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Annotators

import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

vertex_annotator = sv.VertexAnnotator(
    color=sv.Color.GREEN,
    radius=10
)
annotated_frame = vertex_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)

vertex-annotator-example

import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

edge_annotator = sv.EdgeAnnotator(
    color=sv.Color.GREEN,
    thickness=5
)
annotated_frame = edge_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)

edge-annotator-example

import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

vertex_label_annotator = sv.VertexLabelAnnotator(
    color=sv.Color.GREEN,
    text_color=sv.Color.BLACK,
    border_radius=5
)
annotated_frame = vertex_label_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)

vertex-label-annotator-example

VertexAnnotator

Bases: BaseKeyPointAnnotator

A class that specializes in drawing skeleton vertices on images. It uses specified key points to determine the locations where the vertices should be drawn.

Source code in supervision/keypoint/annotators.py 
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class VertexAnnotator(BaseKeyPointAnnotator):
    """
    A class that specializes in drawing skeleton vertices on images. It uses
    specified key points to determine the locations where the vertices should be
    drawn.
    """

    def __init__(
        self,
        color: Color = Color.ROBOFLOW,
        radius: int = 4,
    ) -> None:
        """
        Args:
            color (Color): The color to use for annotating key points.
            radius (int): The radius of the circles used to represent the key
                points.
        """
        self.color = color
        self.radius = radius

    @ensure_cv2_image_for_annotation
    def annotate(self, scene: ImageType, key_points: KeyPoints) -> ImageType:
        """
        Annotates the given scene with skeleton vertices based on the provided key
        points. It draws circles at each key point location.

        Args:
            scene (ImageType): The image where skeleton vertices will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray` or
                `PIL.Image.Image`.
            key_points (KeyPoints): A collection of key points where each key point
                consists of x and y coordinates.

        Returns:
            The annotated image, matching the type of `scene` (`numpy.ndarray`
                or `PIL.Image.Image`)

        Example:
            ```python
            import supervision as sv

            image = ...
            key_points = sv.KeyPoints(...)

            vertex_annotator = sv.VertexAnnotator(
                color=sv.Color.GREEN,
                radius=10
            )
            annotated_frame = vertex_annotator.annotate(
                scene=image.copy(),
                key_points=key_points
            )
            ```

        ![vertex-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/vertex-annotator-example.png)
        """
        assert isinstance(scene, np.ndarray)
        if len(key_points) == 0:
            return scene

        for xy in key_points.xy:
            for x, y in xy:
                cv2.circle(
                    img=scene,
                    center=(int(x), int(y)),
                    radius=self.radius,
                    color=self.color.as_bgr(),
                    thickness=-1,
                )

        return scene

Functions

__init__(color=Color.ROBOFLOW, radius=4)

Parameters:

Name Type Description Default

color

Color

The color to use for annotating key points.

 ROBOFLOW

radius

int

The radius of the circles used to represent the key points.

 4
Source code in supervision/keypoint/annotators.py 
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def __init__(
    self,
    color: Color = Color.ROBOFLOW,
    radius: int = 4,
) -> None:
    """
    Args:
        color (Color): The color to use for annotating key points.
        radius (int): The radius of the circles used to represent the key
            points.
    """
    self.color = color
    self.radius = radius

annotate(scene, key_points)

Annotates the given scene with skeleton vertices based on the provided key points. It draws circles at each key point location.

Parameters:

Name Type Description Default

scene

ImageType

The image where skeleton vertices will be drawn. ImageType is a flexible type, accepting either numpy.ndarray or PIL.Image.Image.

 required

key_points

KeyPoints

A collection of key points where each key point consists of x and y coordinates.

 required

Returns:

Type Description
ImageType

The annotated image, matching the type of scene (numpy.ndarray or PIL.Image.Image)
Example 
import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

vertex_annotator = sv.VertexAnnotator(
    color=sv.Color.GREEN,
    radius=10
)
annotated_frame = vertex_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)

vertex-annotator-example

Source code in supervision/keypoint/annotators.py 
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@ensure_cv2_image_for_annotation
def annotate(self, scene: ImageType, key_points: KeyPoints) -> ImageType:
    """
    Annotates the given scene with skeleton vertices based on the provided key
    points. It draws circles at each key point location.

