zishuo/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-09-26 19:41:29 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #825 from blakeblackshear/release-0.9.0

Browse Source 
Release 0.9.0
This commit is contained in:
Blake Blackshear
2021-10-05 17:59:25 -05:00
committed by GitHub
parent 0877a7dec7 eb48722126
commit c492b30adb
153 changed files with 12607 additions and 10772 deletions
Download Patch File Download Diff File Expand all files Collapse all files

27
.devcontainer/devcontainer.json Normal file
View File

@@ -0,0 +1,27 @@
{
"name": "Frigate Dev",
"dockerComposeFile": "../docker-compose.yml",
"service": "dev",
"workspaceFolder": "/lab/frigate",
"extensions": [
"ms-python.python",
"visualstudioexptteam.vscodeintellicode",
"mhutchie.git-graph",
"ms-azuretools.vscode-docker",
"streetsidesoftware.code-spell-checker",
"eamodio.gitlens",
"esbenp.prettier-vscode",
"ms-python.vscode-pylance"
],
"settings": {
"python.pythonPath": "/usr/bin/python3",
"python.linting.pylintEnabled": true,
"python.linting.enabled": true,
"python.formatting.provider": "black",
"editor.formatOnPaste": false,
"editor.formatOnSave": true,
"editor.formatOnType": true,
"files.trimTrailingWhitespace": true,
"terminal.integrated.shell.linux": "/bin/bash"
}
}

5
.dockerignore
View File

@@ -4,4 +4,7 @@ docs/
debug
config/
*.pyc
.git
.git
core
*.mp4
*.db

28
.github/workflows/push.yml vendored
View File

@@ -1,28 +0,0 @@
name: On push
on:
push:
branches:
- master
- release-0.8.0
jobs:
deploy-docs:
name: Deploy docs
runs-on: ubuntu-latest
defaults:
run:
working-directory: ./docs
steps:
- uses: actions/checkout@master
- uses: actions/setup-node@master
with:
node-version: 12.x
- run: npm install
- name: Build docs
run: npm run build
- name: Deploy documentation
uses: peaceiris/actions-gh-pages@v3
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
publish_dir: ./docs/build

2
.gitignore vendored
View File

@@ -1,5 +1,6 @@
.DS_Store
*.pyc
*.swp
debug
.vscode
config/config.yml
@@ -10,3 +11,4 @@ frigate/version.py
web/build
web/node_modules
web/coverage
core

588
.pylintrc Normal file
View File

@@ -0,0 +1,588 @@
[MASTER]
# A comma-separated list of package or module names from where C extensions may
# be loaded. Extensions are loading into the active Python interpreter and may
# run arbitrary code.
extension-pkg-whitelist=
# Specify a score threshold to be exceeded before program exits with error.
fail-under=10.0
# Add files or directories to the blacklist. They should be base names, not
# paths.
ignore=CVS
# Add files or directories matching the regex patterns to the blacklist. The
# regex matches against base names, not paths.
ignore-patterns=
# Python code to execute, usually for sys.path manipulation such as
# pygtk.require().
#init-hook=
# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
# number of processors available to use.
jobs=1
# Control the amount of potential inferred values when inferring a single
# object. This can help the performance when dealing with large functions or
# complex, nested conditions.
limit-inference-results=100
# List of plugins (as comma separated values of python module names) to load,
# usually to register additional checkers.
load-plugins=
# Pickle collected data for later comparisons.
persistent=yes
# When enabled, pylint would attempt to guess common misconfiguration and emit
# user-friendly hints instead of false-positive error messages.
suggestion-mode=yes
# Allow loading of arbitrary C extensions. Extensions are imported into the
# active Python interpreter and may run arbitrary code.
unsafe-load-any-extension=no
[MESSAGES CONTROL]
# Only show warnings with the listed confidence levels. Leave empty to show
# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
confidence=
# Disable the message, report, category or checker with the given id(s). You
# can either give multiple identifiers separated by comma (,) or put this
# option multiple times (only on the command line, not in the configuration
# file where it should appear only once). You can also use "--disable=all" to
# disable everything first and then reenable specific checks. For example, if
# you want to run only the similarities checker, you can use "--disable=all
# --enable=similarities". If you want to run only the classes checker, but have
# no Warning level messages displayed, use "--disable=all --enable=classes
# --disable=W".
disable=print-statement,
parameter-unpacking,
unpacking-in-except,
old-raise-syntax,
backtick,
long-suffix,
old-ne-operator,
old-octal-literal,
import-star-module-level,
non-ascii-bytes-literal,
raw-checker-failed,
bad-inline-option,
locally-disabled,
file-ignored,
suppressed-message,
useless-suppression,
deprecated-pragma,
use-symbolic-message-instead,
apply-builtin,
basestring-builtin,
buffer-builtin,
cmp-builtin,
coerce-builtin,
execfile-builtin,
file-builtin,
long-builtin,
raw_input-builtin,
reduce-builtin,
standarderror-builtin,
unicode-builtin,
xrange-builtin,
coerce-method,
delslice-method,
getslice-method,
setslice-method,
no-absolute-import,
old-division,
dict-iter-method,
dict-view-method,
next-method-called,
metaclass-assignment,
indexing-exception,
raising-string,
reload-builtin,
oct-method,
hex-method,
nonzero-method,
cmp-method,
input-builtin,
round-builtin,
intern-builtin,
unichr-builtin,
map-builtin-not-iterating,
zip-builtin-not-iterating,
range-builtin-not-iterating,
filter-builtin-not-iterating,
using-cmp-argument,
eq-without-hash,
div-method,
idiv-method,
rdiv-method,
exception-message-attribute,
invalid-str-codec,
sys-max-int,
bad-python3-import,
deprecated-string-function,
deprecated-str-translate-call,
deprecated-itertools-function,
deprecated-types-field,
next-method-defined,
dict-items-not-iterating,
dict-keys-not-iterating,
dict-values-not-iterating,
deprecated-operator-function,
deprecated-urllib-function,
xreadlines-attribute,
deprecated-sys-function,
exception-escape,
comprehension-escape
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option
# multiple time (only on the command line, not in the configuration file where
# it should appear only once). See also the "--disable" option for examples.
enable=c-extension-no-member
[REPORTS]
# Python expression which should return a score less than or equal to 10. You
# have access to the variables 'error', 'warning', 'refactor', and 'convention'
# which contain the number of messages in each category, as well as 'statement'
# which is the total number of statements analyzed. This score is used by the
# global evaluation report (RP0004).
evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
# Template used to display messages. This is a python new-style format string
# used to format the message information. See doc for all details.
#msg-template=
# Set the output format. Available formats are text, parseable, colorized, json
# and msvs (visual studio). You can also give a reporter class, e.g.
# mypackage.mymodule.MyReporterClass.
output-format=text
# Tells whether to display a full report or only the messages.
reports=no
# Activate the evaluation score.
score=yes
[REFACTORING]
# Maximum number of nested blocks for function / method body
max-nested-blocks=5
# Complete name of functions that never returns. When checking for
# inconsistent-return-statements if a never returning function is called then
# it will be considered as an explicit return statement and no message will be
# printed.
never-returning-functions=sys.exit
[SPELLING]
# Limits count of emitted suggestions for spelling mistakes.
max-spelling-suggestions=4
# Spelling dictionary name. Available dictionaries: none. To make it work,
# install the python-enchant package.
spelling-dict=
# List of comma separated words that should not be checked.
spelling-ignore-words=
# A path to a file that contains the private dictionary; one word per line.
spelling-private-dict-file=
# Tells whether to store unknown words to the private dictionary (see the
# --spelling-private-dict-file option) instead of raising a message.
spelling-store-unknown-words=no
[TYPECHECK]
# List of decorators that produce context managers, such as
# contextlib.contextmanager. Add to this list to register other decorators that
# produce valid context managers.
contextmanager-decorators=contextlib.contextmanager
# List of members which are set dynamically and missed by pylint inference
# system, and so shouldn't trigger E1101 when accessed. Python regular
# expressions are accepted.
generated-members=
# Tells whether missing members accessed in mixin class should be ignored. A
# mixin class is detected if its name ends with "mixin" (case insensitive).
ignore-mixin-members=yes
# Tells whether to warn about missing members when the owner of the attribute
# is inferred to be None.
ignore-none=yes
# This flag controls whether pylint should warn about no-member and similar
# checks whenever an opaque object is returned when inferring. The inference
# can return multiple potential results while evaluating a Python object, but
# some branches might not be evaluated, which results in partial inference. In
# that case, it might be useful to still emit no-member and other checks for
# the rest of the inferred objects.
ignore-on-opaque-inference=yes
# List of class names for which member attributes should not be checked (useful
# for classes with dynamically set attributes). This supports the use of
# qualified names.
ignored-classes=optparse.Values,thread._local,_thread._local
# List of module names for which member attributes should not be checked
# (useful for modules/projects where namespaces are manipulated during runtime
# and thus existing member attributes cannot be deduced by static analysis). It
# supports qualified module names, as well as Unix pattern matching.
ignored-modules=
# Show a hint with possible names when a member name was not found. The aspect
# of finding the hint is based on edit distance.
missing-member-hint=yes
# The minimum edit distance a name should have in order to be considered a
# similar match for a missing member name.
missing-member-hint-distance=1
# The total number of similar names that should be taken in consideration when
# showing a hint for a missing member.
missing-member-max-choices=1
# List of decorators that change the signature of a decorated function.
signature-mutators=
[STRING]
# This flag controls whether inconsistent-quotes generates a warning when the
# character used as a quote delimiter is used inconsistently within a module.
check-quote-consistency=no
# This flag controls whether the implicit-str-concat should generate a warning
# on implicit string concatenation in sequences defined over several lines.
check-str-concat-over-line-jumps=no
[FORMAT]
# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
expected-line-ending-format=
# Regexp for a line that is allowed to be longer than the limit.
ignore-long-lines=^\s*(# )?<?https?://\S+>?$
# Number of spaces of indent required inside a hanging or continued line.
indent-after-paren=4
# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
# tab).
indent-string=' '
# Maximum number of characters on a single line.
max-line-length=100
# Maximum number of lines in a module.
max-module-lines=1000
# Allow the body of a class to be on the same line as the declaration if body
# contains single statement.
single-line-class-stmt=no
# Allow the body of an if to be on the same line as the test if there is no
# else.
single-line-if-stmt=no
[SIMILARITIES]
# Ignore comments when computing similarities.
ignore-comments=yes
# Ignore docstrings when computing similarities.
ignore-docstrings=yes
# Ignore imports when computing similarities.
ignore-imports=no
# Minimum lines number of a similarity.
min-similarity-lines=4
[MISCELLANEOUS]
# List of note tags to take in consideration, separated by a comma.
notes=FIXME,
XXX,
TODO
# Regular expression of note tags to take in consideration.
#notes-rgx=
[BASIC]
# Naming style matching correct argument names.
argument-naming-style=snake_case
# Regular expression matching correct argument names. Overrides argument-
# naming-style.
#argument-rgx=
# Naming style matching correct attribute names.
attr-naming-style=snake_case
# Regular expression matching correct attribute names. Overrides attr-naming-
# style.
#attr-rgx=
# Bad variable names which should always be refused, separated by a comma.
bad-names=foo,
bar,
baz,
toto,
tutu,
tata
# Bad variable names regexes, separated by a comma. If names match any regex,
# they will always be refused
bad-names-rgxs=
# Naming style matching correct class attribute names.
class-attribute-naming-style=any
# Regular expression matching correct class attribute names. Overrides class-
# attribute-naming-style.
#class-attribute-rgx=
# Naming style matching correct class names.
class-naming-style=PascalCase
# Regular expression matching correct class names. Overrides class-naming-
# style.
#class-rgx=
# Naming style matching correct constant names.
const-naming-style=UPPER_CASE
# Regular expression matching correct constant names. Overrides const-naming-
# style.
#const-rgx=
# Minimum line length for functions/classes that require docstrings, shorter
# ones are exempt.
docstring-min-length=-1
# Naming style matching correct function names.
function-naming-style=snake_case
# Regular expression matching correct function names. Overrides function-
# naming-style.
#function-rgx=
# Good variable names which should always be accepted, separated by a comma.
good-names=i,
j,
k,
ex,
Run,
_
# Good variable names regexes, separated by a comma. If names match any regex,
# they will always be accepted
good-names-rgxs=
# Include a hint for the correct naming format with invalid-name.
include-naming-hint=no
# Naming style matching correct inline iteration names.
inlinevar-naming-style=any
# Regular expression matching correct inline iteration names. Overrides
# inlinevar-naming-style.
#inlinevar-rgx=
# Naming style matching correct method names.
method-naming-style=snake_case
# Regular expression matching correct method names. Overrides method-naming-
# style.
#method-rgx=
# Naming style matching correct module names.
module-naming-style=snake_case
# Regular expression matching correct module names. Overrides module-naming-
# style.
#module-rgx=
# Colon-delimited sets of names that determine each other's naming style when
# the name regexes allow several styles.
name-group=
# Regular expression which should only match function or class names that do
# not require a docstring.
no-docstring-rgx=^_
# List of decorators that produce properties, such as abc.abstractproperty. Add
# to this list to register other decorators that produce valid properties.
# These decorators are taken in consideration only for invalid-name.
property-classes=abc.abstractproperty
# Naming style matching correct variable names.
variable-naming-style=snake_case
# Regular expression matching correct variable names. Overrides variable-
# naming-style.
#variable-rgx=
[VARIABLES]
# List of additional names supposed to be defined in builtins. Remember that
# you should avoid defining new builtins when possible.
additional-builtins=
# Tells whether unused global variables should be treated as a violation.
allow-global-unused-variables=yes
# List of strings which can identify a callback function by name. A callback
# name must start or end with one of those strings.
callbacks=cb_,
_cb
# A regular expression matching the name of dummy variables (i.e. expected to
# not be used).
dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
# Argument names that match this expression will be ignored. Default to name
# with leading underscore.
ignored-argument-names=_.*|^ignored_|^unused_
# Tells whether we should check for unused import in __init__ files.
init-import=no
# List of qualified module names which can have objects that can redefine
# builtins.
redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
[LOGGING]
# The type of string formatting that logging methods do. `old` means using %
# formatting, `new` is for `{}` formatting.
logging-format-style=fstr
# Logging modules to check that the string format arguments are in logging
# function parameter format.
logging-modules=logging
[DESIGN]
# Maximum number of arguments for function / method.
max-args=5
# Maximum number of attributes for a class (see R0902).
max-attributes=7
# Maximum number of boolean expressions in an if statement (see R0916).
max-bool-expr=5
# Maximum number of branch for function / method body.
max-branches=12
# Maximum number of locals for function / method body.
max-locals=15
# Maximum number of parents for a class (see R0901).
max-parents=7
# Maximum number of public methods for a class (see R0904).
max-public-methods=20
# Maximum number of return / yield for function / method body.
max-returns=6
# Maximum number of statements in function / method body.
max-statements=50
# Minimum number of public methods for a class (see R0903).
min-public-methods=2
[CLASSES]
# List of method names used to declare (i.e. assign) instance attributes.
defining-attr-methods=__init__,
__new__,
setUp,
__post_init__
# List of member names, which should be excluded from the protected access
# warning.
exclude-protected=_asdict,
_fields,
_replace,
_source,
_make
# List of valid names for the first argument in a class method.
valid-classmethod-first-arg=cls
# List of valid names for the first argument in a metaclass class method.
valid-metaclass-classmethod-first-arg=cls
[IMPORTS]
# List of modules that can be imported at any level, not just the top level
# one.
allow-any-import-level=
# Allow wildcard imports from modules that define __all__.
allow-wildcard-with-all=no
# Analyse import fallback blocks. This can be used to support both Python 2 and
# 3 compatible code, which means that the block might have code that exists
# only in one or another interpreter, leading to false positives when analysed.
analyse-fallback-blocks=no
# Deprecated modules which should not be used, separated by a comma.
deprecated-modules=optparse,tkinter.tix
# Create a graph of external dependencies in the given file (report RP0402 must
# not be disabled).
ext-import-graph=
# Create a graph of every (i.e. internal and external) dependencies in the
# given file (report RP0402 must not be disabled).
import-graph=
# Create a graph of internal dependencies in the given file (report RP0402 must
# not be disabled).
int-import-graph=
# Force import order to recognize a module as part of the standard
# compatibility libraries.
known-standard-library=
# Force import order to recognize a module as part of a third party library.
known-third-party=enchant
# Couples of modules and preferred modules, separated by a comma.
preferred-modules=
[EXCEPTIONS]
# Exceptions that will emit a warning when being caught. Defaults to
# "BaseException, Exception".
overgeneral-exceptions=BaseException,
Exception

31
Makefile
View File

@@ -3,7 +3,7 @@ default_target: amd64_frigate
COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
version:
echo "VERSION='0.8.4-$(COMMIT_HASH)'" > frigate/version.py
echo "VERSION='0.9.0-$(COMMIT_HASH)'" > frigate/version.py
web:
docker build --tag frigate-web --file docker/Dockerfile.web web/
@@ -12,11 +12,14 @@ amd64_wheels:
docker build --tag blakeblackshear/frigate-wheels:1.0.3-amd64 --file docker/Dockerfile.wheels .
amd64_ffmpeg:
docker build --tag blakeblackshear/frigate-ffmpeg:1.1.0-amd64 --file docker/Dockerfile.ffmpeg.amd64 .
docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-amd64 --file docker/Dockerfile.ffmpeg.amd64 .
nginx_frigate:
docker buildx build --push --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --tag blakeblackshear/frigate-nginx:1.0.2 --file docker/Dockerfile.nginx .
amd64_frigate: version web
docker build --tag frigate-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.base .
docker build --tag frigate --file docker/Dockerfile.amd64 .
docker build --no-cache --tag frigate-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
docker build --no-cache --tag frigate --file docker/Dockerfile.amd64 .
amd64_all: amd64_wheels amd64_ffmpeg amd64_frigate
@@ -24,11 +27,11 @@ amd64nvidia_wheels:
docker build --tag blakeblackshear/frigate-wheels:1.0.3-amd64nvidia --file docker/Dockerfile.wheels .
amd64nvidia_ffmpeg:
docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
amd64nvidia_frigate: version web
docker build --tag frigate-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.base .
docker build --tag frigate --file docker/Dockerfile.amd64nvidia .
docker build --no-cache --tag frigate-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
docker build --no-cache --tag frigate --file docker/Dockerfile.amd64nvidia .
amd64nvidia_all: amd64nvidia_wheels amd64nvidia_ffmpeg amd64nvidia_frigate
@@ -36,23 +39,23 @@ aarch64_wheels:
docker build --tag blakeblackshear/frigate-wheels:1.0.3-aarch64 --file docker/Dockerfile.wheels .
aarch64_ffmpeg:
docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.3.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
aarch64_frigate: version web
docker build --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.base .
docker build --tag frigate --file docker/Dockerfile.aarch64 .
docker build --no-cache --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
docker build --no-cache --tag frigate --file docker/Dockerfile.aarch64 .
armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
aarch64_all: aarch64_wheels aarch64_ffmpeg aarch64_frigate
armv7_wheels:
docker build --tag blakeblackshear/frigate-wheels:1.0.3-armv7 --file docker/Dockerfile.wheels .
armv7_ffmpeg:
docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-armv7 --file docker/Dockerfile.ffmpeg.armv7 .
docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-armv7 --file docker/Dockerfile.ffmpeg.armv7 .
armv7_frigate: version web
docker build --tag frigate-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.base .
docker build --tag frigate --file docker/Dockerfile.armv7 .
docker build --no-cache --tag frigate-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
docker build --no-cache --tag frigate --file docker/Dockerfile.armv7 .
armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate

8
README.md
View File

@@ -14,25 +14,29 @@ Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but
- 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 clips of detected objects
- Records video with retention settings based on detected objects
- 24/7 recording
- Re-streaming via RTMP to reduce the number of connections to your camera
## Documentation
View the documentation at https://blakeblackshear.github.io/frigate
View the documentation at https://docs.frigate.video
## Donations
If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
## Screenshots
Integration into Home Assistant
<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>
Also comes with a builtin UI:
<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>

29
docker-compose.yml Normal file
View File

@@ -0,0 +1,29 @@
version: "3"
services:
dev:
container_name: frigate-dev
user: vscode
privileged: true
shm_size: "256mb"
build:
context: .
dockerfile: docker/Dockerfile.dev
volumes:
- /etc/localtime:/etc/localtime:ro
- .:/lab/frigate:cached
- ./config/config.yml:/config/config.yml:ro
- ./debug:/media/frigate
- /dev/bus/usb:/dev/bus/usb
- /dev/dri:/dev/dri # for intel hwaccel, needs to be updated for your hardware
ports:
- "1935:1935"
- "5000:5000"
- "5001:5001"
- "8080:8080"
entrypoint: ["sudo", "/init"]
command: /bin/sh -c "while sleep 1000; do :; done"
mqtt:
container_name: mqtt
image: eclipse-mosquitto:1.6
ports:
- "1883:1883"

34
docker/Dockerfile.aarch64
View File

@@ -5,18 +5,24 @@ ENV DEBIAN_FRONTEND=noninteractive
# Install packages for apt repo
RUN apt-get -qq update \
&& apt-get -qq install --no-install-recommends -y \
# ffmpeg runtime dependencies
libgomp1 \
# runtime dependencies
libopenexr24 \
libgstreamer1.0-0 \
libgstreamer-plugins-base1.0-0 \
libopenblas-base \
libjpeg-turbo8 \
libpng16-16 \
libtiff5 \
libdc1394-22 \
## Tensorflow lite
&& pip3 install https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp38-cp38-linux_aarch64.whl \
# ffmpeg runtime dependencies
libgomp1 \
# runtime dependencies
libopenexr24 \
libgstreamer1.0-0 \
libgstreamer-plugins-base1.0-0 \
libopenblas-base \
libjpeg-turbo8 \
libpng16-16 \
libtiff5 \
libdc1394-22 \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)
&& (apt-get autoremove -y; apt-get autoclean -y)
# s6-overlay
ADD https://github.com/just-containers/s6-overlay/releases/download/v2.2.0.3/s6-overlay-aarch64-installer /tmp/
RUN chmod +x /tmp/s6-overlay-aarch64-installer && /tmp/s6-overlay-aarch64-installer /
ENTRYPOINT ["/init"]
CMD ["python3", "-u", "-m", "frigate"]

28
docker/Dockerfile.amd64
View File

@@ -4,15 +4,25 @@ LABEL maintainer "blakeb@blakeshome.com"
# By default, use the i965 driver
ENV LIBVA_DRIVER_NAME=i965
# Install packages for apt repo
RUN wget -qO - https://repositories.intel.com/graphics/intel-graphics.key | apt-key add - \
&& echo 'deb [arch=amd64] https://repositories.intel.com/graphics/ubuntu focal main' > /etc/apt/sources.list.d/intel-graphics.list \
&& apt-key adv --keyserver keyserver.ubuntu.com --recv-keys F63F0F2B90935439 \
&& echo 'deb http://ppa.launchpad.net/kisak/kisak-mesa/ubuntu focal main' > /etc/apt/sources.list.d/kisak-mesa-focal.list
RUN apt-get -qq update \
&& apt-get -qq install --no-install-recommends -y \
# ffmpeg dependencies
libgomp1 \
# VAAPI drivers for Intel hardware accel
libva-drm2 libva2 libmfx1 i965-va-driver vainfo intel-media-va-driver mesa-va-drivers \
## Tensorflow lite
&& wget -q https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
&& python3.8 -m pip install tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
&& rm tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
# ffmpeg dependencies
libgomp1 \
# VAAPI drivers for Intel hardware accel
libva-drm2 libva2 libmfx1 i965-va-driver vainfo intel-media-va-driver-non-free mesa-vdpau-drivers mesa-va-drivers mesa-vdpau-drivers libdrm-radeon1 \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)
&& (apt-get autoremove -y; apt-get autoclean -y)
# s6-overlay
ADD https://github.com/just-containers/s6-overlay/releases/download/v2.2.0.3/s6-overlay-amd64-installer /tmp/
RUN chmod +x /tmp/s6-overlay-amd64-installer && /tmp/s6-overlay-amd64-installer /
ENTRYPOINT ["/init"]
CMD ["python3", "-u", "-m", "frigate"]

16
docker/Dockerfile.amd64nvidia
View File

@@ -4,12 +4,8 @@ LABEL maintainer "blakeb@blakeshome.com"
# Install packages for apt repo
RUN apt-get -qq update \
&& apt-get -qq install --no-install-recommends -y \
# ffmpeg dependencies
libgomp1 \
## Tensorflow lite
&& wget -q https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
&& python3.8 -m pip install tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
&& rm tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl \
# ffmpeg dependencies
libgomp1 \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)
@@ -45,3 +41,11 @@ ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility,video
ENV NVIDIA_REQUIRE_CUDA "cuda>=11.1 brand=tesla,driver>=418,driver<419 brand=tesla,driver>=440,driver<441 brand=tesla,driver>=450,driver<451"
# s6-overlay
ADD https://github.com/just-containers/s6-overlay/releases/download/v2.2.0.3/s6-overlay-amd64-installer /tmp/
RUN chmod +x /tmp/s6-overlay-amd64-installer && /tmp/s6-overlay-amd64-installer /
ENTRYPOINT ["/init"]
CMD ["python3", "-u", "-m", "frigate"]

38
docker/Dockerfile.armv7
View File

@@ -5,20 +5,26 @@ ENV DEBIAN_FRONTEND=noninteractive
# Install packages for apt repo
RUN apt-get -qq update \
&& apt-get -qq install --no-install-recommends -y \
# ffmpeg runtime dependencies
libgomp1 \
# runtime dependencies
libopenexr24 \
libgstreamer1.0-0 \
libgstreamer-plugins-base1.0-0 \
libopenblas-base \
libjpeg-turbo8 \
libpng16-16 \
libtiff5 \
libdc1394-22 \
libaom0 \
libx265-179 \
## Tensorflow lite
&& pip3 install https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp38-cp38-linux_armv7l.whl \
# ffmpeg runtime dependencies
libgomp1 \
# runtime dependencies
libopenexr24 \
libgstreamer1.0-0 \
libgstreamer-plugins-base1.0-0 \
libopenblas-base \
libjpeg-turbo8 \
libpng16-16 \
libtiff5 \
libdc1394-22 \
libaom0 \
libx265-179 \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)
&& (apt-get autoremove -y; apt-get autoclean -y)
# s6-overlay
ADD https://github.com/just-containers/s6-overlay/releases/download/v2.2.0.3/s6-overlay-armhf-installer /tmp/
RUN chmod +x /tmp/s6-overlay-armhf-installer && /tmp/s6-overlay-armhf-installer /
ENTRYPOINT ["/init"]
CMD ["python3", "-u", "-m", "frigate"]

28
docker/Dockerfile.base
View File

@@ -1,8 +1,10 @@
ARG ARCH=amd64
ARG WHEELS_VERSION
ARG FFMPEG_VERSION
ARG NGINX_VERSION
FROM blakeblackshear/frigate-wheels:${WHEELS_VERSION}-${ARCH} as wheels
FROM blakeblackshear/frigate-ffmpeg:${FFMPEG_VERSION}-${ARCH} as ffmpeg
FROM blakeblackshear/frigate-nginx:${NGINX_VERSION} as nginx
FROM frigate-web as web
FROM ubuntu:20.04
@@ -18,33 +20,28 @@ ENV DEBIAN_FRONTEND=noninteractive
# Install packages for apt repo
RUN apt-get -qq update \
&& apt-get upgrade -y \
&& apt-get -qq install --no-install-recommends -y \
gnupg wget unzip tzdata nginx libnginx-mod-rtmp \
&& apt-get -qq install --no-install-recommends -y \
python3-pip \
&& apt-get -qq install --no-install-recommends -y gnupg wget unzip tzdata libxml2 \
&& apt-get -qq install --no-install-recommends -y python3-pip \
&& pip3 install -U /wheels/*.whl \
&& APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn apt-key adv --fetch-keys https://packages.cloud.google.com/apt/doc/apt-key.gpg \
&& echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
&& echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections \
&& apt-get -qq update && apt-get -qq install --no-install-recommends -y \
libedgetpu1-max=15.0 \
&& apt-get -qq update && apt-get -qq install --no-install-recommends -y libedgetpu1-max python3-tflite-runtime python3-pycoral \
&& rm -rf /var/lib/apt/lists/* /wheels \
&& (apt-get autoremove -y; apt-get autoclean -y)
RUN pip3 install \
peewee_migrate \
pydantic \
zeroconf \
voluptuous\
Flask-Sockets \
gevent \
gevent-websocket
ws4py
COPY nginx/nginx.conf /etc/nginx/nginx.conf
COPY --from=nginx /usr/local/nginx/ /usr/local/nginx/
# get model and labels
COPY labelmap.txt /labelmap.txt
RUN wget -q https://github.com/google-coral/test_data/raw/master/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite -O /edgetpu_model.tflite
RUN wget -q https://github.com/google-coral/test_data/raw/master/ssdlite_mobiledet_coco_qat_postprocess.tflite -O /cpu_model.tflite
RUN wget -q https://github.com/google-coral/test_data/raw/release-frogfish/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite -O /edgetpu_model.tflite
RUN wget -q https://github.com/google-coral/test_data/raw/release-frogfish/ssdlite_mobiledet_coco_qat_postprocess.tflite -O /cpu_model.tflite
WORKDIR /opt/frigate/
ADD frigate frigate/
@@ -52,10 +49,7 @@ ADD migrations migrations/
COPY --from=web /opt/frigate/build web/
COPY run.sh /run.sh
RUN chmod +x /run.sh
COPY docker/rootfs/ /
EXPOSE 5000
EXPOSE 1935
CMD ["/run.sh"]

24
docker/Dockerfile.dev Normal file
View File

@@ -0,0 +1,24 @@
FROM frigate:latest
ARG USERNAME=vscode
ARG USER_UID=1000
ARG USER_GID=$USER_UID
# Create the user
RUN groupadd --gid $USER_GID $USERNAME \
&& useradd --uid $USER_UID --gid $USER_GID -m $USERNAME \
#
# [Optional] Add sudo support. Omit if you don't need to install software after connecting.
&& apt-get update \
&& apt-get install -y sudo \
&& echo $USERNAME ALL=\(root\) NOPASSWD:ALL > /etc/sudoers.d/$USERNAME \
&& chmod 0440 /etc/sudoers.d/$USERNAME
RUN apt-get update \
&& apt-get install -y git curl vim htop
RUN pip3 install pylint black
# Install Node 14
RUN curl -sL https://deb.nodesource.com/setup_14.x | bash - \
&& apt-get install -y nodejs

114
docker/Dockerfile.ffmpeg.aarch64
View File

@@ -9,41 +9,41 @@ WORKDIR /tmp/workdir
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get -yqq update && \
apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 && \
apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 xutils-dev && \
apt-get autoremove -y && \
apt-get clean -y
FROM base as build
ENV FFMPEG_VERSION=4.3.1 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
SRC=/usr/local
ENV FFMPEG_VERSION=4.3.2 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.11.0 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
SRC=/usr/local
ARG FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
ARG FREETYPE_SHA256SUM="a45c6b403413abd5706f3582f04c8339d26397c4304b78fa552f2215df64101f freetype-2.11.0.tar.gz"
ARG FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
ARG LIBVIDSTAB_SHA256SUM="14d2a053e56edad4f397be0cb3ef8eb1ec3150404ce99a426c4eb641861dc0bb v1.1.0.tar.gz"
ARG OGG_SHA256SUM="e19ee34711d7af328cb26287f4137e70630e7261b17cbe3cd41011d73a654692 libogg-1.3.2.tar.gz"
@@ -61,27 +61,27 @@ ARG PREFIX=/opt/ffmpeg
ARG LD_LIBRARY_PATH="/opt/ffmpeg/lib:/opt/ffmpeg/lib64:/usr/lib64:/usr/lib:/lib64:/lib"
RUN buildDeps="autoconf \
automake \
cmake \
curl \
bzip2 \
libexpat1-dev \
g++ \
gcc \
git \
gperf \
libtool \
make \
nasm \
perl \
pkg-config \
python \
libssl-dev \
yasm \
linux-headers-raspi2 \
libomxil-bellagio-dev \
zlib1g-dev" && \
RUN buildDeps="autoconf \
automake \
cmake \
curl \
bzip2 \
libexpat1-dev \
g++ \
gcc \
git \
gperf \
libtool \
make \
nasm \
perl \
pkg-config \
python \
libssl-dev \
yasm \
linux-headers-raspi2 \
libomxil-bellagio-dev \
zlib1g-dev" && \
apt-get -yqq update && \
apt-get install -yq --no-install-recommends ${buildDeps}
## opencore-amr https://sourceforge.net/projects/opencore-amr/
@@ -392,6 +392,16 @@ RUN \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/rkmpp && \
mkdir -p ${DIR} && \
cd ${DIR} && \
git clone https://github.com/rockchip-linux/libdrm-rockchip && git clone https://github.com/rockchip-linux/mpp && \
cd libdrm-rockchip && bash autogen.sh && ./configure && make && make install && \
cd ../mpp && cmake -DRKPLATFORM=ON -DHAVE_DRM=ON && make -j6 && make install && \
rm -rf ${DIR}
## ffmpeg https://ffmpeg.org/
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
@@ -434,6 +444,8 @@ RUN \
--enable-libkvazaar \
--enable-libaom \
--extra-libs=-lpthread \
--enable-rkmpp \
--enable-libdrm \
# --enable-omx \
# --enable-omx-rpi \
# --enable-mmal \
@@ -459,7 +471,7 @@ RUN \
cp -r ${PREFIX}/include/libav* ${PREFIX}/include/libpostproc ${PREFIX}/include/libsw* /usr/local/include && \
mkdir -p /usr/local/lib/pkgconfig && \
for pc in ${PREFIX}/lib/pkgconfig/libav*.pc ${PREFIX}/lib/pkgconfig/libpostproc.pc ${PREFIX}/lib/pkgconfig/libsw*.pc; do \
sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
done
FROM base AS release

104
docker/Dockerfile.ffmpeg.amd64
View File

@@ -14,33 +14,33 @@ RUN apt-get -yqq update && \
FROM base as build
ENV FFMPEG_VERSION=4.3.1 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
SRC=/usr/local
ENV FFMPEG_VERSION=4.3.2 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
SRC=/usr/local
ARG FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
ARG FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
@@ -60,27 +60,27 @@ ARG PREFIX=/opt/ffmpeg
ARG LD_LIBRARY_PATH="/opt/ffmpeg/lib:/opt/ffmpeg/lib64:/usr/lib64:/usr/lib:/lib64:/lib"
RUN buildDeps="autoconf \
automake \
cmake \
curl \
bzip2 \
libexpat1-dev \
g++ \
gcc \
git \
gperf \
libtool \
make \
nasm \
perl \
pkg-config \
python \
libssl-dev \
yasm \
libva-dev \
libmfx-dev \
zlib1g-dev" && \
RUN buildDeps="autoconf \
automake \
cmake \
curl \
bzip2 \
libexpat1-dev \
g++ \
gcc \
git \
gperf \
libtool \
make \
nasm \
perl \
pkg-config \
python \
libssl-dev \
yasm \
libva-dev \
libmfx-dev \
zlib1g-dev" && \
apt-get -yqq update && \
apt-get install -yq --no-install-recommends ${buildDeps}
## opencore-amr https://sourceforge.net/projects/opencore-amr/
@@ -450,7 +450,7 @@ RUN \
cp -r ${PREFIX}/include/libav* ${PREFIX}/include/libpostproc ${PREFIX}/include/libsw* /usr/local/include && \
mkdir -p /usr/local/lib/pkgconfig && \
for pc in ${PREFIX}/lib/pkgconfig/libav*.pc ${PREFIX}/lib/pkgconfig/libpostproc.pc ${PREFIX}/lib/pkgconfig/libsw*.pc; do \
sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
done
FROM base AS release
@@ -463,6 +463,6 @@ ENTRYPOINT ["ffmpeg"]
COPY --from=build /usr/local /usr/local/
RUN \
apt-get update -y && \
apt-get install -y --no-install-recommends libva-drm2 libva2 i965-va-driver mesa-va-drivers && \
rm -rf /var/lib/apt/lists/*
apt-get update -y && \
apt-get install -y --no-install-recommends libva-drm2 libva2 i965-va-driver mesa-va-drivers && \
rm -rf /var/lib/apt/lists/*

114
docker/Dockerfile.ffmpeg.amd64nvidia
View File

@@ -37,36 +37,36 @@ FROM devel-base as build
ENV NVIDIA_HEADERS_VERSION=9.1.23.1
ENV FFMPEG_VERSION=4.3.1 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
LIBSRT_VERSION=1.4.1 \
LIBARIBB24_VERSION=1.0.3 \
LIBPNG_VERSION=1.6.9 \
SRC=/usr/local
ENV FFMPEG_VERSION=4.3.2 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
LIBSRT_VERSION=1.4.1 \
LIBARIBB24_VERSION=1.0.3 \
LIBPNG_VERSION=1.6.9 \
SRC=/usr/local
ARG FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
ARG FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
@@ -87,35 +87,35 @@ ARG PREFIX=/opt/ffmpeg
ARG LD_LIBRARY_PATH="/opt/ffmpeg/lib:/opt/ffmpeg/lib64"
RUN buildDeps="autoconf \
automake \
cmake \
curl \
bzip2 \
libexpat1-dev \
g++ \
gcc \
git \
gperf \
libtool \
make \
nasm \
perl \
pkg-config \
python \
libssl-dev \
yasm \
zlib1g-dev" && \
RUN buildDeps="autoconf \
automake \
cmake \
curl \
bzip2 \
libexpat1-dev \
g++ \
gcc \
git \
gperf \
libtool \
make \
nasm \
perl \
pkg-config \
python \
libssl-dev \
yasm \
zlib1g-dev" && \
apt-get -yqq update && \
apt-get install -yq --no-install-recommends ${buildDeps}
RUN \
DIR=/tmp/nv-codec-headers && \
git clone https://github.com/FFmpeg/nv-codec-headers ${DIR} && \
cd ${DIR} && \
git checkout n${NVIDIA_HEADERS_VERSION} && \
make PREFIX="${PREFIX}" && \
make install PREFIX="${PREFIX}" && \
DIR=/tmp/nv-codec-headers && \
git clone https://github.com/FFmpeg/nv-codec-headers ${DIR} && \
cd ${DIR} && \
git checkout n${NVIDIA_HEADERS_VERSION} && \
make PREFIX="${PREFIX}" && \
make install PREFIX="${PREFIX}" && \
rm -rf ${DIR}
## opencore-amr https://sourceforge.net/projects/opencore-amr/
@@ -527,7 +527,7 @@ RUN \
cp -r ${PREFIX}/include/libav* ${PREFIX}/include/libpostproc ${PREFIX}/include/libsw* /usr/local/include && \
mkdir -p /usr/local/lib/pkgconfig && \
for pc in ${PREFIX}/lib/pkgconfig/libav*.pc ${PREFIX}/lib/pkgconfig/libpostproc.pc ${PREFIX}/lib/pkgconfig/libsw*.pc; do \
sed "s:${PREFIX}:/usr/local:g; s:/lib64:/lib:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
sed "s:${PREFIX}:/usr/local:g; s:/lib64:/lib:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
done
@@ -539,7 +539,7 @@ ENV LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64
CMD ["--help"]
ENTRYPOINT ["ffmpeg"]
# copy only needed files, without copying nvidia dev files
# copy only needed files, without copying nvidia dev files
COPY --from=build /usr/local/bin /usr/local/bin/
COPY --from=build /usr/local/share /usr/local/share/
COPY --from=build /usr/local/lib /usr/local/lib/

