Miscellaneous fixes (0.17 beta) (#21350)

* Fix genai callbacks in MQTT

* Cleanup cursor pointer for classification cards

* Cleanup

* Handle unknown SOCs for RKNN converter by only using known SOCs

* don't allow "none" as a classification class name

* change internal port user to admin and default unspecified username to viewer

* keep 5000 as anonymous user

* suppress tensorflow logging during classification training

* Always apply base log level suppressions for noisy third-party libraries even if no specific logConfig is provided

* remove decorator and specifically suppress TFLite delegate creation messages

---------

Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>

docker/main/Dockerfile

@@ -237,8 +237,18 @@ ENV PYTHONWARNINGS="ignore:::numpy.core.getlimits"
 # Set HailoRT to disable logging
 ENV HAILORT_LOGGER_PATH=NONE
 
-# TensorFlow error only
+# TensorFlow C++ logging suppression (must be set before import)
+# TF_CPP_MIN_LOG_LEVEL: 0=all, 1=INFO+, 2=WARNING+, 3=ERROR+ (we use 3 for errors only)
 ENV TF_CPP_MIN_LOG_LEVEL=3
+# Suppress verbose logging from TensorFlow C++ code
+ENV TF_CPP_MIN_VLOG_LEVEL=3
+# Disable oneDNN optimization messages ("optimized with oneDNN...")
+ENV TF_ENABLE_ONEDNN_OPTS=0
+# Suppress AutoGraph verbosity during conversion
+ENV AUTOGRAPH_VERBOSITY=0
+# Google Logging (GLOG) suppression for TensorFlow components
+ENV GLOG_minloglevel=3
+ENV GLOG_logtostderr=0
 
 ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 

docs/static/frigate-api.yaml

@@ -25,7 +25,7 @@ paths:
           description: Authentication Accepted (no response body, different headers depending on auth method)
           headers:
             remote-user:
-              description: Authenticated username or "anonymous" in proxy-only mode
+              description: Authenticated username or "viewer" in proxy-only mode
               schema:
                 type: string
             remote-role:

frigate/api/auth.py

@@ -167,7 +167,7 @@ def allow_any_authenticated():
     Allows:
     - Port 5000 internal requests (remote-user: "anonymous", remote-role: "admin")
     - Authenticated users with JWT tokens (remote-user: username)
-    - Unauthenticated requests when auth is disabled (remote-user: "anonymous")
+    - Unauthenticated requests when auth is disabled (remote-user: "viewer")
 
     Rejects:
     - Requests with no remote-user header (did not pass through /auth endpoint)
@@ -550,7 +550,7 @@ def resolve_role(
             "description": "Authentication Accepted (no response body)",
             "headers": {
                 "remote-user": {
-                    "description": 'Authenticated username or "anonymous" in proxy-only mode',
+                    "description": 'Authenticated username or "viewer" in proxy-only mode',
                     "schema": {"type": "string"},
                 },
                 "remote-role": {
@@ -592,12 +592,12 @@ def auth(request: Request):
     # if auth is disabled, just apply the proxy header map and return success
     if not auth_config.enabled:
         # pass the user header value from the upstream proxy if a mapping is specified
-        # or use anonymous if none are specified
+        # or use viewer if none are specified
         user_header = proxy_config.header_map.user
         success_response.headers["remote-user"] = (
-            request.headers.get(user_header, default="anonymous")
+            request.headers.get(user_header, default="viewer")
             if user_header
-            else "anonymous"
+            else "viewer"
         )
 
         # parse header and resolve a valid role
@@ -712,7 +712,7 @@ def auth(request: Request):
     description="Returns the current authenticated user's profile including username, role, and allowed cameras. This endpoint requires authentication and returns information about the user's permissions.",
 )
 def profile(request: Request):
-    username = request.headers.get("remote-user", "anonymous")
+    username = request.headers.get("remote-user", "viewer")
     role = request.headers.get("remote-role", "viewer")
 
     all_camera_names = set(request.app.frigate_config.cameras.keys())

frigate/comms/mqtt.py

@@ -225,7 +225,8 @@ class MqttClient(Communicator):
             "birdseye_mode",
             "review_alerts",
             "review_detections",
-            "genai",
+            "object_descriptions",
+            "review_descriptions",
         ]
 
         for name in self.config.cameras.keys():

frigate/const.py

@@ -77,6 +77,9 @@ FFMPEG_HWACCEL_RKMPP = "preset-rkmpp"
 FFMPEG_HWACCEL_AMF = "preset-amd-amf"
 FFMPEG_HVC1_ARGS = ["-tag:v", "hvc1"]
 
