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.

Upload a folder with its annotations

report = client.upload_directory(
    "DATASET_ID",
    "datasets/parking",
    work_state="done",
)
print(len(report["items"]), "images stored in", report["requests"], "requests;", report["imported"])

This sends the folder the way dropping it on the dataset's Upload page would: images, plus COCO (polygons and boxes), YOLO (labels/*.txt with data.yaml or classes.txt), Pascal VOC, LabelMe, CSV, metadata.jsonl, a folder per class, or a LabelTify export. The server reads the annotations; the client only splits the folder into requests (at most 100 images and 80 MB each). A COCO file is cut down to each request's images, so polygons stay polygons.

• The dataset must already exist. Create it on the site. Parking polygons need type Instance segmentation. Traffic boxes need Object detection.
• work_state="done" marks every image that received a shape. Images with no shape stay untouched. With any label file in the folder, work_state is required.
• Create the pipeline token on that dataset's Upload page.
• One credit covers 10,000 uploaded images. A free dataset holds 100 photos.
• Frame embeddings follow the dataset setting. Turn them off on the dataset before a large upload when you do not want that charge.
• Use the portable pixel COCO files. The raw Studio shards store boxes as fractions from 0 to 1, and the server divides by width and height again.
• Two images with the same file name in different folders stop the upload before anything is sent; rename one.
• Images over 20 MiB, empty files, and files that are neither images nor annotations are listed in report["files"] and not sent. A zip is sent as it is when it holds at most 200 files and 20 MB; unzip anything bigger.
• If a request fails after its retries, the error says how many images were already stored; run the folder again and exact duplicates are reported instead of stored twice.
• compute_dhash=True also skips near-duplicate frames, like the Upload page. It needs Pillow: pip install "labeltify[images]".

upload_bundle(dataset_id, files, work_state=…) sends one such request from bytes you already have (files is a list of {"filename", "data", "content_type"}).

A deployment behind an access proxy can take extra headers on every request: LabelTifyClient(token, base_url=…, headers={"CF-Access-Client-Id": …, "CF-Access-Client-Secret": …}).

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.