104
docker/Dockerfile.ffmpeg.armv7
View File

@@ -15,33 +15,33 @@ RUN apt-get -yqq update && \
FROM base as build
ENV FFMPEG_VERSION=4.3.1 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.3 \
SRC=/usr/local
ENV FFMPEG_VERSION=4.3.2 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.3 \
SRC=/usr/local
ARG FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
ARG FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
@@ -60,30 +60,30 @@ ARG PREFIX=/opt/ffmpeg
ARG LD_LIBRARY_PATH="/opt/ffmpeg/lib:/opt/ffmpeg/lib64:/usr/lib64:/usr/lib:/lib64:/lib:/opt/vc/lib"
RUN buildDeps="autoconf \
automake \
cmake \
curl \
bzip2 \
libexpat1-dev \
g++ \
gcc \
git \
gperf \
libtool \
make \
nasm \
perl \
pkg-config \
python \
sudo \
libssl-dev \
yasm \
linux-headers-raspi2 \
libomxil-bellagio-dev \
libx265-dev \
libaom-dev \
zlib1g-dev" && \
RUN buildDeps="autoconf \
automake \
cmake \
curl \
bzip2 \
libexpat1-dev \
g++ \
gcc \
git \
gperf \
libtool \
make \
nasm \
perl \
pkg-config \
python \
sudo \
libssl-dev \
yasm \
linux-headers-raspi2 \
libomxil-bellagio-dev \
libx265-dev \
libaom-dev \
zlib1g-dev" && \
apt-get -yqq update && \
apt-get install -yq --no-install-recommends ${buildDeps}
## opencore-amr https://sourceforge.net/projects/opencore-amr/
@@ -471,7 +471,7 @@ RUN \
cp -r ${PREFIX}/include/libav* ${PREFIX}/include/libpostproc ${PREFIX}/include/libsw* /usr/local/include && \
mkdir -p /usr/local/lib/pkgconfig && \
for pc in ${PREFIX}/lib/pkgconfig/libav*.pc ${PREFIX}/lib/pkgconfig/libpostproc.pc ${PREFIX}/lib/pkgconfig/libsw*.pc; do \
sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
done
FROM base AS release

52
docker/Dockerfile.nginx Normal file
View File

@@ -0,0 +1,52 @@
FROM ubuntu:20.04 AS base
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get -yqq update && \
apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 && \
apt-get autoremove -y && \
apt-get clean -y
FROM base as build
ARG NGINX_VERSION=1.18.0
ARG VOD_MODULE_VERSION=1.28
ARG SECURE_TOKEN_MODULE_VERSION=1.4
ARG RTMP_MODULE_VERSION=1.2.1
RUN cp /etc/apt/sources.list /etc/apt/sources.list~ \
&& sed -Ei 's/^# deb-src /deb-src /' /etc/apt/sources.list \
&& apt-get update
RUN apt-get -yqq build-dep nginx
RUN apt-get -yqq install --no-install-recommends curl \
&& mkdir /tmp/nginx \
&& curl -sL https://nginx.org/download/nginx-${NGINX_VERSION}.tar.gz | tar -C /tmp/nginx -zx --strip-components=1 \
&& mkdir /tmp/nginx-vod-module \
&& curl -sL https://github.com/kaltura/nginx-vod-module/archive/refs/tags/${VOD_MODULE_VERSION}.tar.gz | tar -C /tmp/nginx-vod-module -zx --strip-components=1 \
# 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 \
&& mkdir /tmp/nginx-secure-token-module \
&& curl -sL https://github.com/kaltura/nginx-secure-token-module/archive/refs/tags/${SECURE_TOKEN_MODULE_VERSION}.tar.gz | tar -C /tmp/nginx-secure-token-module -zx --strip-components=1 \
&& mkdir /tmp/nginx-rtmp-module \
&& curl -sL https://github.com/arut/nginx-rtmp-module/archive/refs/tags/v${RTMP_MODULE_VERSION}.tar.gz | tar -C /tmp/nginx-rtmp-module -zx --strip-components=1
WORKDIR /tmp/nginx
RUN ./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"
RUN make && make install
RUN rm -rf /usr/local/nginx/html /usr/local/nginx/conf/*.default
FROM base
COPY --from=build /usr/local/nginx /usr/local/nginx
ENTRYPOINT ["/usr/local/nginx/sbin/nginx"]
CMD ["-g", "daemon off;"]

3
docker/Dockerfile.wheels
View File

@@ -34,8 +34,7 @@ RUN pip3 wheel --wheel-dir=/wheels \
matplotlib \
click \
setproctitle \
peewee \
gevent
peewee
FROM scratch

5
docker/rootfs/etc/services.d/nginx/finish Normal file
View File

@@ -0,0 +1,5 @@
#!/usr/bin/execlineb -S1
if { s6-test ${1} -ne 0 }
if { s6-test ${1} -ne 256 }
s6-svscanctl -t /var/run/s6/services

2
docker/rootfs/etc/services.d/nginx/run Normal file
View File

@@ -0,0 +1,2 @@
#!/usr/bin/execlineb -P
/usr/local/nginx/sbin/nginx

73
nginx/nginx.conf → docker/rootfs/usr/local/nginx/conf/nginx.conf
View File

@@ -1,23 +1,23 @@
daemon off;
user root;
worker_processes 1;
error_log /var/log/nginx/error.log warn;
error_log /usr/local/nginx/logs/error.log warn;
pid /var/run/nginx.pid;
load_module "modules/ngx_rtmp_module.so";
events {
worker_connections 1024;
}
http {
include /etc/nginx/mime.types;
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 /var/log/nginx/access.log main;
access_log /usr/local/nginx/logs/access.log main;
sendfile on;
@@ -34,9 +34,56 @@ http {
keepalive 1024;
}
upstream mqtt_ws {
server localhost:5002;
keepalive 1024;
}
upstream jsmpeg {
server localhost:8082;
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 caches
vod_metadata_cache metadata_cache 512m;
vod_mapping_cache mapping_cache 5m;
# gzip manifests
gzip on;
gzip_types application/vnd.apple.mpegurl;
# 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;
location /vod/ {
vod hls;
secure_token $args;
secure_token_types application/vnd.apple.mpegurl;
add_header Access-Control-Allow-Headers '*';
add_header Access-Control-Expose-Headers 'Server,range,Content-Length,Content-Range';
add_header Access-Control-Allow-Methods 'GET, HEAD, OPTIONS';
add_header Access-Control-Allow-Origin '*';
expires -1;
}
location /stream/ {
add_header 'Cache-Control' 'no-cache';
add_header 'Access-Control-Allow-Origin' "$http_origin" always;
@@ -81,6 +128,11 @@ http {
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';
@@ -103,7 +155,15 @@ http {
}
location /ws {
proxy_pass http://frigate_api/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/ {
proxy_pass http://jsmpeg/;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "Upgrade";
@@ -112,6 +172,7 @@ http {
location /api/ {
add_header 'Access-Control-Allow-Origin' '*';
add_header 'Access-Control-Allow-Methods' 'GET, POST, PUT, DELETE, OPTIONS';
add_header Cache-Control "no-store";
proxy_pass http://frigate_api/;
proxy_pass_request_headers on;

95
docs/docs/configuration/advanced.md
View File

@@ -1,50 +1,11 @@
---
id: advanced
title: Advanced
sidebar_label: Advanced
title: Advanced Options
sidebar_label: Advanced Options
---
## Advanced configuration
### `motion`
Global motion detection config. These may also be defined at the camera level.
```yaml
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
# Increasing this value will prevent smaller areas of motion from being detected. Decreasing will make motion detection more sensitive to smaller
# moving objects.
contour_area: 100
# 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: 1/6th of the original frame height)
# 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: 180
```
### `detect`
Global object detection settings. These may also be defined at the camera level.
```yaml
detect:
# Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
max_disappeared: 25
```
### `logger`
Change the default log level for troubleshooting purposes.
@@ -72,48 +33,32 @@ Examples of available modules are:
### `environment_vars`
This section can be used to set environment variables for those unable to modify the environment of the container (ie. within Hass.io)
```yaml
environment_vars:
EXAMPLE_VAR: value
```
This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
### `database`
Event and clip information is managed in a sqlite database at `/media/frigate/clips/frigate.db`. If that database is deleted, clips will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
Event and recording information is managed in a sqlite database at `/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 clips 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.
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 clips.
```yaml
database:
path: /media/frigate/clips/frigate.db
```
### `detectors`
```yaml
detectors:
# Required: name of the detector
coral:
# Required: type of the detector
# Valid values are 'edgetpu' (requires device property below) and 'cpu'.
type: edgetpu
# Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
device: usb
# Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
# This value is only used for CPU types
num_threads: 3
```
This may need to be in a custom location if network storage is used for the media folder.
### `model`
If using a custom model, the width and height will need to be specified.
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:
# Required: height of the trained model
height: 320
# Required: width of the trained model
width: 320
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.

87
docs/docs/configuration/camera_specific.md Normal file
View File

@@ -0,0 +1,87 @@
---
id: camera_specific
title: Camera Specific Configurations
---
### MJPEG Cameras
The input and output parameters need to be adjusted for MJPEG cameras
```yaml
input_args: -avoid_negative_ts make_zero -fflags nobuffer -flags low_delay -strict experimental -fflags +genpts+discardcorrupt -use_wallclock_as_timestamps 1
```
Note that mjpeg cameras require encoding the video into h264 for recording, and rtmp roles. This will use significantly more CPU than if the cameras supported h264 feeds directly.
```yaml
output_args:
record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v libx264 -an
rtmp: -c:v libx264 -an -f flv
```
### 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 -rw_timeout 5000000 -use_wallclock_as_timestamps 1 -f live_flv
```
### Reolink 410/520 (possibly others)
According to [this discussion](https://github.com/blakeblackshear/frigate/issues/1713#issuecomment-932976305), the http video streams seem to be the most reliable for Reolink.
```yaml
cameras:
reolink:
ffmpeg:
hwaccel_args:
input_args:
- -avoid_negative_ts
- make_zero
- -fflags
- nobuffer+genpts+discardcorrupt
- -flags
- low_delay
- -strict
- experimental
- -analyzeduration
- 1000M
- -probesize
- 1000M
- -rw_timeout
- "5000000"
inputs:
- path: http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password
roles:
- record
- rtmp
- path: http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password
roles:
- detect
detect:
width: 640
height: 480
fps: 7
```
![Resolutions](/img/reolink-settings.png)
### Blue Iris RTSP Cameras
You will need to remove `nobuffer` flag for Blue Iris RTSP cameras
```yaml
ffmpeg:
input_args: -avoid_negative_ts make_zero -flags low_delay -strict experimental -fflags +genpts+discardcorrupt -rtsp_transport tcp -stimeout 5000000 -use_wallclock_as_timestamps 1
```
### UDP Only Cameras
If your cameras do not support TCP connections for RTSP, you can use UDP.
```yaml
ffmpeg:
input_args: -avoid_negative_ts make_zero -fflags +genpts+discardcorrupt -rtsp_transport udp -stimeout 5000000 -use_wallclock_as_timestamps 1
```

472
docs/docs/configuration/cameras.md
View File

@@ -5,18 +5,15 @@ title: Cameras
## Setting Up Camera Inputs
Up to 4 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 clips from a higher resolution stream, or vice versa.
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.
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 |
| `clips` | Clips of events from objects detected in the `detect` feed. [docs](#recording-clips) |
| `record` | Saves 60 second segments of the video feed. [docs](#247-recordings) |
| `rtmp` | Broadcast as an RTMP feed for other services to consume. [docs](#rtmp-streams) |
### Example
| Role | Description |
| -------- | ----------------------------------------------------------------------------------------------- |
| `detect` | Main feed for object detection |
| `record` | Saves segments of the video feed based on configuration settings. [docs](/configuration/record) |
| `rtmp` | Broadcast as an RTMP feed for other services to consume. [docs](/configuration/rtmp) |
```yaml
mqtt:
@@ -31,459 +28,18 @@ cameras:
- rtmp
- path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/live
roles:
- clips
- record
width: 1280
height: 720
fps: 5
```
`width`, `height`, and `fps` are only used for the `detect` role. Other streams are passed through, so there is no need to specify the resolution.
## Masks & Zones
### Masks
Masks are used to ignore initial detection in areas of your camera's field of view.
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 video 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. 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](/usage/web)
1. Click the camera you wish to create a mask for
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.yaml` 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"
```
![poly](/img/example-mask-poly.png)
```yaml
# Optional: camera level motion config
motion:
# Optional: motion mask
# NOTE: see docs for more detailed info on creating masks
mask: 0,900,1080,900,1080,1920,0,1920
```
### Zones
Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
During testing, `draw_zones` should be set in the config to draw the zone on the frames so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
To create a zone, follow the same steps above for a "Motion mask", but use the section of the web UI for creating a zone instead.
```yaml
# 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: Coordinates can be generated at https://www.image-map.net/
coordinates: 545,1077,747,939,788,805
# 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
```
## Objects
For a list of available objects, see the [objects documentation](./objects.mdx).
```yaml
# Optional: Camera level object filters config.
objects:
track:
- person
- car
# 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
filters:
person:
min_area: 5000
max_area: 100000
min_score: 0.5
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
```
## Clips
Frigate can save video clips without any CPU overhead for encoding by simply copying the stream directly with FFmpeg. It leverages FFmpeg's segment functionality to maintain a cache of video for each camera. The cache files are written to disk at `/tmp/cache` and do not introduce memory overhead. When an object is being tracked, it will extend the cache to ensure it can assemble a clip when the event ends. Once the event ends, it again uses FFmpeg to assemble a clip by combining the video clips without any encoding by the CPU. Assembled clips are are saved to `/media/frigate/clips`. Clips are retained according to the retention settings defined on the config for each object type.
These clips will not be playable in the web UI or in Home Assistant's media browser unless your camera sends video as h264.
:::caution
Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
:::
```yaml
clips:
# Required: enables clips for the camera (default: shown below)
# This value can be set via MQTT and will be updated in startup based on retained value
enabled: False
# Optional: Number of seconds before the event to include in the clips (default: shown below)
pre_capture: 5
# Optional: Number of seconds after the event to include in the clips (default: shown below)
post_capture: 5
# Optional: Objects to save clips for. (default: all tracked objects)
objects:
- person
# Optional: Restrict clips 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
```
## Snapshots
Frigate can save a snapshot image to `/media/frigate/clips` for each event named as `<camera>-<id>.jpg`.
```yaml
# Optional: Configuration for the jpg snapshots written to the clips directory for each event
snapshots:
# Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
# This value can be set via MQTT and will be updated in startup based on retained value
enabled: False
# 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
```
## 24/7 Recordings
24/7 recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding and are available in Home Assistant's media browser. Each camera supports a configurable retention policy in the config.
:::caution
Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
:::
```yaml
# Optional: 24/7 recording configuration
record:
# Optional: Enable recording (default: global setting)
enabled: False
# Optional: Number of days to retain (default: global setting)
retain_days: 30
```
## RTMP streams
Frigate can re-stream your video feed as a RTMP feed for other applications such as Home Assistant to utilize it at `rtmp://<frigate_host>/live/<camera_name>`. Port 1935 must be open. This allows you to use a video feed for detection in frigate and Home Assistant live view at the same time without having to make two separate connections to the camera. The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
Some video feeds are not compatible with RTMP. If you are experiencing issues, check to make sure your camera feed is h264 with AAC audio. If your camera doesn't support a compatible format for RTMP, you can use the ffmpeg args to re-encode it on the fly at the expense of increased CPU utilization.
## Full example
The following is a full example of all of the options together for a camera configuration
```yaml
cameras:
# Required: name of the camera
back:
# 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: Environment variables that begin with 'FRIGATE_' may 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,clips,rtmp
# NOTICE: In addition to assigning the record, clips, and rtmp roles,
# they must also be enabled in the camera config.
roles:
- detect
- 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:
# Required: width of the frame for the input with the detect role
width: 1280
# Required: height of the frame for the input with the detect role
height: 720
# Optional: desired fps for your camera for the input with the detect role
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
# Frigate will attempt to autodetect if not specified.
fps: 5
# Optional: camera level motion config
motion:
# Optional: motion mask
# NOTE: see docs for more detailed info on creating masks
mask: 0,900,1080,900,1080,1920,0,1920
# 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: Coordinates can be generated at https://www.image-map.net/
coordinates: 545,1077,747,939,788,805
# 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: Camera level detect settings
detect:
# Optional: enables detection for the camera (default: True)
# This value can be set via MQTT and will be updated in startup based on retained value
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: save clips configuration
clips:
# Required: enables clips for the camera (default: shown below)
# This value can be set via MQTT and will be updated in startup based on retained value
enabled: False
# Optional: Number of seconds before the event to include in the clips (default: shown below)
pre_capture: 5
# Optional: Number of seconds after the event to include in the clips (default: shown below)
post_capture: 5
# Optional: Objects to save clips for. (default: all tracked objects)
objects:
- person
# Optional: Restrict clips 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: 24/7 recording configuration
record:
# Optional: Enable recording (default: global setting)
enabled: False
# Optional: Number of days to retain (default: global setting)
retain_days: 30
# Optional: RTMP re-stream configuration
rtmp:
# Required: Enable the live stream (default: True)
enabled: True
# Optional: Configuration for the jpg snapshots written to the clips directory for each event
snapshots:
# Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
# This value can be set via MQTT and will be updated in startup based on retained value
enabled: False
# 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: 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: Restrict mqtt messages to objects that entered any of the listed zones (default: no required zones)
required_zones: []
# Optional: Camera level object filters config.
objects:
track:
- person
- car
# 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
filters:
person:
min_area: 5000
max_area: 100000
min_score: 0.5
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
width: 1280
height: 720
```
## Camera specific configuration
### MJPEG Cameras
The input and output parameters need to be adjusted for MJPEG cameras
Additional cameras are simply added to the config under the `cameras` entry.
```yaml
input_args:
- -avoid_negative_ts
- make_zero
- -fflags
- nobuffer
- -flags
- low_delay
- -strict
- experimental
- -fflags
- +genpts+discardcorrupt
- -r
- "3" # <---- adjust depending on your desired frame rate from the mjpeg image
- -use_wallclock_as_timestamps
- "1"
```
Note that mjpeg cameras require encoding the video into h264 for clips, recording, and rtmp roles. This will use significantly more CPU than if the cameras supported h264 feeds directly.
```yaml
output_args:
record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v libx264 -an
clips: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v libx264 -an
rtmp: -c:v libx264 -an -f flv
```
### 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
- -use_wallclock_as_timestamps
- "1"
```
### Reolink 410/520 (possibly others)
Several users have reported success with the rtmp video from Reolink cameras.
```yaml
ffmpeg:
input_args:
- -avoid_negative_ts
- make_zero
- -fflags
- nobuffer
- -flags
- low_delay
- -strict
- experimental
- -fflags
- +genpts+discardcorrupt
- -rw_timeout
- "5000000"
- -use_wallclock_as_timestamps
- "1"
```
### Blue Iris RTSP Cameras
You will need to remove `nobuffer` flag for Blue Iris RTSP cameras
```yaml
ffmpeg:
input_args:
- -avoid_negative_ts
- make_zero
- -flags
- low_delay
- -strict
- experimental
- -fflags
- +genpts+discardcorrupt
- -rtsp_transport
- tcp
- -stimeout
- "5000000"
- -use_wallclock_as_timestamps
- "1"
mqtt: ...
cameras:
back: ...
front: ...
side: ...
```

20
docs/docs/configuration/detectors.md
View File

@@ -3,13 +3,13 @@ id: detectors
title: Detectors
---
The default config will look for a USB Coral device. If you do not have a Coral, you will need to configure a CPU detector. If you have PCI or multiple Coral devices, you need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
By default, Frigate will use a single CPU detector. If you have a Coral, you will need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
Frigate supports `edgetpu` and `cpu` as detector types. The device value should be specified according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api).
**Note**: There is no support for Nvidia GPUs to perform object detection with tensorflow. It can be used for ffmpeg decoding, but not object detection.
Single USB Coral:
### Single USB Coral
```yaml
detectors:
@@ -18,7 +18,7 @@ detectors:
device: usb
```
Multiple USB Corals:
### Multiple USB Corals
```yaml
detectors:
@@ -30,16 +30,16 @@ detectors:
device: usb:1
```
Native Coral (Dev Board):
### Native Coral (Dev Board)
```yaml
detectors:
coral:
type: edgetpu
device: ''
device: ""
```
Multiple PCIE/M.2 Corals:
### Multiple PCIE/M.2 Corals
```yaml
detectors:
@@ -51,7 +51,7 @@ detectors:
device: pci:1
```
Mixing Corals:
### Mixing Corals
```yaml
detectors:
@@ -63,12 +63,16 @@ detectors:
device: pci
```
CPU Detectors (not recommended):
### CPU Detectors (not recommended)
```yaml
detectors:
cpu1:
type: cpu
num_threads: 3
cpu2:
type: cpu
num_threads: 3
```
When using CPU detectors, you can add a CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.

70
docs/docs/configuration/hardware_acceleration.md Normal file
View File

@@ -0,0 +1,70 @@
---
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 (32-bit OS)
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:
- -c:v
- h264_mmal
```
### Raspberry Pi 3/4 (64-bit OS)
**NOTICE**: If you are using the addon, you may need to turn off `Protection mode` for hardware acceleration.
```yaml
ffmpeg:
hwaccel_args:
- -c:v
- h264_v4l2m2m
```
### Intel-based CPUs (<10th Generation) via Quicksync
```yaml
ffmpeg:
hwaccel_args:
- -hwaccel
- vaapi
- -hwaccel_device
- /dev/dri/renderD128
- -hwaccel_output_format
- yuv420p
```
### Intel-based CPUs (>=10th Generation) via Quicksync
```yaml
ffmpeg:
hwaccel_args:
- -hwaccel
- qsv
- -qsv_device
- /dev/dri/renderD128
```
### AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
**Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
```yaml
ffmpeg:
hwaccel_args:
- -hwaccel
- vaapi
- -hwaccel_device
- /dev/dri/renderD128
```
### NVIDIA GPU
NVIDIA GPU based decoding via NVDEC is supported, but requires special configuration. See the [NVIDIA NVDEC documentation](/configuration/nvdec) for more details.