+# RKNN constants
+SUPPORTED_RK_SOCS = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
+
 # Regex constants
 
 REGEX_CAMERA_NAME = r"^[a-zA-Z0-9_-]+$"

frigate/data_processing/real_time/bird.py

@@ -13,7 +13,7 @@ from frigate.comms.event_metadata_updater import (
 )
 from frigate.config import FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.util.object import calculate_region
 
 from ..types import DataProcessorMetrics
@@ -80,13 +80,14 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         except Exception as e:
             logger.error(f"Failed to download {path}: {e}")
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
-        self.interpreter = Interpreter(
+        # Suppress TFLite delegate creation messages that bypass Python logging
-            model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
+        with suppress_stderr_during("tflite_interpreter_init"):
-            num_threads=2,
+            self.interpreter = Interpreter(
-        )
+                model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
-        self.interpreter.allocate_tensors()
+                num_threads=2,
+            )
+            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
 

frigate/data_processing/real_time/custom_classification.py

@@ -21,7 +21,7 @@ from frigate.config.classification import (
     ObjectClassificationType,
 )
 from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
 from frigate.util.object import box_overlaps, calculate_region
@@ -72,7 +72,6 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         self.last_run = datetime.datetime.now().timestamp()
         self.__build_detector()
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
         try:
             from tflite_runtime.interpreter import Interpreter
@@ -89,11 +88,13 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
-        self.interpreter = Interpreter(
+        # Suppress TFLite delegate creation messages that bypass Python logging
-            model_path=model_path,
+        with suppress_stderr_during("tflite_interpreter_init"):
-            num_threads=2,
+            self.interpreter = Interpreter(
-        )
+                model_path=model_path,
-        self.interpreter.allocate_tensors()
+                num_threads=2,
+            )
+            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
         self.labelmap = load_labels(labelmap_path, prefill=0)
@@ -377,7 +378,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
 
         self.__build_detector()
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
         model_path = os.path.join(self.model_dir, "model.tflite")
         labelmap_path = os.path.join(self.model_dir, "labelmap.txt")
@@ -389,11 +389,13 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
-        self.interpreter = Interpreter(
+        # Suppress TFLite delegate creation messages that bypass Python logging
-            model_path=model_path,
+        with suppress_stderr_during("tflite_interpreter_init"):
-            num_threads=2,
+            self.interpreter = Interpreter(
-        )
+                model_path=model_path,
-        self.interpreter.allocate_tensors()
+                num_threads=2,
+            )
+            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
         self.labelmap = load_labels(labelmap_path, prefill=0)

frigate/detectors/plugins/cpu_tfl.py

@@ -5,7 +5,7 @@ from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 
 from ..detector_utils import tflite_detect_raw, tflite_init
 
@@ -28,12 +28,13 @@ class CpuDetectorConfig(BaseDetectorConfig):
 class CpuTfl(DetectionApi):
     type_key = DETECTOR_KEY
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __init__(self, detector_config: CpuDetectorConfig):
-        interpreter = Interpreter(
+        # Suppress TFLite delegate creation messages that bypass Python logging
-            model_path=detector_config.model.path,
+        with suppress_stderr_during("tflite_interpreter_init"):
-            num_threads=detector_config.num_threads or 3,
+            interpreter = Interpreter(
-        )
+                model_path=detector_config.model.path,
+                num_threads=detector_config.num_threads or 3,
+            )
 
         tflite_init(self, interpreter)
 

frigate/detectors/plugins/rknn.py

@@ -8,7 +8,7 @@ import cv2
 import numpy as np
 from pydantic import Field
 
-from frigate.const import MODEL_CACHE_DIR
+from frigate.const import MODEL_CACHE_DIR, SUPPORTED_RK_SOCS
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import RKNNModelRunner
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
@@ -19,8 +19,6 @@ logger = logging.getLogger(__name__)
 
 DETECTOR_KEY = "rknn"
 
-supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
-
 supported_models = {
     ModelTypeEnum.yologeneric: "^frigate-fp16-yolov9-[cemst]$",
     ModelTypeEnum.yolonas: "^deci-fp16-yolonas_[sml]$",
@@ -82,9 +80,9 @@ class Rknn(DetectionApi):
         except FileNotFoundError:
             raise Exception("Make sure to run docker in privileged mode.")
 
