Keypoint Detection

The sv.KeyPoints class in the Supervision library standardizes results from various keypoint detection and pose estimation models into a consistent format. This class simplifies data manipulation and filtering, providing a uniform API for integration with Supervision keypoints annotators.

UltralyticsInferenceMediaPipe

Use sv.KeyPoints.from_ultralytics method, which accepts YOLOv8 pose result.

import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8s-pose.pt')

result = model(image)[0]
key_points = sv.KeyPoints.from_ultralytics(result)

Use sv.KeyPoints.from_inference method, which accepts Inference pose result.

import cv2
import supervision as sv
from inference import get_model

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_model(model_id=<POSE_MODEL_ID>, api_key=<ROBOFLOW_API_KEY>)

result = model.infer(image)[0]
key_points = sv.KeyPoints.from_inference(result)

Use sv.KeyPoints.from_mediapipe method, which accepts MediaPipe pose result.

import cv2
import mediapipe as mp
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
image_height, image_width, _ = image.shape
mediapipe_image = mp.Image(
    image_format=mp.ImageFormat.SRGB,
    data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

options = mp.tasks.vision.PoseLandmarkerOptions(
    base_options=mp.tasks.BaseOptions(
        model_asset_path="pose_landmarker_heavy.task"
    ),
    running_mode=mp.tasks.vision.RunningMode.IMAGE,
    num_poses=2)

PoseLandmarker = mp.tasks.vision.PoseLandmarker
with PoseLandmarker.create_from_options(options) as landmarker:
    pose_landmarker_result = landmarker.detect(mediapipe_image)

key_points = sv.KeyPoints.from_mediapipe(
    pose_landmarker_result, (image_width, image_height))

Attributes:

Name	Type	Description
xy	ndarray	An array of shape (n, m, 2) containing n detected objects, each composed of m equally-sized sets of keypoints, where each point is [x, y].
confidence	Optional[ndarray]	An array of shape (n, m) containing the confidence scores of each keypoint.
class_id	Optional[ndarray]	An array of shape (n,) containing the class ids of the detected objects.
data	Dict[str, Union[ndarray, List]]	A dictionary containing additional data where each key is a string representing the data type, and the value is either a NumPy array or a list of corresponding data of length n (one entry per detected object).

Source code in supervision/keypoint/core.py

@dataclass
class KeyPoints:
    """
    The `sv.KeyPoints` class in the Supervision library standardizes results from
    various keypoint detection and pose estimation models into a consistent format. This
    class simplifies data manipulation and filtering, providing a uniform API for
    integration with Supervision [keypoints annotators](/latest/keypoint/annotators).

    === "Ultralytics"

        Use [`sv.KeyPoints.from_ultralytics`](/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_ultralytics)
        method, which accepts [YOLOv8](https://github.com/ultralytics/ultralytics)
        pose result.

        ```python
        import cv2
        import supervision as sv
        from ultralytics import YOLO

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = YOLO('yolov8s-pose.pt')

        result = model(image)[0]
        key_points = sv.KeyPoints.from_ultralytics(result)
        ```

    === "Inference"

        Use [`sv.KeyPoints.from_inference`](/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_inference)
        method, which accepts [Inference](https://inference.roboflow.com/) pose result.

        ```python
        import cv2
        import supervision as sv
        from inference import get_model

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = get_model(model_id=<POSE_MODEL_ID>, api_key=<ROBOFLOW_API_KEY>)

        result = model.infer(image)[0]
        key_points = sv.KeyPoints.from_inference(result)
        ```

    === "MediaPipe"

        Use [`sv.KeyPoints.from_mediapipe`](/latest/keypoint/core/#supervision.keypoint.core.KeyPoints.from_mediapipe)
        method, which accepts [MediaPipe](https://github.com/google-ai-edge/mediapipe)
        pose result.

        ```python
        import cv2
        import mediapipe as mp
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        image_height, image_width, _ = image.shape
        mediapipe_image = mp.Image(
            image_format=mp.ImageFormat.SRGB,
            data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

        options = mp.tasks.vision.PoseLandmarkerOptions(
            base_options=mp.tasks.BaseOptions(
                model_asset_path="pose_landmarker_heavy.task"
            ),
            running_mode=mp.tasks.vision.RunningMode.IMAGE,
            num_poses=2)

        PoseLandmarker = mp.tasks.vision.PoseLandmarker
        with PoseLandmarker.create_from_options(options) as landmarker:
            pose_landmarker_result = landmarker.detect(mediapipe_image)

        key_points = sv.KeyPoints.from_mediapipe(
            pose_landmarker_result, (image_width, image_height))
        ```

