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Masks Utils

mask_to_roi

supervision.detection.utils.masks.mask_to_roi(mask: npt.NDArray[np.bool_]) -> tuple[int, int, int, int] | None

Return exclusive (x1, y1, x2, y2) bounds for true mask pixels.

Use this helper when you need NumPy slice semantics. Unlike :func:~supervision.detection.utils.converters.mask_to_xyxy, this function uses exclusive upper bounds (+1) and returns None for empty masks instead of zeros. The inclusive mask_to_xyxy convention stays in place for compatibility with CompactMask and box-based adapters.

Parameters:

Name Type Description Default

mask

NDArray[bool_]

2D boolean array of shape (H, W).

 required

Returns:

Type Description
tuple[int, int, int, int] | None

Exclusive (x1, y1, x2, y2) bounds, or None when the mask
tuple[int, int, int, int] | None

has no true pixels.

Examples:

>>> import numpy as np
>>> from supervision.detection.utils.masks import mask_to_roi
>>> mask = np.zeros((10, 10), dtype=bool)
>>> mask[2:5, 3:6] = True
>>> mask_to_roi(mask)
(3, 2, 6, 5)
Source code in src/supervision/detection/utils/masks.py 
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def mask_to_roi(mask: npt.NDArray[np.bool_]) -> tuple[int, int, int, int] | None:
    """Return exclusive ``(x1, y1, x2, y2)`` bounds for true mask pixels.

    Use this helper when you need NumPy slice semantics. Unlike
    :func:`~supervision.detection.utils.converters.mask_to_xyxy`, this
    function uses exclusive upper bounds (``+1``) and returns ``None`` for
    empty masks instead of zeros. The inclusive ``mask_to_xyxy`` convention
    stays in place for compatibility with CompactMask and box-based adapters.

    Args:
        mask: 2D boolean array of shape ``(H, W)``.

    Returns:
        Exclusive ``(x1, y1, x2, y2)`` bounds, or ``None`` when the mask
        has no true pixels.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> from supervision.detection.utils.masks import mask_to_roi
        >>> mask = np.zeros((10, 10), dtype=bool)
        >>> mask[2:5, 3:6] = True
        >>> mask_to_roi(mask)
        (3, 2, 6, 5)

        ```
    """
    rows = np.flatnonzero(np.any(mask, axis=1))
    if len(rows) == 0:
        return None
    cols = np.flatnonzero(np.any(mask, axis=0))
    return int(cols[0]), int(rows[0]), int(cols[-1]) + 1, int(rows[-1]) + 1

move_masks

supervision.detection.utils.masks.move_masks(masks: npt.NDArray[np.bool_], offset: npt.NDArray[np.integer], resolution_wh: tuple[int, int]) -> npt.NDArray[np.bool_]

Offset the masks in an array by the specified (x, y) amount.

Parameters:

Name Type Description Default

masks

NDArray[bool_]

A 3D array of binary masks corresponding to the predictions. Shape: (N, H, W), where N is the number of predictions, and H, W are the dimensions of each mask.

 required

offset

NDArray[integer]

An array of shape (2,) containing int values [dx, dy]. Supports both positive and negative values for bidirectional movement.

 required

resolution_wh

tuple[int, int]

The width and height of the desired mask resolution.

 required

Returns:

Type Description
NDArray[bool_]

Repositioned masks, optionally padded to the specified shape.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> mask = np.array([[[False, False, False, False],
...                   [False, True,  True,  False],
...                   [False, True,  True,  False],
...                   [False, False, False, False]]], dtype=bool)
>>> offset = np.array([1, 1])
>>> sv.move_masks(mask, offset, resolution_wh=(4, 4))
array([[[False, False, False, False],
        [False, False, False, False],
        [False, False,  True,  True],
        [False, False,  True,  True]]])
>>> offset = np.array([-2, 2])
>>> sv.move_masks(mask, offset, resolution_wh=(4, 4))
array([[[False, False, False, False],
        [False, False, False, False],
        [False, False, False, False],
        [ True, False, False, False]]])
Source code in src/supervision/detection/utils/masks.py 
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def move_masks(
    masks: npt.NDArray[np.bool_],
    offset: npt.NDArray[np.integer],
    resolution_wh: tuple[int, int],
) -> npt.NDArray[np.bool_]:
    """
    Offset the masks in an array by the specified (x, y) amount.

