Annotations can contain annotated data in two forms:
- skeleton data, as provided by the
Skeletonclass, and - volume annotation layers (or volume layers short),
which can be exported as a
SegmentationLayer, seeexport_volume_layer_to_dataset()andtemporary_volume_layer_copy().
Usually, annotations should be created manually in the WEBKNOSSOS interface and can be downloaded using
Annotation.download(). The downloaded instance is not persisted to disk automatically, please use save()
for this purpose. The general purpose file format is .zip files containing an .nml file with
meta-information and the skeleton data and also containing inner .zip files for the volume layers.
For skeleton-only annotations without volume layers .nml files can be used directly. Both formats
are compatible with the WEBKNOSSOS up- and downloads.
To prepare volume annotations in the code for correction of segmentation data in the WEBKNOSSOS interface,
please use add_volume_layer() with the fallback_layer argument, referencing a segmentation layer that
is available on WEBKNOSSOS (e.g. using the Dataset upload before).
Correcting segmentations using fallback layers is much more efficient, adding volume
annotation data programmatically is discouraged therefore.
class-attribute
instance-attribute
annotation_id: Optional[str] = None
property
writable
dataset_name: str
This attribute is a proxy for skeleton.dataset_name.
property
writable
description: Optional[str]
This attribute is a proxy for skeleton.description.
class-attribute
instance-attribute
edit_position: Optional[Vector3] = None
class-attribute
instance-attribute
edit_rotation: Optional[Vector3] = None
class-attribute
instance-attribute
metadata: Dict[str, str] = Factory(dict)
instance-attribute
name: str
property
writable
organization_id: Optional[str]
This attribute is a proxy for skeleton.organization_id.
class-attribute
instance-attribute
owner_name: Optional[str] = None
property
writable
scale: Tuple[float, float, float]
Deprecated, please use voxel_size.
class-attribute
instance-attribute
task_bounding_box: Optional[NDBoundingBox] = None
class-attribute
instance-attribute
time: Optional[int] = ib(factory=time_since_epoch_in_ms)
class-attribute
instance-attribute
user_bounding_boxes: List[NDBoundingBox] = Factory(list)
property
writable
username: Optional[str]
Deprecated, use owner_name instead.
property
writable
voxel_size: Tuple[float, float, float]
This attribute is a proxy for skeleton.voxel_size.
class-attribute
instance-attribute
zoom_level: Optional[float] = None
add_volume_layer(name: str, fallback_layer: Union[Layer, str, None] = None, volume_layer_id: Optional[int] = None) -> None
Adds a volume layer to the annotation, without manual annotations but possibly referring to
segmentation data using the fallback_layer.
To prepare volume annotations in the code for correction of segmentation data in the WEBKNOSSOS interface,
please use the fallback_layer argument, referencing a segmentation layer that is available on WEBKNOSSOS
(e.g. using the Dataset upload before).
Correcting segmentations using fallback layers is much more efficient, adding volume annotation data
programmatically is discouraged therefore.
delete_volume_layer(volume_layer_name: Optional[str] = None, volume_layer_id: Optional[int] = None) -> None
classmethod
download(annotation_id_or_url: str, annotation_type: Union[str, AnnotationType, None] = None, webknossos_url: Optional[str] = None, *, skip_volume_data: bool = False, _return_context: bool = False) -> Union[Annotation, Tuple[Annotation, ContextManager[None]]]
annotation_id_or_urlmay be an annotation id or a full URL to an annotation, e.g.https://webknossos.org/annotations/6114d9410100009f0096c640annotation_typeis no longer required and therefore deprecated and ignoredwebknossos_urlmay be supplied if an annotation id was used and allows to specify in which webknossos instance to search for the annotation. It defaults to the url from your currentwebknossos_context, using https://webknossos.org as a fallback.skip_volume_datacan be set toTrueto omit downloading annotated volume data. They can still be streamed from WEBKNOSSOS usingannotation.get_remote_annotation_dataset()._return_contextshould not be set.
export_volume_layer_to_dataset(dataset: Dataset, layer_name: str = 'volume_layer', volume_layer_name: Optional[str] = None, volume_layer_id: Optional[int] = None) -> SegmentationLayer
Given a dataset, this method will export the specified volume annotation of this annotation into that dataset by creating a new layer. The largest_segment_id is computed automatically, unless provided explicitly.
volume_layer_name or volume_layer_id has to be provided,
if the annotation contains multiple volume layers.
Use get_volume_layer_names() to look up available layers.
get_remote_annotation_dataset() -> Dataset
Returns a streamed dataset of the annotation as shown in webknossos, incorporating fallback layers and potentially mappings. A mapping is currently only incorporated if it is a pinned agglomerate mapping. After an agglomerate mapping was activated in WEBKNOSSOS, it is pinned as soon as the first volume editing action is done. Note that this behavior might change in the future.
get_remote_base_dataset(sharing_token: Optional[str] = None, webknossos_url: Optional[str] = None) -> RemoteDataset
get_volume_layer_names() -> Iterable[str]
get_volume_layer_segments(volume_layer_name: Optional[str] = None, volume_layer_id: Optional[int] = None) -> Dict[int, SegmentInformation]
Returns a dict mapping from segment ids to SegmentInformation.
The dict is mutable, changes to the returned instance are saved in the local annotation.
Changes in a downloaded annotation that are done online in webknossos are not
reflected automatically, the annotation needs to be re-downloaded.
classmethod
load(annotation_path: Union[str, PathLike]) -> Annotation
Loads a .nml file or a .zip file containing an NML and possibly also volume
layers. Returns the Annotation object.
merge_fallback_layer(target: Path, dataset_directory: Path, volume_layer_name: Optional[str] = None, executor: Optional[Executor] = None) -> None
Merge the volume annotation with the fallback layer.
classmethod
open_as_remote_dataset(annotation_id_or_url: str, annotation_type: Union[str, AnnotationType, None] = None, webknossos_url: Optional[str] = None) -> Dataset
save(path: Union[str, PathLike]) -> None
Stores the annotation as a zip or nml at the given path.
temporary_volume_layer_copy(volume_layer_name: Optional[str] = None, volume_layer_id: Optional[int] = None, read_only: bool = True) -> Iterator[SegmentationLayer]
Given a volume annotation path, create a temporary dataset which
contains the volume annotation. Returns the corresponding Layer.
volume_layer_name or volume_layer_id has to be provided,
if the annotation contains multiple volume layers.
upload() -> str
Uploads the annotation to your current webknossos_context.
Bases: Enum
This Enum contains the state of annotations belonging to tasks.
Can be retrieved via Task instances, getting AnnotationInfo from task.get_annotation_infos().
class-attribute
instance-attribute
ACTIVE = 'Active'
class-attribute
instance-attribute
CANCELLED = 'Cancelled'
class-attribute
instance-attribute
FINISHED = 'Finished'
class-attribute
instance-attribute
INITIALIZING = 'Initializing'
Bases: Enum
Annotations can be of different types which has to be specified when using Annotation.download()
with an annotation id.
class-attribute
instance-attribute
EXPLORATIONAL = 'Explorational'
Explorational annotations are all annotations created without the task system, e.g. by uploading an annotation or using the "Create Annotation" Button in the dataset view in webknossos.
class-attribute
instance-attribute
TASK = 'Task'
The Task type is automatically assigned to all annotations that are instances of a task.
See also Task.
instance-attribute
color: Optional[Vector4]
instance-attribute
name: Optional[str]