update index.js to use baseUrl

fixes #544

.devcontainer/devcontainer.json

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

.dockerignore

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

.github/FUNDING.yml

@@ -1,3 +1 @@
-github:
-  - blakeblackshear
-  - paularmstrong
+github: blakeblackshear

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,6 +1,6 @@
 ---
 name: Bug report or Support request
-about: Bug report or Support request
+about: ''
 title: ''
 labels: ''
 assignees: ''
@@ -11,7 +11,7 @@ assignees: ''
 A clear and concise description of what your issue is.
 
 **Version of frigate**
-Output from `/api/version`
+Output from `/version`
 
 **Config file**
 Include your full config file wrapped in triple back ticks.
@@ -26,7 +26,7 @@ Include relevant log output here
 
 **Frigate stats**
 ```json
-Output from frigate's /api/stats endpoint
+Output from frigate's /stats endpoint
 ```
 
 **FFprobe from your camera**

.github/stale.yml

@@ -1,17 +0,0 @@
-# Number of days of inactivity before an issue becomes stale
-daysUntilStale: 30
-# Number of days of inactivity before a stale issue is closed
-daysUntilClose: 3
-# Issues with these labels will never be considered stale
-exemptLabels:
-  - pinned
-  - security
-# Label to use when marking an issue as stale
-staleLabel: stale
-# Comment to post when marking an issue as stale. Set to `false` to disable
-markComment: >
-  This issue has been automatically marked as stale because it has not had
-  recent activity. It will be closed if no further activity occurs. Thank you
-  for your contributions.
-# Comment to post when closing a stale issue. Set to `false` to disable
-closeComment: false

.github/workflows/pull_request.yml

@@ -1,46 +0,0 @@
-name: On pull request
-
-on: pull_request
-
-jobs:
-  web_lint:
-    name: Web - Lint
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@master
-      - uses: actions/setup-node@master
-        with:
-          node-version: 14.x
-      - run: npm install
-        working-directory: ./web
-      - name: Lint
-        run: npm run lint:cmd
-        working-directory: ./web
-
-  web_build:
-    name: Web - Build
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@master
-      - uses: actions/setup-node@master
-        with:
-          node-version: 14.x
-      - run: npm install
-        working-directory: ./web
-      - name: Build
-        run: npm run build
-        working-directory: ./web
-
-  web_test:
-    name: Web - Test
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@master
-      - uses: actions/setup-node@master
-        with:
-          node-version: 14.x
-      - run: npm install
-        working-directory: ./web
-      - name: Test
-        run: npm run test
-        working-directory: ./web

.github/workflows/push.yml

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

.gitignore

@@ -1,6 +1,5 @@
 .DS_Store
 *.pyc
-*.swp
 debug
 .vscode
 config/config.yml
@@ -10,5 +9,3 @@ models
 frigate/version.py
 web/build
 web/node_modules
-web/coverage
-core

.pylintrc

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

Makefile

@@ -3,59 +3,56 @@ default_target: amd64_frigate
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
 
 version:
-	echo "VERSION='0.9.0-$(COMMIT_HASH)'" > frigate/version.py
+	echo "VERSION='0.8.0-$(COMMIT_HASH)'" > frigate/version.py
 
 web:
 	docker build --tag frigate-web --file docker/Dockerfile.web web/
 
 amd64_wheels:
-	docker build --tag blakeblackshear/frigate-wheels:1.0.3-amd64 --file docker/Dockerfile.wheels .
+	docker build --tag blakeblackshear/frigate-wheels:1.0.1-amd64 --file docker/Dockerfile.wheels .
 
 amd64_ffmpeg:
-	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-amd64 --file docker/Dockerfile.ffmpeg.amd64 .
-
-nginx_frigate:
-	docker buildx build --push --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --tag blakeblackshear/frigate-nginx:1.0.2 --file docker/Dockerfile.nginx .
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.1.0-amd64 --file docker/Dockerfile.ffmpeg.amd64 .
 
 amd64_frigate: version web
-	docker build --no-cache --tag frigate-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
-	docker build --no-cache --tag frigate --file docker/Dockerfile.amd64 .
+	docker build --tag frigate-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --build-arg WHEELS_VERSION=1.0.1 --file docker/Dockerfile.base .
+	docker build --tag frigate --file docker/Dockerfile.amd64 .
 
 amd64_all: amd64_wheels amd64_ffmpeg amd64_frigate
 
 amd64nvidia_wheels:
-	docker build --tag blakeblackshear/frigate-wheels:1.0.3-amd64nvidia --file docker/Dockerfile.wheels .
+	docker build --tag blakeblackshear/frigate-wheels:1.0.1-amd64nvidia --file docker/Dockerfile.wheels .
 
 amd64nvidia_ffmpeg:
-	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
 
 amd64nvidia_frigate: version web
-	docker build --no-cache --tag frigate-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
-	docker build --no-cache --tag frigate --file docker/Dockerfile.amd64nvidia .
+	docker build --tag frigate-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.1 --file docker/Dockerfile.base .
+	docker build --tag frigate --file docker/Dockerfile.amd64nvidia .
 
 amd64nvidia_all: amd64nvidia_wheels amd64nvidia_ffmpeg amd64nvidia_frigate
 
 aarch64_wheels:
-	docker build --tag blakeblackshear/frigate-wheels:1.0.3-aarch64 --file docker/Dockerfile.wheels .
+	docker build --tag blakeblackshear/frigate-wheels:1.0.1-aarch64 --file docker/Dockerfile.wheels .
 
