Compare commits

..

7 Commits

Author SHA1 Message Date
Blake Blackshear
68bfa6010d skip frames in the capture thread instead 2020-04-19 10:07:27 -05:00
Blake Blackshear
a810c56811 expose frame time at each step of processing 2020-04-19 07:49:23 -05:00
Blake Blackshear
5333b8ae1b ensure the previous frame is deleted when the new one is stored 2020-04-10 07:05:07 -04:00
Blake Blackshear
2bc57d271c move ffmpeg capture to a separate thread and use a queue 2020-03-14 15:32:51 -05:00
Blake Blackshear
8507bbbb31 make object processor resilient to plasma failures 2020-03-13 16:35:58 -05:00
Blake Blackshear
b6fcb88e5c remove sharedarray references 2020-03-13 15:50:27 -05:00
Blake Blackshear
d3cd4afa65 handle various scenarios with external process failures 2020-03-09 21:12:19 -05:00
388 changed files with 2178 additions and 68197 deletions

View File

@@ -1,78 +0,0 @@
{
"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"]
}
}

View File

@@ -1,13 +0,0 @@
#!/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

View File

@@ -1,17 +0,0 @@
#!/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

View File

@@ -1,16 +1,6 @@
README.md
docs/
diagram.png
.gitignore
debug
config/
*.pyc
.git
core
*.mp4
*.jpg
*.db
*.ts
web/dist/
web/node_modules/
web/.npm
*.pyc

4
.github/FUNDING.yml vendored
View File

@@ -1,3 +1 @@
github:
- blakeblackshear
- NickM-27
github: blakeblackshear

View File

@@ -1,107 +0,0 @@
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

View File

@@ -1 +0,0 @@
blank_issues_enabled: false

View File

@@ -1,82 +0,0 @@
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

View File

@@ -1,84 +0,0 @@
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

View File

@@ -1,20 +0,0 @@
---
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.

View File

@@ -1,107 +0,0 @@
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

View File

@@ -1,96 +0,0 @@
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

View File

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

View File

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

View File

@@ -1,22 +0,0 @@
name: dependabot-auto-merge
on: pull_request
permissions:
contents: write
jobs:
dependabot-auto-merge:
runs-on: ubuntu-latest
if: github.actor == 'dependabot[bot]'
steps:
- name: Get Dependabot metadata
id: metadata
uses: dependabot/fetch-metadata@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 }}

View File

@@ -1,102 +0,0 @@
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

View File

@@ -1,26 +0,0 @@
# 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.*, ',') }}

21
.gitignore vendored
View File

@@ -1,19 +1,4 @@
.DS_Store
*.pyc
*.swp
*.pyc
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/*
.vscode
config/config.yml

588
.pylintrc
View File

@@ -1,588 +0,0 @@
[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

12
.vscode/launch.json vendored
View File

@@ -1,12 +0,0 @@
{
"version": "0.2.0",
"configurations": [
{
"name": "Python: Launch Frigate",
"type": "python",
"request": "launch",
"module": "frigate",
"justMyCode": true
}
]
}

