ultralytics 8.2.46 fix OBB Results xyxy attribute (ultralytics#14020

)

pyproject.toml

@@ -64,7 +64,7 @@ classifiers = [
 
 # Required dependencies ------------------------------------------------------------------------------------------------
 dependencies = [
-    "numpy>=1.23.5,<2.0.0", # temporary patch for compat errors https://github.com/ultralytics/yolov5/actions/runs/9538130424/job/26286956354
+    "numpy>=1.23.0,<2.0.0", # temporary patch for compat errors https://github.com/ultralytics/yolov5/actions/runs/9538130424/job/26286956354
     "matplotlib>=3.3.0",
     "opencv-python>=4.6.0",
     "pillow>=7.1.2",

tests/test_python.py

@@ -236,13 +236,14 @@ def test_results(model):
     results = YOLO(WEIGHTS_DIR / model)([SOURCE, SOURCE], imgsz=160)
     for r in results:
         r = r.cpu().numpy()
+        print(r, len(r), r.path)  # print numpy attributes
         r = r.to(device="cpu", dtype=torch.float32)
         r.save_txt(txt_file=TMP / "runs/tests/label.txt", save_conf=True)
         r.save_crop(save_dir=TMP / "runs/tests/crops/")
         r.tojson(normalize=True)
         r.plot(pil=True)
         r.plot(conf=True, boxes=True)
-        print(r, len(r), r.path)
+        print(r, len(r), r.path)  # print after methods
 
 
 def test_labels_and_crops():

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.45"
+__version__ = "8.2.46"
 
 import os
 

ultralytics/engine/results.py

@@ -743,9 +743,10 @@ def xyxy(self):
 
         Accepts both torch and numpy boxes.
         """
-        x1 = self.xyxyxyxy[..., 0].min(1).values
-        x2 = self.xyxyxyxy[..., 0].max(1).values
-        y1 = self.xyxyxyxy[..., 1].min(1).values
-        y2 = self.xyxyxyxy[..., 1].max(1).values
-        xyxy = [x1, y1, x2, y2]
-        return np.stack(xyxy, axis=-1) if isinstance(self.data, np.ndarray) else torch.stack(xyxy, dim=-1)
+        x = self.xyxyxyxy[..., 0]
+        y = self.xyxyxyxy[..., 1]
+        return (
+            torch.stack([x.amin(1), y.amin(1), x.amax(1), y.amax(1)], -1)
+            if isinstance(x, torch.Tensor)
+            else np.stack([x.min(1), y.min(1), x.max(1), y.max(1)], -1)
+        )