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 view_test(res1, res2):
ds = meta.get_dataset('snemi3d_test')
#volumina_n_layer([ds.inp(0), ds.inp(1), pm_new, pm_new1], ['raw','pm_old', 'pm_new1', 'pm_new2'])
#else:
volumina_n_layer(
[ds.inp(0), ds.inp(1), ds.seg(0), res1, res2],
['raw', 'pmap', 'ws', 'curr_res', 'best_res'])
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')
def view_test_pmaps(new_pmaps):
ds = meta.get_dataset('snemi3d_test')
raw = ds.inp(0)
pm_old = ds.inp(1)
pm_2d = vigra.readHDF5(
'/home/constantin/Work/neurodata_hdd/snemi3d_data/probabilities/pmaps_icv2_test.h5',
'data')
data = [raw, pm_old, pm_2d]
data.extend(new_pmaps)
labels = [
'raw', '3d_v2', '2d', '3d_v3_i1', '3d_v3_i2', '3d_v3_i3', 'ensemble'
]
volumina_n_layer(data, labels)
def view_train():
ds = meta.get_dataset('snemi3d_train')
pmap = vigra.readHDF5('/home/constantin/Downloads/traininf-cst-inv.h5',
'data')
volumina_n_layer([ds.inp(0), ds.inp(1), pmap, ds.seg(0), ds.gt()])
i += 1
mean = np.mean(accuracies)
std = np.std(accuracies)
logging.info("accuracy mean:", mean)
logging.info("accuracy std:", std)
mean_xy = np.mean(accuracies_xy)
std_xy = np.std(accuracies_xy)
mean_z = np.mean(accuracies_z)
std_z = np.std(accuracies_z)
return mean, std, mean_xy, std_xy, mean_z, std_z
if __name__ == '__main__':
meta.load()
ds = meta.get_dataset("snemi3d_train")
local_feats_list_xy = ("prob", "curve")
local_feats_list_z = ("prob", )
#print "Results single RF"
#print cv_singlerf(ds, local_feats_list_z, 1)
print "Results two RF"
print cv_tworf(ds, local_feats_list_xy, local_feats_list_z, 1)
def regfeats():
meta.load()
ds = meta.get_dataset("snemi3d_train")