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Référence pour ultralytics/models/yolo/pose/predict.py

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ultralytics.models.yolo.pose.predict.PosePredictor

Bases : DetectionPredictor

Une classe étendant la classe DetectionPredictor pour la prédiction basée sur un modèle de pose.

Exemple
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.pose import PosePredictor

args = dict(model='yolov8n-pose.pt', source=ASSETS)
predictor = PosePredictor(overrides=args)
predictor.predict_cli()
Code source dans ultralytics/models/yolo/pose/predict.py
class PosePredictor(DetectionPredictor):
    """
    A class extending the DetectionPredictor class for prediction based on a pose model.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.pose import PosePredictor

        args = dict(model='yolov8n-pose.pt', source=ASSETS)
        predictor = PosePredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initializes PosePredictor, sets task to 'pose' and logs a warning for using 'mps' as device."""
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = "pose"
        if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
            LOGGER.warning(
                "WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
                "See https://github.com/ultralytics/ultralytics/issues/4031."
            )

    def postprocess(self, preds, img, orig_imgs):
        """Return detection results for a given input image or list of images."""
        preds = ops.non_max_suppression(
            preds,
            self.args.conf,
            self.args.iou,
            agnostic=self.args.agnostic_nms,
            max_det=self.args.max_det,
            classes=self.args.classes,
            nc=len(self.model.names),
        )

        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

        results = []
        for i, pred in enumerate(preds):
            orig_img = orig_imgs[i]
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
            pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
            pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
            img_path = self.batch[0][i]
            results.append(
                Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts)
            )
        return results

__init__(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Initialise PosePredictor, définit la tâche à 'pose' et enregistre un avertissement pour l'utilisation de 'mps' comme périphérique.

Code source dans ultralytics/models/yolo/pose/predict.py
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """Initializes PosePredictor, sets task to 'pose' and logs a warning for using 'mps' as device."""
    super().__init__(cfg, overrides, _callbacks)
    self.args.task = "pose"
    if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
        LOGGER.warning(
            "WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
            "See https://github.com/ultralytics/ultralytics/issues/4031."
        )

postprocess(preds, img, orig_imgs)

Renvoie les résultats de la détection pour une image d'entrée donnée ou une liste d'images.

Code source dans ultralytics/models/yolo/pose/predict.py
def postprocess(self, preds, img, orig_imgs):
    """Return detection results for a given input image or list of images."""
    preds = ops.non_max_suppression(
        preds,
        self.args.conf,
        self.args.iou,
        agnostic=self.args.agnostic_nms,
        max_det=self.args.max_det,
        classes=self.args.classes,
        nc=len(self.model.names),
    )

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    results = []
    for i, pred in enumerate(preds):
        orig_img = orig_imgs[i]
        pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
        pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
        pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
        img_path = self.batch[0][i]
        results.append(
            Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts)
        )
    return results





Créé le 2023-11-12, Mis à jour le 2023-11-25
Auteurs : glenn-jocher (3)