375
docs/docs/configuration/index.md
View File

@@ -1,13 +1,13 @@
---
id: index
title: Configuration
title: Configuration File
---
For HassOS installations, the default location for the config file is `/config/frigate.yml`.
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`) and named `frigate.yml`.
For all other installations, the default location for the config file is '/config/config.yml'. This can be overridden with the `CONFIG_FILE` environment variable. Camera specific ffmpeg parameters are documented [here](cameras.md).
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:
It is recommended to start with a minimal configuration and add to it as described in [this guide](/guides/getting_started):
```yaml
mqtt:
@@ -20,14 +20,18 @@ cameras:
roles:
- detect
- rtmp
width: 1280
height: 720
fps: 5
detect:
width: 1280
height: 720
```
## Required
### Full configuration reference:
## `mqtt`
:::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.
:::
```yaml
mqtt:
@@ -36,10 +40,10 @@ mqtt:
# Optional: port (default: shown below)
port: 1883
# Optional: topic prefix (default: shown below)
# WARNING: must be unique if you are running multiple instances
# NOTE: must be unique if you are running multiple instances
topic_prefix: frigate
# Optional: client id (default: shown below)
# WARNING: must be unique if you are running multiple instances
# NOTE: must be unique if you are running multiple instances
client_id: frigate
# Optional: user
user: mqtt_user
@@ -47,56 +51,82 @@ mqtt:
# NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}.
# eg. 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
```
## `cameras`
# Optional: Detectors configuration. Defaults to a single CPU detector
detectors:
# Required: name of the detector
coral:
# Required: type of the detector
# Valid values are 'edgetpu' (requires device property below) and 'cpu'.
type: edgetpu
# Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
device: usb
# Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
# This value is only used for CPU types
num_threads: 3
Each of your cameras must be configured. The following is the minimum required to register a camera in Frigate. Check the [camera configuration page](cameras.md) for a complete list of options.
# Optional: Database configuration
database:
# The path to store the SQLite DB (default: shown below)
path: /media/frigate/frigate.db
```yaml
cameras:
# Name of your camera
front_door:
ffmpeg:
inputs:
- path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
roles:
- detect
- rtmp
width: 1280
height: 720
fps: 5
```
# 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: Label name modifications. These are merged into the standard labelmap.
labelmap:
2: vehicle
## Optional
# Optional: logger verbosity settings
logger:
# Optional: Default log verbosity (default: shown below)
default: info
# Optional: Component specific logger overrides
logs:
frigate.event: debug
### `clips`
# Optional: set environment variables
environment_vars:
EXAMPLE_VAR: value
```yaml
clips:
# Optional: Maximum length of time to retain video during long events. (default: shown below)
# NOTE: If an object is being tracked for longer than this amount of time, the cache
# will begin to expire and the resulting clip will be the last x seconds of the event.
max_seconds: 300
# Optional: size of tmpfs mount to create for cache files (default: not set)
# mount -t tmpfs -o size={tmpfs_cache_size} tmpfs /tmp/cache
# NOTICE: Addon users must have Protection mode disabled for the addon when using this setting.
# Also, if you have mounted a tmpfs volume through docker, this value should not be set in your config.
tmpfs_cache_size: 256m
# Optional: Retention settings for clips (default: shown below)
retain:
# Required: Default retention days (default: shown below)
default: 10
# Optional: Per object retention days
objects:
person: 15
```
# Optional: birdseye configuration
birdseye:
# Optional: Enable birdseye view (default: shown below)
enabled: True
# 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
### `ffmpeg`
```yaml
# Optional: ffmpeg configuration
ffmpeg:
# Optional: global ffmpeg args (default: shown below)
global_args: -hide_banner -loglevel warning
@@ -110,22 +140,36 @@ ffmpeg:
# Optional: output args for detect streams (default: shown below)
detect: -f rawvideo -pix_fmt yuv420p
# Optional: output args for record streams (default: shown below)
record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
# Optional: output args for clips streams (default: shown below)
clips: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
# Optional: output args for rtmp streams (default: shown below)
rtmp: -c copy -f flv
```
### `objects`
# 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)
# This value can be set via MQTT and will be updated in startup based on retained value
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
Can be overridden at the camera level. For a list of available objects, see the [objects documentation](./objects.mdx).
```yaml
# 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:
@@ -137,20 +181,207 @@ objects:
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
### `record`
# 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: ~0.17% of the motion frame area)
# Increasing this value will prevent smaller areas of motion from being detected. Decreasing will make motion detection more sensitive to smaller
# moving objects.
contour_area: 100
# 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: 1/6th of the original frame height, but no less than 180)
# 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: 180
# Optional: motion mask
# NOTE: see docs for more detailed info on creating masks
mask: 0,900,1080,900,1080,1920,0,1920
Can be overridden at the camera level. 24/7 recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding and are available in Home Assistant's media browser. Each camera supports a configurable retention policy in the config.
:::caution
Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
:::
```yaml
# Optional: Record configuration
# NOTE: Can be overridden at the camera level
record:
# Optional: Enable recording
# Optional: Enable recording (default: shown below)
enabled: False
# Optional: Number of days to retain
retain_days: 30
# 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.
retain_days: 0
# Optional: Event recording settings
events:
# Optional: Maximum length of time to retain video during long events. (default: shown below)
# NOTE: If an object is being tracked for longer than this amount of time, the retained recordings
# will be the last x seconds of the event unless retain_days under record is > 0.
max_seconds: 300
# 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: 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)
# This value can be set via MQTT and will be updated in startup based on retained value
enabled: False
# 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: Can be overridden at the camera level
rtmp:
# Optional: Enable the RTMP stream (default: True)
enabled: True
# Optional: Live stream configuration for WebUI
# NOTE: Can be overridden at the camera level
live:
# Optional: Set the height of the live 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 live stream. Width is computed to match known aspect ratio.
height: 720
# Optional: Set the encode quality of the live 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:
# 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: Environment variables that begin with 'FRIGATE_' may 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
- 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: Coordinates can be generated at https://www.image-map.net/
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: []
```

77
docs/docs/configuration/masks.md Normal file
View File

@@ -0,0 +1,77 @@
---
id: masks
title: Masks
---
There are two types of masks available:
**Motion masks**: Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the debug feed with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. Over masking will make it more difficult for objects to be tracked. To see this effect, create a mask, and then watch the video feed with `Motion Boxes` enabled again.
**Object filter masks**: Object filter masks are used to filter out false positives for a given object type based on location. These should be used to filter any areas where it is not possible for an object of that type to be. The bottom center of the detected object's bounding box is evaluated against the mask. If it is in a masked area, it is assumed to be a false positive. For example, you may want to mask out rooftops, walls, the sky, treetops for people. For cars, masking locations other than the street or your driveway will tell frigate that anything in your yard is a false positive.
To create a poly mask:
1. Visit the Web UI
1. Click the camera you wish to create a mask for
1. Select "Debug" at the top
1. Expand the "Options" below the video feed
1. Click "Mask & Zone creator"
1. Click "Add" on the type of mask or zone you would like to create
1. Click on the camera's latest image to create a masked area. The yaml representation will be updated in real-time
1. When you've finished creating your mask, click "Copy" and paste the contents into your config file and restart Frigate
Example of a finished row corresponding to the below example image:
```yaml
motion:
mask: "0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"
```
Multiple masks can be listed.
```yaml
motion:
mask:
- 458,1346,336,973,317,869,375,866,432
- 0,461,3,0,1919,0,1919,843,1699,492,1344
```
![poly](/img/example-mask-poly-min.png)
### Further Clarification
This is a response to a [question posed on reddit](https://www.reddit.com/r/homeautomation/comments/ppxdve/replacing_my_doorbell_with_a_security_camera_a_6/hd876w4?utm_source=share&utm_medium=web2x&context=3):
It is helpful to understand a bit about how Frigate uses motion detection and object detection together.
First, Frigate uses motion detection as a first line check to see if there is anything happening in the frame worth checking with object detection.
Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the debug viewer.
After the area with motion is identified, Frigate creates a "region" (the green boxes in the debug viewer) to run object detection on. The models are trained on square images, so these regions are always squares. It adds a margin around the motion area in hopes of capturing a cropped view of the object moving that fills most of the image passed to object detection, but doesn't cut anything off. It also takes into consideration the location of the bounding box from the previous frame if it is tracking an object.
After object detection runs, if there are detected objects that seem to be cut off, Frigate reframes the region and runs object detection again on the same frame to get a better look.
All of this happens for each area of motion and tracked object.
> Are you simply saying that INITIAL triggering of any kind of detection will only happen in un-masked areas, but that once this triggering happens, the masks become irrelevant and object detection takes precedence?
Essentially, yes. I wouldn't describe it as object detection taking precedence though. The motion masks just prevent those areas from being counted as motion. Those masks do not modify the regions passed to object detection in any way, so you can absolutely detect objects in areas masked for motion.
> If so, this is completely expected and intuitive behavior for me. Because obviously if a "foot" starts motion detection the camera should be able to check if it's an entire person before it fully crosses into the zone. The docs imply this is the behavior, so I also don't understand why this would be detrimental to object detection on the whole.
When just a foot is triggering motion, Frigate will zoom in and look only at the foot. If that even qualifies as a person, it will determine the object is being cut off and look again and again until it zooms back out enough to find the whole person.
It is also detrimental to how Frigate tracks a moving object. Motion nearby the bounding box from the previous frame is used to intelligently determine where the region should be in the next frame. With too much masking, tracking is hampered and if an object walks from an unmasked area into a fully masked area, they essentially disappear and will be picked up as a "new" object if they leave the masked area. This is important because Frigate uses the history of scores while tracking an object to determine if it is a false positive or not. It takes a minimum of 3 frames for Frigate to determine is the object type it thinks it is, and the median score must be greater than the threshold. If a person meets this threshold while on the sidewalk before they walk into your stoop, you will get an alert the instant they step a single foot into a zone.
> I thought the main point of this feature was to cut down on CPU use when motion is happening in unnecessary areas.
It is, but the definition of "unnecessary" varies. I want to ignore areas of motion that I know are definitely not being triggered by objects of interest. Timestamps, trees, sky, rooftops. I don't want to ignore motion from objects that I want to track and know where they go.
> For me, giving my masks ANY padding results in a lot of people detection I'm not interested in. I live in the city and catch a lot of the sidewalk on my camera. People walk by my front door all the time and the margin between the sidewalk and actually walking onto my stoop is very thin, so I basically have everything but the exact contours of my stoop masked out. This results in very tidy detections but this info keeps throwing me off. Am I just overthinking it?
This is what `required_zones` are for. You should define a zone (remember this is evaluated based on the bottom center of the bounding box) and make it required to save snapshots and clips (now events in 0.9.0). You can also use this in your conditions for a notification.
> Maybe my specific situation just warrants this. I've just been having a hard time understanding the relevance of this information - it seems to be that it's exactly what would be expected when "masking out" an area of ANY image.
That may be the case for you. Frigate will definitely work harder tracking people on the sidewalk to make sure it doesn't miss anyone who steps foot on your stoop. The trade off with the way you have it now is slower recognition of objects and potential misses. That may be acceptable based on your needs. Also, if your resolution is low enough on the detect stream, your regions may already be so big that they grab the entire object anyway.

21
docs/docs/configuration/nvdec.md
View File

@@ -1,6 +1,6 @@
---
id: nvdec
title: nVidia hardware decoder
title: NVIDIA hardware decoder
---
Certain nvidia cards include a hardware decoder, which can greatly improve the
@@ -9,6 +9,8 @@ ffmpeg with NVDEC support is required. The special docker architecture 'amd64nvi
includes this support for amd64 platforms. An aarch64 for the Jetson, which
also includes NVDEC may be added in the future.
Some more detailed setup instructions are also available in [this issue](https://github.com/blakeblackshear/frigate/issues/1847#issuecomment-932076731).
## Docker setup
### Requirements
@@ -23,7 +25,7 @@ In order to pass NVDEC, the docker engine must be set to `nvidia` and the enviro
In a docker compose file, these lines need to be set:
```
```yaml
services:
frigate:
...
@@ -41,7 +43,7 @@ The decoder you choose will depend on the input video.
A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
```
```shell
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)
@@ -57,10 +59,9 @@ A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the c
For example, for H265 video (hevc), you'll select `hevc_cuvid`. Add
`-c:v hevc_cuvid` to your ffmpeg input arguments:
```
```yaml
ffmpeg:
input_args:
...
input_args: ...
- -c:v
- hevc_cuvid
```
@@ -100,10 +101,10 @@ processes:
To further improve performance, you can set ffmpeg to skip frames in the output,
using the fps filter:
```
output_args:
- -filter:v
- fps=fps=5
```yaml
output_args:
- -filter:v
- fps=fps=5
```
This setting, for example, allows Frigate to consume my 10-15fps camera streams on

24
docs/docs/configuration/objects.mdx
View File

@@ -1,16 +1,15 @@
---
id: objects
title: Default available objects
sidebar_label: Available objects
title: Objects
---
import labels from '../../../labelmap.txt';
import labels from "../../../labelmap.txt";
By default, Frigate includes the following object models from the Google Coral test data.
By default, Frigate includes the following object models from the Google Coral test data. Note that `car` is listed twice because `truck` has been renamed to `car` by default. These object types are frequently confused.
<ul>
{labels.split('\n').map((label) => (
<li>{label.replace(/^\d+\s+/, '')}</li>
{labels.split("\n").map((label) => (
<li>{label.replace(/^\d+\s+/, "")}</li>
))}
</ul>
@@ -22,15 +21,4 @@ Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use yo
- EdgeTPU Model: `/edgetpu_model.tflite`
- Labels: `/labelmap.txt`
You also need to update the model width/height in the config if they differ from the defaults.
### Customizing the Labelmap
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. You must retain the same number of labels, but you can change the names. To change:
- Download the [COCO labelmap](https://dl.google.com/coral/canned_models/coco_labels.txt)
- Modify the label names as desired. For example, change `7 truck` to `7 car`
- Mount the new file at `/labelmap.txt` in the container with an additional volume
```
-v ./config/labelmap.txt:/labelmap.txt
```
You also need to update the [model config](/configuration/advanced#model) if they differ from the defaults.

73
docs/docs/configuration/optimizing.md
View File

@@ -1,73 +0,0 @@
---
id: optimizing
title: Optimizing performance
---
- **Google Coral**: It is strongly recommended to use a Google Coral, Frigate will no longer fall back to CPU in the event one is not found. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU. Frigate should work with any supported Coral device from https://coral.ai
- **Resolution**: For the `detect` input, choose a camera resolution where the smallest object you want to detect barely fits inside a 300x300px square. The model used by Frigate is trained on 300x300px images, so you will get worse performance and no improvement in accuracy by using a larger resolution since Frigate resizes the area where it is looking for objects to 300x300 anyway.
- **FPS**: 5 frames per second should be adequate. Higher frame rates will require more CPU usage without improving detections or accuracy. Reducing the frame rate on your camera will have the greatest improvement on system resources.
- **Hardware Acceleration**: Make sure you configure the `hwaccel_args` for your hardware. They provide a significant reduction in CPU usage if they are available.
- **Masks**: Masks can be used to ignore motion and reduce your idle CPU load. If you have areas with regular motion such as timestamps or trees blowing in the wind, frigate will constantly try to determine if that motion is from a person or other object you are tracking. Those detections not only increase your average CPU usage, but also clog the pipeline for detecting objects elsewhere. If you are experiencing high values for `detection_fps` when no objects of interest are in the cameras, you should use masks to tell frigate to ignore movement from trees, bushes, timestamps, or any part of the image where detections should not be wasted looking for objects.
### FFmpeg Hardware Acceleration
Frigate works on Raspberry Pi 3b/4 and x86 machines. It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible.
Raspberry Pi 3/4 (32-bit OS)
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, ensure you turn off `Protection mode` for hardware acceleration.
```yaml
ffmpeg:
hwaccel_args:
- -c:v
- h264_mmal
```
Raspberry Pi 3/4 (64-bit OS)
**NOTICE**: If you are using the addon, ensure you turn off `Protection mode` for hardware acceleration.
```yaml
ffmpeg:
hwaccel_args:
- -c:v
- h264_v4l2m2m
```
Intel-based CPUs (<10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
```yaml
ffmpeg:
hwaccel_args:
- -hwaccel
- vaapi
- -hwaccel_device
- /dev/dri/renderD128
- -hwaccel_output_format
- yuv420p
```
Intel-based CPUs (>=10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
```yaml
ffmpeg:
hwaccel_args:
- -hwaccel
- qsv
- -qsv_device
- /dev/dri/renderD128
```
AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
**Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
```yaml
ffmpeg:
hwaccel_args:
- -hwaccel
- vaapi
- -hwaccel_device
- /dev/dri/renderD128
```
Nvidia GPU based decoding via NVDEC is supported, but requires special configuration. See the [nvidia NVDEC documentation](/configuration/nvdec) for more details.

25
docs/docs/configuration/record.md Normal file
View File

@@ -0,0 +1,25 @@
---
id: record
title: Recording
---
Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy in the config. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
H265 recordings can be viewed in Edge and Safari only. All other browsers require recordings to be encoded with H264.
## What if I don't want 24/7 recordings?
If you only used clips in previous versions with recordings disabled, you can use the following config to get the same behavior. This is also the default behavior when recordings are enabled.
```yaml
record:
enabled: True
retain_days: 0
events:
retain:
default: 10
```
This configuration will retain recording segments that overlap with events for 10 days. Because multiple events can reference the same recording segments, this avoids storing duplicate footage for overlapping events and reduces overall storage needs.
When `retain_days` is set to `0`, events will have up to `max_seconds` (defaults to 5 minutes) of recordings retained. Increasing `retain_days` to `1` will allow events to exceed the `max_seconds` limitation of up to 1 day.

8
docs/docs/configuration/rtmp.md Normal file
View File

@@ -0,0 +1,8 @@
---
id: rtmp
title: RTMP
---
Frigate can re-stream your video feed as a RTMP feed for other applications such as Home Assistant to utilize it at `rtmp://<frigate_host>/live/<camera_name>`. Port 1935 must be open. This allows you to use a video feed for detection in frigate and Home Assistant live view at the same time without having to make two separate connections to the camera. The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
Some video feeds are not compatible with RTMP. If you are experiencing issues, check to make sure your camera feed is h264 with AAC audio. If your camera doesn't support a compatible format for RTMP, you can use the ffmpeg args to re-encode it on the fly at the expense of increased CPU utilization.

6
docs/docs/configuration/snapshots.md Normal file
View File

@@ -0,0 +1,6 @@
---
id: snapshots
title: Snapshots
---
Frigate can save a snapshot image to `/media/frigate/clips` for each event named as `<camera>-<id>.jpg`.

38
docs/docs/configuration/zones.md Normal file
View File

@@ -0,0 +1,38 @@
---
id: zones
title: Zones
---
Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
During testing, enable the Zones option for the debug feed so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
To create a zone, follow [the steps for a "Motion mask"](/configuration/masks), but use the section of the web UI for creating a zone instead.
### Restricting zones to specific objects
Sometimes you want to limit a zone to specific object types to have more granular control of when events/snapshots are saved. The following example will limit one zone to person objects and the other to cars.
```yaml
camera:
record:
events:
required_zones:
- entire_yard
- front_yard_street
snapshots:
required_zones:
- entire_yard
- front_yard_street
zones:
entire_yard:
coordinates: ... (everywhere you want a person)
objects:
- person
front_yard_street:
coordinates: ... (just the street)
objects:
- car
```
Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. You will get events for person objects that enter anywhere in the yard, and events for cars only if they enter the street.

55
docs/docs/contributing.md
View File

@@ -36,6 +36,59 @@ Fork [blakeblackshear/frigate-hass-integration](https://github.com/blakeblackshe
- [Frigate source code](#frigate-core-web-and-docs)
- GNU make
- Docker
- Extra Coral device (optional, but very helpful to simulate real world performance)
### Setup
#### 1. Build the docker container locally with the appropriate make command
For x86 machines, use `make amd64_frigate`
#### 2. Create a local config file for testing
Place the file at `config/config.yml` in the root of the repo.
Here is an example, but modify for your needs:
```yaml
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
```
These input args tell ffmpeg to read the mp4 file in an infinite loop. You can use any valid ffmpeg input here.
#### 3. Gather some mp4 files for testing
Create and place these files in a `debug` folder in the root of the repo. This is also where recordings will be created if you enable them in your test config. Update your config from step 2 above to point at the right file. You can check the `docker-compose.yml` file in the repo to see how the volumes are mapped.
#### 4. Open the repo with Visual Studio Code
Upon opening, you should be prompted to open the project in a remote container. This will build a container on top of the base frigate container with all the development dependencies installed. This ensures everyone uses a consistent development environment without the need to install any dependencies on your host machine.
#### 5. Run frigate from the command line
VSCode will start the docker compose file for you and open a terminal window connected to `frigate-dev`.
- Run `python3 -m frigate` to start the backend.
- In a separate terminal window inside VS Code, change into the `web` directory and run `npm install && npm start` to start the frontend.
#### 6. Teardown
After closing VSCode, you may still have containers running. To close everything down, just run `docker-compose down -v` to cleanup all containers.
## Web Interface
@@ -109,7 +162,7 @@ npm run test
#### 1. Installation
```console
npm run install
npm install
```
#### 2. Local Development

34
docs/docs/faqs.md Normal file
View File

@@ -0,0 +1,34 @@
---
id: faqs
title: Frequently Asked Questions
---
### Fatal Python error: Bus error
This error message is due to a shm-size that is too small. Try updating your shm-size according to [this guide](/installation#calculating-required-shm-size).
### I am seeing a solid green image for my camera.
A solid green image means that frigate has not received any frames from ffmpeg. Check the logs to see why ffmpeg is exiting and adjust your ffmpeg args accordingly.
### How can I get sound or audio in my recordings?
By default, Frigate removes audio from recordings to reduce the likelihood of failing for invalid data. If you would like to include audio, you need to override the output args to remove `-an` for where you want to include audio. The recommended audio codec is `aac`. Not all audio codecs are supported by RTMP, so you may need to re-encode your audio with `-c:a aac`. The default ffmpeg args are shown [here](configuration/index#ffmpeg).
### My mjpeg stream or snapshots look green and crazy
This almost always means that the width/height defined for your camera are not correct. Double check the resolution with vlc or another player. Also make sure you don't have the width and height values backwards.
![mismatched-resolution](/img/mismatched-resolution-min.jpg)
### I can't view events or recordings in the Web UI.
Ensure your cameras send h264 encoded video
### "[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5639eeb6e140] moov atom not found"
These messages in the logs are expected in certain situations. Frigate checks the integrity of the recordings before storing. Occasionally these cached files will be invalid and cleaned up automatically.
### "On connect called"
If you see repeated "On connect called" messages in your config, check for another instance of frigate. This happens when multiple frigate containers are trying to connect to mqtt with the same client_id.

47
docs/docs/guides/camera_setup.md Normal file
View File

@@ -0,0 +1,47 @@
---
id: camera_setup
title: Camera setup
---
Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but far less compatibility. Safari and Edge are the only browsers able to play H.265. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
- **Recording**: This stream should be the resolution you wish to store for reference. Typically, this will be the highest resolution your camera supports. I recommend setting this feed to 15 fps.
- **Stream Viewing**: This stream will be rebroadcast as is to Home Assistant for viewing with the stream component. Setting this resolution too high will use significant bandwidth when viewing streams in Home Assistant, and they may not load reliably over slower connections.
### Choosing a detect resolution
The ideal resolution for detection is one where the objects you want to detect fit inside the dimensions of the model used by Frigate (320x320). Frigate does not pass the entire camera frame to object detection. It will crop an area of motion from the full frame and look in that portion of the frame. If the area being inspected is larger than 320x320, Frigate must resize it before running object detection. Higher resolutions do not improve the detection accuracy because the additional detail is lost in the resize. Below you can see a reference for how large a 320x320 area is against common resolutions.
Larger resolutions **do** improve performance if the objects are very small in the frame.
![Resolutions](/img/resolutions-min.jpg)
### Example Camera Configuration
For the Dahua/Loryta 5442 camera, I use the following settings:
**Main Stream (Recording)**
- Encode Mode: H.264
- Resolution: 2688\*1520
- Frame Rate(FPS): 15
- I Frame Interval: 30
**Sub Stream 1 (RTMP)**
- Enable: Sub Stream 1
- Encode Mode: H.264
- Resolution: 720\*576
- Frame Rate: 10
- I Frame Interval: 10
**Sub Stream 2 (Detection)**
- Enable: Sub Stream 2
- Encode Mode: H.264
- Resolution: 1280\*720
- Frame Rate: 5
- I Frame Interval: 5

0
docs/docs/configuration/false_positives.md → docs/docs/guides/false_positives.md
View File

193
docs/docs/guides/getting_started.md Normal file
View File

@@ -0,0 +1,193 @@
---
id: getting_started
title: Creating a config file
---
This guide walks through the steps to build a configuration file for Frigate. It assumes that you already have an environment setup as described in [Installation](/installation). You should also configure your cameras according to the [camera setup guide](/guides/camera_setup)
### Step 1: Configure the MQTT server
Frigate requires a functioning MQTT server. Start by adding the mqtt section at the top level in your config:
```yaml
mqtt:
host: <ip of your mqtt server>
```
If using the Mosquitto Addon in Home Assistant, a username and password is required. For example:
```yaml
mqtt:
host: <ip of your mqtt server>
user: <username>
password: <password>
```
Frigate supports many configuration options for mqtt. See the [configuration reference](/configuration/index#full-configuration-reference) for more info.
### Step 2: Configure detectors
By default, Frigate will use a single CPU detector. If you have a USB Coral, you will need to add a detectors section to your config.
```yaml
mqtt:
host: <ip of your mqtt server>
detectors:
coral:
type: edgetpu
device: usb
```
More details on available detectors can be found [here](/configuration/detectors).
### Step 3: Add a minimal camera configuration
Now let's add the first camera:
```yaml
mqtt:
host: <ip of your mqtt server>
detectors:
coral:
type: edgetpu
device: usb
cameras:
camera_1: # <------ Name the camera
ffmpeg:
inputs:
- path: rtsp://10.0.10.10:554/rtsp # <----- Update for your camera
roles:
- detect
- rtmp
detect:
width: 1280 # <---- update for your camera's resolution
height: 720 # <---- update for your camera's resolution
```
### Step 4: Start Frigate
At this point you should be able to start Frigate and see the the video feed in the UI.
If you get a green image from the camera, this means ffmpeg was not able to get the video feed from your camera. Check the logs for error messages from ffmpeg. The default ffmpeg arguments are designed to work with RTSP cameras that support TCP connections. FFmpeg arguments for other types of cameras can be found [here](/configuration/camera_specific).
### Step 5: Configure hardware acceleration (optional)
Now that you have a working camera configuration, you want to setup hardware acceleration to minimize the CPU required to decode your video streams. See the [hardware acceleration](/configuration/hardware_acceleration) config reference for examples applicable to your hardware.
In order to best evaluate the performance impact of hardware acceleration, it is recommended to temporarily disable detection.
```yaml
mqtt: ...
detectors: ...
cameras:
camera_1:
ffmpeg: ...
detect:
enabled: False
...
```
Here is an example configuration with hardware acceleration configured:
```yaml
mqtt: ...
detectors: ...
cameras:
camera_1:
ffmpeg:
inputs: ...
hwaccel_args: -c:v h264_v4l2m2m
detect: ...
```
### Step 6: Setup motion masks
Now that you have optimized your configuration for decoding the video stream, you will want to check to see where to implement motion masks. To do this, navigate to the camera in the UI, select "Debug" at the top, and enable "Motion boxes" in the options below the video feed. Watch for areas that continuously trigger unwanted motion to be detected. Common areas to mask include camera timestamps and trees that frequently blow in the wind. The goal is to avoid wasting object detection cycles looking at these areas.
Now that you know where you need to mask, use the "Mask & Zone creator" in the options pane to generate the coordinates needed for your config file. More information about masks can be found [here](/configuration/masks).
:::caution
Note that motion masks should not be used to mark out areas where you do not want objects to be detected or to reduce false positives. They do not alter the image sent to object detection, so you can still get events and detections in areas with motion masks. These only prevent motion in these areas from initiating object detection.
:::
Your configuration should look similar to this now.
```yaml
mqtt:
host: mqtt.local
detectors:
coral:
type: edgetpu
device: usb
cameras:
camera_1:
ffmpeg:
inputs:
- path: rtsp://10.0.10.10:554/rtsp
roles:
- detect
- rtmp
detect:
width: 1280
height: 720
motion:
mask:
- 0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432
```
### Step 7: Enable recording (optional)
To enable recording video, add the `record` role to a stream and enable it in the config.
```yaml
mqtt: ...
detectors: ...
cameras:
camera_1:
ffmpeg:
inputs:
- path: rtsp://10.0.10.10:554/rtsp
roles:
- detect
- rtmp
- record # <----- Add role
detect: ...
record: # <----- Enable recording
enabled: True
motion: ...
```
By default, Frigate will retain video of all events for 10 days. The full set of options for recording can be found [here](/configuration/index#full-configuration-reference).
### Step 8: Enable snapshots (optional)
To enable snapshots of your events, just enable it in the config.
```yaml
mqtt: ...
detectors: ...
cameras:
camera_1: ...
detect: ...
record: ...
snapshots: # <----- Enable snapshots
enabled: True
motion: ...
```
By default, Frigate will retain snapshots of all events for 10 days. The full set of options for snapshots can be found [here](/configuration/index#full-configuration-reference).

56
docs/docs/guides/ha_notifications.md Normal file
View File

@@ -0,0 +1,56 @@
---
id: ha_notifications
title: Home Assistant notifications
---
The best way to get started with notifications for Frigate is to use the [Blueprint](https://community.home-assistant.io/t/frigate-mobile-app-notifications/311091). You can use the yaml generated from the Blueprint as a starting point and customize from there.
It is generally recommended to trigger notifications based on the `frigate/events` mqtt topic. This provides the event_id needed to fetch [thumbnails/snapshots/clips](/integrations/home-assistant#notification-api) and other useful information to customize when and where you want to receive alerts. The data is published in the form of a change feed, which means you can reference the "previous state" of the object in the `before` section and the "current state" of the object in the `after` section. You can see an example [here](/integrations/mqtt#frigateevents).
Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as frigate finds a "better" image.
```yaml
automation:
- alias: Notify of events
trigger:
platform: mqtt
topic: frigate/events
action:
- service: notify.mobile_app_pixel_3
data_template:
message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
data:
image: 'https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg?format=android'
tag: '{{trigger.payload_json["after"]["id"]}}'
when: '{{trigger.payload_json["after"]["start_time"]|int}}'
```
## Conditions
Conditions with the `before` and `after` values allow a high degree of customization for automations.
When a person enters a zone named yard
```yaml
condition:
- "{{ trigger.payload_json['after']['label'] == 'person' }}"
- "{{ 'yard' in trigger.payload_json['after']['entered_zones'] }}"
```
When a person leaves a zone named yard
```yaml
condition:
- "{{ trigger.payload_json['after']['label'] == 'person' }}"
- "{{ 'yard' in trigger.payload_json['before']['current_zones'] }}"
- "{{ not 'yard' in trigger.payload_json['after']['current_zones'] }}"
```
Notify for dogs in the front with a high top score
```yaml
condition:
- "{{ trigger.payload_json['after']['label'] == 'dog' }}"
- "{{ trigger.payload_json['after']['camera'] == 'front' }}"
- "{{ trigger.payload_json['after']['top_score'] > 0.98 }}"
```

37
docs/docs/guides/stationary_objects.md Normal file
View File

@@ -0,0 +1,37 @@
---
id: stationary_objects
title: Avoiding stationary objects
---
Many people use Frigate to detect cars entering their driveway, and they often run into an issue with repeated events of a parked car being repeatedly detected. This is because object tracking stops when motion ends and the event ends. Motion detection works by determining if a sufficient number of pixels have changed between frames. Shadows or other lighting changes will be detected as motion. This will often cause a new event for a parked car.
You can use zones to restrict events and notifications to objects that have entered specific areas.
:::caution
It is not recommended to use masks to try and eliminate parked cars in your driveway. Masks are designed to prevent motion from triggering object detection and/or to indicate areas that are guaranteed false positives.
Frigate is designed to track objects as they move and over-masking can prevent it from knowing that an object in the current frame is the same as the previous frame. You want Frigate to detect objects everywhere and configure your events and alerts to be based on the location of the object with zones.
:::
To only be notified of cars that enter your driveway from the street, you could create multiple zones that cover your driveway. For cars, you would only notify if `entered_zones` from the events MQTT topic has more than 1 zone.
See [this example](/configuration/zones#restricting-zones-to-specific-objects) from the Zones documentation to see how to restrict zones to certain object types.
![Driveway Zones](/img/driveway_zones-min.png)
To limit snapshots and events, you can list the zone for the entrance of your driveway under `required_zones` in your configuration file. Example below.
```yaml
camera:
record:
events:
required_zones:
- zone_2
zones:
zone_1:
coordinates: ... (parking area)
zone_2:
coordinates: ... (entrance to driveway)
```

51
docs/docs/hardware.md
View File

@@ -5,25 +5,42 @@ title: Recommended hardware
## Cameras
Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, clips, and recordings without re-encoding.
Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
## Computer
I recommend Dahua, Hikvision, and Amcrest in that order. Dahua edges out Hikvision because they are easier to find and order, not because they are better cameras. I personally use Dahua cameras because they are easier to purchase directly. In my experience Dahua and Hikvision both have multiple streams with configurable resolutions and frame rates and rock solid streams. They also both have models with large sensors well known for excellent image quality at night. Not all the models are equal. Larger sensors are better than higher resolutions; especially at night. Amcrest is the fallback recommendation because they are rebranded Dahuas. They are rebranding the lower end models with smaller sensors or less configuration options.
| Name | Inference Speed | Notes |
| ----------------------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| Atomic Pi | 16ms | Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding. |
| Intel NUC NUC7i3BNK | 8-10ms | Great performance. Can handle many cameras at 5fps depending on typical amounts of motion. |
| BMAX B2 Plus | 10-12ms | Good balance of performance and cost. Also capable of running many other services at the same time as frigate. |
| Minisforum GK41 | 9-10ms | Great alternative to a NUC with dual Gigabit NICs. Easily handles several 1080p cameras. |
| Raspberry Pi 3B (32bit) | 60ms | Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0. |
| Raspberry Pi 4 (32bit) | 15-20ms | Can handle a small number of cameras. The 2GB version runs fine. |
| Raspberry Pi 4 (64bit) | 10-15ms | Can handle a small number of cameras. The 2GB version runs fine. |
Many users have reported various issues with Reolink cameras, so I do not recommend them. If you are using Reolink, I suggest the [Reolink specific configuration](configuration/camera_specific#reolink-410520-possibly-others). Wifi cameras are also not recommended. Their streams are less reliable and cause connection loss and/or lost video data.
## Unraid
Here are some of the camera's I recommend:
Many people have powerful enough NAS devices or home servers to also run docker. There is a Unraid Community App.
To install make sure you have the [community app plugin here](https://forums.unraid.net/topic/38582-plug-in-community-applications/). Then search for "Frigate" in the apps section within Unraid - you can see the online store [here](https://unraid.net/community/apps?q=frigate#r)
- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
| Name | Inference Speed | Notes |
| ------------------------------------ | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [M2 Coral Edge TPU](http://coral.ai) | 6.2ms | Install the Coral plugin from Unraid Community App Center [info here](https://forums.unraid.net/topic/98064-support-blakeblackshear-frigate/?do=findComment&comment=949789) |
I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
## Server
My current favorite is the Odyssey X86 Blue J4125 because the Coral M.2 compatibility and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
| Name | Inference Speed | Coral Compatibility | Notes |
| -------------------------------------------------------------------------------------------------------------------------------- | --------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| <a href="https://amzn.to/3oH4BKi" target="_blank" rel="nofollow noopener sponsored">Odyssey X86 Blue J4125</a> (affiliate link) | 9-10ms | M.2 B+M | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
| <a href="https://amzn.to/3oxEC8m" target="_blank" rel="nofollow noopener sponsored">Minisforum GK41</a> (affiliate link) | 9-10ms | USB | Great alternative to a NUC. Easily handles several 1080p cameras. |
| <a href="https://amzn.to/3ixJFlb" target="_blank" rel="nofollow noopener sponsored">Minisforum GK50</a> (affiliate link) | 9-10ms | USB | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
| <a href="https://amzn.to/3l7vCEI" target="_blank" rel="nofollow noopener sponsored">Intel NUC</a> (affiliate link) | 8-10ms | USB | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion. |
| <a href="https://amzn.to/3a6TBh8" target="_blank" rel="nofollow noopener sponsored">BMAX B2 Plus</a> (affiliate link) | 10-12ms | USB | Good balance of performance and cost. Also capable of running many other services at the same time as frigate. |
| <a href="https://amzn.to/2YjpY9m" target="_blank" rel="nofollow noopener sponsored">Atomic Pi</a> (affiliate link) | 16ms | USB | Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding. |
| <a href="https://amzn.to/2WIpwRU" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 3B (32bit)</a> (affiliate link) | 60ms | USB | Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0. |
| <a href="https://amzn.to/2YhSGHH" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 4 (32bit)</a> (affiliate link) | 15-20ms | USB | Can handle a small number of cameras. The 2GB version runs fine. |
| <a href="https://amzn.to/2YhSGHH" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 4 (64bit)</a> (affiliate link) | 10-15ms | USB | Can handle a small number of cameras. The 2GB version runs fine. |
## Google Coral TPU
It is strongly recommended to use a Google Coral. Frigate is designed around the expectation that a Coral is used to achieve very low inference speeds. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU. Frigate should work with any supported Coral device from https://coral.ai
The USB version is compatible with the widest variety of hardware and does not require a driver on the host machine. However, it does lack the automatic throttling features of the other versions.
The PCIe and M.2 versions require installation of a driver on the host. Follow the instructions for your version from https://coral.ai
A single Coral can handle many cameras and will be sufficient for the majority of users. You can calculate the maximum performance of your Coral based on the inference speed reported by Frigate. With an inference speed of 10, your Coral will top out at `1000/10=100`, or 100 frames per second. If your detection fps is regularly getting close to that, you should first consider tuning motion masks. If those are already properly configured, a second Coral may be needed.

13
docs/docs/how-it-works.md
View File

@@ -1,13 +0,0 @@
---
id: how-it-works
title: How Frigate Works
sidebar_label: How it works
---
Frigate is designed to minimize resource and maximize performance by only looking for objects when and where it is necessary
![Diagram](/img/diagram.png)
1. Look for Motion
2. Calculate Detection Regions
3. Run Object Detection

12
docs/docs/index.md
View File

@@ -1,13 +1,12 @@
---
id: index
title: Frigate
sidebar_label: Features
title: Introduction
slug: /
---
A complete and local NVR designed for Home Assistant 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.
Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but strongly recommended. CPU detection should only be used for testing purposes. 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
@@ -15,11 +14,12 @@ Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but
- 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
- 24/7 recording
- Recording with retention based on detected objects
- Re-streaming via RTMP to reduce the number of connections to your camera
- A dynamic combined camera view of all tracked cameras.
## Screenshots
![Media Browser](/img/media_browser.png)
![Media Browser](/img/media_browser-min.png)
![Notification](/img/notification.png)
![Notification](/img/notification-min.png)

125
docs/docs/installation.md
View File

@@ -3,25 +3,44 @@ id: installation
title: Installation
---
Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). See instructions below for installing the HassOS addon.
Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). Note that a Home Assistant Addon is **not** the same thing as the integration. The [integration](integrations/home-assistant) is required to integrate Frigate into Home Assistant.
For Home Assistant users, there is also a [custom component (aka integration)](https://github.com/blakeblackshear/frigate-hass-integration). This custom component adds tighter integration with Home Assistant by automatically setting up camera entities, sensors, media browser for clips and recordings, and a public API to simplify notifications.
## Dependencies
Note that HassOS Addons and custom components are different things. If you are already running Frigate with Docker directly, you do not need the Addon since the Addon would run another instance of Frigate.
**MQTT broker** - Frigate requires an MQTT broker. If using Home Assistant, Frigate and Home Assistant must be connected to the same MQTT broker.
## HassOS Addon
## Preparing your hardware
HassOS users can install via the addon repository. Frigate requires an MQTT server.
### Operating System
1. Navigate to Supervisor > Add-on Store > Repositories
2. Add https://github.com/blakeblackshear/frigate-hass-addons
3. Setup your network configuration in the `Configuration` tab if deisred
4. Create the file `frigate.yml` in your `config` directory with your detailed Frigate configuration
5. Start the addon container
6. If you are using hardware acceleration for ffmpeg, you will need to disable "Protection mode"
Frigate runs best with docker installed on bare metal debian-based distributions. For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer often introduces a sizable amount of overhead for communication with Coral devices, but [not in all circumstances](https://github.com/blakeblackshear/frigate/discussions/1837).
Windows is not officially supported, but some users have had success getting it to run under WSL or Virtualbox. Getting the GPU and/or Coral devices properly passed to Frigate may be difficult or impossible. Search previous discussions or issues for help.
### Calculating required shm-size
Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is 64m.
The default shm-size of 64m is fine for setups with 2 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size.
You can calculate the necessary shm-size for each camera with the following formula:
```
(width * height * 1.5 * 9 + 270480)/1048576 = <shm size in mb>
```
The shm size cannot be set per container for Home Assistant Addons. You must set `default-shm-size` in `/etc/docker/daemon.json` to increase the default shm size. This will increase the shm size for all of your docker containers. This may or may not cause issues with your setup. https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file
### Raspberry Pi 3/4
By default, the Raspberry Pi limits the amount of memory available to the GPU. In order to use ffmpeg hardware acceleration, you must increase the available memory by setting `gpu_mem` to the maximum recommended value in `config.txt` as described in the [official docs](https://www.raspberrypi.org/documentation/computers/config_txt.html#memory-options).
Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with it's own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
## Docker
Running in Docker directly is the recommended install method.
Make sure you choose the right image for your architecture:
| Arch | Image Name |
@@ -41,13 +60,14 @@ services:
privileged: true # this may not be necessary for all setups
restart: unless-stopped
image: blakeblackshear/frigate:<specify_version_tag>
shm_size: "64mb" # update for your cameras based on calculation above
devices:
- /dev/bus/usb:/dev/bus/usb
- /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
- /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
volumes:
- /etc/localtime:/etc/localtime:ro
- <path_to_config_file>:/config/config.yml:ro
- <path_to_directory_for_media>:/media/frigate
- /path/to/your/config.yml:/config/config.yml:ro
- /path/to/your/storage:/media/frigate
- type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
target: /tmp/cache
tmpfs:
@@ -68,8 +88,9 @@ docker run -d \
--mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
--device /dev/bus/usb:/dev/bus/usb \
--device /dev/dri/renderD128 \
-v <path_to_directory_for_media>:/media/frigate \
-v <path_to_config_file>:/config/config.yml:ro \
--shm-size=64m \
-v /path/to/your/storage:/media/frigate \
-v /path/to/your/config.yml:/config/config.yml:ro \
-v /etc/localtime:/etc/localtime:ro \
-e FRIGATE_RTSP_PASSWORD='password' \
-p 5000:5000 \
@@ -77,48 +98,62 @@ docker run -d \
blakeblackshear/frigate:<specify_version_tag>
```
### Calculating shm-size
## Home Assistant Operating System (HassOS)
The default shm-size of 64m is fine for setups with 3 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it could be because you have too many high resolution cameras and you need to specify a higher shm size.
:::caution
You can calculate the necessary shm-size for each camera with the following formula:
Due to limitations in Home Assistant Operating System, Frigate cannot utilize external storage for recordings or snapshots.
```
(width * height * 1.5 * 7 + 270480)/1048576 = <shm size in mb>
:::
:::tip
If possible, it is recommended to run Frigate standalone in Docker and use [Frigate's Proxy Addon](https://github.com/blakeblackshear/frigate-hass-addons/blob/main/frigate_proxy/README.md).
:::
HassOS users can install via the addon repository.
1. Navigate to Supervisor > Add-on Store > Repositories
2. Add https://github.com/blakeblackshear/frigate-hass-addons
3. Install your desired Frigate NVR Addon and navigate to it's page
4. Setup your network configuration in the `Configuration` tab
5. (not for proxy addon) Create the file `frigate.yml` in your `config` directory with your detailed Frigate configuration
6. Start the addon container
7. (not for proxy addon) If you are using hardware acceleration for ffmpeg, you may need to disable "Protection mode"
## Home Assistant Supervised
:::tip
If possible, it is recommended to run Frigate standalone in Docker and use [Frigate's Proxy Addon](https://github.com/blakeblackshear/frigate-hass-addons/blob/main/frigate_proxy/README.md).
:::
When running Home Assistant with the [Supervised install method](https://github.com/home-assistant/supervised-installer), you can get the benefit of running the Addon along with the ability to customize the storage used by Frigate.
In order to customize the storage location for Frigate, simply use `fstab` to mount the drive you want at `/usr/share/hassio/media`. Here is an example fstab entry:
```shell
UUID=1a65fec6-c25f-404a-b3d2-1f2fcf6095c8 /media/data ext4 defaults 0 0
/media/data/homeassistant/media /usr/share/hassio/media none bind 0 0
```
The shm size cannot be set per container for Home Assistant Addons. You must set `default-shm-size` in `/etc/docker/daemon.json` to increase the default shm size. This will increase the shm size for all of your docker containers. This may or may not cause issues with your setup. https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file
Then follow the instructions listed for [Home Assistant Operating System](#home-assistant-operating-system-hassos).
## Kubernetes
Use the [helm chart](https://github.com/blakeblackshear/blakeshome-charts/tree/master/charts/frigate).
## Virtualization
## Unraid
For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices for ffmpeg decoding. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer typically introduces a sizable amount of overhead for communication with Coral devices.
Many people have powerful enough NAS devices or home servers to also run docker. There is a Unraid Community App.
To install make sure you have the [community app plugin here](https://forums.unraid.net/topic/38582-plug-in-community-applications/). Then search for "Frigate" in the apps section within Unraid - you can see the online store [here](https://unraid.net/community/apps?q=frigate#r)
### Proxmox
## Proxmox
Some people have had success running Frigate in LXC directly with the following config:
It is recommended to run Frigate in LXC for maximum performance. See [this discussion](https://github.com/blakeblackshear/frigate/discussions/1111) for more information.
```
arch: amd64
cores: 2
features: nesting=1
hostname: FrigateLXC
memory: 4096
net0: name=eth0,bridge=vmbr0,firewall=1,hwaddr=2E:76:AE:5A:58:48,ip=dhcp,ip6=auto,type=veth
ostype: debian
rootfs: local-lvm:vm-115-disk-0,size=12G
swap: 512
lxc.cgroup.devices.allow: c 189:385 rwm
lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file
lxc.mount.entry: /dev/bus/usb/004/002 dev/bus/usb/004/002 none bind,optional,create=file
lxc.apparmor.profile: unconfined
lxc.cgroup.devices.allow: a
lxc.cap.drop:
```
### ESX
## ESX
For details on running Frigate under ESX, see details [here](https://github.com/blakeblackshear/frigate/issues/305).

55
docs/docs/usage/api.md → docs/docs/integrations/api.md
View File

@@ -5,7 +5,7 @@ title: HTTP API
A web server is available on port 5000 with the following endpoints.
### `/api/<camera_name>`
### `GET /api/<camera_name>`
An mjpeg stream for debugging. Keep in mind the mjpeg endpoint is for debugging only and will put additional load on the system when in use.
@@ -24,7 +24,7 @@ Accepts the following query string parameters:
You can access a higher resolution mjpeg stream by appending `h=height-in-pixels` to the endpoint. For example `http://localhost:5000/api/back?h=1080`. You can also increase the FPS by appending `fps=frame-rate` to the URL such as `http://localhost:5000/api/back?fps=10` or both with `?fps=10&h=1000`.
### `/api/<camera_name>/<object_name>/best.jpg[?h=300&crop=1]`
### `GET /api/<camera_name>/<object_name>/best.jpg[?h=300&crop=1&quality=70]`
The best snapshot for any object type. It is a full resolution image by default.
@@ -32,8 +32,9 @@ Example parameters:
- `h=300`: resizes the image to 300 pixes tall
- `crop=1`: crops the image to the region of the detection rather than returning the entire image
- `quality=70`: sets the jpeg encoding quality (0-100)
### `/api/<camera_name>/latest.jpg[?h=300]`
### `GET /api/<camera_name>/latest.jpg[?h=300]`
The most recent frame that frigate has finished processing. It is a full resolution image by default.
@@ -48,12 +49,13 @@ Accepts the following query string parameters:
| `mask` | int | Overlay the mask on the image (0 or 1) |
| `motion` | int | Draw blue boxes for areas with detected motion (0 or 1) |
| `regions` | int | Draw green boxes for areas where object detection was run (0 or 1) |
| `quality` | int | Jpeg encoding quality (0-100). Defaults to 70. |
Example parameters:
- `h=300`: resizes the image to 300 pixes tall
### `/api/stats`
### `GET /api/stats`
Contains some granular debug info that can be used for sensors in Home Assistant.
@@ -150,15 +152,15 @@ Sample response:
}
```
### `/api/config`
### `GET /api/config`
A json representation of your configuration
### `/api/version`
### `GET /api/version`
Version info
### `/api/events`
### `GET /api/events`
Events from the database. Accepts the following query string parameters:
@@ -174,19 +176,27 @@ Events from the database. Accepts the following query string parameters:
| `has_clip` | int | Filter to events that have clips (0 or 1) |
| `include_thumbnails` | int | Include thumbnails in the response (0 or 1) |
### `/api/events/summary`
### `GET /api/events/summary`
Returns summary data for events in the database. Used by the Home Assistant integration.
### `/api/events/<id>`
### `GET /api/events/<id>`
Returns data for a single event.
### `/api/events/<id>/thumbnail.jpg`
### `DELETE /api/events/<id>`
Permanently deletes the event along with any clips/snapshots.
### `GET /api/events/<id>/thumbnail.jpg`
Returns a thumbnail for the event id optimized for notifications. Works while the event is in progress and after completion. Passing `?format=android` will convert the thumbnail to 2:1 aspect ratio.
### `/api/events/<id>/snapshot.jpg`
### `GET /api/events/<id>/clip.mp4`
Returns the clip for the event id. Works after the event has ended.
### `GET /api/events/<id>/snapshot.jpg`
Returns the snapshot image for the event id. Works while the event is in progress and after completion.
@@ -198,11 +208,24 @@ Accepts the following query string parameters, but they are only applied when an
| `bbox` | int | Show bounding boxes for detected objects (0 or 1) |
| `timestamp` | int | Print the timestamp in the upper left (0 or 1) |
| `crop` | int | Crop the snapshot to the (0 or 1) |
| `quality` | int | Jpeg encoding quality (0-100). Defaults to 70. |
### `/clips/<camera>-<id>.mp4`
Video clip for the given camera and event id.
### `/clips/<camera>-<id>.jpg`
### `GET /clips/<camera>-<id>.jpg`
JPG snapshot for the given camera and event id.
### `GET /vod/<year>-<month>/<day>/<hour>/<camera>/master.m3u8`
HTTP Live Streaming Video on Demand URL for the specified hour and camera. Can be viewed in an application like VLC.
### `GET /vod/event/<event-id>/index.m3u8`
HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed in an application like VLC.
### `GET /vod/event/<event-id>/index.m3u8`
HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed in an application like VLC.
### `GET /vod/<camera>/start/<start-timestamp>/end/<end-timestamp>/index.m3u8`
HTTP Live Streaming Video on Demand URL for the camera with the specified time range. Can be viewed in an application like VLC.

124
docs/docs/usage/home-assistant.md → docs/docs/integrations/home-assistant.md
View File

@@ -1,7 +1,6 @@
---
id: home-assistant
title: Integration with Home Assistant
sidebar_label: Home Assistant
title: Home Assistant Integration
---
The best way to integrate with Home Assistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration).
@@ -70,138 +69,41 @@ Home Assistant > Configuration > Integrations > Frigate > Options
| --------------- | --------------------------------------------------------------------------------- |
| `camera` | Live camera stream (requires RTMP), camera for image of the last detected object. |
| `sensor` | States to monitor Frigate performance, object counts for all zones and cameras. |
| `switch` | Switch entities to toggle detection, clips and snapshots. |
| `switch` | Switch entities to toggle detection, recordings and snapshots. |
| `binary_sensor` | A "motion" binary sensor entity per camera/zone/object. |
## Media Browser Support
The integration provides:
- Rich UI with thumbnails for browsing event clips
- Rich UI for browsing 24/7 recordings by month, day, camera, time
- Browsing event recordings with thumbnails
- Browsing snapshots
- Browsing recordings by month, day, camera, time
This is accessible via "Media Browser" on the left menu panel in Home Assistant.
<a name="api"></a>
## API
## Notification API
- Notification API with public facing endpoints for images in notifications
Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.
### Notifications
Frigate publishes event information in the form of a change feed via MQTT. This
allows lots of customization for notifications to meet your needs. Event changes
are published with `before` and `after` information as shown
[here](#frigateevents). Note that some people may not want to expose frigate to
the web, so you can leverage the HA API that frigate custom_integration ties
into (which is exposed to the web, and thus can be used for mobile notifications
etc):
To load an image taken by frigate from Home Assistants API see below:
To load a thumbnail for an event:
```
https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
```
To load a video clip taken by frigate from Home Assistants API :
To load a snapshot for an event:
```
https://HA_URL/api/frigate/notifications/<event-id>/<camera>/clip.mp4
https://HA_URL/api/frigate/notifications/<event-id>/snapshot.jpg
```
Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as frigate finds a "better" image.
To load a video clip of an event:
```yaml
automation:
- alias: Notify of events
trigger:
platform: mqtt
topic: frigate/events
action:
- service: notify.mobile_app_pixel_3
data_template:
message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
data:
image: 'https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg?format=android'
tag: '{{trigger.payload_json["after"]["id"]}}'
when: '{{trigger.payload_json["after"]["start_time"]|int}}'
```
```yaml
automation:
- alias: When a person enters a zone named yard
trigger:
platform: mqtt
topic: frigate/events
condition:
- "{{ trigger.payload_json['after']['label'] == 'person' }}"
- "{{ 'yard' in trigger.payload_json['after']['entered_zones'] }}"
action:
- service: notify.mobile_app_pixel_3
data_template:
message: "A {{trigger.payload_json['after']['label']}} has entered the yard."
data:
image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
tag: "{{trigger.payload_json['after']['id']}}"
when: '{{trigger.payload_json["after"]["start_time"]|int}}'
```
```yaml
- alias: When a person leaves a zone named yard
trigger:
platform: mqtt
topic: frigate/events
condition:
- "{{ trigger.payload_json['after']['label'] == 'person' }}"
- "{{ 'yard' in trigger.payload_json['before']['current_zones'] }}"
- "{{ not 'yard' in trigger.payload_json['after']['current_zones'] }}"
action:
- service: notify.mobile_app_pixel_3
data_template:
message: "A {{trigger.payload_json['after']['label']}} has left the yard."
data:
image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
tag: "{{trigger.payload_json['after']['id']}}"
when: '{{trigger.payload_json["after"]["start_time"]|int}}'
```
```yaml
- alias: Notify for dogs in the front with a high top score
trigger:
platform: mqtt
topic: frigate/events
condition:
- "{{ trigger.payload_json['after']['label'] == 'dog' }}"
- "{{ trigger.payload_json['after']['camera'] == 'front' }}"
- "{{ trigger.payload_json['after']['top_score'] > 0.98 }}"
action:
- service: notify.mobile_app_pixel_3
data_template:
message: "High confidence dog detection."
data:
image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
tag: "{{trigger.payload_json['after']['id']}}"
when: '{{trigger.payload_json["after"]["start_time"]|int}}'
```
If you are using telegram, you can fetch the image directly from Frigate:
```yaml
automation:
- alias: Notify of events
trigger:
platform: mqtt
topic: frigate/events
action:
- service: notify.telegram_full
data_template:
message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
data:
photo:
# this url should work for addon users
- url: 'http://ccab4aaf-frigate:5000/api/events/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg'
caption: 'A {{trigger.payload_json["after"]["label"]}} was detected on {{ trigger.payload_json["after"]["camera"] }} camera'
https://HA_URL/api/frigate/notifications/<event-id>/clip.mp4
```
<a name="streams"></a>
@@ -282,6 +184,6 @@ which server they are referring to.
## FAQ
### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
#### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
The [HomeKit integration](https://www.home-assistant.io/integrations/homekit/) randomly links one of the binary sensors (motion sensor entities) grouped with the camera device in Home Assistant. You can specify a `linked_motion_sensor` in the Home Assistant [HomeKit configuration](https://www.home-assistant.io/integrations/homekit/#linked_motion_sensor) for each camera.

24
docs/docs/usage/mqtt.md → docs/docs/integrations/mqtt.md
View File

@@ -11,6 +11,10 @@ Designed to be used as an availability topic with Home Assistant. Possible messa
"online": published when frigate is running (on startup)
"offline": published right before frigate stops
### `frigate/restart`
Causes frigate to exit. Docker should be configured to automatically restart the container on exit.
### `frigate/<camera_name>/<object_name>`
Publishes the count of objects for the camera for use as a sensor in Home Assistant.
@@ -32,11 +36,12 @@ Message published for each changed event. The first message is published when th
```json
{
"type": "update", // new, update, or end
"type": "update", // new, update, end
"before": {
"id": "1607123955.475377-mxklsc",
"camera": "front_door",
"frame_time": 1607123961.837752,
"snapshot_time": 1607123961.837752,
"label": "person",
"top_score": 0.958984375,
"false_positive": false,
@@ -48,12 +53,15 @@ Message published for each changed event. The first message is published when th
"region": [264, 450, 667, 853],
"current_zones": ["driveway"],
"entered_zones": ["yard", "driveway"],
"thumbnail": null
"thumbnail": null,
"has_snapshot": false,
"has_clip": false
},
"after": {
"id": "1607123955.475377-mxklsc",
"camera": "front_door",
"frame_time": 1607123962.082975,
"snapshot_time": 1607123961.837752,
"label": "person",
"top_score": 0.958984375,
"false_positive": false,
@@ -65,7 +73,9 @@ Message published for each changed event. The first message is published when th
"region": [218, 440, 693, 915],
"current_zones": ["yard", "driveway"],
"entered_zones": ["yard", "driveway"],
"thumbnail": null
"thumbnail": null,
"has_snapshot": false,
"has_clip": false
}
}
```
@@ -82,13 +92,13 @@ Topic to turn detection for a camera on and off. Expected values are `ON` and `O
Topic with current state of detection for a camera. Published values are `ON` and `OFF`.
### `frigate/<camera_name>/clips/set`
### `frigate/<camera_name>/recordings/set`
Topic to turn clips for a camera on and off. Expected values are `ON` and `OFF`.
Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`.
### `frigate/<camera_name>/clips/state`
### `frigate/<camera_name>/recordings/state`
Topic with current state of clips for a camera. Published values are `ON` and `OFF`.
Topic with current state of recordings for a camera. Published values are `ON` and `OFF`.
### `frigate/<camera_name>/snapshots/set`

28
docs/docs/troubleshooting.md
View File

@@ -1,28 +0,0 @@
---
id: troubleshooting
title: Troubleshooting and FAQ
---
### How can I get sound or audio in my clips and recordings?
By default, Frigate removes audio from clips and recordings to reduce the likelihood of failing for invalid data. If you would like to include audio, you need to override the output args to remove `-an` for where you want to include audio. The recommended audio codec is `aac`. Not all audio codecs are supported by RTMP, so you may need to re-encode your audio with `-c:a aac`. The default ffmpeg args are shown [here](/frigate/configuration/index#ffmpeg).
### My mjpeg stream or snapshots look green and crazy
This almost always means that the width/height defined for your camera are not correct. Double check the resolution with vlc or another player. Also make sure you don't have the width and height values backwards.
![mismatched-resolution](/img/mismatched-resolution.jpg)
### I have clips and snapshots in my clips folder, but I can't view them in the Web UI.
This is usually caused one of two things:
- The permissions on the parent folder don't have execute and nginx returns a 403 error you can see in the browser logs
- In this case, try mounting a volume to `/media/frigate` inside the container instead of `/media/frigate/clips`.
- Your cameras do not send h264 encoded video and the mp4 files are not playable in the browser
### "[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5639eeb6e140] moov atom not found"
These messages in the logs are expected in certain situations. Frigate checks the integrity of the video cache before assembling clips. Occasionally these cached files will be invalid and cleaned up automatically.
### "On connect called"
If you see repeated "On connect called" messages in your config, check for another instance of frigate. This happens when multiple frigate containers are trying to connect to mqtt with the same client_id.

