def set_instance_color(label: Label, category_id: int,
ann_id: int) -> NDArrayU8:
"""Set the color for an instance given its attributes and ID."""
attributes = label.attributes
if attributes is None:
truncated, occluded, crowd, ignored = 0, 0, 0, 0
else:
truncated = int(attributes.get("truncated", False))
occluded = int(attributes.get("occluded", False))
crowd = int(check_crowd(label))
ignored = int(check_ignored(label))
color: NDArrayU8 = np.array(
[
category_id & 255,
(truncated << 3) + (occluded << 2) + (crowd << 1) + ignored,
ann_id >> 8,
ann_id & 255,
],
dtype=np.uint8,
)
return color
def parse_seg_objects(
objects: List[Label],
classes: List[Category],
ignore_unknown_cats: bool = False,
image_size: Optional[ImageSize] = None,
) -> Tuple[List[RLEDict], NDArrayI32, NDArrayI32, List[RLEDict]]:
"""Parse segmentation objects under Scalabel formats."""
rles, labels, ids, ignore_rles = [], [], [], []
class_names = [category.name for category in classes]
for obj in objects:
if obj.rle is not None:
rle = obj.rle
elif obj.poly2d is not None:
assert (
image_size
is not None), "Requires ImageSize for Poly2D conversion to RLE"
rle = mask_to_rle(poly2ds_to_mask(image_size, obj.poly2d))
else:
continue
category = obj.category
if category in class_names:
if check_crowd(obj) or check_ignored(obj):
ignore_rles.append(rle)
else:
rles.append(rle)
labels.append(class_names.index(category))
ids.append(int(obj.id))
else:
if not ignore_unknown_cats:
raise KeyError(f"Unknown category: {category}")
if any(category.isThing is not None and not category.isThing
for category in classes):
rles, labels, ids = combine_stuff_masks(rles, labels, ids, classes)
rles_dict = [rle.dict() for rle in rles]
ignore_rles_dict = [rle.dict() for rle in ignore_rles]
labels_arr: NDArrayI32 = np.array(labels, dtype=np.int32)
ids_arr: NDArrayI32 = np.array(ids, dtype=np.int32)
return (rles_dict, labels_arr, ids_arr, ignore_rles_dict)
def bdd100k2coco_seg_track(mask_base: str,
frames: List[Frame],
config: Config,
nproc: int = NPROC) -> GtType:
"""Converting BDD100K Segmentation Tracking Set to COCO format."""
video_id, image_id, ann_id = 0, 0, 0
img_shape = config.imageSize
frames_list = group_and_sort(frames)
videos: List[VidType] = []
images: List[ImgType] = []
mask_names: List[str] = []
category_ids_list: List[List[int]] = []
instance_ids_list: List[List[int]] = []
annotations_list: List[List[AnnType]] = []
categories = get_leaf_categories(config.categories)
cat_name2id = {cat.name: i + 1 for i, cat in enumerate(categories)}
logger.info("Collecting annotations...")
for video_anns in tqdm(frames_list):
global_instance_id: int = 1
instance_id_maps: Dict[str, int] = {}
video_name = video_anns[0].videoName
video_id += 1
video = VidType(id=video_id, name=video_name)
videos.append(video)
for image_anns in video_anns:
image_id += 1
if img_shape is None:
if image_anns.size is not None:
img_shape = image_anns.size
else:
raise ValueError("Image shape not defined!")
image = ImgType(
video_id=video_id,
frame_id=image_anns.frameIndex,
id=image_id,
file_name=os.path.join(video_name, image_anns.name),
height=img_shape.height,
width=img_shape.width,
)
if image_anns.url is not None:
image["coco_url"] = image_anns.url
images.append(image)
mask_name = os.path.join(
mask_base,
video_name,
# Bitmask in .png format, image in .jpg format
image_anns.name.replace(".jpg", ".png"),
)
mask_names.append(mask_name)
category_ids: List[int] = []
instance_ids: List[int] = []
annotations: List[AnnType] = []
for label in image_anns.labels:
if label.poly2d is None:
continue
if label.category not in cat_name2id:
continue
ann_id += 1
instance_id, global_instance_id = get_bdd100k_instance_id(
instance_id_maps, global_instance_id, label.id)
category_id = cat_name2id[label.category]
annotation = AnnType(
id=ann_id,
image_id=image_id,
instance_id=instance_id,
category_id=category_id,
scalabel_id=label.id,
iscrowd=int(check_crowd(label) or check_ignored(label)),
ignore=0,
)
category_ids.append(category_id)
instance_ids.append(instance_id)
annotations.append(annotation)
category_ids_list.append(category_ids)
instance_ids_list.append(instance_ids)
annotations_list.append(annotations)
annotations = bitmask2coco_with_ids_parallel(
annotations_list,
mask_names,
category_ids_list,
instance_ids_list,
nproc,
)
return GtType(
type="instances",
categories=get_coco_categories(config),
videos=videos,
images=images,
annotations=annotations,
)
def check_bdd100k_crowd(label: Label) -> bool:
"""Check crowd attribute for BDD100K."""
if label.id == "-1":
return True
return check_crowd(label)