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Image Utils

crop_image

supervision.utils.image.crop_image(image, xyxy)

Crop image based on bounding box coordinates.

Parameters:

Name Type Description Default

image

`numpy.ndarray` or `PIL.Image.Image`

The image to crop.

 required

xyxy

`numpy.array`, `list[int]`, or `tuple[int, int, int, int]`

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

 required

Returns:

Type Description
`numpy.ndarray` or `PIL.Image.Image`

Cropped image matching input type.

Examples:

import cv2
import supervision as sv

image = cv2.imread("source.png")
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)

from PIL import Image
import supervision as sv

image = Image.open("source.png")
image.size
# (1920, 1080)

xyxy = (400, 400, 800, 800)
cropped_image = sv.crop_image(image=image, xyxy=xyxy)
cropped_image.size
# (400, 400)

crop-image

Source code in supervision/utils/image.py 
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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 (`numpy.ndarray` or `PIL.Image.Image`): The image to crop.
        xyxy (`numpy.array`, `list[int]`, or `tuple[int, int, int, int]`):
            Bounding box coordinates in `(x_min, y_min, x_max, y_max)` format.

    Returns:
        (`numpy.ndarray` or `PIL.Image.Image`): Cropped image matching input
            type.

    Examples:
        ```python
        import cv2
        import supervision as sv

        image = cv2.imread("source.png")
        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)
        ```

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open("source.png")
        image.size
        # (1920, 1080)

        xyxy = (400, 400, 800, 800)
        cropped_image = sv.crop_image(image=image, xyxy=xyxy)
        cropped_image.size
        # (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, scale_factor)

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

Parameters:

Name Type Description Default

image

`numpy.ndarray` or `PIL.Image.Image`

The image to scale.

 required

scale_factor

`float`

Factor by which to scale the image.

 required

Returns:

Type Description
`numpy.ndarray` or `PIL.Image.Image`

Scaled image matching input type.

Raises:

Type Description
ValueError

If scale factor is non-positive.

Examples:

import cv2
import supervision as sv

image = cv2.imread("source.png")
image.shape
# (1080, 1920, 3)

scaled_image = sv.scale_image(image=image, scale_factor=0.5)
scaled_image.shape
# (540, 960, 3)

from PIL import Image
import supervision as sv

image = Image.open("source.png")
image.size
# (1920, 1080)

scaled_image = sv.scale_image(image=image, scale_factor=0.5)
scaled_image.size
# (960, 540)

scale-image

Source code in 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 (`numpy.ndarray` or `PIL.Image.Image`): The image to scale.
        scale_factor (`float`): Factor by which to scale the image.

    Returns:
        (`numpy.ndarray` or `PIL.Image.Image`): Scaled image matching input
            type.

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

    Examples:
        ```python
        import cv2
        import supervision as sv

        image = cv2.imread("source.png")
        image.shape
        # (1080, 1920, 3)

        scaled_image = sv.scale_image(image=image, scale_factor=0.5)
        scaled_image.shape
        # (540, 960, 3)
        ```

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open("source.png")
        image.size
        # (1920, 1080)

        scaled_image = sv.scale_image(image=image, scale_factor=0.5)
        scaled_image.size
        # (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, resolution_wh, keep_aspect_ratio=False)

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

Parameters:

Name Type Description Default

image

`numpy.ndarray` or `PIL.Image.Image`

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
`numpy.ndarray` or `PIL.Image.Image`

Resized image matching input type.

Examples:

import cv2
import supervision as sv

image = cv2.imread("source.png")
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)

from PIL import Image
import supervision as sv

image = Image.open("source.png")
image.size
# (1920, 1080)

resized_image = sv.resize_image(
    image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
)
resized_image.size
# (1000, 562)

resize-image

Source code in 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 (`numpy.ndarray` or `PIL.Image.Image`): The image to resize.
        resolution_wh (`tuple[int, int]`): Target resolution as `(width, height)`.
        keep_aspect_ratio (`bool`): Flag to maintain original aspect ratio.
            Defaults to `False`.

    Returns:
        (`numpy.ndarray` or `PIL.Image.Image`): Resized image matching input
            type.

    Examples:
        ```python
        import cv2
        import supervision as sv

        image = cv2.imread("source.png")
        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)
        ```

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open("source.png")
        image.size
        # (1920, 1080)

        resized_image = sv.resize_image(
            image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
        )
        resized_image.size
        # (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, resolution_wh, color=Color.BLACK)

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

Parameters:

Name Type Description Default

image

`numpy.ndarray` or `PIL.Image.Image`

The image to resize and pad.

 required

resolution_wh

`tuple[int, int]`

Target resolution as (width, height).

 required

color

`tuple[int, int, int]` or `Color`

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

 BLACK

Returns:

Type Description
`numpy.ndarray` or `PIL.Image.Image`

Letterboxed image matching input type.

