Bump vite from 5.4.0 to 5.4.7 in /web

Bumps [vite](https://github.com/vitejs/vite/tree/HEAD/packages/vite) from 5.4.0 to 5.4.7.
- [Release notes](https://github.com/vitejs/vite/releases)
- [Changelog](https://github.com/vitejs/vite/blob/v5.4.7/packages/vite/CHANGELOG.md)
- [Commits](https://github.com/vitejs/vite/commits/v5.4.7/packages/vite)

---
updated-dependencies:
- dependency-name: vite
  dependency-type: direct:development
...

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

.cspell/frigate-dictionary.txt

@@ -2,7 +2,6 @@ aarch
 absdiff
 airockchip
 Alloc
-alpr
 Amcrest
 amdgpu
 analyzeduration
@@ -13,7 +12,6 @@ argmax
 argmin
 argpartition
 ascontiguousarray
-astype
 authelia
 authentik
 autodetected
@@ -44,7 +42,6 @@ codeproject
 colormap
 colorspace
 comms
-coro
 ctypeslib
 CUDA
 Cuvid
@@ -62,8 +59,6 @@ dsize
 dtype
 ECONNRESET
 edgetpu
-facenet
-fastapi
 faststart
 fflags
 ffprobe
@@ -116,8 +111,6 @@ itemsize
 Jellyfin
 jetson
 jetsons
-jina
-jinaai
 joserfc
 jsmpeg
 jsonify
@@ -191,7 +184,6 @@ openai
 opencv
 openvino
 OWASP
-paddleocr
 paho
 passwordless
 popleft
@@ -201,7 +193,6 @@ poweroff
 preexec
 probesize
 protobuf
-pstate
 psutil
 pubkey
 putenv
@@ -221,7 +212,6 @@ rcond
 RDONLY
 rebranded
 referer
-reindex
 Reolink
 restream
 restreamed
@@ -246,7 +236,6 @@ sleeptime
 SNDMORE
 socs
 sqliteq
-sqlitevecq
 ssdlite
 statm
 stimeout
@@ -281,11 +270,9 @@ unraid
 unreviewed
 userdata
 usermod
-uvicorn
 vaapi
 vainfo
 variations
-vbios
 vconcat
 vitb
 vstream
@@ -313,4 +300,4 @@ yolo
 yolonas
 yolox
 zeep
-zerolatency
+zerolatency

.devcontainer/devcontainer.json

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

.devcontainer/post_create.sh

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

.github/DISCUSSION_TEMPLATE/camera-support.yml

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

.github/DISCUSSION_TEMPLATE/config-support.yml

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

.github/DISCUSSION_TEMPLATE/detector-support.yml

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

.github/DISCUSSION_TEMPLATE/general-support.yml

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

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

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

.github/pull_request_template.md

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

.github/workflows/ci.yml

@@ -6,8 +6,6 @@ on:
     branches:
       - dev
       - master
-    paths-ignore:
-      - "docs/**"
 
 # only run the latest commit to avoid cache overwrites
 concurrency:
@@ -24,8 +22,6 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -47,8 +43,6 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -75,14 +69,21 @@ jobs:
             rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
+      - name: Build and push Rockchip build
+        uses: docker/bake-action@v3
+        with:
+          push: true
+          targets: rk
+          files: docker/rockchip/rk.hcl
+          set: |
+            rk.tags=${{ steps.setup.outputs.image-name }}-rk
+            *.cache-from=type=gha
   jetson_jp4_build:
     runs-on: ubuntu-latest
     name: Jetson Jetpack 4
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -109,8 +110,6 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -139,8 +138,6 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -158,30 +155,6 @@ jobs:
             tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
-  arm64_extra_builds:
-    runs-on: ubuntu-latest
-    name: ARM Extra Build
-    needs:
-      - arm64_build
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-      - name: Set up QEMU and Buildx
-        id: setup
-        uses: ./.github/actions/setup
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push Rockchip build
-        uses: docker/bake-action@v3
-        with:
-          push: true
-          targets: rk
-          files: docker/rockchip/rk.hcl
-          set: |
-            rk.tags=${{ steps.setup.outputs.image-name }}-rk
-            *.cache-from=type=gha
   combined_extra_builds:
     runs-on: ubuntu-latest
     name: Combined Extra Builds
@@ -191,8 +164,6 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up QEMU and Buildx
         id: setup
         uses: ./.github/actions/setup
@@ -234,7 +205,7 @@ jobs:
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
+        uses: docker/login-action@0d4c9c5ea7693da7b068278f7b52bda2a190a446
         with:
           registry: ghcr.io
           username: ${{ github.actor }}

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

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

.github/workflows/pull_request.yml

@@ -1,12 +1,9 @@
 name: On pull request
 
-on:
-  pull_request:
-    paths-ignore:
-      - "docs/**"
+on: pull_request
 
 env:
-  DEFAULT_PYTHON: 3.11
+  DEFAULT_PYTHON: 3.9
 
 jobs:
   build_devcontainer:
@@ -19,8 +16,6 @@ jobs:
       DOCKER_BUILDKIT: "1"
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x
@@ -40,8 +35,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x
@@ -56,8 +49,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 20.x
@@ -73,10 +64,8 @@ jobs:
     steps:
       - name: Check out the repository
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - name: Set up Python ${{ env.DEFAULT_PYTHON }}
-        uses: actions/setup-python@v5.3.0
+        uses: actions/setup-python@v5.1.0
         with:
           python-version: ${{ env.DEFAULT_PYTHON }}
       - name: Install requirements
@@ -96,8 +85,6 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - uses: actions/setup-node@master
         with:
           node-version: 16.x

.github/workflows/release.yml

@@ -11,26 +11,21 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       - id: lowercaseRepo
         uses: ASzc/change-string-case-action@v6
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
+        uses: docker/login-action@0d4c9c5ea7693da7b068278f7b52bda2a190a446
         with:
           registry: ghcr.io
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
       - name: Create tag variables
-        env:
-          TAG: ${{ github.ref_name }}
-          LOWERCASE_REPO: ${{ steps.lowercaseRepo.outputs.lowercase }}
         run: |
-          BUILD_TYPE=$([[ "${TAG}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
+          BUILD_TYPE=$([[ "${{ github.ref_name }}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]] && echo "stable" || echo "beta")
           echo "BUILD_TYPE=${BUILD_TYPE}" >> $GITHUB_ENV
-          echo "BASE=ghcr.io/${LOWERCASE_REPO}" >> $GITHUB_ENV
+          echo "BASE=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}" >> $GITHUB_ENV
           echo "BUILD_TAG=${GITHUB_SHA::7}" >> $GITHUB_ENV
           echo "CLEAN_VERSION=$(echo ${GITHUB_REF##*/} | tr '[:upper:]' '[:lower:]' | sed 's/^[v]//')" >> $GITHUB_ENV
       - name: Tag and push the main image
@@ -39,14 +34,14 @@ jobs:
           STABLE_TAG=${BASE}:stable
           PULL_TAG=${BASE}:${BUILD_TAG}
           docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${VERSION_TAG}
-          for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
+          for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk; do
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${VERSION_TAG}-${variant}
           done
 
