def kd_factory(kd_path, channel='jpg'):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# knossos_utils expects a path to the knossos.conf file
if not kd_path.endswith('knossos.conf'):
kd_path += "/mag1/knossos.conf"
kd = KnossosDataset() # Sets initial values of object
# kd.set_channel(channel) # TODO: currently requires additional adjustment of the data type, i.e. setting the channel explicitely currently leads to uint32 <-> uint64 issues in the CS segmentation
# Initializes the dataset by parsing the knossos.conf in path + "mag1"
kd.initialize_from_knossos_path(kd_path)
return kd
def trafo_objects_to_kd(
realign_map,
obj_ids=None,
label_id=3,
kd_path='/wholebrain/scratch/areaxfs3/knossosdatasets/j0126_realigned_v4b_cbs_ext0_fix/',
path_folder='/wholebrain/scratch/areaxfs3/j0126_cset_paper/obj_mito_1037_3d_8'
):
with open(path_folder + '/direct_map.pkl', 'rb') as f:
obj_map = pkl.load(f)
kd = KnossosDataset()
kd.initialize_from_knossos_path(kd_path)
if obj_ids is None:
obj_ids = list(obj_map.keys())
time_start = time.time()
for i_obj_id, obj_id in enumerate(obj_ids):
voxels = np.array([], dtype=np.int32).reshape(0, 3)
for voxel_file in obj_map[obj_id]:
voxels = np.concatenate([
voxels,
np.load(path_folder + '/voxels/%d_%d.npz' %
(voxel_file[0], voxel_file[1]))["%d" % obj_id]
])
voxels_t, mean_shift = trafo_object(voxels, realign_map)
bb = np.array([np.min(voxels_t, axis=0),
np.max(voxels_t, axis=0)],
dtype=np.int)
if np.product(bb.shape) > 1e10:
print("Obj %d too large" % obj_id)
continue
voxels_t -= bb[0]
vx_cube = np.zeros(bb[1] - bb[0] + 1, dtype=np.uint64)
vx_cube[voxels_t[:, 0], voxels_t[:, 1], voxels_t[:, 2]] = label_id
kd.from_matrix_to_cubes(bb[0],
data=vx_cube,
as_raw=False,
overwrite=False)
dt = time.time() - time_start
print("Written obj %d at bb %s - time / object: %.3fs - eta: %.3fh" %
(obj_id, bb[0], dt / (i_obj_id + 1), dt /
(i_obj_id + 1) * len(obj_ids) / 3600))
def kd_factory(kd_path: str, channel: str = 'jpg'):
"""
Todo:
* Requires additional adjustment of the data type,
i.e. setting the channel explicitly currently leads to uint32 <->
uint64 issues in the CS segmentation.
Args:
kd_path: Path to the KnossosDataset.
channel: Channel which to use. Currently not used.
Returns:
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# knossos_utils expects a path to the knossos.conf file
if not kd_path.endswith('knossos.conf'):
kd_path += "/mag1/knossos.conf"
kd = KnossosDataset() # Sets initial values of object
# kd.set_channel(channel) #
# Initializes the dataset by parsing the knossos.conf in path + "mag1"
kd.initialize_from_knossos_path(kd_path)
return kd
from elektronn2.utils.plotting import scroll_plot, scroll_plot2
path = os.path.expanduser("~/lustre/sdorkenw/j0126_3d_rrbarrier/")
arr = KnossosArray(path, max_ram=1200, n_preload=2)
arr.preload([0, 0, 0], sync=True)
sh = np.array([120, 240, 240])
off = np.array([0, 0, 0]) + np.random.randint(0, 500, size=3)
# a = arr[0:80, 0:220, 0:230]
# a0 = arr[0:100, 0:400, 0:200]
# a1 = arr[0:100, 100:400+100, 100:200+100]
# b = arr[0:640, 0:640, 0:384]
# c = arr[500:600, 500:800, 500:800]
#
kds = KnossosDataset()
kds.initialize_from_knossos_path(path)
get = utils.timeit(kds.from_raw_cubes_to_matrix)
# a0 = arr.cut_slice(sh, off)
# a1 = get(sh[[2,1,0]], off[[2,1,0]])
# a2 = (a1.T).astype(np.float32)/255
# print(np.allclose(a0, a2))
# fig = scroll_plot2([a0, a2], 'ab')
t = 0
out = np.empty(sh, dtype=np.float32)
N = 500
for i in range(N):
t0 = time.time()
a = arr.cut_slice(sh, off, out=out)
t += time.time() - t0