Merge pull request #4055 from blakeblackshear/dev

0.12.0 Release
cleanup hardware recommendations
2025-10-27 01:20:54 +08:00 · 2023-04-08 09:24:50 -05:00 · 2023-04-08 07:44:02 -05:00 · 2023-04-08 07:28:16 -05:00 · 2023-03-30 20:03:42 -05:00 · 2023-03-29 19:08:04 -05:00
389 changed files with 68495 additions and 1558 deletions
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,78 @@
 {
  "name": "Frigate Devcontainer",
  "dockerComposeFile": "../docker-compose.yml",
  "service": "devcontainer",
  "workspaceFolder": "/workspace/frigate",
  "initializeCommand": ".devcontainer/initialize.sh",
  "postCreateCommand": ".devcontainer/post_create.sh",
  "overrideCommand": false,
  "remoteUser": "vscode",
  "features": {
    "ghcr.io/devcontainers/features/common-utils:1": {}
  },
  "forwardPorts": [5000, 5001, 5173, 1935, 8554, 8555],
  "portsAttributes": {
    "5000": {
      "label": "NGINX",
      "onAutoForward": "silent"
    },
    "5001": {
      "label": "Frigate API",
      "onAutoForward": "silent"
    },
    "5173": {
      "label": "Vite Server",
      "onAutoForward": "silent"
    },
    "1935": {
      "label": "RTMP",
      "onAutoForward": "silent"
    },
    "8554": {
      "label": "gortc RTSP",
      "onAutoForward": "silent"
    },
    "8555": {
      "label": "go2rtc WebRTC",
      "onAutoForward": "silent"
    }
  },
  "extensions": [
    "ms-python.vscode-pylance",
    "ms-python.python",
    "visualstudioexptteam.vscodeintellicode",
    "mhutchie.git-graph",
    "ms-azuretools.vscode-docker",
    "streetsidesoftware.code-spell-checker",
    "esbenp.prettier-vscode",
    "dbaeumer.vscode-eslint",
    "mikestead.dotenv",
    "csstools.postcss",
    "blanu.vscode-styled-jsx",
    "bradlc.vscode-tailwindcss"
  ],
  "settings": {
    "remote.autoForwardPorts": false,
    "python.linting.pylintEnabled": true,
    "python.linting.enabled": true,
    "python.formatting.provider": "black",
    "python.languageServer": "Pylance",
    "editor.formatOnPaste": false,
    "editor.formatOnSave": true,
    "editor.formatOnType": true,
    "python.testing.pytestEnabled": false,
    "python.testing.unittestEnabled": true,
    "python.testing.unittestArgs": ["-v", "-s", "./frigate/test"],
    "files.trimTrailingWhitespace": true,
    "eslint.workingDirectories": ["./web"],
    "[json][jsonc]": {
      "editor.defaultFormatter": "esbenp.prettier-vscode"
    },
    "[jsx][js][tsx][ts]": {
      "editor.codeActionsOnSave": ["source.addMissingImports", "source.fixAll"],
      "editor.tabSize": 2
    },
    "cSpell.ignoreWords": ["rtmp"],
    "cSpell.words": ["preact"]
  }
 }
--- a/.devcontainer/initialize.sh
+++ b/.devcontainer/initialize.sh
@@ -0,0 +1,13 @@
 #!/bin/bash
 set -euo pipefail
 # These folders needs to be created and owned by the host user
 mkdir -p debug web/dist
 if [[ -f "config/config.yml" ]]; then
  echo "config/config.yml already exists, skipping initialization" >&2
 else
  echo "initializing config/config.yml" >&2
  cp -fv config/config.yml.example config/config.yml
 fi
--- a/.devcontainer/post_create.sh
+++ b/.devcontainer/post_create.sh
@@ -0,0 +1,17 @@
 #!/bin/bash
 set -euxo pipefail
 # 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
 # create the folders and give the host user permission to write to them.
 sudo mkdir -p /media/frigate
 sudo chown -R "$(id -u):$(id -g)" /media/frigate
 make version
 cd web
 npm install
 npm run build
--- a/.dockerignore
+++ b/.dockerignore
@@ -1 +1,16 @@
-README.md
+README.md
 docs/
 .gitignore
 debug
 config/
 *.pyc
 .git
 core
 *.mp4
 *.jpg
 *.db
 *.ts
 web/dist/
 web/node_modules/
 web/.npm
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -0,0 +1,3 @@
 github:
  - blakeblackshear
  - NickM-27
--- a/.github/ISSUE_TEMPLATE/camera_support_request.yml
+++ b/.github/ISSUE_TEMPLATE/camera_support_request.yml
@@ -0,0 +1,107 @@
 name: Camera Support Request
 description: Support for setting up cameras in Frigate
 title: "[Camera Support]: "
 labels: ["support", "triage"]
 assignees: []
 body:
  - type: textarea
    id: description
    attributes:
      label: Describe the problem you are having
    validations:
      required: true
  - type: input
    id: version
    attributes:
      label: Version
      description: Visible on the Debug page in the Web UI
    validations:
      required: true
  - type: textarea
    id: config
    attributes:
      label: Frigate config file
      description: This will be automatically formatted into code, so no need for backticks.
      render: yaml
    validations:
      required: true
  - type: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
      render: shell
    validations:
      required: true
  - type: textarea
    id: ffprobe
    attributes:
      label: FFprobe output from your camera
      description: Run `ffprobe <camera_url>` and provide output below
      render: shell
    validations:
      required: true
  - type: textarea
    id: stats
    attributes:
      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:
      label: Install method
      options:
        - HassOS Addon
        - Docker Compose
        - Docker CLI
    validations:
      required: true
  - type: dropdown
    id: coral
    attributes:
      label: Coral version
      options:
        - USB
        - PCIe
        - M.2
        - Dev Board
        - Other
        - CPU (no coral)
    validations:
      required: true
  - type: dropdown
    id: network
    attributes:
      label: Network connection
      options:
        - Wired
        - Wireless
        - Mixed
    validations:
      required: true
  - type: input
    id: camera
    attributes:
      label: Camera make and model
      description: Dahua, hikvision, amcrest, reolink, etc and model number
    validations:
      required: true
  - type: textarea
    id: other
    attributes:
      label: Any other information that may be helpful
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1 @@
 blank_issues_enabled: false
--- a/.github/ISSUE_TEMPLATE/config_support_request.yml
+++ b/.github/ISSUE_TEMPLATE/config_support_request.yml
@@ -0,0 +1,82 @@
 name: Config Support Request
 description: Support for Frigate configuration
 title: "[Config Support]: "
 labels: ["support", "triage"]
 assignees: []
 body:
  - type: textarea
    id: description
    attributes:
      label: Describe the problem you are having
    validations:
      required: true
  - type: input
    id: version
    attributes:
      label: Version
      description: Visible on the Debug page in the Web UI
    validations:
      required: true
  - type: textarea
    id: config
    attributes:
      label: Frigate config file
      description: This will be automatically formatted into code, so no need for backticks.
      render: yaml
    validations:
      required: true
  - type: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
      render: shell
    validations:
      required: true
  - type: textarea
    id: stats
    attributes:
      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:
      label: Install method
      options:
        - HassOS Addon
        - Docker Compose
        - Docker CLI
    validations:
      required: true
  - type: dropdown
    id: coral
    attributes:
      label: Coral version
      options:
        - USB
        - PCIe
        - M.2
        - Dev Board
        - Other
        - CPU (no coral)
    validations:
      required: true
  - type: textarea
    id: other
    attributes:
      label: Any other information that may be helpful
--- a/.github/ISSUE_TEMPLATE/detector_support_request.yml
+++ b/.github/ISSUE_TEMPLATE/detector_support_request.yml
@@ -0,0 +1,84 @@
 name: Detector Support Request
 description: Support for setting up object detector in Frigate (Coral, OpenVINO, TensorRT, etc.)
 title: "[Detector Support]: "
 labels: ["support", "triage"]
 assignees: []
 body:
  - type: textarea
    id: description
    attributes:
      label: Describe the problem you are having
    validations:
      required: true
  - type: input
    id: version
    attributes:
      label: Version
      description: Visible on the Debug page in the Web UI
    validations:
      required: true
  - type: textarea
    id: config
    attributes:
      label: Frigate config file
      description: This will be automatically formatted into code, so no need for backticks.
      render: yaml
    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: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: Please copy and paste any relevant log output. 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:
      label: Install method
      options:
        - HassOS Addon
        - Docker Compose
        - Docker CLI
    validations:
      required: true
  - type: dropdown
    id: coral
    attributes:
      label: Coral version
      options:
        - USB
        - PCIe
        - M.2
        - Dev Board
        - Other
        - CPU (no coral)
    validations:
      required: true
  - type: textarea
    id: other
    attributes:
      label: Any other information that may be helpful
--- a/.github/ISSUE_TEMPLATE/feature_request.md
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
 ---
 name: Feature request
 about: Suggest an idea for this project
 title: ''
 labels: enhancement
 assignees: ''
 ---
 **Describe what you are trying to accomplish and why in non technical terms**
 I want to be able to ... so that I can ... 
 **Describe the solution you'd like**
 A clear and concise description of what you want to happen.
 **Describe alternatives you've considered**
 A clear and concise description of any alternative solutions or features you've considered.
 **Additional context**
 Add any other context or screenshots about the feature request here.
--- a/.github/ISSUE_TEMPLATE/general_support_request.yml
+++ b/.github/ISSUE_TEMPLATE/general_support_request.yml
@@ -0,0 +1,107 @@
 name: General Support Request
 description: General support request for Frigate
 title: "[Support]: "
 labels: ["support", "triage"]
 assignees: []
 body:
  - type: textarea
    id: description
    attributes:
      label: Describe the problem you are having
    validations:
      required: true
  - type: input
    id: version
    attributes:
      label: Version
      description: Visible on the Debug page in the Web UI
    validations:
      required: true
  - type: textarea
    id: config
    attributes:
      label: Frigate config file
      description: This will be automatically formatted into code, so no need for backticks.
      render: yaml
    validations:
      required: true
  - type: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
      render: shell
    validations:
      required: true
  - type: textarea
    id: ffprobe
    attributes:
      label: FFprobe output from your camera
      description: Run `ffprobe <camera_url>` and provide output below
      render: shell
    validations:
      required: true
  - type: textarea
    id: stats
    attributes:
      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:
      label: Install method
      options:
        - HassOS Addon
        - Docker Compose
        - Docker CLI
    validations:
      required: true
  - type: dropdown
    id: coral
    attributes:
      label: Coral version
      options:
        - USB
        - PCIe
        - M.2
        - Dev Board
        - Other
        - CPU (no coral)
    validations:
      required: true
  - type: dropdown
    id: network
    attributes:
      label: Network connection
      options:
        - Wired
        - Wireless
        - Mixed
    validations:
      required: true
  - type: input
    id: camera
    attributes:
      label: Camera make and model
      description: Dahua, hikvision, amcrest, reolink, etc and model number
    validations:
      required: true
  - type: textarea
    id: other
    attributes:
      label: Any other information that may be helpful
--- a/.github/ISSUE_TEMPLATE/hwaccel_support_request.yml
+++ b/.github/ISSUE_TEMPLATE/hwaccel_support_request.yml
@@ -0,0 +1,96 @@
 name: Hardware Acceleration Support Request
 description: Support for setting up GPU hardware acceleration in Frigate
 title: "[HW Accel Support]: "
 labels: ["support", "triage"]
 assignees: []
 body:
  - type: textarea
    id: description
    attributes:
      label: Describe the problem you are having
    validations:
      required: true
  - type: input
    id: version
    attributes:
      label: Version
      description: Visible on the Debug page in the Web UI
    validations:
      required: true
  - type: textarea
    id: config
    attributes:
      label: Frigate config file
      description: This will be automatically formatted into code, so no need for backticks.
      render: yaml
    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: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
      render: shell
    validations:
      required: true
  - type: textarea
    id: ffprobe
    attributes:
      label: FFprobe output from your camera
      description: Run `ffprobe <camera_url>` and provide output below
      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:
      label: Install method
      options:
        - HassOS Addon
        - Docker Compose
        - Docker CLI
    validations:
      required: true
  - type: dropdown
    id: network
    attributes:
      label: Network connection
      options:
        - Wired
        - Wireless
        - Mixed
    validations:
      required: true
  - type: input
    id: camera
    attributes:
      label: Camera make and model
      description: Dahua, hikvision, amcrest, reolink, etc and model number
    validations:
      required: true
  - type: textarea
    id: other
    attributes:
      label: Any other information that may be helpful
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -0,0 +1,32 @@
 version: 2
 updates:
  - package-ecosystem: "github-actions"
    directory: "/"
    schedule:
      interval: daily
    open-pull-requests-limit: 10
    target-branch: dev
  - package-ecosystem: "docker"
    directory: "/docker"
    schedule:
      interval: daily
    open-pull-requests-limit: 10
    target-branch: dev
  - package-ecosystem: "pip"
    directory: "/"
    schedule:
      interval: daily
    open-pull-requests-limit: 10
    target-branch: dev
  - package-ecosystem: "npm"
    directory: "/web"
    schedule:
      interval: daily
    open-pull-requests-limit: 10
    target-branch: dev
  - package-ecosystem: "npm"
    directory: "/docs"
    schedule:
      interval: daily
    open-pull-requests-limit: 10
    target-branch: dev
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,67 @@
 name: CI
 on:
  push:
    branches:
      - dev
      - master
 # only run the latest commit to avoid cache overwrites
 concurrency:
  group: ${{ github.ref }}
  cancel-in-progress: true
 env:
  PYTHON_VERSION: 3.9
 jobs:
  multi_arch_build:
    runs-on: ubuntu-latest
    name: Image Build
    steps:
      - name: Remove unnecessary files
        run: |
          sudo rm -rf /usr/share/dotnet
          sudo rm -rf /usr/local/lib/android
          sudo rm -rf /opt/ghc
      - id: lowercaseRepo
        uses: ASzc/change-string-case-action@v5
        with:
          string: ${{ github.repository }}
      - name: Check out code
        uses: actions/checkout@v3
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v2
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v2
      - name: Log in to the Container registry
        uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      - name: Create version file
        run: make version
      - name: Create short sha
        run: echo "SHORT_SHA=${GITHUB_SHA::7}" >> $GITHUB_ENV
      - name: Build and push
        uses: docker/build-push-action@v3
        with:
          context: .
          push: true
          platforms: linux/amd64,linux/arm64,linux/arm/v7
          target: frigate
          tags: |
            ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ github.ref_name }}-${{ env.SHORT_SHA }}
          cache-from: type=gha
          cache-to: type=gha,mode=max
      - name: Build and push TensorRT
        uses: docker/build-push-action@v3
        with:
          context: .
          push: true
          platforms: linux/amd64
          target: frigate-tensorrt
          tags: |
            ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ github.ref_name }}-${{ env.SHORT_SHA }}-tensorrt
          cache-from: type=gha
--- a/.github/workflows/dependabot-auto-merge.yaml
+++ b/.github/workflows/dependabot-auto-merge.yaml
@@ -0,0 +1,22 @@
 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@v1
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
      - name: Enable auto-merge for Dependabot PRs
        if: steps.metadata.outputs.dependency-type == 'direct:development' && (steps.metadata.outputs.update-type == 'version-update:semver-minor' || steps.metadata.outputs.update-type == 'version-update:semver-patch')
        run: gh pr merge --auto --squash "$PR_URL"
        env:
          PR_URL: ${{ github.event.pull_request.html_url }}
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
--- a/.github/workflows/pull_request.yml
+++ b/.github/workflows/pull_request.yml
@@ -0,0 +1,102 @@
 name: On pull request
 on: pull_request
 env:
  DEFAULT_PYTHON: 3.9
 jobs:
  build_devcontainer:
    runs-on: ubuntu-latest
    name: Build Devcontainer
    # The Dockerfile contains features that requires buildkit, and since the
    # devcontainer cli uses docker-compose to build the image, the only way to
    # ensure docker-compose uses buildkit is to explicitly enable it.
    env:
      DOCKER_BUILDKIT: "1"
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@master
        with:
          node-version: 16.x
      - name: Install devcontainer cli
        run: npm install --global @devcontainers/cli
      - name: Build devcontainer
        run: devcontainer build --workspace-folder .
      # It would be nice to also test the following commands, but for some
      # reason they don't work even though in VS Code devcontainer works.
      # - name: Start devcontainer
      #   run: devcontainer up --workspace-folder .
      # - name: Run devcontainer scripts
      #   run: devcontainer run-user-commands --workspace-folder .
  web_lint:
    name: Web - Lint
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@master
        with:
          node-version: 16.x
      - run: npm install
        working-directory: ./web
      - name: Lint
        run: npm run lint
        working-directory: ./web
  web_test:
    name: Web - Test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@master
        with:
          node-version: 16.x
      - run: npm install
        working-directory: ./web
      - name: Test
        run: npm run test
        working-directory: ./web
  python_checks:
    runs-on: ubuntu-latest
    name: Python Checks
    steps:
      - name: Check out the repository
        uses: actions/checkout@v3
      - name: Set up Python ${{ env.DEFAULT_PYTHON }}
        uses: actions/setup-python@v4.5.0
        with:
          python-version: ${{ env.DEFAULT_PYTHON }}
      - name: Install requirements
        run: |
          pip install pip
          pip install -r requirements-dev.txt
      - name: Lint
        run: |
          python3 -m black frigate --check
  python_tests:
    runs-on: ubuntu-latest
    name: Python Tests
    steps:
      - name: Check out code
        uses: actions/checkout@v3
      - uses: actions/setup-node@master
        with:
          node-version: 16.x
      - run: npm install
        working-directory: ./web
      - name: Build web
        run: npm run build
        working-directory: ./web
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v2
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v2
      - name: Build
        run: make
      - name: Run mypy
        run: docker run --rm --entrypoint=python3 frigate:latest -u -m mypy --config-file frigate/mypy.ini frigate
      - name: Run tests
        run: docker run --rm --entrypoint=python3 frigate:latest -u -m unittest
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -0,0 +1,26 @@
 # Close Stale Issues
 # Warns and then closes issues and PRs that have had no activity for a specified amount of time.
 # https://github.com/actions/stale
 name: "Stalebot"
 on:
  schedule:
    - cron: "0 0 * * *" # run stalebot once a day
 jobs:
  stale:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/stale@main
        id: stale
        with:
          stale-issue-message: "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
          close-issue-message: ""
          days-before-stale: 30
          days-before-close: 3
          exempt-draft-pr: true
          exempt-issue-labels: "pinned,security"
          exempt-pr-labels: "pinned,security,dependencies"
          operations-per-run: 120
      - name: Print outputs
        run: echo ${{ join(steps.stale.outputs.*, ',') }}
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,19 @@
-*.pyc 
+.DS_Store
 *.pyc
 *.swp
 debug
 .vscode/*
 !.vscode/launch.json
 config/config.yml
 models
 *.mp4
 *.ts
 *.db
 *.csv
 frigate/version.py
 web/build
 web/node_modules
 web/coverage
 core
 !/web/**/*.ts
 .idea/*
--- a/.pylintrc
+++ b/.pylintrc
@@ -0,0 +1,588 @@
 [MASTER]
