Merge 7af9209e1eb7ed9ed6346360fcf4c6d0da01cfcc into 308e692732e0990cdb3fc944c30bb81cf9e2d079

* only show jwt secret tip for admin users

* fix preview endpoint 403 for viewer role when "all" param is used

* Update docs dependencies

* add warning if ffmpeg isn't selected for reolink http streams

* Update the motion for motion masks

* Also update objects

* Add docs about backchannel and two way talk takeover

* don't require restart when deleting zone or mask

* Ensure motion is correctly set when adjusting masks

* don't use python style raw prefixes in yaml examples in LPR docs

* wording

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

.cspell/frigate-dictionary.txt

@@ -22,6 +22,7 @@ autotrack
 autotracked
 autotracker
 autotracking
+backchannel
 balena
 Beelink
 BGRA
@@ -191,6 +192,7 @@ ONVIF
 openai
 opencv
 openvino
+overfitting
 OWASP
 paddleocr
 paho
@@ -315,4 +317,4 @@ yolo
 yolonas
 yolox
 zeep
-zerolatency
+zerolatency

.github/DISCUSSION_TEMPLATE/beta-support.yml

@@ -0,0 +1,129 @@
+title: "[Beta Support]: "
+labels: ["support", "triage", "beta"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for testing Frigate beta versions! Use this form for support with beta releases.
+
+        **Note:** Beta versions may have incomplete features, known issues, or unexpected behavior. Please check the [release notes](https://github.com/blakeblackshear/frigate/releases) and [recent discussions][discussions] for known beta issues before submitting.
+
+        Before submitting, read the [beta documentation][docs].
+
+        [docs]: https://deploy-preview-19787--frigate-docs.netlify.app/
+  - type: textarea
+    id: description
+    attributes:
+      label: Describe the problem you are having
+      description: Please be as detailed as possible. Include what you expected to happen vs what actually happened.
+    validations:
+      required: true
+  - type: input
+    id: version
+    attributes:
+      label: Beta Version
+      description: Visible on the System page in the Web UI. Please include the full version including the build identifier (eg. 0.17.0-beta1)
+      placeholder: "0.17.0-beta1"
+    validations:
+      required: true
+  - type: dropdown
+    id: issue-category
+    attributes:
+      label: Issue Category
+      description: What area is your issue related to? This helps us understand the context.
+      options:
+        - Object Detection / Detectors
+        - Hardware Acceleration
+        - Configuration / Setup
+        - WebUI / Frontend
+        - Recordings / Storage
+        - Notifications / Events
+        - Integration (Home Assistant, etc)
+        - Performance / Stability
+        - Installation / Updates
+        - Other
+    validations:
+      required: true
+  - type: textarea
+    id: config
+    attributes:
+      label: Frigate config file
+      description: This will be automatically formatted into code, so no need for backticks. Remove any sensitive information like passwords or URLs.
+      render: yaml
+    validations:
+      required: true
+  - type: textarea
+    id: frigatelogs
+    attributes:
+      label: Relevant Frigate log output
+      description: Please copy and paste any relevant Frigate log output. Include logs before and after your exact error when possible. This will be automatically formatted into code, so no need for backticks.
+      render: shell
+    validations:
+      required: true
+  - type: textarea
+    id: go2rtclogs
+    attributes:
+      label: Relevant go2rtc log output (if applicable)
+      description: If your issue involves cameras, streams, or playback, please include go2rtc logs. Logs can be viewed via the Frigate UI, Docker, or the go2rtc dashboard. This will be automatically formatted into code, so no need for backticks.
+      render: shell
+  - type: dropdown
+    id: install-method
+    attributes:
+      label: Install method
+      options:
+        - Home Assistant Add-on
+        - Docker Compose
+        - Docker CLI
+        - Proxmox via Docker
+        - Proxmox via TTeck Script
+        - Windows WSL2
+    validations:
+      required: true
+  - type: textarea
+    id: docker
+    attributes:
+      label: docker-compose file or Docker CLI command
+      description: This will be automatically formatted into code, so no need for backticks. Include relevant environment variables and device mappings.
+      render: yaml
+    validations:
+      required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - Home Assistant OS
+        - Debian
+        - Ubuntu
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
+  - type: input
+    id: hardware
+    attributes:
+      label: CPU / GPU / Hardware
+      description: Provide details about your hardware (e.g., Intel i5-9400, NVIDIA RTX 3060, Raspberry Pi 4, etc)
+      placeholder: "Intel i7-10700, NVIDIA GTX 1660"
+  - type: textarea
+    id: screenshots
+    attributes:
+      label: Screenshots
+      description: Screenshots of the issue, System metrics pages, or any relevant UI. Drag and drop or paste images directly.
+  - type: textarea
+    id: steps-to-reproduce
+    attributes:
+      label: Steps to reproduce
+      description: If applicable, provide detailed steps to reproduce the issue
+      placeholder: |
+        1. Go to '...'
+        2. Click on '...'
+        3. See error
+  - type: textarea
+    id: other
+    attributes:
+      label: Any other information that may be helpful
+      description: Additional context, related issues, when the problem started appearing, etc.

.github/DISCUSSION_TEMPLATE/report-a-bug.yml

@@ -6,6 +6,8 @@ body:
       value: |
         Use this form to submit a reproducible bug in Frigate or Frigate's UI.
 
+        **⚠️ If you are running a beta version (0.17.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
+
         Before submitting your bug report, please ask the AI with the "Ask AI" button on the [official documentation site][ai] about your issue, [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
 
         **If you are unsure if your issue is actually a bug or not, please submit a support request first.**

.github/copilot-instructions.md

@@ -0,0 +1,2 @@
+Never write strings in the frontend directly, always write to and reference the relevant translations file.
+Always conform new and refactored code to the existing coding style in the project.

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  PYTHON_VERSION: 3.9
+  PYTHON_VERSION: 3.11
 
 jobs:
   amd64_build:
@@ -23,7 +23,7 @@ jobs:
     name: AMD64 Build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -47,7 +47,7 @@ jobs:
     name: ARM Build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -82,7 +82,7 @@ jobs:
     name: Jetson Jetpack 6
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -113,7 +113,7 @@ jobs:
       - amd64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -136,7 +136,6 @@ jobs:
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-tensorrt,mode=max
       - name: AMD/ROCm general build
         env:
-          AMDGPU: gfx
           HSA_OVERRIDE: 0
         uses: docker/bake-action@v6
         with:
@@ -155,7 +154,7 @@ jobs:
       - arm64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -180,7 +179,7 @@ jobs:
       - arm64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx

.github/workflows/pull_request.yml

@@ -16,7 +16,7 @@ jobs:
     name: Web - Lint
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           persist-credentials: false
       - uses: actions/setup-node@master
@@ -32,7 +32,7 @@ jobs:
     name: Web - Test
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           persist-credentials: false
       - uses: actions/setup-node@master
@@ -52,7 +52,7 @@ jobs:
     name: Python Checks
     steps:
       - name: Check out the repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
       - name: Set up Python ${{ env.DEFAULT_PYTHON }}
@@ -75,7 +75,7 @@ jobs:
     name: Python Tests
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           persist-credentials: false
       - uses: actions/setup-node@master

.github/workflows/release.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           persist-credentials: false
       - id: lowercaseRepo

.gitignore

@@ -15,6 +15,7 @@ frigate/version.py
 web/build
 web/node_modules
 web/coverage
+web/.env
 core
 !/web/**/*.ts
 .idea/*

LICENSE

@@ -1,6 +1,6 @@
 The MIT License
 
-Copyright (c) 2020 Blake Blackshear
+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
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

Makefile

@@ -14,6 +14,7 @@ push-boards: $(BOARDS:%=push-%)
 
 version:
 	echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
+	echo 'VITE_GIT_COMMIT_HASH=$(COMMIT_HASH)' > web/.env
 
 local: version
 	docker buildx build --target=frigate --file docker/main/Dockerfile . \

README.md

@@ -1,8 +1,10 @@
 <p align="center">
-  <img align="center" alt="logo" src="docs/static/img/frigate.png">
+  <img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
 </p>
 
-# Frigate - NVR With Realtime Object Detection for IP Cameras
+# Frigate NVR™ - Realtime Object Detection for IP Cameras
+
+[![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/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/language-badge.svg" alt="Translation status" />
@@ -12,7 +14,7 @@
 
 A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
 
-Use of a GPU or AI accelerator such as a [Google Coral](https://coral.ai/products/) or [Hailo](https://hailo.ai/) is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
+Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported [object detectors](https://docs.frigate.video/configuration/object_detectors/).
 
 - Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
 - Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
@@ -33,6 +35,15 @@ View the documentation at https://docs.frigate.video
 
 If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
 
+## License
+
+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 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.
+
 ## Screenshots
 
 ### Live dashboard
@@ -66,3 +77,7 @@ We use [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) to support la
 <a href="https://hosted.weblate.org/engage/frigate-nvr/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/multi-auto.svg" alt="Translation status" />
 </a>
+
+---
+
+**Copyright © 2025 Frigate LLC.**

README_CN.md

@@ -1,28 +1,31 @@
 <p align="center">
-  <img align="center" alt="logo" src="docs/static/img/frigate.png">
+  <img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
 </p>
 
-# Frigate - 一个具有实时目标检测的本地NVR
+# Frigate NVR™ - 一个具有实时目标检测的本地 NVR
 
-[English](https://github.com/blakeblackshear/frigate) | \[简体中文\] 
+[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。
+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/)）。它们的性能甚至超过目前的顶级CPU，并且可以以极低的耗电实现更优的性能。
+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。
-- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与Home Assistant紧密集成
+
-- 设计上通过仅在必要时和必要地点寻找物体，最大限度地减少资源使用并最大化性能
+- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与 Home Assistant 紧密集成
+- 设计上通过仅在必要时和必要地点寻找目标，最大限度地减少资源使用并最大化性能
 - 大量利用多进程处理，强调实时性而非处理每一帧
-- 使用非常低开销的运动检测来确定运行物体检测的位置
+- 使用非常低开销的画面变动检测（也叫运动检测）来确定运行目标检测的位置
-- 使用TensorFlow进行物体检测，运行在单独的进程中以达到最大FPS
+- 使用 TensorFlow 进行目标检测，并运行在单独的进程中以达到最大 FPS
-- 通过MQTT进行通信，便于集成到其他系统中
+- 通过 MQTT 进行通信，便于集成到其他系统中
 - 根据检测到的物体设置保留时间进行视频录制
-- 24/7全天候录制
+- 24/7 全天候录制
-- 通过RTSP重新流传输以减少摄像头的连接数
+- 通过 RTSP 重新流传输以减少摄像头的连接数
-- 支持WebRTC和MSE，实现低延迟的实时观看
+- 支持 WebRTC 和 MSE，实现低延迟的实时观看
 
 ## 社区中文翻译文档
 
@@ -32,39 +35,55 @@
 
 如果您想通过捐赠支持开发，请使用 [Github Sponsors](https://github.com/sponsors/blakeblackshear)。
 
+## 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 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
 
 ### 实时监控面板
+
 <div>
 <img width="800" alt="实时监控面板" src="https://github.com/blakeblackshear/frigate/assets/569905/5e713cb9-9db5-41dc-947a-6937c3bc376e">
 </div>
 
 ### 简单的核查工作流程
+
 <div>
 <img width="800" alt="简单的审查工作流程" src="https://github.com/blakeblackshear/frigate/assets/569905/6fed96e8-3b18-40e5-9ddc-31e6f3c9f2ff">
 </div>
 
 ### 多摄像头可按时间轴查看
+
 <div>
 <img width="800" alt="多摄像头可按时间轴查看" src="https://github.com/blakeblackshear/frigate/assets/569905/d6788a15-0eeb-4427-a8d4-80b93cae3d74">
 </div>
 
 ### 内置遮罩和区域编辑器
+
 <div>
 <img width="800" alt="内置遮罩和区域编辑器" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
 </div>
 
-
 ## 翻译
+
 我们使用 [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) 平台提供翻译支持，欢迎参与进来一起完善。
 
-
 ## 非官方中文讨论社区
-欢迎加入中文讨论QQ群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
+
+欢迎加入中文讨论 QQ 群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
 
 Bilibili：https://space.bilibili.com/3546894915602564
 
-
 ## 中文社区赞助商
+
 [![EdgeOne](https://edgeone.ai/media/34fe3a45-492d-4ea4-ae5d-ea1087ca7b4b.png)](https://edgeone.ai/zh?from=github)
 本项目 CDN 加速及安全防护由 Tencent EdgeOne 赞助
+
+---
+
+**Copyright © 2025 Frigate LLC.**

TRADEMARK.md

@@ -0,0 +1,58 @@
+# Trademark Policy
+
+**Last Updated:** November 2025
+
+This document outlines the policy regarding the use of the trademarks associated with the Frigate NVR project.
+
+## 1. Our Trademarks
+
+The following terms and visual assets are trademarks (the "Marks") of **Frigate LLC**:
+
+- **Frigate™**
+- **Frigate NVR™**
+- **Frigate+™**
+- **The Frigate Logo**
+
+**Note on Common Law 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
+
+The software in this repository is licensed under the [MIT License](LICENSE).
+
+**Crucial Distinction:**
+
+- 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 LLC.
+
+## 3. Acceptable Use
+
+You may use the Marks without prior written permission in the following specific contexts:
+
+- **Referential Use:** To truthfully refer to the software (e.g., _"I use Frigate NVR for my home security"_).
+- **Compatibility:** To indicate that your product or project works with the software (e.g., _"MyPlugin for Frigate NVR"_ or _"Compatible with Frigate"_).
+- **Commentary:** In news articles, blog posts, or tutorials discussing the software.
+
+## 4. Prohibited Use
+
+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 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`).
+
+## 5. The Logo
+
+The Frigate logo (the bird icon) is a visual trademark.
+
+- You generally **cannot** use the logo on your own website or product packaging without permission.
+- If you are building a dashboard or integration that interfaces with Frigate, you may use the logo only to represent the Frigate node/service, provided it does not imply you _are_ Frigate.
+
+## 6. Questions & Permissions
+
+If you are unsure if your intended use violates this policy, or if you wish to request a specific license to use the Marks (e.g., for a partnership), please contact us at:
+
+**help@frigate.video**

docker/main/build_pysqlite3.sh

@@ -5,21 +5,27 @@ set -euxo pipefail
 SQLITE3_VERSION="3.46.1"
 PYSQLITE3_VERSION="0.5.3"
 
+# Install libsqlite3-dev if not present (needed for some base images like NVIDIA TensorRT)
+if ! dpkg -l | grep -q libsqlite3-dev; then
+  echo "Installing libsqlite3-dev for compilation..."
+  apt-get update && apt-get install -y libsqlite3-dev && rm -rf /var/lib/apt/lists/*
+fi
+
 # Fetch the pre-built sqlite amalgamation instead of building from source
 if [[ ! -d "sqlite" ]]; then
   mkdir sqlite
   cd sqlite
-  
+
   # Download the pre-built amalgamation from sqlite.org
   # For SQLite 3.46.1, the amalgamation version is 3460100
   SQLITE_AMALGAMATION_VERSION="3460100"
-  
+
   wget https://www.sqlite.org/2024/sqlite-amalgamation-${SQLITE_AMALGAMATION_VERSION}.zip -O sqlite-amalgamation.zip
   unzip sqlite-amalgamation.zip
   mv sqlite-amalgamation-${SQLITE_AMALGAMATION_VERSION}/* .
   rmdir sqlite-amalgamation-${SQLITE_AMALGAMATION_VERSION}
   rm sqlite-amalgamation.zip
-  
+
   cd ../
 fi
 

docker/main/install_deps.sh

@@ -145,6 +145,6 @@ rm -rf /var/lib/apt/lists/*
 
 # Install yq, for frigate-prepare and go2rtc echo source
 curl -fsSL \
-    "https://github.com/mikefarah/yq/releases/download/v4.33.3/yq_linux_$(dpkg --print-architecture)" \
+    "https://github.com/mikefarah/yq/releases/download/v4.48.2/yq_linux_$(dpkg --print-architecture)" \
     --output /usr/local/bin/yq
 chmod +x /usr/local/bin/yq

docker/main/install_memryx.sh

@@ -2,9 +2,9 @@
 set -e
 
 # Download the MxAccl for Frigate github release
-wget https://github.com/memryx/mx_accl_frigate/archive/refs/heads/main.zip -O /tmp/mxaccl.zip
+wget https://github.com/memryx/mx_accl_frigate/archive/refs/tags/v2.1.0.zip -O /tmp/mxaccl.zip
 unzip /tmp/mxaccl.zip -d /tmp
-mv /tmp/mx_accl_frigate-main /opt/mx_accl_frigate
+mv /tmp/mx_accl_frigate-2.1.0 /opt/mx_accl_frigate
 rm /tmp/mxaccl.zip
 
 # Install Python dependencies

docker/main/requirements-wheels.txt

@@ -56,7 +56,7 @@ pywebpush == 2.0.*
 # alpr
 pyclipper == 1.3.*
 shapely == 2.0.*
-Levenshtein==0.26.*
+rapidfuzz==3.12.*
 # HailoRT Wheels
 appdirs==1.4.*
 argcomplete==2.0.*
@@ -81,3 +81,5 @@ librosa==0.11.*
 soundfile==0.13.*
 # DeGirum detector
 degirum == 0.16.*
+# Memory profiling
+memray == 1.15.*

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

@@ -320,6 +320,12 @@ http {
                 add_header Cache-Control "public";
             }
 
+            location /fonts/ {
+                access_log off;
+                expires 1y;
+                add_header Cache-Control "public";
+            }
+
             location /locales/ {
                 access_log off;
                 add_header Cache-Control "public";

docker/memryx/user_installation.sh

@@ -24,10 +24,13 @@ echo "Adding MemryX GPG key and repository..."
 wget -qO- https://developer.memryx.com/deb/memryx.asc | sudo tee /etc/apt/trusted.gpg.d/memryx.asc >/dev/null
 echo 'deb https://developer.memryx.com/deb stable main' | sudo tee /etc/apt/sources.list.d/memryx.list >/dev/null
 
-# Update and install memx-drivers
+# Update and install specific SDK 2.1 packages
-echo "Installing memx-drivers..."
+echo "Installing MemryX SDK 2.1 packages..."
 sudo apt update
-sudo apt install -y memx-drivers
+sudo apt install -y memx-drivers=2.1.* memx-accl=2.1.* mxa-manager=2.1.*
+
+# Hold packages to prevent automatic upgrades
+sudo apt-mark hold memx-drivers memx-accl mxa-manager
 
 # ARM-specific board setup
 if [[ "$arch" == "aarch64" || "$arch" == "arm64" ]]; then
@@ -37,11 +40,5 @@ fi
 
 echo -e "\n\n\033[1;31mYOU MUST RESTART YOUR COMPUTER NOW\033[0m\n\n"
 
-# Install other runtime packages
+echo "MemryX SDK 2.1 installation complete!"
-packages=("memx-accl" "mxa-manager")
-for pkg in "${packages[@]}"; do
-    echo "Installing $pkg..."
-    sudo apt install -y "$pkg"
-done
 
-echo "MemryX installation complete!"

docker/rocm/Dockerfile

@@ -3,7 +3,6 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 ARG ROCM=1
-ARG AMDGPU=gfx900
 ARG HSA_OVERRIDE_GFX_VERSION
 ARG HSA_OVERRIDE
 
@@ -11,11 +10,10 @@ ARG HSA_OVERRIDE
 FROM wget AS rocm
 
 ARG ROCM
-ARG AMDGPU
 
 RUN apt update -qq && \
     apt install -y wget gpg && \
-    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.0.2/ubuntu/jammy/amdgpu-install_7.0.2.70002-1_all.deb && \
+    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.1.1/ubuntu/jammy/amdgpu-install_7.1.1.70101-1_all.deb && \
     apt install -y ./rocm.deb && \
     apt update && \
     apt install -qq -y rocm
@@ -36,7 +34,10 @@ FROM deps AS deps-prelim
 COPY docker/rocm/debian-backports.sources /etc/apt/sources.list.d/debian-backports.sources
 RUN apt-get update && \
     apt-get install -y libnuma1 && \
-    apt-get install -qq -y -t bookworm-backports mesa-va-drivers mesa-vulkan-drivers
+    apt-get install -qq -y -t bookworm-backports mesa-va-drivers mesa-vulkan-drivers && \
+    # Install C++ standard library headers for HIPRTC kernel compilation fallback
+    apt-get install -qq -y libstdc++-12-dev && \
+    rm -rf /var/lib/apt/lists/*
 
 WORKDIR /opt/frigate
 COPY --from=rootfs / /
@@ -54,12 +55,14 @@ RUN pip3 uninstall -y onnxruntime \
 FROM scratch AS rocm-dist
 
 ARG ROCM
-ARG AMDGPU
 
 COPY --from=rocm /opt/rocm-$ROCM/bin/rocminfo /opt/rocm-$ROCM/bin/migraphx-driver /opt/rocm-$ROCM/bin/
-COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*$AMDGPU* /opt/rocm-$ROCM/share/miopen/db/
+# Copy MIOpen database files for gfx10xx and gfx11xx only (RDNA2/RDNA3)
-COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx908* /opt/rocm-$ROCM/share/miopen/db/
+COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx10* /opt/rocm-$ROCM/share/miopen/db/
-COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*$AMDGPU* /opt/rocm-$ROCM/lib/rocblas/library/
+COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx11* /opt/rocm-$ROCM/share/miopen/db/
+# Copy rocBLAS library files for gfx10xx and gfx11xx only
+COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*gfx10* /opt/rocm-$ROCM/lib/rocblas/library/
+COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*gfx11* /opt/rocm-$ROCM/lib/rocblas/library/
 COPY --from=rocm /opt/rocm-dist/ /
 
 #######################################################################

docker/rocm/requirements-wheels-rocm.txt

@@ -1 +1 @@
-onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.0.2/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl
+onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.1.0/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl

docker/rocm/rocm.hcl

@@ -1,8 +1,5 @@
-variable "AMDGPU" {
-  default = "gfx900"
-}
 variable "ROCM" {
-  default = "7.0.2"
+  default = "7.1.1"
 }
 variable "HSA_OVERRIDE_GFX_VERSION" {
   default = ""
@@ -38,7 +35,6 @@ target rocm {
   }
   platforms = ["linux/amd64"]
   args = {
-    AMDGPU = AMDGPU,
     ROCM = ROCM,
     HSA_OVERRIDE_GFX_VERSION = HSA_OVERRIDE_GFX_VERSION,
     HSA_OVERRIDE = HSA_OVERRIDE

docker/rocm/rocm.mk

@@ -1,53 +1,15 @@
 BOARDS += rocm
 
-# AMD/ROCm is chunky so we build couple of smaller images for specific chipsets
-ROCM_CHIPSETS:=gfx900:9.0.0 gfx1030:10.3.0 gfx1100:11.0.0
-
 local-rocm: version
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=frigate:latest-rocm-$(word 1,$(subst :, ,$(chipset))) \
-			--load \
-	&&) true
-	
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
 	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
 		--set rocm.tags=frigate:latest-rocm \
 		--load
 
 build-rocm: version
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
-	&&) true
-
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
 	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
 		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm
 
 push-rocm: build-rocm
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
-			--push \
-	&&) true
-
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
 	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
 		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm \
 		--push

docker/tensorrt/Dockerfile.amd64

@@ -21,7 +21,7 @@ FROM deps AS frigate-tensorrt
 ARG PIP_BREAK_SYSTEM_PACKAGES
 
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
-    pip3 uninstall -y onnxruntime tensorflow-cpu \
+    pip3 uninstall -y onnxruntime \
     && pip3 install -U /deps/trt-wheels/*.whl
 
 COPY --from=rootfs / /

docker/tensorrt/Dockerfile.arm64

@@ -112,7 +112,7 @@ RUN apt-get update \
     && apt-get install -y protobuf-compiler libprotobuf-dev \
     && rm -rf /var/lib/apt/lists/*
 RUN --mount=type=bind,source=docker/tensorrt/requirements-models-arm64.txt,target=/requirements-tensorrt-models.txt \
-    pip3 wheel --wheel-dir=/trt-model-wheels -r /requirements-tensorrt-models.txt
+    pip3 wheel --wheel-dir=/trt-model-wheels --no-deps -r /requirements-tensorrt-models.txt
 
 FROM wget AS jetson-ffmpeg
 ARG DEBIAN_FRONTEND
@@ -145,7 +145,8 @@ COPY --from=trt-wheels /etc/TENSORRT_VER /etc/TENSORRT_VER
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
     --mount=type=bind,from=trt-model-wheels,source=/trt-model-wheels,target=/deps/trt-model-wheels \
     pip3 uninstall -y onnxruntime \
-    && pip3 install -U /deps/trt-wheels/*.whl /deps/trt-model-wheels/*.whl \
+    && pip3 install -U /deps/trt-wheels/*.whl \
+    && pip3 install -U /deps/trt-model-wheels/*.whl \
     && ldconfig
 
 WORKDIR /opt/frigate/

docker/tensorrt/requirements-amd64.txt

@@ -13,7 +13,6 @@ nvidia_cusolver_cu12==11.6.3.*; platform_machine == 'x86_64'
 nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64'
 nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64'
 nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64'
-tensorflow==2.19.*; platform_machine == 'x86_64'
 onnx==1.16.*; platform_machine == 'x86_64'
 onnxruntime-gpu==1.22.*; platform_machine == 'x86_64'
 protobuf==3.20.3; platform_machine == 'x86_64'

docker/tensorrt/requirements-arm64.txt

@@ -1 +1,2 @@
 cuda-python == 12.6.*; platform_machine == 'aarch64'
+numpy == 1.26.*; platform_machine == 'aarch64'

docs/docs/configuration/advanced.md

@@ -25,7 +25,7 @@ Examples of available modules are:
 
 - `frigate.app`
 - `frigate.mqtt`
-- `frigate.object_detection`
+- `frigate.object_detection.base`
 - `detector.<detector_name>`
 - `watchdog.<camera_name>`
 - `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
@@ -53,6 +53,17 @@ environment_vars:
   VARIABLE_NAME: variable_value
 ```
 
+#### TensorFlow Thread Configuration
+
+If you encounter thread creation errors during classification model training, you can limit TensorFlow's thread usage:
+
+```yaml
+environment_vars:
+  TF_INTRA_OP_PARALLELISM_THREADS: "2" # Threads within operations (0 = use default)
+  TF_INTER_OP_PARALLELISM_THREADS: "2" # Threads between operations (0 = use default)
+  TF_DATASET_THREAD_POOL_SIZE: "2" # Data pipeline threads (0 = use default)
+```
+
 ### `database`
 
 Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
@@ -247,7 +258,7 @@ curl -X POST http://frigate_host:5000/api/config/save -d @config.json
 if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
 
 ```bash
-yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
+yq -o=json '.' config.yaml | curl -X POST 'http://frigate_host:5000/api/config/save?save_option=saveonly' --data-binary @-
 ```
 
 ### Via Command Line

docs/docs/configuration/audio_detectors.md

@@ -75,7 +75,13 @@ audio:
 
 ### Audio Transcription
 
-Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI’s open-source Whisper models via `faster-whisper`. To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.
+Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI’s open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
+
+Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.
+
+#### Configuration
+
+To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.
 
 ```yaml
 audio_transcription:
@@ -144,4 +150,28 @@ In order to use transcription and translation for past events, you must enable a
 
 The transcribed/translated speech will appear in the description box in the Tracked Object Details pane. If Semantic Search is enabled, embeddings are generated for the transcription text and are fully searchable using the description search type.
 
-Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.
+:::note
+
+Only one `speech` event may be transcribed at a time. Frigate does not automatically transcribe `speech` events or implement a queue for long-running transcription model inference.
+
+:::
+
+Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a supported Nvidia GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.
+
+#### FAQ
+
+1. Why doesn't Frigate automatically transcribe all `speech` events?
+
+   Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That’s a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.
+
+   Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
+
+   If you hear speech that’s actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
+
+   Other options are being considered for future versions of Frigate to add transcription options that support external `whisper` Docker containers. A single transcription service could then be shared by Frigate and other applications (for example, Home Assistant Voice), and run on more powerful machines when available.
+
+2. Why don't you save live transcription text and use that for `speech` events?
+
+   There’s no guarantee that a `speech` event is even created from the exact audio that went through the transcription model. Live transcription and `speech` event creation are **separate, asynchronous processes**. Even when both are correctly configured, trying to align the **precise start and end time of a speech event** with whatever audio the model happened to be processing at that moment is unreliable.
+
+   Automatically persisting that data would often result in **misaligned, partial, or irrelevant transcripts**, while still incurring all of the CPU, storage, and privacy costs of transcription. That’s why Frigate treats transcription as an **explicit, user-initiated action** rather than an automatic side-effect of every `speech` event.

docs/docs/configuration/custom_classification/object_classification.md

@@ -10,7 +10,6 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
 
 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.
-When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
 
 ## Classes
 
@@ -36,6 +35,15 @@ For object classification:
   - Ideal when multiple attributes can coexist independently.
   - Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
 
+## Assignment Requirements
+
+Sub labels and attributes are only assigned when both conditions are met:
+
+1. **Threshold**: Each classification attempt must have a confidence score that meets or exceeds the configured `threshold` (default: `0.8`).
+2. **Class Consensus**: After at least 3 classification attempts, 60% of attempts must agree on the same class label. If the consensus class is `none`, no assignment is made.
+
+This two-step verification prevents false positives by requiring consistent predictions across multiple frames before assigning a sub label or attribute.
+
 ## Example use cases
 
 ### Sub label
@@ -67,14 +75,18 @@ classification:
 
 ## Training the model
 
-Creating and training the model is done within the Frigate UI using the `Classification` page.
+Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
 
-### Getting Started
+### 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. Include a `none` class for objects that don't fit any specific category.
+
+### Step 2: Assign Training Examples
+
+The system will automatically generate example images from detected objects matching your selected label. You'll be guided through each class one at a time to select which images represent that class. Any images not assigned to a specific class will automatically be assigned to `none` when you complete the last class. Once all images are processed, training will begin automatically.
 
 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.
 
-// TODO add this section once UI is implemented. Explain process of selecting objects and curating training examples.
-
 ### Improving the Model
 
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.

docs/docs/configuration/custom_classification/state_classification.md

@@ -10,7 +10,6 @@ State classification allows you to train a custom MobileNetV2 classification mod
 State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
 
 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.
-When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
 
 ## Classes
 
@@ -49,15 +48,27 @@ classification:
 
 ## Training the model
 
-Creating and training the model is done within the Frigate UI using the `Classification` page.
+Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
 
-### Getting Started
+### Step 1: Name and Define
 
-When choosing a portion of the camera frame for state classification, it is important to make the crop tight around the area of interest to avoid extra signals unrelated to what is being classified.
+Enter a name for your model and define at least 2 classes (states) that represent mutually exclusive states. For example, `open` and `closed` for a door, or `on` and `off` for lights.
 
-// TODO add this section once UI is implemented. Explain process of selecting a crop.
+### Step 2: Select the Crop Area
+
+Choose one or more cameras and draw a rectangle over the area of interest for each camera. The crop should be tight around the region you want to classify to avoid extra signals unrelated to what is being classified. You can drag and resize the rectangle to adjust the crop area.
+
+### Step 3: Assign Training Examples
+
+The system will automatically generate example images from your camera feeds. You'll be guided through each class one at a time to select which images represent that state.
+
+**Important**: All images must be assigned to a state before training can begin. This includes images that may not be optimal, such as when people temporarily block the view, sun glare is present, or other distractions occur. Assign these images to the state that is actually present (based on what you know the state to be), not based on the distraction. This training helps the model correctly identify the state even when such conditions occur during inference.
+
+Once all images are assigned, training will begin automatically.
 