    Attributes:
        xy (np.ndarray): An array of shape `(n, m, 2)` containing
            `n` detected objects, each composed of `m` equally-sized
            sets of keypoints, where each point is `[x, y]`.
        confidence (Optional[np.ndarray]): An array of shape
            `(n, m)` containing the confidence scores of each keypoint.
        class_id (Optional[np.ndarray]): An array of shape
            `(n,)` containing the class ids of the detected objects.
        data (Dict[str, Union[np.ndarray, List]]): A dictionary containing additional
            data where each key is a string representing the data type, and the value
            is either a NumPy array or a list of corresponding data of length `n`
            (one entry per detected object).
    """  # noqa: E501 // docs

    xy: npt.NDArray[np.float32]
    class_id: Optional[npt.NDArray[np.int_]] = None
    confidence: Optional[npt.NDArray[np.float32]] = None
    data: Dict[str, Union[npt.NDArray[Any], List]] = field(default_factory=dict)

    def __post_init__(self):
        validate_keypoints_fields(
            xy=self.xy,
            confidence=self.confidence,
            class_id=self.class_id,
            data=self.data,
        )

    def __len__(self) -> int:
        """
        Returns the number of keypoints in the `sv.KeyPoints` object.
        """
        return len(self.xy)

    def __iter__(
        self,
    ) -> Iterator[
        Tuple[
            np.ndarray,
            Optional[np.ndarray],
            Optional[float],
            Optional[int],
            Optional[int],
            Dict[str, Union[np.ndarray, List]],
        ]
    ]:
        """
        Iterates over the Keypoint object and yield a tuple of
        `(xy, confidence, class_id, data)` for each object detection.
        """
        for i in range(len(self.xy)):
            yield (
                self.xy[i],
                self.confidence[i] if self.confidence is not None else None,
                self.class_id[i] if self.class_id is not None else None,
                get_data_item(self.data, i),
            )

    def __eq__(self, other: KeyPoints) -> bool:
        return all(
            [
                np.array_equal(self.xy, other.xy),
                np.array_equal(self.class_id, other.class_id),
                np.array_equal(self.confidence, other.confidence),
                is_data_equal(self.data, other.data),
            ]
        )

    @classmethod
    def from_inference(cls, inference_result: Union[dict, Any]) -> KeyPoints:
        """
        Create a `sv.KeyPoints` object from the [Roboflow](https://roboflow.com/)
        API inference result or the [Inference](https://inference.roboflow.com/)
        package results.

        Args:
            inference_result (dict, any): The result from the
                Roboflow API or Inference package containing predictions with keypoints.

        Returns:
            A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
                and class names, and confidences of each keypoint.

        Examples:
            ```python
            import cv2
            import supervision as sv
            from inference import get_model

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = get_model(model_id=<POSE_MODEL_ID>, api_key=<ROBOFLOW_API_KEY>)

            result = model.infer(image)[0]
            key_points = sv.KeyPoints.from_inference(result)
            ```

            ```python
            import cv2
            import supervision as sv
            from inference_sdk import InferenceHTTPClient

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            client = InferenceHTTPClient(
                api_url="https://detect.roboflow.com",
                api_key=<ROBOFLOW_API_KEY>
            )

            result = client.infer(image, model_id=<POSE_MODEL_ID>)
            key_points = sv.KeyPoints.from_inference(result)
            ```
        """
        if isinstance(inference_result, list):
            raise ValueError(
                "from_inference() operates on a single result at a time."
                "You can retrieve it like so:  inference_result = model.infer(image)[0]"
            )

        with suppress(AttributeError):
            inference_result = inference_result.dict(exclude_none=True, by_alias=True)

        if not inference_result.get("predictions"):
            return cls.empty()

        xy = []
        confidence = []
        class_id = []
        class_names = []

        for prediction in inference_result["predictions"]:
            prediction_xy = []
            prediction_confidence = []
            for keypoint in prediction["keypoints"]:
                prediction_xy.append([keypoint["x"], keypoint["y"]])
                prediction_confidence.append(keypoint["confidence"])
            xy.append(prediction_xy)
            confidence.append(prediction_confidence)

            class_id.append(prediction["class_id"])
            class_names.append(prediction["class"])

        data = {CLASS_NAME_DATA_FIELD: np.array(class_names)}

        return cls(
            xy=np.array(xy, dtype=np.float32),
            confidence=np.array(confidence, dtype=np.float32),
            class_id=np.array(class_id, dtype=int),
            data=data,
        )

    @classmethod
    def from_mediapipe(
        cls, mediapipe_results, resolution_wh: Tuple[int, int]
    ) -> KeyPoints:
        """
        Creates a `sv.KeyPoints` instance from a
        [MediaPipe](https://github.com/google-ai-edge/mediapipe)
        pose landmark detection inference result.

        Args:
            mediapipe_results (Union[PoseLandmarkerResult, FaceLandmarkerResult, SolutionOutputs]):
                The output results from Mediapipe. It support pose and face landmarks
                from `PoseLandmaker`, `FaceLandmarker` and the legacy ones
                from `Pose` and `FaceMesh`.
            resolution_wh (Tuple[int, int]): A tuple of the form `(width, height)`
                representing the resolution of the frame.

        Returns:
            A `sv.KeyPoints` object containing the keypoint coordinates and
                confidences of each keypoint.

        !!! tip
            Before you start, download model bundles from the
            [MediaPipe website](https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker/index#models).