Examples:

import cv2
import supervision as sv

image = cv2.imread("source.png")
image.shape
# (1080, 1920, 3)

letterboxed_image = sv.letterbox_image(
    image=image, resolution_wh=(1000, 1000)
)
letterboxed_image.shape
# (1000, 1000, 3)

from PIL import Image
import supervision as sv

image = Image.open("source.png")
image.size
# (1920, 1080)

letterboxed_image = sv.letterbox_image(
    image=image, resolution_wh=(1000, 1000)
)
letterboxed_image.size
# (1000, 1000)

letterbox-image

Source code in 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 (`numpy.ndarray` or `PIL.Image.Image`): The image to resize and pad.
        resolution_wh (`tuple[int, int]`): Target resolution as `(width, height)`.
        color (`tuple[int, int, int]` or `Color`): Padding color. If tuple, should
            be in BGR format. Defaults to `Color.BLACK`.

    Returns:
        (`numpy.ndarray` or `PIL.Image.Image`): Letterboxed image matching input
            type.

    Examples:
        ```python
        import cv2
        import supervision as sv

        image = cv2.imread("source.png")
        image.shape
        # (1080, 1920, 3)

        letterboxed_image = sv.letterbox_image(
            image=image, resolution_wh=(1000, 1000)
        )
        letterboxed_image.shape
        # (1000, 1000, 3)
        ```

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open("source.png")
        image.size
        # (1920, 1080)

        letterboxed_image = sv.letterbox_image(
            image=image, resolution_wh=(1000, 1000)
        )
        letterboxed_image.size
        # (1000, 1000)
        ```

    ![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, color=Color.BLACK, opacity=0.5)

Tint image with solid color overlay at specified opacity.

Parameters:

Name Type Description Default

image

`numpy.ndarray` or `PIL.Image.Image`

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
`numpy.ndarray` or `PIL.Image.Image`

Tinted image matching input type.

Raises:

Type Description
ValueError

If opacity is outside range [0.0, 1.0].

Examples:

import cv2
import supervision as sv

image = cv2.imread("source.png")
tinted_image = sv.tint_image(
    image=image, color=sv.Color.ROBOFLOW, opacity=0.5
)
cv2.imwrite("target.png", tinted_image)

from PIL import Image
import supervision as sv

image = Image.open("source.png")
tinted_image = sv.tint_image(
    image=image, color=sv.Color.ROBOFLOW, opacity=0.5
)
tinted_image.save("target.png")

tint-image

Source code in 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 (`numpy.ndarray` or `PIL.Image.Image`): The image to tint.
        color (`Color`): Overlay tint color. Defaults to `Color.BLACK`.
        opacity (`float`): Blend ratio between overlay and image (0.0-1.0).
            Defaults to `0.5`.

    Returns:
        (`numpy.ndarray` or `PIL.Image.Image`): Tinted image matching input
            type.

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

    Examples:
        ```python
        import cv2
        import supervision as sv

        image = cv2.imread("source.png")
        tinted_image = sv.tint_image(
            image=image, color=sv.Color.ROBOFLOW, opacity=0.5
        )
        cv2.imwrite("target.png", tinted_image)
        ```

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open("source.png")
        tinted_image = sv.tint_image(
            image=image, color=sv.Color.ROBOFLOW, opacity=0.5
        )
        tinted_image.save("target.png")
        ```

    ![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)

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

`numpy.ndarray` or `PIL.Image.Image`

The image to convert to grayscale.

 required

Returns:

Type Description
`numpy.ndarray` or `PIL.Image.Image`

3-channel grayscale image matching input type.

Examples:

import cv2
import supervision as sv

image = cv2.imread("source.png")
grayscale_image = sv.grayscale_image(image=image)
cv2.imwrite("target.png", grayscale_image)

from PIL import Image
import supervision as sv

image = Image.open("source.png")
grayscale_image = sv.grayscale_image(image=image)
grayscale_image.save("target.png")

grayscale-image

Source code in 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 (`numpy.ndarray` or `PIL.Image.Image`): The image to convert to
            grayscale.

    Returns:
        (`numpy.ndarray` or `PIL.Image.Image`): 3-channel grayscale image
            matching input type.

    Examples:
        ```python
        import cv2
        import supervision as sv

        image = cv2.imread("source.png")
        grayscale_image = sv.grayscale_image(image=image)
        cv2.imwrite("target.png", grayscale_image)
        ```

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open("source.png")
        grayscale_image = sv.grayscale_image(image=image)
        grayscale_image.save("target.png")
        ```

    ![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)

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

`numpy.ndarray` or `PIL.Image.Image`

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 cv2
import supervision as sv

image = cv2.imread("example.png")
sv.get_image_resolution_wh(image)
# (1920, 1080)

from PIL import Image
import supervision as sv

image = Image.open("example.png")
sv.get_image_resolution_wh(image)
# (1920, 1080)
Source code in 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 (`numpy.ndarray` or `PIL.Image.Image`): Input image.

