administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-02-02 01:05:20 +03:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Merge branch 'release-0.9.0' into fix/shared_memory_exception

Browse Source
This commit is contained in:
mrdrup 2021-06-08 01:31:12 +01:00 committed by GitHub
commit 0a5ece4b60
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
70 changed files with 5108 additions and 2549 deletions
Show Stats 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": "/opt/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"
}
}

1
.dockerignore
View File

@ -5,3 +5,4 @@ debug
config/
*.pyc
.git
core

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

13
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/
@ -14,8 +14,11 @@ amd64_wheels:
amd64_ffmpeg:
docker build --tag blakeblackshear/frigate-ffmpeg:1.1.0-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:1.0.0 --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-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.0 --file docker/Dockerfile.base .
docker build --tag frigate --file docker/Dockerfile.amd64 .
amd64_all: amd64_wheels amd64_ffmpeg amd64_frigate
@ -27,7 +30,7 @@ amd64nvidia_ffmpeg:
docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-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-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.0 --file docker/Dockerfile.base .
docker build --tag frigate --file docker/Dockerfile.amd64nvidia .
amd64nvidia_all: amd64nvidia_wheels amd64nvidia_ffmpeg amd64nvidia_frigate
@ -39,7 +42,7 @@ aarch64_ffmpeg:
docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-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-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.0 --file docker/Dockerfile.base .
docker build --tag frigate --file docker/Dockerfile.aarch64 .
armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
@ -51,7 +54,7 @@ armv7_ffmpeg:
docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-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-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.0 --file docker/Dockerfile.base .
docker build --tag frigate --file docker/Dockerfile.armv7 .
armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate

6
README.md
View File

@ -4,11 +4,11 @@
# Frigate - NVR With Realtime Object Detection for IP Cameras
A complete and local NVR designed for HomeAssistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
- Tight integration with HomeAssistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
- Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection
@ -26,7 +26,7 @@ View the documentation at https://blakeblackshear.github.io/frigate
If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
## Screenshots
Integration into HomeAssistant
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>

31
docker-compose.yml Normal file
View File

@ -0,0 +1,31 @@
version: "3"
services:
dev:
container_name: frigate-dev
user: vscode
build:
context: .
dockerfile: docker/Dockerfile.dev
devices:
- /dev/bus/usb:/dev/bus/usb
- /dev/dri:/dev/dri # for intel hwaccel, needs to be updated for your hardware
volumes:
- /etc/localtime:/etc/localtime:ro
- .:/opt/frigate:cached
- ./config/config.yml:/config/config.yml:ro
- ./debug:/media/frigate
- type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
target: /tmp/cache
tmpfs:
size: 1000000000
ports:
- "1935:1935"
- "5000:5000"
- "5001:5001"
- "8080:8080"
command: /bin/sh -c "sudo /usr/local/nginx/sbin/nginx; while sleep 1000; do :; done"
mqtt:
container_name: mqtt
image: eclipse-mosquitto:1.6
ports:
- "1883:1883"

14
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
@ -18,16 +20,13 @@ 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=15.0 \
&& rm -rf /var/lib/apt/lists/* /wheels \
&& (apt-get autoremove -y; apt-get autoclean -y)
@ -39,7 +38,8 @@ RUN pip3 install \
gevent \
gevent-websocket
COPY nginx/nginx.conf /etc/nginx/nginx.conf
COPY --from=nginx /usr/local/nginx/ /usr/local/nginx/
COPY nginx/nginx.conf /usr/local/nginx/conf/nginx.conf
# get model and labels
COPY labelmap.txt /labelmap.txt

23
docker/Dockerfile.dev Normal file
View File

@ -0,0 +1,23 @@
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 install -y git curl vim
RUN pip3 install pylint black
# Install Node 14
RUN curl -sL https://deb.nodesource.com/setup_14.x | bash - \
&& apt-get install -y nodejs

46
docker/Dockerfile.nginx Normal file
View File

@ -0,0 +1,46 @@
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 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 \
&& 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-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;"]

5
docs/docs/configuration/advanced.md
View File

@ -81,7 +81,7 @@ environment_vars:
### `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 HomeAssistant.
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.
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.
@ -99,7 +99,8 @@ detectors:
# Required: name of the detector
coral:
# Required: type of the detector
# Valid values are 'edgetpu' (requires device property below) and 'cpu'. type: edgetpu
# 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)

49
docs/docs/configuration/cameras.md
View File

@ -62,7 +62,7 @@ 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'
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)
@ -131,7 +131,7 @@ objects:
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 HomeAssistant's media browser unless your camera sends video as h264.
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.
@ -191,7 +191,7 @@ snapshots:
## 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 HomeAssistant's media browser. Each camera supports a configurable retention policy in the config.
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.
@ -208,7 +208,7 @@ record:
## RTMP streams
Frigate can re-stream your video feed as a RTMP feed for other applications such as HomeAssistant 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 HomeAssistant 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.
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.
@ -388,6 +388,37 @@ cameras:
## Camera specific configuration
### 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
- -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
@ -406,7 +437,7 @@ ffmpeg:
- -fflags
- +genpts+discardcorrupt
- -use_wallclock_as_timestamps
- '1'
- "1"
```
### Reolink 410/520 (possibly others)
@ -427,9 +458,9 @@ ffmpeg:
- -fflags
- +genpts+discardcorrupt
- -rw_timeout
- '5000000'
- "5000000"
- -use_wallclock_as_timestamps
- '1'
- "1"
```
### Blue Iris RTSP Cameras
@ -450,7 +481,7 @@ ffmpeg:
- -rtsp_transport
- tcp
- -stimeout
- '5000000'
- "5000000"
- -use_wallclock_as_timestamps
- '1'
- "1"
```

12
docs/docs/configuration/detectors.md
View File

@ -30,6 +30,18 @@ detectors:
device: usb:1
```
Multiple PCIE/M.2 Corals:
```yaml
detectors:
coral1:
type: edgetpu
device: pci:0
coral2:
type: edgetpu
device: pci:1
```
Mixing Corals:
```yaml

36
docs/docs/configuration/index.md
View File

@ -3,7 +3,9 @@ id: index
title: Configuration
---
HassOS users can manage their configuration directly in the addon Configuration tab. For 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 HassOS installations, the default location for the config file is `/config/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).
It is recommended to start with a minimal configuration and add to it:
@ -45,6 +47,17 @@ 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
```
@ -78,11 +91,6 @@ clips:
# 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)
@ -136,3 +144,19 @@ objects:
# Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
threshold: 0.7
```
### `record`
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
record:
# Optional: Enable recording
enabled: False
# Optional: Number of days to retain
retain_days: 30
```

2
docs/docs/configuration/nvdec.md
View File

@ -55,7 +55,7 @@ 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_covid` to your ffmpeg input arguments:
`-c:v hevc_cuvid` to your ffmpeg input arguments:
```
ffmpeg:

2
docs/docs/configuration/optimizing.md
View File

@ -3,7 +3,7 @@ id: optimizing
title: Optimizing performance
---
- **Google Coral**: It is strongly recommended to use a Google Coral, but Frigate will 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
- **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.

53
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
- clips
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 clips and 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

8
docs/docs/hardware.md
View File

@ -5,7 +5,7 @@ title: Recommended hardware
## Cameras
Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and HomeAssistant. 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, clips, and recordings without re-encoding.
## Computer
@ -24,6 +24,6 @@ Cameras that output H.264 video and AAC audio will offer the most compatibility
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)
| 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) |
| 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) |

4
docs/docs/index.md
View File

@ -5,11 +5,11 @@ sidebar_label: Features
slug: /
---
A complete and local NVR designed for HomeAssistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
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.
- Tight integration with HomeAssistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
- Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection

37
docs/docs/installation.md
View File

@ -5,7 +5,7 @@ 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.
For HomeAssistant users, there is also a [custom component (aka integration)](https://github.com/blakeblackshear/frigate-hass-integration). This custom component adds tighter integration with HomeAssistant by automatically setting up camera entities, sensors, media browser for clips and recordings, and a public API to simplify notifications.
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.
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.
@ -14,26 +14,27 @@ Note that HassOS Addons and custom components are different things. If you are a
HassOS users can install via the addon repository. Frigate requires an MQTT server.
1. Navigate to Supervisor > Add-on Store > Repositories
1. Add https://github.com/blakeblackshear/frigate-hass-addons
1. Setup your configuration in the `Configuration` tab
1. Start the addon container
1. If you are using hardware acceleration for ffmpeg, you will need to disable "Protection mode"
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"
## Docker
Make sure you choose the right image for your architecture:
|Arch|Image Name|
|-|-|
|amd64|blakeblackshear/frigate:stable-amd64|
|amd64nvidia|blakeblackshear/frigate:stable-amd64nvidia|
|armv7|blakeblackshear/frigate:stable-armv7|
|aarch64|blakeblackshear/frigate:stable-aarch64|
| Arch | Image Name |
| ----------- | ------------------------------------------ |
| amd64 | blakeblackshear/frigate:stable-amd64 |
| amd64nvidia | blakeblackshear/frigate:stable-amd64nvidia |
| armv7 | blakeblackshear/frigate:stable-armv7 |
| aarch64 | blakeblackshear/frigate:stable-aarch64 |
It is recommended to run with docker-compose:
```yaml
version: '3.9'
version: "3.9"
services:
frigate:
container_name: frigate
@ -52,10 +53,10 @@ services:
tmpfs:
size: 1000000000
ports:
- '5000:5000'
- '1935:1935' # RTMP feeds
- "5000:5000"
- "1935:1935" # RTMP feeds
environment:
FRIGATE_RTSP_PASSWORD: 'password'
FRIGATE_RTSP_PASSWORD: "password"
```
If you can't use docker compose, you can run the container with something similar to this:
@ -66,7 +67,7 @@ docker run -d \
--restart=unless-stopped \
--mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
--device /dev/bus/usb:/dev/bus/usb \
--device /dev/dri/renderD128
--device /dev/dri/renderD128 \
-v <path_to_directory_for_media>:/media/frigate \
-v <path_to_config_file>:/config/config.yml:ro \
-v /etc/localtime:/etc/localtime:ro \
@ -86,7 +87,7 @@ You can calculate the necessary shm-size for each camera with the following form
(width * height * 1.5 * 7 + 270480)/1048576 = <shm size in mb>
```
The shm size cannot be set per container for HomeAssistant 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
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
## Kubernetes
@ -119,5 +120,5 @@ lxc.cap.drop:
```
### ESX
For details on running Frigate under ESX, see details [here](https://github.com/blakeblackshear/frigate/issues/305).
For details on running Frigate under ESX, see details [here](https://github.com/blakeblackshear/frigate/issues/305).

44
docs/docs/usage/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]`
The best snapshot for any object type. It is a full resolution image by default.
@ -33,7 +33,7 @@ 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
### `/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.
@ -53,9 +53,9 @@ 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 HomeAssistant.
Contains some granular debug info that can be used for sensors in Home Assistant.
Sample response:
@ -125,40 +125,40 @@ Sample response:
"total": 1000,
"used": 700,
"free": 300,
"mnt_type": "ext4",
"mnt_type": "ext4"
},
"/media/frigate/recordings": {
"total": 1000,
"used": 700,
"free": 300,
"mnt_type": "ext4",
"mnt_type": "ext4"
},
"/tmp/cache": {
"total": 256,
"used": 100,
"free": 156,
"mnt_type": "tmpfs",
"mnt_type": "tmpfs"
},
"/dev/shm": {
"total": 256,
"used": 100,
"free": 156,
"mnt_type": "tmpfs",
},
"mnt_type": "tmpfs"
}
}
}
}
```
### `/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 +174,23 @@ 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 HomeAssistant integration.
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>/snapshot.jpg`
Returns the snapshot image for the event id. Works while the event is in progress and after completion.
@ -206,3 +210,7 @@ Video clip for the given camera and event id.
### `/clips/<camera>-<id>.jpg`
JPG snapshot for the given camera and event id.
### `/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.

11
docs/docs/usage/home-assistant.md
View File

@ -4,7 +4,7 @@ title: Integration with Home Assistant
sidebar_label: Home Assistant
---
The best way to integrate with HomeAssistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration). When configuring the integration, you will be asked for the `Host` of your frigate instance. This value should be the url you use to access Frigate in the browser and will look like `http://<host>:5000/`. If you are using HassOS with the addon, the host should be `http://ccab4aaf-frigate:5000` (or `http://ccab4aaf-frigate-beta:5000` if your are using the beta version of the addon). HomeAssistant needs access to port 5000 (api) and 1935 (rtmp) for all features. The integration will setup the following entities within HomeAssistant:
The best way to integrate with Home Assistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration). When configuring the integration, you will be asked for the `Host` of your frigate instance. This value should be the url you use to access Frigate in the browser and will look like `http://<host>:5000/`. If you are using HassOS with the addon, the host should be `http://ccab4aaf-frigate:5000` (or `http://ccab4aaf-frigate-beta:5000` if your are using the beta version of the addon). Home Assistant needs access to port 5000 (api) and 1935 (rtmp) for all features. The integration will setup the following entities within Home Assistant:
## Sensors:
@ -30,12 +30,14 @@ The best way to integrate with HomeAssistant is to use the [official integration
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 HomeAssistants API see below:
To load an image taken by frigate from Home Assistants API see below:
```
https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
```
To load a video clip taken by frigate from HomeAssistants API :
To load a video clip taken by frigate from Home Assistants API :
```
https://HA_URL/api/frigate/notifications/<event-id>/<camera>/clip.mp4
```
@ -57,7 +59,6 @@ automation:
tag: '{{trigger.payload_json["after"]["id"]}}'
```
```yaml
automation:
- alias: When a person enters a zone named yard
@ -106,7 +107,7 @@ automation:
action:
- service: notify.mobile_app_pixel_3
data_template:
message: 'High confidence dog detection.'
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']}}"

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

@ -0,0 +1,11 @@
---
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/)

