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PosePredictor

Bases: DetectionPredictor

Source code in ultralytics/yolo/v8/pose/predict.py 
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class PosePredictor(DetectionPredictor):

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = 'pose'

    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))

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

postprocess(preds, img, orig_imgs)

Return detection results for a given input image or list of images.

Source code in ultralytics/yolo/v8/pose/predict.py 
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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))

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



predict

Runs YOLO to predict objects in an image or video.

Source code in ultralytics/yolo/v8/pose/predict.py 
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def predict(cfg=DEFAULT_CFG, use_python=False):
    """Runs YOLO to predict objects in an image or video."""
    model = cfg.model or 'yolov8n-pose.pt'
    source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
        else 'https://ultralytics.com/images/bus.jpg'

    args = dict(model=model, source=source)
    if use_python:
        from ultralytics import YOLO
        YOLO(model)(**args)
    else:
        predictor = PosePredictor(overrides=args)
        predictor.predict_cli()




Created 2023-04-16, Updated 2023-05-17
Authors: Glenn Jocher (3)