# ์ฐธ์กฐ `ultralytics/models/yolo/pose/predict.py`

์ฐธ๊ณ

์ด ํ์ผ์ https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/models/ yolo/pose/predict .py์์ ํ์ธํ  ์ ์์ต๋๋ค. ๋ฌธ์ ๋ฅผ ๋ฐ๊ฒฌํ๋ฉด ํ ๋ฆฌํ์คํธ ๐ ๏ธ ์ ๊ธฐ์ฌํ์ฌ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๋๋ก ๋์์ฃผ์ธ์. ๊ฐ์ฌํฉ๋๋ค ๐!

## `ultralytics.models.yolo.pose.predict.PosePredictor`

๊ธฐ์ง: `DetectionPredictor`

ํฌ์ฆ ๋ชจ๋ธ์ ๊ธฐ๋ฐํ ์์ธก์ ์ํด DetectionPredictor ํด๋์ค๋ฅผ ํ์ฅํ ํด๋์ค์๋๋ค.

์์ 
``````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()
``````
์ ์์ค ์ฝ๋ `ultralytics/models/yolo/pose/predict.py`
 ``` 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``` ``````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 โ ๏ธ 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 ``````

### `__init__(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)`

ํฌ์ฆ ์์ธก๊ธฐ๋ฅผ ์ด๊ธฐํํ๊ณ , ์์์ 'ํฌ์ฆ'๋ก ์ค์ ํ๊ณ , 'mps'๋ฅผ ๋๋ฐ์ด์ค๋ก ์ฌ์ฉํ๋ ๊ฒ์ ๋ํ ๊ฒฝ๊ณ ๋ฅผ ๊ธฐ๋กํฉ๋๋ค.

์ ์์ค ์ฝ๋ `ultralytics/models/yolo/pose/predict.py`
 ```23 24 25 26 27 28 29 30 31``` ``````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 โ ๏ธ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. " "See https://github.com/ultralytics/ultralytics/issues/4031." ) ``````

### `postprocess(preds, img, orig_imgs)`

์ง์ ๋ ์๋ ฅ ์ด๋ฏธ์ง ๋๋ ์ด๋ฏธ์ง ๋ชฉ๋ก์ ๋ํ ๊ฐ์ง ๊ฒฐ๊ณผ๋ฅผ ๋ฐํํฉ๋๋ค.

์ ์์ค ์ฝ๋ `ultralytics/models/yolo/pose/predict.py`
 ```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``` ``````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 ``````

์์ฑ๋จ 2023-11-12, ์๋ฐ์ดํธ๋จ 2023-11-25
์์ฑ์: glenn-jocher (3)