labeltify

Upload frames, and optionally their annotations, into LabelTify from DeepStream, OpenCV, Ultralytics, or any Python pipeline. The only thing it needs is a pipeline token.

pip install labeltify

1. On the dataset Upload page, create a pipeline token.
2. Upload:

from labeltify import LabelTifyClient

client = LabelTifyClient("labeltify_…")  # or set LABELTIFY_TOKEN and call LabelTifyClient()

img = client.upload_image(
    "DATASET_ID",
    jpeg_bytes,
    filename="cam01.jpg",
    camera="cam-01",
    pipeline="ds-prod",
    trigger="low_confidence",
)
client.upload_file("DATASET_ID", "frames/cam02.jpg", camera="cam-02")

The token knows its org, so you don't pass one. Timeouts, 429, and 5xx responses are retried with exponential backoff (max_retries=4 by default). Anything else raises LabelTifyError with the API's message and .status.

Annotations are optional. When you pass labels (COCO, LabelTify, or YOLO JSON bytes), work_state is required: done, in_progress, unsure, or untouched. There is no default; the client raises LabelTifyError before sending if it is missing. Images with no labels stay untouched.

Send model proposals to the Loop review queue:

client.propose(
    "DATASET_ID",
    img["id"],
    [{"type": "bbox", "id": "p1", "classId": "car", "box": {"x": 0.1, "y": 0.1, "w": 0.2, "h": 0.2}, "source": "model", "confidence": 0.4}],
    model_ref="deepstream@1",
    uncertainty=0.8,
)

Boxes are normalized [0,1]. Camera, pipeline, and trigger are stored on the image so Loop can filter by them.

Local API: LabelTifyClient("labeltify_…", base_url="http://localhost:8787").

Develop

pip install -e "clients/python[test]"
pytest clients/python/tests

Bump version in pyproject.toml to publish. Each new version that lands on main is published to PyPI by .github/workflows/publish-python.yml.