 ### Improving the Model
 
 - **Problem framing**: Keep classes visually distinct and state-focused (e.g., `open`, `closed`, `unknown`). Avoid combining object identity with state in a single model unless necessary.
-- **Data collection**: Use the model’s Recent Classifications tab to gather balanced examples across times of day and weather.
+- **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.

docs/docs/configuration/genai.md

@@ -70,7 +70,7 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
 genai:
   provider: ollama
   base_url: http://localhost:11434
-  model: llava:7b
+  model: qwen3-vl:4b
 ```
 
 ## Google Gemini

docs/docs/configuration/genai/config.md

@@ -35,19 +35,18 @@ Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger s
 
 :::tip
 
-If you are trying to use a single model for Frigate and HomeAssistant, it will need to support vision and tools calling. https://github.com/skye-harris/ollama-modelfiles contains optimized model configs for this task.
+If you are trying to use a single model for Frigate and HomeAssistant, it will need to support vision and tools calling. qwen3-VL supports vision and tools simultaneously in Ollama.
 
 :::
 
 The following models are recommended:
 
-| Model             | Notes                                                       |
+| Model             | Notes                                                                |
-| ----------------- | ----------------------------------------------------------- |
+| ----------------- | -------------------------------------------------------------------- |
-| `qwen3-vl`        | Strong visual and situational understanding                 |
+| `qwen3-vl`        | Strong visual and situational understanding, higher vram requirement |
-| `Intern3.5VL`     | Relatively fast with good vision comprehension              |
+| `Intern3.5VL`     | Relatively fast with good vision comprehension                       |
-| `gemma3`          | Strong frame-to-frame understanding, slower inference times |
+| `gemma3`          | Strong frame-to-frame understanding, slower inference times          |
-| `qwen2.5-vl`      | Fast but capable model with good vision comprehension       |
+| `qwen2.5-vl`      | Fast but capable model with good vision comprehension                |
-| `llava-phi3`      | Lightweight and fast model with vision comprehension        |
 
 :::note
 

docs/docs/configuration/genai/review_summaries.md

@@ -111,3 +111,9 @@ review:
 ## 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.
+
+### Requesting Reports Programmatically
+
+Review reports can be requested via the [API](/integrations/api#review-summarization) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
+
+For Home Assistant users, there is a built-in service (`frigate.review_summarize`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -5,7 +5,7 @@ title: Enrichments
 
 # Enrichments
 
-Some of Frigate's enrichments can use a discrete GPU / NPU for accelerated processing.
+Some of Frigate's enrichments can use a discrete GPU or integrated GPU for accelerated processing.
 
 ## Requirements
 
@@ -18,8 +18,10 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
 - **Intel**
 
   - OpenVINO will automatically be detected and used for enrichments in the default Frigate image.
+  - **Note:** Intel NPUs have limited model support for enrichments. GPU is recommended for enrichments when available.
 
 - **Nvidia**
+
   - Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
   - Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
 

docs/docs/configuration/license_plate_recognition.md

@@ -3,18 +3,18 @@ id: license_plate_recognition
 title: License Plate Recognition (LPR)
 ---
 
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a known name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
 
 When a plate is recognized, the details are:
 
-- Added as a `sub_label` (if known) or the `recognized_license_plate` field (if unknown) to a tracked object.
+- Added as a `sub_label` (if [known](#matching)) or the `recognized_license_plate` field (if unknown) to a tracked object.
-- Viewable in the Review Item Details pane in Review (sub labels).
+- Viewable in the Details pane in Review/History.
 - Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
 - Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` (known) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
-- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if known) and `plate`.
+- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
 
 ## Model Requirements
 
@@ -31,6 +31,7 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
 ## Minimum System Requirements
 
 License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
+
 ## Configuration
 
 License plate recognition is disabled by default. Enable it in your config file:
@@ -73,8 +74,8 @@ Fine-tune the LPR feature using these optional parameters at the global level of
   - Default: `small`
   - This can be `small` or `large`.
   - The `small` model is fast and identifies groups of Latin and Chinese characters.
-  - The `large` model identifies Latin characters only, but uses an enhanced text detector and is more capable at finding characters on multi-line plates. It is significantly slower than the `small` model. Note that using the `large` model does not improve _text recognition_, but it may improve _text detection_.
+  - The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. It is significantly slower than the `small` model.
-  - For most users, the `small` model is recommended.
+  - If your country or region does not use multi-line plates, you should use the `small` model as performance is much better for single-line plates.
 
 ### Recognition
 
@@ -106,23 +107,23 @@ Fine-tune the LPR feature using these optional parameters at the global level of
 
 ### Normalization Rules
 
-- **`replace_rules`**: List of regex replacement rules to normalize detected plates. These rules are applied sequentially. Each rule must have a `pattern` (which can be a string or a regex, prepended by `r`) and `replacement` (a string, which also supports [backrefs](https://docs.python.org/3/library/re.html#re.sub) like `\1`). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').
+- **`replace_rules`**: List of regex replacement rules to normalize detected plates. These rules are applied sequentially and are applied _before_ the `format` regex, if specified. Each rule must have a `pattern` (which can be a string or a regex, prepended by `r`) and `replacement` (a string, which also supports [backrefs](https://docs.python.org/3/library/re.html#re.sub) like `\1`). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').
 
 These rules must be defined at the global level of your `lpr` config.
 
 ```yaml
 lpr:
   replace_rules:
-    - pattern: r'[%#*?]' # Remove noise symbols
+    - pattern: "[%#*?]" # Remove noise symbols
       replacement: ""
-    - pattern: r'[= ]' # Normalize = or space to dash
+    - pattern: "[= ]" # Normalize = or space to dash
       replacement: "-"
     - pattern: "O" # Swap 'O' to '0' (common OCR error)
       replacement: "0"
-    - pattern: r'I' # Swap 'I' to '1'
+    - pattern: "I" # Swap 'I' to '1'
       replacement: "1"
-    - pattern: r'(\w{3})(\w{3})' # Split 6 chars into groups (e.g., ABC123 → ABC-123)
+    - pattern: '(\w{3})(\w{3})' # Split 6 chars into groups (e.g., ABC123 → ABC-123) - use single quotes to preserve backslashes
-      replacement: r'\1-\2'
+      replacement: '\1-\2'
 ```
 
 - Rules fire in order: In the example above: clean noise first, then separators, then swaps, then splits.
@@ -177,7 +178,7 @@ lpr:
 
 :::note
 
-If you want to detect cars on cameras but don't want to use resources to run LPR on those cars, you should disable LPR for those specific cameras.
+If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
 
 ```yaml
 cameras:
@@ -305,7 +306,7 @@ With this setup:
 - Review items will always be classified as a `detection`.
 - Snapshots will always be saved.
 - Zones and object masks are **not** used.
-- The `frigate/events` MQTT topic will **not** publish tracked object updates with the license plate bounding box and score, though `frigate/reviews` will publish if recordings are enabled. If a plate is recognized as a known plate, publishing will occur with an updated `sub_label` field. If characters are recognized, publishing will occur with an updated `recognized_license_plate` field.
+- The `frigate/events` MQTT topic will **not** publish tracked object updates with the license plate bounding box and score, though `frigate/reviews` will publish if recordings are enabled. If a plate is recognized as a [known](#matching) plate, publishing will occur with an updated `sub_label` field. If characters are recognized, publishing will occur with an updated `recognized_license_plate` field.
 - License plate snapshots are saved at the highest-scoring moment and appear in Explore.
 - Debug view will not show `license_plate` bounding boxes.
 
@@ -373,9 +374,19 @@ Use `match_distance` to allow small character mismatches. Alternatively, define
 
 Start with ["Why isn't my license plate being detected and recognized?"](#why-isnt-my-license-plate-being-detected-and-recognized). If you are still having issues, work through these steps.
 
-1. Enable debug logs to see exactly what Frigate is doing.
+1. Start with a simplified LPR config.
 
-   - Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only keep this enabled when necessary.
+   - Remove or comment out everything in your LPR config, including `min_area`, `min_plate_length`, `format`, `known_plates`, or `enhancement` values so that the only values left are `enabled` and `debug_save_plates`. This will run LPR with Frigate's default values.
+
+     ```yaml
+     lpr:
+       enabled: true
+       debug_save_plates: true
+     ```
+
+2. Enable debug logs to see exactly what Frigate is doing.
+
+   - Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only keep this enabled when necessary. Restart Frigate after this change.
 
      ```yaml
      logger:
@@ -384,7 +395,7 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
          frigate.data_processing.common.license_plate: debug
      ```
 
-2. Ensure your plates are being _detected_.
+3. Ensure your plates are being _detected_.
 
    If you are using a Frigate+ or `license_plate` detecting model:
 
@@ -397,7 +408,7 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
    - Watch the debug logs for messages from the YOLOv9 plate detector.
    - You may need to adjust your `detection_threshold` if your plates are not being detected.
 
-3. Ensure the characters on detected plates are being _recognized_.
+4. Ensure the characters on detected plates are being _recognized_.
 
    - Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
    - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.

docs/docs/configuration/live.md

@@ -178,6 +178,8 @@ To use the Reolink Doorbell with two way talk, you should use the [recommended R
 
 As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability.
 
+To prevent go2rtc from blocking other applications from accessing your camera's two-way audio, you must configure your stream with `#backchannel=0`. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
+
 ### Streaming options on camera group dashboards
 
 Frigate provides a dialog in the Camera Group Edit pane with several options for streaming on a camera group's dashboard. These settings are _per device_ and are saved in your device's local storage.
@@ -214,6 +216,42 @@ For restreamed cameras, go2rtc remains active but does not use system resources
 
 Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
 
+### Live player error messages
+
+When your browser runs into problems playing back your camera streams, it will log short error messages to the browser console. They indicate playback, codec, or network issues on the client/browser side, not something server side with Frigate itself. Below are the common messages you may see and simple actions you can take to try to resolve them.
+
+- **startup**
+
+  - What it means: The player failed to initialize or connect to the live stream (network or startup error).
+  - What to try: Reload the Live view or click _Reset_. Verify `go2rtc` is running and the camera stream is reachable. Try switching to a different stream from the Live UI dropdown (if available) or use a different browser.
+
+  - Possible console messages from the player code:
+
+    - `Error opening MediaSource.`
+    - `Browser reported a network error.`
+    - `Max error count ${errorCount} exceeded.` (the numeric value will vary)
+
+- **mse-decode**
+
+  - What it means: The browser reported a decoding error while trying to play the stream, which usually is a result of a codec incompatibility or corrupted frames.
+  - What to try: Check the browser console for the supported and negotiated codecs. Ensure your camera/restream is using H.264 video and AAC audio (these are the most compatible). If your camera uses a non-standard audio codec, configure `go2rtc` to transcode the stream to AAC. Try another browser (some browsers have stricter MSE/codec support) and, for iPhone, ensure you're on iOS 17.1 or newer.
+
+  - Possible console messages from the player code:
+
+    - `Safari cannot open MediaSource.`
+    - `Safari reported InvalidStateError.`
+    - `Safari reported decoding errors.`
+
+- **stalled**
+
+  - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
+  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
+
+  - Possible console messages from the player code:
+
+    - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
+    - `Media playback has stalled after <n> seconds due to insufficient buffering or a network interruption.` (the seconds value will vary)
+
 ## Live view FAQ
 
 1. **Why don't I have audio in my Live view?**
@@ -277,3 +315,38 @@ Note that disabling a camera through the config file (`enabled: False`) removes
 7. **My camera streams have lots of visual artifacts / distortion.**
 
    Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
+
+8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
+
+   Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
+
+   To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
+
+   Example: Resolutions from two streams
+
+   - Mismatched (may cause aspect ratio switching on the dashboard):
+
+     - Live/go2rtc stream: 1920x1080 (16:9)
+     - Detect stream: 640x352 (~1.82:1, not 16:9)
+
+   - Matched (prevents switching):
+     - Live/go2rtc stream: 1920x1080 (16:9)
+     - Detect stream: 640x360 (16:9)
+
+   You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
+
+   ```yaml
+   cameras:
+     front_door:
+       detect:
+         width: 640
+         height: 360 # set this to 360 instead of 352
+       ffmpeg:
+         inputs:
+           - path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
+             roles:
+               - record
+           - path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
+             roles:
+               - detect
+   ```

docs/docs/configuration/masks.md

@@ -28,7 +28,6 @@ To create a poly mask:
 5. Click the plus icon under the type of mask or zone you would like to create
 6. Click on the camera's latest image to create the points for a masked area. Click the first point again to close the polygon.
 7. When you've finished creating your mask, press Save.
-8. Restart Frigate to apply your changes.
 
 Your config file will be updated with the relative coordinates of the mask/zone:
 

docs/docs/configuration/object_detectors.md

@@ -3,6 +3,8 @@ id: object_detectors
 title: Object Detectors
 ---
 
+import CommunityBadge from '@site/src/components/CommunityBadge';
+
 # Supported Hardware
 
 :::info
@@ -11,10 +13,10 @@ Frigate supports multiple different detectors that work on different types of ha
 
 **Most Hardware**
 
-- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
+- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
-- [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
+- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
-- [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
+- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
 
 **AMD**
 
@@ -34,16 +36,16 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
 
-**Nvidia Jetson**
+**Nvidia Jetson** <CommunityBadge />
 
 - [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
 - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.
 
-**Rockchip**
+**Rockchip** <CommunityBadge />
 
 - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
 
-**Synaptics**
+**Synaptics** <CommunityBadge />
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
 
@@ -67,12 +69,10 @@ Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8
 
 ## Edge TPU Detector
 
-The Edge TPU detector type runs a TensorFlow Lite model utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
+The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
 
 The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
 
-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`.
-
 :::tip
 
 See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edge TPU is not detected.
@@ -144,6 +144,46 @@ detectors:
     device: pci
 ```
 
+### EdgeTPU Supported Models
+
+| Model                                 | Notes                                       |
+| ------------------------------------- | ------------------------------------------- |
+| [MobileNet v2](#ssdlite-mobilenet-v2) | Default model                               |
+| [YOLOv9](#yolo-v9)                    | More accurate but slower than default model |
+
+#### 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`.
+
+A Tensorflow Lite is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an INT8 precision model.
+
+#### YOLO v9
+
+[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>
+
+After placing the downloaded files for the tflite model and labels in your config folder, you can use the following configuration:
+
+```yaml
+detectors:
+  coral:
+    type: edgetpu
+    device: usb
+
+model:
+  model_type: yolo-generic
+  width: 320 # <--- should match the imgsize of the model, typically 320
+  height: 320 # <--- should match the imgsize of the model, typically 320
+  path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
+  labelmap_path: /labelmap/labels-coco-17.txt
+```
+
+Note that the labelmap uses a subset of the complete COCO label set that has only 17 objects.
+
+</details>
+
 ---
 
 ## Hailo-8
@@ -261,6 +301,8 @@ OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will al
 
 :::tip
 
+**NPU + GPU Systems:** If you have both NPU and GPU available (Intel Core Ultra processors), use NPU for object detection and GPU for enrichments (semantic search, face recognition, etc.) for best performance and compatibility.
+
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
 
 ```yaml
@@ -283,7 +325,7 @@ detectors:
 | [RF-DETR](#rf-detr)                   | ✅  | ✅  | Requires XE iGPU or Arc                                      |
 | [YOLO-NAS](#yolo-nas)                 | ✅  | ✅  |                                                              |
 | [MobileNet v2](#ssdlite-mobilenet-v2) | ✅  | ✅  | Fast and lightweight model, less accurate than larger models |
-| [YOLOX](#yolox)                       | ✅  |  ?  |                                                              |
+| [YOLOX](#yolox)                       | ✅  | ?   |                                                              |
 | [D-FINE](#d-fine)                     | ❌  | ❌  |                                                              |
 
 #### SSDLite MobileNet v2
@@ -360,7 +402,7 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
 
 :::warning
 
-If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
+If you are using a Frigate+ model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
 
 :::
 
@@ -700,7 +742,7 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
 
 :::warning
 
-If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
+If you are using a Frigate+ model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
 
 :::
 
@@ -960,7 +1002,6 @@ model:
   # path: /config/yolov9.zip
   # The .zip file must contain:
   # ├── yolov9.dfp          (a file ending with .dfp)
-  # └── yolov9_post.onnx    (optional; only if the model includes a cropped post-processing network)
 ```
 
 #### YOLOX

docs/docs/configuration/reference.md

@@ -123,7 +123,7 @@ auth:
   # Optional: Refresh time in seconds (default: shown below)
   # When the session is going to expire in less time than this setting,
   # it will be refreshed back to the session_length.
-  refresh_time: 43200 # 12 hours
+  refresh_time: 1800 # 30 minutes
   # Optional: Rate limiting for login failures to help prevent brute force
   # login attacks (default: shown below)
   # See the docs for more information on valid values
@@ -246,7 +246,7 @@ birdseye:
 # Optional: ffmpeg configuration
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
-  # Optional: ffmpeg binry path (default: shown below)
+  # Optional: ffmpeg binary path (default: shown below)
   # can also be set to `7.0` or `5.0` to specify one of the included versions
   # or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
   path: "default"
@@ -700,16 +700,54 @@ genai:
 # Optional: Configuration for audio transcription
 # NOTE: only the enabled option can be overridden at the camera level
 audio_transcription:
-  # Optional: Enable license plate recognition (default: shown below)
+  # Optional: Enable live and speech event audio transcription (default: shown below)
   enabled: False
-  # Optional: The device to run the models on (default: shown below)
+  # Optional: The device to run the models on for live transcription. (default: shown below)
   device: CPU
-  # Optional: Set the model size used for transcription. (default: shown below)
+  # Optional: Set the model size used for live transcription. (default: shown below)
   model_size: small
   # Optional: Set the language used for transcription translation. (default: shown below)
   # List of language codes: https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
   language: en
 
+# Optional: Configuration for classification models
+classification:
+  # Optional: Configuration for bird classification
+  bird:
+    # Optional: Enable bird classification (default: shown below)
+    enabled: False
+    # Optional: Minimum classification score required to be considered a match (default: shown below)
+    threshold: 0.9
+  custom:
+    # Required: name of the classification model
+    model_name:
+      # Optional: Enable running the model (default: shown below)
+      enabled: True
+      # Optional: Name of classification model (default: shown below)
+      name: None
+      # Optional: Classification score threshold to change the state (default: shown below)
+      threshold: 0.8
+      # Optional: Number of classification attempts to save in the recent classifications tab (default: shown below)
+      # NOTE: Defaults to 200 for object classification and 100 for state classification if not specified
+      save_attempts: None
+      # Optional: Object classification configuration
+      object_config:
+        # Required: Object types to classify
+        objects: [dog]
+        # Optional: Type of classification that is applied (default: shown below)
+        classification_type: sub_label
+      # Optional: State classification configuration
+      state_config:
+        # Required: Cameras to run classification on
+        cameras:
+          camera_name:
+            # Required: Crop of image frame on this camera to run classification on
+            crop: [0, 180, 220, 400]
+        # Optional: If classification should be run when motion is detected in the crop (default: shown below)
+        motion: False
+        # Optional: Interval to run classification on in seconds (default: shown below)
+        interval: None
+
 # Optional: Restream configuration
 # Uses https://github.com/AlexxIT/go2rtc (v1.9.10)
 # NOTE: The default go2rtc API port (1984) must be used,
@@ -810,6 +848,8 @@ cameras:
       # NOTE: This must be different than any camera names, but can match with another zone on another
       #       camera.
       front_steps:
+        # Optional: A friendly name or descriptive text for the zones
+        friendly_name: ""
         # Required: List of x,y coordinates to define the polygon of the zone.
         # NOTE: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
         coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
@@ -871,7 +911,7 @@ cameras:
       user: admin
       # Optional: password for login.
       password: admin
-      # Optional: Skip TLS verification from the ONVIF server (default: shown below)
+      # Optional: Skip TLS verification and disable digest authentication for the ONVIF server (default: shown below)
       tls_insecure: False
       # Optional: Ignores time synchronization mismatches between the camera and the server during authentication.
       # Using NTP on both ends is recommended and this should only be set to True in a "safe" environment due to the security risk it represents.
@@ -962,10 +1002,6 @@ ui:
   #    full: 8:15:22 PM Mountain Standard Time
   # (default: shown below).
   time_style: medium
-  # Optional: Ability to manually override the date / time styling to use strftime format
-  # https://www.gnu.org/software/libc/manual/html_node/Formatting-Calendar-Time.html
-  # possible values are shown above (default: not set)
-  strftime_fmt: "%Y/%m/%d %H:%M"
   # Optional: Set the unit system to either "imperial" or "metric" (default: metric)
   # Used in the UI and in MQTT topics
   unit_system: metric

docs/docs/configuration/restream.md

@@ -24,11 +24,12 @@ birdseye:
   restream: True
 ```
 
-:::tip 
+:::tip
 
-To improve connection speed when using Birdseye via restream you can enable a small idle heartbeat by setting `birdseye.idle_heartbeat_fps` to a low value (e.g. `1–2`).  This makes Frigate periodically push the last frame even when no motion is detected, reducing initial connection latency.
+To improve connection speed when using Birdseye via restream you can enable a small idle heartbeat by setting `birdseye.idle_heartbeat_fps` to a low value (e.g. `1–2`). This makes Frigate periodically push the last frame even when no motion is detected, reducing initial connection latency.
 
 :::
+
 ### Securing Restream With Authentication
 
 The go2rtc restream can be secured with RTSP based username / password authentication. Ex:
@@ -159,6 +160,31 @@ go2rtc:
 
 See [this comment](https://github.com/AlexxIT/go2rtc/issues/1217#issuecomment-2242296489) for more information.
 
+## Preventing go2rtc from blocking two-way audio {#two-way-talk-restream}
+
+For cameras that support two-way talk, go2rtc will automatically establish an audio output backchannel when connecting to an RTSP stream. This backchannel blocks access to the camera's audio output for two-way talk functionality, preventing both Frigate and other applications from using it.
+
+To prevent this, you must configure two separate stream instances:
+
+1. One stream instance with `#backchannel=0` for Frigate's viewing, recording, and detection (prevents go2rtc from establishing the blocking backchannel)
+2. A second stream instance without `#backchannel=0` for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
+
+Configuration example:
+
+```yaml
+go2rtc:
+  streams:
+    front_door:
+      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#backchannel=0
+    front_door_twoway:
+      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+```
+
+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.
+
 ## 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:
@@ -169,4 +195,4 @@ NOTE: The output will need to be passed with two curly braces `{{output}}`
 go2rtc:
   streams:
     stream1: exec:ffmpeg -hide_banner -re -stream_loop -1 -i /media/BigBuckBunny.mp4 -c copy -rtsp_transport tcp -f rtsp {{output}}
-```
+```

docs/docs/configuration/semantic_search.md

@@ -78,7 +78,7 @@ Switching between V1 and V2 requires reindexing your embeddings. The embeddings
 
 ### GPU Acceleration
 
-The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU / NPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
+The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
 
 ```yaml
 semantic_search:
@@ -90,7 +90,7 @@ semantic_search:
 
 :::info
 
-If the correct build is used for your GPU / NPU and the `large` model is configured, then the GPU / NPU will be detected and used automatically.
+If the correct build is used for your GPU / NPU and the `large` model is configured, then the GPU will be detected and used automatically.
 Specify the `device` option to target a specific GPU in a multi-GPU system (see [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)).
 If you do not specify a device, the first available GPU will be used.
 
@@ -141,7 +141,7 @@ Triggers are best configured through the Frigate UI.
      Check the `Add Attribute` box to add the trigger's internal ID (e.g., "red_car_alert") to a data attribute on the tracked object that can be processed via the API or MQTT.
 5. Save the trigger to update the configuration and store the embedding in the database.
 
-When a trigger fires, the UI highlights the trigger with a blue dot for 3 seconds for easy identification.
+When a trigger fires, the UI highlights the trigger with a blue dot for 3 seconds for easy identification. Additionally, the UI will show the last date/time and tracked object ID that activated your trigger. The last triggered timestamp is not saved to the database or persisted through restarts of Frigate.
 
 ### Usage and Best Practices
 

docs/docs/configuration/zones.md

@@ -27,6 +27,7 @@ cameras:
           - entire_yard
     zones:
       entire_yard:
+        friendly_name: Entire yard # You can use characters from any language text
         coordinates: ...
 ```
 
@@ -44,8 +45,10 @@ cameras:
           - edge_yard
     zones:
       edge_yard:
+        friendly_name: Edge yard # You can use characters from any language text
         coordinates: ...
       inner_yard:
+        friendly_name: Inner yard # You can use characters from any language text
         coordinates: ...
 ```
 
@@ -59,6 +62,7 @@ cameras:
         - entire_yard
     zones:
       entire_yard:
+        friendly_name: Entire yard
         coordinates: ...
 ```
 
@@ -82,6 +86,7 @@ cameras:
 
 Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. Objects will be tracked for any `person` that enter anywhere in the yard, and for cars only if they enter the street.
 
+
 ### Zone Loitering
 
 Sometimes objects are expected to be passing through a zone, but an object loitering in an area is unexpected. Zones can be configured to have a minimum loitering time after which the object will be considered in the zone.

docs/docs/frigate/hardware.md

@@ -3,6 +3,8 @@ id: hardware
 title: Recommended hardware
 ---
 
+import CommunityBadge from '@site/src/components/CommunityBadge';
+
 ## Cameras
 
 Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
@@ -59,7 +61,7 @@ Frigate supports multiple different detectors that work on different types of ha
 
   - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector)
 
-- [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
+- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
   - [Supports many model architectures](../../configuration/object_detectors#memryx-mx3)
   - Runs best with tiny, small, or medium-size models
 
@@ -84,32 +86,26 @@ Frigate supports multiple different detectors that work on different types of ha
 
 **Nvidia**
 
-- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs and Jetson devices.
+- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs to provide efficient object detection.
+
   - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
   - Runs well with any size models including large
 
-**Rockchip**
+- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
+
+**Rockchip** <CommunityBadge />
 
 - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs to provide efficient object detection.
   - [Supports limited model architectures](../../configuration/object_detectors#choosing-a-model)
   - Runs best with tiny or small size models
   - Runs efficiently on low power hardware
 
-**Synaptics**
+**Synaptics** <CommunityBadge />
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
 
 :::
 
-### Synaptics
-
-- **Synaptics** Default model is **mobilenet**
-
-| Name             | Synaptics SL1680 Inference Time |
-| ---------------- | ------------------------------- |
-| ssd mobilenet    | ~ 25 ms                         |
-| yolov5m          | ~ 118 ms                        |
-
 ### Hailo-8
 
 Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
@@ -163,7 +159,7 @@ Inference speeds vary greatly depending on the CPU or GPU used, some known examp
 | Intel HD 530   | 15 - 35 ms                 |                                                   |                           |                        | Can only run one detector instance |
 | Intel HD 620   | 15 - 25 ms                 |                                                   | 320: ~ 35 ms              |                        |                                    |
 | Intel HD 630   | ~ 15 ms                    |                                                   | 320: ~ 30 ms              |                        |                                    |
-| Intel UHD 730  | ~ 10 ms                    |                                                   | 320: ~ 19 ms 640: ~ 54 ms |                        |                                    |
+| Intel UHD 730  | ~ 10 ms                    | t-320: 14ms s-320: 24ms t-640: 34ms s-640: 65ms   | 320: ~ 19 ms 640: ~ 54 ms |                        |                                    |
 | Intel UHD 770  | ~ 15 ms                    | t-320: ~ 16 ms s-320: ~ 20 ms s-640: ~ 40 ms      | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
 | Intel N100     | ~ 15 ms                    | s-320: 30 ms                                      | 320: ~ 25 ms              |                        | Can only run one detector instance |
 | Intel N150     | ~ 15 ms                    | t-320: 16 ms s-320: 24 ms                         |                           |                        |                                    |
@@ -261,7 +257,7 @@ Inference speeds may vary depending on the host platform. The above data was mea
 
 ### Nvidia Jetson
 
-Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
+Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
 
 Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
 
@@ -282,6 +278,15 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
 
 The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
 
+### Synaptics
+
+- **Synaptics** Default model is **mobilenet**
+
+| Name          | Synaptics SL1680 Inference Time |
+| ------------- | ------------------------------- |
+| ssd mobilenet | ~ 25 ms                         |
+| yolov5m       | ~ 118 ms                        |
+
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/installation.md

@@ -56,7 +56,7 @@ services:
     volumes:
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+      - type: tmpfs # Recommended: 1GB of memory
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -135,6 +135,7 @@ Finally, configure [hardware object detection](/configuration/object_detectors#h
 ### MemryX MX3
 
 The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVMe SSD), and supports a variety of configurations:
+
 - x86 (Intel/AMD) PCs
 - Raspberry Pi 5
 - Orange Pi 5 Plus/Max
@@ -142,7 +143,6 @@ The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVM
 
 #### Configuration
 
-
 #### Installation
 
 To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/get_started/hardware_setup.html).
@@ -156,7 +156,7 @@ Then follow these steps for installing the correct driver/runtime configuration:
 
 #### Setup
 
-To set up Frigate, follow the default installation instructions, for example:   `ghcr.io/blakeblackshear/frigate:stable`
+To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
 
 Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
 
@@ -173,7 +173,7 @@ In your `docker-compose.yml`, also add:
 privileged: true
 
 volumes:
-    /run/mxa_manager:/run/mxa_manager
+  - /run/mxa_manager:/run/mxa_manager
 ```
 
 If you can't use Docker Compose, you can run the container with something similar to this:
@@ -310,7 +310,7 @@ services:
       - /etc/localtime:/etc/localtime:ro
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+      - type: tmpfs # Recommended: 1GB of memory
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -411,7 +411,7 @@ To install make sure you have the [community app plugin here](https://forums.unr
 
 ## Proxmox
 
-[According to Proxmox documentation](https://pve.proxmox.com/pve-docs/pve-admin-guide.html#chapter_pct) it is recommended that you run application containers like Frigate inside a Proxmox QEMU VM. This will give you all the advantages of application containerization, while also providing the benefits that VMs offer, such as strong isolation from the host and the ability to live-migrate, which otherwise isn’t possible with containers.
+[According to Proxmox documentation](https://pve.proxmox.com/pve-docs/pve-admin-guide.html#chapter_pct) it is recommended that you run application containers like Frigate inside a Proxmox QEMU VM. This will give you all the advantages of application containerization, while also providing the benefits that VMs offer, such as strong isolation from the host and the ability to live-migrate, which otherwise isn’t possible with containers. Ensure that ballooning is **disabled**, especially if you are passing through a GPU to the VM.
 
 :::warning
 

docs/docs/guides/configuring_go2rtc.md

@@ -113,7 +113,8 @@ section.
 
 1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
 2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
+3. If your camera supports two-way talk, you must configure your stream with `#backchannel=0` to prevent go2rtc from blocking other applications from accessing the camera's audio output. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
 
 ## Homekit Configuration
 
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to share export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
+To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to share export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.

docs/docs/integrations/mqtt.md

@@ -159,11 +159,44 @@ Message published for updates to tracked object metadata, for example:
 }
 ```
 
+#### Object Classification Update
+
+Message published when [object classification](/configuration/custom_classification/object_classification) reaches consensus on a classification result.
+
+**Sub label type:**
+
+```json
+{
+  "type": "classification",
+  "id": "1607123955.475377-mxklsc",
+  "camera": "front_door_cam",
+  "timestamp": 1607123958.748393,
+  "model": "person_classifier",
+  "sub_label": "delivery_person",
+  "score": 0.87
+}
+```
+
+**Attribute type:**
+
+```json
+{
+  "type": "classification",
+  "id": "1607123955.475377-mxklsc",
+  "camera": "front_door_cam",
+  "timestamp": 1607123958.748393,
+  "model": "helmet_detector",
+  "attribute": "yes",
+  "score": 0.92
+}
+```
+
 ### `frigate/reviews`
 
-Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated. 
+Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated.
 
 An `update` with the same ID will be published when:
+
 - The severity changes from `detection` to `alert`
 - Additional objects are detected
 - An object is recognized via face, lpr, etc.
@@ -308,6 +341,11 @@ Publishes transcribed text for audio detected on this camera.
 