6
docs/docs/usage/mqtt.md
View File

@ -7,17 +7,17 @@ These are the MQTT messages generated by Frigate. The default topic_prefix is `f
### `frigate/available`
Designed to be used as an availability topic with HomeAssistant. Possible message are:
Designed to be used as an availability topic with Home Assistant. Possible message are:
"online": published when frigate is running (on startup)
"offline": published right before frigate stops
### `frigate/<camera_name>/<object_name>`
Publishes the count of objects for the camera for use as a sensor in HomeAssistant.
Publishes the count of objects for the camera for use as a sensor in Home Assistant.
### `frigate/<zone_name>/<object_name>`
Publishes the count of objects for the zone for use as a sensor in HomeAssistant.
Publishes the count of objects for the zone for use as a sensor in Home Assistant.
### `frigate/<camera_name>/<object_name>/snapshot`

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()

197
frigate/app.py
View File

@ -31,7 +31,8 @@ from frigate.zeroconf import broadcast_zeroconf
logger = logging.getLogger(__name__)
class FrigateApp():
class FrigateApp:
def __init__(self):
self.stop_event = mp.Event()
self.config: FrigateConfig = None
@ -54,62 +55,73 @@ 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')
config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
self.config = FrigateConfig(config_file=config_file)
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.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."
)
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)
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')
if not "geventwebsocket.handler" in self.config.logger.logs:
logging.getLogger("geventwebsocket.handler").setLevel("ERROR")
def init_queues(self):
# Queues for clip processing
@ -117,13 +129,15 @@ class FrigateApp():
self.event_processed_queue = mp.Queue()
# 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 = 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()
@ -137,7 +151,13 @@ class FrigateApp():
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,
self.mqtt_client,
)
def init_mqtt(self):
self.mqtt_client = create_mqtt_client(self.config, self.camera_metrics)
@ -148,48 +168,97 @@ class FrigateApp():
self.detection_out_events[name] = mp.Event()
try:
self.detection_shms.append(mp.shared_memory.SharedMemory(name=name, create=True, size=self.config.model.height*self.config.model.width*3))
shm_in = mp.shared_memory.SharedMemory(
name=name,
create=True,
size=self.config.model.height*self.config.model.width * 3,
)
except FileExistsError:
self.detection_shms.append(mp.shared_memory.SharedMemory(name=name))
shm_in = mp.shared_memory.SharedMemory(name=name)
try:
self.detection_shms.append(mp.shared_memory.SharedMemory(name=f"out-{name}", create=True, size=20*6*4))
shm_out = mp.shared_memory.SharedMemory(
name=f"out-{name}", create=True, size=20 * 6 * 4
)
except FileExistsError:
self.detection_shms.append(mp.shared_memory.SharedMemory(name=f"out-{name}"))
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 == "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,
)
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.stop_event,
)
self.detected_frames_processor.start()
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.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):
@ -201,7 +270,13 @@ class FrigateApp():
self.recording_maintainer.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):
@ -247,8 +322,14 @@ class FrigateApp():
signal.signal(signal.SIGTERM, receiveSignal)
server = pywsgi.WSGIServer(('127.0.0.1', 5001), self.flask_app, handler_class=WebSocketHandler)
server.serve_forever()
server = pywsgi.WSGIServer(
("127.0.0.1", 5001), self.flask_app, handler_class=WebSocketHandler
)
try:
server.serve_forever()
except KeyboardInterrupt:
pass
self.stop()

1833
frigate/config.py
View File

File diff suppressed because it is too large Load Diff

6
frigate/const.py
View File

@ -1,3 +1,3 @@
CLIPS_DIR = '/media/frigate/clips'
RECORD_DIR = '/media/frigate/recordings'
CACHE_DIR = '/tmp/cache'
CLIPS_DIR = "/media/frigate/clips"
RECORD_DIR = "/media/frigate/recordings"
CACHE_DIR = "/tmp/cache"

182
frigate/edgetpu.py
View File

@ -1,48 +1,49 @@
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):
self.fps = EventsPerSecond()
@ -57,27 +58,29 @@ 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="/edgetpu_model.tflite",
experimental_delegates=[edge_tpu_delegate],
)
except ValueError:
logger.info("No EdgeTPU detected.")
raise
else:
self.interpreter = tflite.Interpreter(
model_path='/cpu_model.tflite', num_threads=num_threads)
model_path="/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 +88,49 @@ 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']))
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)
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]]
detections[i] = [
label_codes[i],
score,
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_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()}")
@ -114,6 +138,7 @@ 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()
@ -126,21 +151,17 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
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
@ -148,20 +169,29 @@ 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
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):
class EdgeTPUProcess:
def __init__(
self,
name,
detection_queue,
out_events,
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_shape = model_shape
self.tf_device = tf_device
@ -181,11 +211,25 @@ 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_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.name = name
@ -193,11 +237,15 @@ class RemoteObjectDetector():
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)
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):
def detect(self, tensor_input, threshold=0.4):
detections = []
# copy input to shared memory
@ -213,11 +261,9 @@ 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

339
frigate/events.py
View File

@ -20,10 +20,13 @@ from peewee import fn
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 = {}
@ -33,13 +36,17 @@ class EventProcessor(threading.Thread):
self.stop_event = stop_event
def should_create_clip(self, camera, event_data):
if event_data['false_positive']:
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")
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
@ -50,14 +57,14 @@ class EventProcessor(threading.Thread):
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(CACHE_DIR):
files_in_use.append(nt.path.split('/')[-1])
files_in_use.append(nt.path.split("/")[-1])
except:
continue
@ -65,130 +72,158 @@ class EventProcessor(threading.Thread):
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')
basename = os.path.splitext(f)[0]
camera, date = basename.rsplit("-", maxsplit=1)
start_time = datetime.datetime.strptime(date, "%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())
ffprobe_cmd = [
"ffprobe",
"-v",
"error",
"-show_entries",
"format=duration",
"-of",
"default=noprint_wrappers=1:nokey=1",
f"{os.path.join(CACHE_DIR, f)}",
]
p = sp.run(ffprobe_cmd, capture_output=True)
if p.returncode == 0:
duration = float(p.stdout.decode().strip())
else:
logger.info(f"bad file: {f}")
os.remove(os.path.join(CACHE_DIR,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
"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']
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
# if the earliest event is more tha max seconds ago, 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
earliest_event = max(
earliest_event,
datetime.datetime.now().timestamp() - self.config.clips.max_seconds,
)
for f, data in list(self.cached_clips.items()):
if earliest_event-90 > data['start_time']+data['duration']:
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))
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:
if (
cache_usage.used / cache_usage.total > 0.9
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(
"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']))
while cache_usage.used / cache_usage.total > 0.9:
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'])
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:
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.")
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'])
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_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:
if clip["start_time"] + clip["duration"] < playlist_start:
continue
# clip starts after playlist ends, finish
if clip['start_time'] > playlist_end:
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 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'])}")
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"
"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)
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:
@ -199,68 +234,82 @@ class EventProcessor(threading.Thread):
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':
if event_type == "end":
clips_config = self.config.cameras[camera].clips
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 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 clip_created 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'],
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'],
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):
## Expire events from unlisted cameras based on the global config
if media == 'clips':
if media == "clips":
retain_config = self.config.clips.retain
file_extension = 'mp4'
update_params = {'has_clip': False}
file_extension = "mp4"
update_params = {"has_clip": False}
else:
retain_config = self.config.snapshots.retain
file_extension = 'jpg'
update_params = {'has_snapshot': False}
file_extension = "jpg"
update_params = {"has_snapshot": False}
distinct_labels = (Event.select(Event.label)
.where(Event.camera.not_in(self.camera_keys))
.distinct())
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:
@ -268,48 +317,49 @@ class EventCleanup(threading.Thread):
media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}")
media.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':
if media == "clips":
retain_config = camera.clips.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 = Path(
f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
)
media.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()
@ -341,32 +391,23 @@ class EventCleanup(threading.Thread):
media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
media.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...")

459
frigate/http.py
View File

@ -1,21 +1,32 @@
import base64
import datetime
from collections import OrderedDict
from datetime import datetime, timedelta
import json
import glob
import logging
import os
import re
import time
from functools import reduce
from pathlib import Path
import cv2
import gevent
import numpy as np
from flask import (Blueprint, Flask, Response, current_app, jsonify,
make_response, request)
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 peewee import SqliteDatabase, operator, fn, DoesNotExist, Value
from playhouse.shortcuts import model_to_dict
from frigate.const import CLIPS_DIR
from frigate.const import CLIPS_DIR, RECORD_DIR
from frigate.models import Event
from frigate.stats import stats_snapshot
from frigate.util import calculate_region
@ -23,10 +34,11 @@ from frigate.version import VERSION
logger = logging.getLogger(__name__)
bp = Blueprint('frigate', __name__)
ws = Blueprint('ws', __name__)
bp = Blueprint("frigate", __name__)
ws = Blueprint("ws", __name__)
class MqttBackend():
class MqttBackend:
"""Interface for registering and updating WebSocket clients."""
def __init__(self, mqtt_client, topic_prefix):
@ -42,36 +54,48 @@ class MqttBackend():
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)
"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'])
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()
})
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...")
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.")
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)
@ -80,7 +104,14 @@ class MqttBackend():
"""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,
mqtt_client,
):
app = Flask(__name__)
sockets = Sockets(app)
@ -105,14 +136,16 @@ def create_app(frigate_config, database: SqliteDatabase, stats_tracking, detecte
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 = []
@ -126,35 +159,63 @@ def events_summary():
clauses.append((1 == 1))
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 +223,8 @@ 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:
@ -174,18 +236,27 @@ 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):
jpg_bytes = None
try:
@ -193,20 +264,23 @@ def event_snapshot(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),
)
except:
return "Event not found", 404
@ -214,20 +288,21 @@ def event_snapshot(id):
return "Event not found", 404
response = make_response(jpg_bytes)
response.headers['Content-Type'] = 'image/jpg'
response.headers["Content-Type"] = "image/jpg"
return response
@bp.route('/events')
@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 +314,7 @@ 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))
@ -259,116 +334,268 @@ def events():
if len(clauses) == 0:
clauses.append((1 == 1))
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')
@bp.route("/config")
def config():
return jsonify(current_app.frigate_config.to_dict())
@bp.route('/version')
@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])
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), 70])
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)
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)
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])
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), 70])
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):
files = glob.glob(f"{RECORD_DIR}/*/*/*/{camera_name}")
if len(files) == 0:
return jsonify([])
files.sort()
dates = OrderedDict()
for path in files:
first = glob.glob(f"{path}/00.*.mp4")
delay = 0
if len(first) > 0:
delay = int(first[0].strip(path).split(".")[1])
search = re.search(r".+/(\d{4}[-]\d{2})/(\d{2})/(\d{2}).+", path)
if not search:
continue
date = f"{search.group(1)}-{search.group(2)}"
if date not in dates:
dates[date] = OrderedDict()
dates[date][search.group(3)] = {"delay": delay, "events": []}
# 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,
)
e: Event
for e in events:
date = datetime.fromtimestamp(e.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(
e,
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("/vod/<path:path>")
def vod(path):
if not os.path.isdir(f"{RECORD_DIR}/{path}"):
return "Recordings not found.", 404
files = glob.glob(f"{RECORD_DIR}/{path}/*.mp4")
files.sort()
clips = []
durations = []
for filename in files:
clips.append({"type": "source", "path": filename})
video = cv2.VideoCapture(filename)
duration = int(
video.get(cv2.CAP_PROP_FRAME_COUNT) / video.get(cv2.CAP_PROP_FPS) * 1000
)
durations.append(duration)
# Should we cache?
parts = path.split("/", 4)
date = datetime.strptime(f"{parts[0]}-{parts[1]} {parts[2]}", "%Y-%m-%d %H")
return jsonify(
{
"cache": datetime.now() - timedelta(hours=2) > date,
"discontinuity": False,
"durations": durations,
"sequences": [{"clips": clips}],
}
)
def imagestream(detected_frames_processor, camera_name, fps, height, draw_options):
while True:
# max out at specified FPS
gevent.sleep(1/fps)
gevent.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')
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')
@ws.route("/ws")
def echo_socket(socket):
current_app.mqtt_backend.register(socket)

31
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)
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):
@ -61,15 +57,13 @@ 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()
@ -78,6 +72,5 @@ class LogPipe(threading.Thread):
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)

50
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

81
frigate/mqtt.py
View File

@ -7,6 +7,7 @@ from frigate.config import FrigateConfig
logger = logging.getLogger(__name__)
def create_mqtt_client(config: FrigateConfig, camera_metrics):
mqtt_config = config.mqtt
@ -14,18 +15,18 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
payload = message.payload.decode()
logger.debug(f"on_clips_toggle: {message.topic} {payload}")
camera_name = message.topic.split('/')[-3]
camera_name = message.topic.split("/")[-3]
clips_settings = config.cameras[camera_name].clips
if payload == 'ON':
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':
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
clips_settings.enabled = False
else:
logger.warning(f"Received unsupported value at {message.topic}: {payload}")
@ -36,18 +37,18 @@ 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}")
@ -58,20 +59,20 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
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}")
@ -88,22 +89,40 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
elif rc == 5:
logger.error("MQTT Not authorized")
else:
logger.error("Unable to connect to MQTT: Connection refused. Error code: " + str(rc))
logger.error(
"Unable to connect to MQTT: 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.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}/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
)
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 +134,20 @@ 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}/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,
)
return client

