Standardize request handling

frigate/api/classification.py

@@ -39,6 +39,12 @@ def get_faces():
 
 @router.post("/faces/{name}")
 async def register_face(request: Request, name: str, 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.register_face(name, await file.read())
     return JSONResponse(
@@ -48,7 +54,13 @@ async def register_face(request: Request, name: str, file: UploadFile):
 
 
 @router.post("/faces/train/{name}/classify")
-def train_face(name: str, body: dict = None):
+def train_face(request: Request, name: str, body: dict = None):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
     json: dict[str, any] = body or {}
     training_file = os.path.join(
         FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
@@ -82,6 +94,12 @@ def train_face(name: str, body: dict = None):
 
 @router.post("/faces/{name}/delete")
 def deregister_faces(request: Request, name: str, body: dict = None):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
     json: dict[str, any] = body or {}
     list_of_ids = json.get("ids", "")
 

frigate/embeddings/maintainer.py

@@ -4,9 +4,7 @@ import base64
 import datetime
 import logging
 import os
-import random
 import re
-import string
 import threading
 from multiprocessing.synchronize import Event as MpEvent
 from pathlib import Path
@@ -28,7 +26,6 @@ from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
 from frigate.const import (
     CLIPS_DIR,
-    FACE_DIR,
     FRIGATE_LOCALHOST,
     UPDATE_EVENT_DESCRIPTION,
 )
@@ -37,6 +34,7 @@ from frigate.events.types import EventTypeEnum
 from frigate.genai import get_genai_client
 from frigate.models import Event
 from frigate.postprocessing.face_processor import FaceProcessor
+from frigate.postprocessing.processor_api import ProcessorApi
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import serialize
 from frigate.util.image import SharedMemoryFrameManager, area, calculate_region
@@ -75,13 +73,10 @@ class EmbeddingMaintainer(threading.Thread):
         )
         self.embeddings_responder = EmbeddingsResponder()
         self.frame_manager = SharedMemoryFrameManager()
+        self.processors: list[ProcessorApi] = []
 
-        # set face recognition conditions
-        self.face_processor = (
-            FaceProcessor(self.config.face_recognition, db)
-            if self.config.face_recognition.enabled
-            else None
-        )
+        if self.config.face_recognition.enabled:
+            self.processors.append(FaceProcessor(self.config.face_recognition, db))
 
         # create communication for updating event descriptions
         self.requestor = InterProcessRequestor()
@@ -139,55 +134,12 @@ class EmbeddingMaintainer(threading.Thread):
                         self.embeddings.embed_description("", data, upsert=False),
                         pack=False,
                     )
-                elif topic == EmbeddingsRequestEnum.register_face.value:
-                    if not self.face_recognition_enabled:
-                        return {
-                            "message": "Face recognition is not enabled.",
-                            "success": False,
-                        }
-
-                    rand_id = "".join(
-                        random.choices(string.ascii_lowercase + string.digits, k=6)
-                    )
-                    label = data["face_name"]
-                    id = f"{label}-{rand_id}"
-
-                    if data.get("cropped"):
-                        pass
                 else:
-                        img = cv2.imdecode(
-                            np.frombuffer(
-                                base64.b64decode(data["image"]), dtype=np.uint8
-                            ),
-                            cv2.IMREAD_COLOR,
-                        )
-                        face_box = self._detect_face(img)
+                    for processor in self.processors:
+                        resp = processor.handle_request(data)
 
-                        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]]
-                        ret, thumbnail = cv2.imencode(
-                            ".webp", face, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
-                        )
-
-                    # write face to library
-                    folder = os.path.join(FACE_DIR, label)
-                    file = os.path.join(folder, f"{id}.webp")
-                    os.makedirs(folder, exist_ok=True)
-
-                    # save face image
-                    with open(file, "wb") as output:
-                        output.write(thumbnail.tobytes())
-
-                self.face_classifier.clear_classifier()
-                return {
-                    "message": "Successfully registered face.",
-                    "success": True,
-                }
+                        if resp is not None:
+                            return resp
             except Exception as e:
                 logger.error(f"Unable to handle embeddings request {e}")
 

frigate/postprocessing/face_processor.py

@@ -1,7 +1,10 @@
 """Handle processing images for face detection and recognition."""
 
