def update_ids_and_write(seg_map, sub_indices):
pbar = tqdm(sub_indices, desc='Update ids to be globally unique')
for k in pbar:
s = seg_map[k]['input']
seg, offset_zyx = load_inference(s)
offset_xyz = offset_zyx[::-1]
size_xyz = seg.shape[::-1]
bbox = seg_map[k]['bbox']
seg = np.transpose(seg, [2, 1, 0])
# # make labels contiguous and unique globally but keep 0 intact
seg = np.uint32(seg)
zeros = seg == 0
seg, _ = make_labels_contiguous(seg)
seg = np.uint32(seg)
# local_max = np.max(seg)
seg += seg_map[k]['global_offset']
seg[zeros] = 0
# convert to precomputed
precomputed_path = seg_map[k]['output']
#seg_map[i] = (bbox, precomputed_path)
resolution = seg_map[k]['resolution']
chunk_size = seg_map[k]['chunk_size']
cv = prepare_precomputed(precomputed_path, offset_xyz, size_xyz,
resolution, chunk_size)
cv[bbox] = seg
def get_seg_map(input_dir,
output_dir,
resolution,
chunk_size,
sub_indices,
post_clean_up=False):
if not sub_indices.size:
return {}
get_name = lambda s: re.sub('seg-', 'precomputed-', s)
seg_list = glob.glob(os.path.join(input_dir, 'seg*'))
seg_list.sort()
seg_map = dict()
pbar = tqdm(sub_indices, desc='Generating seg map')
for i in pbar:
# for i in tqdm(sub_indices):
# pbar.set_description("Generating Seg Map:")
seg_path = seg_list[i]
if not len(
glob.glob(os.path.join(seg_path, '**/*.npz'), recursive=True)):
continue
seg, offset_zyx = load_inference(seg_path)
offset_xyz = offset_zyx[::-1]
size_xyz = seg.shape[::-1]
bbox = Bbox(a=offset_xyz, b=offset_xyz + size_xyz)
seg = np.transpose(seg, [2, 1, 0])
# make labels contiguous and unique globally but keep 0 intact
min_particle = 1000
if post_clean_up:
clean_up(seg, min_particle)
# non_background = (seg > 0).astype(np.uint8)
# seg = connected_components(non_background)
seg = np.uint32(seg)
#zeros = seg==0
seg, _ = make_labels_contiguous(seg)
seg = np.uint32(seg)
local_max = np.max(seg)
#seg += global_max
# global_max = np.max(seg)
# seg[zeros] = 0
precomputed_path = os.path.join(output_dir,
get_name(os.path.basename(seg_path)))
seg_map[i] = {
'bbox': bbox,
'input': os.path.abspath(seg_path),
'output': os.path.abspath(precomputed_path),
'resolution': resolution,
'chunk_size': chunk_size,
'local_max': local_max
}
return seg_map
def get_seg_map(input_dir, output_dir, resolution, chunk_size, sub_indices):
if not sub_indices.size:
return {}
get_name = lambda s: re.sub('seg-', 'precomputed-', s)
seg_list = glob.glob(os.path.join(input_dir, 'seg*'))
seg_list.sort()
seg_map = dict()
pbar = tqdm(sub_indices, desc='Generating seg map')
for i in pbar:
# for i in tqdm(sub_indices):
# pbar.set_description("Generating Seg Map:")
seg_path = seg_list[i]
seg, offset_zyx = load_inference(seg_path)
offset_xyz = offset_zyx[::-1]
size_xyz = seg.shape[::-1]
bbox = Bbox(a=offset_xyz, b=offset_xyz + size_xyz)
seg = np.transpose(seg, [2, 1, 0])
# make labels contiguous and unique globally but keep 0 intact
seg = np.uint32(seg)
#zeros = seg==0
seg, _ = make_labels_contiguous(seg)
seg = np.uint32(seg)
local_max = np.max(seg)
#seg += global_max
# global_max = np.max(seg)
# seg[zeros] = 0
precomputed_path = os.path.join(output_dir,
get_name(os.path.basename(seg_path)))
seg_map[i] = {
'bbox': bbox,
'input': os.path.abspath(seg_path),
'output': os.path.abspath(precomputed_path),
'resolution': resolution,
'chunk_size': chunk_size,
'local_max': local_max
}
return seg_map