319
Dockerfile Normal file → Executable file
View File

@@ -1,272 +1,59 @@
# syntax=docker/dockerfile:1.2
FROM ubuntu:18.04
LABEL maintainer "blakeb@blakeshome.com"
# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
ARG DEBIAN_FRONTEND=noninteractive
FROM debian:11 AS base
FROM --platform=linux/amd64 debian:11 AS base_amd64
FROM debian:11-slim AS slim-base
FROM slim-base AS wget
ARG DEBIAN_FRONTEND
RUN apt-get update \
&& apt-get install -y wget xz-utils \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /rootfs
FROM base AS nginx
ARG DEBIAN_FRONTEND
# bind /var/cache/apt to tmpfs to speed up nginx build
RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
--mount=type=bind,source=docker/build_nginx.sh,target=/deps/build_nginx.sh \
/deps/build_nginx.sh
FROM wget AS go2rtc
ARG TARGETARCH
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 \
ENV DEBIAN_FRONTEND=noninteractive
# Install packages for apt repo
RUN apt -qq update && apt -qq install --no-install-recommends -y \
software-properties-common \
# apt-transport-https ca-certificates \
build-essential \
gnupg wget unzip \
# libcap-dev \
&& add-apt-repository ppa:deadsnakes/ppa -y \
&& apt -qq install --no-install-recommends -y \
python3.7 \
python3.7-dev \
python3-pip \
ffmpeg \
# VAAPI drivers for Intel hardware accel
libva-drm2 libva2 i965-va-driver vainfo \
&& python3.7 -m pip install -U wheel setuptools \
&& python3.7 -m pip install -U \
opencv-python-headless \
# python-prctl \
numpy \
imutils \
scipy \
&& python3.7 -m pip install -U \
Flask \
paho-mqtt \
PyYAML \
matplotlib \
pyarrow \
&& echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
&& wget -q -O - https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
&& apt -qq update \
&& echo "libedgetpu1-max libedgetpu/accepted-eula boolean true" | debconf-set-selections \
&& apt -qq install --no-install-recommends -y \
libedgetpu1-max \
## Tensorflow lite (python 3.7 only)
&& wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
&& python3.7 -m pip install tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
&& rm tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
&& rm -rf /var/lib/apt/lists/* \
&& npm install -g npm@9
&& (apt-get autoremove -y; apt-get autoclean -y)
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
# get model and labels
RUN wget -q https://github.com/google-coral/edgetpu/raw/master/test_data/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite -O /edgetpu_model.tflite --trust-server-names
RUN wget -q https://dl.google.com/coral/canned_models/coco_labels.txt -O /labelmap.txt --trust-server-names
RUN wget -q https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip -O /cpu_model.zip && \
unzip /cpu_model.zip detect.tflite -d / && \
mv /detect.tflite /cpu_model.tflite && \
rm /cpu_model.zip
WORKDIR /opt/frigate/
COPY frigate frigate/
COPY migrations migrations/
COPY --from=web-build /work/dist/ web/
ADD frigate frigate/
COPY detect_objects.py .
COPY benchmark.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
CMD ["python3.7", "-u", "detect_objects.py"]

674
LICENSE
View File

@@ -1,21 +1,661 @@
The MIT License
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (c) 2020 Blake Blackshear
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
Preamble
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
our General Public Licenses are intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
Developers that use our General Public Licenses protect your rights
with two steps: (1) assert copyright on the software, and (2) offer
you this License which gives you legal permission to copy, distribute
and/or modify the software.
A secondary benefit of defending all users' freedom is that
improvements made in alternate versions of the program, if they
receive widespread use, become available for other developers to
incorporate. Many developers of free software are heartened and
encouraged by the resulting cooperation. However, in the case of
software used on network servers, this result may fail to come about.
The GNU General Public License permits making a modified version and
letting the public access it on a server without ever releasing its
source code to the public.
The GNU Affero General Public License is designed specifically to
ensure that, in such cases, the modified source code becomes available
to the community. It requires the operator of a network server to
provide the source code of the modified version running there to the
users of that server. Therefore, public use of a modified version, on
a publicly accessible server, gives the public access to the source
code of the modified version.
An older license, called the Affero General Public License and
published by Affero, was designed to accomplish similar goals. This is
a different license, not a version of the Affero GPL, but Affero has
released a new version of the Affero GPL which permits relicensing under
this license.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU Affero General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Remote Network Interaction; Use with the GNU General Public License.
Notwithstanding any other provision of this License, if you modify the
Program, your modified version must prominently offer all users
interacting with it remotely through a computer network (if your version
supports such interaction) an opportunity to receive the Corresponding
Source of your version by providing access to the Corresponding Source
from a network server at no charge, through some standard or customary
means of facilitating copying of software. This Corresponding Source
shall include the Corresponding Source for any work covered by version 3
of the GNU General Public License that is incorporated pursuant to the
following paragraph.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the work with which it is combined will remain governed by version
3 of the GNU General Public License.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU Affero General Public License from time to time. Such new versions
will be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU Affero General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU Affero General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU Affero General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.