           # stable tag
           if [[ "${BUILD_TYPE}" == "stable" ]]; then
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${STABLE_TAG}
-            for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
+            for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk; do
               docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${STABLE_TAG}-${variant}
             done
           fi

.github/workflows/stale.yml

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

.gitignore

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

Makefile

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

benchmark.py

@@ -4,7 +4,6 @@ from statistics import mean
 
 import numpy as np
 
-import frigate.util as util
 from frigate.config import DetectorTypeEnum
 from frigate.object_detection import (
     ObjectDetectProcess,
@@ -61,7 +60,7 @@ def start(id, num_detections, detection_queue, event):
     object_detector.cleanup()
     print(f"{id} - Processed for {duration:.2f} seconds.")
     print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
-    print(f"{id} - Average frame processing time: {mean(frame_times) * 1000:.2f}ms")
+    print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
 
 
 ######
@@ -91,7 +90,7 @@ edgetpu_process_2 = ObjectDetectProcess(
 )
 
 for x in range(0, 10):
-    camera_process = util.Process(
+    camera_process = mp.Process(
         target=start, args=(x, 300, detection_queue, events[str(x)])
     )
     camera_process.daemon = True

docker-compose.yml

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

docker/hailo8l/Dockerfile

@@ -5,7 +5,6 @@ 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
 
@@ -17,26 +16,89 @@ RUN mkdir /h8l-wheels
 # Build the wheels
 RUN pip3 wheel --wheel-dir=/h8l-wheels -c /requirements-wheels.txt -r /requirements-wheels-h8l.txt
 
-FROM wget AS hailort
+# Build HailoRT and create wheel
+FROM wheels AS build-hailort
 ARG TARGETARCH
-RUN --mount=type=bind,source=docker/hailo8l/install_hailort.sh,target=/deps/install_hailort.sh \
-    /deps/install_hailort.sh
+
+SHELL ["/bin/bash", "-c"]
+
+# Install necessary APT packages
+RUN apt-get -qq update \
+    && apt-get -qq install -y \
+    apt-transport-https \
+    gnupg \
+    wget \
+    # the key fingerprint can be obtained from https://ftp-master.debian.org/keys.html
+    && wget -qO- "https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xA4285295FC7B1A81600062A9605C66F00D6C9793" | \
+    gpg --dearmor > /usr/share/keyrings/debian-archive-bullseye-stable.gpg \
+    && echo "deb [signed-by=/usr/share/keyrings/debian-archive-bullseye-stable.gpg] http://deb.debian.org/debian bullseye main contrib non-free" | \
+    tee /etc/apt/sources.list.d/debian-bullseye-nonfree.list \
+    && apt-get -qq update \
+    && apt-get -qq install -y \
+    python3.9 \
+    python3.9-dev \
+    build-essential cmake git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Extract Python version and set environment variables
+RUN PYTHON_VERSION=$(python3 --version 2>&1 | awk '{print $2}' | cut -d. -f1,2) && \
+    PYTHON_VERSION_NO_DOT=$(echo $PYTHON_VERSION | sed 's/\.//') && \
+    echo "PYTHON_VERSION=$PYTHON_VERSION" > /etc/environment && \
+    echo "PYTHON_VERSION_NO_DOT=$PYTHON_VERSION_NO_DOT" >> /etc/environment
+
+# Clone and build HailoRT
+RUN . /etc/environment && \
+    git clone https://github.com/hailo-ai/hailort.git /opt/hailort && \
+    cd /opt/hailort && \
+    git checkout v4.17.0 && \
+    cmake -H. -Bbuild -DCMAKE_BUILD_TYPE=Release -DHAILO_BUILD_PYBIND=1 -DPYBIND11_PYTHON_VERSION=${PYTHON_VERSION} && \
+    cmake --build build --config release --target libhailort && \
+    cmake --build build --config release --target _pyhailort && \
+    cp build/hailort/libhailort/bindings/python/src/_pyhailort.cpython-${PYTHON_VERSION_NO_DOT}-$(if [ $TARGETARCH == "amd64" ]; then echo 'x86_64'; else echo 'aarch64'; fi )-linux-gnu.so hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/ && \
+    cp build/hailort/libhailort/src/libhailort.so hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/
+
+RUN ls -ahl /opt/hailort/build/hailort/libhailort/src/
+RUN ls -ahl /opt/hailort/hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/
+
+# Remove the existing setup.py if it exists in the target directory
+RUN rm -f /opt/hailort/hailort/libhailort/bindings/python/platform/setup.py
+
+# Copy generate_wheel_conf.py and setup.py
+COPY docker/hailo8l/pyhailort_build_scripts/generate_wheel_conf.py /opt/hailort/hailort/libhailort/bindings/python/platform/generate_wheel_conf.py
+COPY docker/hailo8l/pyhailort_build_scripts/setup.py /opt/hailort/hailort/libhailort/bindings/python/platform/setup.py
+
+# Run the generate_wheel_conf.py script
+RUN python3 /opt/hailort/hailort/libhailort/bindings/python/platform/generate_wheel_conf.py
+
+# Create a wheel file using pip3 wheel
+RUN cd /opt/hailort/hailort/libhailort/bindings/python/platform && \
+    python3 setup.py bdist_wheel --dist-dir /hailo-wheels
 
 # Use deps as the base image
 FROM deps AS h8l-frigate
 
 # Copy the wheels from the wheels stage
 COPY --from=h8l-wheels /h8l-wheels /deps/h8l-wheels
-COPY --from=hailort /hailo-wheels /deps/hailo-wheels
-COPY --from=hailort /rootfs/ /
+COPY --from=build-hailort /hailo-wheels /deps/hailo-wheels
+COPY --from=build-hailort /etc/environment /etc/environment
+RUN CC=$(python3 -c "import sysconfig; import shlex; cc = sysconfig.get_config_var('CC'); cc_cmd = shlex.split(cc)[0]; print(cc_cmd[:-4] if cc_cmd.endswith('-gcc') else cc_cmd)") && \
+    echo "CC=$CC" >> /etc/environment
 
 # Install the wheels
-RUN 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

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

docker/hailo8l/install_hailort.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-hailo_version="4.20.0"
-
-if [[ "${TARGETARCH}" == "amd64" ]]; then
-    arch="x86_64"
-elif [[ "${TARGETARCH}" == "arm64" ]]; then
-    arch="aarch64"
-fi
-
-wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${TARGETARCH}.tar.gz" |
-    tar -C / -xzf -
-
-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

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

docker/hailo8l/pyhailort_build_scripts/setup.py

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

docker/hailo8l/requirements-wheels-h8l.txt

@@ -1,12 +1,12 @@
-appdirs==1.4.*
-argcomplete==2.0.*
-contextlib2==0.6.*
-distlib==0.3.*
-filelock==3.8.*
-future==0.18.*
-importlib-metadata==5.1.*
-importlib-resources==5.1.*
-netaddr==0.8.*
-netifaces==0.10.*
-verboselogs==1.7.*
-virtualenv==20.17.*
+appdirs==1.4.4
+argcomplete==2.0.0
+contextlib2==0.6.0.post1
+distlib==0.3.6
+filelock==3.8.0
+future==0.18.2
+importlib-metadata==5.1.0
+importlib-resources==5.1.2
+netaddr==0.8.0
+netifaces==0.10.9
+verboselogs==1.7
+virtualenv==20.17.0

docker/hailo8l/user_installation.sh

@@ -2,9 +2,8 @@
 
 # Update package list and install dependencies
 sudo apt-get update
-sudo apt-get install -y build-essential cmake git wget
+sudo apt-get install -y build-essential cmake git wget linux-modules-extra-$(uname -r)
 