 **NOTE:** Requires audio detection and transcription to be enabled
 
+### `frigate/<camera_name>/classification/<model_name>`
+
+Publishes the current state detected by a state classification model for the camera. The topic name includes the model name as configured in your classification settings.
+The published value is the detected state class name (e.g., `open`, `closed`, `on`, `off`). The state is only published when it changes, helping to reduce unnecessary MQTT traffic.
+
 ### `frigate/<camera_name>/enabled/set`
 
 Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.

docs/docs/troubleshooting/memory.md

@@ -0,0 +1,129 @@
+---
+id: memory
+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.
+
+## Enabling Memory Profiling
+
+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:
+
+```bash
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
+```
+
+### Module Names
+
+Frigate processes are named using a module-based naming scheme. Common module names include:
+
+- `frigate.review_segment_manager` - Review segment processing
+- `frigate.recording_manager` - Recording management
+- `frigate.capture` - Camera capture processes (all cameras with this module name)
+- `frigate.process` - Camera processing/tracking (all cameras with this module name)
+- `frigate.output` - Output processing
+- `frigate.audio_manager` - Audio processing
+- `frigate.embeddings` - Embeddings processing
+
+You can also specify the full process name (including camera-specific identifiers) if you want to profile a specific camera:
+
+```bash
+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.
+
+## How It Works
+
+1. **Binary File Creation**: When profiling is enabled, memray creates a binary file (`.bin`) in `/config/memray_reports/` that is updated continuously in real-time as the process runs.
+
+2. **Automatic HTML Generation**: On normal process exit, Frigate automatically:
+
+   - Stops memray tracking
+   - Generates an HTML flamegraph report
+   - Saves it to `/config/memray_reports/<module_name>.html`
+
+3. **Crash Recovery**: If a process crashes (SIGKILL, segfault, etc.), the binary file is preserved with all data up to the crash point. You can manually generate the HTML report from the binary file.
+
+## Viewing Reports
+
+### Automatic Reports
+
+After a process exits normally, you'll find HTML reports in `/config/memray_reports/`. Open these files in a web browser to view interactive flamegraphs showing memory usage patterns.
+
+### Manual Report Generation
+
+If a process crashes or you want to generate a report from an existing binary file, you can manually create the HTML report:
+
+```bash
+memray flamegraph /config/memray_reports/<module_name>.bin
+```
+
+This will generate an HTML file that you can open in your browser.
+
+## Understanding the Reports
+
+Memray flamegraphs show:
+
+- **Memory allocations over time**: See where memory is being allocated in your code
+- **Call stacks**: Understand the full call chain leading to allocations
+- **Memory hotspots**: Identify functions or code paths that allocate the most memory
+- **Memory leaks**: Spot patterns where memory is allocated but not freed
+
+The interactive HTML reports allow you to:
+
+- Zoom into specific time ranges
+- Filter by function names
+- View detailed allocation information
+- Export data for further analysis
+
+## Best Practices
+
+1. **Profile During Issues**: Enable profiling when you're experiencing memory issues, not all the time, as it adds some overhead.
+
+2. **Profile Specific Modules**: Instead of profiling everything, focus on the modules you suspect are causing issues.
+
+3. **Let Processes Run**: Allow processes to run for a meaningful duration to capture representative memory usage patterns.
+
+4. **Check Binary Files**: If HTML reports aren't generated automatically (e.g., after a crash), check for `.bin` files in `/config/memray_reports/` and generate reports manually.
+
+5. **Compare Reports**: Generate reports at different times to compare memory usage patterns and identify trends.
+
+## Troubleshooting
+
+### No Reports Generated
+
+- Check that the environment variable is set correctly
+- Verify the module name matches exactly (case-sensitive)
+- Check logs for memray-related errors
+- Ensure `/config/memray_reports/` directory exists and is writable
+
+### Process Crashed Before Report Generation
+
+- Look for `.bin` files in `/config/memray_reports/`
+- Manually generate HTML reports using: `memray flamegraph <file>.bin`
+- The binary file contains all data up to the crash point
+
+### Reports Show No Data
+
+- Ensure the process ran long enough to generate meaningful data
+- 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/docusaurus.config.ts

@@ -10,7 +10,7 @@ const config: Config = {
   baseUrl: "/",
   onBrokenLinks: "throw",
   onBrokenMarkdownLinks: "warn",
-  favicon: "img/favicon.ico",
+  favicon: "img/branding/favicon.ico",
   organizationName: "blakeblackshear",
   projectName: "frigate",
   themes: [
@@ -116,8 +116,8 @@ const config: Config = {
       title: "Frigate",
       logo: {
         alt: "Frigate",
-        src: "img/logo.svg",
+        src: "img/branding/logo.svg",
-        srcDark: "img/logo-dark.svg",
+        srcDark: "img/branding/logo-dark.svg",
       },
       items: [
         {
@@ -170,7 +170,7 @@ const config: Config = {
           ],
         },
       ],
-      copyright: `Copyright © ${new Date().getFullYear()} Blake Blackshear`,
+      copyright: `Copyright © ${new Date().getFullYear()} Frigate LLC`,
     },
   },
   plugins: [

docs/package.json

@@ -18,14 +18,14 @@
   },
   "dependencies": {
     "@docusaurus/core": "^3.7.0",
-    "@docusaurus/plugin-content-docs": "^3.6.3",
+    "@docusaurus/plugin-content-docs": "^3.7.0",
     "@docusaurus/preset-classic": "^3.7.0",
-    "@docusaurus/theme-mermaid": "^3.6.3",
+    "@docusaurus/theme-mermaid": "^3.7.0",
     "@inkeep/docusaurus": "^2.0.16",
     "@mdx-js/react": "^3.1.0",
     "clsx": "^2.1.1",
-    "docusaurus-plugin-openapi-docs": "^4.3.1",
+    "docusaurus-plugin-openapi-docs": "^4.5.1",
-    "docusaurus-theme-openapi-docs": "^4.3.1",
+    "docusaurus-theme-openapi-docs": "^4.5.1",
     "prism-react-renderer": "^2.4.1",
     "raw-loader": "^4.0.2",
     "react": "^18.3.1",
@@ -44,9 +44,9 @@
     ]
   },
   "devDependencies": {
-    "@docusaurus/module-type-aliases": "^3.4.0",
+    "@docusaurus/module-type-aliases": "^3.7.0",
-    "@docusaurus/types": "^3.4.0",
+    "@docusaurus/types": "^3.7.0",
-    "@types/react": "^18.3.7"
+    "@types/react": "^18.3.27"
   },
   "engines": {
     "node": ">=18.0"

docs/sidebars.ts

@@ -131,6 +131,7 @@ const sidebars: SidebarsConfig = {
       "troubleshooting/recordings",
       "troubleshooting/gpu",
       "troubleshooting/edgetpu",
+      "troubleshooting/memory",
     ],
     Development: [
       "development/contributing",

docs/src/components/CommunityBadge/index.jsx

@@ -0,0 +1,23 @@
+import React from "react";
+
+export default function CommunityBadge() {
+  return (
+    <span
+      title="This detector is maintained by community members who provide code, maintenance, and support. See the contributing boards documentation for more information."
+      style={{
+        display: "inline-block",
+        backgroundColor: "#f1f3f5",
+        color: "#24292f",
+        fontSize: "11px",
+        fontWeight: 600,
+        padding: "2px 6px",
+        borderRadius: "3px",
+        border: "1px solid #d1d9e0",
+        marginLeft: "4px",
+        cursor: "help",
+      }}
+    >
+      Community Supported
+    </span>
+  );
+}

docs/src/components/LanguageAlert/styles.module.css

@@ -1,13 +1,18 @@
 .alert {
-    padding: 12px;
+  padding: 12px;
-    background: #fff8e6;
+  background: #fff8e6;
-    border-bottom: 1px solid #ffd166;
+  border-bottom: 1px solid #ffd166;
-    text-align: center;
+  text-align: center;
-    font-size: 15px;
+  font-size: 15px;
-  }
+}
-  
+
-  .alert a {
+[data-theme="dark"] .alert {
-    color: #1890ff;
+  background: #3b2f0b;
-    font-weight: 500;
+  border-bottom: 1px solid #665c22;
-    margin-left: 6px;
+}
-  }
+
+.alert a {
+  color: #1890ff;
+  font-weight: 500;
+  margin-left: 6px;
+}

docs/static/img/branding/LICENSE.md

@@ -0,0 +1,30 @@
+# COPYRIGHT AND TRADEMARK NOTICE
+
+The images, logos, and icons contained in this directory (the "Brand Assets") are
+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 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
+   following purposes:
+   a. To execute the software interface on a local machine.
+   b. To identify the software in documentation or reviews (nominative use).
+
+3. RESTRICTIONS
+   You may NOT:
+   a. Use these Brand Assets to represent a derivative work (fork) as an official
+   product of Frigate LLC.
+   b. Use these Brand Assets in a way that implies endorsement, sponsorship, or
+   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
+version of the software, you must replace these Brand Assets with your own
+original content.
+
+ALL RIGHTS RESERVED.
+Copyright (c) 2025 Frigate LLC.

frigate/api/app.py

@@ -23,7 +23,7 @@ from markupsafe import escape
 from peewee import SQL, fn, operator
 from pydantic import ValidationError
 
-from frigate.api.auth import require_role
+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.tags import Tags
@@ -37,7 +37,6 @@ from frigate.stats.prometheus import get_metrics, update_metrics
 from frigate.util.builtin import (
     clean_camera_user_pass,
     flatten_config_data,
-    get_tz_modifiers,
     process_config_query_string,
     update_yaml_file_bulk,
 )
@@ -48,6 +47,7 @@ from frigate.util.services import (
     restart_frigate,
     vainfo_hwaccel,
 )
+from frigate.util.time import get_tz_modifiers
 from frigate.version import VERSION
 
 logger = logging.getLogger(__name__)
@@ -56,29 +56,33 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.app])
 
 
-@router.get("/", response_class=PlainTextResponse)
+@router.get(
+    "/", response_class=PlainTextResponse, dependencies=[Depends(allow_public())]
+)
 def is_healthy():
     return "Frigate is running. Alive and healthy!"
 
 
-@router.get("/config/schema.json")
+@router.get("/config/schema.json", dependencies=[Depends(allow_public())])
 def config_schema(request: Request):
     return Response(
         content=request.app.frigate_config.schema_json(), media_type="application/json"
     )
 
 
-@router.get("/version", response_class=PlainTextResponse)
+@router.get(
+    "/version", response_class=PlainTextResponse, dependencies=[Depends(allow_public())]
+)
 def version():
     return VERSION
 
 
-@router.get("/stats")
+@router.get("/stats", dependencies=[Depends(allow_any_authenticated())])
 def stats(request: Request):
     return JSONResponse(content=request.app.stats_emitter.get_latest_stats())
 
 
-@router.get("/stats/history")
+@router.get("/stats/history", dependencies=[Depends(allow_any_authenticated())])
 def stats_history(request: Request, keys: str = None):
     if keys:
         keys = keys.split(",")
@@ -86,7 +90,7 @@ def stats_history(request: Request, keys: str = None):
     return JSONResponse(content=request.app.stats_emitter.get_stats_history(keys))
 
 
-@router.get("/metrics")
+@router.get("/metrics", dependencies=[Depends(allow_any_authenticated())])
 def metrics(request: Request):
     """Expose Prometheus metrics endpoint and update metrics with latest stats"""
     # Retrieve the latest statistics and update the Prometheus metrics
@@ -103,7 +107,7 @@ def metrics(request: Request):
     return Response(content=content, media_type=content_type)
 
 
-@router.get("/config")
+@router.get("/config", dependencies=[Depends(allow_any_authenticated())])
 def config(request: Request):
     config_obj: FrigateConfig = request.app.frigate_config
     config: dict[str, dict[str, Any]] = config_obj.model_dump(
@@ -179,7 +183,37 @@ def config(request: Request):
     return JSONResponse(content=config)
 
 
-@router.get("/config/raw")
+@router.get("/config/raw_paths", dependencies=[Depends(require_role(["admin"]))])
+def config_raw_paths(request: Request):
+    """Admin-only endpoint that returns camera paths and go2rtc streams without credential masking."""
+    config_obj: FrigateConfig = request.app.frigate_config
+
+    raw_paths = {"cameras": {}, "go2rtc": {"streams": {}}}
+
+    # Extract raw camera ffmpeg input paths
+    for camera_name, camera in config_obj.cameras.items():
+        raw_paths["cameras"][camera_name] = {
+            "ffmpeg": {
+                "inputs": [
+                    {"path": input.path, "roles": input.roles}
+                    for input in camera.ffmpeg.inputs
+                ]
+            }
+        }
+
+    # Extract raw go2rtc stream URLs
+    go2rtc_config = config_obj.go2rtc.model_dump(
+        mode="json", warnings="none", exclude_none=True
+    )
+    for stream_name, stream in go2rtc_config.get("streams", {}).items():
+        if stream is None:
+            continue
+        raw_paths["go2rtc"]["streams"][stream_name] = stream
+
+    return JSONResponse(content=raw_paths)
+
+
+@router.get("/config/raw", dependencies=[Depends(allow_any_authenticated())])
 def config_raw():
     config_file = find_config_file()
 
@@ -403,12 +437,13 @@ def config_set(request: Request, body: AppConfigSetBody):
                     settings,
                 )
             else:
-                # Handle nested config updates (e.g., config/classification/custom/{name})
+                # Generic handling for global config updates
                 settings = config.get_nested_object(body.update_topic)
-                if settings:
+
-                    request.app.config_publisher.publisher.publish(
+                # Publish None for removal, actual config for add/update
-                        body.update_topic, settings
+                request.app.config_publisher.publisher.publish(
-                    )
+                    body.update_topic, settings
+                )
 
     return JSONResponse(
         content=(
@@ -421,7 +456,7 @@ def config_set(request: Request, body: AppConfigSetBody):
     )
 
 
-@router.get("/vainfo")
+@router.get("/vainfo", dependencies=[Depends(allow_any_authenticated())])
 def vainfo():
     vainfo = vainfo_hwaccel()
     return JSONResponse(
@@ -441,12 +476,16 @@ def vainfo():
     )
 
 
-@router.get("/nvinfo")
+@router.get("/nvinfo", dependencies=[Depends(allow_any_authenticated())])
 def nvinfo():
     return JSONResponse(content=get_nvidia_driver_info())
 
 
-@router.get("/logs/{service}", tags=[Tags.logs])
+@router.get(
+    "/logs/{service}",
+    tags=[Tags.logs],
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def logs(
     service: str = Path(enum=["frigate", "nginx", "go2rtc"]),
     download: Optional[str] = None,
@@ -554,7 +593,7 @@ def restart():
     )
 
 
-@router.get("/labels")
+@router.get("/labels", dependencies=[Depends(allow_any_authenticated())])
 def get_labels(camera: str = ""):
     try:
         if camera:
@@ -572,7 +611,7 @@ def get_labels(camera: str = ""):
     return JSONResponse(content=labels)
 
 
-@router.get("/sub_labels")
+@router.get("/sub_labels", dependencies=[Depends(allow_any_authenticated())])
 def get_sub_labels(split_joined: Optional[int] = None):
     try:
         events = Event.select(Event.sub_label).distinct()
@@ -603,7 +642,7 @@ def get_sub_labels(split_joined: Optional[int] = None):
     return JSONResponse(content=sub_labels)
 
 
-@router.get("/plus/models")
+@router.get("/plus/models", dependencies=[Depends(allow_any_authenticated())])
 def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
     if not request.app.frigate_config.plus_api.is_active():
         return JSONResponse(
@@ -645,7 +684,9 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
     return JSONResponse(content=validModels)
 
 
-@router.get("/recognized_license_plates")
+@router.get(
+    "/recognized_license_plates", dependencies=[Depends(allow_any_authenticated())]
+)
 def get_recognized_license_plates(split_joined: Optional[int] = None):
     try:
         query = (
@@ -679,7 +720,7 @@ def get_recognized_license_plates(split_joined: Optional[int] = None):
     return JSONResponse(content=recognized_license_plates)
 
 
-@router.get("/timeline")
+@router.get("/timeline", dependencies=[Depends(allow_any_authenticated())])
 def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = None):
     clauses = []
 
@@ -716,7 +757,7 @@ def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = N
     return JSONResponse(content=[t for t in timeline])
 
 
-@router.get("/timeline/hourly")
+@router.get("/timeline/hourly", dependencies=[Depends(allow_any_authenticated())])
 def hourly_timeline(params: AppTimelineHourlyQueryParameters = Depends()):
     """Get hourly summary for timeline."""
     cameras = params.cameras

frigate/api/auth.py

@@ -32,10 +32,178 @@ from frigate.models import User
 
 logger = logging.getLogger(__name__)
 
+
+def require_admin_by_default():
+    """
+    Global admin requirement dependency for all endpoints by default.
+
+    This is set as the default dependency on the FastAPI app to ensure all
+    endpoints require admin access unless explicitly overridden with
+    allow_public(), allow_any_authenticated(), or require_role().
+
+    Port 5000 (internal) always has admin role set by the /auth endpoint,
+    so this check passes automatically for internal requests.
+
+    Certain paths are exempted from the global admin check because they must
+    be accessible before authentication (login, auth) or they have their own
+    route-level authorization dependencies that handle access control.
+    """
+    # Paths that have route-level auth dependencies and should bypass global admin check
+    # These paths still have authorization - it's handled by their route-level dependencies
+    EXEMPT_PATHS = {
+        # Public auth endpoints (allow_public)
+        "/auth",
+        "/auth/first_time_login",
+        "/login",
+        "/logout",
+        # Authenticated user endpoints (allow_any_authenticated)
+        "/profile",
+        # Public info endpoints (allow_public)
+        "/",
+        "/version",
+        "/config/schema.json",
+        # Authenticated user endpoints (allow_any_authenticated)
+        "/metrics",
+        "/stats",
+        "/stats/history",
+        "/config",
+        "/config/raw",
+        "/vainfo",
+        "/nvinfo",
+        "/labels",
+        "/sub_labels",
+        "/plus/models",
+        "/recognized_license_plates",
+        "/timeline",
+        "/timeline/hourly",
+        "/recordings/storage",
+        "/recordings/summary",
+        "/recordings/unavailable",
+        "/go2rtc/streams",
+        "/event_ids",
+        "/events",
+        "/exports",
+    }
+
+    # Path prefixes that should be exempt (for paths with parameters)
+    EXEMPT_PREFIXES = (
+        "/logs/",  # /logs/{service}
+        "/review",  # /review, /review/{id}, /review/summary, /review_ids, etc.
+        "/reviews/",  # /reviews/viewed, /reviews/delete
+        "/events/",  # /events/{id}/thumbnail, /events/summary, etc. (camera-scoped)
+        "/export/",  # /export/{camera}/start/..., /export/{id}/rename, /export/{id}
+        "/go2rtc/streams/",  # /go2rtc/streams/{camera}
+        "/users/",  # /users/{username}/password (has own auth)
+        "/preview/",  # /preview/{file}/thumbnail.jpg
+        "/exports/",  # /exports/{export_id}
+        "/vod/",  # /vod/{camera_name}/...
+        "/notifications/",  # /notifications/pubkey, /notifications/register
+    )
+
+    async def admin_checker(request: Request):
+        path = request.url.path
+
+        # Check exact path matches
+        if path in EXEMPT_PATHS:
+            return
+
+        # Check prefix matches for parameterized paths
+        if path.startswith(EXEMPT_PREFIXES):
+            return
+
+        # Dynamic camera path exemption:
+        # Any path whose first segment matches a configured camera name should
+        # bypass the global admin requirement. These endpoints enforce access
+        # via route-level dependencies (e.g. require_camera_access) to ensure
+        # per-camera authorization. This allows non-admin authenticated users
+        # (e.g. viewer role) to access camera-specific resources without
+        # needing admin privileges.
+        try:
+            if path.startswith("/"):
+                first_segment = path.split("/", 2)[1]
+                if (
+                    first_segment
+                    and first_segment in request.app.frigate_config.cameras
+                ):
+                    return
+        except Exception:
+            pass
+
+        # For all other paths, require admin role
+        # Port 5000 (internal) requests have admin role set automatically
+        role = request.headers.get("remote-role")
+        if role == "admin":
+            return
+
+        raise HTTPException(
+            status_code=403,
+            detail="Access denied. A user with the admin role is required.",
+        )
+
+    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.
+
+    Use this for endpoints that should be publicly accessible without
+    authentication, such as login page, health checks, or pre-auth info.
+
+    Example:
+        @router.get("/public-endpoint", dependencies=[Depends(allow_public())])
+    """
+
+    async def public_checker(request: Request):
+        return  # Always allow
+
+    return public_checker
+
+
+def allow_any_authenticated():
+    """
+    Override dependency to allow any authenticated user (bypass admin requirement).
+
+    Allows:
+    - Port 5000 internal requests (have admin role despite anonymous user)
+    - Any authenticated user with a real username (not "anonymous")
+
+    Rejects:
+    - 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):
+        # 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
+
+    return auth_checker
+
+
 router = APIRouter(tags=[Tags.auth])
 
 
-@router.get("/auth/first_time_login")
+@router.get("/auth/first_time_login", dependencies=[Depends(allow_public())])
 def first_time_login(request: Request):
     """Return whether the admin first-time login help flag is set in config.
 
@@ -143,7 +311,10 @@ def get_jwt_secret() -> str:
             )
             jwt_secret = secrets.token_hex(64)
             try:
-                with open(jwt_secret_file, "w") as f:
+                fd = os.open(
+                    jwt_secret_file, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600
+                )
+                with os.fdopen(fd, "w") as f:
                     f.write(str(jwt_secret))
             except Exception:
                 logger.warning(
@@ -188,9 +359,35 @@ def verify_password(password, password_hash):
     return secrets.compare_digest(password_hash, compare_hash)
 
 
+def validate_password_strength(password: str) -> tuple[bool, Optional[str]]:
+    """
+    Validate password strength.
+
+    Returns a tuple of (is_valid, error_message).
+    """
+    if not password:
+        return False, "Password cannot be empty"
+
+    if len(password) < 8:
+        return False, "Password must be at least 8 characters long"
+
+    if not any(c.isupper() for c in password):
+        return False, "Password must contain at least one uppercase letter"
+
+    if not any(c.isdigit() for c in password):
+        return False, "Password must contain at least one digit"
+
+    if not any(c in '!@#$%^&*(),.?":{}|<>' for c in password):
+        return False, "Password must contain at least one special character"
+
+    return True, None
+
+
 def create_encoded_jwt(user, role, expiration, secret):
     return jwt.encode(
-        {"alg": "HS256"}, {"sub": user, "role": role, "exp": expiration}, secret
+        {"alg": "HS256"},
+        {"sub": user, "role": role, "exp": expiration, "iat": int(time.time())},
+        secret,
     )
 
 
@@ -352,7 +549,7 @@ def resolve_role(
 
 
 # Endpoints
-@router.get("/auth")
+@router.get("/auth", dependencies=[Depends(allow_public())])
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     proxy_config: ProxyConfig = request.app.frigate_config.proxy
@@ -451,13 +648,27 @@ def auth(request: Request):
             return fail_response
 
         # if the jwt cookie is expiring soon
-        elif jwt_source == "cookie" and expiration - JWT_REFRESH <= current_time:
+        if jwt_source == "cookie" and expiration - JWT_REFRESH <= current_time:
             logger.debug("jwt token expiring soon, refreshing cookie")
-            # ensure the user hasn't been deleted
+
+            # Check if password has been changed since token was issued
+            # If so, force re-login by rejecting the refresh
             try:
-                User.get_by_id(user)
+                user_obj = User.get_by_id(user)
+                if user_obj.password_changed_at is not None:
+                    token_iat = int(token.claims.get("iat", 0))
+                    password_changed_timestamp = int(
+                        user_obj.password_changed_at.timestamp()
+                    )
+                    if token_iat < password_changed_timestamp:
+                        logger.debug(
+                            "jwt token issued before password change, rejecting refresh"
+                        )
+                        return fail_response
             except DoesNotExist:
+                logger.debug("user not found")
                 return fail_response
+
             new_expiration = current_time + JWT_SESSION_LENGTH
             new_encoded_jwt = create_encoded_jwt(
                 user, role, new_expiration, request.app.jwt_token
@@ -478,7 +689,7 @@ def auth(request: Request):
         return fail_response
 
 
-@router.get("/profile")
+@router.get("/profile", dependencies=[Depends(allow_any_authenticated())])
 def profile(request: Request):
     username = request.headers.get("remote-user", "anonymous")
     role = request.headers.get("remote-role", "viewer")
@@ -492,7 +703,7 @@ def profile(request: Request):
     )
 
 
-@router.get("/logout")
+@router.get("/logout", dependencies=[Depends(allow_public())])
 def logout(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     response = RedirectResponse("/login", status_code=303)
@@ -503,7 +714,7 @@ def logout(request: Request):
 limiter = Limiter(key_func=get_remote_addr)
 
 
-@router.post("/login")
+@router.post("/login", dependencies=[Depends(allow_public())])
 @limiter.limit(limit_value=rateLimiter.get_limit)
 def login(request: Request, body: AppPostLoginBody):
     JWT_COOKIE_NAME = request.app.frigate_config.auth.cookie_name
@@ -578,13 +789,21 @@ def create_user(
     return JSONResponse(content={"username": body.username})
 
 
-@router.delete("/users/{username}")
+@router.delete("/users/{username}", dependencies=[Depends(require_role(["admin"]))])
-def delete_user(username: str):
+def delete_user(request: Request, username: str):
+    # Prevent deletion of the built-in admin user
+    if username == "admin":
+        return JSONResponse(
+            content={"message": "Cannot delete admin user"}, status_code=403
+        )
+
     User.delete_by_id(username)
     return JSONResponse(content={"success": True})
 
 
-@router.put("/users/{username}/password")
+@router.put(
+    "/users/{username}/password", dependencies=[Depends(allow_any_authenticated())]
+)
 async def update_password(
     request: Request,
     username: str,
@@ -606,10 +825,63 @@ async def update_password(
 
     HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
 
-    password_hash = hash_password(body.password, iterations=HASH_ITERATIONS)
+    try:
-    User.set_by_id(username, {User.password_hash: password_hash})
+        user = User.get_by_id(username)
+    except DoesNotExist:
+        return JSONResponse(content={"message": "User not found"}, status_code=404)
 
-    return JSONResponse(content={"success": True})
+    # 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"},
+                status_code=400,
+            )
+        if not verify_password(body.old_password, user.password_hash):
+            return JSONResponse(
+                content={"message": "Current password is incorrect"},
+                status_code=401,
+            )
+
+    # Validate new password strength
+    is_valid, error_message = validate_password_strength(body.password)
+    if not is_valid:
+        return JSONResponse(
+            content={"message": error_message},
+            status_code=400,
+        )
+
+    password_hash = hash_password(body.password, iterations=HASH_ITERATIONS)
+    User.update(
+        {
+            User.password_hash: password_hash,
+            User.password_changed_at: datetime.now(),
+        }
+    ).where(User.username == username).execute()
+
+    response = JSONResponse(content={"success": True})
+
+    # If user changed their own password, issue a new JWT to keep them logged in
+    if current_username == username:
+        JWT_COOKIE_NAME = request.app.frigate_config.auth.cookie_name
+        JWT_COOKIE_SECURE = request.app.frigate_config.auth.cookie_secure
+        JWT_SESSION_LENGTH = request.app.frigate_config.auth.session_length
+
+        expiration = int(time.time()) + JWT_SESSION_LENGTH
+        encoded_jwt = create_encoded_jwt(
+            username, current_role, expiration, request.app.jwt_token
+        )
+        # Set new JWT cookie on response
+        set_jwt_cookie(
+            response, JWT_COOKIE_NAME, encoded_jwt, expiration, JWT_COOKIE_SECURE
+        )
+
+    return response
 
 
 @router.put(

frigate/api/camera.py

@@ -3,13 +3,23 @@
 import json
 import logging
 import re
+from importlib.util import find_spec
+from pathlib import Path
 from urllib.parse import quote_plus
 
+import httpx
 import requests
-from fastapi import APIRouter, Depends, Request, Response
+from fastapi import APIRouter, Depends, Query, Request, Response
 from fastapi.responses import JSONResponse
+from onvif import ONVIFCamera, ONVIFError
+from zeep.exceptions import Fault, TransportError
+from zeep.transports import AsyncTransport
 
-from frigate.api.auth import require_role
+from frigate.api.auth import (
+    allow_any_authenticated,
+    require_camera_access,
+    require_role,
+)
 from frigate.api.defs.tags import Tags
 from frigate.config.config import FrigateConfig
 from frigate.util.builtin import clean_camera_user_pass
@@ -44,7 +54,7 @@ def _is_valid_host(host: str) -> bool:
         return False
 
 
-@router.get("/go2rtc/streams")
+@router.get("/go2rtc/streams", dependencies=[Depends(allow_any_authenticated())])
 def go2rtc_streams():
     r = requests.get("http://127.0.0.1:1984/api/streams")
     if not r.ok:
@@ -60,7 +70,9 @@ def go2rtc_streams():
     return JSONResponse(content=stream_data)
 
 
-@router.get("/go2rtc/streams/{camera_name}")
+@router.get(
+    "/go2rtc/streams/{camera_name}", dependencies=[Depends(require_camera_access)]
+)
 def go2rtc_camera_stream(request: Request, camera_name: str):
     r = requests.get(
         f"http://127.0.0.1:1984/api/streams?src={camera_name}&video=all&audio=all&microphone"
@@ -155,7 +167,7 @@ def go2rtc_delete_stream(stream_name: str):
         )
 
 
-@router.get("/ffprobe")
+@router.get("/ffprobe", dependencies=[Depends(require_role(["admin"]))])
 def ffprobe(request: Request, paths: str = "", detailed: bool = False):
     path_param = paths
 