11
docs/docs/usage/howtos.md
View File

@@ -1,11 +0,0 @@
---
id: howtos
title: Community Guides
sidebar_label: Community Guides
---
## Communitiy Guides/How-To's
- Best Camera AI Person & Object Detection - How to Setup Frigate w/ Home Assistant - digiblurDIY [YouTube](https://youtu.be/V8vGdoYO6-Y) - [Article](https://www.digiblur.com/2021/05/how-to-setup-frigate-home-assistant.html)
- Even More Free Local Object Detection with Home Assistant - Frigate Install - Everything Smart Home [YouTube](https://youtu.be/pqDCEZSVeRk)
- Home Assistant Frigate integration for local image recognition - KPeyanski [YouTube](https://youtu.be/Q2UT78lFQpo) - [Article](https://peyanski.com/home-assistant-frigate-integration/)

10
docs/docs/usage/web.md
View File

@@ -1,10 +0,0 @@
---
id: web
title: Web Interface
---
Frigate comes bundled with a simple web ui that supports the following:
- Show cameras
- Browse events
- Mask helper

4
docs/docusaurus.config.js
View File

@@ -3,8 +3,8 @@ const path = require('path');
module.exports = {
title: 'Frigate',
tagline: 'NVR With Realtime Object Detection for IP Cameras',
url: 'https://blakeblackshear.github.io',
baseUrl: '/frigate/',
url: 'https://docs.frigate.video',
baseUrl: '/',
onBrokenLinks: 'throw',
onBrokenMarkdownLinks: 'warn',
favicon: 'img/favicon.ico',

9129
docs/package-lock.json generated
View File

File diff suppressed because it is too large Load Diff

4
docs/package.json
View File

@@ -12,8 +12,8 @@
"clear": "docusaurus clear"
},
"dependencies": {
"@docusaurus/core": "2.0.0-alpha.70",
"@docusaurus/preset-classic": "2.0.0-alpha.70",
"@docusaurus/core": "^2.0.0-beta.ff31de0ff",
"@docusaurus/preset-classic": "^2.0.0-beta.ff31de0ff",
"@mdx-js/react": "^1.6.21",
"clsx": "^1.1.1",
"raw-loader": "^4.0.2",

28
docs/sidebars.js
View File

@@ -1,16 +1,34 @@
module.exports = {
docs: {
Frigate: ['index', 'how-it-works', 'hardware', 'installation', 'troubleshooting'],
Frigate: [
'index',
'hardware',
'installation',
],
Guides: [
'guides/camera_setup',
'guides/getting_started',
'guides/false_positives',
'guides/ha_notifications',
'guides/stationary_objects',
],
Configuration: [
'configuration/index',
'configuration/cameras',
'configuration/optimizing',
'configuration/detectors',
'configuration/false_positives',
'configuration/cameras',
'configuration/masks',
'configuration/record',
'configuration/snapshots',
'configuration/objects',
'configuration/rtmp',
'configuration/zones',
'configuration/advanced',
'configuration/hardware_acceleration',
'configuration/nvdec',
'configuration/camera_specific',
],
Usage: ['usage/home-assistant', 'usage/web', 'usage/api', 'usage/mqtt'],
Integrations: ['integrations/home-assistant', 'integrations/api', 'integrations/mqtt'],
Troubleshooting: ['faqs'],
Development: ['contributing'],
},
};

BIN
docs/static/img/driveway_zones-min.png vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 195 KiB

BIN
docs/static/img/driveway_zones.png vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 1.6 MiB

BIN
docs/static/img/example-mask-poly-min.png vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 113 KiB

BIN
docs/static/img/media_browser-min.png vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 133 KiB

BIN
docs/static/img/mismatched-resolution-min.jpg vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 24 KiB

BIN
docs/static/img/notification-min.png vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 98 KiB

BIN
docs/static/img/reolink-settings.png vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 13 KiB

BIN
docs/static/img/resolutions-min.jpg vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 48 KiB

BIN
docs/static/img/resolutions.png vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 65 KiB

8
frigate/__main__.py
View File

@@ -1,4 +1,6 @@
import faulthandler; faulthandler.enable()
import faulthandler
faulthandler.enable()
import sys
import threading
@@ -6,10 +8,10 @@ threading.current_thread().name = "frigate"
from frigate.app import FrigateApp
cli = sys.modules['flask.cli']
cli = sys.modules["flask.cli"]
cli.show_server_banner = lambda *x: None
if __name__ == '__main__':
if __name__ == "__main__":
frigate_app = FrigateApp()
frigate_app.start()

279
frigate/app.py
View File

@@ -2,38 +2,40 @@ import json
import logging
import multiprocessing as mp
import os
import signal
import sys
import threading
from logging.handlers import QueueHandler
from typing import Dict, List
import sys
import signal
import yaml
from gevent import pywsgi
from geventwebsocket.handler import WebSocketHandler
from peewee_migrate import Router
from playhouse.sqlite_ext import SqliteExtDatabase
from playhouse.sqliteq import SqliteQueueDatabase
from frigate.config import FrigateConfig
from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
from frigate.config import DetectorTypeEnum, FrigateConfig
from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
from frigate.edgetpu import EdgeTPUProcess
from frigate.events import EventProcessor, EventCleanup
from frigate.events import EventCleanup, EventProcessor
from frigate.http import create_app
from frigate.log import log_process, root_configurer
from frigate.models import Event
from frigate.mqtt import create_mqtt_client
from frigate.models import Event, Recordings
from frigate.mqtt import MqttSocketRelay, create_mqtt_client
from frigate.object_processing import TrackedObjectProcessor
from frigate.record import RecordingMaintainer
from frigate.output import output_frames
from frigate.record import RecordingCleanup, RecordingMaintainer
from frigate.stats import StatsEmitter, stats_init
from frigate.version import VERSION
from frigate.video import capture_camera, track_camera
from frigate.watchdog import FrigateWatchdog
from frigate.zeroconf import broadcast_zeroconf
logger = logging.getLogger(__name__)
class FrigateApp():
class FrigateApp:
def __init__(self):
self.stop_event = mp.Event()
self.base_config: FrigateConfig = None
self.config: FrigateConfig = None
self.detection_queue = mp.Queue()
self.detectors: Dict[str, EdgeTPUProcess] = {}
@@ -54,148 +56,257 @@ class FrigateApp():
else:
logger.debug(f"Skipping directory: {d}")
tmpfs_size = self.config.clips.tmpfs_cache_size
if tmpfs_size:
logger.info(f"Creating tmpfs of size {tmpfs_size}")
rc = os.system(f"mount -t tmpfs -o size={tmpfs_size} tmpfs {CACHE_DIR}")
if rc != 0:
logger.error(f"Failed to create tmpfs, error code: {rc}")
def init_logger(self):
self.log_process = mp.Process(target=log_process, args=(self.log_queue,), name='log_process')
self.log_process = mp.Process(
target=log_process, args=(self.log_queue,), name="log_process"
)
self.log_process.daemon = True
self.log_process.start()
root_configurer(self.log_queue)
def init_config(self):
config_file = os.environ.get('CONFIG_FILE', '/config/config.yml')
self.config = FrigateConfig(config_file=config_file)
config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
user_config = FrigateConfig.parse_file(config_file)
self.config = user_config.runtime_config
for camera_name in self.config.cameras.keys():
# create camera_metrics
self.camera_metrics[camera_name] = {
'camera_fps': mp.Value('d', 0.0),
'skipped_fps': mp.Value('d', 0.0),
'process_fps': mp.Value('d', 0.0),
'detection_enabled': mp.Value('i', self.config.cameras[camera_name].detect.enabled),
'detection_fps': mp.Value('d', 0.0),
'detection_frame': mp.Value('d', 0.0),
'read_start': mp.Value('d', 0.0),
'ffmpeg_pid': mp.Value('i', 0),
'frame_queue': mp.Queue(maxsize=2),
"camera_fps": mp.Value("d", 0.0),
"skipped_fps": mp.Value("d", 0.0),
"process_fps": mp.Value("d", 0.0),
"detection_enabled": mp.Value(
"i", self.config.cameras[camera_name].detect.enabled
),
"detection_fps": mp.Value("d", 0.0),
"detection_frame": mp.Value("d", 0.0),
"read_start": mp.Value("d", 0.0),
"ffmpeg_pid": mp.Value("i", 0),
"frame_queue": mp.Queue(maxsize=2),
}
def check_config(self):
for name, camera in self.config.cameras.items():
assigned_roles = list(set([r for i in camera.ffmpeg.inputs for r in i.roles]))
if not camera.clips.enabled and 'clips' in assigned_roles:
logger.warning(f"Camera {name} has clips assigned to an input, but clips is not enabled.")
elif camera.clips.enabled and not 'clips' in assigned_roles:
logger.warning(f"Camera {name} has clips enabled, but clips is not assigned to an input.")
assigned_roles = list(
set([r for i in camera.ffmpeg.inputs for r in i.roles])
)
if not camera.record.enabled and "record" in assigned_roles:
logger.warning(
f"Camera {name} has record assigned to an input, but record is not enabled."
)
elif camera.record.enabled and not "record" in assigned_roles:
logger.warning(
f"Camera {name} has record enabled, but record is not assigned to an input."
)
if not camera.record.enabled and 'record' in assigned_roles:
logger.warning(f"Camera {name} has record assigned to an input, but record is not enabled.")
elif camera.record.enabled and not 'record' in assigned_roles:
logger.warning(f"Camera {name} has record enabled, but record is not assigned to an input.")
if not camera.rtmp.enabled and "rtmp" in assigned_roles:
logger.warning(
f"Camera {name} has rtmp assigned to an input, but rtmp is not enabled."
)
elif camera.rtmp.enabled and not "rtmp" in assigned_roles:
logger.warning(
f"Camera {name} has rtmp enabled, but rtmp is not assigned to an input."
)
if not camera.rtmp.enabled and 'rtmp' in assigned_roles:
logger.warning(f"Camera {name} has rtmp assigned to an input, but rtmp is not enabled.")
elif camera.rtmp.enabled and not 'rtmp' in assigned_roles:
logger.warning(f"Camera {name} has rtmp enabled, but rtmp is not assigned to an input.")
def set_log_levels(self):
logging.getLogger().setLevel(self.config.logger.default)
logging.getLogger().setLevel(self.config.logger.default.value.upper())
for log, level in self.config.logger.logs.items():
logging.getLogger(log).setLevel(level)
if not 'geventwebsocket.handler' in self.config.logger.logs:
logging.getLogger('geventwebsocket.handler').setLevel('ERROR')
logging.getLogger(log).setLevel(level.value.upper())
if not "werkzeug" in self.config.logger.logs:
logging.getLogger("werkzeug").setLevel("ERROR")
def init_queues(self):
# Queues for clip processing
self.event_queue = mp.Queue()
self.event_processed_queue = mp.Queue()
self.video_output_queue = mp.Queue(maxsize=len(self.config.cameras.keys()) * 2)
# Queue for cameras to push tracked objects to
self.detected_frames_queue = mp.Queue(maxsize=len(self.config.cameras.keys())*2)
self.detected_frames_queue = mp.Queue(
maxsize=len(self.config.cameras.keys()) * 2
)
def init_database(self):
# Migrate DB location
old_db_path = os.path.join(CLIPS_DIR, "frigate.db")
if not os.path.isfile(self.config.database.path) and os.path.isfile(
old_db_path
):
os.rename(old_db_path, self.config.database.path)
# Migrate DB schema
migrate_db = SqliteExtDatabase(self.config.database.path)
# Run migrations
del(logging.getLogger('peewee_migrate').handlers[:])
del logging.getLogger("peewee_migrate").handlers[:]
router = Router(migrate_db)
router.run()
migrate_db.close()
self.db = SqliteQueueDatabase(self.config.database.path)
models = [Event]
models = [Event, Recordings]
self.db.bind(models)
def init_stats(self):
self.stats_tracking = stats_init(self.camera_metrics, self.detectors)
def init_web_server(self):
self.flask_app = create_app(self.config, self.db, self.stats_tracking, self.detected_frames_processor, self.mqtt_client)
self.flask_app = create_app(
self.config,
self.db,
self.stats_tracking,
self.detected_frames_processor,
)
def init_mqtt(self):
self.mqtt_client = create_mqtt_client(self.config, self.camera_metrics)
def start_mqtt_relay(self):
self.mqtt_relay = MqttSocketRelay(
self.mqtt_client, self.config.mqtt.topic_prefix
)
self.mqtt_relay.start()
def start_detectors(self):
model_path = self.config.model.path
model_shape = (self.config.model.height, self.config.model.width)
for name in self.config.cameras.keys():
self.detection_out_events[name] = mp.Event()
shm_in = mp.shared_memory.SharedMemory(name=name, create=True, size=self.config.model.height*self.config.model.width*3)
shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}", create=True, size=20*6*4)
try:
shm_in = mp.shared_memory.SharedMemory(
name=name,
create=True,
size=self.config.model.height * self.config.model.width * 3,
)
except FileExistsError:
shm_in = mp.shared_memory.SharedMemory(name=name)
try:
shm_out = mp.shared_memory.SharedMemory(
name=f"out-{name}", create=True, size=20 * 6 * 4
)
except FileExistsError:
shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}")
self.detection_shms.append(shm_in)
self.detection_shms.append(shm_out)
for name, detector in self.config.detectors.items():
if detector.type == 'cpu':
self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, 'cpu', detector.num_threads)
if detector.type == 'edgetpu':
self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, detector.device, detector.num_threads)
if detector.type == DetectorTypeEnum.cpu:
self.detectors[name] = EdgeTPUProcess(
name,
self.detection_queue,
self.detection_out_events,
model_path,
model_shape,
"cpu",
detector.num_threads,
)
if detector.type == DetectorTypeEnum.edgetpu:
self.detectors[name] = EdgeTPUProcess(
name,
self.detection_queue,
self.detection_out_events,
model_path,
model_shape,
detector.device,
detector.num_threads,
)
def start_detected_frames_processor(self):
self.detected_frames_processor = TrackedObjectProcessor(self.config, self.mqtt_client, self.config.mqtt.topic_prefix,
self.detected_frames_queue, self.event_queue, self.event_processed_queue, self.stop_event)
self.detected_frames_processor = TrackedObjectProcessor(
self.config,
self.mqtt_client,
self.config.mqtt.topic_prefix,
self.detected_frames_queue,
self.event_queue,
self.event_processed_queue,
self.video_output_queue,
self.stop_event,
)
self.detected_frames_processor.start()
def start_video_output_processor(self):
output_processor = mp.Process(
target=output_frames,
name=f"output_processor",
args=(
self.config,
self.video_output_queue,
),
)
output_processor.daemon = True
self.output_processor = output_processor
output_processor.start()
logger.info(f"Output process started: {output_processor.pid}")
def start_camera_processors(self):
model_shape = (self.config.model.height, self.config.model.width)
for name, config in self.config.cameras.items():
camera_process = mp.Process(target=track_camera, name=f"camera_processor:{name}", args=(name, config, model_shape,
self.detection_queue, self.detection_out_events[name], self.detected_frames_queue,
self.camera_metrics[name]))
camera_process = mp.Process(
target=track_camera,
name=f"camera_processor:{name}",
args=(
name,
config,
model_shape,
self.config.model.merged_labelmap,
self.detection_queue,
self.detection_out_events[name],
self.detected_frames_queue,
self.camera_metrics[name],
),
)
camera_process.daemon = True
self.camera_metrics[name]['process'] = camera_process
self.camera_metrics[name]["process"] = camera_process
camera_process.start()
logger.info(f"Camera processor started for {name}: {camera_process.pid}")
def start_camera_capture_processes(self):
for name, config in self.config.cameras.items():
capture_process = mp.Process(target=capture_camera, name=f"camera_capture:{name}", args=(name, config,
self.camera_metrics[name]))
capture_process = mp.Process(
target=capture_camera,
name=f"camera_capture:{name}",
args=(name, config, self.camera_metrics[name]),
)
capture_process.daemon = True
self.camera_metrics[name]['capture_process'] = capture_process
self.camera_metrics[name]["capture_process"] = capture_process
capture_process.start()
logger.info(f"Capture process started for {name}: {capture_process.pid}")
def start_event_processor(self):
self.event_processor = EventProcessor(self.config, self.camera_metrics, self.event_queue, self.event_processed_queue, self.stop_event)
self.event_processor = EventProcessor(
self.config,
self.camera_metrics,
self.event_queue,
self.event_processed_queue,
self.stop_event,
)
self.event_processor.start()
def start_event_cleanup(self):
self.event_cleanup = EventCleanup(self.config, self.stop_event)
self.event_cleanup.start()
def start_recording_maintainer(self):
self.recording_maintainer = RecordingMaintainer(self.config, self.stop_event)
self.recording_maintainer.start()
def start_recording_cleanup(self):
self.recording_cleanup = RecordingCleanup(self.config, self.stop_event)
self.recording_cleanup.start()
def start_stats_emitter(self):
self.stats_emitter = StatsEmitter(self.config, self.stats_tracking, self.mqtt_client, self.config.mqtt.topic_prefix, self.stop_event)
self.stats_emitter = StatsEmitter(
self.config,
self.stats_tracking,
self.mqtt_client,
self.config.mqtt.topic_prefix,
self.stop_event,
)
self.stats_emitter.start()
def start_watchdog(self):
@@ -204,6 +315,7 @@ class FrigateApp():
def start(self):
self.init_logger()
logger.info(f"Starting Frigate ({VERSION})")
try:
try:
self.init_config()
@@ -223,14 +335,17 @@ class FrigateApp():
self.log_process.terminate()
sys.exit(1)
self.start_detectors()
self.start_video_output_processor()
self.start_detected_frames_processor()
self.start_camera_processors()
self.start_camera_capture_processes()
self.init_stats()
self.init_web_server()
self.start_mqtt_relay()
self.start_event_processor()
self.start_event_cleanup()
self.start_recording_maintainer()
self.start_recording_cleanup()
self.start_stats_emitter()
self.start_watchdog()
# self.zeroconf = broadcast_zeroconf(self.config.mqtt.client_id)
@@ -238,22 +353,26 @@ class FrigateApp():
def receiveSignal(signalNumber, frame):
self.stop()
sys.exit()
signal.signal(signal.SIGTERM, receiveSignal)
server = pywsgi.WSGIServer(('127.0.0.1', 5001), self.flask_app, handler_class=WebSocketHandler)
server.serve_forever()
try:
self.flask_app.run(host="127.0.0.1", port=5001, debug=False)
except KeyboardInterrupt:
pass
self.stop()
def stop(self):
logger.info(f"Stopping...")
self.stop_event.set()
self.mqtt_relay.stop()
self.detected_frames_processor.join()
self.event_processor.join()
self.event_cleanup.join()
self.recording_maintainer.join()
self.recording_cleanup.join()
self.stats_emitter.join()
self.frigate_watchdog.join()
self.db.stop()

1800
frigate/config.py
View File

File diff suppressed because it is too large Load Diff

7
frigate/const.py
View File

@@ -1,3 +1,4 @@
CLIPS_DIR = '/media/frigate/clips'
RECORD_DIR = '/media/frigate/recordings'
CACHE_DIR = '/tmp/cache'
BASE_DIR = "/media/frigate"
CLIPS_DIR = f"{BASE_DIR}/clips"
RECORD_DIR = f"{BASE_DIR}/recordings"
CACHE_DIR = "/tmp/cache"

220
frigate/edgetpu.py
View File

@@ -1,50 +1,51 @@
import datetime
import hashlib
import logging
import multiprocessing as mp
import os
import queue
import threading
import signal
import threading
from abc import ABC, abstractmethod
from multiprocessing.connection import Connection
from setproctitle import setproctitle
from typing import Dict
import numpy as np
import tflite_runtime.interpreter as tflite
from setproctitle import setproctitle
from tflite_runtime.interpreter import load_delegate
from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen
logger = logging.getLogger(__name__)
def load_labels(path, encoding='utf-8'):
"""Loads labels from file (with or without index numbers).
Args:
path: path to label file.
encoding: label file encoding.
Returns:
Dictionary mapping indices to labels.
"""
with open(path, 'r', encoding=encoding) as f:
lines = f.readlines()
if not lines:
return {}
if lines[0].split(' ', maxsplit=1)[0].isdigit():
pairs = [line.split(' ', maxsplit=1) for line in lines]
return {int(index): label.strip() for index, label in pairs}
else:
return {index: line.strip() for index, line in enumerate(lines)}
def load_labels(path, encoding="utf-8"):
"""Loads labels from file (with or without index numbers).
Args:
path: path to label file.
encoding: label file encoding.
Returns:
Dictionary mapping indices to labels.
"""
with open(path, "r", encoding=encoding) as f:
lines = f.readlines()
if not lines:
return {}
if lines[0].split(" ", maxsplit=1)[0].isdigit():
pairs = [line.split(" ", maxsplit=1) for line in lines]
return {int(index): label.strip() for index, label in pairs}
else:
return {index: line.strip() for index, line in enumerate(lines)}
class ObjectDetector(ABC):
@abstractmethod
def detect(self, tensor_input, threshold = .4):
def detect(self, tensor_input, threshold=0.4):
pass
class LocalObjectDetector(ObjectDetector):
def __init__(self, tf_device=None, num_threads=3, labels=None):
def __init__(self, tf_device=None, model_path=None, num_threads=3, labels=None):
self.fps = EventsPerSecond()
if labels is None:
self.labels = {}
@@ -57,27 +58,34 @@ class LocalObjectDetector(ObjectDetector):
edge_tpu_delegate = None
if tf_device != 'cpu':
if tf_device != "cpu":
try:
logger.info(f"Attempting to load TPU as {device_config['device']}")
edge_tpu_delegate = load_delegate('libedgetpu.so.1.0', device_config)
edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
logger.info("TPU found")
self.interpreter = tflite.Interpreter(
model_path='/edgetpu_model.tflite',
experimental_delegates=[edge_tpu_delegate])
model_path=model_path or "/edgetpu_model.tflite",
experimental_delegates=[edge_tpu_delegate],
)
except ValueError:
logger.info("No EdgeTPU detected.")
logger.error(
"No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
)
raise
else:
logger.warning(
"CPU detectors are not recommended and should only be used for testing or for trial purposes."
)
self.interpreter = tflite.Interpreter(
model_path='/cpu_model.tflite', num_threads=num_threads)
model_path=model_path or "/cpu_model.tflite", num_threads=num_threads
)
self.interpreter.allocate_tensors()
self.tensor_input_details = self.interpreter.get_input_details()
self.tensor_output_details = self.interpreter.get_output_details()
def detect(self, tensor_input, threshold=.4):
def detect(self, tensor_input, threshold=0.4):
detections = []
raw_detections = self.detect_raw(tensor_input)
@@ -85,28 +93,51 @@ class LocalObjectDetector(ObjectDetector):
for d in raw_detections:
if d[1] < threshold:
break
detections.append((
self.labels[int(d[0])],
float(d[1]),
(d[2], d[3], d[4], d[5])
))
detections.append(
(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
)
self.fps.update()
return detections
def detect_raw(self, tensor_input):
self.interpreter.set_tensor(self.tensor_input_details[0]['index'], tensor_input)
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
self.interpreter.invoke()
boxes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[0]['index']))
label_codes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[1]['index']))
scores = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[2]['index']))
detections = np.zeros((20,6), np.float32)
for i, score in enumerate(scores):
detections[i] = [label_codes[i], score, boxes[i][0], boxes[i][1], boxes[i][2], boxes[i][3]]
boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
count = int(
self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
)
detections = np.zeros((20, 6), np.float32)
for i in range(count):
if scores[i] < 0.4 or i == 20:
break
detections[i] = [
class_ids[i],
float(scores[i]),
boxes[i][0],
boxes[i][1],
boxes[i][2],
boxes[i][3],
]
return detections
def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.Event], avg_speed, start, model_shape, tf_device, num_threads):
def run_detector(
name: str,
detection_queue: mp.Queue,
out_events: Dict[str, mp.Event],
avg_speed,
start,
model_path,
model_shape,
tf_device,
num_threads,
):
threading.current_thread().name = f"detector:{name}"
logger = logging.getLogger(f"detector.{name}")
logger.info(f"Starting detection process: {os.getpid()}")
@@ -114,33 +145,32 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
listen()
stop_event = mp.Event()
def receiveSignal(signalNumber, frame):
stop_event.set()
signal.signal(signal.SIGTERM, receiveSignal)
signal.signal(signal.SIGINT, receiveSignal)
frame_manager = SharedMemoryFrameManager()
object_detector = LocalObjectDetector(tf_device=tf_device, num_threads=num_threads)
object_detector = LocalObjectDetector(
tf_device=tf_device, model_path=model_path, num_threads=num_threads
)
outputs = {}
for name in out_events.keys():
out_shm = mp.shared_memory.SharedMemory(name=f"out-{name}", create=False)
out_np = np.ndarray((20,6), dtype=np.float32, buffer=out_shm.buf)
outputs[name] = {
'shm': out_shm,
'np': out_np
}
while True:
if stop_event.is_set():
break
out_np = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
outputs[name] = {"shm": out_shm, "np": out_np}
while not stop_event.is_set():
try:
connection_id = detection_queue.get(timeout=5)
except queue.Empty:
continue
input_frame = frame_manager.get(connection_id, (1,model_shape[0],model_shape[1],3))
input_frame = frame_manager.get(
connection_id, (1, model_shape[0], model_shape[1], 3)
)
if input_frame is None:
continue
@@ -148,26 +178,37 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
# detect and send the output
start.value = datetime.datetime.now().timestamp()
detections = object_detector.detect_raw(input_frame)
duration = datetime.datetime.now().timestamp()-start.value
outputs[connection_id]['np'][:] = detections[:]
duration = datetime.datetime.now().timestamp() - start.value
outputs[connection_id]["np"][:] = detections[:]
out_events[connection_id].set()
start.value = 0.0
avg_speed.value = (avg_speed.value*9 + duration)/10
class EdgeTPUProcess():
def __init__(self, name, detection_queue, out_events, model_shape, tf_device=None, num_threads=3):
avg_speed.value = (avg_speed.value * 9 + duration) / 10
class EdgeTPUProcess:
def __init__(
self,
name,
detection_queue,
out_events,
model_path,
model_shape,
tf_device=None,
num_threads=3,
):
self.name = name
self.out_events = out_events
self.detection_queue = detection_queue
self.avg_inference_speed = mp.Value('d', 0.01)
self.detection_start = mp.Value('d', 0.0)
self.avg_inference_speed = mp.Value("d", 0.01)
self.detection_start = mp.Value("d", 0.0)
self.detect_process = None
self.model_path = model_path
self.model_shape = model_shape
self.tf_device = tf_device
self.num_threads = num_threads
self.start_or_restart()
def stop(self):
self.detect_process.terminate()
logging.info("Waiting for detection process to exit gracefully...")
@@ -181,23 +222,42 @@ class EdgeTPUProcess():
self.detection_start.value = 0.0
if (not self.detect_process is None) and self.detect_process.is_alive():
self.stop()
self.detect_process = mp.Process(target=run_detector, name=f"detector:{self.name}", args=(self.name, self.detection_queue, self.out_events, self.avg_inference_speed, self.detection_start, self.model_shape, self.tf_device, self.num_threads))
self.detect_process = mp.Process(
target=run_detector,
name=f"detector:{self.name}",
args=(
self.name,
self.detection_queue,
self.out_events,
self.avg_inference_speed,
self.detection_start,
self.model_path,
self.model_shape,
self.tf_device,
self.num_threads,
),
)
self.detect_process.daemon = True
self.detect_process.start()
class RemoteObjectDetector():
class RemoteObjectDetector:
def __init__(self, name, labels, detection_queue, event, model_shape):
self.labels = load_labels(labels)
self.labels = labels
self.name = name
self.fps = EventsPerSecond()
self.detection_queue = detection_queue
self.event = event
self.shm = mp.shared_memory.SharedMemory(name=self.name, create=False)
self.np_shm = np.ndarray((1,model_shape[0],model_shape[1],3), dtype=np.uint8, buffer=self.shm.buf)
self.out_shm = mp.shared_memory.SharedMemory(name=f"out-{self.name}", create=False)
self.out_np_shm = np.ndarray((20,6), dtype=np.float32, buffer=self.out_shm.buf)
def detect(self, tensor_input, threshold=.4):
self.np_shm = np.ndarray(
(1, model_shape[0], model_shape[1], 3), dtype=np.uint8, buffer=self.shm.buf
)
self.out_shm = mp.shared_memory.SharedMemory(
name=f"out-{self.name}", create=False
)
self.out_np_shm = np.ndarray((20, 6), dtype=np.float32, buffer=self.out_shm.buf)
def detect(self, tensor_input, threshold=0.4):
detections = []
# copy input to shared memory
@@ -213,14 +273,12 @@ class RemoteObjectDetector():
for d in self.out_np_shm:
if d[1] < threshold:
break
detections.append((
self.labels[int(d[0])],
float(d[1]),
(d[2], d[3], d[4], d[5])
))
detections.append(
(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
)
self.fps.update()
return detections
def cleanup(self):
self.shm.unlink()
self.out_shm.unlink()

387
frigate/events.py
View File

@@ -1,29 +1,26 @@
import datetime
import json
import logging
import os
import queue
import subprocess as sp
import threading
import time
from collections import defaultdict
from pathlib import Path
import psutil
import shutil
from frigate.config import FrigateConfig
from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
from frigate.models import Event
from peewee import fn
from frigate.config import EventsConfig, FrigateConfig, RecordConfig
from frigate.const import CLIPS_DIR
from frigate.models import Event
logger = logging.getLogger(__name__)
class EventProcessor(threading.Thread):
def __init__(self, config, camera_processes, event_queue, event_processed_queue, stop_event):
def __init__(
self, config, camera_processes, event_queue, event_processed_queue, stop_event
):
threading.Thread.__init__(self)
self.name = 'event_processor'
self.name = "event_processor"
self.config = config
self.camera_processes = camera_processes
self.cached_clips = {}
@@ -32,292 +29,153 @@ class EventProcessor(threading.Thread):
self.events_in_process = {}
self.stop_event = stop_event
def should_create_clip(self, camera, event_data):
if event_data['false_positive']:
return False
# if there are required zones and there is no overlap
required_zones = self.config.cameras[camera].clips.required_zones
if len(required_zones) > 0 and not set(event_data['entered_zones']) & set(required_zones):
logger.debug(f"Not creating clip for {event_data['id']} because it did not enter required zones")
return False
return True
def refresh_cache(self):
cached_files = os.listdir(CACHE_DIR)
files_in_use = []
for process in psutil.process_iter():
try:
if process.name() != 'ffmpeg':
continue
flist = process.open_files()
if flist:
for nt in flist:
if nt.path.startswith(CACHE_DIR):
files_in_use.append(nt.path.split('/')[-1])
except:
continue
for f in cached_files:
if f in files_in_use or f in self.cached_clips:
continue
camera = '-'.join(f.split('-')[:-1])
start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
ffprobe_cmd = " ".join([
'ffprobe',
'-v',
'error',
'-show_entries',
'format=duration',
'-of',
'default=noprint_wrappers=1:nokey=1',
f"{os.path.join(CACHE_DIR,f)}"
])
p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
(output, err) = p.communicate()
p_status = p.wait()
if p_status == 0:
duration = float(output.decode('utf-8').strip())
else:
logger.info(f"bad file: {f}")
os.remove(os.path.join(CACHE_DIR,f))
continue
self.cached_clips[f] = {
'path': f,
'camera': camera,
'start_time': start_time.timestamp(),
'duration': duration
}
if len(self.events_in_process) > 0:
earliest_event = min(self.events_in_process.values(), key=lambda x:x['start_time'])['start_time']
else:
earliest_event = datetime.datetime.now().timestamp()
# if the earliest event exceeds the max seconds, cap it
max_seconds = self.config.clips.max_seconds
if datetime.datetime.now().timestamp()-earliest_event > max_seconds:
earliest_event = datetime.datetime.now().timestamp()-max_seconds
for f, data in list(self.cached_clips.items()):
if earliest_event-90 > data['start_time']+data['duration']:
del self.cached_clips[f]
logger.debug(f"Cleaning up cached file {f}")
os.remove(os.path.join(CACHE_DIR,f))
# if we are still using more than 90% of the cache, proactively cleanup
cache_usage = shutil.disk_usage("/tmp/cache")
if cache_usage.used/cache_usage.total > .9 and cache_usage.free < 200000000 and len(self.cached_clips) > 0:
logger.warning("More than 90% of the cache is used.")
logger.warning("Consider increasing space available at /tmp/cache or reducing max_seconds in your clips config.")
logger.warning("Proactively cleaning up the cache...")
while cache_usage.used/cache_usage.total > .9:
oldest_clip = min(self.cached_clips.values(), key=lambda x:x['start_time'])
del self.cached_clips[oldest_clip['path']]
os.remove(os.path.join(CACHE_DIR,oldest_clip['path']))
cache_usage = shutil.disk_usage("/tmp/cache")
def create_clip(self, camera, event_data, pre_capture, post_capture):
# get all clips from the camera with the event sorted
sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
# if there are no clips in the cache or we are still waiting on a needed file check every 5 seconds
wait_count = 0
while len(sorted_clips) == 0 or sorted_clips[-1]['start_time'] + sorted_clips[-1]['duration'] < event_data['end_time']+post_capture:
if wait_count > 4:
logger.warning(f"Unable to create clip for {camera} and event {event_data['id']}. There were no cache files for this event.")
return False
logger.debug(f"No cache clips for {camera}. Waiting...")
time.sleep(5)
self.refresh_cache()
# get all clips from the camera with the event sorted
sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
wait_count += 1
playlist_start = event_data['start_time']-pre_capture
playlist_end = event_data['end_time']+post_capture
playlist_lines = []
for clip in sorted_clips:
# clip ends before playlist start time, skip
if clip['start_time']+clip['duration'] < playlist_start:
continue
# clip starts after playlist ends, finish
if clip['start_time'] > playlist_end:
break
playlist_lines.append(f"file '{os.path.join(CACHE_DIR,clip['path'])}'")
# if this is the starting clip, add an inpoint
if clip['start_time'] < playlist_start:
playlist_lines.append(f"inpoint {int(playlist_start-clip['start_time'])}")
# if this is the ending clip, add an outpoint
if clip['start_time']+clip['duration'] > playlist_end:
playlist_lines.append(f"outpoint {int(playlist_end-clip['start_time'])}")
clip_name = f"{camera}-{event_data['id']}"
ffmpeg_cmd = [
'ffmpeg',
'-y',
'-protocol_whitelist',
'pipe,file',
'-f',
'concat',
'-safe',
'0',
'-i',
'-',
'-c',
'copy',
'-movflags',
'+faststart',
f"{os.path.join(CLIPS_DIR, clip_name)}.mp4"
]
p = sp.run(ffmpeg_cmd, input="\n".join(playlist_lines), encoding='ascii', capture_output=True)
if p.returncode != 0:
logger.error(p.stderr)
return False
return True
def run(self):
while True:
if self.stop_event.is_set():
logger.info(f"Exiting event processor...")
break
while not self.stop_event.is_set():
try:
event_type, camera, event_data = self.event_queue.get(timeout=10)
except queue.Empty:
if not self.stop_event.is_set():
self.refresh_cache()
continue
logger.debug(f"Event received: {event_type} {camera} {event_data['id']}")
self.refresh_cache()
if event_type == 'start':
self.events_in_process[event_data['id']] = event_data
if event_type == "start":
self.events_in_process[event_data["id"]] = event_data
if event_type == 'end':
clips_config = self.config.cameras[camera].clips
if event_type == "end":
event_config: EventsConfig = self.config.cameras[camera].record.events
clip_created = False
if self.should_create_clip(camera, event_data):
if clips_config.enabled and (clips_config.objects is None or event_data['label'] in clips_config.objects):
clip_created = self.create_clip(camera, event_data, clips_config.pre_capture, clips_config.post_capture)
if clip_created or event_data['has_snapshot']:
if event_data["has_clip"] or event_data["has_snapshot"]:
Event.create(
id=event_data['id'],
label=event_data['label'],
id=event_data["id"],
label=event_data["label"],
camera=camera,
start_time=event_data['start_time'],
end_time=event_data['end_time'],
top_score=event_data['top_score'],
false_positive=event_data['false_positive'],
zones=list(event_data['entered_zones']),
thumbnail=event_data['thumbnail'],
has_clip=clip_created,
has_snapshot=event_data['has_snapshot'],
start_time=event_data["start_time"] - event_config.pre_capture,
end_time=event_data["end_time"] + event_config.post_capture,
top_score=event_data["top_score"],
false_positive=event_data["false_positive"],
zones=list(event_data["entered_zones"]),
thumbnail=event_data["thumbnail"],
region=event_data["region"],
box=event_data["box"],
area=event_data["area"],
has_clip=event_data["has_clip"],
has_snapshot=event_data["has_snapshot"],
)
del self.events_in_process[event_data['id']]
self.event_processed_queue.put((event_data['id'], camera))
del self.events_in_process[event_data["id"]]
self.event_processed_queue.put((event_data["id"], camera))
logger.info(f"Exiting event processor...")
class EventCleanup(threading.Thread):
def __init__(self, config: FrigateConfig, stop_event):
threading.Thread.__init__(self)
self.name = 'event_cleanup'
self.name = "event_cleanup"
self.config = config
self.stop_event = stop_event
self.camera_keys = list(self.config.cameras.keys())
def expire(self, media):
def expire(self, media_type):
## Expire events from unlisted cameras based on the global config
if media == 'clips':
retain_config = self.config.clips.retain
file_extension = 'mp4'
update_params = {'has_clip': False}
if media_type == "clips":
retain_config = self.config.record.events.retain
file_extension = "mp4"
update_params = {"has_clip": False}
else:
retain_config = self.config.snapshots.retain
file_extension = 'jpg'
update_params = {'has_snapshot': False}
distinct_labels = (Event.select(Event.label)
.where(Event.camera.not_in(self.camera_keys))
.distinct())
file_extension = "jpg"
update_params = {"has_snapshot": False}
distinct_labels = (
Event.select(Event.label)
.where(Event.camera.not_in(self.camera_keys))
.distinct()
)
# loop over object types in db
for l in distinct_labels:
# get expiration time for this label
expire_days = retain_config.objects.get(l.label, retain_config.default)
expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
expire_after = (
datetime.datetime.now() - datetime.timedelta(days=expire_days)
).timestamp()
# grab all events after specific time
expired_events = (
Event.select()
.where(Event.camera.not_in(self.camera_keys),
Event.start_time < expire_after,
Event.label == l.label)
expired_events = Event.select().where(
Event.camera.not_in(self.camera_keys),
Event.start_time < expire_after,
Event.label == l.label,
)
# delete the media from disk
for event in expired_events:
media_name = f"{event.camera}-{event.id}"
media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}")
media.unlink(missing_ok=True)
media_path = Path(
f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
)
media_path.unlink(missing_ok=True)
if file_extension == "jpg":
media_path = Path(
f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"
)
media_path.unlink(missing_ok=True)
# update the clips attribute for the db entry
update_query = (
Event.update(update_params)
.where(Event.camera.not_in(self.camera_keys),
Event.start_time < expire_after,
Event.label == l.label)
update_query = Event.update(update_params).where(
Event.camera.not_in(self.camera_keys),
Event.start_time < expire_after,
Event.label == l.label,
)
update_query.execute()
## Expire events from cameras based on the camera config
for name, camera in self.config.cameras.items():
if media == 'clips':
retain_config = camera.clips.retain
if media_type == "clips":
retain_config = camera.record.events.retain
else:
retain_config = camera.snapshots.retain
# get distinct objects in database for this camera
distinct_labels = (Event.select(Event.label)
.where(Event.camera == name)
.distinct())
distinct_labels = (
Event.select(Event.label).where(Event.camera == name).distinct()
)
# loop over object types in db
for l in distinct_labels:
# get expiration time for this label
expire_days = retain_config.objects.get(l.label, retain_config.default)
expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
expire_after = (
datetime.datetime.now() - datetime.timedelta(days=expire_days)
).timestamp()
# grab all events after specific time
expired_events = (
Event.select()
.where(Event.camera == name,
Event.start_time < expire_after,
Event.label == l.label)
expired_events = Event.select().where(
Event.camera == name,
Event.start_time < expire_after,
Event.label == l.label,
)
# delete the grabbed clips from disk
for event in expired_events:
media_name = f"{event.camera}-{event.id}"
media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}")
media.unlink(missing_ok=True)
media_path = Path(
f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
)
media_path.unlink(missing_ok=True)
if file_extension == "jpg":
media_path = Path(
f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"
)
media_path.unlink(missing_ok=True)
# update the clips attribute for the db entry
update_query = (
Event.update(update_params)
.where( Event.camera == name,
Event.start_time < expire_after,
Event.label == l.label)
update_query = Event.update(update_params).where(
Event.camera == name,
Event.start_time < expire_after,
Event.label == l.label,
)
update_query.execute()
def purge_duplicates(self):
duplicate_query = """with grouped_events as (
select id,
label,
camera,
label,
camera,
has_snapshot,
has_clip,
row_number() over (
@@ -327,46 +185,37 @@ class EventCleanup(threading.Thread):
from event
)
select distinct id, camera, has_snapshot, has_clip from grouped_events
select distinct id, camera, has_snapshot, has_clip from grouped_events
where copy_number > 1;"""
duplicate_events = Event.raw(duplicate_query)
for event in duplicate_events:
logger.debug(f"Removing duplicate: {event.id}")
media_name = f"{event.camera}-{event.id}"
if event.has_snapshot:
media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
media.unlink(missing_ok=True)
if event.has_clip:
media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
media.unlink(missing_ok=True)
media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
media_path.unlink(missing_ok=True)
media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}-clean.png")
media_path.unlink(missing_ok=True)
media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
media_path.unlink(missing_ok=True)
(
Event.delete()
.where(Event.id << [event.id for event in duplicate_events])
.execute()
)
(Event.delete()
.where( Event.id << [event.id for event in duplicate_events] )
.execute())
def run(self):
counter = 0
while(True):
if self.stop_event.is_set():
logger.info(f"Exiting event cleanup...")
break
# only expire events every 5 minutes, but check for stop events every 10 seconds
time.sleep(10)
counter = counter + 1
if counter < 30:
continue
counter = 0
self.expire('clips')
self.expire('snapshots')
# only expire events every 5 minutes
while not self.stop_event.wait(300):
self.expire("clips")
self.expire("snapshots")
self.purge_duplicates()
# drop events from db where has_clip and has_snapshot are false
delete_query = (
Event.delete()
.where( Event.has_clip == False,
Event.has_snapshot == False)
delete_query = Event.delete().where(
Event.has_clip == False, Event.has_snapshot == False
)
delete_query.execute()
logger.info(f"Exiting event cleanup...")

