Move keypoint to detections converstion to sv.KeyPoints.

* Test colab: https://colab.research.google.com/drive/10PMuW0IyaksofqI70NLnB_-Fnr4oKVmk?usp=sharing

docs/how_to/track_objects.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 # Track Objects
@@ -317,7 +318,7 @@ movement patterns and interactions between objects in the video.
 
 ## Tracking Key Points
 
-Keypoint tracking is currently supported via the conversion of `KeyPoints` to `Detections`. This is achieved with the [`keypoints_to_detections`](/latest/utils/datatypes/#supervision.utils.datatypes.keypoints_to_detections) function. We'll use a different video as well as [`DetectionsSmoother`](/latest/detection/tools/smoother/) to stabilize the boxes.
+Keypoint tracking is currently supported via the conversion of `KeyPoints` to `Detections`. This is achieved with the [`KeyPoints.as_detections()`](/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.as_detections) function. We'll use a different video as well as [`DetectionsSmoother`](/latest/detection/tools/smoother/) to stabilize the boxes.
 
 !!! tip
 
@@ -340,7 +341,7 @@ Keypoint tracking is currently supported via the conversion of `KeyPoints` to `D
     def callback(frame: np.ndarray, _: int) -> np.ndarray:
         results = model(frame)[0]
         keypoints = sv.KeyPoints.from_ultralytics(results)
-        detections = sv.keypoints_to_detections(keypoints)
+        detections = keypoints.as_detections()
         detections = tracker.update_with_detections(detections)
         detections = smoother.update_with_detections(detections)
 
@@ -382,7 +383,7 @@ Keypoint tracking is currently supported via the conversion of `KeyPoints` to `D
     def callback(frame: np.ndarray, _: int) -> np.ndarray:
         results = model.infer(frame)[0]
         keypoints = sv.KeyPoints.from_inference(results)
-        detections = sv.keypoints_to_detections(keypoints)
+        detections = keypoints.as_detections()
         detections = tracker.update_with_detections(detections)
         detections = smoother.update_with_detections(detections)
 

docs/keypoint/core.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 # Keypoint Detection

docs/utils/datatypes.md

@@ -1,12 +0,0 @@
----
-comments: true
-status: new
----
-
-# Data Types Utils
-
-<div class="md-typeset">
-    <h2><a href="#supervision.utils.datatypes.keypoints_to_detections">keypoints_to_detections</a></h2>
-</div>
-
-:::supervision.utils.datatypes.keypoints_to_detections

mkdocs.yml

@@ -79,7 +79,6 @@ nav:
           - File: utils/file.md
           - Draw: utils/draw.md
           - Geometry: utils/geometry.md
-          - Datatypes: utils/datatypes.md
       - Assets: assets.md
   - Cookbooks: cookbooks.md
   - Cheatsheet: https://roboflow.github.io/cheatsheet-supervision/

supervision/__init__.py

@@ -100,7 +100,6 @@ from supervision.keypoint.core import KeyPoints
 from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
 from supervision.tracker.byte_tracker.core import ByteTrack
 from supervision.utils.conversion import cv2_to_pillow, pillow_to_cv2
-from supervision.utils.datatypes import keypoints_to_detections
 from supervision.utils.file import list_files_with_extensions
 from supervision.utils.image import (
     ImageSink,

supervision/keypoint/core.py

@@ -2,12 +2,13 @@ from __future__ import annotations
 
 from contextlib import suppress
 from dataclasses import dataclass, field
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, Iterator, List, Optional, Tuple, Union
 
 import numpy as np
 import numpy.typing as npt
 
 from supervision.config import CLASS_NAME_DATA_FIELD
+from supervision.detection.core import Detections
 from supervision.detection.utils import get_data_item, is_data_equal
 from supervision.validators import validate_keypoints_fields
 
