Bump python-multipart from 0.0.12 to 0.0.20 in /docker/main

Bumps [python-multipart](https://github.com/Kludex/python-multipart) from 0.0.12 to 0.0.20.
- [Release notes](https://github.com/Kludex/python-multipart/releases)
- [Changelog](https://github.com/Kludex/python-multipart/blob/master/CHANGELOG.md)
- [Commits](https://github.com/Kludex/python-multipart/compare/0.0.12...0.0.20)

---
updated-dependencies:
- dependency-name: python-multipart
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

.cspell/frigate-dictionary.txt

@@ -2,6 +2,7 @@ aarch
 absdiff
 airockchip
 Alloc
+alpr
 Amcrest
 amdgpu
 analyzeduration
@@ -43,6 +44,7 @@ codeproject
 colormap
 colorspace
 comms
+cooldown
 coro
 ctypeslib
 CUDA
@@ -61,6 +63,7 @@ dsize
 dtype
 ECONNRESET
 edgetpu
+facenet
 fastapi
 faststart
 fflags
@@ -114,6 +117,8 @@ itemsize
 Jellyfin
 jetson
 jetsons
+jina
+jinaai
 joserfc
 jsmpeg
 jsonify
@@ -187,6 +192,7 @@ openai
 opencv
 openvino
 OWASP
+paddleocr
 paho
 passwordless
 popleft
@@ -308,4 +314,4 @@ yolo
 yolonas
 yolox
 zeep
-zerolatency
+zerolatency

.devcontainer/devcontainer.json

@@ -8,9 +8,25 @@
   "overrideCommand": false,
   "remoteUser": "vscode",
   "features": {
-    "ghcr.io/devcontainers/features/common-utils:1": {}
+    "ghcr.io/devcontainers/features/common-utils:2": {}
+    // Uncomment the following lines to use ONNX Runtime with CUDA support
+    // "ghcr.io/devcontainers/features/nvidia-cuda:1": {
+    //   "installCudnn": true,
+    //   "installNvtx": true,
+    //   "installToolkit": true,
+    //   "cudaVersion": "12.5",
+    //   "cudnnVersion": "9.4.0.58"
+    // },
+    // "./features/onnxruntime-gpu": {}
   },
-  "forwardPorts": [8971, 5000, 5001, 5173, 8554, 8555],
+  "forwardPorts": [
+    8971,
+    5000,
+    5001,
+    5173,
+    8554,
+    8555
+  ],
   "portsAttributes": {
     "8971": {
       "label": "External NGINX",
@@ -64,10 +80,18 @@
         "editor.formatOnType": true,
         "python.testing.pytestEnabled": false,
         "python.testing.unittestEnabled": true,
-        "python.testing.unittestArgs": ["-v", "-s", "./frigate/test"],
+        "python.testing.unittestArgs": [
+          "-v",
+          "-s",
+          "./frigate/test"
+        ],
         "files.trimTrailingWhitespace": true,
-        "eslint.workingDirectories": ["./web"],
-        "isort.args": ["--settings-path=./pyproject.toml"],
+        "eslint.workingDirectories": [
+          "./web"
+        ],
+        "isort.args": [
+          "--settings-path=./pyproject.toml"
+        ],
         "[python]": {
           "editor.defaultFormatter": "charliermarsh.ruff",
           "editor.formatOnSave": true,
@@ -86,9 +110,16 @@
           ],
           "editor.tabSize": 2
         },
-        "cSpell.ignoreWords": ["rtmp"],
-        "cSpell.words": ["preact", "astype", "hwaccel", "mqtt"]
+        "cSpell.ignoreWords": [
+          "rtmp"
+        ],
+        "cSpell.words": [
+          "preact",
+          "astype",
+          "hwaccel",
+          "mqtt"
+        ]
       }
     }
   }
-}
+}

.devcontainer/features/onnxruntime-gpu/devcontainer-feature.json

@@ -0,0 +1,22 @@
+{
+  "id": "onnxruntime-gpu",
+  "version": "0.0.1",
+  "name": "ONNX Runtime GPU (Nvidia)",
+  "description": "Installs ONNX Runtime for Nvidia GPUs.",
+  "documentationURL": "",
+  "options": {
+    "version": {
+      "type": "string",
+      "proposals": [
+        "latest",
+        "1.20.1",
+        "1.20.0"
+      ],
+      "default": "latest",
+      "description": "Version of ONNX Runtime to install"
+    }
+  },
+  "installsAfter": [
+    "ghcr.io/devcontainers/features/nvidia-cuda"
+  ]
+}

.devcontainer/features/onnxruntime-gpu/install.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+set -e
+
+VERSION=${VERSION}
+
+python3 -m pip config set global.break-system-packages true
+# if VERSION == "latest" or VERSION is empty, install the latest version
+if [ "$VERSION" == "latest" ] || [ -z "$VERSION" ]; then
+    python3 -m pip install onnxruntime-gpu
+else
+    python3 -m pip install onnxruntime-gpu==$VERSION
+fi
+
+echo "Done!"

.devcontainer/post_create.sh

@@ -19,7 +19,7 @@ sudo chown -R "$(id -u):$(id -g)" /media/frigate
 # When started as a service, LIBAVFORMAT_VERSION_MAJOR is defined in the
 # s6 service file. For dev, where frigate is started from an interactive
 # shell, we define it in .bashrc instead.
-echo 'export LIBAVFORMAT_VERSION_MAJOR=$(/usr/lib/ffmpeg/7.0/bin/ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc
+echo 'export LIBAVFORMAT_VERSION_MAJOR=$("$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)" -version | grep -Po "libavformat\W+\K\d+")' >> "$HOME/.bashrc"
 
 make version
 

.github/pull_request_template.md

@@ -1,5 +1,11 @@
 ## Proposed change
 <!--
+  Thank you!
+
+  If you're introducing a new feature or significantly refactoring existing functionality, 
+  we encourage you to start a discussion first. This helps ensure your idea aligns with 
+  Frigate's development goals.
+
   Describe what this pull request does and how it will benefit users of Frigate.
   Please describe in detail any considerations, breaking changes, etc. that are 
   made in this pull request.

.github/workflows/ci.yml

@@ -42,7 +42,7 @@ jobs:
           tags: ${{ steps.setup.outputs.image-name }}-amd64
           cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
   arm64_build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-22.04-arm
     name: ARM Build
     steps:
       - name: Check out code
@@ -76,36 +76,8 @@ jobs:
             rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
-  jetson_jp4_build:
-    runs-on: ubuntu-22.04
-    name: Jetson Jetpack 4
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-      - name: Set up QEMU and Buildx
-        id: setup
-        uses: ./.github/actions/setup
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push TensorRT (Jetson, Jetpack 4)
-        env:
-          ARCH: arm64
-          BASE_IMAGE: timongentzsch/l4t-ubuntu20-opencv:latest
-          SLIM_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
-          TRT_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: tensorrt
-          files: docker/tensorrt/trt.hcl
-          set: |
-            tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp4
-            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4
-            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4,mode=max
   jetson_jp5_build:
+    if: false
     runs-on: ubuntu-22.04
     name: Jetson Jetpack 5
     steps:
@@ -134,6 +106,35 @@ jobs:
             tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp5
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5,mode=max
+  jetson_jp6_build:
+    runs-on: ubuntu-22.04-arm
+    name: Jetson Jetpack 6
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+      - name: Set up QEMU and Buildx
+        id: setup
+        uses: ./.github/actions/setup
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Build and push TensorRT (Jetson, Jetpack 6)
+        env:
+          ARCH: arm64
+          BASE_IMAGE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
+          SLIM_BASE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
+          TRT_BASE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
+        uses: docker/bake-action@v6
+        with:
+          source: .
+          push: true
+          targets: tensorrt
+          files: docker/tensorrt/trt.hcl
+          set: |
+            tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp6
+            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp6
+            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp6,mode=max
   amd64_extra_builds:
     runs-on: ubuntu-22.04
     name: AMD64 Extra Build
@@ -162,8 +163,22 @@ jobs:
             tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
+      - name: AMD/ROCm general build
+        env:
+          AMDGPU: gfx
+          HSA_OVERRIDE: 0
+        uses: docker/bake-action@v6
+        with:
+          source: .
+          push: true
+          targets: rocm
+          files: docker/rocm/rocm.hcl
+          set: |
+            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
+            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-rocm,mode=max
+            *.cache-from=type=gha
   arm64_extra_builds:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-22.04-arm
     name: ARM Extra Build
     needs:
       - arm64_build
@@ -187,46 +202,6 @@ jobs:
           set: |
             rk.tags=${{ steps.setup.outputs.image-name }}-rk
             *.cache-from=type=gha
-  combined_extra_builds:
-    runs-on: ubuntu-22.04
-    name: Combined Extra Builds
-    needs:
-      - amd64_build
-      - arm64_build
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-      - name: Set up QEMU and Buildx
-        id: setup
-        uses: ./.github/actions/setup
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push Hailo-8l build
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: h8l
-          files: docker/hailo8l/h8l.hcl
-          set: |
-            h8l.tags=${{ steps.setup.outputs.image-name }}-h8l
-            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l
-            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l,mode=max
-      - name: AMD/ROCm general build
-        env:
-          AMDGPU: gfx
-          HSA_OVERRIDE: 0
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: rocm
-          files: docker/rocm/rocm.hcl
-          set: |
-            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
-            *.cache-from=type=gha
   # The majority of users running arm64 are rpi users, so the rpi
   # build should be the primary arm64 image
   assemble_default_build:

.github/workflows/pull_request.yml

@@ -4,9 +4,10 @@ on:
   pull_request:
     paths-ignore:
       - "docs/**"
+      - ".github/**"
 
 env:
-  DEFAULT_PYTHON: 3.9
+  DEFAULT_PYTHON: 3.11
 
 jobs:
   build_devcontainer:
@@ -23,7 +24,7 @@ jobs:
           persist-credentials: false
       - uses: actions/setup-node@master
         with:
-          node-version: 16.x
+          node-version: 20.x
       - name: Install devcontainer cli
         run: npm install --global @devcontainers/cli
       - name: Build devcontainer
@@ -63,6 +64,9 @@ jobs:
           node-version: 20.x
       - run: npm install
         working-directory: ./web
+      - name: Build web
+        run: npm run build
+        working-directory: ./web
       # - name: Test
       #   run: npm run test
       #   working-directory: ./web
@@ -76,7 +80,7 @@ jobs:
         with:
           persist-credentials: false
       - name: Set up Python ${{ env.DEFAULT_PYTHON }}
-        uses: actions/setup-python@v5.3.0
+        uses: actions/setup-python@v5.4.0
         with:
           python-version: ${{ env.DEFAULT_PYTHON }}
       - name: Install requirements
@@ -98,14 +102,6 @@ jobs:
         uses: actions/checkout@v4
         with:
           persist-credentials: false
-      - uses: actions/setup-node@master
-        with:
-          node-version: 16.x
-      - run: npm install
-        working-directory: ./web
-      - name: Build web
-        run: npm run build
-        working-directory: ./web
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v3
       - name: Set up Docker Buildx

.github/workflows/release.yml

@@ -39,14 +39,14 @@ jobs:
           STABLE_TAG=${BASE}:stable
           PULL_TAG=${BASE}:${BUILD_TAG}
           docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${VERSION_TAG}
-          for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
+          for variant in standard-arm64 tensorrt tensorrt-jp5 tensorrt-jp6 rk h8l rocm; do
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${VERSION_TAG}-${variant}
           done
 
           # stable tag
           if [[ "${BUILD_TYPE}" == "stable" ]]; then
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${STABLE_TAG}
-            for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
+            for variant in standard-arm64 tensorrt tensorrt-jp5 tensorrt-jp6 rk h8l rocm; do
               docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${STABLE_TAG}-${variant}
             done
           fi

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.15.0
+VERSION = 0.16.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

docker-compose.yml

@@ -38,4 +38,4 @@ services:
     container_name: mqtt
     image: eclipse-mosquitto:1.6
     ports:
-      - "1883:1883"
+      - "1883:1883"

docker/hailo8l/Dockerfile

@@ -1,40 +0,0 @@
-# syntax=docker/dockerfile:1.6
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-# Build Python wheels
-FROM wheels AS h8l-wheels
-
-COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
-COPY docker/hailo8l/requirements-wheels-h8l.txt /requirements-wheels-h8l.txt
-
-RUN sed -i "/https:\/\//d" /requirements-wheels.txt
-
-# Create a directory to store the built wheels
-RUN mkdir /h8l-wheels
-
-# Build the wheels
-RUN pip3 wheel --wheel-dir=/h8l-wheels -c /requirements-wheels.txt -r /requirements-wheels-h8l.txt
-
-FROM wget AS hailort
-ARG TARGETARCH
-RUN --mount=type=bind,source=docker/hailo8l/install_hailort.sh,target=/deps/install_hailort.sh \
-    /deps/install_hailort.sh
-
-# Use deps as the base image
-FROM deps AS h8l-frigate
-
-# Copy the wheels from the wheels stage
-COPY --from=h8l-wheels /h8l-wheels /deps/h8l-wheels
-COPY --from=hailort /hailo-wheels /deps/hailo-wheels
-COPY --from=hailort /rootfs/ /
-
-# Install the wheels
-RUN pip3 install -U /deps/h8l-wheels/*.whl
-RUN pip3 install -U /deps/hailo-wheels/*.whl
-
-# Copy base files from the rootfs stage
-COPY --from=rootfs / /
-
-# Set workdir
-WORKDIR /opt/frigate/

docker/hailo8l/h8l.hcl

@@ -1,34 +0,0 @@
-target wget {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "wget"
-}
-
-target wheels {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "wheels"
-}
-
-target deps {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "deps"
-}
-
-target rootfs {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "rootfs"
-}
-
-target h8l {
-  dockerfile = "docker/hailo8l/Dockerfile"
-  contexts = {
-    wget = "target:wget"
-    wheels = "target:wheels"
-    deps = "target:deps"
-    rootfs = "target:rootfs"
-  }
-  platforms = ["linux/arm64","linux/amd64"]
-}

docker/hailo8l/h8l.mk

@@ -1,15 +0,0 @@
-BOARDS += h8l
-
-local-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-		--set h8l.tags=frigate:latest-h8l \
-		--load
-
-build-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-		--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l
-
-push-h8l: build-h8l
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-	 	--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l \
-		--push

docker/hailo8l/install_hailort.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-hailo_version="4.19.0"
-
-if [[ "${TARGETARCH}" == "amd64" ]]; then
-    arch="x86_64"
-elif [[ "${TARGETARCH}" == "arm64" ]]; then
-    arch="aarch64"
-fi
-
-wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${TARGETARCH}.tar.gz" |
-    tar -C / -xzf -
-
-mkdir -p /hailo-wheels
-
-wget -P /hailo-wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp39-cp39-linux_${arch}.whl"
-

docker/hailo8l/requirements-wheels-h8l.txt

@@ -1,12 +0,0 @@
-appdirs==1.4.*
-argcomplete==2.0.*
-contextlib2==0.6.*
-distlib==0.3.*
-filelock==3.8.*
-future==0.18.*
-importlib-metadata==5.1.*
-importlib-resources==5.1.*
-netaddr==0.8.*
-netifaces==0.10.*
-verboselogs==1.7.*
-virtualenv==20.17.*

docker/hailo8l/user_installation.sh

@@ -4,6 +4,7 @@
 sudo apt-get update
 sudo apt-get install -y build-essential cmake git wget
 
+hailo_version="4.20.0"
 arch=$(uname -m)
 
 if [[ $arch == "x86_64" ]]; then
@@ -13,7 +14,7 @@ else
 fi
 
 # Clone the HailoRT driver repository
-git clone --depth 1 --branch v4.19.0 https://github.com/hailo-ai/hailort-drivers.git
+git clone --depth 1 --branch v${hailo_version} https://github.com/hailo-ai/hailort-drivers.git
 
 # Build and install the HailoRT driver
 cd hailort-drivers/linux/pcie

docker/main/Dockerfile

@@ -3,14 +3,29 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG BASE_IMAGE=debian:11
-ARG SLIM_BASE=debian:11-slim
+# Globally set pip break-system-packages option to avoid having to specify it every time
+ARG PIP_BREAK_SYSTEM_PACKAGES=1
+
+ARG BASE_IMAGE=debian:12
+ARG SLIM_BASE=debian:12-slim
+
+# A hook that allows us to inject commands right after the base images
+ARG BASE_HOOK=
 
 FROM ${BASE_IMAGE} AS base
+ARG PIP_BREAK_SYSTEM_PACKAGES
+ARG BASE_HOOK
 
-FROM --platform=${BUILDPLATFORM} debian:11 AS base_host
+RUN sh -c "$BASE_HOOK"
+
+FROM --platform=${BUILDPLATFORM} debian:12 AS base_host
+ARG PIP_BREAK_SYSTEM_PACKAGES
 
 FROM ${SLIM_BASE} AS slim-base
+ARG PIP_BREAK_SYSTEM_PACKAGES
+ARG BASE_HOOK
+
+RUN sh -c "$BASE_HOOK"
 
 FROM slim-base AS wget
 ARG DEBIAN_FRONTEND
@@ -24,10 +39,7 @@ ARG DEBIAN_FRONTEND
 ENV CCACHE_DIR /root/.ccache
 ENV CCACHE_MAXSIZE 2G
 
-# bind /var/cache/apt to tmpfs to speed up nginx build
-RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
-    --mount=type=bind,source=docker/main/build_nginx.sh,target=/deps/build_nginx.sh \
-    --mount=type=cache,target=/root/.ccache \
+RUN --mount=type=bind,source=docker/main/build_nginx.sh,target=/deps/build_nginx.sh \
     /deps/build_nginx.sh
 
 FROM wget AS sqlite-vec
@@ -139,24 +151,17 @@ ARG TARGETARCH
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
     && apt-get -qq install -y \
-    apt-transport-https \
-    gnupg \
-    wget \
-    # the key fingerprint can be obtained from https://ftp-master.debian.org/keys.html
-    && wget -qO- "https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xA4285295FC7B1A81600062A9605C66F00D6C9793" | \
-    gpg --dearmor > /usr/share/keyrings/debian-archive-bullseye-stable.gpg \
-    && echo "deb [signed-by=/usr/share/keyrings/debian-archive-bullseye-stable.gpg] http://deb.debian.org/debian bullseye main contrib non-free" | \
-    tee /etc/apt/sources.list.d/debian-bullseye-nonfree.list \
+    apt-transport-https wget \
     && apt-get -qq update \
     && apt-get -qq install -y \
-    python3.9 \
-    python3.9-dev \
+    python3.11 \
+    python3.11-dev \
     # opencv dependencies
     build-essential cmake git pkg-config libgtk-3-dev \
     libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
     libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
     gfortran openexr libatlas-base-dev libssl-dev\
-    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
+    libtbbmalloc2 libtbb-dev libdc1394-dev libopenexr-dev \
     libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
     # sqlite3 dependencies
     tclsh \
@@ -164,8 +169,7 @@ RUN apt-get -qq update \
     gcc gfortran libopenblas-dev liblapack-dev && \
     rm -rf /var/lib/apt/lists/*
 
-# Ensure python3 defaults to python3.9
-RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && python3 get-pip.py "pip"
@@ -180,6 +184,9 @@ RUN /build_pysqlite3.sh
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 
+# Install HailoRT & Wheels
+RUN --mount=type=bind,source=docker/main/install_hailort.sh,target=/deps/install_hailort.sh \
+    /deps/install_hailort.sh
 
 # Collect deps in a single layer
 FROM scratch AS deps-rootfs
@@ -190,6 +197,7 @@ COPY --from=libusb-build /usr/local/lib /usr/local/lib
 COPY --from=tempio /rootfs/ /
 COPY --from=s6-overlay /rootfs/ /
 COPY --from=models /rootfs/ /
+COPY --from=wheels /rootfs/ /
 COPY docker/main/rootfs/ /
 
 
@@ -214,14 +222,22 @@ ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1
 # Set OpenCV ffmpeg loglevel to fatal: https://ffmpeg.org/doxygen/trunk/log_8h.html
 ENV OPENCV_FFMPEG_LOGLEVEL=8
 
+# Set HailoRT to disable logging
+ENV HAILORT_LOGGER_PATH=NONE
+
 ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 
 # Install dependencies
 RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
     /deps/install_deps.sh
 
+ENV DEFAULT_FFMPEG_VERSION="7.0"
+ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:5.0"
+
+RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
+    && python3 get-pip.py "pip"
+
 RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
-    python3 -m pip install --upgrade pip && \
     pip3 install -U /deps/wheels/*.whl
 
 COPY --from=deps-rootfs / /

docker/main/build_nginx.sh

@@ -8,10 +8,16 @@ SECURE_TOKEN_MODULE_VERSION="1.5"
 SET_MISC_MODULE_VERSION="v0.33"
 NGX_DEVEL_KIT_VERSION="v0.3.3"
 
-cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
-sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
-apt-get update
+source /etc/os-release
 