    Args:
        scene (ImageType): The image where skeleton vertices will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray` or
            `PIL.Image.Image`.
        key_points (KeyPoints): A collection of key points where each key point
            consists of x and y coordinates.

    Returns:
        The annotated image, matching the type of `scene` (`numpy.ndarray`
            or `PIL.Image.Image`)

    Example:
        ```python
        import supervision as sv

        image = ...
        key_points = sv.KeyPoints(...)

        vertex_annotator = sv.VertexAnnotator(
            color=sv.Color.GREEN,
            radius=10
        )
        annotated_frame = vertex_annotator.annotate(
            scene=image.copy(),
            key_points=key_points
        )
        ```

    ![vertex-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/vertex-annotator-example.png)
    """
    assert isinstance(scene, np.ndarray)
    if len(key_points) == 0:
        return scene

    for xy in key_points.xy:
        for x, y in xy:
            cv2.circle(
                img=scene,
                center=(int(x), int(y)),
                radius=self.radius,
                color=self.color.as_bgr(),
                thickness=-1,
            )

    return scene

EdgeAnnotator

Bases: BaseKeyPointAnnotator

A class that specializes in drawing skeleton edges on images using specified key points. It connects key points with lines to form the skeleton structure.

Source code in supervision/keypoint/annotators.py 
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class EdgeAnnotator(BaseKeyPointAnnotator):
    """
    A class that specializes in drawing skeleton edges on images using specified key
    points. It connects key points with lines to form the skeleton structure.
    """

    def __init__(
        self,
        color: Color = Color.ROBOFLOW,
        thickness: int = 2,
        edges: Optional[List[Tuple[int, int]]] = None,
    ) -> None:
        """
        Args:
            color (Color): The color to use for the edges.
            thickness (int): The thickness of the edges.
            edges (Optional[List[Tuple[int, int]]]): The edges to draw.
                If set to `None`, will attempt to select automatically.
        """
        self.color = color
        self.thickness = thickness
        self.edges = edges

    @ensure_cv2_image_for_annotation
    def annotate(self, scene: ImageType, key_points: KeyPoints) -> ImageType:
        """
        Annotates the given scene by drawing lines between specified key points to form
        edges.

        Args:
            scene (ImageType): The image where skeleton edges will be drawn. `ImageType`
                is a flexible type, accepting either `numpy.ndarray` or
                `PIL.Image.Image`.
            key_points (KeyPoints): A collection of key points where each key point
                consists of x and y coordinates.

        Returns:
            Returns:
                The annotated image, matching the type of `scene` (`numpy.ndarray`
                    or `PIL.Image.Image`)

        Example:
            ```python
            import supervision as sv

            image = ...
            key_points = sv.KeyPoints(...)

            edge_annotator = sv.EdgeAnnotator(
                color=sv.Color.GREEN,
                thickness=5
            )
            annotated_frame = edge_annotator.annotate(
                scene=image.copy(),
                key_points=key_points
            )
            ```

        ![edge-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/edge-annotator-example.png)
        """
        assert isinstance(scene, np.ndarray)
        if len(key_points) == 0:
            return scene

        for xy in key_points.xy:
            edges = self.edges
            if not edges:
                edges = SKELETONS_BY_VERTEX_COUNT.get(len(xy))
            if not edges:
                warn(f"No skeleton found with {len(xy)} vertices")
                return scene

            for class_a, class_b in edges:
                xy_a = xy[class_a - 1]
                xy_b = xy[class_b - 1]
                missing_a = np.allclose(xy_a, 0)
                missing_b = np.allclose(xy_b, 0)
                if missing_a or missing_b:
                    continue

                cv2.line(
                    img=scene,
                    pt1=(int(xy_a[0]), int(xy_a[1])),
                    pt2=(int(xy_b[0]), int(xy_b[1])),
                    color=self.color.as_bgr(),
                    thickness=self.thickness,
                )

        return scene

Functions

__init__(color=Color.ROBOFLOW, thickness=2, edges=None)

Parameters:

Name Type Description Default

color

Color

The color to use for the edges.