View File

@@ -1,42 +0,0 @@
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

145
README.md
View File

@@ -1,46 +1,127 @@
<p align="center">
<img align="center" alt="logo" src="docs/static/img/frigate.png">
</p>
# Frigate - Realtime Object Detection for IP Cameras
Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
# Frigate - NVR With Realtime Object Detection for IP Cameras
Use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/) is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load.
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.
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.
- Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
- 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
- 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
- Object detection with Tensorflow runs in a separate process
- Object info is published over MQTT for integration into HomeAssistant as a binary sensor
- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously
## Documentation
![Diagram](diagram.png)
View the documentation at https://docs.frigate.video
## Example video (from older version)
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")
## Donations
## Getting Started
Run the container with
```bash
docker run --rm \
--privileged \
--shm-size=512m \ # should work for a 2-3 cameras
-v /dev/bus/usb:/dev/bus/usb \
-v <path_to_config_dir>:/config:ro \
-v /etc/localtime:/etc/localtime:ro \
-p 5000:5000 \
-e FRIGATE_RTSP_PASSWORD='password' \
blakeblackshear/frigate:stable
```
If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
Example docker-compose:
```yaml
frigate:
container_name: frigate
restart: unless-stopped
privileged: true
shm_size: '1g' # should work for 5-7 cameras
image: blakeblackshear/frigate:stable
volumes:
- /dev/bus/usb:/dev/bus/usb
- /etc/localtime:/etc/localtime:ro
- <path_to_config>:/config
ports:
- "5000:5000"
environment:
FRIGATE_RTSP_PASSWORD: "password"
```
## Screenshots
A `config.yml` file must exist in the `config` directory. See example [here](config/config.example.yml) and device specific info can be found [here](docs/DEVICES.md).
Integration into Home Assistant
Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best snapshot for any object type with at `http://localhost:5000/<camera_name>/<object_name>/best.jpg`
<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>
Debug info is available at `http://localhost:5000/debug/stats`
Also comes with a builtin UI:
## Integration with HomeAssistant
```
camera:
- name: Camera Last Person
platform: mqtt
topic: frigate/<camera_name>/person/snapshot
- name: Camera Last Car
platform: mqtt
topic: frigate/<camera_name>/car/snapshot
<div>
<a href="docs/static/img/home-ui.png"><img src="docs/static/img/home-ui.png" height=400></a>
<a href="docs/static/img/camera-ui.png"><img src="docs/static/img/camera-ui.png" height=400></a>
</div>
binary_sensor:
- name: Camera Person
platform: mqtt
state_topic: "frigate/<camera_name>/person"
device_class: motion
availability_topic: "frigate/available"
![Events](docs/static/img/events-ui.png)
automation:
- alias: Alert me if a person is detected while armed away
trigger:
platform: state
entity_id: binary_sensor.camera_person
from: 'off'
to: 'on'
condition:
- condition: state
entity_id: alarm_control_panel.home_alarm
state: armed_away
action:
- service: notify.user_telegram
data:
message: "A person was detected."
data:
photo:
- url: http://<ip>:5000/<camera_name>/person/best.jpg
caption: A person was detected.
sensor:
- platform: rest
name: Frigate Debug
resource: http://localhost:5000/debug/stats
scan_interval: 5
json_attributes:
- back
- coral
value_template: 'OK'
- platform: template
sensors:
back_fps:
value_template: '{{ states.sensor.frigate_debug.attributes["back"]["fps"] }}'
unit_of_measurement: 'FPS'
back_skipped_fps:
value_template: '{{ states.sensor.frigate_debug.attributes["back"]["skipped_fps"] }}'
unit_of_measurement: 'FPS'
back_detection_fps:
value_template: '{{ states.sensor.frigate_debug.attributes["back"]["detection_fps"] }}'
unit_of_measurement: 'FPS'
frigate_coral_fps:
value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["fps"] }}'
unit_of_measurement: 'FPS'
frigate_coral_inference:
value_template: '{{ states.sensor.frigate_debug.attributes["coral"]["inference_speed"] }}'
unit_of_measurement: 'ms'
```
## Using a custom model
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`
## Tips
- Lower the framerate of the video feed on the camera to reduce the CPU usage for capturing the feed

