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Image Utils

crop_image

supervision.utils.image.crop_image(image: ImageType, xyxy: npt.NDArray[int] | list[int] | tuple[int, int, int, int]) -> ImageType

Crop image based on bounding box coordinates.

Parameters:

Name Type Description Default

image

ImageType

The image to crop.

 required

xyxy

NDArray[int] | list[int] | tuple[int, int, int, int]

Bounding box coordinates in (x_min, y_min, x_max, y_max) format.

 required

Returns:

Type Description
ImageType

Cropped image matching input type.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
>>> image.shape
(1080, 1920, 3)
>>> xyxy = (400, 400, 800, 800)
>>> cropped_image = sv.crop_image(image=image, xyxy=xyxy)
>>> cropped_image.shape
(400, 400, 3)

>>> image = np.zeros((1920, 1080), dtype=np.uint8)
>>> image.shape
(1920, 1080)
>>> xyxy = (400, 400, 800, 800)
>>> cropped_image = sv.crop_image(image=image, xyxy=xyxy)
>>> cropped_image.shape
(400, 400)

crop-image

Source code in src/supervision/utils/image.py 
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@ensure_cv2_image_for_standalone_function
def crop_image(
    image: ImageType,
    xyxy: npt.NDArray[int] | list[int] | tuple[int, int, int, int],
) -> ImageType:
    """
    Crop image based on bounding box coordinates.

    Args:
        image: The image to crop.
        xyxy:
            Bounding box coordinates in `(x_min, y_min, x_max, y_max)` format.

    Returns:
        Cropped image matching input
            type.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
        >>> image.shape
        (1080, 1920, 3)
        >>> xyxy = (400, 400, 800, 800)
        >>> cropped_image = sv.crop_image(image=image, xyxy=xyxy)
        >>> cropped_image.shape
        (400, 400, 3)

        ```

        ```pycon
        >>> image = np.zeros((1920, 1080), dtype=np.uint8)
        >>> image.shape
        (1920, 1080)
        >>> xyxy = (400, 400, 800, 800)
        >>> cropped_image = sv.crop_image(image=image, xyxy=xyxy)
        >>> cropped_image.shape
        (400, 400)

        ```

    ![crop-image](https://media.roboflow.com/supervision-docs/supervision-docs-crop-image-2.png){ align=center width="1000" }
    """  # noqa E501 // docs
    if isinstance(xyxy, (list, tuple)):
        xyxy = np.array(xyxy)

    xyxy = np.round(xyxy).astype(int)
    x_min, y_min, x_max, y_max = xyxy.flatten()

    if isinstance(image, np.ndarray):
        return image[y_min:y_max, x_min:x_max]

    if isinstance(image, Image.Image):
        return image.crop((x_min, y_min, x_max, y_max))

    raise TypeError(
        f"`image` must be a numpy.ndarray or PIL.Image.Image. Received {type(image)}"
    )

scale_image

supervision.utils.image.scale_image(image: ImageType, scale_factor: float) -> ImageType

Scale image by given factor. Scale factor > 1.0 zooms in, < 1.0 zooms out.

Parameters:

Name Type Description Default

image

ImageType

The image to scale.

 required

scale_factor

float

Factor by which to scale the image.

 required

Returns:

Type Description
ImageType

Scaled image matching input type.

Raises:

Type Description
ValueError

If scale factor is non-positive.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
>>> image.shape
(1080, 1920, 3)
>>> scaled_image = sv.scale_image(image=image, scale_factor=0.5)
>>> scaled_image.shape
(540, 960, 3)

>>> image = np.zeros((1920, 1080), dtype=np.uint8)
>>> image.shape
(1920, 1080)
>>> scaled_image = sv.scale_image(image=image, scale_factor=0.5)
>>> scaled_image.shape
(960, 540)

scale-image

Source code in src/supervision/utils/image.py 
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@ensure_cv2_image_for_standalone_function
def scale_image(image: ImageType, scale_factor: float) -> ImageType:
    """
    Scale image by given factor. Scale factor > 1.0 zooms in, < 1.0 zooms out.

    Args:
        image: The image to scale.
        scale_factor: Factor by which to scale the image.

    Returns:
        Scaled image matching input
            type.

