Fix trigger sync (#21264)

- don't look for event ids when trigger type is description
- don't try to delete thumbnail whe trigger type is description
- pass correct event ID into thumbnail deletion

.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

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。
-- 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
+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TensorFlow进行物体检测，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
+- 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
 - 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
-- 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
+- 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
+- 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.**

docker/main/requirements-wheels.txt

@@ -21,7 +21,7 @@ onvif-zeep-async == 4.0.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
-peewee_migrate == 1.13.*
+peewee_migrate == 1.14.*
 psutil == 7.1.*
 pydantic == 2.10.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml

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/7.1/ubuntu/jammy/amdgpu-install_7.1.70100-1_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 --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx908* /opt/rocm-$ROCM/share/miopen/db/
-COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*$AMDGPU* /opt/rocm-$ROCM/lib/rocblas/library/
+# Copy MIOpen database files for gfx10xx and gfx11xx only (RDNA2/RDNA3)
+COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx10* /opt/rocm-$ROCM/share/miopen/db/
+COPY --from=rocm /opt/rocm-$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/rocm.hcl

@@ -1,8 +1,5 @@
-variable "AMDGPU" {
-  default = "gfx900"
-}
 variable "ROCM" {
-  default = "7.1.0"
+  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

docs/docs/configuration/audio_detectors.md

@@ -157,3 +157,21 @@ Only one `speech` event may be transcribed at a time. Frigate does not automatic
 :::
 
 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/state_classification.md

@@ -69,4 +69,6 @@ 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/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/license_plate_recognition.md

@@ -107,23 +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) 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)
-      replacement: r'\1-\2'
+    - pattern: '(\w{3})(\w{3})' # Split 6 chars into groups (e.g., ABC123 → ABC-123) - use single quotes to preserve backslashes
+      replacement: '\1-\2'
 ```
 
 - Rules fire in order: In the example above: clean noise first, then separators, then swaps, then splits.
@@ -374,9 +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:
@@ -385,7 +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:
 
@@ -398,7 +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.

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

@@ -13,7 +13,7 @@ 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.
 - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
 - <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).
@@ -69,12 +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.
@@ -146,6 +144,44 @@ 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`.
+
+#### 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
@@ -364,7 +400,7 @@ 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.
 
 :::
 
@@ -704,7 +740,7 @@ 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.
 
 :::
 

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
@@ -710,6 +710,44 @@ audio_transcription:
   # 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,
@@ -873,7 +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.
@@ -964,10 +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/frigate/hardware.md

@@ -159,7 +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                         |                           |                        |                                    |

docs/docs/frigate/installation.md

@@ -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:
@@ -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/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-theme-openapi-docs": "^4.3.1",
+    "docusaurus-plugin-openapi-docs": "^4.5.1",
+    "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/types": "^3.4.0",
-    "@types/react": "^18.3.7"
+    "@docusaurus/module-type-aliases": "^3.7.0",
+    "@docusaurus/types": "^3.7.0",
+    "@types/react": "^18.3.27"
   },
   "engines": {
     "node": ">=18.0"

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

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

frigate/api/auth.py

@@ -55,15 +55,15 @@ def require_admin_by_default():
         "/auth",
         "/auth/first_time_login",
         "/login",
-        # Authenticated user endpoints (allow_any_authenticated)
         "/logout",
+        # Authenticated user endpoints (allow_any_authenticated)
         "/profile",
         # Public info endpoints (allow_public)
         "/",
         "/version",
         "/config/schema.json",
-        "/metrics",
         # Authenticated user endpoints (allow_any_authenticated)
+        "/metrics",
         "/stats",
         "/stats/history",
         "/config",
@@ -76,22 +76,28 @@ def require_admin_by_default():
         "/recognized_license_plates",
         "/timeline",
         "/timeline/hourly",
-        "/events/summary",
         "/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_ids, /review/summary, etc.
+        "/review",  # /review, /review/{id}, /review/summary, /review_ids, etc.
         "/reviews/",  # /reviews/viewed, /reviews/delete
-        "/events/",  # /events/{id}/thumbnail, etc. (camera-scoped)
+        "/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):
@@ -105,6 +111,24 @@ def require_admin_by_default():
         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")
@@ -113,7 +137,7 @@ def require_admin_by_default():
 
         raise HTTPException(
             status_code=403,
-            detail="Admin role required for this endpoint",
+            detail="Access denied. A user with the admin role is required.",
         )
 
     return admin_checker
@@ -287,7 +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(
@@ -332,9 +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,
     )
 
 
@@ -595,13 +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
@@ -636,7 +703,7 @@ def profile(request: Request):
     )
 
 
-@router.get("/logout", dependencies=[Depends(allow_any_authenticated())])
+@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)
@@ -758,10 +825,63 @@ async def update_password(
 
     HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
 
-    password_hash = hash_password(body.password, iterations=HASH_ITERATIONS)
-    User.set_by_id(username, {User.password_hash: password_hash})
+    try:
+        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/classification.py

@@ -710,7 +710,7 @@ 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):
+    if os.path.exists(folder) and not os.listdir(folder) and category.lower() != "none":
         os.rmdir(folder)
 
     return JSONResponse(

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

@@ -70,6 +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.",
 )
@@ -344,7 +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.
