change password button text

Pre-import Interpreter in embeddings maintainer and add defensive lazy imports in classification processors to avoid worker-thread tensorflow imports causing "can't register atexit after shutdown"

frigate/api/auth.py

@@ -893,13 +893,9 @@ async def update_password(
     except DoesNotExist:
         return JSONResponse(content={"message": "User not found"}, status_code=404)
 
-    # Require old_password when:
+    # Require old_password when non-admin user is changing any password
-    # 1. Non-admin user is changing another user's password (admin only action)
+    # Admin users changing passwords do NOT need to provide the current password
-    # 2. Any user is changing their own password
+    if current_role != "admin":
-    is_changing_own_password = current_username == username
-    is_non_admin = current_role != "admin"
-
-    if is_changing_own_password or is_non_admin:
         if not body.old_password:
             return JSONResponse(
                 content={"message": "Current password is required"},

frigate/data_processing/real_time/bird.py

@@ -19,11 +19,6 @@ from frigate.util.object import calculate_region
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
 
-try:
-    from tflite_runtime.interpreter import Interpreter
-except ModuleNotFoundError:
-    from tensorflow.lite.python.interpreter import Interpreter
-
 logger = logging.getLogger(__name__)
 
 
@@ -35,7 +30,7 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         metrics: DataProcessorMetrics,
     ):
         super().__init__(config, metrics)
-        self.interpreter: Interpreter = None
+        self.interpreter: Any | None = None
         self.sub_label_publisher = sub_label_publisher
         self.tensor_input_details: dict[str, Any] = None
         self.tensor_output_details: dict[str, Any] = None
@@ -82,6 +77,11 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
 
     @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
+        try:
+            from tflite_runtime.interpreter import Interpreter
+        except ModuleNotFoundError:
+            from tensorflow.lite.python.interpreter import Interpreter
+
         self.interpreter = Interpreter(
             model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
             num_threads=2,

frigate/data_processing/real_time/custom_classification.py

@@ -29,11 +29,6 @@ from frigate.util.object import box_overlaps, calculate_region
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
 
-try:
-    from tflite_runtime.interpreter import Interpreter
-except ModuleNotFoundError:
-    from tensorflow.lite.python.interpreter import Interpreter
-
 logger = logging.getLogger(__name__)
 
 MAX_OBJECT_CLASSIFICATIONS = 16
@@ -52,7 +47,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         self.requestor = requestor
         self.model_dir = os.path.join(MODEL_CACHE_DIR, self.model_config.name)
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
-        self.interpreter: Interpreter | None = None
+        self.interpreter: Any | None = None
         self.tensor_input_details: dict[str, Any] | None = None
         self.tensor_output_details: dict[str, Any] | None = None
         self.labelmap: dict[int, str] = {}
@@ -74,6 +69,11 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
 
     @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
+        try:
+            from tflite_runtime.interpreter import Interpreter
+        except ModuleNotFoundError:
+            from tensorflow.lite.python.interpreter import Interpreter
+
         model_path = os.path.join(self.model_dir, "model.tflite")
         labelmap_path = os.path.join(self.model_dir, "labelmap.txt")
 
@@ -345,7 +345,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         self.model_config = model_config
         self.model_dir = os.path.join(MODEL_CACHE_DIR, self.model_config.name)
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
-        self.interpreter: Interpreter | None = None
+        self.interpreter: Any | None = None
         self.sub_label_publisher = sub_label_publisher
         self.requestor = requestor
         self.tensor_input_details: dict[str, Any] | None = None
@@ -368,6 +368,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
 
     @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
+        try:
+            from tflite_runtime.interpreter import Interpreter
+        except ModuleNotFoundError:
+            from tensorflow.lite.python.interpreter import Interpreter
+
         model_path = os.path.join(self.model_dir, "model.tflite")
         labelmap_path = os.path.join(self.model_dir, "labelmap.txt")
 

frigate/embeddings/maintainer.py

@@ -146,6 +146,29 @@ class EmbeddingMaintainer(threading.Thread):
         self.detected_license_plates: dict[str, dict[str, Any]] = {}
         self.genai_client = get_genai_client(config)
 