    Raises:
        ValueError: If scale factor is non-positive.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
        >>> image.shape
        (1080, 1920, 3)
        >>> scaled_image = sv.scale_image(image=image, scale_factor=0.5)
        >>> scaled_image.shape
        (540, 960, 3)

        ```

        ```pycon
        >>> image = np.zeros((1920, 1080), dtype=np.uint8)
        >>> image.shape
        (1920, 1080)
        >>> scaled_image = sv.scale_image(image=image, scale_factor=0.5)
        >>> scaled_image.shape
        (960, 540)

        ```

    ![scale-image](https://media.roboflow.com/supervision-docs/supervision-docs-scale-image-2.png){ align=center width="1000" }
    """  # noqa E501 // docs
    if scale_factor <= 0:
        raise ValueError("Scale factor must be positive.")

    width_old, height_old = image.shape[1], image.shape[0]
    width_new = int(width_old * scale_factor)
    height_new = int(height_old * scale_factor)
    return cv2.resize(image, (width_new, height_new), interpolation=cv2.INTER_LINEAR)

resize_image

supervision.utils.image.resize_image(image: ImageType, resolution_wh: tuple[int, int], keep_aspect_ratio: bool = False) -> ImageType

Resize image to specified resolution. Can optionally maintain aspect ratio.

Parameters:

Name Type Description Default

image

ImageType

The image to resize.

 required

resolution_wh

tuple[int, int]

Target resolution as (width, height).

 required

keep_aspect_ratio

bool

Flag to maintain original aspect ratio. Defaults to False.

 False

Returns:

Type Description
ImageType

Resized image matching input type.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
>>> image.shape
(1080, 1920, 3)
>>> resized_image = sv.resize_image(
...     image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
... )
>>> resized_image.shape
(562, 1000, 3)

>>> image = np.zeros((1920, 1080), dtype=np.uint8)
>>> image.shape
(1920, 1080)
>>> resized_image = sv.resize_image(
...     image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
... )
>>> resized_image.shape
(1000, 562)

resize-image

Source code in src/supervision/utils/image.py 
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@ensure_cv2_image_for_standalone_function
def resize_image(
    image: ImageType,
    resolution_wh: tuple[int, int],
    keep_aspect_ratio: bool = False,
) -> ImageType:
    """
    Resize image to specified resolution. Can optionally maintain aspect ratio.

    Args:
        image: The image to resize.
        resolution_wh: Target resolution as `(width, height)`.
        keep_aspect_ratio: Flag to maintain original aspect ratio.
            Defaults to `False`.

    Returns:
        Resized image matching input
            type.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
        >>> image.shape
        (1080, 1920, 3)
        >>> resized_image = sv.resize_image(
        ...     image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
        ... )
        >>> resized_image.shape
        (562, 1000, 3)

        ```

        ```pycon
        >>> image = np.zeros((1920, 1080), dtype=np.uint8)
        >>> image.shape
        (1920, 1080)
        >>> resized_image = sv.resize_image(
        ...     image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
        ... )
        >>> resized_image.shape
        (1000, 562)

        ```

    ![resize-image](https://media.roboflow.com/supervision-docs/supervision-docs-resize-image-2.png){ align=center width="1000" }
    """  # noqa E501 // docs
    if keep_aspect_ratio:
        image_ratio = image.shape[1] / image.shape[0]
        target_ratio = resolution_wh[0] / resolution_wh[1]
        if image_ratio >= target_ratio:
            width_new = resolution_wh[0]
            height_new = int(resolution_wh[0] / image_ratio)
        else:
            height_new = resolution_wh[1]
            width_new = int(resolution_wh[1] * image_ratio)
    else:
        width_new, height_new = resolution_wh

    return cv2.resize(image, (width_new, height_new), interpolation=cv2.INTER_LINEAR)

letterbox_image

supervision.utils.image.letterbox_image(image: ImageType, resolution_wh: tuple[int, int], color: tuple[int, int, int] | Color = Color.BLACK) -> ImageType

Resize image and pad with color to achieve desired resolution while maintaining aspect ratio.

Parameters:

Name Type Description Default

image

ImageType

The image to resize and pad.

 required

resolution_wh

tuple[int, int]

Target resolution as (width, height).

 required

color

tuple[int, int, int] | Color

Padding color. If tuple, should be in BGR format. Defaults to Color.BLACK.

