use same logging pattern in sync_recordings as the other sync functions

* generic job infrastructure

* types and dispatcher changes for jobs

* save data in memory only for completed jobs

* implement media sync job and endpoints

* change logs to debug

* websocket hook and types

* frontend

* i18n

* docs tweaks

* endpoint descriptions

* tweak docs

.github/workflows/pull_request.yml

@@ -19,9 +19,9 @@ jobs:
       - uses: actions/checkout@v6
         with:
           persist-credentials: false
-      - uses: actions/setup-node@v6
+      - uses: actions/setup-node@master
         with:
-          node-version: 20.x
+          node-version: 16.x
       - run: npm install
         working-directory: ./web
       - name: Lint
@@ -35,7 +35,7 @@ jobs:
       - uses: actions/checkout@v6
         with:
           persist-credentials: false
-      - uses: actions/setup-node@v6
+      - uses: actions/setup-node@master
         with:
           node-version: 20.x
       - run: npm install
@@ -78,7 +78,7 @@ jobs:
         uses: actions/checkout@v6
         with:
           persist-credentials: false
-      - uses: actions/setup-node@v6
+      - uses: actions/setup-node@master
         with:
           node-version: 20.x
       - name: Install devcontainer cli

LICENSE

@@ -1,6 +1,6 @@
 The MIT License
 
-Copyright (c) 2026 Frigate, Inc. (Frigate™)
+Copyright (c) 2025 Frigate LLC (Frigate™)
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.17.0
+VERSION = 0.18.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

README.md

@@ -40,7 +40,7 @@ If you would like to make a donation to support development, please use [Github
 This project is licensed under the **MIT License**.
 
 - **Code:** The source code, configuration files, and documentation in this repository are available under the [MIT License](LICENSE). You are free to use, modify, and distribute the code as long as you include the original copyright notice.
-- **Trademarks:** The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are **trademarks of Frigate, Inc.** and are **not** covered by the MIT License.
+- **Trademarks:** The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are **trademarks of Frigate LLC** and are **not** covered by the MIT License.
 
 Please see our [Trademark Policy](TRADEMARK.md) for details on acceptable use of our brand assets.
 
@@ -67,7 +67,7 @@ Please see our [Trademark Policy](TRADEMARK.md) for details on acceptable use of
 ### Built-in mask and zone editor
 
 <div>
-<img width="800" alt="Built-in mask and zone editor" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
+<img width="800" alt="Multi-camera scrubbing" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
 </div>
 
 ## Translations
@@ -80,4 +80,4 @@ We use [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) to support la
 
 ---
 
-**Copyright © 2026 Frigate, Inc.**
+**Copyright © 2025 Frigate LLC.**

README_CN.md

@@ -4,14 +4,14 @@
 
 # Frigate NVR™ - 一个具有实时目标检测的本地 NVR
 
-<a href="https://hosted.weblate.org/engage/frigate-nvr/-/zh_Hans/">
-<img src="https://hosted.weblate.org/widget/frigate-nvr/-/zh_Hans/svg-badge.svg" alt="翻译状态" />
-</a>
-
 [English](https://github.com/blakeblackshear/frigate) | \[简体中文\]
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
+<a href="https://hosted.weblate.org/engage/frigate-nvr/-/zh_Hans/">
+<img src="https://hosted.weblate.org/widget/frigate-nvr/-/zh_Hans/svg-badge.svg" alt="翻译状态" />
+</a>
+
 一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备 AI 目标/物体检测功能。使用 OpenCV 和 TensorFlow 在本地为 IP 摄像头执行实时物体检测。
 
 强烈推荐使用 GPU 或者 AI 加速器（例如[Google Coral 加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)等）。它们的运行效率远远高于现在的顶级 CPU，并且功耗也极低。
@@ -38,10 +38,9 @@
 ## 协议
 
 本项目采用 **MIT 许可证**授权。
-
 **代码部分**：本代码库中的源代码、配置文件和文档均遵循 [MIT 许可证](LICENSE)。您可以自由使用、修改和分发这些代码，但必须保留原始版权声明。
 
-**商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate, Inc. 的商标**，**不在** MIT 许可证覆盖范围内。
+**商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate LLC 的商标**，**不在** MIT 许可证覆盖范围内。
 有关品牌资产的规范使用详情，请参阅我们的[《商标政策》](TRADEMARK.md)。
 
 ## 截图
@@ -87,4 +86,4 @@ Bilibili：https://space.bilibili.com/3546894915602564
 
 ---
 
-**Copyright © 2026 Frigate, Inc.**
+**Copyright © 2025 Frigate LLC.**

TRADEMARK.md

@@ -6,7 +6,7 @@ This document outlines the policy regarding the use of the trademarks associated
 
 ## 1. Our Trademarks
 
-The following terms and visual assets are trademarks (the "Marks") of **Frigate, Inc.**:
+The following terms and visual assets are trademarks (the "Marks") of **Frigate LLC**:
 
 - **Frigate™**
 - **Frigate NVR™**
@@ -14,7 +14,7 @@ The following terms and visual assets are trademarks (the "Marks") of **Frigate,
 - **The Frigate Logo**
 
 **Note on Common Law Rights:**
-Frigate, Inc. asserts all common law rights in these Marks. The absence of a federal registration symbol (®) does not constitute a waiver of our intellectual property rights.
+Frigate LLC asserts all common law rights in these Marks. The absence of a federal registration symbol (®) does not constitute a waiver of our intellectual property rights.
 
 ## 2. Interaction with the MIT License
 
@@ -25,7 +25,7 @@ The software in this repository is licensed under the [MIT License](LICENSE).
 - The **Code** is free to use, modify, and distribute under the MIT terms.
 - The **Brand (Trademarks)** is **NOT** licensed under MIT.
 
-You may not use the Marks in any way that is not explicitly permitted by this policy or by written agreement with Frigate, Inc.
+You may not use the Marks in any way that is not explicitly permitted by this policy or by written agreement with Frigate LLC.
 
 ## 3. Acceptable Use
 
@@ -40,7 +40,7 @@ You may use the Marks without prior written permission in the following specific
 You may **NOT** use the Marks in the following ways:
 
 - **Commercial Products:** You may not use "Frigate" in the name of a commercial product, service, or app (e.g., selling an app named _"Frigate Viewer"_ is prohibited).
-- **Implying Affiliation:** You may not use the Marks in a way that suggests your project is official, sponsored by, or endorsed by Frigate, Inc.
+- **Implying Affiliation:** You may not use the Marks in a way that suggests your project is official, sponsored by, or endorsed by Frigate LLC.
 - **Confusing Forks:** If you fork this repository to create a derivative work, you **must** remove the Frigate logo and rename your project to avoid user confusion. You cannot distribute a modified version of the software under the name "Frigate".
 - **Domain Names:** You may not register domain names containing "Frigate" that are likely to confuse users (e.g., `frigate-official-support.com`).
 

docker/main/Dockerfile

@@ -237,18 +237,8 @@ ENV PYTHONWARNINGS="ignore:::numpy.core.getlimits"
 # Set HailoRT to disable logging
 ENV HAILORT_LOGGER_PATH=NONE
 
-# 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)
+# TensorFlow error 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}"
 

docker/main/requirements-wheels.txt

@@ -48,7 +48,7 @@ onnxruntime == 1.22.*
 transformers == 4.45.*
 # Generative AI
 google-generativeai == 0.8.*
-ollama == 0.6.*
+ollama == 0.5.*
 openai == 1.65.*
 # push notifications
 py-vapid == 1.9.*

docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -55,7 +55,7 @@ function setup_homekit_config() {
 
     if [[ ! -f "${config_path}" ]]; then
         echo "[INFO] Creating empty HomeKit config file..."
-        echo 'homekit: {}' > "${config_path}"
+        echo '{}' > "${config_path}"
     fi
 
     # Convert YAML to JSON for jq processing
@@ -70,14 +70,12 @@ function setup_homekit_config() {
     jq '
         # Keep only the homekit section if it exists, otherwise empty object
         if has("homekit") then {homekit: .homekit} else {homekit: {}} end
-    ' "${temp_json}" > "${cleaned_json}" 2>/dev/null || {
-        echo '{"homekit": {}}' > "${cleaned_json}"
-    }
+    ' "${temp_json}" > "${cleaned_json}" 2>/dev/null || echo '{"homekit": {}}' > "${cleaned_json}"
 
     # Convert back to YAML and write to the config file
     yq eval -P "${cleaned_json}" > "${config_path}" 2>/dev/null || {
         echo "[WARNING] Failed to convert cleaned config to YAML, creating minimal config"
-        echo 'homekit: {}' > "${config_path}"
+        echo '{"homekit": {}}' > "${config_path}"
     }
 
     # Clean up temp files

docker/main/rootfs/usr/local/go2rtc/create_config.py

@@ -22,11 +22,6 @@ sys.path.remove("/opt/frigate")
 
 yaml = YAML()
 
-# Check if arbitrary exec sources are allowed (defaults to False for security)
-ALLOW_ARBITRARY_EXEC = os.environ.get(
-    "GO2RTC_ALLOW_ARBITRARY_EXEC", "false"
-).lower() in ("true", "1", "yes")
-
 FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
 # read docker secret files as env vars too
 if os.path.isdir("/run/secrets"):
@@ -114,26 +109,14 @@ if LIBAVFORMAT_VERSION_MAJOR < 59:
     elif go2rtc_config["ffmpeg"].get("rtsp") is None:
         go2rtc_config["ffmpeg"]["rtsp"] = rtsp_args
 
-
-def is_restricted_source(stream_source: str) -> bool:
-    """Check if a stream source is restricted (echo, expr, or exec)."""
-    return stream_source.strip().startswith(("echo:", "expr:", "exec:"))
-
-
-for name in list(go2rtc_config.get("streams", {})):
+for name in go2rtc_config.get("streams", {}):
     stream = go2rtc_config["streams"][name]
 
     if isinstance(stream, str):
         try:
-            formatted_stream = stream.format(**FRIGATE_ENV_VARS)
-            if not ALLOW_ARBITRARY_EXEC and is_restricted_source(formatted_stream):
-                print(
-                    f"[ERROR] Stream '{name}' uses a restricted source (echo/expr/exec) which is disabled by default for security. "
-                    f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
-                )
-                del go2rtc_config["streams"][name]
-                continue
-            go2rtc_config["streams"][name] = formatted_stream
+            go2rtc_config["streams"][name] = go2rtc_config["streams"][name].format(
+                **FRIGATE_ENV_VARS
+            )
         except KeyError as e:
             print(
                 "[ERROR] Invalid substitution found, see https://docs.frigate.video/configuration/restream#advanced-restream-configurations for more info."
@@ -141,33 +124,15 @@ for name in list(go2rtc_config.get("streams", {})):
             sys.exit(e)
 
     elif isinstance(stream, list):
-        filtered_streams = []
-        for i, stream_item in enumerate(stream):
+        for i, stream in enumerate(stream):
             try:
-                formatted_stream = stream_item.format(**FRIGATE_ENV_VARS)
-                if not ALLOW_ARBITRARY_EXEC and is_restricted_source(formatted_stream):
-                    print(
-                        f"[ERROR] Stream '{name}' item {i + 1} uses a restricted source (echo/expr/exec) which is disabled by default for security. "
-                        f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
-                    )
-                    continue
-
-                filtered_streams.append(formatted_stream)
+                go2rtc_config["streams"][name][i] = stream.format(**FRIGATE_ENV_VARS)
             except KeyError as e:
                 print(
                     "[ERROR] Invalid substitution found, see https://docs.frigate.video/configuration/restream#advanced-restream-configurations for more info."
                 )
                 sys.exit(e)
 
-        if filtered_streams:
-            go2rtc_config["streams"][name] = filtered_streams
-        else:
-            print(
-                f"[ERROR] Stream '{name}' was removed because all sources were restricted (echo/expr/exec). "
-                f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
-            )
-            del go2rtc_config["streams"][name]
-
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, Any] = config.get("birdseye")

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

@@ -18,10 +18,6 @@ proxy_set_header X-Forwarded-User $http_x_forwarded_user;
 proxy_set_header X-Forwarded-Groups $http_x_forwarded_groups;
 proxy_set_header X-Forwarded-Email $http_x_forwarded_email;
 proxy_set_header X-Forwarded-Preferred-Username $http_x_forwarded_preferred_username;
-proxy_set_header X-Auth-Request-User $http_x_auth_request_user;
-proxy_set_header X-Auth-Request-Groups $http_x_auth_request_groups;
-proxy_set_header X-Auth-Request-Email $http_x_auth_request_email;
-proxy_set_header X-Auth-Request-Preferred-Username $http_x_auth_request_preferred_username;
 proxy_set_header X-authentik-username $http_x_authentik_username;
 proxy_set_header X-authentik-groups $http_x_authentik_groups;
 proxy_set_header X-authentik-email $http_x_authentik_email;

docs/docs/configuration/audio_detectors.md

@@ -50,7 +50,7 @@ cameras:
 
 ### Configuring Minimum Volume
 
-The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The Debug view in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are is displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
+The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The Debug view in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are is displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
 
 :::tip
 

docs/docs/configuration/camera_specific.md

@@ -188,10 +188,10 @@ go2rtc:
     # example for connectin to a Reolink camera that supports two way talk
     your_reolink_camera_twt:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
-      - "rtsp://username:password@reolink_ip/Preview_01_sub"
+      - "rtsp://username:password@reolink_ip/Preview_01_sub
     your_reolink_camera_twt_sub:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
-      - "rtsp://username:password@reolink_ip/Preview_01_sub"
+      - "rtsp://username:password@reolink_ip/Preview_01_sub
     # example for connecting to a Reolink NVR
     your_reolink_camera_via_nvr:
       - "ffmpeg:http://reolink_nvr_ip/flv?port=1935&app=bcs&stream=channel3_main.bcs&user=username&password=password" # channel numbers are 0-15
@@ -227,12 +227,6 @@ cameras:
 
 ### Unifi Protect Cameras
 
-:::note 
-
-Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not posible to enable it in standalone mode.
-
-:::
-
 Unifi protect cameras require the rtspx stream to be used with go2rtc.
 To utilize a Unifi protect camera, modify the rtsps link to begin with rtspx.
 Additionally, remove the "?enableSrtp" from the end of the Unifi link.
@@ -258,10 +252,6 @@ ffmpeg:
 
 TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.
 
-### Wyze Wireless Cameras
-
-Some community members have found better performance on Wyze cameras by using an alternative firmware known as [Thingino](https://thingino.com/).
-
 ## USB Cameras (aka Webcams)
 
 To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:

docs/docs/configuration/cameras.md

@@ -94,19 +94,18 @@ This list of working and non-working PTZ cameras is based on user feedback. If y
 The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/) can provide a starting point to determine a camera's compatibility with Frigate's autotracking. Look to see if a camera lists `PTZRelative`, `PTZRelativePanTilt` and/or `PTZRelativeZoom`. These features are required for autotracking, but some cameras still fail to respond even if they claim support. If they are missing, autotracking will not work (though basic PTZ in the WebUI might). Avoid cameras with no database entry unless they are confirmed as working below.
 
 | Brand or specific camera     | PTZ Controls | Autotracking | Notes                                                                                                                                                                                                                                                             |
-| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- |
 | Amcrest                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                                                                                                                                   |
 | Amcrest ASH21                |      ✅      |      ❌      | ONVIF service port: 80                                                                                                                                                                                                                                            |
 | Amcrest IP4M-S2112EW-AI      |      ✅      |      ❌      | FOV relative movement not supported.                                                                                                                                                                                                                              |
 | Amcrest IP5M-1190EW          |      ✅      |      ❌      | ONVIF Port: 80. FOV relative movement not supported.                                                                                                                                                                                                              |
 | Annke CZ504                  |      ✅      |      ✅      | Annke support provide specific firmware ([V5.7.1 build 250227](https://github.com/pierrepinon/annke_cz504/raw/refs/heads/main/digicap_V5-7-1_build_250227.dav)) to fix issue with ONVIF "TranslationSpaceFov"                                                     |
-| Axis Q-6155E                 |      ✅      |      ❌      | ONVIF service port: 80; Camera does not support MoveStatus.                                                                                                                                                                                                       |
 | Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Dahua                        |      ✅      |      ✅      | Some low-end Dahuas (lite series, picoo series (commonly), among others) have been reported to not support autotracking. These models usually don't have a four digit model number with chassis prefix and options postfix (e.g. DH-P5AE-PV vs DH-SD49825GB-HNR). |
 | Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Dahua DH-SD49825GB-HNR       |      ✅      |      ✅      |                                                                                                                                                                                                                                                                   |
 | Dahua DH-P5AE-PV             |      ❌      |      ❌      |                                                                                                                                                                                                                                                                   |
-| Foscam                       |      ✅      |      ❌      | In general support PTZ, but not relative move. There are no official ONVIF certifications and tests available on the ONVIF Conformant Products Database                                                                                                           |
+| Foscam                       |      ✅      |      ❌      | In general support PTZ, but not relative move. There are no official ONVIF certifications and tests available on the ONVIF Conformant Products Database                                                                                                           |     |
 | Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Foscam SD4                   |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |

docs/docs/configuration/custom_classification/object_classification.md

@@ -3,7 +3,7 @@ id: object_classification
 title: Object Classification
 ---
 
-Object classification allows you to train a custom MobileNetV2 classification model to run on tracked objects (persons, cars, animals, etc.) to identify a finer category or attribute for that object. Classification results are visible in the Tracked Object Details pane in Explore, through the `frigate/tracked_object_details` MQTT topic, in Home Assistant sensors via the official Frigate integration, or through the event endpoints in the HTTP API.
+Object classification allows you to train a custom MobileNetV2 classification model to run on tracked objects (persons, cars, animals, etc.) to identify a finer category or attribute for that object.
 
 ## Minimum System Requirements
 
@@ -11,8 +11,6 @@ Object classification models are lightweight and run very fast on CPU. Inference
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 
-A CPU with AVX instructions is required for training and inference.
-
 ## Classes
 
 Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
@@ -33,15 +31,9 @@ For object classification:
   - Example: `cat` → `Leo`, `Charlie`, `None`.
 
 - **Attribute**:
-  - Added as metadata to the object, visible in the Tracked Object Details pane in Explore, `frigate/events` MQTT messages, and the HTTP API response as `<model_name>: <predicted_value>`.
+  - Added as metadata to the object (visible in /events): `<model_name>: <predicted_value>`.
   - Ideal when multiple attributes can coexist independently.
-  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not, and if they are wearing a yellow vest or not.
-
-:::note
-
-A tracked object can only have a single sub label. If you are using Triggers or Face Recognition and you configure an object classification model for `person` using the sub label type, your sub label may not be assigned correctly as it depends on which enrichment completes its analysis first. This could also occur with `car` objects that are assigned a sub label for a delivery carrier. Consider using the `attribute` type instead.
-
-:::
+  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
 
 ## Assignment Requirements
 
@@ -81,17 +73,13 @@ classification:
         classification_type: sub_label # or: attribute
 ```
 
-An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For object classification models, the default is 200.
-
 ## Training the model
 
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
 
 ### Step 1: Name and Define
 
-Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Frigate will automatically include a `none` class for objects that don't fit any specific category.
-
-For example: To classify your two cats, create a model named "Our Cats" and create two classes, "Charlie" and "Leo". A third class, "none", will be created automatically for other neighborhood cats that are not your own.
+Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Include a `none` class for objects that don't fit any specific category.
 
 ### Step 2: Assign Training Examples
 
@@ -99,8 +87,6 @@ The system will automatically generate example images from detected objects matc
 
 When choosing which objects to classify, start with a small number of visually distinct classes and ensure your training samples match camera viewpoints and distances typical for those objects.
 
-If examples for some of your classes do not appear in the grid, you can continue configuring the model without them. New images will begin to appear in the Recent Classifications view. When your missing classes are seen, classify them from this view and retrain your model.
-
 ### Improving the Model
 
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
@@ -108,23 +94,3 @@ If examples for some of your classes do not appear in the grid, you can continue
 - **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
 - **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
 - **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.
-
-## Debugging Classification Models
-
-To troubleshoot issues with object classification models, enable debug logging to see detailed information about classification attempts, scores, and consensus calculations.
-
-Enable debug logs for classification models by adding `frigate.data_processing.real_time.custom_classification: debug` to your `logger` configuration. These logs are verbose, so only keep this enabled when necessary. Restart Frigate after this change.
-
-```yaml
-logger:
-  default: info
-  logs:
-    frigate.data_processing.real_time.custom_classification: debug
-```
-
-The debug logs will show:
-
-- Classification probabilities for each attempt
-- Whether scores meet the threshold requirement
-- Consensus calculations and when assignments are made
-- Object classification history and weighted scores

docs/docs/configuration/custom_classification/state_classification.md

@@ -3,7 +3,7 @@ id: state_classification
 title: State Classification
 ---
 
-State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region. Classification results are available through the `frigate/<camera_name>/classification/<model_name>` MQTT topic and in Home Assistant sensors via the official Frigate integration.
+State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region.
 
 ## Minimum System Requirements
 
@@ -11,8 +11,6 @@ State classification models are lightweight and run very fast on CPU. Inference
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 
-A CPU with AVX instructions is required for training and inference.
-
 ## Classes
 
 Classes are the different states an area on your camera can be in. Each class represents a distinct visual state that the model will learn to recognize.
@@ -48,8 +46,6 @@ classification:
             crop: [0, 180, 220, 400]
 ```
 
-An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For state classification models, the default is 100.
-
 ## Training the model
 
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
@@ -74,34 +70,3 @@ Once some images are assigned, training will begin automatically.
 - **Data collection**: Use the model's Recent Classifications tab to gather balanced examples across times of day and weather.
 - **When to train**: Focus on cases where the model is entirely incorrect or flips between states when it should not. There's no need to train additional images when the model is already working consistently.
 - **Selecting training images**: Images scoring below 100% due to new conditions (e.g., first snow of the year, seasonal changes) or variations (e.g., objects temporarily in view, insects at night) are good candidates for training, as they represent scenarios different from the default state. Training these lower-scoring images that differ from existing training data helps prevent overfitting. Avoid training large quantities of images that look very similar, especially if they already score 100% as this can lead to overfitting.
-
-## Debugging Classification Models
-
-To troubleshoot issues with state classification models, enable debug logging to see detailed information about classification attempts, scores, and state verification.
-
-Enable debug logs for classification models by adding `frigate.data_processing.real_time.custom_classification: debug` to your `logger` configuration. These logs are verbose, so only keep this enabled when necessary. Restart Frigate after this change.
-
-```yaml
-logger:
-  default: info
-  logs:
-    frigate.data_processing.real_time.custom_classification: debug
-```
-
-The debug logs will show:
-
-- Classification probabilities for each attempt
-- Whether scores meet the threshold requirement
-- State verification progress (consecutive detections needed)
-- When state changes are published
-
-### Recent Classifications
-
-For state classification, images are only added to recent classifications under specific circumstances:
-
-- **First detection**: The first classification attempt for a camera is always saved
-- **State changes**: Images are saved when the detected state differs from the current verified state
-- **Pending verification**: Images are saved when there's a pending state change being verified (requires 3 consecutive identical states)
-- **Low confidence**: Images with scores below 100% are saved even if the state matches the current state (useful for training)
-
-Images are **not** saved when the state is stable (detected state matches current state) **and** the score is 100%. This prevents unnecessary storage of redundant high-confidence classifications.

docs/docs/configuration/genai.md

@@ -48,29 +48,15 @@ Using Ollama on CPU is not recommended, high inference times make using Generati
 
 :::
 
-[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
+[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
 
 Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
 
-Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://docs.ollama.com/faq#how-does-ollama-handle-concurrent-requests).
-
-### Model Types: Instruct vs Thinking
-
-Most vision-language models are available as **instruct** models, which are fine-tuned to follow instructions and respond concisely to prompts. However, some models (such as certain Qwen-VL or minigpt variants) offer both **instruct** and **thinking** versions.
-
-- **Instruct models** are always recommended for use with Frigate. These models generate direct, relevant, actionable descriptions that best fit Frigate's object and event summary use case.
-- **Thinking models** are fine-tuned for more free-form, open-ended, and speculative outputs, which are typically not concise and may not provide the practical summaries Frigate expects. For this reason, Frigate does **not** recommend or support using thinking models.
-
-Some models are labeled as **hybrid** (capable of both thinking and instruct tasks). In these cases, Frigate will always use instruct-style prompts and specifically disables thinking-mode behaviors to ensure concise, useful responses.
-
-**Recommendation:**
-Always select the `-instruct` or documented instruct/tagged variant of any model you use in your Frigate configuration. If in doubt, refer to your model provider’s documentation or model library for guidance on the correct model variant to use.
-
-
+Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests).
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/search?c=vision). Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull qwen3-vl:2b-instruct` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
+You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
 
 :::note
 
@@ -78,10 +64,6 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
 
 :::
 
-#### Ollama Cloud models
-
-Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
-
 ### Configuration
 
 ```yaml
@@ -211,7 +193,7 @@ You are also able to define custom prompts in your configuration.
 genai:
   provider: ollama
   base_url: http://localhost:11434
-  model: qwen3-vl:8b-instruct
+  model: llava
 
 objects:
   prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."

docs/docs/configuration/genai/objects.md

@@ -39,10 +39,9 @@ You are also able to define custom prompts in your configuration.
 genai:
   provider: ollama
   base_url: http://localhost:11434
-  model: qwen3-vl:8b-instruct
+  model: llava
 
 objects:
-  genai:
     prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
     object_prompts:
       person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."

docs/docs/configuration/genai/review_summaries.md

@@ -16,13 +16,12 @@ Review summaries provide structured JSON responses that are saved for each revie
 ```
 - `title` (string): A concise, direct title that describes the purpose or overall action (e.g., "Person taking out trash", "Joe walking dog").
 - `scene` (string): A narrative description of what happens across the sequence from start to finish, including setting, detected objects, and their observable actions.
-- `shortSummary` (string): A brief 2-sentence summary of the scene, suitable for notifications. This is a condensed version of the scene description.
 - `confidence` (float): 0-1 confidence in the analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous.
 - `other_concerns` (list): List of user-defined concerns that may need additional investigation.
 - `potential_threat_level` (integer): 0, 1, or 2 as defined below.
 ```
 
-This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will automatically show the title and `shortSummary` when the data is available, while the full `scene` description is available in the UI for detailed review.
+This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will also automatically show the title and description when the data is available.
 
 ### Defining Typical Activity
 
@@ -31,43 +30,40 @@ Each installation and even camera can have different parameters for what is cons
 <details>
   <summary>Default Activity Context Prompt</summary>
 
-```yaml
-review:
-  genai:
-    activity_context_prompt: |
-      ### Normal Activity Indicators (Level 0)
-      - Known/verified people in any zone at any time
-      - People with pets in residential areas
-      - Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
-      - Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
-      - Activity confined to public areas only (sidewalks, streets) without entering property at any time
+```
+### Normal Activity Indicators (Level 0)
+- Known/verified people in any zone at any time
+- People with pets in residential areas
+- Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
+- Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
+- Activity confined to public areas only (sidewalks, streets) without entering property at any time
 
-      ### Suspicious Activity Indicators (Level 1)
-      - **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
-      - **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
-      - Taking items that don't belong to them (packages, objects from porches/driveways)
-      - Climbing or jumping fences/barriers to access property
-      - Attempting to conceal actions or items from view
-      - Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
+### Suspicious Activity Indicators (Level 1)
+- **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
+- **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
+- Taking items that don't belong to them (packages, objects from porches/driveways)
+- Climbing or jumping fences/barriers to access property
+- Attempting to conceal actions or items from view
+- Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
 
-      ### Critical Threat Indicators (Level 2)
-      - Holding break-in tools (crowbars, pry bars, bolt cutters)
-      - Weapons visible (guns, knives, bats used aggressively)
-      - Forced entry in progress
-      - Physical aggression or violence
-      - Active property damage or theft in progress
+### Critical Threat Indicators (Level 2)
+- Holding break-in tools (crowbars, pry bars, bolt cutters)
+- Weapons visible (guns, knives, bats used aggressively)
+- Forced entry in progress
+- Physical aggression or violence
+- Active property damage or theft in progress
 
-      ### Assessment Guidance
-      Evaluate in this order:
+### Assessment Guidance
+Evaluate in this order:
 
-      1. **If person is verified/known** → Level 0 regardless of time or activity
-      2. **If person is unidentified:**
-        - Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
-        - Check actions: If testing doors/handles, taking items, climbing → Level 1
-        - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
-      3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
+1. **If person is verified/known** → Level 0 regardless of time or activity
+2. **If person is unidentified:**
+   - Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
+   - Check actions: If testing doors/handles, taking items, climbing → Level 1
+   - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
+3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
 
-      The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.
+The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.
 ```
 
 </details>
@@ -112,17 +108,6 @@ review:
       - animals in the garden
 ```
 
-### Preferred Language
-
-By default, review summaries are generated in English. You can configure Frigate to generate summaries in your preferred language by setting the `preferred_language` option:
-
-```yaml
-review:
-  genai:
-    enabled: true
-    preferred_language: Spanish
-```
-
 ## Review Reports
 
 Along with individual review item summaries, Generative AI provides the ability to request a report of a given time period. For example, you can get a daily report while on a vacation of any suspicious activity or other concerns that may require review.

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -13,7 +13,7 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
 
 - **AMD**
 
-  - ROCm support in the `-rocm` Frigate image is automatically detected for enrichments, but only some enrichment models are available due to ROCm's focus on LLMs and limited stability with certain neural network models. Frigate disables models that perform poorly or are unstable to ensure reliable operation, so only compatible enrichments may be active.
+  - ROCm will automatically be detected and used for enrichments in the `-rocm` Frigate image.
 
 - **Intel**
 

docs/docs/configuration/hardware_acceleration_video.md

@@ -3,65 +3,78 @@ id: hardware_acceleration_video
 title: Video Decoding
 ---
 
-import CommunityBadge from '@site/src/components/CommunityBadge';
-
 # Video Decoding
 
-It is highly recommended to use an integrated or discrete GPU for hardware acceleration video decoding in Frigate.
+It is highly recommended to use a GPU for hardware acceleration video decoding in Frigate. Some types of hardware acceleration are detected and used automatically, but you may need to update your configuration to enable hardware accelerated decoding in ffmpeg.
 
-Some types of hardware acceleration are detected and used automatically, but you may need to update your configuration to enable hardware accelerated decoding in ffmpeg. To verify that hardware acceleration is working:
-- Check the logs: A message will either say that hardware acceleration was automatically detected, or there will be a warning that no hardware acceleration was automatically detected
-- If hardware acceleration is specified in the config, verification can be done by ensuring the logs are free from errors. There is no CPU fallback for hardware acceleration.
+Depending on your system, these parameters may not be compatible. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
 
-:::info
 
-Frigate supports presets for optimal hardware accelerated video decoding:
+## Raspberry Pi 3/4
 
-**AMD**
+Ensure you increase the allocated RAM for your GPU to at least 128 (`raspi-config` > Performance Options > GPU Memory).
+If you are using the HA Add-on, you may need to use the full access variant and turn off _Protection mode_ for hardware acceleration.
 
-- [AMD](#amd-based-cpus): Frigate can utilize modern AMD integrated GPUs and AMD discrete GPUs to accelerate video decoding.
+```yaml
+# if you want to decode a h264 stream
+ffmpeg:
+  hwaccel_args: preset-rpi-64-h264
 
-**Intel**
+# if you want to decode a h265 (hevc) stream
+ffmpeg:
+  hwaccel_args: preset-rpi-64-h265
+```
 
-- [Intel](#intel-based-cpus): Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video decoding.
+:::note
 
-**Nvidia GPU**
+If running Frigate through Docker, you either need to run in privileged mode or
+map the `/dev/video*` devices to Frigate. With Docker Compose add:
 
-- [Nvidia GPU](#nvidia-gpus): Frigate can utilize most modern Nvidia GPUs to accelerate video decoding.
+```yaml
+services:
+  frigate:
+    ...
+    devices:
+      - /dev/video11:/dev/video11
+```
 
-**Raspberry Pi 3/4**
+Or with `docker run`:
 
-- [Raspberry Pi](#raspberry-pi-34): Frigate can utilize the media engine in the Raspberry Pi 3 and 4 to slightly accelerate video decoding.
+```bash
+docker run -d \
+  --name frigate \
+  ...
+  --device /dev/video11 \
+  ghcr.io/blakeblackshear/frigate:stable
+```
 
-**Nvidia Jetson** <CommunityBadge />
+`/dev/video11` is the correct device (on Raspberry Pi 4B). You can check
+by running the following and looking for `H264`:
 
-- [Jetson](#nvidia-jetson): Frigate can utilize the media engine in Jetson hardware to accelerate video decoding.
+```bash
+for d in /dev/video*; do
+  echo -e "---\n$d"
+  v4l2-ctl --list-formats-ext -d $d
+done
+```
 
-**Rockchip** <CommunityBadge />
-
-- [RKNN](#rockchip-platform): Frigate can utilize the media engine in RockChip SOCs to accelerate video decoding.
-
-**Other Hardware**
-
-Depending on your system, these presets may not be compatible, and you may need to use manual hwaccel args to take advantage of your hardware. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
+Or map in all the `/dev/video*` devices.
 
 :::
 
 ## Intel-based CPUs
 
-Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video decoding.
-
 :::info
 
 **Recommended hwaccel Preset**
 
-| CPU Generation | Intel Driver | Recommended Preset  | Notes                                       |
-| -------------- | ------------ | ------------------- | ------------------------------------------- |
-| gen1 - gen5    | i965         | preset-vaapi        | qsv is not supported, may not support H.265 |
-| gen6 - gen7    | iHD          | preset-vaapi        | qsv is not supported                        |
-| gen8 - gen12   | iHD          | preset-vaapi        | preset-intel-qsv-\* can also be used        |
-| gen13+         | iHD / Xe     | preset-intel-qsv-\* |                                             |
-| Intel Arc GPU  | iHD / Xe     | preset-intel-qsv-\* |                                             |
+| CPU Generation | Intel Driver | Recommended Preset  | Notes                                |
+| -------------- | ------------ | ------------------- | ------------------------------------ |
+| gen1 - gen5    | i965         | preset-vaapi        | qsv is not supported                 |
+| gen6 - gen7    | iHD          | preset-vaapi        | qsv is not supported                 |
+| gen8 - gen12   | iHD          | preset-vaapi        | preset-intel-qsv-\* can also be used |
+| gen13+         | iHD / Xe     | preset-intel-qsv-\* |                                      |
+| Intel Arc GPU  | iHD / Xe     | preset-intel-qsv-\* |                                      |
 
 :::
 
@@ -182,17 +195,15 @@ telemetry:
 
 If you are passing in a device path, make sure you've passed the device through to the container.
 
-## AMD-based CPUs
+## AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
 
-Frigate can utilize modern AMD integrated GPUs and AMD GPUs to accelerate video decoding using VAAPI.
+VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
 
-### Configuring Radeon Driver
+:::note
 
 You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA Add-on users](advanced.md#environment_vars).
 
-### Via VAAPI
-
-VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
+:::
 
 ```yaml
 ffmpeg:
@@ -253,7 +264,7 @@ processes:
 
 :::note
 
-`nvidia-smi` will not show `ffmpeg` processes when run inside the container [due to docker limitations](https://github.com/NVIDIA/nvidia-docker/issues/179#issuecomment-645579458).
+`nvidia-smi` may not show `ffmpeg` processes when run inside the container [due to docker limitations](https://github.com/NVIDIA/nvidia-docker/issues/179#issuecomment-645579458).
 
 :::
 
@@ -289,63 +300,12 @@ If you do not see these processes, check the `docker logs` for the container and
 
 These instructions were originally based on the [Jellyfin documentation](https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-hardware-acceleration-on-docker-linux).
 
-## Raspberry Pi 3/4
-
-Ensure you increase the allocated RAM for your GPU to at least 128 (`raspi-config` > Performance Options > GPU Memory).
-If you are using the HA Add-on, you may need to use the full access variant and turn off _Protection mode_ for hardware acceleration.
-
-```yaml
-# if you want to decode a h264 stream
-ffmpeg:
-  hwaccel_args: preset-rpi-64-h264
-
-# if you want to decode a h265 (hevc) stream
-ffmpeg:
-  hwaccel_args: preset-rpi-64-h265
-```
-
-:::note
-
-If running Frigate through Docker, you either need to run in privileged mode or
-map the `/dev/video*` devices to Frigate. With Docker Compose add:
-
-```yaml
-services:
-  frigate:
-    ...
-    devices:
-      - /dev/video11:/dev/video11
-```
-
-Or with `docker run`:
-
-```bash
-docker run -d \
-  --name frigate \
-  ...
-  --device /dev/video11 \
-  ghcr.io/blakeblackshear/frigate:stable
-```
-
-`/dev/video11` is the correct device (on Raspberry Pi 4B). You can check
-by running the following and looking for `H264`:
-
-```bash
-for d in /dev/video*; do
-  echo -e "---\n$d"
-  v4l2-ctl --list-formats-ext -d $d
-done
-```
-
-Or map in all the `/dev/video*` devices.
-
-:::
-
 # Community Supported
 
-## NVIDIA Jetson
+## NVIDIA Jetson (Orin AGX, Orin NX, Orin Nano\*, Xavier AGX, Xavier NX, TX2, TX1, Nano)
 
-A separate set of docker images is available for Jetson devices. They come with an `ffmpeg` build with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 6.0+ use the `stable-tensorrt-jp6` tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform, but the image will still allow hardware decoding and tensorrt object detection.
+A separate set of docker images is available that is based on Jetpack/L4T. They come with an `ffmpeg` build
+with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 6.0+ use the `stable-tensorrt-jp6` tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform, but the image will still allow hardware decoding and tensorrt object detection.
 
 You will need to use the image with the nvidia container runtime:
 

docs/docs/configuration/live.md

@@ -15,7 +15,7 @@ The jsmpeg live view will use more browser and client GPU resources. Using go2rt
 | ------ | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | jsmpeg | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | Resolution is configurable, but go2rtc is recommended if you want higher resolutions and better frame rates. jsmpeg is Frigate's default without go2rtc configured. |
 | mse    | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured.                                                              |
-| webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                   |
+| webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration, doesn't support h.265. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                   |
 
 ### Camera Settings Recommendations
 
@@ -127,8 +127,7 @@ WebRTC works by creating a TCP or UDP connection on port `8555`. However, it req
   ```
 
 - For access through Tailscale, the Frigate system's Tailscale IP must be added as a WebRTC candidate. Tailscale IPs all start with `100.`, and are reserved within the `100.64.0.0/10` CIDR block.
-
-- Note that some browsers may not support H.265 (HEVC). You can check your browser's current version for H.265 compatibility [here](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness). 
+- Note that WebRTC does not support H.265.
 
 :::tip
 

docs/docs/configuration/object_detectors.md

@@ -146,18 +146,18 @@ detectors:
 
 ### EdgeTPU Supported Models
 
-| Model                   | Notes                                       |
-| ----------------------- | ------------------------------------------- |
-| [Mobiledet](#mobiledet) | Default model                               |
-| [YOLOv9](#yolov9)       | More accurate but slower than default model |
+| Model                                 | Notes                                       |
+| ------------------------------------- | ------------------------------------------- |
+| [MobileNet v2](#ssdlite-mobilenet-v2) | Default model                               |
+| [YOLOv9](#yolo-v9)                    | More accurate but slower than default model |
 
-#### Mobiledet
+#### SSDLite MobileNet v2
 
 A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
 
-#### YOLOv9
+#### YOLO v9
 
-YOLOv9 models that are compiled for TensorFlow Lite and properly quantized are supported, but not included by default. [Download the model](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite), bind mount the file into the container, and provide the path with `model.path`. Note that the linked model requires a 17-label [labelmap file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) that includes only 17 COCO classes.
+[YOLOv9](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite) models that are compiled for Tensorflow Lite and properly quantized are supported, but not included by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`. Note that the model may require a custom label file (eg. [use this 17 label file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) for the model linked above.)
 
 <details>
   <summary>YOLOv9 Setup & Config</summary>
@@ -178,7 +178,7 @@ model:
   labelmap_path: /config/labels-coco17.txt
 ```
 
-Note that due to hardware limitations of the Coral, the labelmap is a subset of the COCO labels and includes only 17 object classes.
+Note that the labelmap uses a subset of the complete COCO label set that has only 17 objects.
 
 </details>
 
@@ -477,7 +477,7 @@ After placing the downloaded onnx model in your config/model_cache folder, you c
 detectors:
   ov:
     type: openvino
-    device: CPU
+    device: GPU
 
 model:
   model_type: dfine
@@ -569,10 +569,10 @@ When using Docker Compose:
 ```yaml
 services:
   frigate:
-    ...
-    devices:
-      - /dev/dri
-      - /dev/kfd
+---
+devices:
+  - /dev/dri
+  - /dev/kfd
 ```
 
 For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
@@ -600,9 +600,9 @@ When using Docker Compose:
 ```yaml
 services:
   frigate:
-    ...
-    environment:
-      HSA_OVERRIDE_GFX_VERSION: "10.0.0"
+
+environment:
+  HSA_OVERRIDE_GFX_VERSION: "10.0.0"
 ```
 
 Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
@@ -1508,17 +1508,17 @@ COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL
 EOF
 ```
 
-### Downloading RF-DETR Model
+### Download RF-DETR Model
 
 RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.
 
 ```sh
-docker build . --build-arg MODEL_SIZE=Nano --rm --output . -f- <<'EOF'
+docker build . --build-arg MODEL_SIZE=Nano --output . -f- <<'EOF'
 FROM python:3.11 AS build
 RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
 COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
 WORKDIR /rfdetr
-RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnx==1.19.1 onnxscript
+RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnxscript
 ARG MODEL_SIZE
 RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
 FROM scratch

docs/docs/configuration/pwa.md

@@ -11,7 +11,7 @@ This adds features including the ability to deep link directly into the app.
 
 In order to install Frigate as a PWA, the following requirements must be met:
 
-- Frigate must be accessed via a secure context (localhost, secure https, VPN, etc.)
+- Frigate must be accessed via a secure context (localhost, secure https, etc.)
 - On Android, Firefox, Chrome, Edge, Opera, and Samsung Internet Browser all support installing PWAs.
 - On iOS 16.4 and later, PWAs can be installed from the Share menu in Safari, Chrome, Edge, Firefox, and Orion.
 
@@ -22,7 +22,3 @@ Installation varies slightly based on the device that is being used:
 - Desktop: Use the install button typically found in right edge of the address bar
 - Android: Use the `Install as App` button in the more options menu for Chrome, and the `Add app to Home screen` button for Firefox
 - iOS: Use the `Add to Homescreen` button in the share menu
-
-## Usage
-
-Once setup, the Frigate app can be used wherever it has access to Frigate. This means it can be setup as local-only, VPN-only, or fully accessible depending on your needs.

docs/docs/configuration/record.md

@@ -139,7 +139,13 @@ record:
 
 :::tip
 
-When using `hwaccel_args` globally hardware encoding is used for time lapse generation. The encoder determines its own behavior so the resulting file size may be undesirably large.
+When using `hwaccel_args`, hardware encoding is used for timelapse generation. This setting can be overridden for a specific camera (e.g., when camera resolution exceeds hardware encoder limits); set `cameras.<camera>.record.export.hwaccel_args` with the appropriate settings. Using an unrecognized value or empty string will fall back to software encoding (libx264).
+
+:::
+
+:::tip
+
+The encoder determines its own behavior so the resulting file size may be undesirably large.
 To reduce the output file size the ffmpeg parameter `-qp n` can be utilized (where `n` stands for the value of the quantisation parameter). The value can be adjusted to get an acceptable tradeoff between quality and file size for the given scenario.
 
 :::
@@ -148,19 +154,16 @@ To reduce the output file size the ffmpeg parameter `-qp n` can be utilized (whe
 
 Apple devices running the Safari browser may fail to playback h.265 recordings. The [apple compatibility option](../configuration/camera_specific.md#h265-cameras-via-safari) should be used to ensure seamless playback on Apple devices.
 
-## Syncing Recordings With Disk
+## Syncing Media Files With Disk
 
-In some cases the recordings files may be deleted but Frigate will not know this has happened. Recordings sync can be enabled which will tell Frigate to check the file system and delete any db entries for files which don't exist.
+Media files (event snapshots, event thumbnails, review thumbnails, previews, exports, and recordings) can become orphaned when database entries are deleted but the corresponding files remain on disk.
 
-```yaml
-record:
-  sync_recordings: True
-```
+Normal operation may leave small numbers of orphaned files until Frigate's scheduled cleanup, but crashes, configuration changes, or upgrades may cause more orphaned files that Frigate does not clean up. This feature checks the file system for media files and removes any that are not referenced in the database.
 
-This feature is meant to fix variations in files, not completely delete entries in the database. If you delete all of your media, don't use `sync_recordings`, just stop Frigate, delete the `frigate.db` database, and restart.
+The Maintenance pane in the Frigate UI or an API endpoint `POST /api/media/sync` can be used to trigger a media sync. When using the API, a job ID is returned and the operation continues on the server. Status can be checked with the `/api/media/sync/status/{job_id}` endpoint.
 
 :::warning
 
-The sync operation uses considerable CPU resources and in most cases is not needed, only enable when necessary.
+This operation uses considerable CPU resources and includes a safety threshold that aborts if more than 50% of files would be deleted. Only run when necessary. If you set `force: true` the safety threshold will be bypassed; do not use `force` unless you are certain the deletions are intended.
 
 :::

docs/docs/configuration/reference.md

@@ -510,8 +510,6 @@ record:
   # Optional: Number of minutes to wait between cleanup runs (default: shown below)
   # This can be used to reduce the frequency of deleting recording segments from disk if you want to minimize i/o
   expire_interval: 60
-  # Optional: Two-way sync recordings database with disk on startup and once a day (default: shown below).
-  sync_recordings: False
   # Optional: Continuous retention settings
   continuous:
     # Optional: Number of days to retain recordings regardless of tracked objects or motion (default: shown below)
@@ -534,6 +532,8 @@ record:
     # The -r (framerate) dictates how smooth the output video is.
     # So the args would be -vf setpts=0.02*PTS -r 30 in that case.
     timelapse_args: "-vf setpts=0.04*PTS -r 30"
+    # Optional: Global hardware acceleration settings for timelapse exports. (default: inherit)
+    hwaccel_args: auto
   # Optional: Recording Preview Settings
   preview:
     # Optional: Quality of recording preview (default: shown below).
@@ -835,6 +835,11 @@ cameras:
       # Optional: camera specific output args (default: inherit)
       # output_args:
 
+    # Optional: camera specific hwaccel args for timelapse export (default: inherit)
+    # record:
+    #   export:
+    #     hwaccel_args:
+
     # Optional: timeout for highest scoring image before allowing it
     # to be replaced by a newer image. (default: shown below)
     best_image_timeout: 60

docs/docs/configuration/restream.md

@@ -185,35 +185,10 @@ In this configuration:
 - `front_door` stream is used by Frigate for viewing, recording, and detection. The `#backchannel=0` parameter prevents go2rtc from establishing the audio output backchannel, so it won't block two-way talk access.
 - `front_door_twoway` stream is used for two-way talk functionality. This stream can be used by Frigate's WebRTC viewer when two-way talk is enabled, or by other applications (like Home Assistant Advanced Camera Card) that need access to the camera's audio output channel.
 
