Bump future from 0.18.2 to 0.18.3 in /docker/hailo8l

Bumps [future](https://github.com/PythonCharmers/python-future) from 0.18.2 to 0.18.3.
- [Release notes](https://github.com/PythonCharmers/python-future/releases)
- [Changelog](https://github.com/PythonCharmers/python-future/blob/master/docs/changelog.rst)
- [Commits](https://github.com/PythonCharmers/python-future/compare/v0.18.2...v0.18.3)

---
updated-dependencies:
- dependency-name: future
  dependency-type: direct:production
...

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

.cspell/frigate-dictionary.txt

@@ -1,311 +1,168 @@
-aarch
-absdiff
-airockchip
-Alloc
-Amcrest
-amdgpu
-analyzeduration
-Annke
-apexcharts
-arange
-argmax
-argmin
-argpartition
-ascontiguousarray
-astype
-authelia
-authentik
-autodetected
-automations
-autotrack
-autotracked
-autotracker
-autotracking
-balena
-Beelink
-BGRA
-BHWC
-blackshear
-blakeblackshear
-bottombar
-buildx
-castable
-cdist
-Celeron
-cgroups
-chipset
-chromadb
-Chromecast
-cmdline
-codeowner
-CODEOWNERS
-codeproject
-colormap
-colorspace
-comms
-coro
-ctypeslib
-CUDA
-Cuvid
-Dahua
-datasheet
-debconf
-deci
-deepstack
-defragment
-devcontainer
-DEVICEMAP
-discardcorrupt
-dpkg
-dsize
-dtype
-ECONNRESET
-edgetpu
-fastapi
-faststart
-fflags
-ffprobe
-fillna
-flac
-foscam
-fourcc
-framebuffer
-fregate
-frégate
-fromarray
-frombuffer
-frontdoor
-fstype
-fullchain
-fullscreen
-genai
-generativeai
-genpts
-getpid
-gpuload
-HACS
-Hailo
-hass
-hconcat
-healthcheck
-hideable
-Hikvision
-homeassistant
-homekit
-homography
-hsize
-hstack
-httpx
-hwaccel
-hwdownload
-hwmap
-hwupload
-iloc
-imagestream
-imdecode
-imencode
-imread
-imutils
-imwrite
-interp
-iostat
-iotop
-itemsize
-Jellyfin
-jetson
-jetsons
-joserfc
-jsmpeg
-jsonify
-Kalman
-keepalive
-keepdims
-labelmap
-letsencrypt
-levelname
-LIBAVFORMAT
-libedgetpu
-libnvinfer
-libva
-libwebp
-libx
-libyolo
-linalg
-localzone
-logpipe
-Loryta
-lstsq
-lsusb
-markupsafe
-maxsplit
-MEMHOSTALLOC
-memlimit
-meshgrid
-metadatas
-migraphx
-minilm
-mjpeg
-mkfifo
-mobiledet
-mobilenet
-modelpath
-mosquitto
-mountpoint
-movflags
-mpegts
-mqtt
-mse
-msenc
-namedtuples
-nbytes
-nchw
-ndarray
-ndimage
-nethogs
-newaxis
-nhwc
-NOBLOCK
-nobuffer
-nokey
-NONBLOCK
-noninteractive
-noprint
-Norfair
-nptype
-NTSC
-numpy
-nvenc
-nvhost
-nvml
-nvmpi
-ollama
-onnx
-onnxruntime
-onvif
-ONVIF
-openai
-opencv
-openvino
-OWASP
-paho
-passwordless
-popleft
-posthog
-postprocess
-poweroff
-preexec
-probesize
-protobuf
-pstate
-psutil
-pubkey
-putenv
-pycache
-pydantic
-pyobj
-pysqlite
-pytz
-pywebpush
-qnap
-quantisation
-Radeon
-radeonsi
-radeontop
-rawvideo
-rcond
-RDONLY
-rebranded
-referer
-reindex
-Reolink
-restream
-restreamed
-restreaming
-rkmpp
-rknn
-rkrga
-rockchip
-rocm
-rocminfo
-rootfs
 rtmp
+edgetpu
+labelmap
+rockchip
+jetson
+rocm
+vaapi
+CUDA
+hwaccel
 RTSP
-ruamel
-scroller
-setproctitle
-setpts
-shms
-SIGUSR
-skylake
-sleeptime
-SNDMORE
-socs
-sqliteq
-sqlitevecq
-ssdlite
-statm
-stimeout
-stylelint
-subclassing
-substream
-superfast
-surveillance
-svscan
-Swipeable
-sysconf
-tailscale
-Tapo
-tensorrt
+Hikvision
+Dahua
+Amcrest
+Reolink
+Loryta
+Beelink
+Celeron
+vaapi
+blakeblackshear
+workdir
+onvif
+autotracking
+openvino
 tflite
+deepstack
+codeproject
+udev
+tailscale
+restream
+restreaming
+webrtc
+ssdlite
+mobilenet
+mosquitto
+datasheet
+Jellyfin
+Radeon
+libva
+Ubiquiti
+Unifi
+Tapo
+Annke
+autotracker
+autotracked
+variations
+ONVIF
+traefik
+devcontainer
+rootfs
+ffprobe
+autotrack
+logpipe
+imread
+imwrite
+imencode
+imutils
 thresholded
 timelapse
-tmpfs
-tobytes
-toggleable
-traefik
-tzlocal
-Ubiquiti
-udev
-udevadm
 ultrafast
-unichip
-unidecode
-Unifi
-unixepoch
-unraid
-unreviewed
-userdata
-usermod
-uvicorn
-vaapi
+sleeptime
+radeontop
 vainfo
-variations
-vbios
-vconcat
-vitb
-vstream
-vsync
-wallclock
-webp
-webpush
-webrtc
+tmpfs
+homography
 websockets
-webui
-werkzeug
-workdir
-WRONLY
-wsgirefserver
-wsgiutils
-wsize
-xaddr
-xmaxs
-xmins
-XPUB
-XSUB
-ymaxs
-ymins
-yolo
-yolonas
-yolox
+LIBAVFORMAT
+NTSC
+onnxruntime
+fourcc
+radeonsi
+paho
+imagestream
+jsonify
+cgroups
+sysconf
+memlimit
+gpuload
+nvml
+setproctitle
+psutil
+Kalman
+frontdoor
+namedtuples
 zeep
-zerolatency
+fflags
+probesize
+wallclock
+rknn
+socs
+pydantic
+shms
+imdecode
+colormap
+webui
+mse
+jsmpeg
+unreviewed
+Chromecast
+Swipeable
+flac
+scroller
+cmdline
+toggleable
+bottombar
+opencv
+apexcharts
+buildx
+mqtt
+rawvideo
+defragment
+Norfair
+subclassing
+yolo
+tensorrt
+blackshear
+stylelint
+HACS
+homeassistant
+hass
+castable
+mobiledet
+framebuffer
+mjpeg
+substream
+codeowner
+noninteractive
+restreamed
+mountpoint
+fstype
+OWASP
+iotop
+letsencrypt
+fullchain
+lsusb
+iostat
+usermod
+balena
+passwordless
+debconf
+dpkg
+poweroff
+surveillance
+qnap
+homekit
+colorspace
+quantisation
+skylake
+Cuvid
+foscam
+onnx
+numpy
+protobuf
+aarch
+amdgpu
+chipset
+referer
+mpegts
+webp
+authelia
+authentik
+unichip
+rebranded
+udevadm
+automations
+unraid
+hideable
+healthcheck
+keepalive

.devcontainer/devcontainer.json

@@ -52,8 +52,7 @@
         "csstools.postcss",
         "blanu.vscode-styled-jsx",
         "bradlc.vscode-tailwindcss",
-        "charliermarsh.ruff",
-        "eamodio.gitlens"
+        "charliermarsh.ruff"
       ],
       "settings": {
         "remote.autoForwardPorts": false,

.devcontainer/post_create.sh

@@ -3,12 +3,10 @@
 set -euxo pipefail
 
 # Cleanup the old github host key
-if [[ -f ~/.ssh/known_hosts ]]; then
-  # Add new github host key
-  sed -i -e '/AAAAB3NzaC1yc2EAAAABIwAAAQEAq2A7hRGmdnm9tUDbO9IDSwBK6TbQa+PXYPCPy6rbTrTtw7PHkccKrpp0yVhp5HdEIcKr6pLlVDBfOLX9QUsyCOV0wzfjIJNlGEYsdlLJizHhbn2mUjvSAHQqZETYP81eFzLQNnPHt4EVVUh7VfDESU84KezmD5QlWpXLmvU31\/yMf+Se8xhHTvKSCZIFImWwoG6mbUoWf9nzpIoaSjB+weqqUUmpaaasXVal72J+UX2B+2RPW3RcT0eOzQgqlJL3RKrTJvdsjE3JEAvGq3lGHSZXy28G3skua2SmVi\/w4yCE6gbODqnTWlg7+wC604ydGXA8VJiS5ap43JXiUFFAaQ==/d' ~/.ssh/known_hosts
-  curl -L https://api.github.com/meta | jq -r '.ssh_keys | .[]' | \
-    sed -e 's/^/github.com /' >> ~/.ssh/known_hosts
-fi
+sed -i -e '/AAAAB3NzaC1yc2EAAAABIwAAAQEAq2A7hRGmdnm9tUDbO9IDSwBK6TbQa+PXYPCPy6rbTrTtw7PHkccKrpp0yVhp5HdEIcKr6pLlVDBfOLX9QUsyCOV0wzfjIJNlGEYsdlLJizHhbn2mUjvSAHQqZETYP81eFzLQNnPHt4EVVUh7VfDESU84KezmD5QlWpXLmvU31\/yMf+Se8xhHTvKSCZIFImWwoG6mbUoWf9nzpIoaSjB+weqqUUmpaaasXVal72J+UX2B+2RPW3RcT0eOzQgqlJL3RKrTJvdsjE3JEAvGq3lGHSZXy28G3skua2SmVi\/w4yCE6gbODqnTWlg7+wC604ydGXA8VJiS5ap43JXiUFFAaQ==/d' ~/.ssh/known_hosts
+# Add new github host key
+curl -L https://api.github.com/meta | jq -r '.ssh_keys | .[]' | \
+  sed -e 's/^/github.com /' >> ~/.ssh/known_hosts
 
 # Frigate normal container runs as root, so it have permission to create
 # the folders. But the devcontainer runs as the host user, so we need to
@@ -19,7 +17,7 @@ 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+")' >> $HOME/.bashrc
+echo 'export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc
 
 make version
 

.github/DISCUSSION_TEMPLATE/camera-support.yml

@@ -90,9 +90,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
     validations:
       required: true
   - type: dropdown
@@ -105,7 +102,7 @@ body:
         - TensorRT
         - RKNN
         - Other
-        - CPU (no coral)
+        - CPU (no Coral)
     validations:
       required: true
   - type: dropdown

.github/DISCUSSION_TEMPLATE/config-support.yml

@@ -76,17 +76,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
-    validations:
-      required: true
-  - type: textarea
-    id: docker
-    attributes:
-      label: docker-compose file or Docker CLI command
-      description: This will be automatically formatted into code, so no need for backticks.
-      render: yaml
     validations:
       required: true
   - type: dropdown

.github/DISCUSSION_TEMPLATE/detector-support.yml

@@ -48,6 +48,28 @@ body:
       render: shell
     validations:
       required: true
+  - type: textarea
+    id: go2rtclogs
+    attributes:
+      label: Relevant go2rtc log output
+      description: Please copy and paste any relevant go2rtc log output. Include logs before and after your exact error when possible. Logs can be viewed via the Frigate UI, Docker, or the go2rtc dashboard. This will be automatically formatted into code, so no need for backticks.
+      render: shell
+    validations:
+      required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - HassOS
+        - Debian
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
   - type: dropdown
     id: install-method
     attributes:
@@ -56,9 +78,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
     validations:
       required: true
   - type: dropdown

.github/DISCUSSION_TEMPLATE/general-support.yml

@@ -68,6 +68,20 @@ body:
       label: Frigate stats
       description: Output from frigate's /api/stats endpoint
       render: json
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - HassOS
+        - Debian
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
   - type: dropdown
     id: install-method
     attributes:
@@ -76,17 +90,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
-    validations:
-      required: true
-  - type: textarea
-    id: docker
-    attributes:
-      label: docker-compose file or Docker CLI command
-      description: This will be automatically formatted into code, so no need for backticks.
-      render: yaml
     validations:
       required: true
   - type: dropdown

.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml

@@ -24,6 +24,12 @@ body:
       description: Visible on the System page in the Web UI. Please include the full version including the build identifier (eg. 0.14.0-ea36ds1)
     validations:
       required: true
+  - type: input
+    attributes:
+      label: In which browser(s) are you experiencing the issue with?
+      placeholder: Google Chrome 88.0.4324.150
+      description: >
+        Provide the full name and don't forget to add the version!
   - type: textarea
     id: config
     attributes:
@@ -64,6 +70,20 @@ body:
       render: shell
     validations:
       required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - HassOS
+        - Debian
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
   - type: dropdown
     id: install-method
     attributes:
@@ -72,22 +92,6 @@ body:
         - HassOS Addon
         - Docker Compose
         - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
-    validations:
-      required: true
-  - type: dropdown
-    id: object-detector
-    attributes:
-      label: Object Detector
-      options:
-        - Coral
-        - OpenVino
-        - TensorRT
-        - RKNN
-        - Other
-        - CPU (no coral)
     validations:
       required: true
   - type: dropdown

.github/pull_request_template.md

@@ -1,32 +0,0 @@
-## Proposed change
-<!--
-  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.
--->
-
-
-## Type of change
-
-- [ ] Dependency upgrade
-- [ ] Bugfix (non-breaking change which fixes an issue)
-- [ ] New feature
-- [ ] Breaking change (fix/feature causing existing functionality to break)
-- [ ] Code quality improvements to existing code
-- [ ] Documentation Update
-
-## Additional information
-
-- This PR fixes or closes issue: fixes #
-- This PR is related to issue: 
-
-## Checklist
-
-<!--
-  Put an `x` in the boxes that apply.
--->
-
-- [ ] The code change is tested and works locally.
-- [ ] Local tests pass. **Your PR cannot be merged unless tests pass**
-- [ ] There is no commented out code in this PR.
-- [ ] The code has been formatted using Ruff (`ruff format frigate`)

.github/workflows/ci.yml

@@ -6,8 +6,6 @@ on:
     branches:
       - dev
       - master
-    paths-ignore:
-      - "docs/**"
 
 # only run the latest commit to avoid cache overwrites
 concurrency:
@@ -24,8 +22,6 @@ jobs:
     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
@@ -47,8 +43,6 @@ jobs:
     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
@@ -75,14 +69,21 @@ 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=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
+      - name: Build and push Rockchip build
+        uses: docker/bake-action@v3
+        with:
+          push: true
+          targets: rk
+          files: docker/rockchip/rk.hcl
+          set: |
+            rk.tags=${{ steps.setup.outputs.image-name }}-rk
+            *.cache-from=type=gha
   jetson_jp4_build:
     runs-on: ubuntu-latest
     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
@@ -109,8 +110,6 @@ jobs:
     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
@@ -139,8 +138,6 @@ jobs:
     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
@@ -158,30 +155,6 @@ 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
-  arm64_extra_builds:
-    runs-on: ubuntu-latest
-    name: ARM Extra Build
-    needs:
-      - 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: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push Rockchip build
-        uses: docker/bake-action@v3
-        with:
-          push: true
-          targets: rk
-          files: docker/rockchip/rk.hcl
-          set: |
-            rk.tags=${{ steps.setup.outputs.image-name }}-rk
-            *.cache-from=type=gha
   combined_extra_builds:
     runs-on: ubuntu-latest
     name: Combined Extra Builds
@@ -191,8 +164,6 @@ jobs:
     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
@@ -208,18 +179,57 @@ jobs:
             h8l.tags=${{ steps.setup.outputs.image-name }}-h8l
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l,mode=max
-      - name: AMD/ROCm general build
-        env:
-          AMDGPU: gfx
-          HSA_OVERRIDE: 0
-        uses: docker/bake-action@v3
-        with:
-          push: true
-          targets: rocm
-          files: docker/rocm/rocm.hcl
-          set: |
-            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
-            *.cache-from=type=gha
+      #- name: AMD/ROCm general build
+      #  env:
+      #    AMDGPU: gfx
+      #    HSA_OVERRIDE: 0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx900
+      #  env:
+      #    AMDGPU: gfx900
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 9.0.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx900
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx1030
+      #  env:
+      #    AMDGPU: gfx1030
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 10.3.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx1030
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx1100
+      #  env:
+      #    AMDGPU: gfx1100
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 11.0.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx1100
+      #      *.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:
@@ -234,7 +244,7 @@ jobs:
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
+        uses: docker/login-action@0d4c9c5ea7693da7b068278f7b52bda2a190a446
         with:
           registry: ghcr.io
           username: ${{ github.actor }}

