Skip to content

Annotators

import supervision as sv

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

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

bounding-box-annotator-example

import supervision as sv

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

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

round-box-annotator-example

import supervision as sv

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

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

box-corner-annotator-example

import supervision as sv

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

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

box-mask-annotator-example

import supervision as sv

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

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

circle-annotator-example

import supervision as sv

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

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

dot-annotator-example

import supervision as sv

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

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

triangle-annotator-example

import supervision as sv

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

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

ellipse-annotator-example

import supervision as sv

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

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

halo-annotator-example

import supervision as sv

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

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

percentage-bar-annotator-example

import supervision as sv

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

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

mask-annotator-example

import supervision as sv

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

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

polygon-annotator-example

import supervision as sv

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

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

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

label-annotator-example

import supervision as sv

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

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

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

label-annotator-example

import supervision as sv

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

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

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

icon-annotator-example

import supervision as sv

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

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

import supervision as sv

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

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

blur-annotator-example

import supervision as sv

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

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

pixelate-annotator-example

import supervision as sv
from ultralytics import YOLO

model = YOLO('yolov8x.pt')

trace_annotator = sv.TraceAnnotator()

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

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

trace-annotator-example

import supervision as sv
from ultralytics import YOLO

model = YOLO('yolov8x.pt')

heat_map_annotator = sv.HeatMapAnnotator()

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

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

heat-map-annotator-example

import supervision as sv

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

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

background-overlay-annotator-example

BoxAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
class BoxAnnotator(BaseAnnotator):
    """
    A class for drawing bounding boxes on an image using provided detections.
    """

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the bounding box lines.

 2
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the bounding box lines.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

The image where bounding boxes will be drawn. ImageType

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

bounding-box-annotator-example

Source code in supervision/annotators/core.py 
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with bounding boxes based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

RoundBoxAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
class RoundBoxAnnotator(BaseAnnotator):
    """
    A class for drawing bounding boxes with round edges on an image
    using provided detections.
    """

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

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

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

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

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

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

        return scene

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the bounding box lines.

 2
color_lookup ColorLookup

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

 CLASS
roundness float

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

 0.6
Source code in supervision/annotators/core.py 
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    roundness: float = 0.6,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the bounding box lines.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        roundness (float): Percent of roundness for edges of bounding box.
            Value must be float 0 < roundness <= 1.0
            By default roundness percent is calculated based on smaller side
            length (width or height).
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup
    if not 0 < roundness <= 1.0:
        raise ValueError("roundness attribute must be float between (0, 1.0]")
    self.roundness: float = roundness

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

round-box-annotator-example

Source code in supervision/annotators/core.py 
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with bounding boxes with rounded edges
    based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

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

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

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

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

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

    return scene

BoxCornerAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
class BoxCornerAnnotator(BaseAnnotator):
    """
    A class for drawing box corners on an image using provided detections.
    """

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the corner lines.

 4
corner_length int

Length of each corner line.

 15
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 4,
    corner_length: int = 15,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the corner lines.
        corner_length (int): Length of each corner line.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.corner_length: int = corner_length
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

box-corner-annotator-example

Source code in supervision/annotators/core.py 
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with box corners based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

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

OrientedBoxAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
class OrientedBoxAnnotator(BaseAnnotator):
    """
    A class for drawing oriented bounding boxes on an image using provided detections.
    """

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

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

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

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

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

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

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

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

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

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

        return scene

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the bounding box lines.

 2
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the bounding box lines.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

oriented_box_annotator = sv.OrientedBoxAnnotator()
annotated_frame = oriented_box_annotator.annotate(
    scene=image.copy(),
    detections=detections
)
Source code in supervision/annotators/core.py 
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with oriented bounding boxes based on the provided detections.

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

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

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

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

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

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

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

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

    return scene

ColorAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
class ColorAnnotator(BaseAnnotator):
    """
    A class for drawing box masks on an image using provided detections.
    """

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
opacity float

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

 0.5
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    opacity: float = 0.5,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.color_lookup: ColorLookup = color_lookup
    self.opacity = opacity

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

box-mask-annotator-example

Source code in supervision/annotators/core.py 
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with box masks based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

CircleAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
class CircleAnnotator(BaseAnnotator):
    """
    A class for drawing circle on an image using provided detections.
    """

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

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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


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

        return scene

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the circle line.

 2
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the circle line.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """

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

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

circle-annotator-example

Source code in supervision/annotators/core.py 
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with circles based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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


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

    return scene

DotAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
class DotAnnotator(BaseAnnotator):
    """
    A class for drawing dots on an image at specific coordinates based on provided
    detections.
    """

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
radius int

Radius of the drawn dots.

 4
position Position

The anchor position for placing the dot.

 CENTER
color_lookup ColorLookup

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

 CLASS
outline_thickness int

Thickness of the outline of the dot.

 0
outline_color Union[Color, ColorPalette]

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

 BLACK
Source code in supervision/annotators/core.py 
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    radius: int = 4,
    position: Position = Position.CENTER,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    outline_thickness: int = 0,
    outline_color: Union[Color, ColorPalette] = Color.BLACK,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        radius (int): Radius of the drawn dots.
        position (Position): The anchor position for placing the dot.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        outline_thickness (int): Thickness of the outline of the dot.
        outline_color (Union[Color, ColorPalette]): The color or color palette to
            use for outline. It is activated by setting outline_thickness to a value
            greater than 0.
    """
    self.color: Union[Color, ColorPalette] = color
    self.radius: int = radius
    self.position: Position = position
    self.color_lookup: ColorLookup = color_lookup
    self.outline_thickness = outline_thickness
    self.outline_color: Union[Color, ColorPalette] = outline_color

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

dot-annotator-example

Source code in supervision/annotators/core.py 
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with dots based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

TriangleAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
class TriangleAnnotator(BaseAnnotator):
    """
    A class for drawing triangle markers on an image at specific coordinates based on
    provided detections.
    """

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
base int

The base width of the triangle.

 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
outline_thickness int

Thickness of the outline of the triangle.