-## Security: Restricted Stream Sources
-
-For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disabled by default in go2rtc. These sources allow arbitrary command execution and can pose security risks if misconfigured.
-
-If you attempt to use these sources in your configuration, the streams will be removed and an error message will be printed in the logs.
-
-To enable these sources, you must set the environment variable `GO2RTC_ALLOW_ARBITRARY_EXEC=true`. This can be done in your Docker Compose file or container environment:
-
-```yaml
-environment:
-  - GO2RTC_ALLOW_ARBITRARY_EXEC=true
-```
-
-:::warning
-
-Enabling arbitrary exec sources allows execution of arbitrary commands through go2rtc stream configurations. Only enable this if you understand the security implications and trust all sources of your configuration.
-
-:::
-
 ## Advanced Restream Configurations
 
 The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
 
-:::warning
-
-The `exec:`, `echo:`, and `expr:` sources are disabled by default for security. You must set `GO2RTC_ALLOW_ARBITRARY_EXEC=true` to use them. See [Security: Restricted Stream Sources](#security-restricted-stream-sources) for more information.
-
-:::
-
 NOTE: The output will need to be passed with two curly braces `{{output}}`
 
 ```yaml

docs/docs/frigate/hardware.md

@@ -20,7 +20,7 @@ Here are some of the cameras I recommend:
 - <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
 - <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
 - <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
-- <a href="https://www.bhphotovideo.com/c/product/1705511-REG/hikvision_colorvu_ds_2cd2387g2p_lsu_sl_8mp_network.html" target="_blank" rel="nofollow noopener">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)
+- <a href="https://amzn.to/4ltOpaC" target="_blank" rel="nofollow noopener sponsored">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)
 
 I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
@@ -38,11 +38,9 @@ If the EQ13 is out of stock, the link below may take you to a suggested alternat
 
 :::
 
-| Name                                                                                                          | Capabilities                                                               | Notes                                               |
-| ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------- |
-| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | Can run object detection on several 1080p cameras with low-medium activity | Dual gigabit NICs for easy isolated camera network. |
-| Intel 1120p ([Amazon](https://www.amazon.com/Beelink-i3-1220P-Computer-Display-Gigabit/dp/B0DDCKT9YP)         | Can handle a large number of 1080p cameras with high activity              |                                                     |
-| Intel 125H  ([Amazon](https://www.amazon.com/MINISFORUM-Pro-125H-Barebone-Computer-HDMI2-1/dp/B0FH21FSZM)     | Can handle a significant number of 1080p cameras with high activity        | Includes NPU for more efficient detection in 0.17+  |
+| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                     |
+| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
+| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
 
 ## Detectors
 
@@ -127,16 +125,10 @@ In real-world deployments, even with multiple cameras running concurrently, Frig
 
 ### Google Coral TPU
 
-:::warning
-
-The Coral is no longer recommended for new Frigate installations, except in deployments with particularly low power requirements or hardware incapable of utilizing alternative AI accelerators for object detection. Instead, we suggest using one of the numerous other supported object detectors. Frigate will continue to provide support for the Coral TPU for as long as practicably possible given its still one of the most power-efficient devices for executing object detection models.
-
-:::
-
 Frigate supports both the USB and M.2 versions of the Google Coral.
 
 - The USB version is compatible with the widest variety of hardware and does not require a driver on the host machine. However, it does lack the automatic throttling features of the other versions.
-- The PCIe and M.2 versions require installation of a driver on the host. https://github.com/jnicolson/gasket-builder should be used.
+- The PCIe and M.2 versions require installation of a driver on the host. Follow the instructions for your version from https://coral.ai
 
 A single Coral can handle many cameras using the default model and will be sufficient for the majority of users. You can calculate the maximum performance of your Coral based on the inference speed reported by Frigate. With an inference speed of 10, your Coral will top out at `1000/10=100`, or 100 frames per second. If your detection fps is regularly getting close to that, you should first consider tuning motion masks. If those are already properly configured, a second Coral may be needed.
 

docs/docs/frigate/installation.md

@@ -94,10 +94,6 @@ $ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576
 
 The shm size cannot be set per container for Home Assistant add-ons. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.
 
-## Extra Steps for Specific Hardware
-
-The following sections contain additional setup steps that are only required if you are using specific hardware. If you are not using any of these hardware types, you can skip to the [Docker](#docker) installation section.
-
 ### Raspberry Pi 3/4
 
 By default, the Raspberry Pi limits the amount of memory available to the GPU. In order to use ffmpeg hardware acceleration, you must increase the available memory by setting `gpu_mem` to the maximum recommended value in `config.txt` as described in the [official docs](https://www.raspberrypi.org/documentation/computers/config_txt.html#memory-options).
@@ -110,107 +106,14 @@ The Hailo-8 and Hailo-8L AI accelerators are available in both M.2 and HAT form
 
 #### Installation
 
-:::warning
+For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simply follow this [guide](https://www.raspberrypi.com/documentation/accessories/ai-kit.html#ai-kit-installation) to install the driver and software.
 
-The Raspberry Pi kernel includes an older version of the Hailo driver that is incompatible with Frigate. You **must** follow the installation steps below to install the correct driver version, and you **must** disable the built-in kernel driver as described in step 1.
+For other installations, follow these steps for installation:
 
-:::
-
-1. **Disable the built-in Hailo driver (Raspberry Pi only)**:
-
-   :::note
-
-   If you are **not** using a Raspberry Pi, skip this step and proceed directly to step 2.
-
-   :::
-
-   If you are using a Raspberry Pi, you need to blacklist the built-in kernel Hailo driver to prevent conflicts. First, check if the driver is currently loaded:
-
-   ```bash
-   lsmod | grep hailo
-   ```
-
-   If it shows `hailo_pci`, unload it:
-
-   ```bash
-   sudo rmmod hailo_pci
-   ```
-
-   Now blacklist the driver to prevent it from loading on boot:
-
-   ```bash
-   echo "blacklist hailo_pci" | sudo tee /etc/modprobe.d/blacklist-hailo_pci.conf
-   ```
-
-   Update initramfs to ensure the blacklist takes effect:
-
-   ```bash
-   sudo update-initramfs -u
-   ```
-
-   Reboot your Raspberry Pi:
-
-   ```bash
-   sudo reboot
-   ```
-
-   After rebooting, verify the built-in driver is not loaded:
-
-   ```bash
-   lsmod | grep hailo
-   ```
-
-   This command should return no results. If it still shows `hailo_pci`, the blacklist did not take effect properly and you may need to check for other Hailo packages installed via apt that are loading the driver.
-
-2. **Run the installation script**:
-
-   Download the installation script:
-
-   ```bash
-   wget https://raw.githubusercontent.com/blakeblackshear/frigate/dev/docker/hailo8l/user_installation.sh
-   ```
-
-   Make it executable:
-
-   ```bash
-   sudo chmod +x user_installation.sh
-   ```
-
-   Run the script:
-
-   ```bash
-   ./user_installation.sh
-   ```
-
-   The script will:
-
-   - Install necessary build dependencies
-   - Clone and build the Hailo driver from the official repository
-   - Install the driver
-   - Download and install the required firmware
-   - Set up udev rules
-
-3. **Reboot your system**:
-
-   After the script completes successfully, reboot to load the firmware:
-
-   ```bash
-   sudo reboot
-   ```
-
-4. **Verify the installation**:
-
-   After rebooting, verify that the Hailo device is available:
-
-   ```bash
-   ls -l /dev/hailo0
-   ```
-
-   You should see the device listed. You can also verify the driver is loaded:
-
-   ```bash
-   lsmod | grep hailo_pci
-   ```
+1. Install the driver from the [Hailo GitHub repository](https://github.com/hailo-ai/hailort-drivers). A convenient script for Linux is available to clone the repository, build the driver, and install it.
+2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/hailo8l/user_installation.sh).
+3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
+4. Run the script with `./user_installation.sh`
 
 #### Setup
 
@@ -399,7 +302,7 @@ services:
     shm_size: "512mb" # update for your cameras based on calculation above
     devices:
       - /dev/bus/usb:/dev/bus/usb # Passes the USB Coral, needs to be modified for other versions
-      - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://github.com/jnicolson/gasket-builder
+      - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
       - /dev/video11:/dev/video11 # For Raspberry Pi 4B
       - /dev/dri/renderD128:/dev/dri/renderD128 # AMD / Intel GPU, needs to be updated for your hardware
       - /dev/accel:/dev/accel # Intel NPU
@@ -465,7 +368,6 @@ There are important limitations in HA OS to be aware of:
 
 - Separate local storage for media is not yet supported by Home Assistant
 - AMD GPUs are not supported because HA OS does not include the mesa driver.
-- Intel NPUs are not supported because HA OS does not include the NPU firmware.
 - Nvidia GPUs are not supported because addons do not support the nvidia runtime.
 
 :::

docs/docs/frigate/updating.md

@@ -5,7 +5,7 @@ title: Updating
 
 # Updating Frigate
 
-The current stable version of Frigate is **0.17.0**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.17.0).
+The current stable version of Frigate is **0.16.2**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.2).
 
 Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups.
 
@@ -33,21 +33,21 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
 2. **Update and Pull the Latest Image**:
 
    - If using Docker Compose:
-     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.17.0` instead of `0.16.3`). For example:
+     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.2` instead of `0.15.2`). For example:
        ```yaml
        services:
          frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.17.0
+           image: ghcr.io/blakeblackshear/frigate:0.16.2
        ```
      - Then pull the image:
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.17.0
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
        ```
      - **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
    - If using `docker run`:
-     - Pull the image with the appropriate tag (e.g., `0.17.0`, `0.17.0-tensorrt`, or `stable`):
+     - Pull the image with the appropriate tag (e.g., `0.16.2`, `0.16.2-tensorrt`, or `stable`):
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.17.0
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
        ```
 
 3. **Start the Container**:
@@ -105,8 +105,8 @@ If an update causes issues:
 1. Stop Frigate.
 2. Restore your backed-up config file and database.
 3. Revert to the previous image version:
-   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.3`) in your `docker run` command.
-   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.3`), and re-run `docker compose up -d`.
+   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.15.2`) in your `docker run` command.
+   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.15.2`), and re-run `docker compose up -d`.
    - For Home Assistant: Reinstall the previous addon version manually via the repository if needed and restart the addon.
 4. Verify the old version is running again.
 

docs/docs/guides/getting_started.md

@@ -134,13 +134,31 @@ Now you should be able to start Frigate by running `docker compose up -d` from w
 
 This section assumes that you already have an environment setup as described in [Installation](../frigate/installation.md). You should also configure your cameras according to the [camera setup guide](/frigate/camera_setup). Pay particular attention to the section on choosing a detect resolution.
 
-### Step 1: Start Frigate
+### Step 1: Add a detect stream
 
-At this point you should be able to start Frigate and a basic config will be created automatically.
+First we will add the detect stream for the camera:
 
-### Step 2: Add a camera
+```yaml
+mqtt:
+  enabled: False
 
-You can click the `Add Camera` button to use the camera setup wizard to get your first camera added into Frigate.
+cameras:
+  name_of_your_camera: # <------ Name the camera
+    enabled: True
+    ffmpeg:
+      inputs:
+        - path: rtsp://10.0.10.10:554/rtsp # <----- The stream you want to use for detection
+          roles:
+            - detect
+```
+
+### Step 2: Start Frigate
+
+At this point you should be able to start Frigate and see the video feed in the UI.
+
+If you get an error image from the camera, this means ffmpeg was not able to get the video feed from your camera. Check the logs for error messages from ffmpeg. The default ffmpeg arguments are designed to work with H264 RTSP cameras that support TCP connections.
+
+FFmpeg arguments for other types of cameras can be found [here](../configuration/camera_specific.md).
 
 ### Step 3: Configure hardware acceleration (recommended)
 
@@ -155,7 +173,7 @@ services:
   frigate:
     ...
     devices:
-      - /dev/dri/renderD128:/dev/dri/renderD128 # for intel & amd hwaccel, needs to be updated for your hardware
+      - /dev/dri/renderD128:/dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
     ...
 ```
 
@@ -184,7 +202,7 @@ services:
     ...
     devices:
       - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
-      - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://github.com/jnicolson/gasket-builder
+      - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
     ...
 ```
 

docs/docs/integrations/home-assistant.md

@@ -245,12 +245,6 @@ To load a preview gif of a review item:
 https://HA_URL/api/frigate/notifications/<review-id>/review_preview.gif
 ```
 
-To load the thumbnail of a review item:
-
-```
-https://HA_URL/api/frigate/notifications/<review-id>/<camera>/review_thumbnail.webp
-```
-
 <a name="streams"></a>
 
 ## RTSP stream

docs/docs/integrations/mqtt.md

@@ -280,7 +280,7 @@ Topic with current state of notifications. Published values are `ON` and `OFF`.
 
 ## Frigate Camera Topics
 
-### `frigate/<camera_name>/status/<role>`
+### `frigate/<camera_name>/<role>/status`
 
 Publishes the current health status of each role that is enabled (`audio`, `detect`, `record`). Possible values are:
 

docs/docs/integrations/third_party_extensions.md

@@ -38,7 +38,3 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht
 ## [Periscope](https://github.com/maksz42/periscope)
 
 [Periscope](https://github.com/maksz42/periscope) is a lightweight Android app that turns old devices into live viewers for Frigate. It works on Android 2.2 and above, including Android TV. It supports authentication and HTTPS.
-
-## [Scrypted - Frigate bridge plugin](https://github.com/apocaliss92/scrypted-frigate-bridge)
-
-[Scrypted - Frigate bridge](https://github.com/apocaliss92/scrypted-frigate-bridge) is an plugin that allows to ingest Frigate detections, motion, videoclips on Scrypted as well as provide templates to export rebroadcast configurations on Frigate.

docs/docs/plus/index.md

@@ -15,11 +15,13 @@ There are three model types offered in Frigate+, `mobiledet`, `yolonas`, and `yo
 
 Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types). You can test model types for compatibility and speed on your hardware by using the base models.
 
-| Model Type  | Description                                                                                                                                                                                                                    |
-| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                                                                                              |
-| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                                                                                   |
-| `yolov9`    | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on Intel, NVidia GPUs, AMD GPUs, Hailo, MemryX, Apple Silicon, and Rockchip NPUs. |
+| Model Type  | Description                                                                                                                                                                                                                        |
+| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                                                                                                  |
+| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                                                                                       |
+| `yolov9`    | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on Intel, NVidia GPUs, AMD GPUs, Hailo, MemryX\*, Apple Silicon\*, and Rockchip NPUs. |
+
+_\* Support coming in 0.17_
 
 ### YOLOv9 Details
 
@@ -37,7 +39,7 @@ If you have a Hailo device, you will need to specify the hardware you have when
 
 #### Rockchip (RKNN) Support
 
-For 0.16, YOLOv9 onnx models will need to be manually converted. First, you will need to configure Frigate to use the model id for your YOLOv9 onnx model so it downloads the model to your `model_cache` directory. From there, you can follow the [documentation](/configuration/object_detectors.md#converting-your-own-onnx-model-to-rknn-format) to convert it. Automatic conversion is available in 0.17 and later.
+For 0.16, YOLOv9 onnx models will need to be manually converted. First, you will need to configure Frigate to use the model id for your YOLOv9 onnx model so it downloads the model to your `model_cache` directory. From there, you can follow the [documentation](/configuration/object_detectors.md#converting-your-own-onnx-model-to-rknn-format) to convert it. Automatic conversion is coming in 0.17.
 
 ## Supported detector types
 
@@ -53,7 +55,7 @@ Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVi
 | [Hailo8/Hailo8L/Hailo8R](/configuration/object_detectors#hailo-8)                | `hailo8l`                 | `yolov9`               |
 | [Rockchip NPU](/configuration/object_detectors#rockchip-platform)\*              | `rknn`                    | `yolov9`               |
 
-_\* Requires manual conversion in 0.16. Automatic conversion available in 0.17 and later._
+_\* Requires manual conversion in 0.16. Automatic conversion coming in 0.17._
 
 ## Improving your model
 

docs/docs/troubleshooting/cpu.md

@@ -1,73 +0,0 @@
----
-id: cpu
-title: High CPU Usage
----
-
-High CPU usage can impact Frigate's performance and responsiveness. This guide outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
-
-## 1. Hardware Acceleration for Video Decoding
-
-**Priority: Critical**
-
-Video decoding is one of the most CPU-intensive tasks in Frigate. While an AI accelerator handles object detection, it does not assist with decoding video streams. Hardware acceleration (hwaccel) offloads this work to your GPU or specialized video decode hardware, significantly reducing CPU usage and enabling you to support more cameras on the same hardware.
-
-### Key Concepts
-
-**Resolution & FPS Impact:** The decoding burden grows exponentially with resolution and frame rate. A 4K stream at 30 FPS requires roughly 4 times the processing power of a 1080p stream at the same frame rate, and doubling the frame rate doubles the decode workload. This is why hardware acceleration becomes critical when working with multiple high-resolution cameras.
-
-**Hardware Acceleration Benefits:** By using dedicated video decode hardware, you can:
-
-- Significantly reduce CPU usage per camera stream
-- Support 2-3x more cameras on the same hardware
-- Free up CPU resources for motion detection and other Frigate processes
-- Reduce system heat and power consumption
-
-### Configuration
-
-Frigate provides preset configurations for common hardware acceleration scenarios. Set up `hwaccel_args` based on your hardware in your [configuration](../configuration/reference) as described in the [getting started guide](../guides/getting_started).
-
-### Troubleshooting Hardware Acceleration
-
-If hardware acceleration isn't working:
-
-1. Check Frigate logs for FFmpeg errors related to hwaccel
-2. Verify the hardware device is accessible inside the container
-3. Ensure your camera streams use H.264 or H.265 codecs (most common)
-4. Try different presets if the automatic detection fails
-5. Check that your GPU drivers are properly installed on the host system
-
-## 2. Detector Selection and Configuration
-
-**Priority: Critical**
-
-Choosing the right detector for your hardware is the single most important factor for detection performance. The detector is responsible for running the AI model that identifies objects in video frames. Different detector types have vastly different performance characteristics and hardware requirements, as detailed in the [hardware documentation](../frigate/hardware).
-
-### Understanding Detector Performance
-
-Frigate uses motion detection as a first-line check before running expensive object detection, as explained in the [motion detection documentation](../configuration/motion_detection). When motion is detected, Frigate creates a "region" (the green boxes in the debug viewer) and sends it to the detector. The detector's inference speed determines how many detections per second your system can handle.
-
-**Calculating Detector Capacity:** Your detector has a finite capacity measured in detections per second. With an inference speed of 10ms, your detector can handle approximately 100 detections per second (1000ms / 10ms = 100).If your cameras collectively require more than this capacity, you'll experience delays, missed detections, or the system will fall behind.
-
-### Choosing the Right Detector
-
-Different detectors have vastly different performance characteristics, see the expected performance for object detectors in [the hardware docs](../frigate/hardware)
-
-### Multiple Detector Instances
-
-When a single detector cannot keep up with your camera count, some detector types (`openvino`, `onnx`) allow you to define multiple detector instances to share the workload. This is particularly useful with GPU-based detectors that have sufficient VRAM to run multiple inference processes.
-
-For detailed instructions on configuring multiple detectors, see the [Object Detectors documentation](../configuration/object_detectors).
-
-
-**When to add a second detector:**
-
-- Skipped FPS is consistently > 0 even during normal activity
-
-### Model Selection and Optimization
-
-The model you use significantly impacts detector performance. Frigate provides default models optimized for each detector type, but you can customize them as described in the [detector documentation](../configuration/object_detectors).
-
-**Model Size Trade-offs:**
-
-- Smaller models (320x320): Faster inference, Frigate is specifically optimized for a 320x320 size model.
-- Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.

docs/docs/troubleshooting/dummy-camera.md

@@ -1,60 +0,0 @@
----
-id: dummy-camera
-title: Analyzing Object Detection
----
-
-When investigating object detection or tracking problems, it can be helpful to replay an exported video as a temporary "dummy" camera. This lets you reproduce issues locally, iterate on configuration (detections, zones, enrichment settings), and capture logs and clips for analysis.
-
-## When to use
-
-- Replaying an exported clip to reproduce incorrect detections
-- Testing configuration changes (model settings, trackers, filters) against a known clip
-- Gathering deterministic logs and recordings for debugging or issue reports
-
-## Example Config
-
-Place the clip you want to replay in a location accessible to Frigate (for example `/media/frigate/` or the repository `debug/` folder when developing). Then add a temporary camera to your `config/config.yml` like this:
-
-```yaml
-cameras:
-  test:
-    ffmpeg:
-      inputs:
-        - path: /media/frigate/car-stopping.mp4
-          input_args: -re -stream_loop -1 -fflags +genpts
-          roles:
-            - detect
-    detect:
-      enabled: true
-    record:
-      enabled: false
-    snapshots:
-      enabled: false
-```
-
-- `-re -stream_loop -1` tells `ffmpeg` to play the file in realtime and loop indefinitely, which is useful for long debugging sessions.
-- `-fflags +genpts` helps generate presentation timestamps when they are missing in the file.
-
-## Steps
-
-1. Export or copy the clip you want to replay to the Frigate host (e.g., `/media/frigate/` or `debug/clips/`).
-2. Add the temporary camera to `config/config.yml` (example above). Use a unique name such as `test` or `replay_camera` so it's easy to remove later.
-   - If you're debugging a specific camera, copy the settings from that camera (frame rate, model/enrichment settings, zones, etc.) into the temporary camera so the replay closely matches the original environment. Leave `record` and `snapshots` disabled unless you are specifically debugging recording or snapshot behavior.
-3. Restart Frigate.
-4. Observe the Debug view in the UI and logs as the clip is replayed. Watch detections, zones, or any feature you're looking to debug, and note any errors in the logs to reproduce the issue.
-5. Iterate on camera or enrichment settings (model, fps, zones, filters) and re-check the replay until the behavior is resolved.
-6. Remove the temporary camera from your config after debugging to avoid spurious telemetry or recordings.
-
-## Variables to consider in object tracking
-
-- The exported video will not always line up exactly with how it originally ran through Frigate (or even with the last loop). Different frames may be used on replay, which can change detections and tracking.
-- Motion detection depends on the frames used; small frame shifts can change motion regions and therefore what gets passed to the detector.
-- Object detection is not deterministic: models and post-processing can yield different results across runs, so you may not get identical detections or track IDs every time.
-
-When debugging, treat the replay as a close approximation rather than a byte-for-byte replay. Capture multiple runs, enable recording if helpful, and examine logs and saved event clips to understand variability.
-
-## Troubleshooting
-
-- No video: verify the path is correct and accessible from the Frigate process/container.
-- FFmpeg errors: check the log output for ffmpeg-specific flags and adjust `input_args` accordingly for your file/container. You may also need to disable hardware acceleration (`hwaccel_args: ""`) for the dummy camera.
-- No detections: confirm the camera `roles` include `detect`, and model/detector configuration is enabled.

docs/docs/troubleshooting/edgetpu.md

@@ -1,6 +1,6 @@
 ---
 id: edgetpu
-title: EdgeTPU Errors
+title: Troubleshooting EdgeTPU
 ---
 
 ## USB Coral Not Detected
@@ -68,7 +68,8 @@ The USB Coral can become stuck and need to be restarted, this can happen for a n
 
 The most common reason for the PCIe Coral not being detected is that the driver has not been installed. This process varies based on what OS and kernel that is being run.
 