.github/workflows/dependabot-auto-merge.yaml

@@ -0,0 +1,24 @@
+name: dependabot-auto-merge
+on: pull_request
+
+permissions:
+  contents: write
+
+jobs:
+  dependabot-auto-merge:
+    runs-on: ubuntu-latest
+    if: github.actor == 'dependabot[bot]'
+    steps:
+      - name: Get Dependabot metadata
+        id: metadata
+        uses: dependabot/fetch-metadata@v2
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+      - name: Enable auto-merge for Dependabot PRs
+        if: steps.metadata.outputs.dependency-type == 'direct:development' && (steps.metadata.outputs.update-type == 'version-update:semver-minor' || steps.metadata.outputs.update-type == 'version-update:semver-patch')
+        run: |
+          gh pr review --approve "$PR_URL"
+          gh pr merge --auto --squash "$PR_URL"
+        env:
+          PR_URL: ${{ github.event.pull_request.html_url }}
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/pull_request.yml

@@ -1,9 +1,6 @@
 name: On pull request
 
-on:
-  pull_request:
-    paths-ignore:
-      - "docs/**"
+on: pull_request
 
 env:
   DEFAULT_PYTHON: 3.9
@@ -19,8 +16,6 @@ jobs:
       DOCKER_BUILDKIT: "1"
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x
@@ -40,8 +35,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x
@@ -56,8 +49,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 20.x
@@ -73,10 +64,8 @@ jobs:
     steps:
       - name: Check out the repository
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up Python ${{ env.DEFAULT_PYTHON }}
-        uses: actions/setup-python@v5.3.0
+        uses: actions/setup-python@v5.1.0
         with:
           python-version: ${{ env.DEFAULT_PYTHON }}
       - name: Install requirements
@@ -96,8 +85,6 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x

.github/workflows/release.yml

@@ -11,26 +11,21 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - id: lowercaseRepo
         uses: ASzc/change-string-case-action@v6
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
+        uses: docker/login-action@0d4c9c5ea7693da7b068278f7b52bda2a190a446
         with:
           registry: ghcr.io
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
       - name: Create tag variables
-        env:
-          TAG: ${{ github.ref_name }}
-          LOWERCASE_REPO: ${{ steps.lowercaseRepo.outputs.lowercase }}
         run: |
-          BUILD_TYPE=$([[ "${TAG}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
+          BUILD_TYPE=$([[ "${{ github.ref_name }}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
           echo "BUILD_TYPE=${BUILD_TYPE}" >> $GITHUB_ENV
-          echo "BASE=ghcr.io/${LOWERCASE_REPO}" >> $GITHUB_ENV
+          echo "BASE=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}" >> $GITHUB_ENV
           echo "BUILD_TAG=${GITHUB_SHA::7}" >> $GITHUB_ENV
           echo "CLEAN_VERSION=$(echo ${GITHUB_REF##*/} | tr '[:upper:]' '[:lower:]' | sed 's/^[v]//')" >> $GITHUB_ENV
       - name: Tag and push the main image
@@ -39,14 +34,14 @@ jobs:
           STABLE_TAG=${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-jp4 tensorrt-jp5 rk; 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-jp4 tensorrt-jp5 rk; 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

.github/workflows/stale.yml

@@ -23,9 +23,7 @@ jobs:
           exempt-pr-labels: "pinned,security,dependencies"
           operations-per-run: 120
       - name: Print outputs
-        env:
-          STALE_OUTPUT: ${{ join(steps.stale.outputs.*, ',') }}
-        run: echo "$STALE_OUTPUT"
+        run: echo ${{ join(steps.stale.outputs.*, ',') }}
 
   # clean_ghcr:
   #   name: Delete outdated dev container images
@@ -40,3 +38,4 @@ jobs:
   #         account-type: personal
   #         token: ${{ secrets.GITHUB_TOKEN }}
   #         token-type: github-token
+

.gitignore

@@ -1,6 +1,5 @@
 .DS_Store
-__pycache__
-.mypy_cache
+*.pyc
 *.swp
 debug
 .vscode/*

.vscode/launch.json

@@ -3,9 +3,10 @@
   "configurations": [
     {
       "name": "Python: Launch Frigate",
-      "type": "debugpy",
+      "type": "python",
       "request": "launch",
-      "module": "frigate"
+      "module": "frigate",
+      "justMyCode": true
     }
   ]
 }

Makefile

@@ -4,6 +4,8 @@ COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
 VERSION = 0.15.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
+CURRENT_UID := $(shell id -u)
+CURRENT_GID := $(shell id -g)
 BOARDS= #Initialized empty
 
 include docker/*/*.mk
@@ -16,38 +18,25 @@ version:
 	echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
 
 local: version
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag frigate:latest \
-		--load
+	docker buildx build --target=frigate --tag frigate:latest --load --file docker/main/Dockerfile .
 
 amd64:
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/amd64
+	docker buildx build --platform linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 arm64:
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/arm64
+	docker buildx build --platform linux/arm64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 build: version amd64 arm64
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/arm64/v8,linux/amd64
+	docker buildx build --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 push: push-boards
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) \
-		--platform linux/arm64/v8,linux/amd64 \
-		--push
+	docker buildx build --push --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 run: local
 	docker run --rm --publish=5000:5000 --volume=${PWD}/config:/config frigate:latest
 
 run_tests: local
-	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
-		python3 -u -m unittest
-	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
-		python3 -u -m mypy --config-file frigate/mypy.ini frigate
+	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m unittest
+	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m mypy --config-file frigate/mypy.ini frigate
 
 .PHONY: run_tests

benchmark.py

@@ -4,7 +4,6 @@ from statistics import mean
 
 import numpy as np
 
-import frigate.util as util
 from frigate.config import DetectorTypeEnum
 from frigate.object_detection import (
     ObjectDetectProcess,
@@ -91,7 +90,7 @@ edgetpu_process_2 = ObjectDetectProcess(
 )
 
 for x in range(0, 10):
-    camera_process = util.Process(
+    camera_process = mp.Process(
         target=start, args=(x, 300, detection_queue, events[str(x)])
     )
     camera_process.daemon = True

cspell.json

@@ -7,8 +7,7 @@
     "*.db",
     "node_modules",
     "__pycache__",
-    "dist",
-    "/audio-labelmap.txt"
+    "dist"
   ],
   "language": "en",
   "dictionaryDefinitions": [

docker-compose.yml

@@ -23,7 +23,7 @@ services:
     #                     count: 1
     #                     capabilities: [gpu]
     environment:
-      YOLO_MODELS: ""
+      YOLO_MODELS: yolov7-320
     devices:
       - /dev/bus/usb:/dev/bus/usb
       # - /dev/dri:/dev/dri # for intel hwaccel, needs to be updated for your hardware

docker/hailo8l/Dockerfile

@@ -16,25 +16,89 @@ 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
+# Build HailoRT and create wheel
+FROM wheels AS build-hailort
 ARG TARGETARCH
-RUN --mount=type=bind,source=docker/hailo8l/install_hailort.sh,target=/deps/install_hailort.sh \
-    /deps/install_hailort.sh
+
+SHELL ["/bin/bash", "-c"]
+
+# Install necessary APT packages
+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-get -qq update \
+    && apt-get -qq install -y \
+    python3.9 \
+    python3.9-dev \
+    build-essential cmake git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Extract Python version and set environment variables
+RUN PYTHON_VERSION=$(python3 --version 2>&1 | awk '{print $2}' | cut -d. -f1,2) && \
+    PYTHON_VERSION_NO_DOT=$(echo $PYTHON_VERSION | sed 's/\.//') && \
+    echo "PYTHON_VERSION=$PYTHON_VERSION" > /etc/environment && \
+    echo "PYTHON_VERSION_NO_DOT=$PYTHON_VERSION_NO_DOT" >> /etc/environment
+
+# Clone and build HailoRT
+RUN . /etc/environment && \
+    git clone https://github.com/hailo-ai/hailort.git /opt/hailort && \
+    cd /opt/hailort && \
+    git checkout v4.17.0 && \
+    cmake -H. -Bbuild -DCMAKE_BUILD_TYPE=Release -DHAILO_BUILD_PYBIND=1 -DPYBIND11_PYTHON_VERSION=${PYTHON_VERSION} && \
+    cmake --build build --config release --target libhailort && \
+    cmake --build build --config release --target _pyhailort && \
+    cp build/hailort/libhailort/bindings/python/src/_pyhailort.cpython-${PYTHON_VERSION_NO_DOT}-$(if [ $TARGETARCH == "amd64" ]; then echo 'x86_64'; else echo 'aarch64'; fi )-linux-gnu.so hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/ && \
+    cp build/hailort/libhailort/src/libhailort.so hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/
+
+RUN ls -ahl /opt/hailort/build/hailort/libhailort/src/
+RUN ls -ahl /opt/hailort/hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/
+
+# Remove the existing setup.py if it exists in the target directory
+RUN rm -f /opt/hailort/hailort/libhailort/bindings/python/platform/setup.py
+
+# Copy generate_wheel_conf.py and setup.py
+COPY docker/hailo8l/pyhailort_build_scripts/generate_wheel_conf.py /opt/hailort/hailort/libhailort/bindings/python/platform/generate_wheel_conf.py
+COPY docker/hailo8l/pyhailort_build_scripts/setup.py /opt/hailort/hailort/libhailort/bindings/python/platform/setup.py
+
+# Run the generate_wheel_conf.py script
+RUN python3 /opt/hailort/hailort/libhailort/bindings/python/platform/generate_wheel_conf.py
+
+# Create a wheel file using pip3 wheel
+RUN cd /opt/hailort/hailort/libhailort/bindings/python/platform && \
+    python3 setup.py bdist_wheel --dist-dir /hailo-wheels
 
 # 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/ /
+COPY --from=build-hailort /hailo-wheels /deps/hailo-wheels
+COPY --from=build-hailort /etc/environment /etc/environment
+RUN CC=$(python3 -c "import sysconfig; import shlex; cc = sysconfig.get_config_var('CC'); cc_cmd = shlex.split(cc)[0]; print(cc_cmd[:-4] if cc_cmd.endswith('-gcc') else cc_cmd)") && \
+    echo "CC=$CC" >> /etc/environment
 
 # Install the wheels
 RUN pip3 install -U /deps/h8l-wheels/*.whl
 RUN pip3 install -U /deps/hailo-wheels/*.whl
 
+RUN . /etc/environment && \
+    mv /usr/local/lib/python${PYTHON_VERSION}/dist-packages/hailo_platform/pyhailort/libhailort.so /usr/lib/${CC} && \
+    cd /usr/lib/${CC}/ && \
+    ln -s libhailort.so libhailort.so.4.17.0
+
 # Copy base files from the rootfs stage
 COPY --from=rootfs / /
 
+# Set environment variables for Hailo SDK
+ENV PATH="/opt/hailort/bin:${PATH}"
+ENV LD_LIBRARY_PATH="/usr/lib/$(if [ $TARGETARCH == "amd64" ]; then echo 'x86_64'; else echo 'aarch64'; fi )-linux-gnu:${LD_LIBRARY_PATH}"
+
 # Set workdir
 WORKDIR /opt/frigate/

docker/hailo8l/h8l.hcl

@@ -1,9 +1,3 @@
-target wget {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "wget"
-}
-
 target wheels {
   dockerfile = "docker/main/Dockerfile"
   platforms = ["linux/arm64","linux/amd64"]
@@ -25,7 +19,6 @@ target rootfs {
 target h8l {
   dockerfile = "docker/hailo8l/Dockerfile"
   contexts = {
-    wget = "target:wget"
     wheels = "target:wheels"
     deps = "target:deps"
     rootfs = "target:rootfs"

docker/hailo8l/h8l.mk

@@ -1,15 +1,10 @@
 BOARDS += h8l
 
 local-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-		--set h8l.tags=frigate:latest-h8l \
-		--load
+	docker buildx bake --load --file=docker/hailo8l/h8l.hcl --set h8l.tags=frigate:latest-h8l h8l
 
 build-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-		--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l
+	docker buildx bake --file=docker/hailo8l/h8l.hcl --set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l h8l
 
 push-h8l: build-h8l
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-	 	--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l \
-		--push
+	docker buildx bake --push --file=docker/hailo8l/h8l.hcl --set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l h8l

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/pyhailort_build_scripts/generate_wheel_conf.py

@@ -0,0 +1,67 @@
+import json
+import os
+import platform
+import sys
+import sysconfig
+
+
+def extract_toolchain_info(compiler):
+    # Remove the "-gcc" or "-g++" suffix if present
+    if compiler.endswith("-gcc") or compiler.endswith("-g++"):
+        compiler = compiler.rsplit("-", 1)[0]
+
+    # Extract the toolchain and ABI part (e.g., "gnu")
+    toolchain_parts = compiler.split("-")
+    abi_conventions = next(
+        (part for part in toolchain_parts if part in ["gnu", "musl", "eabi", "uclibc"]),
+        "",
+    )
+
+    return abi_conventions
+
+
+def generate_wheel_conf():
+    conf_file_path = os.path.join(
+        os.path.abspath(os.path.dirname(__file__)), "wheel_conf.json"
+    )
+
+    # Extract current system and Python version information
+    py_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
+    arch = platform.machine()
+    system = platform.system().lower()
+    libc_version = platform.libc_ver()[1]
+
+    # Get the compiler information
+    compiler = sysconfig.get_config_var("CC")
+    abi_conventions = extract_toolchain_info(compiler)
+
+    # Create the new configuration data
+    new_conf_data = {
+        "py_version": py_version,
+        "arch": arch,
+        "system": system,
+        "libc_version": libc_version,
+        "abi": abi_conventions,
+        "extension": {
+            "posix": "so",
+            "nt": "pyd",  # Windows
+        }[os.name],
+    }
+
+    # If the file exists, load the existing data
+    if os.path.isfile(conf_file_path):
+        with open(conf_file_path, "r") as conf_file:
+            conf_data = json.load(conf_file)
+        # Update the existing data with the new data
+        conf_data.update(new_conf_data)
+    else:
+        # If the file does not exist, use the new data
+        conf_data = new_conf_data
+
+    # Write the updated data to the file
+    with open(conf_file_path, "w") as conf_file:
+        json.dump(conf_data, conf_file, indent=4)
+
+
+if __name__ == "__main__":
+    generate_wheel_conf()

