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- # Ultralytics YOLO 🚀, AGPL-3.0 license
- from ultralytics.engine.results import Results
- from ultralytics.models.yolo.detect.predict import DetectionPredictor
- from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
- 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):
- 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))
- results = []
- is_list = isinstance(orig_imgs, list) # input images are a list, not a torch.Tensor
- for i, pred in enumerate(preds):
- orig_img = orig_imgs[i] if is_list else orig_imgs
- 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
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