Reference for ultralytics/models/yolo/obb/predict.py

class OBBPredictor(DetectionPredictor):
    """A class extending the DetectionPredictor class for prediction based on an Oriented Bounding Box (OBB) model.

    This predictor handles oriented bounding box detection tasks, processing images and returning results with rotated
    bounding boxes.

    Attributes:
        args (namespace): Configuration arguments for the predictor.
        model (torch.nn.Module): The loaded YOLO OBB model.

    Examples:
        >>> from ultralytics.utils import ASSETS
        >>> from ultralytics.models.yolo.obb import OBBPredictor
        >>> args = dict(model="yolo11n-obb.pt", source=ASSETS)
        >>> predictor = OBBPredictor(overrides=args)
        >>> predictor.predict_cli()
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initialize OBBPredictor with optional model and data configuration overrides.

        Args:
            cfg (dict, optional): Default configuration for the predictor.
            overrides (dict, optional): Configuration overrides that take precedence over the default config.
            _callbacks (list, optional): List of callback functions to be invoked during prediction.
        """
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = "obb"

def construct_result(self, pred, img, orig_img, img_path):
    """Construct the result object from the prediction.

    Args:
        pred (torch.Tensor): The predicted bounding boxes, scores, and rotation angles with shape (N, 7) where the
            last dimension contains [x, y, w, h, confidence, class_id, angle].
        img (torch.Tensor): The image after preprocessing with shape (B, C, H, W).
        orig_img (np.ndarray): The original image before preprocessing.
        img_path (str): The path to the original image.

    Returns:
        (Results): The result object containing the original image, image path, class names, and oriented bounding
            boxes.
    """
    rboxes = ops.regularize_rboxes(torch.cat([pred[:, :4], pred[:, -1:]], dim=-1))
    rboxes[:, :4] = ops.scale_boxes(img.shape[2:], rboxes[:, :4], orig_img.shape, xywh=True)
    obb = torch.cat([rboxes, pred[:, 4:6]], dim=-1)
    return Results(orig_img, path=img_path, names=self.model.names, obb=obb)