 BLACK

Returns:

Type Description
ImageType

Letterboxed image matching input type.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
>>> image.shape
(1080, 1920, 3)
>>> letterboxed_image = sv.letterbox_image(
...     image=image, resolution_wh=(1000, 1000)
... )
>>> letterboxed_image.shape
(1000, 1000, 3)

letterbox-image

Source code in src/supervision/utils/image.py 
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@ensure_cv2_image_for_standalone_function
def letterbox_image(
    image: ImageType,
    resolution_wh: tuple[int, int],
    color: tuple[int, int, int] | Color = Color.BLACK,
) -> ImageType:
    """
    Resize image and pad with color to achieve desired resolution while
    maintaining aspect ratio.

    Args:
        image: The image to resize and pad.
        resolution_wh: Target resolution as `(width, height)`.
        color: Padding color. If tuple, should
            be in BGR format. Defaults to `Color.BLACK`.

    Returns:
        Letterboxed image matching input
            type.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
        >>> image.shape
        (1080, 1920, 3)
        >>> letterboxed_image = sv.letterbox_image(
        ...     image=image, resolution_wh=(1000, 1000)
        ... )
        >>> letterboxed_image.shape
        (1000, 1000, 3)

        ```

    ![letterbox-image](https://media.roboflow.com/supervision-docs/supervision-docs-letterbox-image-2.png){ align=center width="1000" }
    """  # noqa E501 // docs
    assert isinstance(image, np.ndarray)
    color = unify_to_bgr(color=color)
    resized_image = resize_image(
        image=image, resolution_wh=resolution_wh, keep_aspect_ratio=True
    )
    height_new, width_new = resized_image.shape[:2]
    padding_top = (resolution_wh[1] - height_new) // 2
    padding_bottom = resolution_wh[1] - height_new - padding_top
    padding_left = (resolution_wh[0] - width_new) // 2
    padding_right = resolution_wh[0] - width_new - padding_left
    image_with_borders = cv2.copyMakeBorder(
        resized_image,
        padding_top,
        padding_bottom,
        padding_left,
        padding_right,
        cv2.BORDER_CONSTANT,
        value=color,
    )

    if image.shape[2] == 4:
        image[:padding_top, :, 3] = 0
        image[height_new - padding_bottom :, :, 3] = 0
        image[:, :padding_left, 3] = 0
        image[:, width_new - padding_right :, 3] = 0

    return image_with_borders

tint_image

supervision.utils.image.tint_image(image: ImageType, color: Color = Color.BLACK, opacity: float = 0.5) -> ImageType

Tint image with solid color overlay at specified opacity.

Parameters:

Name Type Description Default

image

ImageType

The image to tint.

 required

color

Color

Overlay tint color. Defaults to Color.BLACK.

 BLACK

opacity

float

Blend ratio between overlay and image (0.0-1.0). Defaults to 0.5.

 0.5

Returns:

Type Description
ImageType

Tinted image matching input type.

Raises:

Type Description
ValueError

If opacity is outside range [0.0, 1.0].

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> image = np.zeros((100, 100, 3), dtype=np.uint8)
>>> tinted_image = sv.tint_image(
...     image=image, color=sv.Color.ROBOFLOW, opacity=0.5
... )
>>> tinted_image.shape
(100, 100, 3)

tint-image

Source code in src/supervision/utils/image.py 
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@ensure_cv2_image_for_standalone_function
def tint_image(
    image: ImageType,
    color: Color = Color.BLACK,
    opacity: float = 0.5,
) -> ImageType:
    """
    Tint image with solid color overlay at specified opacity.

    Args:
        image: The image to tint.
        color: Overlay tint color. Defaults to `Color.BLACK`.
        opacity: Blend ratio between overlay and image (0.0-1.0).
            Defaults to `0.5`.

    Returns:
        Tinted image matching input
            type.

    Raises:
        ValueError: If opacity is outside range [0.0, 1.0].

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
        >>> tinted_image = sv.tint_image(
        ...     image=image, color=sv.Color.ROBOFLOW, opacity=0.5
        ... )
        >>> tinted_image.shape
        (100, 100, 3)

        ```

    ![tint-image](https://media.roboflow.com/supervision-docs/supervision-docs-tint-image-2.png){ align=center width="1000" }
    """  # noqa E501 // docs
    if not 0.0 <= opacity <= 1.0:
        raise ValueError("opacity must be between 0.0 and 1.0")

    overlay = np.full_like(image, fill_value=color.as_bgr(), dtype=image.dtype)
    cv2.addWeighted(
        src1=overlay, alpha=opacity, src2=image, beta=1 - opacity, gamma=0, dst=image
    )
    return image

grayscale_image

supervision.utils.image.grayscale_image(image: ImageType) -> ImageType

Convert image to 3-channel grayscale. Luminance channel is broadcast to all three channels for compatibility with color-based drawing helpers.

