Annotators

VertexAnnotatorEdgeAnnotator

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
)

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
)

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

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, optional): The color to use for annotating key points.
            radius (int, optional): The radius of the circles used to represent the key
                points.
        """
        self.color = color
        self.radius = radius

    @convert_for_annotation_method
    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 bounding boxes 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)
        """
        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

def __init__(
    self,
    color: Color = Color.ROBOFLOW,
    radius: int = 4,
) -> None:
    """
    Args:
        color (Color, optional): The color to use for annotating key points.
        radius (int, optional): 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 bounding boxes 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
)

Source code in supervision/keypoint/annotators.py

@convert_for_annotation_method
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 bounding boxes 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)
    """
    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

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, optional): The color to use for the edges.
            thickness (int, optional): 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

    @convert_for_annotation_method
    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 bounding boxes 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)
        """
        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

def __init__(
    self,
    color: Color = Color.ROBOFLOW,
    thickness: int = 2,
    edges: Optional[List[Tuple[int, int]]] = None,
) -> None:
    """
    Args:
        color (Color, optional): The color to use for the edges.
        thickness (int, optional): 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 bounding boxes 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
)

Source code in supervision/keypoint/annotators.py

@convert_for_annotation_method
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 bounding boxes 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)
    """
    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

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