docker/hailo8l/pyhailort_build_scripts/setup.py

@@ -0,0 +1,111 @@
+import json
+import os
+
+from setuptools import find_packages, setup
+from wheel.bdist_wheel import bdist_wheel as orig_bdist_wheel
+
+
+class NonPurePythonBDistWheel(orig_bdist_wheel):
+    """Makes the wheel platform-dependent so it can be based on the _pyhailort architecture"""
+
+    def finalize_options(self):
+        orig_bdist_wheel.finalize_options(self)
+        self.root_is_pure = False
+
+
+def _get_hailort_lib_path():
+    lib_filename = "libhailort.so"
+    lib_path = os.path.join(
+        os.path.abspath(os.path.dirname(__file__)),
+        f"hailo_platform/pyhailort/{lib_filename}",
+    )
+    if os.path.exists(lib_path):
+        print(f"Found libhailort shared library at: {lib_path}")
+    else:
+        print(f"Error: libhailort shared library not found at: {lib_path}")
+        raise FileNotFoundError(f"libhailort shared library not found at: {lib_path}")
+    return lib_path
+
+
+def _get_pyhailort_lib_path():
+    conf_file_path = os.path.join(
+        os.path.abspath(os.path.dirname(__file__)), "wheel_conf.json"
+    )
+    if not os.path.isfile(conf_file_path):
+        raise FileNotFoundError(f"Configuration file not found: {conf_file_path}")
+
+    with open(conf_file_path, "r") as conf_file:
+        content = json.load(conf_file)
+        py_version = content["py_version"]
+        arch = content["arch"]
+        system = content["system"]
+        extension = content["extension"]
+        abi = content["abi"]
+
+        # Construct the filename directly
+        lib_filename = f"_pyhailort.cpython-{py_version.split('cp')[1]}-{arch}-{system}-{abi}.{extension}"
+        lib_path = os.path.join(
+            os.path.abspath(os.path.dirname(__file__)),
+            f"hailo_platform/pyhailort/{lib_filename}",
+        )
+
+        if os.path.exists(lib_path):
+            print(f"Found _pyhailort shared library at: {lib_path}")
+        else:
+            print(f"Error: _pyhailort shared library not found at: {lib_path}")
+            raise FileNotFoundError(
+                f"_pyhailort shared library not found at: {lib_path}"
+            )
+
+        return lib_path
+
+
+def _get_package_paths():
+    packages = []
+    pyhailort_lib = _get_pyhailort_lib_path()
+    hailort_lib = _get_hailort_lib_path()
+    if pyhailort_lib:
+        packages.append(pyhailort_lib)
+    if hailort_lib:
+        packages.append(hailort_lib)
+    packages.append(os.path.abspath("hailo_tutorials/notebooks/*"))
+    packages.append(os.path.abspath("hailo_tutorials/hefs/*"))
+    return packages
+
+
+if __name__ == "__main__":
+    setup(
+        author="Hailo team",
+        author_email="contact@hailo.ai",
+        cmdclass={
+            "bdist_wheel": NonPurePythonBDistWheel,
+        },
+        description="HailoRT",
+        entry_points={
+            "console_scripts": [
+                "hailo=hailo_platform.tools.hailocli.main:main",
+            ]
+        },
+        install_requires=[
+            "argcomplete",
+            "contextlib2",
+            "future",
+            "netaddr",
+            "netifaces",
+            "verboselogs",
+            "numpy==1.23.3",
+        ],
+        name="hailort",
+        package_data={
+            "hailo_platform": _get_package_paths(),
+        },
+        packages=find_packages(),
+        platforms=[
+            "linux_x86_64",
+            "linux_aarch64",
+            "win_amd64",
+        ],
+        url="https://hailo.ai/",
+        version="4.17.0",
+        zip_safe=False,
+    )

docker/hailo8l/requirements-wheels-h8l.txt

@@ -1,12 +1,12 @@
-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.*
+appdirs==1.4.4
+argcomplete==2.0.0
+contextlib2==0.6.0.post1
+distlib==0.3.6
+filelock==3.8.0
+future==0.18.3
+importlib-metadata==5.1.0
+importlib-resources==5.1.2
+netaddr==0.8.0
+netifaces==0.10.9
+verboselogs==1.7
+virtualenv==20.17.0

docker/hailo8l/user_installation.sh

@@ -2,7 +2,7 @@
 
 # Update package list and install dependencies
 sudo apt-get update
-sudo apt-get install -y build-essential cmake git wget
+sudo apt-get install -y build-essential cmake git wget linux-modules-extra-$(uname -r)
 
 arch=$(uname -m)
 
@@ -13,7 +13,7 @@ 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 v4.17.0 https://github.com/hailo-ai/hailort-drivers.git
 
 # Build and install the HailoRT driver
 cd hailort-drivers/linux/pcie
@@ -23,26 +23,13 @@ sudo make install
 # Load the Hailo PCI driver
 sudo modprobe hailo_pci
 
-if [ $? -ne 0 ]; then
-  echo "Unable to load hailo_pci module, common reasons for this are:"
-  echo "- Key was rejected by service: Secure Boot is enabling disallowing install."
-  echo "- Permissions are not setup correctly."
-  exit 1
-fi
-
 # Download and install the firmware
 cd ../../
 ./download_firmware.sh
-
-# verify the firmware folder is present
-if [ ! -d /lib/firmware/hailo ]; then
-  sudo mkdir /lib/firmware/hailo
-fi
-sudo mv hailo8_fw.*.bin /lib/firmware/hailo/hailo8_fw.bin
+sudo mv hailo8_fw.4.17.0.bin /lib/firmware/hailo/hailo8_fw.bin
 
 # Install udev rules
 sudo cp ./linux/pcie/51-hailo-udev.rules /etc/udev/rules.d/
 sudo udevadm control --reload-rules && sudo udevadm trigger
 
 echo "HailoRT driver installation complete."
-echo "reboot your system to load the firmware!"

docker/main/Dockerfile

@@ -30,16 +30,6 @@ RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
     --mount=type=cache,target=/root/.ccache \
     /deps/build_nginx.sh
 
-FROM wget AS sqlite-vec
-ARG DEBIAN_FRONTEND
-
-# Build sqlite_vec from source
-COPY docker/main/build_sqlite_vec.sh /deps/build_sqlite_vec.sh
-RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
-    --mount=type=bind,source=docker/main/build_sqlite_vec.sh,target=/deps/build_sqlite_vec.sh \
-    --mount=type=cache,target=/root/.ccache \
-    /deps/build_sqlite_vec.sh
-
 FROM scratch AS go2rtc
 ARG TARGETARCH
 WORKDIR /rootfs/usr/local/go2rtc/bin
@@ -173,7 +163,7 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
 COPY docker/main/requirements.txt /requirements.txt
 RUN pip3 install -r /requirements.txt
 
-# Build pysqlite3 from source
+# Build pysqlite3 from source to support ChromaDB
 COPY docker/main/build_pysqlite3.sh /build_pysqlite3.sh
 RUN /build_pysqlite3.sh
 
@@ -184,7 +174,6 @@ RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 # Collect deps in a single layer
 FROM scratch AS deps-rootfs
 COPY --from=nginx /usr/local/nginx/ /usr/local/nginx/
-COPY --from=sqlite-vec /usr/local/lib/ /usr/local/lib/
 COPY --from=go2rtc /rootfs/ /
 COPY --from=libusb-build /usr/local/lib /usr/local/lib
 COPY --from=tempio /rootfs/ /
@@ -205,17 +194,14 @@ ARG APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn
 ENV NVIDIA_VISIBLE_DEVICES=all
 ENV NVIDIA_DRIVER_CAPABILITIES="compute,video,utility"
 
+# Turn off Chroma Telemetry: https://docs.trychroma.com/telemetry#opting-out
+ENV ANONYMIZED_TELEMETRY=False
+# Allow resetting the chroma database
+ENV ALLOW_RESET=True
 # Disable tokenizer parallelism warning
-# https://stackoverflow.com/questions/62691279/how-to-disable-tokenizers-parallelism-true-false-warning/72926996#72926996
 ENV TOKENIZERS_PARALLELISM=true
-# https://github.com/huggingface/transformers/issues/27214
-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
-
-ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
-ENV LIBAVFORMAT_VERSION_MAJOR=60
+ENV PATH="/usr/lib/btbn-ffmpeg/bin:/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 
 # Install dependencies
 RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
@@ -241,7 +227,7 @@ ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 ENTRYPOINT ["/init"]
 CMD []
 
-HEALTHCHECK --start-period=300s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
+HEALTHCHECK --start-period=120s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
     CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1
 
 # Frigate deps with Node.js and NPM for devcontainer

docker/main/build_sqlite_vec.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-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
-apt-get update
-apt-get -yqq build-dep sqlite3 gettext git
-
-mkdir /tmp/sqlite_vec
-# Grab the sqlite_vec source code.
-wget -nv https://github.com/asg017/sqlite-vec/archive/refs/tags/v${SQLITE_VEC_VERSION}.tar.gz
-tar -zxf v${SQLITE_VEC_VERSION}.tar.gz -C /tmp/sqlite_vec
-
-cd /tmp/sqlite_vec/sqlite-vec-${SQLITE_VEC_VERSION}
-
-mkdir -p vendor
-wget -O sqlite-amalgamation.zip https://www.sqlite.org/2024/sqlite-amalgamation-3450300.zip
-unzip sqlite-amalgamation.zip
-mv sqlite-amalgamation-3450300/* vendor/
-rmdir sqlite-amalgamation-3450300
-rm sqlite-amalgamation.zip
-
-# build loadable module
-make loadable
-
-# install it
-cp dist/vec0.* /usr/local/lib
-

docker/main/install_deps.sh

@@ -8,13 +8,11 @@ apt-get -qq install --no-install-recommends -y \
     apt-transport-https \
     gnupg \
     wget \
-    lbzip2 \
     procps vainfo \
     unzip locales tzdata libxml2 xz-utils \
     python3.9 \
     python3-pip \
     curl \
-    lsof \
     jq \
     nethogs
 
@@ -41,68 +39,39 @@ apt-get -qq install --no-install-recommends --no-install-suggests -y \
 
 # btbn-ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    mkdir -p /usr/lib/ffmpeg/5.0
-    mkdir -p /usr/lib/ffmpeg/7.0
+    mkdir -p /usr/lib/btbn-ffmpeg
     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
+    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
+    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
 fi
 
 # ffmpeg -> arm64
 if [[ "${TARGETARCH}" == "arm64" ]]; then
-    mkdir -p /usr/lib/ffmpeg/5.0
-    mkdir -p /usr/lib/ffmpeg/7.0
+    mkdir -p /usr/lib/btbn-ffmpeg
     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
+    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
+    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
 fi
 
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    # use debian bookworm for amd / intel-i965 driver packages
+    # use debian bookworm for hwaccel 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
     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
-
+        intel-opencl-icd \
+        mesa-va-drivers radeontop libva-drm2 intel-media-va-driver-non-free i965-va-driver libmfx1 intel-gpu-tools
     # 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
-    apt-get -qq update
-    apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        intel-opencl-icd=24.35.30872.31-996~22.04 intel-level-zero-gpu=1.3.29735.27-914~22.04 intel-media-va-driver-non-free=24.3.3-996~22.04 \
-        libmfx1=23.2.2-880~22.04 libmfxgen1=24.2.4-914~22.04 libvpl2=1:2.13.0.0-996~22.04
-
-    rm -f /usr/share/keyrings/intel-graphics.gpg
-    rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
 fi
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        libva-drm2 mesa-va-drivers radeontop
+        libva-drm2 mesa-va-drivers
 fi
 
-# install vulkan
-apt-get -qq install --no-install-recommends --no-install-suggests -y \
-    libvulkan1 mesa-vulkan-drivers
-
 apt-get purge gnupg apt-transport-https xz-utils -y
 apt-get clean autoclean -y
 apt-get autoremove --purge -y

docker/main/requirements-wheels.txt

@@ -1,14 +1,8 @@
 click == 8.1.*
-# FastAPI
-aiohttp == 3.11.2
-starlette == 0.41.2
-starlette-context == 0.3.6
-fastapi == 0.115.*
-uvicorn == 0.30.*
-slowapi == 0.1.*
+Flask == 3.0.*
+Flask_Limiter == 3.8.*
 imutils == 0.5.*
 joserfc == 1.0.*
-pathvalidate == 3.2.*
 markupsafe == 2.1.*
 mypy == 1.6.1
 numpy == 1.26.*
@@ -18,13 +12,15 @@ paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
 peewee_migrate == 1.13.*
-psutil == 6.1.*
+psutil == 5.9.*
 pydantic == 2.8.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
-pytz == 2024.*
+PyYAML == 6.0.*
+pytz == 2024.1
 pyzmq == 26.2.*
 ruamel.yaml == 0.18.*
 tzlocal == 5.2
+types-PyYAML == 6.0.*
 requests == 2.32.*
 types-requests == 2.32.*
 scipy == 1.13.*
@@ -32,16 +28,15 @@ norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
-# OpenVino & ONNX
-openvino == 2024.3.*
-onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'
-onnxruntime == 1.19.* ; platform_machine == 'aarch64'
+onnxruntime == 1.18.*
+openvino == 2024.1.*
 # Embeddings
-transformers == 4.45.*
+onnx_clip == 4.0.*
+chromadb == 0.5.0
 # Generative AI
-google-generativeai == 0.8.*
-ollama == 0.3.*
-openai == 1.51.*
+google-generativeai == 0.6.*
+ollama == 0.2.*
+openai == 1.30.*
 # push notifications
 py-vapid == 1.9.*
-pywebpush == 2.0.*
+pywebpush == 2.0.*

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma-log/consumer-for

@@ -0,0 +1 @@
+chroma

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma-log/pipeline-name

@@ -0,0 +1 @@
+chroma-pipeline

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

@@ -0,0 +1,4 @@
+#!/command/with-contenv bash
+# shellcheck shell=bash
+
+exec logutil-service /dev/shm/logs/chroma

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma-log/type

@@ -0,0 +1 @@
+longrun

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/finish

@@ -0,0 +1,28 @@
+#!/command/with-contenv bash
+# shellcheck shell=bash
+# Take down the S6 supervision tree when the service exits
+
+set -o errexit -o nounset -o pipefail
+
+# Logs should be sent to stdout so that s6 can collect them
+
+declare exit_code_container
+exit_code_container=$(cat /run/s6-linux-init-container-results/exitcode)
+readonly exit_code_container
+readonly exit_code_service="${1}"
+readonly exit_code_signal="${2}"
+readonly service="ChromaDB"
+
+echo "[INFO] Service ${service} exited with code ${exit_code_service} (by signal ${exit_code_signal})"
+
+if [[ "${exit_code_service}" -eq 256 ]]; then
+  if [[ "${exit_code_container}" -eq 0 ]]; then
+    echo $((128 + exit_code_signal)) >/run/s6-linux-init-container-results/exitcode
+  fi
+elif [[ "${exit_code_service}" -ne 0 ]]; then
+  if [[ "${exit_code_container}" -eq 0 ]]; then
+    echo "${exit_code_service}" >/run/s6-linux-init-container-results/exitcode
+  fi
+fi
+
+exec /run/s6/basedir/bin/halt

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/producer-for

@@ -0,0 +1 @@
+chroma-log

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

@@ -0,0 +1,27 @@
+#!/command/with-contenv bash
+# shellcheck shell=bash
+# Start the Frigate service
+
+set -o errexit -o nounset -o pipefail
+
+# Logs should be sent to stdout so that s6 can collect them
+
+# Tell S6-Overlay not to restart this service
+s6-svc -O .
+
+search_enabled=`python3 /usr/local/semantic_search/get_search_settings.py | jq -r .enabled`
+
+# Replace the bash process with the Frigate process, redirecting stderr to stdout
+exec 2>&1
+
+if [[ "$search_enabled" == 'true' ]]; then
+    echo "[INFO] Starting ChromaDB..."
+    exec /usr/local/chroma run --path /config/chroma --host 127.0.0.1
+else
+    while true
+    do
+        sleep 9999
+        continue
+    done
+    exit 0
+fi

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/timeout-kill

@@ -0,0 +1 @@
+120000

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/type

@@ -0,0 +1 @@
+longrun

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

@@ -44,6 +44,8 @@ function migrate_db_path() {
 
 echo "[INFO] Preparing Frigate..."
 migrate_db_path
+export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po 'libavformat\W+\K\d+')
+
 echo "[INFO] Starting Frigate..."
 
 cd /opt/frigate || echo "[ERROR] Failed to change working directory to /opt/frigate"

