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Annotators

import supervision as sv

image = ...
detections = sv.Detections(...)

box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

bounding-box-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

round_box_annotator = sv.RoundBoxAnnotator()
annotated_frame = round_box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

round-box-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

corner_annotator = sv.BoxCornerAnnotator()
annotated_frame = corner_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

box-corner-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

color_annotator = sv.ColorAnnotator()
annotated_frame = color_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

box-mask-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

circle_annotator = sv.CircleAnnotator()
annotated_frame = circle_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

circle-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

dot_annotator = sv.DotAnnotator()
annotated_frame = dot_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

dot-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

triangle_annotator = sv.TriangleAnnotator()
annotated_frame = triangle_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

triangle-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

ellipse_annotator = sv.EllipseAnnotator()
annotated_frame = ellipse_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

ellipse-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

halo_annotator = sv.HaloAnnotator()
annotated_frame = halo_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

halo-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

percentage_bar_annotator = sv.PercentageBarAnnotator()
annotated_frame = percentage_bar_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

percentage-bar-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

mask_annotator = sv.MaskAnnotator()
annotated_frame = mask_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

mask-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

polygon_annotator = sv.PolygonAnnotator()
annotated_frame = polygon_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

polygon-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

labels = [
    f"{class_name} {confidence:.2f}"
    for class_name, confidence
    in zip(detections['class_name'], detections.confidence)
]

label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
annotated_frame = label_annotator.annotate(
    scene=image.copy(),
    detections=detections,
    labels=labels
)

label-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

labels = [
    f"{class_name} {confidence:.2f}"
    for class_name, confidence
    in zip(detections['class_name'], detections.confidence)
]

rich_label_annotator = sv.RichLabelAnnotator(
    font_path="<TTF_FONT_PATH>",
    text_position=sv.Position.CENTER
)
annotated_frame = rich_label_annotator.annotate(
    scene=image.copy(),
    detections=detections,
    labels=labels
)

label-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

icon_paths = [
    "<ICON_PATH>"
    for _ in detections
]

icon_annotator = sv.IconAnnotator()
annotated_frame = icon_annotator.annotate(
    scene=image.copy(),
    detections=detections,
    icon_path=icon_paths
)

icon-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

crop_annotator = sv.CropAnnotator()
annotated_frame = crop_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

import supervision as sv

image = ...
detections = sv.Detections(...)

blur_annotator = sv.BlurAnnotator()
annotated_frame = blur_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

blur-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

pixelate_annotator = sv.PixelateAnnotator()
annotated_frame = pixelate_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

pixelate-annotator-example

import supervision as sv
from ultralytics import YOLO

model = YOLO('yolov8x.pt')

trace_annotator = sv.TraceAnnotator()

video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = sv.get_video_frames_generator(source_path='...')
tracker = sv.ByteTrack()

with sv.VideoSink(target_path='...', video_info=video_info) as sink:
    for frame in frames_generator:
        result = model(frame)[0]
        detections = sv.Detections.from_ultralytics(result)
        detections = tracker.update_with_detections(detections)
        annotated_frame = trace_annotator.annotate(
            scene=frame.copy(),
            detections=detections)
        sink.write_frame(frame=annotated_frame)

trace-annotator-example

import supervision as sv
from ultralytics import YOLO

model = YOLO('yolov8x.pt')

heat_map_annotator = sv.HeatMapAnnotator()

video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = sv.get_video_frames_generator(source_path='...')

with sv.VideoSink(target_path='...', video_info=video_info) as sink:
    for frame in frames_generator:
        result = model(frame)[0]
        detections = sv.Detections.from_ultralytics(result)
        annotated_frame = heat_map_annotator.annotate(
            scene=frame.copy(),
            detections=detections)
        sink.write_frame(frame=annotated_frame)

heat-map-annotator-example

import supervision as sv

image = ...
detections = sv.Detections(...)

background_overlay_annotator = sv.BackgroundOverlayAnnotator()
annotated_frame = background_overlay_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

background-overlay-annotator-example

BoxAnnotator

Bases: BaseAnnotator

A class for drawing bounding boxes on an image using provided detections.

Source code in supervision/annotators/core.py 
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class BoxAnnotator(BaseAnnotator):
    """
    A class for drawing bounding boxes on an image using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        thickness: int = 2,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            thickness (int): Thickness of the bounding box lines.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with bounding boxes based on the provided detections.

        Args:
            scene (ImageType): The image where bounding boxes will be drawn. `ImageType`
            is a flexible type, accepting either `numpy.ndarray` or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            box_annotator = sv.BoxAnnotator()
            annotated_frame = box_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![bounding-box-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/bounding-box-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        for detection_idx in range(len(detections)):
            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            cv2.rectangle(
                img=scene,
                pt1=(x1, y1),
                pt2=(x2, y2),
                color=color.as_bgr(),
                thickness=self.thickness,
            )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, thickness=2, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the bounding box lines.

 2
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the bounding box lines.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with bounding boxes based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

The image where bounding boxes will be drawn. ImageType

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

bounding-box-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with bounding boxes based on the provided detections.

    Args:
        scene (ImageType): The image where bounding boxes will be drawn. `ImageType`
        is a flexible type, accepting either `numpy.ndarray` or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        box_annotator = sv.BoxAnnotator()
        annotated_frame = box_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![bounding-box-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/bounding-box-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    for detection_idx in range(len(detections)):
        x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        cv2.rectangle(
            img=scene,
            pt1=(x1, y1),
            pt2=(x2, y2),
            color=color.as_bgr(),
            thickness=self.thickness,
        )
    return scene

RoundBoxAnnotator

Bases: BaseAnnotator

A class for drawing bounding boxes with round edges on an image using provided detections.

Source code in supervision/annotators/core.py 
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class RoundBoxAnnotator(BaseAnnotator):
    """
    A class for drawing bounding boxes with round edges on an image
    using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        thickness: int = 2,
        color_lookup: ColorLookup = ColorLookup.CLASS,
        roundness: float = 0.6,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            thickness (int): Thickness of the bounding box lines.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
            roundness (float): Percent of roundness for edges of bounding box.
                Value must be float 0 < roundness <= 1.0
                By default roundness percent is calculated based on smaller side
                length (width or height).
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
        self.color_lookup: ColorLookup = color_lookup
        if not 0 < roundness <= 1.0:
            raise ValueError("roundness attribute must be float between (0, 1.0]")
        self.roundness: float = roundness

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with bounding boxes with rounded edges
        based on the provided detections.

        Args:
            scene (ImageType): The image where rounded bounding boxes will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            round_box_annotator = sv.RoundBoxAnnotator()
            annotated_frame = round_box_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![round-box-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/round-box-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        for detection_idx in range(len(detections)):
            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )

            radius = (
                int((x2 - x1) // 2 * self.roundness)
                if abs(x1 - x2) < abs(y1 - y2)
                else int((y2 - y1) // 2 * self.roundness)
            )

            circle_coordinates = [
                ((x1 + radius), (y1 + radius)),
                ((x2 - radius), (y1 + radius)),
                ((x2 - radius), (y2 - radius)),
                ((x1 + radius), (y2 - radius)),
            ]

            line_coordinates = [
                ((x1 + radius, y1), (x2 - radius, y1)),
                ((x2, y1 + radius), (x2, y2 - radius)),
                ((x1 + radius, y2), (x2 - radius, y2)),
                ((x1, y1 + radius), (x1, y2 - radius)),
            ]

            start_angles = (180, 270, 0, 90)
            end_angles = (270, 360, 90, 180)

            for center_coordinates, line, start_angle, end_angle in zip(
                circle_coordinates, line_coordinates, start_angles, end_angles
            ):
                cv2.ellipse(
                    img=scene,
                    center=center_coordinates,
                    axes=(radius, radius),
                    angle=0,
                    startAngle=start_angle,
                    endAngle=end_angle,
                    color=color.as_bgr(),
                    thickness=self.thickness,
                )

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

        return scene

Functions

__init__(color=ColorPalette.DEFAULT, thickness=2, color_lookup=ColorLookup.CLASS, roundness=0.6)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the bounding box lines.

