Label studio export deta YOLO dataset

1. export JSON file with the following function.

import time
from label_studio_sdk import Client

# https://labelstud.io/guide/export
# https://github.com/HumanSignal/label-studio-sdk/blob/master/examples/export_snapshots.py
def ExportSnapshot(LABEL_STUDIO_URL, API_KEY, PROJECT_ID, SAVE_PATH):
    # connect to Label Studio
    ls = Client(url=LABEL_STUDIO_URL, api_key=API_KEY)
    ls.check_connection()

    # get existing project
    project = ls.get_project(PROJECT_ID)

    # get the first tab
    views = project.get_views()
    task_filter_options = {'view': views[0]['id']} if views else {}

    # create new export snapshot
    export_result = project.export_snapshot_create(
        title='Export SDK Snapshot', task_filter_options=task_filter_options
    )
    # assert 'id' in export_result
    export_id = export_result['id']

    # # wait until snapshot is ready
    while project.export_snapshot_status(export_id).is_in_progress():
        time.sleep(1.0)

    # download snapshot file
    status, file_name = project.export_snapshot_download(export_id, export_type='JSON', path=SAVE_PATH)
    assert status == 200
    assert file_name is not None
    print(f"Status of the export is {status}.\nFile name is {file_name}")

2. set LS_UPLOAD_DIR in .zshrc or .bashrc and source ~/.zshrc or source ~/.bashrc

# label studio
export LS_UPLOAD_DIR=/home/epbox/AI/data/media/upload

3. save .xml file of the export project

4. use label-studio-converter to convert JSON to YOLO format

pip install label-studio-converter

label-studio-converter export -i .json --config .xml -o "train" -f YOLO

5. check the convert YOLO train folder

(label) ➜  train -h --filelimit=10 --dirsfirst
train
├── [ 20K]  images [208 entries exceeds filelimit, not opening dir]
├── [ 20K]  labels [208 entries exceeds filelimit, not opening dir]
├── [ 124]  classes.txt
└── [ 840]  notes.json

2 directories, 2 files