+if [[ "$VERSION_ID" == "12" ]]; then
+    sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
+else
+    cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
+    sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
+fi
+
+apt-get update
 apt-get -yqq build-dep nginx
 
 apt-get -yqq install --no-install-recommends ca-certificates wget

docker/main/build_ov_model.py

@@ -4,7 +4,7 @@ from openvino.tools import mo
 ov_model = mo.convert_model(
     "/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
     compress_to_fp16=True,
-    transformations_config="/usr/local/lib/python3.9/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
+    transformations_config="/usr/local/lib/python3.11/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
     tensorflow_object_detection_api_pipeline_config="/models/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config",
     reverse_input_channels=True,
 )

docker/main/build_sqlite_vec.sh

@@ -4,8 +4,15 @@ set -euxo pipefail
 
 SQLITE_VEC_VERSION="0.1.3"
 
-cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
-sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
+source /etc/os-release
+
+if [[ "$VERSION_ID" == "12" ]]; then
+    sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
+else
+    cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
+    sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
+fi
+
 apt-get update
 apt-get -yqq build-dep sqlite3 gettext git
 

docker/main/install_deps.sh

@@ -6,82 +6,66 @@ apt-get -qq update
 
 apt-get -qq install --no-install-recommends -y \
     apt-transport-https \
+    ca-certificates \
     gnupg \
     wget \
     lbzip2 \
     procps vainfo \
     unzip locales tzdata libxml2 xz-utils \
-    python3.9 \
-    python3-pip \
+    python3.11 \
     curl \
     lsof \
     jq \
-    nethogs
+    nethogs \
+    libgl1 \
+    libglib2.0-0 \
+    libusb-1.0.0
 
-# ensure python3 defaults to python3.9
-update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
+update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
 mkdir -p -m 600 /root/.gnupg
 
-# add coral repo
-curl -fsSLo - https://packages.cloud.google.com/apt/doc/apt-key.gpg | \
-    gpg --dearmor -o /etc/apt/trusted.gpg.d/google-cloud-packages-archive-keyring.gpg
-echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | tee /etc/apt/sources.list.d/coral-edgetpu.list
-echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections
+# install coral runtime
+wget -q -O /tmp/libedgetpu1-max.deb "https://github.com/feranick/libedgetpu/releases/download/16.0TF2.17.1-1/libedgetpu1-max_16.0tf2.17.1-1.bookworm_${TARGETARCH}.deb"
+unset DEBIAN_FRONTEND
+yes | dpkg -i /tmp/libedgetpu1-max.deb && export DEBIAN_FRONTEND=noninteractive
+rm /tmp/libedgetpu1-max.deb
 
-# enable non-free repo in Debian
-if grep -q "Debian" /etc/issue; then
-    sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
-fi
-
-# coral drivers
-apt-get -qq update
-apt-get -qq install --no-install-recommends --no-install-suggests -y \
-    libedgetpu1-max python3-tflite-runtime python3-pycoral
-
-# btbn-ffmpeg -> amd64
+# ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
     mkdir -p /usr/lib/ffmpeg/5.0
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
+    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
+    rm -rf ffmpeg.tar.xz
     mkdir -p /usr/lib/ffmpeg/7.0
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
+    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
+    rm -rf ffmpeg.tar.xz
 fi
 
 # ffmpeg -> arm64
 if [[ "${TARGETARCH}" == "arm64" ]]; then
     mkdir -p /usr/lib/ffmpeg/5.0
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
+    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
+    rm -f ffmpeg.tar.xz
     mkdir -p /usr/lib/ffmpeg/7.0
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
+    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
+    rm -f ffmpeg.tar.xz
 fi
 
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    # use debian bookworm for amd / intel-i965 driver packages
-    echo 'deb https://deb.debian.org/debian bookworm main contrib non-free' >/etc/apt/sources.list.d/debian-bookworm.list
-    apt-get -qq update
+    # install amd / intel-i965 driver packages
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
         i965-va-driver intel-gpu-tools onevpl-tools \
         libva-drm2 \
         mesa-va-drivers radeontop
 
-    # something about this dependency requires it to be installed in a separate call rather than in the line above
-    apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        i965-va-driver-shaders
-
     # intel packages use zst compression so we need to update dpkg
     apt-get install -y dpkg
 
-    rm -f /etc/apt/sources.list.d/debian-bookworm.list
-
     # use intel apt intel packages
     wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
     echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list

docker/main/install_hailort.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+set -euxo pipefail
+
+hailo_version="4.20.0"
+
+if [[ "${TARGETARCH}" == "amd64" ]]; then
+    arch="x86_64"
+elif [[ "${TARGETARCH}" == "arm64" ]]; then
+    arch="aarch64"
+fi
+
+wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${TARGETARCH}.tar.gz" | tar -C / -xzf -
+wget -P /wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp311-cp311-linux_${arch}.whl"

docker/main/requirements-wheels.txt

@@ -1,6 +1,7 @@
+aiofiles == 24.1.*
 click == 8.1.*
 # FastAPI
-aiohttp == 3.11.2
+aiohttp == 3.11.3
 starlette == 0.41.2
 starlette-context == 0.3.6
 fastapi == 0.115.*
@@ -9,39 +10,64 @@ slowapi == 0.1.*
 imutils == 0.5.*
 joserfc == 1.0.*
 pathvalidate == 3.2.*
-markupsafe == 2.1.*
+markupsafe == 3.0.*
+python-multipart == 0.0.20
+# General
 mypy == 1.6.1
-numpy == 1.26.*
-onvif_zeep == 0.2.12
-opencv-python-headless == 4.9.0.*
+onvif-zeep-async == 3.1.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
 peewee_migrate == 1.13.*
 psutil == 6.1.*
-pydantic == 2.8.*
+pydantic == 2.10.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
-pytz == 2024.*
+pytz == 2025.*
 pyzmq == 26.2.*
 ruamel.yaml == 0.18.*
 tzlocal == 5.2
 requests == 2.32.*
 types-requests == 2.32.*
-scipy == 1.13.*
 norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
+# Image Manipulation
+numpy == 1.26.*
+opencv-python-headless == 4.11.0.*
+opencv-contrib-python == 4.11.0.*
+scipy == 1.14.*
 # OpenVino & ONNX
-openvino == 2024.3.*
-onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'
-onnxruntime == 1.19.* ; platform_machine == 'aarch64'
+openvino == 2024.4.*
+onnxruntime-openvino == 1.20.* ; platform_machine == 'x86_64'
+onnxruntime == 1.20.* ; platform_machine == 'aarch64'
 # Embeddings
 transformers == 4.45.*
 # Generative AI
 google-generativeai == 0.8.*
 ollama == 0.3.*
-openai == 1.51.*
+openai == 1.65.*
 # push notifications
 py-vapid == 1.9.*
 pywebpush == 2.0.*
+# alpr
+pyclipper == 1.3.*
+shapely == 2.0.*
+Levenshtein==0.26.*
+# HailoRT Wheels
+appdirs==1.4.*
+argcomplete==2.0.*
+contextlib2==0.6.*
+distlib==0.3.*
+filelock==3.8.*
+future==0.18.*
+importlib-metadata==5.1.*
+importlib-resources==5.1.*
+netaddr==0.8.*
+netifaces==0.10.*
+verboselogs==1.7.*
+virtualenv==20.17.*
+prometheus-client == 0.21.*
+# TFLite
+tflite_runtime @ https://github.com/frigate-nvr/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_x86_64.whl; platform_machine == 'x86_64'
+tflite_runtime @ https://github.com/feranick/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_aarch64.whl; platform_machine == 'aarch64'

docker/main/requirements.txt

@@ -1,2 +1,2 @@
-scikit-build == 0.17.*
+scikit-build == 0.18.*
 nvidia-pyindex

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

@@ -43,8 +43,10 @@ function migrate_db_path() {
 }
 
 function set_libva_version() {
-    local ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)
-    export LIBAVFORMAT_VERSION_MAJOR=$($ffmpeg_path -version | grep -Po "libavformat\W+\K\d+")
+    local ffmpeg_path
+    ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)
+    LIBAVFORMAT_VERSION_MAJOR=$("$ffmpeg_path" -version | grep -Po "libavformat\W+\K\d+")
+    export LIBAVFORMAT_VERSION_MAJOR
 }
 
 echo "[INFO] Preparing Frigate..."

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

@@ -44,10 +44,14 @@ function get_ip_and_port_from_supervisor() {
 }
 
 function set_libva_version() {
-    local ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)
-    export LIBAVFORMAT_VERSION_MAJOR=$($ffmpeg_path -version | grep -Po "libavformat\W+\K\d+")
+    local ffmpeg_path
+    ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)
+    LIBAVFORMAT_VERSION_MAJOR=$("$ffmpeg_path" -version | grep -Po "libavformat\W+\K\d+")
+    export LIBAVFORMAT_VERSION_MAJOR
 }
 
+set_libva_version
+
 if [[ -f "/dev/shm/go2rtc.yaml" ]]; then
     echo "[INFO] Removing stale config from last run..."
     rm /dev/shm/go2rtc.yaml
@@ -66,8 +70,6 @@ else
     echo "[WARNING] Unable to remove existing go2rtc config. Changes made to your frigate config file may not be recognized. Please remove the /dev/shm/go2rtc.yaml from your docker host manually."
 fi
 
-set_libva_version
-
 readonly config_path="/config"
 
 if [[ -x "${config_path}/go2rtc" ]]; then

docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py

@@ -1,6 +1,5 @@
 import json
 import os
-import shutil
 import sys
 
 from ruamel.yaml import YAML
@@ -35,10 +34,7 @@ except FileNotFoundError:
 
 path = config.get("ffmpeg", {}).get("path", "default")
 if path == "default":
-    if shutil.which("ffmpeg") is None:
-        print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg")
-    else:
-        print("ffmpeg")
+    print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg")
 elif path in INCLUDED_FFMPEG_VERSIONS:
     print(f"/usr/lib/ffmpeg/{path}/bin/ffmpeg")
 else:

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

@@ -2,7 +2,6 @@
 
 import json
 import os
-import shutil
 import sys
 from pathlib import Path
 
@@ -13,6 +12,7 @@ from frigate.const import (
     BIRDSEYE_PIPE,
     DEFAULT_FFMPEG_VERSION,
     INCLUDED_FFMPEG_VERSIONS,
+    LIBAVFORMAT_VERSION_MAJOR,
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 
@@ -66,29 +66,32 @@ elif go2rtc_config["log"].get("format") is None:
     go2rtc_config["log"]["format"] = "text"
 
 # ensure there is a default webrtc config
-if not go2rtc_config.get("webrtc"):
+if go2rtc_config.get("webrtc") is None:
     go2rtc_config["webrtc"] = {}
 
 # go2rtc should listen on 8555 tcp & udp by default
-if not go2rtc_config["webrtc"].get("listen"):
+if go2rtc_config["webrtc"].get("listen") is None:
     go2rtc_config["webrtc"]["listen"] = ":8555"
 
-if not go2rtc_config["webrtc"].get("candidates", []):
+if go2rtc_config["webrtc"].get("candidates") is None:
     default_candidates = []
     # use internal candidate if it was discovered when running through the add-on
-    internal_candidate = os.environ.get(
-        "FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL", None
-    )
+    internal_candidate = os.environ.get("FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL")
     if internal_candidate is not None:
         default_candidates.append(internal_candidate)
     # should set default stun server so webrtc can work
     default_candidates.append("stun:8555")
 
-    go2rtc_config["webrtc"] = {"candidates": default_candidates}
-else:
-    print(
-        "[INFO] Not injecting WebRTC candidates into go2rtc config as it has been set manually",
-    )
+    go2rtc_config["webrtc"]["candidates"] = default_candidates
+
+# This prevents WebRTC from attempting to establish a connection to the internal
+# docker IPs which are not accessible from outside the container itself and just
+# wastes time during negotiation. Note that this is only necessary because
+# Frigate container doesn't run in host network mode.
+if go2rtc_config["webrtc"].get("filter") is None:
+    go2rtc_config["webrtc"]["filter"] = {"candidates": []}
+elif go2rtc_config["webrtc"]["filter"].get("candidates") is None:
+    go2rtc_config["webrtc"]["filter"]["candidates"] = []
 
 # sets default RTSP response to be equivalent to ?video=h264,h265&audio=aac
 # this means user does not need to specify audio codec when using restream
@@ -112,10 +115,7 @@ else:
 # ensure ffmpeg path is set correctly
 path = config.get("ffmpeg", {}).get("path", "default")
 if path == "default":
-    if shutil.which("ffmpeg") is None:
-        ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
-    else:
-        ffmpeg_path = "ffmpeg"
+    ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
 elif path in INCLUDED_FFMPEG_VERSIONS:
     ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
 else:
@@ -127,14 +127,12 @@ elif go2rtc_config["ffmpeg"].get("bin") is None:
     go2rtc_config["ffmpeg"]["bin"] = ffmpeg_path
 
 # need to replace ffmpeg command when using ffmpeg4
-if int(os.environ.get("LIBAVFORMAT_VERSION_MAJOR", "59") or "59") < 59:
-    if go2rtc_config["ffmpeg"].get("rtsp") is None:
-        go2rtc_config["ffmpeg"]["rtsp"] = (
-            "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
-        )
-else:
+if LIBAVFORMAT_VERSION_MAJOR < 59:
+    rtsp_args = "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
     if go2rtc_config.get("ffmpeg") is None:
-        go2rtc_config["ffmpeg"] = {"path": ""}
+        go2rtc_config["ffmpeg"] = {"rtsp": rtsp_args}
+    elif go2rtc_config["ffmpeg"].get("rtsp") is None:
+        go2rtc_config["ffmpeg"]["rtsp"] = rtsp_args
 
 for name in go2rtc_config.get("streams", {}):
     stream = go2rtc_config["streams"][name]

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

@@ -1,14 +1,16 @@
 ## Send a subrequest to verify if the user is authenticated and has permission to access the resource.
 auth_request /auth;
 
-## Save the upstream metadata response headers from Authelia to variables.
+## Save the upstream metadata response headers from the auth request to variables
 auth_request_set $user $upstream_http_remote_user;
+auth_request_set $role $upstream_http_remote_role;
 auth_request_set $groups $upstream_http_remote_groups;
 auth_request_set $name $upstream_http_remote_name;
 auth_request_set $email $upstream_http_remote_email;
 
 ## Inject the metadata response headers from the variables into the request made to the backend.
 proxy_set_header Remote-User $user;
+proxy_set_header Remote-Role $role;
 proxy_set_header Remote-Groups $groups;
 proxy_set_header Remote-Email $email;
 proxy_set_header Remote-Name $name;

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

@@ -81,6 +81,9 @@ http {
         open_file_cache_errors on;
         aio on;
 
+        # file upload size
+        client_max_body_size 10M;
+
         # https://github.com/kaltura/nginx-vod-module#vod_open_file_thread_pool
         vod_open_file_thread_pool default;
 
@@ -106,6 +109,14 @@ http {
             expires off;
 
             keepalive_disable safari;
+
+            # vod module returns 502 for non-existent media
+            # https://github.com/kaltura/nginx-vod-module/issues/468
+            error_page 502 =404 /vod-not-found;
+        }
+
+        location = /vod-not-found {
+            return 404;
         }
 
         location /stream/ {

docker/rockchip/COCO/coco_subset_20.txt

@@ -0,0 +1,20 @@
+./subset/000000005001.jpg
+./subset/000000038829.jpg
+./subset/000000052891.jpg
+./subset/000000075612.jpg
+./subset/000000098261.jpg
+./subset/000000181542.jpg
+./subset/000000215245.jpg
+./subset/000000277005.jpg
+./subset/000000288685.jpg
+./subset/000000301421.jpg
+./subset/000000334371.jpg
+./subset/000000348481.jpg
+./subset/000000373353.jpg
+./subset/000000397681.jpg
+./subset/000000414673.jpg
+./subset/000000419312.jpg
+./subset/000000465822.jpg
+./subset/000000475732.jpg
+./subset/000000559707.jpg
+./subset/000000574315.jpg

docker/rockchip/Dockerfile

@@ -3,25 +3,32 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
+# Globally set pip break-system-packages option to avoid having to specify it every time
+ARG PIP_BREAK_SYSTEM_PACKAGES=1
+
 FROM wheels as rk-wheels
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt
 RUN sed -i "/https:\/\//d" /requirements-wheels.txt
+RUN sed -i "/onnxruntime/d" /requirements-wheels.txt
 RUN pip3 wheel --wheel-dir=/rk-wheels -c /requirements-wheels.txt -r /requirements-wheels-rk.txt
+RUN rm -rf /rk-wheels/opencv_python-*
 
 FROM deps AS rk-frigate
 ARG TARGETARCH
+ARG PIP_BREAK_SYSTEM_PACKAGES
 
 RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \
-    pip3 install -U /deps/rk-wheels/*.whl
+    pip3 install --no-deps -U /deps/rk-wheels/*.whl
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
+COPY docker/rockchip/COCO /COCO
+COPY docker/rockchip/conv2rknn.py /opt/conv2rknn.py
 
-ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/librknnrt.so /usr/lib/
+ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/librknnrt.so /usr/lib/
 
-RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
-RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe
 ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-7/ffmpeg /usr/lib/ffmpeg/6.0/bin/
 ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-7/ffprobe /usr/lib/ffmpeg/6.0/bin/
-ENV PATH="/usr/lib/ffmpeg/6.0/bin/:${PATH}"
+ENV DEFAULT_FFMPEG_VERSION="6.0"
+ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIONS}"

docker/rockchip/conv2rknn.py

@@ -0,0 +1,82 @@
+import os
+
+import rknn
+import yaml
+from rknn.api import RKNN
+
+try:
+    with open(rknn.__path__[0] + "/VERSION") as file:
+        tk_version = file.read().strip()
+except FileNotFoundError:
+    pass
+
+try:
+    with open("/config/conv2rknn.yaml", "r") as config_file:
+        configuration = yaml.safe_load(config_file)
+except FileNotFoundError:
+    raise Exception("Please place a config.yaml file in /config/conv2rknn.yaml")
+
+if configuration["config"] != None:
+    rknn_config = configuration["config"]
+else:
+    rknn_config = {}
+
+if not os.path.isdir("/config/model_cache/rknn_cache/onnx"):
+    raise Exception(
+        "Place the onnx models you want to convert to rknn format in /config/model_cache/rknn_cache/onnx"
+    )
+
+if "soc" not in configuration:
+    try:
+        with open("/proc/device-tree/compatible") as file:
+            soc = file.read().split(",")[-1].strip("\x00")
+    except FileNotFoundError:
+        raise Exception("Make sure to run docker in privileged mode.")
+
+    configuration["soc"] = [
+        soc,
+    ]
+
+if "quantization" not in configuration:
+    configuration["quantization"] = False
+
+if "output_name" not in configuration:
+    configuration["output_name"] = "{{input_basename}}"
+
+for input_filename in os.listdir("/config/model_cache/rknn_cache/onnx"):
+    for soc in configuration["soc"]:
+        quant = "i8" if configuration["quantization"] else "fp16"
+
+        input_path = "/config/model_cache/rknn_cache/onnx/" + input_filename
+        input_basename = input_filename[: input_filename.rfind(".")]
+
+        output_filename = (
+            configuration["output_name"].format(
+                quant=quant,
+                input_basename=input_basename,
+                soc=soc,
+                tk_version=tk_version,
+            )
+            + ".rknn"
+        )
+        output_path = "/config/model_cache/rknn_cache/" + output_filename
+
+        rknn_config["target_platform"] = soc
+
+        rknn = RKNN(verbose=True)
+        rknn.config(**rknn_config)
+
+        if rknn.load_onnx(model=input_path) != 0:
+            raise Exception("Error loading model.")
+
+        if (
+            rknn.build(
+                do_quantization=configuration["quantization"],
+                dataset="/COCO/coco_subset_20.txt",
+            )
+            != 0
+        ):
+            raise Exception("Error building model.")
+
+        if rknn.export_rknn(output_path) != 0:
+            raise Exception("Error exporting rknn model.")

docker/rockchip/requirements-wheels-rk.txt

@@ -1 +1,2 @@
-rknn-toolkit-lite2 @ https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/rknn_toolkit_lite2-2.0.0b0-cp39-cp39-linux_aarch64.whl
+rknn-toolkit2 == 2.3.0
+rknn-toolkit-lite2 == 2.3.0

docker/rocm/Dockerfile

@@ -2,77 +2,48 @@
 
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
-ARG ROCM=5.7.3
+ARG ROCM=6.3.3
 ARG AMDGPU=gfx900
 ARG HSA_OVERRIDE_GFX_VERSION
 ARG HSA_OVERRIDE
 
 #######################################################################
-FROM ubuntu:focal as rocm
+FROM wget AS rocm
 