523
frigate/object_processing.py
View File

@ -24,44 +24,49 @@ from frigate.util import SharedMemoryFrameManager, draw_box_with_label, calculat
logger = logging.getLogger(__name__)
PATH_TO_LABELS = '/labelmap.txt'
PATH_TO_LABELS = "/labelmap.txt"
LABELS = load_labels(PATH_TO_LABELS)
cmap = plt.cm.get_cmap('tab10', len(LABELS.keys()))
cmap = plt.cm.get_cmap("tab10", len(LABELS.keys()))
COLOR_MAP = {}
for key, val in LABELS.items():
COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
def on_edge(box, frame_shape):
if (
box[0] == 0 or
box[1] == 0 or
box[2] == frame_shape[1]-1 or
box[3] == frame_shape[0]-1
box[0] == 0
or box[1] == 0
or box[2] == frame_shape[1] - 1
or box[3] == frame_shape[0] - 1
):
return True
def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool:
# larger is better
# cutoff images are less ideal, but they should also be smaller?
# better scores are obviously better too
# if the new_thumb is on an edge, and the current thumb is not
if on_edge(new_obj['box'], frame_shape) and not on_edge(current_thumb['box'], frame_shape):
if on_edge(new_obj["box"], frame_shape) and not on_edge(
current_thumb["box"], frame_shape
):
return False
# if the score is better by more than 5%
if new_obj['score'] > current_thumb['score']+.05:
if new_obj["score"] > current_thumb["score"] + 0.05:
return True
# if the area is 10% larger
if new_obj['area'] > current_thumb['area']*1.1:
if new_obj["area"] > current_thumb["area"] * 1.1:
return True
return False
class TrackedObject():
class TrackedObject:
def __init__(self, camera, camera_config: CameraConfig, frame_cache, obj_data):
self.obj_data = obj_data
self.camera = camera
@ -78,33 +83,31 @@ class TrackedObject():
self.previous = self.to_dict()
# start the score history
self.score_history = [self.obj_data['score']]
self.score_history = [self.obj_data["score"]]
def _is_false_positive(self):
# once a true positive, always a true positive
if not self.false_positive:
return False
threshold = self.camera_config.objects.filters[self.obj_data['label']].threshold
if self.computed_score < threshold:
return True
return False
threshold = self.camera_config.objects.filters[self.obj_data["label"]].threshold
return self.computed_score < threshold
def compute_score(self):
scores = self.score_history[:]
# pad with zeros if you dont have at least 3 scores
if len(scores) < 3:
scores += [0.0]*(3 - len(scores))
scores += [0.0] * (3 - len(scores))
return median(scores)
def update(self, current_frame_time, obj_data):
significant_update = False
self.obj_data.update(obj_data)
# if the object is not in the current frame, add a 0.0 to the score history
if self.obj_data['frame_time'] != current_frame_time:
if self.obj_data["frame_time"] != current_frame_time:
self.score_history.append(0.0)
else:
self.score_history.append(self.obj_data['score'])
self.score_history.append(self.obj_data["score"])
# only keep the last 10 scores
if len(self.score_history) > 10:
self.score_history = self.score_history[-10:]
@ -117,27 +120,26 @@ class TrackedObject():
if not self.false_positive:
# determine if this frame is a better thumbnail
if (
self.thumbnail_data is None
or is_better_thumbnail(self.thumbnail_data, self.obj_data, self.camera_config.frame_shape)
if self.thumbnail_data is None or is_better_thumbnail(
self.thumbnail_data, self.obj_data, self.camera_config.frame_shape
):
self.thumbnail_data = {
'frame_time': self.obj_data['frame_time'],
'box': self.obj_data['box'],
'area': self.obj_data['area'],
'region': self.obj_data['region'],
'score': self.obj_data['score']
"frame_time": self.obj_data["frame_time"],
"box": self.obj_data["box"],
"area": self.obj_data["area"],
"region": self.obj_data["region"],
"score": self.obj_data["score"],
}
significant_update = True
# check zones
current_zones = []
bottom_center = (self.obj_data['centroid'][0], self.obj_data['box'][3])
bottom_center = (self.obj_data["centroid"][0], self.obj_data["box"][3])
# check each zone
for name, zone in self.camera_config.zones.items():
contour = zone.contour
# check if the object is in the zone
if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
if cv2.pointPolygonTest(contour, bottom_center, False) >= 0:
# if the object passed the filters once, dont apply again
if name in self.current_zones or not zone_filtered(self, zone.filters):
current_zones.append(name)
@ -151,92 +153,134 @@ class TrackedObject():
return significant_update
def to_dict(self, include_thumbnail: bool = False):
return {
'id': self.obj_data['id'],
'camera': self.camera,
'frame_time': self.obj_data['frame_time'],
'label': self.obj_data['label'],
'top_score': self.top_score,
'false_positive': self.false_positive,
'start_time': self.obj_data['start_time'],
'end_time': self.obj_data.get('end_time', None),
'score': self.obj_data['score'],
'box': self.obj_data['box'],
'area': self.obj_data['area'],
'region': self.obj_data['region'],
'current_zones': self.current_zones.copy(),
'entered_zones': list(self.entered_zones).copy(),
'thumbnail': base64.b64encode(self.get_thumbnail()).decode('utf-8') if include_thumbnail else None
event = {
"id": self.obj_data["id"],
"camera": self.camera,
"frame_time": self.obj_data["frame_time"],
"label": self.obj_data["label"],
"top_score": self.top_score,
"false_positive": self.false_positive,
"start_time": self.obj_data["start_time"],
"end_time": self.obj_data.get("end_time", None),
"score": self.obj_data["score"],
"box": self.obj_data["box"],
"area": self.obj_data["area"],
"region": self.obj_data["region"],
"current_zones": self.current_zones.copy(),
"entered_zones": list(self.entered_zones).copy(),
}
def get_thumbnail(self):
if self.thumbnail_data is None or not self.thumbnail_data['frame_time'] in self.frame_cache:
ret, jpg = cv2.imencode('.jpg', np.zeros((175,175,3), np.uint8))
if include_thumbnail:
event["thumbnail"] = base64.b64encode(self.get_thumbnail()).decode("utf-8")
jpg_bytes = self.get_jpg_bytes(timestamp=False, bounding_box=False, crop=True, height=175)
return event
def get_thumbnail(self):
if (
self.thumbnail_data is None
or self.thumbnail_data["frame_time"] not in self.frame_cache
):
ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
jpg_bytes = self.get_jpg_bytes(
timestamp=False, bounding_box=False, crop=True, height=175
)
if jpg_bytes:
return jpg_bytes
else:
ret, jpg = cv2.imencode('.jpg', np.zeros((175,175,3), np.uint8))
ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
return jpg.tobytes()
def get_jpg_bytes(self, timestamp=False, bounding_box=False, crop=False, height=None):
def get_jpg_bytes(
self, timestamp=False, bounding_box=False, crop=False, height=None
):
if self.thumbnail_data is None:
return None
try:
best_frame = cv2.cvtColor(self.frame_cache[self.thumbnail_data['frame_time']], cv2.COLOR_YUV2BGR_I420)
best_frame = cv2.cvtColor(
self.frame_cache[self.thumbnail_data["frame_time"]],
cv2.COLOR_YUV2BGR_I420,
)
except KeyError:
logger.warning(f"Unable to create jpg because frame {self.thumbnail_data['frame_time']} is not in the cache")
logger.warning(
f"Unable to create jpg because frame {self.thumbnail_data['frame_time']} is not in the cache"
)
return None
if bounding_box:
thickness = 2
color = COLOR_MAP[self.obj_data['label']]
color = COLOR_MAP[self.obj_data["label"]]
# draw the bounding boxes on the frame
box = self.thumbnail_data['box']
draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], self.obj_data['label'], f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}", thickness=thickness, color=color)
box = self.thumbnail_data["box"]
draw_box_with_label(
best_frame,
box[0],
box[1],
box[2],
box[3],
self.obj_data["label"],
f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}",
thickness=thickness,
color=color,
)
if crop:
box = self.thumbnail_data['box']
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 = self.thumbnail_data["box"]
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]]
if height:
width = int(height*best_frame.shape[1]/best_frame.shape[0])
best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
width = int(height * best_frame.shape[1] / best_frame.shape[0])
best_frame = cv2.resize(
best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA
)
if timestamp:
time_to_show = datetime.datetime.fromtimestamp(self.thumbnail_data['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
time_to_show = datetime.datetime.fromtimestamp(
self.thumbnail_data["frame_time"]
).strftime("%m/%d/%Y %H:%M:%S")
size = cv2.getTextSize(
time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2
)
text_width = size[0][0]
desired_size = max(150, 0.33*best_frame.shape[1])
font_scale = desired_size/text_width
cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale, color=(255, 255, 255), thickness=2)
desired_size = max(150, 0.33 * best_frame.shape[1])
font_scale = desired_size / text_width
cv2.putText(
best_frame,
time_to_show,
(5, best_frame.shape[0] - 7),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=font_scale,
color=(255, 255, 255),
thickness=2,
)
ret, jpg = cv2.imencode('.jpg', best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
ret, jpg = cv2.imencode(".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
if ret:
return jpg.tobytes()
else:
return None
def zone_filtered(obj: TrackedObject, object_config):
object_name = obj.obj_data['label']
object_name = obj.obj_data["label"]
if object_name in object_config:
obj_settings = object_config[object_name]
# if the min area is larger than the
# detected object, don't add it to detected objects
if obj_settings.min_area > obj.obj_data['area']:
if obj_settings.min_area > obj.obj_data["area"]:
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.obj_data['area']:
if obj_settings.max_area < obj.obj_data["area"]:
return True
# if the score is lower than the threshold, skip
@ -245,70 +289,109 @@ def zone_filtered(obj: TrackedObject, object_config):
return False
# Maintains the state of a camera
class CameraState():
class CameraState:
def __init__(self, name, config, frame_manager):
self.name = name
self.config = config
self.camera_config = config.cameras[name]
self.frame_manager = frame_manager
self.best_objects: Dict[str, TrackedObject] = {}
self.object_counts = defaultdict(lambda: 0)
self.object_counts = defaultdict(int)
self.tracked_objects: Dict[str, TrackedObject] = {}
self.frame_cache = {}
self.zone_objects = defaultdict(lambda: [])
self.zone_objects = defaultdict(list)
self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
self.current_frame_lock = threading.Lock()
self.current_frame_time = 0.0
self.motion_boxes = []
self.regions = []
self.previous_frame_id = None
self.callbacks = defaultdict(lambda: [])
self.callbacks = defaultdict(list)
def get_current_frame(self, draw_options={}):
with self.current_frame_lock:
frame_copy = np.copy(self._current_frame)
frame_time = self.current_frame_time
tracked_objects = {k: v.to_dict() for k,v in self.tracked_objects.items()}
tracked_objects = {k: v.to_dict() for k, v in self.tracked_objects.items()}
motion_boxes = self.motion_boxes.copy()
regions = self.regions.copy()
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
# draw on the frame
if draw_options.get('bounding_boxes'):
if draw_options.get("bounding_boxes"):
# draw the bounding boxes on the frame
for obj in tracked_objects.values():
thickness = 2
color = COLOR_MAP[obj['label']]
if obj['frame_time'] != frame_time:
if obj["frame_time"] == frame_time:
thickness = 2
color = COLOR_MAP[obj["label"]]
else:
thickness = 1
color = (255,0,0)
color = (255, 0, 0)
# draw the bounding boxes on the frame
box = obj['box']
draw_box_with_label(frame_copy, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
box = obj["box"]
draw_box_with_label(
frame_copy,
box[0],
box[1],
box[2],
box[3],
obj["label"],
f"{obj['score']:.0%} {int(obj['area'])}",
thickness=thickness,
color=color,
)
if draw_options.get('regions'):
if draw_options.get("regions"):
for region in regions:
cv2.rectangle(frame_copy, (region[0], region[1]), (region[2], region[3]), (0,255,0), 2)
cv2.rectangle(
frame_copy,
(region[0], region[1]),
(region[2], region[3]),
(0, 255, 0),
2,
)
if draw_options.get('zones'):
if draw_options.get("zones"):
for name, zone in self.camera_config.zones.items():
thickness = 8 if any([name in obj['current_zones'] for obj in tracked_objects.values()]) else 2
thickness = (
8
if any(
name in obj["current_zones"] for obj in tracked_objects.values()
)
else 2
)
cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
if draw_options.get('mask'):
mask_overlay = np.where(self.camera_config.motion.mask==[0])
frame_copy[mask_overlay] = [0,0,0]
if draw_options.get("mask"):
mask_overlay = np.where(self.camera_config.motion.mask == [0])
frame_copy[mask_overlay] = [0, 0, 0]
if draw_options.get('motion_boxes'):
if draw_options.get("motion_boxes"):
for m_box in motion_boxes:
cv2.rectangle(frame_copy, (m_box[0], m_box[1]), (m_box[2], m_box[3]), (0,0,255), 2)
cv2.rectangle(
frame_copy,
(m_box[0], m_box[1]),
(m_box[2], m_box[3]),
(0, 0, 255),
2,
)
if draw_options.get('timestamp'):
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
cv2.putText(frame_copy, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
if draw_options.get("timestamp"):
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime(
"%m/%d/%Y %H:%M:%S"
)
cv2.putText(
frame_copy,
time_to_show,
(10, 30),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.8,
color=(255, 255, 255),
thickness=2,
)
return frame_copy
@ -319,112 +402,156 @@ class CameraState():
self.callbacks[event_type].append(callback)
def update(self, frame_time, current_detections, motion_boxes, regions):
self.current_frame_time = frame_time
self.motion_boxes = motion_boxes
self.regions = regions
# get the new frame
frame_id = f"{self.name}{frame_time}"
current_frame = self.frame_manager.get(frame_id, self.camera_config.frame_shape_yuv)
current_frame = self.frame_manager.get(
frame_id, self.camera_config.frame_shape_yuv
)
current_ids = current_detections.keys()
previous_ids = self.tracked_objects.keys()
removed_ids = list(set(previous_ids).difference(current_ids))
new_ids = list(set(current_ids).difference(previous_ids))
updated_ids = list(set(current_ids).intersection(previous_ids))
tracked_objects = self.tracked_objects.copy()
current_ids = set(current_detections.keys())
previous_ids = set(tracked_objects.keys())
removed_ids = previous_ids.difference(current_ids)
new_ids = current_ids.difference(previous_ids)
updated_ids = current_ids.intersection(previous_ids)
for id in new_ids:
new_obj = self.tracked_objects[id] = TrackedObject(self.name, self.camera_config, self.frame_cache, current_detections[id])
new_obj = tracked_objects[id] = TrackedObject(
self.name, self.camera_config, self.frame_cache, current_detections[id]
)
# call event handlers
for c in self.callbacks['start']:
for c in self.callbacks["start"]:
c(self.name, new_obj, frame_time)
for id in updated_ids:
updated_obj = self.tracked_objects[id]
updated_obj = tracked_objects[id]
significant_update = updated_obj.update(frame_time, current_detections[id])
if significant_update:
# ensure this frame is stored in the cache
if updated_obj.thumbnail_data['frame_time'] == frame_time and frame_time not in self.frame_cache:
if (
updated_obj.thumbnail_data["frame_time"] == frame_time
and frame_time not in self.frame_cache
):
self.frame_cache[frame_time] = np.copy(current_frame)
updated_obj.last_updated = frame_time
# if it has been more than 5 seconds since the last publish
# and the last update is greater than the last publish
if frame_time - updated_obj.last_published > 5 and updated_obj.last_updated > updated_obj.last_published:
if (
frame_time - updated_obj.last_published > 5
and updated_obj.last_updated > updated_obj.last_published
):
# call event handlers
for c in self.callbacks['update']:
for c in self.callbacks["update"]:
c(self.name, updated_obj, frame_time)
updated_obj.last_published = frame_time
for id in removed_ids:
# publish events to mqtt
removed_obj = self.tracked_objects[id]
if not 'end_time' in removed_obj.obj_data:
removed_obj.obj_data['end_time'] = frame_time
for c in self.callbacks['end']:
removed_obj = tracked_objects[id]
if not "end_time" in removed_obj.obj_data:
removed_obj.obj_data["end_time"] = frame_time
for c in self.callbacks["end"]:
c(self.name, removed_obj, frame_time)
# TODO: can i switch to looking this up and only changing when an event ends?
# maintain best objects
for obj in self.tracked_objects.values():
object_type = obj.obj_data['label']
for obj in tracked_objects.values():
object_type = obj.obj_data["label"]
# if the object's thumbnail is not from the current frame
if obj.false_positive or obj.thumbnail_data['frame_time'] != self.current_frame_time:
if obj.false_positive or obj.thumbnail_data["frame_time"] != frame_time:
continue
if object_type in self.best_objects:
current_best = self.best_objects[object_type]
now = datetime.datetime.now().timestamp()
# if the object is a higher score than the current best score
# or the current object is older than desired, use the new object
if (is_better_thumbnail(current_best.thumbnail_data, obj.thumbnail_data, self.camera_config.frame_shape)
or (now - current_best.thumbnail_data['frame_time']) > self.camera_config.best_image_timeout):
if (
is_better_thumbnail(
current_best.thumbnail_data,
obj.thumbnail_data,
self.camera_config.frame_shape,
)
or (now - current_best.thumbnail_data["frame_time"])
> self.camera_config.best_image_timeout
):
self.best_objects[object_type] = obj
for c in self.callbacks['snapshot']:
for c in self.callbacks["snapshot"]:
c(self.name, self.best_objects[object_type], frame_time)
else:
self.best_objects[object_type] = obj
for c in self.callbacks['snapshot']:
for c in self.callbacks["snapshot"]:
c(self.name, self.best_objects[object_type], frame_time)
# update overall camera state for each object type
obj_counter = Counter()
for obj in self.tracked_objects.values():
if not obj.false_positive:
obj_counter[obj.obj_data['label']] += 1
obj_counter = Counter(
obj.obj_data["label"]
for obj in tracked_objects.values()
if not obj.false_positive
)
# report on detected objects
for obj_name, count in obj_counter.items():
if count != self.object_counts[obj_name]:
self.object_counts[obj_name] = count
for c in self.callbacks['object_status']:
for c in self.callbacks["object_status"]:
c(self.name, obj_name, count)
# expire any objects that are >0 and no longer detected
expired_objects = [obj_name for obj_name, count in self.object_counts.items() if count > 0 and not obj_name in obj_counter]
expired_objects = [
obj_name
for obj_name, count in self.object_counts.items()
if count > 0 and obj_name not in obj_counter
]
for obj_name in expired_objects:
self.object_counts[obj_name] = 0
for c in self.callbacks['object_status']:
for c in self.callbacks["object_status"]:
c(self.name, obj_name, 0)
for c in self.callbacks['snapshot']:
for c in self.callbacks["snapshot"]:
c(self.name, self.best_objects[obj_name], frame_time)
# cleanup thumbnail frame cache
current_thumb_frames = set([obj.thumbnail_data['frame_time'] for obj in self.tracked_objects.values() if not obj.false_positive])
current_best_frames = set([obj.thumbnail_data['frame_time'] for obj in self.best_objects.values()])
thumb_frames_to_delete = [t for t in self.frame_cache.keys() if not t in current_thumb_frames and not t in current_best_frames]
current_thumb_frames = {
obj.thumbnail_data["frame_time"]
for obj in tracked_objects.values()
if not obj.false_positive
}
current_best_frames = {
obj.thumbnail_data["frame_time"] for obj in self.best_objects.values()
}
thumb_frames_to_delete = [
t
for t in self.frame_cache.keys()
if t not in current_thumb_frames and t not in current_best_frames
]
for t in thumb_frames_to_delete:
del self.frame_cache[t]
with self.current_frame_lock:
self.tracked_objects = tracked_objects
self.current_frame_time = frame_time
self.motion_boxes = motion_boxes
self.regions = regions
self._current_frame = current_frame
if not self.previous_frame_id is None:
if self.previous_frame_id is not None:
self.frame_manager.delete(self.previous_frame_id)
self.previous_frame_id = frame_id
class TrackedObjectProcessor(threading.Thread):
def __init__(self, config: FrigateConfig, client, topic_prefix, tracked_objects_queue, event_queue, event_processed_queue, stop_event):
def __init__(
self,
config: FrigateConfig,
client,
topic_prefix,
tracked_objects_queue,
event_queue,
event_processed_queue,
stop_event,
):
threading.Thread.__init__(self)
self.name = "detected_frames_processor"
self.config = config
@ -438,36 +565,55 @@ class TrackedObjectProcessor(threading.Thread):
self.frame_manager = SharedMemoryFrameManager()
def start(camera, obj: TrackedObject, current_frame_time):
self.event_queue.put(('start', camera, obj.to_dict()))
self.event_queue.put(("start", camera, obj.to_dict()))
def update(camera, obj: TrackedObject, current_frame_time):
after = obj.to_dict()
message = { 'before': obj.previous, 'after': after, 'type': 'new' if obj.previous['false_positive'] else 'update' }
self.client.publish(f"{self.topic_prefix}/events", json.dumps(message), retain=False)
message = {
"before": obj.previous,
"after": after,
"type": "new" if obj.previous["false_positive"] else "update",
}
self.client.publish(
f"{self.topic_prefix}/events", json.dumps(message), retain=False
)
obj.previous = after
def end(camera, obj: TrackedObject, current_frame_time):
snapshot_config = self.config.cameras[camera].snapshots
event_data = obj.to_dict(include_thumbnail=True)
event_data['has_snapshot'] = False
event_data["has_snapshot"] = False
if not obj.false_positive:
message = { 'before': obj.previous, 'after': obj.to_dict(), 'type': 'end' }
self.client.publish(f"{self.topic_prefix}/events", json.dumps(message), retain=False)
message = {
"before": obj.previous,
"after": obj.to_dict(),
"type": "end",
}
self.client.publish(
f"{self.topic_prefix}/events", json.dumps(message), retain=False
)
# write snapshot to disk if enabled
if snapshot_config.enabled and self.should_save_snapshot(camera, obj):
jpg_bytes = obj.get_jpg_bytes(
timestamp=snapshot_config.timestamp,
bounding_box=snapshot_config.bounding_box,
crop=snapshot_config.crop,
height=snapshot_config.height
height=snapshot_config.height,
)
if jpg_bytes is None:
logger.warning(f"Unable to save snapshot for {obj.obj_data['id']}.")
logger.warning(
f"Unable to save snapshot for {obj.obj_data['id']}."
)
else:
with open(os.path.join(CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"), 'wb') as j:
with open(
os.path.join(
CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"
),
"wb",
) as j:
j.write(jpg_bytes)
event_data['has_snapshot'] = True
self.event_queue.put(('end', camera, event_data))
event_data["has_snapshot"] = True
self.event_queue.put(("end", camera, event_data))
def snapshot(camera, obj: TrackedObject, current_frame_time):
mqtt_config = self.config.cameras[camera].mqtt
@ -476,24 +622,32 @@ class TrackedObjectProcessor(threading.Thread):
timestamp=mqtt_config.timestamp,
bounding_box=mqtt_config.bounding_box,
crop=mqtt_config.crop,
height=mqtt_config.height
height=mqtt_config.height,
)
if jpg_bytes is None:
logger.warning(f"Unable to send mqtt snapshot for {obj.obj_data['id']}.")
logger.warning(
f"Unable to send mqtt snapshot for {obj.obj_data['id']}."
)
else:
self.client.publish(f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot", jpg_bytes, retain=True)
self.client.publish(
f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot",
jpg_bytes,
retain=True,
)
def object_status(camera, object_name, status):
self.client.publish(f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False)
self.client.publish(
f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False
)
for camera in self.config.cameras.keys():
camera_state = CameraState(camera, self.config, self.frame_manager)
camera_state.on('start', start)
camera_state.on('update', update)
camera_state.on('end', end)
camera_state.on('snapshot', snapshot)
camera_state.on('object_status', object_status)
camera_state.on("start", start)
camera_state.on("update", update)
camera_state.on("end", end)
camera_state.on("snapshot", snapshot)
camera_state.on("object_status", object_status)
self.camera_states[camera] = camera_state
# {
@ -504,13 +658,15 @@ class TrackedObjectProcessor(threading.Thread):
# }
# }
# }
self.zone_data = defaultdict(lambda: defaultdict(lambda: {}))
self.zone_data = defaultdict(lambda: defaultdict(dict))
def should_save_snapshot(self, camera, obj: TrackedObject):
# if there are required zones and there is no overlap
required_zones = self.config.cameras[camera].snapshots.required_zones
if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
logger.debug(f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones")
logger.debug(
f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones"
)
return False
return True
@ -519,7 +675,9 @@ class TrackedObjectProcessor(threading.Thread):
# if there are required zones and there is no overlap
required_zones = self.config.cameras[camera].mqtt.required_zones
if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
logger.debug(f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones")
logger.debug(
f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones"
)
return False
return True
@ -530,7 +688,9 @@ class TrackedObjectProcessor(threading.Thread):
if label in camera_state.best_objects:
best_obj = camera_state.best_objects[label]
best = best_obj.thumbnail_data.copy()
best['frame'] = camera_state.frame_cache.get(best_obj.thumbnail_data['frame_time'])
best["frame"] = camera_state.frame_cache.get(
best_obj.thumbnail_data["frame_time"]
)
return best
else:
return {}
@ -539,46 +699,63 @@ class TrackedObjectProcessor(threading.Thread):
return self.camera_states[camera].get_current_frame(draw_options)
def run(self):
while True:
if self.stop_event.is_set():
logger.info(f"Exiting object processor...")
break
while not self.stop_event.is_set():
try:
camera, frame_time, current_tracked_objects, motion_boxes, regions = self.tracked_objects_queue.get(True, 10)
(
camera,
frame_time,
current_tracked_objects,
motion_boxes,
regions,
) = self.tracked_objects_queue.get(True, 10)
except queue.Empty:
continue
camera_state = self.camera_states[camera]
camera_state.update(frame_time, current_tracked_objects, motion_boxes, regions)
camera_state.update(
frame_time, current_tracked_objects, motion_boxes, regions
)
# update zone counts for each label
# for each zone in the current camera
for zone in self.config.cameras[camera].zones.keys():
# count labels for the camera in the zone
obj_counter = Counter()
for obj in camera_state.tracked_objects.values():
if zone in obj.current_zones and not obj.false_positive:
obj_counter[obj.obj_data['label']] += 1
obj_counter = Counter(
obj.obj_data["label"]
for obj in camera_state.tracked_objects.values()
if zone in obj.current_zones and not obj.false_positive
)
# update counts and publish status
for label in set(list(self.zone_data[zone].keys()) + list(obj_counter.keys())):
for label in set(self.zone_data[zone].keys()) | set(obj_counter.keys()):
# if we have previously published a count for this zone/label
zone_label = self.zone_data[zone][label]
if camera in zone_label:
current_count = sum(zone_label.values())
zone_label[camera] = obj_counter[label] if label in obj_counter else 0
zone_label[camera] = (
obj_counter[label] if label in obj_counter else 0
)
new_count = sum(zone_label.values())
if new_count != current_count:
self.client.publish(f"{self.topic_prefix}/{zone}/{label}", new_count, retain=False)
self.client.publish(
f"{self.topic_prefix}/{zone}/{label}",
new_count,
retain=False,
)
# if this is a new zone/label combo for this camera
else:
if label in obj_counter:
zone_label[camera] = obj_counter[label]
self.client.publish(f"{self.topic_prefix}/{zone}/{label}", obj_counter[label], retain=False)
self.client.publish(
f"{self.topic_prefix}/{zone}/{label}",
obj_counter[label],
retain=False,
)
# cleanup event finished queue
while not self.event_processed_queue.empty():
event_id, camera = self.event_processed_queue.get()
self.camera_states[camera].finished(event_id)
logger.info(f"Exiting object processor...")

