ultralytics 8.0.235 YOLOv8 OBB train, val, predict and export (#4499)

Co-authored-by: Yash Khurana <ykhurana6@gmail.com>
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Co-authored-by: Swamita Gupta <swamita2001@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
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ultralytics/models/yolo/obb/predict.py

@@ -0,0 +1,51 @@
+# 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):
+        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 i, pred in enumerate(preds):
+            orig_img = orig_imgs[i]
+            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape, xywh=True)
+            img_path = self.batch[0][i]
+            # xywh, r, conf, cls
+            obb = torch.cat([pred[:, :4], pred[:, -1:], pred[:, 4:6]], dim=-1)
+            results.append(Results(orig_img, path=img_path, names=self.model.names, obb=obb))
+        return results