Merge remote-tracking branch 'origin/develop' into develop

supervision/detection/core.py

@@ -487,12 +487,18 @@ class Detections:
             transformers_results.__class__.__name__ == "Tensor"
             or "segmentation" in transformers_results
         ):
-            return cls(**process_transformers_v5_segmentation_result(
-                transformers_results, id2label))
+            return cls(
+                **process_transformers_v5_segmentation_result(
+                    transformers_results, id2label
+                )
+            )
 
         if "masks" in transformers_results or "png_string" in transformers_results:
-            return cls(**process_transformers_v4_segmentation_result(
-                    transformers_results, id2label))
+            return cls(
+                **process_transformers_v4_segmentation_result(
+                    transformers_results, id2label
+                )
+            )
 
         if "boxes" in transformers_results:
             return cls(**process_detection_result(transformers_results, id2label))

supervision/detection/tools/transformers.py

@@ -1,5 +1,5 @@
 import io
-from typing import Dict, Optional, Any
+from typing import Any, Dict, Optional
 
 import numpy as np
 from PIL import Image
@@ -11,7 +11,7 @@ from supervision.detection.utils import mask_to_xyxy
 def append_class_names_to_data(
     class_ids: np.ndarray,
     id2label: Optional[Dict[int, str]],
-    data: Optional[Dict[str, Any]] = None
+    data: Optional[Dict[str, Any]] = None,
 ) -> Dict[str, Any]:
     """
     Helper function to create or append to a data dictionary with class names if
@@ -119,7 +119,7 @@ def process_transformers_v4_segmentation_result(
             mask=np.squeeze(masks, axis=1) if boxes is not None else masks,
             confidence=segmentation_result["scores"].cpu().detach().numpy(),
             class_id=class_ids,
-            data=append_class_names_to_data(class_ids, id2label ,{}),
+            data=append_class_names_to_data(class_ids, id2label, {}),
         )
 
 
@@ -202,10 +202,7 @@ def process_png_segmentation_result(
     class_ids = np.array([segment["category_id"] for segment in segments_info])
     label_mask = png_string_to_label_mask(segmentation_result["png_string"])
     masks = np.array(
-        [
-            (label_mask == segment["id"]).astype(bool)
-            for segment in segments_info
-        ]
+        [(label_mask == segment["id"]).astype(bool) for segment in segments_info]
     )
     data = append_class_names_to_data(class_ids, id2label, {})