 2
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
roundness float

Percent of roundness for edges of bounding box. Value must be float 0 < roundness <= 1.0 By default roundness percent is calculated based on smaller side length (width or height).

 0.6
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    roundness: float = 0.6,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the bounding box lines.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        roundness (float): Percent of roundness for edges of bounding box.
            Value must be float 0 < roundness <= 1.0
            By default roundness percent is calculated based on smaller side
            length (width or height).
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup
    if not 0 < roundness <= 1.0:
        raise ValueError("roundness attribute must be float between (0, 1.0]")
    self.roundness: float = roundness

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with bounding boxes with rounded edges based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

round_box_annotator = sv.RoundBoxAnnotator()
annotated_frame = round_box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

round-box-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with bounding boxes with rounded edges
    based on the provided detections.

    Args:
        scene (ImageType): The image where rounded bounding boxes will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        round_box_annotator = sv.RoundBoxAnnotator()
        annotated_frame = round_box_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![round-box-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/round-box-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    for detection_idx in range(len(detections)):
        x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )

        radius = (
            int((x2 - x1) // 2 * self.roundness)
            if abs(x1 - x2) < abs(y1 - y2)
            else int((y2 - y1) // 2 * self.roundness)
        )

        circle_coordinates = [
            ((x1 + radius), (y1 + radius)),
            ((x2 - radius), (y1 + radius)),
            ((x2 - radius), (y2 - radius)),
            ((x1 + radius), (y2 - radius)),
        ]

        line_coordinates = [
            ((x1 + radius, y1), (x2 - radius, y1)),
            ((x2, y1 + radius), (x2, y2 - radius)),
            ((x1 + radius, y2), (x2 - radius, y2)),
            ((x1, y1 + radius), (x1, y2 - radius)),
        ]

        start_angles = (180, 270, 0, 90)
        end_angles = (270, 360, 90, 180)

        for center_coordinates, line, start_angle, end_angle in zip(
            circle_coordinates, line_coordinates, start_angles, end_angles
        ):
            cv2.ellipse(
                img=scene,
                center=center_coordinates,
                axes=(radius, radius),
                angle=0,
                startAngle=start_angle,
                endAngle=end_angle,
                color=color.as_bgr(),
                thickness=self.thickness,
            )

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

    return scene

BoxCornerAnnotator

Bases: BaseAnnotator

A class for drawing box corners on an image using provided detections.

Source code in supervision/annotators/core.py 
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class BoxCornerAnnotator(BaseAnnotator):
    """
    A class for drawing box corners on an image using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        thickness: int = 4,
        corner_length: int = 15,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            thickness (int): Thickness of the corner lines.
            corner_length (int): Length of each corner line.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
        self.corner_length: int = corner_length
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with box corners based on the provided detections.

        Args:
            scene (ImageType): The image where box corners will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            corner_annotator = sv.BoxCornerAnnotator()
            annotated_frame = corner_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![box-corner-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/box-corner-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        for detection_idx in range(len(detections)):
            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            corners = [(x1, y1), (x2, y1), (x1, y2), (x2, y2)]

            for x, y in corners:
                x_end = x + self.corner_length if x == x1 else x - self.corner_length
                cv2.line(
                    scene, (x, y), (x_end, y), color.as_bgr(), thickness=self.thickness
                )

                y_end = y + self.corner_length if y == y1 else y - self.corner_length
                cv2.line(
                    scene, (x, y), (x, y_end), color.as_bgr(), thickness=self.thickness
                )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, thickness=4, corner_length=15, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the corner lines.

 4
corner_length int

Length of each corner line.

 15
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 4,
    corner_length: int = 15,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the corner lines.
        corner_length (int): Length of each corner line.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.corner_length: int = corner_length
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with box corners based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

corner_annotator = sv.BoxCornerAnnotator()
annotated_frame = corner_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

box-corner-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with box corners based on the provided detections.

    Args:
        scene (ImageType): The image where box corners will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        corner_annotator = sv.BoxCornerAnnotator()
        annotated_frame = corner_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![box-corner-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/box-corner-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    for detection_idx in range(len(detections)):
        x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        corners = [(x1, y1), (x2, y1), (x1, y2), (x2, y2)]

        for x, y in corners:
            x_end = x + self.corner_length if x == x1 else x - self.corner_length
            cv2.line(
                scene, (x, y), (x_end, y), color.as_bgr(), thickness=self.thickness
            )

            y_end = y + self.corner_length if y == y1 else y - self.corner_length
            cv2.line(
                scene, (x, y), (x, y_end), color.as_bgr(), thickness=self.thickness
            )
    return scene

OrientedBoxAnnotator

Bases: BaseAnnotator

A class for drawing oriented bounding boxes on an image using provided detections.

Source code in supervision/annotators/core.py 
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class OrientedBoxAnnotator(BaseAnnotator):
    """
    A class for drawing oriented bounding boxes on an image using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        thickness: int = 2,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            thickness (int): Thickness of the bounding box lines.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with oriented bounding boxes based on the provided detections.

        Args:
            scene (ImageType): The image where bounding boxes will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

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

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = YOLO("yolov8n-obb.pt")

            result = model(image)[0]
            detections = sv.Detections.from_ultralytics(result)

            oriented_box_annotator = sv.OrientedBoxAnnotator()
            annotated_frame = oriented_box_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```
        """  # noqa E501 // docs
        assert isinstance(scene, np.ndarray)
        if detections.data is None or ORIENTED_BOX_COORDINATES not in detections.data:
            return scene
        obb_boxes = np.array(detections.data[ORIENTED_BOX_COORDINATES]).astype(int)

        for detection_idx in range(len(detections)):
            obb = obb_boxes[detection_idx]
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )

            cv2.drawContours(scene, [obb], 0, color.as_bgr(), self.thickness)

        return scene

Functions

__init__(color=ColorPalette.DEFAULT, thickness=2, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the bounding box lines.

 2
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the bounding box lines.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with oriented bounding boxes based on the provided detections.

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
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 None

Returns:

Type Description
ImageType

The annotated image, matching the type of scene (numpy.ndarray or PIL.Image.Image)
Example 
import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO("yolov8n-obb.pt")

result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)

oriented_box_annotator = sv.OrientedBoxAnnotator()
annotated_frame = oriented_box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)
Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with oriented bounding boxes based on the provided detections.

    Args:
        scene (ImageType): The image where bounding boxes will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

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

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = YOLO("yolov8n-obb.pt")

        result = model(image)[0]
        detections = sv.Detections.from_ultralytics(result)

        oriented_box_annotator = sv.OrientedBoxAnnotator()
        annotated_frame = oriented_box_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```
    """  # noqa E501 // docs
    assert isinstance(scene, np.ndarray)
    if detections.data is None or ORIENTED_BOX_COORDINATES not in detections.data:
        return scene
    obb_boxes = np.array(detections.data[ORIENTED_BOX_COORDINATES]).astype(int)

    for detection_idx in range(len(detections)):
        obb = obb_boxes[detection_idx]
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )

        cv2.drawContours(scene, [obb], 0, color.as_bgr(), self.thickness)

    return scene

ColorAnnotator

Bases: BaseAnnotator

A class for drawing box masks on an image using provided detections.

