Include necessary cudnn deps in cuda folder

* Add ability to view attempts

* Improve UI

* Cleanup

* Correctly refresh ui when item is deleted

* Select correct library by default

* Add min score

* Cleanup

.cspell/frigate-dictionary.txt

@@ -1,168 +1,316 @@
-rtmp
-edgetpu
-labelmap
-rockchip
-jetson
-rocm
-vaapi
-CUDA
-hwaccel
-RTSP
-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
-ultrafast
-sleeptime
-radeontop
-vainfo
-tmpfs
-homography
-websockets
-LIBAVFORMAT
-NTSC
-onnxruntime
-fourcc
-radeonsi
-paho
-imagestream
-jsonify
-cgroups
-sysconf
-memlimit
-gpuload
-nvml
-setproctitle
-psutil
-Kalman
-frontdoor
-namedtuples
-zeep
-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
+absdiff
+airockchip
+Alloc
+alpr
+Amcrest
 amdgpu
-chipset
-referer
-mpegts
-webp
+analyzeduration
+Annke
+apexcharts
+arange
+argmax
+argmin
+argpartition
+ascontiguousarray
+astype
 authelia
 authentik
-unichip
-rebranded
-udevadm
+autodetected
 automations
-unraid
-hideable
+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
+facenet
+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
-keepalive
+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
+jina
+jinaai
+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
+paddleocr
+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
+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
+tflite
+thresholded
+timelapse
+tmpfs
+tobytes
+toggleable
+traefik
+tzlocal
+Ubiquiti
+udev
+udevadm
+ultrafast
+unichip
+unidecode
+Unifi
+unixepoch
+unraid
+unreviewed
+userdata
+usermod
+uvicorn
+vaapi
+vainfo
+variations
+vbios
+vconcat
+vitb
+vstream
+vsync
+wallclock
+webp
+webpush
+webrtc
+websockets
+webui
+werkzeug
+workdir
+WRONLY
+wsgirefserver
+wsgiutils
+wsize
+xaddr
+xmaxs
+xmins
+XPUB
+XSUB
+ymaxs
+ymins
+yolo
+yolonas
+yolox
+zeep
+zerolatency

.devcontainer/devcontainer.json

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

.devcontainer/post_create.sh

@@ -3,10 +3,12 @@
 set -euxo pipefail
 
 # Cleanup the old github host key
-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
+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
 
 # 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
@@ -17,7 +19,7 @@ sudo chown -R "$(id -u):$(id -g)" /media/frigate
 # When started as a service, LIBAVFORMAT_VERSION_MAJOR is defined in the
 # s6 service file. For dev, where frigate is started from an interactive
 # shell, we define it in .bashrc instead.
-echo 'export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc
+echo 'export LIBAVFORMAT_VERSION_MAJOR=$(/usr/lib/ffmpeg/7.0/bin/ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc
 
 make version
 

.github/DISCUSSION_TEMPLATE/camera-support.yml

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

.github/DISCUSSION_TEMPLATE/config-support.yml

@@ -76,6 +76,17 @@ 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

@@ -48,28 +48,6 @@ 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:
@@ -78,6 +56,9 @@ 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

@@ -68,20 +68,6 @@ 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:
@@ -90,6 +76,17 @@ 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

@@ -24,12 +24,6 @@ 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:
@@ -70,20 +64,6 @@ 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:
@@ -92,6 +72,22 @@ 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

@@ -0,0 +1,32 @@
+## 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

@@ -6,6 +6,8 @@ on:
     branches:
       - dev
       - master
+    paths-ignore:
+      - "docs/**"
 
 # only run the latest commit to avoid cache overwrites
 concurrency:
@@ -22,6 +24,8 @@ 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
@@ -43,6 +47,8 @@ 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
@@ -69,21 +75,14 @@ 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
@@ -110,6 +109,8 @@ 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
@@ -138,6 +139,8 @@ 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
@@ -155,6 +158,30 @@ 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
@@ -164,6 +191,8 @@ 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
@@ -179,57 +208,18 @@ 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 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=${{ steps.setup.outputs.image-name }}-rocm-gfx1100
-      #      *.cache-from=type=gha
+      - name: AMD/ROCm general build
+        env:
+          AMDGPU: gfx
+          HSA_OVERRIDE: 0
+        uses: docker/bake-action@v3
+        with:
+          push: true
+          targets: rocm
+          files: docker/rocm/rocm.hcl
+          set: |
+            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
+            *.cache-from=type=gha
   # The majority of users running arm64 are rpi users, so the rpi
   # build should be the primary arm64 image
   assemble_default_build:
@@ -244,7 +234,7 @@ jobs:
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@0d4c9c5ea7693da7b068278f7b52bda2a190a446
+        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
         with:
           registry: ghcr.io
           username: ${{ github.actor }}

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

@@ -1,24 +0,0 @@
-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

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

@@ -11,21 +11,26 @@ 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@0d4c9c5ea7693da7b068278f7b52bda2a190a446
+        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
         with:
           registry: ghcr.io
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
       - name: Create tag variables
+        env:
+          TAG: ${{ github.ref_name }}
+          LOWERCASE_REPO: ${{ steps.lowercaseRepo.outputs.lowercase }}
         run: |
-          BUILD_TYPE=$([[ "${{ github.ref_name }}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
+          BUILD_TYPE=$([[ "${TAG}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
           echo "BUILD_TYPE=${BUILD_TYPE}" >> $GITHUB_ENV
-          echo "BASE=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}" >> $GITHUB_ENV
+          echo "BASE=ghcr.io/${LOWERCASE_REPO}" >> $GITHUB_ENV
           echo "BUILD_TAG=${GITHUB_SHA::7}" >> $GITHUB_ENV
           echo "CLEAN_VERSION=$(echo ${GITHUB_REF##*/} | tr '[:upper:]' '[:lower:]' | sed 's/^[v]//')" >> $GITHUB_ENV
       - name: Tag and push the main image
@@ -34,14 +39,14 @@ jobs:
           STABLE_TAG=${BASE}:stable
           PULL_TAG=${BASE}:${BUILD_TAG}
           docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${VERSION_TAG}
-          for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk; do
+          for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${VERSION_TAG}-${variant}
           done
 
           # stable tag
           if [[ "${BUILD_TYPE}" == "stable" ]]; then
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${STABLE_TAG}
-            for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk; do
+            for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
               docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${STABLE_TAG}-${variant}
             done
           fi