    Args:
        masks: A 3D array of binary masks corresponding to the
            predictions. Shape: `(N, H, W)`, where N is the number of predictions, and
            H, W are the dimensions of each mask.
        offset: An array of shape `(2,)` containing int values
            `[dx, dy]`. Supports both positive and negative values for bidirectional
            movement.
        resolution_wh: The width and height of the desired mask
            resolution.

    Returns:
        Repositioned masks, optionally padded to the specified shape.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> mask = np.array([[[False, False, False, False],
        ...                   [False, True,  True,  False],
        ...                   [False, True,  True,  False],
        ...                   [False, False, False, False]]], dtype=bool)
        >>> offset = np.array([1, 1])
        >>> sv.move_masks(mask, offset, resolution_wh=(4, 4))
        array([[[False, False, False, False],
                [False, False, False, False],
                [False, False,  True,  True],
                [False, False,  True,  True]]])
        >>> offset = np.array([-2, 2])
        >>> sv.move_masks(mask, offset, resolution_wh=(4, 4))
        array([[[False, False, False, False],
                [False, False, False, False],
                [False, False, False, False],
                [ True, False, False, False]]])

        ```
    """
    mask_array = np.full((masks.shape[0], resolution_wh[1], resolution_wh[0]), False)

    if offset[0] < 0:
        source_x_start = -offset[0]
        source_x_end = min(masks.shape[2], resolution_wh[0] - offset[0])
        destination_x_start = 0
        destination_x_end = min(resolution_wh[0], masks.shape[2] + offset[0])
    else:
        source_x_start = 0
        source_x_end = min(masks.shape[2], resolution_wh[0] - offset[0])
        destination_x_start = offset[0]
        destination_x_end = offset[0] + source_x_end - source_x_start

    if offset[1] < 0:
        source_y_start = -offset[1]
        source_y_end = min(masks.shape[1], resolution_wh[1] - offset[1])
        destination_y_start = 0
        destination_y_end = min(resolution_wh[1], masks.shape[1] + offset[1])
    else:
        source_y_start = 0
        source_y_end = min(masks.shape[1], resolution_wh[1] - offset[1])
        destination_y_start = offset[1]
        destination_y_end = offset[1] + source_y_end - source_y_start

    if source_x_end > source_x_start and source_y_end > source_y_start:
        mask_array[
            :,
            destination_y_start:destination_y_end,
            destination_x_start:destination_x_end,
        ] = masks[:, source_y_start:source_y_end, source_x_start:source_x_end]

    return mask_array

contains_holes

supervision.detection.utils.masks.contains_holes(mask: npt.NDArray[np.bool_]) -> bool

Checks if the binary mask contains holes (background pixels fully enclosed by foreground pixels).

Parameters:

Name Type Description Default

mask

NDArray[bool_]

2D binary mask where True indicates foreground object and False indicates background.

 required

Returns:

Type Description
bool

True if holes are detected, False otherwise.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> mask = np.array([
...     [0, 0, 0, 0, 0],
...     [0, 1, 1, 1, 0],
...     [0, 1, 0, 1, 0],
...     [0, 1, 1, 1, 0],
...     [0, 0, 0, 0, 0]
... ]).astype(bool)
>>> sv.contains_holes(mask=mask)
True
>>> mask = np.array([
...     [0, 0, 0, 0, 0],
...     [0, 1, 1, 1, 0],
...     [0, 1, 1, 1, 0],
...     [0, 1, 1, 1, 0],
...     [0, 0, 0, 0, 0]
... ]).astype(bool)
>>> sv.contains_holes(mask=mask)
False

contains_holes

Source code in src/supervision/detection/utils/masks.py 
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def contains_holes(mask: npt.NDArray[np.bool_]) -> bool:
    """
    Checks if the binary mask contains holes (background pixels fully enclosed by
    foreground pixels).