 # A comma-separated list of package or module names from where C extensions may
 # be loaded. Extensions are loading into the active Python interpreter and may
 # run arbitrary code.
 extension-pkg-whitelist=
 # Specify a score threshold to be exceeded before program exits with error.
 fail-under=10.0
 # Add files or directories to the blacklist. They should be base names, not
 # paths.
 ignore=CVS
 # Add files or directories matching the regex patterns to the blacklist. The
 # regex matches against base names, not paths.
 ignore-patterns=
 # Python code to execute, usually for sys.path manipulation such as
 # pygtk.require().
 #init-hook=
 # Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
 # number of processors available to use.
 jobs=1
 # Control the amount of potential inferred values when inferring a single
 # object. This can help the performance when dealing with large functions or
 # complex, nested conditions.
 limit-inference-results=100
 # List of plugins (as comma separated values of python module names) to load,
 # usually to register additional checkers.
 load-plugins=
 # Pickle collected data for later comparisons.
 persistent=yes
 # When enabled, pylint would attempt to guess common misconfiguration and emit
 # user-friendly hints instead of false-positive error messages.
 suggestion-mode=yes
 # Allow loading of arbitrary C extensions. Extensions are imported into the
 # active Python interpreter and may run arbitrary code.
 unsafe-load-any-extension=no
 [MESSAGES CONTROL]
 # Only show warnings with the listed confidence levels. Leave empty to show
 # all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
 confidence=
 # Disable the message, report, category or checker with the given id(s). You
 # can either give multiple identifiers separated by comma (,) or put this
 # option multiple times (only on the command line, not in the configuration
 # file where it should appear only once). You can also use "--disable=all" to
 # disable everything first and then reenable specific checks. For example, if
 # you want to run only the similarities checker, you can use "--disable=all
 # --enable=similarities". If you want to run only the classes checker, but have
 # no Warning level messages displayed, use "--disable=all --enable=classes
 # --disable=W".
 disable=print-statement,
        parameter-unpacking,
        unpacking-in-except,
        old-raise-syntax,
        backtick,
        long-suffix,
        old-ne-operator,
        old-octal-literal,
        import-star-module-level,
        non-ascii-bytes-literal,
        raw-checker-failed,
        bad-inline-option,
        locally-disabled,
        file-ignored,
        suppressed-message,
        useless-suppression,
        deprecated-pragma,
        use-symbolic-message-instead,
        apply-builtin,
        basestring-builtin,
        buffer-builtin,
        cmp-builtin,
        coerce-builtin,
        execfile-builtin,
        file-builtin,
        long-builtin,
        raw_input-builtin,
        reduce-builtin,
        standarderror-builtin,
        unicode-builtin,
        xrange-builtin,
        coerce-method,
        delslice-method,
        getslice-method,
        setslice-method,
        no-absolute-import,
        old-division,
        dict-iter-method,
        dict-view-method,
        next-method-called,
        metaclass-assignment,
        indexing-exception,
        raising-string,
        reload-builtin,
        oct-method,
        hex-method,
        nonzero-method,
        cmp-method,
        input-builtin,
        round-builtin,
        intern-builtin,
        unichr-builtin,
        map-builtin-not-iterating,
        zip-builtin-not-iterating,
        range-builtin-not-iterating,
        filter-builtin-not-iterating,
        using-cmp-argument,
        eq-without-hash,
        div-method,
        idiv-method,
        rdiv-method,
        exception-message-attribute,
        invalid-str-codec,
        sys-max-int,
        bad-python3-import,
        deprecated-string-function,
        deprecated-str-translate-call,
        deprecated-itertools-function,
        deprecated-types-field,
        next-method-defined,
        dict-items-not-iterating,
        dict-keys-not-iterating,
        dict-values-not-iterating,
        deprecated-operator-function,
        deprecated-urllib-function,
        xreadlines-attribute,
        deprecated-sys-function,
        exception-escape,
        comprehension-escape
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option
 # multiple time (only on the command line, not in the configuration file where
 # it should appear only once). See also the "--disable" option for examples.
 enable=c-extension-no-member
 [REPORTS]
 # Python expression which should return a score less than or equal to 10. You
 # have access to the variables 'error', 'warning', 'refactor', and 'convention'
 # which contain the number of messages in each category, as well as 'statement'
 # which is the total number of statements analyzed. This score is used by the
 # global evaluation report (RP0004).
 evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
 # Template used to display messages. This is a python new-style format string
 # used to format the message information. See doc for all details.
 #msg-template=
 # Set the output format. Available formats are text, parseable, colorized, json
 # and msvs (visual studio). You can also give a reporter class, e.g.
 # mypackage.mymodule.MyReporterClass.
 output-format=text
 # Tells whether to display a full report or only the messages.
 reports=no
 # Activate the evaluation score.
 score=yes
 [REFACTORING]
 # Maximum number of nested blocks for function / method body
 max-nested-blocks=5
 # Complete name of functions that never returns. When checking for
 # inconsistent-return-statements if a never returning function is called then
 # it will be considered as an explicit return statement and no message will be
 # printed.
 never-returning-functions=sys.exit
 [SPELLING]
 # Limits count of emitted suggestions for spelling mistakes.
 max-spelling-suggestions=4
 # Spelling dictionary name. Available dictionaries: none. To make it work,
 # install the python-enchant package.
 spelling-dict=
 # List of comma separated words that should not be checked.
 spelling-ignore-words=
 # A path to a file that contains the private dictionary; one word per line.
 spelling-private-dict-file=
 # Tells whether to store unknown words to the private dictionary (see the
 # --spelling-private-dict-file option) instead of raising a message.
 spelling-store-unknown-words=no
 [TYPECHECK]
 # List of decorators that produce context managers, such as
 # contextlib.contextmanager. Add to this list to register other decorators that
 # produce valid context managers.
 contextmanager-decorators=contextlib.contextmanager
 # List of members which are set dynamically and missed by pylint inference
 # system, and so shouldn't trigger E1101 when accessed. Python regular
 # expressions are accepted.
 generated-members=
 # Tells whether missing members accessed in mixin class should be ignored. A
 # mixin class is detected if its name ends with "mixin" (case insensitive).
 ignore-mixin-members=yes
 # Tells whether to warn about missing members when the owner of the attribute
 # is inferred to be None.
 ignore-none=yes
 # This flag controls whether pylint should warn about no-member and similar
 # checks whenever an opaque object is returned when inferring. The inference
 # can return multiple potential results while evaluating a Python object, but
 # some branches might not be evaluated, which results in partial inference. In
 # that case, it might be useful to still emit no-member and other checks for
 # the rest of the inferred objects.
 ignore-on-opaque-inference=yes
 # List of class names for which member attributes should not be checked (useful
 # for classes with dynamically set attributes). This supports the use of
 # qualified names.
 ignored-classes=optparse.Values,thread._local,_thread._local
 # List of module names for which member attributes should not be checked
 # (useful for modules/projects where namespaces are manipulated during runtime
 # and thus existing member attributes cannot be deduced by static analysis). It
 # supports qualified module names, as well as Unix pattern matching.
 ignored-modules=
 # Show a hint with possible names when a member name was not found. The aspect
 # of finding the hint is based on edit distance.
 missing-member-hint=yes
 # The minimum edit distance a name should have in order to be considered a
 # similar match for a missing member name.
 missing-member-hint-distance=1
 # The total number of similar names that should be taken in consideration when
 # showing a hint for a missing member.
 missing-member-max-choices=1
 # List of decorators that change the signature of a decorated function.
 signature-mutators=
 [STRING]
 # This flag controls whether inconsistent-quotes generates a warning when the
 # character used as a quote delimiter is used inconsistently within a module.
 check-quote-consistency=no
 # This flag controls whether the implicit-str-concat should generate a warning
 # on implicit string concatenation in sequences defined over several lines.
 check-str-concat-over-line-jumps=no
 [FORMAT]
 # Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
 expected-line-ending-format=
 # Regexp for a line that is allowed to be longer than the limit.
 ignore-long-lines=^\s*(# )?<?https?://\S+>?$
 # Number of spaces of indent required inside a hanging or continued line.
 indent-after-paren=4
 # String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
 # tab).
 indent-string='    '
 # Maximum number of characters on a single line.
 max-line-length=100
 # Maximum number of lines in a module.
 max-module-lines=1000
 # Allow the body of a class to be on the same line as the declaration if body
 # contains single statement.
 single-line-class-stmt=no
 # Allow the body of an if to be on the same line as the test if there is no
 # else.
 single-line-if-stmt=no
 [SIMILARITIES]
 # Ignore comments when computing similarities.
 ignore-comments=yes
 # Ignore docstrings when computing similarities.
 ignore-docstrings=yes
 # Ignore imports when computing similarities.
 ignore-imports=no
 # Minimum lines number of a similarity.
 min-similarity-lines=4
 [MISCELLANEOUS]
 # List of note tags to take in consideration, separated by a comma.
 notes=FIXME,
      XXX,
      TODO
 # Regular expression of note tags to take in consideration.
 #notes-rgx=
 [BASIC]
 # Naming style matching correct argument names.
 argument-naming-style=snake_case
 # Regular expression matching correct argument names. Overrides argument-
 # naming-style.
 #argument-rgx=
 # Naming style matching correct attribute names.
 attr-naming-style=snake_case
 # Regular expression matching correct attribute names. Overrides attr-naming-
 # style.
 #attr-rgx=
 # Bad variable names which should always be refused, separated by a comma.
 bad-names=foo,
          bar,
          baz,
          toto,
          tutu,
          tata
 # Bad variable names regexes, separated by a comma. If names match any regex,
 # they will always be refused
 bad-names-rgxs=
 # Naming style matching correct class attribute names.
 class-attribute-naming-style=any
 # Regular expression matching correct class attribute names. Overrides class-
 # attribute-naming-style.
 #class-attribute-rgx=
 # Naming style matching correct class names.
 class-naming-style=PascalCase
 # Regular expression matching correct class names. Overrides class-naming-
 # style.
 #class-rgx=
 # Naming style matching correct constant names.
 const-naming-style=UPPER_CASE
 # Regular expression matching correct constant names. Overrides const-naming-
 # style.
 #const-rgx=
 # Minimum line length for functions/classes that require docstrings, shorter
 # ones are exempt.
 docstring-min-length=-1
 # Naming style matching correct function names.
 function-naming-style=snake_case
 # Regular expression matching correct function names. Overrides function-
 # naming-style.
 #function-rgx=
 # Good variable names which should always be accepted, separated by a comma.
 good-names=i,
           j,
           k,
           ex,
           Run,
           _
 # Good variable names regexes, separated by a comma. If names match any regex,
 # they will always be accepted
 good-names-rgxs=
 # Include a hint for the correct naming format with invalid-name.
 include-naming-hint=no
 # Naming style matching correct inline iteration names.
 inlinevar-naming-style=any
 # Regular expression matching correct inline iteration names. Overrides
 # inlinevar-naming-style.
 #inlinevar-rgx=
 # Naming style matching correct method names.
 method-naming-style=snake_case
 # Regular expression matching correct method names. Overrides method-naming-
 # style.
 #method-rgx=
 # Naming style matching correct module names.
 module-naming-style=snake_case
 # Regular expression matching correct module names. Overrides module-naming-
 # style.
 #module-rgx=
 # Colon-delimited sets of names that determine each other's naming style when
 # the name regexes allow several styles.
 name-group=
 # Regular expression which should only match function or class names that do
 # not require a docstring.
 no-docstring-rgx=^_
 # List of decorators that produce properties, such as abc.abstractproperty. Add
 # to this list to register other decorators that produce valid properties.
 # These decorators are taken in consideration only for invalid-name.
 property-classes=abc.abstractproperty
 # Naming style matching correct variable names.
 variable-naming-style=snake_case
 # Regular expression matching correct variable names. Overrides variable-
 # naming-style.
 #variable-rgx=
 [VARIABLES]
 # List of additional names supposed to be defined in builtins. Remember that
 # you should avoid defining new builtins when possible.
 additional-builtins=
 # Tells whether unused global variables should be treated as a violation.
 allow-global-unused-variables=yes
 # List of strings which can identify a callback function by name. A callback
 # name must start or end with one of those strings.
 callbacks=cb_,
          _cb
 # A regular expression matching the name of dummy variables (i.e. expected to
 # not be used).
 dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
 # Argument names that match this expression will be ignored. Default to name
 # with leading underscore.
 ignored-argument-names=_.*|^ignored_|^unused_
 # Tells whether we should check for unused import in __init__ files.
 init-import=no
 # List of qualified module names which can have objects that can redefine
 # builtins.
 redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
 [LOGGING]
 # The type of string formatting that logging methods do. `old` means using %
 # formatting, `new` is for `{}` formatting.
 logging-format-style=fstr
 # Logging modules to check that the string format arguments are in logging
 # function parameter format.
 logging-modules=logging
 [DESIGN]
 # Maximum number of arguments for function / method.
 max-args=5
 # Maximum number of attributes for a class (see R0902).
 max-attributes=7
 # Maximum number of boolean expressions in an if statement (see R0916).
 max-bool-expr=5
 # Maximum number of branch for function / method body.
 max-branches=12
 # Maximum number of locals for function / method body.
 max-locals=15
 # Maximum number of parents for a class (see R0901).
 max-parents=7
 # Maximum number of public methods for a class (see R0904).
 max-public-methods=20
 # Maximum number of return / yield for function / method body.
 max-returns=6
 # Maximum number of statements in function / method body.
 max-statements=50
 # Minimum number of public methods for a class (see R0903).
 min-public-methods=2
 [CLASSES]
 # List of method names used to declare (i.e. assign) instance attributes.
 defining-attr-methods=__init__,
                      __new__,
                      setUp,
                      __post_init__
 # List of member names, which should be excluded from the protected access
 # warning.
 exclude-protected=_asdict,
                  _fields,
                  _replace,
                  _source,
                  _make
 # List of valid names for the first argument in a class method.
 valid-classmethod-first-arg=cls
 # List of valid names for the first argument in a metaclass class method.
 valid-metaclass-classmethod-first-arg=cls
 [IMPORTS]
 # List of modules that can be imported at any level, not just the top level
 # one.
 allow-any-import-level=
 # Allow wildcard imports from modules that define __all__.
 allow-wildcard-with-all=no
 # Analyse import fallback blocks. This can be used to support both Python 2 and
 # 3 compatible code, which means that the block might have code that exists
 # only in one or another interpreter, leading to false positives when analysed.
 analyse-fallback-blocks=no
 # Deprecated modules which should not be used, separated by a comma.
 deprecated-modules=optparse,tkinter.tix
 # Create a graph of external dependencies in the given file (report RP0402 must
 # not be disabled).
 ext-import-graph=
 # Create a graph of every (i.e. internal and external) dependencies in the
 # given file (report RP0402 must not be disabled).
 import-graph=
 # Create a graph of internal dependencies in the given file (report RP0402 must
 # not be disabled).
 int-import-graph=
 # Force import order to recognize a module as part of the standard
 # compatibility libraries.
 known-standard-library=
 # Force import order to recognize a module as part of a third party library.
 known-third-party=enchant
 # Couples of modules and preferred modules, separated by a comma.
 preferred-modules=
 [EXCEPTIONS]
 # Exceptions that will emit a warning when being caught. Defaults to
 # "BaseException, Exception".
 overgeneral-exceptions=BaseException,
                       Exception
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -0,0 +1,12 @@
 {
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Python: Launch Frigate",
      "type": "python",
      "request": "launch",
      "module": "frigate",
      "justMyCode": true
    }
  ]
 }
--- a/340
+++ b/340
@@ -1,90 +1,272 @@
-FROM ubuntu:16.04
+# syntax=docker/dockerfile:1.2
-# Install system packages
+# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
-RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y python3 \ 
+ARG DEBIAN_FRONTEND=noninteractive
 python3-dev \
 python-pil \
 python-lxml \
 python-tk \
 build-essential \
 cmake \ 
 git \ 
 libgtk2.0-dev \ 
 pkg-config \ 
 libavcodec-dev \ 
 libavformat-dev \ 
 libswscale-dev \ 
 libtbb2 \
 libtbb-dev \ 
 libjpeg-dev \
 libpng-dev \
 libtiff-dev \
 libjasper-dev \
 libdc1394-22-dev \
 x11-apps \
 wget \
 vim \
 ffmpeg \
 unzip \
 && rm -rf /var/lib/apt/lists/* 
-# Install core packages 
+FROM debian:11 AS base
 RUN wget -q -O /tmp/get-pip.py --no-check-certificate https://bootstrap.pypa.io/get-pip.py && python3 /tmp/get-pip.py
 RUN  pip install -U pip \
 numpy \
 matplotlib \
 notebook \
 jupyter \
 pandas \
 moviepy \
 tensorflow \
 keras \
 autovizwidget \
 Flask \
 imutils \
 paho-mqtt
-# Install tensorflow models object detection
+FROM --platform=linux/amd64 debian:11 AS base_amd64
 RUN GIT_SSL_NO_VERIFY=true git clone -q https://github.com/tensorflow/models /usr/local/lib/python3.5/dist-packages/tensorflow/models
 RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/google/protobuf/releases/download/v3.5.1/protobuf-python-3.5.1.tar.gz
-# Download & build protobuf-python
+FROM debian:11-slim AS slim-base
 RUN cd /usr/local/src/ \
 && tar xf protobuf-python-3.5.1.tar.gz \
 && rm protobuf-python-3.5.1.tar.gz \
 && cd /usr/local/src/protobuf-3.5.1/ \
 && ./configure \
 && make \
 && make install \
 && ldconfig \
 && rm -rf /usr/local/src/protobuf-3.5.1/
-# Add dataframe display widget
+FROM slim-base AS wget
-RUN jupyter nbextension enable --py --sys-prefix widgetsnbextension
+ARG DEBIAN_FRONTEND
 RUN apt-get update \
    && apt-get install -y wget xz-utils \
    && rm -rf /var/lib/apt/lists/*
 WORKDIR /rootfs
-# Download & build OpenCV
+FROM base AS nginx
-RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/4.0.1.zip
+ARG DEBIAN_FRONTEND
 RUN cd /usr/local/src/ \
 && unzip 4.0.1.zip \
 && rm 4.0.1.zip \
 && cd /usr/local/src/opencv-4.0.1/ \
 && mkdir build \
 && cd /usr/local/src/opencv-4.0.1/build \ 
 && cmake -D CMAKE_INSTALL_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ .. \
 && make -j4 \
 && make install \
 && rm -rf /usr/local/src/opencv-4.0.1
-# Minimize image size 
+# bind /var/cache/apt to tmpfs to speed up nginx build
-RUN (apt-get autoremove -y; \
+RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
-     apt-get autoclean -y)
+    --mount=type=bind,source=docker/build_nginx.sh,target=/deps/build_nginx.sh \
    /deps/build_nginx.sh
-# Set TF object detection available
+FROM wget AS go2rtc
-ENV PYTHONPATH "$PYTHONPATH:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research/slim"
+ARG TARGETARCH
-RUN cd /usr/local/lib/python3.5/dist-packages/tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=.
+WORKDIR /rootfs/usr/local/go2rtc/bin
 RUN wget -qO go2rtc "https://github.com/AlexxIT/go2rtc/releases/download/v1.2.0/go2rtc_linux_${TARGETARCH}" \
    && chmod +x go2rtc
 ####
 #
 # OpenVino Support
 #
 # 1. Download and convert a model from Intel's Public Open Model Zoo
 # 2. Build libUSB without udev to handle NCS2 enumeration
 #
 ####
 # Download and Convert OpenVino model
 FROM base_amd64 AS ov-converter
 ARG DEBIAN_FRONTEND
 # Install OpenVino Runtime and Dev library
 COPY requirements-ov.txt /requirements-ov.txt
 RUN apt-get -qq update \
    && apt-get -qq install -y wget python3 python3-distutils \
    && wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
    && python3 get-pip.py "pip" \
    && pip install -r /requirements-ov.txt
 # Get OpenVino Model
 RUN mkdir /models \
    && cd /models && omz_downloader --name ssdlite_mobilenet_v2 \
    && cd /models && omz_converter --name ssdlite_mobilenet_v2 --precision FP16
 # libUSB - No Udev
 FROM wget as libusb-build
 ARG TARGETARCH
 ARG DEBIAN_FRONTEND
 # Build libUSB without udev.  Needed for Openvino NCS2 support
 WORKDIR /opt
 RUN apt-get update && apt-get install -y unzip build-essential automake libtool
 RUN wget -q https://github.com/libusb/libusb/archive/v1.0.25.zip -O v1.0.25.zip && \
    unzip v1.0.25.zip && cd libusb-1.0.25 && \
    ./bootstrap.sh && \
    ./configure --disable-udev --enable-shared && \
    make -j $(nproc --all)
 RUN apt-get update && \
    apt-get install -y --no-install-recommends libusb-1.0-0-dev && \
    rm -rf /var/lib/apt/lists/*
 WORKDIR /opt/libusb-1.0.25/libusb
 RUN /bin/mkdir -p '/usr/local/lib' && \
    /bin/bash ../libtool  --mode=install /usr/bin/install -c libusb-1.0.la '/usr/local/lib' && \
    /bin/mkdir -p '/usr/local/include/libusb-1.0' && \
    /usr/bin/install -c -m 644 libusb.h '/usr/local/include/libusb-1.0' && \
    /bin/mkdir -p '/usr/local/lib/pkgconfig' && \
    cd  /opt/libusb-1.0.25/ && \
    /usr/bin/install -c -m 644 libusb-1.0.pc '/usr/local/lib/pkgconfig' && \
    ldconfig
 FROM wget AS models
 # Get model and labels
 RUN wget -qO edgetpu_model.tflite https://github.com/google-coral/test_data/raw/release-frogfish/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite
 RUN wget -qO cpu_model.tflite https://github.com/google-coral/test_data/raw/release-frogfish/ssdlite_mobiledet_coco_qat_postprocess.tflite
 COPY labelmap.txt .