Parameters:

Name Type Description Default

image

ImageType

The image to convert to grayscale.

 required

Returns:

Type Description
ImageType

3-channel grayscale image matching input type.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> image = np.ones((100, 100, 3), dtype=np.uint8) * 128
>>> grayscale_image = sv.grayscale_image(image=image)
>>> grayscale_image.shape
(100, 100, 3)

grayscale-image

Source code in src/supervision/utils/image.py 
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@ensure_cv2_image_for_standalone_function
def grayscale_image(image: ImageType) -> ImageType:
    """
    Convert image to 3-channel grayscale. Luminance channel is broadcast to
    all three channels for compatibility with color-based drawing helpers.

    Args:
        image: The image to convert to
            grayscale.

    Returns:
        3-channel grayscale image
            matching input type.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> image = np.ones((100, 100, 3), dtype=np.uint8) * 128
        >>> grayscale_image = sv.grayscale_image(image=image)
        >>> grayscale_image.shape
        (100, 100, 3)

        ```

    ![grayscale-image](https://media.roboflow.com/supervision-docs/supervision-docs-grayscale-image-2.png){ align=center width="1000" }
    """  # noqa E501 // docs
    grayscaled = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return cv2.cvtColor(grayscaled, cv2.COLOR_GRAY2BGR)

get_image_resolution_wh

supervision.utils.image.get_image_resolution_wh(image: ImageType) -> tuple[int, int]

Get image width and height as a tuple (width, height) for various image formats.

Supports both numpy.ndarray images (with shape (H, W, ...)) and PIL.Image.Image inputs.

Parameters:

Name Type Description Default

image

ImageType

Input image.

 required

Returns:

Type Description
tuple[int, int]

Image resolution as (width, height).

Raises:

Type Description
ValueError

If a numpy.ndarray image has fewer than 2 dimensions.
TypeError

If image is not a supported type (numpy.ndarray or PIL.Image.Image).

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
>>> sv.get_image_resolution_wh(image)
(1920, 1080)
Source code in src/supervision/utils/image.py 
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def get_image_resolution_wh(image: ImageType) -> tuple[int, int]:
    """
    Get image width and height as a tuple `(width, height)` for various image formats.

    Supports both `numpy.ndarray` images (with shape `(H, W, ...)`) and
    `PIL.Image.Image` inputs.

    Args:
        image: Input image.

    Returns:
        Image resolution as `(width, height)`.

    Raises:
        ValueError: If a `numpy.ndarray` image has fewer than 2 dimensions.
        TypeError: If `image` is not a supported type (`numpy.ndarray` or
            `PIL.Image.Image`).

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
        >>> sv.get_image_resolution_wh(image)
        (1920, 1080)

        ```
    """
    if isinstance(image, np.ndarray):
        if image.ndim < 2:
            raise ValueError(
                "NumPy image must have at least 2 dimensions (H, W, ...). "
                f"Received shape: {image.shape}"
            )
        height, width = image.shape[:2]
        return int(width), int(height)

    if isinstance(image, Image.Image):
        width, height = image.size
        return int(width), int(height)

    raise TypeError(
        "`image` must be a numpy.ndarray or PIL.Image.Image. "
        f"Received type: {type(image)}"
    )

ImageSink

supervision.utils.image.ImageSink

Source code in src/supervision/utils/image.py 
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class ImageSink:
    def __init__(
        self,
        target_dir_path: str,
        overwrite: bool = False,
        image_name_pattern: str = "image_{:05d}.png",
    ):
        """
        Initialize context manager for saving images to directory.

        Args:
            target_dir_path (`str`): Target directory path where images will be
                saved.
            overwrite (`bool`): Whether to overwrite existing directory.
                Defaults to `False`.
            image_name_pattern (`str`): File name pattern for saved images.
                Defaults to `"image_{:05d}.png"`.