    Args:
        mask: 2D binary mask where `True` indicates foreground
            object and `False` indicates background.

    Returns:
        True if holes are detected, False otherwise.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> mask = np.array([
        ...     [0, 0, 0, 0, 0],
        ...     [0, 1, 1, 1, 0],
        ...     [0, 1, 0, 1, 0],
        ...     [0, 1, 1, 1, 0],
        ...     [0, 0, 0, 0, 0]
        ... ]).astype(bool)
        >>> sv.contains_holes(mask=mask)
        True
        >>> mask = np.array([
        ...     [0, 0, 0, 0, 0],
        ...     [0, 1, 1, 1, 0],
        ...     [0, 1, 1, 1, 0],
        ...     [0, 1, 1, 1, 0],
        ...     [0, 0, 0, 0, 0]
        ... ]).astype(bool)
        >>> sv.contains_holes(mask=mask)
        False

        ```

    ![contains_holes](https://media.roboflow.com/supervision-docs/contains-holes.png){ align=center width="800" }
    """  # noqa E501 // docs
    mask_uint8 = mask.astype(np.uint8)
    _, hierarchy = cv2.findContours(mask_uint8, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)

    if hierarchy is not None:
        parent_contour_index = 3
        for h in hierarchy[0]:
            if h[parent_contour_index] != -1:
                return True
    return False

contains_multiple_segments

supervision.detection.utils.masks.contains_multiple_segments(mask: npt.NDArray[np.bool_], connectivity: int = 4) -> bool

Checks if the binary mask contains multiple unconnected foreground segments.

Parameters:

Name Type Description Default

mask

NDArray[bool_]

2D binary mask where True indicates foreground object and False indicates background.

 required

connectivity

int

Default: 4 is 4-way connectivity, which means that foreground pixels are the part of the same segment/component if their edges touch. Alternatively: 8 for 8-way connectivity, when foreground pixels are connected by their edges or corners touch.

 4

Returns:

Type Description
bool

True when the mask contains multiple not connected components, False otherwise.

Raises:

Type Description
ValueError

If connectivity(int) parameter value is not 4 or 8.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> mask = np.array([
...     [0, 0, 0, 0, 0, 0],
...     [0, 1, 1, 0, 1, 1],
...     [0, 1, 1, 0, 1, 1],
...     [0, 0, 0, 0, 0, 0],
...     [0, 1, 1, 1, 0, 0],
...     [0, 1, 1, 1, 0, 0]
... ]).astype(bool)
>>> sv.contains_multiple_segments(mask=mask, connectivity=4)
True
>>> mask = np.array([
...     [0, 0, 0, 0, 0, 0],
...     [0, 1, 1, 1, 1, 1],
...     [0, 1, 1, 1, 1, 1],
...     [0, 1, 1, 1, 1, 1],
...     [0, 1, 1, 1, 1, 1],
...     [0, 0, 0, 0, 0, 0]
... ]).astype(bool)
>>> sv.contains_multiple_segments(mask=mask, connectivity=4)
False

contains_multiple_segments

Source code in src/supervision/detection/utils/masks.py 
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def contains_multiple_segments(
    mask: npt.NDArray[np.bool_], connectivity: int = 4
) -> bool:
    """
    Checks if the binary mask contains multiple unconnected foreground segments.