 aarch64_ffmpeg:
-	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
 
 aarch64_frigate: version web
-	docker build --no-cache --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
-	docker build --no-cache --tag frigate --file docker/Dockerfile.aarch64 .
+	docker build --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.1 --file docker/Dockerfile.base .
+	docker build --tag frigate --file docker/Dockerfile.aarch64 .
 
 armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
 
 armv7_wheels:
-	docker build --tag blakeblackshear/frigate-wheels:1.0.3-armv7 --file docker/Dockerfile.wheels .
+	docker build --tag blakeblackshear/frigate-wheels:1.0.1-armv7 --file docker/Dockerfile.wheels .
 
 armv7_ffmpeg:
-	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-armv7 --file docker/Dockerfile.ffmpeg.armv7 .
+	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-armv7 --file docker/Dockerfile.ffmpeg.armv7 .
 
 armv7_frigate: version web
-	docker build --no-cache --tag frigate-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
-	docker build --no-cache --tag frigate --file docker/Dockerfile.armv7 .
+	docker build --tag frigate-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.1 --file docker/Dockerfile.base .
+	docker build --tag frigate --file docker/Dockerfile.armv7 .
 
 armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
 

docker-compose.yml

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

docker/Dockerfile.aarch64

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

docker/Dockerfile.amd64

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

docker/Dockerfile.amd64nvidia

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

docker/Dockerfile.armv7

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

docker/Dockerfile.base

@@ -1,10 +1,8 @@
 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
@@ -20,28 +18,30 @@ 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 libxml2 \
-    && apt-get -qq install --no-install-recommends -y python3-pip \
+    && 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 \
     && 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 python3-tflite-runtime python3-pycoral \
+    && 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)
 
 RUN pip3 install \
     peewee_migrate \
-    pydantic \
     zeroconf \
-    ws4py
+    voluptuous
 
-COPY --from=nginx /usr/local/nginx/ /usr/local/nginx/
+COPY nginx/nginx.conf /etc/nginx/nginx.conf
 
 # get model and labels
 COPY labelmap.txt /labelmap.txt
-RUN wget -q https://github.com/google-coral/test_data/raw/release-frogfish/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite -O /edgetpu_model.tflite
-RUN wget -q https://github.com/google-coral/test_data/raw/release-frogfish/ssdlite_mobiledet_coco_qat_postprocess.tflite -O /cpu_model.tflite
+RUN wget -q https://github.com/google-coral/test_data/raw/master/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite -O /edgetpu_model.tflite
+RUN wget -q https://github.com/google-coral/test_data/raw/master/ssdlite_mobiledet_coco_qat_postprocess.tflite -O /cpu_model.tflite
 
 WORKDIR /opt/frigate/
 ADD frigate frigate/
@@ -49,7 +49,10 @@ ADD migrations migrations/
 
 COPY --from=web /opt/frigate/build web/
 
-COPY docker/rootfs/ /
+COPY run.sh /run.sh
+RUN chmod +x /run.sh
 
 EXPOSE 5000
 EXPOSE 1935
+
+CMD ["/run.sh"]

docker/Dockerfile.dev

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

docker/Dockerfile.ffmpeg.aarch64

@@ -15,33 +15,33 @@ RUN     apt-get -yqq update && \
 
 FROM base as build
 
-ENV     FFMPEG_VERSION=4.3.2 \
-        AOM_VERSION=v1.0.0 \
-        FDKAAC_VERSION=0.1.5 \
-        FREETYPE_VERSION=2.5.5 \
-        FRIBIDI_VERSION=0.19.7 \
-        KVAZAAR_VERSION=1.2.0 \
-        LAME_VERSION=3.100 \
-        LIBPTHREAD_STUBS_VERSION=0.4 \
-        LIBVIDSTAB_VERSION=1.1.0 \
-        LIBXCB_VERSION=1.13.1 \
-        XCBPROTO_VERSION=1.13 \
-        OGG_VERSION=1.3.2 \
-        OPENCOREAMR_VERSION=0.1.5 \
-        OPUS_VERSION=1.2 \
-        OPENJPEG_VERSION=2.1.2 \
-        THEORA_VERSION=1.1.1 \
-        VORBIS_VERSION=1.3.5 \
-        VPX_VERSION=1.8.0 \
-        WEBP_VERSION=1.0.2 \
-        X264_VERSION=20170226-2245-stable \
-        X265_VERSION=3.1.1 \
-        XAU_VERSION=1.0.9 \
-        XORG_MACROS_VERSION=1.19.2 \
-        XPROTO_VERSION=7.0.31 \
-        XVID_VERSION=1.3.4 \
-        LIBZMQ_VERSION=4.3.2 \
-        SRC=/usr/local
+ENV         FFMPEG_VERSION=4.3.1 \
+            AOM_VERSION=v1.0.0 \
+            FDKAAC_VERSION=0.1.5 \
+            FREETYPE_VERSION=2.5.5 \
+            FRIBIDI_VERSION=0.19.7 \
+            KVAZAAR_VERSION=1.2.0 \
+            LAME_VERSION=3.100 \
+            LIBPTHREAD_STUBS_VERSION=0.4 \
+            LIBVIDSTAB_VERSION=1.1.0 \
+            LIBXCB_VERSION=1.13.1 \
+            XCBPROTO_VERSION=1.13 \
+            OGG_VERSION=1.3.2 \
+            OPENCOREAMR_VERSION=0.1.5 \
+            OPUS_VERSION=1.2 \
+            OPENJPEG_VERSION=2.1.2 \
+            THEORA_VERSION=1.1.1 \
+            VORBIS_VERSION=1.3.5 \
+            VPX_VERSION=1.8.0 \
+            WEBP_VERSION=1.0.2 \
+            X264_VERSION=20170226-2245-stable \
+            X265_VERSION=3.1.1 \
+            XAU_VERSION=1.0.9 \
+            XORG_MACROS_VERSION=1.19.2 \
+            XPROTO_VERSION=7.0.31 \
+            XVID_VERSION=1.3.4 \
+            LIBZMQ_VERSION=4.3.2 \
+            SRC=/usr/local
 
