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>
73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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="yolo11n.pt", source=ASSETS)
|
|
predictor = DetectionPredictor(overrides=args)
|
|
predictor.predict_cli()
|
|
```
|
|
"""
|
|
|
|
def postprocess(self, preds, img, orig_imgs, **kwargs):
|
|
"""Post-processes predictions and returns a list of Results objects."""
|
|
preds = ops.non_max_suppression(
|
|
preds,
|
|
self.args.conf,
|
|
self.args.iou,
|
|
self.args.classes,
|
|
self.args.agnostic_nms,
|
|
max_det=self.args.max_det,
|
|
nc=len(self.model.names),
|
|
end2end=getattr(self.model, "end2end", False),
|
|
rotated=self.args.task == "obb",
|
|
)
|
|
|
|
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
|
|
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
|
|
|
|
return self.construct_results(preds, img, orig_imgs, **kwargs)
|
|
|
|
def construct_results(self, preds, img, orig_imgs):
|
|
"""
|
|
Constructs a list of result objects from the predictions.
|
|
|
|
Args:
|
|
preds (List[torch.Tensor]): List of predicted bounding boxes and scores.
|
|
img (torch.Tensor): The image after preprocessing.
|
|
orig_imgs (List[np.ndarray]): List of original images before preprocessing.
|
|
|
|
Returns:
|
|
(list): List of result objects containing the original images, image paths, class names, and bounding boxes.
|
|
"""
|
|
return [
|
|
self.construct_result(pred, img, orig_img, img_path)
|
|
for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
|
|
]
|
|
|
|
def construct_result(self, pred, img, orig_img, img_path):
|
|
"""
|
|
Constructs the result object from the prediction.
|
|
|
|
Args:
|
|
pred (torch.Tensor): The predicted bounding boxes and scores.
|
|
img (torch.Tensor): The image after preprocessing.
|
|
orig_img (np.ndarray): The original image before preprocessing.
|
|
img_path (str): The path to the original image.
|
|
|
|
Returns:
|
|
(Results): The result object containing the original image, image path, class names, and bounding boxes.
|
|
"""
|
|
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
|
|
return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6])
|