715
frigate/http.py
View File

@@ -1,92 +1,56 @@
import base64
import datetime
from collections import OrderedDict
from datetime import datetime, timedelta
import json
import glob
import logging
import os
import re
import subprocess as sp
import time
from functools import reduce
from pathlib import Path
import cv2
import gevent
from flask.helpers import send_file
import numpy as np
from flask import (Blueprint, Flask, Response, current_app, jsonify,
make_response, request)
from flask_sockets import Sockets
from peewee import SqliteDatabase, operator, fn, DoesNotExist
from flask import (
Blueprint,
Flask,
Response,
current_app,
jsonify,
make_response,
request,
)
from peewee import SqliteDatabase, operator, fn, DoesNotExist, Value
from playhouse.shortcuts import model_to_dict
from frigate.const import CLIPS_DIR
from frigate.models import Event
from frigate.const import CLIPS_DIR, RECORD_DIR
from frigate.models import Event, Recordings
from frigate.stats import stats_snapshot
from frigate.util import calculate_region
from frigate.version import VERSION
logger = logging.getLogger(__name__)
bp = Blueprint('frigate', __name__)
ws = Blueprint('ws', __name__)
bp = Blueprint("frigate", __name__)
class MqttBackend():
"""Interface for registering and updating WebSocket clients."""
def __init__(self, mqtt_client, topic_prefix):
self.clients = list()
self.mqtt_client = mqtt_client
self.topic_prefix = topic_prefix
def register(self, client):
"""Register a WebSocket connection for Mqtt updates."""
self.clients.append(client)
def publish(self, message):
try:
json_message = json.loads(message)
json_message = {
'topic': f"{self.topic_prefix}/{json_message['topic']}",
'payload': json_message['payload'],
'retain': json_message.get('retain', False)
}
except:
logger.warning("Unable to parse websocket message as valid json.")
return
logger.debug(f"Publishing mqtt message from websockets at {json_message['topic']}.")
self.mqtt_client.publish(json_message['topic'], json_message['payload'], retain=json_message['retain'])
def run(self):
def send(client, userdata, message):
"""Sends mqtt messages to clients."""
try:
logger.debug(f"Received mqtt message on {message.topic}.")
ws_message = json.dumps({
'topic': message.topic.replace(f"{self.topic_prefix}/",""),
'payload': message.payload.decode()
})
except:
# if the payload can't be decoded don't relay to clients
logger.debug(f"MQTT payload for {message.topic} wasn't text. Skipping...")
return
for client in self.clients:
try:
client.send(ws_message)
except:
logger.debug("Removing websocket client due to a closed connection.")
self.clients.remove(client)
self.mqtt_client.message_callback_add(f"{self.topic_prefix}/#", send)
def start(self):
"""Maintains mqtt subscription in the background."""
gevent.spawn(self.run)
def create_app(frigate_config, database: SqliteDatabase, stats_tracking, detected_frames_processor, mqtt_client):
def create_app(
frigate_config,
database: SqliteDatabase,
stats_tracking,
detected_frames_processor,
):
app = Flask(__name__)
sockets = Sockets(app)
@app.before_request
def _db_connect():
database.connect()
if database.is_closed():
database.connect()
@app.teardown_request
def _db_close(exc):
@@ -98,21 +62,19 @@ def create_app(frigate_config, database: SqliteDatabase, stats_tracking, detecte
app.detected_frames_processor = detected_frames_processor
app.register_blueprint(bp)
sockets.register_blueprint(ws)
app.mqtt_backend = MqttBackend(mqtt_client, frigate_config.mqtt.topic_prefix)
app.mqtt_backend.start()
return app
@bp.route('/')
@bp.route("/")
def is_healthy():
return "Frigate is running. Alive and healthy!"
@bp.route('/events/summary')
@bp.route("/events/summary")
def events_summary():
has_clip = request.args.get('has_clip', type=int)
has_snapshot = request.args.get('has_snapshot', type=int)
has_clip = request.args.get("has_clip", type=int)
has_snapshot = request.args.get("has_snapshot", type=int)
clauses = []
@@ -123,38 +85,66 @@ def events_summary():
clauses.append((Event.has_snapshot == has_snapshot))
if len(clauses) == 0:
clauses.append((1 == 1))
clauses.append((True))
groups = (
Event
.select(
Event.camera,
Event.label,
fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')).alias('day'),
Event.zones,
fn.COUNT(Event.id).alias('count')
)
.where(reduce(operator.and_, clauses))
.group_by(
Event.camera,
Event.label,
fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')),
Event.zones
)
Event.select(
Event.camera,
Event.label,
fn.strftime(
"%Y-%m-%d", fn.datetime(Event.start_time, "unixepoch", "localtime")
).alias("day"),
Event.zones,
fn.COUNT(Event.id).alias("count"),
)
.where(reduce(operator.and_, clauses))
.group_by(
Event.camera,
Event.label,
fn.strftime(
"%Y-%m-%d", fn.datetime(Event.start_time, "unixepoch", "localtime")
),
Event.zones,
)
)
return jsonify([e for e in groups.dicts()])
@bp.route('/events/<id>')
@bp.route("/events/<id>", methods=("GET",))
def event(id):
try:
return model_to_dict(Event.get(Event.id == id))
except DoesNotExist:
return "Event not found", 404
@bp.route('/events/<id>/thumbnail.jpg')
@bp.route("/events/<id>", methods=("DELETE",))
def delete_event(id):
try:
event = Event.get(Event.id == id)
except DoesNotExist:
return make_response(
jsonify({"success": False, "message": "Event" + id + " not found"}), 404
)
media_name = f"{event.camera}-{event.id}"
if event.has_snapshot:
media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
media.unlink(missing_ok=True)
if event.has_clip:
media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
media.unlink(missing_ok=True)
event.delete_instance()
return make_response(
jsonify({"success": True, "message": "Event" + id + " deleted"}), 200
)
@bp.route("/events/<id>/thumbnail.jpg")
def event_thumbnail(id):
format = request.args.get('format', 'ios')
format = request.args.get("format", "ios")
thumbnail_bytes = None
try:
event = Event.get(Event.id == id)
@@ -162,7 +152,8 @@ def event_thumbnail(id):
except DoesNotExist:
# see if the object is currently being tracked
try:
for camera_state in current_app.detected_frames_processor.camera_states.values():
camera_states = current_app.detected_frames_processor.camera_states.values()
for camera_state in camera_states:
if id in camera_state.tracked_objects:
tracked_obj = camera_state.tracked_objects.get(id)
if not tracked_obj is None:
@@ -174,60 +165,114 @@ def event_thumbnail(id):
return "Event not found", 404
# android notifications prefer a 2:1 ratio
if format == 'android':
if format == "android":
jpg_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
img = cv2.imdecode(jpg_as_np, flags=1)
thumbnail = cv2.copyMakeBorder(img, 0, 0, int(img.shape[1]*0.5), int(img.shape[1]*0.5), cv2.BORDER_CONSTANT, (0,0,0))
ret, jpg = cv2.imencode('.jpg', thumbnail, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
thumbnail = cv2.copyMakeBorder(
img,
0,
0,
int(img.shape[1] * 0.5),
int(img.shape[1] * 0.5),
cv2.BORDER_CONSTANT,
(0, 0, 0),
)
ret, jpg = cv2.imencode(".jpg", thumbnail, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
thumbnail_bytes = jpg.tobytes()
response = make_response(thumbnail_bytes)
response.headers['Content-Type'] = 'image/jpg'
response.headers["Content-Type"] = "image/jpg"
return response
@bp.route('/events/<id>/snapshot.jpg')
@bp.route("/events/<id>/snapshot.jpg")
def event_snapshot(id):
download = request.args.get("download", type=bool)
jpg_bytes = None
try:
event = Event.get(Event.id == id)
if not event.has_snapshot:
return "Snapshot not available", 404
# read snapshot from disk
with open(os.path.join(CLIPS_DIR, f"{event.camera}-{id}.jpg"), 'rb') as image_file:
with open(
os.path.join(CLIPS_DIR, f"{event.camera}-{id}.jpg"), "rb"
) as image_file:
jpg_bytes = image_file.read()
except DoesNotExist:
# see if the object is currently being tracked
try:
for camera_state in current_app.detected_frames_processor.camera_states.values():
camera_states = current_app.detected_frames_processor.camera_states.values()
for camera_state in camera_states:
if id in camera_state.tracked_objects:
tracked_obj = camera_state.tracked_objects.get(id)
if not tracked_obj is None:
jpg_bytes = tracked_obj.get_jpg_bytes(
timestamp=request.args.get('timestamp', type=int),
bounding_box=request.args.get('bbox', type=int),
crop=request.args.get('crop', type=int),
height=request.args.get('h', type=int)
timestamp=request.args.get("timestamp", type=int),
bounding_box=request.args.get("bbox", type=int),
crop=request.args.get("crop", type=int),
height=request.args.get("h", type=int),
quality=request.args.get("quality", default=70, type=int),
)
except:
return "Event not found", 404
except:
return "Event not found", 404
if jpg_bytes is None:
return "Event not found", 404
response = make_response(jpg_bytes)
response.headers['Content-Type'] = 'image/jpg'
response.headers["Content-Type"] = "image/jpg"
if download:
response.headers[
"Content-Disposition"
] = f"attachment; filename=snapshot-{id}.jpg"
return response
@bp.route('/events')
@bp.route("/events/<id>/clip.mp4")
def event_clip(id):
download = request.args.get("download", type=bool)
try:
event: Event = Event.get(Event.id == id)
except DoesNotExist:
return "Event not found.", 404
if not event.has_clip:
return "Clip not available", 404
file_name = f"{event.camera}-{id}.mp4"
clip_path = os.path.join(CLIPS_DIR, file_name)
if not os.path.isfile(clip_path):
return recording_clip(event.camera, event.start_time, event.end_time)
response = make_response()
response.headers["Content-Description"] = "File Transfer"
response.headers["Cache-Control"] = "no-cache"
response.headers["Content-Type"] = "video/mp4"
if download:
response.headers["Content-Disposition"] = "attachment; filename=%s" % file_name
response.headers["Content-Length"] = os.path.getsize(clip_path)
response.headers[
"X-Accel-Redirect"
] = f"/clips/{file_name}" # nginx: http://wiki.nginx.org/NginxXSendfile
return response
@bp.route("/events")
def events():
limit = request.args.get('limit', 100)
camera = request.args.get('camera')
label = request.args.get('label')
zone = request.args.get('zone')
after = request.args.get('after', type=float)
before = request.args.get('before', type=float)
has_clip = request.args.get('has_clip', type=int)
has_snapshot = request.args.get('has_snapshot', type=int)
include_thumbnails = request.args.get('include_thumbnails', default=1, type=int)
limit = request.args.get("limit", 100)
camera = request.args.get("camera")
label = request.args.get("label")
zone = request.args.get("zone")
after = request.args.get("after", type=float)
before = request.args.get("before", type=float)
has_clip = request.args.get("has_clip", type=int)
has_snapshot = request.args.get("has_snapshot", type=int)
include_thumbnails = request.args.get("include_thumbnails", default=1, type=int)
clauses = []
excluded_fields = []
@@ -239,7 +284,7 @@ def events():
clauses.append((Event.label == label))
if zone:
clauses.append((Event.zones.cast('text') % f"*\"{zone}\"*"))
clauses.append((Event.zones.cast("text") % f'*"{zone}"*'))
if after:
clauses.append((Event.start_time >= after))
@@ -257,125 +302,427 @@ def events():
excluded_fields.append(Event.thumbnail)
if len(clauses) == 0:
clauses.append((1 == 1))
clauses.append((True))
events = (Event.select()
.where(reduce(operator.and_, clauses))
.order_by(Event.start_time.desc())
.limit(limit))
events = (
Event.select()
.where(reduce(operator.and_, clauses))
.order_by(Event.start_time.desc())
.limit(limit)
)
return jsonify([model_to_dict(e, exclude=excluded_fields) for e in events])
@bp.route('/config')
def config():
return jsonify(current_app.frigate_config.to_dict())
@bp.route('/version')
@bp.route("/config")
def config():
config = current_app.frigate_config.dict()
# add in the ffmpeg_cmds
for camera_name, camera in current_app.frigate_config.cameras.items():
camera_dict = config["cameras"][camera_name]
camera_dict["ffmpeg_cmds"] = camera.ffmpeg_cmds
for cmd in camera_dict["ffmpeg_cmds"]:
cmd["cmd"] = " ".join(cmd["cmd"])
return jsonify(config)
@bp.route("/config/schema")
def config_schema():
return current_app.response_class(
current_app.frigate_config.schema_json(), mimetype="application/json"
)
@bp.route("/version")
def version():
return VERSION
@bp.route('/stats')
@bp.route("/stats")
def stats():
stats = stats_snapshot(current_app.stats_tracking)
return jsonify(stats)
@bp.route('/<camera_name>/<label>/best.jpg')
@bp.route("/<camera_name>/<label>/best.jpg")
def best(camera_name, label):
if camera_name in current_app.frigate_config.cameras:
best_object = current_app.detected_frames_processor.get_best(camera_name, label)
best_frame = best_object.get('frame')
best_frame = best_object.get("frame")
if best_frame is None:
best_frame = np.zeros((720,1280,3), np.uint8)
best_frame = np.zeros((720, 1280, 3), np.uint8)
else:
best_frame = cv2.cvtColor(best_frame, cv2.COLOR_YUV2BGR_I420)
crop = bool(request.args.get('crop', 0, type=int))
crop = bool(request.args.get("crop", 0, type=int))
if crop:
box = best_object.get('box', (0,0,300,300))
region = calculate_region(best_frame.shape, box[0], box[1], box[2], box[3], 1.1)
best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
box = best_object.get("box", (0, 0, 300, 300))
region = calculate_region(
best_frame.shape, box[0], box[1], box[2], box[3], 1.1
)
best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
height = int(request.args.get('h', str(best_frame.shape[0])))
width = int(height*best_frame.shape[1]/best_frame.shape[0])
height = int(request.args.get("h", str(best_frame.shape[0])))
width = int(height * best_frame.shape[1] / best_frame.shape[0])
resize_quality = request.args.get("quality", default=70, type=int)
best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
ret, jpg = cv2.imencode('.jpg', best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
best_frame = cv2.resize(
best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA
)
ret, jpg = cv2.imencode(
".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), resize_quality]
)
response = make_response(jpg.tobytes())
response.headers['Content-Type'] = 'image/jpg'
response.headers["Content-Type"] = "image/jpg"
return response
else:
return "Camera named {} not found".format(camera_name), 404
@bp.route('/<camera_name>')
@bp.route("/<camera_name>")
def mjpeg_feed(camera_name):
fps = int(request.args.get('fps', '3'))
height = int(request.args.get('h', '360'))
fps = int(request.args.get("fps", "3"))
height = int(request.args.get("h", "360"))
draw_options = {
'bounding_boxes': request.args.get('bbox', type=int),
'timestamp': request.args.get('timestamp', type=int),
'zones': request.args.get('zones', type=int),
'mask': request.args.get('mask', type=int),
'motion_boxes': request.args.get('motion', type=int),
'regions': request.args.get('regions', type=int),
"bounding_boxes": request.args.get("bbox", type=int),
"timestamp": request.args.get("timestamp", type=int),
"zones": request.args.get("zones", type=int),
"mask": request.args.get("mask", type=int),
"motion_boxes": request.args.get("motion", type=int),
"regions": request.args.get("regions", type=int),
}
if camera_name in current_app.frigate_config.cameras:
# return a multipart response
return Response(imagestream(current_app.detected_frames_processor, camera_name, fps, height, draw_options),
mimetype='multipart/x-mixed-replace; boundary=frame')
return Response(
imagestream(
current_app.detected_frames_processor,
camera_name,
fps,
height,
draw_options,
),
mimetype="multipart/x-mixed-replace; boundary=frame",
)
else:
return "Camera named {} not found".format(camera_name), 404
@bp.route('/<camera_name>/latest.jpg')
@bp.route("/<camera_name>/latest.jpg")
def latest_frame(camera_name):
draw_options = {
'bounding_boxes': request.args.get('bbox', type=int),
'timestamp': request.args.get('timestamp', type=int),
'zones': request.args.get('zones', type=int),
'mask': request.args.get('mask', type=int),
'motion_boxes': request.args.get('motion', type=int),
'regions': request.args.get('regions', type=int),
"bounding_boxes": request.args.get("bbox", type=int),
"timestamp": request.args.get("timestamp", type=int),
"zones": request.args.get("zones", type=int),
"mask": request.args.get("mask", type=int),
"motion_boxes": request.args.get("motion", type=int),
"regions": request.args.get("regions", type=int),
}
if camera_name in current_app.frigate_config.cameras:
# max out at specified FPS
frame = current_app.detected_frames_processor.get_current_frame(camera_name, draw_options)
if frame is None:
frame = np.zeros((720,1280,3), np.uint8)
resize_quality = request.args.get("quality", default=70, type=int)
height = int(request.args.get('h', str(frame.shape[0])))
width = int(height*frame.shape[1]/frame.shape[0])
if camera_name in current_app.frigate_config.cameras:
frame = current_app.detected_frames_processor.get_current_frame(
camera_name, draw_options
)
if frame is None:
frame = np.zeros((720, 1280, 3), np.uint8)
height = int(request.args.get("h", str(frame.shape[0])))
width = int(height * frame.shape[1] / frame.shape[0])
frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
ret, jpg = cv2.imencode('.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
ret, jpg = cv2.imencode(
".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), resize_quality]
)
response = make_response(jpg.tobytes())
response.headers['Content-Type'] = 'image/jpg'
response.headers["Content-Type"] = "image/jpg"
return response
else:
return "Camera named {} not found".format(camera_name), 404
@bp.route("/<camera_name>/recordings")
def recordings(camera_name):
dates = OrderedDict()
# Retrieve all recordings for this camera
recordings = (
Recordings.select()
.where(Recordings.camera == camera_name)
.order_by(Recordings.start_time.asc())
)
last_end = 0
recording: Recordings
for recording in recordings:
date = datetime.fromtimestamp(recording.start_time)
key = date.strftime("%Y-%m-%d")
hour = date.strftime("%H")
# Create Day Record
if key not in dates:
dates[key] = OrderedDict()
# Create Hour Record
if hour not in dates[key]:
dates[key][hour] = {"delay": {}, "events": []}
# Check for delay
the_hour = datetime.strptime(f"{key} {hour}", "%Y-%m-%d %H").timestamp()
# diff current recording start time and the greater of the previous end time or top of the hour
diff = recording.start_time - max(last_end, the_hour)
# Determine seconds into recording
seconds = 0
if datetime.fromtimestamp(last_end).strftime("%H") == hour:
seconds = int(last_end - the_hour)
# Determine the delay
delay = min(int(diff), 3600 - seconds)
if delay > 1:
# Add an offset for any delay greater than a second
dates[key][hour]["delay"][seconds] = delay
last_end = recording.end_time
# Packing intervals to return all events with same label and overlapping times as one row.
# See: https://blogs.solidq.com/en/sqlserver/packing-intervals/
events = Event.raw(
"""WITH C1 AS
(
SELECT id, label, camera, top_score, start_time AS ts, +1 AS type, 1 AS sub
FROM event
WHERE camera = ?
UNION ALL
SELECT id, label, camera, top_score, end_time + 15 AS ts, -1 AS type, 0 AS sub
FROM event
WHERE camera = ?
),
C2 AS
(
SELECT C1.*,
SUM(type) OVER(PARTITION BY label ORDER BY ts, type DESC
ROWS BETWEEN UNBOUNDED PRECEDING
AND CURRENT ROW) - sub AS cnt
FROM C1
),
C3 AS
(
SELECT id, label, camera, top_score, ts,
(ROW_NUMBER() OVER(PARTITION BY label ORDER BY ts) - 1) / 2 + 1
AS grpnum
FROM C2
WHERE cnt = 0
)
SELECT MIN(id) as id, label, camera, MAX(top_score) as top_score, MIN(ts) AS start_time, max(ts) AS end_time
FROM C3
GROUP BY label, grpnum
ORDER BY start_time;""",
camera_name,
camera_name,
)
event: Event
for event in events:
date = datetime.fromtimestamp(event.start_time)
key = date.strftime("%Y-%m-%d")
hour = date.strftime("%H")
if key in dates and hour in dates[key]:
dates[key][hour]["events"].append(
model_to_dict(
event,
exclude=[
Event.false_positive,
Event.zones,
Event.thumbnail,
Event.has_clip,
Event.has_snapshot,
],
)
)
return jsonify(
[
{
"date": date,
"events": sum([len(value["events"]) for value in hours.values()]),
"recordings": [
{"hour": hour, "delay": value["delay"], "events": value["events"]}
for hour, value in hours.items()
],
}
for date, hours in dates.items()
]
)
@bp.route("/<camera>/start/<int:start_ts>/end/<int:end_ts>/clip.mp4")
@bp.route("/<camera>/start/<float:start_ts>/end/<float:end_ts>/clip.mp4")
def recording_clip(camera, start_ts, end_ts):
download = request.args.get("download", type=bool)
recordings = (
Recordings.select()
.where(
(Recordings.start_time.between(start_ts, end_ts))
| (Recordings.end_time.between(start_ts, end_ts))
| ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
)
.where(Recordings.camera == camera)
.order_by(Recordings.start_time.asc())
)
playlist_lines = []
clip: Recordings
for clip in recordings:
playlist_lines.append(f"file '{clip.path}'")
# if this is the starting clip, add an inpoint
if clip.start_time < start_ts:
playlist_lines.append(f"inpoint {int(start_ts - clip.start_time)}")
# if this is the ending clip, add an outpoint
if clip.end_time > end_ts:
playlist_lines.append(f"outpoint {int(end_ts - clip.start_time)}")
file_name = f"clip_{camera}_{start_ts}-{end_ts}.mp4"
path = f"/tmp/cache/{file_name}"
ffmpeg_cmd = [
"ffmpeg",
"-y",
"-protocol_whitelist",
"pipe,file",
"-f",
"concat",
"-safe",
"0",
"-i",
"-",
"-c",
"copy",
"-movflags",
"+faststart",
path,
]
p = sp.run(
ffmpeg_cmd,
input="\n".join(playlist_lines),
encoding="ascii",
capture_output=True,
)
if p.returncode != 0:
logger.error(p.stderr)
return f"Could not create clip from recordings for {camera}.", 500
response = make_response()
response.headers["Content-Description"] = "File Transfer"
response.headers["Cache-Control"] = "no-cache"
response.headers["Content-Type"] = "video/mp4"
if download:
response.headers["Content-Disposition"] = "attachment; filename=%s" % file_name
response.headers["Content-Length"] = os.path.getsize(path)
response.headers[
"X-Accel-Redirect"
] = f"/cache/{file_name}" # nginx: http://wiki.nginx.org/NginxXSendfile
return response
@bp.route("/vod/<camera>/start/<int:start_ts>/end/<int:end_ts>")
@bp.route("/vod/<camera>/start/<float:start_ts>/end/<float:end_ts>")
def vod_ts(camera, start_ts, end_ts):
recordings = (
Recordings.select()
.where(
Recordings.start_time.between(start_ts, end_ts)
| Recordings.end_time.between(start_ts, end_ts)
| ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
)
.where(Recordings.camera == camera)
.order_by(Recordings.start_time.asc())
)
clips = []
durations = []
recording: Recordings
for recording in recordings:
clip = {"type": "source", "path": recording.path}
duration = int(recording.duration * 1000)
# Determine if offset is needed for first clip
if recording.start_time < start_ts:
offset = int((start_ts - recording.start_time) * 1000)
clip["clipFrom"] = offset
duration -= offset
# Determine if we need to end the last clip early
if recording.end_time > end_ts:
duration -= int((recording.end_time - end_ts) * 1000)
clips.append(clip)
durations.append(duration)
if not clips:
return "No recordings found.", 404
hour_ago = datetime.now() - timedelta(hours=1)
return jsonify(
{
"cache": hour_ago.timestamp() > start_ts,
"discontinuity": False,
"durations": durations,
"sequences": [{"clips": clips}],
}
)
@bp.route("/vod/<year_month>/<day>/<hour>/<camera>")
def vod_hour(year_month, day, hour, camera):
start_date = datetime.strptime(f"{year_month}-{day} {hour}", "%Y-%m-%d %H")
end_date = start_date + timedelta(hours=1) - timedelta(milliseconds=1)
start_ts = start_date.timestamp()
end_ts = end_date.timestamp()
return vod_ts(camera, start_ts, end_ts)
@bp.route("/vod/event/<id>")
def vod_event(id):
try:
event: Event = Event.get(Event.id == id)
except DoesNotExist:
return "Event not found.", 404
if not event.has_clip:
return "Clip not available", 404
clip_path = os.path.join(CLIPS_DIR, f"{event.camera}-{id}.mp4")
if not os.path.isfile(clip_path):
return vod_ts(event.camera, event.start_time, event.end_time)
duration = int((event.end_time - event.start_time) * 1000)
return jsonify(
{
"cache": True,
"discontinuity": False,
"durations": [duration],
"sequences": [{"clips": [{"type": "source", "path": clip_path}]}],
}
)
def imagestream(detected_frames_processor, camera_name, fps, height, draw_options):
while True:
# max out at specified FPS
gevent.sleep(1/fps)
time.sleep(1 / fps)
frame = detected_frames_processor.get_current_frame(camera_name, draw_options)
if frame is None:
frame = np.zeros((height,int(height*16/9),3), np.uint8)
frame = np.zeros((height, int(height * 16 / 9), 3), np.uint8)
width = int(height*frame.shape[1]/frame.shape[0])
width = int(height * frame.shape[1] / frame.shape[0])
frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_LINEAR)
ret, jpg = cv2.imencode('.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
@ws.route('/ws')
def echo_socket(socket):
current_app.mqtt_backend.register(socket)
while not socket.closed:
# Sleep to prevent *constant* context-switches.
gevent.sleep(0.1)
message = socket.receive()
if message:
current_app.mqtt_backend.publish(message)
ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
yield (
b"--frame\r\n"
b"Content-Type: image/jpeg\r\n\r\n" + jpg.tobytes() + b"\r\n\r\n"
)

33
frigate/log.py
View File

@@ -13,38 +13,34 @@ from collections import deque
def listener_configurer():
root = logging.getLogger()
console_handler = logging.StreamHandler()
formatter = logging.Formatter('%(name)-30s %(levelname)-8s: %(message)s')
formatter = logging.Formatter(
"[%(asctime)s] %(name)-30s %(levelname)-8s: %(message)s", "%Y-%m-%d %H:%M:%S"
)
console_handler.setFormatter(formatter)
root.addHandler(console_handler)
root.setLevel(logging.INFO)
def root_configurer(queue):
h = handlers.QueueHandler(queue)
root = logging.getLogger()
root.addHandler(h)
root.setLevel(logging.INFO)
def log_process(log_queue):
stop_event = mp.Event()
def receiveSignal(signalNumber, frame):
stop_event.set()
signal.signal(signal.SIGTERM, receiveSignal)
signal.signal(signal.SIGINT, receiveSignal)
def log_process(log_queue):
threading.current_thread().name = f"logger"
setproctitle("frigate.logger")
listener_configurer()
while True:
if stop_event.is_set() and log_queue.empty():
break
try:
record = log_queue.get(timeout=5)
except queue.Empty:
except (queue.Empty, KeyboardInterrupt):
continue
logger = logging.getLogger(record.name)
logger.handle(record)
# based on https://codereview.stackexchange.com/a/17959
class LogPipe(threading.Thread):
def __init__(self, log_name, level):
@@ -61,23 +57,20 @@ class LogPipe(threading.Thread):
self.start()
def fileno(self):
"""Return the write file descriptor of the pipe
"""
"""Return the write file descriptor of the pipe"""
return self.fdWrite
def run(self):
"""Run the thread, logging everything.
"""
for line in iter(self.pipeReader.readline, ''):
self.deque.append(line.strip('\n'))
"""Run the thread, logging everything."""
for line in iter(self.pipeReader.readline, ""):
self.deque.append(line.strip("\n"))
self.pipeReader.close()
def dump(self):
while len(self.deque) > 0:
self.logger.log(self.level, self.deque.popleft())
def close(self):
"""Close the write end of the pipe.
"""
"""Close the write end of the pipe."""
os.close(self.fdWrite)

13
frigate/models.py
View File

@@ -1,3 +1,4 @@
from numpy import unique
from peewee import *
from playhouse.sqlite_ext import *
@@ -14,3 +15,15 @@ class Event(Model):
thumbnail = TextField()
has_clip = BooleanField(default=True)
has_snapshot = BooleanField(default=True)
region = JSONField()
box = JSONField()
area = IntegerField()
class Recordings(Model):
id = CharField(null=False, primary_key=True, max_length=30)
camera = CharField(index=True, max_length=20)
path = CharField(unique=True)
start_time = DateTimeField()
end_time = DateTimeField()
duration = FloatField()

52
frigate/motion.py
View File

@@ -4,26 +4,37 @@ import numpy as np
from frigate.config import MotionConfig
class MotionDetector():
class MotionDetector:
def __init__(self, frame_shape, config: MotionConfig):
self.config = config
self.frame_shape = frame_shape
self.resize_factor = frame_shape[0]/config.frame_height
self.motion_frame_size = (config.frame_height, config.frame_height*frame_shape[1]//frame_shape[0])
self.resize_factor = frame_shape[0] / config.frame_height
self.motion_frame_size = (
config.frame_height,
config.frame_height * frame_shape[1] // frame_shape[0],
)
self.avg_frame = np.zeros(self.motion_frame_size, np.float)
self.avg_delta = np.zeros(self.motion_frame_size, np.float)
self.motion_frame_count = 0
self.frame_counter = 0
resized_mask = cv2.resize(config.mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
self.mask = np.where(resized_mask==[0])
resized_mask = cv2.resize(
config.mask,
dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
interpolation=cv2.INTER_LINEAR,
)
self.mask = np.where(resized_mask == [0])
def detect(self, frame):
motion_boxes = []
gray = frame[0:self.frame_shape[0], 0:self.frame_shape[1]]
gray = frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]]
# resize frame
resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
resized_frame = cv2.resize(
gray,
dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
interpolation=cv2.INTER_LINEAR,
)
# TODO: can I improve the contrast of the grayscale image here?
@@ -48,7 +59,9 @@ class MotionDetector():
# compute the threshold image for the current frame
# TODO: threshold
current_thresh = cv2.threshold(frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
current_thresh = cv2.threshold(
frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY
)[1]
# black out everything in the avg_delta where there isnt motion in the current frame
avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
@@ -56,7 +69,9 @@ class MotionDetector():
# then look for deltas above the threshold, but only in areas where there is a delta
# in the current frame. this prevents deltas from previous frames from being included
thresh = cv2.threshold(avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.threshold(
avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY
)[1]
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
@@ -70,16 +85,27 @@ class MotionDetector():
contour_area = cv2.contourArea(c)
if contour_area > self.config.contour_area:
x, y, w, h = cv2.boundingRect(c)
motion_boxes.append((int(x*self.resize_factor), int(y*self.resize_factor), int((x+w)*self.resize_factor), int((y+h)*self.resize_factor)))
motion_boxes.append(
(
int(x * self.resize_factor),
int(y * self.resize_factor),
int((x + w) * self.resize_factor),
int((y + h) * self.resize_factor),
)
)
if len(motion_boxes) > 0:
self.motion_frame_count += 1
if self.motion_frame_count >= 10:
# only average in the current frame if the difference persists for a bit
cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
cv2.accumulateWeighted(
resized_frame, self.avg_frame, self.config.frame_alpha
)
else:
# when no motion, just keep averaging the frames together
cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
cv2.accumulateWeighted(
resized_frame, self.avg_frame, self.config.frame_alpha
)
self.motion_frame_count = 0
return motion_boxes

209
frigate/mqtt.py
View File

@@ -1,31 +1,42 @@
import json
import logging
import threading
from wsgiref.simple_server import make_server
import paho.mqtt.client as mqtt
from ws4py.server.wsgirefserver import (
WebSocketWSGIHandler,
WebSocketWSGIRequestHandler,
WSGIServer,
)
from ws4py.server.wsgiutils import WebSocketWSGIApplication
from ws4py.websocket import WebSocket
from frigate.config import FrigateConfig
from frigate.util import restart_frigate
logger = logging.getLogger(__name__)
def create_mqtt_client(config: FrigateConfig, camera_metrics):
mqtt_config = config.mqtt
def on_clips_command(client, userdata, message):
def on_recordings_command(client, userdata, message):
payload = message.payload.decode()
logger.debug(f"on_clips_toggle: {message.topic} {payload}")
logger.debug(f"on_recordings_toggle: {message.topic} {payload}")
camera_name = message.topic.split('/')[-3]
camera_name = message.topic.split("/")[-3]
clips_settings = config.cameras[camera_name].clips
record_settings = config.cameras[camera_name].record
if payload == 'ON':
if not clips_settings.enabled:
logger.info(f"Turning on clips for {camera_name} via mqtt")
clips_settings._enabled = True
elif payload == 'OFF':
if clips_settings.enabled:
logger.info(f"Turning off clips for {camera_name} via mqtt")
clips_settings._enabled = False
if payload == "ON":
if not record_settings.enabled:
logger.info(f"Turning on recordings for {camera_name} via mqtt")
record_settings.enabled = True
elif payload == "OFF":
if record_settings.enabled:
logger.info(f"Turning off recordings for {camera_name} via mqtt")
record_settings.enabled = False
else:
logger.warning(f"Received unsupported value at {message.topic}: {payload}")
@@ -36,74 +47,106 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
payload = message.payload.decode()
logger.debug(f"on_snapshots_toggle: {message.topic} {payload}")
camera_name = message.topic.split('/')[-3]
camera_name = message.topic.split("/")[-3]
snapshots_settings = config.cameras[camera_name].snapshots
if payload == 'ON':
if payload == "ON":
if not snapshots_settings.enabled:
logger.info(f"Turning on snapshots for {camera_name} via mqtt")
snapshots_settings._enabled = True
elif payload == 'OFF':
snapshots_settings.enabled = True
elif payload == "OFF":
if snapshots_settings.enabled:
logger.info(f"Turning off snapshots for {camera_name} via mqtt")
snapshots_settings._enabled = False
snapshots_settings.enabled = False
else:
logger.warning(f"Received unsupported value at {message.topic}: {payload}")
state_topic = f"{message.topic[:-4]}/state"
client.publish(state_topic, payload, retain=True)
def on_detect_command(client, userdata, message):
payload = message.payload.decode()
logger.debug(f"on_detect_toggle: {message.topic} {payload}")
camera_name = message.topic.split('/')[-3]
camera_name = message.topic.split("/")[-3]
detect_settings = config.cameras[camera_name].detect
if payload == 'ON':
if payload == "ON":
if not camera_metrics[camera_name]["detection_enabled"].value:
logger.info(f"Turning on detection for {camera_name} via mqtt")
camera_metrics[camera_name]["detection_enabled"].value = True
detect_settings._enabled = True
elif payload == 'OFF':
detect_settings.enabled = True
elif payload == "OFF":
if camera_metrics[camera_name]["detection_enabled"].value:
logger.info(f"Turning off detection for {camera_name} via mqtt")
camera_metrics[camera_name]["detection_enabled"].value = False
detect_settings._enabled = False
detect_settings.enabled = False
else:
logger.warning(f"Received unsupported value at {message.topic}: {payload}")
state_topic = f"{message.topic[:-4]}/state"
client.publish(state_topic, payload, retain=True)
def on_restart_command(client, userdata, message):
restart_frigate()
def on_connect(client, userdata, flags, rc):
threading.current_thread().name = "mqtt"
if rc != 0:
if rc == 3:
logger.error("MQTT Server unavailable")
logger.error("Unable to connect to MQTT server: MQTT Server unavailable")
elif rc == 4:
logger.error("MQTT Bad username or password")
logger.error("Unable to connect to MQTT server: MQTT Bad username or password")
elif rc == 5:
logger.error("MQTT Not authorized")
logger.error("Unable to connect to MQTT server: MQTT Not authorized")
else:
logger.error("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
logger.error(
"Unable to connect to MQTT server: Connection refused. Error code: "
+ str(rc)
)
logger.info("MQTT connected")
client.subscribe(f"{mqtt_config.topic_prefix}/#")
client.publish(mqtt_config.topic_prefix+'/available', 'online', retain=True)
client.publish(mqtt_config.topic_prefix + "/available", "online", retain=True)
client = mqtt.Client(client_id=mqtt_config.client_id)
client = mqtt.Client(client_id=mqtt_config.client_id)
client.on_connect = on_connect
client.will_set(mqtt_config.topic_prefix+'/available', payload='offline', qos=1, retain=True)
client.will_set(
mqtt_config.topic_prefix + "/available", payload="offline", qos=1, retain=True
)
# register callbacks
for name in config.cameras.keys():
client.message_callback_add(f"{mqtt_config.topic_prefix}/{name}/clips/set", on_clips_command)
client.message_callback_add(f"{mqtt_config.topic_prefix}/{name}/snapshots/set", on_snapshots_command)
client.message_callback_add(f"{mqtt_config.topic_prefix}/{name}/detect/set", on_detect_command)
client.message_callback_add(
f"{mqtt_config.topic_prefix}/{name}/recordings/set", on_recordings_command
)
client.message_callback_add(
f"{mqtt_config.topic_prefix}/{name}/snapshots/set", on_snapshots_command
)
client.message_callback_add(
f"{mqtt_config.topic_prefix}/{name}/detect/set", on_detect_command
)
client.message_callback_add(
f"{mqtt_config.topic_prefix}/restart", on_restart_command
)
if not mqtt_config.tls_ca_certs is None:
if (
not mqtt_config.tls_client_cert is None
and not mqtt_config.tls_client_key is None
):
client.tls_set(
mqtt_config.tls_ca_certs,
mqtt_config.tls_client_cert,
mqtt_config.tls_client_key,
)
else:
client.tls_set(mqtt_config.tls_ca_certs)
if not mqtt_config.tls_insecure is None:
client.tls_insecure_set(mqtt_config.tls_insecure)
if not mqtt_config.user is None:
client.username_pw_set(mqtt_config.user, password=mqtt_config.password)
try:
@@ -115,10 +158,96 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
client.loop_start()
for name in config.cameras.keys():
client.publish(f"{mqtt_config.topic_prefix}/{name}/clips/state", 'ON' if config.cameras[name].clips.enabled else 'OFF', retain=True)
client.publish(f"{mqtt_config.topic_prefix}/{name}/snapshots/state", 'ON' if config.cameras[name].snapshots.enabled else 'OFF', retain=True)
client.publish(f"{mqtt_config.topic_prefix}/{name}/detect/state", 'ON' if config.cameras[name].detect.enabled else 'OFF', retain=True)
client.subscribe(f"{mqtt_config.topic_prefix}/#")
client.publish(
f"{mqtt_config.topic_prefix}/{name}/recordings/state",
"ON" if config.cameras[name].record.enabled else "OFF",
retain=True,
)
client.publish(
f"{mqtt_config.topic_prefix}/{name}/snapshots/state",
"ON" if config.cameras[name].snapshots.enabled else "OFF",
retain=True,
)
client.publish(
f"{mqtt_config.topic_prefix}/{name}/detect/state",
"ON" if config.cameras[name].detect.enabled else "OFF",
retain=True,
)
return client
class MqttSocketRelay:
def __init__(self, mqtt_client, topic_prefix):
self.mqtt_client = mqtt_client
self.topic_prefix = topic_prefix
def start(self):
class MqttWebSocket(WebSocket):
topic_prefix = self.topic_prefix
mqtt_client = self.mqtt_client
def received_message(self, message):
try:
json_message = json.loads(message.data.decode("utf-8"))
json_message = {
"topic": f"{self.topic_prefix}/{json_message['topic']}",
"payload": json_message.get("payload"),
"retain": json_message.get("retain", False),
}
except Exception as e:
logger.warning("Unable to parse websocket message as valid json.")
return
logger.debug(
f"Publishing mqtt message from websockets at {json_message['topic']}."
)
self.mqtt_client.publish(
json_message["topic"],
json_message["payload"],
retain=json_message["retain"],
)
# start a websocket server on 5002
WebSocketWSGIHandler.http_version = "1.1"
self.websocket_server = make_server(
"127.0.0.1",
5002,
server_class=WSGIServer,
handler_class=WebSocketWSGIRequestHandler,
app=WebSocketWSGIApplication(handler_cls=MqttWebSocket),
)
self.websocket_server.initialize_websockets_manager()
self.websocket_thread = threading.Thread(
target=self.websocket_server.serve_forever
)
def send(client, userdata, message):
"""Sends mqtt messages to clients."""
try:
logger.debug(f"Received mqtt message on {message.topic}.")
ws_message = json.dumps(
{
"topic": message.topic.replace(f"{self.topic_prefix}/", ""),
"payload": message.payload.decode(),
}
)
except Exception as e:
# if the payload can't be decoded don't relay to clients
logger.debug(
f"MQTT payload for {message.topic} wasn't text. Skipping..."
)
return
self.websocket_server.manager.broadcast(ws_message)
self.mqtt_client.message_callback_add(f"{self.topic_prefix}/#", send)
self.websocket_thread.start()
def stop(self):
self.websocket_server.manager.close_all()
self.websocket_server.manager.stop()
self.websocket_server.manager.join()
self.websocket_server.shutdown()
self.websocket_thread.join()