 ARG         FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.tar.gz"
 ARG         FRIBIDI_SHA256SUM="3fc96fa9473bd31dcb5500bdf1aa78b337ba13eb8c301e7c28923fea982453a8 0.19.7.tar.gz"
@@ -61,27 +61,27 @@ ARG         PREFIX=/opt/ffmpeg
 ARG         LD_LIBRARY_PATH="/opt/ffmpeg/lib:/opt/ffmpeg/lib64:/usr/lib64:/usr/lib:/lib64:/lib"
 
 
-RUN     buildDeps="autoconf \
-        automake \
-        cmake \
-        curl \
-        bzip2 \
-        libexpat1-dev \
-        g++ \
-        gcc \
-        git \
-        gperf \
-        libtool \
-        make \
-        nasm \
-        perl \
-        pkg-config \
-        python \
-        libssl-dev \
-        yasm \
-        linux-headers-raspi2 \
-        libomxil-bellagio-dev \
-        zlib1g-dev" && \
+RUN      buildDeps="autoconf \
+                    automake \
+                    cmake \
+                    curl \
+                    bzip2 \
+                    libexpat1-dev \
+                    g++ \
+                    gcc \
+                    git \
+                    gperf \
+                    libtool \
+                    make \
+                    nasm \
+                    perl \
+                    pkg-config \
+                    python \
+                    libssl-dev \
+                    yasm \
+                    linux-headers-raspi2 \
+                    libomxil-bellagio-dev \
+                    zlib1g-dev" && \
         apt-get -yqq update && \
         apt-get install -yq --no-install-recommends ${buildDeps}
 ## opencore-amr https://sourceforge.net/projects/opencore-amr/
@@ -459,7 +459,7 @@ RUN \
         cp -r ${PREFIX}/include/libav* ${PREFIX}/include/libpostproc ${PREFIX}/include/libsw* /usr/local/include && \
         mkdir -p /usr/local/lib/pkgconfig && \
         for pc in ${PREFIX}/lib/pkgconfig/libav*.pc ${PREFIX}/lib/pkgconfig/libpostproc.pc ${PREFIX}/lib/pkgconfig/libsw*.pc; do \
-        sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
+          sed "s:${PREFIX}:/usr/local:g" <"$pc" >/usr/local/lib/pkgconfig/"${pc##*/}"; \
         done
 
 FROM        base AS release

docker/Dockerfile.ffmpeg.amd64

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

docker/Dockerfile.ffmpeg.amd64nvidia

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

docker/Dockerfile.ffmpeg.armv7

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

docker/Dockerfile.nginx

@@ -1,52 +0,0 @@
-FROM ubuntu:20.04 AS base
-
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get -yqq update && \
-    apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 && \
-    apt-get autoremove -y && \
-    apt-get clean -y
-
-FROM base as build
-
-ARG NGINX_VERSION=1.18.0
-ARG VOD_MODULE_VERSION=1.28
-ARG SECURE_TOKEN_MODULE_VERSION=1.4
-ARG RTMP_MODULE_VERSION=1.2.1
-
-RUN cp /etc/apt/sources.list /etc/apt/sources.list~ \
-    && sed -Ei 's/^# deb-src /deb-src /' /etc/apt/sources.list \
-    && apt-get update
-
-RUN apt-get -yqq build-dep nginx
-
-RUN apt-get -yqq install --no-install-recommends curl \
-    && mkdir /tmp/nginx \
-    && curl -sL https://nginx.org/download/nginx-${NGINX_VERSION}.tar.gz | tar -C /tmp/nginx -zx --strip-components=1 \
-    && mkdir /tmp/nginx-vod-module \
-    && curl -sL https://github.com/kaltura/nginx-vod-module/archive/refs/tags/${VOD_MODULE_VERSION}.tar.gz | tar -C /tmp/nginx-vod-module -zx --strip-components=1 \
-    # Patch MAX_CLIPS to allow more clips to be added than the default 128
-    && sed -i 's/MAX_CLIPS (128)/MAX_CLIPS (1080)/g' /tmp/nginx-vod-module/vod/media_set.h \
-    && mkdir /tmp/nginx-secure-token-module \
-    && curl -sL https://github.com/kaltura/nginx-secure-token-module/archive/refs/tags/${SECURE_TOKEN_MODULE_VERSION}.tar.gz | tar -C /tmp/nginx-secure-token-module -zx --strip-components=1 \
-    && mkdir /tmp/nginx-rtmp-module \
-    && curl -sL https://github.com/arut/nginx-rtmp-module/archive/refs/tags/v${RTMP_MODULE_VERSION}.tar.gz | tar -C /tmp/nginx-rtmp-module -zx --strip-components=1
-
-WORKDIR /tmp/nginx
-
-RUN ./configure --prefix=/usr/local/nginx \
-    --with-file-aio \
-    --with-http_sub_module \
-    --with-http_ssl_module \
-    --with-threads \
-    --add-module=../nginx-vod-module \
-    --add-module=../nginx-secure-token-module \
-    --add-module=../nginx-rtmp-module \
-    --with-cc-opt="-O3 -Wno-error=implicit-fallthrough"
-
-RUN make && make install
-RUN rm -rf /usr/local/nginx/html /usr/local/nginx/conf/*.default
-
-FROM base
-COPY --from=build /usr/local/nginx /usr/local/nginx
-ENTRYPOINT ["/usr/local/nginx/sbin/nginx"]
-CMD ["-g", "daemon off;"]

