Face recognize api (#17233)

* Add api to run face recognition on image

* Rework save attempts option

* Cleanup mobile object pane buttons

* Adjust api signature

* Remove param

* Cleanup

frigate/api/classification.py

@@ -201,6 +201,22 @@ async def register_face(request: Request, name: str, file: UploadFile):
     )
 
 
+@router.post("/faces/recognize")
+async def recognize_face(request: Request, file: UploadFile):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
+    context: EmbeddingsContext = request.app.embeddings
+    result = context.recognize_face(await file.read())
+    return JSONResponse(
+        status_code=200 if result.get("success", True) else 400,
+        content=result,
+    )
+
+
 @router.post("/faces/{name}/delete", dependencies=[Depends(require_role(["admin"]))])
 def deregister_faces(request: Request, name: str, body: dict = None):
     if not request.app.frigate_config.face_recognition.enabled:

frigate/comms/embeddings_updater.py

@@ -13,6 +13,7 @@ class EmbeddingsRequestEnum(Enum):
     embed_description = "embed_description"
     embed_thumbnail = "embed_thumbnail"
     generate_search = "generate_search"
+    recognize_face = "recognize_face"
     register_face = "register_face"
     reprocess_face = "reprocess_face"
     reprocess_plate = "reprocess_plate"

frigate/config/classification.py

@@ -70,8 +70,8 @@ class FaceRecognitionConfig(FrigateBaseModel):
     min_area: int = Field(
         default=500, title="Min area of face box to consider running face recognition."
     )
-    save_attempts: bool = Field(
+    save_attempts: int = Field(
-        default=True, title="Save images of face detections for training."
+        default=100, ge=0, title="Number of face attempts to save in the train tab."
     )
     blur_confidence_filter: bool = Field(
         default=True, title="Apply blur quality filter to face confidence."

frigate/data_processing/real_time/face.py

@@ -28,7 +28,6 @@ logger = logging.getLogger(__name__)
 
 
 MAX_DETECTION_HEIGHT = 1080
-MAX_FACE_ATTEMPTS = 100
 MIN_MATCHING_FACES = 2
 
 
@@ -407,6 +406,28 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
     def handle_request(self, topic, request_data) -> dict[str, any] | None:
         if topic == EmbeddingsRequestEnum.clear_face_classifier.value:
             self.__clear_classifier()
+        elif topic == EmbeddingsRequestEnum.recognize_face.value:
+            img = cv2.imdecode(
+                np.frombuffer(base64.b64decode(request_data["image"]), dtype=np.uint8),
+                cv2.IMREAD_COLOR,
+            )
+
+            # detect faces with lower confidence since we expect the face
+            # to be visible in uploaded images
+            face_box = self.__detect_face(img, 0.5)
+
+            if not face_box:
+                return {"message": "No face was detected.", "success": False}
+
+            face = img[face_box[1] : face_box[3], face_box[0] : face_box[2]]
+            res = self.__classify_face(face)
+
+            if not res:
+                return {"success": False, "message": "No face was recognized."}
+
+            sub_label, score = res
+
+            return {"success": True, "score": score, "face_name": sub_label}
         elif topic == EmbeddingsRequestEnum.register_face.value:
             rand_id = "".join(
                 random.choices(string.ascii_lowercase + string.digits, k=6)
@@ -490,7 +511,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 )
 
                 # delete oldest face image if maximum is reached
-                if len(files) > MAX_FACE_ATTEMPTS:
+                if len(files) > self.config.face_recognition.save_attempts:
                     os.unlink(os.path.join(folder, files[-1]))
 
     def expire_object(self, object_id: str):

frigate/embeddings/__init__.py

@@ -197,6 +197,14 @@ class EmbeddingsContext:
             },
         )
 
+    def recognize_face(self, image_data: bytes) -> dict[str, any]:
+        return self.requestor.send_data(
+            EmbeddingsRequestEnum.recognize_face.value,
+            {
+                "image": base64.b64encode(image_data).decode("ASCII"),
+            },
+        )
+
     def get_face_ids(self, name: str) -> list[str]:
         sql_query = f"""
             SELECT

web/src/components/overlay/detail/SearchDetailDialog.tsx

@@ -717,7 +717,9 @@ function ObjectDetailsTab({
             draggable={false}
             src={`${apiHost}api/events/${search.id}/thumbnail.webp`}
           />
-          <div className="flex w-full flex-row gap-2">
+          <div
+            className={cn("flex w-full flex-row gap-2", isMobile && "flex-col")}
+          >
             {config?.semantic_search.enabled &&
               search.data.type == "object" && (
                 <Button