.github/workflows/stale.yml

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

.gitignore

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

.vscode/launch.json

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

Makefile

@@ -1,11 +1,9 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.15.0
+VERSION = 0.16.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
-CURRENT_UID := $(shell id -u)
-CURRENT_GID := $(shell id -g)
 BOARDS= #Initialized empty
 
 include docker/*/*.mk
@@ -18,25 +16,38 @@ version:
 	echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
 
 local: version
-	docker buildx build --target=frigate --tag frigate:latest --load --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag frigate:latest \
+		--load
 
 amd64:
-	docker buildx build --platform linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
+		--platform linux/amd64
 
 arm64:
-	docker buildx build --platform linux/arm64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
+		--platform linux/arm64
 
 build: version amd64 arm64
-	docker buildx build --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
+		--platform linux/arm64/v8,linux/amd64
 
 push: push-boards
-	docker buildx build --push --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) \
+		--platform linux/arm64/v8,linux/amd64 \
+		--push
 
 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

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

cspell.json

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

docker-compose.yml

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

docker/hailo8l/Dockerfile

@@ -5,6 +5,7 @@ ARG DEBIAN_FRONTEND=noninteractive
 # Build Python wheels
 FROM wheels AS h8l-wheels
 
+RUN python3 -m pip config set global.break-system-packages true
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 COPY docker/hailo8l/requirements-wheels-h8l.txt /requirements-wheels-h8l.txt
 
@@ -16,89 +17,26 @@ RUN mkdir /h8l-wheels
 # Build the wheels
 RUN pip3 wheel --wheel-dir=/h8l-wheels -c /requirements-wheels.txt -r /requirements-wheels-h8l.txt
 
-# Build HailoRT and create wheel
-FROM wheels AS build-hailort
+FROM wget AS hailort
 ARG TARGETARCH
-
-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
+RUN --mount=type=bind,source=docker/hailo8l/install_hailort.sh,target=/deps/install_hailort.sh \
+    /deps/install_hailort.sh
 
 # Use deps as the base image
 FROM deps AS h8l-frigate
 
 # Copy the wheels from the wheels stage
 COPY --from=h8l-wheels /h8l-wheels /deps/h8l-wheels
-COPY --from=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
+COPY --from=hailort /hailo-wheels /deps/hailo-wheels
+COPY --from=hailort /rootfs/ /
 
 # Install the wheels
+RUN python3 -m pip config set global.break-system-packages true
 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

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

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

docker/hailo8l/install_hailort.sh

@@ -0,0 +1,19 @@
+#!/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}-cp311-cp311-linux_${arch}.whl"
+

docker/hailo8l/pyhailort_build_scripts/generate_wheel_conf.py

@@ -1,67 +0,0 @@
-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

@@ -1,111 +0,0 @@
-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==1.4.4
-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
+appdirs==1.4.*
+argcomplete==2.0.*
+contextlib2==0.6.*
+distlib==0.3.*
+filelock==3.8.*
+future==0.18.*
+importlib-metadata==5.1.*
+importlib-resources==5.1.*
+netaddr==0.8.*
+netifaces==0.10.*
+verboselogs==1.7.*
+virtualenv==20.17.*

docker/hailo8l/user_installation.sh

@@ -2,7 +2,7 @@
 
 # Update package list and install dependencies
 sudo apt-get update
-sudo apt-get install -y build-essential cmake git wget linux-modules-extra-$(uname -r)
+sudo apt-get install -y build-essential cmake git wget
 
 arch=$(uname -m)
 
@@ -13,7 +13,7 @@ else
 fi
 
 # Clone the HailoRT driver repository
-git clone --depth 1 --branch v4.17.0 https://github.com/hailo-ai/hailort-drivers.git
+git clone --depth 1 --branch v4.19.0 https://github.com/hailo-ai/hailort-drivers.git
 
 # Build and install the HailoRT driver
 cd hailort-drivers/linux/pcie
@@ -23,13 +23,26 @@ 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
-sudo mv hailo8_fw.4.17.0.bin /lib/firmware/hailo/hailo8_fw.bin
+
+# 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
 
 # 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

@@ -3,12 +3,12 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG BASE_IMAGE=debian:11
-ARG SLIM_BASE=debian:11-slim
+ARG BASE_IMAGE=debian:12
+ARG SLIM_BASE=debian:12-slim
 
 FROM ${BASE_IMAGE} AS base
 
-FROM --platform=${BUILDPLATFORM} debian:11 AS base_host
+FROM --platform=${BUILDPLATFORM} debian:12 AS base_host
 
 FROM ${SLIM_BASE} AS slim-base
 
@@ -30,6 +30,16 @@ 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
@@ -56,8 +66,8 @@ COPY docker/main/requirements-ov.txt /requirements-ov.txt
 RUN apt-get -qq update \
     && apt-get -qq install -y wget python3 python3-dev python3-distutils gcc pkg-config libhdf5-dev \
     && wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && python3 get-pip.py "pip" \
-    && pip install -r /requirements-ov.txt
+    && python3 get-pip.py "pip" --break-system-packages \
+    && pip install --break-system-packages -r /requirements-ov.txt
 
 # Get OpenVino Model
 RUN --mount=type=bind,source=docker/main/build_ov_model.py,target=/build_ov_model.py \
@@ -129,24 +139,17 @@ ARG TARGETARCH
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
     && apt-get -qq install -y \
-    apt-transport-https \
-    gnupg \
-    wget \
-    # the key fingerprint can be obtained from https://ftp-master.debian.org/keys.html
-    && wget -qO- "https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xA4285295FC7B1A81600062A9605C66F00D6C9793" | \
-    gpg --dearmor > /usr/share/keyrings/debian-archive-bullseye-stable.gpg \
-    && echo "deb [signed-by=/usr/share/keyrings/debian-archive-bullseye-stable.gpg] http://deb.debian.org/debian bullseye main contrib non-free" | \
-    tee /etc/apt/sources.list.d/debian-bullseye-nonfree.list \
+    apt-transport-https wget \
     && apt-get -qq update \
     && apt-get -qq install -y \
-    python3.9 \
-    python3.9-dev \
+    python3 \
+    python3-dev \
     # opencv dependencies
     build-essential cmake git pkg-config libgtk-3-dev \
     libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
     libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
     gfortran openexr libatlas-base-dev libssl-dev\
-    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
+    libtbbmalloc2 libtbb-dev libdc1394-dev libopenexr-dev \
     libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
     # sqlite3 dependencies
     tclsh \
@@ -154,16 +157,13 @@ RUN apt-get -qq update \
     gcc gfortran libopenblas-dev liblapack-dev && \
     rm -rf /var/lib/apt/lists/*
 
-# Ensure python3 defaults to python3.9
-RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
-
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && python3 get-pip.py "pip"
+    && python3 get-pip.py "pip" --break-system-packages
 
 COPY docker/main/requirements.txt /requirements.txt
-RUN pip3 install -r /requirements.txt
+RUN pip3 install -r /requirements.txt --break-system-packages
 
