Remoe Detections.empty metadata field

supervision/detection/core.py

@@ -965,7 +965,7 @@ class Detections:
         )
 
     @classmethod
-    def empty(cls, metadata: Optional[Dict[str, Any]] = None) -> Detections:
+    def empty(cls) -> Detections:
         """
         Create an empty Detections object with no bounding boxes,
             confidences, or class IDs.
@@ -980,14 +980,10 @@ class Detections:
             empty_detections = Detections.empty()
             ```
         """
-        if metadata is not None and not isinstance(metadata, dict):
-            raise TypeError("Metadata must be a dictionary.")
-
         return cls(
             xyxy=np.empty((0, 4), dtype=np.float32),
             confidence=np.array([], dtype=np.float32),
             class_id=np.array([], dtype=int),
-            metadata=metadata if metadata is not None else {},
         )
 
     def is_empty(self) -> bool:
@@ -996,6 +992,7 @@ class Detections:
         """
         empty_detections = Detections.empty()
         empty_detections.data = self.data
+        empty_detections.metadata = self.metadata
         return self == empty_detections
 
     @classmethod
@@ -1052,16 +1049,12 @@ class Detections:
             array([0.1, 0.2, 0.3])
             ```
         """
-        metadata_list = [detections.metadata for detections in detections_list]
-
         detections_list = [
             detections for detections in detections_list if not detections.is_empty()
         ]
 
-        metadata = merge_metadata(metadata_list)
-
         if len(detections_list) == 0:
-            return Detections.empty(metadata=metadata)
+            return Detections.empty()
 
         for detections in detections_list:
             validate_detections_fields(
@@ -1093,6 +1086,9 @@ class Detections:
 
         data = merge_data([d.data for d in detections_list])
 
+        metadata_list = [detections.metadata for detections in detections_list]
+        metadata = merge_metadata(metadata_list)
+
         return cls(
             xyxy=xyxy,
             mask=mask,

supervision/detection/utils.py

@@ -820,8 +820,10 @@ def is_metadata_equal(metadata_a: Dict[str, Any], metadata_b: Dict[str, Any]) ->
     """
     return set(metadata_a.keys()) == set(metadata_b.keys()) and all(
         np.array_equal(metadata_a[key], metadata_b[key])
-        if isinstance(metadata_a[key], np.ndarray)
-        and isinstance(metadata_b[key], np.ndarray)
+        if (
+            isinstance(metadata_a[key], np.ndarray)
+            and isinstance(metadata_b[key], np.ndarray)
+        )
         else metadata_a[key] == metadata_b[key]
         for key in metadata_a
     )
@@ -833,6 +835,9 @@ def merge_data(
     """
     Merges the data payloads of a list of Detections instances.
 
+    Warning: Assumes that empty detections were filtered-out before passing data to
+    this function.
+
     Args:
         data_list: The data payloads of the Detections instances. Each data payload
             is a dictionary with the same keys, and the values are either lists or
@@ -892,6 +897,9 @@ def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
     This function combines the metadata dictionaries. If a key appears in more than one
     dictionary, the values must be identical for the merge to succeed.
 
+    Warning: Assumes that empty detections were filtered-out before passing metadata to
+    this function.
+
     Args:
         metadata_list (List[Dict[str, Any]]): A list of metadata dictionaries to merge.
 
@@ -909,7 +917,7 @@ def merge_metadata(metadata_list: List[Dict[str, Any]]) -> Dict[str, Any]:
     if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets):
         raise ValueError("All metadata dictionaries must have the same keys to merge.")
 
-    merged_metadata = {}
+    merged_metadata: Dict[str, Any] = {}
     for metadata in metadata_list:
         for key, value in metadata.items():
             if key in merged_metadata: