def graph_example(shebang):
input_path = '/home/cpape/Work/data/isbi2012/cluster_example/isbi_train.n5'
input_key = 'volumes/watersheds'
output_path = '/home/cpape/Work/data/isbi2012/cluster_example/graph.n5'
tmp_folder = './tmp'
config_folder = './configs'
max_jobs = 8
global_conf = WatershedLocal.default_global_config()
global_conf.update({'shebang': shebang, 'block_shape': [10, 256, 256]})
with open('./configs/global.config', 'w') as f:
json.dump(global_conf, f)
ret = luigi.build([
GraphWorkflow(input_path=input_path,
input_key=input_key,
graph_path=output_path,
n_scales=1,
config_dir=config_folder,
tmp_folder=tmp_folder,
target='local',
max_jobs=max_jobs)
],
local_scheduler=True)
def ws_example(shebang):
example_path = '/home/cpape/Work/data/isbi2012/cluster_example/isbi_train.n5'
input_key = 'volumes/affinities'
output_key = 'volumes/ws'
max_jobs = 8
global_conf = WatershedLocal.default_global_config()
global_conf.update({'shebang': shebang})
try:
os.mkdir('configs')
except OSError:
pass
with open('./configs/global.config', 'w') as f:
json.dump(global_conf, f)
ret = luigi.build([
WatershedWorkflow(input_path=example_path,
input_key=input_key,
output_path=example_path,
output_key=output_key,
config_dir='./configs',
tmp_folder='./tmp',
target='local',
max_jobs=max_jobs)
],
local_scheduler=True)
if ret:
from cremi_tools.viewer.volumina import view
with z5py.File(example_path) as f:
affs = f[input_key][:3].transpose((1, 2, 3, 0))
ws = f[output_key][:]
view([affs, ws])
def setUp(self):
self._mkdir(self.tmp_folder)
self._mkdir(self.config_folder)
global_config = WatershedLocal.default_global_config()
global_config['shebang'] = '#! /g/kreshuk/pape/Work/software/conda/miniconda3/envs/cluster_env37/bin/python'
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 test_ws_pixel_pitch(self):
config = WatershedLocal.default_task_config()
config['apply_presmooth_2d'] = False
config['apply_dt_2d'] = False
config['apply_ws_2d'] = False
config['pixel_pitch'] = (10, 1 , 1)
with open(os.path.join(self.config_folder, 'watershed.config'), 'w') as f:
json.dump(config, f)
ret = self._run_ws(False)
self.assertTrue(ret)
self._check_result()
def test_ws_2d(self):
config = WatershedLocal.default_task_config()
config['apply_presmooth_2d'] = True
config['apply_dt_2d'] = True
config['apply_ws_2d'] = True
config['threshold'] = 0.25
config['sigma_weights'] = 0.
with open(os.path.join(self.config_folder, 'watershed.config'), 'w') as f:
json.dump(config, f)
ret = self._run_ws()
self.assertTrue(ret)
self._check_result()
def _test_ws_3d(self, two_pass):
config = WatershedLocal.default_task_config()
config['apply_presmooth_2d'] = False
config['apply_dt_2d'] = False
config['apply_ws_2d'] = False
config['sigma_seeds'] = (.5, 2., 2.)
config['sigma_weights'] = (.5, 2., 2.)
config['halo'] = [2, 32, 32]
with open(os.path.join(self.config_folder, 'watershed.config'), 'w') as f:
json.dump(config, f)
ret = self._run_ws(two_pass)
self.assertTrue(ret)
self._check_result()