View File

@@ -3,21 +3,15 @@ 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,
)
from frigate.edgetpu import ObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
my_frame = np.expand_dims(np.full((300, 300, 3), 1, np.uint8), axis=0)
labels = load_labels("/labelmap.txt")
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()
# object_detector = ObjectDetector()
# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
# start = datetime.datetime.now().timestamp()
@@ -43,66 +37,43 @@ labels = load_labels("/labelmap.txt")
# 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')
def start(id, num_detections, detection_queue):
object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue)
start = datetime.datetime.now().timestamp()
# start(1, 1000, edgetpu_process.detection_queue, event)
# print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
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
print(f"{id} - Processed for {duration:.2f} seconds.")
print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
edgetpu_process = EdgeTPUProcess()
# start(1, 1000, edgetpu_process.detect_lock, edgetpu_process.detect_ready, edgetpu_process.frame_ready)
####
# 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"
)
camera_process = mp.Process(target=start, args=(x, 100, edgetpu_process.detection_queue))
camera_process.daemon = True
camera_processes.append(camera_process)
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()
start = datetime.datetime.now().timestamp()
for p in camera_processes:
p.start()
p.start()
for p in camera_processes:
p.join()
p.join()
duration = datetime.datetime.now().timestamp() - start_time
print(f"Total - Processed for {duration:.2f} seconds.")
duration = datetime.datetime.now().timestamp()-start
print(f"Total - Processed for {duration:.2f} seconds.")

BIN
config/back-mask.bmp Normal file

Binary file not shown.

After

Width:  |  Height:  |  Size: 1.8 MiB

129
config/config.example.yml Normal file
View File

@@ -0,0 +1,129 @@
web_port: 5000
mqtt:
host: mqtt.server.com
topic_prefix: frigate
# client_id: frigate # Optional -- set to override default client id of 'frigate' if running multiple instances
# user: username # Optional
#################
## Environment variables that begin with 'FRIGATE_' may be referenced in {}.
## password: '{FRIGATE_MQTT_PASSWORD}'
#################
# password: password # Optional
#################
# Default ffmpeg args. Optional and can be overwritten per camera.
# Should work with most RTSP cameras that send h264 video
# Built from the properties below with:
# "ffmpeg" + global_args + input_args + "-i" + input + output_args
#################
# ffmpeg:
# global_args:
# - -hide_banner
# - -loglevel
# - panic
# hwaccel_args: []
# input_args:
# - -avoid_negative_ts
# - make_zero
# - -fflags
# - nobuffer
# - -flags
# - low_delay
# - -strict
# - experimental
# - -fflags
# - +genpts+discardcorrupt
# - -vsync
# - drop
# - -rtsp_transport
# - tcp
# - -stimeout
# - '5000000'
# - -use_wallclock_as_timestamps
# - '1'
# output_args:
# - -f
# - rawvideo
# - -pix_fmt
# - rgb24
####################
# Global object configuration. Applies to all cameras
# unless overridden at the camera levels.
# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
# All labels from the model are reported over MQTT. These values are used to filter out false positives.
# min_area (optional): minimum width*height of the bounding box for the detected person
# max_area (optional): maximum width*height of the bounding box for the detected person
# threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
####################
objects:
track:
- person
- car
- truck
filters:
person:
min_area: 5000
max_area: 100000
threshold: 0.5
cameras:
back:
ffmpeg:
################
# Source passed to ffmpeg after the -i parameter. Supports anything compatible with OpenCV and FFmpeg.
# Environment variables that begin with 'FRIGATE_' may be referenced in {}
################
input: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
#################
# These values will override default values for just this camera
#################
# global_args: []
# hwaccel_args: []
# input_args: []
# output_args: []
################
## Optionally specify the resolution of the video feed. Frigate will try to auto detect if not specified
################
# height: 1280
# width: 720
################
## Optional mask. Must be the same aspect ratio as your video feed.
##
## The mask works by looking at the bottom center of the bounding box for the detected
## person in the image. If that pixel in the mask is a black pixel, it ignores it as a
## false positive. In my mask, the grass and driveway visible from my backdoor camera
## are white. The garage doors, sky, and trees (anywhere it would be impossible for a
## person to stand) are black.
##
## Masked areas are also ignored for motion detection.
################
# mask: back-mask.bmp
################
# Allows you to limit the framerate within frigate for cameras that do not support
# custom framerates. A value of 1 tells frigate to look at every frame, 2 every 2nd frame,
# 3 every 3rd frame, etc.
################
take_frame: 1
################
# Configuration for the snapshots in the debug view and mqtt
################
snapshots:
show_timestamp: True
################
# Camera level object config. This config is merged with the global config above.
################
objects:
track:
- person
filters:
person:
min_area: 5000
max_area: 100000
threshold: 0.5