-# Build pysqlite3 from source to support ChromaDB
+# Build pysqlite3 from source
 COPY docker/main/build_pysqlite3.sh /build_pysqlite3.sh
 RUN /build_pysqlite3.sh
 
@@ -174,6 +174,7 @@ 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/ /
@@ -194,22 +195,25 @@ 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
 
-ENV PATH="/usr/lib/btbn-ffmpeg/bin:/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
+# Set OpenCV ffmpeg loglevel to fatal: https://ffmpeg.org/doxygen/trunk/log_8h.html
+ENV OPENCV_FFMPEG_LOGLEVEL=8
+
+ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
+ENV LIBAVFORMAT_VERSION_MAJOR=60
 
 # Install dependencies
 RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
     /deps/install_deps.sh
 
 RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
-    python3 -m pip install --upgrade pip && \
-    pip3 install -U /deps/wheels/*.whl
+    python3 -m pip install --upgrade pip --break-system-packages && \
+    pip3 install -U /deps/wheels/*.whl --break-system-packages
 
 COPY --from=deps-rootfs / /
 
@@ -227,7 +231,7 @@ ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 ENTRYPOINT ["/init"]
 CMD []
 
-HEALTHCHECK --start-period=120s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
+HEALTHCHECK --start-period=300s --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
@@ -256,7 +260,7 @@ RUN apt-get update \
     && rm -rf /var/lib/apt/lists/*
 
 RUN --mount=type=bind,source=./docker/main/requirements-dev.txt,target=/workspace/frigate/requirements-dev.txt \
-    pip3 install -r requirements-dev.txt
+    pip3 install -r requirements-dev.txt --break-system-packages
 
 HEALTHCHECK NONE
 

docker/main/build_nginx.sh

@@ -8,8 +8,7 @@ SECURE_TOKEN_MODULE_VERSION="1.5"
 SET_MISC_MODULE_VERSION="v0.33"
 NGX_DEVEL_KIT_VERSION="v0.3.3"
 
-cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
-sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
+sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
 apt-get update
 
 apt-get -yqq build-dep nginx

docker/main/build_ov_model.py

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

docker/main/build_sqlite_vec.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+
+set -euxo pipefail
+
+SQLITE_VEC_VERSION="0.1.3"
+
+sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
+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

@@ -8,70 +8,94 @@ 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 \
     python3-pip \
     curl \
+    lsof \
     jq \
-    nethogs
-
-# ensure python3 defaults to python3.9
-update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
+    nethogs \
+    libgl1 \
+    libglib2.0-0 \
+    libusb-1.0.0
 
 mkdir -p -m 600 /root/.gnupg
 
-# add coral repo
-curl -fsSLo - https://packages.cloud.google.com/apt/doc/apt-key.gpg | \
-    gpg --dearmor -o /etc/apt/trusted.gpg.d/google-cloud-packages-archive-keyring.gpg
-echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | tee /etc/apt/sources.list.d/coral-edgetpu.list
-echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections
+# install coral runtime
+wget -q -O /tmp/libedgetpu1-max.deb "https://github.com/feranick/libedgetpu/releases/download/16.0TF2.17.0-1/libedgetpu1-max_16.0tf2.17.0-1.bookworm_${TARGETARCH}.deb"
+unset DEBIAN_FRONTEND
+yes | dpkg -i /tmp/libedgetpu1-max.deb && export DEBIAN_FRONTEND=noninteractive
+rm /tmp/libedgetpu1-max.deb
 
-# enable non-free repo in Debian
-if grep -q "Debian" /etc/issue; then
-    sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
+# install python3 & tflite runtime
+if [[ "${TARGETARCH}" == "amd64" ]]; then
+    pip3 install --break-system-packages https://github.com/feranick/TFlite-builds/releases/download/v2.17.0/tflite_runtime-2.17.0-cp311-cp311-linux_x86_64.whl
+    pip3 install --break-system-packages https://github.com/feranick/pycoral/releases/download/2.0.2TF2.17.0/pycoral-2.0.2-cp311-cp311-linux_x86_64.whl
 fi
 
-# coral drivers
-apt-get -qq update
-apt-get -qq install --no-install-recommends --no-install-suggests -y \
-    libedgetpu1-max python3-tflite-runtime python3-pycoral
+if [[ "${TARGETARCH}" == "arm64" ]]; then
+    pip3 install --break-system-packages https://github.com/feranick/TFlite-builds/releases/download/v2.17.0/tflite_runtime-2.17.0-cp311-cp311-linux_aarch64.whl
+    pip3 install --break-system-packages https://github.com/feranick/pycoral/releases/download/2.0.2TF2.17.0/pycoral-2.0.2-cp311-cp311-linux_aarch64.whl
+fi
 
 # btbn-ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    mkdir -p /usr/lib/btbn-ffmpeg
+    mkdir -p /usr/lib/ffmpeg/5.0
+    mkdir -p /usr/lib/ffmpeg/7.0
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
+    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
 fi
 
 # ffmpeg -> arm64
 if [[ "${TARGETARCH}" == "arm64" ]]; then
-    mkdir -p /usr/lib/btbn-ffmpeg
+    mkdir -p /usr/lib/ffmpeg/5.0
+    mkdir -p /usr/lib/ffmpeg/7.0
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
+    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
 fi
 
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    # 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
+    # install amd / intel-i965 driver packages
+    apt-get -qq install --no-install-recommends --no-install-suggests -y \
+        i965-va-driver intel-gpu-tools onevpl-tools \
+        libva-drm2 \
+        mesa-va-drivers radeontop
+
+    # intel packages use zst compression so we need to update dpkg
+    apt-get install -y dpkg
+
+    # 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 \
-        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
-    rm -f /etc/apt/sources.list.d/debian-bookworm.list
+        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
+        libva-drm2 mesa-va-drivers radeontop
 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