113
frigate/objects.py
View File

@ -16,17 +16,17 @@ 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
@ -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
continue
new_centroids = np.array([o['centroid'] for o in group])
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]

235
frigate/process_clip.py
View File

@ -16,36 +16,38 @@ from frigate.edgetpu import LocalObjectDetector
from frigate.motion import MotionDetector
from frigate.object_processing import COLOR_MAP, 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)
@ -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))
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,
)
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.")
@ -163,20 +234,23 @@ def process(path, label, threshold, scores, debug_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 = []
@ -184,9 +258,9 @@ def process(path, label, threshold, scores, debug_path):
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).")
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__':
if __name__ == "__main__":
process()

103
frigate/record.py
View File

@ -1,4 +1,5 @@
import datetime
import itertools
import json
import logging
import os
@ -18,41 +19,47 @@ 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(RECORD_DIR)
if os.path.isfile(os.path.join(RECORD_DIR, d)) and d.endswith(".mp4")
]
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])
files_in_use.append(nt.path.split("/")[-1])
except:
continue
@ -60,66 +67,62 @@ class RecordingMaintainer(threading.Thread):
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')
basename = os.path.splitext(f)[0]
camera, date = basename.rsplit("-", maxsplit=1)
start_time = datetime.datetime.strptime(date, "%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())
ffprobe_cmd = [
"ffprobe",
"-v",
"error",
"-show_entries",
"format=duration",
"-of",
"default=noprint_wrappers=1:nokey=1",
f"{os.path.join(RECORD_DIR, f)}",
]
p = sp.run(ffprobe_cmd, capture_output=True)
if p.returncode == 0:
duration = float(p.stdout.decode().strip())
else:
logger.info(f"bad file: {f}")
os.remove(os.path.join(RECORD_DIR,f))
os.remove(os.path.join(RECORD_DIR, f))
continue
directory = os.path.join(RECORD_DIR, start_time.strftime('%Y-%m/%d/%H'), camera)
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')}"
os.rename(os.path.join(RECORD_DIR,f), os.path.join(directory,file_name))
os.rename(os.path.join(RECORD_DIR, f), os.path.join(directory, file_name))
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
delete_before[name] = (
datetime.datetime.now().timestamp()
- SECONDS_IN_DAY * camera.record.retain_days
)
for p in Path('/media/frigate/recordings').rglob("*.mp4"):
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)
def run(self):
counter = 0
self.expire_files()
while(True):
if self.stop_event.is_set():
for counter in itertools.cycle(range(60)):
if self.stop_event.wait(10):
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:
if counter == 0:
self.expire_files()
remove_empty_directories(RECORD_DIR)
counter = 0
self.move_files()

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...")