        Examples:
            ```pycon
            >>> import numpy as np
            >>> import supervision as sv
            >>> import tempfile
            >>> import os
            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     image = np.zeros((100, 100, 3), dtype=np.uint8)
            ...     with sv.ImageSink(target_dir_path=tmpdir, overwrite=True) as sink:
            ...         sink.save_image(image=image)
            ...         sink.save_image(image=image)
            ...     files = sorted(os.listdir(tmpdir))
            ...     len(files)
            2

            ```
        """
        self.target_dir_path = target_dir_path
        self.overwrite = overwrite
        self.image_name_pattern = image_name_pattern
        self.image_count = 0

    def __enter__(self) -> ImageSink:
        if os.path.exists(self.target_dir_path):
            if self.overwrite:
                shutil.rmtree(self.target_dir_path)
                os.makedirs(self.target_dir_path)
        else:
            os.makedirs(self.target_dir_path)

        return self

    def save_image(
        self, image: npt.NDArray[np.uint8], image_name: str | None = None
    ) -> None:
        """
        Save image to target directory with optional custom filename.

        Args:
            image: Image to save with shape `(height, width, 3)`
                in BGR format.
            image_name: Custom filename for saved image. If
                `None`, generates name using `image_name_pattern`. Defaults to
                `None`.
        """
        if image_name is None:
            image_name = self.image_name_pattern.format(self.image_count)

        image_path = os.path.join(self.target_dir_path, image_name)
        cv2.imwrite(image_path, image)
        self.image_count += 1

    def __exit__(
        self,
        exc_type: type[BaseException] | None,
        exc_value: BaseException | None,
        exc_traceback: Any,
    ) -> None:
        pass

Functions

__init__(target_dir_path: str, overwrite: bool = False, image_name_pattern: str = 'image_{:05d}.png')

Initialize context manager for saving images to directory.

Parameters:

Name Type Description Default
target_dir_path
`str`

Target directory path where images will be saved.

 required
overwrite
`bool`

Whether to overwrite existing directory. Defaults to False.

 False
image_name_pattern
`str`

File name pattern for saved images. Defaults to "image_{:05d}.png".

 'image_{:05d}.png'

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> import tempfile
>>> import os
>>> with tempfile.TemporaryDirectory() as tmpdir:
...     image = np.zeros((100, 100, 3), dtype=np.uint8)
...     with sv.ImageSink(target_dir_path=tmpdir, overwrite=True) as sink:
...         sink.save_image(image=image)
...         sink.save_image(image=image)
...     files = sorted(os.listdir(tmpdir))
...     len(files)
2
Source code in src/supervision/utils/image.py 
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def __init__(
    self,
    target_dir_path: str,
    overwrite: bool = False,
    image_name_pattern: str = "image_{:05d}.png",
):
    """
    Initialize context manager for saving images to directory.

    Args:
        target_dir_path (`str`): Target directory path where images will be
            saved.
        overwrite (`bool`): Whether to overwrite existing directory.
            Defaults to `False`.
        image_name_pattern (`str`): File name pattern for saved images.
            Defaults to `"image_{:05d}.png"`.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> import tempfile
        >>> import os
        >>> with tempfile.TemporaryDirectory() as tmpdir:
        ...     image = np.zeros((100, 100, 3), dtype=np.uint8)
        ...     with sv.ImageSink(target_dir_path=tmpdir, overwrite=True) as sink:
        ...         sink.save_image(image=image)
        ...         sink.save_image(image=image)
        ...     files = sorted(os.listdir(tmpdir))
        ...     len(files)
        2

        ```
    """
    self.target_dir_path = target_dir_path
    self.overwrite = overwrite
    self.image_name_pattern = image_name_pattern
    self.image_count = 0

save_image(image: npt.NDArray[np.uint8], image_name: str | None = None) -> None

Save image to target directory with optional custom filename.

Parameters:

Name Type Description Default
image
NDArray[uint8]

Image to save with shape (height, width, 3) in BGR format.

 required
image_name
str | None

Custom filename for saved image. If None, generates name using image_name_pattern. Defaults to None.

 None
Source code in src/supervision/utils/image.py 
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def save_image(
    self, image: npt.NDArray[np.uint8], image_name: str | None = None
) -> None:
    """
    Save image to target directory with optional custom filename.

    Args:
        image: Image to save with shape `(height, width, 3)`
            in BGR format.
        image_name: Custom filename for saved image. If
            `None`, generates name using `image_name_pattern`. Defaults to
            `None`.
    """
    if image_name is None:
        image_name = self.image_name_pattern.format(self.image_count)

    image_path = os.path.join(self.target_dir_path, image_name)
    cv2.imwrite(image_path, image)
    self.image_count += 1

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