42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from ultralytics.engine.predictor import BasePredictor
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from ultralytics.engine.results import Results
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from ultralytics.utils import ops
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class DetectionPredictor(BasePredictor):
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"""
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A class extending the BasePredictor class for prediction based on a detection 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.detect import DetectionPredictor
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args = dict(model="yolo11n.pt", source=ASSETS)
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predictor = DetectionPredictor(overrides=args)
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predictor.predict_cli()
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```
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"""
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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(
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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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)
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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 pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
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return results
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