@@ -452,3 +464,537 @@ def _extract_fps(r_frame_rate: str) -> float | None:
         return round(float(num) / float(den), 2)
     except (ValueError, ZeroDivisionError):
         return None
+
+
+@router.get(
+    "/onvif/probe",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Probe ONVIF device",
+    description=(
+        "Probe an ONVIF device to determine capabilities and optionally test available stream URIs. "
+        "Query params: host (required), port (default 80), username, password, test (boolean), "
+        "auth_type (basic or digest, default basic)."
+    ),
+)
+async def onvif_probe(
+    request: Request,
+    host: str = Query(None),
+    port: int = Query(80),
+    username: str = Query(""),
+    password: str = Query(""),
+    test: bool = Query(False),
+    auth_type: str = Query("basic"),  # Add auth_type parameter
+):
+    """
+    Probe a single ONVIF device to determine capabilities.
+
+    Connects to an ONVIF device and queries for:
+    - Device information (manufacturer, model)
+    - Media profiles count
+    - PTZ support
+    - Available presets
+    - Autotracking support
+
+    Query Parameters:
+        host: Device host/IP address (required)
+        port: Device port (default 80)
+        username: ONVIF username (optional)
+        password: ONVIF password (optional)
+        test: run ffprobe on the stream (optional)
+        auth_type: Authentication type - "basic" or "digest" (default "basic")
+
+    Returns:
+        JSON with device capabilities information
+    """
+    if not host:
+        return JSONResponse(
+            content={"success": False, "message": "host parameter is required"},
+            status_code=400,
+        )
+
+    # Validate host format
+    if not _is_valid_host(host):
+        return JSONResponse(
+            content={"success": False, "message": "Invalid host format"},
+            status_code=400,
+        )
+
+    # Validate auth_type
+    if auth_type not in ["basic", "digest"]:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "auth_type must be 'basic' or 'digest'",
+            },
+            status_code=400,
+        )
+
+    onvif_camera = None
+
+    try:
+        logger.debug(f"Probing ONVIF device at {host}:{port} with {auth_type} auth")
+
+        try:
+            wsdl_base = None
+            spec = find_spec("onvif")
+            if spec and getattr(spec, "origin", None):
+                wsdl_base = str(Path(spec.origin).parent / "wsdl")
+        except Exception:
+            wsdl_base = None
+
+        onvif_camera = ONVIFCamera(
+            host, port, username or "", password or "", wsdl_dir=wsdl_base
+        )
+
+        # Configure digest authentication if requested
+        if auth_type == "digest" and username and password:
+            # Create httpx client with digest auth
+            auth = httpx.DigestAuth(username, password)
+            client = httpx.AsyncClient(auth=auth, timeout=10.0)
+
+            # Replace the transport in the zeep client
+            transport = AsyncTransport(client=client)
+
+            # Update the xaddr before setting transport
+            await onvif_camera.update_xaddrs()
+
+            # Replace transport in all services
+            if hasattr(onvif_camera, "devicemgmt"):
+                onvif_camera.devicemgmt.zeep_client.transport = transport
+            if hasattr(onvif_camera, "media"):
+                onvif_camera.media.zeep_client.transport = transport
+            if hasattr(onvif_camera, "ptz"):
+                onvif_camera.ptz.zeep_client.transport = transport
+
+            logger.debug("Configured digest authentication")
+        else:
+            await onvif_camera.update_xaddrs()
+
+        # Get device information
+        device_info = {
+            "manufacturer": "Unknown",
+            "model": "Unknown",
+            "firmware_version": "Unknown",
+        }
+        try:
+            device_service = await onvif_camera.create_devicemgmt_service()
+
+            # Update transport for device service if digest auth
+            if auth_type == "digest" and username and password:
+                auth = httpx.DigestAuth(username, password)
+                client = httpx.AsyncClient(auth=auth, timeout=10.0)
+                transport = AsyncTransport(client=client)
+                device_service.zeep_client.transport = transport
+
+            device_info_resp = await device_service.GetDeviceInformation()
+            manufacturer = getattr(device_info_resp, "Manufacturer", None) or (
+                device_info_resp.get("Manufacturer")
+                if isinstance(device_info_resp, dict)
+                else None
+            )
+            model = getattr(device_info_resp, "Model", None) or (
+                device_info_resp.get("Model")
+                if isinstance(device_info_resp, dict)
+                else None
+            )
+            firmware = getattr(device_info_resp, "FirmwareVersion", None) or (
+                device_info_resp.get("FirmwareVersion")
+                if isinstance(device_info_resp, dict)
+                else None
+            )
+            device_info.update(
+                {
+                    "manufacturer": manufacturer or "Unknown",
+                    "model": model or "Unknown",
+                    "firmware_version": firmware or "Unknown",
+                }
+            )
+        except Exception as e:
+            logger.debug(f"Failed to get device info: {e}")
+
+        # Get media profiles
+        profiles = []
+        profiles_count = 0
+        first_profile_token = None
+        ptz_config_token = None
+        try:
+            media_service = await onvif_camera.create_media_service()
+
+            # Update transport for media service if digest auth
+            if auth_type == "digest" and username and password:
+                auth = httpx.DigestAuth(username, password)
+                client = httpx.AsyncClient(auth=auth, timeout=10.0)
+                transport = AsyncTransport(client=client)
+                media_service.zeep_client.transport = transport
+
+            profiles = await media_service.GetProfiles()
+            profiles_count = len(profiles) if profiles else 0
+            if profiles and len(profiles) > 0:
+                p = profiles[0]
+                first_profile_token = getattr(p, "token", None) or (
+                    p.get("token") if isinstance(p, dict) else None
+                )
+                # Get PTZ configuration token from the profile
+                ptz_configuration = getattr(p, "PTZConfiguration", None) or (
+                    p.get("PTZConfiguration") if isinstance(p, dict) else None
+                )
+                if ptz_configuration:
+                    ptz_config_token = getattr(ptz_configuration, "token", None) or (
+                        ptz_configuration.get("token")
+                        if isinstance(ptz_configuration, dict)
+                        else None
+                    )
+        except Exception as e:
+            logger.debug(f"Failed to get media profiles: {e}")
+
+        # Check PTZ support and capabilities
+        ptz_supported = False
+        presets_count = 0
+        autotrack_supported = False
+
+        try:
+            ptz_service = await onvif_camera.create_ptz_service()
+
+            # Update transport for PTZ service if digest auth
+            if auth_type == "digest" and username and password:
+                auth = httpx.DigestAuth(username, password)
+                client = httpx.AsyncClient(auth=auth, timeout=10.0)
+                transport = AsyncTransport(client=client)
+                ptz_service.zeep_client.transport = transport
+
+            # Check if PTZ service is available
+            try:
+                await ptz_service.GetServiceCapabilities()
+                ptz_supported = True
+                logger.debug("PTZ service is available")
+            except Exception as e:
+                logger.debug(f"PTZ service not available: {e}")
+                ptz_supported = False
+
+            # Try to get presets if PTZ is supported and we have a profile
+            if ptz_supported and first_profile_token:
+                try:
+                    presets_resp = await ptz_service.GetPresets(
+                        {"ProfileToken": first_profile_token}
+                    )
+                    presets_count = len(presets_resp) if presets_resp else 0
+                    logger.debug(f"Found {presets_count} presets")
+                except Exception as e:
+                    logger.debug(f"Failed to get presets: {e}")
+                    presets_count = 0
+
+            # Check for autotracking support - requires both FOV relative movement and MoveStatus
+            if ptz_supported and first_profile_token and ptz_config_token:
+                # First check for FOV relative movement support
+                pt_r_fov_supported = False
+                try:
+                    config_request = ptz_service.create_type("GetConfigurationOptions")
+                    config_request.ConfigurationToken = ptz_config_token
+                    ptz_config = await ptz_service.GetConfigurationOptions(
+                        config_request
+                    )
+
+                    if ptz_config:
+                        # Check for pt-r-fov support
+                        spaces = getattr(ptz_config, "Spaces", None) or (
+                            ptz_config.get("Spaces")
+                            if isinstance(ptz_config, dict)
+                            else None
+                        )
+
+                        if spaces:
+                            rel_pan_tilt_space = getattr(
+                                spaces, "RelativePanTiltTranslationSpace", None
+                            ) or (
+                                spaces.get("RelativePanTiltTranslationSpace")
+                                if isinstance(spaces, dict)
+                                else None
+                            )
+
+                            if rel_pan_tilt_space:
+                                # Look for FOV space
+                                for i, space in enumerate(rel_pan_tilt_space):
+                                    uri = None
+                                    if isinstance(space, dict):
+                                        uri = space.get("URI")
+                                    else:
+                                        uri = getattr(space, "URI", None)
+
+                                    if uri and "TranslationSpaceFov" in uri:
+                                        pt_r_fov_supported = True
+                                        logger.debug(
+                                            "FOV relative movement (pt-r-fov) supported"
+                                        )
+                                        break
+
+                        logger.debug(f"PTZ config spaces: {ptz_config}")
+                except Exception as e:
+                    logger.debug(f"Failed to check FOV relative movement: {e}")
+                    pt_r_fov_supported = False
+
+                # Now check for MoveStatus support via GetServiceCapabilities
+                if pt_r_fov_supported:
+                    try:
+                        service_capabilities_request = ptz_service.create_type(
+                            "GetServiceCapabilities"
+                        )
+                        service_capabilities = await ptz_service.GetServiceCapabilities(
+                            service_capabilities_request
+                        )
+
+                        # Look for MoveStatus in the capabilities
+                        move_status_capable = False
+                        if service_capabilities:
+                            # Try to find MoveStatus key recursively
+                            def find_move_status(obj, key="MoveStatus"):
+                                if isinstance(obj, dict):
+                                    if key in obj:
+                                        return obj[key]
+                                    for v in obj.values():
+                                        result = find_move_status(v, key)
+                                        if result is not None:
+                                            return result
+                                elif hasattr(obj, key):
+                                    return getattr(obj, key)
+                                elif hasattr(obj, "__dict__"):
+                                    for v in vars(obj).values():
+                                        result = find_move_status(v, key)
+                                        if result is not None:
+                                            return result
+                                return None
+
+                            move_status_value = find_move_status(service_capabilities)
+
+                            # MoveStatus should return "true" if supported
+                            if isinstance(move_status_value, bool):
+                                move_status_capable = move_status_value
+                            elif isinstance(move_status_value, str):
+                                move_status_capable = (
+                                    move_status_value.lower() == "true"
+                                )
+
+                            logger.debug(f"MoveStatus capability: {move_status_value}")
+
+                        # Autotracking is supported if both conditions are met
+                        autotrack_supported = pt_r_fov_supported and move_status_capable
+
+                        if autotrack_supported:
+                            logger.debug(
+                                "Autotracking fully supported (pt-r-fov + MoveStatus)"
+                            )
+                        else:
+                            logger.debug(
+                                f"Autotracking not fully supported - pt-r-fov: {pt_r_fov_supported}, MoveStatus: {move_status_capable}"
+                            )
+                    except Exception as e:
+                        logger.debug(f"Failed to check MoveStatus support: {e}")
+                        autotrack_supported = False
+
+        except Exception as e:
+            logger.debug(f"Failed to probe PTZ service: {e}")
+
+        result = {
+            "success": True,
+            "host": host,
+            "port": port,
+            "manufacturer": device_info["manufacturer"],
+            "model": device_info["model"],
+            "firmware_version": device_info["firmware_version"],
+            "profiles_count": profiles_count,
+            "ptz_supported": ptz_supported,
+            "presets_count": presets_count,
+            "autotrack_supported": autotrack_supported,
+        }
+
+        # Gather RTSP candidates
+        rtsp_candidates: list[dict] = []
+        try:
+            media_service = await onvif_camera.create_media_service()
+
+            # Update transport for media service if digest auth
+            if auth_type == "digest" and username and password:
+                auth = httpx.DigestAuth(username, password)
+                client = httpx.AsyncClient(auth=auth, timeout=10.0)
+                transport = AsyncTransport(client=client)
+                media_service.zeep_client.transport = transport
+
+            if profiles_count and media_service:
+                for p in profiles or []:
+                    token = getattr(p, "token", None) or (
+                        p.get("token") if isinstance(p, dict) else None
+                    )
+                    if not token:
+                        continue
+                    try:
+                        stream_setup = {
+                            "Stream": "RTP-Unicast",
+                            "Transport": {"Protocol": "RTSP"},
+                        }
+                        stream_req = {
+                            "ProfileToken": token,
+                            "StreamSetup": stream_setup,
+                        }
+                        stream_uri_resp = await media_service.GetStreamUri(stream_req)
+                        uri = (
+                            stream_uri_resp.get("Uri")
+                            if isinstance(stream_uri_resp, dict)
+                            else getattr(stream_uri_resp, "Uri", None)
+                        )
+                        if uri:
+                            logger.debug(
+                                f"GetStreamUri returned for token {token}: {uri}"
+                            )
+                            # If credentials were provided, do NOT add the unauthenticated URI.
+                            try:
+                                if isinstance(uri, str) and uri.startswith("rtsp://"):
+                                    if username and password and "@" not in uri:
+                                        # Inject URL-encoded credentials and add only the
+                                        # authenticated version.
+                                        cred = f"{quote_plus(username)}:{quote_plus(password)}@"
+                                        injected = uri.replace(
+                                            "rtsp://", f"rtsp://{cred}", 1
+                                        )
+                                        rtsp_candidates.append(
+                                            {
+                                                "source": "GetStreamUri",
+                                                "profile_token": token,
+                                                "uri": injected,
+                                            }
+                                        )
+                                    else:
+                                        # No credentials provided or URI already contains
+                                        # credentials — add the URI as returned.
+                                        rtsp_candidates.append(
+                                            {
+                                                "source": "GetStreamUri",
+                                                "profile_token": token,
+                                                "uri": uri,
+                                            }
+                                        )
+                                else:
+                                    # Non-RTSP URIs (e.g., http-flv) — add as returned.
+                                    rtsp_candidates.append(
+                                        {
+                                            "source": "GetStreamUri",
+                                            "profile_token": token,
+                                            "uri": uri,
+                                        }
+                                    )
+                            except Exception as e:
+                                logger.debug(
+                                    f"Skipping stream URI for token {token} due to processing error: {e}"
+                                )
+                                continue
+                    except Exception:
+                        logger.debug(
+                            f"GetStreamUri failed for token {token}", exc_info=True
+                        )
+                        continue
+
+            # Add common RTSP patterns as fallback
+            if not rtsp_candidates:
+                common_paths = [
+                    "/h264",
+                    "/live.sdp",
+                    "/media.amp",
+                    "/Streaming/Channels/101",
+                    "/Streaming/Channels/1",
+                    "/stream1",
+                    "/cam/realmonitor?channel=1&subtype=0",
+                    "/11",
+                ]
+                # Use URL-encoded credentials for pattern fallback URIs when provided
+                auth_str = (
+                    f"{quote_plus(username)}:{quote_plus(password)}@"
+                    if username and password
+                    else ""
+                )
+                rtsp_port = 554
+                for path in common_paths:
+                    uri = f"rtsp://{auth_str}{host}:{rtsp_port}{path}"
+                    rtsp_candidates.append({"source": "pattern", "uri": uri})
+        except Exception:
+            logger.debug("Failed to collect RTSP candidates")
+
+        # Optionally test RTSP candidates using ffprobe_stream
+        tested_candidates = []
+        if test and rtsp_candidates:
+            for c in rtsp_candidates:
+                uri = c["uri"]
+                to_test = [uri]
+                try:
+                    if (
+                        username
+                        and password
+                        and isinstance(uri, str)
+                        and uri.startswith("rtsp://")
+                        and "@" not in uri
+                    ):
+                        cred = f"{quote_plus(username)}:{quote_plus(password)}@"
+                        cred_uri = uri.replace("rtsp://", f"rtsp://{cred}", 1)
+                        if cred_uri not in to_test:
+                            to_test.append(cred_uri)
+                except Exception:
+                    pass
+
+                for test_uri in to_test:
+                    try:
+                        probe = ffprobe_stream(
+                            request.app.frigate_config.ffmpeg, test_uri, detailed=False
+                        )
+                        print(probe)
+                        ok = probe is not None and getattr(probe, "returncode", 1) == 0
+                        tested_candidates.append(
+                            {
+                                "uri": test_uri,
+                                "source": c.get("source"),
+                                "ok": ok,
+                                "profile_token": c.get("profile_token"),
+                            }
+                        )
+                    except Exception as e:
+                        logger.debug(f"Unable to probe stream: {e}")
+                        tested_candidates.append(
+                            {
+                                "uri": test_uri,
+                                "source": c.get("source"),
+                                "ok": False,
+                                "profile_token": c.get("profile_token"),
+                            }
+                        )
+
+        result["rtsp_candidates"] = rtsp_candidates
+        if test:
+            result["rtsp_tested"] = tested_candidates
+
+        logger.debug(f"ONVIF probe successful: {result}")
+        return JSONResponse(content=result)
+
+    except ONVIFError as e:
+        logger.warning(f"ONVIF error probing {host}:{port}: {e}")
+        return JSONResponse(
+            content={"success": False, "message": "ONVIF error"},
+            status_code=400,
+        )
+    except (Fault, TransportError) as e:
+        logger.warning(f"Connection error probing {host}:{port}: {e}")
+        return JSONResponse(
+            content={"success": False, "message": "Connection error"},
+            status_code=503,
+        )
+    except Exception as e:
+        logger.warning(f"Error probing ONVIF device at {host}:{port}, {e}")
+        return JSONResponse(
+            content={"success": False, "message": "Probe failed"},
+            status_code=500,
+        )
+
+    finally:
+        # Best-effort cleanup of ONVIF camera client session
+        if onvif_camera is not None:
+            try:
+                # Check if the camera has a close method and call it
+                if hasattr(onvif_camera, "close"):
+                    await onvif_camera.close()
+            except Exception as e:
+                logger.debug(f"Error closing ONVIF camera session: {e}")

frigate/api/classification.py

@@ -31,14 +31,16 @@ 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.const import CLIPS_DIR, FACE_DIR
+from frigate.const import CLIPS_DIR, FACE_DIR, MODEL_CACHE_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event
 from frigate.util.classification import (
     collect_object_classification_examples,
     collect_state_classification_examples,
+    get_dataset_image_count,
+    read_training_metadata,
 )
-from frigate.util.path import get_event_snapshot
+from frigate.util.file import get_event_snapshot
 
 logger = logging.getLogger(__name__)
 
@@ -112,9 +114,18 @@ def reclassify_face(request: Request, body: dict = None):
     context: EmbeddingsContext = request.app.embeddings
     response = context.reprocess_face(training_file)
 
+    if not isinstance(response, dict):
+        return JSONResponse(
+            status_code=500,
+            content={
+                "success": False,
+                "message": "Could not process request.",
+            },
+        )
+
     return JSONResponse(
+        status_code=200 if response.get("success", True) else 400,
         content=response,
-        status_code=200,
     )
 
 
@@ -531,6 +542,7 @@ def transcribe_audio(request: Request, body: AudioTranscriptionBody):
             status_code=409,  # 409 Conflict
         )
     else:
+        logger.debug(f"Failed to transcribe audio, response: {response}")
         return JSONResponse(
             content={
                 "success": False,
@@ -555,23 +567,59 @@ def get_classification_dataset(name: str):
     dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "dataset")
 
     if not os.path.exists(dataset_dir):
-        return JSONResponse(status_code=200, content={})
+        return JSONResponse(
+            status_code=200, content={"categories": {}, "training_metadata": None}
+        )
 
-    for name in os.listdir(dataset_dir):
+    for category_name in os.listdir(dataset_dir):
-        category_dir = os.path.join(dataset_dir, name)
+        category_dir = os.path.join(dataset_dir, category_name)
 
         if not os.path.isdir(category_dir):
             continue
 
-        dataset_dict[name] = []
+        dataset_dict[category_name] = []
 
         for file in filter(
             lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
             os.listdir(category_dir),
         ):
-            dataset_dict[name].append(file)
+            dataset_dict[category_name].append(file)
 
-    return JSONResponse(status_code=200, content=dataset_dict)
+    # Get training metadata
+    metadata = read_training_metadata(sanitize_filename(name))
+    current_image_count = get_dataset_image_count(sanitize_filename(name))
+
+    if metadata is None:
+        training_metadata = {
+            "has_trained": False,
+            "last_training_date": None,
+            "last_training_image_count": 0,
+            "current_image_count": current_image_count,
+            "new_images_count": current_image_count,
+            "dataset_changed": current_image_count > 0,
+        }
+    else:
+        last_training_count = metadata.get("last_training_image_count", 0)
+        # Dataset has changed if count is different (either added or deleted images)
+        dataset_changed = current_image_count != last_training_count
+        # Only show positive count for new images (ignore deletions in the count display)
+        new_images_count = max(0, current_image_count - last_training_count)
+        training_metadata = {
+            "has_trained": True,
+            "last_training_date": metadata.get("last_training_date"),
+            "last_training_image_count": last_training_count,
+            "current_image_count": current_image_count,
+            "new_images_count": new_images_count,
+            "dataset_changed": dataset_changed,
+        }
+
+    return JSONResponse(
+        status_code=200,
+        content={
+            "categories": dataset_dict,
+            "training_metadata": training_metadata,
+        },
+    )
 
 
 @router.get(
@@ -662,12 +710,106 @@ def delete_classification_dataset_images(
         if os.path.isfile(file_path):
             os.unlink(file_path)
 
+    if os.path.exists(folder) and not os.listdir(folder) and category.lower() != "none":
+        os.rmdir(folder)
+
     return JSONResponse(
-        content=({"success": True, "message": "Successfully deleted faces."}),
+        content=({"success": True, "message": "Successfully deleted images."}),
         status_code=200,
     )
 
 
+@router.put(
+    "/classification/{name}/dataset/{old_category}/rename",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Rename a classification category",
+    description="""Renames a classification category for a given classification model.
+    The old category must exist and the new name must be valid. Returns a success message or an error if the name is invalid.""",
+)
+def rename_classification_category(
+    request: Request, name: str, old_category: str, body: dict = None
+):
+    config: FrigateConfig = request.app.frigate_config
+
+    if name not in config.classification.custom:
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"{name} is not a known classification model.",
+                }
+            ),
+            status_code=404,
+        )
+
+    json: dict[str, Any] = body or {}
+    new_category = sanitize_filename(json.get("new_category", ""))
+
+    if not new_category:
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": "New category name is required.",
+                }
+            ),
+            status_code=400,
+        )
+
+    old_folder = os.path.join(
+        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(old_category)
+    )
+    new_folder = os.path.join(
+        CLIPS_DIR, sanitize_filename(name), "dataset", new_category
+    )
+
+    if not os.path.exists(old_folder):
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"Category {old_category} does not exist.",
+                }
+            ),
+            status_code=404,
+        )
+
+    if os.path.exists(new_folder):
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"Category {new_category} already exists.",
+                }
+            ),
+            status_code=400,
+        )
+
+    try:
+        os.rename(old_folder, new_folder)
+        return JSONResponse(
+            content=(
+                {
+                    "success": True,
+                    "message": f"Successfully renamed category to {new_category}.",
+                }
+            ),
+            status_code=200,
+        )
+    except Exception as e:
+        logger.error(f"Error renaming category: {e}")
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": "Failed to rename category",
+                }
+            ),
+            status_code=500,
+        )
+
+
 @router.post(
     "/classification/{name}/dataset/categorize",
     response_model=GenericResponse,
@@ -723,7 +865,47 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
     os.unlink(training_file)
 
     return JSONResponse(
-        content=({"success": True, "message": "Successfully deleted faces."}),
+        content=({"success": True, "message": "Successfully categorized image."}),
+        status_code=200,
+    )
+
+
+@router.post(
+    "/classification/{name}/dataset/{category}/create",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Create an empty classification category folder",
+    description="""Creates an empty folder for a classification category.
+    This is used to create folders for categories that don't have images yet.
+    Returns a success message or an error if the name is invalid.""",
+)
+def create_classification_category(request: Request, name: str, category: str):
+    config: FrigateConfig = request.app.frigate_config
+
+    if name not in config.classification.custom:
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"{name} is not a known classification model.",
+                }
+            ),
+            status_code=404,
+        )
+
+    category_folder = os.path.join(
+        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
+    )
+
+    os.makedirs(category_folder, exist_ok=True)
+
+    return JSONResponse(
+        content=(
+            {
+                "success": True,
+                "message": f"Successfully created category folder: {category}",
+            }
+        ),
         status_code=200,
     )
 
@@ -761,7 +943,7 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
             os.unlink(file_path)
 
     return JSONResponse(
-        content=({"success": True, "message": "Successfully deleted faces."}),
+        content=({"success": True, "message": "Successfully deleted images."}),
         status_code=200,
     )
 
@@ -804,3 +986,44 @@ async def generate_object_examples(request: Request, body: GenerateObjectExample
         content={"success": True, "message": "Example generation completed"},
         status_code=200,
     )
+
+
+@router.delete(
+    "/classification/{name}",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Delete a classification model",
+    description="""Deletes a specific classification model and all its associated data.
+    Works even if the model is not in the config (e.g., partially created during wizard).
+    Returns a success message.""",
+)
+def delete_classification_model(request: Request, name: str):
+    sanitized_name = sanitize_filename(name)
+
+    # Delete the classification model's data directory in clips
+    data_dir = os.path.join(CLIPS_DIR, sanitized_name)
+    if os.path.exists(data_dir):
+        try:
+            shutil.rmtree(data_dir)
+            logger.info(f"Deleted classification data directory for {name}")
+        except Exception as e:
+            logger.debug(f"Failed to delete data directory for {name}: {e}")
+
+    # Delete the classification model's files in model_cache
+    model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
+    if os.path.exists(model_dir):
+        try:
+            shutil.rmtree(model_dir)
+            logger.info(f"Deleted classification model directory for {name}")
+        except Exception as e:
+            logger.debug(f"Failed to delete model directory for {name}: {e}")
+
+    return JSONResponse(
+        content=(
+            {
+                "success": True,
+                "message": f"Successfully deleted classification model {name}.",
+            }
+        ),
+        status_code=200,
+    )

frigate/api/defs/request/app_body.py

@@ -11,6 +11,7 @@ class AppConfigSetBody(BaseModel):
 
 class AppPutPasswordBody(BaseModel):
     password: str
+    old_password: Optional[str] = None
 
 
 class AppPostUsersBody(BaseModel):

frigate/api/defs/request/events_body.py

@@ -29,7 +29,6 @@ class EventsDescriptionBody(BaseModel):
 
 
 class EventsCreateBody(BaseModel):
-    source_type: Optional[str] = "api"
     sub_label: Optional[str] = None
     score: Optional[float] = 0
     duration: Optional[int] = 30

frigate/api/event.py

@@ -2,6 +2,7 @@
 
 import base64
 import datetime
+import json
 import logging
 import os
 import random
@@ -21,6 +22,7 @@ from peewee import JOIN, DoesNotExist, fn, operator
 from playhouse.shortcuts import model_to_dict
 
 from frigate.api.auth import (
+    allow_any_authenticated,
     get_allowed_cameras_for_filter,
     require_camera_access,
     require_role,
@@ -57,8 +59,8 @@ from frigate.const import CLIPS_DIR, TRIGGER_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event, ReviewSegment, Timeline, Trigger
 from frigate.track.object_processing import TrackedObject
-from frigate.util.builtin import get_tz_modifiers
+from frigate.util.file import get_event_thumbnail_bytes
-from frigate.util.path import get_event_thumbnail_bytes
+from frigate.util.time import get_dst_transitions, get_tz_modifiers
 
 logger = logging.getLogger(__name__)
 
@@ -68,6 +70,7 @@ router = APIRouter(tags=[Tags.events])
 @router.get(
     "/events",
     response_model=list[EventResponse],
+    dependencies=[Depends(allow_any_authenticated())],
     summary="Get events",
     description="Returns a list of events.",
 )
@@ -342,7 +345,8 @@ def events(
 @router.get(
     "/events/explore",
     response_model=list[EventResponse],
-    summary="Get summary of objects.",
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get summary of objects",
     description="""Gets a summary of objects from the database.
     Returns a list of objects with a max of `limit` objects for each label.
     """,
@@ -434,7 +438,8 @@ def events_explore(
 @router.get(
     "/event_ids",
     response_model=list[EventResponse],
-    summary="Get events by ids.",
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get events by ids",
     description="""Gets events by a list of ids.
     Returns a list of events.
     """,
@@ -467,7 +472,8 @@ async def event_ids(ids: str, request: Request):
 
 @router.get(
     "/events/search",
-    summary="Search events.",
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Search events",
     description="""Searches for events in the database.
     Returns a list of events.
     """,
@@ -807,13 +813,12 @@ def events_search(
     return JSONResponse(content=processed_events)
 
 
-@router.get("/events/summary")
+@router.get("/events/summary", dependencies=[Depends(allow_any_authenticated())])
 def events_summary(
     params: EventsSummaryQueryParams = Depends(),
     allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ):
     tz_name = params.timezone
-    hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(tz_name)
     has_clip = params.has_clip
     has_snapshot = params.has_snapshot
 
@@ -828,39 +833,98 @@ def events_summary(
     if len(clauses) == 0:
         clauses.append((True))
 
-    groups = (
+    time_range_query = (
         Event.select(
-            Event.camera,
+            fn.MIN(Event.start_time).alias("min_time"),
-            Event.label,
+            fn.MAX(Event.start_time).alias("max_time"),
-            Event.sub_label,
-            Event.data,
-            fn.strftime(
-                "%Y-%m-%d",
-                fn.datetime(
-                    Event.start_time, "unixepoch", hour_modifier, minute_modifier
-                ),
-            ).alias("day"),
-            Event.zones,
-            fn.COUNT(Event.id).alias("count"),
         )
         .where(reduce(operator.and_, clauses) & (Event.camera << allowed_cameras))
-        .group_by(
+        .dicts()
-            Event.camera,
+        .get()
-            Event.label,
-            Event.sub_label,
-            Event.data,
-            (Event.start_time + seconds_offset).cast("int") / (3600 * 24),
-            Event.zones,
-        )
     )
 
-    return JSONResponse(content=[e for e in groups.dicts()])
+    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(tz_name, min_time, max_time)
+
+    grouped: dict[tuple, dict] = {}
+
+    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_groups = (
+            Event.select(
+                Event.camera,
+                Event.label,
+                Event.sub_label,
+                Event.data,
+                fn.strftime(
+                    "%Y-%m-%d",
+                    fn.datetime(
+                        Event.start_time,
+                        "unixepoch",
+                        period_hour_modifier,
+                        period_minute_modifier,
+                    ),
+                ).alias("day"),
+                Event.zones,
+                fn.COUNT(Event.id).alias("count"),
+            )
+            .where(
+                reduce(operator.and_, clauses)
+                & (Event.camera << allowed_cameras)
+                & (Event.start_time >= period_start)
+                & (Event.start_time <= period_end)
+            )
+            .group_by(
+                Event.camera,
+                Event.label,
+                Event.sub_label,
+                Event.data,
+                (Event.start_time + period_offset).cast("int") / (3600 * 24),
+                Event.zones,
+            )
+            .namedtuples()
+        )
+
+        for g in period_groups:
+            key = (
+                g.camera,
+                g.label,
+                g.sub_label,
+                json.dumps(g.data, sort_keys=True) if g.data is not None else None,
+                g.day,
+                json.dumps(g.zones, sort_keys=True) if g.zones is not None else None,
+            )
+
+            if key in grouped:
+                grouped[key]["count"] += int(g.count or 0)
+            else:
+                grouped[key] = {
+                    "camera": g.camera,
+                    "label": g.label,
+                    "sub_label": g.sub_label,
+                    "data": g.data,
+                    "day": g.day,
+                    "zones": g.zones,
+                    "count": int(g.count or 0),
+                }
+
+    return JSONResponse(content=sorted(grouped.values(), key=lambda x: x["day"]))
 
 
 @router.get(
     "/events/{event_id}",
     response_model=EventResponse,
-    summary="Get event by id.",
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get event by id",
     description="Gets an event by its id.",
 )
 async def event(event_id: str, request: Request):
@@ -903,7 +967,8 @@ def set_retain(event_id: str):
 @router.post(
     "/events/{event_id}/plus",
     response_model=EventUploadPlusResponse,
-    summary="Send event to Frigate+.",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Send event to Frigate+",
     description="""Sends an event to Frigate+.
     Returns a success message or an error if the event is not found.
     """,
@@ -1043,6 +1108,7 @@ async def send_to_plus(request: Request, event_id: str, body: SubmitPlusBody = N
 @router.put(
     "/events/{event_id}/false_positive",
     response_model=EventUploadPlusResponse,
+    dependencies=[Depends(require_role(["admin"]))],
     summary="Submit false positive to Frigate+",
     description="""Submit an event as a false positive to Frigate+.
     This endpoint is the same as the standard Frigate+ submission endpoint,
@@ -1141,7 +1207,7 @@ async def false_positive(request: Request, event_id: str):
     "/events/{event_id}/retain",
     response_model=GenericResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Stop event from being retained indefinitely.",
+    summary="Stop event from being retained indefinitely",
     description="""Stops an event from being retained indefinitely.
     Returns a success message or an error if the event is not found.
     NOTE: This is a legacy endpoint and is not supported in the frontend.
@@ -1170,7 +1236,7 @@ async def delete_retain(event_id: str, request: Request):
     "/events/{event_id}/sub_label",
     response_model=GenericResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Set event sub label.",
+    summary="Set event sub label",
     description="""Sets an event's sub label.
     Returns a success message or an error if the event is not found.
     """,
@@ -1229,7 +1295,7 @@ async def set_sub_label(
     "/events/{event_id}/recognized_license_plate",
     response_model=GenericResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Set event license plate.",
+    summary="Set event license plate",
     description="""Sets an event's license plate.
     Returns a success message or an error if the event is not found.
     """,
@@ -1289,7 +1355,7 @@ async def set_plate(
     "/events/{event_id}/description",
     response_model=GenericResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Set event description.",
+    summary="Set event description",
     description="""Sets an event's description.
     Returns a success message or an error if the event is not found.
     """,
@@ -1345,7 +1411,7 @@ async def set_description(
     "/events/{event_id}/description/regenerate",
     response_model=GenericResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Regenerate event description.",
+    summary="Regenerate event description",
     description="""Regenerates an event's description.
     Returns a success message or an error if the event is not found.
     """,
@@ -1397,8 +1463,8 @@ async def regenerate_description(
 @router.post(
     "/description/generate",
     response_model=GenericResponse,
-    # dependencies=[Depends(require_role(["admin"]))],
+    dependencies=[Depends(require_role(["admin"]))],
-    summary="Generate description embedding.",
+    summary="Generate description embedding",
     description="""Generates an embedding for an event's description.
     Returns a success message or an error if the event is not found.
     """,
@@ -1463,7 +1529,7 @@ async def delete_single_event(event_id: str, request: Request) -> dict:
     "/events/{event_id}",
     response_model=GenericResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Delete event.",
+    summary="Delete event",
     description="""Deletes an event from the database.
     Returns a success message or an error if the event is not found.
     """,
@@ -1478,7 +1544,7 @@ async def delete_event(request: Request, event_id: str):
     "/events/",
     response_model=EventMultiDeleteResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Delete events.",
+    summary="Delete events",
     description="""Deletes a list of events from the database.
     Returns a success message or an error if the events are not found.
     """,
@@ -1512,7 +1578,7 @@ async def delete_events(request: Request, body: EventsDeleteBody):
     "/events/{camera_name}/{label}/create",
     response_model=EventCreateResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Create manual event.",
+    summary="Create manual event",
     description="""Creates a manual event in the database.
     Returns a success message or an error if the event is not found.
     NOTES:
@@ -1554,7 +1620,7 @@ def create_event(
             body.score,
             body.sub_label,
             body.duration,
-            body.source_type,
+            "api",
             body.draw,
         ),
         EventMetadataTypeEnum.manual_event_create.value,
@@ -1576,7 +1642,7 @@ def create_event(
     "/events/{event_id}/end",
     response_model=GenericResponse,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="End manual event.",
+    summary="End manual event",
     description="""Ends a manual event.
     Returns a success message or an error if the event is not found.
     NOTE: This should only be used for manual events.
@@ -1586,10 +1652,27 @@ async def end_event(request: Request, event_id: str, body: EventsEndBody):
     try:
         event: Event = Event.get(Event.id == event_id)
         await require_camera_access(event.camera, request=request)
+
+        if body.end_time is not None and body.end_time < event.start_time:
+            return JSONResponse(
+                content=(
+                    {
+                        "success": False,
+                        "message": f"end_time ({body.end_time}) cannot be before start_time ({event.start_time}).",
+                    }
+                ),
+                status_code=400,
+            )
+
         end_time = body.end_time or datetime.datetime.now().timestamp()
         request.app.event_metadata_updater.publish(
             (event_id, end_time), EventMetadataTypeEnum.manual_event_end.value
         )
+    except DoesNotExist:
+        return JSONResponse(
+            content=({"success": False, "message": f"Event {event_id} not found."}),
+            status_code=404,
+        )
     except Exception:
         return JSONResponse(
             content=(
@@ -1608,7 +1691,7 @@ async def end_event(request: Request, event_id: str, body: EventsEndBody):
     "/trigger/embedding",
     response_model=dict,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Create trigger embedding.",
+    summary="Create trigger embedding",
     description="""Creates a trigger embedding for a specific trigger.
     Returns a success message or an error if the trigger is not found.
     """,
@@ -1665,37 +1748,40 @@ def create_trigger_embedding(
             if event.data.get("type") != "object":
                 return
 