@@ -1,42 +1,54 @@
 click == 8.1.*
-Flask == 3.0.*
-Flask_Limiter == 3.8.*
+# FastAPI
+aiohttp == 3.11.2
+starlette == 0.41.2
+starlette-context == 0.3.6
+fastapi == 0.115.*
+uvicorn == 0.30.*
+slowapi == 0.1.*
 imutils == 0.5.*
 joserfc == 1.0.*
+pathvalidate == 3.2.*
 markupsafe == 2.1.*
+python-multipart == 0.0.12
+# General
 mypy == 1.6.1
-numpy == 1.26.*
 onvif_zeep == 0.2.12
-opencv-python-headless == 4.9.0.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
 peewee_migrate == 1.13.*
-psutil == 5.9.*
+psutil == 6.1.*
 pydantic == 2.8.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
-PyYAML == 6.0.*
-pytz == 2024.1
+pytz == 2024.*
 pyzmq == 26.2.*
 ruamel.yaml == 0.18.*
 tzlocal == 5.2
-types-PyYAML == 6.0.*
 requests == 2.32.*
 types-requests == 2.32.*
-scipy == 1.13.*
 norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
-onnxruntime == 1.18.*
-openvino == 2024.1.*
+# Image Manipulation
+numpy == 1.26.*
+opencv-python-headless == 4.10.0.*
+opencv-contrib-python == 4.9.0.*
+scipy == 1.14.*
+# OpenVino & ONNX
+openvino == 2024.4.*
+onnxruntime-openvino == 1.20.* ; platform_machine == 'x86_64'
+onnxruntime == 1.20.* ; platform_machine == 'aarch64'
 # Embeddings
-onnx_clip == 4.0.*
-chromadb == 0.5.0
+transformers == 4.45.*
 # Generative AI
-google-generativeai == 0.6.*
-ollama == 0.2.*
-openai == 1.30.*
+google-generativeai == 0.8.*
+ollama == 0.3.*
+openai == 1.51.*
 # push notifications
 py-vapid == 1.9.*
-pywebpush == 2.0.*
+pywebpush == 2.0.*
+# alpr
+pyclipper == 1.3.*
+shapely == 2.0.*

docker/main/requirements.txt

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

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

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

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

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

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

@@ -1,4 +0,0 @@
-#!/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

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

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

@@ -1,28 +0,0 @@
-#!/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

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

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

@@ -1,27 +0,0 @@
-#!/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

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

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

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

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

@@ -44,8 +44,6 @@ 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

@@ -43,8 +43,6 @@ 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 /dev/shm/logs/chroma)
+dirs=(/dev/shm/logs/frigate /dev/shm/logs/go2rtc /dev/shm/logs/nginx /dev/shm/logs/certsync)
 
 mkdir -p "${dirs[@]}"
 chown nobody:nogroup "${dirs[@]}"

docker/main/rootfs/usr/local/chroma

@@ -1,14 +0,0 @@
-#!/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

@@ -2,19 +2,23 @@
 
 import json
 import os
+import shutil
 import sys
 from pathlib import Path
 
-import yaml
+from ruamel.yaml import YAML
 
 sys.path.insert(0, "/opt/frigate")
-from frigate.const import BIRDSEYE_PIPE  # noqa: E402
-from frigate.ffmpeg_presets import (  # noqa: E402
-    parse_preset_hardware_acceleration_encode,
+from frigate.const import (
+    BIRDSEYE_PIPE,
+    DEFAULT_FFMPEG_VERSION,
+    INCLUDED_FFMPEG_VERSIONS,
 )
+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
@@ -37,7 +41,7 @@ try:
         raw_config = f.read()
 
     if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, any] = yaml.safe_load(raw_config)
+        config: dict[str, any] = yaml.load(raw_config)
     elif config_file.endswith(".json"):
         config: dict[str, any] = json.loads(raw_config)
 except FileNotFoundError:
@@ -105,16 +109,32 @@ 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["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:
+if int(os.environ.get("LIBAVFORMAT_VERSION_MAJOR", "59") or "59") < 59:
+    if go2rtc_config["ffmpeg"].get("rtsp") is None:
         go2rtc_config["ffmpeg"]["rtsp"] = (
             "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
         )
+else:
+    if go2rtc_config.get("ffmpeg") is None:
+        go2rtc_config["ffmpeg"] = {"path": ""}
 
 for name in go2rtc_config.get("streams", {}):
     stream = go2rtc_config["streams"][name]
@@ -145,7 +165,7 @@ 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(config.get('ffmpeg', {}).get('hwaccel_args'), input, '-rtsp_transport tcp -f rtsp {output}')}"
+    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(ffmpeg_path, 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

@@ -81,6 +81,9 @@ http {
         open_file_cache_errors on;
         aio on;
 
+        # file upload size
+        client_max_body_size 10M;
+
         # https://github.com/kaltura/nginx-vod-module#vod_open_file_thread_pool
         vod_open_file_thread_pool default;
 
@@ -104,6 +107,8 @@ http {
 
             add_header Cache-Control "no-store";
             expires off;
+
+            keepalive_disable safari;
         }
 
         location /stream/ {
@@ -224,7 +229,7 @@ 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

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

@@ -1,28 +0,0 @@
-"""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

@@ -7,13 +7,14 @@ FROM wheels as rk-wheels
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt
 RUN sed -i "/https:\/\//d" /requirements-wheels.txt
+RUN python3 -m pip config set global.break-system-packages true
 RUN pip3 wheel --wheel-dir=/rk-wheels -c /requirements-wheels.txt -r /requirements-wheels-rk.txt
 
 FROM deps AS rk-frigate
 ARG TARGETARCH
 
 RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \
-    pip3 install -U /deps/rk-wheels/*.whl
+    pip3 install -U /deps/rk-wheels/*.whl --break-system-packages
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
@@ -22,5 +23,6 @@ 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/btbn-ffmpeg/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffprobe /usr/lib/btbn-ffmpeg/bin/
+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}"

docker/rockchip/requirements-wheels-rk.txt

@@ -1 +1 @@
-rknn-toolkit-lite2 @ https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/rknn_toolkit_lite2-2.0.0b0-cp39-cp39-linux_aarch64.whl
+rknn-toolkit-lite2 @ https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/rknn_toolkit_lite2-2.0.0b0-cp311-cp311-linux_aarch64.whl

docker/rockchip/rk.mk

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

docker/rocm/Dockerfile

@@ -23,18 +23,18 @@ COPY docker/rocm/rocm-pin-600 /etc/apt/preferences.d/
 
 RUN apt-get update
 
-RUN apt-get -y install --no-install-recommends migraphx
+RUN apt-get -y install --no-install-recommends migraphx hipfft roctracer
 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* /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-$ROCM/lib/
 RUN cd /opt/rocm-dist/opt/ && ln -s rocm-$ROCM rocm
 
 RUN mkdir -p /opt/rocm-dist/etc/ld.so.conf.d/
 RUN echo /opt/rocm/lib|tee /opt/rocm-dist/etc/ld.so.conf.d/rocm.conf
 
 #######################################################################
-FROM --platform=linux/amd64 debian:11 as debian-base
+FROM --platform=linux/amd64 debian:12 as debian-base
 
 RUN apt-get update && apt-get -y upgrade
 RUN apt-get -y install --no-install-recommends libelf1 libdrm2 libdrm-amdgpu1 libnuma1 kmod
@@ -51,7 +51,7 @@ COPY --from=rocm /opt/rocm-$ROCM /opt/rocm-$ROCM
 RUN ln -s /opt/rocm-$ROCM /opt/rocm
 
 RUN apt-get -y install g++ cmake
-RUN apt-get -y install python3-pybind11 python3.9-distutils python3-dev
+RUN apt-get -y install python3-pybind11 python3-distutils python3-dev
 
