🚀 Added

• sv.LabelAnnotator allowing to annotate images and videos with text. (#170)

• sv.BoundingBoxAnnotator allowing to annotate images and videos with bounding boxes. (#170)

• sv.BoxCornerAnnotator allowing to annotate images and videos with just bounding box corners. (#170)

• sv.MaskAnnotator allowing to annotate images and videos with segmentation masks. (#170)

• sv.EllipseAnnotator allowing to annotate images and videos with ellipses (sports game style). (#170)

• sv.CircleAnnotator allowing to annotate images and videos with circles. (#386)

• sv.TraceAnnotator allowing to draw path of moving objects on videos. (#354)

• sv.BlurAnnotator allowing to blur objects on images and videos. (#405)

>>> import supervision as sv

>>> image = ...
>>> detections = sv.Detections(...)

>>> bounding_box_annotator = sv.BoundingBoxAnnotator()
>>> annotated_frame = bounding_box_annotator.annotate(
...     scene=image.copy(),
...     detections=detections
... )

• Supervision usage example. You can now learn how to perform traffic flow analysis with Supervision. (#354)

🌱 Changed

• sv.Detections.from_roboflow now does not require class_list to be specified. The class_id value can be extracted directly from the inference response. (#399)

• sv.VideoSink now allows to customize the output codec. (#381)

• sv.InferenceSlicer can now operate in multithreading mode. (#361)

🛠️ Fixed

• sv.Detections.from_deepsparse to allow processing empty deepsparse result object. (#348)

🏆 Contributors

@hardikdava (Hardik Dava), @onuralpszr (Onuralp SEZER), @Killua7362 (Akshay Bhat), @fcakyon (Fatih C. Akyon), @akashAD98 (Akash A Desai), @Rajarshi-Misra, @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)