-            if thumbnail := get_event_thumbnail_bytes(event):
+            # Get the thumbnail
-                cursor = context.db.execute_sql(
+            thumbnail = get_event_thumbnail_bytes(event)
-                    """
+
-                    SELECT thumbnail_embedding FROM vec_thumbnails WHERE id = ?
+            if thumbnail is None:
-                    """,
+                return JSONResponse(
-                    [body.data],
+                    content={
+                        "success": False,
+                        "message": f"Failed to get thumbnail for {body.data} for {body.type} trigger",
+                    },
+                    status_code=400,
                 )
 
-                row = cursor.fetchone() if cursor else None
+            # Try to reuse existing embedding from database
+            cursor = context.db.execute_sql(
+                """
+                SELECT thumbnail_embedding FROM vec_thumbnails WHERE id = ?
+                """,
+                [body.data],
+            )
 
-                if row:
+            row = cursor.fetchone() if cursor else None
-                    query_embedding = row[0]
+
-                    embedding = np.frombuffer(query_embedding, dtype=np.float32)
+            if row:
+                query_embedding = row[0]
+                embedding = np.frombuffer(query_embedding, dtype=np.float32)
             else:
-                # Extract valid thumbnail
+                # Generate new embedding
-                thumbnail = get_event_thumbnail_bytes(event)
-
-                if thumbnail is None:
-                    return JSONResponse(
-                        content={
-                            "success": False,
-                            "message": f"Failed to get thumbnail for {body.data} for {body.type} trigger",
-                        },
-                        status_code=400,
-                    )
-
                 embedding = context.generate_image_embedding(
                     body.data, (base64.b64encode(thumbnail).decode("ASCII"))
                 )
 
-        if embedding is None:
+        if embedding is None or (
+            isinstance(embedding, (list, np.ndarray)) and len(embedding) == 0
+        ):
             return JSONResponse(
                 content={
                     "success": False,
@@ -1723,9 +1809,8 @@ def create_trigger_embedding(
                 logger.debug(
                     f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
                 )
-            except Exception as e:
+            except Exception:
-                logger.error(e.with_traceback())
+                logger.exception(
-                logger.error(
                     f"Failed to write thumbnail for trigger with data {body.data} in {camera_name}"
                 )
 
@@ -1749,8 +1834,8 @@ def create_trigger_embedding(
             status_code=200,
         )
 
-    except Exception as e:
+    except Exception:
-        logger.error(e.with_traceback())
+        logger.exception("Error creating trigger embedding")
         return JSONResponse(
             content={
                 "success": False,
@@ -1764,7 +1849,7 @@ def create_trigger_embedding(
     "/trigger/embedding/{camera_name}/{name}",
     response_model=dict,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Update trigger embedding.",
+    summary="Update trigger embedding",
     description="""Updates a trigger embedding for a specific trigger.
     Returns a success message or an error if the trigger is not found.
     """,
@@ -1831,7 +1916,9 @@ def update_trigger_embedding(
                 body.data, (base64.b64encode(thumbnail).decode("ASCII"))
             )
 
-        if embedding is None:
+        if embedding is None or (
+            isinstance(embedding, (list, np.ndarray)) and len(embedding) == 0
+        ):
             return JSONResponse(
                 content={
                     "success": False,
@@ -1859,9 +1946,8 @@ def update_trigger_embedding(
                     logger.debug(
                         f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
                     )
-                except Exception as e:
+                except Exception:
-                    logger.error(e.with_traceback())
+                    logger.exception(
-                    logger.error(
                         f"Failed to delete thumbnail for trigger with data {trigger.data} in {camera_name}"
                     )
 
@@ -1900,9 +1986,8 @@ def update_trigger_embedding(
                 logger.debug(
                     f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
                 )
-            except Exception as e:
+            except Exception:
-                logger.error(e.with_traceback())
+                logger.exception(
-                logger.error(
                     f"Failed to write thumbnail for trigger with data {body.data} in {camera_name}"
                 )
 
@@ -1914,8 +1999,8 @@ def update_trigger_embedding(
             status_code=200,
         )
 
-    except Exception as e:
+    except Exception:
-        logger.error(e.with_traceback())
+        logger.exception("Error updating trigger embedding")
         return JSONResponse(
             content={
                 "success": False,
@@ -1929,7 +2014,7 @@ def update_trigger_embedding(
     "/trigger/embedding/{camera_name}/{name}",
     response_model=dict,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Delete trigger embedding.",
+    summary="Delete trigger embedding",
     description="""Deletes a trigger embedding for a specific trigger.
     Returns a success message or an error if the trigger is not found.
     """,
@@ -1975,9 +2060,8 @@ def delete_trigger_embedding(
             logger.debug(
                 f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
             )
-        except Exception as e:
+        except Exception:
-            logger.error(e.with_traceback())
+            logger.exception(
-            logger.error(
                 f"Failed to delete thumbnail for trigger with data {trigger.data} in {camera_name}"
             )
 
@@ -1989,8 +2073,8 @@ def delete_trigger_embedding(
             status_code=200,
         )
 
-    except Exception as e:
+    except Exception:
-        logger.error(e.with_traceback())
+        logger.exception("Error deleting trigger embedding")
         return JSONResponse(
             content={
                 "success": False,
@@ -2004,7 +2088,7 @@ def delete_trigger_embedding(
     "/triggers/status/{camera_name}",
     response_model=dict,
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Get triggers status.",
+    summary="Get triggers status",
     description="""Gets the status of all triggers for a specific camera.
     Returns a success message or an error if the camera is not found.
     """,

frigate/api/export.py

@@ -14,6 +14,7 @@ from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
 from frigate.api.auth import (
+    allow_any_authenticated,
     get_allowed_cameras_for_filter,
     require_camera_access,
     require_role,
@@ -34,7 +35,7 @@ from frigate.record.export import (
     PlaybackSourceEnum,
     RecordingExporter,
 )
-from frigate.util.builtin import is_current_hour
+from frigate.util.time import is_current_hour
 
 logger = logging.getLogger(__name__)
 
@@ -44,6 +45,7 @@ router = APIRouter(tags=[Tags.export])
 @router.get(
     "/exports",
     response_model=ExportsResponse,
+    dependencies=[Depends(allow_any_authenticated())],
     summary="Get exports",
     description="""Gets all exports from the database for cameras the user has access to.
     Returns a list of exports ordered by date (most recent first).""",
@@ -272,6 +274,7 @@ async def export_delete(event_id: str, request: Request):
 @router.get(
     "/exports/{export_id}",
     response_model=ExportModel,
+    dependencies=[Depends(allow_any_authenticated())],
     summary="Get a single export",
     description="""Gets a specific export by ID. The user must have access to the camera
     associated with the export.""",

frigate/api/fastapi_app.py

@@ -2,7 +2,7 @@ import logging
 import re
 from typing import Optional
 
-from fastapi import FastAPI, Request
+from fastapi import Depends, FastAPI, Request
 from fastapi.responses import JSONResponse
 from joserfc.jwk import OctKey
 from playhouse.sqliteq import SqliteQueueDatabase
@@ -24,7 +24,7 @@ from frigate.api import (
     preview,
     review,
 )
-from frigate.api.auth import get_jwt_secret, limiter
+from frigate.api.auth import get_jwt_secret, limiter, require_admin_by_default
 from frigate.comms.event_metadata_updater import (
     EventMetadataPublisher,
 )
@@ -62,11 +62,15 @@ def create_fastapi_app(
     stats_emitter: StatsEmitter,
     event_metadata_updater: EventMetadataPublisher,
     config_publisher: CameraConfigUpdatePublisher,
+    enforce_default_admin: bool = True,
 ):
     logger.info("Starting FastAPI app")
     app = FastAPI(
         debug=False,
         swagger_ui_parameters={"apisSorter": "alpha", "operationsSorter": "alpha"},
+        dependencies=[Depends(require_admin_by_default())]
+        if enforce_default_admin
+        else [],
     )
 
     # update the request_address with the x-forwarded-for header from nginx

frigate/api/media.py

@@ -22,7 +22,11 @@ from pathvalidate import sanitize_filename
 from peewee import DoesNotExist, fn, operator
 from tzlocal import get_localzone_name
 
-from frigate.api.auth import get_allowed_cameras_for_filter, require_camera_access
+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 (
     Extension,
     MediaEventsSnapshotQueryParams,
@@ -44,9 +48,9 @@ from frigate.const import (
 )
 from frigate.models import Event, Previews, Recordings, Regions, ReviewSegment
 from frigate.track.object_processing import TrackedObjectProcessor
-from frigate.util.builtin import get_tz_modifiers
+from frigate.util.file import get_event_thumbnail_bytes
 from frigate.util.image import get_image_from_recording
-from frigate.util.path import get_event_thumbnail_bytes
+from frigate.util.time import get_dst_transitions
 
 logger = logging.getLogger(__name__)
 
@@ -393,7 +397,7 @@ async def submit_recording_snapshot_to_plus(
         )
 
 
-@router.get("/recordings/storage")
+@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"
@@ -417,14 +421,13 @@ def get_recordings_storage_usage(request: Request):
     return JSONResponse(content=camera_usages)
 
 
-@router.get("/recordings/summary")
+@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"""
-    hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(params.timezone)
 
     cameras = params.cameras
     if cameras != "all":
@@ -432,43 +435,72 @@ def all_recordings_summary(
         filtered = requested.intersection(allowed_cameras)
         if not filtered:
             return JSONResponse(content={})
-        cameras = ",".join(filtered)
+        camera_list = list(filtered)
     else:
-        cameras = allowed_cameras
+        camera_list = allowed_cameras
 
-    query = (
+    time_range_query = (
         Recordings.select(
-            fn.strftime(
+            fn.MIN(Recordings.start_time).alias("min_time"),
-                "%Y-%m-%d",
+            fn.MAX(Recordings.start_time).alias("max_time"),
-                fn.datetime(
-                    Recordings.start_time + seconds_offset,
-                    "unixepoch",
-                    hour_modifier,
-                    minute_modifier,
-                ),
-            ).alias("day")
         )
-        .group_by(
+        .where(Recordings.camera << camera_list)
-            fn.strftime(
+        .dicts()
-                "%Y-%m-%d",
+        .get()
-                fn.datetime(
-                    Recordings.start_time + seconds_offset,
-                    "unixepoch",
-                    hour_modifier,
-                    minute_modifier,
-                ),
-            )
-        )
-        .order_by(Recordings.start_time.desc())
     )
 
-    if params.cameras != "all":
+    min_time = time_range_query.get("min_time")
-        query = query.where(Recordings.camera << cameras.split(","))
+    max_time = time_range_query.get("max_time")
 
-    recording_days = query.namedtuples()
+    if min_time is None or max_time is None:
-    days = {day.day: True for day in recording_days}
+        return JSONResponse(content={})
 
-    return JSONResponse(content=days)
+    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(
@@ -476,61 +508,103 @@ def all_recordings_summary(
 )
 async def recordings_summary(camera_name: str, timezone: str = "utc"):
     """Returns hourly summary for recordings of given camera"""
-    hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(timezone)
+
-    recording_groups = (
+    time_range_query = (
         Recordings.select(
-            fn.strftime(
+            fn.MIN(Recordings.start_time).alias("min_time"),
-                "%Y-%m-%d %H",
+            fn.MAX(Recordings.start_time).alias("max_time"),
-                fn.datetime(
-                    Recordings.start_time, "unixepoch", hour_modifier, 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)
-        .group_by((Recordings.start_time + seconds_offset).cast("int") / 3600)
+        .dicts()
-        .order_by(Recordings.start_time.desc())
+        .get()
-        .namedtuples()
     )
 
-    event_groups = (
+    min_time = time_range_query.get("min_time")
-        Event.select(
+    max_time = time_range_query.get("max_time")
-            fn.strftime(
+
-                "%Y-%m-%d %H",
+    days: dict[str, dict] = {}
-                fn.datetime(
+
-                    Event.start_time, "unixepoch", hour_modifier, minute_modifier
+    if min_time is None or max_time is None:
-                ),
+        return JSONResponse(content=list(days.values()))
-            ).alias("hour"),
+
-            fn.COUNT(Event.id).alias("count"),
+    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()
         )
-        .where(Event.camera == camera_name, Event.has_clip)
-        .group_by((Event.start_time + seconds_offset).cast("int") / 3600)
-        .namedtuples()
-    )
 
-    event_map = {g.hour: g.count for g in event_groups}
+        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()
+        )
 
-    days = {}
+        event_map = {g.hour: g.count for g in event_groups}
 
-    for recording_group in recording_groups:
+        for recording_group in recording_groups:
-        parts = recording_group.hour.split()
+            parts = recording_group.hour.split()
-        hour = parts[1]
+            hour = parts[1]
-        day = parts[0]
+            day = parts[0]
-        events_count = event_map.get(recording_group.hour, 0)
+            events_count = event_map.get(recording_group.hour, 0)
-        hour_data = {
+            hour_data = {
-            "hour": hour,
+                "hour": hour,
-            "events": events_count,
+                "events": events_count,
-            "motion": recording_group.motion,
+                "motion": recording_group.motion,
-            "objects": recording_group.objects,
+                "objects": recording_group.objects,
-            "duration": round(recording_group.duration),
+                "duration": round(recording_group.duration),
-        }
+            }
-        if day not in days:
+            if day in days:
-            days[day] = {"events": events_count, "hours": [hour_data], "day": day}
+                # merge counts if already present (edge-case at DST boundary)
-        else:
+                days[day]["events"] += events_count or 0
-            days[day]["events"] += events_count
+                days[day]["hours"].append(hour_data)
-            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()))
 
@@ -565,7 +639,11 @@ async def recordings(
     return JSONResponse(content=list(recordings))
 
 
-@router.get("/recordings/unavailable", response_model=list[dict])
+@router.get(
+    "/recordings/unavailable",
+    response_model=list[dict],
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def no_recordings(
     request: Request,
     params: MediaRecordingsAvailabilityQueryParams = Depends(),
@@ -692,6 +770,15 @@ async def recording_clip(
         .order_by(Recordings.start_time.asc())
     )
 
+    if recordings.count() == 0:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "No recordings found for the specified time range",
+            },
+            status_code=400,
+        )
+
     file_name = sanitize_filename(f"playlist_{camera_name}_{start_ts}-{end_ts}.txt")
     file_path = os.path.join(CACHE_DIR, file_name)
     with open(file_path, "w") as file:
@@ -750,7 +837,19 @@ async def recording_clip(
     dependencies=[Depends(require_camera_access)],
     description="Returns an HLS playlist for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.",
 )
-async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
+async def vod_ts(
+    camera_name: str,
+    start_ts: float,
+    end_ts: float,
+    force_discontinuity: bool = False,
+):
+    logger.debug(
+        "VOD: Generating VOD for %s from %s to %s with force_discontinuity=%s",
+        camera_name,
+        start_ts,
+        end_ts,
+        force_discontinuity,
+    )
     recordings = (
         Recordings.select(
             Recordings.path,
@@ -770,10 +869,19 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
 
     clips = []
     durations = []
+    min_duration_ms = 100  # Minimum 100ms to ensure at least one video frame
     max_duration_ms = MAX_SEGMENT_DURATION * 1000
 
     recording: Recordings
     for recording in recordings:
+        logger.debug(
+            "VOD: processing recording: %s start=%s end=%s duration=%s",
+            recording.path,
+            recording.start_time,
+            recording.end_time,
+            recording.duration,
+        )
+
         clip = {"type": "source", "path": recording.path}
         duration = int(recording.duration * 1000)
 
@@ -782,19 +890,35 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
             inpoint = int((start_ts - recording.start_time) * 1000)
             clip["clipFrom"] = inpoint
             duration -= inpoint
+            logger.debug(
+                "VOD: applied clipFrom %sms to %s",
+                inpoint,
+                recording.path,
+            )
 
         # adjust end if recording.end_time is after end_ts
         if recording.end_time > end_ts:
             duration -= int((recording.end_time - end_ts) * 1000)
 
-        if duration <= 0:
+        if duration < min_duration_ms:
-            # skip if the clip has no valid duration
+            # skip if the clip has no valid duration (too short to contain frames)
+            logger.debug(
+                "VOD: skipping recording %s - resulting duration %sms too short",
+                recording.path,
+                duration,
+            )
             continue
 
-        if 0 < duration < max_duration_ms:
+        if min_duration_ms <= duration < max_duration_ms:
             clip["keyFrameDurations"] = [duration]
             clips.append(clip)
             durations.append(duration)
+            logger.debug(
+                "VOD: added clip %s duration_ms=%s clipFrom=%s",
+                recording.path,
+                duration,
+                clip.get("clipFrom"),
+            )
         else:
             logger.warning(f"Recording clip is missing or empty: {recording.path}")
 
@@ -814,7 +938,7 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
     return JSONResponse(
         content={
             "cache": hour_ago.timestamp() > start_ts,
-            "discontinuity": False,
+            "discontinuity": force_discontinuity,
             "consistentSequenceMediaInfo": True,
             "durations": durations,
             "segment_duration": max(durations),
@@ -857,6 +981,7 @@ async def vod_hour(
 
 @router.get(
     "/vod/event/{event_id}",
+    dependencies=[Depends(allow_any_authenticated())],
     description="Returns an HLS playlist for the specified object. Append /master.m3u8 or /index.m3u8 for HLS playback.",
 )
 async def vod_event(
@@ -897,6 +1022,19 @@ async def vod_event(
     return vod_response
 
 
+@router.get(
+    "/vod/clip/{camera_name}/start/{start_ts}/end/{end_ts}",
+    dependencies=[Depends(require_camera_access)],
+    description="Returns an HLS playlist for a timestamp range with HLS discontinuity enabled. Append /master.m3u8 or /index.m3u8 for HLS playback.",
+)
+async def vod_clip(
+    camera_name: str,
+    start_ts: float,
+    end_ts: float,
+):
+    return await vod_ts(camera_name, start_ts, end_ts, force_discontinuity=True)
+
+
 @router.get(
     "/events/{event_id}/snapshot.jpg",
     description="Returns a snapshot image for the specified object id. NOTE: The query params only take affect while the event is in-progress. Once the event has ended the snapshot configuration is used.",
@@ -973,7 +1111,10 @@ async def event_snapshot(
     )
 
 
-@router.get("/events/{event_id}/thumbnail.{extension}")
+@router.get(
+    "/events/{event_id}/thumbnail.{extension}",
+    dependencies=[Depends(require_camera_access)],
+)
 async def event_thumbnail(
     request: Request,
     event_id: str,
@@ -1171,7 +1312,10 @@ def grid_snapshot(
         )
 
 
-@router.get("/events/{event_id}/snapshot-clean.webp")
+@router.get(
+    "/events/{event_id}/snapshot-clean.webp",
+    dependencies=[Depends(require_camera_access)],
+)
 def event_snapshot_clean(request: Request, event_id: str, download: bool = False):
     webp_bytes = None
     try:
@@ -1295,7 +1439,9 @@ def event_snapshot_clean(request: Request, event_id: str, download: bool = False
     )
 
 
-@router.get("/events/{event_id}/clip.mp4")
+@router.get(
+    "/events/{event_id}/clip.mp4", dependencies=[Depends(require_camera_access)]
+)
 async def event_clip(
     request: Request,
     event_id: str,
@@ -1323,7 +1469,9 @@ async def event_clip(
     )
 
 
-@router.get("/events/{event_id}/preview.gif")
+@router.get(
+    "/events/{event_id}/preview.gif", dependencies=[Depends(require_camera_access)]
+)
 def event_preview(request: Request, event_id: str):
     try:
         event: Event = Event.get(Event.id == event_id)
@@ -1676,7 +1824,7 @@ def preview_mp4(
     )
 
 
-@router.get("/review/{event_id}/preview")
+@router.get("/review/{event_id}/preview", dependencies=[Depends(require_camera_access)])
 def review_preview(
     request: Request,
     event_id: str,
@@ -1702,8 +1850,12 @@ def review_preview(
         return preview_mp4(request, review.camera, start_ts, end_ts)
 
 
-@router.get("/preview/{file_name}/thumbnail.jpg")
+@router.get(
-@router.get("/preview/{file_name}/thumbnail.webp")
+    "/preview/{file_name}/thumbnail.jpg", dependencies=[Depends(require_camera_access)]
+)
+@router.get(
+    "/preview/{file_name}/thumbnail.webp", dependencies=[Depends(require_camera_access)]
+)
 def preview_thumbnail(file_name: str):
     """Get a thumbnail from the cached preview frames."""
     if len(file_name) > 1000:

frigate/api/notification.py

@@ -5,11 +5,12 @@ import os
 from typing import Any
 
 from cryptography.hazmat.primitives import serialization
-from fastapi import APIRouter, Request
+from fastapi import APIRouter, Depends, Request
 from fastapi.responses import JSONResponse
 from peewee import DoesNotExist
 from py_vapid import Vapid01, utils
 
+from frigate.api.auth import allow_any_authenticated
 from frigate.api.defs.tags import Tags
 from frigate.const import CONFIG_DIR
 from frigate.models import User
@@ -21,6 +22,7 @@ router = APIRouter(tags=[Tags.notifications])
 
 @router.get(
     "/notifications/pubkey",
+    dependencies=[Depends(allow_any_authenticated())],
     summary="Get VAPID public key",
     description="""Gets the VAPID public key for the notifications.
     Returns the public key or an error if notifications are not enabled.
@@ -47,6 +49,7 @@ def get_vapid_pub_key(request: Request):
 
 @router.post(
     "/notifications/register",
+    dependencies=[Depends(allow_any_authenticated())],
     summary="Register notifications",
     description="""Registers a notifications subscription.
     Returns a success message or an error if the subscription is not provided.

frigate/api/preview.py

@@ -5,10 +5,14 @@ import os
 from datetime import datetime, timedelta, timezone
 
 import pytz
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import JSONResponse
 
-from frigate.api.auth import require_camera_access
+from frigate.api.auth import (
+    allow_any_authenticated,
+    get_allowed_cameras_for_filter,
+    require_camera_access,
+)
 from frigate.api.defs.response.preview_response import (
     PreviewFramesResponse,
     PreviewsResponse,
@@ -26,19 +30,32 @@ router = APIRouter(tags=[Tags.preview])
 @router.get(
     "/preview/{camera_name}/start/{start_ts}/end/{end_ts}",
     response_model=PreviewsResponse,
-    dependencies=[Depends(require_camera_access)],
+    dependencies=[Depends(allow_any_authenticated())],
     summary="Get preview clips for time range",
     description="""Gets all preview clips for a specified camera and time range.
     Returns a list of preview video clips that overlap with the requested time period,
     ordered by start time. Use camera_name='all' to get previews from all cameras.
     Returns an error if no previews are found.""",
 )
-def preview_ts(camera_name: str, start_ts: float, end_ts: float):
+def preview_ts(
+    camera_name: str,
+    start_ts: float,
+    end_ts: float,
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+):
     """Get all mp4 previews relevant for time period."""
     if camera_name != "all":
-        camera_clause = Previews.camera == camera_name
+        if camera_name not in allowed_cameras:
+            raise HTTPException(status_code=403, detail="Access denied for camera")
+        camera_list = [camera_name]
     else:
-        camera_clause = True
+        camera_list = allowed_cameras
+
+    if not camera_list:
+        return JSONResponse(
+            content={"success": False, "message": "No previews found."},
+            status_code=404,
+        )
 
     previews = (
         Previews.select(
@@ -53,7 +70,7 @@ def preview_ts(camera_name: str, start_ts: float, end_ts: float):
             | Previews.end_time.between(start_ts, end_ts)
             | ((start_ts > Previews.start_time) & (end_ts < Previews.end_time))
         )
-        .where(camera_clause)
+        .where(Previews.camera << camera_list)
         .order_by(Previews.start_time.asc())
         .dicts()
         .iterator()
@@ -88,14 +105,21 @@ def preview_ts(camera_name: str, start_ts: float, end_ts: float):
 @router.get(
     "/preview/{year_month}/{day}/{hour}/{camera_name}/{tz_name}",
     response_model=PreviewsResponse,
-    dependencies=[Depends(require_camera_access)],
+    dependencies=[Depends(allow_any_authenticated())],
     summary="Get preview clips for specific hour",
     description="""Gets all preview clips for a specific hour in a given timezone.
     Converts the provided date/time from the specified timezone to UTC and retrieves
     all preview clips for that hour. Use camera_name='all' to get previews from all cameras.
     The tz_name should be a timezone like 'America/New_York' (use commas instead of slashes).""",
 )
-def preview_hour(year_month: str, day: int, hour: int, camera_name: str, tz_name: str):
+def preview_hour(
+    year_month: str,
+    day: int,
+    hour: int,
+    camera_name: str,
+    tz_name: str,
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+):
     """Get all mp4 previews relevant for time period given the timezone"""
     parts = year_month.split("-")
     start_date = (
@@ -106,7 +130,7 @@ def preview_hour(year_month: str, day: int, hour: int, camera_name: str, tz_name
     start_ts = start_date.timestamp()
     end_ts = end_date.timestamp()
 
-    return preview_ts(camera_name, start_ts, end_ts)
+    return preview_ts(camera_name, start_ts, end_ts, allowed_cameras)
 
 
 @router.get(

frigate/api/review.py

@@ -14,6 +14,7 @@ from peewee import Case, DoesNotExist, IntegrityError, fn, operator
 from playhouse.shortcuts import model_to_dict
 
 from frigate.api.auth import (
+    allow_any_authenticated,
     get_allowed_cameras_for_filter,
     get_current_user,
     require_camera_access,
@@ -36,14 +37,18 @@ from frigate.config import FrigateConfig
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Recordings, ReviewSegment, UserReviewStatus
 from frigate.review.types import SeverityEnum
-from frigate.util.builtin import get_tz_modifiers
+from frigate.util.time import get_dst_transitions
 
 logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.review])
 
 
-@router.get("/review", response_model=list[ReviewSegmentResponse])
+@router.get(
+    "/review",
+    response_model=list[ReviewSegmentResponse],
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def review(
     params: ReviewQueryParams = Depends(),
     current_user: dict = Depends(get_current_user),
@@ -152,7 +157,11 @@ async def review(
     return JSONResponse(content=[r for r in review_query])
 
 
-@router.get("/review_ids", response_model=list[ReviewSegmentResponse])
+@router.get(
+    "/review_ids",
+    response_model=list[ReviewSegmentResponse],
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def review_ids(request: Request, ids: str):
     ids = ids.split(",")
 
@@ -186,7 +195,11 @@ async def review_ids(request: Request, ids: str):
         )
 
 
-@router.get("/review/summary", response_model=ReviewSummaryResponse)
+@router.get(
+    "/review/summary",
+    response_model=ReviewSummaryResponse,
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def review_summary(
     params: ReviewSummaryQueryParams = Depends(),
     current_user: dict = Depends(get_current_user),
@@ -197,7 +210,6 @@ async def review_summary(
 
     user_id = current_user["username"]
 
-    hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(params.timezone)
     day_ago = (datetime.datetime.now() - datetime.timedelta(hours=24)).timestamp()
 
     cameras = params.cameras
@@ -329,94 +341,144 @@ async def review_summary(
             )
         clauses.append(reduce(operator.or_, label_clauses))
 
-    day_in_seconds = 60 * 60 * 24
+    # Find the time range of available data
-    last_month_query = (
+    time_range_query = (
         ReviewSegment.select(
-            fn.strftime(
+            fn.MIN(ReviewSegment.start_time).alias("min_time"),
-                "%Y-%m-%d",
+            fn.MAX(ReviewSegment.start_time).alias("max_time"),
-                fn.datetime(
-                    ReviewSegment.start_time,
-                    "unixepoch",
-                    hour_modifier,
-                    minute_modifier,
-                ),
-            ).alias("day"),
-            fn.SUM(
-                Case(
-                    None,
-                    [
-                        (
-                            (ReviewSegment.severity == SeverityEnum.alert)
-                            & (UserReviewStatus.has_been_reviewed == True),
-                            1,
-                        )
-                    ],
-                    0,
-                )
-            ).alias("reviewed_alert"),
-            fn.SUM(
-                Case(
-                    None,
-                    [
-                        (
-                            (ReviewSegment.severity == SeverityEnum.detection)
-                            & (UserReviewStatus.has_been_reviewed == True),
-                            1,
-                        )
-                    ],
-                    0,
-                )
-            ).alias("reviewed_detection"),
-            fn.SUM(
-                Case(
-                    None,
-                    [
-                        (
-                            (ReviewSegment.severity == SeverityEnum.alert),
-                            1,
-                        )
-                    ],
-                    0,
-                )
-            ).alias("total_alert"),
-            fn.SUM(
-                Case(
-                    None,
-                    [
-                        (
-                            (ReviewSegment.severity == SeverityEnum.detection),
-                            1,
-                        )
-                    ],
-                    0,
-                )
-            ).alias("total_detection"),
-        )
-        .left_outer_join(
-            UserReviewStatus,
-            on=(
-                (ReviewSegment.id == UserReviewStatus.review_segment)
-                & (UserReviewStatus.user_id == user_id)
-            ),
         )
         .where(reduce(operator.and_, clauses) if clauses else True)
-        .group_by(
+        .dicts()
-            (ReviewSegment.start_time + seconds_offset).cast("int") / day_in_seconds
+        .get()
-        )
-        .order_by(ReviewSegment.start_time.desc())
     )
 
+    min_time = time_range_query.get("min_time")
+    max_time = time_range_query.get("max_time")
+
     data = {
         "last24Hours": last_24_query,
     }
 