 WORKDIR /opt/build
 
@@ -69,7 +69,12 @@ RUN apt-get -y install libnuma1
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-COPY docker/rocm/rootfs/ /
+
+# Temporarily disabled to see if a new wheel can be built to support py3.11
+#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
 
 #######################################################################
 FROM scratch AS rocm-dist
@@ -79,14 +84,15 @@ 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/
+COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-311-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/
 
 #######################################################################
 FROM deps-prelim AS rocm-prelim-hsa-override0
-
-ENV HSA_ENABLE_SDMA=0
+\
+    ENV HSA_ENABLE_SDMA=0
 
 COPY --from=rocm-dist / /
 
@@ -101,6 +107,3 @@ 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

@@ -0,0 +1 @@
+onnxruntime-rocm @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v1.0.0/onnxruntime_rocm-1.17.3-cp39-cp39-linux_x86_64.whl

docker/rocm/rocm.mk

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

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

@@ -1,20 +0,0 @@
-#!/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

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

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

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

docker/rpi/Dockerfile

@@ -12,5 +12,7 @@ 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/install_deps.sh

@@ -24,7 +24,7 @@ sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
 if [[ "${TARGETARCH}" == "arm64" ]]; then
     # add raspberry pi repo
     gpg --no-default-keyring --keyring /usr/share/keyrings/raspbian.gpg --keyserver keyserver.ubuntu.com --recv-keys 82B129927FA3303E
-    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bullseye main" | tee /etc/apt/sources.list.d/raspi.list
+    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bookworm main" | tee /etc/apt/sources.list.d/raspi.list
     apt-get -qq update
     apt-get -qq install --no-install-recommends --no-install-suggests -y ffmpeg
 fi

docker/rpi/rpi.mk

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

docker/tensorrt/Dockerfile.amd64

@@ -7,15 +7,17 @@ ARG DEBIAN_FRONTEND=noninteractive
 FROM wheels as trt-wheels
 ARG DEBIAN_FRONTEND
 ARG TARGETARCH
+RUN python3 -m pip config set global.break-system-packages true
 
 # Add TensorRT wheels to another folder
 COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
 RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
 FROM tensorrt-base AS frigate-tensorrt
-ENV TRT_VER=8.5.3
+ENV TRT_VER=8.6.1
+RUN python3 -m pip config set global.break-system-packages true
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
-    pip3 install -U /deps/trt-wheels/*.whl && \
+    pip3 install -U /deps/trt-wheels/*.whl --break-system-packages && \
     ldconfig
 
 WORKDIR /opt/frigate/
@@ -26,7 +28,8 @@ 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 \
-    pip3 install -U /deps/trt-wheels/*.whl
+    pip3 install -U /deps/trt-wheels/*.whl --break-system-packages

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
@@ -41,7 +41,11 @@ 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
-RUN pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
+ADD https://nvidia.box.com/shared/static/psl23iw3bh7hlgku0mjo1xekxpego3e3.whl /tmp/onnxruntime_gpu-1.15.1-cp311-cp311-linux_aarch64.whl
+
+RUN pip3 uninstall -y onnxruntime-openvino \
+    && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt \
+    && pip3 install --no-deps /tmp/onnxruntime_gpu-1.15.1-cp311-cp311-linux_aarch64.whl
 
 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND

docker/tensorrt/Dockerfile.base

@@ -3,18 +3,19 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.03-py3
+ARG TRT_BASE=nvcr.io/nvidia/tensorrt:24.10-py3
 
 # Build TensorRT-specific library
 FROM ${TRT_BASE} AS trt-deps
 
 ARG COMPUTE_LEVEL
 
-RUN apt-get update \
-    && apt-get install -y git build-essential cuda-nvcc-* cuda-nvtx-* libnvinfer-dev libnvinfer-plugin-dev libnvparsers-dev libnvonnxparsers-dev \
-    && rm -rf /var/lib/apt/lists/*
-RUN --mount=type=bind,source=docker/tensorrt/detector/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \
-    /tensorrt_libyolo.sh
+# Need to wait for script to be adapted to newer version of tensorrt or perhaps decide that we want to remove the TRT detector in favor of using onnx runtime directly
+#RUN apt-get update \
+#    && apt-get install -y git build-essential cuda-nvcc-* cuda-nvtx-* libnvinfer-dev libnvinfer-plugin-dev libnvparsers-dev libnvonnxparsers-dev \
+#    && rm -rf /var/lib/apt/lists/*
+#RUN --mount=type=bind,source=docker/tensorrt/detector/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \
+#    /tensorrt_libyolo.sh
 
 # Frigate w/ TensorRT Support as separate image
 FROM deps AS tensorrt-base
@@ -22,10 +23,14 @@ FROM deps AS tensorrt-base
 #Disable S6 Global timeout
 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/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/lib/x86_64-linux-gnu/libcudnn* /usr/local/cuda/lib64/
+COPY --from=trt-deps /usr/lib/x86_64-linux-gnu/libnv* /usr/local/cuda/lib64/
+
 COPY docker/tensorrt/detector/rootfs/ /
-ENV YOLO_MODELS="yolov7-320"
+ENV YOLO_MODELS=""
 
 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/ld.so.conf.d/cuda_tensorrt.conf

@@ -1,6 +1,8 @@
 /usr/local/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cudnn/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cuda_runtime/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cublas/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cuda_nvrtc/lib
-/usr/local/lib/python3.9/dist-packages/tensorrt
+/usr/local/cuda/lib64
+/usr/local/lib/python3.11/dist-packages/nvidia/cudnn/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cuda_runtime/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cublas/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cuda_nvrtc/lib
+/usr/local/lib/python3.11/dist-packages/tensorrt
+/usr/local/lib/python3.11/dist-packages/nvidia/cufft/lib

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

@@ -11,6 +11,7 @@ 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}
@@ -19,6 +20,11 @@ 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