 0
outline_color Union[Color, ColorPalette]

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

 BLACK
Source code in supervision/annotators/core.py 
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    base: int = 10,
    height: int = 10,
    position: Position = Position.TOP_CENTER,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    outline_thickness: int = 0,
    outline_color: Union[Color, ColorPalette] = Color.BLACK,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        base (int): The base width of the triangle.
        height (int): The height of the triangle.
        position (Position): The anchor position for placing the triangle.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        outline_thickness (int): Thickness of the outline of the triangle.
        outline_color (Union[Color, ColorPalette]): The color or color palette to
            use for outline. It is activated by setting outline_thickness to a value
            greater than 0.
    """
    self.color: Union[Color, ColorPalette] = color
    self.base: int = base
    self.height: int = height
    self.position: Position = position
    self.color_lookup: ColorLookup = color_lookup
    self.outline_thickness: int = outline_thickness
    self.outline_color: Union[Color, ColorPalette] = outline_color

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

triangle-annotator-example

Source code in supervision/annotators/core.py 
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with triangles based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

EllipseAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
class EllipseAnnotator(BaseAnnotator):
    """
    A class for drawing ellipses on an image using provided detections.
    """

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the ellipse lines.

 2
start_angle int

Starting angle of the ellipse.

 -45
end_angle int

Ending angle of the ellipse.

 235
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    start_angle: int = -45,
    end_angle: int = 235,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the ellipse lines.
        start_angle (int): Starting angle of the ellipse.
        end_angle (int): Ending angle of the ellipse.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.start_angle: int = start_angle
    self.end_angle: int = end_angle
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

ellipse-annotator-example

Source code in supervision/annotators/core.py 
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with ellipses based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

HaloAnnotator

Bases: BaseAnnotator

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

Warning

This annotator uses sv.Detections.mask.

Source code in supervision/annotators/core.py 
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
class HaloAnnotator(BaseAnnotator):
    """
    A class for drawing Halos on an image using provided detections.

    !!! warning

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

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
opacity float

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

 0.8
kernel_size int

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

 40
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    opacity: float = 0.8,
    kernel_size: int = 40,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
        kernel_size (int): The size of the average pooling kernel used for creating
            the halo.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.opacity = opacity
    self.color_lookup: ColorLookup = color_lookup
    self.kernel_size: int = kernel_size

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

halo-annotator-example

Source code in supervision/annotators/core.py 
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with halos based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

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

PercentageBarAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
class PercentageBarAnnotator(BaseAnnotator):
    """
    A class for drawing percentage bars on an image using provided detections.
    """

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

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

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

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

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

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

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
height int

The height in pixels of the percentage bar.

 16
width int

The width in pixels of the percentage bar.

 80
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
border_color Color

The color of the border lines.

 BLACK
position Position

The anchor position of drawing the percentage bar.

 TOP_CENTER
color_lookup ColorLookup

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

 CLASS
border_thickness Optional[int]

The thickness of the border lines.

 None
Source code in supervision/annotators/core.py 
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
def __init__(
    self,
    height: int = 16,
    width: int = 80,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    border_color: Color = Color.BLACK,
    position: Position = Position.TOP_CENTER,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    border_thickness: Optional[int] = None,
):
    """
    Args:
        height (int): The height in pixels of the percentage bar.
        width (int): The width in pixels of the percentage bar.
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        border_color (Color): The color of the border lines.
        position (Position): The anchor position of drawing the percentage bar.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        border_thickness (Optional[int]): The thickness of the border lines.
    """
    self.height: int = height
    self.width: int = width
    self.color: Union[Color, ColorPalette] = color
    self.border_color: Color = border_color
    self.position: Position = position
    self.color_lookup: ColorLookup = color_lookup

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

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

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None
custom_values Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

percentage-bar-example

Source code in supervision/annotators/core.py 
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
    custom_values: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with percentage bars based on the provided
    detections. The percentage bars visually represent the confidence or custom
    values associated with each detection.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

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

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

HeatMapAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
class HeatMapAnnotator(BaseAnnotator):
    """
    A class for drawing heatmaps on an image based on provided detections.
    Heat accumulates over time and is drawn as a semi-transparent overlay
    of blurred circles.
    """

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

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

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

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

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

            model = YOLO('yolov8x.pt')

            heat_map_annotator = sv.HeatMapAnnotator()

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
position Position

The position of the heatmap. Defaults to BOTTOM_CENTER.

 BOTTOM_CENTER
opacity float

Opacity of the overlay mask, between 0 and 1.

 0.2
radius int

Radius of the heat circle.

 40
kernel_size int

Kernel size for blurring the heatmap.

 25
top_hue int

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

 0
low_hue int

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

 125
Source code in supervision/annotators/core.py 
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
def __init__(
    self,
    position: Position = Position.BOTTOM_CENTER,
    opacity: float = 0.2,
    radius: int = 40,
    kernel_size: int = 25,
    top_hue: int = 0,
    low_hue: int = 125,
):
    """
    Args:
        position (Position): The position of the heatmap. Defaults to
            `BOTTOM_CENTER`.
        opacity (float): Opacity of the overlay mask, between 0 and 1.
        radius (int): Radius of the heat circle.
        kernel_size (int): Kernel size for blurring the heatmap.
        top_hue (int): Hue at the top of the heatmap. Defaults to 0 (red).
        low_hue (int): Hue at the bottom of the heatmap. Defaults to 125 (blue).
    """
    self.position = position
    self.opacity = opacity
    self.radius = radius
    self.kernel_size = kernel_size
    self.top_hue = top_hue
    self.low_hue = low_hue
    self.heat_mask: Optional[npt.NDArray[np.float32]] = None

annotate(scene, detections)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required

Returns:

Type Description
ImageType

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

model = YOLO('yolov8x.pt')

heat_map_annotator = sv.HeatMapAnnotator()

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

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

heatmap-annotator-example

Source code in supervision/annotators/core.py 
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
@ensure_cv2_image_for_annotation
def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
    """
    Annotates the scene with a heatmap based on the provided detections.

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

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

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

        model = YOLO('yolov8x.pt')

        heat_map_annotator = sv.HeatMapAnnotator()

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

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

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

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

MaskAnnotator

Bases: BaseAnnotator

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

Warning

This annotator uses sv.Detections.mask.