704
frigate/object_processing.py
View File

File diff suppressed because it is too large Load Diff

123
frigate/objects.py
View File

@@ -16,24 +16,24 @@ from frigate.config import DetectConfig
from frigate.util import draw_box_with_label
class ObjectTracker():
class ObjectTracker:
def __init__(self, config: DetectConfig):
self.tracked_objects = {}
self.disappeared = {}
self.max_disappeared = config.max_disappeared
def register(self, index, obj):
rand_id = ''.join(random.choices(string.ascii_lowercase + string.digits, k=6))
rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
id = f"{obj['frame_time']}-{rand_id}"
obj['id'] = id
obj['start_time'] = obj['frame_time']
obj["id"] = id
obj["start_time"] = obj["frame_time"]
self.tracked_objects[id] = obj
self.disappeared[id] = 0
def deregister(self, id):
del self.tracked_objects[id]
del self.disappeared[id]
def update(self, id, new_obj):
self.disappeared[id] = 0
self.tracked_objects[id].update(new_obj)
@@ -42,97 +42,90 @@ class ObjectTracker():
# group by name
new_object_groups = defaultdict(lambda: [])
for obj in new_objects:
new_object_groups[obj[0]].append({
'label': obj[0],
'score': obj[1],
'box': obj[2],
'area': obj[3],
'region': obj[4],
'frame_time': frame_time
})
new_object_groups[obj[0]].append(
{
"label": obj[0],
"score": obj[1],
"box": obj[2],
"area": obj[3],
"region": obj[4],
"frame_time": frame_time,
}
)
# update any tracked objects with labels that are not
# seen in the current objects and deregister if needed
for obj in list(self.tracked_objects.values()):
if not obj['label'] in new_object_groups:
if self.disappeared[obj['id']] >= self.max_disappeared:
self.deregister(obj['id'])
if not obj["label"] in new_object_groups:
if self.disappeared[obj["id"]] >= self.max_disappeared:
self.deregister(obj["id"])
else:
self.disappeared[obj['id']] += 1
self.disappeared[obj["id"]] += 1
if len(new_objects) == 0:
return
# track objects for each label type
for label, group in new_object_groups.items():
current_objects = [o for o in self.tracked_objects.values() if o['label'] == label]
current_ids = [o['id'] for o in current_objects]
current_centroids = np.array([o['centroid'] for o in current_objects])
current_objects = [
o for o in self.tracked_objects.values() if o["label"] == label
]
current_ids = [o["id"] for o in current_objects]
current_centroids = np.array([o["centroid"] for o in current_objects])
# compute centroids of new objects
for obj in group:
centroid_x = int((obj['box'][0]+obj['box'][2]) / 2.0)
centroid_y = int((obj['box'][1]+obj['box'][3]) / 2.0)
obj['centroid'] = (centroid_x, centroid_y)
centroid_x = int((obj["box"][0] + obj["box"][2]) / 2.0)
centroid_y = int((obj["box"][1] + obj["box"][3]) / 2.0)
obj["centroid"] = (centroid_x, centroid_y)
if len(current_objects) == 0:
for index, obj in enumerate(group):
self.register(index, obj)
return
new_centroids = np.array([o['centroid'] for o in group])
continue
new_centroids = np.array([o["centroid"] for o in group])
# compute the distance between each pair of tracked
# centroids and new centroids, respectively -- our
# goal will be to match each new centroid to an existing
# goal will be to match each current centroid to a new
# object centroid
D = dist.cdist(current_centroids, new_centroids)
# in order to perform this matching we must (1) find the
# smallest value in each row and then (2) sort the row
# indexes based on their minimum values so that the row
# with the smallest value is at the *front* of the index
# list
# in order to perform this matching we must (1) find the smallest
# value in each row (i.e. the distance from each current object to
# the closest new object) and then (2) sort the row indexes based
# on their minimum values so that the row with the smallest
# distance (the best match) is at the *front* of the index list
rows = D.min(axis=1).argsort()
# next, we perform a similar process on the columns by
# finding the smallest value in each column and then
# sorting using the previously computed row index list
# next, we determine which new object each existing object matched
# against, and apply the same sorting as was applied previously
cols = D.argmin(axis=1)[rows]
# in order to determine if we need to update, register,
# or deregister an object we need to keep track of which
# of the rows and column indexes we have already examined
usedRows = set()
usedCols = set()
# many current objects may register with each new object, so only
# match the closest ones. unique returns the indices of the first
# occurrences of each value, and because the rows are sorted by
# distance, this will be index of the closest match
_, index = np.unique(cols, return_index=True)
rows = rows[index]
cols = cols[index]
# loop over the combination of the (row, column) index
# tuples
for (row, col) in zip(rows, cols):
# if we have already examined either the row or
# column value before, ignore it
if row in usedRows or col in usedCols:
continue
# otherwise, grab the object ID for the current row,
# set its new centroid, and reset the disappeared
# counter
# loop over the combination of the (row, column) index tuples
for row, col in zip(rows, cols):
# grab the object ID for the current row, set its new centroid,
# and reset the disappeared counter
objectID = current_ids[row]
self.update(objectID, group[col])
# indicate that we have examined each of the row and
# column indexes, respectively
usedRows.add(row)
usedCols.add(col)
# compute the column index we have NOT yet examined
unusedRows = set(range(0, D.shape[0])).difference(usedRows)
unusedCols = set(range(0, D.shape[1])).difference(usedCols)
# compute the row and column indices we have NOT yet examined
unusedRows = set(range(D.shape[0])).difference(rows)
unusedCols = set(range(D.shape[1])).difference(cols)
# in the event that the number of object centroids is
# equal or greater than the number of input centroids
# we need to check and see if some of these objects have
# potentially disappeared
# equal or greater than the number of input centroids
# we need to check and see if some of these objects have
# potentially disappeared
if D.shape[0] >= D.shape[1]:
for row in unusedRows:
id = current_ids[row]

455
frigate/output.py Normal file
View File

@@ -0,0 +1,455 @@
import datetime
import glob
import logging
import math
import multiprocessing as mp
import queue
import signal
import subprocess as sp
import threading
from multiprocessing import shared_memory
from wsgiref.simple_server import make_server
import cv2
import numpy as np
from setproctitle import setproctitle
from ws4py.server.wsgirefserver import (
WebSocketWSGIHandler,
WebSocketWSGIRequestHandler,
WSGIServer,
)
from ws4py.server.wsgiutils import WebSocketWSGIApplication
from ws4py.websocket import WebSocket
from frigate.config import BirdseyeModeEnum, FrigateConfig
from frigate.util import SharedMemoryFrameManager, copy_yuv_to_position, get_yuv_crop
logger = logging.getLogger(__name__)
class FFMpegConverter:
def __init__(self, in_width, in_height, out_width, out_height, quality):
ffmpeg_cmd = f"ffmpeg -f rawvideo -pix_fmt yuv420p -video_size {in_width}x{in_height} -i pipe: -f mpegts -s {out_width}x{out_height} -codec:v mpeg1video -q {quality} -bf 0 pipe:".split(
" "
)
self.process = sp.Popen(
ffmpeg_cmd,
stdout=sp.PIPE,
stderr=sp.DEVNULL,
stdin=sp.PIPE,
start_new_session=True,
)
def write(self, b):
self.process.stdin.write(b)
def read(self, length):
try:
return self.process.stdout.read1(length)
except ValueError:
return False
def exit(self):
self.process.terminate()
try:
self.process.communicate(timeout=30)
except sp.TimeoutExpired:
self.process.kill()
self.process.communicate()
class BroadcastThread(threading.Thread):
def __init__(self, camera, converter, websocket_server):
super(BroadcastThread, self).__init__()
self.camera = camera
self.converter = converter
self.websocket_server = websocket_server
def run(self):
while True:
buf = self.converter.read(65536)
if buf:
manager = self.websocket_server.manager
with manager.lock:
websockets = manager.websockets.copy()
ws_iter = iter(websockets.values())
for ws in ws_iter:
if (
not ws.terminated
and ws.environ["PATH_INFO"] == f"/{self.camera}"
):
try:
ws.send(buf, binary=True)
except:
pass
elif self.converter.process.poll() is not None:
break
class BirdsEyeFrameManager:
def __init__(self, config, frame_manager: SharedMemoryFrameManager):
self.config = config
self.mode = config.birdseye.mode
self.frame_manager = frame_manager
width = config.birdseye.width
height = config.birdseye.height
self.frame_shape = (height, width)
self.yuv_shape = (height * 3 // 2, width)
self.frame = np.ndarray(self.yuv_shape, dtype=np.uint8)
# initialize the frame as black and with the frigate logo
self.blank_frame = np.zeros(self.yuv_shape, np.uint8)
self.blank_frame[:] = 128
self.blank_frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]] = 16
# find and copy the logo on the blank frame
logo_files = glob.glob("/opt/frigate/web/apple-touch-icon.*.png")
frigate_logo = None
if len(logo_files) > 0:
frigate_logo = cv2.imread(logo_files[0], cv2.IMREAD_UNCHANGED)
if not frigate_logo is None:
transparent_layer = frigate_logo[:, :, 3]
y_offset = height // 2 - transparent_layer.shape[0] // 2
x_offset = width // 2 - transparent_layer.shape[1] // 2
self.blank_frame[
y_offset : y_offset + transparent_layer.shape[1],
x_offset : x_offset + transparent_layer.shape[0],
] = transparent_layer
else:
logger.warning("Unable to read frigate logo")
self.frame[:] = self.blank_frame
self.cameras = {}
for camera, settings in self.config.cameras.items():
# precalculate the coordinates for all the channels
y, u1, u2, v1, v2 = get_yuv_crop(
settings.frame_shape_yuv,
(
0,
0,
settings.frame_shape[1],
settings.frame_shape[0],
),
)
self.cameras[camera] = {
"last_active_frame": 0.0,
"current_frame": 0.0,
"layout_frame": 0.0,
"channel_dims": {
"y": y,
"u1": u1,
"u2": u2,
"v1": v1,
"v2": v2,
},
}
self.camera_layout = []
self.active_cameras = set()
self.layout_dim = 0
self.last_output_time = 0.0
def clear_frame(self):
logger.debug(f"Clearing the birdseye frame")
self.frame[:] = self.blank_frame
def copy_to_position(self, position, camera=None, frame_time=None):
if camera is None:
frame = None
channel_dims = None
else:
try:
frame = self.frame_manager.get(
f"{camera}{frame_time}", self.config.cameras[camera].frame_shape_yuv
)
except FileNotFoundError:
# TODO: better frame management would prevent this edge case
logger.warning(
f"Unable to copy frame {camera}{frame_time} to birdseye."
)
return
channel_dims = self.cameras[camera]["channel_dims"]
copy_yuv_to_position(
self.frame,
self.layout_offsets[position],
self.layout_frame_shape,
frame,
channel_dims,
)
def camera_active(self, object_box_count, motion_box_count):
if self.mode == BirdseyeModeEnum.continuous:
return True
if (
self.mode == BirdseyeModeEnum.motion
and object_box_count + motion_box_count > 0
):
return True
if self.mode == BirdseyeModeEnum.objects and object_box_count > 0:
return True
def update_frame(self):
# determine how many cameras are tracking objects within the last 30 seconds
active_cameras = set(
[
cam
for cam, cam_data in self.cameras.items()
if cam_data["last_active_frame"] > 0
and cam_data["current_frame"] - cam_data["last_active_frame"] < 30
]
)
# if there are no active cameras
if len(active_cameras) == 0:
# if the layout is already cleared
if len(self.camera_layout) == 0:
return False
# if the layout needs to be cleared
else:
self.camera_layout = []
self.layout_dim = 0
self.clear_frame()
return True
# calculate layout dimensions
layout_dim = math.ceil(math.sqrt(len(active_cameras)))
# reset the layout if it needs to be different
if layout_dim != self.layout_dim:
logger.debug(f"Changing layout size from {self.layout_dim} to {layout_dim}")
self.layout_dim = layout_dim
self.camera_layout = [None] * layout_dim * layout_dim
# calculate resolution of each position in the layout
self.layout_frame_shape = (
self.frame_shape[0] // layout_dim, # height
self.frame_shape[1] // layout_dim, # width
)
self.clear_frame()
for cam_data in self.cameras.values():
cam_data["layout_frame"] = 0.0
self.active_cameras = set()
self.layout_offsets = []
# calculate the x and y offset for each position in the layout
for position in range(0, len(self.camera_layout)):
y_offset = self.layout_frame_shape[0] * math.floor(
position / self.layout_dim
)
x_offset = self.layout_frame_shape[1] * (position % self.layout_dim)
self.layout_offsets.append((y_offset, x_offset))
removed_cameras = self.active_cameras.difference(active_cameras)
added_cameras = active_cameras.difference(self.active_cameras)
self.active_cameras = active_cameras
# update each position in the layout
for position, camera in enumerate(self.camera_layout, start=0):
# if this camera was removed, replace it or clear it
if camera in removed_cameras:
# if replacing this camera with a newly added one
if len(added_cameras) > 0:
added_camera = added_cameras.pop()
self.camera_layout[position] = added_camera
self.copy_to_position(
position,
added_camera,
self.cameras[added_camera]["current_frame"],
)
self.cameras[added_camera]["layout_frame"] = self.cameras[
added_camera
]["current_frame"]
# if removing this camera with no replacement
else:
self.camera_layout[position] = None
self.copy_to_position(position)
removed_cameras.remove(camera)
# if an empty spot and there are cameras to add
elif camera is None and len(added_cameras) > 0:
added_camera = added_cameras.pop()
self.camera_layout[position] = added_camera
self.copy_to_position(
position,
added_camera,
self.cameras[added_camera]["current_frame"],
)
self.cameras[added_camera]["layout_frame"] = self.cameras[added_camera][
"current_frame"
]
# if not an empty spot and the camera has a newer frame, copy it
elif (
not camera is None
and self.cameras[camera]["current_frame"]
!= self.cameras[camera]["layout_frame"]
):
self.copy_to_position(
position, camera, self.cameras[camera]["current_frame"]
)
self.cameras[camera]["layout_frame"] = self.cameras[camera][
"current_frame"
]
return True
def update(self, camera, object_count, motion_count, frame_time, frame) -> bool:
# update the last active frame for the camera
self.cameras[camera]["current_frame"] = frame_time
if self.camera_active(object_count, motion_count):
self.cameras[camera]["last_active_frame"] = frame_time
now = datetime.datetime.now().timestamp()
# limit output to 10 fps
if (now - self.last_output_time) < 1 / 10:
return False
# if the frame was updated or the fps is too low, send frame
if self.update_frame() or (now - self.last_output_time) > 1:
self.last_output_time = now
return True
return False
def output_frames(config: FrigateConfig, video_output_queue):
threading.current_thread().name = f"output"
setproctitle(f"frigate.output")
stop_event = mp.Event()
def receiveSignal(signalNumber, frame):
stop_event.set()
signal.signal(signal.SIGTERM, receiveSignal)
signal.signal(signal.SIGINT, receiveSignal)
frame_manager = SharedMemoryFrameManager()
previous_frames = {}
# start a websocket server on 8082
WebSocketWSGIHandler.http_version = "1.1"
websocket_server = make_server(
"127.0.0.1",
8082,
server_class=WSGIServer,
handler_class=WebSocketWSGIRequestHandler,
app=WebSocketWSGIApplication(handler_cls=WebSocket),
)
websocket_server.initialize_websockets_manager()
websocket_thread = threading.Thread(target=websocket_server.serve_forever)
converters = {}
broadcasters = {}
for camera, cam_config in config.cameras.items():
width = int(
cam_config.live.height
* (cam_config.frame_shape[1] / cam_config.frame_shape[0])
)
converters[camera] = FFMpegConverter(
cam_config.frame_shape[1],
cam_config.frame_shape[0],
width,
cam_config.live.height,
cam_config.live.quality,
)
broadcasters[camera] = BroadcastThread(
camera, converters[camera], websocket_server
)
if config.birdseye.enabled:
converters["birdseye"] = FFMpegConverter(
config.birdseye.width,
config.birdseye.height,
config.birdseye.width,
config.birdseye.height,
config.birdseye.quality,
)
broadcasters["birdseye"] = BroadcastThread(
"birdseye", converters["birdseye"], websocket_server
)
websocket_thread.start()
for t in broadcasters.values():
t.start()
birdseye_manager = BirdsEyeFrameManager(config, frame_manager)
while not stop_event.is_set():
try:
(
camera,
frame_time,
current_tracked_objects,
motion_boxes,
regions,
) = video_output_queue.get(True, 10)
except queue.Empty:
continue
frame_id = f"{camera}{frame_time}"
frame = frame_manager.get(frame_id, config.cameras[camera].frame_shape_yuv)
# send camera frame to ffmpeg process if websockets are connected
if any(
ws.environ["PATH_INFO"].endswith(camera) for ws in websocket_server.manager
):
# write to the converter for the camera if clients are listening to the specific camera
converters[camera].write(frame.tobytes())
# update birdseye if websockets are connected
if config.birdseye.enabled and any(
ws.environ["PATH_INFO"].endswith("birdseye")
for ws in websocket_server.manager
):
if birdseye_manager.update(
camera,
len(current_tracked_objects),
len(motion_boxes),
frame_time,
frame,
):
converters["birdseye"].write(birdseye_manager.frame.tobytes())
if camera in previous_frames:
frame_manager.delete(f"{camera}{previous_frames[camera]}")
previous_frames[camera] = frame_time
while not video_output_queue.empty():
(
camera,
frame_time,
current_tracked_objects,
motion_boxes,
regions,
) = video_output_queue.get(True, 10)
frame_id = f"{camera}{frame_time}"
frame = frame_manager.get(frame_id, config.cameras[camera].frame_shape_yuv)
frame_manager.delete(frame_id)
for c in converters.values():
c.exit()
for b in broadcasters.values():
b.join()
websocket_server.manager.close_all()
websocket_server.manager.stop()
websocket_server.manager.join()
websocket_server.shutdown()
websocket_thread.join()
logger.info("exiting output process...")

257
frigate/process_clip.py
View File

@@ -14,39 +14,41 @@ import numpy as np
from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
from frigate.edgetpu import LocalObjectDetector
from frigate.motion import MotionDetector
from frigate.object_processing import COLOR_MAP, CameraState
from frigate.object_processing import CameraState
from frigate.objects import ObjectTracker
from frigate.util import (DictFrameManager, EventsPerSecond,
SharedMemoryFrameManager, draw_box_with_label)
from frigate.video import (capture_frames, process_frames,
start_or_restart_ffmpeg)
from frigate.util import (
DictFrameManager,
EventsPerSecond,
SharedMemoryFrameManager,
draw_box_with_label,
)
from frigate.video import capture_frames, process_frames, start_or_restart_ffmpeg
logging.basicConfig()
logging.root.setLevel(logging.DEBUG)
logger = logging.getLogger(__name__)
def get_frame_shape(source):
ffprobe_cmd = " ".join([
'ffprobe',
'-v',
'panic',
'-show_error',
'-show_streams',
'-of',
'json',
'"'+source+'"'
])
p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
(output, err) = p.communicate()
p_status = p.wait()
info = json.loads(output)
ffprobe_cmd = [
"ffprobe",
"-v",
"panic",
"-show_error",
"-show_streams",
"-of",
"json",
source,
]
p = sp.run(ffprobe_cmd, capture_output=True)
info = json.loads(p.stdout)
video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
video_info = [s for s in info["streams"] if s["codec_type"] == "video"][0]
if video_info["height"] != 0 and video_info["width"] != 0:
return (video_info["height"], video_info["width"], 3)
if video_info['height'] != 0 and video_info['width'] != 0:
return (video_info['height'], video_info['width'], 3)
# fallback to using opencv if ffprobe didnt succeed
video = cv2.VideoCapture(source)
ret, frame = video.read()
@@ -54,14 +56,17 @@ def get_frame_shape(source):
video.release()
return frame_shape
class ProcessClip():
class ProcessClip:
def __init__(self, clip_path, frame_shape, config: FrigateConfig):
self.clip_path = clip_path
self.camera_name = 'camera'
self.camera_name = "camera"
self.config = config
self.camera_config = self.config.cameras['camera']
self.camera_config = self.config.cameras["camera"]
self.frame_shape = self.camera_config.frame_shape
self.ffmpeg_cmd = [c['cmd'] for c in self.camera_config.ffmpeg_cmds if 'detect' in c['roles']][0]
self.ffmpeg_cmd = [
c["cmd"] for c in self.camera_config.ffmpeg_cmds if "detect" in c["roles"]
][0]
self.frame_manager = SharedMemoryFrameManager()
self.frame_queue = mp.Queue()
self.detected_objects_queue = mp.Queue()
@@ -70,37 +75,66 @@ class ProcessClip():
def load_frames(self):
fps = EventsPerSecond()
skipped_fps = EventsPerSecond()
current_frame = mp.Value('d', 0.0)
frame_size = self.camera_config.frame_shape_yuv[0] * self.camera_config.frame_shape_yuv[1]
ffmpeg_process = start_or_restart_ffmpeg(self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size)
capture_frames(ffmpeg_process, self.camera_name, self.camera_config.frame_shape_yuv, self.frame_manager,
self.frame_queue, fps, skipped_fps, current_frame)
current_frame = mp.Value("d", 0.0)
frame_size = (
self.camera_config.frame_shape_yuv[0]
* self.camera_config.frame_shape_yuv[1]
)
ffmpeg_process = start_or_restart_ffmpeg(
self.ffmpeg_cmd, logger, sp.DEVNULL, frame_size
)
capture_frames(
ffmpeg_process,
self.camera_name,
self.camera_config.frame_shape_yuv,
self.frame_manager,
self.frame_queue,
fps,
skipped_fps,
current_frame,
)
ffmpeg_process.wait()
ffmpeg_process.communicate()
def process_frames(self, objects_to_track=['person'], object_filters={}):
def process_frames(self, objects_to_track=["person"], object_filters={}):
mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
mask[:] = 255
motion_detector = MotionDetector(self.frame_shape, mask, self.camera_config.motion)
motion_detector = MotionDetector(
self.frame_shape, mask, self.camera_config.motion
)
object_detector = LocalObjectDetector(labels='/labelmap.txt')
object_detector = LocalObjectDetector(labels="/labelmap.txt")
object_tracker = ObjectTracker(self.camera_config.detect)
process_info = {
'process_fps': mp.Value('d', 0.0),
'detection_fps': mp.Value('d', 0.0),
'detection_frame': mp.Value('d', 0.0)
"process_fps": mp.Value("d", 0.0),
"detection_fps": mp.Value("d", 0.0),
"detection_frame": mp.Value("d", 0.0),
}
stop_event = mp.Event()
model_shape = (self.config.model.height, self.config.model.width)
process_frames(self.camera_name, self.frame_queue, self.frame_shape, model_shape,
self.frame_manager, motion_detector, object_detector, object_tracker,
self.detected_objects_queue, process_info,
objects_to_track, object_filters, mask, stop_event, exit_on_empty=True)
process_frames(
self.camera_name,
self.frame_queue,
self.frame_shape,
model_shape,
self.frame_manager,
motion_detector,
object_detector,
object_tracker,
self.detected_objects_queue,
process_info,
objects_to_track,
object_filters,
mask,
stop_event,
exit_on_empty=True,
)
def top_object(self, debug_path=None):
obj_detected = False
top_computed_score = 0.0
def handle_event(name, obj, frame_time):
nonlocal obj_detected
nonlocal top_computed_score
@@ -108,48 +142,85 @@ class ProcessClip():
top_computed_score = obj.computed_score
if not obj.false_positive:
obj_detected = True
self.camera_state.on('new', handle_event)
self.camera_state.on('update', handle_event)
while(not self.detected_objects_queue.empty()):
camera_name, frame_time, current_tracked_objects, motion_boxes, regions = self.detected_objects_queue.get()
self.camera_state.on("new", handle_event)
self.camera_state.on("update", handle_event)
while not self.detected_objects_queue.empty():
(
camera_name,
frame_time,
current_tracked_objects,
motion_boxes,
regions,
) = self.detected_objects_queue.get()
if not debug_path is None:
self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values())
self.save_debug_frame(
debug_path, frame_time, current_tracked_objects.values()
)
self.camera_state.update(
frame_time, current_tracked_objects, motion_boxes, regions
)
self.camera_state.update(frame_time, current_tracked_objects, motion_boxes, regions)
self.frame_manager.delete(self.camera_state.previous_frame_id)
return {
'object_detected': obj_detected,
'top_score': top_computed_score
}
return {"object_detected": obj_detected, "top_score": top_computed_score}
def save_debug_frame(self, debug_path, frame_time, tracked_objects):
current_frame = cv2.cvtColor(self.frame_manager.get(f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv), cv2.COLOR_YUV2BGR_I420)
current_frame = cv2.cvtColor(
self.frame_manager.get(
f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv
),
cv2.COLOR_YUV2BGR_I420,
)
# draw the bounding boxes on the frame
for obj in tracked_objects:
thickness = 2
color = (0,0,175)
color = (0, 0, 175)
if obj['frame_time'] != frame_time:
if obj["frame_time"] != frame_time:
thickness = 1
color = (255,0,0)
color = (255, 0, 0)
else:
color = (255,255,0)
color = (255, 255, 0)
# draw the bounding boxes on the frame
box = obj['box']
draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['id'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
box = obj["box"]
draw_box_with_label(
current_frame,
box[0],
box[1],
box[2],
box[3],
obj["id"],
f"{int(obj['score']*100)}% {int(obj['area'])}",
thickness=thickness,
color=color,
)
# draw the regions on the frame
region = obj['region']
draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0))
cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", current_frame)
region = obj["region"]
draw_box_with_label(
current_frame,
region[0],
region[1],
region[2],
region[3],
"region",
"",
thickness=1,
color=(0, 255, 0),
)
cv2.imwrite(
f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg",
current_frame,
)
@click.command()
@click.option("-p", "--path", required=True, help="Path to clip or directory to test.")
@click.option("-l", "--label", default='person', help="Label name to detect.")
@click.option("-l", "--label", default="person", help="Label name to detect.")
@click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.")
@click.option("-s", "--scores", default=None, help="File to save csv of top scores")
@click.option("--debug-path", default=None, help="Path to output frames for debugging.")
@@ -159,34 +230,37 @@ def process(path, label, threshold, scores, debug_path):
files = os.listdir(path)
files.sort()
clips = [os.path.join(path, file) for file in files]
elif os.path.isfile(path):
elif os.path.isfile(path):
clips.append(path)
json_config = {
'mqtt': {
'host': 'mqtt'
},
'cameras': {
'camera': {
'ffmpeg': {
'inputs': [
{ 'path': 'path.mp4', 'global_args': '', 'input_args': '', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"cameras": {
"camera": {
"ffmpeg": {
"inputs": [
{
"path": "path.mp4",
"global_args": "",
"input_args": "",
"roles": ["detect"],
}
]
},
'height': 1920,
'width': 1080
"height": 1920,
"width": 1080,
}
}
},
}
results = []
for c in clips:
logger.info(c)
frame_shape = get_frame_shape(c)
json_config['cameras']['camera']['height'] = frame_shape[0]
json_config['cameras']['camera']['width'] = frame_shape[1]
json_config['cameras']['camera']['ffmpeg']['inputs'][0]['path'] = c
json_config["cameras"]["camera"]["height"] = frame_shape[0]
json_config["cameras"]["camera"]["width"] = frame_shape[1]
json_config["cameras"]["camera"]["ffmpeg"]["inputs"][0]["path"] = c
config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config))
@@ -197,12 +271,15 @@ def process(path, label, threshold, scores, debug_path):
results.append((c, process_clip.top_object(debug_path)))
if not scores is None:
with open(scores, 'w') as writer:
with open(scores, "w") as writer:
for result in results:
writer.write(f"{result[0]},{result[1]['top_score']}\n")
positive_count = sum(1 for result in results if result[1]['object_detected'])
print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).")
if __name__ == '__main__':
positive_count = sum(1 for result in results if result[1]["object_detected"])
print(
f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s)."
)
if __name__ == "__main__":
process()

324
frigate/record.py
View File

@@ -1,125 +1,305 @@
import datetime
import json
import itertools
import logging
import os
import queue
import random
import shutil
import string
import subprocess as sp
import threading
import time
from collections import defaultdict
from pathlib import Path
import psutil
from peewee import JOIN, DoesNotExist
from frigate.config import FrigateConfig
from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
from frigate.const import CACHE_DIR, RECORD_DIR
from frigate.models import Event, Recordings
logger = logging.getLogger(__name__)
SECONDS_IN_DAY = 60 * 60 * 24
def remove_empty_directories(directory):
# list all directories recursively and sort them by path,
# longest first
paths = sorted(
[x[0] for x in os.walk(RECORD_DIR)],
key=lambda p: len(str(p)),
reverse=True,
)
for path in paths:
# don't delete the parent
if path == RECORD_DIR:
continue
if len(os.listdir(path)) == 0:
os.rmdir(path)
# list all directories recursively and sort them by path,
# longest first
paths = sorted(
[x[0] for x in os.walk(RECORD_DIR)],
key=lambda p: len(str(p)),
reverse=True,
)
for path in paths:
# don't delete the parent
if path == RECORD_DIR:
continue
if len(os.listdir(path)) == 0:
os.rmdir(path)
class RecordingMaintainer(threading.Thread):
def __init__(self, config: FrigateConfig, stop_event):
threading.Thread.__init__(self)
self.name = 'recording_maint'
self.name = "recording_maint"
self.config = config
self.stop_event = stop_event
def move_files(self):
recordings = [d for d in os.listdir(RECORD_DIR) if os.path.isfile(os.path.join(RECORD_DIR, d)) and d.endswith(".mp4")]
recordings = [
d
for d in os.listdir(CACHE_DIR)
if os.path.isfile(os.path.join(CACHE_DIR, d))
and d.endswith(".mp4")
and not d.startswith("clip_")
]
files_in_use = []
for process in psutil.process_iter():
try:
if process.name() != 'ffmpeg':
if process.name() != "ffmpeg":
continue
flist = process.open_files()
if flist:
for nt in flist:
if nt.path.startswith(RECORD_DIR):
files_in_use.append(nt.path.split('/')[-1])
if nt.path.startswith(CACHE_DIR):
files_in_use.append(nt.path.split("/")[-1])
except:
continue
for f in recordings:
# Skip files currently in use
if f in files_in_use:
continue
camera = '-'.join(f.split('-')[:-1])
start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
ffprobe_cmd = " ".join([
'ffprobe',
'-v',
'error',
'-show_entries',
'format=duration',
'-of',
'default=noprint_wrappers=1:nokey=1',
f"{os.path.join(RECORD_DIR,f)}"
])
p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
(output, err) = p.communicate()
p_status = p.wait()
if p_status == 0:
duration = float(output.decode('utf-8').strip())
else:
logger.info(f"bad file: {f}")
os.remove(os.path.join(RECORD_DIR,f))
cache_path = os.path.join(CACHE_DIR, f)
basename = os.path.splitext(f)[0]
camera, date = basename.rsplit("-", maxsplit=1)
start_time = datetime.datetime.strptime(date, "%Y%m%d%H%M%S")
# Just delete files if recordings are turned off
if (
not camera in self.config.cameras
or not self.config.cameras[camera].record.enabled
):
Path(cache_path).unlink(missing_ok=True)
continue
directory = os.path.join(RECORD_DIR, start_time.strftime('%Y-%m/%d/%H'), camera)
ffprobe_cmd = [
"ffprobe",
"-v",
"error",
"-show_entries",
"format=duration",
"-of",
"default=noprint_wrappers=1:nokey=1",
f"{cache_path}",
]
p = sp.run(ffprobe_cmd, capture_output=True)
if p.returncode == 0:
duration = float(p.stdout.decode().strip())
end_time = start_time + datetime.timedelta(seconds=duration)
else:
logger.warning(f"Discarding a corrupt recording segment: {f}")
Path(cache_path).unlink(missing_ok=True)
continue
directory = os.path.join(
RECORD_DIR, start_time.strftime("%Y-%m/%d/%H"), camera
)
if not os.path.exists(directory):
os.makedirs(directory)
file_name = f"{start_time.strftime('%M.%S.mp4')}"
file_path = os.path.join(directory, file_name)
os.rename(os.path.join(RECORD_DIR,f), os.path.join(directory,file_name))
# copy then delete is required when recordings are stored on some network drives
shutil.copyfile(cache_path, file_path)
os.remove(cache_path)
def expire_files(self):
delete_before = {}
for name, camera in self.config.cameras.items():
delete_before[name] = datetime.datetime.now().timestamp() - SECONDS_IN_DAY*camera.record.retain_days
for p in Path('/media/frigate/recordings').rglob("*.mp4"):
if not p.parent.name in delete_before:
continue
if p.stat().st_mtime < delete_before[p.parent.name]:
p.unlink(missing_ok=True)
rand_id = "".join(
random.choices(string.ascii_lowercase + string.digits, k=6)
)
Recordings.create(
id=f"{start_time.timestamp()}-{rand_id}",
camera=camera,
path=file_path,
start_time=start_time.timestamp(),
end_time=end_time.timestamp(),
duration=duration,
)
def run(self):
counter = 0
self.expire_files()
while(True):
if self.stop_event.is_set():
logger.info(f"Exiting recording maintenance...")
break
# only expire events every 10 minutes, but check for new files every 10 seconds
time.sleep(10)
counter = counter + 1
if counter > 60:
self.expire_files()
remove_empty_directories(RECORD_DIR)
counter = 0
# Check for new files every 5 seconds
while not self.stop_event.wait(5):
self.move_files()
logger.info(f"Exiting recording maintenance...")
class RecordingCleanup(threading.Thread):
def __init__(self, config: FrigateConfig, stop_event):
threading.Thread.__init__(self)
self.name = "recording_cleanup"
self.config = config
self.stop_event = stop_event
def clean_tmp_clips(self):
# delete any clips more than 5 minutes old
for p in Path("/tmp/cache").rglob("clip_*.mp4"):
logger.debug(f"Checking tmp clip {p}.")
if p.stat().st_mtime < (datetime.datetime.now().timestamp() - 60 * 1):
logger.debug("Deleting tmp clip.")
p.unlink(missing_ok=True)
def expire_recordings(self):
logger.debug("Start expire recordings (new).")
logger.debug("Start deleted cameras.")
# Handle deleted cameras
expire_days = self.config.record.retain_days
expire_before = (
datetime.datetime.now() - datetime.timedelta(days=expire_days)
).timestamp()
no_camera_recordings: Recordings = Recordings.select().where(
Recordings.camera.not_in(list(self.config.cameras.keys())),
Recordings.end_time < expire_before,
)
deleted_recordings = set()
for recording in no_camera_recordings:
Path(recording.path).unlink(missing_ok=True)
deleted_recordings.add(recording.id)
logger.debug(f"Expiring {len(deleted_recordings)} recordings")
Recordings.delete().where(Recordings.id << deleted_recordings).execute()
logger.debug("End deleted cameras.")
logger.debug("Start all cameras.")
for camera, config in self.config.cameras.items():
logger.debug(f"Start camera: {camera}.")
# When deleting recordings without events, we have to keep at LEAST the configured max clip duration
min_end = (
datetime.datetime.now()
- datetime.timedelta(seconds=config.record.events.max_seconds)
).timestamp()
expire_days = config.record.retain_days
expire_before = (
datetime.datetime.now() - datetime.timedelta(days=expire_days)
).timestamp()
expire_date = min(min_end, expire_before)
# Get recordings to check for expiration
recordings: Recordings = (
Recordings.select()
.where(
Recordings.camera == camera,
Recordings.end_time < expire_date,
)
.order_by(Recordings.start_time)
)
# Get all the events to check against
events: Event = (
Event.select()
.where(
Event.camera == camera, Event.end_time < expire_date, Event.has_clip
)
.order_by(Event.start_time)
.objects()
)
# loop over recordings and see if they overlap with any non-expired events
event_start = 0
deleted_recordings = set()
for recording in recordings.objects().iterator():
keep = False
# Now look for a reason to keep this recording segment
for idx in range(event_start, len(events)):
event = events[idx]
# if the event starts in the future, stop checking events
# and let this recording segment expire
if event.start_time > recording.end_time:
keep = False
break
# if the event ends after the recording starts, keep it
# and stop looking at events
if event.end_time >= recording.start_time:
keep = True
break
# if the event ends before this recording segment starts, skip
# this event and check the next event for an overlap.
# since the events and recordings are sorted, we can skip events
# that end before the previous recording segment started on future segments
if event.end_time < recording.start_time:
event_start = idx
# Delete recordings outside of the retention window
if not keep:
Path(recording.path).unlink(missing_ok=True)
deleted_recordings.add(recording.id)
logger.debug(f"Expiring {len(deleted_recordings)} recordings")
Recordings.delete().where(Recordings.id << deleted_recordings).execute()
logger.debug(f"End camera: {camera}.")
logger.debug("End all cameras.")
logger.debug("End expire recordings (new).")
def expire_files(self):
logger.debug("Start expire files (legacy).")
default_expire = (
datetime.datetime.now().timestamp()
- SECONDS_IN_DAY * self.config.record.retain_days
)
delete_before = {}
for name, camera in self.config.cameras.items():
delete_before[name] = (
datetime.datetime.now().timestamp()
- SECONDS_IN_DAY * camera.record.retain_days
)
# find all the recordings older than the oldest recording in the db
try:
oldest_recording = (
Recordings.select().order_by(Recordings.start_time.desc()).get()
)
oldest_timestamp = oldest_recording.start_time
except DoesNotExist:
oldest_timestamp = datetime.datetime.now().timestamp()
logger.debug(f"Oldest recording in the db: {oldest_timestamp}")
process = sp.run(
["find", RECORD_DIR, "-type", "f", "-newermt", f"@{oldest_timestamp}"],
capture_output=True,
text=True,
)
files_to_check = process.stdout.splitlines()
for f in files_to_check:
p = Path(f)
if p.stat().st_mtime < delete_before.get(p.parent.name, default_expire):
p.unlink(missing_ok=True)
logger.debug("End expire files (legacy).")
def run(self):
# Expire recordings every minute, clean directories every hour.
for counter in itertools.cycle(range(60)):
if self.stop_event.wait(60):
logger.info(f"Exiting recording cleanup...")
break
self.expire_recordings()
self.clean_tmp_clips()
if counter == 0:
self.expire_files()
remove_empty_directories(RECORD_DIR)

78
frigate/stats.py
View File

@@ -11,14 +11,16 @@ from frigate.version import VERSION
logger = logging.getLogger(__name__)
def stats_init(camera_metrics, detectors):
stats_tracking = {
'camera_metrics': camera_metrics,
'detectors': detectors,
'started': int(time.time())
"camera_metrics": camera_metrics,
"detectors": detectors,
"started": int(time.time()),
}
return stats_tracking
def get_fs_type(path):
bestMatch = ""
fsType = ""
@@ -28,53 +30,62 @@ def get_fs_type(path):
bestMatch = part.mountpoint
return fsType
def stats_snapshot(stats_tracking):
camera_metrics = stats_tracking['camera_metrics']
camera_metrics = stats_tracking["camera_metrics"]
stats = {}
total_detection_fps = 0
for name, camera_stats in camera_metrics.items():
total_detection_fps += camera_stats['detection_fps'].value
total_detection_fps += camera_stats["detection_fps"].value
stats[name] = {
'camera_fps': round(camera_stats['camera_fps'].value, 2),
'process_fps': round(camera_stats['process_fps'].value, 2),
'skipped_fps': round(camera_stats['skipped_fps'].value, 2),
'detection_fps': round(camera_stats['detection_fps'].value, 2),
'pid': camera_stats['process'].pid,
'capture_pid': camera_stats['capture_process'].pid
"camera_fps": round(camera_stats["camera_fps"].value, 2),
"process_fps": round(camera_stats["process_fps"].value, 2),
"skipped_fps": round(camera_stats["skipped_fps"].value, 2),
"detection_fps": round(camera_stats["detection_fps"].value, 2),
"pid": camera_stats["process"].pid,
"capture_pid": camera_stats["capture_process"].pid,
}
stats['detectors'] = {}
stats["detectors"] = {}
for name, detector in stats_tracking["detectors"].items():
stats['detectors'][name] = {
'inference_speed': round(detector.avg_inference_speed.value * 1000, 2),
'detection_start': detector.detection_start.value,
'pid': detector.detect_process.pid
stats["detectors"][name] = {
"inference_speed": round(detector.avg_inference_speed.value * 1000, 2),
"detection_start": detector.detection_start.value,
"pid": detector.detect_process.pid,
}
stats['detection_fps'] = round(total_detection_fps, 2)
stats["detection_fps"] = round(total_detection_fps, 2)
stats['service'] = {
'uptime': (int(time.time()) - stats_tracking['started']),
'version': VERSION,
'storage': {}
stats["service"] = {
"uptime": (int(time.time()) - stats_tracking["started"]),
"version": VERSION,
"storage": {},
}
for path in [RECORD_DIR, CLIPS_DIR, CACHE_DIR, "/dev/shm"]:
storage_stats = shutil.disk_usage(path)
stats['service']['storage'][path] = {
'total': round(storage_stats.total/1000000, 1),
'used': round(storage_stats.used/1000000, 1),
'free': round(storage_stats.free/1000000, 1),
'mount_type': get_fs_type(path)
stats["service"]["storage"][path] = {
"total": round(storage_stats.total / 1000000, 1),
"used": round(storage_stats.used / 1000000, 1),
"free": round(storage_stats.free / 1000000, 1),
"mount_type": get_fs_type(path),
}
return stats
class StatsEmitter(threading.Thread):
def __init__(self, config: FrigateConfig, stats_tracking, mqtt_client, topic_prefix, stop_event):
def __init__(
self,
config: FrigateConfig,
stats_tracking,
mqtt_client,
topic_prefix,
stop_event,
):
threading.Thread.__init__(self)
self.name = 'frigate_stats_emitter'
self.name = "frigate_stats_emitter"
self.config = config
self.stats_tracking = stats_tracking
self.mqtt_client = mqtt_client
@@ -83,10 +94,9 @@ class StatsEmitter(threading.Thread):
def run(self):
time.sleep(10)
while True:
if self.stop_event.is_set():
logger.info(f"Exiting watchdog...")
break
while not self.stop_event.wait(self.config.mqtt.stats_interval):
stats = stats_snapshot(self.stats_tracking)
self.mqtt_client.publish(f"{self.topic_prefix}/stats", json.dumps(stats), retain=False)
time.sleep(self.config.mqtt.stats_interval)
self.mqtt_client.publish(
f"{self.topic_prefix}/stats", json.dumps(stats), retain=False
)
logger.info(f"Exiting watchdog...")

