def bitmask2coco_ins_seg(mask_base: str,
config: Config,
nproc: int = NPROC) -> GtType:
"""Converting BDD100K Instance Segmentation Set to COCO format."""
files = list_files(mask_base, suffix=".png")
images: List[ImgType] = []
logger.info("Collecting bitmasks...")
image_id = 0
for file_ in tqdm(files):
image_id += 1
image = ImgType(
id=image_id,
file_name=file_.replace(".png", ".jpg"),
)
images.append(image)
annotations = bitmask2coco_wo_ids_parallel(mask_base, images, nproc)
return GtType(
type="instances",
categories=get_coco_categories(config),
images=images,
annotations=annotations,
)
def bitmask2coco_pan_seg(
mask_base: str,
pan_mask_base: str,
nproc: int = NPROC,
) -> PanopticGtType:
"""Converting BDD100K Instance Segmentation Set to COCO format."""
files = list_files(mask_base, suffix=".png")
images: List[ImgType] = []
logger.info("Collecting bitmasks...")
image_id = 0
for file_ in tqdm(files):
image_id += 1
image = ImgType(id=image_id, file_name=file_)
images.append(image)
annotations = bitmask2panseg_parallel(mask_base, pan_mask_base, images,
nproc)
categories: List[PanopticCatType] = [
PanopticCatType(
id=label.id,
name=label.name,
supercategory=label.category,
isthing=label.hasInstances,
color=label.color,
) for label in labels
]
return PanopticGtType(
categories=categories,
images=images,
annotations=annotations,
)
def test_bitmask2pan_json(self) -> None:
"""Check bitmask to panoptic json file."""
mask_name = f"{self.cur_dir}/testcases/panseg_bdd100k/{MASK_NAME}"
gt_json_name = f"{self.cur_dir}/testcases/panseg_coco/panseg_coco.json"
image = ImgType(id=255, file_name="panseg_mask.jpg")
pan_ann = bitmask2pan_json(image, mask_name)
with open(gt_json_name, encoding="utf-8") as fp:
gt_pan_ann = json.load(fp)
self.assertDictEqual(dict(pan_ann), gt_pan_ann)
def bitmask2coco_seg_track(
mask_base: str,
shape: Tuple[int, int],
all_files: List[str],
categories: List[CatType],
mask_mode: str = "rle",
nproc: int = 4,
) -> GtType:
"""Converting BDD100K Instance Segmentation Set to COCO format."""
files_list = group_and_sort_files(all_files)
videos: List[VidType] = []
images: List[ImgType] = []
image_ids: List[int] = []
mask_names: List[str] = []
logger.info("Collecting bitmasks...")
video_id, image_id = 0, 0
for files in files_list:
video_name = os.path.split(files[0])[0]
video_id += 1
video = VidType(id=video_id, name=video_name)
videos.append(video)
for frame_id, file_ in tqdm(enumerate(files)):
image_id += 1
image = ImgType(
video_id=video_id,
frame_id=frame_id,
id=image_id,
file_name=file_.replace(".png", ".jpg"),
height=shape[0],
width=shape[1],
)
images.append(image)
image_ids.append(image_id)
mask_name = os.path.join(mask_base, file_.replace(".jpg", ".png"))
mask_names.append(mask_name)
annotations = bitmask2coco_wo_ids_parallel(image_ids, mask_names,
mask_mode, nproc)
return GtType(
type="instances",
categories=categories,
videos=videos,
images=images,
annotations=annotations,
)
def bitmask2coco_ins_seg(
mask_base: str,
shape: Tuple[int, int],
files: List[str],
categories: List[CatType],
mask_mode: str = "rle",
nproc: int = 4,
) -> GtType:
"""Converting BDD100K Instance Segmentation Set to COCO format."""
images: List[ImgType] = []
image_ids: List[int] = []
mask_names: List[str] = []
logger.info("Collecting bitmasks...")
image_id = 0
for file_ in tqdm(files):
image_id += 1
image = ImgType(
id=image_id,
file_name=file_,
height=shape[0],
width=shape[1],
)
images.append(image)
image_ids.append(image_id)
mask_name = os.path.join(mask_base, file_)
mask_names.append(mask_name)
annotations = bitmask2coco_wo_ids_parallel(image_ids, mask_names,
mask_mode, nproc)
return GtType(
type="instances",
categories=categories,
images=images,
annotations=annotations,
)
def bitmask2coco_seg_track(mask_base: str,
config: Config,
nproc: int = NPROC) -> GtType:
"""Converting BDD100K Instance Segmentation Set to COCO format."""
videos: List[VidType] = []
images: List[ImgType] = []
all_files = list_files(mask_base, suffix=".png")
files_list = group_and_sort_files(all_files)
logger.info("Collecting bitmasks...")
video_id, image_id = 0, 0
for files in files_list:
video_name = os.path.split(files[0])[0]
video_id += 1
video = VidType(id=video_id, name=video_name)
videos.append(video)
for frame_id, file_ in tqdm(enumerate(files)):
image_id += 1
image = ImgType(
video_id=video_id,
frame_id=frame_id,
id=image_id,
file_name=file_.replace(".png", ".jpg"),
)
images.append(image)
annotations = bitmask2coco_wo_ids_parallel(mask_base, images, nproc)
return GtType(
type="instances",
categories=get_coco_categories(config),
videos=videos,
images=images,
annotations=annotations,
)
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 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,
)
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,
)