@@ -1,12 +1,10 @@
 # NVidia TensorRT Support (amd64 only)
 --extra-index-url 'https://pypi.nvidia.com'
 numpy < 1.24; platform_machine == 'x86_64'
-tensorrt == 8.5.3.*; platform_machine == 'x86_64'
-cuda-python == 11.8; platform_machine == 'x86_64'
-cython == 0.29.*; platform_machine == 'x86_64'
+tensorrt == 10.5.0; platform_machine == 'x86_64'
+cuda-python == 12.6.*; platform_machine == 'x86_64'
+cython == 3.0.*; platform_machine == 'x86_64'
 nvidia-cuda-runtime-cu12 == 12.1.*; platform_machine == 'x86_64'
-nvidia-cuda-runtime-cu11 == 11.8.*; platform_machine == 'x86_64'
-nvidia-cublas-cu11 == 11.11.3.6; platform_machine == 'x86_64'
-nvidia-cudnn-cu11 == 8.6.0.*; platform_machine == 'x86_64'
-onnx==1.14.0; platform_machine == 'x86_64'
-protobuf==3.20.3; platform_machine == 'x86_64'
+onnx==1.16.*; platform_machine == 'x86_64'
+onnxruntime-gpu==1.20.*; platform_machine == 'x86_64'
+protobuf==3.20.3; platform_machine == 'x86_64'

docker/tensorrt/requirements-arm64.txt

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

docker/tensorrt/trt.mk

@@ -7,20 +7,35 @@ 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 --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt tensorrt
+	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=frigate:latest-tensorrt \
+		--load
 
 local-trt-jp4: version
-	$(JETPACK4_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt-jp4 tensorrt
+	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=frigate:latest-tensorrt-jp4 \
+		--load
 
 local-trt-jp5: version
-	$(JETPACK5_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt-jp5 tensorrt
+	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=frigate:latest-tensorrt-jp5 \
+		--load
 
 build-trt:
-	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(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
+	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=$(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
 
 push-trt: build-trt
-	$(X86_DGPU_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(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
+	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=$(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

docs/README.md

@@ -1,5 +1,10 @@
 # Website
 
-This website is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator.
+This website is built using [Docusaurus 3.5](https://docusaurus.io/docs), 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`
 
-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.
+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.
 
 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, a docker volume mapping can be used to overwrite the included ffmpeg build with an ffmpeg build that works for your specific hardware setup.
+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.
 
 To do this:
 
-1. Download your ffmpeg build and uncompress to a folder on the host (let's use `/home/appdata/frigate/custom-ffmpeg` for this example).
+1. Download your ffmpeg build and uncompress to the Frigate config folder.
 2. Update your docker-compose or docker CLI to include `'/home/appdata/frigate/custom-ffmpeg':'/usr/lib/btbn-ffmpeg':'ro'` in the volume mappings.
 3. Restart Frigate and the custom version will be used if the mapping was done correctly.
 
-NOTE: The folder that is 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`.
+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`.
 
 ### Custom go2rtc version
 
-Frigate currently includes go2rtc v1.9.4, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.2, 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.yaml file updates
+## Validating your config.yml 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 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).
+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).
 
 For example, `1/second;5/minute;20/hour` will rate limit the login endpoint when failures occur more than:
 

docs/docs/configuration/autotracking.md

@@ -41,6 +41,7 @@ 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
@@ -49,6 +50,8 @@ 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

@@ -9,6 +9,12 @@ 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.
@@ -175,7 +181,7 @@ go2rtc:
       - rtspx://192.168.1.1:7441/abcdefghijk
 ```
 
-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#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.
 
@@ -187,4 +193,4 @@ 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 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.`.
+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.`.

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 events 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 tracked objects 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:
 
@@ -46,6 +46,14 @@ 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
@@ -71,29 +79,41 @@ 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            |      ❌      |      ❌      | 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                                                                                                                        |
+| 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                                                                                                                        |
 
 ## Setting up camera groups
 

docs/docs/configuration/face_recognition.md

@@ -0,0 +1,35 @@
+---
+id: face_recognition
+title: Face Recognition
+---
+
+Face recognition allows people to be assigned names and when their face is recognized Frigate will assign the person's name as a sub label. This information is included in the UI, filters, as well as in notifications.
+
+Frigate has support for FaceNet to create face embeddings, which runs locally. Embeddings are then saved to Frigate's database.
+
+## Minimum System Requirements
+
+Face recognition works by running a large AI model locally on your system. Systems without a GPU will not run Face Recognition reliably or at all.
+
+## Configuration
+
+Face recognition is disabled by default and requires semantic search to be enabled, face recognition must be enabled in your config file before it can be used. Semantic Search and face recognition are global configuration settings.
+
+```yaml
+face_recognition:
+  enabled: true
+```
+
+## Dataset
+
+The number of images needed for a sufficient training set for face recognition varies depending on several factors:
+
+- Complexity of the task: A simple task like recognizing faces of known individuals may require fewer images than a complex task like identifying unknown individuals in a large crowd.
+- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
+- Desired accuracy: The higher the desired accuracy, the more images are typically needed.
+
+However, here are some general guidelines:
+
+- Minimum: For basic face recognition tasks, a minimum of 10-20 images per person is often recommended.
+- Recommended: For more robust and accurate systems, 30-50 images per person is a good starting point.
+- Ideal: For optimal performance, especially in challenging conditions, 100 or more images per person can be beneficial.

docs/docs/configuration/genai.md

@@ -3,7 +3,15 @@ id: genai
 title: 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.
+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.
+
+:::
 
 ## Configuration
 
@@ -27,11 +35,21 @@ cameras:
 
 ## Ollama
 
-[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.
+:::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).
 
 ### 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`.
+You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
 
 :::note
 
@@ -46,7 +64,7 @@ genai:
   enabled: True
   provider: ollama
   base_url: http://localhost:11434
-  model: llava
+  model: llava:7b
 ```
 
 ## Google Gemini
@@ -98,17 +116,49 @@ 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 detection 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 tracked object along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows:
 
 ```
-Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background.
+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.
 ```
 
 :::tip
 
-Prompts can use variable replacements like `{label}`, `{sub_label}`, and `{camera}` to substitute information from the detection as part of the prompt.
+Prompts can use variable replacements like `{label}`, `{sub_label}`, and `{camera}` to substitute information from the tracked object as part of the prompt.
 
 :::
 
@@ -120,15 +170,35 @@ genai:
   provider: ollama
   base_url: http://localhost:11434
   model: llava
-  prompt: "Describe the {label} in these images from the {camera} security camera."
+  prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
   object_prompts:
-    person: "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."
+    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
 ```
 
 ### Experiment with prompts
 
-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.
+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.
 
 - OpenAI - [ChatGPT](https://chatgpt.com)
 - Gemini - [Google AI Studio](https://aistudio.google.com)

docs/docs/configuration/hardware_acceleration.md

@@ -65,24 +65,37 @@ 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 is recommended for all generations of Intel-based CPUs.
+VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
 
 ```yaml
 ffmpeg:
   hwaccel_args: preset-vaapi
 ```
 
-:::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.
+### Via Quicksync
 
 #### H.264 streams
 
@@ -218,28 +231,11 @@ docker run -d \
 
 ### Setup Decoder
 
-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`.
+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.
 