    Args:
        mask: 2D binary mask where `True` indicates foreground
            object and `False` indicates background.
        connectivity: Default: 4 is 4-way connectivity, which means that
            foreground pixels are the part of the same segment/component
            if their edges touch.
            Alternatively: 8 for 8-way connectivity, when foreground pixels are
            connected by their edges or corners touch.

    Returns:
        True when the mask contains multiple not connected components, False otherwise.

    Raises:
        ValueError: If connectivity(int) parameter value is not 4 or 8.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> mask = np.array([
        ...     [0, 0, 0, 0, 0, 0],
        ...     [0, 1, 1, 0, 1, 1],
        ...     [0, 1, 1, 0, 1, 1],
        ...     [0, 0, 0, 0, 0, 0],
        ...     [0, 1, 1, 1, 0, 0],
        ...     [0, 1, 1, 1, 0, 0]
        ... ]).astype(bool)
        >>> sv.contains_multiple_segments(mask=mask, connectivity=4)
        True
        >>> mask = np.array([
        ...     [0, 0, 0, 0, 0, 0],
        ...     [0, 1, 1, 1, 1, 1],
        ...     [0, 1, 1, 1, 1, 1],
        ...     [0, 1, 1, 1, 1, 1],
        ...     [0, 1, 1, 1, 1, 1],
        ...     [0, 0, 0, 0, 0, 0]
        ... ]).astype(bool)
        >>> sv.contains_multiple_segments(mask=mask, connectivity=4)
        False

        ```

    ![contains_multiple_segments](https://media.roboflow.com/supervision-docs/contains-multiple-segments.png){ align=center width="800" }
    """  # noqa E501 // docs
    if connectivity != 4 and connectivity != 8:
        raise ValueError(
            "Incorrect connectivity value. Possible connectivity values: 4 or 8."
        )
    mask_uint8 = mask.astype(np.uint8)
    labels = np.zeros_like(mask_uint8, dtype=np.int32)
    number_of_labels, _ = cv2.connectedComponents(
        mask_uint8, labels, connectivity=connectivity
    )
    return bool(number_of_labels > 2)

filter_segments_by_distance

supervision.detection.utils.masks.filter_segments_by_distance(mask: npt.NDArray[np.bool_], absolute_distance: float | None = 100.0, relative_distance: float | None = None, connectivity: int = 8, mode: Literal['edge', 'centroid'] = 'edge') -> npt.NDArray[np.bool_]

Keep the largest connected component and any other components within a distance threshold.

Distance can be absolute in pixels or relative to the image diagonal.

Parameters:

Name Type Description Default

mask

NDArray[bool_]

Boolean mask HxW.

 required

absolute_distance

float | None

Max allowed distance in pixels to the main component. Ignored if relative_distance is provided.

 100.0

relative_distance

float | None

Fraction of the diagonal. If set, threshold = fraction * sqrt(H^2 + W^2).

 None

connectivity

int

Defines which neighboring pixels are considered connected. - 4-connectedness: Only orthogonal neighbors. 

[ ][X][ ]
[X][O][X]
[ ][X][ ]
- 8-connectedness: Includes diagonal neighbors. 
[X][X][X]
[X][O][X]
[X][X][X]
Default is 8.

 8

mode

Literal['edge', 'centroid']

Defines how distance between components is measured. - "edge": Uses distance between nearest edges (via distance transform). - "centroid": Uses distance between component centroids.

 'edge'

Returns:

Type Description
NDArray[bool_]

Boolean mask after filtering.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> mask = np.array([
...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...     [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
...     [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
...     [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
...     [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... ], dtype=bool)
>>> sv.filter_segments_by_distance(
...     mask,
...     absolute_distance=3,
...     mode="edge",
...     connectivity=8
... ).astype(int)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])

The nearby 2x2 block at columns 6-7 is kept because its edge distance is within 3 pixels. The distant block at columns 9-10 is removed.