500
frigate/test/test_config.py
View File

@ -3,24 +3,24 @@ from unittest import TestCase, main
import voluptuous as vol
from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
class TestConfig(TestCase):
def setUp(self):
self.minimal = {
'mqtt': {
'host': 'mqtt'
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920
"height": 1080,
"width": 1920,
}
}
},
}
def test_empty(self):
FRIGATE_CONFIG_SCHEMA({})
@ -32,402 +32,310 @@ class TestConfig(TestCase):
def test_inherit_tracked_objects(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'objects': {
'track': ['person', 'dog']
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920
"height": 1080,
"width": 1920,
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('dog' in frigate_config.cameras['back'].objects.track)
assert "dog" in frigate_config.cameras["back"].objects.track
def test_override_tracked_objects(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'objects': {
'track': ['person', 'dog']
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920,
'objects': {
'track': ['cat']
}
"height": 1080,
"width": 1920,
"objects": {"track": ["cat"]},
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('cat' in frigate_config.cameras['back'].objects.track)
assert "cat" in frigate_config.cameras["back"].objects.track
def test_default_object_filters(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'objects': {
'track': ['person', 'dog']
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920
"height": 1080,
"width": 1920,
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('dog' in frigate_config.cameras['back'].objects.filters)
assert "dog" in frigate_config.cameras["back"].objects.filters
def test_inherit_object_filters(self):
config = {
'mqtt': {
'host': 'mqtt'
"mqtt": {"host": "mqtt"},
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
'objects': {
'track': ['person', 'dog'],
'filters': {
'dog': {
'threshold': 0.7
}
}
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920
"height": 1080,
"width": 1920,
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('dog' in frigate_config.cameras['back'].objects.filters)
assert(frigate_config.cameras['back'].objects.filters['dog'].threshold == 0.7)
assert "dog" in frigate_config.cameras["back"].objects.filters
assert frigate_config.cameras["back"].objects.filters["dog"].threshold == 0.7
def test_override_object_filters(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920,
'objects': {
'track': ['person', 'dog'],
'filters': {
'dog': {
'threshold': 0.7
}
}
}
"height": 1080,
"width": 1920,
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('dog' in frigate_config.cameras['back'].objects.filters)
assert(frigate_config.cameras['back'].objects.filters['dog'].threshold == 0.7)
assert "dog" in frigate_config.cameras["back"].objects.filters
assert frigate_config.cameras["back"].objects.filters["dog"].threshold == 0.7
def test_global_object_mask(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'objects': {
'track': ['person', 'dog']
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920,
'objects': {
'mask': '0,0,1,1,0,1',
'filters': {
'dog': {
'mask': '1,1,1,1,1,1'
}
}
}
"height": 1080,
"width": 1920,
"objects": {
"mask": "0,0,1,1,0,1",
"filters": {"dog": {"mask": "1,1,1,1,1,1"}},
},
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('dog' in frigate_config.cameras['back'].objects.filters)
assert(len(frigate_config.cameras['back'].objects.filters['dog']._raw_mask) == 2)
assert(len(frigate_config.cameras['back'].objects.filters['person']._raw_mask) == 1)
assert "dog" in frigate_config.cameras["back"].objects.filters
assert len(frigate_config.cameras["back"].objects.filters["dog"].raw_mask) == 2
assert (
len(frigate_config.cameras["back"].objects.filters["person"].raw_mask) == 1
)
def test_ffmpeg_params_global(self):
config = {
'ffmpeg': {
'input_args': ['-re']
},
'mqtt': {
'host': 'mqtt'
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"ffmpeg": {"input_args": ["-re"]},
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920,
'objects': {
'track': ['person', 'dog'],
'filters': {
'dog': {
'threshold': 0.7
}
}
}
"height": 1080,
"width": 1920,
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('-re' in frigate_config.cameras['back'].ffmpeg_cmds[0]['cmd'])
assert "-re" in frigate_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
def test_ffmpeg_params_camera(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
],
'input_args': ['-re']
"input_args": ["-re"],
},
"height": 1080,
"width": 1920,
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
'height': 1080,
'width': 1920,
'objects': {
'track': ['person', 'dog'],
'filters': {
'dog': {
'threshold': 0.7
}
}
}
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('-re' in frigate_config.cameras['back'].ffmpeg_cmds[0]['cmd'])
assert "-re" in frigate_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
def test_ffmpeg_params_input(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'], 'input_args': ['-re'] }
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
"input_args": ["-re"],
}
]
},
'height': 1080,
'width': 1920,
'objects': {
'track': ['person', 'dog'],
'filters': {
'dog': {
'threshold': 0.7
}
}
}
"height": 1080,
"width": 1920,
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert('-re' in frigate_config.cameras['back'].ffmpeg_cmds[0]['cmd'])
assert "-re" in frigate_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
def test_inherit_clips_retention(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'clips': {
'retain': {
'default': 20,
'objects': {
'person': 30
}
}
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"clips": {"retain": {"default": 20, "objects": {"person": 30}}},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920
"height": 1080,
"width": 1920,
}
}
},
}
frigate_config = FrigateConfig(config=config)
assert(frigate_config.cameras['back'].clips.retain.objects['person'] == 30)
assert frigate_config.cameras["back"].clips.retain.objects["person"] == 30
def test_roles_listed_twice_throws_error(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'clips': {
'retain': {
'default': 20,
'objects': {
'person': 30
}
}
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] },
{ 'path': 'rtsp://10.0.0.1:554/video2', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"clips": {"retain": {"default": 20, "objects": {"person": 30}}},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]},
{"path": "rtsp://10.0.0.1:554/video2", "roles": ["detect"]},
]
},
'height': 1080,
'width': 1920
"height": 1080,
"width": 1920,
}
}
},
}
self.assertRaises(vol.MultipleInvalid, lambda: FrigateConfig(config=config))
def test_zone_matching_camera_name_throws_error(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'clips': {
'retain': {
'default': 20,
'objects': {
'person': 30
}
}
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"clips": {"retain": {"default": 20, "objects": {"person": 30}}},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920,
'zones': {
'back': {
'coordinates': '1,1,1,1,1,1'
}
}
"height": 1080,
"width": 1920,
"zones": {"back": {"coordinates": "1,1,1,1,1,1"}},
}
}
},
}
self.assertRaises(vol.MultipleInvalid, lambda: FrigateConfig(config=config))
def test_clips_should_default_to_global_objects(self):
config = {
'mqtt': {
'host': 'mqtt'
},
'clips': {
'retain': {
'default': 20,
'objects': {
'person': 30
}
}
},
'objects': {
'track': ['person', 'dog']
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect'] }
"mqtt": {"host": "mqtt"},
"clips": {"retain": {"default": 20, "objects": {"person": 30}}},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
'height': 1080,
'width': 1920,
'clips': {
'enabled': True
}
"height": 1080,
"width": 1920,
"clips": {"enabled": True},
}
}
},
}
config = FrigateConfig(config=config)
assert(config.cameras['back'].clips.objects is None)
assert config.cameras["back"].clips.objects is None
def test_role_assigned_but_not_enabled(self):
json_config = {
'mqtt': {
'host': 'mqtt'
},
'cameras': {
'back': {
'ffmpeg': {
'inputs': [
{ 'path': 'rtsp://10.0.0.1:554/video', 'roles': ['detect', 'rtmp'] },
{ 'path': 'rtsp://10.0.0.1:554/record', 'roles': ['record'] }
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect", "rtmp"],
},
{"path": "rtsp://10.0.0.1:554/record", "roles": ["record"]},
]
},
'height': 1080,
'width': 1920
"height": 1080,
"width": 1920,
}
}
},
}
config = FrigateConfig(config=json_config)
ffmpeg_cmds = config.cameras['back'].ffmpeg_cmds
assert(len(ffmpeg_cmds) == 1)
assert(not 'clips' in ffmpeg_cmds[0]['roles'])
ffmpeg_cmds = config.cameras["back"].ffmpeg_cmds
assert len(ffmpeg_cmds) == 1
assert not "clips" in ffmpeg_cmds[0]["roles"]
if __name__ == '__main__':
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)

237
frigate/util.py
View File

@ -19,9 +19,20 @@ import numpy as np
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 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
@ -32,113 +43,122 @@ 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):
# 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_region_2_rgb(frame, region):
try:
height = frame.shape[0]//3*2
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_x2 = min(width, region[2])
crop_y2 = min(height, region[3])
crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
@ -148,64 +168,65 @@ def yuv_region_2_rgb(frame, region):
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
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)
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
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]
]
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]
]
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]
]
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]
]
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]
]
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 cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
except:
@ -213,23 +234,28 @@ def yuv_region_2_rgb(frame, region):
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
@ -247,19 +273,23 @@ 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
class EventsPerSecond:
def __init__(self, max_events=1000):
self._start = None
@ -274,23 +304,28 @@ 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 +339,15 @@ 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:
@ -326,6 +365,7 @@ class FrameManager(ABC):
def delete(self, name):
pass
class DictFrameManager(FrameManager):
def __init__(self):
self.frames = {}
@ -345,6 +385,7 @@ class DictFrameManager(FrameManager):
def delete(self, name):
del self.frames[name]
class SharedMemoryFrameManager(FrameManager):
def __init__(self):
self.shm_store = {}

350
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
@ -24,13 +19,19 @@ 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]
@ -57,8 +58,11 @@ def filtered(obj, objects_to_track, object_filters):
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:
@ -66,16 +70,20 @@ def filtered(obj, objects_to_track, object_filters):
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 +197,31 @@ 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)
})
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
while not self.stop_event.wait(10):
now = datetime.datetime.now().timestamp()
if not self.capture_thread.is_alive():
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...")
@ -195,23 +232,38 @@ class CameraWatchdog(threading.Thread):
self.ffmpeg_detect_process.communicate()
for p in self.ffmpeg_other_processes:
poll = p['process'].poll()
poll = p["process"].poll()
if poll == None:
continue
p['logpipe'].dump()
p['process'] = start_or_restart_ffmpeg(p['cmd'], self.logger, p['logpipe'], ffmpeg_process=p['process'])
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)
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)
@ -223,29 +275,56 @@ 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,
detection_queue,
result_connection,
detected_objects_queue,
process_info,
):
stop_event = mp.Event()
def receiveSignal(signalNumber, frame):
stop_event.set()
@ -256,79 +335,118 @@ 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.txt", 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 +458,9 @@ 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 +469,9 @@ 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
@ -358,26 +480,43 @@ 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 +535,10 @@ 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)
@ -406,13 +547,22 @@ 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:
@ -426,18 +576,28 @@ 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}")

21
frigate/watchdog.py
View File

@ -7,32 +7,29 @@ import signal
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)
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"])

41
nginx/nginx.conf
View File

@ -1,23 +1,21 @@
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;
@ -37,6 +35,38 @@ http {
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 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;
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;
@ -112,6 +142,7 @@ 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;