 ROBOFLOW

thickness

int

The thickness of the edges.

 2

edges

Optional[List[Tuple[int, int]]]

The edges to draw. If set to None, will attempt to select automatically.

 None
Source code in supervision/keypoint/annotators.py 
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def __init__(
    self,
    color: Color = Color.ROBOFLOW,
    thickness: int = 2,
    edges: Optional[List[Tuple[int, int]]] = None,
) -> None:
    """
    Args:
        color (Color): The color to use for the edges.
        thickness (int): The thickness of the edges.
        edges (Optional[List[Tuple[int, int]]]): The edges to draw.
            If set to `None`, will attempt to select automatically.
    """
    self.color = color
    self.thickness = thickness
    self.edges = edges

annotate(scene, key_points)

Annotates the given scene by drawing lines between specified key points to form edges.

Parameters:

Name Type Description Default

scene

ImageType

The image where skeleton edges will be drawn. ImageType is a flexible type, accepting either numpy.ndarray or PIL.Image.Image.

 required

key_points

KeyPoints

A collection of key points where each key point consists of x and y coordinates.

 required

Returns:

Name Type Description
Returns ImageType

The annotated image, matching the type of scene (numpy.ndarray or PIL.Image.Image)
Example 
import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

edge_annotator = sv.EdgeAnnotator(
    color=sv.Color.GREEN,
    thickness=5
)
annotated_frame = edge_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)

edge-annotator-example

Source code in supervision/keypoint/annotators.py 
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@ensure_cv2_image_for_annotation
def annotate(self, scene: ImageType, key_points: KeyPoints) -> ImageType:
    """
    Annotates the given scene by drawing lines between specified key points to form
    edges.

    Args:
        scene (ImageType): The image where skeleton edges will be drawn. `ImageType`
            is a flexible type, accepting either `numpy.ndarray` or
            `PIL.Image.Image`.
        key_points (KeyPoints): A collection of key points where each key point
            consists of x and y coordinates.

    Returns:
        Returns:
            The annotated image, matching the type of `scene` (`numpy.ndarray`
                or `PIL.Image.Image`)

    Example:
        ```python
        import supervision as sv

        image = ...
        key_points = sv.KeyPoints(...)

        edge_annotator = sv.EdgeAnnotator(
            color=sv.Color.GREEN,
            thickness=5
        )
        annotated_frame = edge_annotator.annotate(
            scene=image.copy(),
            key_points=key_points
        )
        ```

    ![edge-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/edge-annotator-example.png)
    """
    assert isinstance(scene, np.ndarray)
    if len(key_points) == 0:
        return scene

    for xy in key_points.xy:
        edges = self.edges
        if not edges:
            edges = SKELETONS_BY_VERTEX_COUNT.get(len(xy))
        if not edges:
            warn(f"No skeleton found with {len(xy)} vertices")
            return scene

        for class_a, class_b in edges:
            xy_a = xy[class_a - 1]
            xy_b = xy[class_b - 1]
            missing_a = np.allclose(xy_a, 0)
            missing_b = np.allclose(xy_b, 0)
            if missing_a or missing_b:
                continue

            cv2.line(
                img=scene,
                pt1=(int(xy_a[0]), int(xy_a[1])),
                pt2=(int(xy_b[0]), int(xy_b[1])),
                color=self.color.as_bgr(),
                thickness=self.thickness,
            )

    return scene

VertexLabelAnnotator

A class that draws labels of skeleton vertices on images. It uses specified key points to determine the locations where the vertices should be drawn.