 ARG ROCM
+ARG AMDGPU
 
-RUN apt-get update && apt-get -y upgrade
-RUN apt-get -y install gnupg wget
-
-RUN mkdir --parents --mode=0755 /etc/apt/keyrings
-
-RUN wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | tee /etc/apt/keyrings/rocm.gpg > /dev/null
-COPY docker/rocm/rocm.list /etc/apt/sources.list.d/
-COPY docker/rocm/rocm-pin-600 /etc/apt/preferences.d/
-
-RUN apt-get update
-
-RUN apt-get -y install --no-install-recommends migraphx hipfft roctracer
-RUN apt-get -y install --no-install-recommends migraphx-dev
+RUN apt update && \
+    apt install -y wget gpg && \
+    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/$ROCM/ubuntu/jammy/amdgpu-install_6.3.60303-1_all.deb && \
+    apt install -y ./rocm.deb && \
+    apt update && \
+    apt install -y rocm
 
 RUN mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib
-RUN cd /opt/rocm-$ROCM/lib && cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocfft*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/
+RUN cd /opt/rocm-$ROCM/lib && \
+    cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocfft*.so* librocprofiler*.so* libroctx*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/ && \
+    mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib/migraphx/lib && \
+    cp -dpr migraphx/lib/* /opt/rocm-dist/opt/rocm-$ROCM/lib/migraphx/lib
 RUN cd /opt/rocm-dist/opt/ && ln -s rocm-$ROCM rocm
 
 RUN mkdir -p /opt/rocm-dist/etc/ld.so.conf.d/
 RUN echo /opt/rocm/lib|tee /opt/rocm-dist/etc/ld.so.conf.d/rocm.conf
 
-#######################################################################
-FROM --platform=linux/amd64 debian:11 as debian-base
-
-RUN apt-get update && apt-get -y upgrade
-RUN apt-get -y install --no-install-recommends libelf1 libdrm2 libdrm-amdgpu1 libnuma1 kmod
-
-RUN apt-get -y install python3
-
-#######################################################################
-# ROCm does not come with migraphx wrappers for python 3.9, so we build it here
-FROM debian-base as debian-build
-
-ARG ROCM
-
-COPY --from=rocm /opt/rocm-$ROCM /opt/rocm-$ROCM
-RUN ln -s /opt/rocm-$ROCM /opt/rocm
-
-RUN apt-get -y install g++ cmake
-RUN apt-get -y install python3-pybind11 python3.9-distutils python3-dev
-
-WORKDIR /opt/build
-
-COPY docker/rocm/migraphx .
-
-RUN mkdir build && cd build && cmake .. && make install
-
 #######################################################################
 FROM deps AS deps-prelim
 
-# need this to install libnuma1
-RUN apt-get update
-# no ugprade?!?!
-RUN apt-get -y install libnuma1
+RUN apt-get update && apt-get install -y libnuma1
 
-WORKDIR /opt/frigate/
+WORKDIR /opt/frigate
 COPY --from=rootfs / /
 
+RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
+    && python3 get-pip.py "pip" --break-system-packages
+RUN python3 -m pip config set global.break-system-packages true
+
 COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt
-RUN python3 -m pip install --upgrade pip \
-    && pip3 uninstall -y onnxruntime-openvino \
+RUN pip3 uninstall -y onnxruntime-openvino \
     && pip3 install -r /requirements.txt
 
 #######################################################################
@@ -86,12 +57,11 @@ COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*$AMDGPU* /opt/rocm-$ROCM/share
 COPY --from=rocm /opt/rocm-$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 --from=rocm /opt/rocm-dist/ /
-COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-39-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/
 
 #######################################################################
 FROM deps-prelim AS rocm-prelim-hsa-override0
-
 ENV HSA_ENABLE_SDMA=0
+ENV MIGRAPHX_ENABLE_NHWC=1
 
 COPY --from=rocm-dist / /
 

docker/rocm/migraphx/CMakeLists.txt

@@ -1,26 +0,0 @@
-
-cmake_minimum_required(VERSION 3.1)
-
-set(CMAKE_CXX_STANDARD 17)
-set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CXX_EXTENSIONS OFF)
-
-if(NOT CMAKE_BUILD_TYPE)
-  set(CMAKE_BUILD_TYPE Release)
-endif()
-
-SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
-
-project(migraphx_py)
-
-include_directories(/opt/rocm/include)
-
-find_package(pybind11 REQUIRED)
-pybind11_add_module(migraphx migraphx_py.cpp)
-
-target_link_libraries(migraphx PRIVATE /opt/rocm/lib/libmigraphx.so /opt/rocm/lib/libmigraphx_tf.so /opt/rocm/lib/libmigraphx_onnx.so)
-
-install(TARGETS migraphx
-  COMPONENT python
-  LIBRARY DESTINATION /opt/rocm/lib
-)

docker/rocm/migraphx/migraphx_py.cpp

@@ -1,582 +0,0 @@
-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-
-#include <pybind11/pybind11.h>
-#include <pybind11/stl.h>
-#include <pybind11/numpy.h>
-#include <migraphx/program.hpp>
-#include <migraphx/instruction_ref.hpp>
-#include <migraphx/operation.hpp>
-#include <migraphx/quantization.hpp>
-#include <migraphx/generate.hpp>
-#include <migraphx/instruction.hpp>
-#include <migraphx/ref/target.hpp>
-#include <migraphx/stringutils.hpp>
-#include <migraphx/tf.hpp>
-#include <migraphx/onnx.hpp>
-#include <migraphx/load_save.hpp>
-#include <migraphx/register_target.hpp>
-#include <migraphx/json.hpp>
-#include <migraphx/make_op.hpp>
-#include <migraphx/op/common.hpp>
-
-#ifdef HAVE_GPU
-#include <migraphx/gpu/hip.hpp>
-#endif
-
-using half   = half_float::half;
-namespace py = pybind11;
-
-#ifdef __clang__
-#define MIGRAPHX_PUSH_UNUSED_WARNING \
-    _Pragma("clang diagnostic push") \
-        _Pragma("clang diagnostic ignored \"-Wused-but-marked-unused\"")
-#define MIGRAPHX_POP_WARNING _Pragma("clang diagnostic pop")
-#else
-#define MIGRAPHX_PUSH_UNUSED_WARNING
-#define MIGRAPHX_POP_WARNING
-#endif
-#define MIGRAPHX_PYBIND11_MODULE(...) \
-    MIGRAPHX_PUSH_UNUSED_WARNING      \
-    PYBIND11_MODULE(__VA_ARGS__)      \
-    MIGRAPHX_POP_WARNING
-
-#define MIGRAPHX_PYTHON_GENERATE_SHAPE_ENUM(x, t) .value(#x, migraphx::shape::type_t::x)
-namespace migraphx {
-
-migraphx::value to_value(py::kwargs kwargs);
-migraphx::value to_value(py::list lst);
-
-template <class T, class F>
-void visit_py(T x, F f)
-{
-    if(py::isinstance<py::kwargs>(x))
-    {
-        f(to_value(x.template cast<py::kwargs>()));
-    }
-    else if(py::isinstance<py::list>(x))
-    {
-        f(to_value(x.template cast<py::list>()));
-    }
-    else if(py::isinstance<py::bool_>(x))
-    {
-        f(x.template cast<bool>());
-    }
-    else if(py::isinstance<py::int_>(x) or py::hasattr(x, "__index__"))
-    {
-        f(x.template cast<int>());
-    }
-    else if(py::isinstance<py::float_>(x))
-    {
-        f(x.template cast<float>());
-    }
-    else if(py::isinstance<py::str>(x))
-    {
-        f(x.template cast<std::string>());
-    }
-    else if(py::isinstance<migraphx::shape::dynamic_dimension>(x))
-    {
-        f(migraphx::to_value(x.template cast<migraphx::shape::dynamic_dimension>()));
-    }
-    else
-    {
-        MIGRAPHX_THROW("VISIT_PY: Unsupported data type!");
-    }
-}
-
-migraphx::value to_value(py::list lst)
-{
-    migraphx::value v = migraphx::value::array{};
-    for(auto val : lst)
-    {
-        visit_py(val, [&](auto py_val) { v.push_back(py_val); });
-    }
-
-    return v;
-}
-
-migraphx::value to_value(py::kwargs kwargs)
-{
-    migraphx::value v = migraphx::value::object{};
-
-    for(auto arg : kwargs)
-    {
-        auto&& key = py::str(arg.first);
-        auto&& val = arg.second;
-        visit_py(val, [&](auto py_val) { v[key] = py_val; });
-    }
-    return v;
-}
-} // namespace migraphx
-
-namespace pybind11 {
-namespace detail {
-
-template <>
-struct npy_format_descriptor<half>
-{
-    static std::string format()
-    {
-        // following: https://docs.python.org/3/library/struct.html#format-characters
-        return "e";
-    }
-    static constexpr auto name() { return _("half"); }
-};
-
-} // namespace detail
-} // namespace pybind11
-
-template <class F>
-void visit_type(const migraphx::shape& s, F f)
-{
-    s.visit_type(f);
-}
-
-template <class T, class F>
-void visit(const migraphx::raw_data<T>& x, F f)
-{
-    x.visit(f);
-}
-
-template <class F>
-void visit_types(F f)
-{
-    migraphx::shape::visit_types(f);
-}
-
-template <class T>
-py::buffer_info to_buffer_info(T& x)
-{
-    migraphx::shape s = x.get_shape();
-    assert(s.type() != migraphx::shape::tuple_type);
-    if(s.dynamic())
-        MIGRAPHX_THROW("MIGRAPHX PYTHON: dynamic shape argument passed to to_buffer_info");
-    auto strides = s.strides();
-    std::transform(
-        strides.begin(), strides.end(), strides.begin(), [&](auto i) { return i * s.type_size(); });
-    py::buffer_info b;
-    visit_type(s, [&](auto as) {
-        // migraphx use int8_t data to store bool type, we need to
-        // explicitly specify the data type as bool for python
-        if(s.type() == migraphx::shape::bool_type)
-        {
-            b = py::buffer_info(x.data(),
-                                as.size(),
-                                py::format_descriptor<bool>::format(),
-                                s.ndim(),
-                                s.lens(),
-                                strides);
-        }
-        else
-        {
-            b = py::buffer_info(x.data(),
-                                as.size(),
-                                py::format_descriptor<decltype(as())>::format(),
-                                s.ndim(),
-                                s.lens(),
-                                strides);
-        }
-    });
-    return b;
-}
-
-migraphx::shape to_shape(const py::buffer_info& info)
-{
-    migraphx::shape::type_t t;
-    std::size_t n = 0;
-    visit_types([&](auto as) {
-        if(info.format == py::format_descriptor<decltype(as())>::format() or
-           (info.format == "l" and py::format_descriptor<decltype(as())>::format() == "q") or
-           (info.format == "L" and py::format_descriptor<decltype(as())>::format() == "Q"))
-        {
-            t = as.type_enum();
-            n = sizeof(as());
-        }
-        else if(info.format == "?" and py::format_descriptor<decltype(as())>::format() == "b")
-        {
-            t = migraphx::shape::bool_type;
-            n = sizeof(bool);
-        }
-    });
-
-    if(n == 0)
-    {
-        MIGRAPHX_THROW("MIGRAPHX PYTHON: Unsupported data type " + info.format);
-    }
-
-    auto strides = info.strides;
-    std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto i) -> std::size_t {
-        return n > 0 ? i / n : 0;
-    });
-
-    // scalar support
-    if(info.shape.empty())
-    {
-        return migraphx::shape{t};
-    }
-    else
-    {
-        return migraphx::shape{t, info.shape, strides};
-    }
-}
-
-MIGRAPHX_PYBIND11_MODULE(migraphx, m)
-{
-    py::class_<migraphx::shape> shape_cls(m, "shape");
-    shape_cls
-        .def(py::init([](py::kwargs kwargs) {
-            auto v = migraphx::to_value(kwargs);
-            auto t = migraphx::shape::parse_type(v.get("type", "float"));
-            if(v.contains("dyn_dims"))
-            {
-                auto dyn_dims =
-                    migraphx::from_value<std::vector<migraphx::shape::dynamic_dimension>>(
-                        v.at("dyn_dims"));
-                return migraphx::shape(t, dyn_dims);
-            }
-            auto lens = v.get<std::size_t>("lens", {1});
-            if(v.contains("strides"))
-                return migraphx::shape(t, lens, v.at("strides").to_vector<std::size_t>());
-            else
-                return migraphx::shape(t, lens);
-        }))
-        .def("type", &migraphx::shape::type)
-        .def("lens", &migraphx::shape::lens)
-        .def("strides", &migraphx::shape::strides)
-        .def("ndim", &migraphx::shape::ndim)
-        .def("elements", &migraphx::shape::elements)
-        .def("bytes", &migraphx::shape::bytes)
-        .def("type_string", &migraphx::shape::type_string)
-        .def("type_size", &migraphx::shape::type_size)
-        .def("dyn_dims", &migraphx::shape::dyn_dims)
-        .def("packed", &migraphx::shape::packed)
-        .def("transposed", &migraphx::shape::transposed)
-        .def("broadcasted", &migraphx::shape::broadcasted)
-        .def("standard", &migraphx::shape::standard)
-        .def("scalar", &migraphx::shape::scalar)
-        .def("dynamic", &migraphx::shape::dynamic)
-        .def("__eq__", std::equal_to<migraphx::shape>{})
-        .def("__ne__", std::not_equal_to<migraphx::shape>{})
-        .def("__repr__", [](const migraphx::shape& s) { return migraphx::to_string(s); });
-
-    py::enum_<migraphx::shape::type_t>(shape_cls, "type_t")
-        MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_PYTHON_GENERATE_SHAPE_ENUM);
-
-    py::class_<migraphx::shape::dynamic_dimension>(shape_cls, "dynamic_dimension")
-        .def(py::init<>())
-        .def(py::init<std::size_t, std::size_t>())
-        .def(py::init<std::size_t, std::size_t, std::set<std::size_t>>())
-        .def_readwrite("min", &migraphx::shape::dynamic_dimension::min)
-        .def_readwrite("max", &migraphx::shape::dynamic_dimension::max)
-        .def_readwrite("optimals", &migraphx::shape::dynamic_dimension::optimals)
-        .def("is_fixed", &migraphx::shape::dynamic_dimension::is_fixed);
-
-    py::class_<migraphx::argument>(m, "argument", py::buffer_protocol())
-        .def_buffer([](migraphx::argument& x) -> py::buffer_info { return to_buffer_info(x); })
-        .def(py::init([](py::buffer b) {
-            py::buffer_info info = b.request();
-            return migraphx::argument(to_shape(info), info.ptr);
-        }))
-        .def("get_shape", &migraphx::argument::get_shape)
-        .def("data_ptr",
-             [](migraphx::argument& x) { return reinterpret_cast<std::uintptr_t>(x.data()); })
-        .def("tolist",
-             [](migraphx::argument& x) {
-                 py::list l{x.get_shape().elements()};
-                 visit(x, [&](auto data) { l = py::cast(data.to_vector()); });
-                 return l;
-             })
-        .def("__eq__", std::equal_to<migraphx::argument>{})
-        .def("__ne__", std::not_equal_to<migraphx::argument>{})
-        .def("__repr__", [](const migraphx::argument& x) { return migraphx::to_string(x); });
-
-    py::class_<migraphx::target>(m, "target");
-
-    py::class_<migraphx::instruction_ref>(m, "instruction_ref")
-        .def("shape", [](migraphx::instruction_ref i) { return i->get_shape(); })
-        .def("op", [](migraphx::instruction_ref i) { return i->get_operator(); });
-
-    py::class_<migraphx::module, std::unique_ptr<migraphx::module, py::nodelete>>(m, "module")
-        .def("print", [](const migraphx::module& mm) { std::cout << mm << std::endl; })
-        .def(
-            "add_instruction",
-            [](migraphx::module& mm,
-               const migraphx::operation& op,
-               std::vector<migraphx::instruction_ref>& args,
-               std::vector<migraphx::module*>& mod_args) {
-                return mm.add_instruction(op, args, mod_args);
-            },
-            py::arg("op"),
-            py::arg("args"),
-            py::arg("mod_args") = std::vector<migraphx::module*>{})
-        .def(
-            "add_literal",
-            [](migraphx::module& mm, py::buffer data) {
-                py::buffer_info info = data.request();
-                auto literal_shape   = to_shape(info);
-                return mm.add_literal(literal_shape, reinterpret_cast<char*>(info.ptr));
-            },
-            py::arg("data"))
-        .def(
-            "add_parameter",
-            [](migraphx::module& mm, const std::string& name, const migraphx::shape shape) {
-                return mm.add_parameter(name, shape);
-            },
-            py::arg("name"),
-            py::arg("shape"))
-        .def(
-            "add_return",
-            [](migraphx::module& mm, std::vector<migraphx::instruction_ref>& args) {
-                return mm.add_return(args);
-            },
-            py::arg("args"))
-        .def("__repr__", [](const migraphx::module& mm) { return migraphx::to_string(mm); });
-
-    py::class_<migraphx::program>(m, "program")
-        .def(py::init([]() { return migraphx::program(); }))
-        .def("get_parameter_names", &migraphx::program::get_parameter_names)
-        .def("get_parameter_shapes", &migraphx::program::get_parameter_shapes)
-        .def("get_output_shapes", &migraphx::program::get_output_shapes)
-        .def("is_compiled", &migraphx::program::is_compiled)
-        .def(
-            "compile",
-            [](migraphx::program& p,
-               const migraphx::target& t,
-               bool offload_copy,
-               bool fast_math,
-               bool exhaustive_tune) {
-                migraphx::compile_options options;
-                options.offload_copy    = offload_copy;
-                options.fast_math       = fast_math;
-                options.exhaustive_tune = exhaustive_tune;
-                p.compile(t, options);
-            },
-            py::arg("t"),
-            py::arg("offload_copy")    = true,
-            py::arg("fast_math")       = true,
-            py::arg("exhaustive_tune") = false)
-        .def("get_main_module", [](const migraphx::program& p) { return p.get_main_module(); })
-        .def(
-            "create_module",
-            [](migraphx::program& p, const std::string& name) { return p.create_module(name); },
-            py::arg("name"))
-        .def("run",
-             [](migraphx::program& p, py::dict params) {
-                 migraphx::parameter_map pm;
-                 for(auto x : params)
-                 {
-                     std::string key      = x.first.cast<std::string>();
-                     py::buffer b         = x.second.cast<py::buffer>();
-                     py::buffer_info info = b.request();
-                     pm[key]              = migraphx::argument(to_shape(info), info.ptr);
-                 }
-                 return p.eval(pm);
-             })
-        .def("run_async",
-             [](migraphx::program& p,
-                py::dict params,
-                std::uintptr_t stream,
-                std::string stream_name) {
-                 migraphx::parameter_map pm;
-                 for(auto x : params)
-                 {
-                     std::string key      = x.first.cast<std::string>();
-                     py::buffer b         = x.second.cast<py::buffer>();
-                     py::buffer_info info = b.request();
-                     pm[key]              = migraphx::argument(to_shape(info), info.ptr);
-                 }
-                 migraphx::execution_environment exec_env{
-                     migraphx::any_ptr(reinterpret_cast<void*>(stream), stream_name), true};
-                 return p.eval(pm, exec_env);
-             })
-        .def("sort", &migraphx::program::sort)
-        .def("print", [](const migraphx::program& p) { std::cout << p << std::endl; })
-        .def("__eq__", std::equal_to<migraphx::program>{})
-        .def("__ne__", std::not_equal_to<migraphx::program>{})
-        .def("__repr__", [](const migraphx::program& p) { return migraphx::to_string(p); });
-
-    py::class_<migraphx::operation> op(m, "op");
-    op.def(py::init([](const std::string& name, py::kwargs kwargs) {
-          migraphx::value v = migraphx::value::object{};
-          if(kwargs)
-          {
-              v = migraphx::to_value(kwargs);
-          }
-          return migraphx::make_op(name, v);
-      }))
-        .def("name", &migraphx::operation::name);
-
-    py::enum_<migraphx::op::pooling_mode>(op, "pooling_mode")
-        .value("average", migraphx::op::pooling_mode::average)
-        .value("max", migraphx::op::pooling_mode::max)
-        .value("lpnorm", migraphx::op::pooling_mode::lpnorm);
-
-    py::enum_<migraphx::op::rnn_direction>(op, "rnn_direction")
-        .value("forward", migraphx::op::rnn_direction::forward)
-        .value("reverse", migraphx::op::rnn_direction::reverse)
-        .value("bidirectional", migraphx::op::rnn_direction::bidirectional);
-
-    m.def(
-        "argument_from_pointer",
-        [](const migraphx::shape shape, const int64_t address) {
-            return migraphx::argument(shape, reinterpret_cast<void*>(address));
-        },
-        py::arg("shape"),
-        py::arg("address"));
-
-    m.def(
-        "parse_tf",
-        [](const std::string& filename,
-           bool is_nhwc,
-           unsigned int batch_size,
-           std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
-           std::vector<std::string> output_names) {
-            return migraphx::parse_tf(
-                filename, migraphx::tf_options{is_nhwc, batch_size, map_input_dims, output_names});
-        },
-        "Parse tf protobuf (default format is nhwc)",
-        py::arg("filename"),
-        py::arg("is_nhwc")        = true,
-        py::arg("batch_size")     = 1,
-        py::arg("map_input_dims") = std::unordered_map<std::string, std::vector<std::size_t>>(),
-        py::arg("output_names")   = std::vector<std::string>());
-
-    m.def(
-        "parse_onnx",
-        [](const std::string& filename,
-           unsigned int default_dim_value,
-           migraphx::shape::dynamic_dimension default_dyn_dim_value,
-           std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
-           std::unordered_map<std::string, std::vector<migraphx::shape::dynamic_dimension>>
-               map_dyn_input_dims,
-           bool skip_unknown_operators,
-           bool print_program_on_error,
-           int64_t max_loop_iterations) {
-            migraphx::onnx_options options;
-            options.default_dim_value      = default_dim_value;
-            options.default_dyn_dim_value  = default_dyn_dim_value;
-            options.map_input_dims         = map_input_dims;
-            options.map_dyn_input_dims     = map_dyn_input_dims;
-            options.skip_unknown_operators = skip_unknown_operators;
-            options.print_program_on_error = print_program_on_error;
-            options.max_loop_iterations    = max_loop_iterations;
-            return migraphx::parse_onnx(filename, options);
-        },
-        "Parse onnx file",
-        py::arg("filename"),
-        py::arg("default_dim_value")     = 0,
-        py::arg("default_dyn_dim_value") = migraphx::shape::dynamic_dimension{1, 1},
-        py::arg("map_input_dims") = std::unordered_map<std::string, std::vector<std::size_t>>(),
-        py::arg("map_dyn_input_dims") =
-            std::unordered_map<std::string, std::vector<migraphx::shape::dynamic_dimension>>(),
-        py::arg("skip_unknown_operators") = false,
-        py::arg("print_program_on_error") = false,
-        py::arg("max_loop_iterations")    = 10);
-
-    m.def(
-        "parse_onnx_buffer",
-        [](const std::string& onnx_buffer,
-           unsigned int default_dim_value,
-           migraphx::shape::dynamic_dimension default_dyn_dim_value,
-           std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
-           std::unordered_map<std::string, std::vector<migraphx::shape::dynamic_dimension>>
-               map_dyn_input_dims,
-           bool skip_unknown_operators,
-           bool print_program_on_error) {
-            migraphx::onnx_options options;
-            options.default_dim_value      = default_dim_value;
-            options.default_dyn_dim_value  = default_dyn_dim_value;
-            options.map_input_dims         = map_input_dims;
-            options.map_dyn_input_dims     = map_dyn_input_dims;
-            options.skip_unknown_operators = skip_unknown_operators;
-            options.print_program_on_error = print_program_on_error;
-            return migraphx::parse_onnx_buffer(onnx_buffer, options);
-        },
-        "Parse onnx file",
-        py::arg("filename"),
-        py::arg("default_dim_value")     = 0,
-        py::arg("default_dyn_dim_value") = migraphx::shape::dynamic_dimension{1, 1},
-        py::arg("map_input_dims") = std::unordered_map<std::string, std::vector<std::size_t>>(),
-        py::arg("map_dyn_input_dims") =
-            std::unordered_map<std::string, std::vector<migraphx::shape::dynamic_dimension>>(),
-        py::arg("skip_unknown_operators") = false,
-        py::arg("print_program_on_error") = false);
-
-    m.def(
-        "load",
-        [](const std::string& name, const std::string& format) {
-            migraphx::file_options options;
-            options.format = format;
-            return migraphx::load(name, options);
-        },
-        "Load MIGraphX program",
-        py::arg("filename"),
-        py::arg("format") = "msgpack");
-
-    m.def(
-        "save",
-        [](const migraphx::program& p, const std::string& name, const std::string& format) {
-            migraphx::file_options options;
-            options.format = format;
-            return migraphx::save(p, name, options);
-        },
-        "Save MIGraphX program",
-        py::arg("p"),
-        py::arg("filename"),
-        py::arg("format") = "msgpack");
-
-    m.def("get_target", &migraphx::make_target);
-    m.def("create_argument", [](const migraphx::shape& s, const std::vector<double>& values) {
-        if(values.size() != s.elements())
-            MIGRAPHX_THROW("Values and shape elements do not match");
-        migraphx::argument a{s};
-        a.fill(values.begin(), values.end());
-        return a;
-    });
-    m.def("generate_argument", &migraphx::generate_argument, py::arg("s"), py::arg("seed") = 0);
-    m.def("fill_argument", &migraphx::fill_argument, py::arg("s"), py::arg("value"));
-    m.def("quantize_fp16",
-          &migraphx::quantize_fp16,
-          py::arg("prog"),
-          py::arg("ins_names") = std::vector<std::string>{"all"});
-    m.def("quantize_int8",
-          &migraphx::quantize_int8,
-          py::arg("prog"),
-          py::arg("t"),
-          py::arg("calibration") = std::vector<migraphx::parameter_map>{},
-          py::arg("ins_names")   = std::vector<std::string>{"dot", "convolution"});
-
-#ifdef HAVE_GPU
-    m.def("allocate_gpu", &migraphx::gpu::allocate_gpu, py::arg("s"), py::arg("host") = false);
-    m.def("to_gpu", &migraphx::gpu::to_gpu, py::arg("arg"), py::arg("host") = false);
-    m.def("from_gpu", &migraphx::gpu::from_gpu);
-    m.def("gpu_sync", [] { migraphx::gpu::gpu_sync(); });
-#endif
-
-#ifdef VERSION_INFO
-    m.attr("__version__") = VERSION_INFO;
-#else
-    m.attr("__version__") = "dev";
-#endif
-}