+import base64
 import logging
 import os
+import random
+import string
 from typing import Optional
 
 import cv2
@@ -173,20 +176,17 @@ class FaceProcessor(ProcessorApi):
             image, M, (output_width, output_height), flags=cv2.INTER_CUBIC
         )
 
-    def clear_classifier(self) -> None:
+    def __clear_classifier(self) -> None:
         self.face_recognizer = None
         self.label_map = {}
 
-    def detect_faces(self, input: np.ndarray) -> tuple[int, cv2.typing.MatLike] | None:
+    def __detect_face(self, input: np.ndarray) -> tuple[int, int, int, int]:
+        """Detect faces in input image."""
         if not self.face_detector:
             return None
 
         self.face_detector.setInputSize((input.shape[1], input.shape[0]))
-        return self.face_detector.detect(input)
-
-    def _detect_face(self, input: np.ndarray) -> tuple[int, int, int, int]:
-        """Detect faces in input image."""
-        faces = self.detect_faces(input)
+        faces = self.face_detector.detect(input)
 
         if faces is None or faces[1] is None:
             return None
@@ -206,6 +206,23 @@ class FaceProcessor(ProcessorApi):
 
         return face
 
+    def __classify_face(self, face_image: np.ndarray) -> tuple[str, float] | None:
+        if not self.landmark_detector:
+            return None
+
+        if not self.label_map:
+            self.__build_classifier()
+
+        img = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
+        img = self.__align_face(img, img.shape[1], img.shape[0])
+        index, distance = self.recognizer.predict(img)
+
+        if index == -1:
+            return None
+
+        score = 1.0 - (distance / 1000)
+        return self.label_map[index], round(score, 2)
+
     def process_frame(self, obj_data: dict[str, any], frame: np.ndarray) -> bool:
         """Look for faces in image."""
         id = obj_data["id"]
@@ -235,7 +252,7 @@ class FaceProcessor(ProcessorApi):
             rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
             left, top, right, bottom = person_box
             person = rgb[top:bottom, left:right]
-            face_box = self._detect_face(person)
+            face_box = self.__detect_face(person)
 
             if not face_box:
                 logger.debug("Detected no faces for person object.")
@@ -278,7 +295,7 @@ class FaceProcessor(ProcessorApi):
                 max(0, face_box[0]) : min(frame.shape[1], face_box[2]),
             ]
 
-        res = self.face_classifier.classify_face(face_frame)
+        res = self.__classify_face(face_frame)
 
         if not res:
             return False
@@ -328,19 +345,42 @@ class FaceProcessor(ProcessorApi):
 
         return True
 
-    def classify_face(self, face_image: np.ndarray) -> tuple[str, float] | None:
-        if not self.landmark_detector:
-            return None
+    def handle_request(self, request_data) -> dict[str, any] | None:
+        rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
+        label = request_data["face_name"]
+        id = f"{label}-{rand_id}"
 
-        if not self.label_map:
-            self.__build_classifier()
+        if request_data.get("cropped"):
+            thumbnail = request_data["image"]
+        else:
+            img = cv2.imdecode(
+                np.frombuffer(base64.b64decode(request_data["image"]), dtype=np.uint8),
+                cv2.IMREAD_COLOR,
+            )
+            face_box = self.__detect_face(img)
 
-        img = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
-        img = self.__align_face(img, img.shape[1], img.shape[0])
-        index, distance = self.recognizer.predict(img)
+            if not face_box:
+                return {
+                    "message": "No face was detected.",
+                    "success": False,
+                }
 
-        if index == -1:
-            return None
+            face = img[face_box[1] : face_box[3], face_box[0] : face_box[2]]
+            ret, thumbnail = cv2.imencode(
+                ".webp", face, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
+            )
 
-        score = 1.0 - (distance / 1000)
-        return self.label_map[index], round(score, 2)
+        # write face to library
+        folder = os.path.join(FACE_DIR, label)
+        file = os.path.join(folder, f"{id}.webp")
+        os.makedirs(folder, exist_ok=True)
+
+        # save face image
+        with open(file, "wb") as output:
+            output.write(thumbnail.tobytes())
+
+        self.__clear_classifier()
+        return {
+            "message": "Successfully registered face.",
+            "success": True,
+        }

frigate/postprocessing/processor_api.py

@@ -10,10 +10,29 @@ logger = logging.getLogger(__name__)
 
 class ProcessorApi(ABC):
     @abstractmethod
-    def __init__(self, config: FrigateConfig):
+    def __init__(self, config: FrigateConfig) -> None:
         self.config = config
         pass
 
     @abstractmethod
-    def process_frame(self, obj_data: dict[str, any], frame: np.ndarray):
+    def process_frame(self, obj_data: dict[str, any], frame: np.ndarray) -> None:
+        """Processes the frame with object data.
+        Args:
+            obj_data (dict): containing data about focused object in frame.
+            frame (ndarray): full yuv frame.
+
+        Returns:
+            None.
+        """
+        pass
+
+    @abstractmethod
+    def handle_request(self, request_data: dict[str, any]) -> any | None:
+        """Handle metadata requests.
+        Args:
+            request_data (dict): containing data about requested change to process.
+
+        Returns:
+            None if request was not handled, otherwise return response.
+        """
         pass