OBB: Fix distorted plotting (#9899)

ultralytics/data/augment.py

@@ -975,17 +975,22 @@ class Format:
                     1 if self.mask_overlap else nl, img.shape[0] // self.mask_ratio, img.shape[1] // self.mask_ratio
                 )
             labels["masks"] = masks
-        if self.normalize:
-            instances.normalize(w, h)
         labels["img"] = self._format_img(img)
         labels["cls"] = torch.from_numpy(cls) if nl else torch.zeros(nl)
         labels["bboxes"] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
         if self.return_keypoint:
             labels["keypoints"] = torch.from_numpy(instances.keypoints)
+            if self.normalize:
+                labels["keypoints"][..., 0] /= w
+                labels["keypoints"][..., 1] /= h
         if self.return_obb:
             labels["bboxes"] = (
                 xyxyxyxy2xywhr(torch.from_numpy(instances.segments)) if len(instances.segments) else torch.zeros((0, 5))
             )
+        # NOTE: need to normalize obb in xywhr format for width-height consistency
+        if self.normalize:
+            labels["bboxes"][:, [0, 2]] /= w
+            labels["bboxes"][:, [1, 3]] /= h
         # Then we can use collate_fn
         if self.batch_idx:
             labels["batch_idx"] = torch.zeros(nl)

ultralytics/utils/plotting.py

@@ -838,16 +838,16 @@ 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::2] *= h
+                        boxes[..., [0, 2]] *= w  # scale to pixels
+                        boxes[..., [1, 3]] *= h
                     elif scale < 1:  # absolute coords need scale if image scales
                         boxes[..., :4] *= scale
-                boxes[..., 0::2] += x
-                boxes[..., 1::2] += y
+                boxes[..., 0] += x
+                boxes[..., 1] += y
+                is_obb = boxes.shape[-1] == 5  # xywhr
+                boxes = ops.xywhr2xyxyxyxy(boxes) if is_obb else ops.xywh2xyxy(boxes)
                 for j, box in enumerate(boxes.astype(np.int64).tolist()):
                     c = classes[j]
                     color = colors(c)