Source code in supervision/keypoint/annotators.py 
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class VertexLabelAnnotator:
    """
    A class that draws labels of skeleton vertices on images. It uses specified key
    points to determine the locations where the vertices should be drawn.
    """

    def __init__(
        self,
        color: Union[Color, List[Color]] = Color.ROBOFLOW,
        text_color: Union[Color, List[Color]] = Color.WHITE,
        text_scale: float = 0.5,
        text_thickness: int = 1,
        text_padding: int = 10,
        border_radius: int = 0,
        smart_position: bool = False,
    ):
        """
        Args:
            color (Union[Color, List[Color]]): The color to use for each
                keypoint label. If a list is provided, the colors will be used in order
                for each keypoint.
            text_color (Union[Color, List[Color]]): The color to use
                for the labels. If a list is provided, the colors will be used in order
                for each keypoint.
            text_scale (float): The scale of the text.
            text_thickness (int): The thickness of the text.
            text_padding (int): The padding around the text.
            border_radius (int): The radius of the rounded corners of the
                boxes. Set to a high value to produce circles.
            smart_position (bool): Spread out the labels to avoid overlap.
        """
        self.border_radius: int = border_radius
        self.color: Union[Color, List[Color]] = color
        self.text_color: Union[Color, List[Color]] = text_color
        self.text_scale: float = text_scale
        self.text_thickness: int = text_thickness
        self.text_padding: int = text_padding
        self.smart_position = smart_position

    def annotate(
        self,
        scene: ImageType,
        key_points: KeyPoints,
        labels: Optional[List[str]] = None,
    ) -> ImageType:
        """
        A class that draws labels of skeleton vertices on images. It uses specified key
            points to determine the locations where the vertices should be drawn.

        Args:
            scene (ImageType): The image where vertex labels will be drawn. `ImageType`
                is a flexible type, accepting either `numpy.ndarray` or
                `PIL.Image.Image`.
            key_points (KeyPoints): A collection of key points where each key point
                consists of x and y coordinates.
            labels (Optional[List[str]]): A list of labels to be displayed on the
                annotated image. If not provided, keypoint indices will be used.

        Returns:
            The annotated image, matching the type of `scene` (`numpy.ndarray`
                or `PIL.Image.Image`)

        Example:
            ```python
            import supervision as sv

            image = ...
            key_points = sv.KeyPoints(...)

            vertex_label_annotator = sv.VertexLabelAnnotator(
                color=sv.Color.GREEN,
                text_color=sv.Color.BLACK,
                border_radius=5
            )
            annotated_frame = vertex_label_annotator.annotate(
                scene=image.copy(),
                key_points=key_points
            )
            ```

        ![vertex-label-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/vertex-label-annotator-example.png)

        !!! tip

            `VertexLabelAnnotator` allows to customize the color of each keypoint label
            values.

        Example:
            ```python
            import supervision as sv

            image = ...
            key_points = sv.KeyPoints(...)

            LABELS = [
                "nose", "left eye", "right eye", "left ear",
                "right ear", "left shoulder", "right shoulder", "left elbow",
                "right elbow", "left wrist", "right wrist", "left hip",
                "right hip", "left knee", "right knee", "left ankle",
                "right ankle"
            ]

            COLORS = [
                "#FF6347", "#FF6347", "#FF6347", "#FF6347",
                "#FF6347", "#FF1493", "#00FF00", "#FF1493",
                "#00FF00", "#FF1493", "#00FF00", "#FFD700",
                "#00BFFF", "#FFD700", "#00BFFF", "#FFD700",
                "#00BFFF"
            ]
            COLORS = [sv.Color.from_hex(color_hex=c) for c in COLORS]

            vertex_label_annotator = sv.VertexLabelAnnotator(
                color=COLORS,
                text_color=sv.Color.BLACK,
                border_radius=5
            )
            annotated_frame = vertex_label_annotator.annotate(
                scene=image.copy(),
                key_points=key_points,
                labels=labels
            )
            ```
        ![vertex-label-annotator-custom-example](https://media.roboflow.com/
        supervision-annotator-examples/vertex-label-annotator-custom-example.png)
        """
        assert isinstance(scene, np.ndarray)
        font = cv2.FONT_HERSHEY_SIMPLEX

        skeletons_count, points_count, _ = key_points.xy.shape
        if skeletons_count == 0:
            return scene

        anchors = key_points.xy.reshape(points_count * skeletons_count, 2).astype(int)
        mask = np.all(anchors != 0, axis=1)