View File

@@ -1,16 +0,0 @@
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

345
detect_objects.py Normal file
View File

@@ -0,0 +1,345 @@
import os
import sys
import traceback
import signal
import cv2
import time
import datetime
import queue
import yaml
import threading
import multiprocessing as mp
import subprocess as sp
import numpy as np
import logging
from flask import Flask, Response, make_response, jsonify, request
import paho.mqtt.client as mqtt
from frigate.video import track_camera, get_ffmpeg_input, get_frame_shape, CameraCapture, start_or_restart_ffmpeg
from frigate.object_processing import TrackedObjectProcessor
from frigate.util import EventsPerSecond
from frigate.edgetpu import EdgeTPUProcess
FRIGATE_VARS = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')}
with open('/config/config.yml') as f:
CONFIG = yaml.safe_load(f)
MQTT_HOST = CONFIG['mqtt']['host']
MQTT_PORT = CONFIG.get('mqtt', {}).get('port', 1883)
MQTT_TOPIC_PREFIX = CONFIG.get('mqtt', {}).get('topic_prefix', 'frigate')
MQTT_USER = CONFIG.get('mqtt', {}).get('user')
MQTT_PASS = CONFIG.get('mqtt', {}).get('password')
if not MQTT_PASS is None:
MQTT_PASS = MQTT_PASS.format(**FRIGATE_VARS)
MQTT_CLIENT_ID = CONFIG.get('mqtt', {}).get('client_id', 'frigate')
# Set the default FFmpeg config
FFMPEG_CONFIG = CONFIG.get('ffmpeg', {})
FFMPEG_DEFAULT_CONFIG = {
'global_args': FFMPEG_CONFIG.get('global_args',
['-hide_banner','-loglevel','panic']),
'hwaccel_args': FFMPEG_CONFIG.get('hwaccel_args',
[]),
'input_args': FFMPEG_CONFIG.get('input_args',
['-avoid_negative_ts', 'make_zero',
'-fflags', 'nobuffer',
'-flags', 'low_delay',
'-strict', 'experimental',
'-fflags', '+genpts+discardcorrupt',
'-vsync', 'drop',
'-rtsp_transport', 'tcp',
'-stimeout', '5000000',
'-use_wallclock_as_timestamps', '1']),
'output_args': FFMPEG_CONFIG.get('output_args',
['-f', 'rawvideo',
'-pix_fmt', 'rgb24'])
}
GLOBAL_OBJECT_CONFIG = CONFIG.get('objects', {})
WEB_PORT = CONFIG.get('web_port', 5000)
DEBUG = (CONFIG.get('debug', '0') == '1')
def start_plasma_store():
plasma_cmd = ['plasma_store', '-m', '400000000', '-s', '/tmp/plasma']
plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL)
time.sleep(1)
rc = plasma_process.poll()
if rc is not None:
return None
return plasma_process
class CameraWatchdog(threading.Thread):
def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, plasma_process):
threading.Thread.__init__(self)
self.camera_processes = camera_processes
self.config = config
self.tflite_process = tflite_process
self.tracked_objects_queue = tracked_objects_queue
self.plasma_process = plasma_process
def run(self):
time.sleep(10)
while True:
# wait a bit before checking
time.sleep(10)
# check the plasma process
rc = self.plasma_process.poll()
if rc != None:
print(f"plasma_process exited unexpectedly with {rc}")
self.plasma_process = start_plasma_store()
# check the detection process
if (self.tflite_process.detection_start.value > 0.0 and
datetime.datetime.now().timestamp() - self.tflite_process.detection_start.value > 10):
print("Detection appears to be stuck. Restarting detection process")
self.tflite_process.start_or_restart()
elif not self.tflite_process.detect_process.is_alive():
print("Detection appears to have stopped. Restarting detection process")
self.tflite_process.start_or_restart()
# check the camera processes
for name, camera_process in self.camera_processes.items():
process = camera_process['process']
if not process.is_alive():
print(f"Track process for {name} is not alive. Starting again...")
camera_process['process_fps'].value = 0.0
camera_process['detection_fps'].value = 0.0
camera_process['read_start'].value = 0.0
process = mp.Process(target=track_camera, args=(name, self.config[name], GLOBAL_OBJECT_CONFIG, camera_process['frame_queue'],