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

@@ -43,6 +43,8 @@ function get_ip_and_port_from_supervisor() {
     export FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL="${ip_address}:${webrtc_port}"
 }
 
+export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po 'libavformat\W+\K\d+')
+
 if [[ -f "/dev/shm/go2rtc.yaml" ]]; then
     echo "[INFO] Removing stale config from last run..."
     rm /dev/shm/go2rtc.yaml

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

@@ -4,7 +4,7 @@
 
 set -o errexit -o nounset -o pipefail
 
-dirs=(/dev/shm/logs/frigate /dev/shm/logs/go2rtc /dev/shm/logs/nginx /dev/shm/logs/certsync)
+dirs=(/dev/shm/logs/frigate /dev/shm/logs/go2rtc /dev/shm/logs/nginx /dev/shm/logs/certsync /dev/shm/logs/chroma)
 
 mkdir -p "${dirs[@]}"
 chown nobody:nogroup "${dirs[@]}"

docker/main/rootfs/usr/local/chroma

@@ -0,0 +1,14 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-s
+__import__("pysqlite3")
+
+import re
+import sys
+
+sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
+
+from chromadb.cli.cli import app
+
+if __name__ == "__main__":
+    sys.argv[0] = re.sub(r"(-script\.pyw|\.exe)?$", "", sys.argv[0])
+    sys.exit(app())

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

@@ -2,23 +2,19 @@
 
 import json
 import os
-import shutil
 import sys
 from pathlib import Path
 
-from ruamel.yaml import YAML
+import yaml
 
 sys.path.insert(0, "/opt/frigate")
-from frigate.const import (
-    BIRDSEYE_PIPE,
-    DEFAULT_FFMPEG_VERSION,
-    INCLUDED_FFMPEG_VERSIONS,
+from frigate.const import BIRDSEYE_PIPE  # noqa: E402
+from frigate.ffmpeg_presets import (  # noqa: E402
+    parse_preset_hardware_acceleration_encode,
 )
-from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 
 sys.path.remove("/opt/frigate")
 
-yaml = YAML()
 
 FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
 # read docker secret files as env vars too
@@ -41,7 +37,7 @@ try:
         raw_config = f.read()
 
     if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, any] = yaml.load(raw_config)
+        config: dict[str, any] = yaml.safe_load(raw_config)
     elif config_file.endswith(".json"):
         config: dict[str, any] = json.loads(raw_config)
 except FileNotFoundError:
@@ -109,32 +105,16 @@ else:
             **FRIGATE_ENV_VARS
         )
 
-# 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"
-elif path in INCLUDED_FFMPEG_VERSIONS:
-    ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
-else:
-    ffmpeg_path = f"{path}/bin/ffmpeg"
-
-if go2rtc_config.get("ffmpeg") is None:
-    go2rtc_config["ffmpeg"] = {"bin": ffmpeg_path}
-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:
+if int(os.environ["LIBAVFORMAT_VERSION_MAJOR"]) < 59:
+    if go2rtc_config.get("ffmpeg") is None:
+        go2rtc_config["ffmpeg"] = {
+            "rtsp": "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
+        }
+    elif 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 go2rtc_config.get("ffmpeg") is None:
-        go2rtc_config["ffmpeg"] = {"path": ""}
 
 for name in go2rtc_config.get("streams", {}):
     stream = go2rtc_config["streams"][name]
@@ -165,7 +145,7 @@ if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, any] = config.get("birdseye")
 
     input = f"-f rawvideo -pix_fmt yuv420p -video_size {birdseye.get('width', 1280)}x{birdseye.get('height', 720)} -r 10 -i {BIRDSEYE_PIPE}"
-    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(ffmpeg_path, config.get('ffmpeg', {}).get('hwaccel_args', ''), input, '-rtsp_transport tcp -f rtsp {output}')}"
+    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(config.get('ffmpeg', {}).get('hwaccel_args'), input, '-rtsp_transport tcp -f rtsp {output}')}"
 
     if go2rtc_config.get("streams"):
         go2rtc_config["streams"]["birdseye"] = ffmpeg_cmd

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

@@ -104,8 +104,6 @@ http {
 
             add_header Cache-Control "no-store";
             expires off;
-
-            keepalive_disable safari;
         }
 
         location /stream/ {
@@ -226,7 +224,7 @@ http {
 
         location ~* /api/.*\.(jpg|jpeg|png|webp|gif)$ {
             include auth_request.conf;
-            rewrite ^/api/(.*)$ /$1 break;
+            rewrite ^/api/(.*)$ $1 break;
             proxy_pass http://frigate_api;
             include proxy.conf;
         }

docker/main/rootfs/usr/local/nginx/get_tls_settings.py

@@ -3,9 +3,7 @@
 import json
 import os
 
-from ruamel.yaml import YAML
-
-yaml = YAML()
+import yaml
 
 config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
 
@@ -19,7 +17,7 @@ try:
         raw_config = f.read()
 
     if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, any] = yaml.load(raw_config)
+        config: dict[str, any] = yaml.safe_load(raw_config)
     elif config_file.endswith(".json"):
         config: dict[str, any] = json.loads(raw_config)
 except FileNotFoundError:

docker/main/rootfs/usr/local/semantic_search/get_search_settings.py

@@ -0,0 +1,28 @@
+"""Prints the semantic_search config as json to stdout."""
+
+import json
+import os
+
+import yaml
+
+config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
+
+# Check if we can use .yaml instead of .yml
+config_file_yaml = config_file.replace(".yml", ".yaml")
+if os.path.isfile(config_file_yaml):
+    config_file = config_file_yaml
+
+try:
+    with open(config_file) as f:
+        raw_config = f.read()
+
+    if config_file.endswith((".yaml", ".yml")):
+        config: dict[str, any] = yaml.safe_load(raw_config)
+    elif config_file.endswith(".json"):
+        config: dict[str, any] = json.loads(raw_config)
+except FileNotFoundError:
+    config: dict[str, any] = {}
+
+search_config: dict[str, any] = config.get("semantic_search", {"enabled": False})
+
+print(json.dumps(search_config))

docker/rockchip/Dockerfile

@@ -22,6 +22,5 @@ ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/librknnrt
 
 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/6.1-5/ffmpeg /usr/lib/ffmpeg/6.0/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffprobe /usr/lib/ffmpeg/6.0/bin/
-ENV PATH="/usr/lib/ffmpeg/6.0/bin/:${PATH}"
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffmpeg /usr/lib/btbn-ffmpeg/bin/
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffprobe /usr/lib/btbn-ffmpeg/bin/

docker/rockchip/rk.mk

@@ -1,15 +1,10 @@
 BOARDS += rk
 
 local-rk: version
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=frigate:latest-rk \
-		--load
+	docker buildx bake --load --file=docker/rockchip/rk.hcl --set rk.tags=frigate:latest-rk rk
 
 build-rk: version
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk
+	docker buildx bake --file=docker/rockchip/rk.hcl --set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk rk
 
 push-rk: build-rk
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk \
-		--push
+	docker buildx bake --push --file=docker/rockchip/rk.hcl --set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk rk

docker/rocm/Dockerfile

@@ -23,11 +23,11 @@ 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
 RUN apt-get -y install --no-install-recommends migraphx-dev
 
 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* /opt/rocm-dist/opt/rocm-$ROCM/lib/
 RUN cd /opt/rocm-dist/opt/ && ln -s rocm-$ROCM rocm
 
 RUN mkdir -p /opt/rocm-dist/etc/ld.so.conf.d/
@@ -69,11 +69,7 @@ RUN apt-get -y install libnuma1
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-
-COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt
-RUN python3 -m pip install --upgrade pip \
-    && pip3 uninstall -y onnxruntime-openvino \
-    && pip3 install -r /requirements.txt
+COPY docker/rocm/rootfs/ /
 
 #######################################################################
 FROM scratch AS rocm-dist
@@ -83,7 +79,6 @@ ARG AMDGPU
 
 COPY --from=rocm /opt/rocm-$ROCM/bin/rocminfo /opt/rocm-$ROCM/bin/migraphx-driver /opt/rocm-$ROCM/bin/
 COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*$AMDGPU* /opt/rocm-$ROCM/share/miopen/db/
-COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx908* /opt/rocm-$ROCM/share/miopen/db/
 COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*$AMDGPU* /opt/rocm-$ROCM/lib/rocblas/library/
 COPY --from=rocm /opt/rocm-dist/ /
 COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-39-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/
@@ -106,3 +101,6 @@ ENV HSA_OVERRIDE_GFX_VERSION=$HSA_OVERRIDE_GFX_VERSION
 #######################################################################
 FROM rocm-prelim-hsa-override$HSA_OVERRIDE as rocm-deps
 
+# Request yolov8 download at startup
+ENV DOWNLOAD_YOLOV8=1
+

docker/rocm/requirements-wheels-rocm.txt

@@ -1 +0,0 @@
-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

docker/rocm/rocm.mk

@@ -4,50 +4,14 @@ BOARDS += rocm
 ROCM_CHIPSETS:=gfx900:9.0.0 gfx1030:10.3.0 gfx1100:11.0.0
 
 local-rocm: version
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=frigate:latest-rocm-$(word 1,$(subst :, ,$(chipset))) \
-			--load \
-	&&) true
-	
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=frigate:latest-rocm \
-		--load
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --load --file=docker/rocm/rocm.hcl --set rocm.tags=frigate:latest-rocm-$(word 1,$(subst :, ,$(chipset))) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --load --file=docker/rocm/rocm.hcl --set rocm.tags=frigate:latest-rocm rocm
 
 build-rocm: version
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
-	&&) true
-
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm rocm
 
 push-rocm: build-rocm
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
-			--push \
-	&&) true
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --push --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --push --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm rocm
 
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm \
-		--push

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/run

@@ -0,0 +1,20 @@
+#!/command/with-contenv bash
+# shellcheck shell=bash
+# Compile YoloV8 ONNX files into ROCm MIGraphX files
+
+OVERRIDE=$(cd /opt/frigate && python3 -c 'import frigate.detectors.plugins.rocm as rocm; print(rocm.auto_override_gfx_version())')
+
+if ! test -z "$OVERRIDE"; then
+  echo "Using HSA_OVERRIDE_GFX_VERSION=${OVERRIDE}"
+  export HSA_OVERRIDE_GFX_VERSION=$OVERRIDE
+fi
+
+for onnx in /config/model_cache/yolov8/*.onnx
+do
+  mxr="${onnx%.onnx}.mxr"
+  if ! test -f $mxr; then
+    echo "processing $onnx into $mxr"
+    /opt/rocm/bin/migraphx-driver compile $onnx --optimize --gpu --enable-offload-copy --binary -o $mxr
+  fi
+done
+

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/type

@@ -0,0 +1 @@
+oneshot

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/up

@@ -0,0 +1 @@
+/etc/s6-overlay/s6-rc.d/compile-rocm-models/run

docker/rpi/Dockerfile

@@ -12,7 +12,5 @@ RUN rm -rf /usr/lib/btbn-ffmpeg/
 RUN --mount=type=bind,source=docker/rpi/install_deps.sh,target=/deps/install_deps.sh \
     /deps/install_deps.sh
 
-ENV LIBAVFORMAT_VERSION_MAJOR=58
-
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /

docker/rpi/rpi.mk

@@ -1,15 +1,10 @@
 BOARDS += rpi
 
 local-rpi: version
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=frigate:latest-rpi \
-		--load
+	docker buildx bake --load --file=docker/rpi/rpi.hcl --set rpi.tags=frigate:latest-rpi rpi
 
 build-rpi: version
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi
+	docker buildx bake --file=docker/rpi/rpi.hcl --set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi rpi
 
 push-rpi: build-rpi
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi \
-		--push
+	docker buildx bake --push --file=docker/rpi/rpi.hcl --set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi rpi

docker/tensorrt/Dockerfile.amd64

@@ -18,7 +18,6 @@ RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels
     pip3 install -U /deps/trt-wheels/*.whl && \
     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 / /
 
@@ -27,7 +26,6 @@ FROM devcontainer AS devcontainer-trt
 
 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-12.1 /usr/local/cuda
 COPY docker/tensorrt/detector/rootfs/ /
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \

docker/tensorrt/Dockerfile.arm64

@@ -10,8 +10,8 @@ ARG DEBIAN_FRONTEND
 # 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 \
-    wget build-essential cmake git \
+       python3.9 python3.9-dev \
+       wget build-essential cmake git \
     && rm -rf /var/lib/apt/lists/*
 
 # Ensure python3 defaults to python3.9
@@ -41,11 +41,7 @@ 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
-
-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
+RUN pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND

docker/tensorrt/Dockerfile.base

@@ -24,9 +24,8 @@ ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 
 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 docker/tensorrt/detector/rootfs/ /
-ENV YOLO_MODELS=""
+ENV YOLO_MODELS="yolov7-320"
 
 HEALTHCHECK --start-period=600s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
     CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1

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

@@ -11,7 +11,6 @@ set -o errexit -o nounset -o pipefail
 MODEL_CACHE_DIR=${MODEL_CACHE_DIR:-"/config/model_cache/tensorrt"}
 TRT_VER=${TRT_VER:-$(cat /etc/TENSORRT_VER)}
 OUTPUT_FOLDER="${MODEL_CACHE_DIR}/${TRT_VER}"
-YOLO_MODELS=${YOLO_MODELS:-""}
 
 # Create output folder
 mkdir -p ${OUTPUT_FOLDER}
@@ -20,11 +19,6 @@ FIRST_MODEL=true
 MODEL_DOWNLOAD=""
 MODEL_CONVERT=""
 
-if [ -z "$YOLO_MODELS"]; then
-    echo "tensorrt model preparation disabled"
-    exit 0
-fi
-
 for model in ${YOLO_MODELS//,/ }
 do
     # Remove old link in case path/version changed

docker/tensorrt/requirements-amd64.txt

@@ -8,7 +8,5 @@ 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-cufft-cu11==10.*; platform_machine == 'x86_64'
-onnx==1.16.*; platform_machine == 'x86_64'
-onnxruntime-gpu==1.18.*; platform_machine == 'x86_64'
-protobuf==3.20.3; platform_machine == 'x86_64'
+onnx==1.14.0; 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 == 11.7; platform_machine == 'aarch64'

docker/tensorrt/trt.mk

@@ -7,35 +7,20 @@ JETPACK4_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK4_BASE) SLIM_BASE=$(JETPACK4_BAS
 JETPACK5_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK5_BASE) SLIM_BASE=$(JETPACK5_BASE) TRT_BASE=$(JETPACK5_BASE)
 
 local-trt: version
-	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt \
-		--load
+	$(X86_DGPU_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt tensorrt
 
 local-trt-jp4: version
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt-jp4 \
-		--load
+	$(JETPACK4_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt-jp4 tensorrt
 
 local-trt-jp5: version
-	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt-jp5 \
-		--load
+	$(JETPACK5_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt-jp5 tensorrt
 
 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
+	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt tensorrt
+	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 tensorrt
+	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 tensorrt
 
 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
+	$(X86_DGPU_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt tensorrt
+	$(JETPACK4_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 tensorrt
+	$(JETPACK5_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 tensorrt

docs/README.md

@@ -1,10 +1,5 @@
 # Website
 
-This website is built using [Docusaurus 3.5](https://docusaurus.io/docs), a modern static website generator.
+This website is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator.
 
 For installation and contributing instructions, please follow the [Contributing Docs](https://docs.frigate.video/development/contributing).
-
-# Development
-
-1. Run `npm i` to install dependencies
-2. Run `npm run start` to start the website

docs/docs/configuration/advanced.md

@@ -41,7 +41,7 @@ environment_vars:
 
 ### `database`
 
-Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
+Event and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
 
 If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
 
@@ -162,19 +162,19 @@ 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, a docker volume mapping can be used to overwrite the included ffmpeg build with an ffmpeg build that works for your specific hardware setup.
 