        Examples:
            ```python
            import cv2
            import mediapipe as mp
            import supervision as sv

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            image_height, image_width, _ = image.shape
            mediapipe_image = mp.Image(
                image_format=mp.ImageFormat.SRGB,
                data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

            options = mp.tasks.vision.PoseLandmarkerOptions(
                base_options=mp.tasks.BaseOptions(
                    model_asset_path="pose_landmarker_heavy.task"
                ),
                running_mode=mp.tasks.vision.RunningMode.IMAGE,
                num_poses=2)

            PoseLandmarker = mp.tasks.vision.PoseLandmarker
            with PoseLandmarker.create_from_options(options) as landmarker:
                pose_landmarker_result = landmarker.detect(mediapipe_image)

            key_points = sv.KeyPoints.from_mediapipe(
                pose_landmarker_result, (image_width, image_height))
            ```

            ```python
            import cv2
            import mediapipe as mp
            import supervision as sv

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            image_height, image_width, _ = image.shape
            mediapipe_image = mp.Image(
                image_format=mp.ImageFormat.SRGB,
                data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

            options = mp.tasks.vision.FaceLandmarkerOptions(
                base_options=mp.tasks.BaseOptions(
                    model_asset_path="face_landmarker.task"
                ),
                output_face_blendshapes=True,
                output_facial_transformation_matrixes=True,
                num_faces=2)

            FaceLandmarker = mp.tasks.vision.FaceLandmarker
            with FaceLandmarker.create_from_options(options) as landmarker:
                face_landmarker_result = landmarker.detect(mediapipe_image)

            key_points = sv.KeyPoints.from_mediapipe(
                face_landmarker_result, (image_width, image_height))
            ```
        """  # noqa: E501 // docs
        if hasattr(mediapipe_results, "pose_landmarks"):
            results = mediapipe_results.pose_landmarks
            if not isinstance(mediapipe_results.pose_landmarks, list):
                if mediapipe_results.pose_landmarks is None:
                    results = []
                else:
                    results = [
                        [
                            landmark
                            for landmark in mediapipe_results.pose_landmarks.landmark
                        ]
                    ]
        elif hasattr(mediapipe_results, "face_landmarks"):
            results = mediapipe_results.face_landmarks
        elif hasattr(mediapipe_results, "multi_face_landmarks"):
            if mediapipe_results.multi_face_landmarks is None:
                results = []
            else:
                results = [
                    face_landmark.landmark
                    for face_landmark in mediapipe_results.multi_face_landmarks
                ]

        if len(results) == 0:
            return cls.empty()

        xy = []
        confidence = []
        for pose in results:
            prediction_xy = []
            prediction_confidence = []
            for landmark in pose:
                keypoint_xy = [
                    landmark.x * resolution_wh[0],
                    landmark.y * resolution_wh[1],
                ]
                prediction_xy.append(keypoint_xy)
                prediction_confidence.append(landmark.visibility)

            xy.append(prediction_xy)
            confidence.append(prediction_confidence)

        return cls(
            xy=np.array(xy, dtype=np.float32),
            confidence=np.array(confidence, dtype=np.float32),
        )

    @classmethod
    def from_ultralytics(cls, ultralytics_results) -> KeyPoints:
        """
        Creates a `sv.KeyPoints` instance from a
        [YOLOv8](https://github.com/ultralytics/ultralytics) pose inference result.

        Args:
            ultralytics_results (ultralytics.engine.results.Keypoints):
                The output Results instance from YOLOv8

        Returns:
            A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
                and class names, and confidences of each keypoint.

        Examples:
            ```python
            import cv2
            import supervision as sv
            from ultralytics import YOLO

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = YOLO('yolov8s-pose.pt')

            result = model(image)[0]
            key_points = sv.KeyPoints.from_ultralytics(result)
            ```
        """
        if ultralytics_results.keypoints.xy.numel() == 0:
            return cls.empty()

        xy = ultralytics_results.keypoints.xy.cpu().numpy()
        class_id = ultralytics_results.boxes.cls.cpu().numpy().astype(int)
        class_names = np.array([ultralytics_results.names[i] for i in class_id])

        confidence = ultralytics_results.keypoints.conf.cpu().numpy()
        data = {CLASS_NAME_DATA_FIELD: class_names}
        return cls(xy, class_id, confidence, data)

    @classmethod
    def from_yolo_nas(cls, yolo_nas_results) -> KeyPoints:
        """
        Create a `sv.KeyPoints` instance from a [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS-POSE.md)
        pose inference results.

        Args:
            yolo_nas_results (ImagePoseEstimationPrediction): The output object from
                YOLO NAS.

        Returns:
            A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
                and class names, and confidences of each keypoint.

        Examples:
            ```python
            import cv2
            import torch
            import supervision as sv
            import super_gradients

            image = cv2.imread(<SOURCE_IMAGE_PATH>)

            device = "cuda" if torch.cuda.is_available() else "cpu"
            model = super_gradients.training.models.get(
                "yolo_nas_pose_s", pretrained_weights="coco_pose").to(device)

            results = model.predict(image, conf=0.1)
            key_points = sv.KeyPoints.from_yolo_nas(results)
            ```
        """
        if len(yolo_nas_results.prediction.poses) == 0:
            return cls.empty()

        xy = yolo_nas_results.prediction.poses[:, :, :2]
        confidence = yolo_nas_results.prediction.poses[:, :, 2]

        # yolo_nas_results treats params differently.
        # prediction.labels may not exist, whereas class_names might be None
        if hasattr(yolo_nas_results.prediction, "labels"):
            class_id = yolo_nas_results.prediction.labels  # np.array[int]
        else:
            class_id = None

        data = {}
        if class_id is not None and yolo_nas_results.class_names is not None:
            class_names = []
            for c_id in class_id:
                name = yolo_nas_results.class_names[c_id]  # tuple[str]
                class_names.append(name)
            data[CLASS_NAME_DATA_FIELD] = class_names

        return cls(
            xy=xy,
            confidence=confidence,
            class_id=class_id,
            data=data,
        )

    @classmethod
    def from_detectron2(cls, detectron2_results: Any) -> KeyPoints:
        """
        Create a `sv.KeyPoints` object from the
        [Detectron2](https://github.com/facebookresearch/detectron2) inference result.

