def generate_label_views(kzip_path,
ssd_version,
gt_type,
n_voting=40,
nb_views=2,
ws=(256, 128),
comp_window=8e3,
out_path=None,
verbose=False):
"""
Parameters
----------
kzip_path : str
gt_type : str
ssd_version : str
n_voting : int
Number of collected nodes during BFS for majority vote (label smoothing)
nb_views : int
ws: Tuple[int]
comp_window : float
initial_run : bool
if True, will copy SSV from default SSD to SSD with version=gt_type
out_path : str
If given, export mesh colored accoring to GT labels
verbose : bool
Print additional information
Returns
-------
Tuple[np.array]
raw, label and index views
"""
assert gt_type in ["axgt",
"spgt"], "Currently only spine and axon GT is supported"
n_labels = 5 if gt_type == "axgt" else 4
palette = generate_palette(n_labels)
sso_id = int(re.findall("/(\d+).", kzip_path)[0])
sso = SuperSegmentationObject(sso_id, version=ssd_version)
if initial_run: # use default SSD version
orig_sso = SuperSegmentationObject(sso_id)
orig_sso.copy2dir(dest_dir=sso.ssv_dir, safe=False)
if not sso.attr_dict_exists:
msg = 'Attribute dict of original SSV was not copied successfully ' \
'to target SSD.'
raise ValueError(msg)
sso.load_attr_dict()
indices, vertices, normals = sso.mesh
# # Load mesh
vertices = vertices.reshape((-1, 3))
# load skeleton
skel = load_skeleton(kzip_path)
if len(skel) == 1:
skel = list(skel.values())[0]
else:
skel = skel["skeleton"]
skel_nodes = list(skel.getNodes())
node_coords = np.array(
[n.getCoordinate() * sso.scaling for n in skel_nodes])
node_labels = np.array(
[str2intconverter(n.getComment(), gt_type) for n in skel_nodes],
dtype=np.int)
node_coords = node_coords[(node_labels != -1)]
node_labels = node_labels[(node_labels != -1)]
# create KD tree from skeleton node coordinates
tree = KDTree(node_coords)
# transfer labels from skeleton to mesh
dist, ind = tree.query(vertices, k=1)
vertex_labels = node_labels[ind] # retrieving labels of vertices
if n_voting > 0:
vertex_labels = bfs_smoothing(vertices,
vertex_labels,
n_voting=n_voting)
color_array = palette[vertex_labels].astype(np.float32) / 255.
if out_path is not None:
if gt_type == 'spgt': #
colors = [[0.6, 0.6, 0.6, 1], [0.9, 0.2, 0.2, 1],
[0.1, 0.1, 0.1, 1], [0.05, 0.6, 0.6, 1],
[0.9, 0.9, 0.9, 1]]
else: # dendrite, axon, soma, bouton, terminal, background
colors = [[0.6, 0.6, 0.6, 1], [0.9, 0.2, 0.2, 1],
[0.1, 0.1, 0.1, 1], [0.05, 0.6, 0.6, 1],
[0.6, 0.05, 0.05, 1], [0.9, 0.9, 0.9, 1]]
colors = (np.array(colors) * 255).astype(np.uint8)
color_array_mesh = colors[
vertex_labels][:,
0] # TODO: check why only first element, maybe colors introduces an additional axis
write_mesh2kzip("{}/sso_{}_gtlabels.k.zip".format(out_path, sso.id),
sso.mesh[0],
sso.mesh[1],
sso.mesh[2],
color_array_mesh,
ply_fname="gtlabels.ply")
# Initializing mesh object with ground truth coloring
mo = MeshObject("neuron", indices, vertices, color=color_array)
# use downsampled locations for view locations, only if they are close to a
# labeled skeleton node
locs = generate_rendering_locs(vertices, comp_window /
6) # 6 rendering locations per comp.
# window
dist, ind = tree.query(locs)
locs = locs[dist[:, 0] < 2000] #[::3][:5] # TODO add as parameter
# # # To get view locations
# dest_folder = os.path.expanduser("~") + \
# "/spiness_skels/{}/view_imgs_{}/".format(sso_id, n_voting)
# if not os.path.isdir(dest_folder):
# os.makedirs(dest_folder)
# loc_text = ''
# for i, c in enumerate(locs):
# loc_text += str(i) + "\t" + str((c / np.array([10, 10, 20])).astype(np.int)) +'\n' #rescalling to the voxel grid
# with open("{}/viewcoords.txt".format(dest_folder), "w") as f:
# f.write(loc_text)
# # # DEBUG PART END
label_views, rot_mat = _render_mesh_coords(locs,
mo,
depth_map=False,
return_rot_matrices=True,
ws=ws,
smooth_shade=False,
nb_views=nb_views,
comp_window=comp_window,
verbose=verbose)
label_views = remap_rgb_labelviews(label_views[..., :3], palette)[:, None]
# TODO: the 3 neglects the alpha channel, i.e. remapping labels bigger than 256**3 becomes
# invalid
index_views = render_sso_coords_index_views(sso,
locs,
rot_mat=rot_mat,
verbose=verbose,
nb_views=nb_views,
ws=ws,
comp_window=comp_window)
raw_views = render_sso_coords(sso,
locs,
nb_views=nb_views,
ws=ws,
comp_window=comp_window,
verbose=verbose,
rot_mat=rot_mat)
return raw_views, label_views, index_views
args.append(pkl.load(f))
except EOFError:
break
scaling = global_params.config['scaling']
# TODO: This coulb be cunked by loading `mesh_bb` and glia prob. prediction cache arrays
# (might have to be create via `dataset_analysis`)
for cc in args:
svixs = list(cc.nodes())
cc_ix = np.min(svixs)
sso = SuperSegmentationObject(cc_ix,
version="gliaremoval",
nb_cpus=1,
working_dir=global_params.config.working_dir,
create=True,
scaling=scaling,
sv_ids=svixs)
so_cc = nx.Graph()
for e in cc.edges():
so_cc.add_edge(sso.get_seg_obj("sv", e[0]),
sso.get_seg_obj("sv", e[1]))
sso._rag = so_cc
sd = sos_dict_fact(svixs)
sos = init_sos(sd)
sso._objects["sv"] = sos
sso.load_attr_dict()
sso.gliasplit(verbose=False, recompute=False)
with open(path_out_file, "wb") as f:
pkl.dump("0", f)