 To do this:
 
-1. Download your ffmpeg build and uncompress to the Frigate config folder.
+1. Download your ffmpeg build and uncompress to a folder on the host (let's use `/home/appdata/frigate/custom-ffmpeg` for this example).
 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`.
+NOTE: The folder that is mapped from the host needs to be the folder that contains `/bin`. So if the full structure is `/home/appdata/frigate/custom-ffmpeg/bin/ffmpeg` then `/home/appdata/frigate/custom-ffmpeg` needs to be mapped to `/usr/lib/btbn-ffmpeg`.
 
 ### Custom go2rtc version
 
-Frigate currently includes go2rtc v1.9.2, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.4, there may be certain cases where you want to run a different version of go2rtc.
 
 To do this:
 
@@ -183,7 +183,7 @@ To do this:
 3. Give `go2rtc` execute permission.
 4. Restart Frigate and the custom version will be used, you can verify by checking go2rtc logs.
 
-## Validating your config.yml file updates
+## Validating your config.yaml file updates
 
 When frigate starts up, it checks whether your config file is valid, and if it is not, the process exits. To minimize interruptions when updating your config, you have three options -- you can edit the config via the WebUI which has built in validation, use the config API, or you can validate on the command line using the frigate docker container.
 
@@ -211,5 +211,5 @@ docker run                                \
   --entrypoint python3                    \
   ghcr.io/blakeblackshear/frigate:stable  \
   -u -m frigate                           \
-  --validate-config
+  --validate_config
 ```

docs/docs/configuration/authentication.md

@@ -26,7 +26,7 @@ In the event that you are locked out of your instance, you can tell Frigate to r
 
 ## 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).
+In order to limit the risk of brute force attacks, rate limiting is available for login failures. This is implemented with Flask-Limiter, and the string notation for valid values is available in [the documentation](https://flask-limiter.readthedocs.io/en/stable/configuration.html#rate-limit-string-notation).
 
 For example, `1/second;5/minute;20/hour` will rate limit the login endpoint when failures occur more than:
 

docs/docs/configuration/autotracking.md

@@ -41,7 +41,6 @@ cameras:
     ...
     onvif:
       # Required: host of the camera being connected to.
-      # NOTE: HTTP is assumed by default; HTTPS is supported if you specify the scheme, ex: "https://0.0.0.0".
       host: 0.0.0.0
       # Optional: ONVIF port for device (default: shown below).
       port: 8000
@@ -50,8 +49,6 @@ cameras:
       user: admin
       # Optional: password for login.
       password: admin
-      # Optional: Skip TLS verification from the ONVIF server (default: shown below)
-      tls_insecure: False
       # Optional: PTZ camera object autotracking. Keeps a moving object in
       # the center of the frame by automatically moving the PTZ camera.
       autotracking:

docs/docs/configuration/camera_specific.md

@@ -9,12 +9,6 @@ This page makes use of presets of FFmpeg args. For more information on presets,
 
 :::
 
-:::note
-
-Many cameras support encoding options which greatly affect the live view experience, see the [Live view](/configuration/live) page for more info.
-
-:::
-
 ## MJPEG Cameras
 
 Note that mjpeg cameras require encoding the video into h264 for recording, and restream roles. This will use significantly more CPU than if the cameras supported h264 feeds directly. It is recommended to use the restream role to create an h264 restream and then use that as the source for ffmpeg.
@@ -181,7 +175,7 @@ go2rtc:
       - rtspx://192.168.1.1:7441/abcdefghijk
 ```
 
-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-rtsp)
 
 In the Unifi 2.0 update Unifi Protect Cameras had a change in audio sample rate which causes issues for ffmpeg. The input rate needs to be set for record if used directly with unifi protect.
 
@@ -193,4 +187,4 @@ ffmpeg:
 
 ### TP-Link VIGI Cameras
 
-TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.
+TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded events. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.

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 temporarily disabled by using `enabled: False`. Existing events and recordings can still be accessed. Live streams, recording and detecting are not working. Camera specific configurations will be used.
 
 Each role can only be assigned to one input per camera. The options for roles are as follows:
 
@@ -46,14 +46,6 @@ cameras:
   side: ...
 ```
 
-:::note
-
-If you only define one stream in your `inputs` and do not assign a `detect` role to it, Frigate will automatically assign it the `detect` role. Frigate will always decode a stream to support motion detection, Birdseye, the API image endpoints, and other features, even if you have disabled object detection with `enabled: False` in your config's `detect` section.
-
-If you plan to use Frigate for recording only, it is still recommended to define a `detect` role for a low resolution stream to minimize resource usage from the required stream decoding.
-
-:::
-
 For camera model specific settings check the [camera specific](camera_specific.md) infos.
 
 ## Setting up camera PTZ controls
@@ -79,41 +71,29 @@ cameras:
 
 If the ONVIF connection is successful, PTZ controls will be available in the camera's WebUI.
 
-:::tip
-
-If your ONVIF camera does not require authentication credentials, you may still need to specify an empty string for `user` and `password`, eg: `user: ""` and `password: ""`.
-
-:::
-
 An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
 
 ## ONVIF PTZ camera recommendations
 
 This list of working and non-working PTZ cameras is based on user feedback.
 
-| Brand or specific camera     | PTZ Controls | Autotracking | Notes                                                                                                                                           |
-| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------- |
-| Amcrest                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                 |
-| Amcrest ASH21                |      ✅      |      ❌      | ONVIF service port: 80                                                                                                                          |
-| Amcrest IP4M-S2112EW-AI      |      ✅      |      ❌      | FOV relative movement not supported.                                                                                                            |
-| Amcrest IP5M-1190EW          |      ✅      |      ❌      | ONVIF Port: 80. FOV relative movement not supported.                                                                                            |
-| Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                 |
-| Dahua                        |      ✅      |      ✅      |                                                                                                                                                 |
-| Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
-| Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                 |
-| Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                 |
-| Hikvision                    |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
-| Hikvision DS-2DE3A404IWG-E/W |      ✅      |      ✅      |                                                                                                                                                 |
-| Reolink 511WA                |      ✅      |      ❌      | Zoom only                                                                                                                                       |
-| Reolink E1 Pro               |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink E1 Zoom              |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink RLC-823A 16x         |      ✅      |      ❌      |                                                                                                                                                 |
-| Speco O8P32X                 |      ✅      |      ❌      |                                                                                                                                                 |
-| Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable.                                            |
-| Tapo                         |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
-| Uniview IPC672LR-AX4DUPK     |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
-| Uniview IPC6612SR-X33-VG     |      ✅      |      ✅      | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working.                                           |
-| Vikylin PTZ-2804X-I2         |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                        |
+| Brand or specific camera | PTZ Controls | Autotracking | Notes                                                                                                                                           |
+| ------------------------ | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------- |
+| Amcrest                  |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                 |
+| Amcrest ASH21            |      ❌      |      ❌      | No ONVIF support                                                                                                                                |
+| Ctronics PTZ             |      ✅      |      ❌      |                                                                                                                                                 |
+| Dahua                    |      ✅      |      ✅      |                                                                                                                                                 |
+| Foscam R5                |      ✅      |      ❌      |                                                                                                                                                 |
+| Hanwha XNP-6550RH        |      ✅      |      ❌      |                                                                                                                                                 |
+| Hikvision                |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
+| Reolink 511WA            |      ✅      |      ❌      | Zoom only                                                                                                                                       |
+| Reolink E1 Pro           |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink E1 Zoom          |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink RLC-823A 16x     |      ✅      |      ❌      |                                                                                                                                                 |
+| Sunba 405-D20X           |      ✅      |      ❌      |                                                                                                                                                 |
+| Tapo                     |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
+| Uniview IPC672LR-AX4DUPK |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
+| Vikylin PTZ-2804X-I2     |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                        |
 
 ## Setting up camera groups
 

docs/docs/configuration/genai.md

@@ -3,15 +3,7 @@ id: genai
 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.
-
-:::
+Generative AI can be used to automatically generate descriptions based on the thumbnails of your events. This helps with [semantic search](/configuration/semantic_search) in Frigate by providing detailed text descriptions as a basis of the search query.
 
 ## Configuration
 
@@ -35,21 +27,11 @@ cameras:
 
 ## Ollama
 
-:::warning
-
-Using Ollama on CPU is not recommended, high inference times make using Generative AI impractical.
-
-:::
-
-[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
-
-Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
-
-Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests).
+[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance. Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [docker container](https://hub.docker.com/r/ollama/ollama) available.
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
+You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`.
 
 :::note
 
@@ -64,7 +46,7 @@ genai:
   enabled: True
   provider: ollama
   base_url: http://localhost:11434
-  model: llava:7b
+  model: llava
 ```
 
 ## Google Gemini
@@ -116,49 +98,17 @@ genai:
   model: gpt-4o
 ```
 
-## Azure OpenAI
-
-Microsoft offers several vision models through Azure OpenAI. A subscription is required.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
-
-### Create Resource and Get API Key
-
-To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key and resource URL, which must include the `api-version` parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource.
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: azure_openai
-  base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
-  api_key: "{FRIGATE_OPENAI_API_KEY}"
-```
-
-## Usage and Best Practices
-
-Frigate's thumbnail search excels at identifying specific details about tracked objects – for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate’s default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
-
-While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate’s default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what’s happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they’re moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation’s context.
-
-### Using GenAI for notifications
-
-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>`.
-
 ## 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:
+Frigate sends multiple frames from the detection along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows:
 
 ```
-Analyze the sequence of images containing the {label}. Focus on the likely intent or behavior of the {label} based on its actions and movement, rather than describing its appearance or the surroundings. Consider what the {label} is doing, why, and what it might do next.
+Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background.
 ```
 
 :::tip
 
-Prompts can use variable replacements like `{label}`, `{sub_label}`, and `{camera}` to substitute information from the tracked object as part of the prompt.
+Prompts can use variable replacements like `{label}`, `{sub_label}`, and `{camera}` to substitute information from the detection as part of the prompt.
 
 :::
 
@@ -170,35 +120,15 @@ genai:
   provider: ollama
   base_url: http://localhost:11434
   model: llava
-  prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
+  prompt: "Describe the {label} in these images from the {camera} security camera."
   object_prompts:
-    person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
-    car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
-```
-
-Prompts can also be overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire. By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or zones.
-
-Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
-
-```yaml
-cameras:
-  front_door:
-    genai:
-      use_snapshot: True
-      prompt: "Analyze the {label} in these images from the {camera} security camera at the front door. Focus on the actions and potential intent of the {label}."
-      object_prompts:
-        person: "Examine the person in these images. What are they doing, and how might their actions suggest their purpose (e.g., delivering something, approaching, leaving)? If they are carrying or interacting with a package, include details about its source or destination."
-        cat: "Observe the cat in these images. Focus on its movement and intent (e.g., wandering, hunting, interacting with objects). If the cat is near the flower pots or engaging in any specific actions, mention it."
-      objects:
-        - person
-        - cat
-      required_zones:
-        - steps
+    person: "Describe the main person in these images (gender, age, clothing, activity, etc). Do not include where the activity is occurring (sidewalk, concrete, driveway, etc). If delivering a package, include the company the package is from."
+    car: "Label the primary vehicle in these images with just the name of the company if it is a delivery vehicle, or the color make and model."
 ```
 
 ### Experiment with prompts
 
-Many providers also have a public facing chat interface for their models. Download a couple of different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
+Providers also has a public facing chat interface for their models. Download a couple different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
 
 - OpenAI - [ChatGPT](https://chatgpt.com)
 - Gemini - [Google AI Studio](https://aistudio.google.com)

docs/docs/configuration/hardware_acceleration.md

@@ -65,37 +65,24 @@ Or map in all the `/dev/video*` devices.
 
 ## Intel-based CPUs
 
-:::info
-
-**Recommended hwaccel Preset**
-
-| CPU Generation | Intel Driver | Recommended Preset | Notes                               |
-| -------------- | ------------ | ------------------ | ----------------------------------- |
-| gen1 - gen7    | i965         | preset-vaapi       | qsv is not supported                |
-| gen8 - gen12   | iHD          | preset-vaapi       | preset-intel-qsv-* can also be used |
-| gen13+         | iHD / Xe     | preset-intel-qsv-* |                                     |
-| Intel Arc GPU  | iHD / Xe     | preset-intel-qsv-* |                                     |
-
-:::
-
-:::note
-
-The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `frigate.yaml` for HA OS users](advanced.md#environment_vars).
-
-See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/000005505/processors.html) to figure out what generation your CPU is.
-
-:::
-
 ### Via VAAPI
 
-VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
+VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams. VAAPI is recommended for all generations of Intel-based CPUs.
 
 ```yaml
 ffmpeg:
   hwaccel_args: preset-vaapi
 ```
 
-### Via Quicksync
+:::note
+
+With some of the processors, like the J4125, the default driver `iHD` doesn't seem to work correctly for hardware acceleration. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `frigate.yaml` for HA OS users](advanced.md#environment_vars).
+
+:::
+
+### Via Quicksync (>=10th Generation only)
+
+If VAAPI does not work for you, you can try QSV if your processor supports it. QSV must be set specifically based on the video encoding of the stream.
 
 #### H.264 streams
 
@@ -231,11 +218,28 @@ docker run -d \
 
 ### Setup Decoder
 
-Using `preset-nvidia` ffmpeg will automatically select the necessary profile for the incoming video, and will log an error if the profile is not supported by your GPU.
+The decoder you need to pass in the `hwaccel_args` will depend on the input video.
+
+A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get the ones your card supports)
+
+```
+ V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
+ V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
+ V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
+ V..... mjpeg_cuvid          Nvidia CUVID MJPEG decoder (codec mjpeg)
+ V..... mpeg1_cuvid          Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
+ V..... mpeg2_cuvid          Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
+ V..... mpeg4_cuvid          Nvidia CUVID MPEG4 decoder (codec mpeg4)
+ V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)
+ V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)
+ V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)
+```
+
+For example, for H264 video, you'll select `preset-nvidia-h264`.
 
 ```yaml
 ffmpeg:
-  hwaccel_args: preset-nvidia
+  hwaccel_args: preset-nvidia-h264
 ```
 
 If everything is working correctly, you should see a significant improvement in performance.
@@ -366,7 +370,7 @@ Make sure to follow the [Rockchip specific installation instructions](/frigate/i
 
 ### Configuration
 
-Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
+Add one of the following FFmpeg presets to your `config.yaml` to enable hardware video processing:
 
 ```yaml
 # if you try to decode a h264 encoded stream

docs/docs/configuration/index.md

@@ -72,7 +72,7 @@ Here are some common starter configuration examples. Refer to the [reference con
 - Hardware acceleration for decoding video
 - USB Coral detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -95,12 +95,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True
@@ -130,7 +128,7 @@ cameras:
 - VAAPI hardware acceleration for decoding video
 - USB Coral detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -151,12 +149,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True
@@ -186,7 +182,7 @@ cameras:
 - VAAPI hardware acceleration for decoding video
 - OpenVino detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -218,12 +214,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True

docs/docs/configuration/live.md

@@ -11,21 +11,11 @@ Frigate intelligently uses three different streaming technologies to display you
 
 The jsmpeg live view will use more browser and client GPU resources. Using go2rtc is highly recommended and will provide a superior experience.
 
-| Source | Frame Rate                            | Resolution | Audio                        | Requires go2rtc | Notes                                                                                                                                                               |
-| ------ | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| jsmpeg | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | Resolution is configurable, but go2rtc is recommended if you want higher resolutions and better frame rates. jsmpeg is Frigate's default without go2rtc configured. |
-| mse    | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured.                                                              |
-| webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration, doesn't support h.265. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                   |
-
-### Camera Settings Recommendations
-
-If you are using go2rtc, you should adjust the following settings in your camera's firmware for the best experience with Live view:
-
-- Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
-- Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
-- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
-
-The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
+| Source | Latency | Frame Rate                            | Resolution | Audio                        | Requires go2rtc | Other Limitations                                                                    |
+| ------ | ------- | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------ |
+| jsmpeg | low     | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | resolution is configurable, but go2rtc is recommended if you want higher resolutions |
+| mse    | low     | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only                                     |
+| webrtc | lowest  | native                                | native     | yes (depends on audio codec) | yes             | requires extra config, doesn't support h.265                                         |
 
 ### Audio Support
 
@@ -42,15 +32,6 @@ go2rtc:
       - "ffmpeg:http_cam#audio=opus" # <- copy of the stream which transcodes audio to the missing codec (usually will be opus)
 ```
 
-If your camera does not have audio and you are having problems with Live view, you should have go2rtc send video only:
-
-```yaml
-go2rtc:
-  streams:
-    no_audio_camera:
-      - ffmpeg:rtsp://192.168.1.5:554/live0#video=copy
-```
-
 ### Setting Stream 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`.

docs/docs/configuration/motion_detection.md

@@ -13,11 +13,11 @@ Once motion is detected, it tries to group up nearby areas of motion together in
 
 The default motion settings should work well for the majority of cameras, however there are cases where tuning motion detection can lead to better and more optimal results. Each camera has its own environment with different variables that affect motion, this means that the same motion settings will not fit all of your cameras.
 
