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Co-authored-by: Yash Khurana <ykhurana6@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Swamita Gupta <swamita2001@gmail.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Laughing-q <1182102784@qq.com>
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import torch
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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, ops
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class OBBPredictor(DetectionPredictor):
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"""
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A class extending the DetectionPredictor class for prediction based on an Oriented Bounding Box (OBB) 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.obb import OBBPredictor
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args = dict(model='yolov8n-obb.pt', source=ASSETS)
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predictor = OBBPredictor(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 = 'obb'
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def postprocess(self, preds, img, orig_imgs):
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"""Post-processes predictions and returns a list of Results objects."""
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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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nc=len(self.model.names),
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classes=self.args.classes,
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rotated=True)
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if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_imgs[i]
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape, xywh=True)
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img_path = self.batch[0][i]
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# xywh, r, conf, cls
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obb = torch.cat([pred[:, :4], pred[:, -1:], pred[:, 4:6]], dim=-1)
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results.append(Results(orig_img, path=img_path, names=self.model.names, obb=obb))
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return results
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