Source code in supervision/annotators/core.py 
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class ColorAnnotator(BaseAnnotator):
    """
    A class for drawing box masks on an image using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        opacity: float = 0.5,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.color_lookup: ColorLookup = color_lookup
        self.opacity = opacity

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with box masks based on the provided detections.

        Args:
            scene (ImageType): The image where bounding boxes will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            color_annotator = sv.ColorAnnotator()
            annotated_frame = color_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![box-mask-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/box-mask-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        scene_with_boxes = scene.copy()
        for detection_idx in range(len(detections)):
            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            cv2.rectangle(
                img=scene_with_boxes,
                pt1=(x1, y1),
                pt2=(x2, y2),
                color=color.as_bgr(),
                thickness=-1,
            )

        cv2.addWeighted(
            scene_with_boxes, self.opacity, scene, 1 - self.opacity, gamma=0, dst=scene
        )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, opacity=0.5, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
opacity float

Opacity of the overlay mask. Must be between 0 and 1.

 0.5
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    opacity: float = 0.5,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.color_lookup: ColorLookup = color_lookup
    self.opacity = opacity

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with box masks based on the provided detections.

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
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

color_annotator = sv.ColorAnnotator()
annotated_frame = color_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

box-mask-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with box masks based on the provided detections.

    Args:
        scene (ImageType): The image where bounding boxes will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        color_annotator = sv.ColorAnnotator()
        annotated_frame = color_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![box-mask-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/box-mask-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    scene_with_boxes = scene.copy()
    for detection_idx in range(len(detections)):
        x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        cv2.rectangle(
            img=scene_with_boxes,
            pt1=(x1, y1),
            pt2=(x2, y2),
            color=color.as_bgr(),
            thickness=-1,
        )

    cv2.addWeighted(
        scene_with_boxes, self.opacity, scene, 1 - self.opacity, gamma=0, dst=scene
    )
    return scene

CircleAnnotator

Bases: BaseAnnotator

A class for drawing circle on an image using provided detections.

Source code in supervision/annotators/core.py 
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class CircleAnnotator(BaseAnnotator):
    """
    A class for drawing circle on an image using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        thickness: int = 2,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            thickness (int): Thickness of the circle line.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """

        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with circles based on the provided detections.

        Args:
            scene (ImageType): The image where box corners will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            circle_annotator = sv.CircleAnnotator()
            annotated_frame = circle_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```


        ![circle-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/circle-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        for detection_idx in range(len(detections)):
            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
            center = ((x1 + x2) // 2, (y1 + y2) // 2)
            distance = sqrt((x1 - center[0]) ** 2 + (y1 - center[1]) ** 2)
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            cv2.circle(
                img=scene,
                center=center,
                radius=int(distance),
                color=color.as_bgr(),
                thickness=self.thickness,
            )

        return scene

Functions

__init__(color=ColorPalette.DEFAULT, thickness=2, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the circle line.

 2
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the circle line.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """

    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with circles based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

circle_annotator = sv.CircleAnnotator()
annotated_frame = circle_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

circle-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with circles based on the provided detections.

    Args:
        scene (ImageType): The image where box corners will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        circle_annotator = sv.CircleAnnotator()
        annotated_frame = circle_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```


    ![circle-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/circle-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    for detection_idx in range(len(detections)):
        x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
        center = ((x1 + x2) // 2, (y1 + y2) // 2)
        distance = sqrt((x1 - center[0]) ** 2 + (y1 - center[1]) ** 2)
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        cv2.circle(
            img=scene,
            center=center,
            radius=int(distance),
            color=color.as_bgr(),
            thickness=self.thickness,
        )

    return scene

DotAnnotator

Bases: BaseAnnotator

A class for drawing dots on an image at specific coordinates based on provided detections.

Source code in supervision/annotators/core.py 
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class DotAnnotator(BaseAnnotator):
    """
    A class for drawing dots on an image at specific coordinates based on provided
    detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        radius: int = 4,
        position: Position = Position.CENTER,
        color_lookup: ColorLookup = ColorLookup.CLASS,
        outline_thickness: int = 0,
        outline_color: Union[Color, ColorPalette] = Color.BLACK,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            radius (int): Radius of the drawn dots.
            position (Position): The anchor position for placing the dot.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
            outline_thickness (int): Thickness of the outline of the dot.
            outline_color (Union[Color, ColorPalette]): The color or color palette to
                use for outline. It is activated by setting outline_thickness to a value
                greater than 0.
        """
        self.color: Union[Color, ColorPalette] = color
        self.radius: int = radius
        self.position: Position = position
        self.color_lookup: ColorLookup = color_lookup
        self.outline_thickness = outline_thickness
        self.outline_color: Union[Color, ColorPalette] = outline_color

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with dots based on the provided detections.

        Args:
            scene (ImageType): The image where dots will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            dot_annotator = sv.DotAnnotator()
            annotated_frame = dot_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![dot-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/dot-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        xy = detections.get_anchors_coordinates(anchor=self.position)
        for detection_idx in range(len(detections)):
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            center = (int(xy[detection_idx, 0]), int(xy[detection_idx, 1]))

            cv2.circle(scene, center, self.radius, color.as_bgr(), -1)
            if self.outline_thickness:
                outline_color = resolve_color(
                    color=self.outline_color,
                    detections=detections,
                    detection_idx=detection_idx,
                    color_lookup=self.color_lookup
                    if custom_color_lookup is None
                    else custom_color_lookup,
                )
                cv2.circle(
                    scene,
                    center,
                    self.radius,
                    outline_color.as_bgr(),
                    self.outline_thickness,
                )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, radius=4, position=Position.CENTER, color_lookup=ColorLookup.CLASS, outline_thickness=0, outline_color=Color.BLACK)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
radius int

Radius of the drawn dots.

 4
position Position

The anchor position for placing the dot.

 CENTER
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
outline_thickness int

Thickness of the outline of the dot.

 0
outline_color Union[Color, ColorPalette]

The color or color palette to use for outline. It is activated by setting outline_thickness to a value greater than 0.

 BLACK
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    radius: int = 4,
    position: Position = Position.CENTER,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    outline_thickness: int = 0,
    outline_color: Union[Color, ColorPalette] = Color.BLACK,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        radius (int): Radius of the drawn dots.
        position (Position): The anchor position for placing the dot.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        outline_thickness (int): Thickness of the outline of the dot.
        outline_color (Union[Color, ColorPalette]): The color or color palette to
            use for outline. It is activated by setting outline_thickness to a value
            greater than 0.
    """
    self.color: Union[Color, ColorPalette] = color
    self.radius: int = radius
    self.position: Position = position
    self.color_lookup: ColorLookup = color_lookup
    self.outline_thickness = outline_thickness
    self.outline_color: Union[Color, ColorPalette] = outline_color

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with dots based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

dot_annotator = sv.DotAnnotator()
annotated_frame = dot_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

dot-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with dots based on the provided detections.

    Args:
        scene (ImageType): The image where dots will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        dot_annotator = sv.DotAnnotator()
        annotated_frame = dot_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![dot-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/dot-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    xy = detections.get_anchors_coordinates(anchor=self.position)
    for detection_idx in range(len(detections)):
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        center = (int(xy[detection_idx, 0]), int(xy[detection_idx, 1]))

        cv2.circle(scene, center, self.radius, color.as_bgr(), -1)
        if self.outline_thickness:
            outline_color = resolve_color(
                color=self.outline_color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            cv2.circle(
                scene,
                center,
                self.radius,
                outline_color.as_bgr(),
                self.outline_thickness,
            )
    return scene

TriangleAnnotator

Bases: BaseAnnotator

A class for drawing triangle markers on an image at specific coordinates based on provided detections.