-hailo_version="4.20.0"
 arch=$(uname -m)
 
 if [[ $arch == "x86_64" ]]; then
@@ -14,7 +13,7 @@ else
 fi
 
 # Clone the HailoRT driver repository
-git clone --depth 1 --branch v${hailo_version} https://github.com/hailo-ai/hailort-drivers.git
+git clone --depth 1 --branch v4.17.0 https://github.com/hailo-ai/hailort-drivers.git
 
 # Build and install the HailoRT driver
 cd hailort-drivers/linux/pcie
@@ -24,26 +23,13 @@ sudo make install
 # Load the Hailo PCI driver
 sudo modprobe hailo_pci
 
-if [ $? -ne 0 ]; then
-  echo "Unable to load hailo_pci module, common reasons for this are:"
-  echo "- Key was rejected by service: Secure Boot is enabling disallowing install."
-  echo "- Permissions are not setup correctly."
-  exit 1
-fi
-
 # Download and install the firmware
 cd ../../
 ./download_firmware.sh
-
-# verify the firmware folder is present
-if [ ! -d /lib/firmware/hailo ]; then
-  sudo mkdir /lib/firmware/hailo
-fi
-sudo mv hailo8_fw.*.bin /lib/firmware/hailo/hailo8_fw.bin
+sudo mv hailo8_fw.4.17.0.bin /lib/firmware/hailo/hailo8_fw.bin
 
 # Install udev rules
 sudo cp ./linux/pcie/51-hailo-udev.rules /etc/udev/rules.d/
 sudo udevadm control --reload-rules && sudo udevadm trigger
 
 echo "HailoRT driver installation complete."
-echo "reboot your system to load the firmware!"

docker/main/Dockerfile

@@ -3,12 +3,12 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG BASE_IMAGE=debian:12
-ARG SLIM_BASE=debian:12-slim
+ARG BASE_IMAGE=debian:11
+ARG SLIM_BASE=debian:11-slim
 
 FROM ${BASE_IMAGE} AS base
 
-FROM --platform=${BUILDPLATFORM} debian:12 AS base_host
+FROM --platform=${BUILDPLATFORM} debian:11 AS base_host
 
 FROM ${SLIM_BASE} AS slim-base
 
@@ -30,16 +30,6 @@ RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
     --mount=type=cache,target=/root/.ccache \
     /deps/build_nginx.sh
 
-FROM wget AS sqlite-vec
-ARG DEBIAN_FRONTEND
-
-# Build sqlite_vec from source
-COPY docker/main/build_sqlite_vec.sh /deps/build_sqlite_vec.sh
-RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
-    --mount=type=bind,source=docker/main/build_sqlite_vec.sh,target=/deps/build_sqlite_vec.sh \
-    --mount=type=cache,target=/root/.ccache \
-    /deps/build_sqlite_vec.sh
-
 FROM scratch AS go2rtc
 ARG TARGETARCH
 WORKDIR /rootfs/usr/local/go2rtc/bin
@@ -66,8 +56,8 @@ 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" --break-system-packages \
-    && pip install --break-system-packages -r /requirements-ov.txt
+    && python3 get-pip.py "pip" \
+    && pip install -r /requirements-ov.txt
 
 # Get OpenVino Model
 RUN --mount=type=bind,source=docker/main/build_ov_model.py,target=/build_ov_model.py \
@@ -139,17 +129,24 @@ 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 wget \
+    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 \
-    python3-dev \
+    python3.9 \
+    python3.9-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\
-    libtbbmalloc2 libtbb-dev libdc1394-dev libopenexr-dev \
+    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
     libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
     # sqlite3 dependencies
     tclsh \
@@ -157,24 +154,29 @@ 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" --break-system-packages
+    && python3 get-pip.py "pip"
 
 COPY docker/main/requirements.txt /requirements.txt
-RUN pip3 install -r /requirements.txt --break-system-packages
+RUN pip3 install -r /requirements.txt
 
-# Build pysqlite3 from source
+# Build pysqlite3 from source to support ChromaDB
 COPY docker/main/build_pysqlite3.sh /build_pysqlite3.sh
 RUN /build_pysqlite3.sh
 
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 
+COPY docker/main/requirements-wheels-post.txt /requirements-wheels-post.txt
+RUN pip3 wheel --no-deps --wheel-dir=/wheels-post -r /requirements-wheels-post.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/ /
@@ -195,14 +197,12 @@ ARG APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn
 ENV NVIDIA_VISIBLE_DEVICES=all
 ENV NVIDIA_DRIVER_CAPABILITIES="compute,video,utility"
 
+# Turn off Chroma Telemetry: https://docs.trychroma.com/telemetry#opting-out
+ENV ANONYMIZED_TELEMETRY=False
+# Allow resetting the chroma database
+ENV ALLOW_RESET=True
 # Disable tokenizer parallelism warning
-# https://stackoverflow.com/questions/62691279/how-to-disable-tokenizers-parallelism-true-false-warning/72926996#72926996
 ENV TOKENIZERS_PARALLELISM=true
-# https://github.com/huggingface/transformers/issues/27214
-ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1
-
-# Set OpenCV ffmpeg loglevel to fatal: https://ffmpeg.org/doxygen/trunk/log_8h.html
-ENV OPENCV_FFMPEG_LOGLEVEL=8
 
 ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 ENV LIBAVFORMAT_VERSION_MAJOR=60
@@ -212,8 +212,16 @@ RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_de
     /deps/install_deps.sh
 
 RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
-    python3 -m pip install --upgrade pip --break-system-packages && \
-    pip3 install -U /deps/wheels/*.whl --break-system-packages
+    python3 -m pip install --upgrade pip && \
+    pip3 install -U /deps/wheels/*.whl
+
+# We have to uninstall this dependency specifically
+# as it will break onnxruntime-openvino
+RUN pip3 uninstall -y onnxruntime
+
+RUN --mount=type=bind,from=wheels,source=/wheels-post,target=/deps/wheels \
+    python3 -m pip install --upgrade pip && \
+    pip3 install -U /deps/wheels/*.whl
 
 COPY --from=deps-rootfs / /
 
@@ -231,7 +239,7 @@ ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 ENTRYPOINT ["/init"]
 CMD []
 
-HEALTHCHECK --start-period=300s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
+HEALTHCHECK --start-period=120s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
     CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1
 
 # Frigate deps with Node.js and NPM for devcontainer
@@ -260,7 +268,7 @@ 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 --break-system-packages
+    pip3 install -r requirements-dev.txt
 
 HEALTHCHECK NONE
 

docker/main/build_nginx.sh

@@ -8,7 +8,8 @@ SECURE_TOKEN_MODULE_VERSION="1.5"
 SET_MISC_MODULE_VERSION="v0.33"
 NGX_DEVEL_KIT_VERSION="v0.3.3"
 
-sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
+cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
+sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
 apt-get update
 
 apt-get -yqq build-dep 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/python3.11/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
+    transformations_config="/usr/local/lib/python3.9/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

@@ -1,30 +0,0 @@
-#!/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

@@ -8,37 +8,34 @@ apt-get -qq install --no-install-recommends -y \
     apt-transport-https \
     gnupg \
     wget \
-    lbzip2 \
     procps vainfo \
     unzip locales tzdata libxml2 xz-utils \
-    python3 \
+    python3.9 \
     python3-pip \
     curl \
-    lsof \
     jq \
-    nethogs \
-    libgl1 \
-    libglib2.0-0 \
-    libusb-1.0.0
+    nethogs
+
+# ensure python3 defaults to python3.9
+update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
 
 mkdir -p -m 600 /root/.gnupg
 
-# 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
+# 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 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
+# enable non-free repo in Debian
+if grep -q "Debian" /etc/issue; then
+    sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
 fi
 
-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
+# coral drivers
+apt-get -qq update
+apt-get -qq install --no-install-recommends --no-install-suggests -y \
+    libedgetpu1-max python3-tflite-runtime python3-pycoral
 
 # btbn-ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
@@ -47,7 +44,7 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
     tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
     rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
+    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/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
@@ -59,29 +56,34 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
     tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
     rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
+    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/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
-    # install amd / intel-i965 driver packages
+    # use debian bookworm for amd / intel-i965 driver packages
+    echo 'deb https://deb.debian.org/debian bookworm main contrib non-free' >/etc/apt/sources.list.d/debian-bookworm.list
+    apt-get -qq update
     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
+    # 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
 
     # use intel apt intel packages
     wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
     echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
     apt-get -qq update
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        intel-opencl-icd=24.35.30872.31-996~22.04 intel-level-zero-gpu=1.3.29735.27-914~22.04 intel-media-va-driver-non-free=24.3.3-996~22.04 \
-        libmfx1=23.2.2-880~22.04 libmfxgen1=24.2.4-914~22.04 libvpl2=1:2.13.0.0-996~22.04
+        intel-opencl-icd intel-level-zero-gpu intel-media-va-driver-non-free \
+        libmfx1 libmfxgen1 libvpl2
 
     rm -f /usr/share/keyrings/intel-graphics.gpg
     rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
@@ -89,7 +91,7 @@ fi
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        libva-drm2 mesa-va-drivers radeontop
+        libva-drm2 mesa-va-drivers
 fi
 
 # install vulkan

docker/main/requirements-wheels-post.txt

@@ -0,0 +1,3 @@
+# ONNX
+onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'
+onnxruntime == 1.19.* ; platform_machine == 'aarch64'

docker/main/requirements-wheels.txt

@@ -1,54 +1,42 @@
 click == 8.1.*
-# FastAPI
-aiohttp == 3.11.2
-starlette == 0.41.2
-starlette-context == 0.3.6
-fastapi == 0.115.*
-uvicorn == 0.30.*
-slowapi == 0.1.*
+Flask == 3.0.*
+Flask_Limiter == 3.8.*
 imutils == 0.5.*
 joserfc == 1.0.*
-pathvalidate == 3.2.*
 markupsafe == 2.1.*
-python-multipart == 0.0.12
-# General
 mypy == 1.6.1
-onvif-zeep-async == 3.1.*
+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 == 6.1.*
+psutil == 5.9.*
 pydantic == 2.8.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
-pytz == 2024.*
+PyYAML == 6.0.*
+pytz == 2024.1
 pyzmq == 26.2.*
 ruamel.yaml == 0.18.*
 tzlocal == 5.2
+types-PyYAML == 6.0.*
 requests == 2.32.*
 types-requests == 2.32.*
+scipy == 1.13.*
 norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
-# 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'
+# OpenVino (ONNX installed in wheels-post)
+openvino == 2024.3.*
 # Embeddings
-transformers == 4.45.*
+chromadb == 0.5.0
+onnx_clip == 4.0.*
 # Generative AI
-google-generativeai == 0.8.*
-ollama == 0.3.*
-openai == 1.51.*
+google-generativeai == 0.6.*
+ollama == 0.2.*
+openai == 1.30.*
 # push notifications
 py-vapid == 1.9.*
 pywebpush == 2.0.*
-# alpr
-pyclipper == 1.3.*
-shapely == 2.0.*

docker/main/requirements.txt

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

docker/main/rootfs/usr/local/chroma

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

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

@@ -6,7 +6,7 @@ import shutil
 import sys
 from pathlib import Path
 
-from ruamel.yaml import YAML
+import yaml
 
 sys.path.insert(0, "/opt/frigate")
 from frigate.const import (
@@ -18,7 +18,6 @@ from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 
 sys.path.remove("/opt/frigate")
 
-yaml = YAML()
 
 FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
 # read docker secret files as env vars too
@@ -41,7 +40,7 @@ try:
         raw_config = f.read()
 
     if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, any] = yaml.load(raw_config)
+        config: dict[str, any] = yaml.safe_load(raw_config)
     elif config_file.endswith(".json"):
         config: dict[str, any] = json.loads(raw_config)
 except FileNotFoundError:
@@ -165,7 +164,7 @@ if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, any] = config.get("birdseye")
 
     input = f"-f rawvideo -pix_fmt yuv420p -video_size {birdseye.get('width', 1280)}x{birdseye.get('height', 720)} -r 10 -i {BIRDSEYE_PIPE}"
-    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(ffmpeg_path, config.get('ffmpeg', {}).get('hwaccel_args', ''), input, '-rtsp_transport tcp -f rtsp {output}')}"
+    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(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

@@ -81,9 +81,6 @@ 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;
 
@@ -107,8 +104,6 @@ http {
 
             add_header Cache-Control "no-store";
             expires off;
-
-            keepalive_disable safari;
         }
 
         location /stream/ {
@@ -229,7 +224,7 @@ http {
 
         location ~* /api/.*\.(jpg|jpeg|png|webp|gif)$ {
             include auth_request.conf;
-            rewrite ^/api/(.*)$ /$1 break;
+            rewrite ^/api/(.*)$ $1 break;
             proxy_pass http://frigate_api;
             include proxy.conf;
         }

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

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

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

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

docker/rockchip/COCO/coco_subset_20.txt

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

docker/rockchip/Dockerfile

@@ -7,26 +7,21 @@ FROM wheels as rk-wheels
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt
 RUN sed -i "/https:\/\//d" /requirements-wheels.txt
-RUN sed -i "/onnxruntime/d" /requirements-wheels.txt
-RUN 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
-RUN rm -rf /rk-wheels/opencv_python-*
 
 FROM deps AS rk-frigate
 ARG TARGETARCH
 
 RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \
-    pip3 install --no-deps -U /deps/rk-wheels/*.whl --break-system-packages
+    pip3 install -U /deps/rk-wheels/*.whl
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-COPY docker/rockchip/COCO /COCO
-COPY docker/rockchip/conv2rknn.py /opt/conv2rknn.py
 
-ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/librknnrt.so /usr/lib/
+ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/librknnrt.so /usr/lib/
 
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-6/ffmpeg /usr/lib/ffmpeg/6.0/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-6/ffprobe /usr/lib/ffmpeg/6.0/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/conv2rknn.py

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

docker/rockchip/requirements-wheels-rk.txt

@@ -1,2 +1 @@
-rknn-toolkit2 == 2.3.0
-rknn-toolkit-lite2 == 2.3.0
+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

docker/rocm/Dockerfile

@@ -34,7 +34,7 @@ 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:12 as debian-base
+FROM --platform=linux/amd64 debian:11 as debian-base
 
 RUN apt-get update && apt-get -y upgrade
 RUN apt-get -y install --no-install-recommends libelf1 libdrm2 libdrm-amdgpu1 libnuma1 kmod
@@ -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-distutils python3-dev
+RUN apt-get -y install python3-pybind11 python3.9-distutils python3-dev
 
 WORKDIR /opt/build
 
@@ -70,11 +70,10 @@ RUN apt-get -y install libnuma1
 WORKDIR /opt/frigate/
 COPY --from=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
+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
@@ -84,15 +83,14 @@ 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-$ROCM/lib/rocblas/library/
 COPY --from=rocm /opt/rocm-dist/ /
-COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-311-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/
+COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-39-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/
 
 #######################################################################
 FROM deps-prelim AS rocm-prelim-hsa-override0
-\
-    ENV HSA_ENABLE_SDMA=0
+
+ENV HSA_ENABLE_SDMA=0
 
 COPY --from=rocm-dist / /
 

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/ bookworm main" | tee /etc/apt/sources.list.d/raspi.list
+    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bullseye 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/tensorrt/Dockerfile.amd64

@@ -7,19 +7,33 @@ 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=8.6.1
-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 --break-system-packages && \
-    ldconfig
+# Build CuDNN
+FROM wget AS cudnn-deps
 
+ARG COMPUTE_LEVEL
+
+RUN apt-get update \
+    && apt-get install -y git build-essential
+
+RUN wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.1-1_all.deb \
+&& dpkg -i cuda-keyring_1.1-1_all.deb \
+&& apt-get update \
+&& apt-get -y install cuda-toolkit \
+&& rm -rf /var/lib/apt/lists/*
+
+FROM tensorrt-base AS frigate-tensorrt
+ENV TRT_VER=8.5.3
+RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
+    pip3 install -U /deps/trt-wheels/*.whl && \
+    ldconfig
+COPY --from=cudnn-deps /usr/local/cuda-12.6 /usr/local/cuda
+
+ENV LD_LIBRARY_PATH=/usr/local/lib/python3.9/dist-packages/tensorrt:/usr/local/cuda/lib64:/usr/local/lib/python3.9/dist-packages/nvidia/cufft/lib
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 
@@ -28,8 +42,8 @@ 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 --from=cudnn-deps /usr/local/cuda-12.6 /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 --break-system-packages
+    pip3 install -U /deps/trt-wheels/*.whl

docker/tensorrt/Dockerfile.arm64

@@ -10,8 +10,8 @@ ARG DEBIAN_FRONTEND
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
     && apt-get -qq install -y --no-install-recommends \
-    python3.9 python3.9-dev \
-    wget build-essential cmake git \
+       python3.9 python3.9-dev \
+       wget build-essential cmake git \
     && rm -rf /var/lib/apt/lists/*
 
 # Ensure python3 defaults to python3.9
@@ -41,11 +41,7 @@ RUN --mount=type=bind,source=docker/tensorrt/detector/build_python_tensorrt.sh,t
     && TENSORRT_VER=$(cat /etc/TENSORRT_VER) /deps/build_python_tensorrt.sh
 
 COPY docker/tensorrt/requirements-arm64.txt /requirements-tensorrt.txt
-ADD https://nvidia.box.com/shared/static/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
+RUN pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND

docker/tensorrt/Dockerfile.base

@@ -3,7 +3,7 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.12-py3
+ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.03-py3
 
 # Build TensorRT-specific library
 FROM ${TRT_BASE} AS trt-deps
@@ -24,9 +24,8 @@ ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
 
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
-COPY --from=trt-deps /usr/local/cuda-12.* /usr/local/cuda
 COPY docker/tensorrt/detector/rootfs/ /
-ENV YOLO_MODELS=""
+ENV YOLO_MODELS="yolov7-320"
 
 HEALTHCHECK --start-period=600s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
     CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1

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

@@ -1,8 +1,6 @@
 /usr/local/lib
-/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
+/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

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

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

docker/tensorrt/requirements-amd64.txt

@@ -1,14 +1,14 @@
 # NVidia TensorRT Support (amd64 only)
 --extra-index-url 'https://pypi.nvidia.com'
 numpy < 1.24; platform_machine == 'x86_64'
-tensorrt == 8.6.1.*; platform_machine == 'x86_64'
-cuda-python == 11.8.*; platform_machine == 'x86_64'
-cython == 3.0.*; 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'
 nvidia-cuda-runtime-cu12 == 12.1.*; platform_machine == 'x86_64'
 nvidia-cuda-runtime-cu11 == 11.8.*; platform_machine == 'x86_64'
 nvidia-cublas-cu11 == 11.11.3.6; platform_machine == 'x86_64'
 nvidia-cudnn-cu11 == 8.6.0.*; platform_machine == 'x86_64'
 nvidia-cufft-cu11==10.*; platform_machine == 'x86_64'
-onnx==1.16.*; platform_machine == 'x86_64'
-onnxruntime-gpu==1.18.*; platform_machine == 'x86_64'
+onnx==1.14.0; platform_machine == 'x86_64'
+onnxruntime-gpu==1.17.*; 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'

docs/README.md

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

docs/docs/configuration/advanced.md

@@ -174,7 +174,7 @@ NOTE: The folder that is set for the config needs to be the folder that contains
 
 ### Custom go2rtc version
 
-Frigate currently includes go2rtc v1.9.2, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.4, there may be certain cases where you want to run a different version of go2rtc.
 
 To do this:
 
@@ -183,7 +183,7 @@ To do this:
 3. Give `go2rtc` execute permission.
 4. Restart Frigate and the custom version will be used, you can verify by checking go2rtc logs.
 
-## Validating your config.yml file updates
+## Validating your config.yaml file updates
 
 When frigate starts up, it checks whether your config file is valid, and if it is not, the process exits. To minimize interruptions when updating your config, you have three options -- you can edit the config via the WebUI which has built in validation, use the config API, or you can validate on the command line using the frigate docker container.
 
@@ -211,5 +211,5 @@ docker run                                \
   --entrypoint python3                    \
   ghcr.io/blakeblackshear/frigate:stable  \
   -u -m frigate                           \
-  --validate-config
+  --validate_config
 ```

docs/docs/configuration/authentication.md

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

docs/docs/configuration/autotracking.md

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

docs/docs/configuration/camera_specific.md

@@ -9,12 +9,6 @@ This page makes use of presets of FFmpeg args. For more information on presets,
 
 :::
 
-:::note
-
-Many cameras support encoding options which greatly affect the live view experience, see the [Live view](/configuration/live) page for more info.
-
-:::
-
 ## MJPEG Cameras
 
 Note that mjpeg cameras require encoding the video into h264 for recording, and restream roles. This will use significantly more CPU than if the cameras supported h264 feeds directly. It is recommended to use the restream role to create an h264 restream and then use that as the source for ffmpeg.
@@ -67,15 +61,14 @@ ffmpeg:
 
 ### Annke C800
 
-This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be adjusted using the `apple_compatibility` config.
+This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be repackaged and the audio stream has to be converted to aac. Unfortunately direct playback of in the browser is not working (yet), but the downloaded clip can be played locally.
 
