Refactor and enhance Ultralytics model utilities for improved OBB han…

…dling

- Reintroduced the import of `ObjectPrediction` in `base.py` for better modularity.
- Improved code readability in `ultralytics.py` by restructuring the concatenation of OBB data and resizing masks.
- Cleaned up whitespace in `cv.py` to enhance code clarity in the OBB to COCO conversion function.

These changes contribute to a more maintainable and user-friendly codebase for the SAHI library's Ultralytics model integration.

sahi/models/base.py

@@ -5,10 +5,10 @@
 
 import numpy as np
 
+from sahi.prediction import ObjectPrediction
 from sahi.utils.import_utils import is_available
 from sahi.utils.torch import select_device as select_torch_device
 
-from sahi.prediction import ObjectPrediction
 
 class DetectionModel:
     def __init__(

sahi/models/ultralytics.py

@@ -89,13 +89,18 @@ def perform_inference(self, image: np.ndarray):
             prediction_result = [
                 (
                     # Get OBB data: xyxy, conf, cls
-                    torch.cat([
-                        result.obb.xyxy,  # box coordinates
-                        result.obb.conf.unsqueeze(-1),  # confidence scores
-                        result.obb.cls.unsqueeze(-1),  # class ids
-                    ], dim=1) if result.obb is not None else torch.empty((0, 6), device=self.model.device),
+                    torch.cat(
+                        [
+                            result.obb.xyxy,  # box coordinates
+                            result.obb.conf.unsqueeze(-1),  # confidence scores
+                            result.obb.cls.unsqueeze(-1),  # class ids
+                        ],
+                        dim=1,
+                    )
+                    if result.obb is not None
+                    else torch.empty((0, 6), device=self.model.device),
                     # Get OBB points in (N, 4, 2) format
-                    result.obb.xyxyxyxy if result.obb is not None else torch.empty((0, 4, 2), device=self.model.device)
+                    result.obb.xyxyxyxy if result.obb is not None else torch.empty((0, 4, 2), device=self.model.device),
                 )
                 for result in prediction_result
             ]
@@ -197,8 +202,7 @@ def _create_object_prediction_list_from_original_predictions(
                         bool_mask = masks_or_points[pred_ind]
                         # Resize mask to original image size
                         bool_mask = cv2.resize(
-                            bool_mask.astype(np.uint8), 
-                            (self._original_shape[1], self._original_shape[0])
+                            bool_mask.astype(np.uint8), (self._original_shape[1], self._original_shape[0])
                         )
                         segmentation = get_coco_segmentation_from_bool_mask(bool_mask)
                     else:  # is_obb

sahi/utils/cv.py

@@ -691,7 +691,7 @@ def get_bbox_from_coco_segmentation(coco_segmentation):
 def get_coco_segmentation_from_obb_points(obb_points: np.ndarray) -> List[List[float]]:
     """
     Convert OBB (Oriented Bounding Box) points to COCO polygon format.
-    
+
     Args:
         obb_points: np.ndarray
             OBB points tensor from ultralytics.engine.results.OBB
@@ -702,13 +702,13 @@ def get_coco_segmentation_from_obb_points(obb_points: np.ndarray) -> List[List[f
     """
     # Convert from (4,2) to [x1,y1,x2,y2,x3,y3,x4,y4] format
     points = obb_points.reshape(-1).tolist()
-    
+
     # Create polygon from points and close it by repeating first point
     polygons = []
     # Add first point to end to close polygon
     closed_polygon = points + [points[0], points[1]]
     polygons.append(closed_polygon)
-    
+
     return polygons