    Returns:
        (`tuple[int, int]`): 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:
        ```python
        import cv2
        import supervision as sv

        image = cv2.imread("example.png")
        sv.get_image_resolution_wh(image)
        # (1920, 1080)
        ```

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open("example.png")
        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 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:
            ```python
            import supervision as sv

            frames_generator = sv.get_video_frames_generator(
                "source.mp4", stride=2
            )

            with sv.ImageSink(target_dir_path="output_frames") as sink:
                for image in frames_generator:
                    sink.save_image(image=image)

            # Directory structure:
            # output_frames/
            # ├── image_00000.png
            # ├── image_00001.png
            # ├── image_00002.png
            # └── image_00003.png
            ```

            ```python
            import cv2
            import supervision as sv

            image = cv2.imread("source.png")
            crop_boxes = [
                (  0,   0, 400, 400),
                (400,   0, 800, 400),
                (  0, 400, 400, 800),
                (400, 400, 800, 800)
            ]

            with sv.ImageSink(
                target_dir_path="image_crops",
                overwrite=True
            ) as sink:
                for i, xyxy in enumerate(crop_boxes):
                    crop = sv.crop_image(image=image, xyxy=xyxy)
                    sink.save_image(image=crop, image_name=f"crop_{i}.png")

            # Directory structure:
            # image_crops/
            # ├── crop_0.png
            # ├── crop_1.png
            # ├── crop_2.png
            # └── crop_3.png
            ```
        """
        self.target_dir_path = target_dir_path
        self.overwrite = overwrite
        self.image_name_pattern = image_name_pattern
        self.image_count = 0

    def __enter__(self):
        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: np.ndarray, image_name: str | None = None):
        """
        Save image to target directory with optional custom filename.

        Args:
            image (`numpy.array`): Image to save with shape `(height, width, 3)`
                in BGR format.
            image_name (`str` or `None`): 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, exc_value, exc_traceback):
        pass

Functions

__init__(target_dir_path, overwrite=False, image_name_pattern='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 supervision as sv

frames_generator = sv.get_video_frames_generator(
    "source.mp4", stride=2
)

with sv.ImageSink(target_dir_path="output_frames") as sink:
    for image in frames_generator:
        sink.save_image(image=image)

# Directory structure:
# output_frames/
# ├── image_00000.png
# ├── image_00001.png
# ├── image_00002.png
# └── image_00003.png

import cv2
import supervision as sv

image = cv2.imread("source.png")
crop_boxes = [
    (  0,   0, 400, 400),
    (400,   0, 800, 400),
    (  0, 400, 400, 800),
    (400, 400, 800, 800)
]

with sv.ImageSink(
    target_dir_path="image_crops",
    overwrite=True
) as sink:
    for i, xyxy in enumerate(crop_boxes):
        crop = sv.crop_image(image=image, xyxy=xyxy)
        sink.save_image(image=crop, image_name=f"crop_{i}.png")

# Directory structure:
# image_crops/
# ├── crop_0.png
# ├── crop_1.png
# ├── crop_2.png
# └── crop_3.png
Source code in 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:
        ```python
        import supervision as sv

        frames_generator = sv.get_video_frames_generator(
            "source.mp4", stride=2
        )

        with sv.ImageSink(target_dir_path="output_frames") as sink:
            for image in frames_generator:
                sink.save_image(image=image)

        # Directory structure:
        # output_frames/
        # ├── image_00000.png
        # ├── image_00001.png
        # ├── image_00002.png
        # └── image_00003.png
        ```

        ```python
        import cv2
        import supervision as sv

        image = cv2.imread("source.png")
        crop_boxes = [
            (  0,   0, 400, 400),
            (400,   0, 800, 400),
            (  0, 400, 400, 800),
            (400, 400, 800, 800)
        ]

        with sv.ImageSink(
            target_dir_path="image_crops",
            overwrite=True
        ) as sink:
            for i, xyxy in enumerate(crop_boxes):
                crop = sv.crop_image(image=image, xyxy=xyxy)
                sink.save_image(image=crop, image_name=f"crop_{i}.png")

        # Directory structure:
        # image_crops/
        # ├── crop_0.png
        # ├── crop_1.png
        # ├── crop_2.png
        # └── crop_3.png
        ```
    """
    self.target_dir_path = target_dir_path
    self.overwrite = overwrite
    self.image_name_pattern = image_name_pattern
    self.image_count = 0

save_image(image, image_name=None)

Save image to target directory with optional custom filename.

Parameters:

Name Type Description Default
image
`numpy.array`

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

 required
image_name
`str` or `None`

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

 None
Source code in supervision/utils/image.py 
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def save_image(self, image: np.ndarray, image_name: str | None = None):
    """
    Save image to target directory with optional custom filename.

    Args:
        image (`numpy.array`): Image to save with shape `(height, width, 3)`
            in BGR format.
        image_name (`str` or `None`): 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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