def regression_test_nproof(cache_folder, data_folder):
# if the cache does not exist, create it
if not os.path.exists( os.path.join(cache_folder, 'nproof_train') ):
meta = init(cache_folder, data_folder, 'nproof')
else:
meta = MetaSet(cache_folder)
meta.load()
# isbi params
params = ExperimentSettings()
params.rf_cache_folder = os.path.join(cache_folder, "rf_cache")
params.use_2d = False
params.anisotropy_factor = 1.
params.ignore_mask = False
params.n_trees = 500
params.solver = "multicut_fusionmoves"
params.lifted_neighborhood = 2
local_feats_list = ("raw", "prob", "reg", "topo")
lifted_feats_list = ("cluster", "reg")
ds_train = meta.get_dataset('nproof_train')
ds_test = meta.get_dataset('nproof_test')
mc_seg = run_mc( ds_train, ds_test, local_feats_list, params)
lmc_seg = run_lmc(ds_train, ds_test, local_feats_list, lifted_feats_list, params, 2.)
print "Regression Test MC..."
# Eval differences with same parameters and according regression thresholds
# vi-split: 0.31985479849 -> 0.35
vi_split_ref = 0.35
# vi-merge: 0.402968960935 -> 0.45
vi_merge_ref = 0.45
# adapted-ri: 0.122123986224 -> 0.15
adapted_ri_ref = 0.15
regression_test(
vigra.readHDF5(os.path.join(data_folder,'gt_test.h5'), 'data'),
mc_seg,
vi_split_ref,
vi_merge_ref,
adapted_ri_ref
)
print "... passed"
# Eval differences with same parameters and according regression thresholds
# vi-split: 0.332745302066 => 0.4
vi_split_ref = 0.4
# vi-merge: 0.332349723508 => 0.4
vi_merge_ref = 0.4
# adapted-ri: 0.0942531472586 => 0.12
adapted_ri_ref = 0.12
regression_test(
vigra.readHDF5(os.path.join(data_folder,'gt_test.h5'), 'data'),
lmc_seg
)
print "... passed"
def regression_test_isbi(cache_folder, data_folder):
# if the cache does not exist, create it
if not os.path.exists(os.path.join(cache_folder, 'isbi_train')):
meta = init(cache_folder, data_folder, 'isbi')
else:
meta = MetaSet(cache_folder)
meta.load()
# isbi params
params = ExperimentSettings()
params.rf_cache_folder = os.path.join(cache_folder, "rf_cache")
params.use_2d = True
params.anisotropy_factor = 25.
params.learn_2d = True
params.ignore_mask = False
params.n_trees = 500
params.weighting_scheme = "z"
params.solver = "multicut_fusionmoves"
local_feats_list = ("raw", "prob", "reg", "topo")
lifted_feats_list = ("mc", "cluster", "reg")
ds_train = meta.get_dataset('isbi_train')
ds_test = meta.get_dataset('isbi_test')
mc_seg = run_mc(ds_train, ds_test, local_feats_list, params)
lmc_seg = run_lmc(ds_train, ds_test, local_feats_list, lifted_feats_list,
params, 2.)
#vigra.writeHDF5(mc_seg, './cache_isbi/isbi_test/mc_seg.h5', 'data', compression = 'gzip')
#vigra.writeHDF5(lmc_seg, './cache_isbi/isbi_test/lmc_seg.h5', 'data', compression = 'gzip')
print "Regression Test MC..."
# Eval differences with same parameters and according regression thresholds
# vi-split: 0.0718660622942 -> 0.1
vi_split_ref = 0.1
# vi-merge: 0.0811051987574 -> 0.1
vi_merge_ref = 0.1
# adapted-ri: 0.0218391269081 -> 0.05
adapted_ri_ref = 0.05
regression_test(
vigra.readHDF5(os.path.join(data_folder, 'mc_seg.h5'), 'data'), mc_seg,
vi_split_ref, vi_merge_ref, adapted_ri_ref)
print "... passed"
print "Regression Test LMC..."
# Eval differences with same parameters and according regression thresholds
# vi-split: 0.161923549092 -> 0.2
vi_split_ref = 0.2
# vi-merge: 0.0792288680404 -> 0.1
vi_merge_ref = 0.1
# adapted-ri: 0.0334914933439 -> 0.05
adapted_ri_ref = 0.05
regression_test(
vigra.readHDF5(os.path.join(data_folder, 'lmc_seg.h5'), 'data'),
lmc_seg, vi_split_ref, vi_merge_ref, adapted_ri_ref)
print "... passed"
def regression_test_snemi(cache_folder, data_folder):
# if the cache does not exist, create it
if not os.path.exists(os.path.join(cache_folder, 'snmei_train')):
meta = init(cache_folder, data_folder, 'snemi')
else:
meta = MetaSet(cache_folder)
meta.load()
# isbi params
params = ExperimentSettings()
params.rf_cache_folder = os.path.join(cache_folder, "rf_cache")
params.use_2d = True
params.learn_fuzzy = True
params.anisotropy_factor = 5.
params.ignore_mask = False
params.n_trees = 500
params.weighting_scheme = "all"
params.solver = "multicut_exact"
params.lifted_neighborhood = 3
local_feats_list = ("raw", "prob", "reg", "topo")
lifted_feats_list = ("cluster", "reg")
ds_train = meta.get_dataset('snemi_train')
ds_test = meta.get_dataset('snemi_test')
mc_seg = run_mc(ds_train, ds_test, local_feats_list, params)
gamma = 10000.
lmc_seg = run_lmc(ds_train, ds_test, local_feats_list, lifted_feats_list,
params, gamma)
print "Regression Test MC..."
# Eval differences with same parameters and according regression thresholds
# vi-split: 0.0501385345177 -> 0.1
vi_split_ref = 0.1
# vi-merge: 0.049803253098 -> 0.1
vi_merge_ref = 0.1
# adaptred-ri: 0.0170138077554 -> 0.05
adapted_ri_ref = 0.05
regression_test(
vigra.readHDF5(os.path.join(data_folder, 'mc_seg.h5'), 'data'), mc_seg,
vi_split_ref, vi_merge_ref, adapted_ri_ref)
print "... passed"
print "Regression Test LMC..."
# FIXME why are these differences so big?
# vi-split: 0.291149212478 0.141228313621 0.0536859650649
regression_test(
vigra.readHDF5(os.path.join(data_folder, 'lmc_seg.h5'), 'data'),
lmc_seg)
print "... passed"