57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from ultralytics.engine.results import Results
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from ultralytics.models.yolo.detect.predict import DetectionPredictor
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from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
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class PosePredictor(DetectionPredictor):
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"""
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A class extending the DetectionPredictor class for prediction based on a pose model.
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Example:
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```python
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.pose import PosePredictor
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args = dict(model="yolov8n-pose.pt", source=ASSETS)
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predictor = PosePredictor(overrides=args)
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predictor.predict_cli()
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```
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""Initializes PosePredictor, sets task to 'pose' and logs a warning for using 'mps' as device."""
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super().__init__(cfg, overrides, _callbacks)
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self.args.task = "pose"
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if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
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LOGGER.warning(
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"WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
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"See https://github.com/ultralytics/ultralytics/issues/4031."
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)
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def postprocess(self, preds, img, orig_imgs):
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"""Return detection results for a given input image or list of images."""
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preds = ops.non_max_suppression(
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preds,
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self.args.conf,
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self.args.iou,
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agnostic=self.args.agnostic_nms,
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max_det=self.args.max_det,
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classes=self.args.classes,
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nc=len(self.model.names),
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)
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if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
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results = []
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for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
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pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
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pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
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results.append(
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Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts)
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)
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return results
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