        if not np.any(mask):
            return scene

        colors = self.preprocess_and_validate_colors(
            colors=self.color,
            points_count=points_count,
            skeletons_count=skeletons_count,
        )

        text_colors = self.preprocess_and_validate_colors(
            colors=self.text_color,
            points_count=points_count,
            skeletons_count=skeletons_count,
        )

        labels = self.preprocess_and_validate_labels(
            labels=labels, points_count=points_count, skeletons_count=skeletons_count
        )

        anchors = anchors[mask]
        colors = colors[mask]
        text_colors = text_colors[mask]
        labels = labels[mask]

        xyxy = np.array(
            [
                self.get_text_bounding_box(
                    text=label,
                    font=font,
                    text_scale=self.text_scale,
                    text_thickness=self.text_thickness,
                    center_coordinates=tuple(anchor),
                )
                for anchor, label in zip(anchors, labels)
            ]
        )
        xyxy_padded = pad_boxes(xyxy=xyxy, px=self.text_padding)

        if self.smart_position:
            xyxy_padded = spread_out_boxes(xyxy_padded)
            xyxy = pad_boxes(xyxy=xyxy_padded, px=-self.text_padding)

        for text, color, text_color, box, box_padded in zip(
            labels, colors, text_colors, xyxy, xyxy_padded
        ):
            draw_rounded_rectangle(
                scene=scene,
                rect=Rect.from_xyxy(box_padded),
                color=color,
                border_radius=self.border_radius,
            )
            cv2.putText(
                img=scene,
                text=text,
                org=(box[0], box[3]),
                fontFace=font,
                fontScale=self.text_scale,
                color=text_color.as_bgr(),
                thickness=self.text_thickness,
                lineType=cv2.LINE_AA,
            )

        return scene

    @staticmethod
    def get_text_bounding_box(
        text: str,
        font: int,
        text_scale: float,
        text_thickness: int,
        center_coordinates: Tuple[int, int],
    ) -> Tuple[int, int, int, int]:
        text_w, text_h = cv2.getTextSize(
            text=text,
            fontFace=font,
            fontScale=text_scale,
            thickness=text_thickness,
        )[0]
        center_x, center_y = center_coordinates
        return (
            center_x - text_w // 2,
            center_y - text_h // 2,
            center_x + text_w // 2,
            center_y + text_h // 2,
        )

    @staticmethod
    def preprocess_and_validate_labels(
        labels: Optional[List[str]], points_count: int, skeletons_count: int
    ) -> np.ndarray:
        if labels and len(labels) != points_count:
            raise ValueError(
                f"Number of labels ({len(labels)}) must match number of key points "
                f"({points_count})."
            )
        if labels is None:
            labels = [str(i) for i in range(points_count)]

        return np.array(labels * skeletons_count)

    @staticmethod
    def preprocess_and_validate_colors(
        colors: Optional[Union[Color, List[Color]]],
        points_count: int,
        skeletons_count: int,
    ) -> np.ndarray:
        if isinstance(colors, list) and len(colors) != points_count:
            raise ValueError(
                f"Number of colors ({len(colors)}) must match number of key points "
                f"({points_count})."
            )
        return (
            np.array(colors * skeletons_count)
            if isinstance(colors, list)
            else np.array([colors] * points_count * skeletons_count)
        )

Functions

__init__(color=Color.ROBOFLOW, text_color=Color.WHITE, text_scale=0.5, text_thickness=1, text_padding=10, border_radius=0, smart_position=False)

Parameters:

Name Type Description Default

color

Union[Color, List[Color]]

The color to use for each keypoint label. If a list is provided, the colors will be used in order for each keypoint.

 ROBOFLOW

text_color

Union[Color, List[Color]]

The color to use for the labels. If a list is provided, the colors will be used in order for each keypoint.

 WHITE

text_scale

float

The scale of the text.

 0.5

text_thickness

int

The thickness of the text.

 1

text_padding

int

The padding around the text.

 10

border_radius

int

The radius of the rounded corners of the boxes. Set to a high value to produce circles.

 0

smart_position

bool

Spread out the labels to avoid overlap.

