def segtrack_to_bitmasks(
frames: List[Frame],
out_base: str,
ignore_as_class: bool = False,
remove_ignore: bool = False,
nproc: int = 4,
) -> None:
"""Converting segmentation tracking poly2d to bitmasks."""
frames_list = group_and_sort(frames)
categories, name_mapping, ignore_mapping = load_coco_config(
mode="track",
filepath=DEFAULT_COCO_CONFIG,
ignore_as_class=ignore_as_class,
)
out_paths: List[str] = []
colors_list: List[List[np.ndarray]] = []
poly2ds_list: List[List[List[Poly2D]]] = []
logger.info("Preparing annotations for SegTrack to Bitmasks")
for video_anns in tqdm(frames_list):
global_instance_id: int = 1
instance_id_maps: Dict[str, int] = dict()
video_name = video_anns[0].video_name
out_dir = os.path.join(out_base, video_name)
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
for image_anns in video_anns:
# Bitmask in .png format
image_name = image_anns.name.replace(".jpg", ".png")
image_name = os.path.split(image_name)[-1]
out_path = os.path.join(out_dir, image_name)
out_paths.append(out_path)
colors: List[np.ndarray] = []
poly2ds: List[List[Poly2D]] = []
colors_list.append(colors)
poly2ds_list.append(poly2ds)
labels_ = image_anns.labels
if labels_ is None or len(labels_) == 0:
continue
# Scores higher, rendering later
if labels_[0].score is not None:
labels_ = sorted(labels_, key=lambda label: float(label.score))
for label in labels_:
if label.poly2d is None:
continue
category_ignored, category_id = process_category(
label.category,
categories,
name_mapping,
ignore_mapping,
ignore_as_class=ignore_as_class,
)
if category_ignored and remove_ignore:
continue
instance_id, global_instance_id = get_bdd100k_instance_id(
instance_id_maps, global_instance_id, str(label.id))
color = set_instance_color(label, category_id, instance_id,
category_ignored)
colors.append(color)
poly2ds.append(label.poly2d)
logger.info("Start Conversion for SegTrack to Bitmasks")
frames_to_masks(nproc, out_paths, colors_list, poly2ds_list)
def bdd100k2coco_seg_track(
mask_base: str,
shape: Tuple[int, int],
frames: List[Frame],
categories: List[CatType],
name_mapping: Optional[Dict[str, str]] = None,
ignore_mapping: Optional[Dict[str, str]] = None,
ignore_as_class: bool = False,
remove_ignore: bool = False,
mask_mode: str = "rle",
nproc: int = 4,
) -> GtType:
"""Converting BDD100K Segmentation Tracking Set to COCO format."""
frames_list = group_and_sort(frames)
videos: List[VidType] = []
images: List[ImgType] = []
video_id, image_id, ann_id = 0, 0, 0
mask_names: List[str] = []
category_ids_list: List[List[int]] = []
instance_ids_list: List[List[int]] = []
annotations_list: List[List[AnnType]] = []
for video_anns in tqdm(frames_list):
global_instance_id: int = 1
instance_id_maps: Dict[str, int] = dict()
video_name = video_anns[0].video_name
video_id += 1
video = VidType(id=video_id, name=video_name)
videos.append(video)
for image_anns in video_anns:
image_id += 1
image = ImgType(
video_id=video_id,
frame_id=image_anns.frame_index,
id=image_id,
file_name=os.path.join(video_name, image_anns.name),
height=shape[0],
width=shape[1],
)
images.append(image)
mask_name = os.path.join(
mask_base,
video_name,
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.poly_2d is None:
continue
category_ignored, category_id = process_category(
label.category,
categories,
name_mapping,
ignore_mapping,
ignore_as_class=ignore_as_class,
)
if remove_ignore and category_ignored:
continue
scalabel_id = str(label.id)
instance_id, global_instance_id = get_bdd100k_instance_id(
instance_id_maps, global_instance_id, scalabel_id)
ann_id += 1
iscrowd, ignore = get_bdd100k_object_attributes(
label, category_ignored)
annotation = AnnType(
id=ann_id,
image_id=image_id,
category_id=category_id,
instance_id=instance_id,
scalabel_id=scalabel_id,
iscrowd=iscrowd,
ignore=ignore,
)
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,
mask_mode,
nproc,
)
return GtType(
type="instances",
categories=categories,
videos=videos,
images=images,
annotations=annotations,
)
def insseg_to_bitmasks(
frames: List[Frame],
out_base: str,
ignore_as_class: bool = False,
remove_ignore: bool = False,
nproc: int = 4,
) -> None:
"""Converting instance segmentation poly2d to bitmasks."""