+        # Pre-import TensorFlow/tflite on main thread to avoid atexit registration issues
+        # when importing from worker threads later (e.g., during dynamic config updates)
+        if (
+            self.config.classification.bird.enabled
+            or len(self.config.classification.custom) > 0
+        ):
+            try:
+                from tflite_runtime.interpreter import Interpreter  # noqa: F401
+            except ModuleNotFoundError:
+                try:
+                    from tensorflow.lite.python.interpreter import (  # noqa: F401
+                        Interpreter,
+                    )
+
+                    logger.debug(
+                        "Pre-imported TensorFlow Interpreter on main thread for classification models"
+                    )
+                except Exception as e:
+                    logger.warning(
+                        f"Failed to pre-import TensorFlow Interpreter: {e}. "
+                        "Classification models may fail to load if added dynamically."
+                    )
+
         # model runners to share between realtime and post processors
         if self.config.lpr.enabled:
             lpr_model_runner = LicensePlateModelRunner(

frigate/ffmpeg_presets.py

@@ -153,7 +153,7 @@ PRESETS_HW_ACCEL_ENCODE_BIRDSEYE = {
     FFMPEG_HWACCEL_VAAPI: "{0} -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi -hwaccel_device {3} {1} -c:v h264_vaapi -g 50 -bf 0 -profile:v high -level:v 4.1 -sei:v 0 -an -vf format=vaapi|nv12,hwupload {2}",
     "preset-intel-qsv-h264": "{0} -hide_banner {1} -c:v h264_qsv -g 50 -bf 0 -profile:v high -level:v 4.1 -async_depth:v 1 {2}",
     "preset-intel-qsv-h265": "{0} -hide_banner {1} -c:v h264_qsv -g 50 -bf 0 -profile:v main -level:v 4.1 -async_depth:v 1 {2}",
-    FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner {1} -hwaccel cuda -hwaccel_device {3} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {2}",
+    FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner {1} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {2}",
     "preset-jetson-h264": "{0} -hide_banner {1} -c:v h264_nvmpi -profile high {2}",
     "preset-jetson-h265": "{0} -hide_banner {1} -c:v h264_nvmpi -profile main {2}",
     FFMPEG_HWACCEL_RKMPP: "{0} -hide_banner {1} -c:v h264_rkmpp -profile:v high {2}",

frigate/util/classification.py

@@ -499,6 +499,10 @@ def _extract_keyframes(
     """
     Extract keyframes from recordings at specified timestamps and crop to specified regions.
 
+    This implementation batches work by running multiple ffmpeg snapshot commands
+    concurrently, which significantly reduces total runtime compared to
+    processing each timestamp serially.
+
     Args:
         ffmpeg_path: Path to ffmpeg binary
         timestamps: List of timestamp dicts from _select_balanced_timestamps
@@ -508,15 +512,21 @@ def _extract_keyframes(
     Returns:
         List of paths to successfully extracted and cropped keyframe images
     """
-    keyframe_paths = []
+    from concurrent.futures import ThreadPoolExecutor, as_completed
 
-    for idx, ts_info in enumerate(timestamps):
+    if not timestamps:
+        return []
+
+    # Limit the number of concurrent ffmpeg processes so we don't overload the host.
+    max_workers = min(5, len(timestamps))
+
+    def _process_timestamp(idx: int, ts_info: dict) -> tuple[int, str | None]:
         camera = ts_info["camera"]
         timestamp = ts_info["timestamp"]
 
         if camera not in camera_crops:
             logger.warning(f"No crop coordinates for camera {camera}")
-            continue
+            return idx, None
 
         norm_x1, norm_y1, norm_x2, norm_y2 = camera_crops[camera]
 
@@ -533,7 +543,7 @@ def _extract_keyframes(
                 .get()
             )
         except Exception:
-            continue
+            return idx, None
 
         relative_time = timestamp - recording.start_time
 
@@ -547,38 +557,57 @@ def _extract_keyframes(
                 height=None,
             )
 