 # Copy OpenVino model
 COPY --from=ov-converter /models/public/ssdlite_mobilenet_v2/FP16 openvino-model
 RUN wget -q https://github.com/openvinotoolkit/open_model_zoo/raw/master/data/dataset_classes/coco_91cl_bkgr.txt -O openvino-model/coco_91cl_bkgr.txt && \
    sed -i 's/truck/car/g' openvino-model/coco_91cl_bkgr.txt
 FROM wget AS s6-overlay
 ARG TARGETARCH
 RUN --mount=type=bind,source=docker/install_s6_overlay.sh,target=/deps/install_s6_overlay.sh \
    /deps/install_s6_overlay.sh
 FROM base AS wheels
 ARG DEBIAN_FRONTEND
 ARG TARGETARCH
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
    && apt-get -qq install -y \
    apt-transport-https \
    gnupg \
    wget \
    && apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 9165938D90FDDD2E \
    && echo "deb http://raspbian.raspberrypi.org/raspbian/ bullseye main contrib non-free rpi" | tee /etc/apt/sources.list.d/raspi.list \
    && apt-get -qq update \
    && apt-get -qq install -y \
    python3 \
    python3-dev \
    wget \
    # opencv dependencies
    build-essential cmake git pkg-config libgtk-3-dev \
    libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
    libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
    gfortran openexr libatlas-base-dev libssl-dev\
    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
    libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
    # scipy dependencies
    gcc gfortran libopenblas-dev liblapack-dev && \
    rm -rf /var/lib/apt/lists/*
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
    && python3 get-pip.py "pip"
 RUN if [ "${TARGETARCH}" = "arm" ]; \
    then echo "[global]" > /etc/pip.conf \
    && echo "extra-index-url=https://www.piwheels.org/simple" >> /etc/pip.conf; \
    fi
 COPY requirements.txt /requirements.txt
 RUN pip3 install -r requirements.txt
 COPY requirements-wheels.txt /requirements-wheels.txt
 RUN pip3 wheel --wheel-dir=/wheels -r requirements-wheels.txt
 # Make this a separate target so it can be built/cached optionally
 FROM wheels as trt-wheels
 ARG DEBIAN_FRONTEND
 ARG TARGETARCH
 # Add TensorRT wheels to another folder
 COPY requirements-tensorrt.txt /requirements-tensorrt.txt
 RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r requirements-tensorrt.txt
 # Collect deps in a single layer
 FROM scratch AS deps-rootfs
 COPY --from=nginx /usr/local/nginx/ /usr/local/nginx/
 COPY --from=go2rtc /rootfs/ /
 COPY --from=libusb-build /usr/local/lib /usr/local/lib
 COPY --from=s6-overlay /rootfs/ /
 COPY --from=models /rootfs/ /
 COPY docker/rootfs/ /
 # Frigate deps (ffmpeg, python, nginx, go2rtc, s6-overlay, etc)
 FROM slim-base AS deps
 ARG TARGETARCH
 ARG DEBIAN_FRONTEND
 # http://stackoverflow.com/questions/48162574/ddg#49462622
 ARG APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn
 # https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(Native-GPU-Support)
 ENV NVIDIA_VISIBLE_DEVICES=all
 ENV NVIDIA_DRIVER_CAPABILITIES="compute,video,utility"
 ENV PATH="/usr/lib/btbn-ffmpeg/bin:/usr/local/go2rtc/bin:/usr/local/nginx/sbin:${PATH}"
 # Install dependencies
 RUN --mount=type=bind,source=docker/install_deps.sh,target=/deps/install_deps.sh \
    /deps/install_deps.sh
 RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
    pip3 install -U /deps/wheels/*.whl
 COPY --from=deps-rootfs / /
 RUN ldconfig
 EXPOSE 5000
 EXPOSE 1935
 EXPOSE 8554
 EXPOSE 8555/tcp 8555/udp
 # Configure logging to prepend timestamps, log to stdout, keep 0 archives and rotate on 10MB
 ENV S6_LOGGING_SCRIPT="T 1 n0 s10000000 T"
 ENTRYPOINT ["/init"]
 CMD []
 # Frigate deps with Node.js and NPM for devcontainer
 FROM deps AS devcontainer
 # Do not start the actual Frigate service on devcontainer as it will be started by VSCode
 # But start a fake service for simulating the logs
 COPY docker/fake_frigate_run /etc/s6-overlay/s6-rc.d/frigate/run
 # Create symbolic link to the frigate source code, as go2rtc's create_config.sh uses it
 RUN mkdir -p /opt/frigate \
    && ln -svf /workspace/frigate/frigate /opt/frigate/frigate
 # Install Node 16
 RUN apt-get update \
    && apt-get install wget -y \
    && wget -qO- https://deb.nodesource.com/setup_16.x | bash - \
    && apt-get install -y nodejs \
    && rm -rf /var/lib/apt/lists/* \
    && npm install -g npm@9
 WORKDIR /workspace/frigate
 RUN apt-get update \
    && apt-get install make -y \
    && rm -rf /var/lib/apt/lists/*
 RUN --mount=type=bind,source=./requirements-dev.txt,target=/workspace/frigate/requirements-dev.txt \
    pip3 install -r requirements-dev.txt
 CMD ["sleep", "infinity"]
 # Frigate web build
 # force this to run on amd64 because QEMU is painfully slow
 FROM --platform=linux/amd64 node:16 AS web-build
 WORKDIR /work
 COPY web/package.json web/package-lock.json ./
 RUN npm install
 COPY web/ ./
 RUN npm run build \
    && mv dist/BASE_PATH/monacoeditorwork/* dist/assets/ \
    && rm -rf dist/BASE_PATH
 # Collect final files in a single layer
 FROM scratch AS rootfs
 WORKDIR /opt/frigate/
-ADD frigate frigate/
+COPY frigate frigate/
-COPY detect_objects.py .
+COPY migrations migrations/
 COPY --from=web-build /work/dist/ web/
-CMD ["python3", "-u", "detect_objects.py"]
+# Frigate final container
 FROM deps AS frigate
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 # Frigate w/ TensorRT Support as separate image
 FROM frigate AS frigate-tensorrt
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
    pip3 install -U /deps/trt-wheels/*.whl && \
    ln -s libnvrtc.so.11.2 /usr/local/lib/python3.9/dist-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so && \
    ldconfig
 # Dev Container w/ TRT
 FROM devcontainer AS devcontainer-trt
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
    pip3 install -U /deps/trt-wheels/*.whl
--- a/682
+++ b/682
@@ -1,661 +1,21 @@
-                    GNU AFFERO GENERAL PUBLIC LICENSE
+The MIT License
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+
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--- a/42
+++ b/42
@@ -0,0 +1,42 @@
 default_target: local
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
 VERSION = 0.12.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 CURRENT_UID := $(shell id -u)
 CURRENT_GID := $(shell id -g)
 version:
 	echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
 local: version
 	docker buildx build --target=frigate --tag frigate:latest --load .
 local-trt: version
 	docker buildx build --target=frigate-tensorrt --tag frigate:latest-tensorrt --load .
 amd64:
 	docker buildx build --platform linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) .
 	docker buildx build --platform linux/amd64 --target=frigate-tensorrt --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH)-tensorrt .
 arm64:
 	docker buildx build --platform linux/arm64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) .
 armv7:
 	docker buildx build --platform linux/arm/v7 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) .
 build: version amd64 arm64 armv7
 	docker buildx build --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) .
 push: build
 	docker buildx build --push --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) .
 	docker buildx build --push --platform linux/amd64 --target=frigate-tensorrt --tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt .
 run: local
 	docker run --rm --publish=5000:5000 --volume=${PWD}/config/config.yml:/config/config.yml frigate:latest
 run_tests: local
 	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m unittest
 	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m mypy --config-file frigate/mypy.ini frigate
 .PHONY: run_tests
--- a/README.md
+++ b/README.md
@@ -1,147 +1,46 @@
-# Frigate - Realtime Object Detection for RTSP Cameras
+<p align="center">
-Uses OpenCV and Tensorflow to perform realtime object detection locally for RTSP cameras. Designed for integration with HomeAssistant or others via MQTT.
+  <img align="center" alt="logo" src="docs/static/img/frigate.png">
 </p>
- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
+# Frigate - NVR With Realtime Object Detection for IP Cameras
 - Allows you to define specific regions (squares) in the image to look for motion/objects
 - Motion detection runs in a separate process per region and signals to object detection to avoid wasting CPU cycles looking for objects when there is no motion
 - Object detection with Tensorflow runs in a separate process per region
 - Detected objects are placed on a shared mp.Queue and aggregated into a list of recently detected objects in a separate thread
 - A person score is calculated as the sum of all scores/5
 - Motion and object info is published over MQTT for integration into HomeAssistant or others
 - An endpoint is available to view an MJPEG stream for debugging
-![Diagram](diagram.png)
+A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
-## Example video
+Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
 You see multiple bounding boxes because it draws bounding boxes from all frames in the past 1 second where a person was detected. Not all of the bounding boxes were from the current frame.
 [![](http://img.youtube.com/vi/nqHbCtyo4dY/0.jpg)](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
-## Getting Started
+- Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
-Build the container with
+- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
-```
+- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
-docker build -t frigate .
+- Uses a very low overhead motion detection to determine where to run object detection
-```
+- Object detection with TensorFlow runs in separate processes for maximum FPS
 - Communicates over MQTT for easy integration into other systems
 - Records video with retention settings based on detected objects
 - 24/7 recording
 - Re-streaming via RTSP to reduce the number of connections to your camera
 - WebRTC & MSE support for low-latency live view
-Download a model from the [zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
+## Documentation
-Download the cooresponding label map from [here](https://github.com/tensorflow/models/tree/master/research/object_detection/data).
+View the documentation at https://docs.frigate.video
-Run the container with
+## Donations
 ```
 docker run --rm \
 -v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
 -v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
 -v <path_to_config_dir>:/config:ro \
 -p 5000:5000 \
 -e RTSP_URL='<rtsp_url>' \
 -e REGIONS='<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>' \
 -e MQTT_HOST='your.mqtthost.com' \
 -e MQTT_USER='username' \
 -e MQTT_PASS='password' \
 -e MQTT_TOPIC_PREFIX='cameras/1' \
 -e DEBUG='0' \
 frigate:latest
 ```
-Example docker-compose:
+If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
 ```
  frigate:
    container_name: frigate
    restart: unless-stopped
    image: frigate:latest
    volumes:
      - <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro
      - <path_to_labelmap.pbtext>:/label_map.pbtext:ro
      - <path_to_config>:/config
    ports:
      - "127.0.0.1:5000:5000"
    environment:
      RTSP_URL: "<rtsp_url>"
      REGIONS: "<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>"
      MQTT_HOST: "your.mqtthost.com"
      MQTT_USER: "username" #optional
      MQTT_PASS: "password" #optional
      MQTT_TOPIC_PREFIX: "cameras/1"
      DEBUG: "0"
 ```
-Here is an example `REGIONS` env variable:
+## Screenshots
 `350,0,300,5000,200,mask-0-300.bmp:400,350,250,2000,200,mask-350-250.bmp:400,750,250,2000,200,mask-750-250.bmp`
-First region broken down (all are required):
+Integration into Home Assistant
 - `350` - size of the square (350px by 350px)
 - `0` - x coordinate of upper left corner (top left of image is 0,0)
 - `300` - y coordinate of upper left corner (top left of image is 0,0)
 - `5000` - minimum person bounding box size (width*height for bounding box of identified person)
 - `200` - minimum number of changed pixels to trigger motion
 - `mask-0-300.bmp` - a bmp file with the masked regions as pure black, must be the same size as the region
-Mask files go in the `/config` directory.
+<div>
 <a href="docs/static/img/media_browser.png"><img src="docs/static/img/media_browser.png" height=400></a>
 <a href="docs/static/img/notification.png"><img src="docs/static/img/notification.png" height=400></a>
 </div>
-Access the mjpeg stream at http://localhost:5000
+Also comes with a builtin UI:
-## Integration with HomeAssistant
+<div>
-```
+<a href="docs/static/img/home-ui.png"><img src="docs/static/img/home-ui.png" height=400></a>
-camera:
+<a href="docs/static/img/camera-ui.png"><img src="docs/static/img/camera-ui.png" height=400></a>
-  - name: Camera Last Person
+</div>
    platform: generic
    still_image_url: http://<ip>:5000/best_person.jpg
-binary_sensor:
+![Events](docs/static/img/events-ui.png)
  - name: Camera Motion
    platform: mqtt
    state_topic: "cameras/1/motion"
    device_class: motion
    availability_topic: "cameras/1/available"
 sensor:
  - name: Camera Person Score
    platform: mqtt
    state_topic: "cameras/1/objects"
    value_template: '{{ value_json.person }}'
    unit_of_measurement: '%'
    availability_topic: "cameras/1/available"
 ```
 ## Tips
 - Lower the framerate of the RTSP feed on the camera to reduce the CPU usage for capturing the feed
 - Use SSDLite models to reduce CPU usage
 ## Future improvements
 - [ ] Build tensorflow from source for CPU optimizations
 - [ ] Add ability to turn detection on and off via MQTT
 - [ ] MQTT motion occasionally gets stuck ON
 - [ ] Output movie clips of people for notifications, etc.
 - [ ] Integrate with homeassistant push camera
 - [ ] Merge bounding boxes that span multiple regions
 - [ ] Switch to a config file
 - [ ] Allow motion regions to be different than object detection regions
 - [ ] Implement mode to save labeled objects for training
 - [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
 - [ ] Look into GPU accelerated decoding of RTSP stream
 - [ ] Send video over a socket and use JSMPEG
 - [ ] Look into neural compute stick
 ## Building Tensorflow from source for CPU optimizations
 https://www.tensorflow.org/install/source#docker_linux_builds
 used `tensorflow/tensorflow:1.12.0-devel-py3`
 ## Optimizing the graph (cant say I saw much difference in CPU usage)
 https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md#optimizing-for-deployment
 ```
 docker run -it -v ${PWD}:/lab -v ${PWD}/../back_camera_model/models/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb:/frozen_inference_graph.pb:ro tensorflow/tensorflow:1.12.0-devel-py3 bash
 bazel build tensorflow/tools/graph_transforms:transform_graph
 bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
 --in_graph=/frozen_inference_graph.pb \
 --out_graph=/lab/optimized_inception_graph.pb \
 --inputs='image_tensor' \
 --outputs='num_detections,detection_scores,detection_boxes,detection_classes' \
 --transforms='
  strip_unused_nodes(type=float, shape="1,300,300,3")
  remove_nodes(op=Identity, op=CheckNumerics)
  fold_constants(ignore_errors=true)
  fold_batch_norms
  fold_old_batch_norms'
 ```
--- a/benchmark.py
+++ b/benchmark.py
@@ -0,0 +1,108 @@
 import os
 from statistics import mean
 import multiprocessing as mp
 import numpy as np
 import datetime
 from frigate.config import DetectorTypeEnum
 from frigate.object_detection import (
    LocalObjectDetector,
    ObjectDetectProcess,
    RemoteObjectDetector,
    load_labels,
 )
 my_frame = np.expand_dims(np.full((300, 300, 3), 1, np.uint8), axis=0)
 labels = load_labels("/labelmap.txt")
 ######
 # Minimal same process runner
 ######
 # object_detector = LocalObjectDetector()
 # tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
 # start = datetime.datetime.now().timestamp()
 # frame_times = []
 # for x in range(0, 1000):
 #   start_frame = datetime.datetime.now().timestamp()
 #   tensor_input[:] = my_frame
 #   detections = object_detector.detect_raw(tensor_input)
 #   parsed_detections = []
 #   for d in detections:
 #       if d[1] < 0.4:
 #           break
 #       parsed_detections.append((
 #           labels[int(d[0])],
 #           float(d[1]),
 #           (d[2], d[3], d[4], d[5])
 #       ))
 #   frame_times.append(datetime.datetime.now().timestamp()-start_frame)
 # duration = datetime.datetime.now().timestamp()-start
 # print(f"Processed for {duration:.2f} seconds.")
 # print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
 def start(id, num_detections, detection_queue, event):
    object_detector = RemoteObjectDetector(
        str(id), "/labelmap.txt", detection_queue, event
    )
    start = datetime.datetime.now().timestamp()
    frame_times = []
    for x in range(0, num_detections):
        start_frame = datetime.datetime.now().timestamp()
        detections = object_detector.detect(my_frame)
        frame_times.append(datetime.datetime.now().timestamp() - start_frame)
    duration = datetime.datetime.now().timestamp() - start
    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")
 ######
 # Separate process runner
 ######
 # event = mp.Event()
 # detection_queue = mp.Queue()
 # edgetpu_process = EdgeTPUProcess(detection_queue, {'1': event}, 'usb:0')
 # start(1, 1000, edgetpu_process.detection_queue, event)
 # print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
 ####
 # Multiple camera processes
 ####
 camera_processes = []
 events = {}
 for x in range(0, 10):
    events[str(x)] = mp.Event()
 detection_queue = mp.Queue()
 edgetpu_process_1 = ObjectDetectProcess(
    detection_queue, events, DetectorTypeEnum.edgetpu, "usb:0"
 )
 edgetpu_process_2 = ObjectDetectProcess(
    detection_queue, events, DetectorTypeEnum.edgetpu, "usb:1"
 )
 for x in range(0, 10):
    camera_process = mp.Process(
        target=start, args=(x, 300, detection_queue, events[str(x)])
    )
    camera_process.daemon = True
    camera_processes.append(camera_process)
 start_time = datetime.datetime.now().timestamp()
 for p in camera_processes:
    p.start()
 for p in camera_processes:
    p.join()
 duration = datetime.datetime.now().timestamp() - start_time
 print(f"Total - Processed for {duration:.2f} seconds.")
