Function _create_detections

tests/helpers.py:19–76

Create a Detections object from list-based inputs. This is a helper function primarily used for testing purposes to quickly instantiate a Detections object without manually converting lists to numpy arrays. Args: xyxy: Bounding boxes in `(x_min, y_min, x_max, y_max)`

(
    xyxy: list[list[float]],
    mask: list[np.ndarray] | None = None,
    confidence: list[float] | None = None,
    class_id: list[int] | None = None,
    tracker_id: list[int] | None = None,
    data: dict[str, list[Any]] | None = None,
)

def _create_detections(
    xyxy: list[list[float]],
    mask: list[np.ndarray] | None = None,
    confidence: list[float] | None = None,
    class_id: list[int] | None = None,
    tracker_id: list[int] | None = None,
    data: dict[str, list[Any]] | None = None,
) -> Detections:
    """
    Create a Detections object from list-based inputs.

    This is a helper function primarily used for testing purposes to quickly
    instantiate a Detections object without manually converting lists to numpy arrays.

    Args:
        xyxy: Bounding boxes in `(x_min, y_min, x_max, y_max)`
            format.
        mask: Binary masks for each detection.
        confidence: Confidence scores for each detection.
        class_id: Class identifiers for each detection.
        tracker_id: Tracker identifiers for each detection.
        data: Additional data to be associated with
            each detection.

    Returns:
        A Detections object containing the provided data.

    Examples:
        >>> import numpy as np
        >>> detections = _create_detections(
        ...     xyxy=[[0, 0, 10, 10], [20, 20, 30, 30]],
        ...     confidence=[0.5, 0.8],
        ...     class_id=[0, 1]
        ... )
        >>> detections.xyxy
        array([[ 0.,  0., 10., 10.],
               [20., 20., 30., 30.]], dtype=float32)
        >>> detections.confidence
        array([0.5, 0.8], dtype=float32)
        >>> detections.class_id
        array([0, 1])
    """

    def convert_data(data: dict[str, list[Any]]):
        return {k: np.array(v) for k, v in data.items()}

    return Detections(
        xyxy=np.array(xyxy, dtype=np.float32),
        mask=(mask if mask is None else np.array(mask, dtype=bool)),
        confidence=(
            confidence if confidence is None else np.array(confidence, dtype=np.float32)
        ),
        class_id=(class_id if class_id is None else np.array(class_id, dtype=int)),
        tracker_id=(
            tracker_id if tracker_id is None else np.array(tracker_id, dtype=int)
        ),
        data=convert_data(data) if data else {},
    )