-    for e in last_month_query.dicts().iterator():
+    # If no data, return early
-        data[e["day"]] = e
+    if min_time is None or max_time is None:
+        return JSONResponse(content=data)
+
+    # Get DST transition periods
+    dst_periods = get_dst_transitions(params.timezone, min_time, max_time)
+
+    day_in_seconds = 60 * 60 * 24
+
+    # Query each DST period separately with the correct offset
+    for period_start, period_end, period_offset in dst_periods:
+        # Calculate hour/minute modifiers for this period
+        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"
+
+        # Build clauses including time range for this period
+        period_clauses = clauses.copy()
+        period_clauses.append(
+            (ReviewSegment.start_time >= period_start)
+            & (ReviewSegment.start_time <= period_end)
+        )
+
+        period_query = (
+            ReviewSegment.select(
+                fn.strftime(
+                    "%Y-%m-%d",
+                    fn.datetime(
+                        ReviewSegment.start_time,
+                        "unixepoch",
+                        period_hour_modifier,
+                        period_minute_modifier,
+                    ),
+                ).alias("day"),
+                fn.SUM(
+                    Case(
+                        None,
+                        [
+                            (
+                                (ReviewSegment.severity == SeverityEnum.alert)
+                                & (UserReviewStatus.has_been_reviewed == True),
+                                1,
+                            )
+                        ],
+                        0,
+                    )
+                ).alias("reviewed_alert"),
+                fn.SUM(
+                    Case(
+                        None,
+                        [
+                            (
+                                (ReviewSegment.severity == SeverityEnum.detection)
+                                & (UserReviewStatus.has_been_reviewed == True),
+                                1,
+                            )
+                        ],
+                        0,
+                    )
+                ).alias("reviewed_detection"),
+                fn.SUM(
+                    Case(
+                        None,
+                        [
+                            (
+                                (ReviewSegment.severity == SeverityEnum.alert),
+                                1,
+                            )
+                        ],
+                        0,
+                    )
+                ).alias("total_alert"),
+                fn.SUM(
+                    Case(
+                        None,
+                        [
+                            (
+                                (ReviewSegment.severity == SeverityEnum.detection),
+                                1,
+                            )
+                        ],
+                        0,
+                    )
+                ).alias("total_detection"),
+            )
+            .left_outer_join(
+                UserReviewStatus,
+                on=(
+                    (ReviewSegment.id == UserReviewStatus.review_segment)
+                    & (UserReviewStatus.user_id == user_id)
+                ),
+            )
+            .where(reduce(operator.and_, period_clauses))
+            .group_by(
+                (ReviewSegment.start_time + period_offset).cast("int") / day_in_seconds
+            )
+            .order_by(ReviewSegment.start_time.desc())
+        )
+
+        # Merge results from this period
+        for e in period_query.dicts().iterator():
+            day_key = e["day"]
+            if day_key in data:
+                # Merge counts if day already exists (edge case at DST boundary)
+                data[day_key]["reviewed_alert"] += e["reviewed_alert"] or 0
+                data[day_key]["reviewed_detection"] += e["reviewed_detection"] or 0
+                data[day_key]["total_alert"] += e["total_alert"] or 0
+                data[day_key]["total_detection"] += e["total_detection"] or 0
+            else:
+                data[day_key] = e
 
     return JSONResponse(content=data)
 
 
-@router.post("/reviews/viewed", response_model=GenericResponse)
+@router.post(
+    "/reviews/viewed",
+    response_model=GenericResponse,
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def set_multiple_reviewed(
     request: Request,
     body: ReviewModifyMultipleBody,
@@ -515,7 +577,9 @@ def delete_reviews(body: ReviewModifyMultipleBody):
 
 
 @router.get(
-    "/review/activity/motion", response_model=list[ReviewActivityMotionResponse]
+    "/review/activity/motion",
+    response_model=list[ReviewActivityMotionResponse],
+    dependencies=[Depends(allow_any_authenticated())],
 )
 def motion_activity(
     params: ReviewActivityMotionQueryParams = Depends(),
@@ -599,7 +663,11 @@ def motion_activity(
     return JSONResponse(content=normalized)
 
 
-@router.get("/review/event/{event_id}", response_model=ReviewSegmentResponse)
+@router.get(
+    "/review/event/{event_id}",
+    response_model=ReviewSegmentResponse,
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def get_review_from_event(request: Request, event_id: str):
     try:
         review = ReviewSegment.get(
@@ -614,7 +682,11 @@ async def get_review_from_event(request: Request, event_id: str):
         )
 
 
-@router.get("/review/{review_id}", response_model=ReviewSegmentResponse)
+@router.get(
+    "/review/{review_id}",
+    response_model=ReviewSegmentResponse,
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def get_review(request: Request, review_id: str):
     try:
         review = ReviewSegment.get(ReviewSegment.id == review_id)
@@ -627,7 +699,11 @@ async def get_review(request: Request, review_id: str):
         )
 
 
-@router.delete("/review/{review_id}/viewed", response_model=GenericResponse)
+@router.delete(
+    "/review/{review_id}/viewed",
+    response_model=GenericResponse,
+    dependencies=[Depends(allow_any_authenticated())],
+)
 async def set_not_reviewed(
     review_id: str,
     current_user: dict = Depends(get_current_user),
@@ -665,6 +741,7 @@ async def set_not_reviewed(
 
 @router.post(
     "/review/summarize/start/{start_ts}/end/{end_ts}",
+    dependencies=[Depends(allow_any_authenticated())],
     description="Use GenAI to summarize review items over a period of time.",
 )
 def generate_review_summary(request: Request, start_ts: float, end_ts: float):

frigate/camera/maintainer.py

@@ -136,6 +136,7 @@ class CameraMaintainer(threading.Thread):
             self.ptz_metrics[name],
             self.region_grids[name],
             self.stop_event,
+            self.config.logger,
         )
         self.camera_processes[config.name] = camera_process
         camera_process.start()
@@ -156,7 +157,11 @@ class CameraMaintainer(threading.Thread):
             self.frame_manager.create(f"{config.name}_frame{i}", frame_size)
 
         capture_process = CameraCapture(
-            config, count, self.camera_metrics[name], self.stop_event
+            config,
+            count,
+            self.camera_metrics[name],
+            self.stop_event,
+            self.config.logger,
         )
         capture_process.daemon = True
         self.capture_processes[name] = capture_process

frigate/comms/dispatcher.py

@@ -23,6 +23,7 @@ from frigate.const import (
     NOTIFICATION_TEST,
     REQUEST_REGION_GRID,
     UPDATE_AUDIO_ACTIVITY,
+    UPDATE_AUDIO_TRANSCRIPTION_STATE,
     UPDATE_BIRDSEYE_LAYOUT,
     UPDATE_CAMERA_ACTIVITY,
     UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
@@ -61,6 +62,7 @@ class Dispatcher:
         self.model_state: dict[str, ModelStatusTypesEnum] = {}
         self.embeddings_reindex: dict[str, Any] = {}
         self.birdseye_layout: dict[str, Any] = {}
+        self.audio_transcription_state: str = "idle"
         self._camera_settings_handlers: dict[str, Callable] = {
             "audio": self._on_audio_command,
             "audio_transcription": self._on_audio_transcription_command,
@@ -178,6 +180,19 @@ class Dispatcher:
         def handle_model_state() -> None:
             self.publish("model_state", json.dumps(self.model_state.copy()))
 
+        def handle_update_audio_transcription_state() -> None:
+            if payload:
+                self.audio_transcription_state = payload
+                self.publish(
+                    "audio_transcription_state",
+                    json.dumps(self.audio_transcription_state),
+                )
+
+        def handle_audio_transcription_state() -> None:
+            self.publish(
+                "audio_transcription_state", json.dumps(self.audio_transcription_state)
+            )
+
         def handle_update_embeddings_reindex_progress() -> None:
             self.embeddings_reindex = payload
             self.publish(
@@ -264,10 +279,12 @@ class Dispatcher:
             UPDATE_MODEL_STATE: handle_update_model_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,
             NOTIFICATION_TEST: handle_notification_test,
             "restart": handle_restart,
             "embeddingsReindexProgress": handle_embeddings_reindex_progress,
             "modelState": handle_model_state,
+            "audioTranscriptionState": handle_audio_transcription_state,
             "birdseyeLayout": handle_birdseye_layout,
             "onConnect": handle_on_connect,
         }
@@ -590,23 +607,27 @@ class Dispatcher:
         )
         self.publish(f"{camera_name}/snapshots/state", payload, retain=True)
 
-    def _on_ptz_command(self, camera_name: str, payload: str) -> None:
+    def _on_ptz_command(self, camera_name: str, payload: str | bytes) -> None:
         """Callback for ptz topic."""
         try:
-            if "preset" in payload.lower():
+            preset: str = (
+                payload.decode("utf-8") if isinstance(payload, bytes) else payload
+            ).lower()
+
+            if "preset" in preset:
                 command = OnvifCommandEnum.preset
-                param = payload.lower()[payload.index("_") + 1 :]
+                param = preset[preset.index("_") + 1 :]
-            elif "move_relative" in payload.lower():
+            elif "move_relative" in preset:
                 command = OnvifCommandEnum.move_relative
-                param = payload.lower()[payload.index("_") + 1 :]
+                param = preset[preset.index("_") + 1 :]
             else:
-                command = OnvifCommandEnum[payload.lower()]
+                command = OnvifCommandEnum[preset]
                 param = ""
 
             self.onvif.handle_command(camera_name, command, param)
             logger.info(f"Setting ptz command to {command} for {camera_name}")
         except KeyError as k:
-            logger.error(f"Invalid PTZ command {payload}: {k}")
+            logger.error(f"Invalid PTZ command {preset}: {k}")
 
     def _on_birdseye_command(self, camera_name: str, payload: str) -> None:
         """Callback for birdseye topic."""

frigate/comms/webpush.py

@@ -21,7 +21,7 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
 )
-from frigate.const import CONFIG_DIR
+from frigate.const import BASE_DIR, CONFIG_DIR
 from frigate.models import User
 
 logger = logging.getLogger(__name__)
@@ -371,14 +371,39 @@ class WebPushClient(Communicator):
 
         sorted_objects.update(payload["after"]["data"]["sub_labels"])
 
-        image = f"{payload['after']['thumb_path'].replace('/media/frigate', '')}"
+        image = f"{payload['after']['thumb_path'].replace(BASE_DIR, '')}"
         ended = state == "end" or state == "genai"
 
         if state == "genai" and payload["after"]["data"]["metadata"]:
-            title = payload["after"]["data"]["metadata"]["title"]
+            base_title = payload["after"]["data"]["metadata"]["title"]
+            threat_level = payload["after"]["data"]["metadata"].get(
+                "potential_threat_level", 0
+            )
+
+            # Add prefix for threat levels 1 and 2
+            if threat_level == 1:
+                title = f"Needs Review: {base_title}"
+            elif threat_level == 2:
+                title = f"Security Concern: {base_title}"
+            else:
+                title = base_title
+
             message = payload["after"]["data"]["metadata"]["scene"]
         else:
-            title = f"{titlecase(', '.join(sorted_objects).replace('_', ' '))}{' was' if state == 'end' else ''} detected in {titlecase(', '.join(payload['after']['data']['zones']).replace('_', ' '))}"
+            zone_names = payload["after"]["data"]["zones"]
+            formatted_zone_names = []
+
+            for zone_name in zone_names:
+                if zone_name in self.config.cameras[camera].zones:
+                    formatted_zone_names.append(
+                        self.config.cameras[camera]
+                        .zones[zone_name]
+                        .get_formatted_name(zone_name)
+                    )
+                else:
+                    formatted_zone_names.append(titlecase(zone_name.replace("_", " ")))
+
+            title = f"{titlecase(', '.join(sorted_objects).replace('_', ' '))}{' was' if state == 'end' else ''} detected in {', '.join(formatted_zone_names)}"
             message = f"Detected on {camera_name}"
 
         if ended:

frigate/config/auth.py

@@ -20,7 +20,7 @@ class AuthConfig(FrigateBaseModel):
         default=86400, title="Session length for jwt session tokens", ge=60
     )
     refresh_time: int = Field(
-        default=43200,
+        default=1800,
         title="Refresh the session if it is going to expire in this many seconds",
         ge=30,
     )

frigate/config/camera/camera.py

@@ -177,6 +177,12 @@ class CameraConfig(FrigateBaseModel):
     def ffmpeg_cmds(self) -> list[dict[str, list[str]]]:
         return self._ffmpeg_cmds
 
+    def get_formatted_name(self) -> str:
+        """Return the friendly name if set, otherwise return a formatted version of the camera name."""
+        if self.friendly_name:
+            return self.friendly_name
+        return self.name.replace("_", " ").title() if self.name else ""
+
     def create_ffmpeg_cmds(self):
         if "_ffmpeg_cmds" in self:
             return

frigate/config/camera/zone.py

@@ -13,6 +13,9 @@ logger = logging.getLogger(__name__)
 
 
 class ZoneConfig(BaseModel):
+    friendly_name: Optional[str] = Field(
+        None, title="Zone friendly name used in the Frigate UI."
+    )
     filters: dict[str, FilterConfig] = Field(
         default_factory=dict, title="Zone filters."
     )
@@ -53,6 +56,12 @@ class ZoneConfig(BaseModel):
     def contour(self) -> np.ndarray:
         return self._contour
 
+    def get_formatted_name(self, zone_name: str) -> str:
+        """Return the friendly name if set, otherwise return a formatted version of the zone name."""
+        if self.friendly_name:
+            return self.friendly_name
+        return zone_name.replace("_", " ").title()
+
     @field_validator("objects", mode="before")
     @classmethod
     def validate_objects(cls, v):

frigate/config/classification.py

@@ -105,6 +105,11 @@ class CustomClassificationConfig(FrigateBaseModel):
     threshold: float = Field(
         default=0.8, title="Classification score threshold to change the state."
     )
+    save_attempts: int | None = Field(
+        default=None,
+        title="Number of classification attempts to save in the recent classifications tab. If not specified, defaults to 200 for object classification and 100 for state classification.",
+        ge=0,
+    )
     object_config: CustomClassificationObjectConfig | None = Field(default=None)
     state_config: CustomClassificationStateConfig | None = Field(default=None)
 

frigate/config/config.py

@@ -792,6 +792,10 @@ class FrigateConfig(FrigateBaseModel):
             # copy over auth and proxy config in case auth needs to be enforced
             safe_config["auth"] = config.get("auth", {})
             safe_config["proxy"] = config.get("proxy", {})
+
+            # copy over database config for auth and so a new db is not created
+            safe_config["database"] = config.get("database", {})
+
             return cls.parse_object(safe_config, **context)
 
         # Validate and return the config dict.

frigate/config/ui.py

@@ -37,9 +37,6 @@ class UIConfig(FrigateBaseModel):
     time_style: DateTimeStyleEnum = Field(
         default=DateTimeStyleEnum.medium, title="Override UI timeStyle."
     )
-    strftime_fmt: Optional[str] = Field(
-        default=None, title="Override date and time format using strftime syntax."
-    )
     unit_system: UnitSystemEnum = Field(
         default=UnitSystemEnum.metric, title="The unit system to use for measurements."
     )

frigate/const.py

@@ -113,6 +113,7 @@ CLEAR_ONGOING_REVIEW_SEGMENTS = "clear_ongoing_review_segments"
 UPDATE_CAMERA_ACTIVITY = "update_camera_activity"
 UPDATE_AUDIO_ACTIVITY = "update_audio_activity"
 EXPIRE_AUDIO_ACTIVITY = "expire_audio_activity"
+UPDATE_AUDIO_TRANSCRIPTION_STATE = "update_audio_transcription_state"
 UPDATE_EVENT_DESCRIPTION = "update_event_description"
 UPDATE_REVIEW_DESCRIPTION = "update_review_description"
 UPDATE_MODEL_STATE = "update_model_state"

frigate/data_processing/common/audio_transcription/model.py

@@ -4,7 +4,6 @@ import logging
 import os
 
 import sherpa_onnx
-from faster_whisper.utils import download_model
 
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.const import MODEL_CACHE_DIR
@@ -25,6 +24,9 @@ class AudioTranscriptionModelRunner:
 
         if model_size == "large":
             # use the Whisper download function instead of our own
+            # Import dynamically to avoid crashes on systems without AVX support
+            from faster_whisper.utils import download_model
+
             logger.debug("Downloading Whisper audio transcription model")
             download_model(
                 size_or_id="small" if device == "cuda" else "tiny",

frigate/data_processing/common/license_plate/mixin.py

@@ -14,8 +14,8 @@ from typing import Any, List, Optional, Tuple
 
 import cv2
 import numpy as np
-from Levenshtein import distance, jaro_winkler
 from pyclipper import ET_CLOSEDPOLYGON, JT_ROUND, PyclipperOffset
+from rapidfuzz.distance import JaroWinkler, Levenshtein
 from shapely.geometry import Polygon
 
 from frigate.comms.event_metadata_updater import (
@@ -1123,7 +1123,9 @@ class LicensePlateProcessingMixin:
         for i, plate in enumerate(plates):
             merged = False
             for j, cluster in enumerate(clusters):
-                sims = [jaro_winkler(plate["plate"], v["plate"]) for v in cluster]
+                sims = [
+                    JaroWinkler.similarity(plate["plate"], v["plate"]) for v in cluster
+                ]
                 if len(sims) > 0:
                     avg_sim = sum(sims) / len(sims)
                     if avg_sim >= self.cluster_threshold:
@@ -1500,7 +1502,7 @@ class LicensePlateProcessingMixin:
                     and current_time - data["last_seen"]
                     <= self.config.cameras[camera].lpr.expire_time
                 ):
-                    similarity = jaro_winkler(data["plate"], top_plate)
+                    similarity = JaroWinkler.similarity(data["plate"], top_plate)
                     if similarity >= self.similarity_threshold:
                         plate_id = existing_id
                         logger.debug(
@@ -1580,7 +1582,8 @@ class LicensePlateProcessingMixin:
                     for label, plates_list in self.lpr_config.known_plates.items()
                     if any(
                         re.match(f"^{plate}$", rep_plate)
-                        or distance(plate, rep_plate) <= self.lpr_config.match_distance
+                        or Levenshtein.distance(plate, rep_plate)
+                        <= self.lpr_config.match_distance
                         for plate in plates_list
                     )
                 ),

frigate/data_processing/post/audio_transcription.py

@@ -6,15 +6,14 @@ import threading
 import time
 from typing import Optional
 
-from faster_whisper import WhisperModel
 from peewee import DoesNotExist
 
-from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
 from frigate.const import (
     CACHE_DIR,
     MODEL_CACHE_DIR,
+    UPDATE_AUDIO_TRANSCRIPTION_STATE,
     UPDATE_EVENT_DESCRIPTION,
 )
 from frigate.data_processing.types import PostProcessDataEnum
@@ -32,11 +31,13 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
         self,
         config: FrigateConfig,
         requestor: InterProcessRequestor,
+        embeddings,
         metrics: DataProcessorMetrics,
     ):
         super().__init__(config, metrics, None)
         self.config = config
         self.requestor = requestor
+        self.embeddings = embeddings
         self.recognizer = None
         self.transcription_lock = threading.Lock()
         self.transcription_thread = None
@@ -50,6 +51,9 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
 
     def __build_recognizer(self) -> None:
         try:
+            # Import dynamically to avoid crashes on systems without AVX support
+            from faster_whisper import WhisperModel
+
             self.recognizer = WhisperModel(
                 model_size_or_path="small",
                 device="cuda"
@@ -128,10 +132,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
             )
 
             # Embed the description
-            self.requestor.send_data(
+            self.embeddings.embed_description(event_id, transcription)
-                EmbeddingsRequestEnum.embed_description.value,
-                {"id": event_id, "description": transcription},
-            )
 
         except DoesNotExist:
             logger.debug("No recording found for audio transcription post-processing")
@@ -190,6 +191,8 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
                 self.transcription_running = False
                 self.transcription_thread = None
 
+            self.requestor.send_data(UPDATE_AUDIO_TRANSCRIPTION_STATE, "idle")
+
     def handle_request(self, topic: str, request_data: dict[str, any]) -> str | None:
         if topic == "transcribe_audio":
             event = request_data["event"]
@@ -203,6 +206,8 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
 
                 # Mark as running and start the thread
                 self.transcription_running = True
+                self.requestor.send_data(UPDATE_AUDIO_TRANSCRIPTION_STATE, "processing")
+
                 self.transcription_thread = threading.Thread(
                     target=self._transcription_wrapper, args=(event,), daemon=True
                 )

frigate/data_processing/post/object_descriptions.py

@@ -20,8 +20,8 @@ from frigate.genai import GenAIClient
 from frigate.models import Event
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed
+from frigate.util.file import get_event_thumbnail_bytes
 from frigate.util.image import create_thumbnail, ensure_jpeg_bytes
-from frigate.util.path import get_event_thumbnail_bytes
 
 if TYPE_CHECKING:
     from frigate.embeddings import Embeddings

frigate/data_processing/post/review_descriptions.py

@@ -12,10 +12,12 @@ from typing import Any
 
 import cv2
 from peewee import DoesNotExist
+from titlecase import titlecase
 
 from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
+from frigate.config.camera import CameraConfig
 from frigate.config.camera.review import GenAIReviewConfig, ImageSourceEnum
 from frigate.const import CACHE_DIR, CLIPS_DIR, UPDATE_REVIEW_DESCRIPTION
 from frigate.data_processing.types import PostProcessDataEnum
@@ -30,6 +32,7 @@ from ..types import DataProcessorMetrics
 logger = logging.getLogger(__name__)
 
 RECORDING_BUFFER_EXTENSION_PERCENT = 0.10
+MIN_RECORDING_DURATION = 10
 
 
 class ReviewDescriptionProcessor(PostProcessorApi):
@@ -90,7 +93,8 @@ class ReviewDescriptionProcessor(PostProcessorApi):
         pixels_per_image = width * height
         tokens_per_image = pixels_per_image / 1250
         prompt_tokens = 3500
-        available_tokens = context_size * 0.98 - prompt_tokens
+        response_tokens = 300
+        available_tokens = context_size - prompt_tokens - response_tokens
         max_frames = int(available_tokens / tokens_per_image)
 
         return min(max(max_frames, 3), 20)
@@ -129,7 +133,15 @@ class ReviewDescriptionProcessor(PostProcessorApi):
 
             if image_source == ImageSourceEnum.recordings:
                 duration = final_data["end_time"] - final_data["start_time"]
-                buffer_extension = duration * RECORDING_BUFFER_EXTENSION_PERCENT
+                buffer_extension = min(5, duration * RECORDING_BUFFER_EXTENSION_PERCENT)
+
+                # Ensure minimum total duration for short review items
+                # This provides better context for brief events
+                total_duration = duration + (2 * buffer_extension)
+                if total_duration < MIN_RECORDING_DURATION:
+                    # Expand buffer to reach minimum duration, still respecting max of 5s per side
+                    additional_buffer_per_side = (MIN_RECORDING_DURATION - duration) / 2
+                    buffer_extension = min(5, additional_buffer_per_side)
 
                 thumbs = self.get_recording_frames(
                     camera,
@@ -181,7 +193,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     self.requestor,
                     self.genai_client,
                     self.review_desc_speed,
-                    camera,
+                    camera_config,
                     final_data,
                     thumbs,
                     camera_config.review.genai,
@@ -197,10 +209,22 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             logger.debug(
                 f"Found GenAI Review Summary request for {start_ts} to {end_ts}"
             )
-            items: list[dict[str, Any]] = [
+
-                r["data"]["metadata"]
+            # Query all review segments with camera and time information
+            segments: list[dict[str, Any]] = [
+                {
+                    "camera": r["camera"].replace("_", " ").title(),
+                    "start_time": r["start_time"],
+                    "end_time": r["end_time"],
+                    "metadata": r["data"]["metadata"],
+                }
                 for r in (
-                    ReviewSegment.select(ReviewSegment.data)
+                    ReviewSegment.select(
+                        ReviewSegment.camera,
+                        ReviewSegment.start_time,
+                        ReviewSegment.end_time,
+                        ReviewSegment.data,
+                    )
                     .where(
                         (ReviewSegment.data["metadata"].is_null(False))
                         & (ReviewSegment.start_time < end_ts)
@@ -212,21 +236,72 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                 )
             ]
 
-            if len(items) == 0:
+            if len(segments) == 0:
                 logger.debug("No review items with metadata found during time period")
-                return "No activity was found during this time."
+                return "No activity was found during this time period."
 
-            important_items = list(
+            # Identify primary items (important items that need review)
-                filter(
+            primary_segments = [
-                    lambda item: item.get("potential_threat_level", 0) > 0
+                seg
-                    or item.get("other_concerns"),
+                for seg in segments
-                    items,
+                if seg["metadata"].get("potential_threat_level", 0) > 0
-                )
+                or seg["metadata"].get("other_concerns")
-            )
+            ]
 
-            if not important_items:
+            if not primary_segments:
                 return "No concerns were found during this time period."
 
+            # Build hierarchical structure: each primary event with its contextual items
+            events_with_context = []
+
+            for primary_seg in primary_segments:
+                # Start building the primary event structure
+                primary_item = copy.deepcopy(primary_seg["metadata"])
+                primary_item["camera"] = primary_seg["camera"]
+                primary_item["start_time"] = primary_seg["start_time"]
+                primary_item["end_time"] = primary_seg["end_time"]
+
+                # Find overlapping contextual items from other cameras
+                primary_start = primary_seg["start_time"]
+                primary_end = primary_seg["end_time"]
+                primary_camera = primary_seg["camera"]
+                contextual_items = []
+                seen_contextual_cameras = set()
+
+                for seg in segments:
+                    seg_camera = seg["camera"]
+
+                    if seg_camera == primary_camera:
+                        continue
+
+                    if seg in primary_segments:
+                        continue
+
+                    seg_start = seg["start_time"]
+                    seg_end = seg["end_time"]
+
+                    if seg_start < primary_end and primary_start < seg_end:
+                        # Avoid duplicates if same camera has multiple overlapping segments
+                        if seg_camera not in seen_contextual_cameras:
+                            contextual_item = copy.deepcopy(seg["metadata"])
+                            contextual_item["camera"] = seg_camera
+                            contextual_item["start_time"] = seg_start
+                            contextual_item["end_time"] = seg_end
+                            contextual_items.append(contextual_item)
+                            seen_contextual_cameras.add(seg_camera)
+
+                # Add context array to primary item
+                primary_item["context"] = contextual_items
+                events_with_context.append(primary_item)
+
+            total_context_items = sum(
+                len(event.get("context", [])) for event in events_with_context
+            )
+            logger.debug(
+                f"Summary includes {len(events_with_context)} primary events with "
+                f"{total_context_items} total contextual items"
+            )
+
             if self.config.review.genai.debug_save_thumbnails:
                 Path(
                     os.path.join(CLIPS_DIR, "genai-requests", f"{start_ts}-{end_ts}")
@@ -235,7 +310,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             return self.genai_client.generate_review_summary(
                 start_ts,
                 end_ts,
-                important_items,
+                events_with_context,
                 self.config.review.genai.debug_save_thumbnails,
             )
         else:
@@ -410,7 +485,7 @@ def run_analysis(
     requestor: InterProcessRequestor,
     genai_client: GenAIClient,
     review_inference_speed: InferenceSpeed,
-    camera: str,
+    camera_config: CameraConfig,
     final_data: dict[str, str],
     thumbs: list[bytes],
     genai_config: GenAIReviewConfig,
@@ -418,10 +493,19 @@ def run_analysis(
     attribute_labels: list[str],
 ) -> None:
     start = datetime.datetime.now().timestamp()
+
+    # Format zone names using zone config friendly names if available
+    formatted_zones = []
+    for zone_name in final_data["data"]["zones"]:
+        if zone_name in camera_config.zones:
+            formatted_zones.append(
+                camera_config.zones[zone_name].get_formatted_name(zone_name)
+            )
+
     analytics_data = {
         "id": final_data["id"],
-        "camera": camera,
+        "camera": camera_config.get_formatted_name(),
-        "zones": final_data["data"]["zones"],
+        "zones": formatted_zones,
         "start": datetime.datetime.fromtimestamp(final_data["start_time"]).strftime(
             "%A, %I:%M %p"
         ),
@@ -435,14 +519,14 @@ def run_analysis(
 
     for i, verified_label in enumerate(final_data["data"]["verified_objects"]):
         object_type = verified_label.replace("-verified", "").replace("_", " ")
-        name = sub_labels_list[i].replace("_", " ").title()
+        name = titlecase(sub_labels_list[i].replace("_", " "))
         unified_objects.append(f"{name} ({object_type})")
 
     for label in objects_list:
         if "-verified" in label:
             continue
         elif label in labelmap_objects:
-            object_type = label.replace("_", " ").title()
+            object_type = titlecase(label.replace("_", " "))
 
             if label in attribute_labels:
                 unified_objects.append(f"{object_type} (delivery/service)")

frigate/data_processing/post/semantic_trigger.py

@@ -22,7 +22,7 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.embeddings.util import ZScoreNormalization
 from frigate.models import Event, Trigger
 from frigate.util.builtin import cosine_distance
-from frigate.util.path import get_event_thumbnail_bytes
+from frigate.util.file import get_event_thumbnail_bytes
 
 from ..post.api import PostProcessorApi
 from ..types import DataProcessorMetrics

frigate/data_processing/real_time/custom_classification.py

@@ -1,6 +1,7 @@
 """Real time processor that works with classification tflite models."""
 
 import datetime
+import json
 import logging
 import os
 from typing import Any
@@ -21,6 +22,7 @@ from frigate.config.classification import (
 )
 from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
 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
 
@@ -97,6 +99,42 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         if self.inference_speed:
             self.inference_speed.update(duration)
 
+    def _should_save_image(
+        self, camera: str, detected_state: str, score: float = 1.0
+    ) -> bool:
+        """
+        Determine if we should save the image for training.
+        Save when:
+        - State is changing or being verified (regardless of score)
+        - Score is less than 100% (even if state matches, useful for training)
+        Don't save when:
+        - State is stable (matches current_state) AND score is 100%
+        """
+        if camera not in self.state_history:
+            # First detection for this camera, save it
+            return True
+
+        verification = self.state_history[camera]
+        current_state = verification.get("current_state")
+        pending_state = verification.get("pending_state")
+
+        # Save if there's a pending state change being verified
+        if pending_state is not None:
+            return True
+
+        # Save if the detected state differs from the current verified state
+        # (state is changing)
+        if current_state is not None and detected_state != current_state:
+            return True
+
+        # If score is less than 100%, save even if state matches
+        # (useful for training to improve confidence)
+        if score < 1.0:
+            return True
+
+        # Don't save if state is stable (detected_state == current_state) AND score is 100%
+        return False
+
     def verify_state_change(self, camera: str, detected_state: str) -> str | None:
         """
         Verify state change requires 3 consecutive identical states before publishing.
@@ -210,14 +248,22 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                 return
 
         if self.interpreter is None:
-            write_classification_attempt(
+            # When interpreter is None, always save (score is 0.0, which is < 1.0)
-                self.train_dir,
+            if self._should_save_image(camera, "unknown", 0.0):
-                cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
+                save_attempts = (
-                "none-none",
+                    self.model_config.save_attempts
-                now,
+                    if self.model_config.save_attempts is not None
-                "unknown",
+                    else 100
-                0.0,
+                )
-            )
+                write_classification_attempt(
+                    self.train_dir,
+                    cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
+                    "none-none",
+                    now,
+                    "unknown",
+                    0.0,
+                    max_files=save_attempts,
+                )
             return
 
         input = np.expand_dims(resized_frame, axis=0)
@@ -227,18 +273,30 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             self.tensor_output_details[0]["index"]
         )[0]
         probs = res / res.sum(axis=0)
+        logger.debug(
+            f"{self.model_config.name} Ran state classification with probabilities: {probs}"
+        )
         best_id = np.argmax(probs)
         score = round(probs[best_id], 2)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
-        write_classification_attempt(
+        detected_state = self.labelmap[best_id]
-            self.train_dir,
+
-            cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
+        if self._should_save_image(camera, detected_state, score):
-            "none-none",
+            save_attempts = (
-            now,
+                self.model_config.save_attempts
-            self.labelmap[best_id],
+                if self.model_config.save_attempts is not None
-            score,
+                else 100
-        )
+            )
+            write_classification_attempt(
+                self.train_dir,
+                cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
+                "none-none",
+                now,
+                detected_state,
+                score,
+                max_files=save_attempts,
+            )
 
         if score < self.model_config.threshold:
             logger.debug(
@@ -246,7 +304,6 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             )
             return
 
