def neuroproof_lmc(ds_train_str, ds_test_str, seg_id_train, seg_id_test,
local_feats_list, lifted_feats_list, mc_params):
meta.load()
ds_train = meta.get_dataset(ds_train_str)
ds_test = meta.get_dataset(ds_test_str)
ds_train.make_filters(0, mc_params.anisotropy_factor)
ds_test.make_filters(0, mc_params.anisotropy_factor)
ds_train.make_filters(1, mc_params.anisotropy_factor)
ds_test.make_filters(1, mc_params.anisotropy_factor)
mc_node, mc_edges, mc_energy, t_inf = lifted_multicut_workflow(
ds_train,
ds_test,
seg_id_train,
seg_id_test,
local_feats_list,
lifted_feats_list,
mc_params,
gamma=2.,
warmstart=False,
weight_z_lifted=False)
mc_seg = ds_test.project_mc_result(seg_id_test, mc_node)
return mc_seg
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 run_mc(ds_train_name, ds_test_name, mc_params, save_path):
assert os.path.exists(os.path.split(save_path)[0]), "Please choose an existing folder to save your results"
# if you have added multiple segmentations, you can choose on which one to run
# experiments with the seg_id
seg_id = 0
# these strings encode the features that are used for the local features
feature_list = ['raw', 'prob', 'reg']
meta.load()
ds_train = meta.get_dataset(ds_train_name)
ds_test = meta.get_dataset(ds_test_name)
# use this for running the mc without defected slices
mc_nodes, _, _, _ = multicut_workflow(
ds_train, ds_test,
seg_id, seg_id,
feature_list, mc_params)
# use this for running the mc with defected slices
#mc_nodes, _, _, _ = multicut_workflow_with_defect_correction(
# ds_train, ds_test,
# seg_id, seg_id,
# feature_list, mc_params)
segmentation = ds_test.project_mc_result(seg_id, mc_nodes)
vigra.writeHDF5(segmentation, save_path, 'data', compression = 'gzip')
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 snemi3d_lmc(ds_train_str, ds_test_str, seg_id_train, seg_id_test,
local_feats_list, lifted_feats_list, mc_params):
meta.load()
ds_train = meta.get_dataset(ds_train_str)
ds_test = meta.get_dataset(ds_test_str)
ds_train.make_filters(0, mc_params.anisotropy_factor)
ds_test.make_filters(0, mc_params.anisotropy_factor)
ds_train.make_filters(1, mc_params.anisotropy_factor)
ds_test.make_filters(1, mc_params.anisotropy_factor)
mc_node, mc_edges, mc_energy, t_inf = lifted_multicut_workflow(
ds_train, ds_test, seg_id_train, seg_id_test, local_feats_list,
lifted_feats_list, mc_params)
mc_seg = ds_test.project_mc_result(seg_id_test, mc_node)
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_lmc(ds_train_name, ds_test_name, mc_params, save_path):
assert os.path.exists(os.path.split(save_path)[0]), "Please choose an existing folder to save your results"
# if you have added multiple segmentations, you can choose on which one to run
# experiments with the seg_id
seg_id = 0
# these strings encode the features that are used for the local features
feature_list = ['raw', 'prob', 'reg']
# these strings encode the features that will be used for the lifted edges
feature_list_lifted = ['cluster', 'reg']
# this factor determines the weighting of lifted vs. local edge costs
gamma = 2.
meta.load()
ds_train = meta.get_dataset(ds_train_name)
ds_test = meta.get_dataset(ds_test_name)
# need to make filters for the trainset beforehand
ds_train.make_filters(0, mc_params.anisotropy_factor)
ds_train.make_filters(1, mc_params.anisotropy_factor)
# use this for running the mc without defected slices
mc_nodes, _, _, _ = lifted_multicut_workflow(
ds_train, ds_test,
seg_id, seg_id,
feature_list, feature_list_lifted,
mc_params, gamma = gamma)
# use this for running the mc with defected slices
#mc_nodes, _, _, _ = lifted_multicut_workflow_with_defect_correction(
# ds_train, ds_test,
# seg_id, seg_id,
# feature_list, feature_list_lifted,
# mc_params, gamma = gamma)
segmentation = ds_test.project_mc_result(seg_id, mc_nodes)
vigra.writeHDF5(segmentation, save_path, 'data', compression = 'gzip')
# parameters for the Multicut
mc_params = ExperimentSettings()
mc_params.set_rfcache(os.path.join(meta.meta_folder, "rf_cache"))
mc_params.set_nthreads(30)
mc_params.set_anisotropy(1.)
mc_params.set_use2d(False)
mc_params.set_ignore_mask(False)
mc_params.set_fuzzy_learning(True)
mc_params.set_negative_threshold(0.4)
mc_params.set_positive_threshold(0.6)
mc_params.set_ntrees(750)
#mc_params.set_weighting_scheme("all")
mc_params.set_solver("opengm_exact")
mc_params.set_verbose(True)
local_feats_list = ("raw", "prob", "reg", "topo")
seg_id = 0
meta.load()
mc_seg = neuroproof_mc("neuroproof_train", "neuroproof_test",
seg_id, seg_id,
local_feats_list, mc_params)
vigra.writeHDF5(mc_seg, "/home/constantin/Work/home_hdd/results/nature_results/rebuttal/neuroproof/res_mc.h5", "data", compression = 'gzip')
def regfeats():
meta.load()
ds = meta.get_dataset("snemi3d_train")