Source code in supervision/annotators/core.py 
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
class MaskAnnotator(BaseAnnotator):
    """
    A class for drawing masks on an image using provided detections.

    !!! warning

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

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
opacity float

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

 0.5
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    opacity: float = 0.5,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.opacity = opacity
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

mask-annotator-example

Source code in supervision/annotators/core.py 
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with masks based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

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

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

PolygonAnnotator

Bases: BaseAnnotator

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

Warning

This annotator uses sv.Detections.mask.

Source code in supervision/annotators/core.py 
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
class PolygonAnnotator(BaseAnnotator):
    """
    A class for drawing polygons on an image using provided detections.

    !!! warning

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

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

        return scene

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color or color palette to use for annotating detections.

 DEFAULT
thickness int

Thickness of the polygon lines.

 2
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating detections.
        thickness (int): Thickness of the polygon lines.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.thickness: int = thickness
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

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

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

Object detections to annotate.

 required
custom_color_lookup Optional[ndarray]

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

 None

Returns:

Type Description
ImageType

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

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

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

polygon-annotator-example

Source code in supervision/annotators/core.py 
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with polygons based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

    return scene

LabelAnnotator

Bases: BaseAnnotator

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

Source code in supervision/annotators/core.py 
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
class LabelAnnotator(BaseAnnotator):
    """
    A class for annotating labels on an image using provided detections.
    """

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

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

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

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

        Example:
            ```python
            import supervision as sv

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Functions

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

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

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

 DEFAULT
text_color Union[Color, ColorPalette]

The color or color palette to use for the text.

 WHITE
text_scale float

Font scale for the text.

 0.5
text_thickness int

Thickness of the text characters.

 1
text_padding int

Padding around the text within its background box.

 10
text_position Position

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

 TOP_LEFT
color_lookup ColorLookup

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

 CLASS
border_radius int

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

 0
Source code in supervision/annotators/core.py 
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    text_color: Union[Color, ColorPalette] = Color.WHITE,
    text_scale: float = 0.5,
    text_thickness: int = 1,
    text_padding: int = 10,
    text_position: Position = Position.TOP_LEFT,
    color_lookup: ColorLookup = ColorLookup.CLASS,
    border_radius: int = 0,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color or color palette to use for
            annotating the text background.
        text_color (Union[Color, ColorPalette]): The color or color palette to use
            for the text.
        text_scale (float): Font scale for the text.
        text_thickness (int): Thickness of the text characters.
        text_padding (int): Padding around the text within its background box.
        text_position (Position): Position of the text relative to the detection.
            Possible values are defined in the `Position` enum.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
        border_radius (int): The radius to apply round edges. If the selected
            value is higher than the lower dimension, width or height, is clipped.
    """
    self.border_radius: int = border_radius
    self.color: Union[Color, ColorPalette] = color
    self.text_color: Union[Color, ColorPalette] = text_color
    self.text_scale: float = text_scale
    self.text_thickness: int = text_thickness
    self.text_padding: int = text_padding
    self.text_anchor: Position = text_position
    self.color_lookup: ColorLookup = color_lookup

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 Optional[List[str]]

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 
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    labels: Optional[List[str]] = None,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with labels based on the provided detections.

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

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

    Example:
        ```python
        import supervision as sv

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

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

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

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

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

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

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

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

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

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

RichLabelAnnotator

Bases: BaseAnnotator

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 
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
class RichLabelAnnotator(BaseAnnotator):
    """
    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: Union[Color, ColorPalette] = Color.WHITE,
        font_path: Optional[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 (Union[Color, ColorPalette]): The color to use for the text.
            font_path (Optional[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 = self._load_default_font(font_size)
        else:
            self.font = self._load_default_font(font_size)

    @ensure_pil_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        labels: Optional[List[str]] = None,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the given scene with labels based on the provided
        detections, 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 (Optional[List[str]]): Custom labels for each detection.
            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
                Allows to override the default color mapping strategy.

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

        Example:
            ```python
            import supervision as sv

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

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

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

        """
        assert isinstance(scene, Image.Image)
        draw = ImageDraw.Draw(scene)
        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."
            )
        for detection_idx, center_coordinates in enumerate(anchors_coordinates):
            color = resolve_color(
                color=self.color,
                detections=detections,
                detection_idx=detection_idx,
                color_lookup=(
                    self.color_lookup
                    if custom_color_lookup is None
                    else custom_color_lookup
                ),
            )

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

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

            left, top, right, bottom = draw.textbbox((0, 0), text, font=self.font)
            text_width = right - left
            text_height = bottom - top
            text_w_padded = text_width + 2 * self.text_padding
            text_h_padded = text_height + 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 - left
            text_y = text_background_xyxy[1] + self.text_padding - top

            draw.rounded_rectangle(
                text_background_xyxy,
                radius=self.border_radius,
                fill=color.as_rgb(),
                outline=None,
            )
            draw.text(
                xy=(text_x, text_y),
                text=text,
                font=self.font,
                fill=text_color.as_rgb(),
            )
        return scene

    @staticmethod
    def _load_default_font(size):
        """
        PIL either loads a font that accepts a size (e.g. on my machine)
        or raises an error saying `load_default` does not accept arguments
        (e.g. in Colab).
        """
        try:
            font = ImageFont.load_default(size)
        except TypeError:
            font = ImageFont.load_default()
        return font

Functions

__init__(color=ColorPalette.DEFAULT, text_color=Color.WHITE, font_path=None, font_size=10, text_padding=10, text_position=Position.TOP_LEFT, color_lookup=ColorLookup.CLASS, border_radius=0)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

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

 DEFAULT
text_color Union[Color, ColorPalette]

The color to use for the text.

 WHITE
font_path Optional[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.

 None
font_size int

Font size for the text.

 10
text_padding int

Padding around the text within its background box.