@@ -620,3 +621,67 @@ class KeyPoints:
         empty_keypoints = KeyPoints.empty()
         empty_keypoints.data = self.data
         return self == empty_keypoints
+
+    def as_detections(
+        self, selected_keypoint_indices: Optional[Iterable[int]] = None
+    ) -> Detections:
+        """
+        Convert a KeyPoints object to a Detections object. This
+        approximates the bounding box of the detected object by
+        taking the bounding box that fits all keypoints.
+
+        Arguments:
+            selected_keypoint_indices (Optional[Iterable[int]]): The
+                indices of the keypoints to include in the bounding box
+                calculation. This helps focus on a subset of keypoints,
+                e.g. when some are occluded. Captures all keypoints by default.
+
+        Returns:
+            detections (Detections): The converted detections object.
+
+        Example:
+            ```python
+            keypoints = sv.KeyPoints.from_inference(...)
+            detections = keypoints.as_detections()
+            ```
+        """
+        if self.is_empty():
+            return Detections.empty()
+
+        detections_list = []
+        for i, xy in enumerate(self.xy):
+            if selected_keypoint_indices:
+                xy = xy[selected_keypoint_indices]
+
+            # [0, 0] used by some frameworks to indicate missing keypoints
+            xy = xy[~np.all(xy == 0, axis=1)]
+            if len(xy) == 0:
+                xyxy = np.array([[0, 0, 0, 0]], dtype=np.float32)
+            else:
+                x_min = xy[:, 0].min()
+                x_max = xy[:, 0].max()
+                y_min = xy[:, 1].min()
+                y_max = xy[:, 1].max()
+                xyxy = np.array([[x_min, y_min, x_max, y_max]], dtype=np.float32)
+
+            if self.confidence is None:
+                confidence = None
+            else:
+                confidence = self.confidence[i]
+                if selected_keypoint_indices:
+                    confidence = confidence[selected_keypoint_indices]
+                confidence = np.array([confidence.mean()], dtype=np.float32)
+
+            detections_list.append(
+                Detections(
+                    xyxy=xyxy,
+                    confidence=confidence,
+                )
+            )
+
+        detections = Detections.merge(detections_list)
+        detections.class_id = self.class_id
+        detections.data = self.data
+        detections = detections[detections.area > 0]
+
+        return detections

supervision/utils/datatypes.py

@@ -1,72 +0,0 @@
-from typing import Iterable, Optional
-
-import numpy as np
-
-from supervision.detection.core import Detections
-from supervision.keypoint.core import KeyPoints
-
-
-def keypoints_to_detections(
-    keypoints: KeyPoints, selected_keypoint_indices: Optional[Iterable[int]] = None
-) -> Detections:
-    """
-    Convert a KeyPoints object to a Detections object. This
-    approximates the bounding box of the detected object by
-    taking the bounding box that fits all keypoints.
-
-    Arguments:
-        keypoints (KeyPoints): The keypoints to convert to detections.
-        selected_keypoint_indices (Optional[Iterable[int]]): The
-            indices of the keypoints to include in the bounding box
-            calculation. This helps focus on a subset of keypoints,
-            e.g. when some are occluded. Captures all keypoints by default.
-
-    Returns:
-        detections (Detections): The converted detections object.
-
-    Example:
-        ```python
-        keypoints = sv.KeyPoints.from_inference(...)
-        detections = keypoints_to_detections(keypoints)
-        ```
-    """
-    if keypoints.is_empty():
-        return Detections.empty()
-
-    detections_list = []
-    for i, xy in enumerate(keypoints.xy):
-        if selected_keypoint_indices:
-            xy = xy[selected_keypoint_indices]
-
-        # [0, 0] used by some frameworks to indicate missing keypoints
-        xy = xy[~np.all(xy == 0, axis=1)]
-        if len(xy) == 0:
-            xyxy = np.array([[0, 0, 0, 0]], dtype=np.float32)
-        else:
-            x_min = xy[:, 0].min()
-            x_max = xy[:, 0].max()
-            y_min = xy[:, 1].min()
-            y_max = xy[:, 1].max()
-            xyxy = np.array([[x_min, y_min, x_max, y_max]], dtype=np.float32)
-
-        if keypoints.confidence is None:
-            confidence = None
-        else:
-            confidence = keypoints.confidence[i]
-            if selected_keypoint_indices:
-                confidence = confidence[selected_keypoint_indices]
-            confidence = np.array([confidence.mean()], dtype=np.float32)
-
-        detections_list.append(
-            Detections(
-                xyxy=xyxy,
-                confidence=confidence,
-            )
-        )
-
-    detections = Detections.merge(detections_list)
-    detections.class_id = keypoints.class_id
-    detections.data = keypoints.data
-    detections = detections[detections.area > 0]
-
-    return detections