docker/Dockerfile.wheels

@@ -24,7 +24,8 @@ RUN pip3 install scikit-build
 
 RUN pip3 wheel --wheel-dir=/wheels \
     opencv-python-headless \
-    numpy \
+    # pinning due to issue in 1.19.5 https://github.com/numpy/numpy/issues/18131
+    numpy==1.19.4 \
     imutils \
     scipy \
     psutil \

docker/rootfs/etc/services.d/nginx/finish

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

docker/rootfs/etc/services.d/nginx/run

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

docs/.gitignore

@@ -1,20 +0,0 @@
-# Dependencies
-/node_modules
-
-# Production
-/build
-
-# Generated files
-.docusaurus
-.cache-loader
-
-# Misc
-.DS_Store
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
-
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*

docs/README.md

@@ -1,5 +0,0 @@
-# Website
-
-This website is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator.
-
-For installation and contributing instructions, please follow the [Contributing Docs](https://blakeblackshear.github.io/frigate/contributing).

docs/babel.config.js

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

docs/cameras.md

@@ -0,0 +1,42 @@
+# Camera Specific Configuration
+Frigate should work with most RTSP cameras and h264 feeds such as Dahua.
+
+## RTMP Cameras
+The input parameters need to be adjusted for RTMP cameras
+```yaml
+ffmpeg:
+  input_args:
+    - -avoid_negative_ts
+    - make_zero
+    - -fflags
+    - nobuffer
+    - -flags
+    - low_delay
+    - -strict
+    - experimental
+    - -fflags
+    - +genpts+discardcorrupt
+    - -use_wallclock_as_timestamps
+    - '1'
+```
+
+## Blue Iris RTSP Cameras
+You will need to remove `nobuffer` flag for Blue Iris RTSP cameras
+```yaml
+ffmpeg:
+  input_args:
+    - -avoid_negative_ts
+    - make_zero
+    - -flags
+    - low_delay
+    - -strict
+    - experimental
+    - -fflags
+    - +genpts+discardcorrupt
+    - -rtsp_transport
+    - tcp
+    - -stimeout
+    - "5000000"
+    - -use_wallclock_as_timestamps
+    - "1"
+```

docs/docs/configuration/advanced.md

@@ -1,126 +0,0 @@
----
-id: advanced
-title: Advanced
-sidebar_label: Advanced
----
-
-## Advanced configuration
-
-### `motion`
-
-Global motion detection config. These may also be defined at the camera level.
-
-```yaml
-motion:
-  # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
-  # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
-  # The value should be between 1 and 255.
-  threshold: 25
-  # Optional: Minimum size in pixels in the resized motion image that counts as motion (default: ~0.17% of the motion frame area)
-  # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will make motion detection more sensitive to smaller
-  # moving objects.
-  contour_area: 100
-  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
-  # Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
-  # Too low and a fast moving person wont be detected as motion.
-  delta_alpha: 0.2
-  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
-  # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
-  # Low values will cause things like moving shadows to be detected as motion for longer.
-  # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
-  frame_alpha: 0.2
-  # Optional: Height of the resized motion frame  (default: 1/6th of the original frame height, but no less than 180)
-  # This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense of higher CPU usage.
-  # Lower values result in less CPU, but small changes may not register as motion.
-  frame_height: 180
-```
-
-### `detect`
-
-Global object detection settings. These may also be defined at the camera level.
-
-```yaml
-detect:
-  # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
-  max_disappeared: 25
-```
-
-### `logger`
-
-Change the default log level for troubleshooting purposes.
-
-```yaml
-logger:
-  # Optional: default log level (default: shown below)
-  default: info
-  # Optional: module by module log level configuration
-  logs:
-    frigate.mqtt: error
-```
-
-Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
-
-Examples of available modules are:
-
-- `frigate.app`
-- `frigate.mqtt`
-- `frigate.edgetpu`
-- `frigate.zeroconf`
-- `detector.<detector_name>`
-- `watchdog.<camera_name>`
-- `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
-
-### `environment_vars`
-
-This section can be used to set environment variables for those unable to modify the environment of the container (ie. within Hass.io)
-
-```yaml
-environment_vars:
-  EXAMPLE_VAR: value
-```
-
-### `database`
-
-Event and recording information is managed in a sqlite database at `/media/frigate/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
-
-If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
-
-This may need to be in a custom location if network storage is used for the media folder.
-
-```yaml
-database:
-  path: /media/frigate/frigate.db
-```
-
-### `detectors`
-
-```yaml
-detectors:
-  # Required: name of the detector
-  coral:
-    # Required: type of the detector
-    # Valid values are 'edgetpu' (requires device property below) and 'cpu'.
-    type: edgetpu
-    # Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
-    device: usb
-    # Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
-    # This value is only used for CPU types
-    num_threads: 3
-```
-
-### `model`
-
-If using a custom model, the width and height will need to be specified.
-
-The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
-
-```yaml
-model:
-  # Required: height of the trained model
-  height: 320
-  # Required: width of the trained model
-  width: 320
-  # Optional: labelmap overrides
-  labelmap:
-    7: car
-```

