def main():
args = process_command_line()
out_folder = args.output_folder
assert os.path.exists(
out_folder), "Please choose an existing folder for the output"
cache_folder = os.path.join(out_folder, "cache")
# init the cache when running experiments the first time
# if the meta set wasn't saved yet, we need to recreate the cache
if not os.path.exists(os.path.join(cache_folder, "meta_dict.pkl")):
init(args.data_folder, cache_folder, args.snemi_mode)
meta = MetaSet(cache_folder)
meta.load()
ds_train = meta.get_dataset("ds_train")
ds_test = meta.get_dataset("ds_test")
# experiment settings
exp_params = ExperimentSettings()
exp_params.set_rfcache(os.path.join(cache_folder, "rf_cache"))
# use extra 2d features
exp_params.set_use2d(True)
# parameters for learning
exp_params.set_fuzzy_learning(True)
exp_params.set_ntrees(500)
# parameters for lifted multicut
exp_params.set_lifted_neighborhood(3)
# features used
local_feats_list = ("raw", "prob", "reg", "topo")
# we don't use the multicut feature here, because it can take too long
lifted_feats_list = ("cluster", "reg")
if args.snemi_mode:
exp_params.set_anisotropy(5.)
exp_params.set_weighting_scheme("all")
exp_params.set_solver("multicut_exact")
gamma = 10000.
else:
exp_params.set_anisotropy(25.)
exp_params.set_weighting_scheme("z")
exp_params.set_solver("multicut_fusionmoves")
gamma = 2.
seg_id = 0
if args.use_lifted:
print "Starting Lifted Multicut Workflow"
# have to make filters first due to cutouts...
ds_train.make_filters(0, exp_params.anisotropy_factor)
ds_train.make_filters(1, exp_params.anisotropy_factor)
ds_test.make_filters(0, exp_params.anisotropy_factor)
ds_test.make_filters(1, exp_params.anisotropy_factor)
mc_node, mc_edges, mc_energy, t_inf = lifted_multicut_workflow(
ds_train,
ds_test,
seg_id,
seg_id,
local_feats_list,
lifted_feats_list,
exp_params,
gamma=gamma,
weight_z_lifted=True)
save_path = os.path.join(out_folder,
"lifted_multicut_segmentation.tif")
else:
print "Starting Multicut Workflow"
mc_node, mc_edges, mc_energy, t_inf = multicut_workflow(
ds_train, ds_test, seg_id, seg_id, local_feats_list, exp_params)
save_path = os.path.join(out_folder, "multicut_segmentation.tif")
mc_seg = ds_test.project_mc_result(seg_id, mc_node)
print "Saving Result to", save_path
vigra.impex.writeVolume(mc_seg, save_path, '')
anisotropy = 25. # the anisotropy of the data, this is used in the filter calculation
# set to 1. for isotropic data, to the actual degree for mildly anisotropic data or to > 20. to compute filters in 2d
mc_params.set_anisotropy(25.)
# set to true for segmentations with flat superpixels
mc_params.set_use2d(True)
# number of threads used for multithreaded computations
mc_params.set_nthreads(8)
# number of trees used in the random forest
mc_params.set_ntrees(200)
# solver used for the multicut
mc_params.set_solver("multicut_fusionmoves")
# pipeline verbosity
mc_params.set_verbose(True)
# weighting scheme for edge-costs in the mc problem
# set to 'none' for no weighting
# 'z' or 'xyz' or 'all' for flat superpixels (z usually works best)
# 'all' for isotropic data with 3d superpixel
mc_params.set_weighting_scheme("z")
# range of lifted edges
mc_params.set_lifted_neighborhood(3)
# path to save the segmentation result, order has to already exist
save_path = '/path/to/mc_result.h5'
run_lmc('my_train', 'my_test', mc_params, save_path)