        Args:
            detectron2_results (Any): The output of a
                Detectron2 model containing instances with prediction data.

        Returns:
            A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
                and class names, and confidences of each keypoint.

        Example:
            ```python
            import cv2
            import supervision as sv
            from detectron2.engine import DefaultPredictor
            from detectron2.config import get_cfg


            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            cfg = get_cfg()
            cfg.merge_from_file(<CONFIG_PATH>)
            cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
            predictor = DefaultPredictor(cfg)

            result = predictor(image)
            keypoints = sv.KeyPoints.from_detectron2(result)
            ```
        """

        if hasattr(detectron2_results["instances"], "pred_keypoints"):
            if detectron2_results["instances"].pred_keypoints.cpu().numpy().size == 0:
                return cls.empty()
            return cls(
                xy=detectron2_results["instances"]
                .pred_keypoints.cpu()
                .numpy()[:, :, :2],
                confidence=detectron2_results["instances"]
                .pred_keypoints.cpu()
                .numpy()[:, :, 2],
                class_id=detectron2_results["instances"]
                .pred_classes.cpu()
                .numpy()
                .astype(int),
            )
        else:
            return cls.empty()

    def __getitem__(
        self, index: Union[int, slice, List[int], np.ndarray, str]
    ) -> Union[KeyPoints, List, np.ndarray, None]:
        """
        Get a subset of the `sv.KeyPoints` object or access an item from its data field.

        When provided with an integer, slice, list of integers, or a numpy array, this
        method returns a new `sv.KeyPoints` object that represents a subset of the
        original `sv.KeyPoints`. When provided with a string, it accesses the
        corresponding item in the data dictionary.

        Args:
            index (Union[int, slice, List[int], np.ndarray, str]): The index, indices,
                or key to access a subset of the `sv.KeyPoints` or an item from the
                data.

        Returns:
            A subset of the `sv.KeyPoints` object or an item from the data field.

        Examples:
            ```python
            import supervision as sv

            key_points = sv.KeyPoints()

            # access the first keypoint using an integer index
            key_points[0]

            # access the first 10 keypoints using index slice
            key_points[0:10]

            # access selected keypoints using a list of indices
            key_points[[0, 2, 4]]

            # access keypoints with selected class_id
            key_points[key_points.class_id == 0]

            # access keypoints with confidence greater than 0.5
            key_points[key_points.confidence > 0.5]
            ```
        """
        if isinstance(index, str):
            return self.data.get(index)
        if isinstance(index, int):
            index = [index]
        return KeyPoints(
            xy=self.xy[index],
            confidence=self.confidence[index] if self.confidence is not None else None,
            class_id=self.class_id[index] if self.class_id is not None else None,
            data=get_data_item(self.data, index),
        )

    def __setitem__(self, key: str, value: Union[np.ndarray, List]):
        """
        Set a value in the data dictionary of the `sv.KeyPoints` object.

        Args:
            key (str): The key in the data dictionary to set.
            value (Union[np.ndarray, List]): The value to set for the key.

        Examples:
            ```python
            import cv2
            import supervision as sv
            from ultralytics import YOLO

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = YOLO('yolov8s.pt')

            result = model(image)[0]
            keypoints = sv.KeyPoints.from_ultralytics(result)

            keypoints['class_name'] = [
                 model.model.names[class_id]
                 for class_id
                 in keypoints.class_id
             ]
            ```
        """
        if not isinstance(value, (np.ndarray, list)):
            raise TypeError("Value must be a np.ndarray or a list")

        if isinstance(value, list):
            value = np.array(value)

        self.data[key] = value

    @classmethod
    def empty(cls) -> KeyPoints:
        """
        Create an empty Keypoints object with no keypoints.

        Returns:
            An empty `sv.KeyPoints` object.

        Examples:
            ```python
            import supervision as sv

            key_points = sv.KeyPoints.empty()
            ```
        """
        return cls(xy=np.empty((0, 0, 2), dtype=np.float32))

    def is_empty(self) -> bool:
        """
        Returns `True` if the `KeyPoints` object is considered empty.
        """
        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

Functions

__getitem__(index)

Get a subset of the sv.KeyPoints object or access an item from its data field.

When provided with an integer, slice, list of integers, or a numpy array, this method returns a new sv.KeyPoints object that represents a subset of the original sv.KeyPoints. When provided with a string, it accesses the corresponding item in the data dictionary.

Parameters:

Name	Type	Description	Default
index	Union[int, slice, List[int], ndarray, str]	The index, indices, or key to access a subset of the sv.KeyPoints or an item from the data.	required

Returns:

Type	Description
Union[KeyPoints, List, ndarray, None]	A subset of the sv.KeyPoints object or an item from the data field.

Examples:

import supervision as sv

key_points = sv.KeyPoints()

# access the first keypoint using an integer index
key_points[0]

# access the first 10 keypoints using index slice
key_points[0:10]

# access selected keypoints using a list of indices
key_points[[0, 2, 4]]

# access keypoints with selected class_id
key_points[key_points.class_id == 0]

# access keypoints with confidence greater than 0.5
key_points[key_points.confidence > 0.5]

Source code in supervision/keypoint/core.py

def __getitem__(
    self, index: Union[int, slice, List[int], np.ndarray, str]
) -> Union[KeyPoints, List, np.ndarray, None]:
    """
    Get a subset of the `sv.KeyPoints` object or access an item from its data field.