docs/docs/configuration/cameras.md

@@ -1,557 +0,0 @@
----
-id: cameras
-title: Cameras
----
-
-## Setting Up Camera Inputs
-
-Up to 4 inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
-
-Each role can only be assigned to one input per camera. The options for roles are as follows:
-
-| Role     | Description                                                                           |
-| -------- | ------------------------------------------------------------------------------------- |
-| `detect` | Main feed for object detection                                                        |
-| `record` | Saves segments of the video feed based on configuration settings. [docs](#recordings) |
-| `rtmp`   | Broadcast as an RTMP feed for other services to consume. [docs](#rtmp-streams)        |
-
-### Example
-
-```yaml
-mqtt:
-  host: mqtt.server.com
-cameras:
-  back:
-    ffmpeg:
-      inputs:
-        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          roles:
-            - detect
-            - rtmp
-        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/live
-          roles:
-            - record
-    detect:
-      width: 1280
-      height: 720
-      fps: 5
-```
-
-`width`, `height`, and `fps` are only used for the `detect` role. Other streams are passed through, so there is no need to specify the resolution.
-
-## Masks & Zones
-
-### Masks
-
-Masks are used to ignore initial detection in areas of your camera's field of view.
-
-There are two types of masks available:
-
-- **Motion masks**: Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the video feed with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. Over masking will make it more difficult for objects to be tracked. To see this effect, create a mask, and then watch the video feed with `Motion Boxes` enabled again.
-- **Object filter masks**: Object filter masks are used to filter out false positives for a given object type. These should be used to filter any areas where it is not possible for an object of that type to be. The bottom center of the detected object's bounding box is evaluated against the mask. If it is in a masked area, it is assumed to be a false positive. For example, you may want to mask out rooftops, walls, the sky, treetops for people. For cars, masking locations other than the street or your driveway will tell frigate that anything in your yard is a false positive.
-
-To create a poly mask:
-
-1. Visit the [web UI](/usage/web)
-1. Click the camera you wish to create a mask for
-1. Click "Mask & Zone creator"
-1. Click "Add" on the type of mask or zone you would like to create
-1. Click on the camera's latest image to create a masked area. The yaml representation will be updated in real-time
-1. When you've finished creating your mask, click "Copy" and paste the contents into your `config.yaml` file and restart Frigate
-
-Example of a finished row corresponding to the below example image:
-
-```yaml
-motion:
-  mask: "0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"
-```
-
-![poly](/img/example-mask-poly.png)
-
-```yaml
-# Optional: camera level motion config
-motion:
-  # Optional: motion mask
-  # NOTE: see docs for more detailed info on creating masks
-  mask: 0,900,1080,900,1080,1920,0,1920
-```
-
-### Zones
-
-Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
-
-During testing, `draw_zones` should be set in the config to draw the zone on the frames so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
-
-To create a zone, follow the same steps above for a "Motion mask", but use the section of the web UI for creating a zone instead.
-
-```yaml
-# Optional: zones for this camera
-zones:
-  # Required: name of the zone
-  # NOTE: This must be different than any camera names, but can match with another zone on another
-  #       camera.
-  front_steps:
-    # Required: List of x,y coordinates to define the polygon of the zone.
-    # NOTE: Coordinates can be generated at https://www.image-map.net/
-    coordinates: 545,1077,747,939,788,805
-    # Optional: List of objects that can trigger this zone (default: all tracked objects)
-    objects:
-      - person
-    # Optional: Zone level object filters.
-    # NOTE: The global and camera filters are applied upstream.
-    filters:
-      person:
-        min_area: 5000
-        max_area: 100000
-        threshold: 0.7
-```
-
-## Objects
-
-For a list of available objects, see the [objects documentation](./objects.mdx).
-
-```yaml
-# Optional: Camera level object filters config.
-objects:
-  track:
-    - person
-    - car
-  # Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
-  # Checks based on the bottom center of the bounding box of the object.
-  # NOTE: This mask is COMBINED with the object type specific mask below
-  mask: 0,0,1000,0,1000,200,0,200
-  filters:
-    person:
-      min_area: 5000
-      max_area: 100000
-      min_score: 0.5
-      threshold: 0.7
-      # Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
-      # Checks based on the bottom center of the bounding box of the object
-      mask: 0,0,1000,0,1000,200,0,200
-```
-
-## Recordings
-
-24/7 recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding and are available in Home Assistant's media browser. Each camera supports a configurable retention policy in the config.
-
-Exported clips are also created off of these recordings. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
-
-These recordings will not be playable in the web UI or in Home Assistant's media browser unless your camera sends video as h264.
-
-:::caution
-Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
-:::
-
-```yaml
-record:
-  # Optional: Enable recording (default: shown below)
-  enabled: False
-  # Optional: Number of days to retain (default: shown below)
-  retain_days: 0
-  # Optional: Event recording settings
-  events:
-    # Optional: Enable event recording retention settings (default: shown below)
-    enabled: False
-    # Optional: Maximum length of time to retain video during long events. (default: shown below)
-    # NOTE: If an object is being tracked for longer than this amount of time, the cache
-    #       will begin to expire and the resulting clip will be the last x seconds of the event unless retain_days under record is > 0.
-    max_seconds: 300
-    # Optional: Number of seconds before the event to include in the event (default: shown below)
-    pre_capture: 5
-    # Optional: Number of seconds after the event to include in the event (default: shown below)
-    post_capture: 5
-    # Optional: Objects to save event for. (default: all tracked objects)
-    objects:
-      - person
-    # Optional: Restrict event to objects that entered any of the listed zones (default: no required zones)
-    required_zones: []
-    # Optional: Retention settings for event
-    retain:
