|h:ddlmZddlmZmZmZGddeZy))DetectionPredictor) DEFAULT_CFGLOGGERopsc4eZdZdZeddffd ZfdZxZS) PosePredictoraF 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="yolo11n-pose.pt", source=ASSETS) >>> predictor = PosePredictor(overrides=args) >>> predictor.predict_cli() Nct||||d|j_t |jj t r>|jj jdk(rtjdyyy)a 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 (list, optional): List of callback functions to be invoked during prediction. Examples: >>> from ultralytics.utils import ASSETS >>> from ultralytics.models.yolo.pose import PosePredictor >>> args = dict(model="yolo11n-pose.pt", source=ASSETS) >>> predictor = PosePredictor(overrides=args) >>> predictor.predict_cli() posempsz}Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. See https://github.com/ultralytics/ultralytics/issues/4031.N) super__init__argstask isinstancedevicestrlowerrwarning)selfcfg overrides _callbacks __class__s c/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/models/yolo/pose/predict.pyr zPosePredictor.__init__sj& i4  dii&& ,1A1A1G1G1IU1R NNN 2S ,ct|||||}|ddddfjt|g|jj }t j|jdd||j}|j||S)ab 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. N) keypoints) r construct_resultviewlenmodel kpt_shaper scale_coordsshapeupdate)rpredimgorig_imgimg_pathresult pred_kptsrs rr zPosePredictor.construct_result8s~$)$XxH$DABK$$SYF1E1EF $$SYYqr]Ix~~N  * r)__name__ __module__ __qualname____doc__rr r __classcell__)rs@rrrs *'$46rrN)&ultralytics.models.yolo.detect.predictrultralytics.utilsrrrrrrr6s F66I&Ir