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# Ultralytics YOLO 🚀, AGPL-3.0 license
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import ops
class DetectionPredictor(BasePredictor):
"""
A class extending the BasePredictor class for prediction based on a detection model.
Example:
```python
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.detect import DetectionPredictor
args = dict(model='yolov8n.pt', source=ASSETS)
predictor = DetectionPredictor(overrides=args)
predictor.predict_cli()
```
"""
def postprocess(self, preds, img, orig_imgs):
"""Post-processes predictions and returns a list of Results objects."""
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,
)
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)
img_path = self.batch[0][i]
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
return results
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