1367
frigate/test/test_config.py
View File

File diff suppressed because it is too large Load Diff

66
frigate/test/test_copy_yuv_to_position.py Normal file
View File

@@ -0,0 +1,66 @@
import cv2
import numpy as np
from unittest import TestCase, main
from frigate.util import get_yuv_crop, copy_yuv_to_position
class TestCopyYuvToPosition(TestCase):
def setUp(self):
self.source_frame_bgr = np.zeros((400, 800, 3), np.uint8)
self.source_frame_bgr[:] = (0, 0, 255)
self.source_yuv_frame = cv2.cvtColor(
self.source_frame_bgr, cv2.COLOR_BGR2YUV_I420
)
y, u1, u2, v1, v2 = get_yuv_crop(
self.source_yuv_frame.shape,
(
0,
0,
self.source_frame_bgr.shape[1],
self.source_frame_bgr.shape[0],
),
)
self.source_channel_dims = {
"y": y,
"u1": u1,
"u2": u2,
"v1": v1,
"v2": v2,
}
self.dest_frame_bgr = np.zeros((400, 800, 3), np.uint8)
self.dest_frame_bgr[:] = (112, 202, 50)
self.dest_frame_bgr[100:300, 200:600] = (255, 0, 0)
self.dest_yuv_frame = cv2.cvtColor(self.dest_frame_bgr, cv2.COLOR_BGR2YUV_I420)
def test_clear_position(self):
copy_yuv_to_position(self.dest_yuv_frame, (100, 100), (100, 100))
# cv2.imwrite(f"source_frame_yuv.jpg", self.source_yuv_frame)
# cv2.imwrite(f"dest_frame_yuv.jpg", self.dest_yuv_frame)
def test_copy_position(self):
copy_yuv_to_position(
self.dest_yuv_frame,
(100, 100),
(100, 200),
self.source_yuv_frame,
self.source_channel_dims,
)
# cv2.imwrite(f"source_frame_yuv.jpg", self.source_yuv_frame)
# cv2.imwrite(f"dest_frame_yuv.jpg", self.dest_yuv_frame)
def test_copy_position_full_screen(self):
copy_yuv_to_position(
self.dest_yuv_frame,
(0, 0),
(400, 800),
self.source_yuv_frame,
self.source_channel_dims,
)
# cv2.imwrite(f"source_frame_yuv.jpg", self.source_yuv_frame)
# cv2.imwrite(f"dest_frame_yuv.jpg", self.dest_yuv_frame)
if __name__ == "__main__":
main(verbosity=2)

18
frigate/test/test_yuv_region_2_rgb.py
View File

@@ -3,37 +3,39 @@ import numpy as np
from unittest import TestCase, main
from frigate.util import yuv_region_2_rgb
class TestYuvRegion2RGB(TestCase):
def setUp(self):
self.bgr_frame = np.zeros((100, 200, 3), np.uint8)
self.bgr_frame[:] = (0, 0, 255)
self.bgr_frame[5:55, 5:55] = (255,0,0)
self.bgr_frame[5:55, 5:55] = (255, 0, 0)
# cv2.imwrite(f"bgr_frame.jpg", self.bgr_frame)
self.yuv_frame = cv2.cvtColor(self.bgr_frame, cv2.COLOR_BGR2YUV_I420)
def test_crop_yuv(self):
cropped = yuv_region_2_rgb(self.yuv_frame, (10,10,50,50))
cropped = yuv_region_2_rgb(self.yuv_frame, (10, 10, 50, 50))
# ensure the upper left pixel is blue
assert(np.all(cropped[0, 0] == [0, 0, 255]))
assert np.all(cropped[0, 0] == [0, 0, 255])
def test_crop_yuv_out_of_bounds(self):
cropped = yuv_region_2_rgb(self.yuv_frame, (0,0,200,200))
cropped = yuv_region_2_rgb(self.yuv_frame, (0, 0, 200, 200))
# cv2.imwrite(f"cropped.jpg", cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
# ensure the upper left pixel is red
# the yuv conversion has some noise
assert(np.all(cropped[0, 0] == [255, 1, 0]))
assert np.all(cropped[0, 0] == [255, 1, 0])
# ensure the bottom right is black
assert(np.all(cropped[199, 199] == [0, 0, 0]))
assert np.all(cropped[199, 199] == [0, 0, 0])
def test_crop_yuv_portrait(self):
bgr_frame = np.zeros((1920, 1080, 3), np.uint8)
bgr_frame[:] = (0, 0, 255)
bgr_frame[5:55, 5:55] = (255,0,0)
bgr_frame[5:55, 5:55] = (255, 0, 0)
# cv2.imwrite(f"bgr_frame.jpg", self.bgr_frame)
yuv_frame = cv2.cvtColor(bgr_frame, cv2.COLOR_BGR2YUV_I420)
cropped = yuv_region_2_rgb(yuv_frame, (0, 852, 648, 1500))
# cv2.imwrite(f"cropped.jpg", cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
if __name__ == '__main__':
if __name__ == "__main__":
main(verbosity=2)

587
frigate/util.py
View File

@@ -1,8 +1,10 @@
import collections
import copy
import datetime
import hashlib
import json
import logging
import math
import signal
import subprocess as sp
import threading
@@ -15,13 +17,139 @@ from typing import AnyStr
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import psutil
logger = logging.getLogger(__name__)
def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
def deep_merge(dct1: dict, dct2: dict, override=False, merge_lists=False) -> dict:
"""
:param dct1: First dict to merge
:param dct2: Second dict to merge
:param override: if same key exists in both dictionaries, should override? otherwise ignore. (default=True)
:return: The merge dictionary
"""
merged = copy.deepcopy(dct1)
for k, v2 in dct2.items():
if k in merged:
v1 = merged[k]
if isinstance(v1, dict) and isinstance(v2, collections.Mapping):
merged[k] = deep_merge(v1, v2, override)
elif isinstance(v1, list) and isinstance(v2, list):
if merge_lists:
merged[k] = v1 + v2
else:
if override:
merged[k] = copy.deepcopy(v2)
else:
merged[k] = copy.deepcopy(v2)
return merged
def draw_timestamp(
frame,
timestamp,
timestamp_format,
font_effect=None,
font_thickness=2,
font_color=(255, 255, 255),
position="tl",
):
time_to_show = datetime.datetime.fromtimestamp(timestamp).strftime(timestamp_format)
# calculate a dynamic font size
size = cv2.getTextSize(
time_to_show,
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1.0,
thickness=font_thickness,
)
text_width = size[0][0]
desired_size = max(150, 0.33 * frame.shape[1])
font_scale = desired_size / text_width
# calculate the actual size with the dynamic scale
size = cv2.getTextSize(
time_to_show,
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale,
thickness=font_thickness,
)
image_width = frame.shape[1]
image_height = frame.shape[0]
text_width = size[0][0]
text_height = size[0][1]
line_height = text_height + size[1]
if position == "tl":
text_offset_x = 0
text_offset_y = 0 if 0 < line_height else 0 - (line_height + 8)
elif position == "tr":
text_offset_x = image_width - text_width
text_offset_y = 0 if 0 < line_height else 0 - (line_height + 8)
elif position == "bl":
text_offset_x = 0
text_offset_y = image_height - (line_height + 8)
elif position == "br":
text_offset_x = image_width - text_width
text_offset_y = image_height - (line_height + 8)
if font_effect == "solid":
# make the coords of the box with a small padding of two pixels
timestamp_box_coords = np.array(
[
[text_offset_x, text_offset_y],
[text_offset_x + text_width, text_offset_y],
[text_offset_x + text_width, text_offset_y + line_height + 8],
[text_offset_x, text_offset_y + line_height + 8],
]
)
cv2.fillPoly(
frame,
[timestamp_box_coords],
# inverse color of text for background for max. contrast
(255 - font_color[0], 255 - font_color[1], 255 - font_color[2]),
)
elif font_effect == "shadow":
cv2.putText(
frame,
time_to_show,
(text_offset_x + 3, text_offset_y + line_height),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale,
color=(255 - font_color[0], 255 - font_color[1], 255 - font_color[2]),
thickness=font_thickness,
)
cv2.putText(
frame,
time_to_show,
(text_offset_x, text_offset_y + line_height - 3),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale,
color=font_color,
thickness=font_thickness,
)
def draw_box_with_label(
frame,
x_min,
y_min,
x_max,
y_max,
label,
info,
thickness=2,
color=None,
position="ul",
):
if color is None:
color = (0,0,255)
color = (0, 0, 255)
display_text = "{}: {}".format(label, info)
cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, thickness)
font_scale = 0.5
@@ -32,208 +160,350 @@ def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thicknes
text_height = size[0][1]
line_height = text_height + size[1]
# set the text start position
if position == 'ul':
if position == "ul":
text_offset_x = x_min
text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
elif position == 'ur':
text_offset_x = x_max - (text_width+8)
text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
elif position == 'bl':
text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
elif position == "ur":
text_offset_x = x_max - (text_width + 8)
text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
elif position == "bl":
text_offset_x = x_min
text_offset_y = y_max
elif position == 'br':
text_offset_x = x_max - (text_width+8)
elif position == "br":
text_offset_x = x_max - (text_width + 8)
text_offset_y = y_max
# make the coords of the box with a small padding of two pixels
textbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y + line_height))
textbox_coords = (
(text_offset_x, text_offset_y),
(text_offset_x + text_width + 2, text_offset_y + line_height),
)
cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
cv2.putText(
frame,
display_text,
(text_offset_x, text_offset_y + line_height - 3),
font,
fontScale=font_scale,
color=(0, 0, 0),
thickness=2,
)
def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
# size is the longest edge and divisible by 4
size = int(max(xmax-xmin, ymax-ymin)//4*4*multiplier)
size = int(max(xmax - xmin, ymax - ymin) // 4 * 4 * multiplier)
# dont go any smaller than 300
if size < 300:
size = 300
# x_offset is midpoint of bounding box minus half the size
x_offset = int((xmax-xmin)/2.0+xmin-size/2.0)
x_offset = int((xmax - xmin) / 2.0 + xmin - size / 2.0)
# if outside the image
if x_offset < 0:
x_offset = 0
elif x_offset > (frame_shape[1]-size):
x_offset = max(0, (frame_shape[1]-size))
elif x_offset > (frame_shape[1] - size):
x_offset = max(0, (frame_shape[1] - size))
# y_offset is midpoint of bounding box minus half the size
y_offset = int((ymax-ymin)/2.0+ymin-size/2.0)
y_offset = int((ymax - ymin) / 2.0 + ymin - size / 2.0)
# # if outside the image
if y_offset < 0:
y_offset = 0
elif y_offset > (frame_shape[0]-size):
y_offset = max(0, (frame_shape[0]-size))
elif y_offset > (frame_shape[0] - size):
y_offset = max(0, (frame_shape[0] - size))
return (x_offset, y_offset, x_offset + size, y_offset + size)
return (x_offset, y_offset, x_offset+size, y_offset+size)
def get_yuv_crop(frame_shape, crop):
# crop should be (x1,y1,x2,y2)
frame_height = frame_shape[0]//3*2
frame_height = frame_shape[0] // 3 * 2
frame_width = frame_shape[1]
# compute the width/height of the uv channels
uv_width = frame_width//2 # width of the uv channels
uv_height = frame_height//4 # height of the uv channels
uv_width = frame_width // 2 # width of the uv channels
uv_height = frame_height // 4 # height of the uv channels
# compute the offset for upper left corner of the uv channels
uv_x_offset = crop[0]//2 # x offset of the uv channels
uv_y_offset = crop[1]//4 # y offset of the uv channels
uv_x_offset = crop[0] // 2 # x offset of the uv channels
uv_y_offset = crop[1] // 4 # y offset of the uv channels
# compute the width/height of the uv crops
uv_crop_width = (crop[2] - crop[0])//2 # width of the cropped uv channels
uv_crop_height = (crop[3] - crop[1])//4 # height of the cropped uv channels
uv_crop_width = (crop[2] - crop[0]) // 2 # width of the cropped uv channels
uv_crop_height = (crop[3] - crop[1]) // 4 # height of the cropped uv channels
# ensure crop dimensions are multiples of 2 and 4
y = (
crop[0],
crop[1],
crop[0] + uv_crop_width*2,
crop[1] + uv_crop_height*4
)
y = (crop[0], crop[1], crop[0] + uv_crop_width * 2, crop[1] + uv_crop_height * 4)
u1 = (
0 + uv_x_offset,
0 + uv_x_offset,
frame_height + uv_y_offset,
0 + uv_x_offset + uv_crop_width,
frame_height + uv_y_offset + uv_crop_height
0 + uv_x_offset + uv_crop_width,
frame_height + uv_y_offset + uv_crop_height,
)
u2 = (
uv_width + uv_x_offset,
uv_width + uv_x_offset,
frame_height + uv_y_offset,
uv_width + uv_x_offset + uv_crop_width,
frame_height + uv_y_offset + uv_crop_height
uv_width + uv_x_offset + uv_crop_width,
frame_height + uv_y_offset + uv_crop_height,
)
v1 = (
0 + uv_x_offset,
frame_height + uv_height + uv_y_offset,
0 + uv_x_offset + uv_crop_width,
frame_height + uv_height + uv_y_offset + uv_crop_height
0 + uv_x_offset,
frame_height + uv_height + uv_y_offset,
0 + uv_x_offset + uv_crop_width,
frame_height + uv_height + uv_y_offset + uv_crop_height,
)
v2 = (
uv_width + uv_x_offset,
frame_height + uv_height + uv_y_offset,
uv_width + uv_x_offset + uv_crop_width,
frame_height + uv_height + uv_y_offset + uv_crop_height
uv_width + uv_x_offset,
frame_height + uv_height + uv_y_offset,
uv_width + uv_x_offset + uv_crop_width,
frame_height + uv_height + uv_y_offset + uv_crop_height,
)
return y, u1, u2, v1, v2
def yuv_crop_and_resize(frame, region, height=None):
# Crops and resizes a YUV frame while maintaining aspect ratio
# https://stackoverflow.com/a/57022634
height = frame.shape[0] // 3 * 2
width = frame.shape[1]
# get the crop box if the region extends beyond the frame
crop_x1 = max(0, region[0])
crop_y1 = max(0, region[1])
# ensure these are a multiple of 4
crop_x2 = min(width, region[2])
crop_y2 = min(height, region[3])
crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
y, u1, u2, v1, v2 = get_yuv_crop(frame.shape, crop_box)
# if the region starts outside the frame, indent the start point in the cropped frame
y_channel_x_offset = abs(min(0, region[0]))
y_channel_y_offset = abs(min(0, region[1]))
uv_channel_x_offset = y_channel_x_offset // 2
uv_channel_y_offset = y_channel_y_offset // 4
# create the yuv region frame
# make sure the size is a multiple of 4
# TODO: this should be based on the size after resize now
size = (region[3] - region[1]) // 4 * 4
yuv_cropped_frame = np.zeros((size + size // 2, size), np.uint8)
# fill in black
yuv_cropped_frame[:] = 128
yuv_cropped_frame[0:size, 0:size] = 16
# copy the y channel
yuv_cropped_frame[
y_channel_y_offset : y_channel_y_offset + y[3] - y[1],
y_channel_x_offset : y_channel_x_offset + y[2] - y[0],
] = frame[y[1] : y[3], y[0] : y[2]]
uv_crop_width = u1[2] - u1[0]
uv_crop_height = u1[3] - u1[1]
# copy u1
yuv_cropped_frame[
size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
] = frame[u1[1] : u1[3], u1[0] : u1[2]]
# copy u2
yuv_cropped_frame[
size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
size // 2
+ uv_channel_x_offset : size // 2
+ uv_channel_x_offset
+ uv_crop_width,
] = frame[u2[1] : u2[3], u2[0] : u2[2]]
# copy v1
yuv_cropped_frame[
size
+ size // 4
+ uv_channel_y_offset : size
+ size // 4
+ uv_channel_y_offset
+ uv_crop_height,
0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
] = frame[v1[1] : v1[3], v1[0] : v1[2]]
# copy v2
yuv_cropped_frame[
size
+ size // 4
+ uv_channel_y_offset : size
+ size // 4
+ uv_channel_y_offset
+ uv_crop_height,
size // 2
+ uv_channel_x_offset : size // 2
+ uv_channel_x_offset
+ uv_crop_width,
] = frame[v2[1] : v2[3], v2[0] : v2[2]]
return yuv_cropped_frame
def copy_yuv_to_position(
destination_frame,
destination_offset,
destination_shape,
source_frame=None,
source_channel_dim=None,
):
# get the coordinates of the channels for this position in the layout
y, u1, u2, v1, v2 = get_yuv_crop(
destination_frame.shape,
(
destination_offset[1],
destination_offset[0],
destination_offset[1] + destination_shape[1],
destination_offset[0] + destination_shape[0],
),
)
# clear y
destination_frame[
y[1] : y[3],
y[0] : y[2],
] = 16
# clear u1
destination_frame[u1[1] : u1[3], u1[0] : u1[2]] = 128
# clear u2
destination_frame[u2[1] : u2[3], u2[0] : u2[2]] = 128
# clear v1
destination_frame[v1[1] : v1[3], v1[0] : v1[2]] = 128
# clear v2
destination_frame[v2[1] : v2[3], v2[0] : v2[2]] = 128
if not source_frame is None:
# calculate the resized frame, maintaining the aspect ratio
source_aspect_ratio = source_frame.shape[1] / (source_frame.shape[0] // 3 * 2)
dest_aspect_ratio = destination_shape[1] / destination_shape[0]
if source_aspect_ratio <= dest_aspect_ratio:
y_resize_height = int(destination_shape[0] // 4 * 4)
y_resize_width = int((y_resize_height * source_aspect_ratio) // 4 * 4)
else:
y_resize_width = int(destination_shape[1] // 4 * 4)
y_resize_height = int((y_resize_width / source_aspect_ratio) // 4 * 4)
uv_resize_width = int(y_resize_width // 2)
uv_resize_height = int(y_resize_height // 4)
y_y_offset = int((destination_shape[0] - y_resize_height) / 4 // 4 * 4)
y_x_offset = int((destination_shape[1] - y_resize_width) / 2 // 4 * 4)
uv_y_offset = y_y_offset // 4
uv_x_offset = y_x_offset // 2
interpolation = cv2.INTER_LINEAR
# resize/copy y channel
destination_frame[
y[1] + y_y_offset : y[1] + y_y_offset + y_resize_height,
y[0] + y_x_offset : y[0] + y_x_offset + y_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["y"][1] : source_channel_dim["y"][3],
source_channel_dim["y"][0] : source_channel_dim["y"][2],
],
dsize=(y_resize_width, y_resize_height),
interpolation=interpolation,
)
# resize/copy u1
destination_frame[
u1[1] + uv_y_offset : u1[1] + uv_y_offset + uv_resize_height,
u1[0] + uv_x_offset : u1[0] + uv_x_offset + uv_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["u1"][1] : source_channel_dim["u1"][3],
source_channel_dim["u1"][0] : source_channel_dim["u1"][2],
],
dsize=(uv_resize_width, uv_resize_height),
interpolation=interpolation,
)
# resize/copy u2
destination_frame[
u2[1] + uv_y_offset : u2[1] + uv_y_offset + uv_resize_height,
u2[0] + uv_x_offset : u2[0] + uv_x_offset + uv_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["u2"][1] : source_channel_dim["u2"][3],
source_channel_dim["u2"][0] : source_channel_dim["u2"][2],
],
dsize=(uv_resize_width, uv_resize_height),
interpolation=interpolation,
)
# resize/copy v1
destination_frame[
v1[1] + uv_y_offset : v1[1] + uv_y_offset + uv_resize_height,
v1[0] + uv_x_offset : v1[0] + uv_x_offset + uv_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["v1"][1] : source_channel_dim["v1"][3],
source_channel_dim["v1"][0] : source_channel_dim["v1"][2],
],
dsize=(uv_resize_width, uv_resize_height),
interpolation=interpolation,
)
# resize/copy v2
destination_frame[
v2[1] + uv_y_offset : v2[1] + uv_y_offset + uv_resize_height,
v2[0] + uv_x_offset : v2[0] + uv_x_offset + uv_resize_width,
] = cv2.resize(
source_frame[
source_channel_dim["v2"][1] : source_channel_dim["v2"][3],
source_channel_dim["v2"][0] : source_channel_dim["v2"][2],
],
dsize=(uv_resize_width, uv_resize_height),
interpolation=interpolation,
)
def yuv_region_2_rgb(frame, region):
try:
height = frame.shape[0]//3*2
width = frame.shape[1]
# get the crop box if the region extends beyond the frame
crop_x1 = max(0, region[0])
crop_y1 = max(0, region[1])
# ensure these are a multiple of 4
crop_x2 = min(width, region[2])
crop_y2 = min(height, region[3])
crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
y, u1, u2, v1, v2 = get_yuv_crop(frame.shape, crop_box)
# if the region starts outside the frame, indent the start point in the cropped frame
y_channel_x_offset = abs(min(0, region[0]))
y_channel_y_offset = abs(min(0, region[1]))
uv_channel_x_offset = y_channel_x_offset//2
uv_channel_y_offset = y_channel_y_offset//4
# create the yuv region frame
# make sure the size is a multiple of 4
size = (region[3] - region[1])//4*4
yuv_cropped_frame = np.zeros((size+size//2, size), np.uint8)
# fill in black
yuv_cropped_frame[:] = 128
yuv_cropped_frame[0:size,0:size] = 16
# copy the y channel
yuv_cropped_frame[
y_channel_y_offset:y_channel_y_offset + y[3] - y[1],
y_channel_x_offset:y_channel_x_offset + y[2] - y[0]
] = frame[
y[1]:y[3],
y[0]:y[2]
]
uv_crop_width = u1[2] - u1[0]
uv_crop_height = u1[3] - u1[1]
# copy u1
yuv_cropped_frame[
size + uv_channel_y_offset:size + uv_channel_y_offset + uv_crop_height,
0 + uv_channel_x_offset:0 + uv_channel_x_offset + uv_crop_width
] = frame[
u1[1]:u1[3],
u1[0]:u1[2]
]
# copy u2
yuv_cropped_frame[
size + uv_channel_y_offset:size + uv_channel_y_offset + uv_crop_height,
size//2 + uv_channel_x_offset:size//2 + uv_channel_x_offset + uv_crop_width
] = frame[
u2[1]:u2[3],
u2[0]:u2[2]
]
# copy v1
yuv_cropped_frame[
size+size//4 + uv_channel_y_offset:size+size//4 + uv_channel_y_offset + uv_crop_height,
0 + uv_channel_x_offset:0 + uv_channel_x_offset + uv_crop_width
] = frame[
v1[1]:v1[3],
v1[0]:v1[2]
]
# copy v2
yuv_cropped_frame[
size+size//4 + uv_channel_y_offset:size+size//4 + uv_channel_y_offset + uv_crop_height,
size//2 + uv_channel_x_offset:size//2 + uv_channel_x_offset + uv_crop_width
] = frame[
v2[1]:v2[3],
v2[0]:v2[2]
]
# TODO: does this copy the numpy array?
yuv_cropped_frame = yuv_crop_and_resize(frame, region)
return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
except:
print(f"frame.shape: {frame.shape}")
print(f"region: {region}")
raise
def intersection(box_a, box_b):
return (
max(box_a[0], box_b[0]),
max(box_a[1], box_b[1]),
min(box_a[2], box_b[2]),
min(box_a[3], box_b[3])
min(box_a[3], box_b[3]),
)
def area(box):
return (box[2]-box[0] + 1)*(box[3]-box[1] + 1)
return (box[2] - box[0] + 1) * (box[3] - box[1] + 1)
def intersection_over_union(box_a, box_b):
# determine the (x, y)-coordinates of the intersection rectangle
intersect = intersection(box_a, box_b)
# compute the area of intersection rectangle
inter_area = max(0, intersect[2] - intersect[0] + 1) * max(0, intersect[3] - intersect[1] + 1)
inter_area = max(0, intersect[2] - intersect[0] + 1) * max(
0, intersect[3] - intersect[1] + 1
)
if inter_area == 0:
return 0.0
# compute the area of both the prediction and ground-truth
# rectangles
box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1)
@@ -247,25 +517,39 @@ def intersection_over_union(box_a, box_b):
# return the intersection over union value
return iou
def clipped(obj, frame_shape):
# if the object is within 5 pixels of the region border, and the region is not on the edge
# consider the object to be clipped
box = obj[2]
region = obj[4]
if ((region[0] > 5 and box[0]-region[0] <= 5) or
(region[1] > 5 and box[1]-region[1] <= 5) or
(frame_shape[1]-region[2] > 5 and region[2]-box[2] <= 5) or
(frame_shape[0]-region[3] > 5 and region[3]-box[3] <= 5)):
if (
(region[0] > 5 and box[0] - region[0] <= 5)
or (region[1] > 5 and box[1] - region[1] <= 5)
or (frame_shape[1] - region[2] > 5 and region[2] - box[2] <= 5)
or (frame_shape[0] - region[3] > 5 and region[3] - box[3] <= 5)
):
return True
else:
return False
def restart_frigate():
proc = psutil.Process(1)
# if this is running via s6, sigterm pid 1
if proc.name() == "s6-svscan":
proc.terminate()
# otherwise, just try and exit frigate
else:
os.kill(os.getpid(), signal.SIGTERM)
class EventsPerSecond:
def __init__(self, max_events=1000):
self._start = None
self._max_events = max_events
self._timestamps = []
def start(self):
self._start = datetime.datetime.now().timestamp()
@@ -274,23 +558,28 @@ class EventsPerSecond:
self.start()
self._timestamps.append(datetime.datetime.now().timestamp())
# truncate the list when it goes 100 over the max_size
if len(self._timestamps) > self._max_events+100:
self._timestamps = self._timestamps[(1-self._max_events):]
if len(self._timestamps) > self._max_events + 100:
self._timestamps = self._timestamps[(1 - self._max_events) :]
def eps(self, last_n_seconds=10):
if self._start is None:
self.start()
# compute the (approximate) events in the last n seconds
# compute the (approximate) events in the last n seconds
now = datetime.datetime.now().timestamp()
seconds = min(now-self._start, last_n_seconds)
return len([t for t in self._timestamps if t > (now-last_n_seconds)]) / seconds
seconds = min(now - self._start, last_n_seconds)
return (
len([t for t in self._timestamps if t > (now - last_n_seconds)]) / seconds
)
def print_stack(sig, frame):
traceback.print_stack(frame)
def listen():
signal.signal(signal.SIGUSR1, print_stack)
def create_mask(frame_shape, mask):
mask_img = np.zeros(frame_shape, np.uint8)
mask_img[:] = 255
@@ -304,11 +593,15 @@ def create_mask(frame_shape, mask):
return mask_img
def add_mask(mask, mask_img):
points = mask.split(',')
contour = np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
points = mask.split(",")
contour = np.array(
[[int(points[i]), int(points[i + 1])] for i in range(0, len(points), 2)]
)
cv2.fillPoly(mask_img, pts=[contour], color=(0))
class FrameManager(ABC):
@abstractmethod
def create(self, name, size) -> AnyStr:
@@ -326,29 +619,31 @@ class FrameManager(ABC):
def delete(self, name):
pass
class DictFrameManager(FrameManager):
def __init__(self):
self.frames = {}
def create(self, name, size) -> AnyStr:
mem = bytearray(size)
self.frames[name] = mem
return mem
def get(self, name, shape):
mem = self.frames[name]
return np.ndarray(shape, dtype=np.uint8, buffer=mem)
def close(self, name):
pass
def delete(self, name):
del self.frames[name]
class SharedMemoryFrameManager(FrameManager):
def __init__(self):
self.shm_store = {}
def create(self, name, size) -> AnyStr:
shm = shared_memory.SharedMemory(name=name, create=True, size=size)
self.shm_store[name] = shm