-- In most cases https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
+- In most cases [the Coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) show how to install the driver for the PCIe based Coral.
+- For some newer Linux distros (for example, Ubuntu 22.04+), https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
 
 ## Attempting to load TPU as pci & Fatal Python error: Illegal instruction
 

docs/docs/troubleshooting/gpu.md

@@ -1,6 +1,6 @@
 ---
 id: gpu
-title: GPU Errors
+title: Troubleshooting GPU
 ---
 
 ## OpenVINO

docs/docs/troubleshooting/memory.md

@@ -1,6 +1,6 @@
 ---
 id: memory
-title: Memory Usage
+title: Memory Troubleshooting
 ---
 
 Frigate includes built-in memory profiling using [memray](https://bloomberg.github.io/memray/) to help diagnose memory issues. This feature allows you to profile specific Frigate modules to identify memory leaks, excessive allocations, or other memory-related problems.
@@ -9,20 +9,8 @@ Frigate includes built-in memory profiling using [memray](https://bloomberg.gith
 
 Memory profiling is controlled via the `FRIGATE_MEMRAY_MODULES` environment variable. Set it to a comma-separated list of module names you want to profile:
 
-```yaml
-# docker-compose example
-services:
-  frigate:
-    ...
-    environment:
-      - FRIGATE_MEMRAY_MODULES=frigate.embeddings,frigate.capture
-```
-
 ```bash
-# docker run example
-docker run -e FRIGATE_MEMRAY_MODULES="frigate.embeddings" \
-   ...
-   --name frigate <frigate_image>
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
 ```
 
 ### Module Names
@@ -40,7 +28,7 @@ Frigate processes are named using a module-based naming scheme. Common module na
 You can also specify the full process name (including camera-specific identifiers) if you want to profile a specific camera:
 
 ```bash
-FRIGATE_MEMRAY_MODULES=frigate.capture:front_door
+export FRIGATE_MEMRAY_MODULES="frigate.capture:front_door"
 ```
 
 When you specify a module name (e.g., `frigate.capture`), all processes with that module prefix will be profiled. For example, `frigate.capture` will profile all camera capture processes.
@@ -67,20 +55,11 @@ After a process exits normally, you'll find HTML reports in `/config/memray_repo
 
 If a process crashes or you want to generate a report from an existing binary file, you can manually create the HTML report:
 
-- Run `memray` inside the Frigate container:
-
 ```bash
-docker-compose exec frigate memray flamegraph /config/memray_reports/<module_name>.bin
-# or
-docker exec -it <container_name_or_id> memray flamegraph /config/memray_reports/<module_name>.bin
+memray flamegraph /config/memray_reports/<module_name>.bin
 ```
 
-- You can also copy the `.bin` file to the host and run `memray` locally if you have it installed:
-
-```bash
-docker cp <container_name_or_id>:/config/memray_reports/<module_name>.bin /tmp/
-memray flamegraph /tmp/<module_name>.bin
-```
+This will generate an HTML file that you can open in your browser.
 
 ## Understanding the Reports
 
@@ -131,4 +110,20 @@ The interactive HTML reports allow you to:
 - Check that memray is properly installed (included by default in Frigate)
 - Verify the process actually started and ran (check process logs)
 
+## Example Usage
+
+```bash
+# Enable profiling for review and capture modules
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
+
+# Start Frigate
+# ... let it run for a while ...
+
+# Check for reports
+ls -lh /config/memray_reports/
+
+# If a process crashed, manually generate report
+memray flamegraph /config/memray_reports/frigate_capture_front_door.bin
+```
+
 For more information about memray and interpreting reports, see the [official memray documentation](https://bloomberg.github.io/memray/).

docs/docs/troubleshooting/recordings.md

@@ -1,6 +1,6 @@
 ---
 id: recordings
-title: Recordings Errors
+title: Troubleshooting Recordings
 ---
 
 ## I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?

docs/docusaurus.config.ts

@@ -170,7 +170,7 @@ const config: Config = {
           ],
         },
       ],
-      copyright: `Copyright © ${new Date().getFullYear()} Frigate, Inc.`,
+      copyright: `Copyright © ${new Date().getFullYear()} Frigate LLC`,
     },
   },
   plugins: [

docs/sidebars.ts

@@ -129,27 +129,9 @@ const sidebars: SidebarsConfig = {
     Troubleshooting: [
       "troubleshooting/faqs",
       "troubleshooting/recordings",
-      "troubleshooting/dummy-camera",
-      {
-        type: "category",
-        label: "Troubleshooting Hardware",
-        link: {
-          type: "generated-index",
-          title: "Troubleshooting Hardware",
-          description: "Troubleshooting Problems with Hardware",
-        },
-        items: ["troubleshooting/gpu", "troubleshooting/edgetpu"],
-      },
-      {
-        type: "category",
-        label: "Troubleshooting Resource Usage",
-        link: {
-          type: "generated-index",
-          title: "Troubleshooting Resource Usage",
-          description: "Troubleshooting issues with resource usage",
-        },
-        items: ["troubleshooting/cpu", "troubleshooting/memory"],
-      },
+      "troubleshooting/gpu",
+      "troubleshooting/edgetpu",
+      "troubleshooting/memory",
     ],
     Development: [
       "development/contributing",

docs/static/frigate-api.yaml

@@ -17,25 +17,20 @@ paths:
       summary: Authenticate request
       description: |-
         Authenticates the current request based on proxy headers or JWT token.
+        Returns user role and permissions for camera access.
         This endpoint verifies authentication credentials and manages JWT token refresh.
-        On success, no JSON body is returned; authentication state is communicated via response headers and cookies.
       operationId: auth_auth_get
       responses:
+        "200":
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
         "202":
-          description: Authentication Accepted (no response body, different headers depending on auth method)
-          headers:
-            remote-user:
-              description: Authenticated username or "viewer" in proxy-only mode
-              schema:
-                type: string
-            remote-role:
-              description: Resolved role (e.g., admin, viewer, or custom)
-              schema:
-                type: string
-            Set-Cookie:
-              description: May include refreshed JWT cookie ("frigate-token") when applicable
-              schema:
-                type: string
+          description: Authentication Accepted
+          content:
+            application/json:
+              schema: {}
         "401":
           description: Authentication Failed
   /profile:
@@ -331,6 +326,59 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
+  /media/sync:
+    post:
+      tags:
+        - App
+      summary: Start media sync job
+      description: |-
+        Start an asynchronous media sync job to find and (optionally) remove orphaned media files.
+        Returns 202 with job details when queued, or 409 if a job is already running.
+      operationId: sync_media_media_sync_post
+      requestBody:
+        required: true
+        content:
+          application/json:
+      responses:
+        "202":
+          description: Accepted - Job queued
+        "409":
+          description: Conflict - Job already running
+        "422":
+          description: Validation Error
+
+  /media/sync/current:
+    get:
+      tags:
+        - App
+      summary: Get current media sync job
+      description: |-
+        Retrieve the current running media sync job, if any. Returns the job details or null when no job is active.
+      operationId: get_media_sync_current_media_sync_current_get
+      responses:
+        "200":
+          description: Successful Response
+        "422":
+          description: Validation Error
+
+  /media/sync/status/{job_id}:
+    get:
+      tags:
+        - App
+      summary: Get media sync job status
+      description: |-
+        Get status and results for the specified media sync job id. Returns 200 with job details including results, or 404 if the job is not found.
+      operationId: get_media_sync_status_media_sync_status__job_id__get
+      parameters:
+        - name: job_id
+          in: path
+      responses:
+        "200":
+          description: Successful Response
+        "404":
+          description: Not Found - Job not found
+        "422":
+          description: Validation Error
   /faces/train/{name}/classify:
     post:
       tags:
@@ -616,32 +664,6 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
-  /classification/attributes:
-    get:
-      tags:
-        - Classification
-      summary: Get custom classification attributes
-      description: |-
-        Returns custom classification attributes for a given object type.
-        Only includes models with classification_type set to 'attribute'.
-        By default returns a flat sorted list of all attribute labels.
-        If group_by_model is true, returns attributes grouped by model name.
-      operationId: get_custom_attributes_classification_attributes_get
-      parameters:
-        - name: object_type
-          in: query
-          schema:
-            type: string
-        - name: group_by_model
-          in: query
-          schema:
-            type: boolean
-            default: false
-      responses:
-        "200":
-          description: Successful Response
-        "422":
-          description: Validation Error
   /classification/{name}/dataset:
     get:
       tags:
@@ -2938,42 +2960,6 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
-  /events/{event_id}/attributes:
-    post:
-      tags:
-        - Events
-      summary: Set custom classification attributes
-      description: |-
-        Sets an event's custom classification attributes for all attribute-type
-        models that apply to the event's object type.
-            Returns a success message or an error if the event is not found.
-      operationId: set_attributes_events__event_id__attributes_post
-      parameters:
-        - name: event_id
-          in: path
-          required: true
-          schema:
-            type: string
-            title: Event Id
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: "#/components/schemas/EventsAttributesBody"
-      responses:
-        "200":
-          description: Successful Response
-          content:
-            application/json:
-              schema:
-                $ref: "#/components/schemas/GenericResponse"
-        "422":
-          description: Validation Error
-          content:
-            application/json:
-              schema:
-                $ref: "#/components/schemas/HTTPValidationError"
   /events/{event_id}/description:
     post:
       tags:
@@ -5021,18 +5007,6 @@ components:
       required:
         - subLabel
       title: EventsSubLabelBody
-    EventsAttributesBody:
-      properties:
-        attributes:
-          type: object
-          title: Attributes
-          description: Object with model names as keys and attribute values
-          additionalProperties:
-            type: string
-      type: object
-      required:
-        - attributes
-      title: EventsAttributesBody
     ExportModel:
       properties:
         id:

docs/static/img/branding/LICENSE.md

@@ -1,12 +1,12 @@
 # COPYRIGHT AND TRADEMARK NOTICE
 
 The images, logos, and icons contained in this directory (the "Brand Assets") are
-proprietary to Frigate, Inc. and are NOT covered by the MIT License governing the
+proprietary to Frigate LLC and are NOT covered by the MIT License governing the
 rest of this repository.
 
 1. TRADEMARK STATUS
    The "Frigate" name and the accompanying logo are common law trademarks™ of
-   Frigate, Inc. Frigate, Inc. reserves all rights to these marks.
+   Frigate LLC. Frigate LLC reserves all rights to these marks.
 
 2. LIMITED PERMISSION FOR USE
    Permission is hereby granted to display these Brand Assets strictly for the
@@ -17,9 +17,9 @@ rest of this repository.
 3. RESTRICTIONS
    You may NOT:
    a. Use these Brand Assets to represent a derivative work (fork) as an official
-   product of Frigate, Inc.
+   product of Frigate LLC.
    b. Use these Brand Assets in a way that implies endorsement, sponsorship, or
-   commercial affiliation with Frigate, Inc.
+   commercial affiliation with Frigate LLC.
    c. Modify or alter the Brand Assets.
 
 If you fork this repository with the intent to distribute a modified or competing
@@ -27,4 +27,4 @@ version of the software, you must replace these Brand Assets with your own
 original content.
 
 ALL RIGHTS RESERVED.
-Copyright (c) 2026 Frigate, Inc.
+Copyright (c) 2025 Frigate LLC.

frigate/api/app.py

@@ -25,15 +25,22 @@ from pydantic import ValidationError
 
 from frigate.api.auth import allow_any_authenticated, allow_public, require_role
 from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
-from frigate.api.defs.request.app_body import AppConfigSetBody
+from frigate.api.defs.request.app_body import AppConfigSetBody, MediaSyncBody
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateTopic,
 )
+from frigate.ffmpeg_presets import FFMPEG_HWACCEL_VAAPI, _gpu_selector
+from frigate.jobs.media_sync import (
+    get_current_media_sync_job,
+    get_media_sync_job_by_id,
+    start_media_sync_job,
+)
 from frigate.models import Event, Timeline
 from frigate.stats.prometheus import get_metrics, update_metrics
+from frigate.types import JobStatusTypesEnum
 from frigate.util.builtin import (
     clean_camera_user_pass,
     flatten_config_data,
@@ -458,7 +465,15 @@ def config_set(request: Request, body: AppConfigSetBody):
 
 @router.get("/vainfo", dependencies=[Depends(allow_any_authenticated())])
 def vainfo():
-    vainfo = vainfo_hwaccel()
+    # Use LibvaGpuSelector to pick an appropriate libva device (if available)
+    selected_gpu = ""
+    try:
+        selected_gpu = _gpu_selector.get_gpu_arg(FFMPEG_HWACCEL_VAAPI, 0) or ""
+    except Exception:
+        selected_gpu = ""
+
+    # If selected_gpu is empty, pass None to vainfo_hwaccel to run plain `vainfo`.
+    vainfo = vainfo_hwaccel(device_name=selected_gpu or None)
     return JSONResponse(
         content={
             "return_code": vainfo.returncode,
@@ -593,6 +608,98 @@ def restart():
     )
 
 
+@router.post(
+    "/media/sync",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Start media sync job",
+    description="""Start an asynchronous media sync job to find and (optionally) remove orphaned media files.
+    Returns 202 with job details when queued, or 409 if a job is already running.""",
+)
+def sync_media(body: MediaSyncBody = Body(...)):
+    """Start async media sync job - remove orphaned files.
+
+    Syncs specified media types: event snapshots, event thumbnails, review thumbnails,
+    previews, exports, and/or recordings. Job runs in background; use /media/sync/current
+    or /media/sync/status/{job_id} to check status.
+
+    Args:
+        body: MediaSyncBody with dry_run flag and media_types list.
+              media_types can include: 'all', 'event_snapshots', 'event_thumbnails',
+              'review_thumbnails', 'previews', 'exports', 'recordings'
+
+    Returns:
+        202 Accepted with job_id, or 409 Conflict if job already running.
+    """
+    job_id = start_media_sync_job(
+        dry_run=body.dry_run, media_types=body.media_types, force=body.force
+    )
+
+    if job_id is None:
+        # A job is already running
+        current = get_current_media_sync_job()
+        return JSONResponse(
+            content={
+                "error": "A media sync job is already running",
+                "current_job_id": current.id if current else None,
+            },
+            status_code=409,
+        )
+
+    return JSONResponse(
+        content={
+            "job": {
+                "job_type": "media_sync",
+                "status": JobStatusTypesEnum.queued,
+                "id": job_id,
+            }
+        },
+        status_code=202,
+    )
+
+
+@router.get(
+    "/media/sync/current",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Get current media sync job",
+    description="""Retrieve the current running media sync job, if any. Returns the job details
+    or null when no job is active.""",
+)
+def get_media_sync_current():
+    """Get the current running media sync job, if any."""
+    job = get_current_media_sync_job()
+
+    if job is None:
+        return JSONResponse(content={"job": None}, status_code=200)
+
+    return JSONResponse(
+        content={"job": job.to_dict()},
+        status_code=200,
+    )
+
+
+@router.get(
+    "/media/sync/status/{job_id}",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Get media sync job status",
+    description="""Get status and results for the specified media sync job id. Returns 200 with
+    job details including results, or 404 if the job is not found.""",
+)
+def get_media_sync_status(job_id: str):
+    """Get the status of a specific media sync job."""
+    job = get_media_sync_job_by_id(job_id)
+
+    if job is None:
+        return JSONResponse(
+            content={"error": "Job not found"},
+            status_code=404,
+        )
+
+    return JSONResponse(
+        content={"job": job.to_dict()},
+        status_code=200,
+    )
+
+
 @router.get("/labels", dependencies=[Depends(allow_any_authenticated())])
 def get_labels(camera: str = ""):
     try:

frigate/api/auth.py

@@ -143,6 +143,17 @@ def require_admin_by_default():
     return admin_checker
 
 
+def _is_authenticated(request: Request) -> bool:
+    """
+    Helper to determine if a request is from an authenticated user.
+
+    Returns True if the request has a valid authenticated user (not anonymous).
+    Port 5000 internal requests are considered anonymous despite having admin role.
+    """
+    username = request.headers.get("remote-user")
+    return username is not None and username != "anonymous"
+
+
 def allow_public():
     """
     Override dependency to allow unauthenticated access to an endpoint.
@@ -162,24 +173,27 @@ def allow_public():
 
 def allow_any_authenticated():
     """
-    Override dependency to allow any request that passed through the /auth endpoint.
+    Override dependency to allow any authenticated user (bypass admin requirement).
 
     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: "viewer")
+    - Port 5000 internal requests (have admin role despite anonymous user)
+    - Any authenticated user with a real username (not "anonymous")
 
     Rejects:
-    - Requests with no remote-user header (did not pass through /auth endpoint)
+    - Port 8971 requests with anonymous user (auth disabled, no proxy auth)
 
     Example:
         @router.get("/authenticated-endpoint", dependencies=[Depends(allow_any_authenticated())])
     """
 
     async def auth_checker(request: Request):
-        # Ensure a remote-user has been set by the /auth endpoint
-        username = request.headers.get("remote-user")
-        if username is None:
+        # Port 5000 requests have admin role and should be allowed
+        role = request.headers.get("remote-role")
+        if role == "admin":
+            return
+
+        # Otherwise require a real authenticated user (not anonymous)
+        if not _is_authenticated(request):
             raise HTTPException(status_code=401, detail="Authentication required")
         return
 
@@ -539,32 +553,7 @@ def resolve_role(
     "/auth",
     dependencies=[Depends(allow_public())],
     summary="Authenticate request",
-    description=(
-        "Authenticates the current request based on proxy headers or JWT token. "
-        "This endpoint verifies authentication credentials and manages JWT token refresh. "
-        "On success, no JSON body is returned; authentication state is communicated via response headers and cookies."
-    ),
-    status_code=202,
-    responses={
-        202: {
-            "description": "Authentication Accepted (no response body)",
-            "headers": {
-                "remote-user": {
-                    "description": 'Authenticated username or "viewer" in proxy-only mode',
-                    "schema": {"type": "string"},
-                },
-                "remote-role": {
-                    "description": "Resolved role (e.g., admin, viewer, or custom)",
-                    "schema": {"type": "string"},
-                },
-                "Set-Cookie": {
-                    "description": "May include refreshed JWT cookie when applicable",
-                    "schema": {"type": "string"},
-                },
-            },
-        },
-        401: {"description": "Authentication Failed"},
-    },
+    description="Authenticates the current request based on proxy headers or JWT token. Returns user role and permissions for camera access.",
 )
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
@@ -592,12 +581,12 @@ 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 viewer if none are specified
+        # or use anonymous if none are specified
         user_header = proxy_config.header_map.user
         success_response.headers["remote-user"] = (
-            request.headers.get(user_header, default="viewer")
+            request.headers.get(user_header, default="anonymous")
             if user_header
-            else "viewer"
+            else "anonymous"
         )
 
         # parse header and resolve a valid role
@@ -709,10 +698,10 @@ def auth(request: Request):
     "/profile",
     dependencies=[Depends(allow_any_authenticated())],
     summary="Get user profile",
-    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.",
+    description="Returns the current authenticated user's profile including username, role, and allowed cameras.",
 )
 def profile(request: Request):
-    username = request.headers.get("remote-user", "viewer")
+    username = request.headers.get("remote-user", "anonymous")
     role = request.headers.get("remote-role", "viewer")
 
     all_camera_names = set(request.app.frigate_config.cameras.keys())
@@ -728,7 +717,7 @@ def profile(request: Request):
     "/logout",
     dependencies=[Depends(allow_public())],
     summary="Logout user",
-    description="Logs out the current user by clearing the session cookie. After logout, subsequent requests will require re-authentication.",
+    description="Logs out the current user by clearing the session cookie.",
 )
 def logout(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
@@ -744,7 +733,7 @@ limiter = Limiter(key_func=get_remote_addr)
     "/login",
     dependencies=[Depends(allow_public())],
     summary="Login with credentials",
-    description='Authenticates a user with username and password. Returns a JWT token as a secure HTTP-only cookie that can be used for subsequent API requests. The JWT token can also be retrieved from the response and used as a Bearer token in the Authorization header.\n\nExample using Bearer token:\n```\ncurl -H "Authorization: Bearer <token_value>" https://frigate_ip:8971/api/profile\n```',
+    description="Authenticates a user with username and password. Returns a JWT token as a secure HTTP-only cookie that can be used for subsequent API requests. The token can also be retrieved and used as a Bearer token in the Authorization header.",
 )
 @limiter.limit(limit_value=rateLimiter.get_limit)
 def login(request: Request, body: AppPostLoginBody):
@@ -787,7 +776,7 @@ def login(request: Request, body: AppPostLoginBody):
     "/users",
     dependencies=[Depends(require_role(["admin"]))],
     summary="Get all users",
-    description="Returns a list of all users with their usernames and roles. Requires admin role. Each user object contains the username and assigned role.",
+    description="Returns a list of all users with their usernames and roles. Requires admin role.",
 )
 def get_users():
     exports = (
@@ -800,7 +789,7 @@ def get_users():
     "/users",
     dependencies=[Depends(require_role(["admin"]))],
     summary="Create new user",
-    description='Creates a new user with the specified username, password, and role. Requires admin role. Password must meet strength requirements: minimum 8 characters, at least one uppercase letter, at least one digit, and at least one special character (!@#$%^&*(),.?":{} |<>).',
+    description="Creates a new user with the specified username, password, and role. Requires admin role. Password must meet strength requirements.",
 )
 def create_user(
     request: Request,
@@ -834,7 +823,7 @@ def create_user(
     "/users/{username}",
     dependencies=[Depends(require_role(["admin"]))],
     summary="Delete user",
-    description="Deletes a user by username. The built-in admin user cannot be deleted. Requires admin role. Returns success message or error if user not found.",
+    description="Deletes a user by username. The built-in admin user cannot be deleted. Requires admin role.",
 )
 def delete_user(request: Request, username: str):
     # Prevent deletion of the built-in admin user
@@ -851,7 +840,7 @@ def delete_user(request: Request, username: str):
     "/users/{username}/password",
     dependencies=[Depends(allow_any_authenticated())],
     summary="Update user password",
-    description="Updates a user's password. Users can only change their own password unless they have admin role. Requires the current password to verify identity for non-admin users. Password must meet strength requirements: minimum 8 characters, at least one uppercase letter, at least one digit, and at least one special character (!@#$%^&*(),.?\":{} |<>). If user changes their own password, a new JWT cookie is automatically issued.",
+    description="Updates a user's password. Users can only change their own password unless they have admin role. Requires the current password to verify identity. Password must meet strength requirements (minimum 8 characters, uppercase letter, digit, and special character).",
 )
 async def update_password(
     request: Request,
@@ -879,9 +868,13 @@ async def update_password(
     except DoesNotExist:
         return JSONResponse(content={"message": "User not found"}, status_code=404)
 
-    # Require old_password when non-admin user is changing any password
-    # Admin users changing passwords do NOT need to provide the current password
-    if current_role != "admin":
+    # Require old_password when:
+    # 1. Non-admin user is changing another user's password (admin only action)
+    # 2. Any user is changing their own password
+    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"},
@@ -933,7 +926,7 @@ async def update_password(
     "/users/{username}/role",
     dependencies=[Depends(require_role(["admin"]))],
     summary="Update user role",
-    description="Updates a user's role. The built-in admin user's role cannot be modified. Requires admin role. Valid roles are defined in the configuration.",
+    description="Updates a user's role. The built-in admin user's role cannot be modified. Requires admin role.",
 )
 async def update_role(
     request: Request,

frigate/api/classification.py

@@ -31,7 +31,6 @@ from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
 from frigate.config.camera import DetectConfig
-from frigate.config.classification import ObjectClassificationType
 from frigate.const import CLIPS_DIR, FACE_DIR, MODEL_CACHE_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event
@@ -40,7 +39,6 @@ from frigate.util.classification import (
     collect_state_classification_examples,
     get_dataset_image_count,
     read_training_metadata,
-    write_training_metadata,
 )
 from frigate.util.file import get_event_snapshot
 