-        if soc not in supported_socs:
+        if soc not in SUPPORTED_RK_SOCS:
             raise Exception(
-                f"Your SoC is not supported. Your SoC is: {soc}. Currently these SoCs are supported: {supported_socs}."
+                f"Your SoC is not supported. Your SoC is: {soc}. Currently these SoCs are supported: {SUPPORTED_RK_SOCS}."
             )
 
         return soc

frigate/embeddings/onnx/face_embedding.py

@@ -8,7 +8,7 @@ import numpy as np
 from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors.detection_runners import get_optimized_runner
 from frigate.embeddings.types import EnrichmentModelTypeEnum
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.util.downloader import ModelDownloader
 
 from ...config import FaceRecognitionConfig
@@ -57,17 +57,18 @@ class FaceNetEmbedding(BaseEmbedding):
             self._load_model_and_utils()
             logger.debug(f"models are already downloaded for {self.model_name}")
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def _load_model_and_utils(self):
         if self.runner is None:
             if self.downloader:
                 self.downloader.wait_for_download()
 
-            self.runner = Interpreter(
+            # Suppress TFLite delegate creation messages that bypass Python logging
-                model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
+            with suppress_stderr_during("tflite_interpreter_init"):
-                num_threads=2,
+                self.runner = Interpreter(
-            )
+                    model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
-            self.runner.allocate_tensors()
+                    num_threads=2,
+                )
+                self.runner.allocate_tensors()
             self.tensor_input_details = self.runner.get_input_details()
             self.tensor_output_details = self.runner.get_output_details()
 

frigate/events/audio.py

@@ -34,7 +34,7 @@ from frigate.data_processing.real_time.audio_transcription import (
     AudioTranscriptionRealTimeProcessor,
 )
 from frigate.ffmpeg_presets import parse_preset_input
-from frigate.log import LogPipe, redirect_output_to_logger
+from frigate.log import LogPipe, suppress_stderr_during
 from frigate.object_detection.base import load_labels
 from frigate.util.builtin import get_ffmpeg_arg_list
 from frigate.util.process import FrigateProcess
@@ -367,17 +367,17 @@ class AudioEventMaintainer(threading.Thread):
 
 
 class AudioTfl:
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __init__(self, stop_event: threading.Event, num_threads=2):
         self.stop_event = stop_event
         self.num_threads = num_threads
         self.labels = load_labels("/audio-labelmap.txt", prefill=521)
-        self.interpreter = Interpreter(
+        # Suppress TFLite delegate creation messages that bypass Python logging
-            model_path="/cpu_audio_model.tflite",
+        with suppress_stderr_during("tflite_interpreter_init"):
-            num_threads=self.num_threads,
+            self.interpreter = Interpreter(
-        )
+                model_path="/cpu_audio_model.tflite",
-
+                num_threads=self.num_threads,
-        self.interpreter.allocate_tensors()
+            )
+            self.interpreter.allocate_tensors()
 
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()

frigate/log.py

@@ -80,10 +80,15 @@ def apply_log_levels(default: str, log_levels: dict[str, LogLevel]) -> None:
     log_levels = {
         "absl": LogLevel.error,
         "httpx": LogLevel.error,
+        "h5py": LogLevel.error,
+        "keras": LogLevel.error,
         "matplotlib": LogLevel.error,
         "tensorflow": LogLevel.error,
+        "tensorflow.python": LogLevel.error,
         "werkzeug": LogLevel.error,
         "ws4py": LogLevel.error,
+        "PIL": LogLevel.warning,
+        "numba": LogLevel.warning,
         **log_levels,
     }
 