Source code in supervision/annotators/core.py 
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class TriangleAnnotator(BaseAnnotator):
    """
    A class for drawing triangle markers on an image at specific coordinates based on
    provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        base: int = 10,
        height: int = 10,
        position: Position = Position.TOP_CENTER,
        color_lookup: ColorLookup = ColorLookup.CLASS,
        outline_thickness: int = 0,
        outline_color: Union[Color, ColorPalette] = Color.BLACK,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            base (int): The base width of the triangle.
            height (int): The height of the triangle.
            position (Position): The anchor position for placing the triangle.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
            outline_thickness (int): Thickness of the outline of the triangle.
            outline_color (Union[Color, ColorPalette]): The color or color palette to
                use for outline. It is activated by setting outline_thickness to a value
                greater than 0.
        """
        self.color: Union[Color, ColorPalette] = color
        self.base: int = base
        self.height: int = height
        self.position: Position = position
        self.color_lookup: ColorLookup = color_lookup
        self.outline_thickness: int = outline_thickness
        self.outline_color: Union[Color, ColorPalette] = outline_color

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with triangles based on the provided detections.

        Args:
            scene (ImageType): The image where triangles will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            triangle_annotator = sv.TriangleAnnotator()
            annotated_frame = triangle_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![triangle-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/triangle-annotator-example.png)
        """
        assert isinstance(scene, np.ndarray)
        xy = detections.get_anchors_coordinates(anchor=self.position)
        for detection_idx in range(len(detections)):
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            tip_x, tip_y = int(xy[detection_idx, 0]), int(xy[detection_idx, 1])
            vertices = np.array(
                [
                    [tip_x - self.base // 2, tip_y - self.height],
                    [tip_x + self.base // 2, tip_y - self.height],
                    [tip_x, tip_y],
                ],
                np.int32,
            )

            cv2.fillPoly(scene, [vertices], color.as_bgr())
            if self.outline_thickness:
                outline_color = resolve_color(
                    color=self.outline_color,
                    detections=detections,
                    detection_idx=detection_idx,
                    color_lookup=self.color_lookup
                    if custom_color_lookup is None
                    else custom_color_lookup,
                )
                cv2.polylines(
                    scene,
                    [vertices],
                    True,
                    outline_color.as_bgr(),
                    thickness=self.outline_thickness,
                )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, base=10, height=10, position=Position.TOP_CENTER, color_lookup=ColorLookup.CLASS, outline_thickness=0, outline_color=Color.BLACK)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
base int

The base width of the triangle.

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height int

The height of the triangle.

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position Position

The anchor position for placing the triangle.

 TOP_CENTER
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
outline_thickness int

Thickness of the outline of the triangle.

 0
outline_color Union[Color, ColorPalette]

The color or color palette to use for outline. It is activated by setting outline_thickness to a value greater than 0.

 BLACK
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    base: int = 10,
    height: int = 10,
    position: Position = Position.TOP_CENTER,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    outline_thickness: int = 0,
    outline_color: Union[Color, ColorPalette] = Color.BLACK,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        base (int): The base width of the triangle.
        height (int): The height of the triangle.
        position (Position): The anchor position for placing the triangle.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        outline_thickness (int): Thickness of the outline of the triangle.
        outline_color (Union[Color, ColorPalette]): The color or color palette to
            use for outline. It is activated by setting outline_thickness to a value
            greater than 0.
    """
    self.color: Union[Color, ColorPalette] = color
    self.base: int = base
    self.height: int = height
    self.position: Position = position
    self.color_lookup: ColorLookup = color_lookup
    self.outline_thickness: int = outline_thickness
    self.outline_color: Union[Color, ColorPalette] = outline_color

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with triangles based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

triangle_annotator = sv.TriangleAnnotator()
annotated_frame = triangle_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

triangle-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with triangles based on the provided detections.

    Args:
        scene (ImageType): The image where triangles will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        triangle_annotator = sv.TriangleAnnotator()
        annotated_frame = triangle_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![triangle-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/triangle-annotator-example.png)
    """
    assert isinstance(scene, np.ndarray)
    xy = detections.get_anchors_coordinates(anchor=self.position)
    for detection_idx in range(len(detections)):
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        tip_x, tip_y = int(xy[detection_idx, 0]), int(xy[detection_idx, 1])
        vertices = np.array(
            [
                [tip_x - self.base // 2, tip_y - self.height],
                [tip_x + self.base // 2, tip_y - self.height],
                [tip_x, tip_y],
            ],
            np.int32,
        )

        cv2.fillPoly(scene, [vertices], color.as_bgr())
        if self.outline_thickness:
            outline_color = resolve_color(
                color=self.outline_color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            cv2.polylines(
                scene,
                [vertices],
                True,
                outline_color.as_bgr(),
                thickness=self.outline_thickness,
            )
    return scene

EllipseAnnotator

Bases: BaseAnnotator

A class for drawing ellipses on an image using provided detections.

Source code in supervision/annotators/core.py 
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class EllipseAnnotator(BaseAnnotator):
    """
    A class for drawing ellipses on an image using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        thickness: int = 2,
        start_angle: int = -45,
        end_angle: int = 235,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            thickness (int): Thickness of the ellipse lines.
            start_angle (int): Starting angle of the ellipse.
            end_angle (int): Ending angle of the ellipse.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
        self.start_angle: int = start_angle
        self.end_angle: int = end_angle
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with ellipses based on the provided detections.

        Args:
            scene (ImageType): The image where ellipses will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            ellipse_annotator = sv.EllipseAnnotator()
            annotated_frame = ellipse_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![ellipse-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/ellipse-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        for detection_idx in range(len(detections)):
            x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            center = (int((x1 + x2) / 2), y2)
            width = x2 - x1
            cv2.ellipse(
                scene,
                center=center,
                axes=(int(width), int(0.35 * width)),
                angle=0.0,
                startAngle=self.start_angle,
                endAngle=self.end_angle,
                color=color.as_bgr(),
                thickness=self.thickness,
                lineType=cv2.LINE_4,
            )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, thickness=2, start_angle=-45, end_angle=235, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the ellipse lines.

 2
start_angle int

Starting angle of the ellipse.

 -45
end_angle int

Ending angle of the ellipse.

 235
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    start_angle: int = -45,
    end_angle: int = 235,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the ellipse lines.
        start_angle (int): Starting angle of the ellipse.
        end_angle (int): Ending angle of the ellipse.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.start_angle: int = start_angle
    self.end_angle: int = end_angle
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with ellipses based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

ellipse_annotator = sv.EllipseAnnotator()
annotated_frame = ellipse_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

ellipse-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with ellipses based on the provided detections.

    Args:
        scene (ImageType): The image where ellipses will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        ellipse_annotator = sv.EllipseAnnotator()
        annotated_frame = ellipse_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![ellipse-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/ellipse-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    for detection_idx in range(len(detections)):
        x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        center = (int((x1 + x2) / 2), y2)
        width = x2 - x1
        cv2.ellipse(
            scene,
            center=center,
            axes=(int(width), int(0.35 * width)),
            angle=0.0,
            startAngle=self.start_angle,
            endAngle=self.end_angle,
            color=color.as_bgr(),
            thickness=self.thickness,
            lineType=cv2.LINE_4,
        )
    return scene

HaloAnnotator

Bases: BaseAnnotator

A class for drawing Halos on an image using provided detections.

Warning

This annotator uses sv.Detections.mask.

Source code in supervision/annotators/core.py 
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class HaloAnnotator(BaseAnnotator):
    """
    A class for drawing Halos on an image using provided detections.

