Skip to content

Reference for ultralytics/models/yolo/obb/predict.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/obb/predict.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.yolo.obb.predict.OBBPredictor 

OBBPredictor(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Bases: DetectionPredictor

A class extending the DetectionPredictor class for prediction based on an Oriented Bounding Box (OBB) model.

Example 
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.obb import OBBPredictor

args = dict(model="yolov8n-obb.pt", source=ASSETS)
predictor = OBBPredictor(overrides=args)
predictor.predict_cli()
Source code in ultralytics/models/yolo/obb/predict.py 
25
26
27
28
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """Initializes OBBPredictor with optional model and data configuration overrides."""
    super().__init__(cfg, overrides, _callbacks)
    self.args.task = "obb"

postprocess 

postprocess(preds, img, orig_imgs)

Post-processes predictions and returns a list of Results objects.

Source code in ultralytics/models/yolo/obb/predict.py 
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
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,
        nc=len(self.model.names),
        classes=self.args.classes,
        rotated=True,
    )

    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 pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
        rboxes = ops.regularize_rboxes(torch.cat([pred[:, :4], pred[:, -1:]], dim=-1))
        rboxes[:, :4] = ops.scale_boxes(img.shape[2:], rboxes[:, :4], orig_img.shape, xywh=True)
        # xywh, r, conf, cls
        obb = torch.cat([rboxes, pred[:, 4:6]], dim=-1)
        results.append(Results(orig_img, path=img_path, names=self.model.names, obb=obb))
    return results



📅 Created 11 months ago ✏️ Updated 3 months ago
jk4e glenn-jocher Burhan-Q