2
run.sh
View File

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

459
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"
}
@ -4118,6 +4117,74 @@
"integrity": "sha512-GmVAWB+JuFKqSbzlofYK4qxk955gEv4Kd9/aj2hLOxneXMAm/J7OXcl5DlElS9tmkqwCcxGysSZGOrjzNvmjFQ==",
"dev": true
},
"@videojs/http-streaming": {
"version": "2.6.4",
"resolved": "https://registry.npmjs.org/@videojs/http-streaming/-/http-streaming-2.6.4.tgz",
"integrity": "sha512-sFVE0MVXhawAkET8EgiUSMvDDv6u3uGidtO0BvNXG0/qKWlze/zEzhvLsyPU4HmLFRnffKeHK5RE2XpO5vHY8Q==",
"requires": {
"@babel/runtime": "^7.12.5",
"@videojs/vhs-utils": "^3.0.0",
"aes-decrypter": "3.1.2",
"global": "^4.4.0",
"m3u8-parser": "4.5.2",
"mpd-parser": "0.15.4",
"mux.js": "5.10.0",
"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": "3.0.2",
"resolved": "https://registry.npmjs.org/@videojs/vhs-utils/-/vhs-utils-3.0.2.tgz",
"integrity": "sha512-r8Yas1/tNGsGRNoIaDJuiWiQgM0P2yaEnobgzw5JcBiEqxnS8EXoUm4QtKH7nJtnppZ1yqBx1agBZCvBMKXA2w==",
"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": "2.5.1",
"resolved": "https://registry.npmjs.org/@videojs/xhr/-/xhr-2.5.1.tgz",
"integrity": "sha512-wV9nGESHseSK+S9ePEru2+OJZ1jq/ZbbzniGQ4weAmTIepuBMSYPx5zrxxQA0E786T5ykpO8ts+LayV+3/oI2w==",
"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"
}
}
}
},
"abab": {
"version": "2.0.5",
"resolved": "https://registry.npmjs.org/abab/-/abab-2.0.5.tgz",
@ -4163,6 +4230,28 @@
"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",
@ -4658,16 +4747,42 @@
"dev": true
},
"browserslist": {
"version": "4.16.1",
"resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.16.1.tgz",
"integrity": "sha512-UXhDrwqsNcpTYJBTZsbGATDxZbiVDsx6UjpmRUmtnP10pr8wAYr5LgFoEFw9ixriQH2mv/NX2SfGzE/o8GndLA==",
"version": "4.16.6",
"resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.16.6.tgz",
"integrity": "sha512-Wspk/PqO+4W9qp5iUTJsa1B/QrYn1keNCcEP5OvP7WBwT4KaDly0uONYmC6Xa3Z5IqnUgS0KcgLYu1l74x0ZXQ==",
"dev": true,
"requires": {
"caniuse-lite": "^1.0.30001173",
"colorette": "^1.2.1",
"electron-to-chromium": "^1.3.634",
"caniuse-lite": "^1.0.30001219",
"colorette": "^1.2.2",
"electron-to-chromium": "^1.3.723",
"escalade": "^3.1.1",
"node-releases": "^1.1.69"
"node-releases": "^1.1.71"
},
"dependencies": {
"caniuse-lite": {
"version": "1.0.30001230",
"resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001230.tgz",
"integrity": "sha512-5yBd5nWCBS+jWKTcHOzXwo5xzcj4ePE/yjtkZyUV1BTUmrBaA9MRGC+e7mxnqXSA90CmCA8L3eKLaSUkt099IQ==",
"dev": true
},
"colorette": {
"version": "1.2.2",
"resolved": "https://registry.npmjs.org/colorette/-/colorette-1.2.2.tgz",
"integrity": "sha512-MKGMzyfeuutC/ZJ1cba9NqcNpfeqMUcYmyF1ZFY6/Cn7CNSAKx6a+s48sqLqyAiZuaP2TcqMhoo+dlwFnVxT9w==",
"dev": true
},
"electron-to-chromium": {
"version": "1.3.739",
"resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.3.739.tgz",
"integrity": "sha512-+LPJVRsN7hGZ9EIUUiWCpO7l4E3qBYHNadazlucBfsXBbccDFNKUBAgzE68FnkWGJPwD/AfKhSzL+G+Iqb8A4A==",
"dev": true
},
"node-releases": {
"version": "1.1.72",
"resolved": "https://registry.npmjs.org/node-releases/-/node-releases-1.1.72.tgz",
"integrity": "sha512-LLUo+PpH3dU6XizX3iVoubUNheF/owjXCZZ5yACDxNnPtgFuludV1ZL3ayK1kVep42Rmm0+R9/Y60NQbZ2bifw==",
"dev": true
}
}
},
"bser": {
@ -4763,6 +4878,14 @@
"integrity": "sha1-G2gcIf+EAzyCZUMJBolCDRhxUdw=",
"dev": true
},
"chainsaw": {
"version": "0.0.9",
"resolved": "https://registry.npmjs.org/chainsaw/-/chainsaw-0.0.9.tgz",
"integrity": "sha1-EaBRAtHEx4W20EFdM21aOhYSkT4=",
"requires": {
"traverse": ">=0.3.0 <0.4"
}
},
"chalk": {
"version": "2.4.2",
"resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz",
@ -5103,6 +5226,11 @@
"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",
@ -5271,6 +5399,11 @@
"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",
@ -5328,12 +5461,6 @@
"safer-buffer": "^2.1.0"
}
},
"electron-to-chromium": {
"version": "1.3.641",
"resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.3.641.tgz",
"integrity": "sha512-b0DLhsHSHESC1I+Nx6n4w4Lr61chMd3m/av1rZQhS2IXTzaS5BMM5N+ldWdMIlni9CITMRM09m8He4+YV/92TA==",
"dev": true
},
"emittery": {
"version": "0.7.2",
"resolved": "https://registry.npmjs.org/emittery/-/emittery-0.7.2.tgz",
@ -5964,6 +6091,11 @@
"integrity": "sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw==",
"dev": true
},
"estree-walker": {
"version": "0.6.1",
"resolved": "https://registry.npmjs.org/estree-walker/-/estree-walker-0.6.1.tgz",
"integrity": "sha512-SqmZANLWS0mnatqbSfRP5g8OXZC12Fgg1IwNtLsyHDzJizORW4khDfjPqJZsemPWBB2uqykUah5YpQ6epsqC/w=="
},
"esutils": {
"version": "2.0.3",
"resolved": "https://registry.npmjs.org/esutils/-/esutils-2.0.3.tgz",
@ -6435,6 +6567,22 @@
"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",
@ -6537,6 +6685,14 @@
}
}
},
"hashish": {
"version": "0.0.4",
"resolved": "https://registry.npmjs.org/hashish/-/hashish-0.0.4.tgz",
"integrity": "sha1-bWC8b/r3Ebav1g5CbQd5iAFOZVQ=",
"requires": {
"traverse": ">=0.2.4"
}
},
"himalaya": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/himalaya/-/himalaya-1.1.0.tgz",
@ -6544,9 +6700,9 @@
"dev": true
},
"hosted-git-info": {
"version": "2.8.8",
"resolved": "https://registry.npmjs.org/hosted-git-info/-/hosted-git-info-2.8.8.tgz",
"integrity": "sha512-f/wzC2QaWBs7t9IYqB4T3sR1xviIViXJRJTWBlx2Gf3g0Xi5vI7Yy4koXQ1c9OYDGHN9sBy1DQ2AB8fqZBWhUg==",
"version": "2.8.9",
"resolved": "https://registry.npmjs.org/hosted-git-info/-/hosted-git-info-2.8.9.tgz",
"integrity": "sha512-mxIDAb9Lsm6DoOJ7xH+5+X4y1LU/4Hi50L9C5sIswK3JzULS4bwk1FvjdBgvYR4bzT4tuUQiC15FE2f5HbLvYw==",
"dev": true
},
"html-encoding-sniffer": {
@ -6693,6 +6849,11 @@
"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",
@ -6876,6 +7037,11 @@
"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",
@ -8596,6 +8762,11 @@
"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",
@ -8680,9 +8851,9 @@
}
},
"lodash": {
"version": "4.17.20",
"resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.20.tgz",
"integrity": "sha512-PlhdFcillOINfeV7Ni6oF1TAEayyZBoZ8bcshTHqOYJYlrqzRK5hagpagky5o4HfCzzd1TRkXPMFq6cKk9rGmA==",
"version": "4.17.21",
"resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.21.tgz",
"integrity": "sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==",
"dev": true
},
"lodash.difference": {
@ -8757,11 +8928,31 @@
"integrity": "sha1-wNjq82BZ9wV5bh40SBHPTEmNOiY=",
"dev": true
},
"m3u8-parser": {
"version": "4.5.2",
"resolved": "https://registry.npmjs.org/m3u8-parser/-/m3u8-parser-4.5.2.tgz",
"integrity": "sha512-sN/lu3TiRxmG2RFjZxo5c0/7Dr4RrEztl43jXrWwj5gFZ7vfa2iIxGfiPx485dm5QCazaIcKk+vNkUso8Aq0Ag==",
"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",
"integrity": "sha512-4CrMT5DOHTDk4HYDlzmwu4FVCcIYI8gauveasrdCu2IKIFOJ3f0v/8MDGJCDL9oD2ppz/Av1b0Nj345H9M+XIA==",
"dev": true,
"requires": {
"sourcemap-codec": "^1.4.4"
}
@ -8856,6 +9047,14 @@
"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",
@ -8916,6 +9115,28 @@
"integrity": "sha512-Xm9jdWvqFrlV0k965eY5AlCpWIIUBY2ExzGbEG+byMs+mZI4J7zvaUOLpQ8MTFgkpgyEnu4qUhuZT/Or3QeRiA==",
"dev": true
},
"mpd-parser": {
"version": "0.15.4",
"resolved": "https://registry.npmjs.org/mpd-parser/-/mpd-parser-0.15.4.tgz",
"integrity": "sha512-YcOclxKc5gnT87UQYwRoPJpWOFvQORwN+bXYmTWCJ4U2pCSS7jjtPrIhoOLHFAyekj48CHTX4hjGBV/VSNsUsg==",
"requires": {
"@babel/runtime": "^7.12.5",
"@videojs/vhs-utils": "^3.0.0",
"global": "^4.4.0",
"xmldom": "^0.4.0"
},
"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",
@ -8935,10 +9156,18 @@
"minimatch": "^3.0.4"
}
},
"mux.js": {
"version": "5.10.0",
"resolved": "https://registry.npmjs.org/mux.js/-/mux.js-5.10.0.tgz",
"integrity": "sha512-kLzvYsHYBwNa+ckkmpxWV3eImwntJbrwd1KbN4WR0hLe+dK/KB82aCuC0fQzAI2hkjYszdlSGsAWFgYdiFBUuA==",
"requires": {
"@babel/runtime": "^7.11.2"
}
},
"nanoid": {
"version": "3.1.20",
"resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.1.20.tgz",
"integrity": "sha512-a1cQNyczgKbLX9jwbS/+d7W8fX/RfgYR7lVWwWOGIPNgK2m0MWvrGF6/m4kk6U3QcFMnZf3RIhL0v2Jgh/0Uxw==",
"version": "3.1.23",
"resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.1.23.tgz",
"integrity": "sha512-FiB0kzdP0FFVGDKlRLEQ1BgDzU87dy5NnzjeW9YZNt+/c3+q82EQDUwniSAUxp/F0gFNI1ZhKU1FqYsMuqZVnw==",
"dev": true
},
"nanomatch": {
@ -9014,12 +9243,6 @@
"which": "^2.0.2"
}
},
"node-releases": {
"version": "1.1.69",
"resolved": "https://registry.npmjs.org/node-releases/-/node-releases-1.1.69.tgz",
"integrity": "sha512-DGIjo79VDEyAnRlfSqYTsy+yoHd2IOjJiKUozD2MV2D85Vso6Bug56mb9tT/fY5Urt0iqk01H7x+llAruDR2zA==",
"dev": true
},
"normalize-package-data": {
"version": "2.5.0",
"resolved": "https://registry.npmjs.org/normalize-package-data/-/normalize-package-data-2.5.0.tgz",
@ -9381,6 +9604,14 @@
"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",
@ -9448,20 +9679,20 @@
"dev": true
},
"postcss": {
"version": "8.2.2",
"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.2.2.tgz",
"integrity": "sha512-HM1NDNWLgglJPQQMNwvLxgH2KcrKZklKLi/xXYIOaqQB57p/pDWEJNS83PVICYsn1Dg/9C26TiejNr422/ePaQ==",
"version": "8.2.10",
"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.2.10.tgz",
"integrity": "sha512-b/h7CPV7QEdrqIxtAf2j31U5ef05uBDuvoXv6L51Q4rcS1jdlXAVKJv+atCFdUXYl9dyTHGyoMzIepwowRJjFw==",
"dev": true,
"requires": {
"colorette": "^1.2.1",
"nanoid": "^3.1.20",
"colorette": "^1.2.2",
"nanoid": "^3.1.22",
"source-map": "^0.6.1"
},
"dependencies": {
"source-map": {
"version": "0.6.1",
"resolved": "https://registry.npmjs.org/source-map/-/source-map-0.6.1.tgz",
"integrity": "sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==",
"colorette": {
"version": "1.2.2",
"resolved": "https://registry.npmjs.org/colorette/-/colorette-1.2.2.tgz",
"integrity": "sha512-MKGMzyfeuutC/ZJ1cba9NqcNpfeqMUcYmyF1ZFY6/Cn7CNSAKx6a+s48sqLqyAiZuaP2TcqMhoo+dlwFnVxT9w==",
"dev": true
}
}
@ -9743,6 +9974,11 @@
"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",
@ -9971,8 +10207,7 @@
"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",
@ -10046,6 +10281,14 @@
}
}
},
"remove": {
"version": "0.1.5",
"resolved": "https://registry.npmjs.org/remove/-/remove-0.1.5.tgz",
"integrity": "sha1-CV/9gn1lyfQa2X0z5BanWBEHmVU=",
"requires": {
"seq": ">= 0.3.5"
}
},
"remove-trailing-separator": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/remove-trailing-separator/-/remove-trailing-separator-1.1.0.tgz",
@ -10230,6 +10473,23 @@
}
}
},
"rollup-plugin-replace": {
"version": "2.2.0",
"resolved": "https://registry.npmjs.org/rollup-plugin-replace/-/rollup-plugin-replace-2.2.0.tgz",
"integrity": "sha512-/5bxtUPkDHyBJAKketb4NfaeZjL5yLZdeUihSfbF2PQMz+rSTEb8ARKoOl3UBT4m7/X+QOXJo3sLTcq+yMMYTA==",
"requires": {
"magic-string": "^0.25.2",
"rollup-pluginutils": "^2.6.0"
}
},
"rollup-pluginutils": {
"version": "2.8.2",
"resolved": "https://registry.npmjs.org/rollup-pluginutils/-/rollup-pluginutils-2.8.2.tgz",
"integrity": "sha512-EEp9NhnUkwY8aif6bxgovPHMoMoNr2FulJziTndpt5H9RdwC47GSGuII9XxpSdzVGM0GWrNPHV6ie1LTNJPaLQ==",
"requires": {
"estree-walker": "^0.6.1"
}
},
"rsvp": {
"version": "4.8.5",
"resolved": "https://registry.npmjs.org/rsvp/-/rsvp-4.8.5.tgz",
@ -10242,12 +10502,28 @@
"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",
@ -10490,6 +10766,15 @@
"lru-cache": "^6.0.0"
}
},
"seq": {
"version": "0.3.5",
"resolved": "https://registry.npmjs.org/seq/-/seq-0.3.5.tgz",
"integrity": "sha1-rgKvOkJHk9jMvyEtaRdODFTf/jg=",
"requires": {
"chainsaw": ">=0.0.7 <0.1",
"hashish": ">=0.0.2 <0.1"
}
},
"set-blocking": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/set-blocking/-/set-blocking-2.0.0.tgz",
@ -10809,8 +11094,7 @@
"sourcemap-codec": {
"version": "1.4.8",
"resolved": "https://registry.npmjs.org/sourcemap-codec/-/sourcemap-codec-1.4.8.tgz",
"integrity": "sha512-9NykojV5Uih4lgo5So5dtw+f0JgJX30KCNI8gwhz2J9A15wD0Ml6tjHKwf6fTSa6fAdVBdZeNOs9eJ71qCk8vA==",
"dev": true
"integrity": "sha512-9NykojV5Uih4lgo5So5dtw+f0JgJX30KCNI8gwhz2J9A15wD0Ml6tjHKwf6fTSa6fAdVBdZeNOs9eJ71qCk8vA=="
},
"spdx-correct": {
"version": "3.1.1",
@ -11291,6 +11575,11 @@
"punycode": "^2.1.1"
}
},
"traverse": {
"version": "0.3.9",
"resolved": "https://registry.npmjs.org/traverse/-/traverse-0.3.9.tgz",
"integrity": "sha1-cXuPIgzAu3tE5AUUwisui7xw2Lk="
},
"ts-morph": {
"version": "9.1.0",
"resolved": "https://registry.npmjs.org/ts-morph/-/ts-morph-9.1.0.tgz",
@ -11557,6 +11846,11 @@
"integrity": "sha1-2pN/emLiH+wf0Y1Js1wpNQZ6bHI=",
"dev": true
},
"url-toolkit": {
"version": "2.2.2",
"resolved": "https://registry.npmjs.org/url-toolkit/-/url-toolkit-2.2.2.tgz",
"integrity": "sha512-l25w6Sy+Iy3/IbogunxhWwljPaDnqpiKvrQRoLBm6DfISco7NyRIS7Zf6+Oxhy1T8kHxWdwLND7ZZba6NjXMug=="
},
"use": {
"version": "3.1.1",
"resolved": "https://registry.npmjs.org/use/-/use-3.1.1.tgz",
@ -11631,6 +11925,74 @@
"extsprintf": "^1.2.0"
}
},
"video.js": {
"version": "7.11.8",
"resolved": "https://registry.npmjs.org/video.js/-/video.js-7.11.8.tgz",
"integrity": "sha512-iQmNYB+pdgu8b45Za1AKSa5J7uDyHIqfJy+picw4voKfjErXK/BEvs+A3f99Ck7SCZU4cmMmX/s17AwaaNs+1w==",
"requires": {
"@babel/runtime": "^7.9.2",
"@videojs/http-streaming": "2.6.4",
"@videojs/xhr": "2.5.1",
"global": "4.3.2",
"keycode": "^2.2.0",
"remove": "^0.1.5",
"rollup-plugin-replace": "^2.2.0",
"safe-json-parse": "4.0.0",
"videojs-font": "3.2.0",
"videojs-vtt.js": "^0.15.2"
}
},
"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-mobile-ui": {
"version": "0.5.3",
"resolved": "https://registry.npmjs.org/videojs-mobile-ui/-/videojs-mobile-ui-0.5.3.tgz",
"integrity": "sha512-rY+JFLUq2edqoWB4CHVxPLYQEYhSNdGylGe44MEdfxzqYaEgkf/qyDlmmpdN9BFIQ6vJ7eaQBxgTOHha8UpOGA==",
"requires": {
"global": "^4.3.2",
"video.js": "^5.19.2 || ^6.6.0 || ^7.0.0"
}
},
"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": "2.0.0",
"resolved": "https://registry.npmjs.org/videojs-seek-buttons/-/videojs-seek-buttons-2.0.0.tgz",
"integrity": "sha512-fSq2COvwTT5OwD5urc3E+ktQRwdjptXNaeuv1Tld2yfoV1ep9Am9gE/O07ADgHJVedFatVUXnifTh6wlUWSyTA==",
"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",
@ -11767,9 +12129,9 @@
}
},
"ws": {
"version": "7.4.3",
"resolved": "https://registry.npmjs.org/ws/-/ws-7.4.3.tgz",
"integrity": "sha512-hr6vCR76GsossIRsr8OLR9acVVm1jyfEWvhbNjtgPOrfvAlKzvyeg/P6r8RuDjRyrcQoPQT7K0DGEPc7Ae6jzA==",
"version": "7.4.6",
"resolved": "https://registry.npmjs.org/ws/-/ws-7.4.6.tgz",
"integrity": "sha512-YmhHDO4MzaDLB+M9ym/mDA5z0naX8j7SIlT8f8z+I0VtzsRbekxEutHSme7NPS2qE8StCYQNUnfWdXta/Yu85A==",
"dev": true
},
"xml-name-validator": {
@ -11784,6 +12146,11 @@
"integrity": "sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw==",
"dev": true
},
"xmldom": {
"version": "0.4.0",
"resolved": "https://registry.npmjs.org/xmldom/-/xmldom-0.4.0.tgz",
"integrity": "sha512-2E93k08T30Ugs+34HBSTQLVtpi6mCddaY8uO+pMNk1pqSjV5vElzn4mmh6KLxN3hki8rNcHSYzILoh3TEWORvA=="
},
"xtend": {
"version": "4.0.2",
"resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",