-            if image_data:
+            if not image_data:
-                nparr = np.frombuffer(image_data, np.uint8)
+                return idx, None
-                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
 
-                if img is not None:
+            nparr = np.frombuffer(image_data, np.uint8)
-                    height, width = img.shape[:2]
+            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
 
-                    x1 = int(norm_x1 * width)
+            if img is None:
-                    y1 = int(norm_y1 * height)
+                return idx, None
-                    x2 = int(norm_x2 * width)
-                    y2 = int(norm_y2 * height)
 
-                    x1_clipped = max(0, min(x1, width))
+            height, width = img.shape[:2]
-                    y1_clipped = max(0, min(y1, height))
-                    x2_clipped = max(0, min(x2, width))
-                    y2_clipped = max(0, min(y2, height))
 
-                    if x2_clipped > x1_clipped and y2_clipped > y1_clipped:
+            x1 = int(norm_x1 * width)
-                        cropped = img[y1_clipped:y2_clipped, x1_clipped:x2_clipped]
+            y1 = int(norm_y1 * height)
-                        resized = cv2.resize(cropped, (224, 224))
+            x2 = int(norm_x2 * width)
+            y2 = int(norm_y2 * height)
 
-                        output_path = os.path.join(output_dir, f"frame_{idx:04d}.jpg")
+            x1_clipped = max(0, min(x1, width))
-                        cv2.imwrite(output_path, resized)
+            y1_clipped = max(0, min(y1, height))
-                        keyframe_paths.append(output_path)
+            x2_clipped = max(0, min(x2, width))
+            y2_clipped = max(0, min(y2, height))
 
+            if x2_clipped <= x1_clipped or y2_clipped <= y1_clipped:
+                return idx, None
+
+            cropped = img[y1_clipped:y2_clipped, x1_clipped:x2_clipped]
+            resized = cv2.resize(cropped, (224, 224))
+
+            output_path = os.path.join(output_dir, f"frame_{idx:04d}.jpg")
+            cv2.imwrite(output_path, resized)
+            return idx, output_path
         except Exception as e:
             logger.debug(
                 f"Failed to extract frame from {recording.path} at {relative_time}s: {e}"
             )
-            continue
+            return idx, None
 
-    return keyframe_paths
+    keyframes_with_index: list[tuple[int, str]] = []
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        future_to_idx = {
+            executor.submit(_process_timestamp, idx, ts_info): idx
+            for idx, ts_info in enumerate(timestamps)
+        }
+
+        for future in as_completed(future_to_idx):
+            _, path = future.result()
+            if path:
+                keyframes_with_index.append((future_to_idx[future], path))
+
+    keyframes_with_index.sort(key=lambda item: item[0])
+    return [path for _, path in keyframes_with_index]
 
 
 def _select_distinct_images(

web/public/locales/en/views/settings.json

@@ -679,7 +679,7 @@
       "desc": "Manage this Frigate instance's user accounts."
     },
     "addUser": "Add User",
-    "updatePassword": "Update Password",
+    "updatePassword": "Reset Password",
     "toast": {
       "success": {
         "createUser": "User {{user}} created successfully",
@@ -700,7 +700,7 @@
       "role": "Role",
       "noUsers": "No users found.",
       "changeRole": "Change user role",
-      "password": "Password",
+      "password": "Reset Password",
       "deleteUser": "Delete user"
     },
     "dialog": {

web/src/App.tsx

@@ -14,6 +14,7 @@ import ProtectedRoute from "@/components/auth/ProtectedRoute";
 import { AuthProvider } from "@/context/auth-context";
 import useSWR from "swr";
 import { FrigateConfig } from "./types/frigateConfig";
+import ActivityIndicator from "@/components/indicators/activity-indicator";
 