 10
text_position Position

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

 TOP_LEFT
color_lookup ColorLookup

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

 CLASS
border_radius int

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

 0
Source code in supervision/annotators/core.py 
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    text_color: Union[Color, ColorPalette] = Color.WHITE,
    font_path: Optional[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 (Union[Color, ColorPalette]): The color to use for the text.
        font_path (Optional[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 = self._load_default_font(font_size)
    else:
        self.font = self._load_default_font(font_size)

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

Annotates the given scene with labels based on the provided detections, with support for Unicode characters.

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 Optional[List[str]]

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)
]

rich_label_annotator = sv.RichLabelAnnotator(font_path="path/to/font.ttf")
annotated_frame = label_annotator.annotate(
    scene=image.copy(),
    detections=detections,
    labels=labels
)
Source code in supervision/annotators/core.py 
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
@ensure_pil_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    labels: Optional[List[str]] = None,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the given scene with labels based on the provided
    detections, 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 (Optional[List[str]]): Custom labels for each detection.
        custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
            Allows to override the default color mapping strategy.

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

    Example:
        ```python
        import supervision as sv

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

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

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

    """
    assert isinstance(scene, Image.Image)
    draw = ImageDraw.Draw(scene)
    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."
        )
    for detection_idx, center_coordinates in enumerate(anchors_coordinates):
        color = resolve_color(
            color=self.color,
            detections=detections,
            detection_idx=detection_idx,
            color_lookup=(
                self.color_lookup
                if custom_color_lookup is None
                else custom_color_lookup
            ),
        )

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

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

        left, top, right, bottom = draw.textbbox((0, 0), text, font=self.font)
        text_width = right - left
        text_height = bottom - top
        text_w_padded = text_width + 2 * self.text_padding
        text_h_padded = text_height + 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 - left
        text_y = text_background_xyxy[1] + self.text_padding - top

        draw.rounded_rectangle(
            text_background_xyxy,
            radius=self.border_radius,
            fill=color.as_rgb(),
            outline=None,
        )
        draw.text(
            xy=(text_x, text_y),
            text=text,
            font=self.font,
            fill=text_color.as_rgb(),
        )
    return scene

IconAnnotator

Bases: BaseAnnotator

A class for drawing an icon on an image, using provided detections.

Source code in supervision/annotators/core.py 
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
class IconAnnotator(BaseAnnotator):
    """
    A class for drawing an icon on an image, using provided detections.
    """

    def __init__(
        self,
        icon_resolution_wh: Tuple[int, int] = (64, 64),
        icon_position: Position = Position.TOP_CENTER,
        offset_xy: Tuple[int, int] = (0, 0),
    ):
        """
        Args:
            icon_resolution_wh (Tuple[int, int]): The size of drawn icons.
                All icons will be resized to this resolution, keeping the aspect ratio.
            icon_position (Position): The position of the icon.
            offset_xy (Tuple[int, int]): The offset to apply to the icon position,
                in pixels. Can be both positive and negative.
        """
        self.icon_resolution_wh = icon_resolution_wh
        self.position = icon_position
        self.offset_xy = offset_xy

    @ensure_cv2_image_for_annotation
    def annotate(
        self, scene: ImageType, detections: Detections, icon_path: Union[str, List[str]]
    ) -> ImageType:
        """
        Annotates the given scene with given icons.

        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.
            icon_path (Union[str, List[str]]): The path to the PNG image to use as an
                icon. Must be a single path or a list of paths, one for each detection.
                Pass an empty string `""` to draw nothing.

        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(...)

            available_icons = ["roboflow.png", "lenny.png"]
            icon_paths = [np.random.choice(available_icons) for _ in detections]

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

        ![icon-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/icon-annotator-example.png)
        """
        assert isinstance(scene, np.ndarray)
        if isinstance(icon_path, list) and len(icon_path) != len(detections):
            raise ValueError(
                f"The number of icon paths provided ({len(icon_path)}) does not match "
                f"the number of detections ({len(detections)}). Either provide a single"
                f" icon path or one for each detection."
            )

        xy = detections.get_anchors_coordinates(anchor=self.position).astype(int)

        for detection_idx in range(len(detections)):
            current_path = (
                icon_path if isinstance(icon_path, str) else icon_path[detection_idx]
            )
            if current_path == "":
                continue
            icon = self._load_icon(current_path)
            icon_h, icon_w = icon.shape[:2]

            x = int(xy[detection_idx, 0] - icon_w / 2 + self.offset_xy[0])
            y = int(xy[detection_idx, 1] - icon_h / 2 + self.offset_xy[1])

            scene[:] = overlay_image(scene, icon, (x, y))
        return scene

    @lru_cache
    def _load_icon(self, icon_path: str) -> np.ndarray:
        icon = cv2.imread(icon_path, cv2.IMREAD_UNCHANGED)
        if icon is None:
            raise FileNotFoundError(
                f"Error: Couldn't load the icon image from {icon_path}"
            )
        icon = letterbox_image(image=icon, resolution_wh=self.icon_resolution_wh)
        return icon

Functions

__init__(icon_resolution_wh=(64, 64), icon_position=Position.TOP_CENTER, offset_xy=(0, 0))

Parameters:

Name Type Description Default
icon_resolution_wh Tuple[int, int]

The size of drawn icons. All icons will be resized to this resolution, keeping the aspect ratio.

 (64, 64)
icon_position Position

The position of the icon.

 TOP_CENTER
offset_xy Tuple[int, int]

The offset to apply to the icon position, in pixels. Can be both positive and negative.

 (0, 0)
Source code in supervision/annotators/core.py 
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
def __init__(
    self,
    icon_resolution_wh: Tuple[int, int] = (64, 64),
    icon_position: Position = Position.TOP_CENTER,
    offset_xy: Tuple[int, int] = (0, 0),
):
    """
    Args:
        icon_resolution_wh (Tuple[int, int]): The size of drawn icons.
            All icons will be resized to this resolution, keeping the aspect ratio.
        icon_position (Position): The position of the icon.
        offset_xy (Tuple[int, int]): The offset to apply to the icon position,
            in pixels. Can be both positive and negative.
    """
    self.icon_resolution_wh = icon_resolution_wh
    self.position = icon_position
    self.offset_xy = offset_xy

annotate(scene, detections, icon_path)

Annotates the given scene with given icons.