     """,
@@ -436,7 +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.
     """,
@@ -469,7 +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.
     """,
@@ -919,7 +923,8 @@ def events_summary(
 @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):
@@ -962,7 +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.
     """,
@@ -1102,6 +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,
@@ -1200,7 +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.
@@ -1229,7 +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.
     """,
@@ -1288,7 +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.
     """,
@@ -1348,7 +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.
     """,
@@ -1404,7 +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.
     """,
@@ -1456,8 +1463,8 @@ async def regenerate_description(
 @router.post(
     "/description/generate",
     response_model=GenericResponse,
-    # dependencies=[Depends(require_role(["admin"]))],
-    summary="Generate description embedding.",
+    dependencies=[Depends(require_role(["admin"]))],
+    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.
     """,
@@ -1522,7 +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.
     """,
@@ -1537,7 +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.
     """,
@@ -1571,7 +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:
@@ -1613,7 +1620,7 @@ def create_event(
             body.score,
             body.sub_label,
             body.duration,
-            body.source_type,
+            "api",
             body.draw,
         ),
         EventMetadataTypeEnum.manual_event_create.value,
@@ -1635,7 +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.
@@ -1645,10 +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=(
@@ -1667,7 +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.
     """,
@@ -1724,37 +1748,40 @@ def create_trigger_embedding(
             if event.data.get("type") != "object":
                 return
 
-            if thumbnail := get_event_thumbnail_bytes(event):
-                cursor = context.db.execute_sql(
-                    """
-                    SELECT thumbnail_embedding FROM vec_thumbnails WHERE id = ?
-                    """,
-                    [body.data],
+            # Get the thumbnail
+            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,
                 )
 
-                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:
-                    query_embedding = row[0]
-                    embedding = np.frombuffer(query_embedding, dtype=np.float32)
+            row = cursor.fetchone() if cursor else None
+
+            if row:
+                query_embedding = row[0]
+                embedding = np.frombuffer(query_embedding, dtype=np.float32)
             else:
-                # Extract valid thumbnail
-                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,
-                    )
-
+                # Generate new embedding
                 embedding = context.generate_image_embedding(
                     body.data, (base64.b64encode(thumbnail).decode("ASCII"))
                 )
 
-        if not embedding:
+        if embedding is None or (
+            isinstance(embedding, (list, np.ndarray)) and len(embedding) == 0
+        ):
             return JSONResponse(
                 content={
                     "success": False,
@@ -1822,7 +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.
     """,
@@ -1889,7 +1916,9 @@ def update_trigger_embedding(
                 body.data, (base64.b64encode(thumbnail).decode("ASCII"))
             )
 
-        if not embedding:
+        if embedding is None or (
+            isinstance(embedding, (list, np.ndarray)) and len(embedding) == 0
+        ):
             return JSONResponse(
                 content={
                     "success": False,
@@ -1985,7 +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.
     """,
@@ -2059,7 +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,
@@ -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/media.py

@@ -837,7 +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,
@@ -862,6 +874,14 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
 
     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)
 
@@ -870,6 +890,11 @@ 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:
@@ -877,12 +902,23 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
 
         if duration < min_duration_ms:
             # 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 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}")
 
@@ -902,7 +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),
@@ -945,6 +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(
@@ -985,6 +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.",

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

@@ -577,7 +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(),
@@ -739,6 +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/comms/dispatcher.py

@@ -607,23 +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 :]
-            elif "move_relative" in payload.lower():
+                param = preset[preset.index("_") + 1 :]
+            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__)
@@ -371,7 +371,7 @@ 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"]:
@@ -390,7 +390,20 @@ class WebPushClient(Communicator):
 
             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/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/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/data_processing/post/review_descriptions.py

@@ -209,10 +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)
@@ -224,21 +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(
-                filter(
-                    lambda item: item.get("potential_threat_level", 0) > 0
-                    or item.get("other_concerns"),
-                    items,
-                )
-            )
+            # Identify primary items (important items that need review)
+            primary_segments = [
+                seg
+                for seg in segments
+                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}")
@@ -247,7 +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:

frigate/data_processing/real_time/custom_classification.py

@@ -99,6 +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.