camera_process['frame_shape'], self.tflite_process.detection_queue, self.tracked_objects_queue,
camera_process['process_fps'], camera_process['detection_fps'],
camera_process['read_start'], camera_process['detection_frame']))
process.daemon = True
camera_process['process'] = process
process.start()
print(f"Track process started for {name}: {process.pid}")
if not camera_process['capture_thread'].is_alive():
frame_shape = camera_process['frame_shape']
frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
ffmpeg_process = start_or_restart_ffmpeg(camera_process['ffmpeg_cmd'], frame_size)
camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, camera_process['frame_queue'],
camera_process['take_frame'], camera_process['camera_fps'], camera_process['detection_frame'])
camera_capture.start()
camera_process['ffmpeg_process'] = ffmpeg_process
camera_process['capture_thread'] = camera_capture
def main():
# connect to mqtt and setup last will
def on_connect(client, userdata, flags, rc):
print("On connect called")
if rc != 0:
if rc == 3:
print ("MQTT Server unavailable")
elif rc == 4:
print ("MQTT Bad username or password")
elif rc == 5:
print ("MQTT Not authorized")
else:
print ("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
# publish a message to signal that the service is running
client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
client = mqtt.Client(client_id=MQTT_CLIENT_ID)
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, MQTT_PORT, 60)
client.loop_start()
plasma_process = start_plasma_store()
##
# Setup config defaults for cameras
##
for name, config in CONFIG['cameras'].items():
config['snapshots'] = {
'show_timestamp': config.get('snapshots', {}).get('show_timestamp', True)
}
# Queue for cameras to push tracked objects to
tracked_objects_queue = mp.SimpleQueue()
# Start the shared tflite process
tflite_process = EdgeTPUProcess()
# start the camera processes
camera_processes = {}
for name, config in CONFIG['cameras'].items():
# Merge the ffmpeg config with the global config
ffmpeg = config.get('ffmpeg', {})
ffmpeg_input = get_ffmpeg_input(ffmpeg['input'])
ffmpeg_global_args = ffmpeg.get('global_args', FFMPEG_DEFAULT_CONFIG['global_args'])
ffmpeg_hwaccel_args = ffmpeg.get('hwaccel_args', FFMPEG_DEFAULT_CONFIG['hwaccel_args'])
ffmpeg_input_args = ffmpeg.get('input_args', FFMPEG_DEFAULT_CONFIG['input_args'])
ffmpeg_output_args = ffmpeg.get('output_args', FFMPEG_DEFAULT_CONFIG['output_args'])
ffmpeg_cmd = (['ffmpeg'] +
ffmpeg_global_args +
ffmpeg_hwaccel_args +
ffmpeg_input_args +
['-i', ffmpeg_input] +
ffmpeg_output_args +
['pipe:'])
if 'width' in config and 'height' in config:
frame_shape = (config['height'], config['width'], 3)
else:
frame_shape = get_frame_shape(ffmpeg_input)
frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
take_frame = config.get('take_frame', 1)
detection_frame = mp.Value('d', 0.0)
ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_size)
frame_queue = mp.SimpleQueue()
camera_fps = EventsPerSecond()
camera_fps.start()
camera_capture = CameraCapture(name, ffmpeg_process, frame_shape, frame_queue, take_frame, camera_fps, detection_frame)
camera_capture.start()
camera_processes[name] = {
'camera_fps': camera_fps,
'take_frame': take_frame,
'process_fps': mp.Value('d', 0.0),
'detection_fps': mp.Value('d', 0.0),
'detection_frame': detection_frame,
'read_start': mp.Value('d', 0.0),
'ffmpeg_process': ffmpeg_process,
'ffmpeg_cmd': ffmpeg_cmd,
'frame_queue': frame_queue,
'frame_shape': frame_shape,
'capture_thread': camera_capture
}
camera_process = mp.Process(target=track_camera, args=(name, config, GLOBAL_OBJECT_CONFIG, frame_queue, frame_shape,
tflite_process.detection_queue, tracked_objects_queue, camera_processes[name]['process_fps'],
