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Annotation Types

Annotation Types are something that can be configured for a project either during or after the Guideline Agreement Process (GAP). During the GAP a set of taxonomies are defined which will be used in order to annotate all the inputs that are uploaded to the platform.

Annotation Types are identifiers that each map to one of these taxonomies and allows you to programmatically define, for every scene that you create, what taxonomies that you would like that scene to be annotated with. You do not need to use annotation types in a project but by skipping annotation types your inputs would always be created for all taxonomies available in the specified batch.

Add additional Annotation Types

It is possible to add additional Annotation Types after a project is created, and make them available to future batches that you create. If you would like Annotation Types to be added to your project please get in touch with Kognic's Professional Services team.

Examples​

For the following examples we will be creating cameras_sequence scenes, however the procedure would be identical for any other scene type. We will also assume that the project example_project_id is configured with the Annotation Types: static_objects, and dynamic_objects, and that they are also available in the batch example_batch_id.

Get Annotation Types for Project​

from kognic.io.client import KognicIOClient

client = KognicIOClient()
project_annotation_types = client.project.get_annotation_types(project="example_project_id")

This will return a list of all Annotation Types available in the project.

Get Annotation Types for a specified Project Batch​

from kognic.io.client import KognicIOClient

client = KognicIOClient()
batch_annotation_types = client.project.get_annotation_types(project="example_project_id",
                                                             batch="example_batch_id")

This will return a list of all Annotation Types available in the specified batch. Note that this list does not need to contain all Annotation Types in the project.

Create inputs for specific Annotation Types​

from kognic.io.client import KognicIOClient
from kognic.io.model.scene.cameras_sequence import CamerasSequence

client = KognicIOClient()
scene = CamerasSequence(external_id=..., frames=...)
client.cameras_sequence.create(scene,
                               project="example_project_id",
                               batch="example_batch_id",
                               annotation_types=["static_objects", "dynamic_objects"])

The above example will create a new scene which will be annotated for the annotation types specified. If one or more of the specified annotation types would not be available in the specified batch the validation in the API would fail.

Specifying batch is optional

In these examples we have specified which batch the inputs should be created for, but this is optional. If no batch is specified the inputs will be created in the latest batch with status open.

Create inputs for all Annotation Types in batch​

from kognic.io.client import KognicIOClient
from kognic.io.model.scene.cameras_sequence import CamerasSequence

client = KognicIOClient()
scene = CamerasSequence(external_id=..., frames=...)
client.cameras_sequence.create(scene,
                               project="example_project_id",
                               batch="example_batch_id")

The above example will create a new scene which will be annotated for all Annotation Types available in the batch example_batch_id. However, this way it is not explicit what Annotation Types that the scene will be annotated with, and you would not get an error if e.g. static_objects was missing from the specified batch.

Always specify Annotation Types

In order to get the best possible validation it is recommended that you always specify annotation types when you create inputs.

Add/remove annotation types for an input (deprecated)​

Deprecated

Removing annotation types has been deprecated in favor of Deleting Inputs and will be removed in the near future.

Deprecated

Adding annotation types has been deprecated in favor of Creating Inputs from Scene and will be removed in the near future.

Adding an annotation type to a scene means that an annotation will be produced for that scene with the specified annotation type. In the same way, removing annotation types from a scene means that annotations will not be produced for that scene with the specified annotation types.

In the case when multiple annotation types are annotated in the same task, it is enough to specify one annotation type when adding but all annotation types must be specified when removing.

Note that it is currently not possible to add an annotation type that has already been removed from a scene.

from kognic.io.client import KognicIOClient

client = KognicIOClient()
scene_uuid = 'cca60a67-cb68-4645-8bae-00c6e6415555'

# Add an annotation type to a scene
client.input.add_annotation_type(scene_uuid=scene_uuid, annotation_type="annotation-type")

# Remove annotation types from a scene
annotation_types = ["annotation-type-1", "annotation-type-2", ...]
client.input.remove_annotation_types(scene_uuid=scene_uuid, annotation_type=annotation_types)