def isbi12_multicut(ds_train_str, ds_test_str,
seg_id_train, seg_id_test,
local_feats_list, mc_params):
meta.load()
ds_train = meta.get_dataset(ds_train_str)
ds_test = meta.get_dataset(ds_test_str)
mc_node, mc_edges, mc_energy, t_inf = multicut_workflow(
ds_train, ds_test,
seg_id_train, seg_id_test,
local_feats_list, mc_params)
if ds_test_str == "isbi2012_train":
return eval_lazy(mc_edges, ds_test._rag(seg_id_test) )
else:
assert ds_test_str == "isbi2012_test"
res_folder = "/home/consti/Work/nature_experiments/results/isbi12"
mc_seg = ds_test.project_mc_result( seg_id_test, mc_node )
# save segmentation result
seg_name = "_".join( ["mcresult", str(seg_id_test), "seg"] ) + ".h5"
seg_path = os.path.join(res_folder, seg_name)
vigra.writeHDF5(mc_seg, seg_path, "data")
# save binary edges
edge_name = "_".join( ["mcresult", str(seg_id_test), "edges"] ) + ".tif"
edge_path = os.path.join(res_folder, edge_name)
edge_vol = edges_to_binary(ds_test._rag(seg_id_test), mc_edges)
vigra.impex.writeVolume(edge_vol, edge_path, '', dtype = np.uint8 )
return 0, 0
def snemi3d_mc(ds_train_str, ds_test_str, seg_id_train, seg_id_test,
local_feats_list, mc_params):
meta.load()
ds_train = meta.get_dataset(ds_train_str)
ds_test = meta.get_dataset(ds_test_str)
mc_node, mc_edges, mc_energy, t_inf = multicut_workflow(
ds_train, ds_test, seg_id_train, seg_id_test, local_feats_list,
mc_params)
mc_seg = ds_test.project_mc_result(seg_id_test, mc_node)
print mc_energy, t_inf
return mc_seg
def neuroproof_mc(ds_train_str, ds_test_str,
seg_id_train, seg_id_test,
local_feats_list, mc_params):
meta.load()
ds_train = meta.get_dataset(ds_train_str)
ds_test = meta.get_dataset(ds_test_str)
mc_node, mc_edges, mc_energy, t_inf = multicut_workflow(
ds_train, ds_test,
seg_id_train, seg_id_test,
local_feats_list, mc_params)
print np.unique(mc_node).shape
mc_seg = ds_test.project_mc_result(seg_id_test, mc_node)
return mc_seg
def run_and_eval_mc(sample):
# Experiment settings
exp_params = ExperimentSettings()
exp_params.set_ntrees(300)
exp_params.set_anisotropy(25.)
exp_params.set_weighting_scheme("z")
exp_params.set_solver("multicut_fusionmoves")
local_feats_list = ("prob", "reg")
sample.make_filters(1, 25.)
mc_node, mc_edges, mc_energy, t_inf = multicut_workflow(
sample.get_cutout(0), sample.get_cutout(1),
0,0,
local_feats_list, exp_params)
mc_seg = sample.get_cutout(1).project_mc_result(0, mc_node)
print "MCSEG"