回传数据解析，兼容v5和v10

ultralytics/models/yolo/obb/predict.py

@@ -0,0 +1,53 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import torch
+
+from ultralytics.engine.results import Results
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, ops
+
+
+class OBBPredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on an Oriented Bounding Box (OBB) model.
+
+    Example:
+        ```python
+        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()
+        ```
+    """
+
+    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"
+
+    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