Ë
    Fêñig  ã                  óB   — d dl mZ d dlmZ d dlmZmZ  G d„ de«      Zy)é    )Úannotations)ÚDetectionPredictor)ÚDEFAULT_CFGÚopsc                  ó6   ‡ — e Zd ZdZeddfdˆ fd„Zˆ fd„Zˆ xZS )ÚPosePredictoraA  A class extending the DetectionPredictor class for prediction based on a pose model.

    This class specializes in pose estimation, handling keypoints detection alongside standard object detection
    capabilities inherited from DetectionPredictor.

    Attributes:
        args (namespace): Configuration arguments for the predictor.
        model (torch.nn.Module): The loaded YOLO pose model with keypoint detection capabilities.

    Methods:
        construct_result: Construct the result object from the prediction, including keypoints.

    Examples:
        >>> from ultralytics.utils import ASSETS
        >>> from ultralytics.models.yolo.pose import PosePredictor
        >>> args = dict(model="yolo26n-pose.pt", source=ASSETS)
        >>> predictor = PosePredictor(overrides=args)
        >>> predictor.predict_cli()
    Nc                óJ   •— t         ‰| �  |||«       d| j                  _        y)aÝ  Initialize PosePredictor for pose estimation tasks.

        Sets up a PosePredictor instance, configuring it for pose detection tasks and handling device-specific warnings
        for Apple MPS.

        Args:
            cfg (Any): Configuration for the predictor.
            overrides (dict, optional): Configuration overrides that take precedence over cfg.
            _callbacks (dict, optional): Dictionary of callback functions to be invoked during prediction.
        ÚposeN)ÚsuperÚ__init__ÚargsÚtask)ÚselfÚcfgÚ	overridesÚ
_callbacksÚ	__class__s       €úf/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/models/yolo/pose/predict.pyr   zPosePredictor.__init__   s!   ø€ ô 	‰Ñ˜˜i¨Ô4Øˆ�	‰	�ó    c                ó&  •— t         ‰| �  ||||«      } |dd…dd…f   j                  |j                  d   g| j                  j
                  ¢­Ž }t        j                  |j                  dd ||j                  «      }|j                  |¬«       |S )aY  Construct the result object from the prediction, including keypoints.

        Extends the parent class implementation by extracting keypoint data from predictions and adding them to the
        result object.

        Args:
            pred (torch.Tensor): The predicted bounding boxes, scores, and keypoints with shape (N, 6+K*D) where N is
                the number of detections, K is the number of keypoints, and D is the keypoint dimension.
            img (torch.Tensor): The processed input image tensor with shape (B, C, H, W).
            orig_img (np.ndarray): The original unprocessed image as a numpy array.
            img_path (str): The path to the original image file.

        Returns:
            (Results): The result object containing the original image, image path, class names, bounding boxes, and
                keypoints.
        Né   r   é   )Ú	keypoints)	r   Úconstruct_resultÚviewÚshapeÚmodelÚ	kpt_shaper   Úscale_coordsÚupdate)r   ÚpredÚimgÚorig_imgÚimg_pathÚresultÚ	pred_kptsr   s          €r   r   zPosePredictor.construct_result,   s‚   ø€ ô" ‘Ñ)¨$°°X¸xÓHˆà$�Dš˜A™B˜‘K×$Ñ$ T§Z¡Z°¡]ÐJ°T·Z±Z×5IÑ5IÒJˆ	ä×$Ñ$ S§Y¡Y¨q¨r ]°I¸x¿~¹~ÓNˆ	Ø�‰ 	ˆÔ*Øˆr   )r   zdict | None)Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   r   r   Ú__classcell__)r   s   @r   r   r   	   s!   ø„ ñð( '°$ÐRVö  ÷ð r   r   N)Ú
__future__r   Ú&ultralytics.models.yolo.detect.predictr   Úultralytics.utilsr   r   r   © r   r   ú<module>r0      s   ðõ #å Eß .ô:Ð&õ :r   