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Annotators

import supervision as sv

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

bounding_box_annotator = sv.BoundingBoxAnnotator()
annotated_frame = bounding_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=".../font.ttf",
    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(...)

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 = 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 = 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

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
class BoundingBoxAnnotator(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 (str): 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

    @convert_for_annotation_method
    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(...)

            bounding_box_annotator = sv.BoundingBoxAnnotator()
            annotated_frame = bounding_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)
        """
        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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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(...)

bounding_box_annotator = sv.BoundingBoxAnnotator()
annotated_frame = bounding_box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)

bounding-box-annotator-example

Source code in supervision/annotators/core.py
@convert_for_annotation_method
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(...)

        bounding_box_annotator = sv.BoundingBoxAnnotator()
        annotated_frame = bounding_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)
    """
    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

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
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 (str): 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

    @convert_for_annotation_method
    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)
        """

        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 str

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
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 (str): 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
@convert_for_annotation_method
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)
    """

    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

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
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 (str): 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

    @convert_for_annotation_method
    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)
        """
        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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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
@convert_for_annotation_method
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)
    """
    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

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
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 (str): 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

    @convert_for_annotation_method
    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

        if detections.data is None or ORIENTED_BOX_COORDINATES not in detections.data:
            return scene

        for detection_idx in range(len(detections)):
            bbox = np.int0(detections.data.get(ORIENTED_BOX_COORDINATES)[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, [bbox], 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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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
@convert_for_annotation_method
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

    if detections.data is None or ORIENTED_BOX_COORDINATES not in detections.data:
        return scene

    for detection_idx in range(len(detections)):
        bbox = np.int0(detections.data.get(ORIENTED_BOX_COORDINATES)[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, [bbox], 0, color.as_bgr(), self.thickness)

    return scene

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
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 (str): 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

    @convert_for_annotation_method
    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)
        """
        mask_image = 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,
                pt1=(x1, y1),
                pt2=(x2, y2),
                color=color.as_bgr(),
                thickness=-1,
            )
        scene = cv2.addWeighted(
            scene, self.opacity, mask_image, 1 - self.opacity, gamma=0
        )
        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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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
@convert_for_annotation_method
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)
    """
    mask_image = 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,
            pt1=(x1, y1),
            pt2=(x2, y2),
            color=color.as_bgr(),
            thickness=-1,
        )
    scene = cv2.addWeighted(
        scene, self.opacity, mask_image, 1 - self.opacity, gamma=0
    )
    return scene

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
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 (str): 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

    @convert_for_annotation_method
    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)
        """
        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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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
@convert_for_annotation_method
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)
    """
    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

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
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,
    ):
        """
        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`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.radius: int = radius
        self.position: Position = position
        self.color_lookup: ColorLookup = color_lookup

    @convert_for_annotation_method
    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)
        """
        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)
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, radius=4, position=Position.CENTER, color_lookup=ColorLookup.CLASS)

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
Source code in supervision/annotators/core.py
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    radius: int = 4,
    position: Position = Position.CENTER,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    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`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.radius: int = radius
    self.position: Position = position
    self.color_lookup: ColorLookup = color_lookup

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
@convert_for_annotation_method
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)
    """
    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)
    return scene

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
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,
    ):
        """
        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`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.base: int = base
        self.height: int = height
        self.position: Position = position
        self.color_lookup: ColorLookup = color_lookup

    @convert_for_annotation_method
    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)
        """
        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())

        return scene

Functions

__init__(color=ColorPalette.DEFAULT, base=10, height=10, position=Position.TOP_CENTER, color_lookup=ColorLookup.CLASS)

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.

10
height int

The height of the triangle.

10
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
Source code in supervision/annotators/core.py
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,
):
    """
    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`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.base: int = base
    self.height: int = height
    self.position: Position = position
    self.color_lookup: ColorLookup = color_lookup

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
@convert_for_annotation_method
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)
    """
    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())

    return scene

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
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 (str): 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

    @convert_for_annotation_method
    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)
        """
        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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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
@convert_for_annotation_method
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)
    """
    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

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
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 (str): 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

    @convert_for_annotation_method
    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)
        """
        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]
        scene = np.uint8(scene * (1 - alpha_mask) + colored_mask * self.opacity)
        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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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
@convert_for_annotation_method
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)
    """
    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]
    scene = np.uint8(scene * (1 - alpha_mask) + colored_mask * self.opacity)
    return scene

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py
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: 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 (str): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
            border_thickness (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)

    @convert_for_annotation_method
    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)
        """
        self.validate_custom_values(
            custom_values=custom_values, detections_count=len(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]

            value = (
                custom_values[detection_idx]
                if custom_values is not None
                else 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_count: int
    ) -> None:
        if custom_values is not None:
            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) != detections_count:
                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 str

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

CLASS
border_thickness int

The thickness of the border lines.