    When provided with an integer, slice, list of integers, or a numpy array, this
    method returns a new `sv.KeyPoints` object that represents a subset of the
    original `sv.KeyPoints`. When provided with a string, it accesses the
    corresponding item in the data dictionary.

    Args:
        index (Union[int, slice, List[int], np.ndarray, str]): The index, indices,
            or key to access a subset of the `sv.KeyPoints` or an item from the
            data.

    Returns:
        A subset of the `sv.KeyPoints` object or an item from the data field.

    Examples:
        ```python
        import supervision as sv

        key_points = sv.KeyPoints()

        # access the first keypoint using an integer index
        key_points[0]

        # access the first 10 keypoints using index slice
        key_points[0:10]

        # access selected keypoints using a list of indices
        key_points[[0, 2, 4]]

        # access keypoints with selected class_id
        key_points[key_points.class_id == 0]

        # access keypoints with confidence greater than 0.5
        key_points[key_points.confidence > 0.5]
        ```
    """
    if isinstance(index, str):
        return self.data.get(index)
    if isinstance(index, int):
        index = [index]
    return KeyPoints(
        xy=self.xy[index],
        confidence=self.confidence[index] if self.confidence is not None else None,
        class_id=self.class_id[index] if self.class_id is not None else None,
        data=get_data_item(self.data, index),
    )

__iter__()

Iterates over the Keypoint object and yield a tuple of (xy, confidence, class_id, data) for each object detection.

Source code in supervision/keypoint/core.py

def __iter__(
    self,
) -> Iterator[
    Tuple[
        np.ndarray,
        Optional[np.ndarray],
        Optional[float],
        Optional[int],
        Optional[int],
        Dict[str, Union[np.ndarray, List]],
    ]
]:
    """
    Iterates over the Keypoint object and yield a tuple of
    `(xy, confidence, class_id, data)` for each object detection.
    """
    for i in range(len(self.xy)):
        yield (
            self.xy[i],
            self.confidence[i] if self.confidence is not None else None,
            self.class_id[i] if self.class_id is not None else None,
            get_data_item(self.data, i),
        )

__len__()

Returns the number of keypoints in the sv.KeyPoints object.

Source code in supervision/keypoint/core.py

def __len__(self) -> int:
    """
    Returns the number of keypoints in the `sv.KeyPoints` object.
    """
    return len(self.xy)

__setitem__(key, value)

Set a value in the data dictionary of the sv.KeyPoints object.

Parameters:

Name	Type	Description	Default
key	str	The key in the data dictionary to set.	required
value	Union[ndarray, List]	The value to set for the key.	required

Examples:

import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8s.pt')

result = model(image)[0]
keypoints = sv.KeyPoints.from_ultralytics(result)

keypoints['class_name'] = [
     model.model.names[class_id]
     for class_id
     in keypoints.class_id
 ]

Source code in supervision/keypoint/core.py

def __setitem__(self, key: str, value: Union[np.ndarray, List]):
    """
    Set a value in the data dictionary of the `sv.KeyPoints` object.

    Args:
        key (str): The key in the data dictionary to set.
        value (Union[np.ndarray, List]): The value to set for the key.

    Examples:
        ```python
        import cv2
        import supervision as sv
        from ultralytics import YOLO

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = YOLO('yolov8s.pt')

        result = model(image)[0]
        keypoints = sv.KeyPoints.from_ultralytics(result)

        keypoints['class_name'] = [
             model.model.names[class_id]
             for class_id
             in keypoints.class_id
         ]
        ```
    """
    if not isinstance(value, (np.ndarray, list)):
        raise TypeError("Value must be a np.ndarray or a list")

    if isinstance(value, list):
        value = np.array(value)

    self.data[key] = value

as_detections(selected_keypoint_indices=None)

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.

Parameters:

Name	Type	Description	Default
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.	None

Returns:

Name	Type	Description
detections	Detections	The converted detections object.

Example

keypoints = sv.KeyPoints.from_inference(...)
detections = keypoints.as_detections()

Source code in supervision/keypoint/core.py

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

empty() classmethod

Create an empty Keypoints object with no keypoints.

Returns:

Type	Description
KeyPoints	An empty sv.KeyPoints object.

Examples:

import supervision as sv

key_points = sv.KeyPoints.empty()

Source code in supervision/keypoint/core.py

@classmethod
def empty(cls) -> KeyPoints:
    """
    Create an empty Keypoints object with no keypoints.

    Returns:
        An empty `sv.KeyPoints` object.

    Examples:
        ```python
        import supervision as sv

        key_points = sv.KeyPoints.empty()
        ```
    """
    return cls(xy=np.empty((0, 0, 2), dtype=np.float32))

from_detectron2(detectron2_results) classmethod

Create a sv.KeyPoints object from the Detectron2 inference result.

Parameters:

Name	Type	Description	Default
detectron2_results	Any	The output of a Detectron2 model containing instances with prediction data.	required

Returns:

Type	Description
KeyPoints	A sv.KeyPoints object containing the keypoint coordinates, class IDs, and class names, and confidences of each keypoint.

