1. Task Assignment

To reach the tasks and assignments repositories go to tasks and assignments.

To reach the tasks and assignments entities go to tasks and assignments.

1.1. Item Review

The Annotation Studio is built for realtime review, task assignment and feedback.

Each item can be classified in 3 ways:

• Discarded: Items that are not relevant for labeling

• Complete (or an alternate custom status created by the task creator): Items after an annotation process

• Approved (or an alternate custom status created by the task creator): Completed items after a QA process
  #### Prep

import dtlpy as dl
if dl.token_expired():
    dl.login()
project = dl.projects.get(project_name='<project_name>')
dataset = project.datasets.get(dataset_name='<dataset_name>')

1.1.1. Single status update

# Mark single item as completed
item = dataset.items.get(item_id='<my-item-id>')
item.update_status(status=dl.ItemStatus.COMPLETED)
# In the same way you can update to another status
item.update_status(status=dl.ItemStatus.APPROVED)
item.update_status(status=dl.ItemStatus.DISCARDED)

1.1.2. Clear status

# Clear status for completed/approved/discarded
item.update_status(dl.ITEM_STATUS_COMPLETED, clear=True)

1.2. Bulk status update

# With items list
filters = dl.Filters(field='<annotated>', values=True)
items = dataset.items.list(filters=filters)
dataset.items.update_status(status=dl.ItemStatus.APPROVED, items=items)
# With filters
filters = dl.Filters(field='<annotated>', values=True)
dataset.items.update_status(status=dl.ItemStatus.DISCARDED, filters=filters)
# With list of item ids
item_ids = ['<id1>', '<id2>', '<id3>']
dataset.items.update_status(status=dl.ItemStatus.COMPLETED, item_ids=item_ids)

1.2.1. Example

To mark an entire task as completed use the following:

task = dataset.tasks.get(task_name='<my-task-name>')
dataset.items.update_status(status=dl.ItemStatus.COMPLETED, items=task.get_items())