--- a/config/config.yml.example
+++ b/config/config.yml.example
@@ -0,0 +1,16 @@
 mqtt:
  host: mqtt
 cameras:
  test:
    ffmpeg:
      inputs:
        - path: /media/frigate/car-stopping.mp4
          input_args: -re -stream_loop -1 -fflags +genpts
          roles:
            - detect
            - rtmp
    detect:
      height: 1080
      width: 1920
      fps: 5
--- a/config/mask-0-300.bmp
+++ b/config/mask-0-300.bmp
--- a/config/mask-350-250.bmp
+++ b/config/mask-350-250.bmp
--- a/config/mask-750-250.bmp
+++ b/config/mask-750-250.bmp
--- a/detect_objects.py
+++ b/detect_objects.py
@@ -1,247 +0,0 @@
 import os
 import cv2
 import imutils
 import time
 import datetime
 import ctypes
 import logging
 import multiprocessing as mp
 import threading
 import json
 from contextlib import closing
 import numpy as np
 from object_detection.utils import visualization_utils as vis_util
 from flask import Flask, Response, make_response, send_file
 import paho.mqtt.client as mqtt
 from frigate.util import tonumpyarray
 from frigate.mqtt import MqttMotionPublisher, MqttObjectPublisher
 from frigate.objects import ObjectParser, ObjectCleaner, BestPersonFrame
 from frigate.motion import detect_motion
 from frigate.video import fetch_frames, FrameTracker
 from frigate.object_detection import detect_objects
 RTSP_URL = os.getenv('RTSP_URL')
 MQTT_HOST = os.getenv('MQTT_HOST')
 MQTT_USER = os.getenv('MQTT_USER')
 MQTT_PASS = os.getenv('MQTT_PASS')
 MQTT_TOPIC_PREFIX = os.getenv('MQTT_TOPIC_PREFIX')
 # REGIONS = "350,0,300,50:400,350,250,50:400,750,250,50"
 # REGIONS = "400,350,250,50"
 REGIONS = os.getenv('REGIONS')
 DEBUG = (os.getenv('DEBUG') == '1')
 def main():
    DETECTED_OBJECTS = []
    recent_motion_frames = {}
    # Parse selected regions
    regions = []
    for region_string in REGIONS.split(':'):
        region_parts = region_string.split(',')
        region_mask_image = cv2.imread("/config/{}".format(region_parts[5]), cv2.IMREAD_GRAYSCALE)
        region_mask = np.where(region_mask_image==[0])
        regions.append({
            'size': int(region_parts[0]),
            'x_offset': int(region_parts[1]),
            'y_offset': int(region_parts[2]),
            'min_person_area': int(region_parts[3]),
            'min_object_size': int(region_parts[4]),
            'mask': region_mask,
            # Event for motion detection signaling
            'motion_detected': mp.Event(),
            # create shared array for storing 10 detected objects
            # note: this must be a double even though the value you are storing
            #       is a float. otherwise it stops updating the value in shared
            #       memory. probably something to do with the size of the memory block
            'output_array': mp.Array(ctypes.c_double, 6*10)
        })
    # capture a single frame and check the frame shape so the correct array
    # size can be allocated in memory
    video = cv2.VideoCapture(RTSP_URL)
    ret, frame = video.read()
    if ret:
        frame_shape = frame.shape
    else:
        print("Unable to capture video stream")
        exit(1)
    video.release()
    # compute the flattened array length from the array shape
    flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2]
    # create shared array for storing the full frame image data
    shared_arr = mp.Array(ctypes.c_uint16, flat_array_length)
    # create shared value for storing the frame_time
    shared_frame_time = mp.Value('d', 0.0)
    # Lock to control access to the frame
    frame_lock = mp.Lock()
    # Condition for notifying that a new frame is ready
    frame_ready = mp.Condition()
    # Condition for notifying that motion status changed globally
    motion_changed = mp.Condition()
    # Condition for notifying that objects were parsed
    objects_parsed = mp.Condition()
    # Queue for detected objects
    object_queue = mp.Queue()
    # shape current frame so it can be treated as an image
    frame_arr = tonumpyarray(shared_arr).reshape(frame_shape)
    # start the process to capture frames from the RTSP stream and store in a shared array
    capture_process = mp.Process(target=fetch_frames, args=(shared_arr, 
        shared_frame_time, frame_lock, frame_ready, frame_shape, RTSP_URL))
    capture_process.daemon = True
    # for each region, start a separate process for motion detection and object detection
    detection_processes = []
    motion_processes = []
    for region in regions:
        detection_process = mp.Process(target=detect_objects, args=(shared_arr, 
            object_queue,
            shared_frame_time,
            frame_lock, frame_ready,
            region['motion_detected'],
            frame_shape, 
            region['size'], region['x_offset'], region['y_offset'],
            region['min_person_area'],
            DEBUG))
        detection_process.daemon = True
        detection_processes.append(detection_process)
        motion_process = mp.Process(target=detect_motion, args=(shared_arr,
            shared_frame_time,
            frame_lock, frame_ready,
            region['motion_detected'],
            motion_changed,
            frame_shape, 
            region['size'], region['x_offset'], region['y_offset'],
            region['min_object_size'], region['mask'],
            DEBUG))
        motion_process.daemon = True
        motion_processes.append(motion_process)
    # start a thread to store recent motion frames for processing
    frame_tracker = FrameTracker(frame_arr, shared_frame_time, frame_ready, frame_lock, 
        recent_motion_frames, motion_changed, [region['motion_detected'] for region in regions])
    frame_tracker.start()
    # start a thread to store the highest scoring recent person frame
    best_person_frame = BestPersonFrame(objects_parsed, recent_motion_frames, DETECTED_OBJECTS, 
        motion_changed, [region['motion_detected'] for region in regions])
    best_person_frame.start()
    # start a thread to parse objects from the queue
    object_parser = ObjectParser(object_queue, objects_parsed, DETECTED_OBJECTS)
    object_parser.start()
    # start a thread to expire objects from the detected objects list
    object_cleaner = ObjectCleaner(objects_parsed, DETECTED_OBJECTS)
    object_cleaner.start()
    # connect to mqtt and setup last will
    def on_connect(client, userdata, flags, rc): 
        print("On connect called")
        # publish a message to signal that the service is running
        client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
    client = mqtt.Client()
    client.on_connect = on_connect
    client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
    if not MQTT_USER is None:
        client.username_pw_set(MQTT_USER, password=MQTT_PASS)
    client.connect(MQTT_HOST, 1883, 60)
    client.loop_start()
    # start a thread to publish object scores (currently only person)
    mqtt_publisher = MqttObjectPublisher(client, MQTT_TOPIC_PREFIX, objects_parsed, DETECTED_OBJECTS)
    mqtt_publisher.start()
    # start thread to publish motion status
    mqtt_motion_publisher = MqttMotionPublisher(client, MQTT_TOPIC_PREFIX, motion_changed,
        [region['motion_detected'] for region in regions])
    mqtt_motion_publisher.start()
    # start the process of capturing frames
    capture_process.start()
    print("capture_process pid ", capture_process.pid)
    # start the object detection processes
    for detection_process in detection_processes:
        detection_process.start()
        print("detection_process pid ", detection_process.pid)
    # start the motion detection processes
    for motion_process in motion_processes:
        motion_process.start()
        print("motion_process pid ", motion_process.pid)
    # create a flask app that encodes frames a mjpeg on demand
    app = Flask(__name__)
    @app.route('/best_person.jpg')
    def best_person():
        frame = np.zeros(frame_shape, np.uint8) if best_person_frame.best_frame is None else best_person_frame.best_frame
        ret, jpg = cv2.imencode('.jpg', frame)
        response = make_response(jpg.tobytes())
        response.headers['Content-Type'] = 'image/jpg'
        return response
    @app.route('/')
    def index():
        # return a multipart response
        return Response(imagestream(),
                        mimetype='multipart/x-mixed-replace; boundary=frame')
    def imagestream():
        while True:
            # max out at 5 FPS
            time.sleep(0.2)
            # make a copy of the current detected objects
            detected_objects = DETECTED_OBJECTS.copy()
            # lock and make a copy of the current frame
            with frame_lock:
                frame = frame_arr.copy()
            # convert to RGB for drawing
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            # draw the bounding boxes on the screen
            for obj in detected_objects:
                vis_util.draw_bounding_box_on_image_array(frame,
                    obj['ymin'],
                    obj['xmin'],
                    obj['ymax'],
                    obj['xmax'],
                    color='red',
                    thickness=2,
                    display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
                    use_normalized_coordinates=False)
            for region in regions:
                color = (255,255,255)
                if region['motion_detected'].is_set():
                    color = (0,255,0)
                cv2.rectangle(frame, (region['x_offset'], region['y_offset']), 
                    (region['x_offset']+region['size'], region['y_offset']+region['size']), 
                    color, 2)
            # convert back to BGR
            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
            # encode the image into a jpg
            ret, jpg = cv2.imencode('.jpg', frame)
            yield (b'--frame\r\n'
                b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
    app.run(host='0.0.0.0', debug=False)
    capture_process.join()
    for detection_process in detection_processes:
        detection_process.join()
    for motion_process in motion_processes:
        motion_process.join()
    frame_tracker.join()
    best_person_frame.join()
    object_parser.join()
    object_cleaner.join()
    mqtt_publisher.join()
 if __name__ == '__main__':
    main()
--- a/diagram.png
+++ b/diagram.png
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,39 @@
 version: "3"
 services:
  devcontainer:
    container_name: frigate-devcontainer
    # add groups from host for render, plugdev, video
    group_add:
      - "109" # render
      - "110" # render
      - "44"  # video
      - "46"  # plugdev
    shm_size: "256mb"
    build:
      context: .
      # Use target devcontainer-trt for TensorRT dev
      target: devcontainer
    deploy:
          resources:
              reservations:
                  devices:
                      - driver: nvidia
                        count: 1
                        capabilities: [gpu]
    devices:
      - /dev/bus/usb:/dev/bus/usb
      # - /dev/dri:/dev/dri # for intel hwaccel, needs to be updated for your hardware
    volumes:
      - .:/workspace/frigate:cached
      - ./web/dist:/opt/frigate/web:cached
      - /etc/localtime:/etc/localtime:ro
      - ./config/config.yml:/config/config.yml:ro
      - ./debug:/media/frigate
      # Create the trt-models folder using the documented method of generating TRT models
      # - ./debug/trt-models:/trt-models
      - /dev/bus/usb:/dev/bus/usb
  mqtt:
    container_name: mqtt
    image: eclipse-mosquitto:1.6
    ports:
      - "1883:1883"
--- a/docker/build_nginx.sh
+++ b/docker/build_nginx.sh
@@ -0,0 +1,66 @@
 #!/bin/bash
 set -euxo pipefail
 NGINX_VERSION="1.22.1"
 VOD_MODULE_VERSION="1.30"
 SECURE_TOKEN_MODULE_VERSION="1.4"
 RTMP_MODULE_VERSION="1.2.1"
 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
 apt-get -yqq install --no-install-recommends ca-certificates wget
 update-ca-certificates -f
 mkdir /tmp/nginx
 wget -nv https://nginx.org/download/nginx-${NGINX_VERSION}.tar.gz
 tar -zxf nginx-${NGINX_VERSION}.tar.gz -C /tmp/nginx --strip-components=1
 rm nginx-${NGINX_VERSION}.tar.gz
 mkdir /tmp/nginx-vod-module
 wget -nv https://github.com/kaltura/nginx-vod-module/archive/refs/tags/${VOD_MODULE_VERSION}.tar.gz
 tar -zxf ${VOD_MODULE_VERSION}.tar.gz -C /tmp/nginx-vod-module --strip-components=1
 rm ${VOD_MODULE_VERSION}.tar.gz
    # Patch MAX_CLIPS to allow more clips to be added than the default 128
 sed -i 's/MAX_CLIPS (128)/MAX_CLIPS (1080)/g' /tmp/nginx-vod-module/vod/media_set.h
 patch -d /tmp/nginx-vod-module/ -p1 << 'EOF'
 --- a/vod/avc_hevc_parser.c       2022-06-27 11:38:10.000000000 +0000
 +++ b/vod/avc_hevc_parser.c       2023-01-16 11:25:10.900521298 +0000
@@ -3,6 +3,9 @@
 bool_t
 avc_hevc_parser_rbsp_trailing_bits(bit_reader_state_t* reader)
 {
 +	// https://github.com/blakeblackshear/frigate/issues/4572
 +	return TRUE;
 +
 	uint32_t one_bit;
 	if (reader->stream.eof_reached)
 EOF
 mkdir /tmp/nginx-secure-token-module
 wget https://github.com/kaltura/nginx-secure-token-module/archive/refs/tags/${SECURE_TOKEN_MODULE_VERSION}.tar.gz
 tar -zxf ${SECURE_TOKEN_MODULE_VERSION}.tar.gz -C /tmp/nginx-secure-token-module --strip-components=1
 rm ${SECURE_TOKEN_MODULE_VERSION}.tar.gz
 mkdir /tmp/nginx-rtmp-module
 wget -nv https://github.com/arut/nginx-rtmp-module/archive/refs/tags/v${RTMP_MODULE_VERSION}.tar.gz
 tar -zxf v${RTMP_MODULE_VERSION}.tar.gz -C /tmp/nginx-rtmp-module --strip-components=1
 rm v${RTMP_MODULE_VERSION}.tar.gz
 cd /tmp/nginx
 ./configure --prefix=/usr/local/nginx \
    --with-file-aio \
    --with-http_sub_module \
    --with-http_ssl_module \
    --with-threads \
    --add-module=../nginx-vod-module \
    --add-module=../nginx-secure-token-module \
    --add-module=../nginx-rtmp-module \
    --with-cc-opt="-O3 -Wno-error=implicit-fallthrough"
 make -j$(nproc) && make install
 rm -rf /usr/local/nginx/html /usr/local/nginx/conf/*.default
--- a/docker/fake_frigate_run
+++ b/docker/fake_frigate_run
@@ -0,0 +1,13 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 # Start the fake Frigate service
 set -o errexit -o nounset -o pipefail
 # Tell S6-Overlay not to restart this service
 s6-svc -O .
 while true; do
  echo "[INFO] The fake Frigate service is running..."
  sleep 5s
 done
--- a/docker/install_deps.sh
+++ b/docker/install_deps.sh
@@ -0,0 +1,91 @@
 #!/bin/bash
 set -euxo pipefail
 apt-get -qq update
 apt-get -qq install --no-install-recommends -y \
    apt-transport-https \
    gnupg \
    wget \
    procps vainfo \
    unzip locales tzdata libxml2 xz-utils \
    python3-pip \
    curl \
    jq
 mkdir -p -m 600 /root/.gnupg
 # add coral repo
 wget --quiet -O /usr/share/keyrings/google-edgetpu.gpg https://packages.cloud.google.com/apt/doc/apt-key.gpg
 echo "deb [signed-by=/usr/share/keyrings/google-edgetpu.gpg] https://packages.cloud.google.com/apt coral-edgetpu-stable main" | tee /etc/apt/sources.list.d/coral-edgetpu.list
 echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections
 # enable non-free repo
 sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
 # coral drivers
 apt-get -qq update
 apt-get -qq install --no-install-recommends --no-install-suggests -y \
    libedgetpu1-max python3-tflite-runtime python3-pycoral
 # btbn-ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
    mkdir -p /usr/lib/btbn-ffmpeg
    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
 fi
 # ffmpeg -> arm32
 if [[ "${TARGETARCH}" == "arm" ]]; then
    # add raspberry pi repo
    gpg --no-default-keyring --keyring /usr/share/keyrings/raspbian.gpg --keyserver keyserver.ubuntu.com --recv-keys 9165938D90FDDD2E
    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] http://raspbian.raspberrypi.org/raspbian/ bullseye main contrib non-free rpi" | tee /etc/apt/sources.list.d/raspi.list
    apt-get -qq update
    apt-get -qq install --no-install-recommends --no-install-suggests -y ffmpeg
 fi
 # ffmpeg -> arm64
 if [[ "${TARGETARCH}" == "arm64" ]]; then
    # add raspberry pi repo
    gpg --no-default-keyring --keyring /usr/share/keyrings/raspbian.gpg --keyserver keyserver.ubuntu.com --recv-keys 82B129927FA3303E
    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bullseye main" | tee /etc/apt/sources.list.d/raspi.list
    apt-get -qq update
    apt-get -qq install --no-install-recommends --no-install-suggests -y ffmpeg
 fi
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
    # Use debian testing repo only for hwaccel packages
    echo 'deb http://deb.debian.org/debian testing main non-free' >/etc/apt/sources.list.d/debian-testing.list
    apt-get -qq update
    # intel-opencl-icd specifically for GPU support in OpenVino
    apt-get -qq install --no-install-recommends --no-install-suggests -y \
        intel-opencl-icd \
        mesa-va-drivers libva-drm2 intel-media-va-driver-non-free i965-va-driver libmfx1 radeontop intel-gpu-tools
    # something about this dependency requires it to be installed in a separate call rather than in the line above
    apt-get -qq install --no-install-recommends --no-install-suggests -y \
        i965-va-driver-shaders
    rm -f /etc/apt/sources.list.d/debian-testing.list
 fi
 if [[ "${TARGETARCH}" == "arm64" ]]; then
    apt-get -qq install --no-install-recommends --no-install-suggests -y \
        libva-drm2 mesa-va-drivers
 fi
 # not sure why 32bit arm requires all these
 if [[ "${TARGETARCH}" == "arm" ]]; then
    apt-get -qq install --no-install-recommends --no-install-suggests -y \
        libgtk-3-dev \
        libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
        libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
        gfortran openexr libatlas-base-dev libtbb-dev libdc1394-22-dev libopenexr-dev \
        libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev
 fi
 apt-get purge gnupg apt-transport-https wget xz-utils -y
 apt-get clean autoclean -y
 apt-get autoremove --purge -y
 rm -rf /var/lib/apt/lists/*
--- a/docker/install_s6_overlay.sh
+++ b/docker/install_s6_overlay.sh
@@ -0,0 +1,21 @@
 #!/bin/bash
 set -euxo pipefail
 s6_version="3.1.4.1"
 if [[ "${TARGETARCH}" == "amd64" ]]; then
    s6_arch="x86_64"
 elif [[ "${TARGETARCH}" == "arm" ]]; then
    s6_arch="armhf"
 elif [[ "${TARGETARCH}" == "arm64" ]]; then
    s6_arch="aarch64"
 fi
 mkdir -p /rootfs/
 wget -qO- "https://github.com/just-containers/s6-overlay/releases/download/v${s6_version}/s6-overlay-noarch.tar.xz" |
    tar -C /rootfs/ -Jxpf -
 wget -qO- "https://github.com/just-containers/s6-overlay/releases/download/v${s6_version}/s6-overlay-${s6_arch}.tar.xz" |
    tar -C /rootfs/ -Jxpf -
--- a/docker/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf
+++ b/docker/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf
@@ -0,0 +1,5 @@
 /usr/local/lib/python3.9/dist-packages/nvidia/cudnn/lib
 /usr/local/lib/python3.9/dist-packages/nvidia/cuda_runtime/lib
 /usr/local/lib/python3.9/dist-packages/nvidia/cublas/lib
 /usr/local/lib/python3.9/dist-packages/nvidia/cuda_nvrtc/lib
 /usr/local/lib/python3.9/dist-packages/tensorrt
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/consumer-for
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/consumer-for
@@ -0,0 +1 @@
 frigate
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/dependencies.d/log-prepare
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/dependencies.d/log-prepare
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/pipeline-name
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/pipeline-name
@@ -0,0 +1 @@
 frigate-pipeline
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/run
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/run
@@ -0,0 +1,4 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 exec logutil-service /dev/shm/logs/frigate
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/type
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate-log/type
@@ -0,0 +1 @@
 longrun
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/dependencies.d/go2rtc
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/dependencies.d/go2rtc
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/finish
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/finish
@@ -0,0 +1,28 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 # Take down the S6 supervision tree when the service exits
 set -o errexit -o nounset -o pipefail
 # Logs should be sent to stdout so that s6 can collect them
 declare exit_code_container
 exit_code_container=$(cat /run/s6-linux-init-container-results/exitcode)
 readonly exit_code_container
 readonly exit_code_service="${1}"
 readonly exit_code_signal="${2}"
 readonly service="Frigate"
 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
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/producer-for
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/producer-for
@@ -0,0 +1 @@
 frigate-log
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/run
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/run
@@ -0,0 +1,18 @@
 #!/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 .
 echo "[INFO] Starting Frigate..."
 cd /opt/frigate || echo "[ERROR] Failed to change working directory to /opt/frigate"
 # Replace the bash process with the Frigate process, redirecting stderr to stdout
 exec 2>&1
 exec python3 -u -m frigate
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/timeout-kill
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/timeout-kill
@@ -0,0 +1 @@
 120000
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/type
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/frigate/type
@@ -0,0 +1 @@
 longrun
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/dependencies.d/go2rtc
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/dependencies.d/go2rtc
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/finish
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/finish
@@ -0,0 +1,12 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 set -o errexit -o nounset -o pipefail
 # Logs should be sent to stdout so that s6 can collect them
 readonly exit_code_service="${1}"
 readonly exit_code_signal="${2}"
 readonly service="go2rtc-healthcheck"
 echo "[INFO] The ${service} service exited with code ${exit_code_service} (by signal ${exit_code_signal})"
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/producer-for
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/producer-for
@@ -0,0 +1 @@
 go2rtc-log
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/run
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/run
@@ -0,0 +1,22 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 # Start the go2rtc-healthcheck service
 set -o errexit -o nounset -o pipefail
 # Logs should be sent to stdout so that s6 can collect them
 # Give some additional time for go2rtc to start before start pinging
 sleep 10s
 echo "[INFO] Starting go2rtc healthcheck service..."
 while sleep 30s; do
    # Check if the service is running
    if ! curl --connect-timeout 10 --fail --silent --show-error --output /dev/null http://127.0.0.1:1984/api/streams 2>&1; then
        echo "[ERROR] The go2rtc service is not responding to ping, restarting..."
