, block_shape, tmp_dir):
from cluster_tools.dt_components import make_batch_jobs
make_batch_jobs(path,
aff_key='predictions/affs_glia',
out_key='segmentations/dt_components',
mask_key='masks/minfilter_mask',
cache_folder=os.path.join(tmp_dir, 'tmp_ws'),
n_jobs=n_jobs,
block_shape=block_shape,
ws_key='segmentations/watershed2',
executable=EXECUTABLE,
use_bsub=True)
def make_minfilter_scripts(path, n_jobs, chunks, filter_shape, block_shape):
from cluster_tools.minfilter import make_batch_jobs
make_batch_jobs(path,
'masks/initial_mask',
path,
'masks/minfilter_mask',
chunks,
filter_shape,
block_shape,
os.path.join(tmp_dir, 'tmp_files', 'tmp_minfilter'),
n_jobs=n_jobs,
executable=EXECUTABLE,
use_bsub=True,
eta=5)
def make_graph_scripts(path, n_scales, n_jobs, n_threads, block_shape,
tmp_dir):
from cluster_tools.graph import make_batch_jobs
make_batch_jobs(path,
'segmentations/watershed_glia2',
os.path.join(tmp_dir, 'tmp_files', 'graph.n5'),
os.path.join(tmp_dir, 'tmp_files', 'tmp_graph'),
block_shape,
n_scales,
n_jobs,
EXECUTABLE,
use_bsub=False,
n_threads_merge=16,
eta=[30, 10, 60, 10])
def make_graph_scripts(path, n_scales, n_jobs, n_threads, block_shape,
tmp_dir):
sys.path.append('../../..')
from cluster_tools.graph import make_batch_jobs
make_batch_jobs(path,
'segmentations/watershed2',
os.path.join(tmp_dir, 'tmp_files', 'graph.n5'),
os.path.join(tmp_dir, 'tmp_files', 'tmp_graph'),
block_shape,
n_scales,
n_jobs,
EXECUTABLE,
use_bsub=True,
n_threads_merge=n_threads,
eta=[10, 10, 10, 10])
def make_cost_scripts(path, n_jobs, n_threads, tmp_dir):
from cluster_tools.costs import make_batch_jobs
rf_path = ''
make_batch_jobs(os.path.join(tmp_dir, 'tmp_files', 'features.n5'),
'features',
os.path.join(tmp_dir, 'tmp_files', 'graph.n5'),
'graph',
rf_path,
os.path.join(tmp_dir, 'tmp_files', 'costs.n5'),
'costs',
n_jobs,
os.path.join(tmp_dir, 'tmp_files', 'tmp_costs'),
n_threads,
executable=EXECUTABLE,
use_bsub=True,
eta=5)
def make_projection_scripts(path, n_jobs, block_shape, tmp_dir):
from cluster_tools.label_projection import make_batch_jobs
chunks = [bs // 2 for bs in block_shape]
# chunks = block_shape
make_batch_jobs(path,
'segmentations/watershed_glia2',
path,
'segmentations/multicut_glia',
path,
'node_labelings/multicut_glia',
os.path.join(tmp_dir, 'tmp_files', 'tmp_projection'),
block_shape,
chunks,
n_jobs,
executable=EXECUTABLE,
use_bsub=True,
eta=5)
def make_ws_scripts(path, aff_path, n_jobs, block_shape, tmp_dir):
from cluster_tools.masked_watershed import make_batch_jobs
chunks = [bs // 2 for bs in block_shape]
# chunks = block_shape
make_batch_jobs(aff_path,
'predictions/affs_glia',
path,
'masks/minfilter_mask',
path,
'segmentations/watershed_glia2',
os.path.join(tmp_dir, 'tmp_files', 'tmp_ws'),
block_shape,
chunks,
n_jobs,
EXECUTABLE,
use_bsub=True,
n_threads_ufd=12,
eta=[180, 30, 30, 60])
def make_multicut_scripts(path, n_scales, n_jobs, n_threads, block_shape,
tmp_dir):
from cluster_tools.multicut import make_batch_jobs
make_batch_jobs(os.path.join(tmp_dir, 'tmp_files', 'graph.n5'),
'graph',
os.path.join(tmp_dir, 'tmp_files', 'tmp_mc',
'merged_graph.n5', 's0'),
'costs',
path,
'node_labelings/multicut_glia',
block_shape,
n_scales,
os.path.join(tmp_dir, 'tmp_files', 'tmp_m'),
n_jobs,
n_threads=n_threads,
executable=EXECUTABLE,
use_bsub=True,
eta=[90, 90, 180])
def make_projection_scripts(path, n_jobs, block_shape, tmp_dir, res_key):
sys.path.append('../../..')
from cluster_tools.label_projection import make_batch_jobs
chunks = [bs // 2 for bs in block_shape]
# chunks = block_shape
make_batch_jobs(path,
'segmentations/watershed2',
path,
'segmentations/%s' % res_key,
path,
'node_labelings/%s' % res_key,
os.path.join(tmp_dir, 'tmp_files', 'tmp_projection'),
block_shape,
chunks,
n_jobs,
executable=EXECUTABLE,
use_bsub=True,
eta=5)
def make_multicut_scripts(path, n_scales, n_jobs, n_threads, block_shape,
tmp_dir, res_key):
sys.path.append('../../..')
from cluster_tools.multicut import make_batch_jobs
make_batch_jobs(os.path.join(tmp_dir, 'tmp_files', 'graph.n5'),
'graph',
os.path.join(tmp_dir, 'tmp_files', 'costs.n5'),
'costs',
path,
'node_labelings/%s' % res_key,
block_shape,
n_scales,
os.path.join(tmp_dir, 'tmp_files', 'tmp_mc'),
n_jobs,
n_threads=n_threads,
executable=EXECUTABLE,
use_bsub=True,
eta=[5, 5, 15])
def make_feature_scripts(path, aff_path, n_jobs1, n_jobs2, n_threads,
block_shape, tmp_dir):
from cluster_tools.features import make_batch_jobs
make_batch_jobs(os.path.join(tmp_dir, 'tmp_files', 'graph.n5'),
'graph',
os.path.join(tmp_dir, 'tmp_files', 'features.n5'),
'features',
aff_path,
'predictions/affs_glia',
path,
'segmentations/watershed_glia2',
os.path.join(tmp_dir, 'tmp_files', 'tmp_features'),
block_shape,
n_jobs1,
n_jobs2,
n_threads2=n_threads,
executable=EXECUTABLE,
use_bsub=True,
eta=[60, 15])