-Before tuning motion it is important to understand the goal. In an optimal configuration, motion from people and cars would be detected, but not grass moving, lighting changes, timestamps, etc. If your motion detection is too sensitive, you will experience higher CPU loads and greater false positives from the increased rate of object detection. If it is not sensitive enough, you will miss objects that you want to track.
+Before tuning motion it is important to understand the goal. In an optimal configuration, motion from people and cars would be detected, but not grass moving, lighting changes, timestamps, etc. If your motion detection is too sensitive, you will experience higher CPU loads and greater false positives from the increased rate of object detection. If it is not sensitive enough, you will miss events.
 
 ## Create Motion Masks
 
-First, mask areas with regular motion not caused by the objects you want to detect. The best way to find candidates for motion masks is by watching the debug stream with motion boxes enabled. Good use cases for motion masks are timestamps or tree limbs and large bushes that regularly move due to wind. When possible, avoid creating motion masks that would block motion detection for objects you want to track **even if they are in locations where you don't want alerts or detections**. Motion masks should not be used to avoid detecting objects in specific areas. More details can be found [in the masks docs.](/configuration/masks.md).
+First, mask areas with regular motion not caused by the objects you want to detect. The best way to find candidates for motion masks is by watching the debug stream with motion boxes enabled. Good use cases for motion masks are timestamps or tree limbs and large bushes that regularly move due to wind. When possible, avoid creating motion masks that would block motion detection for objects you want to track **even if they are in locations where you don't want events**. Motion masks should not be used to avoid detecting objects in specific areas. More details can be found [in the masks docs.](/configuration/masks.md).
 
 ## Prepare For Testing
 
@@ -29,7 +29,7 @@ Now that things are set up, find a time to tune that represents normal circumsta
 
 :::note
 
-Remember that motion detection is just used to determine when object detection should be used. You should aim to have motion detection sensitive enough that you won't miss objects you want to detect with object detection. The goal is to prevent object detection from running constantly for every small pixel change in the image. Windy days are still going to result in lots of motion being detected.
+Remember that motion detection is just used to determine when object detection should be used. You should aim to have motion detection sensitive enough that you won't miss events from objects you want to detect with object detection. The goal is to prevent object detection from running constantly for every small pixel change in the image. Windy days are still going to result in lots of motion being detected.
 
 :::
 
@@ -92,15 +92,9 @@ motion:
   lightning_threshold: 0.8
 ```
 
-:::warning
+:::tip
 
-Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
-
-:::
-
-:::note
-
-Lightning threshold does not stop motion based recordings from being saved.
+Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these events are not missed.
 
 :::
 

docs/docs/configuration/object_detectors.md

@@ -3,39 +3,37 @@ id: object_detectors
 title: Object Detectors
 ---
 
-# Supported Hardware
+# Officially Supported Detectors
 
-:::info
+Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `openvino`, `tensorrt`, `rknn`, and `hailo8l`. 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 supports multiple different detectors that work on different types of hardware:
+## CPU Detector (not recommended)
 
-**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.
+The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
 
-**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.
+:::tip
 
-**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.
+If you do not have GPU or Edge TPU hardware, using the [OpenVINO Detector](#openvino-detector) is often more efficient than using the CPU detector.
 
 :::
 
-# Officially Supported Detectors
+The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
 
-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.
+A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+
+```yaml
+detectors:
+  cpu1:
+    type: cpu
+    num_threads: 3
+    model:
+      path: "/custom_model.tflite"
+  cpu2:
+    type: cpu
+    num_threads: 3
+```
+
+When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
 
 ## Edge TPU Detector
 
@@ -124,22 +122,6 @@ The OpenVINO device to be used is specified using the `"device"` attribute accor
 
 OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html)
 
-:::tip
-
-When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
-
-```yaml
-detectors:
-  ov_0:
-    type: openvino
-    device: GPU
-  ov_1:
-    type: openvino
-    device: GPU
-```
-
-:::
-
 ### Supported Models
 
 #### SSDLite MobileNet v2
@@ -167,7 +149,7 @@ 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).
+[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
 
@@ -223,7 +205,7 @@ The model used for TensorRT must be preprocessed on the same hardware platform t
 
 The Frigate image will generate model files during startup if the specified model is not found. Processed models are stored in the `/config/model_cache` folder. Typically the `/config` path is mapped to a directory on the host already and the `model_cache` does not need to be mapped separately unless the user wants to store it in a different location on the host.
 
-By default, no models will be generated, but this can be overridden by specifying the `YOLO_MODELS` environment variable in Docker. One or more models may be listed in a comma-separated format, and each one will be generated. Models will only be generated if the corresponding `{model}.trt` file is not present in the `model_cache` folder, so you can force a model to be regenerated by deleting it from your Frigate data folder.
+By default, the `yolov7-320` model will be generated, but this can be overridden by specifying the `YOLO_MODELS` environment variable in Docker. One or more models may be listed in a comma-separated format, and each one will be generated. To select no model generation, set the variable to an empty string, `YOLO_MODELS=""`. Models will only be generated if the corresponding `{model}.trt` file is not present in the `model_cache` folder, so you can force a model to be regenerated by deleting it from your Frigate data folder.
 
 If you have a Jetson device with DLAs (Xavier or Orin), you can generate a model that will run on the DLA by appending `-dla` to your model name, e.g. specify `YOLO_MODELS=yolov7-320-dla`. The model will run on DLA0 (Frigate does not currently support DLA1). DLA-incompatible layers will fall back to running on the GPU.
 
@@ -264,7 +246,7 @@ An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yol
 ```yml
 frigate:
   environment:
-    - YOLO_MODELS=yolov7-320,yolov7x-640
+    - YOLO_MODELS=yolov4-608,yolov7x-640
     - USE_FP16=false
 ```
 
@@ -296,220 +278,6 @@ model:
   height: 320
 ```
 
-## AMD/ROCm GPU detector
-
-### 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`.
-
-### Docker settings for GPU access
-
-ROCm needs access to the `/dev/kfd` and `/dev/dri` devices. When docker or frigate is not run under root then also `video` (and possibly `render` and `ssl/_ssl`) groups should be added.
-
-When running docker directly the following flags should be added for device access:
-
-```bash
-$ docker run --device=/dev/kfd --device=/dev/dri  \
-    ...
-```
-
-When using docker compose:
-
-```yaml
-services:
-  frigate:
----
-devices:
-  - /dev/dri
-  - /dev/kfd
-```
-
-For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
-
-### Docker settings for overriding the GPU chipset
-
-Your GPU might work just fine without any special configuration but in many cases they need manual settings. AMD/ROCm software stack comes with a limited set of GPU drivers and for newer or missing models you will have to override the chipset version to an older/generic version to get things working.
-
-Also AMD/ROCm does not "officially" support integrated GPUs. It still does work with most of them just fine but requires special settings. One has to configure the `HSA_OVERRIDE_GFX_VERSION` environment variable. See the [ROCm bug report](https://github.com/ROCm/ROCm/issues/1743) for context and examples.
-
-For the rocm frigate build there is some automatic detection:
-
-- gfx90c -> 9.0.0
-- gfx1031 -> 10.3.0
-- gfx1103 -> 11.0.0
-
-If you have something else you might need to override the `HSA_OVERRIDE_GFX_VERSION` at Docker launch. Suppose the version you want is `9.0.0`, then you should configure it from command line as:
-
-```bash
-$ docker run -e HSA_OVERRIDE_GFX_VERSION=9.0.0 \
-    ...
-```
-
-When using docker compose:
-
-```yaml
-services:
-  frigate:
-...
-environment:
-  HSA_OVERRIDE_GFX_VERSION: "9.0.0"
-```
-
-Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
-
-- first make sure that rocm environment is running properly by running `/opt/rocm/bin/rocminfo` in the frigate container -- it should list both the CPU and the GPU with their properties
-- find the chipset version you have (gfxNNN) from the output of the `rocminfo` (see below)
-- use a search engine to query what `HSA_OVERRIDE_GFX_VERSION` you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
-- override the `HSA_OVERRIDE_GFX_VERSION` with relevant value
-- if things are not working check the frigate docker logs
-
-#### Figuring out if AMD/ROCm is working and found your GPU
-
-```bash
-$ docker exec -it frigate /opt/rocm/bin/rocminfo
-```
-
-#### Figuring out your AMD GPU chipset version:
-
-We unset the `HSA_OVERRIDE_GFX_VERSION` to prevent an existing override from messing up the result:
-
-```bash
-$ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo |grep gfx)'
-```
-
-### 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.
-
-## 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.
-
-:::info
-
-If the correct build is used for your GPU then the GPU will be detected and used automatically.
-
-- **AMD**
-
-  - ROCm will automatically be detected and used with the ONNX detector in the `-rocm` Frigate image.
-
-- **Intel**
-
-  - OpenVINO will automatically be detected and used with the ONNX detector in the default Frigate image.
-
-- **Nvidia**
-  - Nvidia GPUs will automatically be detected and used with the ONNX detector in the `-tensorrt` Frigate image.
-  - Jetson devices will automatically be detected and used with the ONNX detector in the `-tensorrt-jp(4/5)` Frigate image.
-
-:::
-
-:::tip
-
-When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
-
-```yaml
-detectors:
-  onnx_0:
-    type: onnx
-  onnx_1:
-    type: onnx
-```
-
-:::
-
-### 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:
-  onnx:
-    type: onnx
-
-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
-  input_tensor: nchw
-  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.
-
-## CPU Detector (not recommended)
-
-The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
-
-:::danger
-
-The CPU detector is not recommended for general use. If you do not have GPU or Edge TPU hardware, using the [OpenVINO Detector](#openvino-detector) in CPU mode is often more efficient than using the CPU detector.
-
-:::
-
-The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
-
-A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
-
-```yaml
-detectors:
-  cpu1:
-    type: cpu
-    num_threads: 3
-    model:
-      path: "/custom_model.tflite"
-  cpu2:
-    type: cpu
-    num_threads: 3
-```
-
-When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
-
 ## Deepstack / CodeProject.AI Server Detector
 
 The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
@@ -621,9 +389,7 @@ $ cat /sys/kernel/debug/rknpu/load
 
 ## Hailo-8l
 
-This detector is available for use with Hailo-8 AI Acceleration Module.
-
-See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the hailo8.
+This detector is available if you are using the Raspberry Pi 5 with Hailo-8L AI Kit. This has not been tested using the Hailo-8L with other hardware.
 
 ### Configuration
 

docs/docs/configuration/object_filters.md

@@ -20,13 +20,15 @@ For object filters in your configuration, any single detection below `min_score`
 
 In frame 2, the score is below the `min_score` value, so Frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.
 
+show image of snapshot vs event with differing scores
+
 ### Minimum Score
 
-Any detection below `min_score` will be immediately thrown out and never tracked because it is considered a false positive. If `min_score` is too low then false positives may be detected and tracked which can confuse the object tracker and may lead to wasted resources. If `min_score` is too high then lower scoring true positives like objects that are further away or partially occluded may be thrown out which can also confuse the tracker and cause valid tracked objects to be lost or disjointed.
+Any detection below `min_score` will be immediately thrown out and never tracked because it is considered a false positive. If `min_score` is too low then false positives may be detected and tracked which can confuse the object tracker and may lead to wasted resources. If `min_score` is too high then lower scoring true positives like objects that are further away or partially occluded may be thrown out which can also confuse the tracker and cause valid events to be lost or disjointed.
 
 ### Threshold
 
-`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
+`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an event. If `threshold` is too high then true positive events may be missed due to the object never scoring high enough.
 
 ## Object Shape
 
@@ -50,7 +52,7 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
 
 ### Zones
 
-[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
+[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create events for objects that enter the zone.
 
 ### Object Masks
 

docs/docs/configuration/objects.md

@@ -5,7 +5,7 @@ title: Available Objects
 
 import labels from "../../../labelmap.txt";
 
-Frigate includes the object labels listed below from the Google Coral test data.
+Frigate includes the object models listed below from the Google Coral test data.
 
 Please note:
 

docs/docs/configuration/pwa.md

@@ -1,24 +0,0 @@
----
-id: pwa
-title: Installing Frigate App
----
-
-Frigate supports being installed as a [Progressive Web App](https://web.dev/explore/progressive-web-apps) on Desktop, Android, and iOS.
-
-This adds features including the ability to deep link directly into the app.
-
-## Requirements
-
-In order to install Frigate as a PWA, the following requirements must be met:
-
-- Frigate must be accessed via a secure context (localhost, secure https, etc.)
-- On Android, Firefox, Chrome, Edge, Opera, and Samsung Internet Browser all support installing PWAs.
-- On iOS 16.4 and later, PWAs can be installed from the Share menu in Safari, Chrome, Edge, Firefox, and Orion.
-
-## Installation
-
-Installation varies slightly based on the device that is being used:
-
-- Desktop: Use the install button typically found in right edge of the address bar
-- Android: Use the `Install as App` button in the more options menu
-- iOS: Use the `Add to Homescreen` button in the share menu

docs/docs/configuration/record.md

@@ -3,7 +3,7 @@ id: record
 title: Recording
 ---
 
-Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy in the config. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
+Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy in the config. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
 
 New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
 
@@ -13,7 +13,7 @@ H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other br
 
 ### Most conservative: Ensure all video is saved
 
-For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion and overlapping with alerts or detections will be retained until 30 days have passed.
+For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion and overlapping with events will be retained until 30 days have passed.
 
 ```yaml
 record:
@@ -21,13 +21,9 @@ record:
   retain:
     days: 3
     mode: all
-  alerts:
+  events:
     retain:
-      days: 30
-      mode: motion
-  detections:
-    retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
@@ -41,28 +37,25 @@ record:
   retain:
     days: 3
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-      mode: motion
-  detections:
-    retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
-### Minimum: Alerts only
+### Minimum: Events only
 
-If you only want to retain video that occurs during a tracked object, this config will discard video unless an alert is ongoing.
+If you only want to retain video that occurs during an event, this config will discard video unless an event is ongoing.
 
 ```yaml
 record:
   enabled: True
   retain:
     days: 0
-  alerts:
+    mode: all
+  events:
     retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
@@ -72,7 +65,7 @@ As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 h
 
 ## Configuring Recording Retention
 
-Frigate supports both continuous and tracked object based recordings with separate retention modes and retention periods.
+Frigate supports both continuous and event based recordings with separate retention modes and retention periods.
 