 const Live = lazy(() => import("@/pages/Live"));
 const Events = lazy(() => import("@/pages/Events"));
@@ -50,6 +51,13 @@ function DefaultAppView() {
   const { data: config } = useSWR<FrigateConfig>("config", {
     revalidateOnFocus: false,
   });
+
+  // Compute required roles for main routes, ensuring we have config first
+  // to prevent race condition where custom roles are temporarily unavailable
+  const mainRouteRoles = config?.auth?.roles
+    ? Object.keys(config.auth.roles)
+    : undefined;
+
   return (
     <div className="size-full overflow-hidden">
       {isDesktop && <Sidebar />}
@@ -68,13 +76,11 @@ function DefaultAppView() {
           <Routes>
             <Route
               element={
-                <ProtectedRoute
+                mainRouteRoles ? (
-                  requiredRoles={
+                  <ProtectedRoute requiredRoles={mainRouteRoles} />
-                    config?.auth.roles
+                ) : (
-                      ? Object.keys(config.auth.roles)
+                  <ActivityIndicator className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2" />
-                      : ["admin", "viewer"]
+                )
-                  }
-                />
               }
             >
               <Route index element={<Live />} />

web/src/components/classification/wizard/Step3ChooseExamples.tsx

@@ -141,7 +141,37 @@ export default function Step3ChooseExamples({
       );
       await Promise.all(categorizePromises);
 
-      // Step 2.5: Create empty folders for classes that don't have any images
+      // Step 2.5: Delete any unselected images from train folder
+      // For state models, all images must be classified, so unselected images should be removed
+      // For object models, unselected images are assigned to "none" so they're already categorized
+      if (step1Data.modelType === "state") {
+        try {
+          // Fetch current train images to see what's left after categorization
+          const trainImagesResponse = await axios.get<string[]>(
+            `/classification/${step1Data.modelName}/train`,
+          );
+          const remainingTrainImages = trainImagesResponse.data || [];
+
+          const categorizedImageNames = new Set(Object.keys(classifications));
+          const unselectedImages = remainingTrainImages.filter(
+            (imageName) => !categorizedImageNames.has(imageName),
+          );
+
+          if (unselectedImages.length > 0) {
+            await axios.post(
+              `/classification/${step1Data.modelName}/train/delete`,
+              {
+                ids: unselectedImages,
+              },
+            );
+          }
+        } catch (error) {
+          // Silently fail - unselected images will remain but won't cause issues
+          // since the frontend filters out images that don't match expected format
+        }
+      }
+
+      // Step 2.6: Create empty folders for classes that don't have any images
       // This ensures all classes are available in the dataset view later
       const classesWithImages = new Set(
         Object.values(classifications).filter((c) => c && c !== "none"),

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

@@ -49,6 +49,29 @@ export default function DetailActionsMenu({
     search.data?.type === "audio" ? null : [`review/event/${search.id}`],
   );
 
+  // don't render menu at all if no options are available
+  const hasSemanticSearchOption =
+    config?.semantic_search.enabled &&
+    setSimilarity !== undefined &&
+    search.data?.type === "object";
+
+  const hasReviewItem = !!(reviewItem && reviewItem.id);
+
+  const hasAdminTriggerOption =
+    isAdmin &&
+    config?.semantic_search.enabled &&
+    search.data?.type === "object";
+
+  if (
+    !search.has_snapshot &&
+    !search.has_clip &&
+    !hasSemanticSearchOption &&
+    !hasReviewItem &&
+    !hasAdminTriggerOption
+  ) {
+    return null;
+  }
+
   return (
     <DropdownMenu open={isOpen} onOpenChange={setIsOpen}>
       <DropdownMenuTrigger>

web/src/views/classification/ModelTrainingView.tsx

@@ -866,6 +866,12 @@ function TrainGrid({
           };
         })
         .filter((data) => {
+          // Ignore images that don't match the expected format (event-camera-timestamp-state-score.webp)
+          // Expected format has 5 parts when split by "-", and score should be a valid number
+          if (data.score === undefined || isNaN(data.score) || !data.name) {
+            return false;
+          }
+
           if (!trainFilter) {
             return true;
           }