    !!! warning

        This annotator uses `sv.Detections.mask`.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        opacity: float = 0.8,
        kernel_size: int = 40,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
            kernel_size (int): The size of the average pooling kernel used for creating
                the halo.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.opacity = opacity
        self.color_lookup: ColorLookup = color_lookup
        self.kernel_size: int = kernel_size

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with halos based on the provided detections.

        Args:
            scene (ImageType): The image where masks will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            halo_annotator = sv.HaloAnnotator()
            annotated_frame = halo_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![halo-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/halo-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        if detections.mask is None:
            return scene
        colored_mask = np.zeros_like(scene, dtype=np.uint8)
        fmask = np.array([False] * scene.shape[0] * scene.shape[1]).reshape(
            scene.shape[0], scene.shape[1]
        )

        for detection_idx in np.flip(np.argsort(detections.area)):
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            mask = detections.mask[detection_idx]
            fmask = np.logical_or(fmask, mask)
            color_bgr = color.as_bgr()
            colored_mask[mask] = color_bgr

        colored_mask = cv2.blur(colored_mask, (self.kernel_size, self.kernel_size))
        colored_mask[fmask] = [0, 0, 0]
        gray = cv2.cvtColor(colored_mask, cv2.COLOR_BGR2GRAY)
        alpha = self.opacity * gray / gray.max()
        alpha_mask = alpha[:, :, np.newaxis]
        blended_scene = np.uint8(scene * (1 - alpha_mask) + colored_mask * self.opacity)
        np.copyto(scene, blended_scene)
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, opacity=0.8, kernel_size=40, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
opacity float

Opacity of the overlay mask. Must be between 0 and 1.

 0.8
kernel_size int

The size of the average pooling kernel used for creating the halo.

 40
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    opacity: float = 0.8,
    kernel_size: int = 40,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
        kernel_size (int): The size of the average pooling kernel used for creating
            the halo.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.opacity = opacity
    self.color_lookup: ColorLookup = color_lookup
    self.kernel_size: int = kernel_size

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with halos based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

halo_annotator = sv.HaloAnnotator()
annotated_frame = halo_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

halo-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with halos based on the provided detections.

    Args:
        scene (ImageType): The image where masks will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        halo_annotator = sv.HaloAnnotator()
        annotated_frame = halo_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![halo-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/halo-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    if detections.mask is None:
        return scene
    colored_mask = np.zeros_like(scene, dtype=np.uint8)
    fmask = np.array([False] * scene.shape[0] * scene.shape[1]).reshape(
        scene.shape[0], scene.shape[1]
    )

    for detection_idx in np.flip(np.argsort(detections.area)):
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        mask = detections.mask[detection_idx]
        fmask = np.logical_or(fmask, mask)
        color_bgr = color.as_bgr()
        colored_mask[mask] = color_bgr

    colored_mask = cv2.blur(colored_mask, (self.kernel_size, self.kernel_size))
    colored_mask[fmask] = [0, 0, 0]
    gray = cv2.cvtColor(colored_mask, cv2.COLOR_BGR2GRAY)
    alpha = self.opacity * gray / gray.max()
    alpha_mask = alpha[:, :, np.newaxis]
    blended_scene = np.uint8(scene * (1 - alpha_mask) + colored_mask * self.opacity)
    np.copyto(scene, blended_scene)
    return scene

PercentageBarAnnotator

Bases: BaseAnnotator

A class for drawing percentage bars on an image using provided detections.

Source code in supervision/annotators/core.py 
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class PercentageBarAnnotator(BaseAnnotator):
    """
    A class for drawing percentage bars on an image using provided detections.
    """

    def __init__(
        self,
        height: int = 16,
        width: int = 80,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        border_color: Color = Color.BLACK,
        position: Position = Position.TOP_CENTER,
        color_lookup: ColorLookup = ColorLookup.CLASS,
        border_thickness: Optional[int] = None,
    ):
        """
        Args:
            height (int): The height in pixels of the percentage bar.
            width (int): The width in pixels of the percentage bar.
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            border_color (Color): The color of the border lines.
            position (Position): The anchor position of drawing the percentage bar.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
            border_thickness (Optional[int]): The thickness of the border lines.
        """
        self.height: int = height
        self.width: int = width
        self.color: Union[Color, ColorPalette] = color
        self.border_color: Color = border_color
        self.position: Position = position
        self.color_lookup: ColorLookup = color_lookup

        if border_thickness is None:
            self.border_thickness = int(0.15 * self.height)

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
        custom_values: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with percentage bars based on the provided
        detections. The percentage bars visually represent the confidence or custom
        values associated with each detection.

        Args:
            scene (ImageType): The image where percentage bars will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.
            custom_values (Optional[np.ndarray]): Custom values array to use instead
                of the default detection confidences. This array should have the
                same length as the number of detections and contain a value between
                0 and 1 (inclusive) for each detection, representing the percentage
                to be displayed.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            percentage_bar_annotator = sv.PercentageBarAnnotator()
            annotated_frame = percentage_bar_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![percentage-bar-example](https://media.roboflow.com/
        supervision-annotator-examples/percentage-bar-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        self.validate_custom_values(custom_values=custom_values, detections=detections)

        anchors = detections.get_anchors_coordinates(anchor=self.position)
        for detection_idx in range(len(detections)):
            anchor = anchors[detection_idx]
            border_coordinates = self.calculate_border_coordinates(
                anchor_xy=(int(anchor[0]), int(anchor[1])),
                border_wh=(self.width, self.height),
                position=self.position,
            )
            border_width = border_coordinates[1][0] - border_coordinates[0][0]

            if custom_values is not None:
                value = custom_values[detection_idx]
            else:
                assert detections.confidence is not None  # MyPy type hint
                value = detections.confidence[detection_idx]

            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            cv2.rectangle(
                img=scene,
                pt1=border_coordinates[0],
                pt2=(
                    border_coordinates[0][0] + int(border_width * value),
                    border_coordinates[1][1],
                ),
                color=color.as_bgr(),
                thickness=-1,
            )
            cv2.rectangle(
                img=scene,
                pt1=border_coordinates[0],
                pt2=border_coordinates[1],
                color=self.border_color.as_bgr(),
                thickness=self.border_thickness,
            )
        return scene

    @staticmethod
    def calculate_border_coordinates(
        anchor_xy: Tuple[int, int], border_wh: Tuple[int, int], position: Position
    ) -> Tuple[Tuple[int, int], Tuple[int, int]]:
        cx, cy = anchor_xy
        width, height = border_wh

        if position == Position.TOP_LEFT:
            return (cx - width, cy - height), (cx, cy)
        elif position == Position.TOP_CENTER:
            return (cx - width // 2, cy), (cx + width // 2, cy - height)
        elif position == Position.TOP_RIGHT:
            return (cx, cy), (cx + width, cy - height)
        elif position == Position.CENTER_LEFT:
            return (cx - width, cy - height // 2), (cx, cy + height // 2)
        elif position == Position.CENTER or position == Position.CENTER_OF_MASS:
            return (
                (cx - width // 2, cy - height // 2),
                (cx + width // 2, cy + height // 2),
            )
        elif position == Position.CENTER_RIGHT:
            return (cx, cy - height // 2), (cx + width, cy + height // 2)
        elif position == Position.BOTTOM_LEFT:
            return (cx - width, cy), (cx, cy + height)
        elif position == Position.BOTTOM_CENTER:
            return (cx - width // 2, cy), (cx + width // 2, cy + height)
        elif position == Position.BOTTOM_RIGHT:
            return (cx, cy), (cx + width, cy + height)

    @staticmethod
    def validate_custom_values(
        custom_values: Optional[Union[np.ndarray, List[float]]], detections: Detections
    ) -> None:
        if custom_values is None:
            if detections.confidence is None:
                raise ValueError(
                    "The provided detections do not contain confidence values. "
                    "Please provide `custom_values` or ensure that the detections "
                    "contain confidence values (e.g. by using a different model)."
                )

        else:
            if not isinstance(custom_values, (np.ndarray, list)):
                raise TypeError(
                    "custom_values must be either a numpy array or a list of floats."
                )

            if len(custom_values) != len(detections):
                raise ValueError(
                    "The length of custom_values must match the number of detections."
                )

            if not all(0 <= value <= 1 for value in custom_values):
                raise ValueError("All values in custom_values must be between 0 and 1.")