@@ -212,14 +248,22 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                 return
 
         if self.interpreter is None:
-            write_classification_attempt(
-                self.train_dir,
-                cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
-                "none-none",
-                now,
-                "unknown",
-                0.0,
-            )
+            # When interpreter is None, always save (score is 0.0, which is < 1.0)
+            if self._should_save_image(camera, "unknown", 0.0):
+                save_attempts = (
+                    self.model_config.save_attempts
+                    if self.model_config.save_attempts is not None
+                    else 100
+                )
+                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)
@@ -236,14 +280,23 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         score = round(probs[best_id], 2)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
-        write_classification_attempt(
-            self.train_dir,
-            cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
-            "none-none",
-            now,
-            self.labelmap[best_id],
-            score,
-        )
+        detected_state = self.labelmap[best_id]
+
+        if self._should_save_image(camera, detected_state, score):
+            save_attempts = (
+                self.model_config.save_attempts
+                if self.model_config.save_attempts is not None
+                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(
@@ -251,7 +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:
@@ -442,6 +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),
@@ -449,6 +506,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                 now,
                 "unknown",
                 0.0,
+                max_files=save_attempts,
             )
             return
 
@@ -466,6 +524,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         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),
@@ -473,7 +536,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             now,
             self.labelmap[best_id],
             score,
-            max_files=200,
+            max_files=save_attempts,
         )
 
         if score < self.model_config.threshold:
@@ -576,14 +639,14 @@ 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
     try:
+        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:

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]
-        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]
-        )
+        if self.model_type == ModelTypeEnum.yologeneric:
+            # Multi-tensor YOLO model with (non-standard B(H*W)C output format).
+            # (the comments indicate the shape of tensors,
+            # using "2100" as the anchor count (for image size of 320x320),
+            # "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 scores[i] < 0.4 or i == 20:
-                break
-            detections[i] = [
-                class_ids[i],
-                float(scores[i]),
-                boxes[i][0],
-                boxes[i][1],
-                boxes[i][2],
-                boxes[i][3],
+            if not np.any(mask):
+                return detections  # empty results
+
+            max_scores_filtered_shiftedpositive = (
+                (max_scores_quantized[mask] - self.scores_zero_point)
+                * self.scores_scale
+            ) + self.logit_shift_to_positive_values  # (N,1) shifted logit values
+            scores_output_quantized_filtered = scores_output_quantized[mask]
+
+            # 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/embeddings/embeddings.py

@@ -12,9 +12,6 @@ from peewee import DoesNotExist, IntegrityError
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
-from frigate.comms.embeddings_updater import (
-    EmbeddingsRequestEnum,
-)
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
 from frigate.config.classification import SemanticSearchModelEnum
@@ -495,44 +492,49 @@ class Embeddings:
                         or thumbnail_missing
                     ):
                         existing_trigger.embedding = self._calculate_trigger_embedding(
-                            trigger
+                            trigger, trigger_name, camera.name
                         )
                         needs_embedding_update = True
 
                     if needs_embedding_update:
                         existing_trigger.save()
+                    continue
                 else:
                     # Create new trigger
                     try:
-                        try:
-                            event: Event = Event.get(Event.id == trigger.data)
-                        except DoesNotExist:
-                            logger.warning(
-                                f"Event ID {trigger.data} for trigger {trigger_name} does not exist."
+                        # For thumbnail triggers, validate the event exists
+                        if trigger.type == "thumbnail":
+                            try:
+                                event: Event = Event.get(Event.id == trigger.data)
+                            except DoesNotExist:
+                                logger.warning(
+                                    f"Event ID {trigger.data} for trigger {trigger_name} does not exist."
+                                )
+                                continue
+
+                            # Skip the event if not an object
+                            if event.data.get("type") != "object":
+                                logger.warning(
+                                    f"Event ID {trigger.data} for trigger {trigger_name} is not a tracked object."
+                                )
+                                continue
+
+                            thumbnail = get_event_thumbnail_bytes(event)
+
+                            if not thumbnail:
+                                logger.warning(
+                                    f"Unable to retrieve thumbnail for event ID {trigger.data} for {trigger_name}."
+                                )
+                                continue
+
+                            self.write_trigger_thumbnail(
+                                camera.name, trigger.data, thumbnail
                             )
-                            continue
-
-                        # Skip the event if not an object
-                        if event.data.get("type") != "object":
-                            logger.warning(
-                                f"Event ID {trigger.data} for trigger {trigger_name} is not a tracked object."
-                            )
-                            continue
-
-                        thumbnail = get_event_thumbnail_bytes(event)
-
-                        if not thumbnail:
-                            logger.warning(
-                                f"Unable to retrieve thumbnail for event ID {trigger.data} for {trigger_name}."