docker/rocm/requirements-wheels-rocm.txt

@@ -1 +1 @@
-onnxruntime-rocm @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v1.0.0/onnxruntime_rocm-1.17.3-cp39-cp39-linux_x86_64.whl
+onnxruntime-rocm @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v6.3.3/onnxruntime_rocm-1.20.1-cp311-cp311-linux_x86_64.whl

docker/rocm/rocm-pin-600

@@ -1,3 +0,0 @@
-Package: *
-Pin: release o=repo.radeon.com
-Pin-Priority: 600

docker/rocm/rocm.hcl

@@ -2,7 +2,7 @@ variable "AMDGPU" {
   default = "gfx900"
 }
 variable "ROCM" {
-  default = "5.7.3"
+  default = "6.3.3"
 }
 variable "HSA_OVERRIDE_GFX_VERSION" {
   default = ""
@@ -10,6 +10,13 @@ variable "HSA_OVERRIDE_GFX_VERSION" {
 variable "HSA_OVERRIDE" {
   default = "1"
 }
+
+target wget {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/amd64"]
+  target = "wget"
+}
+
 target deps {
   dockerfile = "docker/main/Dockerfile"
   platforms = ["linux/amd64"]
@@ -26,6 +33,7 @@ target rocm {
   dockerfile = "docker/rocm/Dockerfile"
   contexts = {
     deps = "target:deps",
+    wget = "target:wget",
     rootfs = "target:rootfs"
   }
   platforms = ["linux/amd64"]

docker/rocm/rocm.list

@@ -1 +0,0 @@
-deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/5.7.3 focal main

docker/rpi/Dockerfile

@@ -6,11 +6,12 @@ ARG DEBIAN_FRONTEND=noninteractive
 FROM deps AS rpi-deps
 ARG TARGETARCH
 
-RUN rm -rf /usr/lib/btbn-ffmpeg/
-
 # Install dependencies
 RUN --mount=type=bind,source=docker/rpi/install_deps.sh,target=/deps/install_deps.sh \
     /deps/install_deps.sh
 
+ENV DEFAULT_FFMPEG_VERSION="rpi"
+ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIONS}"
+
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /

docker/rpi/install_deps.sh

@@ -18,13 +18,17 @@ apt-get -qq install --no-install-recommends -y \
 mkdir -p -m 600 /root/.gnupg
 
 # enable non-free repo
-sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
+echo "deb http://deb.debian.org/debian bookworm main contrib non-free non-free-firmware" | tee -a /etc/apt/sources.list
+apt update
 
 # ffmpeg -> arm64
 if [[ "${TARGETARCH}" == "arm64" ]]; then
     # add raspberry pi repo
     gpg --no-default-keyring --keyring /usr/share/keyrings/raspbian.gpg --keyserver keyserver.ubuntu.com --recv-keys 82B129927FA3303E
-    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bullseye main" | tee /etc/apt/sources.list.d/raspi.list
+    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bookworm main" | tee /etc/apt/sources.list.d/raspi.list
     apt-get -qq update
     apt-get -qq install --no-install-recommends --no-install-suggests -y ffmpeg
+    mkdir -p /usr/lib/ffmpeg/rpi/bin
+    ln -svf /usr/bin/ffmpeg /usr/lib/ffmpeg/rpi/bin/ffmpeg
+    ln -svf /usr/bin/ffprobe /usr/lib/ffmpeg/rpi/bin/ffprobe
 fi

docker/tensorrt/Dockerfile.amd64

@@ -3,22 +3,17 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-# Make this a separate target so it can be built/cached optionally
-FROM wheels as trt-wheels
-ARG DEBIAN_FRONTEND
-ARG TARGETARCH
-
-# Add TensorRT wheels to another folder
-COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
-RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
+# Globally set pip break-system-packages option to avoid having to specify it every time
+ARG PIP_BREAK_SYSTEM_PACKAGES=1
 
 FROM tensorrt-base AS frigate-tensorrt
-ENV TRT_VER=8.5.3
-RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
-    pip3 install -U /deps/trt-wheels/*.whl && \
-    ldconfig
+ARG PIP_BREAK_SYSTEM_PACKAGES
+ENV TRT_VER=8.6.1
+
+# Install TensorRT wheels
+COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
+RUN pip3 install -U -r /requirements-tensorrt.txt && ldconfig
 
-ENV LD_LIBRARY_PATH=/usr/local/lib/python3.9/dist-packages/tensorrt:/usr/local/cuda/lib64:/usr/local/lib/python3.9/dist-packages/nvidia/cufft/lib
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 

docker/tensorrt/Dockerfile.arm64

@@ -7,20 +7,25 @@ ARG BASE_IMAGE
 FROM ${BASE_IMAGE} AS build-wheels
 ARG DEBIAN_FRONTEND
 
+# Add deadsnakes PPA for python3.11
+RUN apt-get -qq update && \
+        apt-get -qq install -y --no-install-recommends \
+        software-properties-common \
+        && add-apt-repository ppa:deadsnakes/ppa
+
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
     && apt-get -qq install -y --no-install-recommends \
-    python3.9 python3.9-dev \
+    python3.11 python3.11-dev \
     wget build-essential cmake git \
     && rm -rf /var/lib/apt/lists/*
 
-# Ensure python3 defaults to python3.9
-RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
+# Ensure python3 defaults to python3.11
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && python3 get-pip.py "pip"
 
-
 FROM build-wheels AS trt-wheels
 ARG DEBIAN_FRONTEND
 ARG TARGETARCH
@@ -41,11 +46,12 @@ RUN --mount=type=bind,source=docker/tensorrt/detector/build_python_tensorrt.sh,t
     && TENSORRT_VER=$(cat /etc/TENSORRT_VER) /deps/build_python_tensorrt.sh
 
 COPY docker/tensorrt/requirements-arm64.txt /requirements-tensorrt.txt
-ADD https://nvidia.box.com/shared/static/9aemm4grzbbkfaesg5l7fplgjtmswhj8.whl /tmp/onnxruntime_gpu-1.15.1-cp39-cp39-linux_aarch64.whl
+# See https://elinux.org/Jetson_Zoo#ONNX_Runtime
+ADD https://nvidia.box.com/shared/static/9yvw05k6u343qfnkhdv2x6xhygze0aq1.whl /tmp/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl
 
 RUN pip3 uninstall -y onnxruntime-openvino \
     && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt \
-    && pip3 install --no-deps /tmp/onnxruntime_gpu-1.15.1-cp39-cp39-linux_aarch64.whl
+    && pip3 install --no-deps /tmp/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl
 
 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND
@@ -67,11 +73,21 @@ RUN --mount=type=bind,source=docker/tensorrt/build_jetson_ffmpeg.sh,target=/deps
 # Frigate w/ TensorRT for NVIDIA Jetson platforms
 FROM tensorrt-base AS frigate-tensorrt
 RUN apt-get update \
-    && apt-get install -y python-is-python3 libprotobuf17  \
+    && apt-get install -y python-is-python3 libprotobuf23  \
     && rm -rf /var/lib/apt/lists/*
 
-RUN rm -rf /usr/lib/btbn-ffmpeg/
 COPY --from=jetson-ffmpeg /rootfs /
+ENV DEFAULT_FFMPEG_VERSION="jetson"
+ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIONS}"
+
+# ffmpeg runtime dependencies
+RUN apt-get -qq update \
+    && apt-get -qq install -y --no-install-recommends \
+    libx264-163 libx265-199 libegl1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Fixes "Error loading shared libs"
+RUN mkdir -p /etc/ld.so.conf.d && echo /usr/lib/ffmpeg/jetson/lib/ > /etc/ld.so.conf.d/ffmpeg.conf
 
 COPY --from=trt-wheels /etc/TENSORRT_VER /etc/TENSORRT_VER
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
@@ -81,3 +97,6 @@ RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
+
+# Fixes "Error importing detector runtime: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: cannot allocate memory in static TLS block"
+ENV LD_PRELOAD /usr/lib/aarch64-linux-gnu/libstdc++.so.6

docker/tensorrt/Dockerfile.base

@@ -3,11 +3,12 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.03-py3
+ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.12-py3
 
 # Build TensorRT-specific library
 FROM ${TRT_BASE} AS trt-deps
 
+ARG TARGETARCH
 ARG COMPUTE_LEVEL
 
 RUN apt-get update \
@@ -16,15 +17,26 @@ RUN apt-get update \
 RUN --mount=type=bind,source=docker/tensorrt/detector/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \
     /tensorrt_libyolo.sh
 
+# COPY required individual CUDA deps
+RUN mkdir -p /usr/local/cuda-deps
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+      cp /usr/local/cuda-12.3/targets/x86_64-linux/lib/libcurand.so.* /usr/local/cuda-deps/ && \
+      cp /usr/local/cuda-12.3/targets/x86_64-linux/lib/libnvrtc.so.* /usr/local/cuda-deps/ ; \
+    fi
+
 # Frigate w/ TensorRT Support as separate image
 FROM deps AS tensorrt-base
 
 #Disable S6 Global timeout
 ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 
+# COPY TensorRT Model Generation Deps
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
-COPY --from=trt-deps /usr/local/cuda-12.* /usr/local/cuda
+
+# COPY Individual CUDA deps folder
+COPY --from=trt-deps /usr/local/cuda-deps /usr/local/cuda
+
 COPY docker/tensorrt/detector/rootfs/ /
 ENV YOLO_MODELS=""
 

docker/tensorrt/build_jetson_ffmpeg.sh

@@ -5,7 +5,7 @@
 
 set -euxo pipefail
 
-INSTALL_PREFIX=/rootfs/usr/local
+INSTALL_PREFIX=/rootfs/usr/lib/ffmpeg/jetson
 
 apt-get -qq update
 apt-get -qq install -y --no-install-recommends build-essential ccache clang cmake pkg-config
@@ -14,14 +14,27 @@ apt-get -qq install -y --no-install-recommends libx264-dev libx265-dev
 pushd /tmp
 
 # Install libnvmpi to enable nvmpi decoders (h264_nvmpi, hevc_nvmpi)
-if [ -e /usr/local/cuda-10.2 ]; then
+if [ -e /usr/local/cuda-12 ]; then
+    # assume Jetpack 6.2
+    apt-key adv --fetch-key https://repo.download.nvidia.com/jetson/jetson-ota-public.asc
+    echo "deb https://repo.download.nvidia.com/jetson/common r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+    echo "deb https://repo.download.nvidia.com/jetson/t234 r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+    echo "deb https://repo.download.nvidia.com/jetson/ffmpeg r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+
+    mkdir -p /opt/nvidia/l4t-packages/
+    touch /opt/nvidia/l4t-packages/.nv-l4t-disable-boot-fw-update-in-preinstall
+
+    apt-get update
+    apt-get -qq install -y --no-install-recommends -o Dpkg::Options::="--force-confold" nvidia-l4t-jetson-multimedia-api
+elif [ -e /usr/local/cuda-10.2 ]; then
     # assume Jetpack 4.X
     wget -q https://developer.nvidia.com/embedded/L4T/r32_Release_v5.0/T186/Jetson_Multimedia_API_R32.5.0_aarch64.tbz2 -O jetson_multimedia_api.tbz2
+    tar xaf jetson_multimedia_api.tbz2 -C / && rm jetson_multimedia_api.tbz2
 else
     # assume Jetpack 5.X
     wget -q https://developer.nvidia.com/downloads/embedded/l4t/r35_release_v3.1/release/jetson_multimedia_api_r35.3.1_aarch64.tbz2 -O jetson_multimedia_api.tbz2
+    tar xaf jetson_multimedia_api.tbz2 -C / && rm jetson_multimedia_api.tbz2
 fi
-tar xaf jetson_multimedia_api.tbz2 -C / && rm jetson_multimedia_api.tbz2
 
 wget -q https://github.com/AndBobsYourUncle/jetson-ffmpeg/archive/9c17b09.zip -O jetson-ffmpeg.zip
 unzip jetson-ffmpeg.zip && rm jetson-ffmpeg.zip && mv jetson-ffmpeg-* jetson-ffmpeg && cd jetson-ffmpeg

docker/tensorrt/detector/build_python_tensorrt.sh

@@ -6,23 +6,23 @@ mkdir -p /trt-wheels
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then
 
-  # NVIDIA supplies python-tensorrt for python3.8, but frigate uses python3.9,
+  # NVIDIA supplies python-tensorrt for python3.10, but frigate uses python3.11,
   # so we must build python-tensorrt ourselves.
 