Example

import cv2
import supervision as sv
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg


image = cv2.imread(<SOURCE_IMAGE_PATH>)
cfg = get_cfg()
cfg.merge_from_file(<CONFIG_PATH>)
cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
predictor = DefaultPredictor(cfg)

result = predictor(image)
keypoints = sv.KeyPoints.from_detectron2(result)

Source code in supervision/keypoint/core.py

@classmethod
def from_detectron2(cls, detectron2_results: Any) -> KeyPoints:
    """
    Create a `sv.KeyPoints` object from the
    [Detectron2](https://github.com/facebookresearch/detectron2) inference result.

    Args:
        detectron2_results (Any): The output of a
            Detectron2 model containing instances with prediction data.

    Returns:
        A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
            and class names, and confidences of each keypoint.

    Example:
        ```python
        import cv2
        import supervision as sv
        from detectron2.engine import DefaultPredictor
        from detectron2.config import get_cfg


        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        cfg = get_cfg()
        cfg.merge_from_file(<CONFIG_PATH>)
        cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
        predictor = DefaultPredictor(cfg)

        result = predictor(image)
        keypoints = sv.KeyPoints.from_detectron2(result)
        ```
    """

    if hasattr(detectron2_results["instances"], "pred_keypoints"):
        if detectron2_results["instances"].pred_keypoints.cpu().numpy().size == 0:
            return cls.empty()
        return cls(
            xy=detectron2_results["instances"]
            .pred_keypoints.cpu()
            .numpy()[:, :, :2],
            confidence=detectron2_results["instances"]
            .pred_keypoints.cpu()
            .numpy()[:, :, 2],
            class_id=detectron2_results["instances"]
            .pred_classes.cpu()
            .numpy()
            .astype(int),
        )
    else:
        return cls.empty()

from_inference(inference_result) classmethod

Create a sv.KeyPoints object from the Roboflow API inference result or the Inference package results.

Parameters:

Name	Type	Description	Default
inference_result	(dict, any)	The result from the Roboflow API or Inference package containing predictions with keypoints.	required

Returns:

Type	Description
KeyPoints	A sv.KeyPoints object containing the keypoint coordinates, class IDs, and class names, and confidences of each keypoint.

Examples:

import cv2
import supervision as sv
from inference import get_model

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_model(model_id=<POSE_MODEL_ID>, api_key=<ROBOFLOW_API_KEY>)

result = model.infer(image)[0]
key_points = sv.KeyPoints.from_inference(result)

import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image = cv2.imread(<SOURCE_IMAGE_PATH>)
client = InferenceHTTPClient(
    api_url="https://detect.roboflow.com",
    api_key=<ROBOFLOW_API_KEY>
)

result = client.infer(image, model_id=<POSE_MODEL_ID>)
key_points = sv.KeyPoints.from_inference(result)

Source code in supervision/keypoint/core.py

@classmethod
def from_inference(cls, inference_result: Union[dict, Any]) -> KeyPoints:
    """
    Create a `sv.KeyPoints` object from the [Roboflow](https://roboflow.com/)
    API inference result or the [Inference](https://inference.roboflow.com/)
    package results.

    Args:
        inference_result (dict, any): The result from the
            Roboflow API or Inference package containing predictions with keypoints.

    Returns:
        A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
            and class names, and confidences of each keypoint.

    Examples:
        ```python
        import cv2
        import supervision as sv
        from inference import get_model

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = get_model(model_id=<POSE_MODEL_ID>, api_key=<ROBOFLOW_API_KEY>)

        result = model.infer(image)[0]
        key_points = sv.KeyPoints.from_inference(result)
        ```

        ```python
        import cv2
        import supervision as sv
        from inference_sdk import InferenceHTTPClient

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        client = InferenceHTTPClient(
            api_url="https://detect.roboflow.com",
            api_key=<ROBOFLOW_API_KEY>
        )

        result = client.infer(image, model_id=<POSE_MODEL_ID>)
        key_points = sv.KeyPoints.from_inference(result)
        ```
    """
    if isinstance(inference_result, list):
        raise ValueError(
            "from_inference() operates on a single result at a time."
            "You can retrieve it like so:  inference_result = model.infer(image)[0]"
        )

    with suppress(AttributeError):
        inference_result = inference_result.dict(exclude_none=True, by_alias=True)

    if not inference_result.get("predictions"):
        return cls.empty()

    xy = []
    confidence = []
    class_id = []
    class_names = []

    for prediction in inference_result["predictions"]:
        prediction_xy = []
        prediction_confidence = []
        for keypoint in prediction["keypoints"]:
            prediction_xy.append([keypoint["x"], keypoint["y"]])
            prediction_confidence.append(keypoint["confidence"])
        xy.append(prediction_xy)
        confidence.append(prediction_confidence)

        class_id.append(prediction["class_id"])
        class_names.append(prediction["class"])

    data = {CLASS_NAME_DATA_FIELD: np.array(class_names)}

    return cls(
        xy=np.array(xy, dtype=np.float32),
        confidence=np.array(confidence, dtype=np.float32),
        class_id=np.array(class_id, dtype=int),
        data=data,
    )

from_mediapipe(mediapipe_results, resolution_wh) classmethod

Creates a sv.KeyPoints instance from a MediaPipe pose landmark detection inference result.