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
icon_path Union[str, List[str]]

The path to the PNG image to use as an icon. Must be a single path or a list of paths, one for each detection. Pass an empty string "" to draw nothing.

 required

Returns:

Type Description
ImageType

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

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

available_icons = ["roboflow.png", "lenny.png"]
icon_paths = [np.random.choice(available_icons) for _ in detections]

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

icon-annotator-example

Source code in supervision/annotators/core.py 
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
@ensure_cv2_image_for_annotation
def annotate(
    self, scene: ImageType, detections: Detections, icon_path: Union[str, List[str]]
) -> ImageType:
    """
    Annotates the given scene with given icons.

    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.
        icon_path (Union[str, List[str]]): The path to the PNG image to use as an
            icon. Must be a single path or a list of paths, one for each detection.
            Pass an empty string `""` to draw nothing.

    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(...)

        available_icons = ["roboflow.png", "lenny.png"]
        icon_paths = [np.random.choice(available_icons) for _ in detections]

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

    ![icon-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/icon-annotator-example.png)
    """
    assert isinstance(scene, np.ndarray)
    if isinstance(icon_path, list) and len(icon_path) != len(detections):
        raise ValueError(
            f"The number of icon paths provided ({len(icon_path)}) does not match "
            f"the number of detections ({len(detections)}). Either provide a single"
            f" icon path or one for each detection."
        )

    xy = detections.get_anchors_coordinates(anchor=self.position).astype(int)

    for detection_idx in range(len(detections)):
        current_path = (
            icon_path if isinstance(icon_path, str) else icon_path[detection_idx]
        )
        if current_path == "":
            continue
        icon = self._load_icon(current_path)
        icon_h, icon_w = icon.shape[:2]

        x = int(xy[detection_idx, 0] - icon_w / 2 + self.offset_xy[0])
        y = int(xy[detection_idx, 1] - icon_h / 2 + self.offset_xy[1])

        scene[:] = overlay_image(scene, icon, (x, y))
    return scene

BlurAnnotator

Bases: BaseAnnotator

A class for blurring regions in an image using provided detections.

Source code in supervision/annotators/core.py 
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
class BlurAnnotator(BaseAnnotator):
    """
    A class for blurring regions in an image using provided detections.
    """

    def __init__(self, kernel_size: int = 15):
        """
        Args:
            kernel_size (int): The size of the average pooling kernel used for blurring.
        """
        self.kernel_size: int = kernel_size

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
    ) -> ImageType:
        """
        Annotates the given scene by blurring regions based on the provided detections.

        Args:
            scene (ImageType): The image where blurring will be applied.
                `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

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

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

        ![blur-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/blur-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        image_height, image_width = scene.shape[:2]
        clipped_xyxy = clip_boxes(
            xyxy=detections.xyxy, resolution_wh=(image_width, image_height)
        ).astype(int)

        for x1, y1, x2, y2 in clipped_xyxy:
            roi = scene[y1:y2, x1:x2]
            roi = cv2.blur(roi, (self.kernel_size, self.kernel_size))
            scene[y1:y2, x1:x2] = roi

        return scene

Functions

__init__(kernel_size=15)

Parameters:

Name Type Description Default
kernel_size int

The size of the average pooling kernel used for blurring.

 15
Source code in supervision/annotators/core.py 
1537
1538
1539
1540
1541
1542
def __init__(self, kernel_size: int = 15):
    """
    Args:
        kernel_size (int): The size of the average pooling kernel used for blurring.
    """
    self.kernel_size: int = kernel_size

annotate(scene, detections)

Annotates the given scene by blurring regions based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

The image where blurring will be applied. 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

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

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

blur-annotator-example

Source code in supervision/annotators/core.py 
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
) -> ImageType:
    """
    Annotates the given scene by blurring regions based on the provided detections.

    Args:
        scene (ImageType): The image where blurring will be applied.
            `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

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

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

    ![blur-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/blur-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    image_height, image_width = scene.shape[:2]
    clipped_xyxy = clip_boxes(
        xyxy=detections.xyxy, resolution_wh=(image_width, image_height)
    ).astype(int)

    for x1, y1, x2, y2 in clipped_xyxy:
        roi = scene[y1:y2, x1:x2]
        roi = cv2.blur(roi, (self.kernel_size, self.kernel_size))
        scene[y1:y2, x1:x2] = roi

    return scene

PixelateAnnotator

Bases: BaseAnnotator

A class for pixelating regions in an image using provided detections.

Source code in supervision/annotators/core.py 
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
class PixelateAnnotator(BaseAnnotator):
    """
    A class for pixelating regions in an image using provided detections.
    """

    def __init__(self, pixel_size: int = 20):
        """
        Args:
            pixel_size (int): The size of the pixelation.
        """
        self.pixel_size: int = pixel_size

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
    ) -> ImageType:
        """
        Annotates the given scene by pixelating regions based on the provided
            detections.

        Args:
            scene (ImageType): The image where pixelating will be applied.
                `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

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

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

        ![pixelate-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/pixelate-annotator-example-10.png)
        """
        assert isinstance(scene, np.ndarray)
        image_height, image_width = scene.shape[:2]
        clipped_xyxy = clip_boxes(
            xyxy=detections.xyxy, resolution_wh=(image_width, image_height)
        ).astype(int)

        for x1, y1, x2, y2 in clipped_xyxy:
            roi = scene[y1:y2, x1:x2]
            scaled_up_roi = cv2.resize(
                src=roi, dsize=None, fx=1 / self.pixel_size, fy=1 / self.pixel_size
            )
            scaled_down_roi = cv2.resize(
                src=scaled_up_roi,
                dsize=(roi.shape[1], roi.shape[0]),
                interpolation=cv2.INTER_NEAREST,
            )

            scene[y1:y2, x1:x2] = scaled_down_roi

        return scene

Functions

__init__(pixel_size=20)

Parameters:

Name Type Description Default
pixel_size int

The size of the pixelation.

 20
Source code in supervision/annotators/core.py 
1820
1821
1822
1823
1824
1825
def __init__(self, pixel_size: int = 20):
    """
    Args:
        pixel_size (int): The size of the pixelation.
    """
    self.pixel_size: int = pixel_size

annotate(scene, detections)

Annotates the given scene by pixelating regions based on the provided detections.

