9181ff62f5
Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
49 lines
2.1 KiB
Python
49 lines
2.1 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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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="yolo11n-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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"""Initializes PosePredictor, sets task to 'pose' and logs a warning for using 'mps' as device."""
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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(
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"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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)
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def construct_result(self, pred, img, orig_img, img_path):
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"""
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Constructs the result object from the prediction.
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Args:
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pred (torch.Tensor): The predicted bounding boxes, scores, and keypoints.
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img (torch.Tensor): The image after preprocessing.
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orig_img (np.ndarray): The original image before preprocessing.
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img_path (str): The path to the original image.
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Returns:
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(Results): The result object containing the original image, image path, class names, bounding boxes, and keypoints.
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"""
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result = super().construct_result(pred, img, orig_img, img_path)
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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, orig_img.shape)
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result.update(keypoints=pred_kpts)
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return result
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