 ```yaml
 cameras:
   annkec800: # <------ Name the camera
     ffmpeg:
-      apple_compatibility: true  # <- Adds compatibility with MacOS and iPhone
       output_args:
-        record: preset-record-generic-audio-aac
+        record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -tag:v hvc1 -bsf:v hevc_mp4toannexb -c:a aac
 
       inputs:
         - path: rtsp://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
@@ -157,9 +150,7 @@ cameras:
 
 #### Reolink Doorbell
 
-The reolink doorbell supports two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
-
-Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
+The reolink doorbell supports 2-way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
 
 ```yaml
 go2rtc:
@@ -184,7 +175,7 @@ go2rtc:
       - rtspx://192.168.1.1:7441/abcdefghijk
 ```
 
-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#source-rtsp)
 
 In the Unifi 2.0 update Unifi Protect Cameras had a change in audio sample rate which causes issues for ffmpeg. The input rate needs to be set for record if used directly with unifi protect.
 

docs/docs/configuration/cameras.md

@@ -79,41 +79,29 @@ cameras:
 
 If the ONVIF connection is successful, PTZ controls will be available in the camera's WebUI.
 
-:::tip
-
-If your ONVIF camera does not require authentication credentials, you may still need to specify an empty string for `user` and `password`, eg: `user: ""` and `password: ""`.
-
-:::
-
 An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
 
 ## ONVIF PTZ camera recommendations
 
 This list of working and non-working PTZ cameras is based on user feedback.
 
-| Brand or specific camera     | PTZ Controls | Autotracking | Notes                                                                                                                                           |
-| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------- |
-| Amcrest                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                 |
-| Amcrest ASH21                |      ✅      |      ❌      | ONVIF service port: 80                                                                                                                          |
-| Amcrest IP4M-S2112EW-AI      |      ✅      |      ❌      | FOV relative movement not supported.                                                                                                            |
-| Amcrest IP5M-1190EW          |      ✅      |      ❌      | ONVIF Port: 80. FOV relative movement not supported.                                                                                            |
-| Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                 |
-| Dahua                        |      ✅      |      ✅      |                                                                                                                                                 |
-| Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
-| Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                 |
-| Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                 |
-| Hikvision                    |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
-| Hikvision DS-2DE3A404IWG-E/W |      ✅      |      ✅      |                                                                                                                                                 |
-| Reolink 511WA                |      ✅      |      ❌      | Zoom only                                                                                                                                       |
-| Reolink E1 Pro               |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink E1 Zoom              |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink RLC-823A 16x         |      ✅      |      ❌      |                                                                                                                                                 |
-| Speco O8P32X                 |      ✅      |      ❌      |                                                                                                                                                 |
-| Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable.                                            |
-| Tapo                         |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
-| Uniview IPC672LR-AX4DUPK     |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
-| Uniview IPC6612SR-X33-VG     |      ✅      |      ✅      | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working.                                           |
-| Vikylin PTZ-2804X-I2         |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                        |
+| Brand or specific camera | PTZ Controls | Autotracking | Notes                                                                                                                                           |
+| ------------------------ | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------- |
+| Amcrest                  |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                 |
+| Amcrest ASH21            |      ❌      |      ❌      | No ONVIF support                                                                                                                                |
+| Ctronics PTZ             |      ✅      |      ❌      |                                                                                                                                                 |
+| Dahua                    |      ✅      |      ✅      |                                                                                                                                                 |
+| Foscam R5                |      ✅      |      ❌      |                                                                                                                                                 |
+| Hanwha XNP-6550RH        |      ✅      |      ❌      |                                                                                                                                                 |
+| Hikvision                |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
+| Reolink 511WA            |      ✅      |      ❌      | Zoom only                                                                                                                                       |
+| Reolink E1 Pro           |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink E1 Zoom          |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink RLC-823A 16x     |      ✅      |      ❌      |                                                                                                                                                 |
+| Sunba 405-D20X           |      ✅      |      ❌      |                                                                                                                                                 |
+| Tapo                     |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
+| Uniview IPC672LR-AX4DUPK |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
+| Vikylin PTZ-2804X-I2     |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                        |
 
 ## Setting up camera groups
 

docs/docs/configuration/face_recognition.md

@@ -1,35 +0,0 @@
----
-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

@@ -3,15 +3,9 @@ id: genai
 title: Generative AI
 ---
 
-Generative AI can be used to automatically generate descriptive text based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate to provide more context about your tracked objects. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
+Generative AI can be used to automatically generate descriptions based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate by providing detailed text descriptions as a basis of the search query.
 
-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.
-
-:::
+Semantic Search must be enabled to use Generative AI. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
 
 ## Configuration
 
@@ -35,21 +29,11 @@ cameras:
 
 ## Ollama
 
-:::warning
-
-Using Ollama on CPU is not recommended, high inference times make using Generative AI impractical.
-
-:::
-
-[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
-
-Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
-
-Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests).
+[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance. Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [docker container](https://hub.docker.com/r/ollama/ollama) available.
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
+You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`.
 
 :::note
 
@@ -64,7 +48,7 @@ genai:
   enabled: True
   provider: ollama
   base_url: http://localhost:11434
-  model: llava:7b
+  model: llava
 ```
 
 ## Google Gemini
@@ -116,44 +100,12 @@ genai:
   model: gpt-4o
 ```
 
-## Azure OpenAI
-
-Microsoft offers several vision models through Azure OpenAI. A subscription is required.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
-
-### Create Resource and Get API Key
-
-To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key and resource URL, which must include the `api-version` parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource.
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: azure_openai
-  base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
-  api_key: "{FRIGATE_OPENAI_API_KEY}"
-```
-
-## Usage and Best Practices
-
-Frigate's thumbnail search excels at identifying specific details about tracked objects – for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate’s default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
-
-While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate’s default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what’s happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they’re moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation’s context.
-
-### Using GenAI for notifications
-
-Frigate provides an [MQTT topic](/integrations/mqtt), `frigate/tracked_object_update`, that is updated with a JSON payload containing `event_id` and `description` when your AI provider returns a description for a tracked object. This description could be used directly in notifications, such as sending alerts to your phone or making audio announcements. If additional details from the tracked object are needed, you can query the [HTTP API](/integrations/api/event-events-event-id-get) using the `event_id`, eg: `http://frigate_ip:5000/api/events/<event_id>`.
-
 ## Custom Prompts
 
 Frigate sends multiple frames from the tracked object along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows:
 
 ```
-Analyze the sequence of images containing the {label}. Focus on the likely intent or behavior of the {label} based on its actions and movement, rather than describing its appearance or the surroundings. Consider what the {label} is doing, why, and what it might do next.
+Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background.
 ```
 
 :::tip
@@ -170,30 +122,22 @@ genai:
   provider: ollama
   base_url: http://localhost:11434
   model: llava
-  prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
+  prompt: "Describe the {label} in these images from the {camera} security camera."
   object_prompts:
-    person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
-    car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
+    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)."
+    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."
 ```
 
-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.
+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.
 
