docs/docs/configuration/bird_classification.md

@@ -1,31 +0,0 @@
----
-id: bird_classification
-title: Bird Classification
----
-
-Bird classification identifies known birds using a quantized Tensorflow model. When a known bird is recognized, its common name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
-
-## Minimum System Requirements
-
-Bird classification runs a lightweight tflite model on the CPU, there are no significantly different system requirements than running Frigate itself.
-
-## Model
-
-The classification model used is the MobileNet INat Bird Classification, [available identifiers can be found here.](https://raw.githubusercontent.com/google-coral/test_data/master/inat_bird_labels.txt)
-
-## Configuration
-
-Bird classification is disabled by default, it must be enabled in your config file before it can be used. Bird classification is a global configuration setting.
-
-```yaml
-classification:
-  bird:
-    enabled: true
-```
-
-## Advanced Configuration
-
-Fine-tune bird classification with these optional parameters:
-
-- `threshold`: Classification confidence score required to set the sub label on the object.
-  - Default: `0.9`.

docs/sidebars.ts

@@ -33,12 +33,11 @@ const sidebars: SidebarsConfig = {
         "configuration/object_detectors",
         "configuration/audio_detectors",
       ],
-      Enrichments: [
+      Classifiers: [
         "configuration/semantic_search",
         "configuration/genai",
         "configuration/face_recognition",
         "configuration/license_plate_recognition",
-        "configuration/bird_classification",
       ],
       Cameras: [
         "configuration/cameras",

frigate/api/classification.py

@@ -198,16 +198,6 @@ async def register_face(request: Request, name: str, file: UploadFile):
 
     context: EmbeddingsContext = request.app.embeddings
     result = context.register_face(name, await file.read())
-
-    if not isinstance(result, dict):
-        return JSONResponse(
-            status_code=500,
-            content={
-                "success": False,
-                "message": "Could not process request. Try restarting Frigate.",
-            },
-        )
-
     return JSONResponse(
         status_code=200 if result.get("success", True) else 400,
         content=result,
@@ -224,16 +214,6 @@ async def recognize_face(request: Request, file: UploadFile):
 
     context: EmbeddingsContext = request.app.embeddings
     result = context.recognize_face(await file.read())
-
-    if not isinstance(result, dict):
-        return JSONResponse(
-            status_code=500,
-            content={
-                "success": False,
-                "message": "Could not process request. Try restarting Frigate.",
-            },
-        )
-
     return JSONResponse(
         status_code=200 if result.get("success", True) else 400,
         content=result,

frigate/data_processing/common/face/model.py

@@ -1,7 +1,5 @@
 import logging
 import os
-import queue
-import threading
 from abc import ABC, abstractmethod
 
 import cv2
@@ -20,7 +18,10 @@ class FaceRecognizer(ABC):
 
     def __init__(self, config: FrigateConfig) -> None:
         self.config = config
-        self.init_landmark_detector()
+        self.landmark_detector = cv2.face.createFacemarkLBF()
+        self.landmark_detector.loadModel(
+            os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
+        )
 
     @abstractmethod
     def build(self) -> None:
@@ -36,13 +37,6 @@ class FaceRecognizer(ABC):
     def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
         pass
 
-    def init_landmark_detector(self) -> None:
-        landmark_model = os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
-
-        if os.path.exists(landmark_model):
-            self.landmark_detector = cv2.face.createFacemarkLBF()
-            self.landmark_detector.loadModel(landmark_model)
-
     def align_face(
         self,
         image: np.ndarray,
@@ -136,7 +130,6 @@ class LBPHRecognizer(FaceRecognizer):
 
     def build(self):
         if not self.landmark_detector:
-            self.init_landmark_detector()
             return None
 
         labels = []
@@ -208,69 +201,45 @@ class ArcFaceRecognizer(FaceRecognizer):
         super().__init__(config)
         self.mean_embs: dict[int, np.ndarray] = {}
         self.face_embedder: ArcfaceEmbedding = ArcfaceEmbedding()
-        self.model_builder_queue: queue.Queue | None = None
 