os.makedirs(out_base, exist_ok=True)
categories, name_mapping, ignore_mapping = load_coco_config(
mode="track",
filepath=DEFAULT_COCO_CONFIG,
ignore_as_class=ignore_as_class,
)
out_paths: List[str] = []
colors_list: List[List[np.ndarray]] = []
poly2ds_list: List[List[List[Poly2D]]] = []
logger.info("Preparing annotations for InsSeg to Bitmasks")
for image_anns in tqdm(frames):
ann_id = 0
# Bitmask in .png format
image_name = image_anns.name.replace(".jpg", ".png")
image_name = os.path.split(image_name)[-1]
out_path = os.path.join(out_base, image_name)
out_paths.append(out_path)
colors: List[np.ndarray] = []
poly2ds: List[List[Poly2D]] = []
colors_list.append(colors)
poly2ds_list.append(poly2ds)
labels_ = image_anns.labels
if labels_ is None or len(labels_) == 0:
continue
# Scores higher, rendering later
if labels_[0].score is not None:
labels_ = sorted(labels_, key=lambda label: float(label.score))
for label in labels_:
if label.poly2d is None:
continue
category_ignored, category_id = process_category(
label.category,
categories,
name_mapping,
ignore_mapping,
ignore_as_class=ignore_as_class,
)
if remove_ignore and category_ignored:
continue
ann_id += 1
color = set_instance_color(label, category_id, ann_id,
category_ignored)
colors.append(color)
poly2ds.append(label.poly2d)
logger.info("Start conversion for InsSeg to Bitmasks")
frames_to_masks(nproc, out_paths, colors_list, poly2ds_list)
def bdd100k2coco_ins_seg(
mask_base: str,
shape: Tuple[int, int],
frames: List[Frame],
categories: List[CatType],
name_mapping: Optional[Dict[str, str]] = None,
ignore_mapping: Optional[Dict[str, str]] = None,
ignore_as_class: bool = False,
remove_ignore: bool = False,
mask_mode: str = "rle",
nproc: int = 4,
) -> GtType:
"""Converting BDD100K Instance Segmentation Set to COCO format."""
image_id, ann_id = 0, 0
images: List[ImgType] = []
mask_names: List[str] = []
category_ids_list: List[List[int]] = []
instance_ids_list: List[List[int]] = []
annotations_list: List[List[AnnType]] = []
logger.info("Collecting annotations...")
for image_anns in tqdm(frames):
instance_id = 0
image_id += 1
image = ImgType(
id=image_id,
file_name=image_anns.name,
height=shape[0],
width=shape[1],
)
images.append(image)
mask_name = os.path.join(
mask_base,
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.poly_2d is None:
continue
category_ignored, category_id = process_category(
label.category,
categories,
name_mapping,
ignore_mapping,
ignore_as_class=ignore_as_class,
)
if category_ignored and remove_ignore:
continue
ann_id += 1
instance_id += 1
iscrowd, ignore = get_bdd100k_object_attributes(
label, category_ignored)
annotation = AnnType(
id=ann_id,
image_id=image_id,
category_id=category_id,
scalabel_id=str(label.id),
iscrowd=iscrowd,
ignore=ignore,
)
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,
mask_mode,
nproc,
)
return GtType(
type="instances",
categories=categories,
images=images,
annotations=annotations,
)