refactor(keypoints): remove deprecated RF-DETR keypoint conversion logic

Author: Borda

src/supervision/key_points/core.py

@@ -788,66 +788,6 @@ def from_transformers(cls, transformers_results: Any) -> KeyPoints:
         else:
             return cls.empty()
 
-    @classmethod
-    def from_rfdetr(cls, detections: Detections) -> KeyPoints:
-        """
-        Create a `sv.KeyPoints` object from RF-DETR keypoint predictions.
-
-        RF-DETR stores keypoints under `sv.Detections.data["keypoints"]` with shape
-        `(N, K, 3)`, where each keypoint is `(x, y, confidence)`.
-
-        Args:
-            detections: RF-DETR detections containing keypoints in
-                `detections.data["keypoints"]`.
-
-        Returns:
-            A `sv.KeyPoints` object containing xy coordinates, confidence, and class IDs.
-
-        Raises:
-            ValueError: If keypoints are missing or have invalid shape.
-
-        Examples:
-            ```pycon
-            >>> import numpy as np
-            >>> import supervision as sv
-            >>> detections = sv.Detections(
-            ...     xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
-            ...     class_id=np.array([0], dtype=int),
-            ...     data={"keypoints": np.array([[[1, 2, 0.9], [3, 4, 0.8]]], dtype=np.float32)},
-            ... )
-            >>> key_points = sv.KeyPoints.from_rfdetr(detections)
-            >>> key_points.xy.shape
-            (1, 2, 2)
-            >>> key_points.confidence.shape
-            (1, 2)
-
-            ```
-        """
-        if "keypoints" not in detections.data:
-            raise ValueError(
-                "RF-DETR keypoints are missing. Expected detections.data['keypoints'] with shape (N, K, 3)."
-            )
-
-        keypoints = np.asarray(detections.data["keypoints"], dtype=np.float32)
-        if keypoints.ndim != 3 or keypoints.shape[-1] != 3:
-            raise ValueError(
-                "RF-DETR keypoints must have shape (N, K, 3) with (x, y, confidence) values."
-            )
-
-        xy = keypoints[..., :2].astype(np.float32, copy=False)
-        confidence = keypoints[..., 2].astype(np.float32, copy=False)
-        class_id = (
-            detections.class_id.astype(int, copy=False)
-            if detections.class_id is not None
-            else None
-        )
-
-        return cls(
-            xy=xy,
-            confidence=confidence,
-            class_id=class_id,
-        )
-
     def _get_by_2d_bool_mask(self, mask: npt.NDArray[np.bool_]) -> KeyPoints:
         """Filter keypoints using a 2D boolean mask of shape `(n, m)`.
 

tests/key_points/test_from_rfdetr.py

@@ -36,7 +36,7 @@ def test_keypoints_from_rfdetr_missing_keypoints_raises_clear_error() -> None:
         class_id=np.array([0], dtype=int),
     )
 
-    with pytest.raises(ValueError, match="detections.data\\['keypoints'\\]"):
+    with pytest.raises(ValueError, match=r"detections\.data\['keypoints'\]"):
         sv.KeyPoints.from_rfdetr(detections)
 
 
@@ -57,11 +57,17 @@ def test_keypoint_annotator_uses_vertex_and_edge_rendering() -> None:
     scene = np.zeros((32, 32, 3), dtype=np.uint8)
     detections = sv.Detections(
         xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
-        data={"keypoints": np.array([[[10.0, 10.0, 0.9], [20.0, 20.0, 0.8]]], dtype=np.float32)},
+        data={
+            "keypoints": np.array(
+                [[[10.0, 10.0, 0.9], [20.0, 20.0, 0.8]]], dtype=np.float32
+            )
+        },
     )
     key_points = sv.KeyPoints.from_rfdetr(detections)
 
     scene = sv.VertexAnnotator().annotate(scene=scene, key_points=key_points)
-    scene = sv.EdgeAnnotator(edges=[(1, 2)]).annotate(scene=scene, key_points=key_points)
+    scene = sv.EdgeAnnotator(edges=[(1, 2)]).annotate(
+        scene=scene, key_points=key_points
+    )
 
     assert np.any(scene != 0)