-                            )
-                            continue
-
-                        self.write_trigger_thumbnail(
-                            camera.name, trigger.data, thumbnail
-                        )
 
                         # Calculate embedding for new trigger
-                        embedding = self._calculate_trigger_embedding(trigger)
+                        embedding = self._calculate_trigger_embedding(
+                            trigger, trigger_name, camera.name
+                        )
 
                         Trigger.create(
                             camera=camera.name,
@@ -558,7 +560,11 @@ class Embeddings:
                     Trigger.camera == camera.name, Trigger.name.in_(triggers_to_remove)
                 ).execute()
                 for trigger_name in triggers_to_remove:
-                    self.remove_trigger_thumbnail(camera.name, trigger_name)
+                    # Only remove thumbnail files for thumbnail triggers
+                    if existing_triggers[trigger_name].type == "thumbnail":
+                        self.remove_trigger_thumbnail(
+                            camera.name, existing_triggers[trigger_name].data
+                        )
 
     def write_trigger_thumbnail(
         self, camera: str, event_id: str, thumbnail: bytes
@@ -588,14 +594,13 @@ class Embeddings:
                 f"Failed to delete thumbnail for trigger with data {event_id} in {camera}: {e}"
             )
 
-    def _calculate_trigger_embedding(self, trigger) -> bytes:
+    def _calculate_trigger_embedding(
+        self, trigger, trigger_name: str, camera_name: str
+    ) -> bytes:
         """Calculate embedding for a trigger based on its type and data."""
         if trigger.type == "description":
-            logger.debug(f"Generating embedding for trigger description {trigger.name}")
-            embedding = self.requestor.send_data(
-                EmbeddingsRequestEnum.embed_description.value,
-                {"id": None, "description": trigger.data, "upsert": False},
-            )
+            logger.debug(f"Generating embedding for trigger description {trigger_name}")
+            embedding = self.embed_description(None, trigger.data, upsert=False)
             return embedding.astype(np.float32).tobytes()
 
         elif trigger.type == "thumbnail":
@@ -615,28 +620,21 @@ class Embeddings:
 
                 try:
                     with open(
-                        os.path.join(
-                            TRIGGER_DIR, trigger.camera, f"{trigger.data}.webp"
-                        ),
+                        os.path.join(TRIGGER_DIR, camera_name, f"{trigger.data}.webp"),
                         "rb",
                     ) as f:
                         thumbnail = f.read()
                 except Exception as e:
                     logger.error(
-                        f"Failed to read thumbnail for trigger {trigger.name} with ID {trigger.data}: {e}"
+                        f"Failed to read thumbnail for trigger {trigger_name} with ID {trigger.data}: {e}"
                     )
                     return b""
 
                 logger.debug(
-                    f"Generating embedding for trigger thumbnail {trigger.name} with ID {trigger.data}"
+                    f"Generating embedding for trigger thumbnail {trigger_name} with ID {trigger.data}"
                 )
-                embedding = self.requestor.send_data(
-                    EmbeddingsRequestEnum.embed_thumbnail.value,
-                    {
-                        "id": str(trigger.data),
-                        "thumbnail": str(thumbnail),
-                        "upsert": False,
-                    },
+                embedding = self.embed_thumbnail(
+                    str(trigger.data), thumbnail, upsert=False
                 )
                 return embedding.astype(np.float32).tobytes()
 

frigate/genai/__init__.py

@@ -177,56 +177,60 @@ Each line represents a detection state, not necessarily unique individuals. Pare
         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.
-Time range: {time_range}.
-Input: JSON list with "title", "scene", "confidence", "potential_threat_level" (1-2), "other_concerns".
+You are a security officer writing a concise security report.
 
-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
-  - Start with:
-    # Security Summary - {time_range}
-  - Write a 1-2 sentence situational overview capturing the general pattern of the period.
+Report Structure - Use this EXACT format:
 
-- Event details
-  - 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).
+# Security Summary - {time_range}
 
-- Threat levels
-  - Always show the threat level for each event using these labels:
-    - Threat level 0: "Normal"
-    - Threat level 1: "Needs review"
-    - Threat level 2: "Security concern"
-  - Format as (threat level: Normal), (threat level: Needs review), or (threat level: Security concern).
-  - If multiple events at the same time share the same threat level, only state it once.
+## Overview
+[Write 1-2 sentences summarizing the overall activity pattern during this period.]
 
-- Final assessment
-  - End with a Final Assessment section.
-  - If all events are threat level 0:
-    Final assessment: Only normal residential activity observed during this period.
-  - If threat level 1 events are present:
-    Final assessment: Some activity requires review but no security concerns identified.
-  - If threat level 2 events are present, clearly summarize them as Security concerns requiring immediate attention.
+---
 
-- Conciseness
-  - Do not repeat benign clothing/appearance details unless they distinguish individuals.
-  - Summarize similar routine events instead of restating full scene descriptions.