390
frigate/video.py
View File

@@ -1,12 +1,7 @@
import base64
import copy
import ctypes
import datetime
import itertools
import json
import logging
import multiprocessing as mp
import os
import queue
import subprocess as sp
import signal
@@ -16,7 +11,7 @@ from collections import defaultdict
from setproctitle import setproctitle
from typing import Dict, List
import cv2
from cv2 import cv2
import numpy as np
from frigate.config import CameraConfig
@@ -24,19 +19,25 @@ from frigate.edgetpu import RemoteObjectDetector
from frigate.log import LogPipe
from frigate.motion import MotionDetector
from frigate.objects import ObjectTracker
from frigate.util import (EventsPerSecond, FrameManager,
SharedMemoryFrameManager, area, calculate_region,
clipped, draw_box_with_label, intersection,
intersection_over_union, listen, yuv_region_2_rgb)
from frigate.util import (
EventsPerSecond,
FrameManager,
SharedMemoryFrameManager,
calculate_region,
clipped,
listen,
yuv_region_2_rgb,
)
logger = logging.getLogger(__name__)
def filtered(obj, objects_to_track, object_filters):
object_name = obj[0]
if not object_name in objects_to_track:
return True
if object_name in object_filters:
obj_settings = object_filters[object_name]
@@ -44,7 +45,7 @@ def filtered(obj, objects_to_track, object_filters):
# detected object, don't add it to detected objects
if obj_settings.min_area > obj[3]:
return True
# if the detected object is larger than the
# max area, don't add it to detected objects
if obj_settings.max_area < obj[3]:
@@ -53,29 +54,36 @@ def filtered(obj, objects_to_track, object_filters):
# if the score is lower than the min_score, skip
if obj_settings.min_score > obj[1]:
return True
if not obj_settings.mask is None:
# compute the coordinates of the object and make sure
# the location isnt outside the bounds of the image (can happen from rounding)
y_location = min(int(obj[2][3]), len(obj_settings.mask)-1)
x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(obj_settings.mask[0])-1)
y_location = min(int(obj[2][3]), len(obj_settings.mask) - 1)
x_location = min(
int((obj[2][2] - obj[2][0]) / 2.0) + obj[2][0],
len(obj_settings.mask[0]) - 1,
)
# if the object is in a masked location, don't add it to detected objects
if obj_settings.mask[y_location][x_location] == 0:
return True
return False
def create_tensor_input(frame, model_shape, region):
cropped_frame = yuv_region_2_rgb(frame, region)
# Resize to 300x300 if needed
if cropped_frame.shape != (model_shape[0], model_shape[1], 3):
cropped_frame = cv2.resize(cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR)
cropped_frame = cv2.resize(
cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR
)
# Expand dimensions since the model expects images to have shape: [1, height, width, 3]
return np.expand_dims(cropped_frame, axis=0)
def stop_ffmpeg(ffmpeg_process, logger):
logger.info("Terminating the existing ffmpeg process...")
ffmpeg_process.terminate()
@@ -88,18 +96,43 @@ def stop_ffmpeg(ffmpeg_process, logger):
ffmpeg_process.communicate()
ffmpeg_process = None
def start_or_restart_ffmpeg(ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None):
if not ffmpeg_process is None:
def start_or_restart_ffmpeg(
ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None
):
if ffmpeg_process is not None:
stop_ffmpeg(ffmpeg_process, logger)
if frame_size is None:
process = sp.Popen(ffmpeg_cmd, stdout = sp.DEVNULL, stderr=logpipe, stdin = sp.DEVNULL, start_new_session=True)
process = sp.Popen(
ffmpeg_cmd,
stdout=sp.DEVNULL,
stderr=logpipe,
stdin=sp.DEVNULL,
start_new_session=True,
)
else:
process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stderr=logpipe, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True)
process = sp.Popen(
ffmpeg_cmd,
stdout=sp.PIPE,
stderr=logpipe,
stdin=sp.DEVNULL,
bufsize=frame_size * 10,
start_new_session=True,
)
return process
def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager,
frame_queue, fps:mp.Value, skipped_fps: mp.Value, current_frame: mp.Value):
def capture_frames(
ffmpeg_process,
camera_name,
frame_shape,
frame_manager: FrameManager,
frame_queue,
fps: mp.Value,
skipped_fps: mp.Value,
current_frame: mp.Value,
):
frame_size = frame_shape[0] * frame_shape[1]
frame_rate = EventsPerSecond()
@@ -119,7 +152,9 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
logger.info(f"{camera_name}: ffmpeg sent a broken frame. {e}")
if ffmpeg_process.poll() != None:
logger.info(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
logger.info(
f"{camera_name}: ffmpeg process is not running. exiting capture thread..."
)
frame_manager.delete(frame_name)
break
continue
@@ -138,8 +173,11 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
# add to the queue
frame_queue.put(current_frame.value)
class CameraWatchdog(threading.Thread):
def __init__(self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event):
def __init__(
self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event
):
threading.Thread.__init__(self)
self.logger = logging.getLogger(f"watchdog.{camera_name}")
self.camera_name = camera_name
@@ -159,32 +197,38 @@ class CameraWatchdog(threading.Thread):
self.start_ffmpeg_detect()
for c in self.config.ffmpeg_cmds:
if 'detect' in c['roles']:
if "detect" in c["roles"]:
continue
logpipe = LogPipe(f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}", logging.ERROR)
self.ffmpeg_other_processes.append({
'cmd': c['cmd'],
'logpipe': logpipe,
'process': start_or_restart_ffmpeg(c['cmd'], self.logger, logpipe)
})
time.sleep(10)
while True:
if self.stop_event.is_set():
stop_ffmpeg(self.ffmpeg_detect_process, self.logger)
for p in self.ffmpeg_other_processes:
stop_ffmpeg(p['process'], self.logger)
p['logpipe'].close()
self.logpipe.close()
break
logpipe = LogPipe(
f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}",
logging.ERROR,
)
self.ffmpeg_other_processes.append(
{
"cmd": c["cmd"],
"logpipe": logpipe,
"process": start_or_restart_ffmpeg(c["cmd"], self.logger, logpipe),
}
)
time.sleep(10)
while not self.stop_event.wait(10):
now = datetime.datetime.now().timestamp()
if not self.capture_thread.is_alive():
self.logger.error(
f"FFMPEG process crashed unexpectedly for {self.camera_name}."
)
self.logger.error(
"The following ffmpeg logs include the last 100 lines prior to exit."
)
self.logger.error("You may have invalid args defined for this camera.")
self.logpipe.dump()
self.start_ffmpeg_detect()
elif now - self.capture_thread.current_frame.value > 20:
self.logger.info(f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg...")
self.logger.info(
f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg..."
)
self.ffmpeg_detect_process.terminate()
try:
self.logger.info("Waiting for ffmpeg to exit gracefully...")
@@ -193,25 +237,40 @@ class CameraWatchdog(threading.Thread):
self.logger.info("FFmpeg didnt exit. Force killing...")
self.ffmpeg_detect_process.kill()
self.ffmpeg_detect_process.communicate()
for p in self.ffmpeg_other_processes:
poll = p['process'].poll()
if poll == None:
poll = p["process"].poll()
if poll is None:
continue
p['logpipe'].dump()
p['process'] = start_or_restart_ffmpeg(p['cmd'], self.logger, p['logpipe'], ffmpeg_process=p['process'])
# wait a bit before checking again
time.sleep(10)
p["logpipe"].dump()
p["process"] = start_or_restart_ffmpeg(
p["cmd"], self.logger, p["logpipe"], ffmpeg_process=p["process"]
)
stop_ffmpeg(self.ffmpeg_detect_process, self.logger)
for p in self.ffmpeg_other_processes:
stop_ffmpeg(p["process"], self.logger)
p["logpipe"].close()
self.logpipe.close()
def start_ffmpeg_detect(self):
ffmpeg_cmd = [c['cmd'] for c in self.config.ffmpeg_cmds if 'detect' in c['roles']][0]
self.ffmpeg_detect_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.logger, self.logpipe, self.frame_size)
ffmpeg_cmd = [
c["cmd"] for c in self.config.ffmpeg_cmds if "detect" in c["roles"]
][0]
self.ffmpeg_detect_process = start_or_restart_ffmpeg(
ffmpeg_cmd, self.logger, self.logpipe, self.frame_size
)
self.ffmpeg_pid.value = self.ffmpeg_detect_process.pid
self.capture_thread = CameraCapture(self.camera_name, self.ffmpeg_detect_process, self.frame_shape, self.frame_queue,
self.camera_fps)
self.capture_thread = CameraCapture(
self.camera_name,
self.ffmpeg_detect_process,
self.frame_shape,
self.frame_queue,
self.camera_fps,
)
self.capture_thread.start()
class CameraCapture(threading.Thread):
def __init__(self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps):
threading.Thread.__init__(self)
@@ -223,32 +282,60 @@ class CameraCapture(threading.Thread):
self.skipped_fps = EventsPerSecond()
self.frame_manager = SharedMemoryFrameManager()
self.ffmpeg_process = ffmpeg_process
self.current_frame = mp.Value('d', 0.0)
self.current_frame = mp.Value("d", 0.0)
self.last_frame = 0
def run(self):
self.skipped_fps.start()
capture_frames(self.ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue,
self.fps, self.skipped_fps, self.current_frame)
capture_frames(
self.ffmpeg_process,
self.camera_name,
self.frame_shape,
self.frame_manager,
self.frame_queue,
self.fps,
self.skipped_fps,
self.current_frame,
)
def capture_camera(name, config: CameraConfig, process_info):
stop_event = mp.Event()
def receiveSignal(signalNumber, frame):
stop_event.set()
signal.signal(signal.SIGTERM, receiveSignal)
signal.signal(signal.SIGINT, receiveSignal)
frame_queue = process_info['frame_queue']
camera_watchdog = CameraWatchdog(name, config, frame_queue, process_info['camera_fps'], process_info['ffmpeg_pid'], stop_event)
frame_queue = process_info["frame_queue"]
camera_watchdog = CameraWatchdog(
name,
config,
frame_queue,
process_info["camera_fps"],
process_info["ffmpeg_pid"],
stop_event,
)
camera_watchdog.start()
camera_watchdog.join()
def track_camera(name, config: CameraConfig, model_shape, detection_queue, result_connection, detected_objects_queue, process_info):
def track_camera(
name,
config: CameraConfig,
model_shape,
labelmap,
detection_queue,
result_connection,
detected_objects_queue,
process_info,
):
stop_event = mp.Event()
def receiveSignal(signalNumber, frame):
stop_event.set()
signal.signal(signal.SIGTERM, receiveSignal)
signal.signal(signal.SIGINT, receiveSignal)
@@ -256,79 +343,118 @@ def track_camera(name, config: CameraConfig, model_shape, detection_queue, resul
setproctitle(f"frigate.process:{name}")
listen()
frame_queue = process_info['frame_queue']
detection_enabled = process_info['detection_enabled']
frame_queue = process_info["frame_queue"]
detection_enabled = process_info["detection_enabled"]
frame_shape = config.frame_shape
objects_to_track = config.objects.track
object_filters = config.objects.filters
motion_detector = MotionDetector(frame_shape, config.motion)
object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection, model_shape)
object_detector = RemoteObjectDetector(
name, labelmap, detection_queue, result_connection, model_shape
)
object_tracker = ObjectTracker(config.detect)
frame_manager = SharedMemoryFrameManager()
process_frames(name, frame_queue, frame_shape, model_shape, frame_manager, motion_detector, object_detector,
object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, detection_enabled, stop_event)
process_frames(
name,
frame_queue,
frame_shape,
model_shape,
frame_manager,
motion_detector,
object_detector,
object_tracker,
detected_objects_queue,
process_info,
objects_to_track,
object_filters,
detection_enabled,
stop_event,
)
logger.info(f"{name}: exiting subprocess")
def reduce_boxes(boxes):
if len(boxes) == 0:
return []
reduced_boxes = cv2.groupRectangles([list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2)[0]
reduced_boxes = cv2.groupRectangles(
[list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2
)[0]
return [tuple(b) for b in reduced_boxes]
# modified from https://stackoverflow.com/a/40795835
def intersects_any(box_a, boxes):
for box in boxes:
if box_a[2] < box[0] or box_a[0] > box[2] or box_a[1] > box[3] or box_a[3] < box[1]:
if (
box_a[2] < box[0]
or box_a[0] > box[2]
or box_a[1] > box[3]
or box_a[3] < box[1]
):
continue
return True
def detect(object_detector, frame, model_shape, region, objects_to_track, object_filters):
def detect(
object_detector, frame, model_shape, region, objects_to_track, object_filters
):
tensor_input = create_tensor_input(frame, model_shape, region)
detections = []
region_detections = object_detector.detect(tensor_input)
for d in region_detections:
box = d[2]
size = region[2]-region[0]
size = region[2] - region[0]
x_min = int((box[1] * size) + region[0])
y_min = int((box[0] * size) + region[1])
x_max = int((box[3] * size) + region[0])
y_max = int((box[2] * size) + region[1])
det = (d[0],
det = (
d[0],
d[1],
(x_min, y_min, x_max, y_max),
(x_max-x_min)*(y_max-y_min),
region)
(x_max - x_min) * (y_max - y_min),
region,
)
# apply object filters
if filtered(det, objects_to_track, object_filters):
continue
detections.append(det)
return detections
def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_shape,
frame_manager: FrameManager, motion_detector: MotionDetector,
object_detector: RemoteObjectDetector, object_tracker: ObjectTracker,
detected_objects_queue: mp.Queue, process_info: Dict,
objects_to_track: List[str], object_filters, detection_enabled: mp.Value, stop_event,
exit_on_empty: bool = False):
fps = process_info['process_fps']
detection_fps = process_info['detection_fps']
current_frame_time = process_info['detection_frame']
def process_frames(
camera_name: str,
frame_queue: mp.Queue,
frame_shape,
model_shape,
frame_manager: FrameManager,
motion_detector: MotionDetector,
object_detector: RemoteObjectDetector,
object_tracker: ObjectTracker,
detected_objects_queue: mp.Queue,
process_info: Dict,
objects_to_track: List[str],
object_filters,
detection_enabled: mp.Value,
stop_event,
exit_on_empty: bool = False,
):
fps = process_info["process_fps"]
detection_fps = process_info["detection_fps"]
current_frame_time = process_info["detection_frame"]
fps_tracker = EventsPerSecond()
fps_tracker.start()
while True:
if stop_event.is_set():
break
while not stop_event.is_set():
if exit_on_empty and frame_queue.empty():
logger.info(f"Exiting track_objects...")
break
@@ -340,7 +466,9 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
current_frame_time.value = frame_time
frame = frame_manager.get(f"{camera_name}{frame_time}", (frame_shape[0]*3//2, frame_shape[1]))
frame = frame_manager.get(
f"{camera_name}{frame_time}", (frame_shape[0] * 3 // 2, frame_shape[1])
)
if frame is None:
logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
@@ -349,7 +477,9 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
if not detection_enabled.value:
fps.value = fps_tracker.eps()
object_tracker.match_and_update(frame_time, [])
detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects, [], []))
detected_objects_queue.put(
(camera_name, frame_time, object_tracker.tracked_objects, [], [])
)
detection_fps.value = object_detector.fps.eps()
frame_manager.close(f"{camera_name}{frame_time}")
continue
@@ -358,27 +488,44 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
motion_boxes = motion_detector.detect(frame)
# only get the tracked object boxes that intersect with motion
tracked_object_boxes = [obj['box'] for obj in object_tracker.tracked_objects.values() if intersects_any(obj['box'], motion_boxes)]
tracked_object_boxes = [
obj["box"]
for obj in object_tracker.tracked_objects.values()
if intersects_any(obj["box"], motion_boxes)
]
# combine motion boxes with known locations of existing objects
combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
# compute regions
regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
for a in combined_boxes]
regions = [
calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2)
for a in combined_boxes
]
# combine overlapping regions
combined_regions = reduce_boxes(regions)
# re-compute regions
regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
for a in combined_regions]
regions = [
calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
for a in combined_regions
]
# resize regions and detect
detections = []
for region in regions:
detections.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
detections.extend(
detect(
object_detector,
frame,
model_shape,
region,
objects_to_track,
object_filters,
)
)
#########
# merge objects, check for clipped objects and look again up to 4 times
#########
@@ -396,8 +543,10 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
for group in detected_object_groups.values():
# apply non-maxima suppression to suppress weak, overlapping bounding boxes
boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1])
for o in group]
boxes = [
(o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
for o in group
]
confidences = [o[1] for o in group]
idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
@@ -406,17 +555,26 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
if clipped(obj, frame_shape):
box = obj[2]
# calculate a new region that will hopefully get the entire object
region = calculate_region(frame_shape,
box[0], box[1],
box[2], box[3])
region = calculate_region(
frame_shape, box[0], box[1], box[2], box[3]
)
regions.append(region)
selected_objects.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
selected_objects.extend(
detect(
object_detector,
frame,
model_shape,
region,
objects_to_track,
object_filters,
)
)
refining = True
else:
selected_objects.append(obj)
selected_objects.append(obj)
# set the detections list to only include top, complete objects
# and new detections
detections = selected_objects
@@ -426,18 +584,28 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
# Limit to the detections overlapping with motion areas
# to avoid picking up stationary background objects
detections_with_motion = [d for d in detections if intersects_any(d[2], motion_boxes)]
detections_with_motion = [
d for d in detections if intersects_any(d[2], motion_boxes)
]
# now that we have refined our detections, we need to track objects
object_tracker.match_and_update(frame_time, detections_with_motion)
# add to the queue if not full
if(detected_objects_queue.full()):
if detected_objects_queue.full():
frame_manager.delete(f"{camera_name}{frame_time}")
continue
else:
fps_tracker.update()
fps.value = fps_tracker.eps()
detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects, motion_boxes, regions))
detected_objects_queue.put(
(
camera_name,
frame_time,
object_tracker.tracked_objects,
motion_boxes,
regions,
)
)
detection_fps.value = object_detector.fps.eps()
frame_manager.close(f"{camera_name}{frame_time}")

27
frigate/watchdog.py
View File

@@ -5,34 +5,35 @@ import time
import os
import signal
from frigate.util import (
restart_frigate,
)
logger = logging.getLogger(__name__)
class FrigateWatchdog(threading.Thread):
def __init__(self, detectors, stop_event):
threading.Thread.__init__(self)
self.name = 'frigate_watchdog'
self.name = "frigate_watchdog"
self.detectors = detectors
self.stop_event = stop_event
def run(self):
time.sleep(10)
while True:
# wait a bit before checking
time.sleep(10)
if self.stop_event.is_set():
logger.info(f"Exiting watchdog...")
break
while not self.stop_event.wait(10):
now = datetime.datetime.now().timestamp()
# check the detection processes
for detector in self.detectors.values():
detection_start = detector.detection_start.value
if (detection_start > 0.0 and
now - detection_start > 10):
logger.info("Detection appears to be stuck. Restarting detection process...")
if detection_start > 0.0 and now - detection_start > 10:
logger.info(
"Detection appears to be stuck. Restarting detection process..."
)
detector.start_or_restart()
elif not detector.detect_process.is_alive():
logger.info("Detection appears to have stopped. Exiting frigate...")
os.kill(os.getpid(), signal.SIGTERM)
restart_frigate()
logger.info(f"Exiting watchdog...")

1
frigate/zeroconf.py
View File

@@ -31,6 +31,7 @@ def get_local_ip() -> str:
finally:
sock.close()
def broadcast_zeroconf(frigate_id):
zeroconf = Zeroconf(interfaces=InterfaceChoice.Default, ip_version=IPVersion.V4Only)

6
migrations/001_create_events_table.py
View File

@@ -32,10 +32,14 @@ except ImportError:
SQL = pw.SQL
def migrate(migrator, database, fake=False, **kwargs):
migrator.sql('CREATE TABLE IF NOT EXISTS "event" ("id" VARCHAR(30) NOT NULL PRIMARY KEY, "label" VARCHAR(20) NOT NULL, "camera" VARCHAR(20) NOT NULL, "start_time" DATETIME NOT NULL, "end_time" DATETIME NOT NULL, "top_score" REAL NOT NULL, "false_positive" INTEGER NOT NULL, "zones" JSON NOT NULL, "thumbnail" TEXT NOT NULL)')
migrator.sql(
'CREATE TABLE IF NOT EXISTS "event" ("id" VARCHAR(30) NOT NULL PRIMARY KEY, "label" VARCHAR(20) NOT NULL, "camera" VARCHAR(20) NOT NULL, "start_time" DATETIME NOT NULL, "end_time" DATETIME NOT NULL, "top_score" REAL NOT NULL, "false_positive" INTEGER NOT NULL, "zones" JSON NOT NULL, "thumbnail" TEXT NOT NULL)'
)
migrator.sql('CREATE INDEX IF NOT EXISTS "event_label" ON "event" ("label")')
migrator.sql('CREATE INDEX IF NOT EXISTS "event_camera" ON "event" ("camera")')
def rollback(migrator, database, fake=False, **kwargs):
pass

9
migrations/002_add_clip_snapshot.py
View File

@@ -35,7 +35,12 @@ SQL = pw.SQL
def migrate(migrator, database, fake=False, **kwargs):
migrator.add_fields(Event, has_clip=pw.BooleanField(default=True), has_snapshot=pw.BooleanField(default=True))
migrator.add_fields(
Event,
has_clip=pw.BooleanField(default=True),
has_snapshot=pw.BooleanField(default=True),
)
def rollback(migrator, database, fake=False, **kwargs):
migrator.remove_fields(Event, ['has_clip', 'has_snapshot'])
migrator.remove_fields(Event, ["has_clip", "has_snapshot"])

44
migrations/003_create_recordings_table.py Normal file
View File

@@ -0,0 +1,44 @@
"""Peewee migrations -- 003_create_recordings_table.py.
Some examples (model - class or model name)::
> Model = migrator.orm['model_name'] # Return model in current state by name
> migrator.sql(sql) # Run custom SQL
> migrator.python(func, *args, **kwargs) # Run python code
> migrator.create_model(Model) # Create a model (could be used as decorator)
> migrator.remove_model(model, cascade=True) # Remove a model
> migrator.add_fields(model, **fields) # Add fields to a model
> migrator.change_fields(model, **fields) # Change fields
> migrator.remove_fields(model, *field_names, cascade=True)
> migrator.rename_field(model, old_field_name, new_field_name)
> migrator.rename_table(model, new_table_name)
> migrator.add_index(model, *col_names, unique=False)
> migrator.drop_index(model, *col_names)
> migrator.add_not_null(model, *field_names)
> migrator.drop_not_null(model, *field_names)
> migrator.add_default(model, field_name, default)
"""
import peewee as pw
from frigate.models import Recordings
SQL = pw.SQL
def migrate(migrator, database, fake=False, **kwargs):
migrator.create_model(Recordings)
def add_index():
# First add the index here, because there is a bug in peewee_migrate
# when trying to create an multi-column index in the same migration
# as the table: https://github.com/klen/peewee_migrate/issues/19
Recordings.add_index("start_time", "end_time")
Recordings.create_table()
migrator.python(add_index)
def rollback(migrator, database, fake=False, **kwargs):
migrator.remove_model(Recordings)

48
migrations/004_add_bbox_region_area.py Normal file
View File

@@ -0,0 +1,48 @@
"""Peewee migrations -- 004_add_bbox_region_area.py.
Some examples (model - class or model name)::
> Model = migrator.orm['model_name'] # Return model in current state by name
> migrator.sql(sql) # Run custom SQL
> migrator.python(func, *args, **kwargs) # Run python code
> migrator.create_model(Model) # Create a model (could be used as decorator)
> migrator.remove_model(model, cascade=True) # Remove a model
> migrator.add_fields(model, **fields) # Add fields to a model
> migrator.change_fields(model, **fields) # Change fields
> migrator.remove_fields(model, *field_names, cascade=True)
> migrator.rename_field(model, old_field_name, new_field_name)
> migrator.rename_table(model, new_table_name)
> migrator.add_index(model, *col_names, unique=False)
> migrator.drop_index(model, *col_names)
> migrator.add_not_null(model, *field_names)
> migrator.drop_not_null(model, *field_names)
> migrator.add_default(model, field_name, default)
"""
import datetime as dt
import peewee as pw
from playhouse.sqlite_ext import *
from decimal import ROUND_HALF_EVEN
from frigate.models import Event
try:
import playhouse.postgres_ext as pw_pext
except ImportError:
pass
SQL = pw.SQL
def migrate(migrator, database, fake=False, **kwargs):
migrator.add_fields(
Event,
region=JSONField(default=[]),
box=JSONField(default=[]),
area=pw.IntegerField(default=0),
)
def rollback(migrator, database, fake=False, **kwargs):
migrator.remove_fields(Event, ["region", "box", "area"])

4
run.sh
View File

@@ -1,4 +0,0 @@
#!/usr/bin/env bash
service nginx start
exec python3 -u -m frigate

2
web/.eslintrc.js
View File

@@ -118,7 +118,7 @@ module.exports = {
'import/no-unresolved': 'error',
'react-hooks/exhaustive-deps': 'error',
// 'react-hooks/exhaustive-deps': 'error',
'jest/consistent-test-it': ['error', { fn: 'test' }],
'jest/no-test-prefixes': 'error',

3
web/jest.config.js
View File

@@ -6,4 +6,7 @@ module.exports = {
setupFilesAfterEnv: ['<rootDir>/config/setupTests.js'],
testEnvironment: 'jsdom',
timers: 'fake',
moduleNameMapper: {
'\\.(scss|sass|css)$': '<rootDir>/src/__mocks__/styleMock.js'
}
};

337
web/package-lock.json generated
View File

@@ -2873,7 +2873,6 @@
"version": "7.12.13",
"resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.12.13.tgz",
"integrity": "sha512-8+3UMPBrjFa/6TtKi/7sehPKqfAm4g6K+YQjyyFOLUTxzOngcRZTlAVY8sc2CORJYqdHQY8gRPHmn+qo15rCBw==",
"dev": true,
"requires": {
"regenerator-runtime": "^0.13.4"
}
@@ -2943,6 +2942,25 @@
"minimist": "^1.2.0"
}
},
"@cycjimmy/awesome-js-funcs": {
"version": "2.5.0",
"resolved": "https://registry.npmjs.org/@cycjimmy/awesome-js-funcs/-/awesome-js-funcs-2.5.0.tgz",
"integrity": "sha512-xy4MkuL8FcmRfWm0FwdHUCWB+EQ/ljCuTAf6d3SdaXPDgD4celrq0/vJT9ekLF0E6Au4fLbvRh1G8sXnGwDUkg=="
},
"@cycjimmy/jsmpeg-player": {
"version": "5.0.1",
"resolved": "https://registry.npmjs.org/@cycjimmy/jsmpeg-player/-/jsmpeg-player-5.0.1.tgz",
"integrity": "sha512-x2kCCEfmXinebXC3d8XBJmOeFP+Jw0f4peT2rAmNvhggP1hYPvACP2ioobxeZvp255rPQTt/RPNi9y2SJ5ATMQ==",
"requires": {
"@cycjimmy/awesome-js-funcs": "^2.3.0",
"@cycjimmy/sass-lib": "^1.0.3"
}
},
"@cycjimmy/sass-lib": {
"version": "1.0.3",
"resolved": "https://registry.npmjs.org/@cycjimmy/sass-lib/-/sass-lib-1.0.3.tgz",
"integrity": "sha512-OTr8uMjeRapr0bWTKs782Jj84/xQt1XvVl5ZyYW1yi4sEbqY1FfW1FYLeflBp4gSU99o4c/nPi2Fx5aRxna5dw=="
},
"@dsherret/to-absolute-glob": {
"version": "2.0.2",
"resolved": "https://registry.npmjs.org/@dsherret/to-absolute-glob/-/to-absolute-glob-2.0.2.tgz",
@@ -4118,6 +4136,79 @@
"integrity": "sha512-GmVAWB+JuFKqSbzlofYK4qxk955gEv4Kd9/aj2hLOxneXMAm/J7OXcl5DlElS9tmkqwCcxGysSZGOrjzNvmjFQ==",
"dev": true
},
"@videojs/http-streaming": {
"version": "2.10.2",
"resolved": "https://registry.npmjs.org/@videojs/http-streaming/-/http-streaming-2.10.2.tgz",
"integrity": "sha512-JTAlAUHzj0sTsge2WBh4DWKM2I5BDFEZYOvzxmsK/ySILmI0GRyjAHx9uid68ZECQ2qbEAIRmZW5lWp0R5PeNA==",
"requires": {
"@babel/runtime": "^7.12.5",
"@videojs/vhs-utils": "3.0.3",
"aes-decrypter": "3.1.2",
"global": "^4.4.0",
"m3u8-parser": "4.7.0",
"mpd-parser": "0.19.0",
"mux.js": "5.13.0",
"video.js": "^6 || ^7"
},
"dependencies": {
"global": {
"version": "4.4.0",
"resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz",
"integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==",
"requires": {
"min-document": "^2.19.0",
"process": "^0.11.10"
}
}
}
},
"@videojs/vhs-utils": {
"version": "3.0.3",
"resolved": "https://registry.npmjs.org/@videojs/vhs-utils/-/vhs-utils-3.0.3.tgz",
"integrity": "sha512-bU7daxDHhzcTDbmty1cXjzsTYvx2cBGbA8hG5H2Gvpuk4sdfuvkZtMCwtCqL59p6dsleMPspyaNS+7tWXx2Y0A==",
"requires": {
"@babel/runtime": "^7.12.5",
"global": "^4.4.0",
"url-toolkit": "^2.2.1"
},
"dependencies": {
"global": {
"version": "4.4.0",
"resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz",
"integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==",
"requires": {
"min-document": "^2.19.0",
"process": "^0.11.10"
}
}
}
},
"@videojs/xhr": {
"version": "2.6.0",
"resolved": "https://registry.npmjs.org/@videojs/xhr/-/xhr-2.6.0.tgz",
"integrity": "sha512-7J361GiN1tXpm+gd0xz2QWr3xNWBE+rytvo8J3KuggFaLg+U37gZQ2BuPLcnkfGffy2e+ozY70RHC8jt7zjA6Q==",
"requires": {
"@babel/runtime": "^7.5.5",
"global": "~4.4.0",
"is-function": "^1.0.1"
},
"dependencies": {
"global": {
"version": "4.4.0",
"resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz",
"integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==",
"requires": {
"min-document": "^2.19.0",
"process": "^0.11.10"
}
}
}
},
"@xmldom/xmldom": {
"version": "0.7.4",
"resolved": "https://registry.npmjs.org/@xmldom/xmldom/-/xmldom-0.7.4.tgz",
"integrity": "sha512-wdxC79cvO7PjSM34jATd/RYZuYWQ8y/R7MidZl1NYYlbpFn1+spfjkiR3ZsJfcaTs2IyslBN7VwBBJwrYKM+zw=="
},
"abab": {
"version": "2.0.5",
"resolved": "https://registry.npmjs.org/abab/-/abab-2.0.5.tgz",
@@ -4163,6 +4254,28 @@
"integrity": "sha512-OPdCF6GsMIP+Az+aWfAAOEt2/+iVDKE7oy6lJ098aoe59oAmK76qV6Gw60SbZ8jHuG2wH058GF4pLFbYamYrVA==",
"dev": true
},
"aes-decrypter": {
"version": "3.1.2",
"resolved": "https://registry.npmjs.org/aes-decrypter/-/aes-decrypter-3.1.2.tgz",
"integrity": "sha512-42nRwfQuPRj9R1zqZBdoxnaAmnIFyDi0MNyTVhjdFOd8fifXKKRfwIHIZ6AMn1or4x5WONzjwRTbTWcsIQ0O4A==",
"requires": {
"@babel/runtime": "^7.12.5",
"@videojs/vhs-utils": "^3.0.0",
"global": "^4.4.0",
"pkcs7": "^1.0.4"
},
"dependencies": {
"global": {
"version": "4.4.0",
"resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz",
"integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==",
"requires": {
"min-document": "^2.19.0",
"process": "^0.11.10"
}
}
}
},
"ajv": {
"version": "6.12.6",
"resolved": "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz",
@@ -5129,6 +5242,11 @@
"whatwg-url": "^8.0.0"
}
},
"date-fns": {
"version": "2.21.3",
"resolved": "https://registry.npmjs.org/date-fns/-/date-fns-2.21.3.tgz",
"integrity": "sha512-HeYdzCaFflc1i4tGbj7JKMjM4cKGYoyxwcIIkHzNgCkX8xXDNJDZXgDDVchIWpN4eQc3lH37WarduXFZJOtxfw=="
},
"debug": {
"version": "4.3.1",
"resolved": "https://registry.npmjs.org/debug/-/debug-4.3.1.tgz",
@@ -5297,6 +5415,11 @@
"integrity": "sha512-TvrjBckDy2c6v6RLxPv5QXOnU+SmF9nBII5621Ve5fu6Z/BDrENurBEvlC1f44lKEUVqOpK4w9E5Idc5/EgkLQ==",
"dev": true
},
"dom-walk": {
"version": "0.1.2",
"resolved": "https://registry.npmjs.org/dom-walk/-/dom-walk-0.1.2.tgz",
"integrity": "sha512-6QvTW9mrGeIegrFXdtQi9pk7O/nSK6lSdXW2eqUspN5LWD7UTji2Fqw5V2YLjBpHEoU9Xl/eUWNpDeZvoyOv2w=="
},
"domconstants": {
"version": "0.1.2",
"resolved": "https://registry.npmjs.org/domconstants/-/domconstants-0.1.2.tgz",
@@ -6447,14 +6570,30 @@
}
},
"glob-parent": {
"version": "5.1.1",
"resolved": "https://registry.npmjs.org/glob-parent/-/glob-parent-5.1.1.tgz",
"integrity": "sha512-FnI+VGOpnlGHWZxthPGR+QhR78fuiK0sNLkHQv+bL9fQi57lNNdquIbna/WrfROrolq8GK5Ek6BiMwqL/voRYQ==",
"version": "5.1.2",
"resolved": "https://registry.npmjs.org/glob-parent/-/glob-parent-5.1.2.tgz",
"integrity": "sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==",
"dev": true,
"requires": {
"is-glob": "^4.0.1"
}
},
"global": {
"version": "4.3.2",
"resolved": "https://registry.npmjs.org/global/-/global-4.3.2.tgz",
"integrity": "sha1-52mJJopsdMOJCLEwWxD8DjlOnQ8=",
"requires": {
"min-document": "^2.19.0",
"process": "~0.5.1"
},
"dependencies": {
"process": {
"version": "0.5.2",
"resolved": "https://registry.npmjs.org/process/-/process-0.5.2.tgz",
"integrity": "sha1-FjjYqONML0QKkduVq5rrZ3/Bhc8="
}
}
},
"globals": {
"version": "11.12.0",
"resolved": "https://registry.npmjs.org/globals/-/globals-11.12.0.tgz",
@@ -6713,6 +6852,11 @@
"integrity": "sha1-8w9xbI4r00bHtn0985FVZqfAVgc=",
"dev": true
},
"individual": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/individual/-/individual-2.0.0.tgz",
"integrity": "sha1-gzsJfa0jKU52EXqY+zjg2a1hu5c="
},
"inflight": {
"version": "1.0.6",
"resolved": "https://registry.npmjs.org/inflight/-/inflight-1.0.6.tgz",
@@ -6896,6 +7040,11 @@
"integrity": "sha512-zymm5+u+sCsSWyD9qNaejV3DFvhCKclKdizYaJUuHA83RLjb7nSuGnddCHGv0hk+KY7BMAlsWeK4Ueg6EV6XQg==",
"dev": true
},
"is-function": {
"version": "1.0.2",
"resolved": "https://registry.npmjs.org/is-function/-/is-function-1.0.2.tgz",
"integrity": "sha512-lw7DUp0aWXYg+CBCN+JKkcE0Q2RayZnSvnZBlwgxHBQhqt5pZNVy4Ri7H9GmmXkdu7LUthszM+Tor1u/2iBcpQ=="
},
"is-generator-fn": {
"version": "2.1.0",
"resolved": "https://registry.npmjs.org/is-generator-fn/-/is-generator-fn-2.1.0.tgz",
@@ -8616,6 +8765,11 @@
"object.assign": "^4.1.2"
}
},
"keycode": {
"version": "2.2.0",
"resolved": "https://registry.npmjs.org/keycode/-/keycode-2.2.0.tgz",
"integrity": "sha1-PQr1bce4uOXLqNCpfxByBO7CKwQ="
},
"kind-of": {
"version": "6.0.3",
"resolved": "https://registry.npmjs.org/kind-of/-/kind-of-6.0.3.tgz",
@@ -8777,6 +8931,27 @@
"integrity": "sha1-wNjq82BZ9wV5bh40SBHPTEmNOiY=",
"dev": true
},
"m3u8-parser": {
"version": "4.7.0",
"resolved": "https://registry.npmjs.org/m3u8-parser/-/m3u8-parser-4.7.0.tgz",
"integrity": "sha512-48l/OwRyjBm+QhNNigEEcRcgbRvnUjL7rxs597HmW9QSNbyNvt+RcZ9T/d9vxi9A9z7EZrB1POtZYhdRlwYQkQ==",
"requires": {
"@babel/runtime": "^7.12.5",
"@videojs/vhs-utils": "^3.0.0",
"global": "^4.4.0"
},
"dependencies": {
"global": {
"version": "4.4.0",
"resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz",
"integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==",
"requires": {
"min-document": "^2.19.0",
"process": "^0.11.10"
}
}
}
},
"magic-string": {
"version": "0.25.7",
"resolved": "https://registry.npmjs.org/magic-string/-/magic-string-0.25.7.tgz",
@@ -8876,6 +9051,14 @@
"integrity": "sha512-OqbOk5oEQeAZ8WXWydlu9HJjz9WVdEIvamMCcXmuqUYjTknH/sqsWvhQ3vgwKFRR1HpjvNBKQ37nbJgYzGqGcg==",
"dev": true
},
"min-document": {
"version": "2.19.0",
"resolved": "https://registry.npmjs.org/min-document/-/min-document-2.19.0.tgz",
"integrity": "sha1-e9KC4/WELtKVu3SM3Z8f+iyCRoU=",
"requires": {
"dom-walk": "^0.1.0"
}
},
"min-indent": {
"version": "1.0.1",
"resolved": "https://registry.npmjs.org/min-indent/-/min-indent-1.0.1.tgz",
@@ -8936,6 +9119,28 @@
"integrity": "sha512-Xm9jdWvqFrlV0k965eY5AlCpWIIUBY2ExzGbEG+byMs+mZI4J7zvaUOLpQ8MTFgkpgyEnu4qUhuZT/Or3QeRiA==",
"dev": true
},
"mpd-parser": {
"version": "0.19.0",
"resolved": "https://registry.npmjs.org/mpd-parser/-/mpd-parser-0.19.0.tgz",
"integrity": "sha512-FDLIXtZMZs99fv5iXNFg94quNFT26tobo0NUgHu7L3XgZvEq1NBarf5yxDFFJ1zzfbcmtj+NRaml6nYIxoPWvw==",
"requires": {
"@babel/runtime": "^7.12.5",
"@videojs/vhs-utils": "^3.0.2",
"@xmldom/xmldom": "^0.7.2",
"global": "^4.4.0"
},
"dependencies": {
"global": {
"version": "4.4.0",
"resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz",
"integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==",
"requires": {
"min-document": "^2.19.0",
"process": "^0.11.10"
}
}
}
},
"ms": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/ms/-/ms-2.0.0.tgz",
@@ -8955,6 +9160,14 @@
"minimatch": "^3.0.4"
}
},
"mux.js": {
"version": "5.13.0",
"resolved": "https://registry.npmjs.org/mux.js/-/mux.js-5.13.0.tgz",
"integrity": "sha512-PkmnzHcTQjUBEHp3KRPQAFoNkJtKlpCEvsYtXDfDrC+/WqbMuxHvoYfmAbHVAH7Sa/KliPVU0dT1ureO8wilog==",
"requires": {
"@babel/runtime": "^7.11.2"
}
},
"nanoid": {
"version": "3.1.23",
"resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.1.23.tgz",
@@ -9357,9 +9570,9 @@
"dev": true
},
"path-parse": {
"version": "1.0.6",
"resolved": "https://registry.npmjs.org/path-parse/-/path-parse-1.0.6.tgz",
"integrity": "sha512-GSmOT2EbHrINBf9SR7CDELwlJ8AENk3Qn7OikK4nFYAu3Ote2+JYNVvkpAEQm3/TLNEJFD/xZJjzyxg3KBWOzw==",
"version": "1.0.7",
"resolved": "https://registry.npmjs.org/path-parse/-/path-parse-1.0.7.tgz",
"integrity": "sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==",
"dev": true
},
"path-type": {
@@ -9395,6 +9608,14 @@
"node-modules-regexp": "^1.0.0"
}
},
"pkcs7": {
"version": "1.0.4",
"resolved": "https://registry.npmjs.org/pkcs7/-/pkcs7-1.0.4.tgz",
"integrity": "sha512-afRERtHn54AlwaF2/+LFszyAANTCggGilmcmILUzEjvs3XgFZT+xE6+QWQcAGmu4xajy+Xtj7acLOPdx5/eXWQ==",
"requires": {
"@babel/runtime": "^7.5.5"
}
},
"pkg-dir": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/pkg-dir/-/pkg-dir-2.0.0.tgz",
@@ -9757,6 +9978,11 @@
"integrity": "sha1-t+PqQkNaTJsnWdmeDyAesZWALuE=",
"dev": true
},
"process": {
"version": "0.11.10",
"resolved": "https://registry.npmjs.org/process/-/process-0.11.10.tgz",
"integrity": "sha1-czIwDoQBYb2j5podHZGn1LwW8YI="
},
"progress": {
"version": "2.0.3",
"resolved": "https://registry.npmjs.org/progress/-/progress-2.0.3.tgz",
@@ -9985,8 +10211,7 @@
"regenerator-runtime": {
"version": "0.13.7",
"resolved": "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.13.7.tgz",
"integrity": "sha512-a54FxoJDIr27pgf7IgeQGxmqUNYrcV338lf/6gH456HZ/PhX+5BcwHXG9ajESmwe6WRO0tAzRUrRmNONWgkrew==",
"dev": true
"integrity": "sha512-a54FxoJDIr27pgf7IgeQGxmqUNYrcV338lf/6gH456HZ/PhX+5BcwHXG9ajESmwe6WRO0tAzRUrRmNONWgkrew=="
},
"regenerator-transform": {
"version": "0.14.5",
@@ -10256,12 +10481,28 @@
"integrity": "sha512-zb/1OuZ6flOlH6tQyMPUrE3x3Ulxjlo9WIVXR4yVYi4H9UXQaeIsPbLn2R3O3vQCnDKkAl2qHiuocKKX4Tz/Sw==",
"dev": true
},
"rust-result": {
"version": "1.0.0",
"resolved": "https://registry.npmjs.org/rust-result/-/rust-result-1.0.0.tgz",
"integrity": "sha1-NMdbLm3Dn+WHXlveyFteD5FTb3I=",
"requires": {
"individual": "^2.0.0"
}
},
"safe-buffer": {
"version": "5.1.2",
"resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz",
"integrity": "sha512-Gd2UZBJDkXlY7GbJxfsE8/nvKkUEU1G38c1siN6QP6a9PT9MmHB8GnpscSmMJSoF8LOIrt8ud/wPtojys4G6+g==",
"dev": true
},
"safe-json-parse": {
"version": "4.0.0",
"resolved": "https://registry.npmjs.org/safe-json-parse/-/safe-json-parse-4.0.0.tgz",
"integrity": "sha1-fA9XjPzNEtM6ccDgVBPi7KFx6qw=",
"requires": {
"rust-result": "^1.0.0"
}
},
"safe-regex": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/safe-regex/-/safe-regex-1.1.0.tgz",
@@ -11571,6 +11812,11 @@
"integrity": "sha1-2pN/emLiH+wf0Y1Js1wpNQZ6bHI=",
"dev": true
},
"url-toolkit": {
"version": "2.2.3",
"resolved": "https://registry.npmjs.org/url-toolkit/-/url-toolkit-2.2.3.tgz",
"integrity": "sha512-Da75SQoxsZ+2wXS56CZBrj2nukQ4nlGUZUP/dqUBG5E1su5GKThgT94Q00x81eVII7AyS1Pn+CtTTZ4Z0pLUtQ=="
},
"use": {
"version": "3.1.1",
"resolved": "https://registry.npmjs.org/use/-/use-3.1.1.tgz",
@@ -11645,6 +11891,79 @@
"extsprintf": "^1.2.0"
}
},
"video.js": {
"version": "7.15.4",
"resolved": "https://registry.npmjs.org/video.js/-/video.js-7.15.4.tgz",
"integrity": "sha512-hghxkgptLUvfkpktB4wxcIVF3VpY/hVsMkrjHSv0jpj1bW9Jplzdt8IgpTm9YhlB1KYAp07syVQeZcBFUBwhkw==",
"requires": {
"@babel/runtime": "^7.12.5",
"@videojs/http-streaming": "2.10.2",
"@videojs/vhs-utils": "^3.0.3",
"@videojs/xhr": "2.6.0",
"aes-decrypter": "3.1.2",
"global": "^4.4.0",
"keycode": "^2.2.0",
"m3u8-parser": "4.7.0",
"mpd-parser": "0.19.0",
"mux.js": "5.13.0",
"safe-json-parse": "4.0.0",
"videojs-font": "3.2.0",
"videojs-vtt.js": "^0.15.3"
},
"dependencies": {
"global": {
"version": "4.4.0",
"resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz",
"integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==",
"requires": {
"min-document": "^2.19.0",
"process": "^0.11.10"
}
}
}
},
"videojs-font": {
"version": "3.2.0",
"resolved": "https://registry.npmjs.org/videojs-font/-/videojs-font-3.2.0.tgz",
"integrity": "sha512-g8vHMKK2/JGorSfqAZQUmYYNnXmfec4MLhwtEFS+mMs2IDY398GLysy6BH6K+aS1KMNu/xWZ8Sue/X/mdQPliA=="
},
"videojs-playlist": {
"version": "4.3.1",
"resolved": "https://registry.npmjs.org/videojs-playlist/-/videojs-playlist-4.3.1.tgz",
"integrity": "sha512-fxI3T6mWHKaXRwTQyJeq5I0b8GM9Q4S/p92Aq7O1xAT+X8jYxYSIN15xi32a1F5adEGPRqct+yMl5MkXO9x9cQ==",
"requires": {
"global": "^4.3.2",
"video.js": "^6 || ^7"
}
},
"videojs-seek-buttons": {
"version": "2.0.1",
"resolved": "https://registry.npmjs.org/videojs-seek-buttons/-/videojs-seek-buttons-2.0.1.tgz",
"integrity": "sha512-FIWIy0l1cy8zbJEcjBpL7m8t54219HNPRLfGcvs++CV3J7E6HbmF1bVwVMyh3Iev/Y95s0tnn0x5P6w/HTulfw==",
"requires": {
"global": "^4.4.0",
"video.js": "^6 || ^7"
},
"dependencies": {
"global": {
"version": "4.4.0",
"resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz",
"integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==",
"requires": {
"min-document": "^2.19.0",
"process": "^0.11.10"
}
}
}
},
"videojs-vtt.js": {
"version": "0.15.3",
"resolved": "https://registry.npmjs.org/videojs-vtt.js/-/videojs-vtt.js-0.15.3.tgz",
"integrity": "sha512-5FvVsICuMRx6Hd7H/Y9s9GDeEtYcXQWzGMS+sl4UX3t/zoHp3y+isSfIPRochnTH7h+Bh1ILyC639xy9Z6kPag==",
"requires": {
"global": "^4.3.1"
}
},
"w3c-hr-time": {
"version": "1.0.2",
"resolved": "https://registry.npmjs.org/w3c-hr-time/-/w3c-hr-time-1.0.2.tgz",

7
web/package.json
View File

@@ -11,11 +11,16 @@
"test": "jest"
},
"dependencies": {
"@cycjimmy/jsmpeg-player": "^5.0.1",
"date-fns": "^2.21.3",
"idb-keyval": "^5.0.2",
"immer": "^8.0.1",
"preact": "^10.5.9",
"preact-async-route": "^2.2.1",
"preact-router": "^3.2.1"
"preact-router": "^3.2.1",
"video.js": "^7.15.4",
"videojs-playlist": "^4.3.1",
"videojs-seek-buttons": "^2.0.1"
},
"devDependencies": {
"@babel/eslint-parser": "^7.12.13",

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