|h6zddlmZddlmZmZmZddlZddlZddl m Z ddl m Z m Z ddlmZmZmZGdde Zy) )Path)AnyDictTupleN)DetectionValidator)LOGGERops) OKS_SIGMA PoseMetricskpt_iouc :eZdZdZddfd Zdeeefdeeefffd ZdefdZ de jjddffd Z d e jdeee jfffd Zd edeeefdeeefffd Zdeeefdeeefdeeefffd Zd eee jfdeeefdeeej(fffd Zdeee jfdedeeefdeddf dZdeee jfdeddfdZdeeefdeeefffd ZxZS) PoseValidatora A class extending the DetectionValidator class for validation based on a pose model. This validator is specifically designed for pose estimation tasks, handling keypoints and implementing specialized metrics for pose evaluation. Attributes: sigma (np.ndarray): Sigma values for OKS calculation, either OKS_SIGMA or ones divided by number of keypoints. kpt_shape (List[int]): Shape of the keypoints, typically [17, 3] for COCO format. args (dict): Arguments for the validator including task set to "pose". metrics (PoseMetrics): Metrics object for pose evaluation. Methods: preprocess: Preprocess batch by converting keypoints data to float and moving it to the device. get_desc: Return description of evaluation metrics in string format. init_metrics: Initialize pose estimation metrics for YOLO model. _prepare_batch: Prepare a batch for processing by converting keypoints to float and scaling to original dimensions. _prepare_pred: Prepare and scale keypoints in predictions for pose processing. _process_batch: Return correct prediction matrix by computing Intersection over Union (IoU) between detections and ground truth. plot_val_samples: Plot and save validation set samples with ground truth bounding boxes and keypoints. plot_predictions: Plot and save model predictions with bounding boxes and keypoints. save_one_txt: Save YOLO pose detections to a text file in normalized coordinates. pred_to_json: Convert YOLO predictions to COCO JSON format. eval_json: Evaluate object detection model using COCO JSON format. Examples: >>> from ultralytics.models.yolo.pose import PoseValidator >>> args = dict(model="yolo11n-pose.pt", data="coco8-pose.yaml") >>> validator = PoseValidator(args=args) >>> validator() NreturncJt|||||d|_d|_d|j_t |_t|jjtr>|jjjdk(rtjdyyy)a Initialize a PoseValidator object for pose estimation validation. This validator is specifically designed for pose estimation tasks, handling keypoints and implementing specialized metrics for pose evaluation. Args: dataloader (torch.utils.data.DataLoader, optional): Dataloader to be used for validation. save_dir (Path | str, optional): Directory to save results. args (dict, optional): Arguments for the validator including task set to "pose". _callbacks (list, optional): List of callback functions to be executed during validation. Examples: >>> from ultralytics.models.yolo.pose import PoseValidator >>> args = dict(model="yolo11n-pose.pt", data="coco8-pose.yaml") >>> validator = PoseValidator(args=args) >>> validator() Notes: This class extends DetectionValidator with pose-specific functionality. It initializes with sigma values for OKS calculation and sets up PoseMetrics for evaluation. A warning is displayed when using Apple MPS due to a known bug with pose models. Nposempsz}Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. See https://github.com/ultralytics/ultralytics/issues/4031.)super__init__sigma kpt_shapeargstaskr metrics isinstancedevicestrlowerrwarning)self dataloadersave_dirr _callbacks __class__s _/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/models/yolo/pose/val.pyrzPoseValidator.