None
Source code in supervision/annotators/core.py
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: 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 (str): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        border_thickness (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
@convert_for_annotation_method
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)
    """
    self.validate_custom_values(
        custom_values=custom_values, detections_count=len(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]

        value = (
            custom_values[detection_idx]
            if custom_values is not None
            else 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

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
class HeatMapAnnotator:
    """
    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.heat_mask = None
        self.top_hue = top_hue
        self.low_hue = low_hue

    @convert_for_annotation_method
    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 = 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)
        """

        if self.heat_mask is None:
            self.heat_mask = np.zeros(scene.shape[:2])
        mask = np.zeros(scene.shape[:2])
        for xy in detections.get_anchors_coordinates(self.position):
            cv2.circle(mask, (int(xy[0]), int(xy[1])), self.radius, 1, -1)
        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
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.heat_mask = None
    self.top_hue = top_hue
    self.low_hue = low_hue

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 = 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
@convert_for_annotation_method
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 = 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)
    """

    if self.heat_mask is None:
        self.heat_mask = np.zeros(scene.shape[:2])
    mask = np.zeros(scene.shape[:2])
    for xy in detections.get_anchors_coordinates(self.position):
        cv2.circle(mask, (int(xy[0]), int(xy[1])), self.radius, 1, -1)
    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

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
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 (str): 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

    @convert_for_annotation_method
    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)
        """
        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()

        scene = cv2.addWeighted(colored_mask, self.opacity, scene, 1 - self.opacity, 0)
        return scene.astype(np.uint8)

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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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
@convert_for_annotation_method
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)
    """
    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()

    scene = cv2.addWeighted(colored_mask, self.opacity, scene, 1 - self.opacity, 0)
    return scene.astype(np.uint8)

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
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 (str): 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

    @convert_for_annotation_method
    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)
        """
        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 str

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

CLASS
Source code in supervision/annotators/core.py
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 (str): 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
@convert_for_annotation_method
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)
    """
    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

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

Source code in supervision/annotators/core.py
class LabelAnnotator:
    """
    A class for annotating labels on an image using provided detections.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        text_color: Color = 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 (Color): The color 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 (str): 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: Color = 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

    @convert_for_annotation_method
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        labels: 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 (List[str]): Optional. 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)
        """
        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 provided ({len(labels)}) does not match the "
                f"number of detections ({len(detections)}). Each detection should have "
                f"a corresponding label. This discrepancy can occur if the labels and "
                f"detections are not aligned or if an incorrect number of labels has "
                f"been provided. Please ensure that the labels array has the same "
                f"length as the Detections object."
            )

        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
                ),
            )

            if labels is not None:
                text = labels[detection_idx]
            elif detections[CLASS_NAME_DATA_FIELD] is not None:
                text = detections[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=self.text_color.as_rgb(),
                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 Color

The color 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 str

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
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    text_color: Color = 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 (Color): The color 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 (str): 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: Color = 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

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

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

Parameters:

Name Type Description Default
scene ImageType

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

required
detections Detections

Object detections to annotate.

required
labels List[str]

Optional. Custom labels for each detection.

None
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(...)

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

Source code in supervision/annotators/core.py
@convert_for_annotation_method
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    labels: 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 (List[str]): Optional. 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)
    """
    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 provided ({len(labels)}) does not match the "
            f"number of detections ({len(detections)}). Each detection should have "
            f"a corresponding label. This discrepancy can occur if the labels and "
            f"detections are not aligned or if an incorrect number of labels has "
            f"been provided. Please ensure that the labels array has the same "
            f"length as the Detections object."
        )

    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
            ),
        )

        if labels is not None:
            text = labels[detection_idx]
        elif detections[CLASS_NAME_DATA_FIELD] is not None:
            text = detections[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=self.text_color.as_rgb(),
            thickness=self.text_thickness,
            lineType=cv2.LINE_AA,
        )
    return scene

A class for annotating labels on an image using provided detections, with support for Unicode characters by using a custom font.

Source code in supervision/annotators/core.py
class RichLabelAnnotator:
    """
    A class for annotating labels on an image using provided detections,
    with support for Unicode characters by using a custom font.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        text_color: Color = Color.WHITE,
        font_path: str = None,
        font_size: int = 10,
        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 (Color): The color to use for the text.
            font_path (str): Path to the font file (e.g., ".ttf" or ".otf") to use for
                rendering text. If `None`, the default PIL font will be used.
            font_size (int): Font size for the text.
            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.color = color
        self.text_color = text_color
        self.text_padding = text_padding
        self.text_anchor = text_position
        self.color_lookup = color_lookup
        self.border_radius = border_radius
        if font_path is not None:
            try:
                self.font = ImageFont.truetype(font_path, font_size)
            except OSError:
                print(f"Font path '{font_path}' not found. Using PIL's default font.")
                self.font = ImageFont.load_default(size=font_size)
        else:
            self.font = ImageFont.load_default(size=font_size)

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

        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 (List[str]): Optional. 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)
            ]

            rich_label_annotator = sv.RichLabelAnnotator(font_path="path/to/font.ttf")
            annotated_frame = label_annotator.annotate(
                scene=image.copy(),
                detections=detections,
                labels=labels