@@ -624,59 +622,6 @@ def get_classification_dataset(name: str):
     )
 
 
-@router.get(
-    "/classification/attributes",
-    summary="Get custom classification attributes",
-    description="""Returns custom classification attributes for a given object type.
-    Only includes models with classification_type set to 'attribute'.
-    By default returns a flat sorted list of all attribute labels.
-    If group_by_model is true, returns attributes grouped by model name.""",
-)
-def get_custom_attributes(
-    request: Request, object_type: str = None, group_by_model: bool = False
-):
-    models_with_attributes = {}
-
-    for (
-        model_key,
-        model_config,
-    ) in request.app.frigate_config.classification.custom.items():
-        if (
-            not model_config.enabled
-            or not model_config.object_config
-            or model_config.object_config.classification_type
-            != ObjectClassificationType.attribute
-        ):
-            continue
-
-        model_objects = getattr(model_config.object_config, "objects", []) or []
-        if object_type is not None and object_type not in model_objects:
-            continue
-
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
-        if not os.path.exists(dataset_dir):
-            continue
-
-        attributes = []
-        for category_name in os.listdir(dataset_dir):
-            category_dir = os.path.join(dataset_dir, category_name)
-            if os.path.isdir(category_dir) and category_name != "none":
-                attributes.append(category_name)
-
-        if attributes:
-            model_name = model_config.name or model_key
-            models_with_attributes[model_name] = sorted(attributes)
-
-    if group_by_model:
-        return JSONResponse(content=models_with_attributes)
-    else:
-        # Flatten to a unique sorted list
-        all_attributes = set()
-        for attributes in models_with_attributes.values():
-            all_attributes.update(attributes)
-        return JSONResponse(content=sorted(list(all_attributes)))
-
-
 @router.get(
     "/classification/{name}/train",
     summary="Get classification train images",
@@ -843,12 +788,6 @@ def rename_classification_category(
 
     try:
         os.rename(old_folder, new_folder)
-
-        # Mark dataset as ready to train by resetting training metadata
-        # This ensures the dataset is marked as changed after renaming
-        sanitized_name = sanitize_filename(name)
-        write_training_metadata(sanitized_name, 0)
-
         return JSONResponse(
             content=(
                 {

frigate/api/defs/query/events_query_parameters.py

@@ -12,7 +12,6 @@ class EventsQueryParams(BaseModel):
     labels: Optional[str] = "all"
     sub_label: Optional[str] = "all"
     sub_labels: Optional[str] = "all"
-    attributes: Optional[str] = "all"
     zone: Optional[str] = "all"
     zones: Optional[str] = "all"
     limit: Optional[int] = 100
@@ -59,8 +58,6 @@ class EventsSearchQueryParams(BaseModel):
     limit: Optional[int] = 50
     cameras: Optional[str] = "all"
     labels: Optional[str] = "all"
-    sub_labels: Optional[str] = "all"
-    attributes: Optional[str] = "all"
     zones: Optional[str] = "all"
     after: Optional[float] = None
     before: Optional[float] = None

frigate/api/defs/query/media_query_parameters.py

@@ -1,8 +1,7 @@
 from enum import Enum
-from typing import Optional, Union
+from typing import Optional
 
 from pydantic import BaseModel
-from pydantic.json_schema import SkipJsonSchema
 
 
 class Extension(str, Enum):
@@ -48,15 +47,3 @@ class MediaMjpegFeedQueryParams(BaseModel):
     mask: Optional[int] = None
     motion: Optional[int] = None
     regions: Optional[int] = None
-
-
-class MediaRecordingsSummaryQueryParams(BaseModel):
-    timezone: str = "utc"
-    cameras: Optional[str] = "all"
-
-
-class MediaRecordingsAvailabilityQueryParams(BaseModel):
-    cameras: str = "all"
-    before: Union[float, SkipJsonSchema[None]] = None
-    after: Union[float, SkipJsonSchema[None]] = None
-    scale: int = 30

frigate/api/defs/query/recordings_query_parameters.py

@@ -0,0 +1,21 @@
+from typing import Optional, Union
+
+from pydantic import BaseModel
+from pydantic.json_schema import SkipJsonSchema
+
+
+class MediaRecordingsSummaryQueryParams(BaseModel):
+    timezone: str = "utc"
+    cameras: Optional[str] = "all"
+
+
+class MediaRecordingsAvailabilityQueryParams(BaseModel):
+    cameras: str = "all"
+    before: Union[float, SkipJsonSchema[None]] = None
+    after: Union[float, SkipJsonSchema[None]] = None
+    scale: int = 30
+
+
+class RecordingsDeleteQueryParams(BaseModel):
+    keep: Optional[str] = None
+    cameras: Optional[str] = "all"

frigate/api/defs/request/app_body.py

@@ -1,6 +1,6 @@
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 
 class AppConfigSetBody(BaseModel):
@@ -27,3 +27,16 @@ class AppPostLoginBody(BaseModel):
 
 class AppPutRoleBody(BaseModel):
     role: str
+
+
+class MediaSyncBody(BaseModel):
+    dry_run: bool = Field(
+        default=True, description="If True, only report orphans without deleting them"
+    )
+    media_types: List[str] = Field(
+        default=["all"],
+        description="Types of media to sync: 'all', 'event_snapshots', 'event_thumbnails', 'review_thumbnails', 'previews', 'exports', 'recordings'",
+    )
+    force: bool = Field(
+        default=False, description="If True, bypass safety threshold checks"
+    )

frigate/api/defs/request/events_body.py

@@ -24,13 +24,6 @@ class EventsLPRBody(BaseModel):
     )
 
 
-class EventsAttributesBody(BaseModel):
-    attributes: List[str] = Field(
-        title="Selected classification attributes for the event",
-        default_factory=list,
-    )
-
-
 class EventsDescriptionBody(BaseModel):
     description: Union[str, None] = Field(title="The description of the event")
 

frigate/api/defs/request/export_case_body.py

@@ -0,0 +1,35 @@
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class ExportCaseCreateBody(BaseModel):
+    """Request body for creating a new export case."""
+
+    name: str = Field(max_length=100, description="Friendly name of the export case")
+    description: Optional[str] = Field(
+        default=None, description="Optional description of the export case"
+    )
+
+
+class ExportCaseUpdateBody(BaseModel):
+    """Request body for updating an existing export case."""
+
+    name: Optional[str] = Field(
+        default=None,
+        max_length=100,
+        description="Updated friendly name of the export case",
+    )
+    description: Optional[str] = Field(
+        default=None, description="Updated description of the export case"
+    )
+
+
+class ExportCaseAssignBody(BaseModel):
+    """Request body for assigning or unassigning an export to a case."""
+
+    export_case_id: Optional[str] = Field(
+        default=None,
+        max_length=30,
+        description="Case ID to assign to the export, or null to unassign",
+    )

frigate/api/defs/request/export_recordings_body.py

@@ -16,5 +16,11 @@ class ExportRecordingsBody(BaseModel):
     source: PlaybackSourceEnum = Field(
         default=PlaybackSourceEnum.recordings, title="Playback source"
     )
-    name: Optional[str] = Field(title="Friendly name", default=None, max_length=256)
+    name: str = Field(title="Friendly name", default=None, max_length=256)
     image_path: Union[str, SkipJsonSchema[None]] = None
+    export_case_id: Optional[str] = Field(
+        default=None,
+        title="Export case ID",
+        max_length=30,
+        description="ID of the export case to assign this export to",
+    )

frigate/api/defs/response/export_case_response.py

@@ -0,0 +1,22 @@
+from typing import List, Optional
+
+from pydantic import BaseModel, Field
+
+
+class ExportCaseModel(BaseModel):
+    """Model representing a single export case."""
+
+    id: str = Field(description="Unique identifier for the export case")
+    name: str = Field(description="Friendly name of the export case")
+    description: Optional[str] = Field(
+        default=None, description="Optional description of the export case"
+    )
+    created_at: float = Field(
+        description="Unix timestamp when the export case was created"
+    )
+    updated_at: float = Field(
+        description="Unix timestamp when the export case was last updated"
+    )
+
+
+ExportCasesResponse = List[ExportCaseModel]

frigate/api/defs/response/export_response.py

@@ -15,6 +15,9 @@ class ExportModel(BaseModel):
     in_progress: bool = Field(
         description="Whether the export is currently being processed"
     )
+    export_case_id: Optional[str] = Field(
+        default=None, description="ID of the export case this export belongs to"
+    )
 
 
 class StartExportResponse(BaseModel):

frigate/api/defs/tags.py

@@ -3,13 +3,14 @@ from enum import Enum
 
 class Tags(Enum):
     app = "App"
+    auth = "Auth"
     camera = "Camera"
-    preview = "Preview"
+    events = "Events"
+    export = "Export"
+    classification = "Classification"
     logs = "Logs"
     media = "Media"
     notifications = "Notifications"
+    preview = "Preview"
+    recordings = "Recordings"
     review = "Review"
-    export = "Export"
-    events = "Events"
-    classification = "Classification"
-    auth = "Auth"

frigate/api/event.py

@@ -37,7 +37,6 @@ from frigate.api.defs.query.regenerate_query_parameters import (
     RegenerateQueryParameters,
 )
 from frigate.api.defs.request.events_body import (
-    EventsAttributesBody,
     EventsCreateBody,
     EventsDeleteBody,
     EventsDescriptionBody,
@@ -56,7 +55,6 @@ from frigate.api.defs.response.event_response import (
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.comms.event_metadata_updater import EventMetadataTypeEnum
-from frigate.config.classification import ObjectClassificationType
 from frigate.const import CLIPS_DIR, TRIGGER_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event, ReviewSegment, Timeline, Trigger
@@ -101,8 +99,6 @@ def events(
     if sub_labels == "all" and sub_label != "all":
         sub_labels = sub_label
 
-    attributes = unquote(params.attributes)
-
     zone = params.zone
     zones = params.zones
 
@@ -191,17 +187,6 @@ def events(
         sub_label_clause = reduce(operator.or_, sub_label_clauses)
         clauses.append((sub_label_clause))
 
-    if attributes != "all":
-        # Custom classification results are stored as data[model_name] = result_value
-        filtered_attributes = attributes.split(",")
-        attribute_clauses = []
-
-        for attr in filtered_attributes:
-            attribute_clauses.append(Event.data.cast("text") % f'*:"{attr}"*')
-
-        attribute_clause = reduce(operator.or_, attribute_clauses)
-        clauses.append(attribute_clause)
-
     if recognized_license_plate != "all":
         filtered_recognized_license_plates = recognized_license_plate.split(",")
 
@@ -507,8 +492,6 @@ def events_search(
     # Filters
     cameras = params.cameras
     labels = params.labels
-    sub_labels = params.sub_labels
-    attributes = params.attributes
     zones = params.zones
     after = params.after
     before = params.before
@@ -583,38 +566,6 @@ def events_search(
     if labels != "all":
         event_filters.append((Event.label << labels.split(",")))
 
-    if sub_labels != "all":
-        # use matching so joined sub labels are included
-        # for example a sub label 'bob' would get events
-        # with sub labels 'bob' and 'bob, john'
-        sub_label_clauses = []
-        filtered_sub_labels = sub_labels.split(",")
-
-        if "None" in filtered_sub_labels:
-            filtered_sub_labels.remove("None")
-            sub_label_clauses.append((Event.sub_label.is_null()))
-
-        for label in filtered_sub_labels:
-            sub_label_clauses.append(
-                (Event.sub_label.cast("text") == label)
-            )  # include exact matches
-
-            # include this label when part of a list
-            sub_label_clauses.append((Event.sub_label.cast("text") % f"*{label},*"))
-            sub_label_clauses.append((Event.sub_label.cast("text") % f"*, {label}*"))
-
-        event_filters.append((reduce(operator.or_, sub_label_clauses)))
-
-    if attributes != "all":
-        # Custom classification results are stored as data[model_name] = result_value
-        filtered_attributes = attributes.split(",")
-        attribute_clauses = []
-
-        for attr in filtered_attributes:
-            attribute_clauses.append(Event.data.cast("text") % f'*:"{attr}"*')
-
-        event_filters.append(reduce(operator.or_, attribute_clauses))
-
     if zones != "all":
         zone_clauses = []
         filtered_zones = zones.split(",")
@@ -1400,107 +1351,6 @@ async def set_plate(
     )
 
 
-@router.post(
-    "/events/{event_id}/attributes",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Set custom classification attributes",
-    description=(
-        "Sets an event's custom classification attributes for all attribute-type "
-        "models that apply to the event's object type."
-    ),
-)
-async def set_attributes(
-    request: Request,
-    event_id: str,
-    body: EventsAttributesBody,
-):
-    try:
-        event: Event = Event.get(Event.id == event_id)
-        await require_camera_access(event.camera, request=request)
-    except DoesNotExist:
-        return JSONResponse(
-            content=({"success": False, "message": f"Event {event_id} not found."}),
-            status_code=404,
-        )
-
-    object_type = event.label
-    selected_attributes = set(body.attributes or [])
-    applied_updates: list[dict[str, str | float | None]] = []
-
-    for (
-        model_key,
-        model_config,
-    ) in request.app.frigate_config.classification.custom.items():
-        # Only apply to enabled attribute classifiers that target this object type
-        if (
-            not model_config.enabled
-            or not model_config.object_config
-            or model_config.object_config.classification_type
-            != ObjectClassificationType.attribute
-            or object_type not in (model_config.object_config.objects or [])
-        ):
-            continue
-
-        # Get available labels from dataset directory
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
-        available_labels = set()
-
-        if os.path.exists(dataset_dir):
-            for category_name in os.listdir(dataset_dir):
-                category_dir = os.path.join(dataset_dir, category_name)
-                if os.path.isdir(category_dir):
-                    available_labels.add(category_name)
-
-        if not available_labels:
-            logger.warning(
-                "No dataset found for custom attribute model %s at %s",
-                model_key,
-                dataset_dir,
-            )
-            continue
-
-        # Find all selected attributes that apply to this model
-        model_name = model_config.name or model_key
-        matching_attrs = selected_attributes & available_labels
-
-        if matching_attrs:
-            # Publish updates for each selected attribute
-            for attr in matching_attrs:
-                request.app.event_metadata_updater.publish(
-                    (event_id, model_name, attr, 1.0),
-                    EventMetadataTypeEnum.attribute.value,
-                )
-                applied_updates.append(
-                    {"model": model_name, "label": attr, "score": 1.0}
-                )
-        else:
-            # Clear this model's attribute
-            request.app.event_metadata_updater.publish(
-                (event_id, model_name, None, None),
-                EventMetadataTypeEnum.attribute.value,
-            )
-            applied_updates.append({"model": model_name, "label": None, "score": None})
-
-    if len(applied_updates) == 0:
-        return JSONResponse(
-            content={
-                "success": False,
-                "message": "No matching attributes found for this object type.",
-            },
-            status_code=400,
-        )
-
-    return JSONResponse(
-        content={
-            "success": True,
-            "message": f"Updated {len(applied_updates)} attribute(s)",
-            "applied": applied_updates,
-        },
-        status_code=200,
-    )
-
-
 @router.post(
     "/events/{event_id}/description",
     response_model=GenericResponse,

frigate/api/export.py

@@ -4,10 +4,10 @@ import logging
 import random
 import string
 from pathlib import Path
-from typing import List
+from typing import List, Optional
 
 import psutil
-from fastapi import APIRouter, Depends, Request
+from fastapi import APIRouter, Depends, Query, Request
 from fastapi.responses import JSONResponse
 from pathvalidate import sanitize_filepath
 from peewee import DoesNotExist
@@ -19,8 +19,17 @@ from frigate.api.auth import (
     require_camera_access,
     require_role,
 )
+from frigate.api.defs.request.export_case_body import (
+    ExportCaseAssignBody,
+    ExportCaseCreateBody,
+    ExportCaseUpdateBody,
+)
 from frigate.api.defs.request.export_recordings_body import ExportRecordingsBody
 from frigate.api.defs.request.export_rename_body import ExportRenameBody
+from frigate.api.defs.response.export_case_response import (
+    ExportCaseModel,
+    ExportCasesResponse,
+)
 from frigate.api.defs.response.export_response import (
     ExportModel,
     ExportsResponse,
@@ -29,7 +38,7 @@ from frigate.api.defs.response.export_response import (
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.const import CLIPS_DIR, EXPORT_DIR
-from frigate.models import Export, Previews, Recordings
+from frigate.models import Export, ExportCase, Previews, Recordings
 from frigate.record.export import (
     PlaybackFactorEnum,
     PlaybackSourceEnum,
@@ -52,17 +61,182 @@ router = APIRouter(tags=[Tags.export])
 )
 def get_exports(
     allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+    export_case_id: Optional[str] = None,
+    cameras: Optional[str] = Query(default="all"),
+    start_date: Optional[float] = None,
+    end_date: Optional[float] = None,
 ):
-    exports = (
-        Export.select()
-        .where(Export.camera << allowed_cameras)
-        .order_by(Export.date.desc())
-        .dicts()
-        .iterator()
-    )
+    query = Export.select().where(Export.camera << allowed_cameras)
+
+    if export_case_id is not None:
+        if export_case_id == "unassigned":
+            query = query.where(Export.export_case.is_null(True))
+        else:
+            query = query.where(Export.export_case == export_case_id)
+
+    if cameras and cameras != "all":
+        requested = set(cameras.split(","))
+        filtered_cameras = list(requested.intersection(allowed_cameras))
+        if not filtered_cameras:
+            return JSONResponse(content=[])
+        query = query.where(Export.camera << filtered_cameras)
+
+    if start_date is not None:
+        query = query.where(Export.date >= start_date)
+
+    if end_date is not None:
+        query = query.where(Export.date <= end_date)
+
+    exports = query.order_by(Export.date.desc()).dicts().iterator()
     return JSONResponse(content=[e for e in exports])
 
 
+@router.get(
+    "/cases",
+    response_model=ExportCasesResponse,
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get export cases",
+    description="Gets all export cases from the database.",
+)
+def get_export_cases():
+    cases = (
+        ExportCase.select().order_by(ExportCase.created_at.desc()).dicts().iterator()
+    )
+    return JSONResponse(content=[c for c in cases])
+
+
+@router.post(
+    "/cases",
+    response_model=ExportCaseModel,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Create export case",
+    description="Creates a new export case.",
+)
+def create_export_case(body: ExportCaseCreateBody):
+    case = ExportCase.create(
+        id="".join(random.choices(string.ascii_lowercase + string.digits, k=12)),
+        name=body.name,
+        description=body.description,
+        created_at=Path().stat().st_mtime,
+        updated_at=Path().stat().st_mtime,
+    )
+    return JSONResponse(content=model_to_dict(case))
+
+
+@router.get(
+    "/cases/{case_id}",
+    response_model=ExportCaseModel,
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get a single export case",
+    description="Gets a specific export case by ID.",
+)
+def get_export_case(case_id: str):
+    try:
+        case = ExportCase.get(ExportCase.id == case_id)
+        return JSONResponse(content=model_to_dict(case))
+    except DoesNotExist:
+        return JSONResponse(
+            content={"success": False, "message": "Export case not found"},
+            status_code=404,
+        )
+
+
+@router.patch(
+    "/cases/{case_id}",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Update export case",
+    description="Updates an existing export case.",
+)
+def update_export_case(case_id: str, body: ExportCaseUpdateBody):
+    try:
+        case = ExportCase.get(ExportCase.id == case_id)
+    except DoesNotExist:
+        return JSONResponse(
+            content={"success": False, "message": "Export case not found"},
+            status_code=404,
+        )
+
+    if body.name is not None:
+        case.name = body.name
+    if body.description is not None:
+        case.description = body.description
+
+    case.save()
+
+    return JSONResponse(
+        content={"success": True, "message": "Successfully updated export case."}
+    )
+
+
+@router.delete(
+    "/cases/{case_id}",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Delete export case",
+    description="""Deletes an export case.\n    Exports that reference this case will have their export_case set to null.\n    """,
+)
+def delete_export_case(case_id: str):
+    try:
+        case = ExportCase.get(ExportCase.id == case_id)
+    except DoesNotExist:
+        return JSONResponse(
+            content={"success": False, "message": "Export case not found"},
+            status_code=404,
+        )
+
+    # Unassign exports from this case but keep the exports themselves
+    Export.update(export_case=None).where(Export.export_case == case).execute()
+
+    case.delete_instance()
+
+    return JSONResponse(
+        content={"success": True, "message": "Successfully deleted export case."}
+    )
+
+
+@router.patch(
+    "/export/{export_id}/case",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Assign export to case",
+    description=(
+        "Assigns an export to a case, or unassigns it if export_case_id is null."
+    ),
+)
+async def assign_export_case(
+    export_id: str,
+    body: ExportCaseAssignBody,
+    request: Request,
+):
+    try:
+        export: Export = Export.get(Export.id == export_id)
+        await require_camera_access(export.camera, request=request)
+    except DoesNotExist:
+        return JSONResponse(
+            content={"success": False, "message": "Export not found."},
+            status_code=404,
+        )
+
+    if body.export_case_id is not None:
+        try:
+            ExportCase.get(ExportCase.id == body.export_case_id)
+        except DoesNotExist:
+            return JSONResponse(
+                content={"success": False, "message": "Export case not found."},
+                status_code=404,
+            )
+        export.export_case = body.export_case_id
+    else:
+        export.export_case = None
+
+    export.save()
+
+    return JSONResponse(
+        content={"success": True, "message": "Successfully updated export case."}
+    )
+
+
 @router.post(
     "/export/{camera_name}/start/{start_time}/end/{end_time}",
     response_model=StartExportResponse,
@@ -93,6 +267,16 @@ def export_recording(
     friendly_name = body.name
     existing_image = sanitize_filepath(body.image_path) if body.image_path else None
 
+    export_case_id = body.export_case_id
+    if export_case_id is not None:
+        try:
+            ExportCase.get(ExportCase.id == export_case_id)
+        except DoesNotExist:
+            return JSONResponse(
+                content={"success": False, "message": "Export case not found"},
+                status_code=404,
+            )
+
     # Ensure that existing_image is a valid path
     if existing_image and not existing_image.startswith(CLIPS_DIR):
         return JSONResponse(
@@ -161,6 +345,7 @@ def export_recording(
             if playback_source in PlaybackSourceEnum.__members__.values()
             else PlaybackSourceEnum.recordings
         ),
+        export_case_id,
     )
     exporter.start()
     return JSONResponse(

frigate/api/fastapi_app.py

@@ -22,6 +22,7 @@ from frigate.api import (
     media,
     notification,
     preview,
+    record,
     review,
 )
 from frigate.api.auth import get_jwt_secret, limiter, require_admin_by_default
@@ -128,6 +129,7 @@ def create_fastapi_app(
     app.include_router(export.router)
     app.include_router(event.router)
     app.include_router(media.router)
+    app.include_router(record.router)
     # App Properties
     app.frigate_config = frigate_config
     app.embeddings = embeddings

frigate/api/media.py

@@ -8,9 +8,8 @@ import os
 import subprocess as sp
 import time
 from datetime import datetime, timedelta, timezone
-from functools import reduce
 from pathlib import Path as FilePath
-from typing import Any, List
+from typing import Any
 from urllib.parse import unquote
 
 import cv2
@@ -19,12 +18,11 @@ import pytz
 from fastapi import APIRouter, Depends, Path, Query, Request, Response
 from fastapi.responses import FileResponse, JSONResponse, StreamingResponse
 from pathvalidate import sanitize_filename
-from peewee import DoesNotExist, fn, operator
+from peewee import DoesNotExist, fn
 from tzlocal import get_localzone_name
 
 from frigate.api.auth import (
     allow_any_authenticated,
-    get_allowed_cameras_for_filter,
     require_camera_access,
 )
 from frigate.api.defs.query.media_query_parameters import (
@@ -32,8 +30,6 @@ from frigate.api.defs.query.media_query_parameters import (
     MediaEventsSnapshotQueryParams,
     MediaLatestFrameQueryParams,
     MediaMjpegFeedQueryParams,
-    MediaRecordingsAvailabilityQueryParams,
-    MediaRecordingsSummaryQueryParams,
 )
 from frigate.api.defs.tags import Tags
 from frigate.camera.state import CameraState
@@ -44,13 +40,11 @@ from frigate.const import (
     INSTALL_DIR,
     MAX_SEGMENT_DURATION,
     PREVIEW_FRAME_TYPE,
-    RECORD_DIR,
 )
 from frigate.models import Event, Previews, Recordings, Regions, ReviewSegment
 from frigate.track.object_processing import TrackedObjectProcessor
 from frigate.util.file import get_event_thumbnail_bytes
 from frigate.util.image import get_image_from_recording
-from frigate.util.time import get_dst_transitions
 
 logger = logging.getLogger(__name__)
 
