Convert face crops to RGB before embedding (#23712)

* Convert face crops to RGB before embedding

Face crops flow through the cv2 pipeline as BGR arrays, but
_process_image passes ndarrays to PIL without any channel conversion,
so the FaceNet and ArcFace embedders receive BGR input while both
models expect RGB. The error is symmetric between enrollment and
recognition so it partially cancels, but it still costs accuracy.

* Move BGR to RGB conversion into a shared helper

Deduplicate the channel swap from both _preprocess_inputs methods
into a BaseEmbedding._bgr_to_rgb static helper, as suggested in
review.

frigate/embeddings/onnx/base_embedding.py

@@ -57,6 +57,12 @@ class BaseEmbedding(ABC):
     def _preprocess_inputs(self, raw_inputs: Any) -> Any:
         pass
 
+    @staticmethod
+    def _bgr_to_rgb(frame: Any) -> Any:
+        if isinstance(frame, np.ndarray) and frame.ndim == 3:
+            return np.ascontiguousarray(frame[:, :, ::-1])
+        return frame
+
     def _process_image(self, image, output: str = "RGB") -> Image.Image:
         if isinstance(image, str):
             if image.startswith("http"):

frigate/embeddings/onnx/face_embedding.py

@@ -73,7 +73,7 @@ class FaceNetEmbedding(BaseEmbedding):
             self.tensor_output_details = self.runner.get_output_details()
 
     def _preprocess_inputs(self, raw_inputs):
-        pil = self._process_image(raw_inputs[0])
+        pil = self._process_image(self._bgr_to_rgb(raw_inputs[0]))
 
         # handle images larger than input size
         width, height = pil.size
@@ -159,7 +159,7 @@ class ArcfaceEmbedding(BaseEmbedding):
             )
 
     def _preprocess_inputs(self, raw_inputs):
-        pil = self._process_image(raw_inputs[0])
+        pil = self._process_image(self._bgr_to_rgb(raw_inputs[0]))
 
         # handle images larger than input size
         width, height = pil.size