-      # Required: Default retention days (default: shown below)
-      default: 10
-      # Optional: Per object retention days
-      objects:
-        person: 15
-```
-
-## Snapshots
-
-Frigate can save a snapshot image to `/media/frigate/clips` for each event named as `<camera>-<id>.jpg`.
-
-```yaml
-# Optional: Configuration for the jpg snapshots written to the clips directory for each event
-snapshots:
-  # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
-  # This value can be set via MQTT and will be updated in startup based on retained value
-  enabled: False
-  # Optional: Enable writing a clean copy png snapshot to /media/frigate/clips (default: shown below)
-  # Only works if snapshots are enabled. This image is intended to be used for training purposes.
-  clean_copy: True
-  # Optional: print a timestamp on the snapshots (default: shown below)
-  timestamp: False
-  # Optional: draw bounding box on the snapshots (default: shown below)
-  bounding_box: False
-  # Optional: crop the snapshot (default: shown below)
-  crop: False
-  # Optional: height to resize the snapshot to (default: original size)
-  height: 175
-  # Optional: jpeg encode quality (default: shown below)
-  quality: 70
-  # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
-  required_zones: []
-  # Optional: Camera override for retention settings (default: global values)
-  retain:
-    # Required: Default retention days (default: shown below)
-    default: 10
-    # Optional: Per object retention days
-    objects:
-      person: 15
-```
-
-## RTMP streams
-
-Frigate can re-stream your video feed as a RTMP feed for other applications such as Home Assistant to utilize it at `rtmp://<frigate_host>/live/<camera_name>`. Port 1935 must be open. This allows you to use a video feed for detection in frigate and Home Assistant live view at the same time without having to make two separate connections to the camera. The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
-
-Some video feeds are not compatible with RTMP. If you are experiencing issues, check to make sure your camera feed is h264 with AAC audio. If your camera doesn't support a compatible format for RTMP, you can use the ffmpeg args to re-encode it on the fly at the expense of increased CPU utilization.
-
-## Timestamp style configuration
-
-For the debug view and snapshots it is possible to embed a timestamp in the feed. In some instances the default position obstructs important space, visibility or contrast is too low because of color or the datetime format does not match ones desire.
-
-```yaml
-# Optional: in-feed timestamp style configuration
-timestamp_style:
-  # Optional: Position of the timestamp (default: shown below)
-  #           "tl" (top left), "tr" (top right), "bl" (bottom left), "br" (bottom right)
-  position: "tl"
-  # Optional: Format specifier conform to the Python package "datetime" (default: shown below)
-  #           Additional Examples:
-  #             german: "%d.%m.%Y %H:%M:%S"
-  format: "%m/%d/%Y %H:%M:%S"
-  # Optional: Color of font
-  color:
-    # All Required when color is specified (default: shown below)
-    red: 255
-    green: 255
-    blue: 255
-  # Optional: Scale factor for font (default: shown below)
-  scale: 1.0
-  # Optional: Line thickness of font (default: shown below)
-  thickness: 2
-  # Optional: Effect of lettering (default: shown below)
-  #           None (No effect),
-  #           "solid" (solid background in inverse color of font)
-  #           "shadow" (shadow for font)
-  effect: None
-```
-
-## Full example
-
-The following is a full example of all of the options together for a camera configuration
-
-```yaml
-cameras:
-  # Required: name of the camera
-  back:
-    # Required: ffmpeg settings for the camera
-    ffmpeg:
-      # Required: A list of input streams for the camera. See documentation for more information.
-      inputs:
-        # Required: the path to the stream
-        # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}
-        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          # Required: list of roles for this stream. valid values are: detect,record,rtmp
-          # NOTICE: In addition to assigning the record, and rtmp roles,
-          # they must also be enabled in the camera config.
-          roles:
-            - detect
-            - rtmp
-          # Optional: stream specific global args (default: inherit)
-          global_args:
-          # Optional: stream specific hwaccel args (default: inherit)
-          hwaccel_args:
-          # Optional: stream specific input args (default: inherit)
-          input_args:
-      # Optional: camera specific global args (default: inherit)
-      global_args:
-      # Optional: camera specific hwaccel args (default: inherit)
-      hwaccel_args:
-      # Optional: camera specific input args (default: inherit)
-      input_args:
-      # Optional: camera specific output args (default: inherit)
-      output_args:
-
-    # Required: Camera level detect settings
-    detect:
-      # Optional: width of the frame for the input with the detect role (default: shown below)
-      width: 1280
-      # Optional: height of the frame for the input with the detect role (default: shown below)
-      height: 720
-      # Optional: desired fps for your camera for the input with the detect role (default: shown below)
-      # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
-      fps: 5
-      # Optional: enables detection for the camera (default: True)
-      # This value can be set via MQTT and will be updated in startup based on retained value
-      enabled: True
-      # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
-      max_disappeared: 25
-
-    # Optional: camera level motion config
-    motion:
-      # Optional: motion mask
-      # NOTE: see docs for more detailed info on creating masks
-      mask: 0,900,1080,900,1080,1920,0,1920
-
-    # Optional: timeout for highest scoring image before allowing it
-    # to be replaced by a newer image. (default: shown below)
-    best_image_timeout: 60
-
-    # Optional: zones for this camera
-    zones:
-      # Required: name of the zone
-      # NOTE: This must be different than any camera names, but can match with another zone on another
-      #       camera.
-      front_steps:
-        # Required: List of x,y coordinates to define the polygon of the zone.
-        # NOTE: Coordinates can be generated at https://www.image-map.net/
-        coordinates: 545,1077,747,939,788,805
-        # Optional: List of objects that can trigger this zone (default: all tracked objects)
-        objects:
-          - person
-        # Optional: Zone level object filters.
-        # NOTE: The global and camera filters are applied upstream.
-        filters:
-          person:
-            min_area: 5000
