def test_serialization_without_labels(self):
"""
Users can opt to serialize the Rag without serializing the labels,
but then they can't use superpixel features on the deserialized Rag.
"""
import h5py
superpixels = generate_random_voronoi((100,200), 200)
original_rag = Rag( superpixels )
tmp_dir = tempfile.mkdtemp()
filepath = os.path.join(tmp_dir, 'test_rag.h5')
rag_groupname = 'saved_rag'
# Serialize with labels
with h5py.File(filepath, 'w') as f:
rag_group = f.create_group(rag_groupname)
original_rag.serialize_hdf5(rag_group, store_labels=False) # Don't store
# Deserialize
with h5py.File(filepath, 'r') as f:
rag_group = f[rag_groupname]
deserialized_rag = Rag.deserialize_hdf5(rag_group)
assert deserialized_rag.label_img.dtype == original_rag.label_img.dtype
assert deserialized_rag.label_img.shape == original_rag.label_img.shape
assert deserialized_rag.label_img.axistags == original_rag.label_img.axistags
#assert (deserialized_rag.label_img == original_rag.label_img).all() # not stored
assert (deserialized_rag.sp_ids == original_rag.sp_ids).all()
assert deserialized_rag.max_sp == original_rag.max_sp
assert deserialized_rag.num_sp == original_rag.num_sp
assert deserialized_rag.num_edges == original_rag.num_edges
assert (deserialized_rag.edge_ids == original_rag.edge_ids).all()
# Check some features
# For simplicity, just make values identical to superpixels
values = superpixels.astype(np.float32)
feature_names = ['standard_edge_mean', 'standard_edge_count']
features_df_original = original_rag.compute_features(values, feature_names)
features_df_deserialized = deserialized_rag.compute_features(values, feature_names)
assert (features_df_original.values == features_df_deserialized.values).all()
try:
deserialized_rag.compute_features(values, ['standard_sp_count'])
except NotImplementedError:
pass
except:
raise
else:
assert False, "Shouldn't be able to use superpixels if labels weren't serialized/deserialized!"
def test_serialization_with_external_labels(self):
"""
Users can opt to serialize the Rag without serializing the labels,
but then they can't use superpixel features on the deserialized Rag.
When deserializing, they can provide the labels from an external source,
as tested here.
"""
import h5py
superpixels = generate_random_voronoi((100,200), 200)
original_rag = Rag( superpixels )
tmp_dir = tempfile.mkdtemp()
filepath = os.path.join(tmp_dir, 'test_rag.h5')
rag_groupname = 'saved_rag'
# Serialize with labels
with h5py.File(filepath, 'w') as f:
rag_group = f.create_group(rag_groupname)
original_rag.serialize_hdf5(rag_group, store_labels=False)
# Deserialize
with h5py.File(filepath, 'r') as f:
rag_group = f[rag_groupname]
deserialized_rag = Rag.deserialize_hdf5(rag_group, label_img=superpixels) # Provide labels explicitly
assert deserialized_rag.label_img.dtype == original_rag.label_img.dtype
assert deserialized_rag.label_img.shape == original_rag.label_img.shape
assert deserialized_rag.label_img.axistags == original_rag.label_img.axistags
assert (deserialized_rag.label_img == original_rag.label_img).all()
assert (deserialized_rag.sp_ids == original_rag.sp_ids).all()
assert deserialized_rag.max_sp == original_rag.max_sp
assert deserialized_rag.num_sp == original_rag.num_sp
assert deserialized_rag.num_edges == original_rag.num_edges
assert (deserialized_rag.edge_ids == original_rag.edge_ids).all()
# Check some features
# For simplicity, just make values identical to superpixels
values = superpixels.astype(np.float32)
feature_names = ['standard_edge_mean', 'standard_sp_count']
features_df_original = original_rag.compute_features(values, feature_names)
features_df_deserialized = deserialized_rag.compute_features(values, feature_names)
assert (features_df_original.values == features_df_deserialized.values).all()
def test_serialization_with_labels(self):
"""
Serialize the rag and labels to hdf5,
then deserialize it and make sure nothing was lost.
"""
import h5py
superpixels = generate_random_voronoi((100,200), 200)
original_rag = Rag( superpixels )
tmp_dir = tempfile.mkdtemp()
filepath = os.path.join(tmp_dir, 'test_rag.h5')
rag_groupname = 'saved_rag'
# Serialize with labels
with h5py.File(filepath, 'w') as f:
rag_group = f.create_group(rag_groupname)
original_rag.serialize_hdf5(rag_group, store_labels=True)
# Deserialize
with h5py.File(filepath, 'r') as f:
rag_group = f[rag_groupname]
deserialized_rag = Rag.deserialize_hdf5(rag_group)
assert deserialized_rag.label_img.dtype == original_rag.label_img.dtype
assert deserialized_rag.label_img.shape == original_rag.label_img.shape
assert deserialized_rag.label_img.axistags == original_rag.label_img.axistags
assert (deserialized_rag.label_img == original_rag.label_img).all()
assert (deserialized_rag.sp_ids == original_rag.sp_ids).all()
assert deserialized_rag.max_sp == original_rag.max_sp
assert deserialized_rag.num_sp == original_rag.num_sp
assert deserialized_rag.num_edges == original_rag.num_edges
assert (deserialized_rag.edge_ids == original_rag.edge_ids).all()
# Check some features
# For simplicity, just make values identical to superpixels
values = superpixels.astype(np.float32)
feature_names = ['standard_edge_mean', 'standard_sp_count']
features_df_original = original_rag.compute_features(values, feature_names)
features_df_deserialized = deserialized_rag.compute_features(values, feature_names)
assert (features_df_original.values == features_df_deserialized.values).all()