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56 lines
2.4 KiB
56 lines
2.4 KiB
# 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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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("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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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(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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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
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shape = orig_img.shape
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], 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, shape)
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path = self.batch[0]
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img_path = path[i] if isinstance(path, list) else path
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results.append(
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Results(orig_img=orig_img,
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path=img_path,
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names=self.model.names,
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boxes=pred[:, :6],
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keypoints=pred_kpts))
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return results
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