 ```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}."
+      prompt: "Describe the {label} in these images from the {camera} security camera at the front door of a house, aimed outward toward the street."
       object_prompts:
-        person: "Examine the person in these images. What are they doing, and how might their actions suggest their purpose (e.g., delivering something, approaching, leaving)? If they are carrying or interacting with a package, include details about its source or destination."
-        cat: "Observe the cat in these images. Focus on its movement and intent (e.g., wandering, hunting, interacting with objects). If the cat is near the flower pots or engaging in any specific actions, mention it."
-      objects:
-        - person
-        - cat
-      required_zones:
-        - steps
+        person: "Describe the main person in these images (gender, age, clothing, activity, etc). Do not include where the activity is occurring (sidewalk, concrete, driveway, etc). If delivering a package, include the company the package is from."
+        cat: "Describe the cat in these images (color, size, tail). Indicate whether or not the cat is by the flower pots. If the cat is chasing a mouse, make up a name for the mouse."
 ```
 
 ### Experiment with prompts

docs/docs/configuration/hardware_acceleration.md

@@ -65,8 +65,6 @@ Or map in all the `/dev/video*` devices.
 
 ## Intel-based CPUs
 
-:::info
-
 **Recommended hwaccel Preset**
 
 | CPU Generation | Intel Driver | Recommended Preset | Notes                               |
@@ -76,13 +74,11 @@ Or map in all the `/dev/video*` devices.
 | 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.
+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.)
 
 :::
 
@@ -175,16 +171,6 @@ For more information on the various values across different distributions, see h
 
 Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
 
-#### Stats for SR-IOV devices
-
-When using virtualized GPUs via SR-IOV, additional args are needed for GPU stats to function. This can be enabled with the following config:
-
-```yaml
-telemetry:
-  stats:
-    sriov: True
-```
-
 ## AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
 
 VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
@@ -241,11 +227,28 @@ docker run -d \
 
 ### Setup Decoder
 
-Using `preset-nvidia` ffmpeg will automatically select the necessary profile for the incoming video, and will log an error if the profile is not supported by your GPU.
+The decoder you need to pass in the `hwaccel_args` will depend on the input video.
+
+A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get the ones your card supports)
+
+```
+ V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
+ V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
+ V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
+ V..... mjpeg_cuvid          Nvidia CUVID MJPEG decoder (codec mjpeg)
+ V..... mpeg1_cuvid          Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
+ V..... mpeg2_cuvid          Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
+ V..... mpeg4_cuvid          Nvidia CUVID MPEG4 decoder (codec mpeg4)
+ V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)
+ V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)
+ V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)
+```
+
+For example, for H264 video, you'll select `preset-nvidia-h264`.
 
 ```yaml
 ffmpeg:
-  hwaccel_args: preset-nvidia
+  hwaccel_args: preset-nvidia-h264
 ```
 
 If everything is working correctly, you should see a significant improvement in performance.
@@ -376,7 +379,7 @@ Make sure to follow the [Rockchip specific installation instructions](/frigate/i
 
 ### Configuration
 
-Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
+Add one of the following FFmpeg presets to your `config.yaml` to enable hardware video processing:
 
 ```yaml
 # if you try to decode a h264 encoded stream

docs/docs/configuration/index.md

@@ -203,13 +203,14 @@ detectors:
   ov:
     type: openvino
     device: AUTO
+    model:
+      path: /openvino-model/ssdlite_mobilenet_v2.xml
 
 model:
   width: 300
   height: 300
   input_tensor: nhwc
   input_pixel_format: bgr
-  path: /openvino-model/ssdlite_mobilenet_v2.xml
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 
 record:

docs/docs/configuration/license_plate_recognition.md

@@ -1,45 +0,0 @@
----
-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

@@ -11,25 +11,15 @@ Frigate intelligently uses three different streaming technologies to display you
 
 The jsmpeg live view will use more browser and client GPU resources. Using go2rtc is highly recommended and will provide a superior experience.
 
-| Source | Frame Rate                            | Resolution | Audio                        | Requires go2rtc | Notes                                                                                                                                                               |
-| ------ | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| jsmpeg | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | Resolution is configurable, but go2rtc is recommended if you want higher resolutions and better frame rates. jsmpeg is Frigate's default without go2rtc configured. |
-| mse    | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured.                                                              |
-| webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration, doesn't support h.265. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                   |
-
-### Camera Settings Recommendations
-
-If you are using go2rtc, you should adjust the following settings in your camera's firmware for the best experience with Live view:
-
-- Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
-- Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
-- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
-
-The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
+| Source | Latency | Frame Rate                            | Resolution | Audio                        | Requires go2rtc | Other Limitations                                                                    |
+| ------ | ------- | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------ |
+| jsmpeg | low     | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | resolution is configurable, but go2rtc is recommended if you want higher resolutions |
+| mse    | low     | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only                                     |
+| webrtc | lowest  | native                                | native     | yes (depends on audio codec) | yes             | requires extra config, doesn't support h.265                                         |
 
 ### Audio Support
 
-MSE Requires PCMA/PCMU or AAC audio, WebRTC requires PCMA/PCMU or opus audio. If you want to support both MSE and WebRTC then your restream config needs to make sure both are enabled.
+MSE Requires AAC audio, WebRTC requires PCMU/PCMA, or opus audio. If you want to support both MSE and WebRTC then your restream config needs to make sure both are enabled.
 
 ```yaml
 go2rtc:
@@ -42,15 +32,6 @@ go2rtc:
       - "ffmpeg:http_cam#audio=opus" # <- copy of the stream which transcodes audio to the missing codec (usually will be opus)
 ```
 
-If your camera does not have audio and you are having problems with Live view, you should have go2rtc send video only:
-
-```yaml
-go2rtc:
-  streams:
-    no_audio_camera:
-      - ffmpeg:rtsp://192.168.1.5:554/live0#video=copy
-```
-
 ### Setting Stream For Live UI
 
 There may be some cameras that you would prefer to use the sub stream for live view, but the main stream for recording. This can be done via `live -> stream_name`.
@@ -138,13 +119,3 @@ services:
 :::
 
