def get_skeleton(seg_fn, out_folder, bfs, res, edgTh, modified_bfs, opt):
if opt == '0': # mesh -> skeleton
seg = ReadH5(seg_fn, 'main')
CreateSkeleton(seg, out_folder, res, res)
elif opt == '1': # skeleton -> dense graph
import networkx as nx
print('read skel')
skel = ReadSkeletons(out_folder,
skeleton_algorithm='thinning',
downsample_resolution=res,
read_edges=True)[1]
print('save node positions')
node_pos = np.stack(skel.get_nodes()).astype(int)
WriteH5(out_folder + 'node_pos.h5', node_pos)
print('generate dt for edge width')
seg = ReadH5(seg_fn, 'main')
sz = seg.shape
bb = GetBbox(seg > 0)
seg_b = seg[bb[0]:bb[1] + 1, bb[2]:bb[3] + 1, bb[4]:bb[5] + 1]
dt = distance_transform_cdt(seg_b, return_distances=True)
print('generate graph')
new_graph, wt_dict, th_dict, ph_dict = GetGraphFromSkeleton(skel, dt=dt, dt_bb=[bb[x] for x in [0,2,4]],\
modified_bfs=modified_bfs)
print('save as a networkx object')
edge_list = GetEdgeList(new_graph, wt_dict, th_dict, ph_dict)
G = nx.Graph(shape=sz)
# add edge attributes
G.add_edges_from(edge_list)
nx.write_gpickle(G, out_folder + 'graph-%s.obj' % (bfs))
elif opt == '2': # reduced graph
import networkx as nx
G = nx.read_gpickle(out_folder + 'graph-%s.obj' % (bfs))
n0 = len(G.nodes())
G = ShrinkGraph_v2(G, threshold=edgTh)
n1 = len(G.nodes())
print('#nodes: %d -> %d' % (n0, n1))
nx.write_gpickle(
G,
out_folder + 'graph-%s-%d-%d.obj' % (bfs, edgTh[0], 10 * edgTh[1]))
elif opt == '3': # generate h5 for visualization
import networkx as nx
G = nx.read_gpickle(out_folder + 'graph-%s-%d-%d.obj' %
(bfs, edgTh[0], 10 * edgTh[1]))
pos = ReadH5(out_folder + 'node_pos.h5', 'main')
vis = Graph2H5(G, pos)
WriteH5(
out_folder + 'graph-%s-%d-%d.h5' % (bfs, edgTh[0], 10 * edgTh[1]),
vis)
def compute_ibex_skeleton_graphs(skeleton, out_folder, input_resolution, downsample_fac):
input_resolution = np.array(input_resolution)
downsample_fac = np.array(downsample_fac)
downsample_res = input_resolution * downsample_fac
# skeleton_ids = np.unique(skeleton)
# skeleton_ids = skeleton_ids[skeleton_ids > 0]
#
# locations = scipy.ndimage.find_objects(skeleton)
#
# ibex_skeletons = []
# for idx, skeleton_id in enumerate(skeleton_ids):
# loc = locations[int(skeleton_id) - 1]
# start_pos = np.array([loc[0].start, loc[1].start, loc[2].start], dtype=np.uint16)
# skel_mask = (skeleton[loc] == int(skeleton_id))
# CreateSkeleton(skeleton, out_folder, input_resolution, downsample_res)
# ibex_skeletons.append(ReadSkeletons(out_folder, skeleton_algorithm='thinning', downsample_resolution=downsample_res, read_edges=True)[0])
CreateSkeleton(skeleton, out_folder, input_resolution, downsample_res)
ibex_skeletons = ReadSkeletons(out_folder, skeleton_algorithm='thinning', downsample_resolution=downsample_res, read_edges=True)
keep_indices = [0] + [i for i, skel in enumerate(ibex_skeletons) if skel.get_nodes().shape[0] > 0]
ibex_skeletons = [ibex_skeletons[i] for i in keep_indices]
return ibex_skeletons
def compute_thinned_nodes(process_id, seg_ids, skel_vol_full, temp_folder, input_resolution, downsample_fac, output_file_name):
process_id = str(process_id)
out_folder = temp_folder + '/' + process_id
if not os.path.isdir(out_folder):
os.makedirs(out_folder)
input_resolution = np.array(input_resolution).astype(np.uint8)
downsample_fac = np.array(downsample_fac).astype(np.uint8)
graphs = {}
with h5py.File(temp_folder + '/(' + process_id + ')' + output_file_name , 'w') as hf_nodes:
locations = scipy.ndimage.find_objects(skel_vol_full)
for idx, seg_id in enumerate(seg_ids):
loc = locations[int(seg_id) - 1]
start_pos = np.array([loc[0].start, loc[1].start, loc[2].start], dtype=np.uint16)
skel_mask = (skel_vol_full[loc] == int(seg_id))
try:
CreateSkeleton(skel_mask, out_folder, input_resolution, input_resolution*downsample_fac)
skel_obj = ReadSkeletons(out_folder, skeleton_algorithm='thinning', downsample_resolution=input_resolution*downsample_fac, read_edges=True)[1]
nodes = start_pos + np.stack(skel_obj.get_nodes()).astype(np.uint16)
except:
continue
hf_nodes.create_dataset('allNodes' + str(seg_id), data=nodes, compression='gzip')
import networkx as nx
from scipy.ndimage.morphology import distance_transform_cdt
opt = sys.argv[1]
res = [120, 128, 128] # z,y,x
out_folder = '../tmp/demo/'
bfs = 'bfs'
modified_bfs = False
edgTh = [40, 1] # threshold
# 3d segment volume
seg_fn = '/mnt/coxfs01/donglai/data/JWR/snow_cell/cell128nm/neuron/cell26_d.h5'
if opt == '0': # mesh -> skeleton
seg = ReadH5(seg_fn, 'main')
CreateSkeleton(seg, out_folder, res, res)
elif opt == '1': # skeleton -> dense graph
print('read skel')
skel = ReadSkeletons(out_folder,
skeleton_algorithm='thinning',
downsample_resolution=res,
read_edges=True)[1]
print('save node positions')
node_pos = np.stack(skel.get_nodes()).astype(int)
WriteH5(out_folder + 'node_pos.h5', node_pos)
print('generate dt for edge width')
seg = ReadH5(seg_fn, 'main')
sz = seg.shape
args = get_args() # get args
seg_fn = args.seg
output_folder = args.out
res = [int(i) for i in args.res.split(':')]
dendrite_ids = np.array([int(i) for i in args.ids.split(':')])
print('--Load segmentation volume..')