        # We can also use -r instead of -t to send kill signal rather than term
        s6-svc -t /var/run/service/go2rtc 2>&1
        # Give some additional time to go2rtc to restart before start pinging again
        sleep 10s
    fi
 done
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/timeout-kill
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/timeout-kill
@@ -0,0 +1 @@
 5000
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/type
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-healthcheck/type
@@ -0,0 +1 @@
 longrun
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/consumer-for
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/consumer-for
@@ -0,0 +1,2 @@
 go2rtc
 go2rtc-healthcheck
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/dependencies.d/log-prepare
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/dependencies.d/log-prepare
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/pipeline-name
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/pipeline-name
@@ -0,0 +1 @@
 go2rtc-pipeline
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/run
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/run
@@ -0,0 +1,4 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 exec logutil-service /dev/shm/logs/go2rtc
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/type
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc-log/type
@@ -0,0 +1 @@
 longrun
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/dependencies.d/base
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/dependencies.d/base
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/finish
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/finish
@@ -0,0 +1,12 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 set -o errexit -o nounset -o pipefail
 # Logs should be sent to stdout so that s6 can collect them
 readonly exit_code_service="${1}"
 readonly exit_code_signal="${2}"
 readonly service="go2rtc"
 echo "[INFO] The ${service} service exited with code ${exit_code_service} (by signal ${exit_code_signal})"
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/producer-for
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/producer-for
@@ -0,0 +1 @@
 go2rtc-log
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run
@@ -0,0 +1,70 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 # Start the go2rtc service
 set -o errexit -o nounset -o pipefail
 # Logs should be sent to stdout so that s6 can collect them
 function get_ip_and_port_from_supervisor() {
    local ip_address
    # Example: 192.168.1.10/24
    local ip_regex='^([0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})/[0-9]{1,2}$'
    if ip_address=$(
        curl -fsSL \
            -H "Authorization: Bearer ${SUPERVISOR_TOKEN}" \
            -H "Content-Type: application/json" \
            http://supervisor/network/interface/default/info |
            jq --exit-status --raw-output '.data.ipv4.address[0]'
    ) && [[ "${ip_address}" =~ ${ip_regex} ]]; then
        ip_address="${BASH_REMATCH[1]}"
        echo "[INFO] Got IP address from supervisor: ${ip_address}"
    else
        echo "[WARN] Failed to get IP address from supervisor"
        return 0
    fi
    local webrtc_port
    local port_regex='^([0-9]{1,5})$'
    if webrtc_port=$(
        curl -fsSL \
            -H "Authorization: Bearer ${SUPERVISOR_TOKEN}" \
            -H "Content-Type: application/json" \
            http://supervisor/addons/self/info |
            jq --exit-status --raw-output '.data.network["8555/tcp"]'
    ) && [[ "${webrtc_port}" =~ ${port_regex} ]]; then
        webrtc_port="${BASH_REMATCH[1]}"
        echo "[INFO] Got WebRTC port from supervisor: ${webrtc_port}"
    else
        echo "[WARN] Failed to get WebRTC port from supervisor"
        return 0
    fi
    export FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL="${ip_address}:${webrtc_port}"
 }
 if [[ ! -f "/dev/shm/go2rtc.yaml" ]]; then
    echo "[INFO] Preparing go2rtc config..."
    if [[ -n "${SUPERVISOR_TOKEN:-}" ]]; then
        # Running as a Home Assistant add-on, infer the IP address and port
        get_ip_and_port_from_supervisor
    fi
    python3 /usr/local/go2rtc/create_config.py
 fi
 readonly config_path="/config"
 if [[ -x "${config_path}/go2rtc" ]]; then
  readonly binary_path="${config_path}/go2rtc"
  echo "[WARN] Using go2rtc binary from '${binary_path}' instead of the embedded one"
 else
  readonly binary_path="/usr/local/go2rtc/bin/go2rtc"
 fi
 echo "[INFO] Starting go2rtc..."
 # Replace the bash process with the go2rtc process, redirecting stderr to stdout
 exec 2>&1
 exec "${binary_path}" -config=/dev/shm/go2rtc.yaml
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/timeout-kill
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/timeout-kill
@@ -0,0 +1 @@
 30000
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/type
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/type
@@ -0,0 +1 @@
 longrun
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/dependencies.d/base
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/dependencies.d/base
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/run
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/run
@@ -0,0 +1,11 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 # Prepare the logs folder for s6-log
 set -o errexit -o nounset -o pipefail
 dirs=(/dev/shm/logs/frigate /dev/shm/logs/go2rtc /dev/shm/logs/nginx)
 mkdir -p "${dirs[@]}"
 chown nobody:nogroup "${dirs[@]}"
 chmod 02755 "${dirs[@]}"
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/type
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/type
@@ -0,0 +1 @@
 oneshot
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/up
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/up
@@ -0,0 +1 @@
 /etc/s6-overlay/s6-rc.d/log-prepare/run
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/consumer-for
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/consumer-for
@@ -0,0 +1 @@
 nginx
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/dependencies.d/log-prepare
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/dependencies.d/log-prepare
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/pipeline-name
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/pipeline-name
@@ -0,0 +1 @@
 nginx-pipeline
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/run
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/run
@@ -0,0 +1,4 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 exec logutil-service /dev/shm/logs/nginx
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/type
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx-log/type
@@ -0,0 +1 @@
 longrun
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/dependencies.d/frigate
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/dependencies.d/frigate
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/finish
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/finish
@@ -0,0 +1,30 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 # Take down the S6 supervision tree when the service fails
 set -o errexit -o nounset -o pipefail
 # Logs should be sent to stdout so that s6 can collect them
 declare exit_code_container
 exit_code_container=$(cat /run/s6-linux-init-container-results/exitcode)
 readonly exit_code_container
 readonly exit_code_service="${1}"
 readonly exit_code_signal="${2}"
 readonly service="NGINX"
 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 $((128 + exit_code_signal)) >/run/s6-linux-init-container-results/exitcode
  fi
  if [[ "${exit_code_signal}" -eq 15 ]]; then
    exec /run/s6/basedir/bin/halt
  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
  exec /run/s6/basedir/bin/halt
 fi
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/producer-for
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/producer-for
@@ -0,0 +1 @@
 nginx-log
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/run
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/run
@@ -0,0 +1,13 @@
 #!/command/with-contenv bash
 # shellcheck shell=bash
 # Start the NGINX service
 set -o errexit -o nounset -o pipefail
 # Logs should be sent to stdout so that s6 can collect them
 echo "[INFO] Starting NGINX..."
 # Replace the bash process with the NGINX process, redirecting stderr to stdout
 exec 2>&1
 exec nginx
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/timeout-kill
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/timeout-kill
@@ -0,0 +1 @@
 30000
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/type
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/nginx/type
@@ -0,0 +1 @@
 longrun
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/user/contents.d/frigate-pipeline
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/user/contents.d/frigate-pipeline
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/user/contents.d/go2rtc-pipeline
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/user/contents.d/go2rtc-pipeline
--- a/docker/rootfs/etc/s6-overlay/s6-rc.d/user/contents.d/nginx-pipeline
+++ b/docker/rootfs/etc/s6-overlay/s6-rc.d/user/contents.d/nginx-pipeline
--- a/docker/rootfs/usr/local/go2rtc/create_config.py
+++ b/docker/rootfs/usr/local/go2rtc/create_config.py
@@ -0,0 +1,106 @@
 """Creates a go2rtc config file."""
 import json
 import os
 import sys
 import yaml
 sys.path.insert(0, "/opt/frigate")
 from frigate.const import BIRDSEYE_PIPE, BTBN_PATH
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 sys.path.remove("/opt/frigate")
 FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
 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
 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)
 go2rtc_config: dict[str, any] = config.get("go2rtc", {})
 # Need to enable CORS for go2rtc so the frigate integration / card work automatically
 if go2rtc_config.get("api") is None:
    go2rtc_config["api"] = {"origin": "*"}
 elif go2rtc_config["api"].get("origin") is None:
    go2rtc_config["api"]["origin"] = "*"
 # we want to ensure that logs are easy to read
 if go2rtc_config.get("log") is None:
    go2rtc_config["log"] = {"format": "text"}
 elif go2rtc_config["log"].get("format") is None:
    go2rtc_config["log"]["format"] = "text"
 if not go2rtc_config.get("webrtc", {}).get("candidates", []):
    default_candidates = []
    # use internal candidate if it was discovered when running through the add-on
    internal_candidate = os.environ.get(
        "FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL", None
    )
    if internal_candidate is not None:
        default_candidates.append(internal_candidate)
    # should set default stun server so webrtc can work
    default_candidates.append("stun:8555")
    go2rtc_config["webrtc"] = {"candidates": default_candidates}
 else:
    print(
        "[INFO] Not injecting WebRTC candidates into go2rtc config as it has been set manually",
    )
 # sets default RTSP response to be equivalent to ?video=h264,h265&audio=aac
 # this means user does not need to specify audio codec when using restream
 # as source for frigate and the integration supports HLS playback
 if go2rtc_config.get("rtsp") is None:
    go2rtc_config["rtsp"] = {"default_query": "mp4"}
 elif go2rtc_config["rtsp"].get("default_query") is None:
    go2rtc_config["rtsp"]["default_query"] = "mp4"
 # need to replace ffmpeg command when using ffmpeg4
 if not os.path.exists(BTBN_PATH):
    if go2rtc_config.get("ffmpeg") is None:
        go2rtc_config["ffmpeg"] = {
            "rtsp": "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
        }
    elif go2rtc_config["ffmpeg"].get("rtsp") is None:
        go2rtc_config["ffmpeg"][
            "rtsp"
        ] = "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
 for name in go2rtc_config.get("streams", {}):
    stream = go2rtc_config["streams"][name]
    if isinstance(stream, str):
        go2rtc_config["streams"][name] = go2rtc_config["streams"][name].format(
            **FRIGATE_ENV_VARS
        )
    elif isinstance(stream, list):
        for i, stream in enumerate(stream):
            go2rtc_config["streams"][name][i] = stream.format(**FRIGATE_ENV_VARS)
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
    birdseye: dict[str, any] = config.get("birdseye")
    input = f"-f rawvideo -pix_fmt yuv420p -video_size {birdseye.get('width', 1280)}x{birdseye.get('height', 720)} -r 10 -i {BIRDSEYE_PIPE}"
    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(config.get('ffmpeg', {}).get('hwaccel_args'), input, '-rtsp_transport tcp -f rtsp {output}')}"
    if go2rtc_config.get("streams"):
        go2rtc_config["streams"]["birdseye"] = ffmpeg_cmd
    else:
        go2rtc_config["streams"] = {"birdseye": ffmpeg_cmd}
 # Write go2rtc_config to /dev/shm/go2rtc.yaml
 with open("/dev/shm/go2rtc.yaml", "w") as f:
    yaml.dump(go2rtc_config, f)
--- a/docker/rootfs/usr/local/nginx/conf/nginx.conf
+++ b/docker/rootfs/usr/local/nginx/conf/nginx.conf
@@ -0,0 +1,271 @@
 daemon off;
 user root;
 worker_processes auto;
 error_log /dev/stdout warn;
 pid /var/run/nginx.pid;
 events {
    worker_connections 1024;
 }
 http {
    include mime.types;
    default_type application/octet-stream;
    log_format main '$remote_addr - $remote_user [$time_local] "$request" '
                        '$status $body_bytes_sent "$http_referer" '
                        '"$http_user_agent" "$http_x_forwarded_for"';
    access_log /dev/stdout main;
    # send headers in one piece, it is better than sending them one by one
    tcp_nopush on;
    sendfile on;
    keepalive_timeout 65;
    gzip on;
    gzip_comp_level 6;
    gzip_types text/plain text/css application/json application/x-javascript application/javascript text/javascript image/svg+xml image/x-icon image/bmp image/png image/gif image/jpeg image/jpg;
    gzip_proxied no-cache no-store private expired auth;
    gzip_vary on;
    upstream frigate_api {
        server 127.0.0.1:5001;
        keepalive 1024;
    }
    upstream mqtt_ws {
        server 127.0.0.1:5002;
        keepalive 1024;
    }
    upstream jsmpeg {
        server 127.0.0.1:8082;
        keepalive 1024;
    }
    upstream go2rtc {
        server 127.0.0.1:1984;
        keepalive 1024;
    }
    server {
        listen 5000;
        # vod settings
        vod_base_url '';
        vod_segments_base_url '';
        vod_mode mapped;
        vod_max_mapping_response_size 1m;
        vod_upstream_location /api;
        vod_align_segments_to_key_frames on;
        vod_manifest_segment_durations_mode accurate;
        vod_ignore_edit_list on;
        vod_segment_duration 10000;
        vod_hls_mpegts_align_frames off;
        vod_hls_mpegts_interleave_frames on;
        # file handle caching / aio
        open_file_cache max=1000 inactive=5m;
        open_file_cache_valid 2m;
        open_file_cache_min_uses 1;
        open_file_cache_errors on;
        aio on;
        # https://github.com/kaltura/nginx-vod-module#vod_open_file_thread_pool
        vod_open_file_thread_pool default;
        # vod caches
        vod_metadata_cache metadata_cache 512m;
        vod_mapping_cache mapping_cache 5m 10m;
        # gzip manifests
        gzip on;
        gzip_types application/vnd.apple.mpegurl;
        location /vod/ {
            aio threads;
            vod hls;
            secure_token $args;
            secure_token_types application/vnd.apple.mpegurl;
            add_header Access-Control-Allow-Headers '*';
            add_header Access-Control-Expose-Headers 'Server,range,Content-Length,Content-Range';
            add_header Access-Control-Allow-Methods 'GET, HEAD, OPTIONS';
            add_header Access-Control-Allow-Origin '*';
            add_header Cache-Control "no-store";
            expires off;
        }
        location /stream/ {
            add_header Cache-Control "no-store";
            expires off;
            add_header 'Access-Control-Allow-Origin' "$http_origin" always;
            add_header 'Access-Control-Allow-Credentials' 'true';
            add_header 'Access-Control-Expose-Headers' 'Content-Length';
            if ($request_method = 'OPTIONS') {
                add_header 'Access-Control-Allow-Origin' "$http_origin";
                add_header 'Access-Control-Max-Age' 1728000;
                add_header 'Content-Type' 'text/plain charset=UTF-8';
                add_header 'Content-Length' 0;
                return 204;
            }
            types {
                application/dash+xml mpd;
                application/vnd.apple.mpegurl m3u8;
                video/mp2t ts;
                image/jpeg jpg;
            }
            root /tmp;
        }
        location /clips/ {
            add_header 'Access-Control-Allow-Origin' "$http_origin" always;
            add_header 'Access-Control-Allow-Credentials' 'true';
            add_header 'Access-Control-Expose-Headers' 'Content-Length';
            if ($request_method = 'OPTIONS') {
                add_header 'Access-Control-Allow-Origin' "$http_origin";
                add_header 'Access-Control-Max-Age' 1728000;
                add_header 'Content-Type' 'text/plain charset=UTF-8';
                add_header 'Content-Length' 0;
                return 204;
            }
            types {
                video/mp4 mp4;
                image/jpeg jpg;
            }
            autoindex on;
            root /media/frigate;
        }
        location /cache/ {
            internal; # This tells nginx it's not accessible from the outside
            alias /tmp/cache/;
        }
        location /recordings/ {
            add_header 'Access-Control-Allow-Origin' "$http_origin" always;
            add_header 'Access-Control-Allow-Credentials' 'true';
            add_header 'Access-Control-Expose-Headers' 'Content-Length';
            if ($request_method = 'OPTIONS') {
                add_header 'Access-Control-Allow-Origin' "$http_origin";
                add_header 'Access-Control-Max-Age' 1728000;
                add_header 'Content-Type' 'text/plain charset=UTF-8';
                add_header 'Content-Length' 0;
                return 204;
            }
            types {
                video/mp4 mp4;
            }
            autoindex on;
            autoindex_format json;
            root /media/frigate;
        }
        location /ws {
            proxy_pass http://mqtt_ws/;
            proxy_http_version 1.1;
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection "Upgrade";
            proxy_set_header Host $host;
        }
        location /live/jsmpeg/ {
            proxy_pass http://jsmpeg/;
            proxy_http_version 1.1;
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection "Upgrade";
            proxy_set_header Host $host;
        }
        location /live/mse/ {
            proxy_pass http://go2rtc/;
            proxy_http_version 1.1;
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection "Upgrade";
            proxy_set_header Host $host;
        }
        location /live/webrtc/ {
            proxy_pass http://go2rtc/;
            proxy_http_version 1.1;
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection "Upgrade";
            proxy_set_header Host $host;
        }
        location ~* /api/.*\.(jpg|jpeg|png)$ {
            add_header 'Access-Control-Allow-Origin' '*';
            add_header 'Access-Control-Allow-Methods' 'GET, POST, PUT, DELETE, OPTIONS';
            rewrite ^/api/(.*)$ $1 break;
            proxy_pass http://frigate_api;
            proxy_pass_request_headers on;
            proxy_set_header Host $host;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header X-Forwarded-Proto $scheme;
        }
        location /api/ {
            add_header Cache-Control "no-store";
            expires off;
            add_header 'Access-Control-Allow-Origin' '*';
            add_header 'Access-Control-Allow-Methods' 'GET, POST, PUT, DELETE, OPTIONS';
            proxy_pass http://frigate_api/;
            proxy_pass_request_headers on;
            proxy_set_header Host $host;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header X-Forwarded-Proto $scheme;
        }
        location / {
            add_header Cache-Control "no-store";
            expires off;
            location /assets/ {
                access_log off;
                expires 1y;
                add_header Cache-Control "public";
            }
            sub_filter 'href="/BASE_PATH/' 'href="$http_x_ingress_path/';
            sub_filter 'url(/BASE_PATH/' 'url($http_x_ingress_path/';
            sub_filter '"/BASE_PATH/dist/' '"$http_x_ingress_path/dist/';
            sub_filter '"/BASE_PATH/js/' '"$http_x_ingress_path/js/';
            sub_filter '"/BASE_PATH/assets/' '"$http_x_ingress_path/assets/';
            sub_filter '"/BASE_PATH/monacoeditorwork/' '"$http_x_ingress_path/assets/';
            sub_filter 'return"/BASE_PATH/"' 'return window.baseUrl';
            sub_filter '<body>' '<body><script>window.baseUrl="$http_x_ingress_path/";</script>';
            sub_filter_types text/css application/javascript;
            sub_filter_once off;
            root /opt/frigate/web;
            try_files $uri $uri/ /index.html;
        }
    }
 }
 rtmp {
    server {
        listen 1935;
        chunk_size 4096;
        allow publish 127.0.0.1;
        deny publish all;
        allow play all;
        application live {
            live on;
            record off;
            meta copy;
        }
    }
 }
--- a/docker/tensorrt_models.sh
+++ b/docker/tensorrt_models.sh
@@ -0,0 +1,34 @@
 #!/bin/bash
 set -euxo pipefail
 CUDA_HOME=/usr/local/cuda
 LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
 OUTPUT_FOLDER=/tensorrt_models
 echo "Generating the following TRT Models: ${YOLO_MODELS:="yolov4-tiny-288,yolov4-tiny-416,yolov7-tiny-416"}"
 # Create output folder
 mkdir -p ${OUTPUT_FOLDER}
 # Install packages
 pip install --upgrade pip && pip install onnx==1.9.0 protobuf==3.20.3
 # Clone tensorrt_demos repo
 git clone --depth 1 https://github.com/yeahme49/tensorrt_demos.git /tensorrt_demos
 # Build libyolo
 cd /tensorrt_demos/plugins && make all
 cp libyolo_layer.so ${OUTPUT_FOLDER}/libyolo_layer.so
 # Download yolo weights
 cd /tensorrt_demos/yolo && ./download_yolo.sh
 # Build trt engine
 cd /tensorrt_demos/yolo
 for model in ${YOLO_MODELS//,/ }
 do
    python3 yolo_to_onnx.py -m ${model}
    python3 onnx_to_tensorrt.py -m ${model}
    cp /tensorrt_demos/yolo/${model}.trt ${OUTPUT_FOLDER}/${model}.trt;
 done
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -0,0 +1,20 @@
 # Dependencies
 /node_modules
 # Production
 /build
 # Generated files
 .docusaurus
 .cache-loader
 # Misc
 .DS_Store
 .env.local
 .env.development.local
 .env.test.local
 .env.production.local
 npm-debug.log*
 yarn-debug.log*
 yarn-error.log*
--- a/docs/README.md
+++ b/docs/README.md
@@ -0,0 +1,5 @@
 # Website
 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://blakeblackshear.github.io/frigate/contributing).