 See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.8.3#module-webrtc) for more information about this.
-
-### Two way talk
-
-For devices that support two way talk, Frigate can be configured to use the feature from the camera's Live view in the Web UI. You should:
-
-- Set up go2rtc with [WebRTC](#webrtc-extra-configuration).
-- Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
-- For the Home Assistant Frigate card, [follow the docs](https://github.com/dermotduffy/frigate-hass-card?tab=readme-ov-file#using-2-way-audio) for the correct source.
-
-To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)

docs/docs/configuration/motion_detection.md

@@ -92,16 +92,10 @@ motion:
   lightning_threshold: 0.8
 ```
 
-:::warning
+:::tip
 
 Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
 
 :::
 
-:::note
-
-Lightning threshold does not stop motion based recordings from being saved.
-
-:::
-
 Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in no motion detection. This is done via the `lightning_threshold` configuration. It is defined as the percentage of the image used to detect lightning or other substantial changes where motion detection needs to recalibrate. Increasing this value will make motion detection more likely to consider lightning or IR mode changes as valid motion. Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching a doorbell camera.

docs/docs/configuration/object_detectors.md

@@ -5,8 +5,6 @@ title: Object Detectors
 
 # Supported Hardware
 
-:::info
-
 Frigate supports multiple different detectors that work on different types of hardware:
 
 **Most Hardware**
@@ -22,20 +20,43 @@ Frigate supports multiple different detectors that work on different types of ha
 - [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.
+- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Nvidia GPUs, using one of many default models.
+- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image 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.
+# Officially Supported Detectors
+
+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.
+
+## 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"`.
+
+:::tip
+
+If you do not have GPU or Edge TPU hardware, using the [OpenVINO Detector](#openvino-detector) is often more efficient than using the CPU detector.
 
 :::
 
-# Officially Supported Detectors
+The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
 
-Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, `rocm`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+
+```yaml
+detectors:
+  cpu1:
+    type: cpu
+    num_threads: 3
+    model:
+      path: "/custom_model.tflite"
+  cpu2:
+    type: cpu
+    num_threads: 3
+```
+
+When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
 
 ## Edge TPU Detector
 
@@ -144,9 +165,7 @@ detectors:
 
 #### SSDLite MobileNet v2
 
-An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
-
-Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:
+An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model. Use the model configuration shown below when using the OpenVINO detector with the default model.
 
 ```yaml
 detectors:
@@ -169,7 +188,7 @@ This detector also supports YOLOX. Frigate does not come with any YOLOX models p
 
 #### YOLO-NAS
 
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
 
 :::warning
 
@@ -225,7 +244,7 @@ The model used for TensorRT must be preprocessed on the same hardware platform t
 
 The Frigate image will generate model files during startup if the specified model is not found. Processed models are stored in the `/config/model_cache` folder. Typically the `/config` path is mapped to a directory on the host already and the `model_cache` does not need to be mapped separately unless the user wants to store it in a different location on the host.
 
-By default, no models will be generated, but this can be overridden by specifying the `YOLO_MODELS` environment variable in Docker. One or more models may be listed in a comma-separated format, and each one will be generated. Models will only be generated if the corresponding `{model}.trt` file is not present in the `model_cache` folder, so you can force a model to be regenerated by deleting it from your Frigate data folder.
+By default, the `yolov7-320` model will be generated, but this can be overridden by specifying the `YOLO_MODELS` environment variable in Docker. One or more models may be listed in a comma-separated format, and each one will be generated. To select no model generation, set the variable to an empty string, `YOLO_MODELS=""`. Models will only be generated if the corresponding `{model}.trt` file is not present in the `model_cache` folder, so you can force a model to be regenerated by deleting it from your Frigate data folder.
 
 If you have a Jetson device with DLAs (Xavier or Orin), you can generate a model that will run on the DLA by appending `-dla` to your model name, e.g. specify `YOLO_MODELS=yolov7-320-dla`. The model will run on DLA0 (Frigate does not currently support DLA1). DLA-incompatible layers will fall back to running on the GPU.
 
@@ -256,7 +275,6 @@ yolov4x-mish-640
 yolov7-tiny-288
 yolov7-tiny-416
 yolov7-640
-yolov7-416
 yolov7-320
 yolov7x-640
 yolov7x-320
@@ -267,7 +285,7 @@ An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yol
 ```yml
 frigate:
   environment:
-    - YOLO_MODELS=yolov7-320,yolov7x-640
+    - YOLO_MODELS=yolov4-608,yolov7x-640
     - USE_FP16=false
 ```
 
@@ -285,8 +303,6 @@ The TensorRT detector can be selected by specifying `tensorrt` as the model type
 
 The TensorRT detector uses `.trt` model files that are located in `/config/model_cache/tensorrt` by default. These model path and dimensions used will depend on which model you have generated.
 
-Use the config below to work with generated TRT models:
-
 ```yaml
 detectors:
   tensorrt:
@@ -402,7 +418,7 @@ After placing the downloaded onnx model in your config folder, you can use the f
 
 ```yaml
 detectors:
-  rocm:
+  onnx:
     type: rocm
 
 model:
@@ -420,24 +436,6 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
 
 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:
@@ -480,42 +478,12 @@ model:
   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
-  cpu2:
-    type: cpu
-    num_threads: 3
-
-model:
-  path: "/custom_model.tflite"
-```
-
-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.
@@ -550,7 +518,7 @@ Hardware accelerated object detection is supported on the following SoCs:
 - RK3576
 - RK3588
 
-This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.3.0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
+This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.0.0.beta0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
 
 ### Prerequisites
 
@@ -623,48 +591,12 @@ $ cat /sys/kernel/debug/rknpu/load
 :::
 
 - All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
-- You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2`. Note, that there is only post-processing for the supported models.
-
-### Converting your own onnx model to rknn format
-
-To convert a onnx model to the rknn format using the [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2/) you have to:
-
-- Place one ore more models in onnx format in the directory `config/model_cache/rknn_cache/onnx` on your docker host (this might require `sudo` privileges).
-- Save the configuration file under `config/conv2rknn.yaml` (see below for details).
-- Run `docker exec <frigate_container_id> python3 /opt/conv2rknn.py`. If the conversion was successful, the rknn models will be placed in `config/model_cache/rknn_cache`.
-
-This is an example configuration file that you need to adjust to your specific onnx model:
-
-```yaml
-soc: ["rk3562","rk3566", "rk3568", "rk3576", "rk3588"]
-quantization: false
-
-output_name: "{input_basename}"
-
-config:
-  mean_values: [[0, 0, 0]]
-  std_values: [[255, 255, 255]]
-  quant_img_rgb2bgr: true
-```
-
-Explanation of the paramters:
-
-- `soc`: A list of all SoCs you want to build the rknn model for. If you don't specify this parameter, the script tries to find out your SoC and builds the rknn model for this one.
-- `quantization`: true: 8 bit integer (i8) quantization, false: 16 bit float (fp16). Default: false.
-- `output_name`: The output name of the model. The following variables are available:
-  - `quant`: "i8" or "fp16" depending on the config
-  - `input_basename`: the basename of the input model (e.g. "my_model" if the input model is calles "my_model.onnx")
-  - `soc`: the SoC this model was build for (e.g. "rk3588")
-  - `tk_version`: Version of `rknn-toolkit2` (e.g. "2.3.0")
-  - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
-- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.0_EN.pdf).
+- You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
 
 ## Hailo-8l
 
 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
 
 ```yaml
@@ -672,6 +604,8 @@ detectors:
   hailo8l:
     type: hailo8l
     device: PCIe
+    model:
+      path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
 
 model:
   width: 300
@@ -679,5 +613,4 @@ model:
   input_tensor: nhwc
   input_pixel_format: bgr
   model_type: ssd
-  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
 ```

docs/docs/configuration/objects.md

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

docs/docs/configuration/pwa.md

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