 :::tip
 
@@ -93,28 +86,25 @@ record:
 
 Continuous recording supports different retention modes [which are described below](#what-do-the-different-retain-modes-mean)
 
-### Object Recording
+### Event Recording
 
-The number of days to record review items can be specified for review items classified as alerts as well as tracked objects.
+If you only used clips in previous versions with recordings disabled, you can use the following config to get the same behavior. This is also the default behavior when recordings are enabled.
 
 ```yaml
 record:
   enabled: True
-  alerts:
+  events:
     retain:
-      days: 10 # <- number of days to keep alert recordings
-  detections:
-    retain:
-      days: 10 # <- number of days to keep detections recordings
+      default: 10 # <- number of days to keep event recordings
 ```
 
-This configuration will retain recording segments that overlap with alerts and detections for 10 days. Because multiple tracked objects can reference the same recording segments, this avoids storing duplicate footage for overlapping tracked objects and reduces overall storage needs.
+This configuration will retain recording segments that overlap with events and have active tracked objects for 10 days. Because multiple events can reference the same recording segments, this avoids storing duplicate footage for overlapping events and reduces overall storage needs.
 
 **WARNING**: Recordings still must be enabled in the config. If a camera has recordings disabled in the config, enabling via the methods listed above will have no effect.
 
 ## What do the different retain modes mean?
 
-Frigate saves from the stream with the `record` role in 10 second segments. These options determine which recording segments are kept for continuous recording (but can also affect tracked objects).
+Frigate saves from the stream with the `record` role in 10 second segments. These options determine which recording segments are kept for continuous recording (but can also affect events).
 
 Let's say you have Frigate configured so that your doorbell camera would retain the last **2** days of continuous recording.
 
@@ -122,7 +112,11 @@ Let's say you have Frigate configured so that your doorbell camera would retain
 - With the `motion` option the only parts of those 48 hours would be segments that Frigate detected motion. This is the middle ground option that won't keep all 48 hours, but will likely keep all segments of interest along with the potential for some extra segments.
 - With the `active_objects` option the only segments that would be kept are those where there was a true positive object that was not considered stationary.
 
-The same options are available with alerts and detections, except it will only save the recordings when it overlaps with a review item of that type.
+The same options are available with events. Let's consider a scenario where you drive up and park in your driveway, go inside, then come back out 4 hours later.
+
+- With the `all` option all segments for the duration of the event would be saved for the event. This event would have 4 hours of footage.
+- With the `motion` option all segments for the duration of the event with motion would be saved. This means any segment where a car drove by in the street, person walked by, lighting changed, etc. would be saved.
+- With the `active_objects` it would only keep segments where the object was active. In this case the only segments that would be saved would be the ones where the car was driving up, you going inside, you coming outside, and the car driving away. Essentially reducing the 4 hours to a minute or two of event footage.
 
 A configuration example of the above retain modes where all `motion` segments are stored for 7 days and `active objects` are stored for 14 days would be as follows:
 
@@ -132,18 +126,33 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 14
-      mode: active_objects
-  detections:
-    retain:
-      days: 14
+      default: 14
       mode: active_objects
 ```
 
 The above configuration example can be added globally or on a per camera basis.
 
+### Object Specific Retention
+
+You can also set specific retention length for an object type. The below configuration example builds on from above but also specifies that recordings of dogs only need to be kept for 2 days and recordings of cars should be kept for 7 days.
+
+```yaml
+record:
+  enabled: True
+  retain:
+    days: 7
+    mode: motion
+  events:
+    retain:
+      default: 14
+      mode: active_objects
+      objects:
+        dog: 2
+        car: 7
+```
+
 ## Can I have "continuous" recordings, but only at certain times?
 
 Using Frigate UI, HomeAssistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
@@ -154,7 +163,7 @@ Footage can be exported from Frigate by right-clicking (desktop) or long pressin
 
 ### Time-lapse export
 
-Time lapse exporting is available only via the [HTTP API](../integrations/api/export-recording-export-camera-name-start-start-time-end-end-time-post.api.mdx).
+Time lapse exporting is available only via the [HTTP API](../integrations/api.md#post-apiexportcamerastartstart-timestampendend-timestamp).
 
 When exporting a time-lapse the default speed-up is 25x with 30 FPS. This means that every 25 seconds of (real-time) recording is condensed into 1 second of time-lapse video (always without audio) with a smoothness of 30 FPS.
 

docs/docs/configuration/reference.md

@@ -138,16 +138,6 @@ model:
   # Optional: Label name modifications. These are merged into the standard labelmap.
   labelmap:
     2: vehicle
-  # Optional: Map of object labels to their attribute labels (default: depends on model)
-  attributes_map:
-    person:
-      - amazon
-      - face
-    car:
-      - amazon
-      - fedex
-      - license_plate
-      - ups
 
 # Optional: Audio Events Configuration
 # NOTE: Can be overridden at the camera level
@@ -220,10 +210,6 @@ birdseye:
 # Optional: ffmpeg configuration
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
-  # Optional: ffmpeg binry path (default: shown below)
-  # can also be set to `7.0` or `5.0` to specify one of the included versions
-  # or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
-  path: "default"
   # Optional: global ffmpeg args (default: shown below)
   global_args: -hide_banner -loglevel warning -threads 2
   # Optional: global hwaccel args (default: auto detect)
@@ -285,13 +271,13 @@ detect:
   # especially when using separate streams for detect and record.
   # Use this setting to make the timeline bounding boxes more closely align
   # with the recording. The value can be positive or negative.
-  # TIP: Imagine there is an tracked object clip with a person walking from left to right.
-  #      If the tracked object lifecycle bounding box is consistently to the left of the person
+  # TIP: Imagine there is an event clip with a person walking from left to right.
+  #      If the event timeline bounding box is consistently to the left of the person
   #      then the value should be decreased. Similarly, if a person is walking from
   #      left to right and the bounding box is consistently ahead of the person
   #      then the value should be increased.
   # TIP: This offset is dynamic so you can change the value and it will update existing
-  #      tracked objects, this makes it easy to tune.
+  #      events, this makes it easy to tune.
   # WARNING: Fast moving objects will likely not have the bounding box align.
   annotation_offset: 0
 
@@ -334,9 +320,6 @@ review:
       - car
       - person
     # Optional: required zones for an object to be marked as an alert (default: none)
-    # NOTE: when settings required zones globally, this zone must exist on all cameras
-    #       or the config will be considered invalid. In that case the required_zones
-    #       should be configured at the camera level.
     required_zones:
       - driveway
   # Optional: detections configuration
@@ -346,20 +329,12 @@ review:
       - car
       - person
     # Optional: required zones for an object to be marked as a detection (default: none)
-    # NOTE: when settings required zones globally, this zone must exist on all cameras
-    #       or the config will be considered invalid. In that case the required_zones
-    #       should be configured at the camera level.
     required_zones:
       - driveway
 
 # Optional: Motion configuration
 # NOTE: Can be overridden at the camera level
 motion:
-  # Optional: enables detection for the camera (default: True)
-  # NOTE: Motion detection is required for object detection,
-  #       setting this to False and leaving detect enabled
-  #       will result in an error on startup.
-  enabled: False
   # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
   # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
   # The value should be between 1 and 255.
@@ -419,9 +394,9 @@ record:
   sync_recordings: False
   # Optional: Retention settings for recording
   retain:
-    # Optional: Number of days to retain recordings regardless of tracked objects (default: shown below)
-    # NOTE: This should be set to 0 and retention should be defined in alerts and detections section below
-    #       if you only want to retain recordings of alerts and detections.
+    # Optional: Number of days to retain recordings regardless of events (default: shown below)
+    # NOTE: This should be set to 0 and retention should be defined in events section below
+    #       if you only want to retain recordings of events.
     days: 0
     # Optional: Mode for retention. Available options are: all, motion, and active_objects
     #   all - save all recording segments regardless of activity
@@ -444,48 +419,34 @@ record:
     # Optional: Quality of recording preview (default: shown below).
     # Options are: very_low, low, medium, high, very_high
     quality: medium
-  # Optional: alert recording settings
-  alerts:
-    # Optional: Number of seconds before the alert to include (default: shown below)
+  # Optional: Event recording settings
+  events:
+    # Optional: Number of seconds before the event to include (default: shown below)
     pre_capture: 5
-    # Optional: Number of seconds after the alert to include (default: shown below)
+    # Optional: Number of seconds after the event to include (default: shown below)
     post_capture: 5
-    # Optional: Retention settings for recordings of alerts
+    # Optional: Objects to save recordings for. (default: all tracked objects)
+    objects:
+      - person
+    # Optional: Retention settings for recordings of events
     retain:
-      # Required: Retention days (default: shown below)
-      days: 14
+      # Required: Default retention days (default: shown below)
+      default: 10
       # Optional: Mode for retention. (default: shown below)
-      #   all - save all recording segments for alerts regardless of activity
-      #   motion - save all recordings segments for alerts with any detected motion
-      #   active_objects - save all recording segments for alerts with active/moving objects
-      #
-      # NOTE: If the retain mode for the camera is more restrictive than the mode configured
-      #       here, the segments will already be gone by the time this mode is applied.
-      #       For example, if the camera retain mode is "motion", the segments without motion are
-      #       never stored, so setting the mode to "all" here won't bring them back.
-      mode: motion
-  # Optional: detection recording settings
-  detections:
-    # Optional: Number of seconds before the detection to include (default: shown below)
-    pre_capture: 5
-    # Optional: Number of seconds after the detection to include (default: shown below)
-    post_capture: 5
-    # Optional: Retention settings for recordings of detections
-    retain:
-      # Required: Retention days (default: shown below)
-      days: 14
-      # Optional: Mode for retention. (default: shown below)
-      #   all - save all recording segments for detections regardless of activity
-      #   motion - save all recordings segments for detections with any detected motion
-      #   active_objects - save all recording segments for detections with active/moving objects
+      #   all - save all recording segments for events regardless of activity
+      #   motion - save all recordings segments for events with any detected motion
+      #   active_objects - save all recording segments for event with active/moving objects
       #
       # NOTE: If the retain mode for the camera is more restrictive than the mode configured
       #       here, the segments will already be gone by the time this mode is applied.
       #       For example, if the camera retain mode is "motion", the segments without motion are
       #       never stored, so setting the mode to "all" here won't bring them back.
       mode: motion
+      # Optional: Per object retention days
+      objects:
+        person: 15
 
-# Optional: Configuration for the jpg snapshots written to the clips directory for each tracked object
+# Optional: Configuration for the jpg snapshots written to the clips directory for each event
 # NOTE: Can be overridden at the camera level
 snapshots:
   # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
@@ -516,19 +477,16 @@ snapshots:
 semantic_search:
   # Optional: Enable semantic search (default: shown below)
   enabled: False
-  # Optional: Re-index embeddings database from historical tracked objects (default: shown below)
+  # Optional: Re-index embeddings database from historical events (default: shown below)
   reindex: 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 AI generated tracked object descriptions
+# Optional: Configuration for AI generated event 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.
 genai:
-  # Optional: Enable AI description generation (default: shown below)
+  # Optional: Enable Google Gemini description generation (default: shown below)
   enabled: False
   # Required if enabled: Provider must be one of ollama, gemini, or openai
   provider: ollama
@@ -548,12 +506,10 @@ genai:
 # Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
 go2rtc:
 
-# Optional: Live stream configuration for WebUI.
-# NOTE: Can be overridden at the camera level
+# Optional: jsmpeg stream configuration for WebUI
 live:
-  # Optional: Set the name of the stream 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.
+  # Optional: Set the name of the stream that should be used for live view
+  # in frigate WebUI. (default: name of camera)
   stream_name: camera_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
@@ -686,7 +642,6 @@ cameras:
     # to enable PTZ controls.
     onvif:
       # Required: host of the camera being connected to.
-      # NOTE: HTTP is assumed by default; HTTPS is supported if you specify the scheme, ex: "https://0.0.0.0".
       host: 0.0.0.0
       # Optional: ONVIF port for device (default: shown below).
       port: 8000
@@ -695,8 +650,6 @@ cameras:
       user: admin
       # Optional: password for login.
       password: admin
-      # Optional: Skip TLS verification from the ONVIF server (default: shown below)
-      tls_insecure: False
       # Optional: Ignores time synchronization mismatches between the camera and the server during authentication.
       # Using NTP on both ends is recommended and this should only be set to True in a "safe" environment due to the security risk it represents.
       ignore_time_mismatch: False
@@ -741,28 +694,6 @@ cameras:
       # By default the cameras are sorted alphabetically.
       order: 0
 
-    # Optional: Configuration for AI generated tracked object descriptions
-    genai:
-      # Optional: Enable AI description generation (default: shown below)
-      enabled: False
-      # Optional: Use the object snapshot instead of thumbnails for description generation (default: shown below)
-      use_snapshot: False
-      # Optional: The default prompt for generating descriptions. Can use replacement
-      # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
-      prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
-      # Optional: Object specific prompts to customize description results
-      # Format: {label}: {prompt}
-      object_prompts:
-        person: "My special person prompt."
-      # Optional: objects to generate descriptions for (default: all objects that are tracked)
-      objects:
-        - person
-        - cat
-      # Optional: Restrict generation to objects that entered any of the listed zones (default: none, all zones qualify)
-      required_zones: []
-      # Optional: Save thumbnails sent to generative AI for review/debugging purposes (default: shown below)
-      debug_save_thumbnails: False
-
 # Optional
 ui:
   # Optional: Set a timezone to use in the UI (default: use browser local time)
@@ -825,7 +756,7 @@ camera_groups:
       - side_cam
       - front_doorbell_cam
     # Required: icon used for group
-    icon: LuCar
+    icon: car
     # Required: index of this group
     order: 0
 ```

docs/docs/configuration/restream.md

@@ -7,7 +7,7 @@ title: Restream
 
 Frigate can restream your video feed as an RTSP feed for other applications such as Home Assistant to utilize it at `rtsp://<frigate_host>:8554/<camera_name>`. Port 8554 must be open. [This allows you to use a video feed for detection in Frigate and Home Assistant live view at the same time without having to make two separate connections to the camera](#reduce-connections-to-camera). The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
 
-Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.2) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#configuration) for more advanced configurations and features.
+Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.4) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#configuration) for more advanced configurations and features.
 
 :::note
 
@@ -21,7 +21,7 @@ Birdseye RTSP restream can be accessed at `rtsp://<frigate_host>:8554/birdseye`.
 
 ```yaml
 birdseye:
-  restream: True
+  restream: true
 ```
 
 ### Securing Restream With Authentication
@@ -132,31 +132,9 @@ cameras:
             - detect
 ```
 
-## Handling Complex Passwords
-
-go2rtc expects URL-encoded passwords in the config, [urlencoder.org](https://urlencoder.org) can be used for this purpose. 
-
-For example:
-
-```yaml
-go2rtc:
-  streams:
-    my_camera: rtsp://username:$@foo%@192.168.1.100
-```
-
-becomes
-
-```yaml
-go2rtc:
-  streams:
-    my_camera: rtsp://username:$%40foo%25@192.168.1.100
-```
-
-See [this comment(https://github.com/AlexxIT/go2rtc/issues/1217#issuecomment-2242296489) for more information.
-
 ## Advanced Restream Configurations
 
-The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
 
 NOTE: The output will need to be passed with two curly braces `{{output}}`
 

docs/docs/configuration/review.md

@@ -7,13 +7,13 @@ The Review page of the Frigate UI is for quickly reviewing historical footage of
 
 Review items are filterable by date, object type, and camera.
 
-### Review items vs. tracked objects (formerly "events")
+### Review items vs. events
 
 In Frigate 0.13 and earlier versions, the UI presented "events". An event was synonymous with a tracked or detected object. In Frigate 0.14 and later, a review item is a time period where any number of tracked objects were active.
 
 For example, consider a situation where two people walked past your house. One was walking a dog. At the same time, a car drove by on the street behind them.
 
-In this scenario, Frigate 0.13 and earlier would show 4 "events" in the UI - one for each person, another for the dog, and yet another for the car. You would have had 4 separate videos to watch even though they would have all overlapped.
+In this scenario, Frigate 0.13 and earlier would show 4 events in the UI - one for each person, another for the dog, and yet another for the car. You would have had 4 separate videos to watch even though they would have all overlapped.
 
 In 0.14 and later, all of that is bundled into a single review item which starts and ends to capture all of that activity. Reviews for a single camera cannot overlap. Once you have watched that time period on that camera, it is marked as reviewed.
 
@@ -41,6 +41,8 @@ review:
 
 By default all detections that do not qualify as an alert qualify as a detection. However, detections can further be filtered to only include certain labels or certain zones.
 
+By default a review item will only be marked as an alert if a person or car is detected. This can be configured to include any object or audio label using the following config:
+
 ```yaml
 # can be overridden at the camera level
 review:

docs/docs/configuration/semantic_search.md

@@ -3,23 +3,11 @@ id: semantic_search
 title: Using 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.
-
-Semantic Search is accessed via the _Explore_ view in the Frigate UI.
-
-## Minimum System Requirements
-
-Semantic Search works by running a large AI model locally on your system. Small or underpowered systems like a Raspberry Pi will not run Semantic Search reliably or at all.
-
-A minimum of 8GB of RAM is required to use Semantic Search. A GPU is not strictly required but will provide a significant performance increase over CPU-only systems.
-
-For best performance, 16GB or more of RAM and a dedicated GPU are recommended.
+Semantic search works by embedding images and/or text into a vector representation identified by numbers. Frigate has support for two such models which both run locally: [OpenAI CLIP](https://openai.com/research/clip) and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). Embeddings are then saved to a local instance of [ChromaDB](https://trychroma.com).
 