--- a/docs/babel.config.js
+++ b/docs/babel.config.js
@@ -0,0 +1,3 @@
 module.exports = {
  presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
 };
--- a/docs/docs/configuration/advanced.md
+++ b/docs/docs/configuration/advanced.md
@@ -0,0 +1,110 @@
 ---
 id: advanced
 title: Advanced Options
 sidebar_label: Advanced Options
 ---
 ### `logger`
 Change the default log level for troubleshooting purposes.
 ```yaml
 logger:
  # Optional: default log level (default: shown below)
  default: info
  # Optional: module by module log level configuration
  logs:
    frigate.mqtt: error
 ```
 Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
 Examples of available modules are:
 - `frigate.app`
 - `frigate.mqtt`
 - `frigate.object_detection`
 - `frigate.zeroconf`
 - `detector.<detector_name>`
 - `watchdog.<camera_name>`
 - `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
 ### `environment_vars`
 This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
 Example:
 ```yaml
 environment_vars:
  VARIABLE_NAME: variable_value
 ```
 ### `database`
 Event and recording information is managed in a sqlite database at `/media/frigate/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
 If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
 This may need to be in a custom location if network storage is used for the media folder.
 ```yaml
 database:
  path: /path/to/frigate.db
 ```
 ### `model`
 If using a custom model, the width and height will need to be specified.
 Custom models may also require different input tensor formats. The colorspace conversion supports RGB, BGR, or YUV frames to be sent to the object detector. The input tensor shape parameter is an enumeration to match what specified by the model.
 | Tensor Dimension | Description    |
 | :--------------: | -------------- |
 |        N         | Batch Size     |
 |        H         | Model Height   |
 |        W         | Model Width    |
 |        C         | Color Channels |
 | Available Input Tensor Shapes |
 | :---------------------------: |
 |            "nhwc"             |
 |            "nchw"             |
 ```yaml
 # Optional: model config
 model:
  path: /path/to/model
  width: 320
  height: 320
  input_tensor: "nhwc"
  input_pixel_format: "bgr"
 ```
 The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
 ```yaml
 model:
  labelmap:
    2: vehicle
    3: vehicle
    5: vehicle
    7: vehicle
    15: animal
    16: animal
    17: animal
 ```
 Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
 ## Custom ffmpeg build
 Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, a docker volume mapping can be used to overwrite the included ffmpeg build with an ffmpeg build that works for your specific hardware setup.
 To do this:
 1. Download your ffmpeg build and uncompress to a folder on the host (let's use `/home/appdata/frigate/custom-ffmpeg` for this example).
 2. Update your docker-compose or docker CLI to include `'/home/appdata/frigate/custom-ffmpeg':'/usr/lib/btbn-ffmpeg':'ro'` in the volume mappings.
 3. Restart Frigate and the custom version will be used if the mapping was done correctly.
 NOTE: The folder that is mapped from the host needs to be the folder that contains `/bin`. So if the full structure is `/home/appdata/frigate/custom-ffmpeg/bin/ffmpeg` then `/home/appdata/frigate/custom-ffmpeg` needs to be mapped to `/usr/lib/btbn-ffmpeg`.
--- a/docs/docs/configuration/birdseye.md
+++ b/docs/docs/configuration/birdseye.md
@@ -0,0 +1,35 @@
 # Birdseye
 Birdseye allows a heads-up view of your cameras to see what is going on around your property / space without having to watch all cameras that may have nothing happening. Birdseye allows specific modes that intelligently show and disappear based on what you care about. 
 ### Birdseye Modes
 Birdseye offers different modes to customize which cameras show under which circumstances.
 - **continuous:** All cameras are always included
 - **motion:** Cameras that have detected motion within the last 30 seconds are included
 - **objects:** Cameras that have tracked an active object within the last 30 seconds are included
 ### Custom Birdseye Icon
 A custom icon can be added to the birdseye background by providing a 180x180 image named `custom.png` inside of the Frigate `media` folder. The file must be a png with the icon as transparent, any non-transparent pixels will be white when displayed in the birdseye view.
 ### Birdseye view override at camera level
 If you want to include a camera in Birdseye view only for specific circumstances, or just don't include it at all, the Birdseye setting can be set at the camera level.
 ```yaml
 # Include all cameras by default in Birdseye view
 birdseye:
  enabled: True
  mode: continuous
 cameras:
  front:
    # Only include the "front" camera in Birdseye view when objects are detected
    birdseye:
      mode: objects
  back:
    # Exclude the "back" camera from Birdseye view
    birdseye:
      enabled: False
 ```
--- a/docs/docs/configuration/camera_specific.md
+++ b/docs/docs/configuration/camera_specific.md
@@ -0,0 +1,153 @@
 ---
 id: camera_specific
 title: Camera Specific Configurations
 ---
 :::note
 This page makes use of presets of FFmpeg args. For more information on presets, see the [FFmpeg Presets](/configuration/ffmpeg_presets) page.
 :::
 ## 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.
 ```yaml
 go2rtc:
  streams:
    mjpeg_cam: "ffmpeg:{your_mjpeg_stream_url}#video=h264#hardware" # <- use hardware acceleration to create an h264 stream usable for other components.
 cameras:
  ...
  mjpeg_cam:
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/mjpeg_cam
          roles:
            - detect
            - record
 ```
 ## JPEG Stream Cameras
 Cameras using a live changing jpeg image will need input parameters as below
 ```yaml
 input_args: preset-http-jpeg-generic
 ```
 Outputting the stream will have the same args and caveats as per [MJPEG Cameras](#mjpeg-cameras)
 ## RTMP Cameras
 The input parameters need to be adjusted for RTMP cameras
 ```yaml
 ffmpeg:
  input_args: preset-rtmp-generic
 ```
 ## UDP Only Cameras
 If your cameras do not support TCP connections for RTSP, you can use UDP.
 ```yaml
 ffmpeg:
  input_args: preset-rtsp-udp
 ```
 ## Model/vendor specific setup
 ### Annke C800
 This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be repackaged and the audio stream has to be converted to aac. Unfortunately direct playback of in the browser is not working (yet), but the downloaded clip can be played locally.
 ```yaml
 cameras:
  annkec800: # <------ Name the camera
    ffmpeg:
      output_args:
        record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -tag:v hvc1 -bsf:v hevc_mp4toannexb -c:a aac
        rtmp: -c:v copy -c:a aac -f flv
      inputs:
        - path: rtsp://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
          roles:
            - detect
            - record
            - rtmp
    rtmp:
      enabled: False # <-- RTMP should be disabled if your stream is not H264
    detect:
      width: # <---- update for your camera's resolution
      height: # <---- update for your camera's resolution
 ```
 ### Blue Iris RTSP Cameras
 You will need to remove `nobuffer` flag for Blue Iris RTSP cameras
 ```yaml
 ffmpeg:
  input_args: preset-rtsp-blue-iris
 ```
 ### Reolink Cameras
 Reolink has older cameras (ex: 410 & 520) as well as newer camera (ex: 520a & 511wa) which support different subsets of options. In both cases using the http stream is recommended.
 Frigate works much better with newer reolink cameras that are setup with the below options:
 If available, recommended settings are:
 - `On, fluency first` this sets the camera to CBR (constant bit rate)
 - `Interframe Space 1x` this sets the iframe interval to the same as the frame rate
 According to [this discussion](https://github.com/blakeblackshear/frigate/issues/3235#issuecomment-1135876973), the http video streams seem to be the most reliable for Reolink.
 ```yaml
 go2rtc:
  streams:
    reolink: 
      - http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password
      - "ffmpeg:reolink#audio=opus"
    reolink_sub: 
      - http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password
 cameras:
  reolink:
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/reolink?video=copy&audio=aac
          input_args: preset-rtsp-restream
          roles:
            - record
        - path: rtsp://127.0.0.1:8554/reolink_sub?video=copy
          input_args: preset-rtsp-restream
          roles:
            - detect
 ```
 ### Unifi Protect Cameras
 Unifi protect cameras require the rtspx stream to be used with go2rtc.
 To utilize a Unifi protect camera, modify the rtsps link to begin with rtspx.
 Additionally, remove the "?enableSrtp" from the end of the Unifi link.
 ```yaml
 go2rtc:
  streams:
    front:
      - rtspx://192.168.1.1:7441/abcdefghijk
 ```
 [See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.2.0#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 and rtmp if used directly with unifi protect.
 ```yaml
 ffmpeg:
  output_args:
    record: preset-record-ubiquiti
    rtmp: preset-rtmp-ubiquiti # recommend using go2rtc instead
 ```
--- a/docs/docs/configuration/cameras.md
+++ b/docs/docs/configuration/cameras.md
@@ -0,0 +1,50 @@
 ---
 id: cameras
 title: Cameras
 ---
 ## Setting Up Camera Inputs
 Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
 A camera is enabled by default but can be temporarily disabled by using `enabled: False`. Existing events and recordings can still be accessed. Live streams, recording and detecting are not working. Camera specific configurations will be used.
 Each role can only be assigned to one input per camera. The options for roles are as follows:
 | Role       | Description                                                                              |
 | ---------- | ---------------------------------------------------------------------------------------- |
 | `detect`   | Main feed for object detection                                                           |
 | `record`   | Saves segments of the video feed based on configuration settings. [docs](record.md)      |
 | `rtmp`     | Deprecated: Broadcast as an RTMP feed for other services to consume. [docs](restream.md) |
 ```yaml
 mqtt:
  host: mqtt.server.com
 cameras:
  back:
    enabled: True
    ffmpeg:
      inputs:
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
          roles:
            - detect
            - rtmp # <- deprecated, recommend using restream instead
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/live
          roles:
            - record
    detect:
      width: 1280
      height: 720
 ```
 Additional cameras are simply added to the config under the `cameras` entry.
 ```yaml
 mqtt: ...
 cameras:
  back: ...
  front: ...
  side: ...
 ```
 For camera model specific settings check the [camera specific](camera_specific.md) infos.
--- a/docs/docs/configuration/detectors.md
+++ b/docs/docs/configuration/detectors.md
@@ -0,0 +1,258 @@
 ---
 id: detectors
 title: Detectors
 ---
 Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `openvino`, 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.
 ## 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"`.
 The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
 A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
 ```yaml
 detectors:
  cpu1:
    type: cpu
    num_threads: 3
    model:
      path: "/custom_model.tflite"
  cpu2:
    type: cpu
    num_threads: 3
 ```
 When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
 ## Edge-TPU Detector
 The EdgeTPU detector type runs a TensorFlow Lite model utilizing the Google Coral delegate for hardware acceleration. To configure an EdgeTPU detector, set the `"type"` attribute to `"edgetpu"`.
 The EdgeTPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
 A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
 ### Single USB Coral
 ```yaml
 detectors:
  coral:
    type: edgetpu
    device: usb
    model:
      path: "/custom_model.tflite"
 ```
 ### Multiple USB Corals
 ```yaml
 detectors:
  coral1:
    type: edgetpu
    device: usb:0
  coral2:
    type: edgetpu
    device: usb:1
 ```
 ### Native Coral (Dev Board)
 _warning: may have [compatibility issues](https://github.com/blakeblackshear/frigate/issues/1706) after `v0.9.x`_
 ```yaml
 detectors:
  coral:
    type: edgetpu
    device: ""
 ```
 ### Multiple PCIE/M.2 Corals
 ```yaml
 detectors:
  coral1:
    type: edgetpu
    device: pci:0
  coral2:
    type: edgetpu
    device: pci:1
 ```
 ### Mixing Corals
 ```yaml
 detectors:
  coral_usb:
    type: edgetpu
    device: usb
  coral_pci:
    type: edgetpu
    device: pci
 ```
 ## OpenVINO Detector
 The OpenVINO detector type runs an OpenVINO IR model on Intel CPU, GPU and VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
 The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Working_with_devices.html). Other supported devices could be `AUTO`, `CPU`, `GPU`, `MYRIAD`, etc. If not specified, the default OpenVINO device will be selected by the `AUTO` plugin.
 OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. A supported Intel platform is required to use the `GPU` device with OpenVINO. The `MYRIAD` device may be run on any platform, including Arm devices. For detailed system requirements, see [OpenVINO System Requirements](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
 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:
  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
  labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 ```
 This detector also supports some YOLO variants: YOLOX, YOLOv5, and YOLOv8 specifically. Other YOLO variants are not officially supported/tested. Frigate does not come with any yolo models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/index.md#full-configuration-reference) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
 ```yaml
 detectors:
  ov:
    type: openvino
    device: AUTO
    model:
      path: /path/to/yolox_tiny.xml
 model:
  width: 416
  height: 416
  input_tensor: nchw
  input_pixel_format: bgr
  model_type: yolox
  labelmap_path: /path/to/coco_80cl.txt
 ```
 ### Intel NCS2 VPU and Myriad X Setup
 Intel produces a neural net inference accelleration chip called Myriad X. This chip was sold in their Neural Compute Stick 2 (NCS2) which has been discontinued. If intending to use the MYRIAD device for accelleration, additional setup is required to pass through the USB device. The host needs a udev rule installed to handle the NCS2 device.
 ```bash
 sudo usermod -a -G users "$(whoami)"
 cat <<EOF > 97-myriad-usbboot.rules
 SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
 SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
 EOF
 sudo cp 97-myriad-usbboot.rules /etc/udev/rules.d/
 sudo udevadm control --reload-rules
 sudo udevadm trigger
 ```
 Additionally, the Frigate docker container needs to run with the following configuration:
 ```bash
 --device-cgroup-rule='c 189:\* rmw' -v /dev/bus/usb:/dev/bus/usb
 ```
 or in your compose file:
 ```yml
 device_cgroup_rules:
  - "c 189:* rmw"
 volumes:
  - /dev/bus/usb:/dev/bus/usb
 ```
 ## NVidia TensorRT Detector
 NVidia GPUs may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt` tag suffix. This detector is designed to work with Yolo models for object detection.
 ### Minimum Hardware Support
 The TensorRT detector uses the 11.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be `>=450.80.02`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
 > **TODO:** NVidia claims support on compute 3.5 and 3.7, but marks it as deprecated. This would have some, but not all, Kepler GPUs as possibly working. This needs testing before making any claims of support.
 To use the TensorRT detector, make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
 There are improved capabilities in newer GPU architectures that TensorRT can benefit from, such as INT8 operations and Tensor cores. The features compatible with your hardware will be optimized when the model is converted to a trt file. Currently the script provided for generating the model provides a switch to enable/disable FP16 operations. If you wish to use newer features such as INT8 optimization, more work is required.
 #### Compatibility References:
 [NVIDIA TensorRT Support Matrix](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-841/support-matrix/index.html)
 [NVIDIA CUDA Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html)
 [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus)
 ### Generate Models
 The model used for TensorRT must be preprocessed on the same hardware platform that they will run on. This means that each user must run additional setup to generate a model file for the TensorRT library. A script is provided that will build several common models.
 To generate model files, create a new folder to save the models, download the script, and launch a docker container that will run the script.
 ```bash
 mkdir trt-models
 wget https://raw.githubusercontent.com/blakeblackshear/frigate/docker/tensorrt_models.sh
 chmod +x tensorrt_models.sh
 docker run --gpus=all --rm -it -v `pwd`/trt-models:/tensorrt_models -v `pwd`/tensorrt_models.sh:/tensorrt_models.sh nvcr.io/nvidia/tensorrt:22.07-py3 /tensorrt_models.sh
 ```
 The `trt-models` folder can then be mapped into your Frigate container as `trt-models` and the models referenced from the config.
 If your GPU does not support FP16 operations, you can pass the environment variable `-e USE_FP16=False` to the `docker run` command to disable it.
 Specific models can be selected by passing an environment variable to the `docker run` command. Use the form `-e YOLO_MODELS=yolov4-416,yolov4-tiny-416` to select one or more model names. The models available are shown below.
 ```
 yolov3-288
 yolov3-416
 yolov3-608
 yolov3-spp-288
 yolov3-spp-416
 yolov3-spp-608
 yolov3-tiny-288
 yolov3-tiny-416
 yolov4-288
 yolov4-416
 yolov4-608
 yolov4-csp-256
 yolov4-csp-512
 yolov4-p5-448
 yolov4-p5-896
 yolov4-tiny-288
 yolov4-tiny-416
 yolov4x-mish-320
 yolov4x-mish-640
 yolov7-tiny-288
 yolov7-tiny-416
 yolov7-640
 yolov7-320
 yolov7x-640
 yolov7x-320
 ```
 ### Configuration Parameters
 The TensorRT detector can be selected by specifying `tensorrt` as the model type. The GPU will need to be passed through to the docker container using the same methods described in the [Hardware Acceleration](hardware_acceleration.md#nvidia-gpu) section. If you pass through multiple GPUs, you can select which GPU is used for a detector with the `device` configuration parameter. The `device` parameter is an integer value of the GPU index, as shown by `nvidia-smi` within the container.
 The TensorRT detector uses `.trt` model files that are located in `/trt-models/` by default. These model file path and dimensions used will depend on which model you have generated.
 ```yaml
 detectors:
  tensorrt:
    type: tensorrt
    device: 0 #This is the default, select the first GPU
 model:
  path: /trt-models/yolov7-tiny-416.trt
  input_tensor: nchw
  input_pixel_format: rgb
  width: 416
  height: 416
 ```
--- a/docs/docs/configuration/ffmpeg_presets.md
+++ b/docs/docs/configuration/ffmpeg_presets.md
@@ -0,0 +1,79 @@
 ---
 id: ffmpeg_presets
 title: FFmpeg presets
 ---
 Some presets of FFmpeg args are provided by default to make the configuration easier. All presets can be seen in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).