 False
Source code in supervision/keypoint/annotators.py 
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def __init__(
    self,
    color: Union[Color, List[Color]] = Color.ROBOFLOW,
    text_color: Union[Color, List[Color]] = Color.WHITE,
    text_scale: float = 0.5,
    text_thickness: int = 1,
    text_padding: int = 10,
    border_radius: int = 0,
    smart_position: bool = False,
):
    """
    Args:
        color (Union[Color, List[Color]]): The color to use for each
            keypoint label. If a list is provided, the colors will be used in order
            for each keypoint.
        text_color (Union[Color, List[Color]]): The color to use
            for the labels. If a list is provided, the colors will be used in order
            for each keypoint.
        text_scale (float): The scale of the text.
        text_thickness (int): The thickness of the text.
        text_padding (int): The padding around the text.
        border_radius (int): The radius of the rounded corners of the
            boxes. Set to a high value to produce circles.
        smart_position (bool): Spread out the labels to avoid overlap.
    """
    self.border_radius: int = border_radius
    self.color: Union[Color, List[Color]] = color
    self.text_color: Union[Color, List[Color]] = text_color
    self.text_scale: float = text_scale
    self.text_thickness: int = text_thickness
    self.text_padding: int = text_padding
    self.smart_position = smart_position

annotate(scene, key_points, labels=None)

A class that draws labels of skeleton vertices on images. It uses specified key points to determine the locations where the vertices should be drawn.

Parameters:

Name Type Description Default

scene

ImageType

The image where vertex labels will be drawn. ImageType is a flexible type, accepting either numpy.ndarray or PIL.Image.Image.

 required

key_points

KeyPoints

A collection of key points where each key point consists of x and y coordinates.

 required

labels

Optional[List[str]]

A list of labels to be displayed on the annotated image. If not provided, keypoint indices will be used.

 None

Returns:

Type Description
ImageType

The annotated image, matching the type of scene (numpy.ndarray or PIL.Image.Image)
Example 
import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

vertex_label_annotator = sv.VertexLabelAnnotator(
    color=sv.Color.GREEN,
    text_color=sv.Color.BLACK,
    border_radius=5
)
annotated_frame = vertex_label_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)

vertex-label-annotator-example

Tip

VertexLabelAnnotator allows to customize the color of each keypoint label values.

Example 
import supervision as sv

image = ...
key_points = sv.KeyPoints(...)

LABELS = [
    "nose", "left eye", "right eye", "left ear",
    "right ear", "left shoulder", "right shoulder", "left elbow",
    "right elbow", "left wrist", "right wrist", "left hip",
    "right hip", "left knee", "right knee", "left ankle",
    "right ankle"
]

COLORS = [
    "#FF6347", "#FF6347", "#FF6347", "#FF6347",
    "#FF6347", "#FF1493", "#00FF00", "#FF1493",
    "#00FF00", "#FF1493", "#00FF00", "#FFD700",
    "#00BFFF", "#FFD700", "#00BFFF", "#FFD700",
    "#00BFFF"
]
COLORS = [sv.Color.from_hex(color_hex=c) for c in COLORS]

vertex_label_annotator = sv.VertexLabelAnnotator(
    color=COLORS,
    text_color=sv.Color.BLACK,
    border_radius=5
)
annotated_frame = vertex_label_annotator.annotate(
    scene=image.copy(),
    key_points=key_points,
    labels=labels
)

vertex-label-annotator-custom-example

Source code in supervision/keypoint/annotators.py 
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def annotate(
    self,
    scene: ImageType,
    key_points: KeyPoints,
    labels: Optional[List[str]] = None,
) -> ImageType:
    """
    A class that draws labels of skeleton vertices on images. It uses specified key
        points to determine the locations where the vertices should be drawn.

    Args:
        scene (ImageType): The image where vertex labels will be drawn. `ImageType`
            is a flexible type, accepting either `numpy.ndarray` or
            `PIL.Image.Image`.
        key_points (KeyPoints): A collection of key points where each key point
            consists of x and y coordinates.
        labels (Optional[List[str]]): A list of labels to be displayed on the
            annotated image. If not provided, keypoint indices will be used.