   # Get python-tensorrt source
-  mkdir /workspace
+  mkdir -p /workspace
   cd /workspace
-  git clone -b ${TENSORRT_VER} https://github.com/NVIDIA/TensorRT.git --depth=1
+  git clone -b release/8.6 https://github.com/NVIDIA/TensorRT.git --depth=1
 
   # Collect dependencies
   EXT_PATH=/workspace/external && mkdir -p $EXT_PATH
-  pip3 install pybind11 && ln -s /usr/local/lib/python3.9/dist-packages/pybind11 $EXT_PATH/pybind11
-  ln -s /usr/include/python3.9 $EXT_PATH/python3.9
+  pip3 install pybind11 && ln -s /usr/local/lib/python3.11/dist-packages/pybind11 $EXT_PATH/pybind11
+  ln -s /usr/include/python3.11 $EXT_PATH/python3.11
   ln -s /usr/include/aarch64-linux-gnu/NvOnnxParser.h /workspace/TensorRT/parsers/onnx/
 
   # Build wheel
   cd /workspace/TensorRT/python
-  EXT_PATH=$EXT_PATH PYTHON_MAJOR_VERSION=3 PYTHON_MINOR_VERSION=9 TARGET_ARCHITECTURE=aarch64 /bin/bash ./build.sh
-  mv build/dist/*.whl /trt-wheels/
+  EXT_PATH=$EXT_PATH PYTHON_MAJOR_VERSION=3 PYTHON_MINOR_VERSION=11 TARGET_ARCHITECTURE=aarch64 TENSORRT_MODULE=tensorrt /bin/bash ./build.sh
+  mv build/bindings_wheel/dist/*.whl /trt-wheels/
 
 fi

docker/tensorrt/detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf

@@ -1,6 +1,8 @@
 /usr/local/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cudnn/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cuda_runtime/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cublas/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cuda_nvrtc/lib
-/usr/local/lib/python3.9/dist-packages/tensorrt
+/usr/local/cuda
+/usr/local/lib/python3.11/dist-packages/nvidia/cudnn/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cuda_runtime/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cublas/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cuda_nvrtc/lib
+/usr/local/lib/python3.11/dist-packages/tensorrt
+/usr/local/lib/python3.11/dist-packages/nvidia/cufft/lib

docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run

@@ -20,7 +20,7 @@ FIRST_MODEL=true
 MODEL_DOWNLOAD=""
 MODEL_CONVERT=""
 
-if [ -z "$YOLO_MODELS"]; then
+if [ -z "$YOLO_MODELS" ]; then
     echo "tensorrt model preparation disabled"
     exit 0
 fi
@@ -64,7 +64,7 @@ fi
 # order to run libyolo here.
 # On Jetpack 5.0, these libraries are not mounted by the runtime and are supplied by the image.
 if [[ "$(arch)" == "aarch64" ]]; then
-    if [[ ! -e /usr/lib/aarch64-linux-gnu/tegra ]]; then
+    if [[ ! -e /usr/lib/aarch64-linux-gnu/tegra && ! -e /usr/lib/aarch64-linux-gnu/tegra-egl ]]; then
         echo "ERROR: Container must be launched with nvidia runtime"
         exit 1
     elif [[ ! -e /usr/lib/aarch64-linux-gnu/libnvinfer.so.8 ||

docker/tensorrt/requirements-amd64.txt

@@ -1,14 +1,17 @@
 # NVidia TensorRT Support (amd64 only)
 --extra-index-url 'https://pypi.nvidia.com'
 numpy < 1.24; platform_machine == 'x86_64'
-tensorrt == 8.5.3.*; platform_machine == 'x86_64'
-cuda-python == 11.8; platform_machine == 'x86_64'
-cython == 0.29.*; platform_machine == 'x86_64'
+tensorrt == 8.6.1; platform_machine == 'x86_64'
+tensorrt_bindings == 8.6.1; platform_machine == 'x86_64'
+cuda-python == 11.8.*; platform_machine == 'x86_64'
+cython == 3.0.*; platform_machine == 'x86_64'
 nvidia-cuda-runtime-cu12 == 12.1.*; platform_machine == 'x86_64'
 nvidia-cuda-runtime-cu11 == 11.8.*; platform_machine == 'x86_64'
 nvidia-cublas-cu11 == 11.11.3.6; platform_machine == 'x86_64'
 nvidia-cudnn-cu11 == 8.6.0.*; platform_machine == 'x86_64'
+nvidia-cudnn-cu12 == 9.5.0.*; platform_machine == 'x86_64'
 nvidia-cufft-cu11==10.*; platform_machine == 'x86_64'
+nvidia-cufft-cu12==11.*; platform_machine == 'x86_64'
 onnx==1.16.*; platform_machine == 'x86_64'
-onnxruntime-gpu==1.18.*; platform_machine == 'x86_64'
+onnxruntime-gpu==1.20.*; platform_machine == 'x86_64'
 protobuf==3.20.3; platform_machine == 'x86_64'

docker/tensorrt/requirements-arm64.txt

@@ -1 +1 @@
-cuda-python == 11.7; platform_machine == 'aarch64'
+cuda-python == 12.6.*; platform_machine == 'aarch64'

docker/tensorrt/trt.hcl

@@ -13,13 +13,29 @@ variable "TRT_BASE" {
 variable "COMPUTE_LEVEL" {
   default = ""
 }
+variable "BASE_HOOK" {
+  # Ensure an up-to-date python 3.11 is available in jetson images
+  default = <<EOT
+if grep -iq \"ubuntu\" /etc/os-release; then
+  . /etc/os-release
+
+  # Add the deadsnakes PPA repository
+  echo "deb https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu $VERSION_CODENAME main" >> /etc/apt/sources.list.d/deadsnakes.list
+  echo "deb-src https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu $VERSION_CODENAME main" >> /etc/apt/sources.list.d/deadsnakes.list
+
+  # Add deadsnakes signing key
+  apt-key adv --keyserver keyserver.ubuntu.com --recv-keys F23C5A6CF475977595C89F51BA6932366A755776
+fi
+EOT
+}
 
 target "_build_args" {
   args = {
     BASE_IMAGE = BASE_IMAGE,
     SLIM_BASE = SLIM_BASE,
     TRT_BASE = TRT_BASE,
-    COMPUTE_LEVEL = COMPUTE_LEVEL
+    COMPUTE_LEVEL = COMPUTE_LEVEL,
+    BASE_HOOK = BASE_HOOK
   }
   platforms = ["linux/${ARCH}"]
 }
@@ -79,7 +95,6 @@ target "tensorrt" {
     wget = "target:wget",
     tensorrt-base = "target:tensorrt-base",
     rootfs = "target:rootfs"
-    wheels = "target:wheels"
   }
   target = "frigate-tensorrt"
   inherits = ["_build_args"]

docker/tensorrt/trt.mk

@@ -1,41 +1,41 @@
 BOARDS += trt
 
-JETPACK4_BASE ?= timongentzsch/l4t-ubuntu20-opencv:latest	# L4T 32.7.1 JetPack 4.6.1
 JETPACK5_BASE ?= nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime	# L4T 35.3.1 JetPack 5.1.1
+JETPACK6_BASE ?= nvcr.io/nvidia/tensorrt:23.12-py3-igpu
 X86_DGPU_ARGS := ARCH=amd64 COMPUTE_LEVEL="50 60 70 80 90"
-JETPACK4_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK4_BASE) SLIM_BASE=$(JETPACK4_BASE) TRT_BASE=$(JETPACK4_BASE)
 JETPACK5_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK5_BASE) SLIM_BASE=$(JETPACK5_BASE) TRT_BASE=$(JETPACK5_BASE)
+JETPACK6_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK6_BASE) SLIM_BASE=$(JETPACK6_BASE) TRT_BASE=$(JETPACK6_BASE)
 
 local-trt: version
 	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
 		--set tensorrt.tags=frigate:latest-tensorrt \
 		--load
 
-local-trt-jp4: version
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt-jp4 \
-		--load
-
 local-trt-jp5: version
 	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
 		--set tensorrt.tags=frigate:latest-tensorrt-jp5 \
 		--load
 
+local-trt-jp6: version
+	$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=frigate:latest-tensorrt-jp6 \
+		--load
+
 build-trt:
 	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
 		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4
 	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
 		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5
+	$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp6
 
 push-trt: build-trt
 	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
 		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt \
 		--push
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 \
-		--push
 	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
 		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 \
 		--push
+	$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp6 \
+		--push

docs/docs/configuration/advanced.md

@@ -4,7 +4,9 @@ title: Advanced Options
 sidebar_label: Advanced Options
 ---
 
-### `logger`
+### Logging
+
+#### Frigate `logger`
 
 Change the default log level for troubleshooting purposes.
 
@@ -28,6 +30,18 @@ Examples of available modules are:
 - `watchdog.<camera_name>`
 - `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
 
+#### Go2RTC Logging
+
+See [the go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#module-log) for logging configuration
+
+```yaml
+go2rtc:
+  streams:
+    # ...
+  log:
+    exec: trace
+```
+
 ### `environment_vars`
 
 This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
@@ -162,15 +176,13 @@ listen [::]:5000 ipv6only=off;
 
 ### Custom ffmpeg build
 
-Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, statically built ffmpeg binary can be downloaded to /config and used.
+Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, statically built `ffmpeg` and `ffprobe` binaries can be placed in `/config/custom-ffmpeg/bin` for Frigate to use.
 
 To do this:
 
-1. Download your ffmpeg build and uncompress to the Frigate config folder.
-2. Update your docker-compose or docker CLI to include `'/home/appdata/frigate/custom-ffmpeg':'/usr/lib/btbn-ffmpeg':'ro'` in the volume mappings.
-3. Restart Frigate and the custom version will be used if the mapping was done correctly.
-
-NOTE: The folder that is set for the config needs to be the folder that contains `/bin`. So if the full structure is `/home/appdata/frigate/custom-ffmpeg/bin/ffmpeg` then the `ffmpeg -> path` field should be `/config/custom-ffmpeg/bin`.
+1. Download your ffmpeg build and uncompress it to the `/config/custom-ffmpeg` folder. Verify that both the `ffmpeg` and `ffprobe` binaries are located in `/config/custom-ffmpeg/bin`.
+2. Update the `ffmpeg.path` in your Frigate config to `/config/custom-ffmpeg`.
+3. Restart Frigate and the custom version will be used if the steps above were done correctly.
 
 ### Custom go2rtc version
 
@@ -178,7 +190,7 @@ Frigate currently includes go2rtc v1.9.2, there may be certain cases where you w
 
 To do this:
 
-1. Download the go2rtc build to the /config folder.
+1. Download the go2rtc build to the `/config` folder.
 2. Rename the build to `go2rtc`.
 3. Give `go2rtc` execute permission.
 4. Restart Frigate and the custom version will be used, you can verify by checking go2rtc logs.
@@ -189,16 +201,16 @@ When frigate starts up, it checks whether your config file is valid, and if it i
 
 ### Via API
 
-Frigate can accept a new configuration file as JSON at the `/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
+Frigate can accept a new configuration file as JSON at the `/api/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
 
 ```bash
-curl -X POST http://frigate_host:5000/config/save -d @config.json
+curl -X POST http://frigate_host:5000/api/config/save -d @config.json
 ```
 
 if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
 
 ```bash
-yq r -j config.yml | curl -X POST http://frigate_host:5000/config/save -d @-
+yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
 ```
 
 ### Via Command Line

docs/docs/configuration/authentication.md

@@ -24,6 +24,11 @@ On startup, an admin user and password are generated and printed in the logs. It
 
 In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup using the `reset_admin_password` setting in your config file.
 
+```yaml
+auth:
+  reset_admin_password: true
+```
+
 ## Login failure rate limiting
 
 In order to limit the risk of brute force attacks, rate limiting is available for login failures. This is implemented with SlowApi, and the string notation for valid values is available in [the documentation](https://limits.readthedocs.io/en/stable/quickstart.html#examples).
@@ -92,15 +97,35 @@ python3 -c 'import secrets; print(secrets.token_hex(64))'
 
 ### Header mapping
 
-If you have disabled Frigate's authentication and your proxy supports passing a header with the authenticated username, you can use the `header_map` config to specify the header name so it is passed to Frigate. For example, the following will map the `X-Forwarded-User` value. Header names are not case sensitive.
+If you have disabled Frigate's authentication and your proxy supports passing a header with authenticated usernames and/or roles, you can use the `header_map` config to specify the header name so it is passed to Frigate. For example, the following will map the `X-Forwarded-User` and `X-Forwarded-Role` values. Header names are not case sensitive.
 
 ```yaml
 proxy:
   ...
   header_map:
     user: x-forwarded-user
+    role: x-forwarded-role
 ```
 
+Frigate supports both `admin` and `viewer` roles (see below). When using port `8971`, Frigate validates these headers and subsequent requests use the headers `remote-user` and `remote-role` for authorization.
+
+#### Port Considerations
+
+**Authenticated Port (8971)**
+
+- Header mapping is **fully supported**.
+- The `remote-role` header determines the user’s privileges:
+  - **admin** → Full access (user management, configuration changes).
+  - **viewer** → Read-only access.
+- Ensure your **proxy sends both user and role headers** for proper role enforcement.
+
+**Unauthenticated Port (5000)**
+
+- Headers are **ignored** for role enforcement.
+- All requests are treated as **anonymous**.
+- The `remote-role` value is **overridden** to **admin-level access**.
+- This design ensures **unauthenticated internal use** within a trusted network.
+
 Note that only the following list of headers are permitted by default:
 
 ```
@@ -121,8 +146,6 @@ X-authentik-uid
 
 If you would like to add more options, you can overwrite the default file with a docker bind mount at `/usr/local/nginx/conf/proxy_trusted_headers.conf`. Reference the source code for the default file formatting.
 
-Future versions of Frigate may leverage group and role headers for authorization in Frigate as well.
-
 ### Login page redirection
 
 Frigate gracefully performs login page redirection that should work with most authentication proxies. If your reverse proxy returns a `Location` header on `401`, `302`, or `307` unauthorized responses, Frigate's frontend will automatically detect it and redirect to that URL.
@@ -130,3 +153,31 @@ Frigate gracefully performs login page redirection that should work with most au
 ### Custom logout url
 
 If your reverse proxy has a dedicated logout url, you can specify using the `logout_url` config option. This will update the link for the `Logout` link in the UI.
+
+## User Roles
+
+Frigate supports user roles to control access to certain features in the UI and API, such as managing users or modifying configuration settings. Roles are assigned to users in the database or through proxy headers and are enforced when accessing the UI or API through the authenticated port (`8971`).
+
+### Supported Roles
+
+- **admin**: Full access to all features, including user management and configuration.
+- **viewer**: Read-only access to the UI and API, including viewing cameras, review items, and historical footage. Configuration editor and settings in the UI are inaccessible.
+
+### Role Enforcement
+
+When using the authenticated port (`8971`), roles are validated via the JWT token or proxy headers (e.g., `remote-role`).
+
+On the internal **unauthenticated** port (`5000`), roles are **not enforced**. All requests are treated as **anonymous**, granting access equivalent to the **admin** role without restrictions.
+
+To use role-based access control, you must connect to Frigate via the **authenticated port (`8971`)** directly or through a reverse proxy.
+
+### Role Visibility in the UI
+
+- When logged in via port `8971`, your **username and role** are displayed in the **account menu** (bottom corner).
+- When using port `5000`, the UI will always display "anonymous" for the username and "admin" for the role.
+
+### Managing User Roles
+
+1. Log in as an **admin** user via port `8971`.
+2. Navigate to **Settings > Users**.
+3. Edit a user’s role by selecting **admin** or **viewer**.

docs/docs/configuration/autotracking.md

@@ -167,3 +167,7 @@ To maintain object tracking during PTZ moves, Frigate tracks the motion of your
 ### Calibration seems to have completed, but the camera is not actually moving to track my object. Why?
 
 Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
+
+### Frigate reports an error saying that calibration has failed. Why?
+
+Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner. You can try running without calibration (just remove the `movement_weights` line from your config and restart), but if calibration fails, this often means that autotracking will behave unpredictably.

docs/docs/configuration/camera_specific.md

@@ -22,7 +22,7 @@ Note that mjpeg cameras require encoding the video into h264 for recording, and
 ```yaml
 go2rtc:
   streams:
-    mjpeg_cam: "ffmpeg:{your_mjpeg_stream_url}#video=h264#hardware" # <- use hardware acceleration to create an h264 stream usable for other components.
+    mjpeg_cam: "ffmpeg:http://your_mjpeg_stream_url#video=h264#hardware" # <- use hardware acceleration to create an h264 stream usable for other components.
 
 cameras:
   ...
@@ -65,19 +65,32 @@ ffmpeg:
 
 ## Model/vendor specific setup
 
+### Amcrest & Dahua
+
+Amcrest & Dahua cameras should be connected to via RTSP using the following format:
+
+```
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=0 # this is the main stream
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=1 # this is the sub stream, typically supporting low resolutions only
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=2 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=3 # new higher end cameras support a fourth stream with another mid resolution (1280x720, 1920x1080)
+
+```
+
 ### Annke C800
 
-This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be repackaged and the audio stream has to be converted to aac. Unfortunately direct playback of in the browser is not working (yet), but the downloaded clip can be played locally.
+This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be adjusted using the `apple_compatibility` config.
 
 ```yaml
 cameras:
   annkec800: # <------ Name the camera
     ffmpeg:
+      apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
       output_args:
-        record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -tag:v hvc1 -bsf:v hevc_mp4toannexb -c:a aac
+        record: preset-record-generic-audio-aac
 
       inputs:
-        - path: rtsp://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
+        - path: rtsp://USERNAME:PASSWORD@CAMERA-IP/H264/ch1/main/av_stream # <----- Update for your camera
           roles:
             - detect
             - record
@@ -95,6 +108,29 @@ ffmpeg:
   input_args: preset-rtsp-blue-iris
 ```
 
+### Hikvision Cameras
+
+Hikvision cameras should be connected to via RTSP using the following format:
+
+```
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/101 # this is the main stream
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/102 # this is the sub stream, typically supporting low resolutions only
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/103 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
+```
+
+:::note
+
+[Some users have reported](https://www.reddit.com/r/frigate_nvr/comments/1hg4ze7/hikvision_security_settings) that newer Hikvision cameras require adjustments to the security settings:
+
+```
+RTSP Authentication - digest/basic
+RTSP Digest Algorithm - MD5
+WEB Authentication - digest/basic
+WEB Digest Algorithm  - MD5
+```
+
+:::
+
 ### Reolink Cameras
 
 Reolink has older cameras (ex: 410 & 520) as well as newer camera (ex: 520a & 511wa) which support different subsets of options. In both cases using the http stream is recommended.

docs/docs/configuration/cameras.md

@@ -7,7 +7,7 @@ title: Camera Configuration
 
 Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
 
-A camera is enabled by default but can be temporarily disabled by using `enabled: False`. Existing tracked objects and recordings can still be accessed. Live streams, recording and detecting are not working. Camera specific configurations will be used.
+A camera is enabled by default but can be disabled by using `enabled: False`. Cameras that are disabled through the configuration file will not appear in the Frigate UI and will not consume system resources.
 
 Each role can only be assigned to one input per camera. The options for roles are as follows:
 

docs/docs/configuration/face_recognition.md

@@ -0,0 +1,56 @@
+---
+id: face_recognition
+title: Face Recognition
+---
+
+Face recognition allows people to be assigned names and when their face is recognized Frigate will assign the person's name as a sub label. This information is included in the UI, filters, as well as in notifications.
+
+Frigate has support for CV2 Local Binary Pattern Face Recognizer to recognize faces, which runs locally. A lightweight face landmark detection model is also used to align faces before running them through the face recognizer.
+
+## Configuration
+
+Face recognition is disabled by default, face recognition must be enabled in your config file before it can be used. Face recognition is a global configuration setting.
+
+```yaml
+face_recognition:
+  enabled: true
+```
+
+## Dataset
+
+The number of images needed for a sufficient training set for face recognition varies depending on several factors:
+
+- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
+- Desired accuracy: The higher the desired accuracy, the more images are typically needed.
+
+However, here are some general guidelines:
+
+- Minimum: For basic face recognition tasks, a minimum of 10-20 images per person is often recommended.
+- Recommended: For more robust and accurate systems, 30-50 images per person is a good starting point.
+- Ideal: For optimal performance, especially in challenging conditions, 100 or more images per person can be beneficial.
+
+## Creating a Robust Training Set
+
+The accuracy of face recognition is heavily dependent on the quality of data given to it for training. It is recommended to build the face training library in phases.
+
+:::tip
+
+When choosing images to include in the face training set it is recommended to always follow these recommendations:
+
+- If it is difficult to make out details in a persons face it will not be helpful in training.
+- Avoid images with under/over-exposure.
+- Avoid blurry / pixelated images.
+- Be careful when uploading images of people when they are wearing clothing that covers a lot of their face as this may confuse the training.
+- Do not upload too many images at the same time, it is recommended to train 4-6 images for each person each day so it is easier to know if the previously added images helped or hurt performance.
+
+:::
+
+### Step 1 - Building a Strong Foundation
+
+When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-2 photos taken by a smartphone for each person. It is important that the person's face in the photo is straight-on and not turned which will ensure a good starting point.
+
+Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle. Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step.
+
+### Step 2 - Expanding The Dataset
+
+Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.

docs/docs/configuration/genai.md

@@ -5,19 +5,13 @@ title: Generative AI
 
 Generative AI can be used to automatically generate descriptive text based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate to provide more context about your tracked objects. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
 
-Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle. Descriptions can also be regenerated manually via the Frigate UI.
-
-:::info
-
-Semantic Search must be enabled to use Generative AI.
-
-:::
+Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle, or can optionally be sent earlier after a number of significantly changed frames, for example in use in more real-time notifications. Descriptions can also be regenerated manually via the Frigate UI. Note that if you are manually entering a description for tracked objects prior to its end, this will be overwritten by the generated response.
 
 ## Configuration
 
-Generative AI can be enabled for all cameras or only for specific cameras. There are currently 3 providers available to integrate with Frigate.
+Generative AI can be enabled for all cameras or only for specific cameras. There are currently 3 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI section below.
 
-If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
+To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
 
 ```yaml
 genai:
@@ -116,7 +110,7 @@ genai:
   model: gpt-4o
 ```
 
-::: note
+:::note
 
 To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
 
@@ -154,6 +148,15 @@ While generating simple descriptions of detected objects is useful, understandin
 
 Frigate provides an [MQTT topic](/integrations/mqtt), `frigate/tracked_object_update`, that is updated with a JSON payload containing `event_id` and `description` when your AI provider returns a description for a tracked object. This description could be used directly in notifications, such as sending alerts to your phone or making audio announcements. If additional details from the tracked object are needed, you can query the [HTTP API](/integrations/api/event-events-event-id-get) using the `event_id`, eg: `http://frigate_ip:5000/api/events/<event_id>`.
 
+If looking to get notifications earlier than when an object ceases to be tracked, an additional send trigger can be configured of `after_significant_updates`.
+
+```yaml
+genai:
+  send_triggers:
+    tracked_object_end: true # default
+    after_significant_updates: 3 # how many updates to a tracked object before we should send an image
+```
+
 ## Custom Prompts
 
 Frigate sends multiple frames from the tracked object along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows:

docs/docs/configuration/hardware_acceleration.md

@@ -175,6 +175,16 @@ For more information on the various values across different distributions, see h
 
 Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
 
+#### Stats for SR-IOV devices
+
+When using virtualized GPUs via SR-IOV, additional args are needed for GPU stats to function. This can be enabled with the following config:
+
+```yaml
+telemetry:
+  stats:
+    sriov: True
+```
+
 ## AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
 
 VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
@@ -285,10 +295,8 @@ These instructions were originally based on the [Jellyfin documentation](https:/
 ## NVIDIA Jetson (Orin AGX, Orin NX, Orin Nano\*, Xavier AGX, Xavier NX, TX2, TX1, Nano)
 
 A separate set of docker images is available that is based on Jetpack/L4T. They come with an `ffmpeg` build
-with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 4.6, use the
-`stable-tensorrt-jp4` tagged image, or if your Jetson host is running Jetpack 5.0+, use the `stable-tensorrt-jp5`
-tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform,
-but the image will still allow hardware decoding and tensorrt object detection.
+with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 5.0+ use the `stable-tensorrt-jp5`
+tagged image, or if your Jetson host is running Jetpack 6.0+ use the `stable-tensorrt-jp6` tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform, but the image will still allow hardware decoding and tensorrt object detection.
 
