Face recognition fixes (#15222)

* Fix nginx max upload size

* Close upload dialog when done and add toasts

* Formatting

* fix ruff

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -81,6 +81,9 @@ http {
         open_file_cache_errors on;
         aio on;
 
+        # file upload size
+        client_max_body_size 10M;
+
         # https://github.com/kaltura/nginx-vod-module#vod_open_file_thread_pool
         vod_open_file_thread_pool default;
 
@@ -246,8 +249,6 @@ http {
             proxy_no_cache $should_not_cache;
             add_header X-Cache-Status $upstream_cache_status;
 
-            client_max_body_size 10M;
-
             location /api/vod/ {
                 include auth_request.conf;
                 proxy_pass http://frigate_api/vod/;

frigate/util/model.py

@@ -162,7 +162,9 @@ class FaceClassificationModel:
     def __init__(self, config: FaceRecognitionConfig, db: SqliteQueueDatabase):
         self.config = config
         self.db = db
-        self.recognizer = cv2.face.LBPHFaceRecognizer_create(radius=4, threshold=(1 - config.threshold) * 1000)
+        self.recognizer = cv2.face.LBPHFaceRecognizer_create(
+            radius=4, threshold=(1 - config.threshold) * 1000
+        )
         self.label_map: dict[int, str] = {}
 
     def __build_classifier(self) -> None:
@@ -190,11 +192,12 @@ class FaceClassificationModel:
         if not self.label_map:
             self.__build_classifier()
 
-        index, distance = self.recognizer.predict(cv2.equalizeHist(cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)))
+        index, distance = self.recognizer.predict(
+            cv2.equalizeHist(cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY))
+        )
 
         if index == -1:
             return None
 
         score = 1.0 - (distance / 1000)
         return self.label_map[index], round(score, 2)
-

web/src/pages/FaceLibrary.tsx

@@ -20,7 +20,7 @@ export default function FaceLibrary() {
 
   // face data
 
-  const { data: faceData } = useSWR("faces");
+  const { data: faceData, mutate: refreshFaces } = useSWR("faces");
 
   const faces = useMemo<string[]>(
     () => (faceData ? Object.keys(faceData) : []),
@@ -47,13 +47,36 @@ export default function FaceLibrary() {
     (file: File) => {
       const formData = new FormData();
       formData.append("file", file);
-      axios.post(`faces/${pageToggle}`, formData, {
+      axios
+        .post(`faces/${pageToggle}`, formData, {
           headers: {
             "Content-Type": "multipart/form-data",
           },
+        })
+        .then((resp) => {
+          if (resp.status == 200) {
+            setUpload(false);
+            refreshFaces();
+            toast.success(
+              "Successfully uploaded iamge. View the file in the /exports folder.",
+              { position: "top-center" },
+            );
+          }
+        })
+        .catch((error) => {
+          if (error.response?.data?.message) {
+            toast.error(
+              `Failed to upload image: ${error.response.data.message}`,
+              { position: "top-center" },
+            );
+          } else {
+            toast.error(`Failed to upload image: ${error.message}`, {
+              position: "top-center",
+            });
+          }
         });
     },
-    [pageToggle],
+    [pageToggle, refreshFaces],
   );
 
   return (