@@ -318,3 +323,31 @@ def suppress_os_output(func: Callable) -> Callable:
         return result
 
     return wrapper
+
+
+@contextmanager
+def suppress_stderr_during(operation_name: str) -> Generator[None, None, None]:
+    """
+    Context manager to suppress stderr output during a specific operation.
+
+    Useful for silencing LLVM debug output, CUDA messages, and other native
+    library logging that cannot be controlled via Python logging or environment
+    variables. Completely redirects file descriptor 2 (stderr) to /dev/null.
+
+    Usage:
+        with suppress_stderr_during("model_conversion"):
+            converter = tf.lite.TFLiteConverter.from_keras_model(model)
+            tflite_model = converter.convert()
+
+    Args:
+        operation_name: Name of the operation for debugging purposes
+    """
+    original_stderr_fd = os.dup(2)
+    devnull = os.open(os.devnull, os.O_WRONLY)
+    try:
+        os.dup2(devnull, 2)
+        yield
+    finally:
+        os.dup2(original_stderr_fd, 2)
+        os.close(devnull)
+        os.close(original_stderr_fd)

frigate/util/classification.py

@@ -19,7 +19,7 @@ from frigate.const import (
     PROCESS_PRIORITY_LOW,
     UPDATE_MODEL_STATE,
 )
-from frigate.log import redirect_output_to_logger
+from frigate.log import redirect_output_to_logger, suppress_stderr_during
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.types import ModelStatusTypesEnum
 from frigate.util.downloader import ModelDownloader
@@ -250,15 +250,20 @@ class ClassificationTrainingProcess(FrigateProcess):
             logger.debug(f"Converting {self.model_name} to TFLite...")
 
             # convert model to tflite
-            converter = tf.lite.TFLiteConverter.from_keras_model(model)
+            # Suppress stderr during conversion to avoid LLVM debug output
-            converter.optimizations = [tf.lite.Optimize.DEFAULT]
+            # (fully_quantize, inference_type, MLIR optimization messages, etc)
-            converter.representative_dataset = (
+            with suppress_stderr_during("tflite_conversion"):
-                self.__generate_representative_dataset_factory(dataset_dir)
+                converter = tf.lite.TFLiteConverter.from_keras_model(model)
-            )
+                converter.optimizations = [tf.lite.Optimize.DEFAULT]
-            converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+                converter.representative_dataset = (
-            converter.inference_input_type = tf.uint8
+                    self.__generate_representative_dataset_factory(dataset_dir)
-            converter.inference_output_type = tf.uint8
+                )
-            tflite_model = converter.convert()
+                converter.target_spec.supported_ops = [
+                    tf.lite.OpsSet.TFLITE_BUILTINS_INT8
+                ]
+                converter.inference_input_type = tf.uint8
+                converter.inference_output_type = tf.uint8
+                tflite_model = converter.convert()
 
             # write model
             model_path = os.path.join(model_dir, "model.tflite")

frigate/util/process.py

@@ -65,10 +65,15 @@ class FrigateProcess(BaseProcess):
         logging.basicConfig(handlers=[], force=True)
         logging.getLogger().addHandler(QueueHandler(self.__log_queue))
 
+        # Always apply base log level suppressions for noisy third-party libraries
+        # even if no specific logConfig is provided
         if logConfig:
             frigate.log.apply_log_levels(
                 logConfig.default.value.upper(), logConfig.logs
             )
+        else:
+            # Apply default INFO level with standard library suppressions
+            frigate.log.apply_log_levels("INFO", {})
 
         self._setup_memray()
 

frigate/util/rknn_converter.py

@@ -8,6 +8,7 @@ import time
 from pathlib import Path
 from typing import Optional
 
+from frigate.const import SUPPORTED_RK_SOCS
 from frigate.util.file import FileLock
 
 logger = logging.getLogger(__name__)
@@ -68,9 +69,20 @@ def is_rknn_compatible(model_path: str, model_type: str | None = None) -> bool:
         True if the model is RKNN-compatible, False otherwise
     """
     soc = get_soc_type()
+
     if soc is None:
         return False
 
+    # Check if the SoC is actually a supported RK device
+    # This prevents false positives on non-RK devices (e.g., macOS Docker)
+    # where /proc/device-tree/compatible might exist but contain non-RK content
+    if soc not in SUPPORTED_RK_SOCS:
+        logger.debug(
+            f"SoC '{soc}' is not a supported RK device for RKNN conversion. "
+            f"Supported SoCs: {SUPPORTED_RK_SOCS}"
+        )
+        return False
+
     if not model_type:
         model_type = get_rknn_model_type(model_path)
 