Parameters:

Name Type Description Default
scene ImageType

The image where pixelating will be applied. 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

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

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

pixelate-annotator-example

Source code in supervision/annotators/core.py 
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
) -> ImageType:
    """
    Annotates the given scene by pixelating regions based on the provided
        detections.

    Args:
        scene (ImageType): The image where pixelating will be applied.
            `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

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

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

    ![pixelate-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/pixelate-annotator-example-10.png)
    """
    assert isinstance(scene, np.ndarray)
    image_height, image_width = scene.shape[:2]
    clipped_xyxy = clip_boxes(
        xyxy=detections.xyxy, resolution_wh=(image_width, image_height)
    ).astype(int)

    for x1, y1, x2, y2 in clipped_xyxy:
        roi = scene[y1:y2, x1:x2]
        scaled_up_roi = cv2.resize(
            src=roi, dsize=None, fx=1 / self.pixel_size, fy=1 / self.pixel_size
        )
        scaled_down_roi = cv2.resize(
            src=scaled_up_roi,
            dsize=(roi.shape[1], roi.shape[0]),
            interpolation=cv2.INTER_NEAREST,
        )

        scene[y1:y2, x1:x2] = scaled_down_roi

    return scene

TraceAnnotator

Bases: BaseAnnotator

A class for drawing trace paths on an image based on detection coordinates.

Warning

This annotator uses the sv.Detections.tracker_id. Read here to learn how to plug tracking into your inference pipeline.

Source code in supervision/annotators/core.py 
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
class TraceAnnotator(BaseAnnotator):
    """
    A class for drawing trace paths on an image based on detection coordinates.

    !!! warning

        This annotator uses the `sv.Detections.tracker_id`. Read
        [here](/latest/trackers/) to learn how to plug
        tracking into your inference pipeline.
    """

    def __init__(
        self,
        color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        position: Position = Position.CENTER,
        trace_length: int = 30,
        thickness: int = 2,
        color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            color (Union[Color, ColorPalette]): The color to draw the trace, can be
                a single color or a color palette.
            position (Position): The position of the trace.
                Defaults to `CENTER`.
            trace_length (int): The maximum length of the trace in terms of historical
                points. Defaults to `30`.
            thickness (int): The thickness of the trace lines. Defaults to `2`.
            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.color: Union[Color, ColorPalette] = color
        self.trace = Trace(max_size=trace_length, anchor=position)
        self.thickness = thickness
        self.color_lookup: ColorLookup = color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Draws trace paths on the frame based on the detection coordinates provided.

        Args:
            scene (ImageType): The image on which the traces will be drawn.
                `ImageType` is a flexible type, accepting either `numpy.ndarray`
                or `PIL.Image.Image`.
            detections (Detections): The detections which include coordinates for
                which the traces will be drawn.
            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
            from ultralytics import YOLO

            model = YOLO('yolov8x.pt')
            trace_annotator = sv.TraceAnnotator()

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

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

        ![trace-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/trace-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        if detections.tracker_id is None:
            raise ValueError(
                "The `tracker_id` field is missing in the provided detections."
                " See more: https://supervision.roboflow.com/latest/how_to/track_objects"
            )

        self.trace.put(detections)
        for detection_idx in range(len(detections)):
            tracker_id = int(detections.tracker_id[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,
            )
            xy = self.trace.get(tracker_id=tracker_id)
            if len(xy) > 1:
                scene = cv2.polylines(
                    scene,
                    [xy.astype(np.int32)],
                    False,
                    color=color.as_bgr(),
                    thickness=self.thickness,
                )
        return scene

Functions

__init__(color=ColorPalette.DEFAULT, position=Position.CENTER, trace_length=30, thickness=2, color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
color Union[Color, ColorPalette]

The color to draw the trace, can be a single color or a color palette.

 DEFAULT
position Position

The position of the trace. Defaults to CENTER.

 CENTER
trace_length int

The maximum length of the trace in terms of historical points. Defaults to 30.

 30
thickness int

The thickness of the trace lines. Defaults to 2.

 2
color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
def __init__(
    self,
    color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    position: Position = Position.CENTER,
    trace_length: int = 30,
    thickness: int = 2,
    color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        color (Union[Color, ColorPalette]): The color to draw the trace, can be
            a single color or a color palette.
        position (Position): The position of the trace.
            Defaults to `CENTER`.
        trace_length (int): The maximum length of the trace in terms of historical
            points. Defaults to `30`.
        thickness (int): The thickness of the trace lines. Defaults to `2`.
        color_lookup (ColorLookup): Strategy for mapping colors to annotations.
            Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.color: Union[Color, ColorPalette] = color
    self.trace = Trace(max_size=trace_length, anchor=position)
    self.thickness = thickness
    self.color_lookup: ColorLookup = color_lookup

annotate(scene, detections, custom_color_lookup=None)

Draws trace paths on the frame based on the detection coordinates provided.

Parameters:

Name Type Description Default
scene ImageType

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

 required
detections Detections

The detections which include coordinates for which the traces will be drawn.

 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
from ultralytics import YOLO

model = YOLO('yolov8x.pt')
trace_annotator = sv.TraceAnnotator()

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

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

trace-annotator-example

Source code in supervision/annotators/core.py 
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Draws trace paths on the frame based on the detection coordinates provided.

    Args:
        scene (ImageType): The image on which the traces will be drawn.
            `ImageType` is a flexible type, accepting either `numpy.ndarray`
            or `PIL.Image.Image`.
        detections (Detections): The detections which include coordinates for
            which the traces will be drawn.
        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
        from ultralytics import YOLO

        model = YOLO('yolov8x.pt')
        trace_annotator = sv.TraceAnnotator()

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

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

    ![trace-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/trace-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    if detections.tracker_id is None:
        raise ValueError(
            "The `tracker_id` field is missing in the provided detections."
            " See more: https://supervision.roboflow.com/latest/how_to/track_objects"
        )

    self.trace.put(detections)
    for detection_idx in range(len(detections)):
        tracker_id = int(detections.tracker_id[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,
        )
        xy = self.trace.get(tracker_id=tracker_id)
        if len(xy) > 1:
            scene = cv2.polylines(
                scene,
                [xy.astype(np.int32)],
                False,
                color=color.as_bgr(),
                thickness=self.thickness,
            )
    return scene

CropAnnotator

Bases: BaseAnnotator

A class for drawing scaled up crops of detections on the scene.

