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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):
"""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
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