Functions

__init__(height=16, width=80, color=ColorPalette.DEFAULT, border_color=Color.BLACK, position=Position.TOP_CENTER, color_lookup=ColorLookup.CLASS, border_thickness=None)

Parameters:

Name Type Description Default
height int

The height in pixels of the percentage bar.

 16
width int

The width in pixels of the percentage bar.

 80
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
border_color Color

The color of the border lines.

 BLACK
position Position

The anchor position of drawing the percentage bar.

 TOP_CENTER
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
border_thickness Optional[int]

The thickness of the border lines.

 None
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    height: int = 16,
    width: int = 80,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    border_color: Color = Color.BLACK,
    position: Position = Position.TOP_CENTER,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    border_thickness: Optional[int] = None,
):
    """
    Args:
        height (int): The height in pixels of the percentage bar.
        width (int): The width in pixels of the percentage bar.
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        border_color (Color): The color of the border lines.
        position (Position): The anchor position of drawing the percentage bar.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        border_thickness (Optional[int]): The thickness of the border lines.
    """
    self.height: int = height
    self.width: int = width
    self.color: Union[Color, ColorPalette] = color
    self.border_color: Color = border_color
    self.position: Position = position
    self.color_lookup: ColorLookup = color_lookup

    if border_thickness is None:
        self.border_thickness = int(0.15 * self.height)

annotate(scene, detections, custom_color_lookup=None, custom_values=None)

Annotates the given scene with percentage bars based on the provided detections. The percentage bars visually represent the confidence or custom values associated with each detection.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 None
custom_values Optional[ndarray]

Custom values array to use instead of the default detection confidences. This array should have the same length as the number of detections and contain a value between 0 and 1 (inclusive) for each detection, representing the percentage to be displayed.

 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 = ...
detections = sv.Detections(...)

percentage_bar_annotator = sv.PercentageBarAnnotator()
annotated_frame = percentage_bar_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

percentage-bar-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
    custom_values: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with percentage bars based on the provided
    detections. The percentage bars visually represent the confidence or custom
    values associated with each detection.

    Args:
        scene (ImageType): The image where percentage bars will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.
        custom_values (Optional[np.ndarray]): Custom values array to use instead
            of the default detection confidences. This array should have the
            same length as the number of detections and contain a value between
            0 and 1 (inclusive) for each detection, representing the percentage
            to be displayed.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        percentage_bar_annotator = sv.PercentageBarAnnotator()
        annotated_frame = percentage_bar_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![percentage-bar-example](https://media.roboflow.com/
    supervision-annotator-examples/percentage-bar-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    self.validate_custom_values(custom_values=custom_values, detections=detections)

    anchors = detections.get_anchors_coordinates(anchor=self.position)
    for detection_idx in range(len(detections)):
        anchor = anchors[detection_idx]
        border_coordinates = self.calculate_border_coordinates(
            anchor_xy=(int(anchor[0]), int(anchor[1])),
            border_wh=(self.width, self.height),
            position=self.position,
        )
        border_width = border_coordinates[1][0] - border_coordinates[0][0]

        if custom_values is not None:
            value = custom_values[detection_idx]
        else:
            assert detections.confidence is not None  # MyPy type hint
            value = detections.confidence[detection_idx]

        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        cv2.rectangle(
            img=scene,
            pt1=border_coordinates[0],
            pt2=(
                border_coordinates[0][0] + int(border_width * value),
                border_coordinates[1][1],
            ),
            color=color.as_bgr(),
            thickness=-1,
        )
        cv2.rectangle(
            img=scene,
            pt1=border_coordinates[0],
            pt2=border_coordinates[1],
            color=self.border_color.as_bgr(),
            thickness=self.border_thickness,
        )
    return scene

HeatMapAnnotator

Bases: BaseAnnotator

A class for drawing heatmaps on an image based on provided detections. Heat accumulates over time and is drawn as a semi-transparent overlay of blurred circles.

Source code in supervision/annotators/core.py 
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class HeatMapAnnotator(BaseAnnotator):
    """
    A class for drawing heatmaps on an image based on provided detections.
    Heat accumulates over time and is drawn as a semi-transparent overlay
    of blurred circles.
    """

    def __init__(
        self,
        position: Position = Position.BOTTOM_CENTER,
        opacity: float = 0.2,
        radius: int = 40,
        kernel_size: int = 25,
        top_hue: int = 0,
        low_hue: int = 125,
    ):
        """
        Args:
            position (Position): The position of the heatmap. Defaults to
                `BOTTOM_CENTER`.
            opacity (float): Opacity of the overlay mask, between 0 and 1.
            radius (int): Radius of the heat circle.
            kernel_size (int): Kernel size for blurring the heatmap.
            top_hue (int): Hue at the top of the heatmap. Defaults to 0 (red).
            low_hue (int): Hue at the bottom of the heatmap. Defaults to 125 (blue).
        """
        self.position = position
        self.opacity = opacity
        self.radius = radius
        self.kernel_size = kernel_size
        self.top_hue = top_hue
        self.low_hue = low_hue
        self.heat_mask: Optional[npt.NDArray[np.float32]] = None

    @ensure_cv2_image_for_annotation
    def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
        """
        Annotates the scene with a heatmap based on the provided detections.

        Args:
            scene (ImageType): The image where the heatmap will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.

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

        Example:
            ```python
            import supervision as sv
            from ultralytics import YOLO

            model = YOLO('yolov8x.pt')

            heat_map_annotator = sv.HeatMapAnnotator()

            video_info = sv.VideoInfo.from_video_path(video_path='...')
            frames_generator = sv.get_video_frames_generator(source_path='...')

            with sv.VideoSink(target_path='...', video_info=video_info) as sink:
               for frame in frames_generator:
                   result = model(frame)[0]
                   detections = sv.Detections.from_ultralytics(result)
                   annotated_frame = heat_map_annotator.annotate(
                       scene=frame.copy(),
                       detections=detections)
                   sink.write_frame(frame=annotated_frame)
            ```

        ![heatmap-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/heat-map-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        if self.heat_mask is None:
            self.heat_mask = np.zeros(scene.shape[:2], dtype=np.float32)

        mask = np.zeros(scene.shape[:2])
        for xy in detections.get_anchors_coordinates(self.position):
            x, y = int(xy[0]), int(xy[1])
            cv2.circle(
                img=mask,
                center=(x, y),
                radius=self.radius,
                color=(1,),
                thickness=-1,  # fill
            )
        self.heat_mask = mask + self.heat_mask
        temp = self.heat_mask.copy()
        temp = self.low_hue - temp / temp.max() * (self.low_hue - self.top_hue)
        temp = temp.astype(np.uint8)
        if self.kernel_size is not None:
            temp = cv2.blur(temp, (self.kernel_size, self.kernel_size))
        hsv = np.zeros(scene.shape)
        hsv[..., 0] = temp
        hsv[..., 1] = 255
        hsv[..., 2] = 255
        temp = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)
        mask = cv2.cvtColor(self.heat_mask.astype(np.uint8), cv2.COLOR_GRAY2BGR) > 0
        scene[mask] = cv2.addWeighted(temp, self.opacity, scene, 1 - self.opacity, 0)[
            mask
        ]
        return scene

Functions

__init__(position=Position.BOTTOM_CENTER, opacity=0.2, radius=40, kernel_size=25, top_hue=0, low_hue=125)

Parameters:

Name Type Description Default
position Position

The position of the heatmap. Defaults to BOTTOM_CENTER.