 ## Configuration
 
-Semantic Search is disabled by default, and must be enabled in your config file or in the UI's Settings page before it can be used. Semantic Search is a global configuration setting.
+Semantic Search is a global configuration setting.
 
 ```yaml
 semantic_search:
@@ -29,64 +17,22 @@ semantic_search:
 
 :::tip
 
-The embeddings database can be re-indexed from the existing tracked objects in your database by adding `reindex: True` to your `semantic_search` configuration or by toggling the switch on the Search Settings page in the UI and restarting Frigate. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing. Make sure to turn the UI's switch off or set the config back to `False` before restarting Frigate again.
-
-If you are enabling Semantic Search for the first time, be advised that Frigate does not automatically index older tracked objects. You will need to enable the `reindex` feature in order to do that.
+The embeddings database can be re-indexed from the existing detections in your database by adding `reindex: True` to your `semantic_search` configuration. Depending on the number of detections you have, it can take up to 30 minutes to complete and may max out your CPU while indexing. Make sure to set the config back to `False` before restarting Frigate again.
 
 :::
 
-### Jina AI CLIP
+### OpenAI CLIP
 
-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.
+This 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 detections to encode the thumbnail image and store it in Chroma. When searching detections via text in the search box, frigate will perform a `text -> image` similarity search against this embedding. When clicking "FIND SIMILAR" next to a detection, 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.
+### all-MiniLM-L6-v2
 
-Differently weighted versions of the Jina model are available and can be selected by setting the `model_size` config option as `small` or `large`:
+This is a sentence embedding model that has been fine tuned on over 1 billion sentence pairs. This model is used to embed detection descriptions and perform searches against them. Descriptions can be created and/or modified on the search page when clicking on the info icon next to a detection. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate event descriptions.
 
-```yaml
-semantic_search:
-  enabled: True
-  model_size: small
-```
+## Usage Tips
 
-- 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.
-
-### 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.
-
-```yaml
-semantic_search:
-  enabled: True
-  model_size: large
-```
-
-:::info
-
-If the correct build is used for your GPU and the `large` model is configured, then the GPU will be detected and used automatically.
-
-**NOTE:** Object detection and Semantic Search are independent features. If you want to use your GPU with Semantic Search, you must choose the appropriate Frigate Docker image for your GPU.
-
-- **AMD**
-
-  - ROCm will automatically be detected and used for Semantic Search in the `-rocm` Frigate image.
-
-- **Intel**
-
-  - OpenVINO will automatically be detected and used for Semantic Search in the default Frigate image.
-
-- **Nvidia**
-  - Nvidia GPUs will automatically be detected and used for Semantic Search in the `-tensorrt` Frigate image.
-  - Jetson devices will automatically be detected and used for Semantic Search in the `-tensorrt-jp(4/5)` Frigate image.
-
-:::
-
-## Usage and Best Practices
-
-1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
-2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
-3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the the ones without them are more relevant.
-4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
-5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
-6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.
+1. Semantic search is used in conjunction with the other filters available on the search page. Use a combination of traditional filtering and semantic search for the best results.
+2. The comparison between text and image embedding distances generally means that results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" filter to help find what you are looking for.
+3. Make your search language and tone closely match your descriptions. If you are using thumbnail search, phrase your query as an image caption.
+4. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
+5. Experiment! Find a detection you want to test and start typing keywords to see what works for you.

docs/docs/configuration/snapshots.md

@@ -3,7 +3,7 @@ id: snapshots
 title: Snapshots
 ---
 
-Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>.jpg`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)
+Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>.jpg`. They are also accessible [via the api](../integrations/api.md#get-apieventsidsnapshotjpg)
 
 For users with Frigate+ enabled, snapshots are accessible in the UI in the Frigate+ pane to allow for quick submission to the Frigate+ service.
 

docs/docs/configuration/zones.md

@@ -64,7 +64,7 @@ cameras:
 
 ### Restricting zones to specific objects
 
-Sometimes you want to limit a zone to specific object types to have more granular control of when alerts, detections, and snapshots are saved. The following example will limit one zone to person objects and the other to cars.
+Sometimes you want to limit a zone to specific object types to have more granular control of when events/snapshots are saved. The following example will limit one zone to person objects and the other to cars.
 
 ```yaml
 cameras:
@@ -80,7 +80,7 @@ cameras:
           - car
 ```
 
-Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. Objects will be tracked for any `person` that enter anywhere in the yard, and for cars only if they enter the street.
+Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. You will get events for person objects that enter anywhere in the yard, and events for cars only if they enter the street.
 
 ### Zone Loitering
 

docs/docs/development/contributing.md

@@ -193,7 +193,7 @@ npm run test
 #### 1. Installation
 
 ```console
-cd docs && npm install
+npm install
 ```
 
 #### 2. Local Development

docs/docs/frigate/camera_setup.md

@@ -28,7 +28,7 @@ For the Dahua/Loryta 5442 camera, I use the following settings:
 - Encode Mode: H.264
 - Resolution: 2688\*1520
 - Frame Rate(FPS): 15
-- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](../configuration/live) for more info)
+- I Frame Interval: 30
 
 **Sub Stream (Detection)**
 

docs/docs/frigate/glossary.md

@@ -16,6 +16,10 @@ A box returned from the object detection model that outlines an object in the fr
 - A gray thin line indicates that object is detected as being stationary
 - A thick line indicates that object is the subject of autotracking (when enabled).
 
+## Event
+
+The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Events are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
+
 ## False Positive
 
 An incorrect detection of an object type. For example a dog being detected as a person, a chair being detected as a dog, etc. A person being detected in an area you want to ignore is not a false positive.
@@ -60,10 +64,6 @@ The threshold is the median score that an object must reach in order to be consi
 
 The top score for an object is the highest median score for an object.
 
-## Tracked Object ("event" in previous versions)
-
-The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Tracked objects are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
-
 ## Zone
 
 Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create an [event](#event). [See the zone docs for more info](/configuration/zones)

docs/docs/frigate/hardware.md

@@ -69,7 +69,6 @@ Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known
 | 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          |                                                                       |
 
 ### TensorRT - Nvidia GPU
 
@@ -88,10 +87,6 @@ Inference speeds will vary greatly depending on the GPU and the model used.
 | Quadro P400 2GB | 20 - 25 ms      |
 | Quadro P2000    | ~ 12 ms         |
 
-#### AMD GPUs
-
-With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many AMD GPUs.
-
 ### Community Supported:
 
 #### Nvidia Jetson

docs/docs/frigate/installation.md

@@ -73,23 +73,23 @@ Users of the Snapcraft build of Docker cannot use storage locations outside your
 
 Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is **64MB**.
 
-The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in docker-compose).
+The default shm size of **64MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in docker-compose).
 
-The Frigate container also stores logs in shm, which can take up to **40MB**, so make sure to take this into account in your math as well.
+The Frigate container also stores logs in shm, which can take up to **30MB**, so make sure to take this into account in your math as well.
 
-You can calculate the **minimum** shm size for each camera with the following formula using the resolution specified for detect:
+You can calculate the necessary shm size for each camera with the following formula using the resolution specified for detect:
 
 ```console
 # Replace <width> and <height>
-$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 20 + 270480) / 1048576))'
+$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 9 + 270480) / 1048576))'
 
-# Example for 1280x720, including logs
-$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 20 + 270480) / 1048576)) + 40'
-46.63MB
+# Example for 1280x720
+$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 9 + 270480) / 1048576))'
+12.12MB
 
 # Example for eight cameras detecting at 1280x720, including logs
-$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576) * 8 + 40))'
-253MB
+$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 9 + 270480) / 1048576) * 8 + 30))'
+126.99MB
 ```
 
 The shm size cannot be set per container for Home Assistant add-ons. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.
@@ -112,8 +112,8 @@ 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).
-3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
-4. Run the script with `./user_installation.sh`
+3. Ensure it has execution permissions with `sudo chmod +x install_hailo8l_driver.sh`
+4. Run the script with `./install_hailo8l_driver.sh`
 
 #### Setup
 
@@ -193,9 +193,8 @@ services:
     container_name: frigate
     privileged: true # this may not be necessary for all setups
     restart: unless-stopped
-    stop_grace_period: 30s # allow enough time to shut down the various services
     image: ghcr.io/blakeblackshear/frigate:stable
-    shm_size: "512mb" # update for your cameras based on calculation above
+    shm_size: "64mb" # update for your cameras based on calculation above
     devices:
       - /dev/bus/usb:/dev/bus/usb # Passes the USB Coral, needs to be modified for other versions
       - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
@@ -225,7 +224,6 @@ If you can't use docker compose, you can run the container with something simila
 docker run -d \
   --name frigate \
   --restart=unless-stopped \
-  --stop-timeout 30 \
   --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
   --device /dev/bus/usb:/dev/bus/usb \
   --device /dev/dri/renderD128 \
@@ -252,7 +250,10 @@ 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-rk` - Frigate build for SBCs with Rockchip SoC
-- `stable-rocm` - Frigate build for [AMD GPUs](../configuration/object_detectors.md#amdrocm-gpu-detector)
+- `stable-rocm` - Frigate build for [AMD GPUs and iGPUs](../configuration/object_detectors.md#amdrocm-gpu-detector), all drivers
+  - `stable-rocm-gfx900` - AMD gfx900 driver only
+  - `stable-rocm-gfx1030` - AMD gfx1030 driver only
+  - `stable-rocm-gfx1100` - AMD gfx1100 driver only
   - `stable-h8l` - Frigate build for the Hailo-8L M.2 PICe Raspberry Pi 5 hat
 
 ## Home Assistant Addon

docs/docs/guides/configuring_go2rtc.md

@@ -13,15 +13,7 @@ Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect
 
 # Setup a go2rtc stream
 
-First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#module-streams), not just rtsp.
-
-:::tip
-
-For the best experience, you should set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
-
-See [the live view docs](../configuration/live.md#setting-stream-for-live-ui) for more information.
-
-:::
+First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. If you set the stream name under go2rtc to match the name of your camera, it will automatically be mapped and you will get additional live view options for the camera. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#module-streams), not just rtsp.
 
 ```yaml
 go2rtc:
@@ -30,7 +22,7 @@ go2rtc:
       - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
 ```
 
-After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
+The easiest live view to get working is MSE. After adding this to the config, restart Frigate and try to watch the live stream by selecting MSE in the dropdown after clicking on the camera.
 
 
 ### What if my video doesn't play?
@@ -47,14 +39,14 @@ After adding this to the config, restart Frigate and try to watch the live strea
 
 - Check Video Codec:
     - If the camera stream works in go2rtc but not in your browser, the video codec might be unsupported.
-    - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#codecs-madness) in go2rtc documentation.
-    - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
+    - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#codecs-madness) in go2rtc documentation.
+    - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
         ```yaml
         go2rtc:
           streams:
             back:
               - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-              - "ffmpeg:back#video=h264#hardware"
+              - "ffmpeg:back#video=h264"
         ```
 
 - Switch to FFmpeg if needed: 
@@ -66,8 +58,9 @@ After adding this to the config, restart Frigate and try to watch the live strea
               - ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
         ```
 
-    - If you can see the video but do not have audio, this is most likely because your camera's audio stream codec is not AAC.
-    - If possible, update your camera's audio settings to AAC in your camera's firmware.
+- If you can see the video but do not have audio, this is most likely because your
+camera's audio stream is not AAC.
+    - If possible, update your camera's audio settings to AAC.
     - If your cameras do not support AAC audio, you will need to tell go2rtc to re-encode the audio to AAC on demand if you want audio. This will use additional CPU and add some latency. To add AAC audio on demand, you can update your go2rtc config as follows:
         ```yaml
         go2rtc:
@@ -84,7 +77,7 @@ After adding this to the config, restart Frigate and try to watch the live strea
           streams:
             back:
               - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-              - "ffmpeg:back#video=h264#audio=aac#hardware"
+              - "ffmpeg:back#video=h264#audio=aac"
         ```
 
         When using the ffmpeg module, you would add AAC audio like this:
@@ -93,7 +86,7 @@ After adding this to the config, restart Frigate and try to watch the live strea
         go2rtc:
           streams:
             back:
-              - "ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#video=copy#audio=copy#audio=aac#hardware"
+              - "ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#video=copy#audio=copy#audio=aac"
         ```
 
 :::warning
@@ -109,4 +102,4 @@ section.
 ## Next steps
 
 1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
-2. You may also prefer to [setup WebRTC](/configuration/live#webrtc-extra-configuration) for slightly lower latency than MSE. Note that WebRTC only supports h264 and specific audio formats and may require opening ports on your router.
+1. You may also prefer to [setup WebRTC](/configuration/live#webrtc-extra-configuration) for slightly lower latency than MSE. Note that WebRTC only supports h264 and specific audio formats.

docs/docs/guides/getting_started.md

@@ -115,7 +115,6 @@ services:
   frigate:
     container_name: frigate
     restart: unless-stopped
-    stop_grace_period: 30s
     image: ghcr.io/blakeblackshear/frigate:stable
     volumes:
       - ./config:/config
@@ -239,7 +238,7 @@ Now that you know where you need to mask, use the "Mask & Zone creator" in the o
 
 :::warning
 
-Note that motion masks should not be used to mark out areas where you do not want objects to be detected or to reduce false positives. They do not alter the image sent to object detection, so you can still get tracked objects, alerts, and detections in areas with motion masks. These only prevent motion in these areas from initiating object detection.
+Note that motion masks should not be used to mark out areas where you do not want objects to be detected or to reduce false positives. They do not alter the image sent to object detection, so you can still get events and detections in areas with motion masks. These only prevent motion in these areas from initiating object detection.
 
 :::
 
@@ -295,21 +294,11 @@ cameras:
 
 If you don't have separate streams for detect and record, you would just add the record role to the list on the first input.
 
-:::note
-
-If you only define one stream in your `inputs` and do not assign a `detect` role to it, Frigate will automatically assign it the `detect` role. Frigate will always decode a stream to support motion detection, Birdseye, the API image endpoints, and other features, even if you have disabled object detection with `enabled: False` in your config's `detect` section.
-
-If you only plan to use Frigate for recording, it is still recommended to define a `detect` role for a low resolution stream to minimize resource usage from the required stream decoding.
-
-:::
-
-By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/reference.md).
+By default, Frigate will retain video of all events for 10 days. The full set of options for recording can be found [here](../configuration/reference.md).
 
 ### Step 7: Complete config
 
-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.
+At this point you have a complete config with basic functionality. You can see the [full config reference](../configuration/reference.md) for a complete list of configuration options.
 
 ### Follow up
 
@@ -320,3 +309,4 @@ Now that you have a working install, you can use the following documentation for
 3. [Review](../configuration/review.md)
 4. [Masks](../configuration/masks.md)
 5. [Home Assistant Integration](../integrations/home-assistant.md) - Integrate with Home Assistant
+