def _load_instances_items(self):
items = {}
instances_dir = osp.join(self._annotations_dir, MapillaryVistasPath.INSTANCES_DIR)
for instance_path in find_images(instances_dir, recursive=True):
item_id = osp.splitext(osp.relpath(instance_path, instances_dir))[0]
mask = load_image(instance_path, dtype=np.uint32)
annotations = []
for uval in np.unique(mask):
label_id, instance_id = uval >> 8, uval & 255
annotations.append(
Mask(
image=self._lazy_extract_mask(mask, uval),
label=label_id, id=instance_id
)
)
items[item_id] = DatasetItem(id=item_id, subset=self._subset,
annotations=annotations)
class_dir = osp.join(self._annotations_dir, MapillaryVistasPath.CLASS_DIR)
for class_path in find_images(class_dir, recursive=True):
item_id = osp.splitext(osp.relpath(class_path, class_dir))[0]
if item_id in items:
continue
from PIL import Image as PILImage
class_mask = np.array(PILImage.open(class_path))
classes = np.unique(class_mask)
annotations = []
for label_id in classes:
annotations.append(Mask(label=label_id,
image=self._lazy_extract_mask(class_mask, label_id))
)
items[item_id] = DatasetItem(id=item_id, subset=self._subset,
annotations=annotations)
for image_path in find_images(self._images_dir, recursive=True):
item_id = osp.splitext(osp.relpath(image_path, self._images_dir))[0]
image = Image(path=image_path)
if item_id in items:
items[item_id].image = image
else:
items[item_id] = DatasetItem(id=item_id, subset=self._subset,
image=image)
self._load_polygons(items)
return items.values()
def __call__(
self,
tfds_example: Any,
item: DatasetItem,
state: namespace,
) -> None:
if self.filename_feature_name:
filename = tfds_example[self.filename_feature_name].numpy() \
.decode('UTF-8')
else:
filename = None
item.image = ByteImage(data=tfds_example[self.feature_name].numpy(),
path=filename)