+## Timeline
+
+[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 += f"\n{item}"
+        timeline_summary_prompt += "\n\nEvents:\n"
+        for event in events:
+            timeline_summary_prompt += f"\n{event}\n"
 
         if debug_save:
             with open(

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

frigate/motion/improved_motion.py

@@ -239,6 +239,12 @@ class ImprovedMotionDetector(MotionDetector):
         )
         self.mask = np.where(resized_mask == [0])
 
+        # Reset motion detection state when mask changes
+        # so motion detection can quickly recalibrate with the new mask
+        self.avg_frame = np.zeros(self.motion_frame_size, np.float32)
+        self.calibrating = True
+        self.motion_frame_count = 0
+
     def stop(self) -> None:
         """stop the motion detector."""
         pass

frigate/ptz/onvif.py

@@ -95,12 +95,21 @@ class OnvifController:
 
         cam = self.camera_configs[cam_name]
         try:
+            user = cam.onvif.user
+            password = cam.onvif.password
+
+            if user is not None and isinstance(user, bytes):
+                user = user.decode("utf-8")
+
+            if password is not None and isinstance(password, bytes):
+                password = password.decode("utf-8")
+
             self.cams[cam_name] = {
                 "onvif": ONVIFCamera(
                     cam.onvif.host,
                     cam.onvif.port,
-                    cam.onvif.user,
-                    cam.onvif.password,
+                    user,
+                    password,
                     wsdl_dir=str(Path(find_spec("onvif").origin).parent / "wsdl"),
                     adjust_time=cam.onvif.ignore_time_mismatch,
                     encrypt=not cam.onvif.tls_insecure,
@@ -190,7 +199,11 @@ class OnvifController:
         ptz: ONVIFService = await onvif.create_ptz_service()
         self.cams[camera_name]["ptz"] = ptz
 
-        imaging: ONVIFService = await onvif.create_imaging_service()
+        try:
+            imaging: ONVIFService = await onvif.create_imaging_service()
+        except (Fault, ONVIFError, TransportError, Exception) as e:
+            logger.debug(f"Imaging service not supported for {camera_name}: {e}")
+            imaging = None
         self.cams[camera_name]["imaging"] = imaging
         try:
             video_sources = await media.GetVideoSources()
@@ -321,9 +334,15 @@ class OnvifController:
             presets = []
 
         for preset in presets:
-            self.cams[camera_name]["presets"][
-                (getattr(preset, "Name") or f"preset {preset['token']}").lower()
-            ] = preset["token"]
+            # Ensure preset name is a Unicode string and handle UTF-8 characters correctly
+            preset_name = getattr(preset, "Name") or f"preset {preset['token']}"
+
+            if isinstance(preset_name, bytes):
+                preset_name = preset_name.decode("utf-8")
+
+            # Convert to lowercase while preserving UTF-8 characters
+            preset_name_lower = preset_name.lower()
+            self.cams[camera_name]["presets"][preset_name_lower] = preset["token"]
 
         # get list of supported features
         supported_features = []
@@ -381,7 +400,10 @@ class OnvifController:
                             f"Disabling autotracking zooming for {camera_name}: Absolute zoom not supported. Exception: {e}"
                         )
 
-        if self.cams[camera_name]["video_source_token"] is not None:
+        if (
+            self.cams[camera_name]["video_source_token"] is not None
+            and imaging is not None
+        ):
             try:
                 imaging_capabilities = await imaging.GetImagingSettings(
                     {"VideoSourceToken": self.cams[camera_name]["video_source_token"]}
@@ -421,6 +443,7 @@ class OnvifController:
         if (
             "focus" in self.cams[camera_name]["features"]
             and self.cams[camera_name]["video_source_token"]
+            and self.cams[camera_name]["imaging"] is not None
         ):
             try:
                 stop_request = self.cams[camera_name]["imaging"].create_type("Stop")
@@ -555,6 +578,11 @@ class OnvifController:
         self.cams[camera_name]["active"] = False
 
     async def _move_to_preset(self, camera_name: str, preset: str) -> None:
+        if isinstance(preset, bytes):
+            preset = preset.decode("utf-8")
+
+        preset = preset.lower()
+
         if preset not in self.cams[camera_name]["presets"]:
             logger.error(f"{preset} is not a valid preset for {camera_name}")
             return
@@ -648,6 +676,7 @@ class OnvifController:
         if (
             "focus" not in self.cams[camera_name]["features"]
             or not self.cams[camera_name]["video_source_token"]
+            or self.cams[camera_name]["imaging"] is None
         ):
             logger.error(f"{camera_name} does not support ONVIF continuous focus.")