 BOTTOM_CENTER
opacity float

Opacity of the overlay mask, between 0 and 1.

 0.2
radius int

Radius of the heat circle.

 40
kernel_size int

Kernel size for blurring the heatmap.

 25
top_hue int

Hue at the top of the heatmap. Defaults to 0 (red).

 0
low_hue int

Hue at the bottom of the heatmap. Defaults to 125 (blue).

 125
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    position: Position = Position.BOTTOM_CENTER,
    opacity: float = 0.2,
    radius: int = 40,
    kernel_size: int = 25,
    top_hue: int = 0,
    low_hue: int = 125,
):
    """
    Args:
        position (Position): The position of the heatmap. Defaults to
            `BOTTOM_CENTER`.
        opacity (float): Opacity of the overlay mask, between 0 and 1.
        radius (int): Radius of the heat circle.
        kernel_size (int): Kernel size for blurring the heatmap.
        top_hue (int): Hue at the top of the heatmap. Defaults to 0 (red).
        low_hue (int): Hue at the bottom of the heatmap. Defaults to 125 (blue).
    """
    self.position = position
    self.opacity = opacity
    self.radius = radius
    self.kernel_size = kernel_size
    self.top_hue = top_hue
    self.low_hue = low_hue
    self.heat_mask: Optional[npt.NDArray[np.float32]] = None

annotate(scene, detections)

Annotates the scene with a heatmap based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required

Returns:

Type Description
ImageType

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

model = YOLO('yolov8x.pt')

heat_map_annotator = sv.HeatMapAnnotator()

video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = sv.get_video_frames_generator(source_path='...')

with sv.VideoSink(target_path='...', video_info=video_info) as sink:
   for frame in frames_generator:
       result = model(frame)[0]
       detections = sv.Detections.from_ultralytics(result)
       annotated_frame = heat_map_annotator.annotate(
           scene=frame.copy(),
           detections=detections)
       sink.write_frame(frame=annotated_frame)

heatmap-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
    """
    Annotates the scene with a heatmap based on the provided detections.

    Args:
        scene (ImageType): The image where the heatmap will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.

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

    Example:
        ```python
        import supervision as sv
        from ultralytics import YOLO

        model = YOLO('yolov8x.pt')

        heat_map_annotator = sv.HeatMapAnnotator()

        video_info = sv.VideoInfo.from_video_path(video_path='...')
        frames_generator = sv.get_video_frames_generator(source_path='...')

        with sv.VideoSink(target_path='...', video_info=video_info) as sink:
           for frame in frames_generator:
               result = model(frame)[0]
               detections = sv.Detections.from_ultralytics(result)
               annotated_frame = heat_map_annotator.annotate(
                   scene=frame.copy(),
                   detections=detections)
               sink.write_frame(frame=annotated_frame)
        ```

    ![heatmap-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/heat-map-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    if self.heat_mask is None:
        self.heat_mask = np.zeros(scene.shape[:2], dtype=np.float32)

    mask = np.zeros(scene.shape[:2])
    for xy in detections.get_anchors_coordinates(self.position):
        x, y = int(xy[0]), int(xy[1])
        cv2.circle(
            img=mask,
            center=(x, y),
            radius=self.radius,
            color=(1,),
            thickness=-1,  # fill
        )
    self.heat_mask = mask + self.heat_mask
    temp = self.heat_mask.copy()
    temp = self.low_hue - temp / temp.max() * (self.low_hue - self.top_hue)
    temp = temp.astype(np.uint8)
    if self.kernel_size is not None:
        temp = cv2.blur(temp, (self.kernel_size, self.kernel_size))
    hsv = np.zeros(scene.shape)
    hsv[..., 0] = temp
    hsv[..., 1] = 255
    hsv[..., 2] = 255
    temp = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)
    mask = cv2.cvtColor(self.heat_mask.astype(np.uint8), cv2.COLOR_GRAY2BGR) > 0
    scene[mask] = cv2.addWeighted(temp, self.opacity, scene, 1 - self.opacity, 0)[
        mask
    ]
    return scene

MaskAnnotator

Bases: BaseAnnotator

A class for drawing masks on an image using provided detections.

Warning

This annotator uses sv.Detections.mask.

Source code in supervision/annotators/core.py 
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class MaskAnnotator(BaseAnnotator):
    """
    A class for drawing masks on an image using provided detections.

    !!! warning

        This annotator uses `sv.Detections.mask`.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        opacity: float = 0.5,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.opacity = opacity
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with masks based on the provided detections.

        Args:
            scene (ImageType): The image where masks will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            mask_annotator = sv.MaskAnnotator()
            annotated_frame = mask_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![mask-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/mask-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        if detections.mask is None:
            return scene

        colored_mask = np.array(scene, copy=True, dtype=np.uint8)

        for detection_idx in np.flip(np.argsort(detections.area)):
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            mask = detections.mask[detection_idx]
            colored_mask[mask] = color.as_bgr()

        cv2.addWeighted(
            colored_mask, self.opacity, scene, 1 - self.opacity, 0, dst=scene
        )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, opacity=0.5, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
opacity float

Opacity of the overlay mask. Must be between 0 and 1.

 0.5
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    opacity: float = 0.5,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.opacity = opacity
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with masks based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

mask_annotator = sv.MaskAnnotator()
annotated_frame = mask_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

mask-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with masks based on the provided detections.

    Args:
        scene (ImageType): The image where masks will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        mask_annotator = sv.MaskAnnotator()
        annotated_frame = mask_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![mask-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/mask-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    if detections.mask is None:
        return scene

    colored_mask = np.array(scene, copy=True, dtype=np.uint8)

    for detection_idx in np.flip(np.argsort(detections.area)):
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        mask = detections.mask[detection_idx]
        colored_mask[mask] = color.as_bgr()

    cv2.addWeighted(
        colored_mask, self.opacity, scene, 1 - self.opacity, 0, dst=scene
    )
    return scene

PolygonAnnotator

Bases: BaseAnnotator

A class for drawing polygons on an image using provided detections.

Warning

This annotator uses sv.Detections.mask.

Source code in supervision/annotators/core.py 
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class PolygonAnnotator(BaseAnnotator):
    """
    A class for drawing polygons on an image using provided detections.

    !!! warning

        This annotator uses `sv.Detections.mask`.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        thickness: int = 2,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating detections.
            thickness (int): Thickness of the polygon lines.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.thickness: int = thickness
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with polygons based on the provided detections.

        Args:
            scene (ImageType): The image where polygons will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            polygon_annotator = sv.PolygonAnnotator()
            annotated_frame = polygon_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```

        ![polygon-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/polygon-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        if detections.mask is None:
            return scene

        for detection_idx in range(len(detections)):
            mask = detections.mask[detection_idx]
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup,
            )
            for polygon in mask_to_polygons(mask=mask):
                scene = draw_polygon(
                    scene=scene,
                    polygon=polygon,
                    color=color,
                    thickness=self.thickness,
                )

        return scene

Functions

__init__(color=ColorPalette.DEFAULT, thickness=2, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the polygon lines.

 2
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the polygon lines.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

Annotates the given scene with polygons based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

Custom color lookup array. Allows to override the default color mapping strategy.