-            max_area: 100000
-            threshold: 0.7
-
-    # Optional: 24/7 recording configuration
-    record:
-      # Optional: Enable recording (default: global setting)
-      enabled: False
-      # Optional: Number of days to retain (default: global setting)
-      retain_days: 30
-      # Optional: Event recording settings
-      events:
-        # Required: enables event recordings for the camera (default: shown below)
-        # This value can be set via MQTT and will be updated in startup based on retained value
-        enabled: False
-        # Optional: Number of seconds before the event to include (default: shown below)
-        pre_capture: 5
-        # Optional: Number of seconds after the event to include (default: shown below)
-        post_capture: 5
-        # Optional: Objects to save events for. (default: all tracked objects)
-        objects:
-          - person
-        # Optional: Restrict events to objects that entered any of the listed zones (default: no required zones)
-        required_zones: []
-        # Optional: Camera override for retention settings (default: global values)
-        retain:
-          # Required: Default retention days (default: shown below)
-          default: 10
-          # Optional: Per object retention days
-          objects:
-            person: 15
-
-    # Optional: RTMP re-stream configuration
-    rtmp:
-      # Required: Enable the RTMP stream (default: True)
-      enabled: True
-
-    # Optional: Live stream configuration for WebUI
-    live:
-      # Optional: Set the height of the live stream. (default: 720)
-      # This must be less than or equal to the height of the detect stream. Lower resolutions
-      # reduce bandwidth required for viewing the live stream. Width is computed to match known aspect ratio.
-      height: 720
-      # Optional: Set the encode quality of the live stream (default: shown below)
-      # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
-      quality: 8
-
-    # Optional: Configuration for the jpg snapshots written to the clips directory for each event
-    snapshots:
-      # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
-      # This value can be set via MQTT and will be updated in startup based on retained value
-      enabled: False
-      # Optional: print a timestamp on the snapshots (default: shown below)
-      timestamp: False
-      # Optional: draw bounding box on the snapshots (default: shown below)
-      bounding_box: False
-      # Optional: crop the snapshot (default: shown below)
-      crop: False
-      # Optional: height to resize the snapshot to (default: original size)
-      height: 175
-      # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
-      required_zones: []
-      # Optional: Camera override for retention settings (default: global values)
-      retain:
-        # Required: Default retention days (default: shown below)
-        default: 10
-        # Optional: Per object retention days
-        objects:
-          person: 15
-
-    # Optional: Configuration for the jpg snapshots published via MQTT
-    mqtt:
-      # Optional: Enable publishing snapshot via mqtt for camera (default: shown below)
-      # NOTE: Only applies to publishing image data to MQTT via 'frigate/<camera_name>/<object_name>/snapshot'.
-      # All other messages will still be published.
-      enabled: True
-      # Optional: print a timestamp on the snapshots (default: shown below)
-      timestamp: True
-      # Optional: draw bounding box on the snapshots (default: shown below)
-      bounding_box: True
-      # Optional: crop the snapshot (default: shown below)
-      crop: True
-      # Optional: height to resize the snapshot to (default: shown below)
-      height: 270
-      # Optional: jpeg encode quality (default: shown below)
-      quality: 70
-      # Optional: Restrict mqtt messages to objects that entered any of the listed zones (default: no required zones)
-      required_zones: []
-
-    # Optional: Camera level object filters config.
-    objects:
-      track:
-        - person
-        - car
-      # Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
-      # Checks based on the bottom center of the bounding box of the object.
-      # NOTE: This mask is COMBINED with the object type specific mask below
-      mask: 0,0,1000,0,1000,200,0,200
-      filters:
-        person:
-          min_area: 5000
-          max_area: 100000
-          min_score: 0.5
-          threshold: 0.7
-          # Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
-          # Checks based on the bottom center of the bounding box of the object
-          mask: 0,0,1000,0,1000,200,0,200
-
-    # Optional: In-feed timestamp style configuration
-    timestamp_style:
-    # Optional: Position of the timestamp (default: shown below)
-    #           "tl" (top left), "tr" (top right), "bl" (bottom left), "br" (bottom right)
-    position: "tl"
-    # Optional: Format specifier conform to the Python package "datetime" (default: shown below)
-    #           Additional Examples:
-    #             german: "%d.%m.%Y %H:%M:%S"
-    format: "%m/%d/%Y %H:%M:%S"
-    # Optional: Color of font
-    color:
-      # All Required when color is specified (default: shown below)
-      red: 255
-      green: 255
-      blue: 255
-    # Optional: Scale factor for font (default: shown below)
-    scale: 1.0
-    # Optional: Line thickness of font (default: shown below)
-    thickness: 2
-    # Optional: Effect of lettering (default: shown below)
-    #           None (No effect),
-    #           "solid" (solid background in inverse color of font)
-    #           "shadow" (shadow for font)
-    effect: None
-```
-
-## 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 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
-  rtmp: -c:v libx264 -an -f flv
-```
-
-### RTMP Cameras
-
-The input parameters need to be adjusted for RTMP cameras
-
-```yaml
-ffmpeg:
-  input_args:
-    - -avoid_negative_ts
-    - make_zero
-    - -fflags
-    - nobuffer
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -use_wallclock_as_timestamps
-    - "1"
-```
-
-### Reolink 410/520 (possibly others)
-
-Several users have reported success with the rtmp video from Reolink cameras.
-
-```yaml
-ffmpeg:
-  input_args:
-    - -avoid_negative_ts
-    - make_zero
-    - -fflags
-    - nobuffer
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -rw_timeout
-    - "5000000"
-    - -use_wallclock_as_timestamps
-    - "1"
-```
-
-### Blue Iris RTSP Cameras
-
-You will need to remove `nobuffer` flag for Blue Iris RTSP cameras
-
-```yaml
-ffmpeg:
-  input_args:
-    - -avoid_negative_ts
-    - make_zero
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -rtsp_transport
-    - tcp
-    - -stimeout
-    - "5000000"
-    - -use_wallclock_as_timestamps
-    - "1"
-```