9
web/package.json
View File

@ -11,11 +11,16 @@
"test": "jest"
},
"dependencies": {
"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": "^7.11.8",
"videojs-mobile-ui": "^0.5.3",
"videojs-playlist": "^4.3.1",
"videojs-seek-buttons": "^2.0.0"
},
"devDependencies": {
"@babel/eslint-parser": "^7.12.13",
@ -35,7 +40,7 @@
"eslint-plugin-jest": "^24.1.3",
"eslint-plugin-testing-library": "^3.10.1",
"jest": "^26.6.3",
"postcss": "^8.2.2",
"postcss": "^8.2.10",
"postcss-cli": "^8.3.1",
"prettier": "^2.2.1",
"rimraf": "^3.0.2",

1
web/public/index.html
View File

@ -15,6 +15,7 @@
</head>
<body>
<div id="root" class="z-0"></div>
<div id="dialogs" class="z-0"></div>
<div id="menus" class="z-0"></div>
<div id="tooltips" class="z-0"></div>
<noscript>You need to enable JavaScript to run this app.</noscript>

1
web/src/App.jsx
View File

@ -29,6 +29,7 @@ export default function App() {
<AsyncRoute path="/cameras/:camera" getComponent={Routes.getCamera} />
<AsyncRoute path="/events/:eventId" getComponent={Routes.getEvent} />
<AsyncRoute path="/events" getComponent={Routes.getEvents} />
<AsyncRoute path="/recording/:camera/:date?/:hour?/:seconds?" getComponent={Routes.getRecording} />
<AsyncRoute path="/debug" getComponent={Routes.getDebug} />
<AsyncRoute path="/styleguide" getComponent={Routes.getStyleGuide} />
<Cameras default path="/" />

26
web/src/Sidebar.jsx
View File

@ -9,7 +9,7 @@ import NavigationDrawer, { Destination, Separator } from './components/Navigatio
export default function Sidebar() {
const { data: config } = useConfig();
const cameras = useMemo(() => Object.keys(config.cameras), [config]);
const cameras = useMemo(() => Object.entries(config.cameras), [config]);
return (
<NavigationDrawer header={<Header />}>
@ -19,7 +19,7 @@ export default function Sidebar() {
matches ? (
<Fragment>
<Separator />
{cameras.map((camera) => (
{cameras.map(([camera]) => (
<Destination href={`/cameras/${camera}`} text={camera} />
))}
<Separator />
@ -27,6 +27,28 @@ export default function Sidebar() {
) : null
}
</Match>
<Match path="/recording/:camera/:date?/:hour?/:seconds?">
{({ matches }) =>
matches ? (
<Fragment>
<Separator />
{cameras.map(([camera, conf]) => {
if (conf.record.enabled) {
return (
<Destination
path={`/recording/${camera}/:date?/:hour?/:seconds?`}
href={`/recording/${camera}`}
text={camera}
/>
);
}
return null;
})}
<Separator />
</Fragment>
) : null
}
</Match>
<Destination href="/events" text="Events" />
<Destination href="/debug" text="Debug" />
<Separator />

11
web/src/__tests__/Sidebar.test.jsx
View File

@ -9,8 +9,8 @@ describe('Sidebar', () => {
jest.spyOn(Api, 'useConfig').mockImplementation(() => ({
data: {
cameras: {
front: { name: 'front', objects: { track: ['taco', 'cat', 'dog'] } },
side: { name: 'side', objects: { track: ['taco', 'cat', 'dog'] } },
front: { name: 'front', objects: { track: ['taco', 'cat', 'dog'] }, record: { enabled: true } },
side: { name: 'side', objects: { track: ['taco', 'cat', 'dog'] }, record: { enabled: false } },
},
},
}));
@ -30,4 +30,11 @@ describe('Sidebar', () => {
expect(screen.queryByRole('link', { name: 'front' })).toBeInTheDocument();
expect(screen.queryByRole('link', { name: 'side' })).toBeInTheDocument();
});
test('render cameras if in record route', async () => {
window.history.replaceState({}, 'Front Recordings', '/recording/front');
render(<Sidebar />);
expect(screen.queryByRole('link', { name: 'front' })).toBeInTheDocument();
expect(screen.queryByRole('link', { name: 'side' })).not.toBeInTheDocument();
});
});

5
web/src/api/index.jsx
View File

@ -110,6 +110,11 @@ export function useEvent(eventId, fetchId) {
return useFetch(url, fetchId);
}
export function useRecording(camera, fetchId) {
const url = `/api/${camera}/recordings`;
return useFetch(url, fetchId);
}
export function useConfig(searchParams, fetchId) {
const url = `/api/config${searchParams ? `?${searchParams.toString()}` : ''}`;
return useFetch(url, fetchId);

2
web/src/components/Button.jsx
View File

@ -66,7 +66,7 @@ export default function Button({
let classes = `whitespace-nowrap flex items-center space-x-1 ${className} ${ButtonTypes[type]} ${
ButtonColors[disabled ? 'disabled' : color][type]
} font-sans inline-flex font-bold uppercase text-xs px-2 py-2 rounded outline-none focus:outline-none ring-opacity-50 transition-shadow transition-colors ${
} font-sans inline-flex font-bold uppercase text-xs px-1.5 md:px-2 py-2 rounded outline-none focus:outline-none ring-opacity-50 transition-shadow transition-colors ${
disabled ? 'cursor-not-allowed' : 'focus:ring-2 cursor-pointer'
}`;

47
web/src/components/Dialog.jsx Normal file
View File

@ -0,0 +1,47 @@
import { h, Fragment } from 'preact';
import Button from './Button';
import Heading from './Heading';
import { createPortal } from 'preact/compat';
import { useState, useEffect } from 'preact/hooks';
export default function Dialog({ actions = [], portalRootID = 'dialogs', title, text }) {
const portalRoot = portalRootID && document.getElementById(portalRootID);
const [show, setShow] = useState(false);
useEffect(() => {
window.requestAnimationFrame(() => {
setShow(true);
});
}, []);
const dialog = (
<Fragment>
<div
data-testid="scrim"
key="scrim"
className="absolute inset-0 z-10 flex justify-center items-center bg-black bg-opacity-40"
>
<div
role="modal"
className={`absolute rounded shadow-2xl bg-white dark:bg-gray-700 max-w-sm text-gray-900 dark:text-white transition-transform transition-opacity duration-75 transform scale-90 opacity-0 ${
show ? 'scale-100 opacity-100' : ''
}`}
>
<div className="p-4">
<Heading size="lg">{title}</Heading>
<p>{text}</p>
</div>
<div className="p-2 flex justify-start flex-row-reverse space-x-2">
{actions.map(({ color, text, onClick, ...props }, i) => (
<Button className="ml-2" color={color} key={i} onClick={onClick} type="text" {...props}>
{text}
</Button>
))}
</div>
</div>
</div>
</Fragment>
);
return portalRoot ? createPortal(dialog, portalRoot) : dialog;
}

111
web/src/components/RecordingPlaylist.jsx Normal file
View File

@ -0,0 +1,111 @@
import { h } from 'preact';
import { useState } from 'preact/hooks';
import { addSeconds, differenceInSeconds, fromUnixTime, format, parseISO, startOfHour } from 'date-fns';
import ArrowDropdown from '../icons/ArrowDropdown';
import ArrowDropup from '../icons/ArrowDropup';
import Link from '../components/Link';
import Menu from '../icons/Menu';
import MenuOpen from '../icons/MenuOpen';
import { useApiHost } from '../api';
export default function RecordingPlaylist({ camera, recordings, selectedDate, selectedHour }) {
const [active, setActive] = useState(true);
const toggle = () => setActive(!active);
const result = [];
for (const recording of recordings.slice().reverse()) {
const date = parseISO(recording.date);
result.push(
<ExpandableList
title={format(date, 'MMM d, yyyy')}
events={recording.events}
selected={recording.date === selectedDate}
>
{recording.recordings.map((item, i) => (
<div className="mb-2 w-full">
<div
className={`flex w-full text-md text-white px-8 py-2 mb-2 ${
i === 0 ? 'border-t border-white border-opacity-50' : ''
}`}
>
<div className="flex-1">
<Link href={`/recording/${camera}/${recording.date}/${item.hour}`} type="text">
{item.hour}:00
</Link>
</div>
<div className="flex-1 text-right">{item.events.length} Events</div>
</div>
{item.events.map((event) => (
<EventCard camera={camera} event={event} delay={item.delay} />
))}
</div>
))}
</ExpandableList>
);
}
const openClass = active ? '-left-6' : 'right-0';
return (
<div className="flex absolute inset-y-0 right-0 w-9/12 md:w-1/2 lg:w-3/5 max-w-md text-base text-white font-sans">
<div
onClick={toggle}
className={`absolute ${openClass} cursor-pointer items-center self-center rounded-tl-lg rounded-bl-lg border border-r-0 w-6 h-20 py-7 bg-gray-800 bg-opacity-70`}
>
{active ? <Menu /> : <MenuOpen />}
</div>
<div
className={`w-full h-full bg-gray-800 bg-opacity-70 border-l overflow-x-hidden overflow-y-auto${
active ? '' : ' hidden'
}`}
>
{result}
</div>
</div>
);
}
export function ExpandableList({ title, events = 0, children, selected = false }) {
const [active, setActive] = useState(selected);
const toggle = () => setActive(!active);
return (
<div className={`w-full text-sm ${active ? 'border-b border-white border-opacity-50' : ''}`}>
<div className="flex items-center w-full p-2 cursor-pointer md:text-lg" onClick={toggle}>
<div className="flex-1 font-bold">{title}</div>
<div className="flex-1 text-right mr-4">{events} Events</div>
<div className="w-6 md:w-10 h-6 md:h-10">{active ? <ArrowDropup /> : <ArrowDropdown />}</div>
</div>
<div className={`bg-gray-800 bg-opacity-50 ${active ? '' : 'hidden'}`}>{children}</div>
</div>
);
}
export function EventCard({ camera, event, delay }) {
const apiHost = useApiHost();
const start = fromUnixTime(event.start_time);
const end = fromUnixTime(event.end_time);
const duration = addSeconds(new Date(0), differenceInSeconds(end, start));
const seconds = Math.max(differenceInSeconds(start, startOfHour(start)) - delay - 10, 0);
return (
<Link className="" href={`/recording/${camera}/${format(start, 'yyyy-MM-dd')}/${format(start, 'HH')}/${seconds}`}>
<div className="flex flex-row mb-2">
<div className="w-28 mr-4">
<img className="antialiased" src={`${apiHost}/api/events/${event.id}/thumbnail.jpg`} />
</div>
<div className="flex flex-row w-full border-b">
<div className="w-full text-gray-700 font-semibold relative pt-0">
<div className="flex flex-row items-center">
<div className="flex-1">
<div className="text-2xl text-white leading-tight capitalize">{event.label}</div>
<div className="text-xs md:text-normal text-gray-300">Start: {format(start, 'HH:mm:ss')}</div>
<div className="text-xs md:text-normal text-gray-300">Duration: {format(duration, 'mm:ss')}</div>
</div>
<div className="text-lg text-white text-right leading-tight">{(event.top_score * 100).toFixed(1)}%</div>
</div>
</div>
</div>
<div className="w-6" />
</div>
</Link>
);
}