             return

frigate/storage.py

@@ -5,7 +5,7 @@ import shutil
 import threading
 from pathlib import Path
 
-from peewee import fn
+from peewee import SQL, fn
 
 from frigate.config import FrigateConfig
 from frigate.const import RECORD_DIR
@@ -44,13 +44,19 @@ class StorageMaintainer(threading.Thread):
                     )
                 }
 
-                # calculate MB/hr
+                # calculate MB/hr from last 100 segments
                 try:
-                    bandwidth = round(
-                        Recordings.select(fn.AVG(bandwidth_equation))
+                    # Subquery to get last 100 segments, then average their bandwidth
+                    last_100 = (
+                        Recordings.select(bandwidth_equation.alias("bw"))
                         .where(Recordings.camera == camera, Recordings.segment_size > 0)
+                        .order_by(Recordings.start_time.desc())
                         .limit(100)
-                        .scalar()
+                        .alias("recent")
+                    )
+
+                    bandwidth = round(
+                        Recordings.select(fn.AVG(SQL("bw"))).from_(last_100).scalar()
                         * 3600,
                         2,
                     )

frigate/track/object_processing.py

@@ -78,6 +78,8 @@ class TrackedObjectProcessor(threading.Thread):
             [
                 CameraConfigUpdateEnum.add,
                 CameraConfigUpdateEnum.enabled,
+                CameraConfigUpdateEnum.motion,
+                CameraConfigUpdateEnum.objects,
                 CameraConfigUpdateEnum.remove,
                 CameraConfigUpdateEnum.zones,
             ],

frigate/util/classification.py

@@ -330,7 +330,7 @@ def collect_state_classification_examples(
     1. Queries review items from specified cameras
     2. Selects 100 balanced timestamps across the data
     3. Extracts keyframes from recordings (cropped to specified regions)
-    4. Selects 20 most visually distinct images
+    4. Selects 24 most visually distinct images
     5. Saves them to the dataset directory
 
     Args:
@@ -660,7 +660,6 @@ def collect_object_classification_examples(
     Args:
         model_name: Name of the classification model
         label: Object label to collect (e.g., "person", "car")
-        cameras: List of camera names to collect examples from
     """
     dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
     temp_dir = os.path.join(dataset_dir, "temp")

frigate/util/process.py

@@ -14,6 +14,7 @@ from setproctitle import setproctitle
 
 import frigate.log
 from frigate.config.logger import LoggerConfig
+from frigate.const import CONFIG_DIR
 
 
 class BaseProcess(mp.Process):
@@ -92,7 +93,7 @@ class FrigateProcess(BaseProcess):
         try:
             import memray
 
-            reports_dir = pathlib.Path("/config/memray_reports")
+            reports_dir = pathlib.Path(CONFIG_DIR) / "memray_reports"
             reports_dir.mkdir(parents=True, exist_ok=True)
             safe_name = (
                 process_name.replace(":", "_").replace("/", "_").replace("\\", "_")
@@ -126,7 +127,7 @@ class FrigateProcess(BaseProcess):
             self.__memray_tracker.__exit__(None, None, None)
             self.__memray_tracker = None
 
-            reports_dir = pathlib.Path("/config/memray_reports")
+            reports_dir = pathlib.Path(CONFIG_DIR) / "memray_reports"
             html_file = reports_dir / f"{safe_name}.html"
 
             result = subprocess.run(

frigate/video.py

@@ -124,45 +124,50 @@ def capture_frames(
         config_subscriber.check_for_updates()
         return config.enabled
 
-    while not stop_event.is_set():
-        if not get_enabled_state():
-            logger.debug(f"Stopping capture thread for disabled {config.name}")
-            break
-
-        fps.value = frame_rate.eps()
-        skipped_fps.value = skipped_eps.eps()
-        current_frame.value = datetime.now().timestamp()
-        frame_name = f"{config.name}_frame{frame_index}"
-        frame_buffer = frame_manager.write(frame_name)
-        try:
-            frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
-        except Exception:
-            # shutdown has been initiated
-            if stop_event.is_set():
+    try:
+        while not stop_event.is_set():
+            if not get_enabled_state():
+                logger.debug(f"Stopping capture thread for disabled {config.name}")
                 break
 
-            logger.error(f"{config.name}: Unable to read frames from ffmpeg process.")
+            fps.value = frame_rate.eps()
+            skipped_fps.value = skipped_eps.eps()
+            current_frame.value = datetime.now().timestamp()
+            frame_name = f"{config.name}_frame{frame_index}"
+            frame_buffer = frame_manager.write(frame_name)
+            try:
+                frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
+            except Exception:
+                # shutdown has been initiated
+                if stop_event.is_set():
+                    break
 
-            if ffmpeg_process.poll() is not None:
                 logger.error(
-                    f"{config.name}: ffmpeg process is not running. exiting capture thread..."
+                    f"{config.name}: Unable to read frames from ffmpeg process."
                 )
-                break
 
-            continue
+                if ffmpeg_process.poll() is not None:
+                    logger.error(
+                        f"{config.name}: ffmpeg process is not running. exiting capture thread..."
+                    )
+                    break
 
-        frame_rate.update()
+                continue
 
-        # don't lock the queue to check, just try since it should rarely be full
-        try:
-            # add to the queue
-            frame_queue.put((frame_name, current_frame.value), False)
-            frame_manager.close(frame_name)
-        except queue.Full:
-            # if the queue is full, skip this frame
-            skipped_eps.update()
+            frame_rate.update()
 
-        frame_index = 0 if frame_index == shm_frame_count - 1 else frame_index + 1
+            # don't lock the queue to check, just try since it should rarely be full
+            try:
+                # add to the queue
+                frame_queue.put((frame_name, current_frame.value), False)
+                frame_manager.close(frame_name)
+            except queue.Full:
+                # if the queue is full, skip this frame
+                skipped_eps.update()
+
+            frame_index = 0 if frame_index == shm_frame_count - 1 else frame_index + 1
+    finally:
+        config_subscriber.stop()
 
 
 class CameraWatchdog(threading.Thread):
@@ -234,6 +239,16 @@ class CameraWatchdog(threading.Thread):
                 else:
                     self.ffmpeg_detect_process.wait()
 
+        # Wait for old capture thread to fully exit before starting a new one
+        if self.capture_thread is not None and self.capture_thread.is_alive():
+            self.logger.info("Waiting for capture thread to exit...")