@@ -397,333 +391,6 @@ async def submit_recording_snapshot_to_plus(
         )
 
 
-@router.get("/recordings/storage", dependencies=[Depends(allow_any_authenticated())])
-def get_recordings_storage_usage(request: Request):
-    recording_stats = request.app.stats_emitter.get_latest_stats()["service"][
-        "storage"
-    ][RECORD_DIR]
-
-    if not recording_stats:
-        return JSONResponse({})
-
-    total_mb = recording_stats["total"]
-
-    camera_usages: dict[str, dict] = (
-        request.app.storage_maintainer.calculate_camera_usages()
-    )
-
-    for camera_name in camera_usages.keys():
-        if camera_usages.get(camera_name, {}).get("usage"):
-            camera_usages[camera_name]["usage_percent"] = (
-                camera_usages.get(camera_name, {}).get("usage", 0) / total_mb
-            ) * 100
-
-    return JSONResponse(content=camera_usages)
-
-
-@router.get("/recordings/summary", dependencies=[Depends(allow_any_authenticated())])
-def all_recordings_summary(
-    request: Request,
-    params: MediaRecordingsSummaryQueryParams = Depends(),
-    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
-):
-    """Returns true/false by day indicating if recordings exist"""
-
-    cameras = params.cameras
-    if cameras != "all":
-        requested = set(unquote(cameras).split(","))
-        filtered = requested.intersection(allowed_cameras)
-        if not filtered:
-            return JSONResponse(content={})
-        camera_list = list(filtered)
-    else:
-        camera_list = allowed_cameras
-
-    time_range_query = (
-        Recordings.select(
-            fn.MIN(Recordings.start_time).alias("min_time"),
-            fn.MAX(Recordings.start_time).alias("max_time"),
-        )
-        .where(Recordings.camera << camera_list)
-        .dicts()
-        .get()
-    )
-
-    min_time = time_range_query.get("min_time")
-    max_time = time_range_query.get("max_time")
-
-    if min_time is None or max_time is None:
-        return JSONResponse(content={})
-
-    dst_periods = get_dst_transitions(params.timezone, min_time, max_time)
-
-    days: dict[str, bool] = {}
-
-    for period_start, period_end, period_offset in dst_periods:
-        hours_offset = int(period_offset / 60 / 60)
-        minutes_offset = int(period_offset / 60 - hours_offset * 60)
-        period_hour_modifier = f"{hours_offset} hour"
-        period_minute_modifier = f"{minutes_offset} minute"
-
-        period_query = (
-            Recordings.select(
-                fn.strftime(
-                    "%Y-%m-%d",
-                    fn.datetime(
-                        Recordings.start_time,
-                        "unixepoch",
-                        period_hour_modifier,
-                        period_minute_modifier,
-                    ),
-                ).alias("day")
-            )
-            .where(
-                (Recordings.camera << camera_list)
-                & (Recordings.end_time >= period_start)
-                & (Recordings.start_time <= period_end)
-            )
-            .group_by(
-                fn.strftime(
-                    "%Y-%m-%d",
-                    fn.datetime(
-                        Recordings.start_time,
-                        "unixepoch",
-                        period_hour_modifier,
-                        period_minute_modifier,
-                    ),
-                )
-            )
-            .order_by(Recordings.start_time.desc())
-            .namedtuples()
-        )
-
-        for g in period_query:
-            days[g.day] = True
-
-    return JSONResponse(content=dict(sorted(days.items())))
-
-
-@router.get(
-    "/{camera_name}/recordings/summary", dependencies=[Depends(require_camera_access)]
-)
-async def recordings_summary(camera_name: str, timezone: str = "utc"):
-    """Returns hourly summary for recordings of given camera"""
-
-    time_range_query = (
-        Recordings.select(
-            fn.MIN(Recordings.start_time).alias("min_time"),
-            fn.MAX(Recordings.start_time).alias("max_time"),
-        )
-        .where(Recordings.camera == camera_name)
-        .dicts()
-        .get()
-    )
-
-    min_time = time_range_query.get("min_time")
-    max_time = time_range_query.get("max_time")
-
-    days: dict[str, dict] = {}
-
-    if min_time is None or max_time is None:
-        return JSONResponse(content=list(days.values()))
-
-    dst_periods = get_dst_transitions(timezone, min_time, max_time)
-
-    for period_start, period_end, period_offset in dst_periods:
-        hours_offset = int(period_offset / 60 / 60)
-        minutes_offset = int(period_offset / 60 - hours_offset * 60)
-        period_hour_modifier = f"{hours_offset} hour"
-        period_minute_modifier = f"{minutes_offset} minute"
-
-        recording_groups = (
-            Recordings.select(
-                fn.strftime(
-                    "%Y-%m-%d %H",
-                    fn.datetime(
-                        Recordings.start_time,
-                        "unixepoch",
-                        period_hour_modifier,
-                        period_minute_modifier,
-                    ),
-                ).alias("hour"),
-                fn.SUM(Recordings.duration).alias("duration"),
-                fn.SUM(Recordings.motion).alias("motion"),
-                fn.SUM(Recordings.objects).alias("objects"),
-            )
-            .where(
-                (Recordings.camera == camera_name)
-                & (Recordings.end_time >= period_start)
-                & (Recordings.start_time <= period_end)
-            )
-            .group_by((Recordings.start_time + period_offset).cast("int") / 3600)
-            .order_by(Recordings.start_time.desc())
-            .namedtuples()
-        )
-
-        event_groups = (
-            Event.select(
-                fn.strftime(
-                    "%Y-%m-%d %H",
-                    fn.datetime(
-                        Event.start_time,
-                        "unixepoch",
-                        period_hour_modifier,
-                        period_minute_modifier,
-                    ),
-                ).alias("hour"),
-                fn.COUNT(Event.id).alias("count"),
-            )
-            .where(Event.camera == camera_name, Event.has_clip)
-            .where(
-                (Event.start_time >= period_start) & (Event.start_time <= period_end)
-            )
-            .group_by((Event.start_time + period_offset).cast("int") / 3600)
-            .namedtuples()
-        )
-
-        event_map = {g.hour: g.count for g in event_groups}
-
-        for recording_group in recording_groups:
-            parts = recording_group.hour.split()
-            hour = parts[1]
-            day = parts[0]
-            events_count = event_map.get(recording_group.hour, 0)
-            hour_data = {
-                "hour": hour,
-                "events": events_count,
-                "motion": recording_group.motion,
-                "objects": recording_group.objects,
-                "duration": round(recording_group.duration),
-            }
-            if day in days:
-                # merge counts if already present (edge-case at DST boundary)
-                days[day]["events"] += events_count or 0
-                days[day]["hours"].append(hour_data)
-            else:
-                days[day] = {
-                    "events": events_count or 0,
-                    "hours": [hour_data],
-                    "day": day,
-                }
-
-    return JSONResponse(content=list(days.values()))
-
-
-@router.get("/{camera_name}/recordings", dependencies=[Depends(require_camera_access)])
-async def recordings(
-    camera_name: str,
-    after: float = (datetime.now() - timedelta(hours=1)).timestamp(),
-    before: float = datetime.now().timestamp(),
-):
-    """Return specific camera recordings between the given 'after'/'end' times. If not provided the last hour will be used"""
-    recordings = (
-        Recordings.select(
-            Recordings.id,
-            Recordings.start_time,
-            Recordings.end_time,
-            Recordings.segment_size,
-            Recordings.motion,
-            Recordings.objects,
-            Recordings.duration,
-        )
-        .where(
-            Recordings.camera == camera_name,
-            Recordings.end_time >= after,
-            Recordings.start_time <= before,
-        )
-        .order_by(Recordings.start_time)
-        .dicts()
-        .iterator()
-    )
-
-    return JSONResponse(content=list(recordings))
-
-
-@router.get(
-    "/recordings/unavailable",
-    response_model=list[dict],
-    dependencies=[Depends(allow_any_authenticated())],
-)
-async def no_recordings(
-    request: Request,
-    params: MediaRecordingsAvailabilityQueryParams = Depends(),
-    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
-):
-    """Get time ranges with no recordings."""
-    cameras = params.cameras
-    if cameras != "all":
-        requested = set(unquote(cameras).split(","))
-        filtered = requested.intersection(allowed_cameras)
-        if not filtered:
-            return JSONResponse(content=[])
-        cameras = ",".join(filtered)
-    else:
-        cameras = allowed_cameras
-
-    before = params.before or datetime.datetime.now().timestamp()
-    after = (
-        params.after
-        or (datetime.datetime.now() - datetime.timedelta(hours=1)).timestamp()
-    )
-    scale = params.scale
-
-    clauses = [(Recordings.end_time >= after) & (Recordings.start_time <= before)]
-    if cameras != "all":
-        camera_list = cameras.split(",")
-        clauses.append((Recordings.camera << camera_list))
-    else:
-        camera_list = allowed_cameras
-
-    # Get recording start times
-    data: list[Recordings] = (
-        Recordings.select(Recordings.start_time, Recordings.end_time)
-        .where(reduce(operator.and_, clauses))
-        .order_by(Recordings.start_time.asc())
-        .dicts()
-        .iterator()
-    )
-
-    # Convert recordings to list of (start, end) tuples
-    recordings = [(r["start_time"], r["end_time"]) for r in data]
-
-    # Iterate through time segments and check if each has any recording
-    no_recording_segments = []
-    current = after
-    current_gap_start = None
-
-    while current < before:
-        segment_end = min(current + scale, before)
-
-        # Check if this segment overlaps with any recording
-        has_recording = any(
-            rec_start < segment_end and rec_end > current
-            for rec_start, rec_end in recordings
-        )
-
-        if not has_recording:
-            # This segment has no recordings
-            if current_gap_start is None:
-                current_gap_start = current  # Start a new gap
-        else:
-            # This segment has recordings
-            if current_gap_start is not None:
-                # End the current gap and append it
-                no_recording_segments.append(
-                    {"start_time": int(current_gap_start), "end_time": int(current)}
-                )
-                current_gap_start = None
-
-        current = segment_end
-
-    # Append the last gap if it exists
-    if current_gap_start is not None:
-        no_recording_segments.append(
-            {"start_time": int(current_gap_start), "end_time": int(before)}
-        )
-
-    return JSONResponse(content=no_recording_segments)
-
-
 @router.get(
     "/{camera_name}/start/{start_ts}/end/{end_ts}/clip.mp4",
     dependencies=[Depends(require_camera_access)],
@@ -1935,7 +1602,7 @@ async def label_clip(request: Request, camera_name: str, label: str):
     try:
         event = event_query.get()
 
-        return await event_clip(request, event.id, 0)
+        return await event_clip(request, event.id)
     except DoesNotExist:
         return JSONResponse(
             content={"success": False, "message": "Event not found"}, status_code=404

frigate/api/record.py

@@ -0,0 +1,479 @@
+"""Recording APIs."""
+
+import logging
+from datetime import datetime, timedelta
+from functools import reduce
+from pathlib import Path
+from typing import List
+from urllib.parse import unquote
+
+from fastapi import APIRouter, Depends, Request
+from fastapi import Path as PathParam
+from fastapi.responses import JSONResponse
+from peewee import fn, operator
+
+from frigate.api.auth import (
+    allow_any_authenticated,
+    get_allowed_cameras_for_filter,
+    require_camera_access,
+    require_role,
+)
+from frigate.api.defs.query.recordings_query_parameters import (
+    MediaRecordingsAvailabilityQueryParams,
+    MediaRecordingsSummaryQueryParams,
+    RecordingsDeleteQueryParams,
+)
+from frigate.api.defs.response.generic_response import GenericResponse
+from frigate.api.defs.tags import Tags
+from frigate.const import RECORD_DIR
+from frigate.models import Event, Recordings
+from frigate.util.time import get_dst_transitions
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(tags=[Tags.recordings])
+
+
+@router.get("/recordings/storage", dependencies=[Depends(allow_any_authenticated())])
+def get_recordings_storage_usage(request: Request):
+    recording_stats = request.app.stats_emitter.get_latest_stats()["service"][
+        "storage"
+    ][RECORD_DIR]
+
+    if not recording_stats:
+        return JSONResponse({})
+
+    total_mb = recording_stats["total"]
+
+    camera_usages: dict[str, dict] = (
+        request.app.storage_maintainer.calculate_camera_usages()
+    )
+
+    for camera_name in camera_usages.keys():
+        if camera_usages.get(camera_name, {}).get("usage"):
+            camera_usages[camera_name]["usage_percent"] = (
+                camera_usages.get(camera_name, {}).get("usage", 0) / total_mb
+            ) * 100
+
+    return JSONResponse(content=camera_usages)
+
+
+@router.get("/recordings/summary", dependencies=[Depends(allow_any_authenticated())])
+def all_recordings_summary(
+    request: Request,
+    params: MediaRecordingsSummaryQueryParams = Depends(),
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+):
+    """Returns true/false by day indicating if recordings exist"""
+
+    cameras = params.cameras
+    if cameras != "all":
+        requested = set(unquote(cameras).split(","))
+        filtered = requested.intersection(allowed_cameras)
+        if not filtered:
+            return JSONResponse(content={})
+        camera_list = list(filtered)
+    else:
+        camera_list = allowed_cameras
+
+    time_range_query = (
+        Recordings.select(
+            fn.MIN(Recordings.start_time).alias("min_time"),
+            fn.MAX(Recordings.start_time).alias("max_time"),
+        )
+        .where(Recordings.camera << camera_list)
+        .dicts()
+        .get()
+    )
+
+    min_time = time_range_query.get("min_time")
+    max_time = time_range_query.get("max_time")
+
+    if min_time is None or max_time is None:
+        return JSONResponse(content={})
+
+    dst_periods = get_dst_transitions(params.timezone, min_time, max_time)
+
+    days: dict[str, bool] = {}
+
+    for period_start, period_end, period_offset in dst_periods:
+        hours_offset = int(period_offset / 60 / 60)
+        minutes_offset = int(period_offset / 60 - hours_offset * 60)
+        period_hour_modifier = f"{hours_offset} hour"
+        period_minute_modifier = f"{minutes_offset} minute"
+
+        period_query = (
+            Recordings.select(
+                fn.strftime(
+                    "%Y-%m-%d",
+                    fn.datetime(
+                        Recordings.start_time,
+                        "unixepoch",
+                        period_hour_modifier,
+                        period_minute_modifier,
+                    ),
+                ).alias("day")
+            )
+            .where(
+                (Recordings.camera << camera_list)
+                & (Recordings.end_time >= period_start)
+                & (Recordings.start_time <= period_end)
+            )
+            .group_by(
+                fn.strftime(
+                    "%Y-%m-%d",
+                    fn.datetime(
+                        Recordings.start_time,
+                        "unixepoch",
+                        period_hour_modifier,
+                        period_minute_modifier,
+                    ),
+                )
+            )
+            .order_by(Recordings.start_time.desc())
+            .namedtuples()
+        )
+
+        for g in period_query:
+            days[g.day] = True
+
+    return JSONResponse(content=dict(sorted(days.items())))
+
+
+@router.get(
+    "/{camera_name}/recordings/summary", dependencies=[Depends(require_camera_access)]
+)
+async def recordings_summary(camera_name: str, timezone: str = "utc"):
+    """Returns hourly summary for recordings of given camera"""
+
+    time_range_query = (
+        Recordings.select(
+            fn.MIN(Recordings.start_time).alias("min_time"),
+            fn.MAX(Recordings.start_time).alias("max_time"),
+        )
+        .where(Recordings.camera == camera_name)
+        .dicts()
+        .get()
+    )
+
+    min_time = time_range_query.get("min_time")
+    max_time = time_range_query.get("max_time")
+
+    days: dict[str, dict] = {}
+
+    if min_time is None or max_time is None:
+        return JSONResponse(content=list(days.values()))
+
+    dst_periods = get_dst_transitions(timezone, min_time, max_time)
+
+    for period_start, period_end, period_offset in dst_periods:
+        hours_offset = int(period_offset / 60 / 60)
+        minutes_offset = int(period_offset / 60 - hours_offset * 60)
+        period_hour_modifier = f"{hours_offset} hour"
+        period_minute_modifier = f"{minutes_offset} minute"
+
+        recording_groups = (
+            Recordings.select(
+                fn.strftime(
+                    "%Y-%m-%d %H",
+                    fn.datetime(
+                        Recordings.start_time,
+                        "unixepoch",
+                        period_hour_modifier,
+                        period_minute_modifier,
+                    ),
+                ).alias("hour"),
+                fn.SUM(Recordings.duration).alias("duration"),
+                fn.SUM(Recordings.motion).alias("motion"),
+                fn.SUM(Recordings.objects).alias("objects"),
+            )
+            .where(
+                (Recordings.camera == camera_name)
+                & (Recordings.end_time >= period_start)
+                & (Recordings.start_time <= period_end)
+            )
+            .group_by((Recordings.start_time + period_offset).cast("int") / 3600)
+            .order_by(Recordings.start_time.desc())
+            .namedtuples()
+        )
+
+        event_groups = (
+            Event.select(
+                fn.strftime(
+                    "%Y-%m-%d %H",
+                    fn.datetime(
+                        Event.start_time,
+                        "unixepoch",
+                        period_hour_modifier,
+                        period_minute_modifier,
+                    ),
+                ).alias("hour"),
+                fn.COUNT(Event.id).alias("count"),
+            )
+            .where(Event.camera == camera_name, Event.has_clip)
+            .where(
+                (Event.start_time >= period_start) & (Event.start_time <= period_end)
+            )
+            .group_by((Event.start_time + period_offset).cast("int") / 3600)
+            .namedtuples()
+        )
+
+        event_map = {g.hour: g.count for g in event_groups}
+
+        for recording_group in recording_groups:
+            parts = recording_group.hour.split()
+            hour = parts[1]
+            day = parts[0]
+            events_count = event_map.get(recording_group.hour, 0)
+            hour_data = {
+                "hour": hour,
+                "events": events_count,
+                "motion": recording_group.motion,
+                "objects": recording_group.objects,
+                "duration": round(recording_group.duration),
+            }
+            if day in days:
+                # merge counts if already present (edge-case at DST boundary)
+                days[day]["events"] += events_count or 0
+                days[day]["hours"].append(hour_data)
+            else:
+                days[day] = {
+                    "events": events_count or 0,
+                    "hours": [hour_data],
+                    "day": day,
+                }
+
+    return JSONResponse(content=list(days.values()))
+
+
+@router.get("/{camera_name}/recordings", dependencies=[Depends(require_camera_access)])
+async def recordings(
+    camera_name: str,
+    after: float = (datetime.now() - timedelta(hours=1)).timestamp(),
+    before: float = datetime.now().timestamp(),
+):
+    """Return specific camera recordings between the given 'after'/'end' times. If not provided the last hour will be used"""
+    recordings = (
+        Recordings.select(
+            Recordings.id,
+            Recordings.start_time,
+            Recordings.end_time,
+            Recordings.segment_size,
+            Recordings.motion,
+            Recordings.objects,
+            Recordings.duration,
+        )
+        .where(
+            Recordings.camera == camera_name,
+            Recordings.end_time >= after,
+            Recordings.start_time <= before,
+        )
+        .order_by(Recordings.start_time)
+        .dicts()
+        .iterator()
+    )
+
+    return JSONResponse(content=list(recordings))
+
+
+@router.get(
+    "/recordings/unavailable",
+    response_model=list[dict],
+    dependencies=[Depends(allow_any_authenticated())],
+)
+async def no_recordings(
+    request: Request,
+    params: MediaRecordingsAvailabilityQueryParams = Depends(),
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+):
+    """Get time ranges with no recordings."""
+    cameras = params.cameras
+    if cameras != "all":
+        requested = set(unquote(cameras).split(","))
+        filtered = requested.intersection(allowed_cameras)
+        if not filtered:
+            return JSONResponse(content=[])
+        cameras = ",".join(filtered)
+    else:
+        cameras = allowed_cameras
+
+    before = params.before or datetime.datetime.now().timestamp()
+    after = (
+        params.after
+        or (datetime.datetime.now() - datetime.timedelta(hours=1)).timestamp()
+    )
+    scale = params.scale
+
+    clauses = [(Recordings.end_time >= after) & (Recordings.start_time <= before)]
+    if cameras != "all":
+        camera_list = cameras.split(",")
+        clauses.append((Recordings.camera << camera_list))
+    else:
+        camera_list = allowed_cameras
+
+    # Get recording start times
+    data: list[Recordings] = (
+        Recordings.select(Recordings.start_time, Recordings.end_time)
+        .where(reduce(operator.and_, clauses))
+        .order_by(Recordings.start_time.asc())
+        .dicts()
+        .iterator()
+    )
+
+    # Convert recordings to list of (start, end) tuples
+    recordings = [(r["start_time"], r["end_time"]) for r in data]
+
+    # Iterate through time segments and check if each has any recording
+    no_recording_segments = []
+    current = after
+    current_gap_start = None
+
+    while current < before:
+        segment_end = min(current + scale, before)
+
+        # Check if this segment overlaps with any recording
+        has_recording = any(
+            rec_start < segment_end and rec_end > current
+            for rec_start, rec_end in recordings
+        )
+
+        if not has_recording:
+            # This segment has no recordings
+            if current_gap_start is None:
+                current_gap_start = current  # Start a new gap
+        else:
+            # This segment has recordings
+            if current_gap_start is not None:
+                # End the current gap and append it
+                no_recording_segments.append(
+                    {"start_time": int(current_gap_start), "end_time": int(current)}
+                )
+                current_gap_start = None
+
+        current = segment_end
+
+    # Append the last gap if it exists
+    if current_gap_start is not None:
+        no_recording_segments.append(
+            {"start_time": int(current_gap_start), "end_time": int(before)}
+        )
+
+    return JSONResponse(content=no_recording_segments)
+
+
+@router.delete(
+    "/recordings/start/{start}/end/{end}",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Delete recordings",
+    description="""Deletes recordings within the specified time range.
+    Recordings can be filtered by cameras and kept based on motion, objects, or audio attributes.
+    """,
+)
+async def delete_recordings(
+    start: float = PathParam(..., description="Start timestamp (unix)"),
+    end: float = PathParam(..., description="End timestamp (unix)"),
+    params: RecordingsDeleteQueryParams = Depends(),
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
+):
+    """Delete recordings in the specified time range."""
+    if start >= end:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "Start time must be less than end time.",
+            },
+            status_code=400,
+        )
+
+    cameras = params.cameras
+
+    if cameras != "all":
+        requested = set(cameras.split(","))
+        filtered = requested.intersection(allowed_cameras)
+
+        if not filtered:
+            return JSONResponse(
+                content={
+                    "success": False,
+                    "message": "No valid cameras found in the request.",
+                },
+                status_code=400,
+            )
+
+        camera_list = list(filtered)
+    else:
+        camera_list = allowed_cameras
+
+    # Parse keep parameter
+    keep_set = set()
+
+    if params.keep:
+        keep_set = set(params.keep.split(","))
+
+    # Build query to find overlapping recordings
+    clauses = [
+        (
+            Recordings.start_time.between(start, end)
+            | Recordings.end_time.between(start, end)
+            | ((start > Recordings.start_time) & (end < Recordings.end_time))
+        ),
+        (Recordings.camera << camera_list),
+    ]
+
+    keep_clauses = []
+
+    if "motion" in keep_set:
+        keep_clauses.append(Recordings.motion.is_null(False) & (Recordings.motion > 0))
+
+    if "object" in keep_set:
+        keep_clauses.append(
+            Recordings.objects.is_null(False) & (Recordings.objects > 0)
+        )
+
+    if "audio" in keep_set:
+        keep_clauses.append(Recordings.dBFS.is_null(False))
+
+    if keep_clauses:
+        keep_condition = reduce(operator.or_, keep_clauses)
+        clauses.append(~keep_condition)
+
+    recordings_to_delete = (
+        Recordings.select(Recordings.id, Recordings.path)
+        .where(reduce(operator.and_, clauses))
+        .dicts()
+        .iterator()
+    )
+
+    recording_ids = []
+    deleted_count = 0
+    error_count = 0
+
+    for recording in recordings_to_delete:
+        recording_ids.append(recording["id"])
+
+        try:
+            Path(recording["path"]).unlink(missing_ok=True)
+            deleted_count += 1
+        except Exception as e:
+            logger.error(f"Failed to delete recording file {recording['path']}: {e}")
+            error_count += 1
+
+    if recording_ids:
+        max_deletes = 100000
+        recording_ids_list = list(recording_ids)
+
+        for i in range(0, len(recording_ids_list), max_deletes):
+            Recordings.delete().where(
+                Recordings.id << recording_ids_list[i : i + max_deletes]
+            ).execute()
+
+    message = f"Successfully deleted {deleted_count} recording(s)."
+
+    if error_count > 0:
+        message += f" {error_count} file deletion error(s) occurred."
+
+    return JSONResponse(
+        content={"success": True, "message": message},
+        status_code=200,
+    )