 You will need to use the image with the nvidia container runtime:
 

docs/docs/configuration/license_plate_recognition.md

@@ -0,0 +1,152 @@
+---
+id: license_plate_recognition
+title: License Plate Recognition (LPR)
+---
+
+Frigate can recognize license plates on vehicles and automatically add the detected characters or recognized name as a `sub_label` to objects that are of type `car`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+
+LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. However, LPR does not run on stationary vehicles.
+
+When a plate is recognized, the detected characters or recognized name is:
+
+- Added as a `sub_label` to the `car` tracked object.
+- Viewable in the Review Item Details pane in Review and the Tracked Object Details pane in Explore.
+- Filterable through the More Filters menu in Explore.
+- Published via the `frigate/events` MQTT topic as a `sub_label` for the tracked object.
+
+## Model Requirements
+
+Users running a Frigate+ model (or any custom model that natively detects license plates) should ensure that `license_plate` is added to the [list of objects to track](https://docs.frigate.video/plus/#available-label-types) either globally or for a specific camera. This will improve the accuracy and performance of the LPR model.
+
+Users without a model that detects license plates can still run LPR. Frigate uses a lightweight YOLOv9 license plate detection model that runs on your CPU. In this case, you should _not_ define `license_plate` in your list of objects to track.
+
+:::note
+
+Frigate needs to first detect a `car` before it can recognize a license plate. If you're using a dedicated LPR camera or have a zoomed-in view, make sure the camera captures enough of the `car` for Frigate to detect it reliably.
+
+:::
+
+## Minimum System Requirements
+
+License plate recognition works by running AI models locally on your system. The models are relatively lightweight and run on your CPU. At least 4GB of RAM is required.
+
+## Configuration
+
+License plate recognition is disabled by default. Enable it in your config file:
+
+```yaml
+lpr:
+  enabled: True
+```
+
+Ensure that your camera is configured to detect objects of type `car`, and that a car is actually being detected by Frigate. Otherwise, LPR will not run.
+
+Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
+
+## Advanced Configuration
+
+Fine-tune the LPR feature using these optional parameters:
+
+### Detection
+
+- **`detection_threshold`**: License plate object detection confidence score required before recognition runs.
+  - Default: `0.7`
+  - Note: If you are using a Frigate+ model and you set the `threshold` in your objects config for `license_plate` higher than this value, recognition will never run. It's best to ensure these values match, or this `detection_threshold` is lower than your object config `threshold`.
+- **`min_area`**: Defines the minimum size (in pixels) a license plate must be before recognition runs.
+  - Default: `1000` pixels.
+  - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
+
+### Recognition
+
+- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a sub label.
+  - Default: `0.9`.
+- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a sub label to an object.
+  - Use this to filter out short, incomplete, or incorrect detections.
+- **`format`**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
+  - `"^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$"` matches plates like "B AB 1234" or "M X 7"
+  - `"^[A-Z]{2}[0-9]{2} [A-Z]{3}$"` matches plates like "AB12 XYZ" or "XY68 ABC"
+  - Websites like https://regex101.com/ can help test regular expressions for your plates.
+
+### Matching
+
+- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` objects when a recognized plate matches a known value.
+  - These labels appear in the UI, filters, and notifications.
+- **`match_distance`**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
+  - For example, setting `match_distance: 1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`.
+  - This parameter will _not_ operate on known plates that are defined as regular expressions. You should define the full string of your plate in `known_plates` in order to use `match_distance`.
+
+## Configuration Examples
+
+```yaml
+lpr:
+  enabled: True
+  min_area: 1500 # Ignore plates smaller than 1500 pixels
+  min_plate_length: 4 # Only recognize plates with 4 or more characters
+  known_plates:
+    Wife's Car:
+      - "ABC-1234"
+      - "ABC-I234" # Accounts for potential confusion between the number one (1) and capital letter I
+    Johnny:
+      - "J*N-*234" # Matches JHN-1234 and JMN-I234, but also note that "*" matches any number of characters
+    Sally:
+      - "[S5]LL 1234" # Matches both SLL 1234 and 5LL 1234
+    Work Trucks:
+      - "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z
+```
+
+```yaml
+lpr:
+  enabled: True
+  min_area: 4000 # Run recognition on larger plates only
+  recognition_threshold: 0.85
+  format: "^[A-Z]{2} [A-Z][0-9]{4}$" # Only recognize plates that are two letters, followed by a space, followed by a single letter and 4 numbers
+  match_distance: 1 # Allow one character variation in plate matching
+  known_plates:
+    Delivery Van:
+      - "RJ K5678"
+      - "UP A1234"
+    Supervisor:
+      - "MN D3163"
+```
+
+## FAQ
+
+### Why isn't my license plate being detected and recognized?
+
+Ensure that:
+
+- Your camera has a clear, human-readable, well-lit view of the plate. If you can't read the plate, Frigate certainly won't be able to. This may require changing video size, quality, or frame rate settings on your camera, depending on your scene and how fast the vehicles are traveling.
+- The plate is large enough in the image (try adjusting `min_area`) or increasing the resolution of your camera's stream.
+- A `car` is detected first, as LPR only runs on recognized vehicles.
+
+If you are using a Frigate+ model or a custom model that detects license plates, ensure that `license_plate` is added to your list of objects to track.
+If you are using the free model that ships with Frigate, you should _not_ add `license_plate` to the list of objects to track.
+
+### Can I run LPR without detecting `car` objects?
+
+No, Frigate requires a `car` to be detected first before recognizing a license plate.
+
+### How can I improve detection accuracy?
+
+- Use high-quality cameras with good resolution.
+- Adjust `detection_threshold` and `recognition_threshold` values.
+- Define a `format` regex to filter out invalid detections.
+
+### Does LPR work at night?
+
+Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night.
+
+### How can I match known plates with minor variations?
+
+Use `match_distance` to allow small character mismatches. Alternatively, define multiple variations in `known_plates`.
+
+### How do I debug LPR issues?
+
+- View MQTT messages for `frigate/events` to verify detected plates.
+- Adjust `detection_threshold` and `recognition_threshold` settings.
+- If you are using a Frigate+ model or a model that detects license plates, watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected with a `car`.
+- 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 enable this when necessary.
+
+### Will LPR slow down my system?
+
+LPR runs on the CPU, so performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU for optimal results.

docs/docs/configuration/live.md

@@ -3,9 +3,9 @@ id: live
 title: Live View
 ---
 
-Frigate intelligently displays your camera streams on the Live view dashboard. Your camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion is detected, cameras seamlessly switch to a live stream.
+Frigate intelligently displays your camera streams on the Live view dashboard. By default, Frigate employs "smart streaming" where camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion or active objects are detected, cameras seamlessly switch to a live stream.
 
-## Live View technologies
+### Live View technologies
 
 Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be configured as shown in the [step by step guide](/guides/configuring_go2rtc).
 
@@ -51,19 +51,32 @@ go2rtc:
       - ffmpeg:rtsp://192.168.1.5:554/live0#video=copy
 ```
 
-### Setting Stream For Live UI
+### Setting Streams For Live UI
 
-There may be some cameras that you would prefer to use the sub stream for live view, but the main stream for recording. This can be done via `live -> stream_name`.
+You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the `live -> streams` list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
+
+Additionally, when creating and editing camera groups in the UI, you can choose the stream you want to use for your camera group's Live dashboard.
+
+:::note
+
+Frigate's default dashboard ("All Cameras") will always use the first entry you've defined in `streams:` when playing live streams from your cameras.
+
+:::
+
+Configure the `streams` option with a "friendly name" for your stream followed by the go2rtc stream name.
+
+Using Frigate's internal version of go2rtc is required to use this feature. You cannot specify paths in the `streams` configuration, only go2rtc stream names.
 
 ```yaml
 go2rtc:
   streams:
     test_cam:
-      - rtsp://192.168.1.5:554/live0 # <- stream which supports video & aac audio.
+      - rtsp://192.168.1.5:554/live_main # <- stream which supports video & aac audio.
       - "ffmpeg:test_cam#audio=opus" # <- copy of the stream which transcodes audio to opus for webrtc
     test_cam_sub:
-      - rtsp://192.168.1.5:554/substream # <- stream which supports video & aac audio.
-      - "ffmpeg:test_cam_sub#audio=opus" # <- copy of the stream which transcodes audio to opus for webrtc
+      - rtsp://192.168.1.5:554/live_sub # <- stream which supports video & aac audio.
+    test_cam_another_sub:
+      - rtsp://192.168.1.5:554/live_alt # <- stream which supports video & aac audio.
 
 cameras:
   test_cam:
@@ -80,7 +93,10 @@ cameras:
           roles:
             - detect
     live:
-      stream_name: test_cam_sub
+      streams: # <--- Multiple streams for Frigate 0.16 and later
+        Main Stream: test_cam # <--- Specify a "friendly name" followed by the go2rtc stream name
+        Sub Stream: test_cam_sub
+        Special Stream: test_cam_another_sub
 ```
 
 ### WebRTC extra configuration:
@@ -101,6 +117,7 @@ WebRTC works by creating a TCP or UDP connection on port `8555`. However, it req
   ```
 
 - For access through Tailscale, the Frigate system's Tailscale IP must be added as a WebRTC candidate. Tailscale IPs all start with `100.`, and are reserved within the `100.64.0.0/10` CIDR block.
+- Note that WebRTC does not support H.265.
 
 :::tip
 
@@ -148,3 +165,64 @@ For devices that support two way talk, Frigate can be configured to use the feat
 - For the Home Assistant Frigate card, [follow the docs](https://github.com/dermotduffy/frigate-hass-card?tab=readme-ov-file#using-2-way-audio) for the correct source.
 
 To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)
+
+### 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.
+
+- Stream selection using the `live -> streams` configuration option (see _Setting Streams For Live UI_ above)
+- Streaming type:
+  - _No streaming_: Camera images will only update once per minute and no live streaming will occur.
+  - _Smart Streaming_ (default, recommended setting): Smart streaming will update your camera image once per minute when no detectable activity is occurring to conserve bandwidth and resources, since a static picture is the same as a streaming image with no motion or objects. When motion or objects are detected, the image seamlessly switches to a live stream.
+  - _Continuous Streaming_: Camera image will always be a live stream when visible on the dashboard, even if no activity is being detected. Continuous streaming may cause high bandwidth usage and performance issues. **Use with caution.**
+- _Compatibility mode_: Enable this option only if your camera's live stream is displaying color artifacts and has a diagonal line on the right side of the image. Before enabling this, try setting your camera's `detect` width and height to a standard aspect ratio (for example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your config fails to resolve the color artifacts and diagonal line.
+
+:::note
+
+The default dashboard ("All Cameras") will always use Smart Streaming and the first entry set in your `streams` configuration, if defined. Use a camera group if you want to change any of these settings from the defaults.
+
+:::
+
+### Disabling cameras
+
+Cameras can be temporarily disabled through the Frigate UI and through [MQTT](/integrations/mqtt#frigatecamera_nameenabledset) to conserve system resources. When disabled, Frigate's ffmpeg processes are terminated — recording stops, object detection is paused, and the Live dashboard displays a blank image with a disabled message. Review items, tracked objects, and historical footage for disabled cameras can still be accessed via the UI.
+
+For restreamed cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
+
+Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
+
+## Live view FAQ
+
+1. **Why don't I have audio in my Live view?**
+
+   You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
+
+   Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
+
+2. **Frigate shows that my live stream is in "low bandwidth mode". What does this mean?**
+
+   Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
+
+   When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. You can also try using the _Reset_ button to force a reload of your stream.
+
+   If you are still experiencing Frigate falling back to low bandwidth mode, you may need to adjust your camera's settings per the recommendations above or ensure you have enough bandwidth available.
+
+3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
+
+   On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
+
+4. **I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?**
+
+   This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
+
+5. **How does "smart streaming" work?**
+
+   Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
+
+   This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
+
+   This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
+
+6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?**
+
+   If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.

docs/docs/configuration/metrics.md

@@ -0,0 +1,99 @@
+---
+id: metrics
+title: Metrics
+---
+
+# Metrics
+
+Frigate exposes Prometheus metrics at the `/api/metrics` endpoint that can be used to monitor the performance and health of your Frigate instance.
+
+## Available Metrics
+
+### System Metrics
+- `frigate_cpu_usage_percent{pid="", name="", process="", type="", cmdline=""}` - Process CPU usage percentage
+- `frigate_mem_usage_percent{pid="", name="", process="", type="", cmdline=""}` - Process memory usage percentage
+- `frigate_gpu_usage_percent{gpu_name=""}` - GPU utilization percentage
+- `frigate_gpu_mem_usage_percent{gpu_name=""}` - GPU memory usage percentage
+
+### Camera Metrics
+- `frigate_camera_fps{camera_name=""}` - Frames per second being consumed from your camera
+- `frigate_detection_fps{camera_name=""}` - Number of times detection is run per second
+- `frigate_process_fps{camera_name=""}` - Frames per second being processed
+- `frigate_skipped_fps{camera_name=""}` - Frames per second skipped for processing
+- `frigate_detection_enabled{camera_name=""}` - Detection enabled status for camera
+- `frigate_audio_dBFS{camera_name=""}` - Audio dBFS for camera
+- `frigate_audio_rms{camera_name=""}` - Audio RMS for camera
+
+### Detector Metrics
+- `frigate_detector_inference_speed_seconds{name=""}` - Time spent running object detection in seconds
+- `frigate_detection_start{name=""}` - Detector start time (unix timestamp)
+
+### Storage Metrics
+- `frigate_storage_free_bytes{storage=""}` - Storage free bytes
+- `frigate_storage_total_bytes{storage=""}` - Storage total bytes
+- `frigate_storage_used_bytes{storage=""}` - Storage used bytes
+- `frigate_storage_mount_type{mount_type="", storage=""}` - Storage mount type info
+
+### Service Metrics
+- `frigate_service_uptime_seconds` - Uptime in seconds
+- `frigate_service_last_updated_timestamp` - Stats recorded time (unix timestamp)
+- `frigate_device_temperature{device=""}` - Device Temperature
+
+### Event Metrics
+- `frigate_camera_events{camera="", label=""}` - Count of camera events since exporter started
+
+## Configuring Prometheus
+
+To scrape metrics from Frigate, add the following to your Prometheus configuration:
+
+```yaml
+scrape_configs:
+  - job_name: 'frigate'
+    metrics_path: '/api/metrics'
+    static_configs:
+      - targets: ['frigate:5000']
+    scrape_interval: 15s
+```
+
+## Example Queries
+
+Here are some example PromQL queries that might be useful:
+
+```promql
+# Average CPU usage across all processes
+avg(frigate_cpu_usage_percent)
+
+# Total GPU memory usage
+sum(frigate_gpu_mem_usage_percent)
+
+# Detection FPS by camera
+rate(frigate_detection_fps{camera_name="front_door"}[5m])
+
+# Storage usage percentage
+(frigate_storage_used_bytes / frigate_storage_total_bytes) * 100
+
+# Event count by camera in last hour
+increase(frigate_camera_events[1h])
+```
+
+## Grafana Dashboard
+
+You can use these metrics to create Grafana dashboards to monitor your Frigate instance. Here's an example of metrics you might want to track:
+
+- CPU, Memory and GPU usage over time
+- Camera FPS and detection rates
+- Storage usage and trends
+- Event counts by camera
+- System temperatures
+
+A sample Grafana dashboard JSON will be provided in a future update.
+
+## Metric Types
+
+The metrics exposed by Frigate use the following Prometheus metric types:
+
+- **Counter**: Cumulative values that only increase (e.g., `frigate_camera_events`)
+- **Gauge**: Values that can go up and down (e.g., `frigate_cpu_usage_percent`)
+- **Info**: Key-value pairs for metadata (e.g., `frigate_storage_mount_type`)
+
+For more information about Prometheus metric types, see the [Prometheus documentation](https://prometheus.io/docs/concepts/metric_types/).

docs/docs/configuration/notifications.md

@@ -11,14 +11,38 @@ Frigate offers native notifications using the [WebPush Protocol](https://web.dev
 
 In order to use notifications the following requirements must be met:
 
-- Frigate must be accessed via a secure https connection
+- Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)).
 - A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
-- In order for notifications to be usable externally, Frigate must be accessible externally
+- In order for notifications to be usable externally, Frigate must be accessible externally.
+- For iOS devices, some users have also indicated that the Notifications switch needs to be enabled in iOS Settings --> Apps --> Safari --> Advanced --> Features.
 
 ### Configuration
 
 To configure notifications, go to the Frigate WebUI -> Settings -> Notifications and enable, then fill out the fields and save.
 
+Optionally, you can change the default cooldown period for notifications through the `cooldown` parameter in your config file. This parameter can also be overridden at the camera level.
+
+Notifications will be prevented if either:
+
+- The global cooldown period hasn't elapsed since any camera's last notification
+- The camera-specific cooldown period hasn't elapsed for the specific camera
+
+```yaml
+notifications:
+  enabled: True
+  email: "johndoe@gmail.com"
+  cooldown: 10 # wait 10 seconds before sending another notification from any camera
+```
+
+```yaml
+cameras:
+  doorbell:
+    ...
+    notifications:
+      enabled: True
+      cooldown: 30 # wait 30 seconds before sending another notification from the doorbell camera
+```
+
 ### Registration
 
 Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
@@ -39,4 +63,4 @@ Different platforms handle notifications differently, some settings changes may
 
 ### Android
 
-Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.
+Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.

docs/docs/configuration/object_detectors.md

@@ -10,32 +10,46 @@ title: Object Detectors
 Frigate supports multiple different detectors that work on different types of hardware:
 
 **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.
-- [Hailo](#hailo-8l): The Hailo8 AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering 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.
 
 **AMD**
+
 - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
 - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
 
 **Intel**
+
 - [OpenVino](#openvino-detector): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
 - [ONNX](#onnx): OpenVINO will automatically be detected and used as a detector in the default Frigate image when a supported ONNX model is configured.
 
 **Nvidia**
+
 - [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Nvidia GPUs and Jetson devices, using one of many default models.
 - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` or `-tensorrt-jp(4/5)` Frigate images when a supported ONNX model is configured.
 