    Returns:
        The annotated image, matching the type of `scene` (`numpy.ndarray`
            or `PIL.Image.Image`)

    Example:
        ```python
        import supervision as sv

        image = ...
        key_points = sv.KeyPoints(...)

        vertex_label_annotator = sv.VertexLabelAnnotator(
            color=sv.Color.GREEN,
            text_color=sv.Color.BLACK,
            border_radius=5
        )
        annotated_frame = vertex_label_annotator.annotate(
            scene=image.copy(),
            key_points=key_points
        )
        ```

    ![vertex-label-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/vertex-label-annotator-example.png)

    !!! tip

        `VertexLabelAnnotator` allows to customize the color of each keypoint label
        values.

    Example:
        ```python
        import supervision as sv

        image = ...
        key_points = sv.KeyPoints(...)

        LABELS = [
            "nose", "left eye", "right eye", "left ear",
            "right ear", "left shoulder", "right shoulder", "left elbow",
            "right elbow", "left wrist", "right wrist", "left hip",
            "right hip", "left knee", "right knee", "left ankle",
            "right ankle"
        ]

        COLORS = [
            "#FF6347", "#FF6347", "#FF6347", "#FF6347",
            "#FF6347", "#FF1493", "#00FF00", "#FF1493",
            "#00FF00", "#FF1493", "#00FF00", "#FFD700",
            "#00BFFF", "#FFD700", "#00BFFF", "#FFD700",
            "#00BFFF"
        ]
        COLORS = [sv.Color.from_hex(color_hex=c) for c in COLORS]

        vertex_label_annotator = sv.VertexLabelAnnotator(
            color=COLORS,
            text_color=sv.Color.BLACK,
            border_radius=5
        )
        annotated_frame = vertex_label_annotator.annotate(
            scene=image.copy(),
            key_points=key_points,
            labels=labels
        )
        ```
    ![vertex-label-annotator-custom-example](https://media.roboflow.com/
    supervision-annotator-examples/vertex-label-annotator-custom-example.png)
    """
    assert isinstance(scene, np.ndarray)
    font = cv2.FONT_HERSHEY_SIMPLEX

    skeletons_count, points_count, _ = key_points.xy.shape
    if skeletons_count == 0:
        return scene

    anchors = key_points.xy.reshape(points_count * skeletons_count, 2).astype(int)
    mask = np.all(anchors != 0, axis=1)

    if not np.any(mask):
        return scene

    colors = self.preprocess_and_validate_colors(
        colors=self.color,
        points_count=points_count,
        skeletons_count=skeletons_count,
    )

    text_colors = self.preprocess_and_validate_colors(
        colors=self.text_color,
        points_count=points_count,
        skeletons_count=skeletons_count,
    )

    labels = self.preprocess_and_validate_labels(
        labels=labels, points_count=points_count, skeletons_count=skeletons_count
    )

    anchors = anchors[mask]
    colors = colors[mask]
    text_colors = text_colors[mask]
    labels = labels[mask]

    xyxy = np.array(
        [
            self.get_text_bounding_box(
                text=label,
                font=font,
                text_scale=self.text_scale,
                text_thickness=self.text_thickness,
                center_coordinates=tuple(anchor),
            )
            for anchor, label in zip(anchors, labels)
        ]
    )
    xyxy_padded = pad_boxes(xyxy=xyxy, px=self.text_padding)

    if self.smart_position:
        xyxy_padded = spread_out_boxes(xyxy_padded)
        xyxy = pad_boxes(xyxy=xyxy_padded, px=-self.text_padding)

    for text, color, text_color, box, box_padded in zip(
        labels, colors, text_colors, xyxy, xyxy_padded
    ):
        draw_rounded_rectangle(
            scene=scene,
            rect=Rect.from_xyxy(box_padded),
            color=color,
            border_radius=self.border_radius,
        )
        cv2.putText(
            img=scene,
            text=text,
            org=(box[0], box[3]),
            fontFace=font,
            fontScale=self.text_scale,
            color=text_color.as_bgr(),
            thickness=self.text_thickness,
            lineType=cv2.LINE_AA,
        )

    return scene

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