 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 = ...
detections = sv.Detections(...)

polygon_annotator = sv.PolygonAnnotator()
annotated_frame = polygon_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

polygon-annotator-example

Source code in supervision/annotators/core.py 
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@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with polygons based on the provided detections.

    Args:
        scene (ImageType): The image where polygons will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): Object detections to annotate.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

        image = ...
        detections = sv.Detections(...)

        polygon_annotator = sv.PolygonAnnotator()
        annotated_frame = polygon_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```

    ![polygon-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/polygon-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    if detections.mask is None:
        return scene

    for detection_idx in range(len(detections)):
        mask = detections.mask[detection_idx]
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=self.color_lookup
            if custom_color_lookup is None
            else custom_color_lookup,
        )
        for polygon in mask_to_polygons(mask=mask):
            scene = draw_polygon(
                scene=scene,
                polygon=polygon,
                color=color,
                thickness=self.thickness,
            )

    return scene

LabelAnnotator

Bases: BaseAnnotator

A class for annotating labels on an image using provided detections.

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class LabelAnnotator(BaseAnnotator):
    """
    A class for annotating labels on an image using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        text_color: Union[Color, ColorPalette] = Color.WHITE,
        text_scale: float = 0.5,
        text_thickness: int = 1,
        text_padding: int = 10,
        text_position: Position = Position.TOP_LEFT,
        color_lookup: ColorLookup = ColorLookup.CLASS,
        border_radius: int = 0,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color or color palette to use for
                annotating the text background.
            text_color (Union[Color, ColorPalette]): The color or color palette to use
                for the text.
            text_scale (float): Font scale for the text.
            text_thickness (int): Thickness of the text characters.
            text_padding (int): Padding around the text within its background box.
            text_position (Position): Position of the text relative to the detection.
                Possible values are defined in the `Position` enum.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
            border_radius (int): The radius to apply round edges. If the selected
                value is higher than the lower dimension, width or height, is clipped.
        """
        self.border_radius: int = border_radius
        self.color: Union[Color, ColorPalette] = color
        self.text_color: Union[Color, ColorPalette] = text_color
        self.text_scale: float = text_scale
        self.text_thickness: int = text_thickness
        self.text_padding: int = text_padding
        self.text_anchor: Position = text_position
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        labels: Optional[List[str]] = None,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with labels based on the provided detections.

        Args:
            scene (ImageType): The image where labels will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): Object detections to annotate.
            labels (Optional[List[str]]): Custom labels for each detection.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

            image = ...
            detections = sv.Detections(...)

            labels = [
                f"{class_name} {confidence:.2f}"
                for class_name, confidence
                in zip(detections['class_name'], detections.confidence)
            ]

            label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
            annotated_frame = label_annotator.annotate(
                scene=image.copy(),
                detections=detections,
                labels=labels
            )
            ```

        ![label-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/label-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        font = cv2.FONT_HERSHEY_SIMPLEX
        anchors_coordinates = detections.get_anchors_coordinates(
            anchor=self.text_anchor
        ).astype(int)
        if labels is not None and len(labels) != len(detections):
            raise ValueError(
                f"The number of labels ({len(labels)}) does not match the "
                f"number of detections ({len(detections)}). Each detection "
                f"should have exactly 1 label."
            )

        for detection_idx, center_coordinates in enumerate(anchors_coordinates):
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=(
                    self.color_lookup
                    if custom_color_lookup is None
                    else custom_color_lookup
                ),
            )

            text_color = resolve_color(
                color=self.text_color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=(
                    self.color_lookup
                    if custom_color_lookup is None
                    else custom_color_lookup
                ),
            )

            if labels is not None:
                text = labels[detection_idx]
            elif CLASS_NAME_DATA_FIELD in detections.data:
                text = detections.data[CLASS_NAME_DATA_FIELD][detection_idx]
            elif detections.class_id is not None:
                text = str(detections.class_id[detection_idx])
            else:
                text = str(detection_idx)

            text_w, text_h = cv2.getTextSize(
                text=text,
                fontFace=font,
                fontScale=self.text_scale,
                thickness=self.text_thickness,
            )[0]
            text_w_padded = text_w + 2 * self.text_padding
            text_h_padded = text_h + 2 * self.text_padding
            text_background_xyxy = resolve_text_background_xyxy(
                center_coordinates=tuple(center_coordinates),
                text_wh=(text_w_padded, text_h_padded),
                position=self.text_anchor,
            )

            text_x = text_background_xyxy[0] + self.text_padding
            text_y = text_background_xyxy[1] + self.text_padding + text_h

            self.draw_rounded_rectangle(
                scene=scene,
                xyxy=text_background_xyxy,
                color=color.as_bgr(),
                border_radius=self.border_radius,
            )
            cv2.putText(
                img=scene,
                text=text,
                org=(text_x, text_y),
                fontFace=font,
                fontScale=self.text_scale,
                color=text_color.as_bgr(),
                thickness=self.text_thickness,
                lineType=cv2.LINE_AA,
            )
        return scene

    @staticmethod
    def draw_rounded_rectangle(
        scene: np.ndarray,
        xyxy: Tuple[int, int, int, int],
        color: Tuple[int, int, int],
        border_radius: int,
    ) -> np.ndarray:
        x1, y1, x2, y2 = xyxy
        width = x2 - x1
        height = y2 - y1

        border_radius = min(border_radius, min(width, height) // 2)

        rectangle_coordinates = [
            ((x1 + border_radius, y1), (x2 - border_radius, y2)),
            ((x1, y1 + border_radius), (x2, y2 - border_radius)),
        ]
        circle_centers = [
            (x1 + border_radius, y1 + border_radius),
            (x2 - border_radius, y1 + border_radius),
            (x1 + border_radius, y2 - border_radius),
            (x2 - border_radius, y2 - border_radius),
        ]

        for coordinates in rectangle_coordinates:
            cv2.rectangle(
                img=scene,
                pt1=coordinates[0],
                pt2=coordinates[1],
                color=color,
                thickness=-1,
            )
        for center in circle_centers:
            cv2.circle(
                img=scene,
                center=center,
                radius=border_radius,
                color=color,
                thickness=-1,
            )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, text_color=Color.WHITE, text_scale=0.5, text_thickness=1, text_padding=10, text_position=Position.TOP_LEFT, color_lookup=ColorLookup.CLASS, border_radius=0)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating the text background.

 DEFAULT
text_color Union[Color, ColorPalette]

The color or color palette to use for the text.

 WHITE
text_scale float

Font scale for the text.

 0.5
text_thickness int

Thickness of the text characters.

 1
text_padding int

Padding around the text within its background box.

 10
text_position Position

Position of the text relative to the detection. Possible values are defined in the Position enum.

 TOP_LEFT
color_lookup ColorLookup

Strategy for mapping colors to annotations. Options are INDEX, CLASS, TRACK.

 CLASS
border_radius int

The radius to apply round edges. If the selected value is higher than the lower dimension, width or height, is clipped.

 0
Source code in supervision/annotators/core.py 
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def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    text_color: Union[Color, ColorPalette] = Color.WHITE,
    text_scale: float = 0.5,
    text_thickness: int = 1,
    text_padding: int = 10,
    text_position: Position = Position.TOP_LEFT,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    border_radius: int = 0,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating the text background.
        text_color (Union[Color, ColorPalette]): The color or color palette to use
            for the text.
        text_scale (float): Font scale for the text.
        text_thickness (int): Thickness of the text characters.
        text_padding (int): Padding around the text within its background box.
        text_position (Position): Position of the text relative to the detection.
            Possible values are defined in the `Position` enum.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        border_radius (int): The radius to apply round edges. If the selected
            value is higher than the lower dimension, width or height, is clipped.
    """
    self.border_radius: int = border_radius
    self.color: Union[Color,