+            self.capture_thread.join(timeout=5)
+
+            if self.capture_thread.is_alive():
+                self.logger.warning(
+                    f"Capture thread for {self.config.name} did not exit in time"
+                )
+
         self.logger.error(
             "The following ffmpeg logs include the last 100 lines prior to exit."
         )
@@ -756,6 +771,7 @@ def process_frames(
             camera_enabled = camera_config.enabled
 
         if "motion" in updated_configs:
+            motion_detector.config = camera_config.motion
             motion_detector.update_mask()
 
         if (

migrations/001_create_events_table.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/002_add_clip_snapshot.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/003_create_recordings_table.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/004_add_bbox_region_area.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/005_make_end_time_nullable.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/006_add_motion_active_objects.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/007_add_retain_indefinitely.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/008_add_sub_label.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/009_add_object_filter_ratio.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/010_add_plus_image_id.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/011_update_indexes.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/012_add_segment_size.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/013_create_timeline_table.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/014_event_updates_for_fp.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/015_event_refactor.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/016_sublabel_increase.py

@@ -4,8 +4,8 @@ from frigate.models import Event
 
 
 def migrate(migrator, database, fake=False, **kwargs):
-    migrator.change_columns(Event, sub_label=pw.CharField(max_length=100, null=True))
+    migrator.change_fields(Event, sub_label=pw.CharField(max_length=100, null=True))
 
 
 def rollback(migrator, database, fake=False, **kwargs):
-    migrator.change_columns(Event, sub_label=pw.CharField(max_length=20, null=True))
+    migrator.change_fields(Event, sub_label=pw.CharField(max_length=20, null=True))

migrations/017_update_indexes.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/018_add_dbfs.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/019_create_regions_table.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/020_update_index_recordings.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/021_create_previews_table.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/022_create_review_segment_table.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/023_add_regions.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/024_create_export_table.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/025_create_user_table.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/026_add_notification_tokens.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/027_create_explore_index.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/028_optional_event_thumbnail.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/029_add_user_role.py

@@ -5,7 +5,7 @@ Some examples (model - class or model name)::
     > Model = migrator.orm['model_name']            # Return model in current state by name
 
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/030_create_user_review_status.py

@@ -8,7 +8,7 @@ Some examples (model - class or model_name)::
 
     > Model = migrator.orm['model_name']            # Return model in current state by name
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model
@@ -54,7 +54,9 @@ def migrate(migrator, database, fake=False, **kwargs):
 
     # Migrate existing has_been_reviewed data to UserReviewStatus for all users
     def migrate_data():
-        all_users = list(User.select())
+        # Use raw SQL to avoid ORM issues with columns that don't exist yet
+        cursor = database.execute_sql('SELECT "username" FROM "user"')
+        all_users = cursor.fetchall()
         if not all_users:
             return
 
@@ -63,7 +65,7 @@ def migrate(migrator, database, fake=False, **kwargs):
         )
         reviewed_segment_ids = [row[0] for row in cursor.fetchall()]
         # also migrate for anonymous (unauthenticated users)
-        usernames = [user.username for user in all_users] + ["anonymous"]
+        usernames = [user[0] for user in all_users] + ["anonymous"]
 
         for segment_id in reviewed_segment_ids:
             for username in usernames:
@@ -74,7 +76,7 @@ def migrate(migrator, database, fake=False, **kwargs):
                 )
 
     if not fake:  # Only run data migration if not faking
-        migrator.python(migrate_data)
+        migrator.run(migrate_data)
 
     migrator.sql('ALTER TABLE "reviewsegment" DROP COLUMN "has_been_reviewed"')
 

migrations/031_create_trigger_table.py

@@ -6,7 +6,7 @@ Some examples (model - class or model_name)::
 
     > Model = migrator.orm['model_name']            # Return model in current state by name
     > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
+    > migrator.run(func, *args, **kwargs)           # Run python code
     > migrator.create_model(Model)                  # Create a model (could be used as decorator)
     > migrator.remove_model(model, cascade=True)    # Remove a model
     > migrator.add_fields(model, **fields)          # Add fields to a model

migrations/032_add_password_changed_at.py

@@ -0,0 +1,42 @@
+"""Peewee migrations -- 032_add_password_changed_at.py.