Parameters:

Name	Type	Description	Default
mediapipe_results	Union[PoseLandmarkerResult, FaceLandmarkerResult, SolutionOutputs]	The output results from Mediapipe. It support pose and face landmarks from PoseLandmaker, FaceLandmarker and the legacy ones from Pose and FaceMesh.	required
resolution_wh	Tuple[int, int]	A tuple of the form (width, height) representing the resolution of the frame.	required

Returns:

Type	Description
KeyPoints	A sv.KeyPoints object containing the keypoint coordinates and confidences of each keypoint.

Tip

Before you start, download model bundles from the MediaPipe website.

Examples:

import cv2
import mediapipe as mp
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
image_height, image_width, _ = image.shape
mediapipe_image = mp.Image(
    image_format=mp.ImageFormat.SRGB,
    data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

options = mp.tasks.vision.PoseLandmarkerOptions(
    base_options=mp.tasks.BaseOptions(
        model_asset_path="pose_landmarker_heavy.task"
    ),
    running_mode=mp.tasks.vision.RunningMode.IMAGE,
    num_poses=2)

PoseLandmarker = mp.tasks.vision.PoseLandmarker
with PoseLandmarker.create_from_options(options) as landmarker:
    pose_landmarker_result = landmarker.detect(mediapipe_image)

key_points = sv.KeyPoints.from_mediapipe(
    pose_landmarker_result, (image_width, image_height))

import cv2
import mediapipe as mp
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
image_height, image_width, _ = image.shape
mediapipe_image = mp.Image(
    image_format=mp.ImageFormat.SRGB,
    data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

options = mp.tasks.vision.FaceLandmarkerOptions(
    base_options=mp.tasks.BaseOptions(
        model_asset_path="face_landmarker.task"
    ),
    output_face_blendshapes=True,
    output_facial_transformation_matrixes=True,
    num_faces=2)

FaceLandmarker = mp.tasks.vision.FaceLandmarker
with FaceLandmarker.create_from_options(options) as landmarker:
    face_landmarker_result = landmarker.detect(mediapipe_image)

key_points = sv.KeyPoints.from_mediapipe(
    face_landmarker_result, (image_width, image_height))

Source code in supervision/keypoint/core.py

@classmethod
def from_mediapipe(
    cls, mediapipe_results, resolution_wh: Tuple[int, int]
) -> KeyPoints:
    """
    Creates a `sv.KeyPoints` instance from a
    [MediaPipe](https://github.com/google-ai-edge/mediapipe)
    pose landmark detection inference result.

    Args:
        mediapipe_results (Union[PoseLandmarkerResult, FaceLandmarkerResult, SolutionOutputs]):
            The output results from Mediapipe. It support pose and face landmarks
            from `PoseLandmaker`, `FaceLandmarker` and the legacy ones
            from `Pose` and `FaceMesh`.
        resolution_wh (Tuple[int, int]): A tuple of the form `(width, height)`
            representing the resolution of the frame.

    Returns:
        A `sv.KeyPoints` object containing the keypoint coordinates and
            confidences of each keypoint.

    !!! tip
        Before you start, download model bundles from the
        [MediaPipe website](https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker/index#models).

    Examples:
        ```python
        import cv2
        import mediapipe as mp
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        image_height, image_width, _ = image.shape
        mediapipe_image = mp.Image(
            image_format=mp.ImageFormat.SRGB,
            data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

        options = mp.tasks.vision.PoseLandmarkerOptions(
            base_options=mp.tasks.BaseOptions(
                model_asset_path="pose_landmarker_heavy.task"
            ),
            running_mode=mp.tasks.vision.RunningMode.IMAGE,
            num_poses=2)

        PoseLandmarker = mp.tasks.vision.PoseLandmarker
        with PoseLandmarker.create_from_options(options) as landmarker:
            pose_landmarker_result = landmarker.detect(mediapipe_image)

        key_points = sv.KeyPoints.from_mediapipe(
            pose_landmarker_result, (image_width, image_height))
        ```

        ```python
        import cv2
        import mediapipe as mp
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        image_height, image_width, _ = image.shape
        mediapipe_image = mp.Image(
            image_format=mp.ImageFormat.SRGB,
            data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

        options = mp.tasks.vision.FaceLandmarkerOptions(
            base_options=mp.tasks.BaseOptions(
                model_asset_path="face_landmarker.task"
            ),
            output_face_blendshapes=True,
            output_facial_transformation_matrixes=True,
            num_faces=2)

        FaceLandmarker = mp.tasks.vision.FaceLandmarker
        with FaceLandmarker.create_from_options(options) as landmarker:
            face_landmarker_result = landmarker.detect(mediapipe_image)

        key_points = sv.KeyPoints.from_mediapipe(
            face_landmarker_result, (image_width, image_height))
        ```
    """  # noqa: E501 // docs
    if hasattr(mediapipe_results, "pose_landmarks"):
        results = mediapipe_results.pose_landmarks
        if not isinstance(mediapipe_results.pose_landmarks, list):
            if mediapipe_results.pose_landmarks is None:
                results = []
            else:
                results = [
                    [
                        landmark
                        for landmark in mediapipe_results.pose_landmarks.landmark
                    ]
                ]
    elif hasattr(mediapipe_results, "face_landmarks"):
        results = mediapipe_results.face_landmarks
    elif hasattr(mediapipe_results, "multi_face_landmarks"):
        if mediapipe_results.multi_face_landmarks is None:
            results = []
        else:
            results = [
                face_landmark.landmark
                for face_landmark in mediapipe_results.multi_face_landmarks
            ]

    if len(results) == 0:
        return cls.empty()

    xy = []
    confidence = []
    for pose in results:
        prediction_xy = []
        prediction_confidence = []
        for landmark in pose:
            keypoint_xy = [
                landmark.x * resolution_wh[0],
                landmark.y * resolution_wh[1],
            ]
            prediction_xy.append(keypoint_xy)
            prediction_confidence.append(landmark.visibility)

        xy.append(prediction_xy)
        confidence.append(prediction_confidence)

    return cls(
        xy=np.array(xy, dtype=np.float32),
        confidence=np.array(confidence, dtype=np.float32),
    )

from_ultralytics(ultralytics_results) classmethod

Creates a sv.KeyPoints instance from a YOLOv8 pose inference result.

