Spaces:
Sleeping
Sleeping
File size: 2,375 Bytes
e5dd705 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
# Ultralytics YOLO 🚀, AGPL-3.0 license
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.yolo.v8.detect.predict import DetectionPredictor
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
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()
if __name__ == '__main__':
predict()
|