frigate/app.py

@@ -100,10 +100,6 @@ class FrigateApp:
             )
             if (
                 config.semantic_search.enabled
-                or any(
-                    c.objects.genai.enabled or c.review.genai.enabled
-                    for c in config.cameras.values()
-                )
                 or config.lpr.enabled
                 or config.face_recognition.enabled
                 or len(config.classification.custom) > 0

frigate/camera/__init__.py

@@ -19,6 +19,8 @@ class CameraMetrics:
     process_pid: Synchronized
     capture_process_pid: Synchronized
     ffmpeg_pid: Synchronized
+    reconnects_last_hour: Synchronized
+    stalls_last_hour: Synchronized
 
     def __init__(self, manager: SyncManager):
         self.camera_fps = manager.Value("d", 0)
@@ -35,6 +37,8 @@ class CameraMetrics:
         self.process_pid = manager.Value("i", 0)
         self.capture_process_pid = manager.Value("i", 0)
         self.ffmpeg_pid = manager.Value("i", 0)
+        self.reconnects_last_hour = manager.Value("i", 0)
+        self.stalls_last_hour = manager.Value("i", 0)
 
 
 class PTZMetrics:

frigate/comms/dispatcher.py

@@ -28,6 +28,7 @@ from frigate.const import (
     UPDATE_CAMERA_ACTIVITY,
     UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
     UPDATE_EVENT_DESCRIPTION,
+    UPDATE_JOB_STATE,
     UPDATE_MODEL_STATE,
     UPDATE_REVIEW_DESCRIPTION,
     UPSERT_REVIEW_SEGMENT,
@@ -60,6 +61,7 @@ class Dispatcher:
         self.camera_activity = CameraActivityManager(config, self.publish)
         self.audio_activity = AudioActivityManager(config, self.publish)
         self.model_state: dict[str, ModelStatusTypesEnum] = {}
+        self.job_state: dict[str, dict[str, Any]] = {}  # {job_type: job_data}
         self.embeddings_reindex: dict[str, Any] = {}
         self.birdseye_layout: dict[str, Any] = {}
         self.audio_transcription_state: str = "idle"
@@ -180,6 +182,19 @@ class Dispatcher:
         def handle_model_state() -> None:
             self.publish("model_state", json.dumps(self.model_state.copy()))
 
+        def handle_update_job_state() -> None:
+            if payload and isinstance(payload, dict):
+                job_type = payload.get("job_type")
+                if job_type:
+                    self.job_state[job_type] = payload
+                    self.publish(
+                        "job_state",
+                        json.dumps(self.job_state),
+                    )
+
+        def handle_job_state() -> None:
+            self.publish("job_state", json.dumps(self.job_state.copy()))
+
         def handle_update_audio_transcription_state() -> None:
             if payload:
                 self.audio_transcription_state = payload
@@ -277,6 +292,7 @@ class Dispatcher:
             UPDATE_EVENT_DESCRIPTION: handle_update_event_description,
             UPDATE_REVIEW_DESCRIPTION: handle_update_review_description,
             UPDATE_MODEL_STATE: handle_update_model_state,
+            UPDATE_JOB_STATE: handle_update_job_state,
             UPDATE_EMBEDDINGS_REINDEX_PROGRESS: handle_update_embeddings_reindex_progress,
             UPDATE_BIRDSEYE_LAYOUT: handle_update_birdseye_layout,
             UPDATE_AUDIO_TRANSCRIPTION_STATE: handle_update_audio_transcription_state,
@@ -284,6 +300,7 @@ class Dispatcher:
             "restart": handle_restart,
             "embeddingsReindexProgress": handle_embeddings_reindex_progress,
             "modelState": handle_model_state,
+            "jobState": handle_job_state,
             "audioTranscriptionState": handle_audio_transcription_state,
             "birdseyeLayout": handle_birdseye_layout,
             "onConnect": handle_on_connect,

frigate/comms/mqtt.py

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

frigate/comms/webpush.py

@@ -388,7 +388,7 @@ class WebPushClient(Communicator):
             else:
                 title = base_title
 
-            message = payload["after"]["data"]["metadata"]["shortSummary"]
+            message = payload["after"]["data"]["metadata"]["scene"]
         else:
             zone_names = payload["after"]["data"]["zones"]
             formatted_zone_names = []

frigate/config/camera/record.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Optional
+from typing import Optional, Union
 
 from pydantic import Field
 
@@ -70,13 +70,13 @@ class RecordExportConfig(FrigateBaseModel):
     timelapse_args: str = Field(
         default=DEFAULT_TIME_LAPSE_FFMPEG_ARGS, title="Timelapse Args"
     )
+    hwaccel_args: Union[str, list[str]] = Field(
+        default="auto", title="Export-specific FFmpeg hardware acceleration arguments."
+    )
 
 
 class RecordConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Enable record on all cameras.")
-    sync_recordings: bool = Field(
-        default=False, title="Sync recordings with disk on startup and once a day."
-    )
     expire_interval: int = Field(
         default=60,
         title="Number of minutes to wait between cleanup runs.",

frigate/config/config.py

@@ -28,7 +28,6 @@ from frigate.util.builtin import (
     get_ffmpeg_arg_list,
 )
 from frigate.util.config import (
-    CURRENT_CONFIG_VERSION,
     StreamInfoRetriever,
     convert_area_to_pixels,
     find_config_file,
@@ -77,12 +76,11 @@ logger = logging.getLogger(__name__)
 
 yaml = YAML()
 
-DEFAULT_CONFIG = f"""
+DEFAULT_CONFIG = """
 mqtt:
   enabled: False
 
-cameras: {{}}  # No cameras defined, UI wizard should be used
-version: {CURRENT_CONFIG_VERSION}
+cameras: {}  # No cameras defined, UI wizard should be used
 """
 
 DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
@@ -525,6 +523,14 @@ class FrigateConfig(FrigateBaseModel):
             if camera_config.ffmpeg.hwaccel_args == "auto":
                 camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args
 
+            # Resolve export hwaccel_args: camera export -> camera ffmpeg -> global ffmpeg
+            # This allows per-camera override for exports (e.g., when camera resolution
+            # exceeds hardware encoder limits)
+            if camera_config.record.export.hwaccel_args == "auto":
+                camera_config.record.export.hwaccel_args = (
+                    camera_config.ffmpeg.hwaccel_args
+                )
+
             for input in camera_config.ffmpeg.inputs:
                 need_detect_dimensions = "detect" in input.roles and (
                     camera_config.detect.height is None
@@ -755,7 +761,8 @@ class FrigateConfig(FrigateBaseModel):
             if new_config and f.tell() == 0:
                 f.write(DEFAULT_CONFIG)
                 logger.info(
-                    "Created default config file, see the getting started docs for configuration: https://docs.frigate.video/guides/getting_started"
+                    "Created default config file, see the getting started docs \
+                    for configuration https://docs.frigate.video/guides/getting_started"
                 )
 
             f.seek(0)

frigate/const.py

@@ -77,9 +77,6 @@ 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_-]+$"
@@ -122,6 +119,7 @@ UPDATE_REVIEW_DESCRIPTION = "update_review_description"
 UPDATE_MODEL_STATE = "update_model_state"
 UPDATE_EMBEDDINGS_REINDEX_PROGRESS = "handle_embeddings_reindex_progress"
 UPDATE_BIRDSEYE_LAYOUT = "update_birdseye_layout"
+UPDATE_JOB_STATE = "update_job_state"
 NOTIFICATION_TEST = "notification_test"
 
 # IO Nice Values

frigate/data_processing/common/license_plate/mixin.py

@@ -374,9 +374,6 @@ class LicensePlateProcessingMixin:
                             combined_plate = re.sub(
                                 pattern, replacement, combined_plate
                             )
-                            logger.debug(
-                                f"{camera}: Processing replace rule: '{pattern}' -> '{replacement}', result: '{combined_plate}'"
-                            )
                     except re.error as e:
                         logger.warning(
                             f"{camera}: Invalid regex in replace_rules '{pattern}': {e}"
@@ -384,7 +381,7 @@ class LicensePlateProcessingMixin:
 
             if combined_plate != original_combined:
                 logger.debug(
-                    f"{camera}: All rules applied: '{original_combined}' -> '{combined_plate}'"
+                    f"{camera}: Rules applied: '{original_combined}' -> '{combined_plate}'"
                 )
 
             # Compute the combined area for qualifying boxes

frigate/data_processing/post/audio_transcription.py

@@ -131,9 +131,8 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
                 },
             )
 
-            # Embed the description if semantic search is enabled
-            if self.config.semantic_search.enabled:
-                self.embeddings.embed_description(event_id, transcription)
+            # Embed the description
+            self.embeddings.embed_description(event_id, transcription)
 
         except DoesNotExist:
             logger.debug("No recording found for audio transcription post-processing")

frigate/data_processing/post/object_descriptions.py

@@ -86,11 +86,7 @@ class ObjectDescriptionProcessor(PostProcessorApi):
                 and data["id"] not in self.early_request_sent
             ):
                 if data["has_clip"] and data["has_snapshot"]:
-                    try:
-                        event: Event = Event.get(Event.id == data["id"])
-                    except DoesNotExist:
-                        logger.error(f"Event {data['id']} not found")
-                        return
+                    event: Event = Event.get(Event.id == data["id"])
 
                     if (
                         not camera_config.objects.genai.objects
@@ -135,8 +131,6 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             )
         ):
             self._process_genai_description(event, camera_config, thumbnail)
-        else:
-            self.cleanup_event(event.id)
 
     def __regenerate_description(self, event_id: str, source: str, force: bool) -> None:
         """Regenerate the description for an event."""
@@ -210,17 +204,6 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             )
         return None
 
-    def cleanup_event(self, event_id: str) -> None:
-        """Clean up tracked event data to prevent memory leaks.
-
-        This should be called when an event ends, regardless of whether
-        genai processing is triggered.
-        """
-        if event_id in self.tracked_events:
-            del self.tracked_events[event_id]
-        if event_id in self.early_request_sent:
-            del self.early_request_sent[event_id]
-
     def _read_and_crop_snapshot(self, event: Event) -> bytes | None:
         """Read, decode, and crop the snapshot image."""
 
@@ -316,8 +299,9 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             ),
         ).start()
 
-        # Clean up tracked events and early request state
-        self.cleanup_event(event.id)
+        # Delete tracked events based on the event_id
+        if event.id in self.tracked_events:
+            del self.tracked_events[event.id]
 
     def _genai_embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
         """Embed the description for an event."""

frigate/data_processing/post/review_descriptions.py

@@ -92,7 +92,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
 
         pixels_per_image = width * height
         tokens_per_image = pixels_per_image / 1250
-        prompt_tokens = 3800
+        prompt_tokens = 3500
         response_tokens = 300
         available_tokens = context_size - prompt_tokens - response_tokens
         max_frames = int(available_tokens / tokens_per_image)
@@ -311,7 +311,6 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                 start_ts,
                 end_ts,
                 events_with_context,
-                self.config.review.genai.preferred_language,
                 self.config.review.genai.debug_save_thumbnails,
             )
         else:

frigate/data_processing/post/types.py

@@ -8,9 +8,6 @@ class ReviewMetadata(BaseModel):
     scene: str = Field(
         description="A comprehensive description of the setting and entities, including relevant context and plausible inferences if supported by visual evidence."
     )
-    shortSummary: str = Field(
-        description="A brief 2-sentence summary of the scene, suitable for notifications. Should capture the key activity and context without full detail."
-    )
     confidence: float = Field(
         description="A float between 0 and 1 representing your overall confidence in this analysis."
     )

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 suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 from frigate.util.object import calculate_region
 
 from ..types import DataProcessorMetrics
@@ -80,14 +80,13 @@ 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:
-        # Suppress TFLite delegate creation messages that bypass Python logging
-        with suppress_stderr_during("tflite_interpreter_init"):
-            self.interpreter = Interpreter(
-                model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
-                num_threads=2,
-            )
-            self.interpreter.allocate_tensors()
+        self.interpreter = Interpreter(
+            model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
+            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 suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
 from frigate.util.object import box_overlaps, calculate_region
@@ -52,7 +52,7 @@ 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
+        self.interpreter: Interpreter | 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] = {}
@@ -72,12 +72,8 @@ 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
-        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")
 
@@ -88,13 +84,11 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
-        # Suppress TFLite delegate creation messages that bypass Python logging
-        with suppress_stderr_during("tflite_interpreter_init"):
-            self.interpreter = Interpreter(
-                model_path=model_path,
-                num_threads=2,
-            )
-            self.interpreter.allocate_tensors()
+        self.interpreter = Interpreter(
+            model_path=model_path,
+            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)
@@ -230,34 +224,28 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         if not should_run:
             return
 
+        x, y, x2, y2 = calculate_region(
+            frame.shape,
+            crop[0],
+            crop[1],
+            crop[2],
+            crop[3],
+            224,
+            1.0,
+        )
+
         rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
-        height, width = rgb.shape[:2]
+        frame = rgb[
+            y:y2,
+            x:x2,
+        ]
 
-        # Convert normalized crop coordinates to pixel values
-        x1 = int(camera_config.crop[0] * width)
-        y1 = int(camera_config.crop[1] * height)
-        x2 = int(camera_config.crop[2] * width)
-        y2 = int(camera_config.crop[3] * height)
-
-        # Clip coordinates to frame boundaries
-        x1 = max(0, min(x1, width))
-        y1 = max(0, min(y1, height))
-        x2 = max(0, min(x2, width))
-        y2 = max(0, min(y2, height))
-
-        if x2 <= x1 or y2 <= y1:
-            logger.warning(
-                f"Invalid crop coordinates for {camera}: [{x1}, {y1}, {x2}, {y2}]"
-            )
-            return
-
-        frame = rgb[y1:y2, x1:x2]
-
-        try:
-            resized_frame = cv2.resize(frame, (224, 224))
-        except Exception:
-            logger.warning("Failed to resize image for state classification")
-            return
+        if frame.shape != (224, 224):
+            try:
+                resized_frame = cv2.resize(frame, (224, 224))
+            except Exception:
+                logger.warning("Failed to resize image for state classification")
+                return
 
         if self.interpreter is None:
             # When interpreter is None, always save (score is 0.0, which is < 1.0)
@@ -357,7 +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
+        self.interpreter: Interpreter | None = None
         self.sub_label_publisher = sub_label_publisher
         self.requestor = requestor
         self.tensor_input_details: dict[str, Any] | None = None
@@ -378,6 +366,7 @@ 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")
@@ -389,13 +378,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
-        # Suppress TFLite delegate creation messages that bypass Python logging
-        with suppress_stderr_during("tflite_interpreter_init"):
-            self.interpreter = Interpreter(
-                model_path=model_path,
-                num_threads=2,
-            )
-            self.interpreter.allocate_tensors()
+        self.interpreter = Interpreter(
+            model_path=model_path,
+            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)
@@ -521,13 +508,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                 0.0,
                 max_files=save_attempts,
             )
-
-            # Still track history even when model doesn't exist to respect MAX_OBJECT_CLASSIFICATIONS
-            # Add an entry with "unknown" label so the history limit is enforced
-            if object_id not in self.classification_history:
-                self.classification_history[object_id] = []
-
-            self.classification_history[object_id].append(("unknown", 0.0, now))
             return
 
         input = np.expand_dims(resized_crop, axis=0)
@@ -669,5 +649,5 @@ def write_classification_attempt(
 
         if len(files) > max_files:
             os.unlink(os.path.join(folder, files[-1]))
-    except (FileNotFoundError, OSError):
+    except FileNotFoundError:
         pass

frigate/detectors/detection_runners.py

@@ -131,7 +131,6 @@ class ONNXModelRunner(BaseModelRunner):
 
         return model_type in [
             EnrichmentModelTypeEnum.paddleocr.value,
-            EnrichmentModelTypeEnum.yolov9_license_plate.value,
             EnrichmentModelTypeEnum.jina_v1.value,
             EnrichmentModelTypeEnum.jina_v2.value,
             EnrichmentModelTypeEnum.facenet.value,
@@ -139,31 +138,8 @@ class ONNXModelRunner(BaseModelRunner):
             ModelTypeEnum.dfine.value,
         ]
 
-    @staticmethod
-    def is_concurrent_model(model_type: str | None) -> bool:
-        """Check if model requires thread locking for concurrent inference.
-
-        Some models (like JinaV2) share one runner between text and vision embeddings
-        called from different threads, requiring thread synchronization.
-        """
-        if not model_type:
-            return False
-
-        # Import here to avoid circular imports
-        from frigate.embeddings.types import EnrichmentModelTypeEnum
-
-        return model_type == EnrichmentModelTypeEnum.jina_v2.value
-
-    def __init__(self, ort: ort.InferenceSession, model_type: str | None = None):
+    def __init__(self, ort: ort.InferenceSession):
         self.ort = ort
-        self.model_type = model_type
-
-        # Thread lock to prevent concurrent inference (needed for JinaV2 which shares
-        # one runner between text and vision embeddings called from different threads)
-        if self.is_concurrent_model(model_type):
-            self._inference_lock = threading.Lock()
-        else:
-            self._inference_lock = None
 
     def get_input_names(self) -> list[str]:
         return [input.name for input in self.ort.get_inputs()]
@@ -173,10 +149,6 @@ class ONNXModelRunner(BaseModelRunner):
         return self.ort.get_inputs()[0].shape[3]
 
     def run(self, input: dict[str, Any]) -> Any | None:
-        if self._inference_lock:
-            with self._inference_lock:
-                return self.ort.run(None, input)
-
         return self.ort.run(None, input)
 
 
@@ -197,7 +169,6 @@ class CudaGraphRunner(BaseModelRunner):
 
         return model_type not in [
             ModelTypeEnum.yolonas.value,
-            ModelTypeEnum.dfine.value,
             EnrichmentModelTypeEnum.paddleocr.value,
             EnrichmentModelTypeEnum.jina_v1.value,
             EnrichmentModelTypeEnum.jina_v2.value,
@@ -603,6 +574,5 @@ def get_optimized_runner(
             ),
             providers=providers,
             provider_options=options,
-        ),
-        model_type=model_type,
+        )
     )

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 suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 
 from ..detector_utils import tflite_detect_raw, tflite_init
 
@@ -28,13 +28,12 @@ class CpuDetectorConfig(BaseDetectorConfig):
 class CpuTfl(DetectionApi):
     type_key = DETECTOR_KEY
 
+    @redirect_output_to_logger(logger, logging.DEBUG)
     def __init__(self, detector_config: CpuDetectorConfig):
-        # Suppress TFLite delegate creation messages that bypass Python logging
-        with suppress_stderr_during("tflite_interpreter_init"):
-            interpreter = Interpreter(
-                model_path=detector_config.model.path,
-                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, SUPPORTED_RK_SOCS
+from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import RKNNModelRunner
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
@@ -19,6 +19,8 @@ logger = logging.getLogger(__name__)
 
 DETECTOR_KEY = "rknn"
 
+supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
+
 supported_models = {
     ModelTypeEnum.yologeneric: "^frigate-fp16-yolov9-[cemst]$",
     ModelTypeEnum.yolonas: "^deci-fp16-yolonas_[sml]$",
@@ -80,9 +82,9 @@ class Rknn(DetectionApi):
         except FileNotFoundError:
             raise Exception("Make sure to run docker in privileged mode.")
 