camera_processes[name]['detection_fps'],
camera_processes[name]['read_start'], camera_processes[name]['detection_frame']))
camera_process.daemon = True
camera_processes[name]['process'] = camera_process
for name, camera_process in camera_processes.items():
camera_process['process'].start()
print(f"Camera_process started for {name}: {camera_process['process'].pid}")
object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue)
object_processor.start()
camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, plasma_process)
camera_watchdog.start()
# create a flask app that encodes frames a mjpeg on demand
app = Flask(__name__)
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
@app.route('/')
def ishealthy():
# return a healh
return "Frigate is running. Alive and healthy!"
@app.route('/debug/stack')
def processor_stack():
frame = sys._current_frames().get(object_processor.ident, None)
if frame:
return "<br>".join(traceback.format_stack(frame)), 200
else:
return "no frame found", 200
@app.route('/debug/print_stack')
def print_stack():
pid = int(request.args.get('pid', 0))
if pid == 0:
return "missing pid", 200
else:
os.kill(pid, signal.SIGUSR1)
return "check logs", 200
@app.route('/debug/stats')
def stats():
stats = {}
total_detection_fps = 0
for name, camera_stats in camera_processes.items():
total_detection_fps += camera_stats['detection_fps'].value
capture_thread = camera_stats['capture_thread']
stats[name] = {
'camera_fps': round(capture_thread.fps.eps(), 2),
'process_fps': round(camera_stats['process_fps'].value, 2),
'skipped_fps': round(capture_thread.skipped_fps.eps(), 2),
'detection_fps': round(camera_stats['detection_fps'].value, 2),
'read_start': camera_stats['read_start'].value,
'pid': camera_stats['process'].pid,
'ffmpeg_pid': camera_stats['ffmpeg_process'].pid,
'frame_info': {
'read': capture_thread.current_frame,
'detect': camera_stats['detection_frame'].value,
'process': object_processor.camera_data[name]['current_frame_time']
}
}
stats['coral'] = {
'fps': round(total_detection_fps, 2),
'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2),
'detection_start': tflite_process.detection_start.value,
'pid': tflite_process.detect_process.pid
}
rc = camera_watchdog.plasma_process.poll()
stats['plasma_store_rc'] = rc
return jsonify(stats)
@app.route('/<camera_name>/<label>/best.jpg')
def best(camera_name, label):
if camera_name in CONFIG['cameras']:
best_frame = object_processor.get_best(camera_name, label)
if best_frame is None:
best_frame = np.zeros((720,1280,3), np.uint8)
best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)
ret, jpg = cv2.imencode('.jpg', best_frame)
response = make_response(jpg.tobytes())
response.headers['Content-Type'] = 'image/jpg'
return response
else:
return "Camera named {} not found".format(camera_name), 404
@app.route('/<camera_name>')
def mjpeg_feed(camera_name):
fps = int(request.args.get('fps', '3'))
height = int(request.args.get('h', '360'))
if camera_name in CONFIG['cameras']:
# return a multipart response
return Response(imagestream(camera_name, fps, height),
mimetype='multipart/x-mixed-replace; boundary=frame')
else:
return "Camera named {} not found".format(camera_name), 404
def imagestream(camera_name, fps, height):
while True:
# max out at specified FPS
time.sleep(1/fps)
frame = object_processor.get_current_frame(camera_name)
if frame is None:
frame = np.zeros((height,int(height*16/9),3), np.uint8)
frame = cv2.resize(frame, dsize=(int(height*16/9), height), interpolation=cv2.INTER_LINEAR)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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', port=WEB_PORT, debug=False)
object_processor.join()
plasma_process.terminate()
if __name__ == '__main__':
main()

