|
|
# 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
|
|
|
|