 ### Hwaccel Presets
 It is highly recommended to use hwaccel presets in the config. These presets not only replace the longer args, but they also give Frigate hints of what hardware is available and allows Frigate to make other optimizations using the GPU such as when encoding the birdseye restream or when scaling a stream that has a size different than the native stream size.
 See [the hwaccel docs](/configuration/hardware_acceleration.md) for more info on how to setup hwaccel for your GPU / iGPU.
 | Preset                | Usage                        | Other Notes                                           |
 | --------------------- | ---------------------------- | ----------------------------------------------------- |
 | preset-rpi-32-h264    | 32 bit Rpi with h264 stream  |                                                       |
 | preset-rpi-64-h264    | 64 bit Rpi with h264 stream  |                                                       |
 | preset-vaapi          | Intel & AMD VAAPI            | Check hwaccel docs to ensure correct driver is chosen |
 | preset-intel-qsv-h264 | Intel QSV with h264 stream   | If issues occur recommend using vaapi preset instead  |
 | preset-intel-qsv-h265 | Intel QSV with h265 stream   | If issues occur recommend using vaapi preset instead  |
 | preset-nvidia-h264    | Nvidia GPU with h264 stream  |                                                       |
 | preset-nvidia-h265    | Nvidia GPU with h265 stream  |                                                       |
 | preset-nvidia-mjpeg   | Nvidia GPU with mjpeg stream | Recommend restreaming mjpeg and using nvidia-h264     |
 ### Input Args Presets
 Input args presets help make the config more readable and handle use cases for different types of streams to ensure maximum compatibility.
 See [the camera specific docs](/configuration/camera_specific.md) for more info on non-standard cameras and recommendations for using them in Frigate.
 | Preset                           | Usage                     | Other Notes                                                                                      |
 | -------------------------------- | ------------------------- | ------------------------------------------------------------------------------------------------ |
 | preset-http-jpeg-generic         | HTTP Live Jpeg            | Recommend restreaming live jpeg instead                                                          |
 | preset-http-mjpeg-generic        | HTTP Mjpeg Stream         | Recommend restreaming mjpeg stream instead                                                       |
 | preset-http-reolink              | Reolink HTTP-FLV Stream   | Only for reolink http, not when restreaming as rtsp                                              |
 | preset-rtmp-generic              | RTMP Stream               |                                                                                                  |
 | preset-rtsp-generic              | RTSP Stream               | This is the default when nothing is specified                                                    |
 | preset-rtsp-restream             | RTSP Stream from restream | Use for rtsp restream as source for frigate                                                      |
 | preset-rtsp-restream-low-latency | RTSP Stream from restream | Use for rtsp restream as source for frigate to lower latency, may cause issues with some cameras |
 | preset-rtsp-udp                  | RTSP Stream via UDP       | Use when camera is UDP only                                                                      |
 | preset-rtsp-blue-iris            | Blue Iris RTSP Stream     | Use when consuming a stream from Blue Iris                                                       |
 :::caution
 It is important to be mindful of input args when using restream because you can have a mix of protocols. `http` and `rtmp` presets cannot be used with `rtsp` streams. For example, when using a reolink cam with the rtsp restream as a source for record the preset-http-reolink will cause a crash. In this case presets will need to be set at the stream level. See the example below.
 :::
 ```yaml
 go2rtc:
  streams:
    reolink_cam: http://192.168.0.139/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=admin&password=password
 cameras:
  reolink_cam:
    ffmpeg:
      inputs:
        - path: http://192.168.0.139/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=admin&password=password
          input_args: preset-http-reolink
          roles:
            - detect
        - path: rtsp://127.0.0.1:8554/reolink_cam
          input_args: preset-rtsp-generic
          roles:
            - record
 ```
 ### Output Args Presets
 Output args presets help make the config more readable and handle use cases for different types of streams to ensure consistent recordings.
 | Preset                           | Usage                             | Other Notes                                   |
 | -------------------------------- | --------------------------------- | --------------------------------------------- |
 | preset-record-generic            | Record WITHOUT audio              | This is the default when nothing is specified |
 | preset-record-generic-audio-aac  | Record WITH aac audio             | Use this to enable audio in recordings        |
 | preset-record-generic-audio-copy | Record WITH original audio        | Use this to enable audio in recordings        |
 | preset-record-mjpeg              | Record an mjpeg stream            | Recommend restreaming mjpeg stream instead    |
 | preset-record-jpeg               | Record live jpeg                  | Recommend restreaming live jpeg instead       |
 | preset-record-ubiquiti           | Record ubiquiti stream with audio | Recordings with ubiquiti non-standard audio   |
--- a/docs/docs/configuration/hardware_acceleration.md
+++ b/docs/docs/configuration/hardware_acceleration.md
@@ -0,0 +1,163 @@
 ---
 id: hardware_acceleration
 title: Hardware Acceleration
 ---
 It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
 ### Raspberry Pi 3/4
 Ensure you increase the allocated RAM for your GPU to at least 128 (raspi-config > Performance Options > GPU Memory).
 **NOTICE**: If you are using the addon, you may need to turn off `Protection mode` for hardware acceleration.
 ```yaml
 ffmpeg:
  hwaccel_args: preset-rpi-64-h264
 ```
 ### Intel-based CPUs (<10th Generation) via VAAPI
 VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams. VAAPI is recommended for all generations of Intel-based CPUs if QSV does not work.
 ```yaml
 ffmpeg:
  hwaccel_args: preset-vaapi
 ```
 **NOTICE**: With some of the processors, like the J4125, the default driver `iHD` doesn't seem to work correctly for hardware acceleration. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the frigate.yml for HA OS users](advanced.md#environment_vars).
 ### Intel-based CPUs (>=10th Generation) via Quicksync
 QSV must be set specifically based on the video encoding of the stream.
 #### H.264 streams
 ```yaml
 ffmpeg:
  hwaccel_args: preset-intel-qsv-h264
 ```
 #### H.265 streams
 ```yaml
 ffmpeg:
  hwaccel_args: preset-intel-qsv-h265
 ```
 ### 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.
 **Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
 ```yaml
 ffmpeg:
  hwaccel_args: preset-vaapi
 ```
 ### NVIDIA GPUs
 While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
 A more complete list of cards and ther compatible drivers is available in the [driver release readme](https://download.nvidia.com/XFree86/Linux-x86_64/525.85.05/README/supportedchips.html).
 If your distribution does not offer NVIDIA driver packages, you can [download them here](https://www.nvidia.com/en-us/drivers/unix/).
 #### Docker Configuration
 Additional configuration is needed for the Docker container to be able to access the NVIDIA GPU. The supported method for this is to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) and specify the GPU to Docker. How you do this depends on how Docker is being run:
 ##### Docker Compose
 ```yaml
 services:
  frigate:
    ...
    image: ghcr.io/blakeblackshear/frigate:stable
    deploy:    # <------------- Add this section
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ['0'] # this is only needed when using multiple GPUs
              count: 1 # number of GPUs
              capabilities: [gpu]
 ```
 ##### Docker Run CLI
 ```bash
 docker run -d \
  --name frigate \
  ...
  --gpus=all \
  ghcr.io/blakeblackshear/frigate:stable
 ```
 #### Setup Decoder
 The decoder you need to pass in the `hwaccel_args` will depend on the input video.
 A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get the ones your card supports)
 ```
 V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
 V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
 V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
 V..... mjpeg_cuvid          Nvidia CUVID MJPEG decoder (codec mjpeg)
 V..... mpeg1_cuvid          Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
 V..... mpeg2_cuvid          Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
 V..... mpeg4_cuvid          Nvidia CUVID MPEG4 decoder (codec mpeg4)
 V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)
 V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)
 V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)
 ```
 For example, for H264 video, you'll select `preset-nvidia-h264`.
 ```yaml
 ffmpeg:
  hwaccel_args: preset-nvidia-h264
 ```
 If everything is working correctly, you should see a significant improvement in performance.
 Verify that hardware decoding is working by running `nvidia-smi`, which should show `ffmpeg`
 processes:
 :::note
 `nvidia-smi` may not show `ffmpeg` processes when run inside the container [due to docker limitations](https://github.com/NVIDIA/nvidia-docker/issues/179#issuecomment-645579458).
 :::
 ```
 +-----------------------------------------------------------------------------+
 | NVIDIA-SMI 455.38       Driver Version: 455.38       CUDA Version: 11.1     |
 |-------------------------------+----------------------+----------------------+
 | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
 |                               |                      |               MIG M. |
 |===============================+======================+======================|
 |   0  GeForce GTX 166...  Off  | 00000000:03:00.0 Off |                  N/A |
 | 38%   41C    P2    36W / 125W |   2082MiB /  5942MiB |      5%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 +-----------------------------------------------------------------------------+
 | Processes:                                                                  |
 |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
 |        ID   ID                                                   Usage      |
 |=============================================================================|
 |    0   N/A  N/A     12737      C   ffmpeg                            249MiB |
 |    0   N/A  N/A     12751      C   ffmpeg                            249MiB |
 |    0   N/A  N/A     12772      C   ffmpeg                            249MiB |
 |    0   N/A  N/A     12775      C   ffmpeg                            249MiB |
 |    0   N/A  N/A     12800      C   ffmpeg                            249MiB |
 |    0   N/A  N/A     12811      C   ffmpeg                            417MiB |
 |    0   N/A  N/A     12827      C   ffmpeg                            417MiB |
 +-----------------------------------------------------------------------------+
 ```
 If you do not see these processes, check the `docker logs` for the container and look for decoding errors.
 These instructions were originally based on the [Jellyfin documentation](https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-hardware-acceleration-on-docker-linux).
--- a/docs/docs/configuration/index.md
+++ b/docs/docs/configuration/index.md
@@ -0,0 +1,537 @@
 ---
 id: index
 title: Configuration File
 ---
 For Home Assistant Addon installations, the config file needs to be in the root of your Home Assistant config directory (same location as `configuration.yaml`). It can be named `frigate.yml` or `frigate.yaml`, but if both files exist `frigate.yaml` will be preferred and `frigate.yml` will be ignored.
 For all other installation types, the config file should be mapped to `/config/config.yml` inside the container.
 It is recommended to start with a minimal configuration and add to it as described in [this guide](../guides/getting_started.md):
 ```yaml
 mqtt:
  host: mqtt.server.com
 cameras:
  back:
    ffmpeg:
      inputs:
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
          roles:
            - detect
    detect:
      width: 1280
      height: 720
 ```
 ### VSCode Configuration Schema
 VSCode (and VSCode addon) supports the JSON schemas which will automatically validate the config. This can be added by adding `# yaml-language-server: $schema=http://frigate_host:5000/api/config/schema.json` to the top of the config file. `frigate_host` being the IP address of Frigate or `ccab4aaf-frigate` if running in the addon.
 ### Full configuration reference:
 :::caution
 It is not recommended to copy this full configuration file. Only specify values that are different from the defaults. Configuration options and default values may change in future versions.
 :::
 **Note:** The following values will be replaced at runtime by using environment variables
 - `{FRIGATE_MQTT_USER}`
 - `{FRIGATE_MQTT_PASSWORD}`
 - `{FRIGATE_RTSP_USER}`
 - `{FRIGATE_RTSP_PASSWORD}`
 for example:
 ```yaml
 mqtt:
  user: "{FRIGATE_MQTT_USER}"
  password: "{FRIGATE_MQTT_PASSWORD}"
 ```
 ```yaml
 - path: rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:8554/unicast
 ```
 ```yaml
 mqtt:
  # Optional: Enable mqtt server (default: shown below)
  enabled: True
  # Required: host name
  host: mqtt.server.com
  # Optional: port (default: shown below)
  port: 1883
  # Optional: topic prefix (default: shown below)
  # NOTE: must be unique if you are running multiple instances
  topic_prefix: frigate
  # Optional: client id (default: shown below)
  # NOTE: must be unique if you are running multiple instances
  client_id: frigate
  # Optional: user
  # NOTE: MQTT user can be specified with an environment variables that must begin with 'FRIGATE_'.
  #       e.g. user: '{FRIGATE_MQTT_USER}'
  user: mqtt_user
  # Optional: password
  # NOTE: MQTT password can be specified with an environment variables that must begin with 'FRIGATE_'.
  #       e.g. password: '{FRIGATE_MQTT_PASSWORD}'
  password: password
  # Optional: tls_ca_certs for enabling TLS using self-signed certs (default: None)
  tls_ca_certs: /path/to/ca.crt
  # Optional: tls_client_cert and tls_client key in order to use self-signed client
  # certificates (default: None)
  # NOTE: certificate must not be password-protected
  #       do not set user and password when using a client certificate
  tls_client_cert: /path/to/client.crt
  tls_client_key: /path/to/client.key
  # Optional: tls_insecure (true/false) for enabling TLS verification of
  # the server hostname in the server certificate (default: None)
  tls_insecure: false
  # Optional: interval in seconds for publishing stats (default: shown below)
  stats_interval: 60
 # Optional: Detectors configuration. Defaults to a single CPU detector
 detectors:
  # Required: name of the detector
  detector_name:
    # Required: type of the detector
    # Frigate provided types include 'cpu', 'edgetpu', and 'openvino' (default: shown below)
    # Additional detector types can also be plugged in.
    # Detectors may require additional configuration.
    # Refer to the Detectors configuration page for more information.
    type: cpu
 # Optional: Database configuration
 database:
  # The path to store the SQLite DB (default: shown below)
  path: /media/frigate/frigate.db
 # Optional: model modifications
 model:
  # Optional: path to the model (default: automatic based on detector)
  path: /edgetpu_model.tflite
  # Optional: path to the labelmap (default: shown below)
  labelmap_path: /labelmap.txt
  # Required: Object detection model input width (default: shown below)
  width: 320
  # Required: Object detection model input height (default: shown below)
  height: 320
  # Optional: Object detection model input colorspace
  # Valid values are rgb, bgr, or yuv. (default: shown below)
  input_pixel_format: rgb
  # Optional: Object detection model input tensor format
  # Valid values are nhwc or nchw (default: shown below)
  input_tensor: nhwc
  # Optional: Object detection model type, currently only used with the OpenVINO detector
  # Valid values are ssd, yolox, yolov5, or yolov8 (default: shown below)
  model_type: ssd
  # Optional: Label name modifications. These are merged into the standard labelmap.
  labelmap:
    2: vehicle
 # Optional: logger verbosity settings
 logger:
  # Optional: Default log verbosity (default: shown below)
  default: info
  # Optional: Component specific logger overrides
  logs:
    frigate.event: debug
 # Optional: set environment variables
 environment_vars:
  EXAMPLE_VAR: value
 # Optional: birdseye configuration
 # NOTE: Can (enabled, mode) be overridden at the camera level
 birdseye:
  # Optional: Enable birdseye view (default: shown below)
  enabled: True
  # Optional: Restream birdseye via RTSP (default: shown below)
  # NOTE: Enabling this will set birdseye to run 24/7 which may increase CPU usage somewhat.
  restream: False
  # Optional: Width of the output resolution (default: shown below)
  width: 1280
  # Optional: Height of the output resolution (default: shown below)
  height: 720
  # Optional: Encoding quality of the mpeg1 feed (default: shown below)
  # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
  quality: 8
  # Optional: Mode of the view. Available options are: objects, motion, and continuous
  #   objects - cameras are included if they have had a tracked object within the last 30 seconds
  #   motion - cameras are included if motion was detected in the last 30 seconds
  #   continuous - all cameras are included always
  mode: objects
 # Optional: ffmpeg configuration
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
  # Optional: global ffmpeg args (default: shown below)
  global_args: -hide_banner -loglevel warning -threads 2
  # Optional: global hwaccel args (default: shown below)
  # NOTE: See hardware acceleration docs for your specific device
  hwaccel_args: []
  # Optional: global input args (default: shown below)
  input_args: preset-rtsp-generic
  # Optional: global output args
  output_args:
    # Optional: output args for detect streams (default: shown below)
    detect: -threads 2 -f rawvideo -pix_fmt yuv420p
    # Optional: output args for record streams (default: shown below)
    record: preset-record-generic
    # Optional: output args for rtmp streams (default: shown below)
    rtmp: preset-rtmp-generic
 # Optional: Detect configuration
 # NOTE: Can be overridden at the camera level
 detect:
  # Optional: width of the frame for the input with the detect role (default: shown below)
  width: 1280
  # Optional: height of the frame for the input with the detect role (default: shown below)
  height: 720
  # Optional: desired fps for your camera for the input with the detect role (default: shown below)
  # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
  fps: 5
  # Optional: enables detection for the camera (default: True)
  enabled: True
  # Optional: Number of frames without a detection before Frigate considers an object to be gone. (default: 5x the frame rate)
  max_disappeared: 25
  # Optional: Configuration for stationary object tracking
  stationary:
    # Optional: Frequency for confirming stationary objects (default: shown below)
    # When set to 0, object detection will not confirm stationary objects until movement is detected.
    # If set to 10, object detection will run to confirm the object still exists on every 10th frame.
    interval: 0
    # Optional: Number of frames without a position change for an object to be considered stationary (default: 10x the frame rate or 10s)
    threshold: 50
    # Optional: Define a maximum number of frames for tracking a stationary object (default: not set, track forever)
    # This can help with false positives for objects that should only be stationary for a limited amount of time.
    # It can also be used to disable stationary object tracking. For example, you may want to set a value for person, but leave
    # car at the default.
    # WARNING: Setting these values overrides default behavior and disables stationary object tracking.
    #          There are very few situations where you would want it disabled. It is NOT recommended to
    #          copy these values from the example config into your config unless you know they are needed.
    max_frames:
      # Optional: Default for all object types (default: not set, track forever)
      default: 3000
      # Optional: Object specific values
      objects:
        person: 1000
 # Optional: Object configuration
 # NOTE: Can be overridden at the camera level
 objects:
  # Optional: list of objects to track from labelmap.txt (default: shown below)
  track:
    - person
  # Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
  # Checks based on the bottom center of the bounding box of the object.
  # NOTE: This mask is COMBINED with the object type specific mask below
  mask: 0,0,1000,0,1000,200,0,200
  # Optional: filters to reduce false positives for specific object types
  filters:
    person:
      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
      min_area: 5000
      # Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
      max_area: 100000
      # Optional: minimum width/height of the bounding box for the detected object (default: 0)
      min_ratio: 0.5
      # Optional: maximum width/height of the bounding box for the detected object (default: 24000000)
      max_ratio: 2.0
      # Optional: minimum score for the object to initiate tracking (default: shown below)
      min_score: 0.5
      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
      threshold: 0.7
      # Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
      # Checks based on the bottom center of the bounding box of the object
      mask: 0,0,1000,0,1000,200,0,200
 # Optional: Motion configuration
 # NOTE: Can be overridden at the camera level
 motion:
  # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
  # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
  # The value should be between 1 and 255.
  threshold: 25
  # Optional: Minimum size in pixels in the resized motion image that counts as motion (default: 30)
  # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
  # make motion detection more sensitive to smaller moving objects.
  # As a rule of thumb:
  #  - 15 - high sensitivity
  #  - 30 - medium sensitivity
  #  - 50 - low sensitivity
  contour_area: 30
  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
  # Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
  # Too low and a fast moving person wont be detected as motion.
  delta_alpha: 0.2
  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
  # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
  # Low values will cause things like moving shadows to be detected as motion for longer.
  # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
  frame_alpha: 0.2
  # Optional: Height of the resized motion frame  (default: 50)
  # This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense
  # of higher CPU usage. Lower values result in less CPU, but small changes may not register as motion.
  frame_height: 50
  # Optional: motion mask
  # NOTE: see docs for more detailed info on creating masks
  mask: 0,900,1080,900,1080,1920,0,1920
  # Optional: improve contrast (default: shown below)
  # Enables dynamic contrast improvement. This should help improve night detections at the cost of making motion detection more sensitive
  # for daytime.
  improve_contrast: False
  # Optional: Delay when updating camera motion through MQTT from ON -> OFF (default: shown below).
  mqtt_off_delay: 30
 # Optional: Record configuration
 # NOTE: Can be overridden at the camera level
 record:
  # Optional: Enable recording (default: shown below)
  # WARNING: If recording is disabled in the config, turning it on via
  #          the UI or MQTT later will have no effect.
  enabled: False
  # Optional: Number of minutes to wait between cleanup runs (default: shown below)
  # This can be used to reduce the frequency of deleting recording segments from disk if you want to minimize i/o
  expire_interval: 60
  # Optional: Retention settings for recording
  retain:
    # Optional: Number of days to retain recordings regardless of events (default: shown below)
    # NOTE: This should be set to 0 and retention should be defined in events section below
    #       if you only want to retain recordings of events.
    days: 0
    # Optional: Mode for retention. Available options are: all, motion, and active_objects
    #   all - save all recording segments regardless of activity
    #   motion - save all recordings segments with any detected motion
    #   active_objects - save all recording segments with active/moving objects
    # NOTE: this mode only applies when the days setting above is greater than 0
    mode: all
  # Optional: Event recording settings
  events:
    # Optional: Number of seconds before the event to include (default: shown below)
    pre_capture: 5
    # Optional: Number of seconds after the event to include (default: shown below)
    post_capture: 5
    # Optional: Objects to save recordings for. (default: all tracked objects)
    objects:
      - person
    # Optional: Restrict recordings to objects that entered any of the listed zones (default: no required zones)
    required_zones: []
    # Optional: Retention settings for recordings of events
    retain:
      # Required: Default retention days (default: shown below)
      default: 10
      # Optional: Mode for retention. (default: shown below)
      #   all - save all recording segments for events regardless of activity
      #   motion - save all recordings segments for events with any detected motion
      #   active_objects - save all recording segments for event with active/moving objects
      #
      # NOTE: If the retain mode for the camera is more restrictive than the mode configured
      #       here, the segments will already be gone by the time this mode is applied.
      #       For example, if the camera retain mode is "motion", the segments without motion are
      #       never stored, so setting the mode to "all" here won't bring them back.
      mode: motion
      # Optional: Per object retention days
      objects:
        person: 15
 # Optional: Configuration for the jpg snapshots written to the clips directory for each event
 # NOTE: Can be overridden at the camera level
 snapshots:
  # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
  enabled: False
  # Optional: save a clean PNG copy of the snapshot image (default: shown below)
  clean_copy: True
  # Optional: print a timestamp on the snapshots (default: shown below)
  timestamp: False
  # Optional: draw bounding box on the snapshots (default: shown below)
  bounding_box: False
  # Optional: crop the snapshot (default: shown below)
  crop: False
  # Optional: height to resize the snapshot to (default: original size)
  height: 175
  # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
  required_zones: []
  # Optional: Camera override for retention settings (default: global values)
  retain:
    # Required: Default retention days (default: shown below)
    default: 10
    # Optional: Per object retention days
    objects:
      person: 15
 # Optional: RTMP configuration
 # NOTE: RTMP is deprecated in favor of restream
 # NOTE: Can be overridden at the camera level
 rtmp:
  # Optional: Enable the RTMP stream (default: False)
  enabled: False
 # Optional: Restream configuration
 # Uses https://github.com/AlexxIT/go2rtc (v1.2.0)
 go2rtc:
 # Optional: jsmpeg stream configuration for WebUI
 live:
  # Optional: Set the name of the stream that should be used for live view
  # in frigate WebUI. (default: name of camera)
  stream_name: camera_name
  # Optional: Set the height of the jsmpeg stream. (default: 720)
  # This must be less than or equal to the height of the detect stream. Lower resolutions
  # reduce bandwidth required for viewing the jsmpeg stream. Width is computed to match known aspect ratio.
  height: 720
  # Optional: Set the encode quality of the jsmpeg stream (default: shown below)
  # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
  quality: 8
 # Optional: in-feed timestamp style configuration
 # NOTE: Can be overridden at the camera level
 timestamp_style:
  # Optional: Position of the timestamp (default: shown below)
  #           "tl" (top left), "tr" (top right), "bl" (bottom left), "br" (bottom right)
  position: "tl"
  # Optional: Format specifier conform to the Python package "datetime" (default: shown below)
  #           Additional Examples:
  #             german: "%d.%m.%Y %H:%M:%S"
  format: "%m/%d/%Y %H:%M:%S"
  # Optional: Color of font
  color:
    # All Required when color is specified (default: shown below)
    red: 255
    green: 255
    blue: 255
  # Optional: Line thickness of font (default: shown below)
  thickness: 2
  # Optional: Effect of lettering (default: shown below)
  #           None (No effect),
  #           "solid" (solid background in inverse color of font)
  #           "shadow" (shadow for font)
  effect: None
 # Required
 cameras:
  # Required: name of the camera
  back:
    # Optional: Enable/Disable the camera (default: shown below).
    # If disabled: config is used but no live stream and no capture etc.
    # Events/Recordings are still viewable.
    enabled: True
    # Required: ffmpeg settings for the camera
    ffmpeg:
      # Required: A list of input streams for the camera. See documentation for more information.
      inputs:
        # Required: the path to the stream
        # NOTE: path may include environment variables, which must begin with 'FRIGATE_' and be referenced in {}
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
          # Required: list of roles for this stream. valid values are: detect,record,rtmp
          # NOTICE: In addition to assigning the record and rtmp roles,
          # they must also be enabled in the camera config.
          roles:
            - detect
            - record
            - rtmp
          # Optional: stream specific global args (default: inherit)
          # global_args:
          # Optional: stream specific hwaccel args (default: inherit)
          # hwaccel_args:
          # Optional: stream specific input args (default: inherit)
          # input_args:
      # Optional: camera specific global args (default: inherit)
      # global_args:
      # Optional: camera specific hwaccel args (default: inherit)
      # hwaccel_args:
      # Optional: camera specific input args (default: inherit)
      # input_args:
      # Optional: camera specific output args (default: inherit)
      # output_args:
    # Optional: timeout for highest scoring image before allowing it
    # to be replaced by a newer image. (default: shown below)
    best_image_timeout: 60
    # Optional: zones for this camera
    zones:
      # Required: name of the zone
      # NOTE: This must be different than any camera names, but can match with another zone on another
      #       camera.
      front_steps:
        # Required: List of x,y coordinates to define the polygon of the zone.
        # NOTE: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
        coordinates: 545,1077,747,939,788,805
        # Optional: List of objects that can trigger this zone (default: all tracked objects)
        objects:
          - person
        # Optional: Zone level object filters.
        # NOTE: The global and camera filters are applied upstream.
        filters:
          person:
            min_area: 5000
            max_area: 100000
            threshold: 0.7
    # Optional: Configuration for the jpg snapshots published via MQTT
    mqtt:
      # Optional: Enable publishing snapshot via mqtt for camera (default: shown below)
      # NOTE: Only applies to publishing image data to MQTT via 'frigate/<camera_name>/<object_name>/snapshot'.
      # All other messages will still be published.
      enabled: True
      # Optional: print a timestamp on the snapshots (default: shown below)
      timestamp: True
      # Optional: draw bounding box on the snapshots (default: shown below)
      bounding_box: True
      # Optional: crop the snapshot (default: shown below)
      crop: True
      # Optional: height to resize the snapshot to (default: shown below)
      height: 270
      # Optional: jpeg encode quality (default: shown below)
      quality: 70
      # Optional: Restrict mqtt messages to objects that entered any of the listed zones (default: no required zones)
      required_zones: []
    # Optional: Configuration for how camera is handled in the GUI.
    ui:
      # Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
      # By default the cameras are sorted alphabetically.
      order: 0
      # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
      dashboard: True
 # Optional
 ui:
  # Optional: Set the default live mode for cameras in the UI (default: shown below)
  live_mode: mse
  # Optional: Set a timezone to use in the UI (default: use browser local time)
  # timezone: America/Denver
  # Optional: Use an experimental recordings / camera view UI (default: shown below)
  use_experimental: False
  # Optional: Set the time format used.
  # Options are browser, 12hour, or 24hour (default: shown below)
  time_format: browser
  # Optional: Set the date style for a specified length.
  # Options are: full, long, medium, short
  # Examples:
  #    short: 2/11/23
  #    medium: Feb 11, 2023
  #    full: Saturday, February 11, 2023
  # (default: shown below).
  date_style: short
  # Optional: Set the time style for a specified length.
  # Options are: full, long, medium, short
  # Examples:
  #    short: 8:14 PM
  #    medium: 8:15:22 PM
  #    full: 8:15:22 PM Mountain Standard Time
  # (default: shown below).
  time_style: medium
  # Optional: Ability to manually override the date / time styling to use strftime format
  # https://www.gnu.org/software/libc/manual/html_node/Formatting-Calendar-Time.html
  # possible values are shown above (default: not set)
  strftime_fmt: "%Y/%m/%d %H:%M"
 # Optional: Telemetry configuration
 telemetry:
  # Optional: Enable the latest version outbound check (default: shown below)
  # NOTE: If you use the HomeAssistant integration, disabling this will prevent it from reporting new versions
  version_check: True
 ```
--- a/docs/docs/configuration/live.md
+++ b/docs/docs/configuration/live.md
@@ -0,0 +1,104 @@
 ---
 id: live
 title: Live View
 ---
 Frigate has different live view options, some of which require the bundled `go2rtc` to be configured as shown in the [step by step guide](/guides/configuring_go2rtc).
 ## Live View Options
 Live view options can be selected while viewing the live stream. The options are:
 | Source | Latency | Frame Rate                            | Resolution     | Audio                        | Requires go2rtc | Other Limitations                            |
 | ------ | ------- | ------------------------------------- | -------------- | ---------------------------- | --------------- | -------------------------------------------- |
 | jsmpeg | low     | same as `detect -> fps`, capped at 10 | same as detect | no                           | no              | none                                         |
 | mse    | low     | native                                | native         | yes (depends on audio codec) | yes             | not supported on iOS, 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 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:
  streams:
    rtsp_cam: # <- for RTSP streams
      - rtsp://192.168.1.5:554/live0 # <- stream which supports video & aac audio
      - "ffmpeg:rtsp_cam#audio=opus" # <- copy of the stream which transcodes audio to the missing codec (usually will be opus)
    http_cam: # <- for http streams
      - http://192.168.50.155/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=user&password=password # <- stream which supports video & aac audio
      - "ffmpeg:http_cam#audio=opus" # <- copy of the stream which transcodes audio to the missing codec (usually will be opus)
 ```
 ### 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`.
 ```yaml
 go2rtc:
  streams:
    rtsp_cam:
      - rtsp://192.168.1.5:554/live0 # <- stream which supports video & aac audio.
      - "ffmpeg:rtsp_cam#audio=opus" # <- copy of the stream which transcodes audio to opus
    rtsp_cam_sub:
      - rtsp://192.168.1.5:554/substream # <- stream which supports video & aac audio.
      - "ffmpeg:rtsp_cam_sub#audio=opus" # <- copy of the stream which transcodes audio to opus
 cameras:
  test_cam:
    ffmpeg:
      output_args:
        record: preset-record-generic-audio-copy
      inputs:
        - path: rtsp://127.0.0.1:8554/test_cam # <--- the name here must match the name of the camera in restream
          input_args: preset-rtsp-restream
          roles:
            - record
        - path: rtsp://127.0.0.1:8554/test_cam_sub # <--- the name here must match the name of the camera_sub in restream
          input_args: preset-rtsp-restream
          roles:
            - detect
    live:
      stream_name: rtsp_cam_sub
 ```
 ### WebRTC extra configuration:
 WebRTC works by creating a TCP or UDP connection on port `8555`. However, it requires additional configuration:
 - For external access, over the internet, setup your router to forward port `8555` to port `8555` on the Frigate device, for both TCP and UDP.
 - For internal/local access, unless you are running through the add-on, you will also need to set the WebRTC candidates list in the go2rtc config. For example, if `192.168.1.10` is the local IP of the device running Frigate:
  ```yaml title="/config/frigate.yaml"
  go2rtc:
    streams:
      test_cam: ...
    webrtc:
      candidates:
        - 192.168.1.10:8555
        - stun:8555
  ```
 :::tip
 This extra configuration may not be required if Frigate has been installed as a Home Assistant add-on, as Frigate uses the Supervisor's API to generate a WebRTC candidate.
 However, it is recommended if issues occur to define the candidates manually. You should do this if the Frigate add-on fails to generate a valid candidate. If an error occurs you will see some warnings like the below in the add-on logs page during the initialization:
 ```log
 [WARN] Failed to get IP address from supervisor
 [WARN] Failed to get WebRTC port from supervisor
 ```
 :::
 :::note
 If you are having difficulties getting WebRTC to work and you are running Frigate with docker, you may want to try changing the container network mode:
 - `network: host`, in this mode you don't need to forward any ports. The services inside of the Frigate container will have full access to the network interfaces of your host machine as if they were running natively and not in a container. Any port conflicts will need to be resolved. This network mode is recommended by go2rtc, but we recommend you only use it if necessary.
 - `network: bridge` creates a virtual network interface for the container, and the container will have full access to it. You also don't need to forward any ports, however, the IP for accessing Frigate locally will differ from the IP of the host machine. Your router will see Frigate as if it was a new device connected in the network.
 :::
 See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.2.0#module-webrtc) for more information about this.
--- a/docs/docs/configuration/masks.md
+++ b/docs/docs/configuration/masks.md
@@ -0,0 +1,77 @@
 ---
 id: masks
 title: Masks
 ---
 There are two types of masks available:
 **Motion masks**: Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the debug feed with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. Over masking will make it more difficult for objects to be tracked. To see this effect, create a mask, and then watch the video feed with `Motion Boxes` enabled again.
 **Object filter masks**: Object filter masks are used to filter out false positives for a given object type based on location. These should be used to filter any areas where it is not possible for an object of that type to be. The bottom center of the detected object's bounding box is evaluated against the mask. If it is in a masked area, it is assumed to be a false positive. For example, you may want to mask out rooftops, walls, the sky, treetops for people. For cars, masking locations other than the street or your driveway will tell Frigate that anything in your yard is a false positive.
 To create a poly mask:
 1. Visit the Web UI
 1. Click the camera you wish to create a mask for
 1. Select "Debug" at the top
 1. Expand the "Options" below the video feed
 1. Click "Mask & Zone creator"
 1. Click "Add" on the type of mask or zone you would like to create
 1. Click on the camera's latest image to create a masked area. The yaml representation will be updated in real-time
 1. When you've finished creating your mask, click "Copy" and paste the contents into your config file and restart Frigate
 Example of a finished row corresponding to the below example image:
 ```yaml
 motion:
  mask: "0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"
 ```
 Multiple masks can be listed.
 ```yaml
 motion:
  mask:
    - 458,1346,336,973,317,869,375,866,432
    - 0,461,3,0,1919,0,1919,843,1699,492,1344
 ```
 ![poly](/img/example-mask-poly-min.png)
 ### Further Clarification
 This is a response to a [question posed on reddit](https://www.reddit.com/r/homeautomation/comments/ppxdve/replacing_my_doorbell_with_a_security_camera_a_6/hd876w4?utm_source=share&utm_medium=web2x&context=3):
 It is helpful to understand a bit about how Frigate uses motion detection and object detection together.
 First, Frigate uses motion detection as a first line check to see if there is anything happening in the frame worth checking with object detection.
 Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the debug viewer.
 After the area with motion is identified, Frigate creates a "region" (the green boxes in the debug viewer) to run object detection on. The models are trained on square images, so these regions are always squares. It adds a margin around the motion area in hopes of capturing a cropped view of the object moving that fills most of the image passed to object detection, but doesn't cut anything off. It also takes into consideration the location of the bounding box from the previous frame if it is tracking an object.
 After object detection runs, if there are detected objects that seem to be cut off, Frigate reframes the region and runs object detection again on the same frame to get a better look.
 All of this happens for each area of motion and tracked object.
 > Are you simply saying that INITIAL triggering of any kind of detection will only happen in un-masked areas, but that once this triggering happens, the masks become irrelevant and object detection takes precedence?
 Essentially, yes. I wouldn't describe it as object detection taking precedence though. The motion masks just prevent those areas from being counted as motion. Those masks do not modify the regions passed to object detection in any way, so you can absolutely detect objects in areas masked for motion.
 > If so, this is completely expected and intuitive behavior for me. Because obviously if a "foot" starts motion detection the camera should be able to check if it's an entire person before it fully crosses into the zone. The docs imply this is the behavior, so I also don't understand why this would be detrimental to object detection on the whole.
 When just a foot is triggering motion, Frigate will zoom in and look only at the foot. If that even qualifies as a person, it will determine the object is being cut off and look again and again until it zooms back out enough to find the whole person.
 It is also detrimental to how Frigate tracks a moving object. Motion nearby the bounding box from the previous frame is used to intelligently determine where the region should be in the next frame. With too much masking, tracking is hampered and if an object walks from an unmasked area into a fully masked area, they essentially disappear and will be picked up as a "new" object if they leave the masked area. This is important because Frigate uses the history of scores while tracking an object to determine if it is a false positive or not. It takes a minimum of 3 frames for Frigate to determine is the object type it thinks it is, and the median score must be greater than the threshold. If a person meets this threshold while on the sidewalk before they walk into your stoop, you will get an alert the instant they step a single foot into a zone.
 > I thought the main point of this feature was to cut down on CPU use when motion is happening in unnecessary areas.
 It is, but the definition of "unnecessary" varies. I want to ignore areas of motion that I know are definitely not being triggered by objects of interest. Timestamps, trees, sky, rooftops. I don't want to ignore motion from objects that I want to track and know where they go.
 > For me, giving my masks ANY padding results in a lot of people detection I'm not interested in. I live in the city and catch a lot of the sidewalk on my camera. People walk by my front door all the time and the margin between the sidewalk and actually walking onto my stoop is very thin, so I basically have everything but the exact contours of my stoop masked out. This results in very tidy detections but this info keeps throwing me off. Am I just overthinking it?
 This is what `required_zones` are for. You should define a zone (remember this is evaluated based on the bottom center of the bounding box) and make it required to save snapshots and clips (now events in 0.9.0). You can also use this in your conditions for a notification.
 > Maybe my specific situation just warrants this. I've just been having a hard time understanding the relevance of this information - it seems to be that it's exactly what would be expected when "masking out" an area of ANY image.
 That may be the case for you. Frigate will definitely work harder tracking people on the sidewalk to make sure it doesn't miss anyone who steps foot on your stoop. The trade off with the way you have it now is slower recognition of objects and potential misses. That may be acceptable based on your needs. Also, if your resolution is low enough on the detect stream, your regions may already be so big that they grab the entire object anyway.
--- a/docs/docs/configuration/objects.mdx
+++ b/docs/docs/configuration/objects.mdx
@@ -0,0 +1,28 @@
 ---
 id: objects
 title: Objects
 ---
 import labels from "../../../labelmap.txt";
 Frigate includes the object models listed below from the Google Coral test data.
 Please note: 
 - `car` is listed twice because `truck` has been renamed to `car` by default. These object types are frequently confused.
 - `person` is the only tracked object by default. See the [full configuration reference](index.md#full-configuration-reference) for an example of expanding the list of tracked objects.
 <ul>
  {labels.split("\n").map((label) => (
    <li>{label.replace(/^\d+\s+/, "")}</li>
  ))}
 </ul>
 ## Custom Models
 Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
 - CPU Model: `/cpu_model.tflite`
 - EdgeTPU Model: `/edgetpu_model.tflite`
 - Labels: `/labelmap.txt`
 You also need to update the [model config](advanced.md#model) if they differ from the defaults.
--- a/Show More
+++ b/Show More