docs(cookbooks): add Oriented Bounding Boxes cookbook (#2314)
- Added an end-to-end cookbook focused on oriented bounding boxes (OBB), demonstrating how OBB detections differ from axis-aligned boxes, why oriented overlap and NMS matter, how to use footprint-based filtering with `Detections.area`, and how to export annotations in YOLO OBB format. - Added visual examples that clearly compare axis-aligned and oriented boxes, including a close-up showing how axis-aligned envelopes can significantly overestimate object footprints for angled objects. - Improved the cookbook narrative to center on the practical consequences of using oriented versus axis-aligned boxes, including tighter localization and more appropriate NMS behavior for densely packed, rotated objects. - Updated cookbook references, naming, dependency versions, image attribution, and changelog links to align with the released 0.29.0 documentation. - Fixed the 0.29.0 changelog by removing a duplicate `Detections.area` entry and keeping the more accurate correctness-fix classification. --------- Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com> Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
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Agis Kounelis
GitHub
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description: "Full version history of the supervision Python library — release notes, breaking changes, new features, and deprecations for every version."
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date_modified: 2026-06-15
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date_modified: 2026-06-16
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---
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# Changelog
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@ -21,6 +21,10 @@ date_modified: 2026-06-15
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### 0.29.0 <small>Jun 15, 2026</small>
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- Added [#2314](https://github.com/roboflow/supervision/pull/2314): new cookbook **Oriented Bounding Boxes** showing how an oriented box differs from an axis-aligned one on a marina of boats: DOTA-pretrained detection, the effect on [`with_nms`](https://supervision.roboflow.com/0.29.0/detection/core/#supervision.detection.core.Detections.with_nms) and [`Detections.area`](https://supervision.roboflow.com/0.29.0/detection/core/#supervision.detection.core.Detections.area), and YOLO OBB dataset export.
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- Fixed [#2306](https://github.com/roboflow/supervision/pull/2306): [`sv.Detections.area`](https://supervision.roboflow.com/0.29.0/detection/core/#supervision.detection.core.Detections.area) now returns the rotated body's area for detections carrying `data["xyxyxyxy"]` (oriented box corners) instead of the area of the derived axis-aligned bounding box, which overestimates by up to ~2x at 45° rotation. Affects annotator z-ordering inside [`MaskAnnotator`](https://supervision.roboflow.com/0.29.0/detection/annotators/#supervision.annotators.core.MaskAnnotator) and [`HaloAnnotator`](https://supervision.roboflow.com/0.29.0/detection/annotators/#supervision.annotators.core.HaloAnnotator), and any user code that filters or sorts OBB detections by area. The mask path and the non-OBB AABB fallback are unchanged.
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- Added [#2277](https://github.com/roboflow/supervision/pull/2277), [#2286](https://github.com/roboflow/supervision/pull/2286): [`sv.VertexEllipseAreaAnnotator`](https://supervision.roboflow.com/0.29.0/keypoint/annotators/#supervision.key_points.annotators.VertexEllipseAreaAnnotator), [`sv.VertexEllipseOutlineAnnotator`](https://supervision.roboflow.com/0.29.0/keypoint/annotators/#supervision.key_points.annotators.VertexEllipseOutlineAnnotator), and [`sv.VertexEllipseHaloAnnotator`](https://supervision.roboflow.com/0.29.0/keypoint/annotators/#supervision.key_points.annotators.VertexEllipseHaloAnnotator) for visualizing keypoint uncertainty as covariance ellipses. Requires models that output keypoint uncertainty (e.g. RF-DETR keypoint models).
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- Added [#2303](https://github.com/roboflow/supervision/pull/2303): [`sv.oriented_box_non_max_suppression`](https://supervision.roboflow.com/0.29.0/detection/utils/iou_and_nms/#supervision.detection.utils.iou_and_nms.oriented_box_non_max_suppression) and [`sv.oriented_box_non_max_merge`](https://supervision.roboflow.com/0.29.0/detection/utils/iou_and_nms/#supervision.detection.utils.iou_and_nms.oriented_box_non_max_merge) for performing NMS and NMM directly on oriented bounding boxes using oriented-box IoU instead of axis-aligned IoU.
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@ -45,8 +49,6 @@ date_modified: 2026-06-15
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- Changed [#2325](https://github.com/roboflow/supervision/pull/2325): [`sv.VertexEllipseAreaAnnotator`](https://supervision.roboflow.com/0.29.0/keypoint/annotators/#supervision.key_points.annotators.VertexEllipseAreaAnnotator), [`sv.VertexEllipseOutlineAnnotator`](https://supervision.roboflow.com/0.29.0/keypoint/annotators/#supervision.key_points.annotators.VertexEllipseOutlineAnnotator), and [`sv.VertexEllipseHaloAnnotator`](https://supervision.roboflow.com/0.29.0/keypoint/annotators/#supervision.key_points.annotators.VertexEllipseHaloAnnotator) now draw sigma levels level-by-level (outermost first) across all points, ensuring correct visual layering when ellipses overlap.
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- Changed [#2306](https://github.com/roboflow/supervision/pull/2306): [`sv.Detections.area`](https://supervision.roboflow.com/0.29.0/detection/core/#supervision.detection.core.Detections.area) now returns the polygon area of the rotated bounding box (via the shoelace formula) when oriented box coordinates are present, instead of the axis-aligned box area.
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- Changed [#2256](https://github.com/roboflow/supervision/pull/2256): [`sv.InferenceSlicer`](https://supervision.roboflow.com/0.29.0/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) now detects OBB outputs from callbacks and automatically falls back to sequential processing to avoid thread-safety issues when `thread_workers > 1`.
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- Changed [#2324](https://github.com/roboflow/supervision/pull/2324): Project-wide deprecation policy unified to a minimum 3-minor-release window. All current deprecations (including `KeyPoints.confidence` and `validate_*` helpers) are scheduled for removal in `0.32.0`.
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<p class="card repo-card" data-name="Memory-Efficient Instance Segmentation"
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data-labels="COMPACT MASK,SAM3,SEGMENTATION" data-version="v0.28.0" data-author="Borda"></p>
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</a>
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<a href="../notebooks/oriented-bounding-boxes/">
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<p class="card repo-card" data-name="Oriented Bounding Boxes for Densely Packed Objects"
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data-labels="OBB,DETECTIONS,NMS,DATASET" data-version="v0.29.0" data-author="kounelisagis"></p>
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</a>
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</div>
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</div>
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</section>
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@ -158,7 +158,9 @@ def pillow_to_cv2(image: Image.Image) -> npt.NDArray[np.uint8]:
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"""
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scene = np.array(image)
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scene = cv2.cvtColor(scene, cv2.COLOR_RGB2BGR)
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return scene.astype(np.uint8)
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# cvtColor already returns uint8 here, so astype is a no-op other than the
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# full-image copy it forces; copy=False keeps the dtype guard without it.
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return scene.astype(np.uint8, copy=False)
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def cv2_to_pillow(image: npt.NDArray[np.uint8]) -> Image.Image:
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