-        if soc not in SUPPORTED_RK_SOCS:
+        if soc not in supported_socs:
             raise Exception(
-                f"Your SoC is not supported. Your SoC is: {soc}. Currently these SoCs are supported: {SUPPORTED_RK_SOCS}."
+                f"Your SoC is not supported. Your SoC is: {soc}. Currently these SoCs are supported: {supported_socs}."
             )
 
         return soc

frigate/embeddings/maintainer.py

@@ -203,9 +203,7 @@ class EmbeddingMaintainer(threading.Thread):
         # post processors
         self.post_processors: list[PostProcessorApi] = []
 
-        if self.genai_client is not None and any(
-            c.review.genai.enabled_in_config for c in self.config.cameras.values()
-        ):
+        if any(c.review.genai.enabled_in_config for c in self.config.cameras.values()):
             self.post_processors.append(
                 ReviewDescriptionProcessor(
                     self.config, self.requestor, self.metrics, self.genai_client
@@ -246,9 +244,7 @@ class EmbeddingMaintainer(threading.Thread):
             )
             self.post_processors.append(semantic_trigger_processor)
 
-        if self.genai_client is not None and any(
-            c.objects.genai.enabled_in_config for c in self.config.cameras.values()
-        ):
+        if any(c.objects.genai.enabled_in_config for c in self.config.cameras.values()):
             self.post_processors.append(
                 ObjectDescriptionProcessor(
                     self.config,
@@ -526,8 +522,6 @@ class EmbeddingMaintainer(threading.Thread):
                     )
                 elif isinstance(processor, ObjectDescriptionProcessor):
                     if not updated_db:
-                        # Still need to cleanup tracked events even if not processing
-                        processor.cleanup_event(event_id)
                         continue
 
                     processor.process_data(
@@ -633,7 +627,7 @@ class EmbeddingMaintainer(threading.Thread):
 
         camera, frame_name, _, _, motion_boxes, _ = data
 
-        if not camera or len(motion_boxes) == 0 or camera not in self.config.cameras:
+        if not camera or len(motion_boxes) == 0:
             return
 
         camera_config = self.config.cameras[camera]

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 suppress_stderr_during
+from frigate.log import redirect_output_to_logger
 from frigate.util.downloader import ModelDownloader
 
 from ...config import FaceRecognitionConfig
@@ -57,18 +57,17 @@ 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()
 
-            # Suppress TFLite delegate creation messages that bypass Python logging
-            with suppress_stderr_during("tflite_interpreter_init"):
-                self.runner = Interpreter(
-                    model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
-                    num_threads=2,
-                )
-                self.runner.allocate_tensors()
+            self.runner = Interpreter(
+                model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
+                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/embeddings/onnx/jina_v1_embedding.py

@@ -186,9 +186,6 @@ class JinaV1ImageEmbedding(BaseEmbedding):
                 download_func=self._download_model,
             )
             self.downloader.ensure_model_files()
-            # Avoid lazy loading in worker threads: block until downloads complete
-            # and load the model on the main thread during initialization.
-            self._load_model_and_utils()
         else:
             self.downloader = None
             ModelDownloader.mark_files_state(

frigate/embeddings/onnx/jina_v2_embedding.py

@@ -3,7 +3,6 @@
 import io
 import logging
 import os
-import threading
 
 import numpy as np
 from PIL import Image
@@ -54,11 +53,6 @@ class JinaV2Embedding(BaseEmbedding):
         self.tokenizer = None
         self.image_processor = None
         self.runner = None
-
-        # Lock to prevent concurrent calls (text and vision share this instance)
-        self._call_lock = threading.Lock()
-
-        # download the model and tokenizer
         files_names = list(self.download_urls.keys()) + [self.tokenizer_file]
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -71,9 +65,6 @@ class JinaV2Embedding(BaseEmbedding):
                 download_func=self._download_model,
             )
             self.downloader.ensure_model_files()
-            # Avoid lazy loading in worker threads: block until downloads complete
-            # and load the model on the main thread during initialization.
-            self._load_model_and_utils()
         else:
             self.downloader = None
             ModelDownloader.mark_files_state(
@@ -206,40 +197,37 @@ class JinaV2Embedding(BaseEmbedding):
     def __call__(
         self, inputs: list[str] | list[Image.Image] | list[str], embedding_type=None
     ) -> list[np.ndarray]:
-        # Lock the entire call to prevent race conditions when text and vision
-        # embeddings are called concurrently from different threads
-        with self._call_lock:
-            self.embedding_type = embedding_type
-            if not self.embedding_type:
-                raise ValueError(
-                    "embedding_type must be specified either in __init__ or __call__"
-                )
+        self.embedding_type = embedding_type
+        if not self.embedding_type:
+            raise ValueError(
+                "embedding_type must be specified either in __init__ or __call__"
+            )
 
-            self._load_model_and_utils()
-            processed = self._preprocess_inputs(inputs)
-            batch_size = len(processed)
+        self._load_model_and_utils()
+        processed = self._preprocess_inputs(inputs)
+        batch_size = len(processed)
 
-            # Prepare ONNX inputs with matching batch sizes
-            onnx_inputs = {}
-            if self.embedding_type == "text":
-                onnx_inputs["input_ids"] = np.stack([x[0] for x in processed])
-                onnx_inputs["pixel_values"] = np.zeros(
-                    (batch_size, 3, 512, 512), dtype=np.float32
-                )
-            elif self.embedding_type == "vision":
-                onnx_inputs["input_ids"] = np.zeros((batch_size, 16), dtype=np.int64)
-                onnx_inputs["pixel_values"] = np.stack([x[0] for x in processed])
-            else:
-                raise ValueError("Invalid embedding type")
+        # Prepare ONNX inputs with matching batch sizes
+        onnx_inputs = {}
+        if self.embedding_type == "text":
+            onnx_inputs["input_ids"] = np.stack([x[0] for x in processed])
+            onnx_inputs["pixel_values"] = np.zeros(
+                (batch_size, 3, 512, 512), dtype=np.float32
+            )
+        elif self.embedding_type == "vision":
+            onnx_inputs["input_ids"] = np.zeros((batch_size, 16), dtype=np.int64)
+            onnx_inputs["pixel_values"] = np.stack([x[0] for x in processed])
+        else:
+            raise ValueError("Invalid embedding type")
 
-            # Run inference
-            outputs = self.runner.run(onnx_inputs)
-            if self.embedding_type == "text":
-                embeddings = outputs[2]  # text embeddings
-            elif self.embedding_type == "vision":
-                embeddings = outputs[3]  # image embeddings
-            else:
-                raise ValueError("Invalid embedding type")
+        # Run inference
+        outputs = self.runner.run(onnx_inputs)
+        if self.embedding_type == "text":
+            embeddings = outputs[2]  # text embeddings
+        elif self.embedding_type == "vision":
+            embeddings = outputs[3]  # image embeddings
+        else:
+            raise ValueError("Invalid embedding type")
 
-            embeddings = self._postprocess_outputs(embeddings)
-            return [embedding for embedding in embeddings]
+        embeddings = self._postprocess_outputs(embeddings)
+        return [embedding for embedding in embeddings]

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, suppress_stderr_during
+from frigate.log import LogPipe, redirect_output_to_logger
 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)
-        # Suppress TFLite delegate creation messages that bypass Python logging
-        with suppress_stderr_during("tflite_interpreter_init"):
-            self.interpreter = Interpreter(
-                model_path="/cpu_audio_model.tflite",
-                num_threads=self.num_threads,
-            )
-            self.interpreter.allocate_tensors()
+        self.interpreter = Interpreter(
+            model_path="/cpu_audio_model.tflite",
+            num_threads=self.num_threads,
+        )
+
+        self.interpreter.allocate_tensors()
 
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()

frigate/events/maintainer.py

@@ -46,7 +46,7 @@ def should_update_state(prev_event: Event, current_event: Event) -> bool:
     if prev_event["sub_label"] != current_event["sub_label"]:
         return True
 
-    if set(prev_event["current_zones"]) != set(current_event["current_zones"]):
+    if len(prev_event["current_zones"]) < len(current_event["current_zones"]):
         return True
 
     return False

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} -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} -hwaccel cuda -hwaccel_device {3} -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/genai/__init__.py

@@ -101,7 +101,6 @@ When forming your description:
 Your response MUST be a flat JSON object with:
 - `title` (string): A concise, direct title that describes the primary action or event in the sequence, not just what you literally see. Use spatial context when available to make titles more meaningful. When multiple objects/actions are present, prioritize whichever is most prominent or occurs first. Use names from "Objects in Scene" based on what you visually observe. If you see both a name and an unidentified object of the same type but visually observe only one person/object, use ONLY the name. Examples: "Joe walking dog", "Person taking out trash", "Vehicle arriving in driveway", "Joe accessing vehicle", "Person leaving porch for driveway".
 - `scene` (string): A narrative description of what happens across the sequence from start to finish, in chronological order. Start by describing how the sequence begins, then describe the progression of events. **Describe all significant movements and actions in the order they occur.** For example, if a vehicle arrives and then a person exits, describe both actions sequentially. **Only describe actions you can actually observe happening in the frames provided.** Do not infer or assume actions that aren't visible (e.g., if you see someone walking but never see them sit, don't say they sat down). Include setting, detected objects, and their observable actions. Avoid speculation or filling in assumed behaviors. Your description should align with and support the threat level you assign.
-- `shortSummary` (string): A brief 2-sentence summary of the scene, suitable for notifications. Should capture the key activity and context without full detail. This should be a condensed version of the scene description above.
 - `confidence` (float): 0-1 confidence in your analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous. Lower confidence when the sequence is unclear, objects are partially obscured, or context is ambiguous.
 - `potential_threat_level` (integer): 0, 1, or 2 as defined in "Normal Activity Patterns for This Property" above. Your threat level must be consistent with your scene description and the guidance above.
 {get_concern_prompt()}
@@ -179,7 +178,6 @@ Each line represents a detection state, not necessarily unique individuals. Pare
         start_ts: float,
         end_ts: float,
         events: list[dict[str, Any]],
-        preferred_language: str | None,
         debug_save: bool,
     ) -> str | None:
         """Generate a summary of review item descriptions over a period of time."""
@@ -193,8 +191,6 @@ Input format: Each event is a JSON object with:
 - "title", "scene", "confidence", "potential_threat_level" (0-2), "other_concerns", "camera", "time", "start_time", "end_time"
 - "context": array of related events from other cameras that occurred during overlapping time periods
 
-**Note: Use the "scene" field for event descriptions in the report. Ignore any "shortSummary" field if present.**
-
 Report Structure - Use this EXACT format:
 
 # Security Summary - {time_range}
@@ -236,9 +232,6 @@ Guidelines:
         for event in events:
             timeline_summary_prompt += f"\n{event}\n"
 
-        if preferred_language:
-            timeline_summary_prompt += f"\nProvide your answer in {preferred_language}"
-
         if debug_save:
             with open(
                 os.path.join(

frigate/genai/ollama.py

@@ -3,7 +3,7 @@
 import logging
 from typing import Any, Optional
 
-from httpx import RemoteProtocolError, TimeoutException
+from httpx import TimeoutException
 from ollama import Client as ApiClient
 from ollama import ResponseError
 
@@ -68,12 +68,7 @@ class OllamaClient(GenAIClient):
                 f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}"
             )
             return result["response"].strip()
-        except (
-            TimeoutException,
-            ResponseError,
-            RemoteProtocolError,
-            ConnectionError,
-        ) as e:
+        except (TimeoutException, ResponseError, ConnectionError) as e:
             logger.warning("Ollama returned an error: %s", str(e))
             return None
 

frigate/jobs/job.py

@@ -0,0 +1,21 @@
+"""Generic base class for long-running background jobs."""
+
+from dataclasses import asdict, dataclass, field
+from typing import Any, Optional
+
+
+@dataclass
+class Job:
+    """Base class for long-running background jobs."""
+
+    id: str = field(default_factory=lambda: __import__("uuid").uuid4().__str__()[:12])
+    job_type: str = ""  # Must be set by subclasses
+    status: str = "queued"  # queued, running, success, failed, cancelled
+    results: Optional[dict[str, Any]] = None
+    start_time: Optional[float] = None
+    end_time: Optional[float] = None
+    error_message: Optional[str] = None
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for WebSocket transmission."""
+        return asdict(self)

frigate/jobs/manager.py

@@ -0,0 +1,70 @@
+"""Generic job management for long-running background tasks."""
+
+import threading
+from typing import Optional
+
+from frigate.jobs.job import Job
+from frigate.types import JobStatusTypesEnum
+
+# Global state and locks for enforcing single concurrent job per job type
+_job_locks: dict[str, threading.Lock] = {}
+_current_jobs: dict[str, Optional[Job]] = {}
+# Keep completed jobs for retrieval, keyed by (job_type, job_id)
+_completed_jobs: dict[tuple[str, str], Job] = {}
+
+
+def _get_lock(job_type: str) -> threading.Lock:
+    """Get or create a lock for the specified job type."""
+    if job_type not in _job_locks:
+        _job_locks[job_type] = threading.Lock()
+    return _job_locks[job_type]
+
+
+def set_current_job(job: Job) -> None:
+    """Set the current job for a given job type."""
+    lock = _get_lock(job.job_type)
+    with lock:
+        # Store the previous job if it was completed
+        old_job = _current_jobs.get(job.job_type)
+        if old_job and old_job.status in (
+            JobStatusTypesEnum.success,
+            JobStatusTypesEnum.failed,
+            JobStatusTypesEnum.cancelled,
+        ):
+            _completed_jobs[(job.job_type, old_job.id)] = old_job
+        _current_jobs[job.job_type] = job
+
+
+def clear_current_job(job_type: str, job_id: Optional[str] = None) -> None:
+    """Clear the current job for a given job type, optionally checking the ID."""
+    lock = _get_lock(job_type)
+    with lock:
+        if job_type in _current_jobs:
+            current = _current_jobs[job_type]
+            if current is None or (job_id is None or current.id == job_id):
+                _current_jobs[job_type] = None
+
+
+def get_current_job(job_type: str) -> Optional[Job]:
+    """Get the current running/queued job for a given job type, if any."""
+    lock = _get_lock(job_type)
+    with lock:
+        return _current_jobs.get(job_type)
+
+
+def get_job_by_id(job_type: str, job_id: str) -> Optional[Job]:
+    """Get job by ID. Checks current job first, then completed jobs."""
+    lock = _get_lock(job_type)
+    with lock:
+        # Check if it's the current job
+        current = _current_jobs.get(job_type)
+        if current and current.id == job_id:
+            return current
+        # Check if it's a completed job
+        return _completed_jobs.get((job_type, job_id))
+
+
+def job_is_running(job_type: str) -> bool:
+    """Check if a job of the given type is currently running or queued."""
+    job = get_current_job(job_type)
+    return job is not None and job.status in ("queued", "running")

frigate/jobs/media_sync.py

@@ -0,0 +1,135 @@
+"""Media sync job management with background execution."""
+
+import logging
+import threading
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Optional
+
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.const import UPDATE_JOB_STATE
+from frigate.jobs.job import Job
+from frigate.jobs.manager import (
+    get_current_job,
+    get_job_by_id,
+    job_is_running,
+    set_current_job,
+)
+from frigate.types import JobStatusTypesEnum
+from frigate.util.media import sync_all_media
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class MediaSyncJob(Job):
+    """In-memory job state for media sync operations."""
+
+    job_type: str = "media_sync"
+    dry_run: bool = False
+    media_types: list[str] = field(default_factory=lambda: ["all"])
+    force: bool = False
+
+
+class MediaSyncRunner(threading.Thread):
+    """Thread-based runner for media sync jobs."""
+
+    def __init__(self, job: MediaSyncJob) -> None:
+        super().__init__(daemon=True, name="media_sync")
+        self.job = job
+        self.requestor = InterProcessRequestor()
+
+    def run(self) -> None:
+        """Execute the media sync job and broadcast status updates."""
+        try:
+            # Update job status to running
+            self.job.status = JobStatusTypesEnum.running
+            self.job.start_time = datetime.now().timestamp()
+            self._broadcast_status()
+
+            # Execute sync with provided parameters
+            logger.debug(
+                f"Starting media sync job {self.job.id}: "
+                f"media_types={self.job.media_types}, "
+                f"dry_run={self.job.dry_run}, "
+                f"force={self.job.force}"
+            )
+
+            results = sync_all_media(
+                dry_run=self.job.dry_run,
+                media_types=self.job.media_types,
+                force=self.job.force,
+            )
+
+            # Store results and mark as complete
+            self.job.results = results.to_dict()
+            self.job.status = JobStatusTypesEnum.success
+            self.job.end_time = datetime.now().timestamp()
+
+            logger.debug(f"Media sync job {self.job.id} completed successfully")
+            self._broadcast_status()
+
+        except Exception as e:
+            logger.error(f"Media sync job {self.job.id} failed: {e}", exc_info=True)
+            self.job.status = JobStatusTypesEnum.failed
+            self.job.error_message = str(e)
+            self.job.end_time = datetime.now().timestamp()
+            self._broadcast_status()
+
+        finally:
+            if self.requestor:
+                self.requestor.stop()
+
+    def _broadcast_status(self) -> None:
+        """Broadcast job status update via IPC to all WebSocket subscribers."""
+        try:
+            self.requestor.send_data(
+                UPDATE_JOB_STATE,
+                self.job.to_dict(),
+            )
+        except Exception as e:
+            logger.warning(f"Failed to broadcast media sync status: {e}")
+
+
+def start_media_sync_job(
+    dry_run: bool = False,
+    media_types: Optional[list[str]] = None,
+    force: bool = False,
+) -> Optional[str]:
+    """Start a new media sync job if none is currently running.
+
+    Returns job ID on success, None if job already running.
+    """
+    # Check if a job is already running
+    if job_is_running("media_sync"):
+        current = get_current_job("media_sync")
+        logger.warning(
+            f"Media sync job {current.id} is already running. Rejecting new request."
+        )
+        return None
+
+    # Create and start new job
+    job = MediaSyncJob(
+        dry_run=dry_run,
+        media_types=media_types or ["all"],
+        force=force,
+    )
+
+    logger.debug(f"Creating new media sync job: {job.id}")
+    set_current_job(job)
+
+    # Start the background runner
+    runner = MediaSyncRunner(job)
+    runner.start()
+
+    return job.id
+
+
+def get_current_media_sync_job() -> Optional[MediaSyncJob]:
+    """Get the current running/queued media sync job, if any."""
+    return get_current_job("media_sync")
+
+
+def get_media_sync_job_by_id(job_id: str) -> Optional[MediaSyncJob]:
+    """Get media sync job by ID. Currently only tracks the current job."""
+    return get_job_by_id("media_sync", job_id)

frigate/log.py

@@ -80,15 +80,10 @@ 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,
     }
 
@@ -323,31 +318,3 @@ 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/models.py

@@ -80,6 +80,14 @@ class Recordings(Model):
     regions = IntegerField(null=True)
 
 
+class ExportCase(Model):
+    id = CharField(null=False, primary_key=True, max_length=30)
+    name = CharField(index=True, max_length=100)
+    description = TextField(null=True)
+    created_at = DateTimeField()
+    updated_at = DateTimeField()
+
+
 class Export(Model):
     id = CharField(null=False, primary_key=True, max_length=30)
     camera = CharField(index=True, max_length=20)
@@ -88,6 +96,12 @@ class Export(Model):
     video_path = CharField(unique=True)
     thumb_path = CharField(unique=True)
     in_progress = BooleanField()
+    export_case = ForeignKeyField(
+        ExportCase,
+        null=True,
+        backref="exports",
+        column_name="export_case_id",
+    )
 
 
 class ReviewSegment(Model):

frigate/output/output.py

@@ -139,11 +139,9 @@ class OutputProcess(FrigateProcess):
             if CameraConfigUpdateEnum.add in updates:
                 for camera in updates["add"]:
                     jsmpeg_cameras[camera] = JsmpegCamera(
-                        self.config.cameras[camera], self.stop_event, websocket_server
-                    )
-                    preview_recorders[camera] = PreviewRecorder(
-                        self.config.cameras[camera]
+                        cam_config, self.stop_event, websocket_server
                     )
+                    preview_recorders[camera] = PreviewRecorder(cam_config)
                     preview_write_times[camera] = 0
 
                     if (

frigate/record/cleanup.py

@@ -13,9 +13,8 @@ from playhouse.sqlite_ext import SqliteExtDatabase
 from frigate.config import CameraConfig, FrigateConfig, RetainModeEnum
 from frigate.const import CACHE_DIR, CLIPS_DIR, MAX_WAL_SIZE, RECORD_DIR
 from frigate.models import Previews, Recordings, ReviewSegment, UserReviewStatus
-from frigate.record.util import remove_empty_directories, sync_recordings
 from frigate.util.builtin import clear_and_unlink
-from frigate.util.time import get_tomorrow_at_time
+from frigate.util.media import remove_empty_directories
 
 logger = logging.getLogger(__name__)
 
@@ -119,7 +118,6 @@ class RecordingCleanup(threading.Thread):
                 Recordings.path,
                 Recordings.objects,
                 Recordings.motion,
-                Recordings.dBFS,
             )
             .where(
                 (Recordings.camera == config.name)
@@ -127,7 +125,6 @@ class RecordingCleanup(threading.Thread):
                     (
                         (Recordings.end_time < continuous_expire_date)
                         & (Recordings.motion == 0)
-                        & (Recordings.dBFS == 0)
                     )
                     | (Recordings.end_time < motion_expire_date)
                 )
@@ -187,7 +184,6 @@ class RecordingCleanup(threading.Thread):
                     mode == RetainModeEnum.motion
                     and recording.motion == 0
                     and recording.objects == 0
-                    and recording.dBFS == 0
                 )
                 or (mode == RetainModeEnum.active_objects and recording.objects == 0)
             ):
@@ -350,11 +346,6 @@ class RecordingCleanup(threading.Thread):
         logger.debug("End expire recordings.")
 
     def run(self) -> None:
-        # on startup sync recordings with disk if enabled
-        if self.config.record.sync_recordings:
-            sync_recordings(limited=False)
-            next_sync = get_tomorrow_at_time(3)
-
         # Expire tmp clips every minute, recordings and clean directories every hour.
         for counter in itertools.cycle(range(self.config.record.expire_interval)):
             if self.stop_event.wait(60):
@@ -363,14 +354,6 @@ class RecordingCleanup(threading.Thread):
 
             self.clean_tmp_previews()
 
-            if (
-                self.config.record.sync_recordings
-                and datetime.datetime.now().astimezone(datetime.timezone.utc)
-                > next_sync
-            ):
-                sync_recordings(limited=True)
-                next_sync = get_tomorrow_at_time(3)
-
             if counter == 0:
                 self.clean_tmp_clips()
                 self.expire_recordings()

frigate/record/export.py

@@ -64,6 +64,7 @@ class RecordingExporter(threading.Thread):
         end_time: int,
         playback_factor: PlaybackFactorEnum,
         playback_source: PlaybackSourceEnum,
+        export_case_id: Optional[str] = None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -75,6 +76,7 @@ class RecordingExporter(threading.Thread):
         self.end_time = end_time
         self.playback_factor = playback_factor
         self.playback_source = playback_source
+        self.export_case_id = export_case_id
 
         # ensure export thumb dir
         Path(os.path.join(CLIPS_DIR, "export")).mkdir(exist_ok=True)
@@ -226,7 +228,7 @@ class RecordingExporter(threading.Thread):
             ffmpeg_cmd = (
                 parse_preset_hardware_acceleration_encode(
                     self.config.ffmpeg.ffmpeg_path,
-                    self.config.ffmpeg.hwaccel_args,
+                    self.config.cameras[self.camera].record.export.hwaccel_args,
                     f"-an {ffmpeg_input}",
                     f"{self.config.cameras[self.camera].record.export.timelapse_args} -movflags +faststart",
                     EncodeTypeEnum.timelapse,
@@ -317,7 +319,7 @@ class RecordingExporter(threading.Thread):
             ffmpeg_cmd = (
                 parse_preset_hardware_acceleration_encode(
                     self.config.ffmpeg.ffmpeg_path,
-                    self.config.ffmpeg.hwaccel_args,
+                    self.config.cameras[self.camera].record.export.hwaccel_args,
                     f"{TIMELAPSE_DATA_INPUT_ARGS} {ffmpeg_input}",
                     f"{self.config.cameras[self.camera].record.export.timelapse_args} -movflags +faststart {video_path}",
                     EncodeTypeEnum.timelapse,
@@ -348,17 +350,20 @@ class RecordingExporter(threading.Thread):
         video_path = f"{EXPORT_DIR}/{self.camera}_{filename_start_datetime}-{filename_end_datetime}_{cleaned_export_id}.mp4"
         thumb_path = self.save_thumbnail(self.export_id)
 
-        Export.insert(
-            {
-                Export.id: self.export_id,
-                Export.camera: self.camera,
-                Export.name: export_name,
-                Export.date: self.start_time,
-                Export.video_path: video_path,
-                Export.thumb_path: thumb_path,
-                Export.in_progress: True,
-            }
-        ).execute()
+        export_values = {
+            Export.id: self.export_id,
+            Export.camera: self.camera,
+            Export.name: export_name,
+            Export.date: self.start_time,
+            Export.video_path: video_path,
+            Export.thumb_path: thumb_path,
+            Export.in_progress: True,
+        }
+
+        if self.export_case_id is not None:
+            export_values[Export.export_case] = self.export_case_id
+
+        Export.insert(export_values).execute()
 
         try:
             if self.playback_source == PlaybackSourceEnum.recordings:

frigate/record/maintainer.py

@@ -67,7 +67,7 @@ class SegmentInfo:
         if (
             not keep
             and retain_mode == RetainModeEnum.motion
-            and (self.motion_count > 0 or self.average_dBFS != 0)
+            and (self.motion_count > 0 or self.average_dBFS > 0)
         ):
             keep = True