Custom functions to work with SuperAnnotate in Databricks
Project description
Superannotate Databricks Connector
SuperAnnotate is the cornerstone of your data labeling pipeline. It brings you a cutting-edge annotation tool for all types of data including image, video, text, LiDAR, audio, and more.
This Python package provides a set of utilities for working with SuperAnnotate data on Databricks. It includes functionality to process SuperAnnotate data and save it to Delta tables.
Features
Convert superannotate annotation data to Apache Spark™ Data Frames. Project types supported: - Vector - Text
Example notebooks.
Copy the notebooks in the demo folder to your databricks workspace to get started with SuperAnnotate quickly!
Installation
pip install superannotate_databricks_connector
Tests
Run tests by building the Dockerfile.test file using
docker build -f Dockerfile.test -t test_package .
If you are running the tests for the first you first have to build the base dockerfile containing pyspark.
docker build -f Dockerfile.spark -t spark_docker_base .
Build package
In the main directory, run the following to generate a .whl file.
python -m build
Usage
First import the required function
from superannotate_databricks_conector.vector import get_vector_dataframe
from superannotate import SAClient
You can then convert your annotations to a spark dataframe
sa = SAClient(token="<TOKEN>")
annotations = sa.get_annotations("<PROJECT_NAME>)
df = get_vector_dataframe(annotations, spark)
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