59
web/src/components/VideoPlayer.jsx Normal file
View File

@ -0,0 +1,59 @@
import { h, Component } from 'preact';
import videojs from 'video.js';
import 'videojs-mobile-ui';
import 'videojs-playlist';
import 'videojs-seek-buttons';
import 'video.js/dist/video-js.css';
import 'videojs-seek-buttons/dist/videojs-seek-buttons.css';
const defaultOptions = {
controls: true,
playbackRates: [0.5, 1, 2, 4, 8],
fluid: true,
};
export default class VideoPlayer extends Component {
componentDidMount() {
const { options, onReady = () => {} } = this.props;
const videoJsOptions = {
...defaultOptions,
...options,
};
this.player = videojs(this.videoNode, videoJsOptions, function onPlayerReady() {
onReady(this);
});
this.player.seekButtons({
forward: 30,
back: 10,
});
this.player.mobileUi({
fullscreen: {
iOS: true,
},
});
}
componentWillUnmount() {
const { onDispose = () => {} } = this.props;
if (this.player) {
this.player.dispose();
onDispose();
}
}
// shouldComponentUpdate() {
// return false;
// }
render() {
const { style, children } = this.props;
return (
<div style={style}>
<div data-vjs-player>
<video ref={(node) => (this.videoNode = node)} className="video-js vjs-default-skin" controls playsinline />
{children}
</div>
</div>
);
}
}

38
web/src/components/__tests__/Dialog.test.jsx Normal file
View File

@ -0,0 +1,38 @@
import { h } from 'preact';
import Dialog from '../Dialog';
import { fireEvent, render, screen } from '@testing-library/preact';
describe('Dialog', () => {
let portal;
beforeAll(() => {
portal = document.createElement('div');
portal.id = 'dialogs';
document.body.appendChild(portal);
});
afterAll(() => {
document.body.removeChild(portal);
});
test('renders to a portal', async () => {
render(<Dialog title="Tacos" text="This is the dialog" />);
expect(screen.getByText('Tacos')).toBeInTheDocument();
expect(screen.getByRole('modal').closest('#dialogs')).not.toBeNull();
});
test('renders action buttons', async () => {
const handleClick = jest.fn();
render(
<Dialog
actions={[
{ color: 'red', text: 'Delete' },
{ text: 'Okay', onClick: handleClick },
]}
title="Tacos"
/>
);
fireEvent.click(screen.getByRole('button', { name: 'Okay' }));
expect(handleClick).toHaveBeenCalled();
});
});

13
web/src/icons/Delete.jsx Normal file
View File

@ -0,0 +1,13 @@
import { h } from 'preact';
import { memo } from 'preact/compat';
export function Delete({ className = '' }) {
return (
<svg className={`fill-current ${className}`} viewBox="0 0 24 24">
<path d="M0 0h24v24H0V0z" fill="none" />
<path d="M6 21h12V7H6v14zM19 4h-3.5l-1-1h-5l-1 1H5v2h14V4z" />
</svg>
);
}
export default memo(Delete);

4
web/src/index.css
View File

@ -25,3 +25,7 @@
transform: rotate(360deg);
}
}
.video-js.vjs-has-started .vjs-touch-overlay {
display: none;
}

14
web/src/routes/Cameras.jsx
View File

@ -16,19 +16,25 @@ export default function Cameras() {
<ActivityIndicator />
) : (
<div className="grid grid-cols-1 3xl:grid-cols-3 md:grid-cols-2 gap-4">
{Object.keys(config.cameras).map((camera) => (
<Camera name={camera} />
{Object.entries(config.cameras).map(([camera, conf]) => (
<Camera name={camera} conf={conf} />
))}
</div>
);
}
function Camera({ name }) {
function Camera({ name, conf }) {
const { payload: detectValue, send: sendDetect } = useDetectState(name);
const { payload: clipValue, send: sendClips } = useClipsState(name);
const { payload: snapshotValue, send: sendSnapshots } = useSnapshotsState(name);
const href = `/cameras/${name}`;
const buttons = useMemo(() => [{ name: 'Events', href: `/events?camera=${name}` }], [name]);
const buttons = useMemo(() => {
const result = [{ name: 'Events', href: `/events?camera=${name}` }];
if (conf.record.enabled) {
result.push({ name: 'Recordings', href: `/recording/${name}` });
}
return result;
}, [name, conf.record.enabled]);
const icons = useMemo(
() => [
{

61
web/src/routes/Event.jsx
View File

@ -1,5 +1,10 @@
import { h, Fragment } from 'preact';
import { useCallback, useState } from 'preact/hooks';
import { route } from 'preact-router';
import ActivityIndicator from '../components/ActivityIndicator';
import Button from '../components/Button';
import Delete from '../icons/Delete'
import Dialog from '../components/Dialog';
import Heading from '../components/Heading';
import Link from '../components/Link';
import { FetchStatus, useApiHost, useEvent } from '../api';
@ -8,9 +13,39 @@ import { Table, Thead, Tbody, Th, Tr, Td } from '../components/Table';
export default function Event({ eventId }) {
const apiHost = useApiHost();
const { data, status } = useEvent(eventId);
const [showDialog, setShowDialog] = useState(false);
const [deleteStatus, setDeleteStatus] = useState(FetchStatus.NONE);
const handleClickDelete = () => {
setShowDialog(true);
};
const handleDismissDeleteDialog = () => {
setShowDialog(false);
};
const handleClickDeleteDialog = useCallback(async () => {
let success;
try {
const response = await fetch(`${apiHost}/api/events/${eventId}`, { method: 'DELETE' });
success = await (response.status < 300 ? response.json() : { success: true });
setDeleteStatus(success ? FetchStatus.LOADED : FetchStatus.ERROR);
} catch (e) {
setDeleteStatus(FetchStatus.ERROR);
}
if (success) {
setDeleteStatus(FetchStatus.LOADED);
setShowDialog(false);
route('/events', true);
}
}, [apiHost, eventId, setShowDialog]);
if (status !== FetchStatus.LOADED) {
return <ActivityIndicator />;
return <ActivityIndicator />
}
const startime = new Date(data.start_time * 1000);
@ -18,9 +53,27 @@ export default function Event({ eventId }) {
return (
<div className="space-y-4">
<Heading>
{data.camera} {data.label} <span className="text-sm">{startime.toLocaleString()}</span>
</Heading>
<div className="flex">
<Heading className="flex-grow">
{data.camera} {data.label} <span className="text-sm">{startime.toLocaleString()}</span>
</Heading>
<Button className="self-start" color="red" onClick={handleClickDelete}>
<Delete className="w-6" /> Delete event
</Button>
{showDialog ? (
<Dialog
onDismiss={handleDismissDeleteDialog}
title="Delete Event?"
text="This event will be permanently deleted along with any related clips and snapshots"
actions={[
deleteStatus !== FetchStatus.LOADING
? { text: 'Delete', color: 'red', onClick: handleClickDeleteDialog }
: { text: 'Deleting…', color: 'red', disabled: true },
{ text: 'Cancel', onClick: handleDismissDeleteDialog },
]}
/>
) : null}
</div>
<Table class="w-full">
<Thead>

97
web/src/routes/Recording.jsx Normal file
View File

@ -0,0 +1,97 @@
import { h } from 'preact';
import { closestTo, format, parseISO } from 'date-fns';
import ActivityIndicator from '../components/ActivityIndicator';
import Heading from '../components/Heading';
import RecordingPlaylist from '../components/RecordingPlaylist';
import VideoPlayer from '../components/VideoPlayer';
import { FetchStatus, useApiHost, useRecording } from '../api';
export default function Recording({ camera, date, hour, seconds }) {
const apiHost = useApiHost();
const { data, status } = useRecording(camera);
if (status !== FetchStatus.LOADED) {
return <ActivityIndicator />;
}
if (data.length === 0) {
return (
<div className="space-y-4">
<Heading>{camera} Recordings</Heading>
<div class="bg-yellow-100 border-l-4 border-yellow-500 text-yellow-700 p-4" role="alert">
<p class="font-bold">No Recordings Found</p>
<p>Make sure you have enabled the record role in your configuration for the {camera} camera.</p>
</div>
</div>
);
}
const recordingDates = data.map((item) => item.date);
const selectedDate = closestTo(
date ? parseISO(date) : new Date(),
recordingDates.map((i) => parseISO(i))
);
const selectedKey = format(selectedDate, 'yyyy-MM-dd');
const [year, month, day] = selectedKey.split('-');
const playlist = [];
const hours = [];
for (const item of data) {
if (item.date === selectedKey) {
for (const recording of item.recordings) {
playlist.push({
name: `${selectedKey} ${recording.hour}:00`,
description: `${camera} recording @ ${recording.hour}:00.`,
sources: [
{
src: `${apiHost}/vod/${year}-${month}/${day}/${recording.hour}/${camera}/index.m3u8`,
type: 'application/vnd.apple.mpegurl',
},
],
});
hours.push(recording.hour);
}
}
}
const selectedHour = hours.indexOf(hour);
if (this.player) {
this.player.playlist([]);
this.player.playlist(playlist);
this.player.playlist.autoadvance(0);
if (selectedHour !== -1) {
this.player.playlist.currentItem(selectedHour);
if (seconds !== undefined) {
this.player.currentTime(seconds);
}
}
}
return (
<div className="space-y-4">
<Heading>{camera} Recordings</Heading>
<VideoPlayer
onReady={(player) => {
if (player.playlist) {
player.playlist(playlist);
player.playlist.autoadvance(0);
if (selectedHour !== -1) {
player.playlist.currentItem(selectedHour);
if (seconds !== undefined) {
player.currentTime(seconds);
}
}
this.player = player;
}
}}
onDispose={() => {
this.player = null;
}}
>
<RecordingPlaylist camera={camera} recordings={data} selectedDate={selectedKey} selectedHour={hour} />
</VideoPlayer>
</div>
);
}

26
web/src/routes/StyleGuide.jsx
View File

@ -2,6 +2,7 @@ import { h } from 'preact';
import ArrowDropdown from '../icons/ArrowDropdown';
import ArrowDropup from '../icons/ArrowDropup';
import Button from '../components/Button';
import Dialog from '../components/Dialog';
import Heading from '../components/Heading';
import Select from '../components/Select';
import Switch from '../components/Switch';
@ -10,6 +11,7 @@ import { useCallback, useState } from 'preact/hooks';
export default function StyleGuide() {
const [switches, setSwitches] = useState({ 0: false, 1: true, 2: false, 3: false });
const [showDialog, setShowDialog] = useState(false);
const handleSwitch = useCallback(
(id, checked) => {
@ -18,6 +20,10 @@ export default function StyleGuide() {
[switches]
);
const handleDismissDialog = () => {
setShowDialog(false);
};
return (
<div>
<Heading size="md">Button</Heading>
@ -59,6 +65,26 @@ export default function StyleGuide() {
</Button>
</div>
<Heading size="md">Dialog</Heading>
<Button
onClick={() => {
setShowDialog(true);
}}
>
Show Dialog
</Button>
{showDialog ? (
<Dialog
onDismiss={handleDismissDialog}
title="This is a dialog"
text="Would you like to see more?"
actions={[
{ text: 'Yes', color: 'red', onClick: handleDismissDialog },
{ text: 'No', onClick: handleDismissDialog },
]}
/>
) : null}
<Heading size="md">Switch</Heading>
<div className="flex-col space-y-4 max-w-4xl">
<Switch label="Disabled, off" labelPosition="after" />

12
web/src/routes/__tests__/Cameras.test.jsx
View File

@ -12,8 +12,8 @@ describe('Cameras Route', () => {
useConfigMock = jest.spyOn(Api, 'useConfig').mockImplementation(() => ({
data: {
cameras: {
front: { name: 'front', objects: { track: ['taco', 'cat', 'dog'] } },
side: { name: 'side', objects: { track: ['taco', 'cat', 'dog'] } },
front: { name: 'front', objects: { track: ['taco', 'cat', 'dog'] }, record: { enabled: true } },
side: { name: 'side', objects: { track: ['taco', 'cat', 'dog'] }, record: { enabled: false } },
},
},
status: 'loaded',
@ -41,6 +41,14 @@ describe('Cameras Route', () => {
expect(screen.queryByText('side').closest('a')).toHaveAttribute('href', '/cameras/side');
});
test('shows recordings link', async () => {
render(<Cameras />);
expect(screen.queryByLabelText('Loading…')).not.toBeInTheDocument();
expect(screen.queryAllByText('Recordings')).toHaveLength(1);
});
test('buttons toggle detect, clips, and snapshots', async () => {
const sendDetect = jest.fn();
const sendClips = jest.fn();

5
web/src/routes/index.js
View File

@ -18,6 +18,11 @@ export async function getEvents(url, cb, props) {
return module.default;
}
export async function getRecording(url, cb, props) {
const module = await import('./Recording.jsx');
return module.default;
}
export async function getDebug(url, cb, props) {
const module = await import('./Debug.jsx');
return module.default;