     def clear(self) -> None:
         self.mean_embs = {}
 
-    def run_build_task(self) -> None:
-        self.model_builder_queue = queue.Queue()
-
-        def build_model():
-            face_embeddings_map: dict[str, list[np.ndarray]] = {}
-            idx = 0
-
-            dir = "/media/frigate/clips/faces"
-            for name in os.listdir(dir):
-                if name == "train":
-                    continue
-
-                face_folder = os.path.join(dir, name)
-
-                if not os.path.isdir(face_folder):
-                    continue
-
-                face_embeddings_map[name] = []
-                for image in os.listdir(face_folder):
-                    img = cv2.imread(os.path.join(face_folder, image))
-
-                    if img is None:
-                        continue
-
-                    img = self.align_face(img, img.shape[1], img.shape[0])
-                    emb = self.face_embedder([img])[0].squeeze()
-                    face_embeddings_map[name].append(emb)
-
-                idx += 1
-
-            self.model_builder_queue.put(face_embeddings_map)
-
-        thread = threading.Thread(target=build_model, daemon=True)
-        thread.start()
-
     def build(self):
         if not self.landmark_detector:
-            self.init_landmark_detector()
             return None
 
-        if self.model_builder_queue is not None:
+        face_embeddings_map: dict[str, list[np.ndarray]] = {}
-            try:
+        idx = 0
-                face_embeddings_map: dict[str, list[np.ndarray]] = (
+
-                    self.model_builder_queue.get(timeout=0.1)
+        dir = "/media/frigate/clips/faces"
-                )
+        for name in os.listdir(dir):
-                self.model_builder_queue = None
+            if name == "train":
-            except queue.Empty:
+                continue
-                return
+
-        else:
+            face_folder = os.path.join(dir, name)
-            self.run_build_task()
+
-            return
+            if not os.path.isdir(face_folder):
+                continue
+
+            face_embeddings_map[name] = []
+            for image in os.listdir(face_folder):
+                img = cv2.imread(os.path.join(face_folder, image))
+
+                if img is None:
+                    continue
+
+                img = self.align_face(img, img.shape[1], img.shape[0])
+                emb = self.face_embedder([img])[0].squeeze()
+                face_embeddings_map[name].append(emb)
+
+            idx += 1
 
         if not face_embeddings_map:
             return
 
         for name, embs in face_embeddings_map.items():
-            if embs:
+            self.mean_embs[name] = stats.trim_mean(embs, 0.15)
-                self.mean_embs[name] = stats.trim_mean(embs, 0.15)
 
         logger.debug("Finished building ArcFace model")
 

web/src/views/settings/ClassificationSettingsView.tsx

@@ -176,8 +176,8 @@ export default function ClassificationSettingsView({
       })
       .finally(() => {
         addMessage(
-          "search_settings_restart",
+          "search_settings",
-          `Restart required (Classification settings changed)`,
+          `Restart Required (Classification settings changed)`,
           undefined,
           "search_settings",
         );

web/src/views/settings/FrigatePlusSettingsView.tsx

@@ -131,6 +131,12 @@ export default function FrigatePlusSettingsView({
             position: "top-center",
           });
           setChangedValue(false);
+          addMessage(
+            "plus_restart",
+            "Restart required (Frigate+ model changed)",
+            undefined,
+            "plus_restart",
+          );
           updateConfig();
         } else {
           toast.error(
@@ -154,12 +160,6 @@ export default function FrigatePlusSettingsView({
         );
       })
       .finally(() => {
-        addMessage(
-          "plus_restart",
-          "Restart required (Frigate+ model changed)",
-          undefined,
-          "plus_restart",
-        );
         setIsLoading(false);
       });
   }, [updateConfig, addMessage, frigatePlusSettings, t]);