Source code in supervision/annotators/core.py 
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
class CropAnnotator(BaseAnnotator):
    """
    A class for drawing scaled up crops of detections on the scene.
    """

    def __init__(
        self,
        position: Position = Position.TOP_CENTER,
        scale_factor: float = 2.0,
        border_color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
        border_thickness: int = 2,
        border_color_lookup: ColorLookup = ColorLookup.CLASS,
    ):
        """
        Args:
            position (Position): The anchor position for placing the cropped and scaled
                part of the detection in the scene.
            scale_factor (float): The factor by which to scale the cropped image part. A
                factor of 2, for example, would double the size of the cropped area,
                allowing for a closer view of the detection.
            border_color (Union[Color, ColorPalette]): The color or color palette to
                use for annotating border around the cropped area.
            border_thickness (int): The thickness of the border around the cropped area.
            border_color_lookup (ColorLookup): Strategy for mapping colors to
                annotations. Options are `INDEX`, `CLASS`, `TRACK`.
        """
        self.position: Position = position
        self.scale_factor: float = scale_factor
        self.border_color: Union[Color, ColorPalette] = border_color
        self.border_thickness: int = border_thickness
        self.border_color_lookup: ColorLookup = border_color_lookup

    @ensure_cv2_image_for_annotation
    def annotate(
        self,
        scene: ImageType,
        detections: Detections,
        custom_color_lookup: Optional[np.ndarray] = None,
    ) -> ImageType:
        """
        Annotates the provided scene with scaled and cropped parts of the image based
        on the provided detections. Each detection is cropped from the original scene
        and scaled according to the annotator's scale factor before being placed back
        onto the scene at the specified position.


        Args:
            scene (ImageType): The image where cropped detection will be placed.
                `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.

        Example:
            ```python
            import supervision as sv

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

            crop_annotator = sv.CropAnnotator()
            annotated_frame = crop_annotator.annotate(
                scene=image.copy(),
                detections=detections
            )
            ```
        """
        assert isinstance(scene, np.ndarray)
        crops = [
            crop_image(image=scene, xyxy=xyxy) for xyxy in detections.xyxy.astype(int)
        ]
        resized_crops = [
            scale_image(image=crop, scale_factor=self.scale_factor) for crop in crops
        ]
        anchors = detections.get_anchors_coordinates(anchor=self.position).astype(int)

        for idx, (resized_crop, anchor) in enumerate(zip(resized_crops, anchors)):
            crop_wh = resized_crop.shape[1], resized_crop.shape[0]
            (x1, y1), (x2, y2) = self.calculate_crop_coordinates(
                anchor=anchor, crop_wh=crop_wh, position=self.position
            )
            scene = overlay_image(image=scene, overlay=resized_crop, anchor=(x1, y1))
            color = resolve_color(
                color=self.border_color,
                detections=detections,
                detection_idx=idx,
                color_lookup=self.border_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.border_thickness,
            )

        return scene

    @staticmethod
    def calculate_crop_coordinates(
        anchor: Tuple[int, int], crop_wh: Tuple[int, int], position: Position
    ) -> Tuple[Tuple[int, int], Tuple[int, int]]:
        anchor_x, anchor_y = anchor
        width, height = crop_wh

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

Functions

__init__(position=Position.TOP_CENTER, scale_factor=2.0, border_color=ColorPalette.DEFAULT, border_thickness=2, border_color_lookup=ColorLookup.CLASS)

Parameters:

Name Type Description Default
position Position

The anchor position for placing the cropped and scaled part of the detection in the scene.

 TOP_CENTER
scale_factor float

The factor by which to scale the cropped image part. A factor of 2, for example, would double the size of the cropped area, allowing for a closer view of the detection.

 2.0
border_color Union[Color, ColorPalette]

The color or color palette to use for annotating border around the cropped area.

 DEFAULT
border_thickness int

The thickness of the border around the cropped area.

 2
border_color_lookup ColorLookup

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

 CLASS
Source code in supervision/annotators/core.py 
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
def __init__(
    self,
    position: Position = Position.TOP_CENTER,
    scale_factor: float = 2.0,
    border_color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
    border_thickness: int = 2,
    border_color_lookup: ColorLookup = ColorLookup.CLASS,
):
    """
    Args:
        position (Position): The anchor position for placing the cropped and scaled
            part of the detection in the scene.
        scale_factor (float): The factor by which to scale the cropped image part. A
            factor of 2, for example, would double the size of the cropped area,
            allowing for a closer view of the detection.
        border_color (Union[Color, ColorPalette]): The color or color palette to
            use for annotating border around the cropped area.
        border_thickness (int): The thickness of the border around the cropped area.
        border_color_lookup (ColorLookup): Strategy for mapping colors to
            annotations. Options are `INDEX`, `CLASS`, `TRACK`.
    """
    self.position: Position = position
    self.scale_factor: float = scale_factor
    self.border_color: Union[Color, ColorPalette] = border_color
    self.border_thickness: int = border_thickness
    self.border_color_lookup: ColorLookup = border_color_lookup

annotate(scene, detections, custom_color_lookup=None)

Annotates the provided scene with scaled and cropped parts of the image based on the provided detections. Each detection is cropped from the original scene and scaled according to the annotator's scale factor before being placed back onto the scene at the specified position.

Parameters:

Name Type Description Default
scene ImageType

The image where cropped detection will be placed. 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.
Example 
import supervision as sv

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

crop_annotator = sv.CropAnnotator()
annotated_frame = crop_annotator.annotate(
    scene=image.copy(),
    detections=detections
)
Source code in supervision/annotators/core.py 
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
@ensure_cv2_image_for_annotation
def annotate(
    self,
    scene: ImageType,
    detections: Detections,
    custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
    """
    Annotates the provided scene with scaled and cropped parts of the image based
    on the provided detections. Each detection is cropped from the original scene
    and scaled according to the annotator's scale factor before being placed back
    onto the scene at the specified position.