 **Rockchip**
+
 - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
 
 **For Testing**
+
 - [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
 
 :::
 
+:::note
+
+Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
+
+This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
+
+:::
+
 # Officially Supported Detectors
 
-Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, `rocm`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
 
 ## Edge TPU Detector
 
@@ -115,6 +129,111 @@ detectors:
     type: edgetpu
     device: pci
 ```
+---
+
+
+## Hailo-8
+
+This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleration Modules. The integration automatically detects your hardware architecture via the Hailo CLI and selects the appropriate default model if no custom model is specified.
+
+See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the Hailo hardware.
+
+### Configuration
+
+When configuring the Hailo detector, you have two options to specify the model: a local **path** or a **URL**.  
+If both are provided, the detector will first check for the model at the given local path. If the file is not found, it will download the model from the specified URL. The model file is cached under `/config/model_cache/hailo`.
+
+#### YOLO 
+
+Use this configuration for YOLO-based models. When no custom model path or URL is provided, the detector automatically downloads the default model based on the detected hardware:
+- **Hailo-8 hardware:** Uses **YOLOv6n** (default: `yolov6n.hef`)
+- **Hailo-8L hardware:** Uses **YOLOv6n** (default: `yolov6n.hef`)
+
+```yaml
+detectors:
+  hailo8l:
+    type: hailo8l
+    device: PCIe
+
+model:
+  width: 320
+  height: 320
+  input_tensor: nhwc
+  input_pixel_format: rgb
+  input_dtype: int
+  model_type: yolo-generic
+
+  # The detector automatically selects the default model based on your hardware:
+  # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
+  # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
+  #
+  # Optionally, you can specify a local model path to override the default.
+  # If a local path is provided and the file exists, it will be used instead of downloading.
+  # Example:
+  # path: /config/model_cache/hailo/yolov6n.hef
+  #
+  # You can also override using a custom URL:
+  # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
+  # just make sure to give it the write configuration based on the model
+```
+
+#### SSD
+
+For SSD-based models, provide either a model path or URL to your compiled SSD model. The integration will first check the local path before downloading if necessary.
+
+```yaml
+detectors:
+  hailo8l:
+    type: hailo8l
+    device: PCIe
+
+model:
+  width: 300
+  height: 300
+  input_tensor: nhwc
+  input_pixel_format: rgb
+  model_type: ssd
+  # Specify the local model path (if available) or URL for SSD MobileNet v1.
+  # Example with a local path:
+  # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
+  #
+  # Or override using a custom URL:
+  # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
+```
+
+#### Custom Models
+
+The Hailo detector supports all YOLO models compiled for Hailo hardware that include post-processing. You can specify a custom URL or a local path to download or use your model directly. If both are provided, the detector checks the local path first.
+
+```yaml
+detectors:
+  hailo8l:
+    type: hailo8l
+    device: PCIe
+
+model:
+  width: 640
+  height: 640
+  input_tensor: nhwc
+  input_pixel_format: rgb
+  input_dtype: int
+  model_type: yolo-generic
+  # Optional: Specify a local model path.
+  # path: /config/model_cache/hailo/custom_model.hef
+  #
+  # Alternatively, or as a fallback, provide a custom URL:
+  # path: https://custom-model-url.com/path/to/model.hef
+```
+For additional ready-to-use models, please visit: https://github.com/hailo-ai/hailo_model_zoo
+
+Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-processing. You're welcome to choose any of these pre-configured models for your implementation. 
+
+> **Note:**  
+> The config.path parameter can accept either a local file path or a URL ending with .hef. When provided, the detector will first check if the path is a local file path. If the file exists locally, it will use it directly. If the file is not found locally or if a URL was provided, it will attempt to download the model from the specified URL.
+
+---
+
+
 
 ## OpenVINO Detector
 
@@ -169,15 +288,7 @@ This detector also supports YOLOX. Frigate does not come with any YOLOX models p
 
 #### YOLO-NAS
 
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
@@ -199,13 +310,43 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
+#### YOLOv9
+
+[YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
+
+:::tip
+
+The YOLOv9 detector has been designed to support YOLOv9 models, but may support other YOLO model architectures as well.
+
+:::
+
+After placing the downloaded onnx model in your config folder, you can use the following configuration:
+
+```yaml
+detectors:
+  ov:
+    type: openvino
+    device: GPU
+
+model:
+  model_type: yolov9
+  width: 640 # <--- should match the imgsize set during model export
+  height: 640 # <--- should match the imgsize set during model export
+  input_tensor: nchw
+  input_dtype: float
+  path: /config/model_cache/yolov9-t.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
+Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
+
 ## NVidia TensorRT Detector
 
 Nvidia GPUs may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt`. This detector is designed to work with Yolo models for object detection.
 
 ### Minimum Hardware Support
 
-The TensorRT detector uses the 12.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be `>=530`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
+The TensorRT detector uses the 12.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be `>=545`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
 
 To use the TensorRT detector, make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
 
@@ -233,6 +374,8 @@ If your GPU does not support FP16 operations, you can pass the environment varia
 
 Specific models can be selected by passing an environment variable to the `docker run` command or in your `docker-compose.yml` file. Use the form `-e YOLO_MODELS=yolov4-416,yolov4-tiny-416` to select one or more model names. The models available are shown below.
 
+<details>
+<summary>Available Models</summary>
 ```
 yolov3-288
 yolov3-416
@@ -261,6 +404,7 @@ yolov7-320
 yolov7x-640
 yolov7x-320
 ```
+</details>
 
 An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yolov7x-640` models for a Pascal card would look something like this:
 
@@ -305,7 +449,7 @@ model:
 
 ### Setup
 
-The `rocm` detector supports running YOLO-NAS models on AMD GPUs. Use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
+Support for AMD GPUs is provided using the [ONNX detector](#ONNX). In order to utilize the AMD GPU for object detection use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
 
 ### Docker settings for GPU access
 
@@ -355,7 +499,7 @@ When using docker compose:
 ```yaml
 services:
   frigate:
-...
+
 environment:
   HSA_OVERRIDE_GFX_VERSION: "9.0.0"
 ```
@@ -384,41 +528,13 @@ $ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/
 
 ### Supported Models
 
-There is no default model provided, the following formats are supported:
-
-#### YOLO-NAS
-
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  rocm:
-    type: rocm
-
-model:
-  model_type: yolonas
-  width: 320 # <--- should match whatever was set in notebook
-  height: 320 # <--- should match whatever was set in notebook
-  input_pixel_format: bgr
-  path: /config/yolo_nas_s.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
+See [ONNX supported models](#supported-models) for supported models, there are some caveats:
+- D-FINE models are not supported
+- YOLO-NAS models are known to not run well on integrated GPUs
 
 ## ONNX
 
-ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
+ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
 
 :::info
 
@@ -458,15 +574,7 @@ There is no default model provided, the following formats are supported:
 
 #### YOLO-NAS
 
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
@@ -485,6 +593,62 @@ model:
   labelmap_path: /labelmap/coco-80.txt
 ```
 
+#### YOLOv9
+
+[YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
+
+:::tip
+
+The YOLOv9 detector has been designed to support YOLOv9 models, but may support other YOLO model architectures as well.
+
+:::
+
+After placing the downloaded onnx model in your config folder, you can use the following configuration:
+
+```yaml
+detectors:
+  onnx:
+    type: onnx
+
+model:
+  model_type: yolov9
+  width: 640 # <--- should match the imgsize set during model export
+  height: 640 # <--- should match the imgsize set during model export
+  input_tensor: nchw
+  input_dtype: float
+  path: /config/model_cache/yolov9-t.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
+Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
+
+#### D-FINE
+
+[D-FINE](https://github.com/Peterande/D-FINE) is the [current state of the art](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=d-fine-redefine-regression-task-in-detrs-as) at the time of writing. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
+
+:::warning
+
+D-FINE is currently not supported on OpenVINO
+
+:::
+
+After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:
+
+```yaml
+detectors:
+  onnx:
+    type: onnx
+
+model:
+  model_type: dfine
+  width: 640
+  height: 640
+  input_tensor: nchw
+  input_dtype: float
+  path: /config/model_cache/dfine_m_obj2coco.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 ## CPU Detector (not recommended)
@@ -550,7 +714,7 @@ Hardware accelerated object detection is supported on the following SoCs:
 - RK3576
 - RK3588
 
-This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.0.0.beta0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
+This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.3.0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
 
 ### Prerequisites
 
@@ -625,25 +789,79 @@ $ cat /sys/kernel/debug/rknpu/load
 - All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
 - You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
 
-## Hailo-8l
+### Converting your own onnx model to rknn format
 
-This detector is available for use with Hailo-8 AI Acceleration Module.
+To convert a onnx model to the rknn format using the [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2/) you have to:
 
-See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the hailo8.
+- Place one ore more models in onnx format in the directory `config/model_cache/rknn_cache/onnx` on your docker host (this might require `sudo` privileges).
+- Save the configuration file under `config/conv2rknn.yaml` (see below for details).
+- Run `docker exec <frigate_container_id> python3 /opt/conv2rknn.py`. If the conversion was successful, the rknn models will be placed in `config/model_cache/rknn_cache`.
 
-### Configuration
+This is an example configuration file that you need to adjust to your specific onnx model:
 
 ```yaml
-detectors:
-  hailo8l:
-    type: hailo8l
-    device: PCIe
+soc: ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
+quantization: false
 
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
-  input_pixel_format: bgr
-  model_type: ssd
-  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
+output_name: "{input_basename}"
+
+config:
+  mean_values: [[0, 0, 0]]
+  std_values: [[255, 255, 255]]
+  quant_img_rgb2bgr: true
 ```
+
+Explanation of the paramters:
+
+- `soc`: A list of all SoCs you want to build the rknn model for. If you don't specify this parameter, the script tries to find out your SoC and builds the rknn model for this one.
+- `quantization`: true: 8 bit integer (i8) quantization, false: 16 bit float (fp16). Default: false.
+- `output_name`: The output name of the model. The following variables are available:
+  - `quant`: "i8" or "fp16" depending on the config
+  - `input_basename`: the basename of the input model (e.g. "my_model" if the input model is calles "my_model.onnx")
+  - `soc`: the SoC this model was build for (e.g. "rk3588")
+  - `tk_version`: Version of `rknn-toolkit2` (e.g. "2.3.0")
+  - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
+- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.0_EN.pdf).
+
+# Models
+
+Some model types are not included in Frigate by default.
+
+## Downloading Models
+
+Here are some tips for getting different model types
+
+### Downloading D-FINE Model
+
+To export as ONNX:
+
+1. Clone: https://github.com/Peterande/D-FINE and install all dependencies.
+2. Select and download a checkpoint from the [readme](https://github.com/Peterande/D-FINE).
+3. Modify line 58 of `tools/deployment/export_onnx.py` and change batch size to 1: `data = torch.rand(1, 3, 640, 640)`
+4. Run the export, making sure you select the right config, for your checkpoint.
+
+Example:
+
+```
+python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth
+```
+
+:::tip
+
+Model export has only been tested on Linux (or WSL2). Not all dependencies are in `requirements.txt`. Some live in the deployment folder, and some are still missing entirely and must be installed manually.
+
+Make sure you change the batch size to 1 before exporting.
+
+:::
+
+### Downloading YOLO-NAS Model
+
+You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
+
+:::warning
+
+The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+
+:::
+
+The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.

docs/docs/configuration/object_filters.md

@@ -34,7 +34,7 @@ False positives can also be reduced by filtering a detection based on its shape.
 
 ### Object Area
 
-`min_area` and `max_area` filter on the area of an objects bounding box in pixels and can be used to reduce false positives that are outside the range of expected sizes. For example when a leaf is detected as a dog or when a large tree is detected as a person, these can be reduced by adding a `min_area` / `max_area` filter.
+`min_area` and `max_area` filter on the area of an objects bounding box and can be used to reduce false positives that are outside the range of expected sizes. For example when a leaf is detected as a dog or when a large tree is detected as a person, these can be reduced by adding a `min_area` / `max_area` filter. These values can either be in pixels or as a percentage of the frame (for example, 0.12 represents 12% of the frame).
 
 ### Object Proportions
 

docs/docs/configuration/record.md

@@ -183,6 +183,8 @@ record:
   sync_recordings: True
 ```
 
+This feature is meant to fix variations in files, not completely delete entries in the database. If you delete all of your media, don't use `sync_recordings`, just stop Frigate, delete the `frigate.db` database, and restart.
+
 :::warning
 
 The sync operation uses considerable CPU resources and in most cases is not needed, only enable when necessary.

docs/docs/configuration/reference.md

@@ -46,6 +46,11 @@ mqtt:
   tls_insecure: false
   # Optional: interval in seconds for publishing stats (default: shown below)
   stats_interval: 60
+  # Optional: QoS level for subscriptions and publishing (default: shown below)
+  # 0 = at most once
+  # 1 = at least once
+  # 2 = exactly once
+  qos: 0
 
 # Optional: Detectors configuration. Defaults to a single CPU detector
 detectors:
@@ -244,10 +249,14 @@ ffmpeg:
   # If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
   # NOTE: this can be a useful setting for Wireless / Battery cameras to reduce how much footage is potentially lost during a connection timeout.
   retry_interval: 10
+  # Optional: Set tag on HEVC (H.265) recording stream to improve compatibility with Apple players. (default: shown below)
+  apple_compatibility: false
 
 # Optional: Detect configuration
 # NOTE: Can be overridden at the camera level
 detect:
+  # Optional: enables detection for the camera (default: shown below)
+  enabled: False
   # Optional: width of the frame for the input with the detect role (default: use native stream resolution)
   width: 1280
   # Optional: height of the frame for the input with the detect role (default: use native stream resolution)
@@ -255,8 +264,6 @@ detect:
   # Optional: desired fps for your camera for the input with the detect role (default: shown below)
   # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
   fps: 5
-  # Optional: enables detection for the camera (default: True)
-  enabled: True
   # Optional: Number of consecutive detection hits required for an object to be initialized in the tracker. (default: 1/2 the frame rate)
   min_initialized: 2
   # Optional: Number of frames without a detection before Frigate considers an object to be gone. (default: 5x the frame rate)
@@ -310,9 +317,11 @@ objects:
   # Optional: filters to reduce false positives for specific object types
   filters:
     person:
-      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
+      # Optional: minimum size of the bounding box for the detected object (default: 0).
+      # Can be specified as an integer for width*height in pixels or as a decimal representing the percentage of the frame (0.000001 to 0.99).
       min_area: 5000
-      # Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
+      # Optional: maximum size of the bounding box for the detected object (default: 24000000).
+      # Can be specified as an integer for width*height in pixels or as a decimal representing the percentage of the frame (0.000001 to 0.99).
       max_area: 100000
       # Optional: minimum width/height of the bounding box for the detected object (default: 0)
       min_ratio: 0.5
@@ -331,6 +340,8 @@ objects:
 review:
   # Optional: alerts configuration
   alerts:
+    # Optional: enables alerts for the camera (default: shown below)
+    enabled: True
     # Optional: labels that qualify as an alert (default: shown below)
     labels:
       - car
@@ -343,6 +354,8 @@ review:
       - driveway
   # Optional: detections configuration
   detections:
+    # Optional: enables detections for the camera (default: shown below)
+    enabled: True
     # Optional: labels that qualify as a detection (default: all labels that are tracked / listened to)
     labels:
       - car
@@ -400,12 +413,15 @@ motion:
   mqtt_off_delay: 30
 
 # Optional: Notification Configuration
+# NOTE: Can be overridden at the camera level (except email)
 notifications:
   # Optional: Enable notification service (default: shown below)
   enabled: False
   # Optional: Email for push service to reach out to
   # NOTE: This is required to use notifications
   email: "admin@example.com"
+  # Optional: Cooldown time for notifications in seconds (default: shown below)
+  cooldown: 0
 
 # Optional: Record configuration
 # NOTE: Can be overridden at the camera level
@@ -520,12 +536,40 @@ semantic_search:
   enabled: False
   # Optional: Re-index embeddings database from historical tracked objects (default: shown below)
   reindex: False
+  # Optional: Set the model used for embeddings. (default: shown below)
+  model: "jinav1"
   # Optional: Set the model size used for embeddings. (default: shown below)
   # NOTE: small model runs on CPU and large model runs on GPU
   model_size: "small"
 
+# Optional: Configuration for face recognition capability
+face_recognition:
+  # Optional: Enable semantic search (default: shown below)
+  enabled: False
+  # Optional: Set the model size used for embeddings. (default: shown below)
+  # NOTE: small model runs on CPU and large model runs on GPU
+  model_size: "small"
+
+# Optional: Configuration for license plate recognition capability
+lpr:
+  # Optional: Enable license plate recognition (default: shown below)
+  enabled: False
+  # Optional: License plate object confidence score required to begin running recognition (default: shown below)
+  detection_threshold: 0.7
+  # Optional: Minimum area of license plate to begin running recognition (default: shown below)
+  min_area: 1000
+  # Optional: Recognition confidence score required to add the plate to the object as a sub label (default: shown below)
+  recognition_threshold: 0.9
+  # Optional: Minimum number of characters a license plate must have to be added to the object as a sub label (default: shown below)
+  min_plate_length: 4
+  # Optional: Regular expression for the expected format of a license plate (default: shown below)
+  format: None
+  # Optional: Allow this number of missing/incorrect characters to still cause a detected plate to match a known plate
+  match_distance: 1
+  # Optional: Known plates to track (strings or regular expressions) (default: shown below)
+  known_plates: {}
+
 # Optional: Configuration for AI generated tracked object descriptions
-# NOTE: Semantic Search must be enabled for this to do anything.
 # WARNING: Depending on the provider, this will send thumbnails over the internet
 # to Google or OpenAI's LLMs to generate descriptions. It can be overridden at
 # the camera level (enabled: False) to enhance privacy for indoor cameras.
@@ -549,16 +593,18 @@ genai:
 # Optional: Restream configuration
 # Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
 # NOTE: The default go2rtc API port (1984) must be used,
-#       changing this port for the integrated go2rtc instance is not supported. 
+#       changing this port for the integrated go2rtc instance is not supported.
 go2rtc:
 
 # Optional: Live stream configuration for WebUI.
 # NOTE: Can be overridden at the camera level
 live:
-  # Optional: Set the name of the stream configured in go2rtc
+  # Optional: Set the streams configured in go2rtc
   # that should be used for live view in frigate WebUI. (default: name of camera)
   # NOTE: In most cases this should be set at the camera level only.
-  stream_name: camera_name
+  streams:
+    main_stream: main_stream_name
+    sub_stream: sub_stream_name
   # Optional: Set the height of the jsmpeg stream. (default: 720)
   # This must be less than or equal to the height of the detect stream. Lower resolutions
   # reduce bandwidth required for viewing the jsmpeg stream. Width is computed to match known aspect ratio.
@@ -643,7 +689,10 @@ cameras:
       front_steps:
         # Required: List of x,y coordinates to define the polygon of the zone.
         # NOTE: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
-        coordinates: 0.284,0.997,0.389,0.869,0.410,0.745
+        coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
+        # Optional: The real-world distances of a 4-sided zone used for zones with speed estimation enabled (default: none)
+        # List distances in order of the zone points coordinates and use the unit system defined in the ui config
+        distances: 10,15,12,11
         # Optional: Number of consecutive frames required for object to be considered present in the zone (default: shown below).
         inertia: 3
         # Optional: Number of seconds that an object must loiter to be considered in the zone (default: shown below)
@@ -764,6 +813,12 @@ cameras:
         - cat
       # Optional: Restrict generation to objects that entered any of the listed zones (default: none, all zones qualify)
       required_zones: []
+      # Optional: What triggers to use to send frames for a tracked object to generative AI (default: shown below)
+      send_triggers:
+        # Once the object is no longer tracked
+        tracked_object_end: True
+        # Optional: After X many significant updates are received (default: shown below)
+        after_significant_updates: None
       # Optional: Save thumbnails sent to generative AI for review/debugging purposes (default: shown below)
       debug_save_thumbnails: False
 
@@ -794,6 +849,9 @@ ui:
   # 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
 
 # Optional: Telemetry configuration
 telemetry:
@@ -807,11 +865,13 @@ telemetry:
     - lo
   # Optional: Configure system stats
   stats:
-    # Enable AMD GPU stats (default: shown below)
+    # Optional: Enable AMD GPU stats (default: shown below)
     amd_gpu_stats: True
-    # Enable Intel GPU stats (default: shown below)
+    # Optional: Enable Intel GPU stats (default: shown below)
     intel_gpu_stats: True
-    # Enable network bandwidth stats monitoring for camera ffmpeg processes, go2rtc, and object detectors. (default: shown below)
+    # Optional: Treat GPU as SR-IOV to fix GPU stats (default: shown below)
+    sriov: False
+    # Optional: Enable network bandwidth stats monitoring for camera ffmpeg processes, go2rtc, and object detectors. (default: shown below)
     # NOTE: The container must either be privileged or have cap_net_admin, cap_net_raw capabilities enabled.
     network_bandwidth: False
   # Optional: Enable the latest version outbound check (default: shown below)

docs/docs/configuration/semantic_search.md

@@ -1,11 +1,11 @@
 ---
 id: semantic_search
-title: Using Semantic Search
+title: Semantic Search
 ---
 
 Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
 
-Frigate uses [Jina AI's CLIP model](https://huggingface.co/jinaai/jina-clip-v1) to create and save embeddings to Frigate's database. All of this runs locally.
+Frigate uses models from [Jina AI](https://huggingface.co/jinaai) to create and save embeddings to Frigate's database. All of this runs locally.
 
 Semantic Search is accessed via the _Explore_ view in the Frigate UI.
 
@@ -35,23 +35,47 @@ If you are enabling Semantic Search for the first time, be advised that Frigate
 
 :::
 
-### Jina AI CLIP
+### Jina AI CLIP (version 1)
 
-The vision model is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails.
+The [V1 model from Jina](https://huggingface.co/jinaai/jina-clip-v1) has a vision model which is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails.
 
-The text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions.
+The V1 text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions.
 
-Differently weighted versions of the Jina model are available and can be selected by setting the `model_size` config option as `small` or `large`:
+Differently weighted versions of the Jina models are available and can be selected by setting the `model_size` config option as `small` or `large`:
 
 ```yaml
 semantic_search:
   enabled: True
+  model: "jinav1"
   model_size: small
 ```
 
 - Configuring the `large` model employs the full Jina model and will automatically run on the GPU if applicable.
 - Configuring the `small` model employs a quantized version of the Jina model that uses less RAM and runs on CPU with a very negligible difference in embedding quality.
 
+### Jina AI CLIP (version 2)
+
+Frigate also supports the [V2 model from Jina](https://huggingface.co/jinaai/jina-clip-v2), which introduces multilingual support (89 languages). In contrast, the V1 model only supports English.
+
+V2 offers only a 3% performance improvement over V1 in both text-image and text-text retrieval tasks, an upgrade that is unlikely to yield noticeable real-world benefits. Additionally, V2 has _significantly_ higher RAM and GPU requirements, leading to increased inference time and memory usage. If you plan to use V2, ensure your system has ample RAM and a discrete GPU. CPU inference (with the `small` model) using V2 is not recommended.
+
+To use the V2 model, update the `model` parameter in your config:
+
+```yaml
+semantic_search:
+  enabled: True
+  model: "jinav2"
+  model_size: large
+```
+
+For most users, especially native English speakers, the V1 model remains the recommended choice.
+
+:::note
+
+Switching between V1 and V2 requires reindexing your embeddings. To do this, set `reindex: True` in your Semantic Search configuration and restart Frigate. The embeddings from V1 and V2 are incompatible, and failing to reindex will result in incorrect search results.
+
+:::
+
 ### GPU Acceleration
 
 The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used.

docs/docs/configuration/zones.md

@@ -122,16 +122,61 @@ cameras:
           - car
 ```
 
