Programmatic Label & Review

It may be useful to programmatically add labels to your uploaded data or perform a review on queued tasks. This scenario may arise if you have an automated way of reviewing data or if you want to bulk-process tasks. 
Please see the detailed reference documentation for put_tasks here.
You can use put_tasks only on tasks assigned to your API key.
Visit our documentation on assigning tasks to learn how to assign tasks to your API key.
First, perform the standard RedBrick AI SDK set-up to create a project object.
project = redbrick.get_project(org_id, project_id, api_key)
Next, you need to get a list of tasks you want to label/review. You can do this by: 
1.Searching for the task_id through the RedBrick AI UI.
2.Retrieving the task_id from your filename/custom name from the Items List, using search_tasks.
3.Retrieving tasks assigned to your API key using get_tasks.
Programmatically label tasks
Add your annotations within the series field, along with the task_id. Please refer to the reference documentation for the format of the annotations in series.
 The corresponding task must be queued in the Label stage and assigned to your API key. 
tasks = [
    {
        "taskId": "...",
        "series": [{...}]
    },
]
​
# Submit tasks with new labels
project.labeling.put_tasks("Label", tasks)
​
# Save tasks as draft with new labels
project.labeling.put_tasks("Label", tasks, finalize=False)
Programmatically review tasks
Add your review decision in the review_result argument, along with the task_id. The corresponding task must be queued in the Review stage that you specify in stage_name and must be assigned to your API key.
# Set review_result to True if you want to accept the tasks
project.review.put_tasks("Review_1", [{taskId: "..."}], review_result=True)
​
# Set review_result to False if you want to reject the tasks
project.review.put_tasks("Review_1", [{taskId: "..."}], review_result=False)
​
# Add labels if you want to accept the tasks with correction
project.review.put_tasks("Review_1", [{taskId: "...", series: [{...}]}])
Re-annotate Ground Truth Tasks
Once your task goes through all workflow stages, it will be stored in the Ground Truth stage. If you notice issues with one or more of your Ground Truth tasks, you can either directly correct them in the Ground Truth stage through the UI or send them back to the Label stage for correction.
First, get a list of the task_ids you want to send back to Label. You can do this by exporting only ground truth tasks and filtering them. Then, use move_tasks_to_start to send them back to Label.
task_ids = ["...", "..."]
project.labeling.move_tasks_to_start(task_ids=task_ids)
All corresponding Tasks need to be in the Ground Truth stage. This function will not work for tasks queued in Review. 
Importing data & annotations
Assigning & querying tasks
Last modified 2mo ago