diff --git a/ultralytics/models/yolo/obb/val.py b/ultralytics/models/yolo/obb/val.py
index cc337ab321c..f0c008aafff 100644
--- a/ultralytics/models/yolo/obb/val.py
+++ b/ultralytics/models/yolo/obb/val.py
@@ -117,11 +117,10 @@ def pred_to_json(self, predn, filename):
 
     def save_one_txt(self, predn, save_conf, shape, file):
         """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
-        gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
-        for *xyxy, conf, cls, angle in predn.tolist():
-            xywha = torch.tensor([*xyxy, angle]).view(1, 5)
-            xywha[:, :4] /= gn
-            xyxyxyxy = ops.xywhr2xyxyxyxy(xywha).view(-1).tolist()  # normalized xywh
+        gn = torch.tensor(shape)[[1, 0]]  # normalization gain whwh
+        for *xywh, conf, cls, angle in predn.tolist():
+            xywha = torch.tensor([*xywh, angle]).view(1, 5)
+            xyxyxyxy = (ops.xywhr2xyxyxyxy(xywha) / gn).view(-1).tolist()  # normalized xywh
             line = (cls, *xyxyxyxy, conf) if save_conf else (cls, *xyxyxyxy)  # label format
             with open(file, "a") as f:
                 f.write(("%g " * len(line)).rstrip() % line + "\n")
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py
index 51170e9dce4..1deb9efbaf6 100644
--- a/ultralytics/utils/ops.py
+++ b/ultralytics/utils/ops.py
@@ -541,7 +541,7 @@ def xyxyxyxy2xywhr(corners):
     )  # rboxes
 
 
-def xywhr2xyxyxyxy(center):
+def xywhr2xyxyxyxy(rboxes):
     """
     Convert batched Oriented Bounding Boxes (OBB) from [xywh, rotation] to [xy1, xy2, xy3, xy4]. Rotation values should
     be in degrees from 0 to 90.
@@ -552,11 +552,11 @@ def xywhr2xyxyxyxy(center):
     Returns:
         (numpy.ndarray | torch.Tensor): Converted corner points of shape (n, 4, 2) or (b, n, 4, 2).
     """
-    is_numpy = isinstance(center, np.ndarray)
+    is_numpy = isinstance(rboxes, np.ndarray)
     cos, sin = (np.cos, np.sin) if is_numpy else (torch.cos, torch.sin)
 
-    ctr = center[..., :2]
-    w, h, angle = (center[..., i : i + 1] for i in range(2, 5))
+    ctr = rboxes[..., :2]
+    w, h, angle = (rboxes[..., i : i + 1] for i in range(2, 5))
     cos_value, sin_value = cos(angle), sin(angle)
     vec1 = [w / 2 * cos_value, w / 2 * sin_value]
     vec2 = [-h / 2 * sin_value, h / 2 * cos_value]
diff --git a/ultralytics/utils/plotting.py b/ultralytics/utils/plotting.py
index d591f402fd0..e5c0c415aef 100644
--- a/ultralytics/utils/plotting.py
+++ b/ultralytics/utils/plotting.py
@@ -706,16 +706,16 @@ def plot_images(
             if len(bboxes):
                 boxes = bboxes[idx]
                 conf = confs[idx] if confs is not None else None  # check for confidence presence (label vs pred)
+                is_obb = boxes.shape[-1] == 5  # xywhr
+                boxes = ops.xywhr2xyxyxyxy(boxes) if is_obb else ops.xywh2xyxy(boxes)
                 if len(boxes):
                     if boxes[:, :4].max() <= 1.1:  # if normalized with tolerance 0.1
-                        boxes[:, [0, 2]] *= w  # scale to pixels
-                        boxes[:, [1, 3]] *= h
+                        boxes[..., 0::2] *= w  # scale to pixels
+                        boxes[..., 1::2] *= h
                     elif scale < 1:  # absolute coords need scale if image scales
-                        boxes[:, :4] *= scale
-                boxes[:, 0] += x
-                boxes[:, 1] += y
-                is_obb = boxes.shape[-1] == 5  # xywhr
-                boxes = ops.xywhr2xyxyxyxy(boxes) if is_obb else ops.xywh2xyxy(boxes)
+                        boxes[..., :4] *= scale
+                boxes[..., 0::2] += x
+                boxes[..., 1::2] += y
                 for j, box in enumerate(boxes.astype(np.int64).tolist()):
                     c = classes[j]
                     color = colors(c)