roboflow/supervision · error · ValueError

xyxyxyxy must have shape (N, 4, 2); got {corners.shape}

Error message

xyxyxyxy must have shape (N, 4, 2); got {corners.shape}

What it means

Raised by the private helper _oriented_box_anchors in boxes.py when the corner array is not shaped (N, 4, 2). The helper computes box centers and half-side vectors by indexing corners[:, 1], corners[:, 2], etc., so exactly 4 corner points per box are required. Note the message names the parameter 'xyxyxyxy' even though the local variable is 'corners' — same requirement either way.

Source

Thrown at src/supervision/detection/utils/boxes.py:385

        The anchor always lies on the box; the effect is cosmetic for static
        images but visible on rotating objects in video.

    Examples:
        ```pycon
        >>> import numpy as np
        >>> from supervision.detection.utils.boxes import _oriented_box_anchors
        >>> from supervision.geometry.core import Position
        >>> corners = np.array(
        ...     [[[0, 0], [10, 0], [10, 4], [0, 4]]], dtype=np.float32
        ... )
        >>> _oriented_box_anchors(corners, Position.BOTTOM_CENTER)
        array([[5., 4.]])

        ```
    """
    corners = np.asarray(xyxyxyxy, dtype=np.float64)
    if corners.ndim != 3 or corners.shape[-2:] != (4, 2):
        raise ValueError(f"xyxyxyxy must have shape (N, 4, 2); got {corners.shape}")
    if anchor not in _ANCHOR_OFFSETS:
        raise ValueError(f"{anchor} is not supported.")
    sx, sy = _ANCHOR_OFFSETS[anchor]

    center = corners.mean(axis=1)
    # Two perpendicular half-side vectors per box.
    half_side_a = (corners[:, 1] - corners[:, 0]) / 2
    half_side_b = (corners[:, 2] - corners[:, 1]) / 2

    # Map each box's own sides onto the image axes: the side more aligned with
    # the x-axis plays the role of width, the other of height. This makes the
    # offsets collapse to the axis-aligned frame when the box is not rotated.
    is_width = np.abs(half_side_a[:, 0]) >= np.abs(half_side_b[:, 0])
    width = np.where(is_width[:, None], half_side_a, half_side_b)
    height = np.where(is_width[:, None], half_side_b, half_side_a)
    # Point width toward +x and height toward +y so the offset signs are stable.
    width = np.where((width[:, 0] < 0)[:, None], -width, width)
    height = np.where((height[:, 1] < 0)[:, None], -height, height)

View on GitHub (pinned to 7f254d9784)

Solutions

1. Reshape input to (N, 4, 2): flat.reshape(-1, 4, 2) or box[np.newaxis] for a single box.
2. If you hit this via an annotator, fix the ORIENTED_BOX_COORDINATES data field attached to your Detections so it has shape (N, 4, 2).
3. Prefer the public API path (annotators) instead of calling the underscore-prefixed helper directly.

Example fix

# before
anchors = _oriented_box_anchors(box_4x2, Position.BOTTOM_CENTER)  # ValueError

# after
anchors = _oriented_box_anchors(box_4x2[np.newaxis], Position.BOTTOM_CENTER)

corners = np.asarray(corners, dtype=np.float64).reshape(-1, 4, 2)
anchors = _oriented_box_anchors(corners, position)

def is_xyxyxyxy(a) -> bool:
    a = np.asarray(a)
    return a.ndim == 3 and a.shape[-2:] == (4, 2)

When it happens

Trigger: Calling _oriented_box_anchors (directly, or indirectly through an annotator that places labels on rotated boxes) with an un-batched (4, 2) array, an (N, 8) flattened array, or an array with the wrong corner count.

Common situations: Custom annotation code that holds OBB corners in a non-standard layout; downstream code reshaping model output incorrectly before annotation; usually reached via BoxAnnotator/LabelAnnotator with oriented detections rather than called directly.

Related errors

• corners must have shape (N, 4, 2); got {corners.shape}
• xyxyxyxy must have shape (N, 4, 2); got {xyxyxyxy.shape}
• {anchor} is not supported.
• Confusion matrix must have shape (..., 3), got {confusion_ma
• Value must be a np.ndarray or a list

AI-assisted analysis of roboflow/supervision@7f254d9784 (2026-08-15). Data as JSON: /api/errors/40ff817f784b929f. Report an issue: GitHub.