-        detected_state = self.labelmap[best_id]
         verified_state = self.verify_state_change(camera, detected_state)
 
         if verified_state is not None:
@@ -281,6 +338,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         config: FrigateConfig,
         model_config: CustomClassificationConfig,
         sub_label_publisher: EventMetadataPublisher,
+        requestor: InterProcessRequestor,
         metrics: DataProcessorMetrics,
     ):
         super().__init__(config, metrics)
@@ -289,6 +347,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
         self.interpreter: Interpreter | None = None
         self.sub_label_publisher = sub_label_publisher
+        self.requestor = requestor
         self.tensor_input_details: dict[str, Any] | None = None
         self.tensor_output_details: dict[str, Any] | None = None
         self.classification_history: dict[str, list[tuple[str, float, float]]] = {}
@@ -398,9 +457,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         if obj_data.get("end_time") is not None:
             return
 
-        if obj_data.get("stationary"):
-            return
-
         object_id = obj_data["id"]
 
         if (
@@ -418,8 +474,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             obj_data["box"][2],
             obj_data["box"][3],
             max(
-                obj_data["box"][1] - obj_data["box"][0],
+                obj_data["box"][2] - obj_data["box"][0],
-                obj_data["box"][3] - obj_data["box"][2],
+                obj_data["box"][3] - obj_data["box"][1],
             ),
             1.0,
         )
@@ -438,6 +494,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                 return
 
         if self.interpreter is None:
+            save_attempts = (
+                self.model_config.save_attempts
+                if self.model_config.save_attempts is not None
+                else 200
+            )
             write_classification_attempt(
                 self.train_dir,
                 cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
@@ -445,6 +506,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                 now,
                 "unknown",
                 0.0,
+                max_files=save_attempts,
             )
             return
 
@@ -455,10 +517,18 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             self.tensor_output_details[0]["index"]
         )[0]
         probs = res / res.sum(axis=0)
+        logger.debug(
+            f"{self.model_config.name} Ran object classification with probabilities: {probs}"
+        )
         best_id = np.argmax(probs)
         score = round(probs[best_id], 2)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
+        save_attempts = (
+            self.model_config.save_attempts
+            if self.model_config.save_attempts is not None
+            else 200
+        )
         write_classification_attempt(
             self.train_dir,
             cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
@@ -466,6 +536,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             now,
             self.labelmap[best_id],
             score,
+            max_files=save_attempts,
         )
 
         if score < self.model_config.threshold:
@@ -479,6 +550,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         )
 
         if consensus_label is not None:
+            camera = obj_data["camera"]
+
             if (
                 self.model_config.object_config.classification_type
                 == ObjectClassificationType.sub_label
@@ -487,6 +560,20 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                     (object_id, consensus_label, consensus_score),
                     EventMetadataTypeEnum.sub_label,
                 )
+                self.requestor.send_data(
+                    "tracked_object_update",
+                    json.dumps(
+                        {
+                            "type": TrackedObjectUpdateTypesEnum.classification,
+                            "id": object_id,
+                            "camera": camera,
+                            "timestamp": now,
+                            "model": self.model_config.name,
+                            "sub_label": consensus_label,
+                            "score": consensus_score,
+                        }
+                    ),
+                )
             elif (
                 self.model_config.object_config.classification_type
                 == ObjectClassificationType.attribute
@@ -500,6 +587,20 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                     ),
                     EventMetadataTypeEnum.attribute.value,
                 )
+                self.requestor.send_data(
+                    "tracked_object_update",
+                    json.dumps(
+                        {
+                            "type": TrackedObjectUpdateTypesEnum.classification,
+                            "id": object_id,
+                            "camera": camera,
+                            "timestamp": now,
+                            "model": self.model_config.name,
+                            "attribute": consensus_label,
+                            "score": consensus_score,
+                        }
+                    ),
+                )
 
     def handle_request(self, topic, request_data):
         if topic == EmbeddingsRequestEnum.reload_classification_model.value:
@@ -529,6 +630,7 @@ def write_classification_attempt(
     timestamp: float,
     label: str,
     score: float,
+    max_files: int = 100,
 ) -> None:
     if "-" in label:
         label = label.replace("-", "_")
@@ -537,12 +639,15 @@ def write_classification_attempt(
     os.makedirs(folder, exist_ok=True)
     cv2.imwrite(file, frame)
 
-    files = sorted(
-        filter(lambda f: (f.endswith(".webp")), os.listdir(folder)),
-        key=lambda f: os.path.getctime(os.path.join(folder, f)),
-        reverse=True,
-    )
-
     # delete oldest face image if maximum is reached
-    if len(files) > 100:
+    try:
-        os.unlink(os.path.join(folder, files[-1]))
+        files = sorted(
+            filter(lambda f: (f.endswith(".webp")), os.listdir(folder)),
+            key=lambda f: os.path.getctime(os.path.join(folder, f)),
+            reverse=True,
+        )
+
+        if len(files) > max_files:
+            os.unlink(os.path.join(folder, files[-1]))
+    except FileNotFoundError:
+        pass

frigate/data_processing/real_time/face.py

@@ -166,6 +166,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
         camera = obj_data["camera"]
 
         if not self.config.cameras[camera].face_recognition.enabled:
+            logger.debug(f"Face recognition disabled for camera {camera}, skipping")
             return
 
         start = datetime.datetime.now().timestamp()
@@ -208,6 +209,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             person_box = obj_data.get("box")
 
             if not person_box:
+                logger.debug(f"No person box available for {id}")
                 return
 
             rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
@@ -233,7 +235,8 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
             try:
                 face_frame = cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)
-            except Exception:
+            except Exception as e:
+                logger.debug(f"Failed to convert face frame color for {id}: {e}")
                 return
         else:
             # don't run for object without attributes
@@ -251,6 +254,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
             # no faces detected in this frame
             if not face:
+                logger.debug(f"No face attributes found for {id}")
                 return
 
             face_box = face.get("box")
@@ -274,6 +278,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
         res = self.recognizer.classify(face_frame)
 
         if not res:
+            logger.debug(f"Face recognizer returned no result for {id}")
             self.__update_metrics(datetime.datetime.now().timestamp() - start)
             return
 
@@ -330,6 +335,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
     def handle_request(self, topic, request_data) -> dict[str, Any] | None:
         if topic == EmbeddingsRequestEnum.clear_face_classifier.value:
             self.recognizer.clear()
+            return {"success": True, "message": "Face classifier cleared."}
         elif topic == EmbeddingsRequestEnum.recognize_face.value:
             img = cv2.imdecode(
                 np.frombuffer(base64.b64decode(request_data["image"]), dtype=np.uint8),
@@ -417,7 +423,10 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             res = self.recognizer.classify(img)
 
             if not res:
-                return
+                return {
+                    "message": "Model is still training, please try again in a few moments.",
+                    "success": False,
+                }
 
             sub_label, score = res
 
@@ -436,6 +445,13 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 )
                 shutil.move(current_file, new_file)
 
+            return {
+                "message": f"Successfully reprocessed face. Result: {sub_label} (score: {score:.2f})",
+                "success": True,
+                "face_name": sub_label,
+                "score": score,
+            }
+
     def expire_object(self, object_id: str, camera: str):
         if object_id in self.person_face_history:
             self.person_face_history.pop(object_id)

frigate/detectors/detection_runners.py

@@ -3,6 +3,7 @@
 import logging
 import os
 import platform
+import threading
 from abc import ABC, abstractmethod
 from typing import Any
 
@@ -161,12 +162,12 @@ class CudaGraphRunner(BaseModelRunner):
     """
 
     @staticmethod
-    def is_complex_model(model_type: str) -> bool:
+    def is_model_supported(model_type: str) -> bool:
         # Import here to avoid circular imports
         from frigate.detectors.detector_config import ModelTypeEnum
         from frigate.embeddings.types import EnrichmentModelTypeEnum
 
-        return model_type in [
+        return model_type not in [
             ModelTypeEnum.yolonas.value,
             EnrichmentModelTypeEnum.paddleocr.value,
             EnrichmentModelTypeEnum.jina_v1.value,
@@ -234,11 +235,36 @@ class OpenVINOModelRunner(BaseModelRunner):
         # Import here to avoid circular imports
         from frigate.embeddings.types import EnrichmentModelTypeEnum
 
-        return model_type in [EnrichmentModelTypeEnum.paddleocr.value]
+        return model_type in [
+            EnrichmentModelTypeEnum.paddleocr.value,
+            EnrichmentModelTypeEnum.jina_v2.value,
+        ]
+
+    @staticmethod
+    def is_model_npu_supported(model_type: str) -> bool:
+        # Import here to avoid circular imports
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+        return model_type not in [
+            EnrichmentModelTypeEnum.paddleocr.value,
+            EnrichmentModelTypeEnum.jina_v1.value,
+            EnrichmentModelTypeEnum.jina_v2.value,
+            EnrichmentModelTypeEnum.arcface.value,
+        ]
 
     def __init__(self, model_path: str, device: str, model_type: str, **kwargs):
         self.model_path = model_path
         self.device = device
+        self.model_type = model_type
+
+        if device == "NPU" and not OpenVINOModelRunner.is_model_npu_supported(
+            model_type
+        ):
+            logger.warning(
+                f"OpenVINO model {model_type} is not supported on NPU, using GPU instead"
+            )
+            device = "GPU"
+
         self.complex_model = OpenVINOModelRunner.is_complex_model(model_type)
 
         if not os.path.isfile(model_path):
@@ -266,6 +292,10 @@ class OpenVINOModelRunner(BaseModelRunner):
         self.infer_request = self.compiled_model.create_infer_request()
         self.input_tensor: ov.Tensor | None = None
 
+        # Thread lock to prevent concurrent inference (needed for JinaV2 which shares
+        # one runner between text and vision embeddings called from different threads)
+        self._inference_lock = threading.Lock()
+
         if not self.complex_model:
             try:
                 input_shape = self.compiled_model.inputs[0].get_shape()
@@ -309,57 +339,81 @@ class OpenVINOModelRunner(BaseModelRunner):
         Returns:
             List of output tensors
         """
-        # Handle single input case for backward compatibility
+        # Lock prevents concurrent access to infer_request
-        if (
+        # Needed for JinaV2: genai thread (text) + embeddings thread (vision)
-            len(inputs) == 1
+        with self._inference_lock:
-            and len(self.compiled_model.inputs) == 1
+            from frigate.embeddings.types import EnrichmentModelTypeEnum
-            and self.input_tensor is not None
+
-        ):
+            if self.model_type in [EnrichmentModelTypeEnum.arcface.value]:
-            # Single input case - use the pre-allocated tensor for efficiency
+                # For face recognition models, create a fresh infer_request
-            input_data = list(inputs.values())[0]
+                # for each inference to avoid state pollution that causes incorrect results.
-            np.copyto(self.input_tensor.data, input_data)
+                self.infer_request = self.compiled_model.create_infer_request()
-            self.infer_request.infer(self.input_tensor)
+
-        else:
+            # Handle single input case for backward compatibility
-            if self.complex_model:
+            if (
+                len(inputs) == 1
+                and len(self.compiled_model.inputs) == 1
+                and self.input_tensor is not None
+            ):
+                # Single input case - use the pre-allocated tensor for efficiency
+                input_data = list(inputs.values())[0]
+                np.copyto(self.input_tensor.data, input_data)
+                self.infer_request.infer(self.input_tensor)
+            else:
+                if self.complex_model:
+                    try:
+                        # This ensures the model starts with a clean state for each sequence
+                        # Important for RNN models like PaddleOCR recognition
+                        self.infer_request.reset_state()
+                    except Exception:
+                        # this will raise an exception for models with AUTO set as the device
+                        pass
+
+                # Multiple inputs case - set each input by name
+                for input_name, input_data in inputs.items():
+                    # Find the input by name and its index
+                    input_port = None
+                    input_index = None
+                    for idx, port in enumerate(self.compiled_model.inputs):
+                        if port.get_any_name() == input_name:
+                            input_port = port
+                            input_index = idx
+                            break
+
+                    if input_port is None:
+                        raise ValueError(f"Input '{input_name}' not found in model")
+
+                    # Create tensor with the correct element type
+                    input_element_type = input_port.get_element_type()
+
+                    # Ensure input data matches the expected dtype to prevent type mismatches
+                    # that can occur with models like Jina-CLIP v2 running on OpenVINO
+                    expected_dtype = input_element_type.to_dtype()
+                    if input_data.dtype != expected_dtype:
+                        logger.debug(
+                            f"Converting input '{input_name}' from {input_data.dtype} to {expected_dtype}"
+                        )
+                        input_data = input_data.astype(expected_dtype)
+
+                    input_tensor = ov.Tensor(input_element_type, input_data.shape)
+                    np.copyto(input_tensor.data, input_data)
+
+                    # Set the input tensor for the specific port index
+                    self.infer_request.set_input_tensor(input_index, input_tensor)
+
+                # Run inference
                 try:
-                    # This ensures the model starts with a clean state for each sequence
+                    self.infer_request.infer()
-                    # Important for RNN models like PaddleOCR recognition
+                except Exception as e:
-                    self.infer_request.reset_state()
+                    logger.error(f"Error during OpenVINO inference: {e}")
-                except Exception:
+                    return []
-                    # this will raise an exception for models with AUTO set as the device
-                    pass
 
-            # Multiple inputs case - set each input by name
+            # Get all output tensors
-            for input_name, input_data in inputs.items():
+            outputs = []
-                # Find the input by name and its index
+            for i in range(len(self.compiled_model.outputs)):
-                input_port = None
+                outputs.append(self.infer_request.get_output_tensor(i).data)
-                input_index = None
-                for idx, port in enumerate(self.compiled_model.inputs):
-                    if port.get_any_name() == input_name:
-                        input_port = port
-                        input_index = idx
-                        break
 
-                if input_port is None:
+            return outputs
-                    raise ValueError(f"Input '{input_name}' not found in model")
-
-                # Create tensor with the correct element type
-                input_element_type = input_port.get_element_type()
-                input_tensor = ov.Tensor(input_element_type, input_data.shape)
-                np.copyto(input_tensor.data, input_data)
-
-                # Set the input tensor for the specific port index
-                self.infer_request.set_input_tensor(input_index, input_tensor)
-
-            # Run inference
-            self.infer_request.infer()
-
-        # Get all output tensors
-        outputs = []
-        for i in range(len(self.compiled_model.outputs)):
-            outputs.append(self.infer_request.get_output_tensor(i).data)
-
-        return outputs
 
 
 class RKNNModelRunner(BaseModelRunner):
@@ -487,7 +541,7 @@ def get_optimized_runner(
             return OpenVINOModelRunner(model_path, device, model_type, **kwargs)
 
     if (
-        not CudaGraphRunner.is_complex_model(model_type)
+        CudaGraphRunner.is_model_supported(model_type)
         and providers[0] == "CUDAExecutionProvider"
     ):
         options[0] = {

frigate/detectors/plugins/edgetpu_tfl.py

@@ -1,19 +1,20 @@
 import logging
+import math
 import os
 
+import cv2
 import numpy as np
 from pydantic import Field
 from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
 
 try:
     from tflite_runtime.interpreter import Interpreter, load_delegate
 except ModuleNotFoundError:
     from tensorflow.lite.python.interpreter import Interpreter, load_delegate
 
-
 logger = logging.getLogger(__name__)
 
 DETECTOR_KEY = "edgetpu"
@@ -26,6 +27,10 @@ class EdgeTpuDetectorConfig(BaseDetectorConfig):
 
 class EdgeTpuTfl(DetectionApi):
     type_key = DETECTOR_KEY
+    supported_models = [
+        ModelTypeEnum.ssd,
+        ModelTypeEnum.yologeneric,
+    ]
 
     def __init__(self, detector_config: EdgeTpuDetectorConfig):
         device_config = {}
@@ -63,31 +68,294 @@ class EdgeTpuTfl(DetectionApi):
 
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
+        self.model_width = detector_config.model.width
+        self.model_height = detector_config.model.height
+
+        self.min_score = 0.4
+        self.max_detections = 20
+
         self.model_type = detector_config.model.model_type
+        self.model_requires_int8 = self.tensor_input_details[0]["dtype"] == np.int8
+
+        if self.model_type == ModelTypeEnum.yologeneric:
+            logger.debug("Using YOLO preprocessing/postprocessing")
+
+            if len(self.tensor_output_details) not in [2, 3]:
+                logger.error(
+                    f"Invalid count of output tensors in YOLO model. Found {len(self.tensor_output_details)}, expecting 2 or 3."
+                )
+                raise
+
+            self.reg_max = 16  # = 64 dfl_channels // 4 # YOLO standard
+            self.min_logit_value = np.log(
+                self.min_score / (1 - self.min_score)
+            )  # for filtering
+            self._generate_anchors_and_strides()  # decode bounding box DFL
+            self.project = np.arange(
+                self.reg_max, dtype=np.float32
+            )  # for decoding bounding box DFL information
+
+            # Determine YOLO tensor indices and quantization scales for
+            # boxes and class_scores the tensor ordering and names are
+            # not reliable, so use tensor shape to detect which tensor
+            # holds boxes or class scores.
+            # The tensors have shapes (B, N, C)
+            # where N is the number of candidates (=2100 for 320x320)
+            # this may guess wrong if the number of classes is exactly 64
+            output_boxes_index = None
+            output_classes_index = None
+            for i, x in enumerate(self.tensor_output_details):
+                # the nominal index seems to start at 1 instead of 0
+                if len(x["shape"]) == 3 and x["shape"][2] == 64:
+                    output_boxes_index = i
+                elif len(x["shape"]) == 3 and x["shape"][2] > 1:
+                    # require the number of classes to be more than 1
+                    # to differentiate from (not used) max score tensor
+                    output_classes_index = i
+            if output_boxes_index is None or output_classes_index is None:
+                logger.warning("Unrecognized model output, unexpected tensor shapes.")
+                output_classes_index = (
+                    0
+                    if (output_boxes_index is None or output_classes_index == 1)
+                    else 1
+                )  # 0 is default guess
+                output_boxes_index = 1 if (output_boxes_index == 0) else 0
+
+            scores_details = self.tensor_output_details[output_classes_index]
+            self.scores_tensor_index = scores_details["index"]
+            self.scores_scale, self.scores_zero_point = scores_details["quantization"]
+            # calculate the quantized version of the min_score
+            self.min_score_quantized = int(
+                (self.min_logit_value / self.scores_scale) + self.scores_zero_point
+            )
+            self.logit_shift_to_positive_values = (
+                max(0, math.ceil((128 + self.scores_zero_point) * self.scores_scale))
+                + 1
+            )  # round up
+
+            boxes_details = self.tensor_output_details[output_boxes_index]
+            self.boxes_tensor_index = boxes_details["index"]
+            self.boxes_scale, self.boxes_zero_point = boxes_details["quantization"]
+
+        elif self.model_type == ModelTypeEnum.ssd:
+            logger.debug("Using SSD preprocessing/postprocessing")
+
+            # SSD model indices (4 outputs: boxes, class_ids, scores, count)
+            for x in self.tensor_output_details:
+                if len(x["shape"]) == 3:
+                    self.output_boxes_index = x["index"]
+                elif len(x["shape"]) == 1:
+                    self.output_count_index = x["index"]
+
+            self.output_class_ids_index = None
+            self.output_class_scores_index = None
+
+        else:
+            raise Exception(
+                f"{self.model_type} is currently not supported for edgetpu. See the docs for more info on supported models."
+            )
+
+    def _generate_anchors_and_strides(self):
+        # for decoding the bounding box DFL information into xy coordinates
+        all_anchors = []
+        all_strides = []
+        strides = (8, 16, 32)  # YOLO's small, medium, large detection heads
+
+        for stride in strides:
+            feat_h, feat_w = self.model_height // stride, self.model_width // stride
+
+            grid_y, grid_x = np.meshgrid(
+                np.arange(feat_h, dtype=np.float32),
+                np.arange(feat_w, dtype=np.float32),
+                indexing="ij",
+            )
+
+            grid_coords = np.stack((grid_x.flatten(), grid_y.flatten()), axis=1)
+            anchor_points = grid_coords + 0.5
+
+            all_anchors.append(anchor_points)
+            all_strides.append(np.full((feat_h * feat_w, 1), stride, dtype=np.float32))
+
+        self.anchors = np.concatenate(all_anchors, axis=0)
+        self.anchor_strides = np.concatenate(all_strides, axis=0)
+
+    def determine_indexes_for_non_yolo_models(self):
+        """Legacy method for SSD models."""
+        if (
+            self.output_class_ids_index is None
+            or self.output_class_scores_index is None
+        ):
+            for i in range(4):
+                index = self.tensor_output_details[i]["index"]
+                if (
+                    index != self.output_boxes_index
+                    and index != self.output_count_index
+                ):
+                    if (
+                        np.mod(np.float32(self.interpreter.tensor(index)()[0][0]), 1)
+                        == 0.0
+                    ):
+                        self.output_class_ids_index = index
+                    else:
+                        self.output_scores_index = index
+
+    def pre_process(self, tensor_input):
+        if self.model_requires_int8:
+            tensor_input = np.bitwise_xor(tensor_input, 128).view(
+                np.int8
+            )  # shift by -128
+        return tensor_input
 
     def detect_raw(self, tensor_input):
+        tensor_input = self.pre_process(tensor_input)
+
         self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
         self.interpreter.invoke()
 
-        boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
+        if self.model_type == ModelTypeEnum.yologeneric:
-        class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
+            # Multi-tensor YOLO model with (non-standard B(H*W)C output format).
-        scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
+            # (the comments indicate the shape of tensors,
-        count = int(
+            # using "2100" as the anchor count (for image size of 320x320),
-            self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
+            # "NC" as number of classes,
-        )
+            # "N" as the count that survive after min-score filtering)
+            # TENSOR A) class scores (1, 2100, NC) with logit values
+            # TENSOR B) box coordinates (1, 2100, 64) encoded as dfl scores
+            # Recommend that the model clamp the logit values in tensor (A)
+            # to the range [-4,+4] to preserve precision from [2%,98%]
+            # and because NMS requires the min_score parameter to be >= 0
 
-        detections = np.zeros((20, 6), np.float32)
+            # don't dequantize scores data yet, wait until the low-confidence
+            # candidates are filtered out from the overall result set.
+            # This reduces the work and makes post-processing faster.
+            # this method works with raw quantized numbers when possible,
+            # which relies on the value of the scale factor to be >0.
+            # This speeds up max and argmax operations.
+            # Get max confidence for each detection and create the mask
+            detections = np.zeros(
+                (self.max_detections, 6), np.float32
+            )  # initialize zero results
+            scores_output_quantized = self.interpreter.get_tensor(
+                self.scores_tensor_index
+            )[0]  # (2100, NC)
+            max_scores_quantized = np.max(scores_output_quantized, axis=1)  # (2100,)
+            mask = max_scores_quantized >= self.min_score_quantized  # (2100,)
 
-        for i in range(count):
+            if not np.any(mask):
-            if scores[i] < 0.4 or i == 20:
+                return detections  # empty results
-                break
+
-            detections[i] = [
+            max_scores_filtered_shiftedpositive = (
-                class_ids[i],
+                (max_scores_quantized[mask] - self.scores_zero_point)
-                float(scores[i]),
+                * self.scores_scale
-                boxes[i][0],
+            ) + self.logit_shift_to_positive_values  # (N,1) shifted logit values
-                boxes[i][1],
+            scores_output_quantized_filtered = scores_output_quantized[mask]
-                boxes[i][2],
+
-                boxes[i][3],
+            # dequantize boxes. NMS needs them to be in float format
+            # remove candidates with probabilities < threshold
+            boxes_output_quantized_filtered = (
+                self.interpreter.get_tensor(self.boxes_tensor_index)[0]
+            )[mask]  # (N, 64)
+            boxes_output_filtered = (
+                boxes_output_quantized_filtered.astype(np.float32)
+                - self.boxes_zero_point
+            ) * self.boxes_scale
+
+            # 2. Decode DFL to distances (ltrb)
+            dfl_distributions = boxes_output_filtered.reshape(
+                -1, 4, self.reg_max
+            )  # (N, 4, 16)
+
+            # Softmax over the 16 bins
+            dfl_max = np.max(dfl_distributions, axis=2, keepdims=True)
+            dfl_exp = np.exp(dfl_distributions - dfl_max)
+            dfl_probs = dfl_exp / np.sum(dfl_exp, axis=2, keepdims=True)  # (N, 4, 16)
+
+            # Weighted sum: (N, 4, 16) * (16,) -> (N, 4)
+            distances = np.einsum("pcr,r->pc", dfl_probs, self.project)
+
+            # Calculate box corners in pixel coordinates
+            anchors_filtered = self.anchors[mask]
+            anchor_strides_filtered = self.anchor_strides[mask]
+            x1y1 = (
+                anchors_filtered - distances[:, [0, 1]]
+            ) * anchor_strides_filtered  # (N, 2)
+            x2y2 = (
+                anchors_filtered + distances[:, [2, 3]]
+            ) * anchor_strides_filtered  # (N, 2)
+            boxes_filtered_decoded = np.concatenate((x1y1, x2y2), axis=-1)  # (N, 4)
+
+            # 9. Apply NMS. Use logit scores here to defer sigmoid()
+            # until after filtering out redundant boxes
+            # Shift the logit scores to be non-negative (required by cv2)
+            indices = cv2.dnn.NMSBoxes(
+                bboxes=boxes_filtered_decoded,
+                scores=max_scores_filtered_shiftedpositive,
+                score_threshold=(
+                    self.min_logit_value + self.logit_shift_to_positive_values
+                ),
+                nms_threshold=0.4,  # should this be a model config setting?
+            )
+            num_detections = len(indices)
+            if num_detections == 0:
+                return detections  # empty results
+
+            nms_indices = np.array(indices, dtype=np.int32).ravel()  # or .flatten()
+            if num_detections > self.max_detections:
+                nms_indices = nms_indices[: self.max_detections]
+                num_detections = self.max_detections
+            kept_logits_quantized = scores_output_quantized_filtered[nms_indices]
+            class_ids_post_nms = np.argmax(kept_logits_quantized, axis=1)
+
+            # Extract the final boxes and scores using fancy indexing
+            final_boxes = boxes_filtered_decoded[nms_indices]
+            final_scores_logits = (
+                max_scores_filtered_shiftedpositive[nms_indices]
+                - self.logit_shift_to_positive_values
+            )  # Unshifted logits
+
+            # Detections array format: [class_id, score, ymin, xmin, ymax, xmax]
+            detections[:num_detections, 0] = class_ids_post_nms
+            detections[:num_detections, 1] = 1.0 / (
+                1.0 + np.exp(-final_scores_logits)
+            )  # sigmoid
+            detections[:num_detections, 2] = final_boxes[:, 1] / self.model_height
+            detections[:num_detections, 3] = final_boxes[:, 0] / self.model_width
+            detections[:num_detections, 4] = final_boxes[:, 3] / self.model_height
+            detections[:num_detections, 5] = final_boxes[:, 2] / self.model_width
+            return detections
+
+        elif self.model_type == ModelTypeEnum.ssd:
+            self.determine_indexes_for_non_yolo_models()
+            boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
+            class_ids = self.interpreter.tensor(
+                self.tensor_output_details[1]["index"]
+            )()[0]
+            scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[
+                0
             ]
+            count = int(
+                self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
+            )
 
-        return detections
+            detections = np.zeros((self.max_detections, 6), np.float32)
+
+            for i in range(count):
+                if scores[i] < self.min_score:
+                    break
+                if i == self.max_detections:
+                    logger.debug(f"Too many detections ({count})!")
+                    break
+                detections[i] = [
+                    class_ids[i],
+                    float(scores[i]),
+                    boxes[i][0],
+                    boxes[i][1],
+                    boxes[i][2],
+                    boxes[i][3],
+                ]
+
+            return detections
+
+        else:
+            raise Exception(
+                f"{self.model_type} is currently not supported for edgetpu. See the docs for more info on supported models."
+            )

frigate/detectors/plugins/memryx.py

@@ -2,7 +2,6 @@ import glob
 import logging
 import os
 import shutil
-import time
 import urllib.request
 import zipfile
 from queue import Queue
@@ -17,7 +16,7 @@ from frigate.detectors.detector_config import (
     BaseDetectorConfig,
     ModelTypeEnum,
 )
-from frigate.util.model import post_process_yolo
+from frigate.util.file import FileLock
 
 logger = logging.getLogger(__name__)
 
@@ -55,6 +54,9 @@ class MemryXDetector(DetectionApi):
             )
             return
 
+        # Initialize stop_event as None, will be set later by set_stop_event()
+        self.stop_event = None
+
         model_cfg = getattr(detector_config, "model", None)
 
         # Check if model_type was explicitly set by the user
@@ -177,44 +179,14 @@ class MemryXDetector(DetectionApi):
             logger.error(f"Failed to initialize MemryX model: {e}")
             raise
 
-    def _acquire_file_lock(self, lock_path: str, timeout: int = 60, poll: float = 0.2):
-        """
-        Create an exclusive lock file. Blocks (with polling) until it can acquire,
-        or raises TimeoutError. Uses only stdlib (os.O_EXCL).
-        """
-        start = time.time()
-        while True:
-            try:
-                fd = os.open(lock_path, os.O_CREAT | os.O_EXCL | os.O_RDWR)
-                os.close(fd)
-                return
-            except FileExistsError:
-                if time.time() - start > timeout:
-                    raise TimeoutError(f"Timeout waiting for lock: {lock_path}")
-                time.sleep(poll)
-
-    def _release_file_lock(self, lock_path: str):
-        """Best-effort removal of the lock file."""
-        try:
-            os.remove(lock_path)
-        except FileNotFoundError:
-            pass
-
-    def load_yolo_constants(self):
-        base = f"{self.cache_dir}/{self.model_folder}"
-        # constants for yolov9 post-processing
-        self.const_A = np.load(f"{base}/_model_22_Constant_9_output_0.npy")
-        self.const_B = np.load(f"{base}/_model_22_Constant_10_output_0.npy")
-        self.const_C = np.load(f"{base}/_model_22_Constant_12_output_0.npy")
-
     def check_and_prepare_model(self):
         if not os.path.exists(self.cache_dir):
             os.makedirs(self.cache_dir, exist_ok=True)
 
         lock_path = os.path.join(self.cache_dir, f".{self.model_folder}.lock")
-        self._acquire_file_lock(lock_path)
+        lock = FileLock(lock_path, timeout=60)
 
-        try:
+        with lock:
             # ---------- CASE 1: user provided a custom model path ----------
             if self.memx_model_path:
                 if not self.memx_model_path.endswith(".zip"):
@@ -258,7 +230,6 @@ class MemryXDetector(DetectionApi):
 
                 # Handle post model requirements by model type
                 if self.memx_model_type in [
-                    ModelTypeEnum.yologeneric,
                     ModelTypeEnum.yolonas,
                     ModelTypeEnum.ssd,
                 ]:
@@ -267,7 +238,10 @@ class MemryXDetector(DetectionApi):
                             f"No *_post.onnx file found in custom model zip for {self.memx_model_type.name}."
                         )
                     self.memx_post_model = post_candidates[0]
-                elif self.memx_model_type == ModelTypeEnum.yolox:
+                elif self.memx_model_type in [
+                    ModelTypeEnum.yolox,
+                    ModelTypeEnum.yologeneric,
+                ]:
                     # Explicitly ignore any post model even if present
                     self.memx_post_model = None
                 else:
@@ -295,8 +269,6 @@ class MemryXDetector(DetectionApi):
                 logger.info("Using cached models.")
                 self.memx_model_path = dfp_path
                 self.memx_post_model = post_path
-                if self.memx_model_type == ModelTypeEnum.yologeneric:
-                    self.load_yolo_constants()
                 return
 
             # ---------- CASE 3: download MemryX model (no cache) ----------
@@ -325,9 +297,6 @@ class MemryXDetector(DetectionApi):
                     else None
                 )
 
-                if self.memx_model_type == ModelTypeEnum.yologeneric:
-                    self.load_yolo_constants()
-
             finally:
                 if os.path.exists(zip_path):
                     try:
@@ -338,9 +307,6 @@ class MemryXDetector(DetectionApi):
                             f"Failed to remove downloaded zip {zip_path}: {e}"
                         )
 