seg = ReadH5(seg_fn, 'main')
if args.cs == 1: # only needed if no skeleton created yet
print("\n--Create skeletons for given ids:")
for i, did in enumerate(tqdm(dendrite_ids)):
blockPrint()
dendrite_folder = '{}/skels/{}/'.format(output_folder, did)
CreateSkeleton(seg == did, dendrite_folder, res, res)
enablePrint()
print("\n--Analyse skeletons for given ids:")
lookuptable = np.zeros((dendrite_ids.shape[0], 8))
for i, did in enumerate(tqdm(dendrite_ids)):
blockPrint()
dendrite_folder = '{}/skels/{}/'.format(output_folder, did)
# load skeleton of given seg id, return it and its graph
G, skel = skeleton2graph(did, dendrite_folder, seg, res)
# %% get longest axis
if args.task == 0 or args.task == 3: # distance based methods
# main_G, _, _, endnodes = search_longest_path_exhaustive(G)
weight = 'weight' # longest path based on edge parameter weigth
elif args.task == 1 or args.task == 4:
def compute_skel_graph(process_id, seg_ids, skel_vol_full, temp_folder, input_resolution, downsample_fac, output_file_name, save_graph, use_spline=True):
process_id = str(process_id)
out_folder = temp_folder + '/' + process_id
if not os.path.isdir(out_folder):
os.makedirs(out_folder)
input_resolution = np.array(input_resolution).astype(np.uint8)
downsample_fac = np.array(downsample_fac).astype(np.uint8)
graphs = {}
edge_lists = {}
with h5py.File(temp_folder + '/(' + process_id + ')' + output_file_name , 'w') as hf_nodes:
locations = scipy.ndimage.find_objects(skel_vol_full)
for idx, seg_id in enumerate(seg_ids):
loc = locations[int(seg_id) - 1]
start_pos = np.array([loc[0].start, loc[1].start, loc[2].start], dtype=np.uint16)
skel_mask = (skel_vol_full[loc] == int(seg_id))
try:
CreateSkeleton(skel_mask, out_folder, input_resolution, input_resolution*downsample_fac)
skel_obj = ReadSkeletons(out_folder, skeleton_algorithm='thinning', downsample_resolution=input_resolution*downsample_fac, read_edges=True)[1]
nodes = np.stack(skel_obj.get_nodes()).astype(np.uint16)
if nodes.shape[0] < 10:
# print('skipped skel: {} (too small!)'.format(seg_id))
continue
graph, wt_dict, th_dict, ph_dict = GetGraphFromSkeleton(skel_obj, modified_bfs=False)
edge_list = GetEdgeList(graph, wt_dict, th_dict, ph_dict)
except:
# print('Catched exp in skel: ', seg_id)
#traceback.print_exc(file=sys.stdout)
continue
if save_graph is True:
if use_spline is True:
_, graph = upsample_skeleton_using_splines(edge_list, nodes, skel_mask.shape, return_graph=True, start_pos=start_pos)
graphs[seg_id] = graph
else:
g = nx.Graph()
nodes_shifted = nodes + start_pos
for long_sec in edge_list:
path = long_sec[2]['path']
g.add_edges_from([(tuple(nodes_shifted[path[i]]), tuple(nodes_shifted[path[i + 1]])) for i in range(len(path)-1)])
graphs[seg_id] = g
edge_lists[seg_id] = edge_list
g = nx.Graph()
g.add_edges_from(edge_list)
j_ids = [x for x in g.nodes() if g.degree(x) > 2]
e_ids = [x for x in g.nodes() if g.degree(x) == 1]
nodes = nodes + start_pos
if len(j_ids) > 0:
junctions = nodes[j_ids]
hf_nodes.create_dataset('j' + str(seg_id), data=junctions, compression='gzip')
if len(e_ids) > 0:
end_points = nodes[e_ids]
hf_nodes.create_dataset('e' + str(seg_id), data=end_points, compression='gzip')
hf_nodes.create_dataset('allNodes' + str(seg_id), data=nodes, compression='gzip')
if save_graph is True:
with open(temp_folder + '/(' + process_id + ')graph.h5', 'wb') as pfile:
pickle.dump(graphs, pfile, protocol=pickle.HIGHEST_PROTOCOL)
with open(temp_folder + '/(' + process_id + ')edge_list.pkl', 'wb') as pfile:
pickle.dump(edge_lists, pfile, protocol=pickle.HIGHEST_PROTOCOL)