Parameters:

Name	Type	Description	Default
ultralytics_results	Keypoints	The output Results instance from YOLOv8	required

Returns:

Type	Description
KeyPoints	A sv.KeyPoints object containing the keypoint coordinates, class IDs, and class names, and confidences of each keypoint.

Examples:

import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8s-pose.pt')

result = model(image)[0]
key_points = sv.KeyPoints.from_ultralytics(result)

Source code in supervision/keypoint/core.py

@classmethod
def from_ultralytics(cls, ultralytics_results) -> KeyPoints:
    """
    Creates a `sv.KeyPoints` instance from a
    [YOLOv8](https://github.com/ultralytics/ultralytics) pose inference result.

    Args:
        ultralytics_results (ultralytics.engine.results.Keypoints):
            The output Results instance from YOLOv8

    Returns:
        A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
            and class names, and confidences of each keypoint.

    Examples:
        ```python
        import cv2
        import supervision as sv
        from ultralytics import YOLO

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = YOLO('yolov8s-pose.pt')

        result = model(image)[0]
        key_points = sv.KeyPoints.from_ultralytics(result)
        ```
    """
    if ultralytics_results.keypoints.xy.numel() == 0:
        return cls.empty()

    xy = ultralytics_results.keypoints.xy.cpu().numpy()
    class_id = ultralytics_results.boxes.cls.cpu().numpy().astype(int)
    class_names = np.array([ultralytics_results.names[i] for i in class_id])

    confidence = ultralytics_results.keypoints.conf.cpu().numpy()
    data = {CLASS_NAME_DATA_FIELD: class_names}
    return cls(xy, class_id, confidence, data)

from_yolo_nas(yolo_nas_results) classmethod

Create a sv.KeyPoints instance from a YOLO-NAS pose inference results.

Parameters:

Name	Type	Description	Default
yolo_nas_results	ImagePoseEstimationPrediction	The output object from YOLO NAS.	required

Returns:

Type	Description
KeyPoints	A sv.KeyPoints object containing the keypoint coordinates, class IDs, and class names, and confidences of each keypoint.

Examples:

import cv2
import torch
import supervision as sv
import super_gradients

image = cv2.imread(<SOURCE_IMAGE_PATH>)

device = "cuda" if torch.cuda.is_available() else "cpu"
model = super_gradients.training.models.get(
    "yolo_nas_pose_s", pretrained_weights="coco_pose").to(device)

results = model.predict(image, conf=0.1)
key_points = sv.KeyPoints.from_yolo_nas(results)

Source code in supervision/keypoint/core.py

@classmethod
def from_yolo_nas(cls, yolo_nas_results) -> KeyPoints:
    """
    Create a `sv.KeyPoints` instance from a [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS-POSE.md)
    pose inference results.

    Args:
        yolo_nas_results (ImagePoseEstimationPrediction): The output object from
            YOLO NAS.

    Returns:
        A `sv.KeyPoints` object containing the keypoint coordinates, class IDs,
            and class names, and confidences of each keypoint.

    Examples:
        ```python
        import cv2
        import torch
        import supervision as sv
        import super_gradients

        image = cv2.imread(<SOURCE_IMAGE_PATH>)

        device = "cuda" if torch.cuda.is_available() else "cpu"
        model = super_gradients.training.models.get(
            "yolo_nas_pose_s", pretrained_weights="coco_pose").to(device)

        results = model.predict(image, conf=0.1)
        key_points = sv.KeyPoints.from_yolo_nas(results)
        ```
    """
    if len(yolo_nas_results.prediction.poses) == 0:
        return cls.empty()

    xy = yolo_nas_results.prediction.poses[:, :, :2]
    confidence = yolo_nas_results.prediction.poses[:, :, 2]

    # yolo_nas_results treats params differently.
    # prediction.labels may not exist, whereas class_names might be None
    if hasattr(yolo_nas_results.prediction, "labels"):
        class_id = yolo_nas_results.prediction.labels  # np.array[int]
    else:
        class_id = None

    data = {}
    if class_id is not None and yolo_nas_results.class_names is not None:
        class_names = []
        for c_id in class_id:
            name = yolo_nas_results.class_names[c_id]  # tuple[str]
            class_names.append(name)
        data[CLASS_NAME_DATA_FIELD] = class_names

    return cls(
        xy=xy,
        confidence=confidence,
        class_id=class_id,
        data=data,
    )

is_empty()

Returns True if the KeyPoints object is considered empty.

Source code in supervision/keypoint/core.py

def is_empty(self) -> bool:
    """
    Returns `True` if the `KeyPoints` object is considered empty.
    """
    empty_keypoints = KeyPoints.empty()
    empty_keypoints.data = self.data
    return self == empty_keypoints

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