Handle larger images

frigate/embeddings/functions/onnx.py

@@ -193,6 +193,17 @@ class GenericONNXEmbedding:
                 raise ValueError("Face embedding does not support batch inputs.")
 
             pil = self._process_image(raw_inputs)
+
+            # handle images larger than input size
+            width, height = pil.size
+            if width > 112 or height > 112:
+                if width > height:
+                    new_height = int(((width / height) * 112) // 4 * 4)
+                    pil = pil.resize((112, new_height))
+                else:
+                    new_width = int(((height / width) * 112) // 4 * 4)
+                    pil = pil.resize((new_width, 112))
+
             og = np.array(pil).astype(np.float32)
 
             # Image must be 112x112

frigate/embeddings/maintainer.py

@@ -26,7 +26,7 @@ from frigate.events.types import EventTypeEnum
 from frigate.genai import get_genai_client
 from frigate.models import Event
 from frigate.util.builtin import serialize
-from frigate.util.image import SharedMemoryFrameManager, calculate_region, area
+from frigate.util.image import SharedMemoryFrameManager, area, calculate_region
 
 from .embeddings import Embeddings
 
@@ -318,7 +318,10 @@ class EmbeddingMaintainer(threading.Thread):
         face_box = face.get("box")
 
         # check that face is valid
-        if not face_box or area(face_box) < self.config.semantic_search.face_recognition.min_area:
+        if (
+            not face_box
+            or area(face_box) < self.config.semantic_search.face_recognition.min_area
+        ):
             return
 
         face_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)