 ```yaml
 ffmpeg:
-  hwaccel_args: preset-nvidia-h264
+  hwaccel_args: preset-nvidia
 ```
 
 If everything is working correctly, you should see a significant improvement in performance.
@@ -370,7 +366,7 @@ Make sure to follow the [Rockchip specific installation instructions](/frigate/i
 
 ### Configuration
 
-Add one of the following FFmpeg presets to your `config.yaml` to enable hardware video processing:
+Add one of the following FFmpeg presets to your `config.yml` 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 was during any event for 30 days
+- Continue to keep all video if it qualified as an alert or detection for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -95,10 +95,12 @@ record:
   retain:
     days: 7
     mode: motion
-  events:
+  alerts:
     retain:
-      default: 30
-      mode: motion
+      days: 30
+  detections:
+    retain:
+      days: 30
 
 snapshots:
   enabled: True
@@ -128,7 +130,7 @@ 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 was during any event for 30 days
+- Continue to keep all video if it qualified as an alert or detection for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -149,10 +151,12 @@ record:
   retain:
     days: 7
     mode: motion
-  events:
+  alerts:
     retain:
-      default: 30
-      mode: motion
+      days: 30
+  detections:
+    retain:
+      days: 30
 
 snapshots:
   enabled: True
@@ -182,7 +186,7 @@ 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 was during any event for 30 days
+- Continue to keep all video if it qualified as an alert or detection for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -214,10 +218,12 @@ record:
   retain:
     days: 7
     mode: motion
-  events:
+  alerts:
     retain:
-      default: 30
-      mode: motion
+      days: 30
+  detections:
+    retain:
+      days: 30
 
 snapshots:
   enabled: True

docs/docs/configuration/license_plate_recognition.md

@@ -0,0 +1,45 @@
+---
+id: license_plate_recognition
+title: License Plate Recognition (LPR)
+---
+
+Frigate can recognize license plates on vehicles and automatically add the detected characters as a `sub_label` to objects that are of type `car`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street with a dedicated LPR camera.
+
+Users running a Frigate+ model should ensure that `license_plate` is added to the [list of objects to track](https://docs.frigate.video/plus/#available-label-types) either globally or for a specific camera. This will improve the accuracy and performance of the LPR model.
+
+LPR is most effective when the vehicle’s license plate is fully visible to the camera. For moving vehicles, Frigate will attempt to read the plate continuously, refining its detection and keeping the most confident result. LPR will not run on stationary vehicles.
+
+## Minimum System Requirements
+
+License plate recognition works by running AI models locally on your system. The models are relatively lightweight and run on your CPU. At least 4GB of RAM is required.
+
+## Configuration
+
+License plate recognition is disabled by default. Enable it in your config file:
+
+```yaml
+lpr:
+  enabled: true
+```
+
+## Advanced Configuration
+
+Several options are available to fine-tune the LPR feature. For example, you can adjust the `min_area` setting, which defines the minimum size in pixels a license plate must be before LPR runs. The default is 500 pixels.
+
+Additionally, you can define `known_plates` as strings or regular expressions, allowing Frigate to label tracked vehicles with custom sub_labels when a recognized plate is detected. This information is then accessible in the UI, filters, and notifications.
+
+```yaml
+lpr:
+  enabled: true
+  min_area: 500
+  known_plates:
+    Wife's Car:
+      - "ABC-1234"
+      - "ABC-I234"
+    Johnny:
+      - "J*N-*234" # Using wildcards for H/M and 1/I
+    Sally:
+      - "[S5]LL-1234" # Matches SLL-1234 and 5LL-1234
+```
+
+In this example, "Wife's Car" will appear as the label for any vehicle matching the plate "ABC-1234." The model might occasionally interpret the digit 1 as a capital I (e.g., "ABC-I234"), so both variations are listed. Similarly, multiple possible variations are specified for Johnny and Sally.

docs/docs/configuration/live.md

@@ -11,11 +11,21 @@ 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 | 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                                         |
+| 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.
 
 ### Audio Support
 
@@ -32,6 +42,15 @@ 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 events.
+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.
 
 ## 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 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).
+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).
 
 ## 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 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.
+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.
 
 :::
 
@@ -92,9 +92,15 @@ motion:
   lightning_threshold: 0.8
 ```
 
-:::tip
+:::warning
 
-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.
+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.
 
 :::
 

docs/docs/configuration/object_detectors.md

@@ -3,37 +3,39 @@ id: object_detectors
 title: Object Detectors
 ---
 
-# Officially Supported Detectors
+# Supported Hardware
 
-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.
+:::info
 
-## CPU Detector (not recommended)
+Frigate supports multiple different detectors that work on different types of hardware:
 
-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"`.
+**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.
 
-:::tip
+**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.
 
-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.
+**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.
 
 :::
 
-The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
+# Officially Supported Detectors
 
-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.
+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.
 
 ## Edge TPU Detector
 
@@ -122,6 +124,22 @@ 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
@@ -149,7 +167,7 @@ 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/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/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
 
 :::warning
 
@@ -205,7 +223,7 @@ 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, 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.
+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.
 
 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.
 
@@ -246,7 +264,7 @@ An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yol
 ```yml
 frigate:
   environment:
-    - YOLO_MODELS=yolov4-608,yolov7x-640
+    - YOLO_MODELS=yolov7-320,yolov7x-640
     - USE_FP16=false
 ```
 
@@ -278,6 +296,220 @@ 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.
@@ -389,7 +621,9 @@ $ cat /sys/kernel/debug/rknpu/load
 
 ## Hailo-8l
 
-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.
+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.
 
 ### Configuration
 

docs/docs/configuration/object_filters.md

@@ -20,15 +20,13 @@ 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 events 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 tracked objects 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 event. If `threshold` is too high then true positive events 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 tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
 
 ## Object Shape
 
@@ -52,7 +50,7 @@ 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 events 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 tracked objects 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 models listed below from the Google Coral test data.
+Frigate includes the object labels listed below from the Google Coral test data.
 
 Please note:
 

docs/docs/configuration/pwa.md

@@ -0,0 +1,24 @@
+---
+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 event 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 tracked object 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 events 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 alerts or detections will be retained until 30 days have passed.
 
 ```yaml
 record:
@@ -21,9 +21,13 @@ record:
   retain:
     days: 3
     mode: all
-  events:
+  alerts:
     retain:
-      default: 30
+      days: 30
+      mode: motion
+  detections:
+    retain:
+      days: 30
       mode: motion
 ```
 
@@ -37,25 +41,28 @@ record:
   retain:
     days: 3
     mode: motion
-  events:
+  alerts:
     retain:
-      default: 30
+      days: 30
+      mode: motion
+  detections:
+    retain:
+      days: 30
       mode: motion
 ```
 
-### Minimum: Events only
+### Minimum: Alerts only
 
-If you only want to retain video that occurs during an event, this config will discard video unless an event is ongoing.
+If you only want to retain video that occurs during a tracked object, this config will discard video unless an alert is ongoing.
 