Source code in src/supervision/detection/utils/masks.py 
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def filter_segments_by_distance(
    mask: npt.NDArray[np.bool_],
    absolute_distance: float | None = 100.0,
    relative_distance: float | None = None,
    connectivity: int = 8,
    mode: Literal["edge", "centroid"] = "edge",
) -> npt.NDArray[np.bool_]:
    """
    Keep the largest connected component and any other components within a distance
    threshold.

    Distance can be absolute in pixels or relative to the image diagonal.

    Args:
        mask: Boolean mask HxW.
        absolute_distance: Max allowed distance in pixels to the main component.
            Ignored if `relative_distance` is provided.
        relative_distance: Fraction of the diagonal. If set, threshold = fraction * sqrt(H^2 + W^2).
        connectivity: Defines which neighboring pixels are considered connected.
            - 4-connectedness: Only orthogonal neighbors.
              ```
              [ ][X][ ]
              [X][O][X]
              [ ][X][ ]
              ```
            - 8-connectedness: Includes diagonal neighbors.
              ```
              [X][X][X]
              [X][O][X]
              [X][X][X]
              ```
            Default is 8.
        mode: Defines how distance between components is measured.
            - "edge": Uses distance between nearest edges (via distance transform).
            - "centroid": Uses distance between component centroids.

    Returns:
        Boolean mask after filtering.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> mask = np.array([
        ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ...     [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
        ...     [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
        ...     [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
        ...     [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
        ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
        ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
        ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ... ], dtype=bool)
        >>> sv.filter_segments_by_distance(
        ...     mask,
        ...     absolute_distance=3,
        ...     mode="edge",
        ...     connectivity=8
        ... ).astype(int)
        array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
               [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])

        ```

        The nearby 2x2 block at columns 6-7 is kept because its edge distance
        is within 3 pixels. The distant block at columns 9-10 is removed.
    """  # noqa E501 // docs
    if mask.dtype != bool:
        raise TypeError("mask must be boolean")

    height, width = mask.shape
    if not np.any(mask):
        return cast(npt.NDArray[np.bool_], mask.copy())

    image = cast(npt.NDArray[np.uint8], mask.astype(np.uint8))
    components = cv2.connectedComponentsWithStats(image, connectivity=connectivity)
    num_labels = int(components[0])
    labels = cast(npt.NDArray[np.int32], components[1])
    stats = cast(npt.NDArray[np.int32], components[2])
    centroids = cast(npt.NDArray[np.float64], components[3])

    if num_labels <= 1:
        return cast(npt.NDArray[np.bool_], mask.copy())

    areas = stats[1:, cv2.CC_STAT_AREA]
    main_label = 1 + int(np.argmax(areas))

    if relative_distance is not None:
        diagonal = float(np.hypot(height, width))
        threshold = float(relative_distance) * diagonal
    elif absolute_distance is not None:
        threshold = float(absolute_distance)
    else:
        raise ValueError("Either absolute_distance or relative_distance must be set.")

    keep_labels: npt.NDArray[np.bool_] = np.zeros(num_labels, dtype=bool)
    keep_labels[main_label] = True

    if mode == "centroid":
        differences = centroids[1:] - centroids[main_label]
        distances = np.sqrt(np.sum(differences**2, axis=1))
        nearby = 1 + np.where(distances <= threshold)[0]
        keep_labels[nearby] = True
    elif mode == "edge":
        main_mask = (labels == main_label).astype(np.uint8)
        inverse = 1 - main_mask
        distance_transform = cv2.distanceTransform(inverse, cv2.DIST_L2, 3)
        for label in range(1, num_labels):
            if label == main_label:
                continue
            component = labels == label
            if not np.any(component):
                continue
            min_distance = float(distance_transform[component].min())
            if min_distance <= threshold:
                keep_labels[label] = True
    else:
        raise ValueError("mode must be 'edge' or 'centroid'")

    return keep_labels[labels]

calculate_masks_centroids

supervision.detection.utils.masks.calculate_masks_centroids(masks: npt.NDArray[np.bool_] | CompactMask) -> npt.NDArray[np.int_]

Calculate the centroids of binary masks in a tensor.

