def _prepare_val_data(
self, data: Tuple[Tuple[int, str], Tuple[str, BinaryIO]]
) -> Tuple[Tuple[Label, str], Tuple[str, BinaryIO]]:
label_data, image_data = data
_, wnid = label_data
label = Label.from_category(self._wnid_to_category[wnid], categories=self._categories)
return (label, wnid), image_data
def _prepare_sample(self, data: Tuple[str, Any]) -> Dict[str, Any]:
path, buffer = data
category = pathlib.Path(path).parent.name
return dict(
label=Label.from_category(category, categories=self._categories),
path=path,
image=EncodedImage.from_file(buffer),
)
def _prepare_sample(
self, data: Tuple[str, Tuple[str, BinaryIO]]) -> Dict[str, Any]:
id, (path, buffer) = data
return dict(
label=Label.from_category(id.split("/", 1)[0],
categories=self._categories),
path=path,
image=EncodedImage.from_file(buffer),
)
def _prepare_train_data(
self, data: Tuple[str, BinaryIO]
) -> Tuple[Tuple[Label, str], Tuple[str, BinaryIO]]:
path = pathlib.Path(data[0])
wnid = cast(Match[str],
self._TRAIN_IMAGE_NAME_PATTERN.match(path.name))["wnid"]
label = Label.from_category(self.info.extra.wnid_to_category[wnid],
categories=self.categories)
return (label, wnid), data
def _prepare_sample(
data: Tuple[str, BinaryIO],
*,
root: pathlib.Path,
categories: List[str],
) -> Dict[str, Any]:
path, buffer = data
category = pathlib.Path(path).relative_to(root).parts[0]
return dict(
path=path,
data=EncodedData.from_file(buffer),
label=Label.from_category(category, categories=categories),
)
def _prepare_sample(
self, data: Tuple[Tuple[str, List[str]], Tuple[str, BinaryIO]]
) -> Dict[str, Any]:
(_, joint_categories_data), image_data = data
_, *joint_categories = joint_categories_data
path, buffer = image_data
category = pathlib.Path(path).parent.name
return dict(
joint_categories={
category
for category in joint_categories if category
},
label=Label.from_category(category, categories=self.categories),
path=path,
image=EncodedImage.from_file(buffer),
)
def _prepare_sample(
self, data: Tuple[Tuple[str, str], Tuple[Tuple[str, BinaryIO],
Tuple[str, BinaryIO]]]
) -> Dict[str, Any]:
key, (image_data, ann_data) = data
category, _ = key
image_path, image_buffer = image_data
ann_path, ann_buffer = ann_data
image = EncodedImage.from_file(image_buffer)
ann = read_mat(ann_buffer)
return dict(
label=Label.from_category(category, categories=self._categories),
image_path=image_path,
image=image,
ann_path=ann_path,
bounding_box=BoundingBox(ann["box_coord"].astype(
np.int64).squeeze()[[2, 0, 3, 1]],
format="xyxy",
image_size=image.image_size),
contour=_Feature(ann["obj_contour"].T),
)