+
+Some examples (model - class or model name)::
+
+    > Model = migrator.orm['model_name']            # Return model in current state by name
+
+    > migrator.sql(sql)                             # Run custom SQL
+    > migrator.run(func, *args, **kwargs)           # Run python code
+    > migrator.create_model(Model)                  # Create a model (could be used as decorator)
+    > migrator.remove_model(model, cascade=True)    # Remove a model
+    > migrator.add_fields(model, **fields)          # Add fields to a model
+    > migrator.change_fields(model, **fields)       # Change fields
+    > migrator.remove_fields(model, *field_names, cascade=True)
+    > migrator.rename_field(model, old_field_name, new_field_name)
+    > migrator.rename_table(model, new_table_name)
+    > migrator.add_index(model, *col_names, unique=False)
+    > migrator.drop_index(model, *col_names)
+    > migrator.add_not_null(model, *field_names)
+    > migrator.drop_not_null(model, *field_names)
+    > migrator.add_default(model, field_name, default)
+
+"""
+
+import peewee as pw
+
+SQL = pw.SQL
+
+
+def migrate(migrator, database, fake=False, **kwargs):
+    migrator.sql(
+        """
+        ALTER TABLE user ADD COLUMN password_changed_at DATETIME NULL
+        """
+    )
+
+
+def rollback(migrator, database, fake=False, **kwargs):
+    migrator.sql(
+        """
+        ALTER TABLE user DROP COLUMN password_changed_at
+        """
+    )

web/public/locales/bg/components/auth.json

@@ -1,5 +1,6 @@
 {
   "form": {
-    "user": "Потребителско име"
+    "user": "Потребителско име",
+    "password": "Парола"
   }
 }

web/public/locales/bg/components/dialog.json

@@ -10,6 +10,7 @@
     "select": "Избери"
   },
   "restart": {
-    "title": "Сигурен ли сте, че искате да рестартирате Frigate?"
+    "title": "Сигурен ли сте, че искате да рестартирате Frigate?",
+    "button": "Рестартирай"
   }
 }

web/public/locales/bg/components/icons.json

@@ -1,5 +1,8 @@
 {
   "iconPicker": {
-    "selectIcon": "Изберете иконка"
+    "selectIcon": "Изберете иконка",
+    "search": {
+      "placeholder": "Потърси за икона…"
+    }
   }
 }

web/public/locales/bg/components/input.json

@@ -1,7 +1,10 @@
 {
   "button": {
     "downloadVideo": {
-      "label": "Свали видео"
+      "label": "Свали видео",
+      "toast": {
+        "success": "Вашето видео за преглеждане почна да се изтегля."
+      }
     }
   }
 }

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

@@ -1 +1,3 @@
-{}
+{
+  "documentTitle": "Модели за класификация"
+}

web/public/locales/bg/views/configEditor.json

@@ -1 +1,4 @@
-{}
+{
+  "documentTitle": "Настройки на конфигурацията - Фригейт",
+  "configEditor": "Настройки на конфигурацията"
+}

web/public/locales/bg/views/events.json

@@ -9,5 +9,7 @@
     "aria": "Избери събития",
     "noFoundForTimePeriod": "Няма намерени събития за този времеви период."
   },
-  "allCameras": "Всички камери"
+  "allCameras": "Всички камери",
+  "alerts": "Известия",
+  "detections": "Засичания"
 }

web/public/locales/bg/views/explore.json

@@ -8,5 +8,7 @@
     }
   },
   "trackedObjectsCount_one": "{{count}} проследен обект ",
-  "trackedObjectsCount_other": "{{count}} проследени обекта "
+  "trackedObjectsCount_other": "{{count}} проследени обекта ",
+  "documentTitle": "Разгледай - Фригейт",
+  "generativeAI": "Генериращ Изкъствен Интелект"
 }

web/public/locales/bg/views/exports.json

@@ -1,3 +1,4 @@
 {
-  "documentTitle": "Експорт - Frigate"
+  "documentTitle": "Експорт - Frigate",
+  "search": "Търси"
 }

web/public/locales/bg/views/faceLibrary.json

@@ -10,5 +10,9 @@
       "deletedName_one": "{{count}} лице бе изтрито успешно.",
       "deletedName_other": "{{count}} лица бяха изтрити успешно."
     }
+  },
+  "description": {
+    "addFace": "Добавете нова колекция във библиотеката за лица при качването на първата ви снимка.",
+    "placeholder": "Напишете име за тази колекция"
   }
 }

web/public/locales/bg/views/live.json

@@ -64,5 +64,6 @@
   "cameraSettings": {
     "cameraEnabled": "Камерата е включена"
   },
-  "documentTitle": "Наживо - Frigate"
+  "documentTitle": "Наживо - Frigate",
+  "documentTitle.withCamera": "{{camera}} - На живо - Фригейт"
 }

web/public/locales/bg/views/search.json

@@ -1,5 +1,7 @@
 {
   "button": {
     "save": "Запазване на търсенето"
-  }
+  },
+  "search": "Търси",
+  "savedSearches": "Запазени търсения"
 }