-### Loitering Time
+### Speed Estimation
 
-Zones support a `loitering_time` configuration which can be used to only consider an object as part of a zone if they loiter in the zone for the specified number of seconds. This can be used, for example, to create alerts for cars that stop on the street but not cars that just drive past your camera.
+Frigate can be configured to estimate the speed of objects moving through a zone. This works by combining data from Frigate's object tracker and "real world" distance measurements of the edges of the zone. The recommended use case for this feature is to track the speed of vehicles on a road as they move through the zone.
+
+Your zone must be defined with exactly 4 points and should be aligned to the ground where objects are moving.
+
+![Ground plane 4-point zone](/img/ground-plane.jpg)
+
+Speed estimation requires a minimum number of frames for your object to be tracked before a valid estimate can be calculated, so create your zone away from places where objects enter and exit for the best results. _Your zone should not take up the full frame._ An object's speed is tracked while it is in the zone and then saved to Frigate's database.
+
+Accurate real-world distance measurements are required to estimate speeds. These distances can be specified in your zone config through the `distances` field.
 
 ```yaml
 cameras:
   name_of_your_camera:
     zones:
-      front_yard:
-        loitering_time: 5 # unit is in seconds
-        objects:
-          - person
+      street:
+        coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
+        distances: 10,12,11,13.5 # in meters or feet
+```
+
+Each number in the `distance` field represents the real-world distance between the points in the `coordinates` list. So in the example above, the distance between the first two points ([0.033,0.306] and [0.324,0.138]) is 10. The distance between the second and third set of points ([0.324,0.138] and [0.439,0.185]) is 12, and so on. The fastest and most accurate way to configure this is through the Zone Editor in the Frigate UI.
+
+The `distance` values are measured in meters (metric) or feet (imperial), depending on how `unit_system` is configured in your `ui` config:
+
+```yaml
+ui:
+  # can be "metric" or "imperial", default is metric
+  unit_system: metric
+```
+
+The average speed of your object as it moved through your zone is saved in Frigate's database and can be seen in the UI in the Tracked Object Details pane in Explore. Current estimated speed can also be seen on the debug view as the third value in the object label (see the caveats below). Current estimated speed, average estimated speed, and velocity angle (the angle of the direction the object is moving relative to the frame) of tracked objects is also sent through the `events` MQTT topic. See the [MQTT docs](../integrations/mqtt.md#frigateevents).
+
+These speed values are output as a number in miles per hour (mph) or kilometers per hour (kph). For miles per hour, set `unit_system` to `imperial`. For kilometers per hour, set `unit_system` to `metric`.
+
+#### Best practices and caveats
+
+- Speed estimation works best with a straight road or path when your object travels in a straight line across that path. Avoid creating your zone near intersections or anywhere that objects would make a turn. If the bounding box changes shape (either because the object made a turn or became partially obscured, for example), speed estimation will not be accurate.
+- Create a zone where the bottom center of your object's bounding box travels directly through it and does not become obscured at any time. See the photo example above.
+- Depending on the size and location of your zone, you may want to decrease the zone's `inertia` value from the default of 3.
+- The more accurate your real-world dimensions can be measured, the more accurate speed estimation will be. However, due to the way Frigate's tracking algorithm works, you may need to tweak the real-world distance values so that estimated speeds better match real-world speeds.
+- Once an object leaves the zone, speed accuracy will likely decrease due to perspective distortion and misalignment with the calibrated area. Therefore, speed values will show as a zero through MQTT and will not be visible on the debug view when an object is outside of a speed tracking zone.
+- The speeds are only an _estimation_ and are highly dependent on camera position, zone points, and real-world measurements. This feature should not be used for law enforcement.
+
+### Speed Threshold
+
+Zones can be configured with a minimum speed requirement, meaning an object must be moving at or above this speed to be considered inside the zone. Zone `distances` must be defined as described above.
+
+```yaml
+cameras:
+  name_of_your_camera:
+    zones:
+      sidewalk:
+        coordinates: ...
+        distances: ...
+        inertia: 1
+        speed_threshold: 20 # unit is in kph or mph, depending on how unit_system is set (see above)
 ```

docs/docs/development/contributing.md

@@ -34,7 +34,7 @@ Fork [blakeblackshear/frigate-hass-integration](https://github.com/blakeblackshe
 ### Prerequisites
 
 - GNU make
-- Docker
+- Docker (including buildx plugin)
 - An extra detector (Coral, OpenVINO, etc.) is optional but recommended to simulate real world performance.
 
 :::note

docs/docs/frigate/hardware.md

@@ -13,20 +13,19 @@ Many users have reported various issues with Reolink cameras, so I do not recomm
 
 Here are some of the camera's I recommend:
 
-- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
-- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
-- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
+- <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
+- <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
+- <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
 
 I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
 ## Server
 
-My current favorite is the Beelink EQ12 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
+My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
-| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                                                                   |
-| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
-| Beelink EQ12 (<a href="https://amzn.to/3OlTMJY" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                               |
-| Intel NUC (<a href="https://amzn.to/3psFlHi" target="_blank" rel="nofollow noopener sponsored">Amazon</a>)    | 5-10ms                | USB                 | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion. Requires extra parts. |
+| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                     |
+| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
+| Beelink EQ13 (<a href="https://amzn.to/4iQaBKu" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
 
 ## Detectors
 
@@ -52,24 +51,25 @@ The OpenVINO detector type is able to run on:
 
 More information is available [in the detector docs](/configuration/object_detectors#openvino-detector)
 
-Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known examples are below:
+Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:
 
-| Name                 | Inference Speed | Notes                                                                 |
-| -------------------- | --------------- | --------------------------------------------------------------------- |
-| Intel NCS2 VPU       | 60 - 65 ms      | May vary based on host device                                         |
-| Intel Celeron J4105  | ~ 25 ms         | Inference speeds on CPU were 150 - 200 ms                             |
-| Intel Celeron N3060  | 130 - 150 ms    | Inference speeds on CPU were ~ 550 ms                                 |
-| Intel Celeron N3205U | ~ 120 ms        | Inference speeds on CPU were ~ 380 ms                                 |
-| Intel Celeron N4020  | 50 - 200 ms     | Inference speeds on CPU were ~ 800 ms, greatly depends on other loads |
-| Intel i3 6100T       | 15 - 35 ms      | Inference speeds on CPU were 60 - 120 ms                              |
-| Intel i3 8100        | ~ 15 ms         | Inference speeds on CPU were ~ 65 ms                                  |
-| Intel i5 4590        | ~ 20 ms         | Inference speeds on CPU were ~ 230 ms                                 |
-| Intel i5 6500        | ~ 15 ms         | Inference speeds on CPU were ~ 150 ms                                 |
-| Intel i5 7200u       | 15 - 25 ms      | Inference speeds on CPU were ~ 150 ms                                 |
-| Intel i5 7500        | ~ 15 ms         | Inference speeds on CPU were ~ 260 ms                                 |
-| Intel i5 1135G7      | 10 - 15 ms      |                                                                       |
-| Intel i5 12600K      | ~ 15 ms         | Inference speeds on CPU were ~ 35 ms                                  |
-| Intel Arc A750       | ~ 4 ms          |                                                                       |
+| Name                 | MobileNetV2 Inference Time | YOLO-NAS Inference Time   | Notes                                  |
+| -------------------- | -------------------------- | ------------------------- | -------------------------------------- |
+| Intel Celeron J4105  | ~ 25 ms                    |                           | Can only run one detector instance     |
+| Intel Celeron N3060  | 130 - 150 ms               |                           | Can only run one detector instance     |
+| Intel Celeron N3205U | ~ 120 ms                   |                           | Can only run one detector instance     |
+| Intel Celeron N4020  | 50 - 200 ms                |                           | Inference speed depends on other loads |
+| Intel i3 6100T       | 15 - 35 ms                 |                           | Can only run one detector instance     |
+| Intel i3 8100        | ~ 15 ms                    |                           |                                        |
+| Intel i5 4590        | ~ 20 ms                    |                           |                                        |
+| Intel i5 6500        | ~ 15 ms                    |                           |                                        |
+| Intel i5 7200u       | 15 - 25 ms                 |                           |                                        |
+| Intel i5 7500        | ~ 15 ms                    |                           |                                        |
+| Intel i5 1135G7      | 10 - 15 ms                 |                           |                                        |
+| Intel i3 12000       |                            | 320: ~ 19 ms 640: ~ 54 ms |                                        |
+| Intel i5 12600K      | ~ 15 ms                    | 320: ~ 20 ms 640: ~ 46 ms |                                        |
+| Intel Arc A380       | ~ 6 ms                     | 320: ~ 10 ms              |                                        |
+| Intel Arc A750       | ~ 4 ms                     | 320: ~ 8 ms               |                                        |
 
 ### TensorRT - Nvidia GPU
 
@@ -78,29 +78,46 @@ The TensortRT detector is able to run on x86 hosts that have an Nvidia GPU which
 Inference speeds will vary greatly depending on the GPU and the model used.
 `tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
 
-| Name            | Inference Speed |
-| --------------- | --------------- |
-| GTX 1060 6GB    | ~ 7 ms          |
-| GTX 1070        | ~ 6 ms          |
-| GTX 1660 SUPER  | ~ 4 ms          |
-| RTX 3050        | 5 - 7 ms        |
-| RTX 3070 Mobile | ~ 5 ms          |
-| Quadro P400 2GB | 20 - 25 ms      |
-| Quadro P2000    | ~ 12 ms         |
+| Name            | YoloV7 Inference Time | YOLO-NAS Inference Time   |
+| --------------- | --------------------- | ------------------------- |
+| GTX 1060 6GB    | ~ 7 ms                |                           |
+| GTX 1070        | ~ 6 ms                |                           |
+| GTX 1660 SUPER  | ~ 4 ms                |                           |
+| RTX 3050        | 5 - 7 ms              | 320: ~ 10 ms 640: ~ 16 ms |
+| RTX 3070 Mobile | ~ 5 ms                |                           |
+| Quadro P400 2GB | 20 - 25 ms            |                           |
+| Quadro P2000    | ~ 12 ms               |                           |
 
-#### AMD GPUs
+### AMD GPUs
 
-With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many AMD GPUs.
+With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
 
-### Community Supported:
+### Hailo-8
 
-#### Nvidia Jetson
+| Name            | Hailo‑8 Inference Time | Hailo‑8L Inference Time |
+| --------------- | ---------------------- | ----------------------- |
+| ssd mobilenet v1| ~ 6 ms                 | ~ 10 ms                 |
+| yolov6n         | ~ 7 ms                 | ~ 11 ms                 |
+
+
+Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
+
+**Default Model Configuration:**
+- **Hailo-8L:** Default model is **YOLOv6n**.
+- **Hailo-8:** Default model is **YOLOv6n**.
+
+In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms—with dual PCIe lanes—yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
+
+
+## Community Supported Detectors
+
+### Nvidia Jetson
 
 Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
 
 Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
 
-#### Rockchip platform
+### Rockchip platform
 
 Frigate supports hardware video processing on all Rockchip boards. However, hardware object detection is only supported on these boards:
 
@@ -112,12 +129,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
 
 The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
 
-#### Hailo-8l PCIe
-
-Frigate supports the Hailo-8l M.2 card on any hardware but currently it is only tested on the Raspberry Pi5 PCIe hat from the AI kit.
-
-The inference time for the Hailo-8L chip at time of writing is around 17-21 ms for the SSD MobileNet Version 1 model.
-
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/installation.md

@@ -80,12 +80,12 @@ The Frigate container also stores logs in shm, which can take up to **40MB**, so
 You can calculate the **minimum** shm size for each camera with the following formula using the resolution specified for detect:
 
 ```console
-# Replace <width> and <height>
+# Template for one camera without logs, replace <width> and <height>
 $ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 20 + 270480) / 1048576))'
 
 # Example for 1280x720, including logs
-$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 20 + 270480) / 1048576)) + 40'
-46.63MB
+$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 20 + 270480) / 1048576 + 40))'
+66.63MB
 
 # Example for eight cameras detecting at 1280x720, including logs
 $ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576) * 8 + 40))'
@@ -100,9 +100,9 @@ By default, the Raspberry Pi limits the amount of memory available to the GPU. I
 
 Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with it's own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
 
-### Hailo-8L
+### Hailo-8
 
-The Hailo-8L is an M.2 card typically connected to a carrier board for PCIe, which then connects to the Raspberry Pi 5 as part of the AI Kit. However, it can also be used on other boards equipped with an M.2 M key edge connector.
+The Hailo-8 and Hailo-8L AI accelerators are available in both M.2 and HAT form factors for the Raspberry Pi. The M.2 version typically connects to a carrier board for PCIe, which then interfaces with the Raspberry Pi 5 as part of the AI Kit. The HAT version can be mounted directly onto compatible Raspberry Pi models. Both form factors have been successfully tested on x86 platforms as well, making them versatile options for various computing environments.
 
 #### Installation
 
@@ -111,13 +111,13 @@ For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simpl
 For other installations, follow these steps for installation:
 
 1. Install the driver from the [Hailo GitHub repository](https://github.com/hailo-ai/hailort-drivers). A convenient script for Linux is available to clone the repository, build the driver, and install it.
-2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/41c9b13d2fffce508b32dfc971fa529b49295fbd/docker/hailo8l/user_installation.sh).
+2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/hailo8l/user_installation.sh).
 3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
 4. Run the script with `./user_installation.sh`
 
 #### Setup
 
-To set up Frigate, follow the default installation instructions, but use a Docker image with the `-h8l` suffix, for example: `ghcr.io/blakeblackshear/frigate:stable-h8l`
+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:
 
@@ -250,7 +250,7 @@ The official docker image tags for the current stable version are:
 The community supported docker image tags for the current stable version are:
 
 - `stable-tensorrt-jp5` - Frigate build optimized for nvidia Jetson devices running Jetpack 5
-- `stable-tensorrt-jp4` - Frigate build optimized for nvidia Jetson devices running Jetpack 4.6
+- `stable-tensorrt-jp6` - Frigate build optimized for nvidia Jetson devices running Jetpack 6
 - `stable-rk` - Frigate build for SBCs with Rockchip SoC
 - `stable-rocm` - Frigate build for [AMD GPUs](../configuration/object_detectors.md#amdrocm-gpu-detector)
   - `stable-h8l` - Frigate build for the Hailo-8L M.2 PICe Raspberry Pi 5 hat

docs/docs/guides/configuring_go2rtc.md

@@ -7,7 +7,7 @@ title: Configuring go2rtc
 
 Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect directly to your cameras. However, adding go2rtc to your configuration is required for the following features:
 
-- WebRTC or MSE for live viewing with higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream
+- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
 - Live stream support for cameras in Home Assistant Integration
 - RTSP relay for use with other consumers to reduce the number of connections to your camera streams
 

docs/docs/guides/getting_started.md

@@ -151,8 +151,6 @@ cameras:
         - path: rtsp://10.0.10.10:554/rtsp # <----- The stream you want to use for detection
           roles:
             - detect
-    detect:
-      enabled: False # <---- disable detection until you have a working camera feed
 ```
 
 ### Step 2: Start Frigate
@@ -177,7 +175,7 @@ services:
   frigate:
     ...
     devices:
-      - /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
+      - /dev/dri/renderD128:/dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
     ...
 ```
 
@@ -307,7 +305,7 @@ By default, Frigate will retain video of all tracked objects for 10 days. The fu
 
 ### Step 7: Complete config
 
-At this point you have a complete config with basic functionality. 
+At this point you have a complete config with basic functionality.
 - View [common configuration examples](../configuration/index.md#common-configuration-examples) for a list of common configuration examples.
 - View [full config reference](../configuration/reference.md) for a complete list of configuration options.
 

docs/docs/integrations/home-assistant.md

@@ -97,13 +97,13 @@ services:
 
 If you are using HassOS with the addon, the URL should be one of the following depending on which addon version you are using. Note that if you are using the Proxy Addon, you do NOT point the integration at the proxy URL. Just enter the URL used to access Frigate directly from your network.
 
-| Addon Version                  | URL                                    |
-| ------------------------------ | -------------------------------------- |
-| Frigate NVR                    | `http://ccab4aaf-frigate:5000`         |
-| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`      |
-| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`    |
-| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000` |
-| Frigate NVR HailoRT Beta       | `http://ccab4aaf-frigate-hailo-beta:5000`    |
+| Addon Version                  | URL                                       |
+| ------------------------------ | ----------------------------------------- |
+| Frigate NVR                    | `http://ccab4aaf-frigate:5000`            |
+| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`         |
+| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`       |
+| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000`    |
+| Frigate NVR HailoRT Beta       | `http://ccab4aaf-frigate-hailo-beta:5000` |
 
 ### Frigate running on a separate machine
 
@@ -301,3 +301,7 @@ which server they are referring to.
 #### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
 
 The [HomeKit integration](https://www.home-assistant.io/integrations/homekit/) randomly links one of the binary sensors (motion sensor entities) grouped with the camera device in Home Assistant. You can specify a `linked_motion_sensor` in the Home Assistant [HomeKit configuration](https://www.home-assistant.io/integrations/homekit/#linked_motion_sensor) for each camera.
+
+#### I have set up automations based on the occupancy sensors. Sometimes the automation runs because the sensors are turned on, but then I look at Frigate I can't find the object that triggered the sensor. Is this a bug?
+
+No. The occupancy sensors have fewer checks in place because they are often used for things like turning the lights on where latency needs to be as low as possible. So false positives can sometimes trigger these sensors. If you want false positive filtering, you should use an mqtt sensor on the `frigate/events` or `frigate/reviews` topic.

docs/docs/integrations/mqtt.md

@@ -52,7 +52,9 @@ Message published for each changed tracked object. The first message is publishe
     "attributes": {
       "face": 0.64
     }, // attributes with top score that have been identified on the object at any point
-    "current_attributes": [] // detailed data about the current attributes in this frame
+    "current_attributes": [], // detailed data about the current attributes in this frame
+    "current_estimated_speed": 0.71, // current estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
+    "velocity_angle": 180 // direction of travel relative to the frame for objects moving through zones with speed estimation enabled
   },
   "after": {
     "id": "1607123955.475377-mxklsc",
@@ -89,7 +91,9 @@ Message published for each changed tracked object. The first message is publishe
         "box": [442, 506, 534, 524],
         "score": 0.86
       }
-    ]
+    ],
+    "current_estimated_speed": 0.77, // current estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
+    "velocity_angle": 180 // direction of travel relative to the frame for objects moving through zones with speed estimation enabled
   }
 }
 ```
@@ -218,6 +222,14 @@ Publishes the rms value for audio detected on this camera.
 
 **NOTE:** Requires audio detection to be enabled
 
+### `frigate/<camera_name>/enabled/set`
+
+Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/enabled/state`
+
+Topic with current state of processing for a camera. Published values are `ON` and `OFF`.
+
 ### `frigate/<camera_name>/detect/set`
 
 Topic to turn object detection for a camera on and off. Expected values are `ON` and `OFF`.
@@ -312,6 +324,22 @@ Topic with current state of the PTZ autotracker for a camera. Published values a
 
 Topic to determine if PTZ autotracker is actively tracking an object. Published values are `ON` and `OFF`.
 
+### `frigate/<camera_name>/review_alerts/set`
+
+Topic to turn review alerts for a camera on or off. Expected values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/review_alerts/state`
+
+Topic with current state of review alerts for a camera. Published values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/review_detections/set`
+
+Topic to turn review detections for a camera on or off. Expected values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/review_detections/state`
+
+Topic with current state of review detections for a camera. Published values are `ON` and `OFF`.
+
 ### `frigate/<camera_name>/birdseye/set`
 
 Topic to turn Birdseye for a camera on and off. Expected values are `ON` and `OFF`. Birdseye mode
@@ -337,3 +365,19 @@ the camera to be removed from the view._
 ### `frigate/<camera_name>/birdseye_mode/state`
 
 Topic with current state of the Birdseye mode for a camera. Published values are `CONTINUOUS`, `MOTION`, `OBJECTS`.
+
+### `frigate/<camera_name>/notifications/set`
+
+Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/notifications/state`
+
+Topic with current state of notifications. Published values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/notifications/suspend`
+
+Topic to suspend notifications for a certain number of minutes. Expected value is an integer.
+
+### `frigate/<camera_name>/notifications/suspended`
+
+Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if notifications are not suspended.