def main():
parser = argparse.ArgumentParser()
parser.add_argument("cvpath", help="Path to precomputed volume to create")
parser.add_argument("meshes", nargs="+", help="Mesh files to convert")
parser.add_argument("--initial-id",
default=1,
type=int,
help="Initial ID for meshes")
parser.add_argument("--resolution",
nargs=3,
default=[4, 4, 40],
help="Voxel resolution (full res)")
parser.add_argument("--volume-size",
nargs=3,
default=[248832, 134144, 7063],
help="Extent of segmentation (full res)")
args = parser.parse_args()
cvpath = args.cvpath
mesh_path = os.path.join(cvpath, "mesh")
seg_props_path = os.path.join(cvpath, "seg_props")
os.makedirs(mesh_path, exist_ok=True)
os.makedirs(seg_props_path, exist_ok=True)
# Convert the entire space into units of "one entire brain" in case neuroglancer tries to load the image layer
resolution = np.asarray(args.resolution) * np.asarray(args.volume_size)
size = np.asarray([1, 1, 1])
with open(os.path.join(cvpath, "info"), "w") as f:
info = {
"data_type":
"uint64",
"scales": [{
"key": "fake",
"encoding": "raw",
"voxel_offset": [0, 0, 0],
"resolution": resolution.tolist(),
"size": size.tolist(),
"chunk_sizes": [[256, 256, 16]]
}],
"mesh":
"mesh",
"segment_properties":
"seg_props",
"type":
"segmentation",
"num_channels":
1
}
f.write(json.dumps(info))
with open(os.path.join(mesh_path, "info"), "w") as f:
info = {"@type": "neuroglancer_legacy_mesh"}
f.write(json.dumps(info))
segment_props = {
"@type": "neuroglancer_segment_properties",
'inline': {
'ids': [],
'properties': [{
"id": "source",
"type": "label",
"values": []
}]
}
}
mesh_id = args.initial_id
for meshfile in args.meshes:
# This will work when https://github.com/seung-lab/cloud-volume/pull/413 is merged:
#cv_mesh = Mesh.from_obj(text=Path(meshfile).read_text(), segid=mesh_id)
# Until then, use trimesh:
tmesh = trimesh.load_mesh(meshfile)
cv_mesh = Mesh(vertices=tmesh.vertices,
faces=tmesh.faces,
segid=mesh_id)
ngl_mesh_file = "%d.frag" % mesh_id
with open(os.path.join(mesh_path, "%d:0" % (mesh_id)), "w") as f:
info = {"fragments": [ngl_mesh_file]}
f.write(json.dumps(info))
with open(os.path.join(mesh_path, ngl_mesh_file), "wb") as f:
f.write(cv_mesh.to_precomputed())
segment_props['inline']['ids'].append(f'{mesh_id}')
segment_props['inline']['properties'][0]['values'].append(
os.path.basename(meshfile))
mesh_id += 1
with open(os.path.join(seg_props_path, "info"), "w") as f:
f.write(json.dumps(segment_props))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("meshcsv", help="Mesh files to convert")
parser.add_argument("meshdir", help="Mesh files to convert")
parser.add_argument("cvpath", help="Path to precomputed volume to create")
parser.add_argument("--resolution",
nargs=3,
default=[4, 4, 40],
help="Voxel resolution (full res)")
parser.add_argument("--volume-size",
nargs=3,
default=[248832, 134144, 7063],
help="Extent of segmentation (full res)")
args = parser.parse_args()
df = pandas.read_csv(args.meshcsv, header=1)
cvpath = args.cvpath
mesh_path = os.path.join(cvpath, "mesh")
seg_props_path = os.path.join(cvpath, "seg_props")
os.makedirs(mesh_path, exist_ok=True)
os.makedirs(seg_props_path, exist_ok=True)
segment_props = {
"@type": "neuroglancer_segment_properties",
'inline': {
'ids': [],
'properties': [{
"id": "source",
"type": "label",
"values": []
}]
}
}
# Set size to the max value in the polygons
size = np.asarray([0, 0, 0])
for _, row in df.iterrows():
meshfile = os.path.join(args.meshdir, "%s.obj" % row['Material'])
mesh_id = row['Nr']
mesh_name = row['Material']
if os.path.exists(meshfile):
# This will work when https://github.com/seung-lab/cloud-volume/pull/413 is merged:
#cv_mesh = Mesh.from_obj(text=Path(meshfile).read_text(), segid=mesh_id)
# Until then, use trimesh:
tmesh = trimesh.load_mesh(meshfile)
vertices = np.asarray(tmesh.vertices)
size[0] = max(size[0], np.max(vertices[:, 0]))
size[1] = max(size[1], np.max(vertices[:, 1]))
size[2] = max(size[2], np.max(vertices[:, 2]))
cv_mesh = Mesh(vertices=tmesh.vertices,
faces=tmesh.faces,
segid=mesh_id)
ngl_mesh_file = "%d.frag" % mesh_id
with open(os.path.join(mesh_path, "%d:0" % (mesh_id)), "w") as f:
info = {"fragments": [ngl_mesh_file]}
f.write(json.dumps(info))
with open(os.path.join(mesh_path, ngl_mesh_file), "wb") as f:
f.write(cv_mesh.to_precomputed())
segment_props['inline']['ids'].append(f'{mesh_id}')
segment_props['inline']['properties'][0]['values'].append(
mesh_name)
else:
print(f'{meshfile} does not exist. Skipping.')
with open(os.path.join(seg_props_path, "info"), "w") as f:
f.write(json.dumps(segment_props))
# Convert the entire space into units of "one entire brain" in case neuroglancer tries to load the image layer
# resolution = np.asarray(args.resolution) * np.asarray(args.volume_size)
resolution = size
size = np.asarray([1, 1, 1])
with open(os.path.join(cvpath, "info"), "w") as f:
info = {
"data_type":
"uint64",
"scales": [{
"key": "fake",
"encoding": "raw",
"voxel_offset": [0, 0, 0],
"resolution": resolution.tolist(),
"size": size.tolist(),
"chunk_sizes": [[256, 256, 16]]
}],
"mesh":
"mesh",
"segment_properties":
"seg_props",
"type":
"segmentation",
"num_channels":
1
}
f.write(json.dumps(info))
with open(os.path.join(mesh_path, "info"), "w") as f:
info = {"@type": "neuroglancer_legacy_mesh"}
f.write(json.dumps(info))