    Args:
        scene (ImageType): The image where cropped detection will be placed.
            `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.

    Example:
        ```python
        import supervision as sv

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

        crop_annotator = sv.CropAnnotator()
        annotated_frame = crop_annotator.annotate(
            scene=image.copy(),
            detections=detections
        )
        ```
    """
    assert isinstance(scene, np.ndarray)
    crops = [
        crop_image(image=scene, xyxy=xyxy) for xyxy in detections.xyxy.astype(int)
    ]
    resized_crops = [
        scale_image(image=crop, scale_factor=self.scale_factor) for crop in crops
    ]
    anchors = detections.get_anchors_coordinates(anchor=self.position).astype(int)

    for idx, (resized_crop, anchor) in enumerate(zip(resized_crops, anchors)):
        crop_wh = resized_crop.shape[1], resized_crop.shape[0]
        (x1, y1), (x2, y2) = self.calculate_crop_coordinates(
            anchor=anchor, crop_wh=crop_wh, position=self.position
        )
        scene = overlay_image(image=scene, overlay=resized_crop, anchor=(x1, y1))
        color = resolve_color(
            color=self.border_color,
            detections=detections,
            detection_idx=idx,
            color_lookup=self.border_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.border_thickness,
        )

    return scene

BackgroundOverlayAnnotator

Bases: BaseAnnotator

A class for drawing a colored overlay on the background of an image outside the region of detections.

If masks are provided, the background is colored outside the masks. If masks are not provided, the background is colored outside the bounding boxes.

You can use the force_box parameter to force the annotator to use bounding boxes.

Warning

This annotator uses sv.Detections.mask.

Source code in supervision/annotators/core.py 
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
class BackgroundOverlayAnnotator(BaseAnnotator):
    """
    A class for drawing a colored overlay on the background of an image outside
    the region of detections.

    If masks are provided, the background is colored outside the masks.
    If masks are not provided, the background is colored outside the bounding boxes.

    You can use the `force_box` parameter to force the annotator to use bounding boxes.

    !!! warning

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

    def __init__(
        self,
        color: Color = Color.BLACK,
        opacity: float = 0.5,
        force_box: bool = False,
    ):
        """
        Args:
            color (Color): The color to use for annotating detections.
            opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
            force_box (bool): If `True`, forces the annotator to use bounding boxes when
                masks are provided in the supplied sv.Detections.
        """
        self.color: Color = color
        self.opacity = opacity
        self.force_box = force_box

    @ensure_cv2_image_for_annotation
    def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
        """
        Applies a colored overlay to the scene outside of the detected regions.

        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.

        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(...)

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

        ![background-overlay-annotator-example](https://media.roboflow.com/
        supervision-annotator-examples/background-color-annotator-example-purple.png)
        """
        assert isinstance(scene, np.ndarray)
        colored_mask = np.full_like(scene, self.color.as_bgr(), dtype=np.uint8)

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

        if detections.mask is None or self.force_box:
            for x1, y1, x2, y2 in detections.xyxy.astype(int):
                colored_mask[y1:y2, x1:x2] = scene[y1:y2, x1:x2]
        else:
            for mask in detections.mask:
                colored_mask[mask] = scene[mask]

        np.copyto(scene, colored_mask)
        return scene

Functions

__init__(color=Color.BLACK, opacity=0.5, force_box=False)

Parameters:

Name Type Description Default
color Color

The color to use for annotating detections.

 BLACK
opacity float

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

 0.5
force_box bool

If True, forces the annotator to use bounding boxes when masks are provided in the supplied sv.Detections.

 False
Source code in supervision/annotators/core.py 
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
def __init__(
    self,
    color: Color = Color.BLACK,
    opacity: float = 0.5,
    force_box: bool = False,
):
    """
    Args:
        color (Color): The color to use for annotating detections.
        opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
        force_box (bool): If `True`, forces the annotator to use bounding boxes when
            masks are provided in the supplied sv.Detections.
    """
    self.color: Color = color
    self.opacity = opacity
    self.force_box = force_box

annotate(scene, detections)

Applies a colored overlay to the scene outside of the detected regions.

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

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(...)

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

background-overlay-annotator-example

Source code in supervision/annotators/core.py 
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
@ensure_cv2_image_for_annotation
def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
    """
    Applies a colored overlay to the scene outside of the detected regions.

    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.

    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(...)

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

    ![background-overlay-annotator-example](https://media.roboflow.com/
    supervision-annotator-examples/background-color-annotator-example-purple.png)
    """
    assert isinstance(scene, np.ndarray)
    colored_mask = np.full_like(scene, self.color.as_bgr(), dtype=np.uint8)

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

    if detections.mask is None or self.force_box:
        for x1, y1, x2, y2 in detections.xyxy.astype(int):
            colored_mask[y1:y2, x1:x2] = scene[y1:y2, x1:x2]
    else:
        for mask in detections.mask:
            colored_mask[mask] = scene[mask]

    np.copyto(scene, colored_mask)
    return scene

ColorLookup

Bases: Enum

Enumeration class to define strategies for mapping colors to annotations.

This enum supports three different lookup strategies
  • INDEX: Colors are determined by the index of the detection within the scene.
  • CLASS: Colors are determined by the class label of the detected object.
  • TRACK: Colors are determined by the tracking identifier of the object.
Source code in supervision/annotators/utils.py 
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
class ColorLookup(Enum):
    """
    Enumeration class to define strategies for mapping colors to annotations.

    This enum supports three different lookup strategies:
        - `INDEX`: Colors are determined by the index of the detection within the scene.
        - `CLASS`: Colors are determined by the class label of the detected object.
        - `TRACK`: Colors are determined by the tracking identifier of the object.
    """

    INDEX = "index"
    CLASS = "class"
    TRACK = "track"

    @classmethod
    def list(cls):
        return list(map(lambda c: c.value, cls))

Comments