View File

Before

Width:  |  Height:  |  Size: 132 KiB

After

Width:  |  Height:  |  Size: 132 KiB

View File

@@ -1,39 +0,0 @@
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"

View File

@@ -1,66 +0,0 @@
#!/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

View File

@@ -1,13 +0,0 @@
#!/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

View File

@@ -1,91 +0,0 @@
#!/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/*

View File

@@ -1,21 +0,0 @@
#!/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 -

View File

@@ -1,5 +0,0 @@
/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

View File

@@ -1 +0,0 @@
frigate-pipeline

View File

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

View File

@@ -1 +0,0 @@
longrun

View File

@@ -1,28 +0,0 @@
#!/command/with-contenv bash
# shellcheck shell=bash
# Take down the S6 supervision tree when the service exits
set -o errexit -o nounset -o pipefail
# Logs should be sent to stdout so that s6 can collect them
declare exit_code_container
exit_code_container=$(cat /run/s6-linux-init-container-results/exitcode)
readonly exit_code_container
readonly exit_code_service="${1}"
readonly exit_code_signal="${2}"
readonly service="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

View File

@@ -1 +0,0 @@
frigate-log

View File

@@ -1,18 +0,0 @@
#!/command/with-contenv bash
# shellcheck shell=bash
# Start the Frigate service
set -o errexit -o nounset -o pipefail
# Logs should be sent to stdout so that s6 can collect them
# Tell S6-Overlay not to restart this service
s6-svc -O .
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

View File

@@ -1 +0,0 @@
longrun

View File

@@ -1,12 +0,0 @@
#!/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})"

View File

@@ -1,22 +0,0 @@
#!/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

View File

@@ -1,2 +0,0 @@
go2rtc
go2rtc-healthcheck

View File

@@ -1 +0,0 @@
go2rtc-pipeline

View File

@@ -1,4 +0,0 @@
#!/command/with-contenv bash
# shellcheck shell=bash
exec logutil-service /dev/shm/logs/go2rtc

View File

@@ -1 +0,0 @@
longrun

View File

@@ -1,12 +0,0 @@
#!/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})"

View File

@@ -1 +0,0 @@
go2rtc-log

View File

@@ -1,70 +0,0 @@
#!/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

View File

@@ -1 +0,0 @@
longrun

View File

@@ -1,11 +0,0 @@
#!/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[@]}"

View File

@@ -1 +0,0 @@
oneshot

View File

@@ -1 +0,0 @@
/etc/s6-overlay/s6-rc.d/log-prepare/run

View File

@@ -1 +0,0 @@
nginx-pipeline

View File

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

View File

@@ -1 +0,0 @@
longrun

View File

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

View File

@@ -1 +0,0 @@
nginx-log

View File

@@ -1,13 +0,0 @@
#!/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

View File

@@ -1 +0,0 @@
longrun

View File

@@ -1,106 +0,0 @@
"""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)

View File

@@ -1,271 +0,0 @@
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;
}
}
}

View File

@@ -1,34 +0,0 @@
#!/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

20
docs/.gitignore vendored
View File

@@ -1,20 +0,0 @@
# 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*

74
docs/DEVICES.md Normal file
View File

@@ -0,0 +1,74 @@
# Configuration Examples
### Default (most RTSP cameras)
This is the default ffmpeg command and should work with most RTSP cameras that send h264 video
```yaml
ffmpeg:
global_args:
- -hide_banner
- -loglevel
- panic
hwaccel_args: []
input_args:
- -avoid_negative_ts
- make_zero
- -fflags
- nobuffer
- -flags
- low_delay
- -strict
- experimental
- -fflags
- +genpts+discardcorrupt
- -vsync
- drop
- -rtsp_transport
- tcp
- -stimeout
- '5000000'
- -use_wallclock_as_timestamps
- '1'
output_args:
- -vf
- mpdecimate
- -f
- rawvideo
- -pix_fmt
- rgb24
```
### RTMP Cameras
The input parameters need to be adjusted for RTMP cameras
```yaml
ffmpeg:
input_args:
- -avoid_negative_ts
- make_zero
- -fflags
- nobuffer
- -flags
- low_delay
- -strict
- experimental
- -fflags
- +genpts+discardcorrupt
- -vsync
- drop
- -use_wallclock_as_timestamps
- '1'
```
### Hardware Acceleration
Intel Quicksync
```yaml
ffmpeg:
hwaccel_args:
- -hwaccel
- vaapi
- -hwaccel_device
- /dev/dri/renderD128
- -hwaccel_output_format
- yuv420p
```

View File

@@ -1,5 +0,0 @@
# 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).

View File

@@ -1,3 +0,0 @@
module.exports = {
presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
};

View File

@@ -1,110 +0,0 @@
---
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`.

View File

@@ -1,35 +0,0 @@
# 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
```

View File

@@ -1,153 +0,0 @@
---
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
```

View File

@@ -1,50 +0,0 @@
---
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.

View File

@@ -1,258 +0,0 @@
---
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
```

View File

@@ -1,79 +0,0 @@
---
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 |

View File

@@ -1,163 +0,0 @@
---
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).

View File

@@ -1,537 +0,0 @@
---
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
```

View File

@@ -1,104 +0,0 @@
---
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.

View File

@@ -1,77 +0,0 @@
---
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.

View File

@@ -1,28 +0,0 @@
---
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.

Some files were not shown because too many files have changed in this diff Show More