 ```yaml
 record:
   enabled: True
   retain:
     days: 0
-    mode: all
-  events:
+  alerts:
     retain:
-      default: 30
+      days: 30
       mode: motion
 ```
 
@@ -65,7 +72,7 @@ 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 event based recordings with separate retention modes and retention periods.
+Frigate supports both continuous and tracked object based recordings with separate retention modes and retention periods.
 
 :::tip
 
@@ -86,25 +93,28 @@ record:
 
 Continuous recording supports different retention modes [which are described below](#what-do-the-different-retain-modes-mean)
 
-### Event Recording
+### Object Recording
 
-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.
+The number of days to record review items can be specified for review items classified as alerts as well as tracked objects.
 
 ```yaml
 record:
   enabled: True
-  events:
+  alerts:
     retain:
-      default: 10 # <- number of days to keep event recordings
+      days: 10 # <- number of days to keep alert recordings
+  detections:
+    retain:
+      days: 10 # <- number of days to keep detections recordings
 ```
 
-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.
+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.
 
 **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 events).
+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).
 
 Let's say you have Frigate configured so that your doorbell camera would retain the last **2** days of continuous recording.
 
@@ -112,11 +122,7 @@ 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 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.
+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.
 
 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:
 
@@ -126,33 +132,18 @@ record:
   retain:
     days: 7
     mode: motion
-  events:
+  alerts:
     retain:
-      default: 14
+      days: 14
+      mode: active_objects
+  detections:
+    retain:
+      days: 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.
@@ -163,7 +154,7 @@ 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.md#post-apiexportcamerastartstart-timestampendend-timestamp).
+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).
 
 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

@@ -138,6 +138,16 @@ 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
@@ -210,6 +220,10 @@ 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)
@@ -271,13 +285,13 @@ 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 event clip with a person walking from left to right.
-  #      If the event timeline bounding box is consistently to the left of the person
+  # 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
   #      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
-  #      events, this makes it easy to tune.
+  #      tracked objects, this makes it easy to tune.
   # WARNING: Fast moving objects will likely not have the bounding box align.
   annotation_offset: 0
 
@@ -320,6 +334,9 @@ 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
@@ -329,12 +346,20 @@ 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.
@@ -394,9 +419,9 @@ record:
   sync_recordings: False
   # Optional: Retention settings for recording
   retain:
-    # 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.
+    # 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.
     days: 0
     # Optional: Mode for retention. Available options are: all, motion, and active_objects
     #   all - save all recording segments regardless of activity
@@ -419,34 +444,48 @@ record:
     # Optional: Quality of recording preview (default: shown below).
     # Options are: very_low, low, medium, high, very_high
     quality: medium
-  # Optional: Event recording settings
-  events:
-    # Optional: Number of seconds before the event to include (default: shown below)
+  # Optional: alert recording settings
+  alerts:
+    # Optional: Number of seconds before the alert to include (default: shown below)
     pre_capture: 5
-    # Optional: Number of seconds after the event to include (default: shown below)
+    # Optional: Number of seconds after the alert to include (default: shown below)
     post_capture: 5
-    # Optional: Objects to save recordings for. (default: all tracked objects)
-    objects:
-      - person
-    # Optional: Retention settings for recordings of events
+    # Optional: Retention settings for recordings of alerts
     retain:
-      # Required: Default retention days (default: shown below)
-      default: 10
+      # Required: Retention days (default: shown below)
+      days: 14
       # Optional: Mode for retention. (default: shown below)
-      #   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
+      #   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
       #
       # 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 event
+# Optional: Configuration for the jpg snapshots written to the clips directory for each tracked object
 # NOTE: Can be overridden at the camera level
 snapshots:
   # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
@@ -477,16 +516,27 @@ snapshots:
 semantic_search:
   # Optional: Enable semantic search (default: shown below)
   enabled: False
-  # Optional: Re-index embeddings database from historical events (default: shown below)
+  # Optional: Re-index embeddings database from historical tracked objects (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 event descriptions
+# Optional: Configuration for face recognition capability
+face_recognition:
+  # Optional: Enable semantic search (default: shown below)
+  enabled: False
+  # Optional: Set the model size used for embeddings. (default: shown below)
+  # NOTE: small model runs on CPU and large model runs on GPU
+  model_size: "small"
+
+# Optional: Configuration for AI generated tracked object descriptions
 # NOTE: Semantic Search must be enabled for this to do anything.
 # WARNING: Depending on the provider, this will send thumbnails over the internet
 # to Google or OpenAI's LLMs to generate descriptions. It can be overridden at
 # the camera level (enabled: False) to enhance privacy for indoor cameras.
 genai:
-  # Optional: Enable Google Gemini description generation (default: shown below)
+  # Optional: Enable AI description generation (default: shown below)
   enabled: False
   # Required if enabled: Provider must be one of ollama, gemini, or openai
   provider: ollama
@@ -506,10 +556,12 @@ genai:
 # Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
 go2rtc:
 
-# Optional: jsmpeg stream configuration for WebUI
+# Optional: Live stream configuration for WebUI.
+# NOTE: Can be overridden at the camera level
 live:
-  # Optional: Set the name of the stream that should be used for live view
-  # in frigate WebUI. (default: name of camera)
+  # 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.
   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
@@ -642,6 +694,7 @@ 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
@@ -650,6 +703,8 @@ 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
@@ -694,6 +749,28 @@ 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)
@@ -756,7 +833,7 @@ camera_groups:
       - side_cam
       - front_doorbell_cam
     # Required: icon used for group
-    icon: car
+    icon: LuCar
     # 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/v1.9.4) 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.
+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.
 
 :::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
@@ -132,9 +132,31 @@ 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/v1.9.4#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#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. events
+### Review items vs. tracked objects (formerly "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.
 
@@ -41,8 +41,6 @@ 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

@@ -3,11 +3,23 @@ id: semantic_search
 title: Using Semantic Search
 ---
 
-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).
+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.
 
 ## Configuration
 
-Semantic Search is a global configuration setting.
+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.
 
 ```yaml
 semantic_search:
@@ -17,22 +29,64 @@ semantic_search:
 
 :::tip
 
-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.
+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.
 
 :::
 
-### OpenAI CLIP
+### Jina AI CLIP
 
-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 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.
 
-### all-MiniLM-L6-v2
+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.
 
-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.
+Differently weighted versions of the Jina model are available and can be selected by setting the `model_size` config option as `small` or `large`:
 
-## Usage Tips
+```yaml
+semantic_search:
+  enabled: True
+  model_size: small
+```
 
-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.
+- 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.

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.md#get-apieventsidsnapshotjpg)
+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)
 
 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 events/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 alerts, detections, and 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`. You will get events for person objects that enter anywhere in the yard, and events 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`. Objects will be tracked for any `person` that enter anywhere in the yard, and 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
-npm install
+cd docs && 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
+- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](../configuration/live) for more info)
 
 **Sub Stream (Detection)**