def box_track_to_bdd100k(dataset, results, out_base, nproc):
bdd100k = []
track_base = osp.join(out_base, "box_track")
if not osp.exists(track_base):
os.makedirs(track_base)
print(f'\nStart converting to BDD100K box tracking format')
for idx, track_dict in tqdm(enumerate(results['track_results'])):
img_name = dataset.data_infos[idx]['file_name']
frame_index = dataset.data_infos[idx]['frame_id']
vid_name = os.path.split(img_name)[0]
frame = Frame(
name=img_name,
video_name=vid_name,
frame_index=frame_index,
labels=[])
for id_, instance in track_dict.items():
bbox = instance['bbox']
cls_ = instance['label']
label = Label(
id=id_,
score=bbox[-1],
box2d=bbox_to_box2d(xyxy2xywh(bbox)),
category=CATEGORIES[cls_ + 1])
frame.labels.append(label)
bdd100k.append(frame)
print(f'\nWriting the converted json')
out_path = osp.join(out_base, "box_track.json")
save(out_path, bdd100k)
def main() -> None:
"""Main."""
args = parse_args()
assert os.path.isdir(args.input)
dataset = load(args.input, args.nproc)
if args.config is not None:
bdd100k_config = load_bdd100k_config(args.config)
elif dataset.config is not None:
bdd100k_config = BDD100KConfig(config=dataset.config)
else:
bdd100k_config = load_bdd100k_config(args.mode)
categories = get_leaf_categories(bdd100k_config.scalabel.categories)
convert_funcs: Dict[str, ToRLEFunc] = dict(
ins_seg=insseg_to_rle,
sem_seg=semseg_to_rle,
drivable=semseg_to_rle,
seg_track=segtrack_to_rle,
)
if args.mode == "ins_seg":
assert args.score_file is not None
frames = load(args.score_file).frames
assert all(
os.path.exists(
os.path.join(args.input, frame.name.replace(".jpg", ".png")))
for frame in frames), "Missing some bitmasks."
elif args.mode in ("sem_seg", "drivable", "seg_track"):
files = list_files(args.input)
frames = []
for file in files:
if not file.endswith(".png") and not file.endswith(".jpg"):
continue
frame = Frame(name=file.replace(".png", ".jpg"), labels=[])
frames.append(frame)
else:
return
if args.nproc > 1:
with Pool(args.nproc) as pool:
frames = pool.map(
partial(
convert_funcs[args.mode],
input_dir=args.input,
categories=categories,
),
tqdm(frames),
)
else:
frames = [
convert_funcs[args.mode](frame, args.input, categories)
for frame in tqdm(frames)
]
save(args.output, frames)
def det_to_bdd(self, results, out_base, nproc):
bdd100k = []
ann_id = 0
print(f'\nStart converting to BDD100K detection format')
for idx, bboxes_list in tqdm(enumerate(results['bbox_result'])):
img_name = self.data_infos[idx]['file_name']
frame = Frame(name=img_name, labels=[])
for cls_, bboxes in enumerate(bboxes_list):
for bbox in bboxes:
ann_id += 1
label = Label(id=ann_id,
score=bbox[-1],
box2d=bbox_to_box2d(xyxy2xywh(bbox)),
category=CATEGORIES[cls_ + 1])
frame.labels.append(label)
bdd100k.append(frame)
print(f'\nWriting the converted json')
out_path = osp.join(out_base, "det.json")
save(out_path, bdd100k)
def ins_seg_to_bdd100k(dataset, results, out_base, nproc=4):
bdd100k = []
bitmask_base = osp.join(out_base, "ins_seg")
if not osp.exists(bitmask_base):
os.makedirs(bitmask_base)
if 'bbox_results' in results and 'segm_results' in results:
results = [[bbox, segm] for bbox, segm in zip(results['bbox_results'],
results['segm_results'])]
track_dicts = []
img_names = [
dataset.data_infos[idx]['file_name'] for idx in range(len(results))
]
print(f'\nStart converting to BDD100K instance segmentation format')
ann_id = 0
for idx, [bboxes_list, segms_list] in enumerate(results):
index = 0
frame = Frame(name=img_names[idx], labels=[])
track_dict = {}
for cls_, (bboxes, segms) in enumerate(zip(bboxes_list, segms_list)):
for bbox, segm in zip(bboxes, segms):
ann_id += 1
index += 1
label = Label(id=str(ann_id), index=index, score=bbox[-1])
frame.labels.append(label)
instance = {'bbox': bbox, 'segm': segm, 'label': cls_}
track_dict[index] = instance
bdd100k.append(frame)
track_dicts.append(track_dict)
print(f'\nWriting the converted json')
out_path = osp.join(out_base, 'ins_seg.json')
save(out_path, bdd100k)
mask_merge_parallel(track_dicts, img_names, bitmask_base, nproc)