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

@@ -139,6 +139,7 @@
         "nameOnlyNumbers": "Model name cannot contain only numbers",
         "classRequired": "At least 1 class is required",
         "classesUnique": "Class names must be unique",
+        "noneNotAllowed": "The class 'none' is not allowed",
         "stateRequiresTwoClasses": "State models require at least 2 classes",
         "objectLabelRequired": "Please select an object label",
         "objectTypeRequired": "Please select a classification type"

web/src/components/card/ClassificationCard.tsx

@@ -40,6 +40,7 @@ type ClassificationCardProps = {
   data: ClassificationItemData;
   threshold?: ClassificationThreshold;
   selected: boolean;
+  clickable: boolean;
   i18nLibrary: string;
   showArea?: boolean;
   count?: number;
@@ -56,6 +57,7 @@ export const ClassificationCard = forwardRef<
     data,
     threshold,
     selected,
+    clickable,
     i18nLibrary,
     showArea = true,
     count,
@@ -101,11 +103,12 @@ export const ClassificationCard = forwardRef<
     <div
       ref={ref}
       className={cn(
-        "relative flex size-full cursor-pointer flex-col overflow-hidden rounded-lg outline outline-[3px]",
+        "relative flex size-full flex-col overflow-hidden rounded-lg outline outline-[3px]",
         className,
         selected
           ? "shadow-selected outline-selected"
           : "outline-transparent duration-500",
+        clickable && "cursor-pointer",
       )}
       onClick={(e) => {
         const isMeta = e.metaKey || e.ctrlKey;
@@ -289,6 +292,7 @@ export function GroupedClassificationCard({
         data={bestItem}
         threshold={threshold}
         selected={selectedItems.includes(bestItem.filename)}
+        clickable={true}
         i18nLibrary={i18nLibrary}
         count={group.length}
         onClick={(_, meta) => {
@@ -413,6 +417,7 @@ export function GroupedClassificationCard({
                     data={data}
                     threshold={threshold}
                     selected={false}
+                    clickable={false}
                     i18nLibrary={i18nLibrary}
                     onClick={() => {}}
                   >

web/src/components/classification/wizard/Step1NameAndDefine.tsx

@@ -94,7 +94,14 @@ export default function Step1NameAndDefine({
       objectLabel: z.string().optional(),
       objectType: z.enum(["sub_label", "attribute"]).optional(),
       classes: z
-        .array(z.string())
+        .array(
+          z
+            .string()
+            .refine(
+              (val) => val.trim().toLowerCase() !== "none",
+              t("wizard.step1.errors.noneNotAllowed"),
+            ),
+        )
         .min(1, t("wizard.step1.errors.classRequired"))
         .refine(
           (classes) => {
@@ -467,6 +474,7 @@ export default function Step1NameAndDefine({
                           )}
                         </div>
                       </FormControl>
+                      <FormMessage />
                     </FormItem>
                   )}
                 />

web/src/pages/FaceLibrary.tsx

@@ -1026,6 +1026,7 @@ function FaceGrid({
               filepath: `clips/faces/${pageToggle}/${image}`,
             }}
             selected={selectedFaces.includes(image)}
+            clickable={selectedFaces.length > 0}
             i18nLibrary="views/faceLibrary"
             onClick={(data, meta) => onClickFaces([data.filename], meta)}
           >

web/src/views/classification/ModelTrainingView.tsx

@@ -804,6 +804,7 @@ function DatasetGrid({
               name: "",
             }}
             showArea={false}
+            clickable={selectedImages.length > 0}
             selected={selectedImages.includes(image)}
             i18nLibrary="views/classificationModel"
             onClick={(data, _) => onClickImages([data.filename], true)}
@@ -962,6 +963,7 @@ function StateTrainGrid({
             data={data}
             threshold={threshold}
             selected={selectedImages.includes(data.filename)}
+            clickable={selectedImages.length > 0}
             i18nLibrary="views/classificationModel"
             showArea={false}
             onClick={(data, meta) => onClickImages([data.filename], meta)}