Parameters:

Name Type Description Default

masks

NDArray[bool_] | CompactMask

A 3D NumPy array of shape (num_masks, height, width). Each 2D array in the tensor represents a binary mask. Also accepts a :class:~supervision.detection.compact_mask.CompactMask.

 required

Returns:

Type Description
NDArray[int_]

A 2D NumPy array of shape (num_masks, 2), where each row contains the x and y coordinates (in that order) of the centroid of the corresponding mask.

Examples:

>>> import numpy as np
>>> import supervision as sv
>>> masks = np.zeros((1, 10, 10), dtype=bool)
>>> masks[0, 2:6, 2:6] = True
>>> sv.calculate_masks_centroids(masks)
array([[4, 4]])
Source code in src/supervision/detection/utils/masks.py 
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def calculate_masks_centroids(
    masks: npt.NDArray[np.bool_] | CompactMask,
) -> npt.NDArray[np.int_]:
    """
    Calculate the centroids of binary masks in a tensor.

    Args:
        masks: A 3D NumPy array of shape (num_masks, height, width).
            Each 2D array in the tensor represents a binary mask.
            Also accepts a :class:`~supervision.detection.compact_mask.CompactMask`.

    Returns:
        A 2D NumPy array of shape (num_masks, 2), where each row contains the x and y
            coordinates (in that order) of the centroid of the corresponding mask.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> import supervision as sv
        >>> masks = np.zeros((1, 10, 10), dtype=bool)
        >>> masks[0, 2:6, 2:6] = True
        >>> sv.calculate_masks_centroids(masks)
        array([[4, 4]])

        ```
    """
    if isinstance(masks, CompactMask):
        # Compute centroids per-crop to avoid materialising the full (N, H, W) array.
        n = len(masks)
        if n == 0:
            return cast(npt.NDArray[np.int_], np.empty((0, 2), dtype=int))

        centroids: npt.NDArray[np.float64] = np.zeros((n, 2), dtype=np.float64)
        for i in range(n):
            crop = masks.crop(i)
            crop_h, crop_w = crop.shape
            x1 = int(masks.offsets[i, 0])
            y1 = int(masks.offsets[i, 1])
            total = int(crop.sum())
            if total == 0:
                centroids[i] = [0.0, 0.0]
                continue
            # Match the +0.5 offset used by the dense implementation.
            crop_rows, crop_cols = np.indices((crop_h, crop_w))
            cx = float(np.sum((crop_cols + 0.5)[crop])) / total + x1
            cy = float(np.sum((crop_rows + 0.5)[crop])) / total + y1
            centroids[i] = [cx, cy]

        return cast(npt.NDArray[np.int_], centroids.astype(int))

    _num_masks, height, width = masks.shape
    total_pixels: npt.NDArray[np.int64] = count_mask_pixels(masks)

    # offset for 1-based indexing
    vertical_indices, horizontal_indices = np.indices((height, width)) + 0.5
    # avoid division by zero for empty masks
    total_pixels[total_pixels == 0] = 1

    def sum_over_mask(
        indices: npt.NDArray[np.floating], axis: tuple[list[int], list[int]]
    ) -> npt.NDArray[np.floating]:
        return cast(npt.NDArray[np.floating], np.tensordot(masks, indices, axes=axis))

    aggregation_axis: tuple[list[int], list[int]] = ([1, 2], [0, 1])
    centroid_x = sum_over_mask(horizontal_indices, aggregation_axis) / total_pixels
    centroid_y = sum_over_mask(vertical_indices, aggregation_axis) / total_pixels

    return cast(
        npt.NDArray[np.int_], np.column_stack((centroid_x, centroid_y)).astype(int)
    )

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