__init__1s0 XtZ@  "} dii&& ,1A1A1G1G1IU1R NNN 2S ,batchct||}|dj|jj |d<|S)zSPreprocess batch by converting keypoints data to float and moving it to the device. keypoints)r preprocesstorfloat)rr&r#s r$r)zPoseValidator.preprocessTs>"5)";/224;;?EEGk r%c ddzS)z:Return description of evaluation metrics in string format.z,%22s%11s%11s%11s%11s%11s%11s%11s%11s%11s%11s) ClassImages InstanceszBox(PRmAP50 mAP50-95)zPose(Pr0r1r2)rs r$get_desczPoseValidator.get_descZs$ )  r%modelct|||jd|_|jddgk(}|jd}|r t|_yt j ||z |_y)z Initialize evaluation metrics for YOLO pose validation. Args: model (torch.nn.Module): Model to validate. rrN)r init_metricsdatarr nponesr)rr5is_posenkptr#s r$r9zPoseValidator.init_metricsjs^ U#;/..RG+~~a ")Y rwwt}t/C r%predsct||}|D]1}|jdjdg|j|d<3|S)a Postprocess YOLO predictions to extract and reshape keypoints for pose estimation. This method extends the parent class postprocessing by extracting keypoints from the 'extra' field of predictions and reshaping them according to the keypoint shape configuration. The keypoints are reshaped from a flattened format to the proper dimensional structure (typically [N, 17, 3] for COCO pose format). Args: preds (torch.Tensor): Raw prediction tensor from the YOLO pose model containing bounding boxes, confidence scores, class predictions, and keypoint data. Returns: (Dict[torch.Tensor]): Dict of processed prediction dictionaries, each containing: - 'bboxes': Bounding box coordinates - 'conf': Confidence scores - 'cls': Class predictions - 'keypoints': Reshaped keypoint coordinates with shape (-1, *self.kpt_shape) Note: If no keypoints are present in a prediction (empty keypoints), that prediction is skipped and continues to the next one. The keypoints are extracted from the 'extra' field which contains additional task-specific data beyond basic detection. extrar()r postprocesspopviewr)rr?predr#s r$rCzPoseValidator.postprocesswsS2#E* LD 6 1 6 6r KDNN KD  L r%sict|||}|d|d|k(}|d\}}|j}|dxx|zcc<|dxx|zcc<tj|d||d|d}||d<|S) a` Prepare a batch for processing by converting keypoints to float and scaling to original dimensions. Args: si (int): Batch index. batch (Dict[str, Any]): Dictionary containing batch data with keys like 'keypoints', 'batch_idx', etc. Returns: (Dict[str, Any]): Prepared batch with keypoints scaled to original image dimensions. Notes: This method extends the parent class's _prepare_batch method by adding keypoint processing. Keypoints are scaled from normalized coordinates to original image dimensions. r( batch_idximgsz).r). ori_shape ratio_padrM)r_prepare_batchcloner scale_coords)rrGr&pbatchkptshwr#s r$rOzPoseValidator._prepare_batchs'E2[!% "4":;g1zz| V   V  wvk7JV\]hVij"{ r%rFrRct|||}tj|d|j dj |d|d|d<|S)am Prepare and scale keypoints in predictions for pose processing. This method extends the parent class's _prepare_pred method to handle keypoint scaling. It first calls the parent method to get the basic prediction boxes, then extracts and scales the keypoint coordinates to match the original image dimensions. Args: pred (Dict[str, torch.Tensor]): Post-processed predictions from the model. pbatch (Dict[str, Any]): Processed batch dictionary containing image information including: - imgsz: Image size used for inference - ori_shape: Original image shape - ratio_pad: Ratio and padding information for coordinate scaling Returns: (Dict[str, Any]): Processed prediction dictionary with keypoints scaled to original image dimensions. rJr(rLrMrN)r _prepare_predr rQgetrP)rrFrRprednr#s r$rWzPoseValidator._prepare_preds]$%dF3 -- 7OTXXk288:F;>#aΛ4QU1a[4!""1a[    5sD-statscv|jddz }|jdz }t| |||ddgddgS) z7Evaluate object detection model using COCO JSON format.rwz)annotations/person_keypoints_val2017.jsonzpredictions.jsonrr(BoxPose)suffix)r:r!r coco_evaluate)rr anno_json pred_jsonr#s r$ eval_jsonzPoseValidator.eval_json!sMIIf%(SS MM$66 w$UIy6;BWafhn`o$ppr%)NNNN)rN)__name__ __module__ __qualname____doc__rrrrr)r4r|nnModuler9TensorrCrrOrWr;ndarrayrbrfrrrrr __classcell__)r#s@r$rrs D!FS#X4S>  # D%((// Dd D$sELL7H2I<T#s(^S#X2$sCx.$sCx.TRUWZRZ^0Dell):$;DcNW[\_acakak\kWl<.$sELL'8"9.d.SXY\^aYaSb.jn.sw.0#$sELL'8"9#S#T#JqtCH~q$sCx.qqr%r)pathlibrtypingrrrrkr;r|ultralytics.models.yolo.detectrultralytics.utilsrr ultralytics.utils.metricsr r r rr3r%r$rs3## =)EEWq&Wqr%