-        finally:
-            self._release_file_lock(lock_path)
-
     def send_input(self, connection_id, tensor_input: np.ndarray):
         """Pre-process (if needed) and send frame to MemryX input queue"""
         if tensor_input is None:
@@ -399,26 +365,43 @@ class MemryXDetector(DetectionApi):
     def process_input(self):
         """Input callback function: wait for frames in the input queue, preprocess, and send to MX3 (return)"""
         while True:
+            # Check if shutdown is requested
+            if self.stop_event and self.stop_event.is_set():
+                logger.debug("[process_input] Stop event detected, returning None")
+                return None
             try:
-                # Wait for a frame from the queue (blocking call)
+                # Wait for a frame from the queue with timeout to check stop_event periodically
-                frame = self.capture_queue.get(
+                frame = self.capture_queue.get(block=True, timeout=0.5)
-                    block=True
-                )  # Blocks until data is available
 
                 return frame
 
             except Exception as e:
-                logger.info(f"[process_input] Error processing input: {e}")
+                # Silently handle queue.Empty timeouts (expected during normal operation)
-                time.sleep(0.1)  # Prevent busy waiting in case of error
+                # Log any other unexpected exceptions
+                if "Empty" not in str(type(e).__name__):
+                    logger.warning(f"[process_input] Unexpected error: {e}")
+                # Loop continues and will check stop_event at the top
 
     def receive_output(self):
         """Retrieve processed results from MemryX output queue + a copy of the original frame"""
-        connection_id = (
+        try:
-            self.capture_id_queue.get()
+            # Get connection ID with timeout
-        )  # Get the corresponding connection ID
+            connection_id = self.capture_id_queue.get(
-        detections = self.output_queue.get()  # Get detections from MemryX
+                block=True, timeout=1.0
+            )  # Get the corresponding connection ID
+            detections = self.output_queue.get()  # Get detections from MemryX
 
-        return connection_id, detections
+            return connection_id, detections
+
+        except Exception as e:
+            # On timeout or stop event, return None
+            if self.stop_event and self.stop_event.is_set():
+                logger.debug("[receive_output] Stop event detected, exiting")
+            # Silently handle queue.Empty timeouts, they're expected during normal operation
+            elif "Empty" not in str(type(e).__name__):
+                logger.warning(f"[receive_output] Error receiving output: {e}")
+
+            return None, None
 
     def post_process_yolonas(self, output):
         predictions = output[0]
@@ -625,127 +608,232 @@ class MemryXDetector(DetectionApi):
 
         self.output_queue.put(final_detections)
 
-    def onnx_reshape_with_allowzero(
+    def _generate_anchors(self, sizes=[80, 40, 20]):
-        self, data: np.ndarray, shape: np.ndarray, allowzero: int = 0
+        """Generate anchor points for YOLOv9 style processing"""
+        yscales = []
+        xscales = []
+        for s in sizes:
+            r = np.arange(s) + 0.5
+            yscales.append(np.repeat(r, s))
+            xscales.append(np.repeat(r[None, ...], s, axis=0).flatten())
+
+        yscales = np.concatenate(yscales)
+        xscales = np.concatenate(xscales)
+        anchors = np.stack([xscales, yscales], axis=1)
+        return anchors
+
+    def _generate_scales(self, sizes=[80, 40, 20]):
+        """Generate scaling factors for each detection level"""
+        factors = [8, 16, 32]
+        s = np.concatenate([np.ones([int(s * s)]) * f for s, f in zip(sizes, factors)])
+        return s[:, None]
+
+    @staticmethod
+    def _softmax(x: np.ndarray, axis: int) -> np.ndarray:
+        """Efficient softmax implementation"""
+        x = x - np.max(x, axis=axis, keepdims=True)
+        np.exp(x, out=x)
+        x /= np.sum(x, axis=axis, keepdims=True)
+        return x
+
+    def dfl(self, x: np.ndarray) -> np.ndarray:
+        """Distribution Focal Loss decoding - YOLOv9 style"""
+        x = x.reshape(-1, 4, 16)
+        weights = np.arange(16, dtype=np.float32)
+        p = self._softmax(x, axis=2)
+        p = p * weights[None, None, :]
+        out = np.sum(p, axis=2, keepdims=False)
+        return out
+
+    def dist2bbox(
+        self, x: np.ndarray, anchors: np.ndarray, scales: np.ndarray
     ) -> np.ndarray:
-        shape = shape.astype(int)
+        """Convert distances to bounding boxes - YOLOv9 style"""
-        input_shape = data.shape
+        lt = x[:, :2]
-        output_shape = []
+        rb = x[:, 2:]
 
-        for i, dim in enumerate(shape):
+        x1y1 = anchors - lt
-            if dim == 0 and allowzero == 0:
+        x2y2 = anchors + rb
-                output_shape.append(input_shape[i])  # Copy dimension from input
-            else:
-                output_shape.append(dim)
 
-        # Now let NumPy infer any -1 if needed
+        wh = x2y2 - x1y1
-        reshaped = np.reshape(data, output_shape)
+        c_xy = (x1y1 + x2y2) / 2
 
-        return reshaped
+        out = np.concatenate([c_xy, wh], axis=1)
+        out = out * scales
+        return out
+
+    def post_process_yolo_optimized(self, outputs):
+        """
+        Custom YOLOv9 post-processing optimized for MemryX ONNX outputs.
+        Implements DFL decoding, confidence filtering, and NMS in pure NumPy.
+        """
+        # YOLOv9 outputs: 6 outputs (lbox, lcls, mbox, mcls, sbox, scls)
+        conv_out1, conv_out2, conv_out3, conv_out4, conv_out5, conv_out6 = outputs
+
+        # Determine grid sizes based on input resolution
+        # YOLOv9 uses 3 detection heads with strides [8, 16, 32]
+        # Grid sizes = input_size / stride
+        sizes = [
+            self.memx_model_height
+            // 8,  # Large objects (e.g., 80 for 640x640, 40 for 320x320)
+            self.memx_model_height
+            // 16,  # Medium objects (e.g., 40 for 640x640, 20 for 320x320)
+            self.memx_model_height
+            // 32,  # Small objects (e.g., 20 for 640x640, 10 for 320x320)
+        ]
+
+        # Generate anchors and scales if not already done
+        if not hasattr(self, "anchors"):
+            self.anchors = self._generate_anchors(sizes)
+            self.scales = self._generate_scales(sizes)
+
+        # Process outputs in YOLOv9 format: reshape and moveaxis for ONNX format
+        lbox = np.moveaxis(conv_out1, 1, -1)  # Large boxes
+        lcls = np.moveaxis(conv_out2, 1, -1)  # Large classes
+        mbox = np.moveaxis(conv_out3, 1, -1)  # Medium boxes
+        mcls = np.moveaxis(conv_out4, 1, -1)  # Medium classes
+        sbox = np.moveaxis(conv_out5, 1, -1)  # Small boxes
+        scls = np.moveaxis(conv_out6, 1, -1)  # Small classes
+
+        # Determine number of classes dynamically from the class output shape
+        # lcls shape should be (batch, height, width, num_classes)
+        num_classes = lcls.shape[-1]
+
+        # Validate that all class outputs have the same number of classes
+        if not (mcls.shape[-1] == num_classes and scls.shape[-1] == num_classes):
+            raise ValueError(
+                f"Class output shapes mismatch: lcls={lcls.shape}, mcls={mcls.shape}, scls={scls.shape}"
+            )
+
+        # Concatenate boxes and classes
+        boxes = np.concatenate(
+            [
+                lbox.reshape(-1, 64),  # 64 is for 4 bbox coords * 16 DFL bins
+                mbox.reshape(-1, 64),
+                sbox.reshape(-1, 64),
+            ],
+            axis=0,
+        )
+
+        classes = np.concatenate(
+            [
+                lcls.reshape(-1, num_classes),
+                mcls.reshape(-1, num_classes),
+                scls.reshape(-1, num_classes),
+            ],
+            axis=0,
+        )
+
+        # Apply sigmoid to classes
+        classes = self.sigmoid(classes)
+
+        # Apply DFL to box predictions
+        boxes = self.dfl(boxes)
+
+        # YOLOv9 postprocessing with confidence filtering and NMS
+        confidence_thres = 0.4
+        iou_thres = 0.6
+
+        # Find the class with the highest score for each detection
+        max_scores = np.max(classes, axis=1)  # Maximum class score for each detection
+        class_ids = np.argmax(classes, axis=1)  # Index of the best class
+
+        # Filter out detections with scores below the confidence threshold
+        valid_indices = np.where(max_scores >= confidence_thres)[0]
+        if len(valid_indices) == 0:
+            # Return empty detections array
+            final_detections = np.zeros((20, 6), np.float32)
+            return final_detections
+
+        # Select only valid detections
+        valid_boxes = boxes[valid_indices]
+        valid_class_ids = class_ids[valid_indices]
+        valid_scores = max_scores[valid_indices]
+
+        # Convert distances to actual bounding boxes using anchors and scales
+        valid_boxes = self.dist2bbox(
+            valid_boxes, self.anchors[valid_indices], self.scales[valid_indices]
+        )
+
+        # Convert bounding box coordinates from (x_center, y_center, w, h) to (x_min, y_min, x_max, y_max)
+        x_center, y_center, width, height = (
+            valid_boxes[:, 0],
+            valid_boxes[:, 1],
+            valid_boxes[:, 2],
+            valid_boxes[:, 3],
+        )
+        x_min = x_center - width / 2
+        y_min = y_center - height / 2
+        x_max = x_center + width / 2
+        y_max = y_center + height / 2
+
+        # Convert to format expected by cv2.dnn.NMSBoxes: [x, y, width, height]
+        boxes_for_nms = []
+        scores_for_nms = []
+
+        for i in range(len(valid_indices)):
+            # Ensure coordinates are within bounds and positive
+            x_min_clipped = max(0, x_min[i])
+            y_min_clipped = max(0, y_min[i])
+            x_max_clipped = min(self.memx_model_width, x_max[i])
+            y_max_clipped = min(self.memx_model_height, y_max[i])
+
+            width_clipped = x_max_clipped - x_min_clipped
+            height_clipped = y_max_clipped - y_min_clipped
+
+            if width_clipped > 0 and height_clipped > 0:
+                boxes_for_nms.append(
+                    [x_min_clipped, y_min_clipped, width_clipped, height_clipped]
+                )
+                scores_for_nms.append(float(valid_scores[i]))
+
+        final_detections = np.zeros((20, 6), np.float32)
+
+        if len(boxes_for_nms) == 0:
+            return final_detections
+
+        # Apply NMS using OpenCV
+        indices = cv2.dnn.NMSBoxes(
+            boxes_for_nms, scores_for_nms, confidence_thres, iou_thres
+        )
+
+        if len(indices) > 0:
+            # Flatten indices if they are returned as a list of arrays
+            if isinstance(indices[0], list) or isinstance(indices[0], np.ndarray):
+                indices = [i[0] for i in indices]
+
+            # Limit to top 20 detections
+            indices = indices[:20]
+
+            # Convert to Frigate format: [class_id, confidence, y_min, x_min, y_max, x_max] (normalized)
+            for i, idx in enumerate(indices):
+                class_id = valid_class_ids[idx]
+                confidence = valid_scores[idx]
+
+                # Get the box coordinates
+                box = boxes_for_nms[idx]
+                x_min_norm = box[0] / self.memx_model_width
+                y_min_norm = box[1] / self.memx_model_height
+                x_max_norm = (box[0] + box[2]) / self.memx_model_width
+                y_max_norm = (box[1] + box[3]) / self.memx_model_height
+
+                final_detections[i] = [
+                    class_id,
+                    confidence,
+                    y_min_norm,  # Frigate expects y_min first
+                    x_min_norm,
+                    y_max_norm,
+                    x_max_norm,
+                ]
+
+        return final_detections
 
     def process_output(self, *outputs):
         """Output callback function -- receives frames from the MX3 and triggers post-processing"""
         if self.memx_model_type == ModelTypeEnum.yologeneric:
-            if not self.memx_post_model:
+            # Use complete YOLOv9-style postprocessing (includes NMS)
-                conv_out1 = outputs[0]
+            final_detections = self.post_process_yolo_optimized(outputs)
-                conv_out2 = outputs[1]
-                conv_out3 = outputs[2]
-                conv_out4 = outputs[3]
-                conv_out5 = outputs[4]
-                conv_out6 = outputs[5]
 
-                concat_1 = self.onnx_concat([conv_out1, conv_out2], axis=1)
-                concat_2 = self.onnx_concat([conv_out3, conv_out4], axis=1)
-                concat_3 = self.onnx_concat([conv_out5, conv_out6], axis=1)
-
-                shape = np.array([1, 144, -1], dtype=np.int64)
-
-                reshaped_1 = self.onnx_reshape_with_allowzero(
-                    concat_1, shape, allowzero=0
-                )
-                reshaped_2 = self.onnx_reshape_with_allowzero(
-                    concat_2, shape, allowzero=0
-                )
-                reshaped_3 = self.onnx_reshape_with_allowzero(
-                    concat_3, shape, allowzero=0
-                )
-
-                concat_4 = self.onnx_concat([reshaped_1, reshaped_2, reshaped_3], 2)
-
-                axis = 1
-                split_sizes = [64, 80]
-
-                # Calculate indices at which to split
-                indices = np.cumsum(split_sizes)[
-                    :-1
-                ]  # [64] — split before the second chunk
-
-                # Perform split along axis 1
-                split_0, split_1 = np.split(concat_4, indices, axis=axis)
-
-                num_boxes = 2100 if self.memx_model_height == 320 else 8400
-                shape1 = np.array([1, 4, 16, num_boxes])
-                reshape_4 = self.onnx_reshape_with_allowzero(
-                    split_0, shape1, allowzero=0
-                )
-
-                transpose_1 = reshape_4.transpose(0, 2, 1, 3)
-
-                axis = 1  # As per ONNX softmax node
-
-                # Subtract max for numerical stability
-                x_max = np.max(transpose_1, axis=axis, keepdims=True)
-                x_exp = np.exp(transpose_1 - x_max)
-                x_sum = np.sum(x_exp, axis=axis, keepdims=True)
-                softmax_output = x_exp / x_sum
-
-                # Weight W from the ONNX initializer (1, 16, 1, 1) with values 0 to 15
-                W = np.arange(16, dtype=np.float32).reshape(
-                    1, 16, 1, 1
-                )  # (1, 16, 1, 1)
-
-                # Apply 1x1 convolution: this is a weighted sum over channels
-                conv_output = np.sum(
-                    softmax_output * W, axis=1, keepdims=True
-                )  # shape: (1, 1, 4, 8400)
-
-                shape2 = np.array([1, 4, num_boxes])
-                reshape_5 = self.onnx_reshape_with_allowzero(
-                    conv_output, shape2, allowzero=0
-                )
-
-                # ONNX Slice — get first 2 channels: [0:2] along axis 1
-                slice_output1 = reshape_5[:, 0:2, :]  # Result: (1, 2, 8400)
-
-                # Slice channels 2 to 4 → axis = 1
-                slice_output2 = reshape_5[:, 2:4, :]
-
-                # Perform Subtraction
-                sub_output = self.const_A - slice_output1  # Equivalent to ONNX Sub
-
-                # Perform the ONNX-style Add
-                add_output = self.const_B + slice_output2
-
-                sub1 = add_output - sub_output
-
-                add1 = sub_output + add_output
-
-                div_output = add1 / 2.0
-
-                concat_5 = self.onnx_concat([div_output, sub1], axis=1)
-
-                # Expand B to (1, 1, 8400) so it can broadcast across axis=1 (4 channels)
-                const_C_expanded = self.const_C[:, np.newaxis, :]  # Shape: (1, 1, 8400)
-
-                # Perform ONNX-style element-wise multiplication
-                mul_output = concat_5 * const_C_expanded  # Result: (1, 4, 8400)
-
-                sigmoid_output = self.sigmoid(split_1)
-                outputs = self.onnx_concat([mul_output, sigmoid_output], axis=1)
-
-            final_detections = post_process_yolo(
-                outputs, self.memx_model_width, self.memx_model_height
-            )
             self.output_queue.put(final_detections)
 
         elif self.memx_model_type == ModelTypeEnum.yolonas:
@@ -762,6 +850,19 @@ class MemryXDetector(DetectionApi):
                 f"{self.memx_model_type} is currently not supported for memryx. See the docs for more info on supported models."
             )
 
+    def set_stop_event(self, stop_event):
+        """Set the stop event for graceful shutdown."""
+        self.stop_event = stop_event
+
+    def shutdown(self):
+        """Gracefully shutdown the MemryX accelerator"""
+        try:
+            if hasattr(self, "accl") and self.accl is not None:
+                self.accl.shutdown()
+                logger.info("MemryX accelerator shutdown complete")
+        except Exception as e:
+            logger.error(f"Error during MemryX shutdown: {e}")
+
     def detect_raw(self, tensor_input: np.ndarray):
         """Removed synchronous detect_raw() function so that we only use async"""
         return 0

frigate/embeddings/embeddings.py

@@ -29,7 +29,7 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event, Trigger
 from frigate.types import ModelStatusTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, serialize
-from frigate.util.path import get_event_thumbnail_bytes
+from frigate.util.file import get_event_thumbnail_bytes
 
 from .onnx.jina_v1_embedding import JinaV1ImageEmbedding, JinaV1TextEmbedding
 from .onnx.jina_v2_embedding import JinaV2Embedding
@@ -472,7 +472,7 @@ class Embeddings:
                                 )
                                 thumbnail_missing = True
                         except DoesNotExist:
-                            logger.warning(
+                            logger.debug(
                                 f"Event ID {trigger.data} for trigger {trigger_name} does not exist."
                             )
                             continue

frigate/embeddings/maintainer.py

@@ -62,8 +62,8 @@ from frigate.events.types import EventTypeEnum, RegenerateDescriptionEnum
 from frigate.genai import get_genai_client
 from frigate.models import Event, Recordings, ReviewSegment, Trigger
 from frigate.util.builtin import serialize
+from frigate.util.file import get_event_thumbnail_bytes
 from frigate.util.image import SharedMemoryFrameManager
-from frigate.util.path import get_event_thumbnail_bytes
 
 from .embeddings import Embeddings
 
@@ -158,11 +158,13 @@ class EmbeddingMaintainer(threading.Thread):
         self.realtime_processors: list[RealTimeProcessorApi] = []
 
         if self.config.face_recognition.enabled:
+            logger.debug("Face recognition enabled, initializing FaceRealTimeProcessor")
             self.realtime_processors.append(
                 FaceRealTimeProcessor(
                     self.config, self.requestor, self.event_metadata_publisher, metrics
                 )
             )
+            logger.debug("FaceRealTimeProcessor initialized successfully")
 
         if self.config.classification.bird.enabled:
             self.realtime_processors.append(
@@ -193,6 +195,7 @@ class EmbeddingMaintainer(threading.Thread):
                     self.config,
                     model_config,
                     self.event_metadata_publisher,
+                    self.requestor,
                     self.metrics,
                 )
             )
@@ -224,7 +227,9 @@ class EmbeddingMaintainer(threading.Thread):
             for c in self.config.cameras.values()
         ):
             self.post_processors.append(
-                AudioTranscriptionPostProcessor(self.config, self.requestor, metrics)
+                AudioTranscriptionPostProcessor(
+                    self.config, self.requestor, self.embeddings, metrics
+                )
             )
 
         semantic_trigger_processor: SemanticTriggerProcessor | None = None
@@ -283,44 +288,66 @@ class EmbeddingMaintainer(threading.Thread):
         logger.info("Exiting embeddings maintenance...")
 
     def _check_classification_config_updates(self) -> None:
-        """Check for classification config updates and add new processors."""
+        """Check for classification config updates and add/remove processors."""
         topic, model_config = self.classification_config_subscriber.check_for_update()
 
-        if topic and model_config:
+        if topic:
             model_name = topic.split("/")[-1]
-            self.config.classification.custom[model_name] = model_config
 
-            # Check if processor already exists
+            if model_config is None:
-            for processor in self.realtime_processors:
+                self.realtime_processors = [
-                if isinstance(
+                    processor
-                    processor,
+                    for processor in self.realtime_processors
-                    (
+                    if not (
-                        CustomStateClassificationProcessor,
+                        isinstance(
-                        CustomObjectClassificationProcessor,
+                            processor,
-                    ),
+                            (
-                ):
+                                CustomStateClassificationProcessor,
-                    if processor.model_config.name == model_name:
+                                CustomObjectClassificationProcessor,
-                        logger.debug(
+                            ),
-                            f"Classification processor for model {model_name} already exists, skipping"
                         )
-                        return
+                        and processor.model_config.name == model_name
+                    )
+                ]
 
-            if model_config.state_config is not None:
+                logger.info(
-                processor = CustomStateClassificationProcessor(
+                    f"Successfully removed classification processor for model: {model_name}"
-                    self.config, model_config, self.requestor, self.metrics
                 )
             else:
-                processor = CustomObjectClassificationProcessor(
+                self.config.classification.custom[model_name] = model_config
-                    self.config,
-                    model_config,
-                    self.event_metadata_publisher,
-                    self.metrics,
-                )
 
-            self.realtime_processors.append(processor)
+                # Check if processor already exists
-            logger.info(
+                for processor in self.realtime_processors:
-                f"Added classification processor for model: {model_name} (type: {type(processor).__name__})"
+                    if isinstance(
-            )
+                        processor,
+                        (
+                            CustomStateClassificationProcessor,
+                            CustomObjectClassificationProcessor,
+                        ),
+                    ):
+                        if processor.model_config.name == model_name:
+                            logger.debug(
+                                f"Classification processor for model {model_name} already exists, skipping"
+                            )
+                            return
+
+                if model_config.state_config is not None:
+                    processor = CustomStateClassificationProcessor(
+                        self.config, model_config, self.requestor, self.metrics
+                    )
+                else:
+                    processor = CustomObjectClassificationProcessor(
+                        self.config,
+                        model_config,
+                        self.event_metadata_publisher,
+                        self.requestor,
+                        self.metrics,
+                    )
+
+                self.realtime_processors.append(processor)
+                logger.info(
+                    f"Added classification processor for model: {model_name} (type: {type(processor).__name__})"
+                )
 
     def _process_requests(self) -> None:
         """Process embeddings requests"""
@@ -374,7 +401,14 @@ class EmbeddingMaintainer(threading.Thread):
 
         source_type, _, camera, frame_name, data = update
 
+        logger.debug(
+            f"Received update - source_type: {source_type}, camera: {camera}, data label: {data.get('label') if data else 'None'}"
+        )
+
         if not camera or source_type != EventTypeEnum.tracked_object:
+            logger.debug(
+                f"Skipping update - camera: {camera}, source_type: {source_type}"
+            )
             return
 
         if self.config.semantic_search.enabled:
@@ -384,6 +418,9 @@ class EmbeddingMaintainer(threading.Thread):
 
         # no need to process updated objects if no processors are active
         if len(self.realtime_processors) == 0 and len(self.post_processors) == 0:
+            logger.debug(
+                f"No processors active - realtime: {len(self.realtime_processors)}, post: {len(self.post_processors)}"
+            )
             return
 
         # Create our own thumbnail based on the bounding box and the frame time
@@ -392,6 +429,7 @@ class EmbeddingMaintainer(threading.Thread):
                 frame_name, camera_config.frame_shape_yuv
             )
         except FileNotFoundError:
+            logger.debug(f"Frame {frame_name} not found for camera {camera}")
             pass
 
         if yuv_frame is None:
@@ -400,7 +438,11 @@ class EmbeddingMaintainer(threading.Thread):
             )
             return
 
+        logger.debug(
+            f"Processing {len(self.realtime_processors)} realtime processors for object {data.get('id')} (label: {data.get('label')})"
+        )
         for processor in self.realtime_processors:
+            logger.debug(f"Calling process_frame on {processor.__class__.__name__}")
             processor.process_frame(data, yuv_frame)
 
         for processor in self.post_processors:

frigate/events/cleanup.py

@@ -12,7 +12,7 @@ from frigate.config import FrigateConfig
 from frigate.const import CLIPS_DIR
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event, Timeline
-from frigate.util.path import delete_event_snapshot, delete_event_thumbnail
+from frigate.util.file import delete_event_snapshot, delete_event_thumbnail
 
 logger = logging.getLogger(__name__)
 

frigate/genai/__init__.py

@@ -51,8 +51,7 @@ class GenAIClient:
         def get_concern_prompt() -> str:
             if concerns:
                 concern_list = "\n    - ".join(concerns)
-                return f"""
+                return f"""- `other_concerns` (list of strings): Include a list of any of the following concerns that are occurring:
-- `other_concerns` (list of strings): Include a list of any of the following concerns that are occurring:
     - {concern_list}"""
             else:
                 return ""
@@ -70,7 +69,7 @@ class GenAIClient:
                 return "\n- (No objects detected)"
 
         context_prompt = f"""
-Your task is to analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"].replace("_", " ")} security camera.
+Your task is to analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"]} security camera.
 
 ## Normal Activity Patterns for This Property
 
@@ -100,8 +99,8 @@ When forming your description:
 ## Response Format
 
 Your response MUST be a flat JSON object with:
-- `title` (string): A concise, direct title that describes the purpose or overall action, not just what you literally see. 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", "Joe accessing vehicle", "Joe and person on front porch".
+- `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. **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.
+- `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.
 - `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()}
@@ -110,11 +109,11 @@ Your response MUST be a flat JSON object with:
 
 - Frame 1 = earliest, Frame {len(thumbnails)} = latest
 - Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds
-- Zones involved: {", ".join(z.replace("_", " ").title() for z in review_data["zones"]) or "None"}
+- Zones involved: {", ".join(review_data["zones"]) if review_data["zones"] else "None"}
 
 ## Objects in Scene
 
-Each line represents a detection state, not necessarily unique individuals. Objects with names in parentheses (e.g., "Name (person)") are verified identities. Objects without names (e.g., "Person") are detected but not identified.
+Each line represents a detection state, not necessarily unique individuals. Parentheses indicate object type or category, use only the name/label in your response, not the parentheses.
 
 **CRITICAL: When you see both recognized and unrecognized entries of the same type (e.g., "Joe (person)" and "Person"), visually count how many distinct people/objects you actually see based on appearance and clothing. If you observe only ONE person throughout the sequence, use ONLY the recognized name (e.g., "Joe"). The same person may be recognized in some frames but not others. Only describe both if you visually see MULTIPLE distinct people with clearly different appearances.**
 
@@ -178,50 +177,60 @@ Each line represents a detection state, not necessarily unique individuals. Obje
         self,
         start_ts: float,
         end_ts: float,
-        segments: list[dict[str, Any]],
+        events: list[dict[str, Any]],
         debug_save: bool,
     ) -> str | None:
         """Generate a summary of review item descriptions over a period of time."""
         time_range = f"{datetime.datetime.fromtimestamp(start_ts).strftime('%B %d, %Y at %I:%M %p')} to {datetime.datetime.fromtimestamp(end_ts).strftime('%B %d, %Y at %I:%M %p')}"
         timeline_summary_prompt = f"""
-You are a security officer.
+You are a security officer writing a concise security report.
-Time range: {time_range}.
-Input: JSON list with "title", "scene", "confidence", "potential_threat_level" (1-2), "other_concerns".
 
-Task: Write a concise, human-presentable security report in markdown format.
+Time range: {time_range}
 
-Rules for the report:
+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
 
-- Title & overview
+Report Structure - Use this EXACT format:
-  - Start with:
-    # Security Summary - {time_range}
-  - Write a 1-2 sentence situational overview capturing the general pattern of the period.
 
-- Event details
+# Security Summary - {time_range}
-  - Present events in chronological order as a bullet list.
-  - **If multiple events occur within the same minute or overlapping time range, COMBINE them into a single bullet.**
-    - Summarize the distinct activities as sub-points under the shared timestamp.
-  - If no timestamp is given, preserve order but label as “Time not specified.”
-  - Use bold timestamps for clarity.
-  - Group bullets under subheadings when multiple events fall into the same category (e.g., Vehicle Activity, Porch Activity, Unusual Behavior).
 
-- Threat levels
+## Overview
-  - Always show (threat level: X) for each event.
+[Write 1-2 sentences summarizing the overall activity pattern during this period.]
-  - If multiple events at the same time share the same threat level, only state it once.
 
-- Final assessment
+---
-  - End with a Final Assessment section.
-  - If all events are threat level 1 with no escalation:
-    Final assessment: Only normal residential activity observed during this period.
-  - If threat level 2+ events are present, clearly summarize them as Potential concerns requiring review.
 
-- Conciseness
+## Timeline
-  - Do not repeat benign clothing/appearance details unless they distinguish individuals.
+
-  - Summarize similar routine events instead of restating full scene descriptions.
+[Group events by time periods (e.g., "Morning (6:00 AM - 12:00 PM)", "Afternoon (12:00 PM - 5:00 PM)", "Evening (5:00 PM - 9:00 PM)", "Night (9:00 PM - 6:00 AM)"). Use appropriate time blocks based on when events occurred.]
+
+### [Time Block Name]
+
+**HH:MM AM/PM** | [Camera Name] | [Threat Level Indicator]
+- [Event title]: [Clear description incorporating contextual information from the "context" array]
+- Context: [If context array has items, mention them here, e.g., "Delivery truck present on Front Driveway Cam (HH:MM AM/PM)"]
+- Assessment: [Brief assessment incorporating context - if context explains the event, note it here]
+
+[Repeat for each event in chronological order within the time block]
+
+---
+
+## Summary
+[One sentence summarizing the period. If all events are normal/explained: "Routine activity observed." If review needed: "Some activity requires review but no security concerns." If security concerns: "Security concerns requiring immediate attention."]
+
+Guidelines:
+- List ALL events in chronological order, grouped by time blocks
+- Threat level indicators: ✓ Normal, ⚠️ Needs review, 🔴 Security concern
+- Integrate contextual information naturally - use the "context" array to enrich each event's description
+- If context explains the event (e.g., delivery truck explains person at door), describe it accordingly (e.g., "delivery person" not "unidentified person")
+- Be concise but informative - focus on what happened and what it means
+- If contextual information makes an event clearly normal, reflect that in your assessment
+- Only create time blocks that have events - don't create empty sections
 """
 
-        for item in segments:
+        timeline_summary_prompt += "\n\nEvents:\n"
-            timeline_summary_prompt += f"\n{item}"
+        for event in events:
+            timeline_summary_prompt += f"\n{event}\n"
 
         if debug_save:
             with open(

frigate/genai/openai.py

@@ -18,6 +18,7 @@ class OpenAIClient(GenAIClient):
     """Generative AI client for Frigate using OpenAI."""
 
     provider: OpenAI
+    context_size: Optional[int] = None
 
     def _init_provider(self):
         """Initialize the client."""
@@ -69,5 +70,33 @@ class OpenAIClient(GenAIClient):
 
     def get_context_size(self) -> int:
         """Get the context window size for OpenAI."""
-        # OpenAI GPT-4 Vision models have 128K token context window
+        if self.context_size is not None:
-        return 128000
+            return self.context_size
+
+        try:
+            models = self.provider.models.list()
+            for model in models.data:
+                if model.id == self.genai_config.model:
+                    if hasattr(model, "max_model_len") and model.max_model_len:
+                        self.context_size = model.max_model_len
+                        logger.debug(
+                            f"Retrieved context size {self.context_size} for model {self.genai_config.model}"
+                        )
+                        return self.context_size
+
+        except Exception as e:
+            logger.debug(
+                f"Failed to fetch model context size from API: {e}, using default"
+            )
+
+        # Default to 128K for ChatGPT models, 8K for others
+        model_name = self.genai_config.model.lower()
+        if "gpt" in model_name:
+            self.context_size = 128000
+        else:
+            self.context_size = 8192
+
+        logger.debug(
+            f"Using default context size {self.context_size} for model {self.genai_config.model}"
+        )
+        return self.context_size

frigate/models.py

@@ -133,6 +133,7 @@ class User(Model):
         default="admin",
     )
     password_hash = CharField(null=False, max_length=120)
+    password_changed_at = DateTimeField(null=True)
     notification_tokens = JSONField()
 
     @classmethod