docs/docs/configuration/detectors.md

@@ -1,74 +0,0 @@
----
-id: detectors
-title: Detectors
----
-
-The default config will look for a USB Coral device. If you do not have a Coral, you will need to configure a CPU detector. If you have PCI or multiple Coral devices, you need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
-
-Frigate supports `edgetpu` and `cpu` as detector types. The device value should be specified according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api).
-
-**Note**: There is no support for Nvidia GPUs to perform object detection with tensorflow. It can be used for ffmpeg decoding, but not object detection.
-
-Single USB Coral:
-
-```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
-```
-
-Multiple USB Corals:
-
-```yaml
-detectors:
-  coral1:
-    type: edgetpu
-    device: usb:0
-  coral2:
-    type: edgetpu
-    device: usb:1
-```
-
-Native Coral (Dev Board):
-
-```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: ''
-```
-
-Multiple PCIE/M.2 Corals:
-
-```yaml
-detectors:
-  coral1:
-    type: edgetpu
-    device: pci:0
-  coral2:
-    type: edgetpu
-    device: pci:1
-```
-
-Mixing Corals:
-
-```yaml
-detectors:
-  coral_usb:
-    type: edgetpu
-    device: usb
-  coral_pci:
-    type: edgetpu
-    device: pci
-```
-
-CPU Detectors (not recommended):
-
-```yaml
-detectors:
-  cpu1:
-    type: cpu
-  cpu2:
-    type: cpu
-```

docs/docs/configuration/false_positives.md

@@ -1,19 +0,0 @@
----
-id: false_positives
-title: Reducing false positives
----
-
-Tune your object filters to adjust false positives: `min_area`, `max_area`, `min_score`, `threshold`.
-
-For object filters in your configuration, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:
-
-| Frame | Current Score | Score History                     | Computed Score | Detected Object |
-| ----- | ------------- | --------------------------------- | -------------- | --------------- |
-| 1     | 0.7           | 0.0, 0, 0.7                       | 0.0            | No              |
-| 2     | 0.55          | 0.0, 0.7, 0.0                     | 0.0            | No              |
-| 3     | 0.85          | 0.7, 0.0, 0.85                    | 0.7            | No              |
-| 4     | 0.90          | 0.7, 0.85, 0.95, 0.90             | 0.875          | Yes             |
-| 5     | 0.88          | 0.7, 0.85, 0.95, 0.90, 0.88       | 0.88           | Yes             |
-| 6     | 0.95          | 0.7, 0.85, 0.95, 0.90, 0.88, 0.95 | 0.89           | Yes             |
-
-In frame 2, the score is below the `min_score` value, so frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.

docs/docs/configuration/index.md

@@ -1,235 +0,0 @@
----
-id: index
-title: Configuration
----
-
-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:
-
-```yaml
-mqtt:
-  host: mqtt.server.com
-cameras:
-  back:
-    ffmpeg:
-      inputs:
-        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          roles:
-            - detect
-            - rtmp
-    detect:
-      width: 1280
-      height: 720
-      fps: 5
-```
-
-## Required
-
-## `mqtt`
-
-```yaml
-mqtt:
-  # Required: host name
-  host: mqtt.server.com
-  # Optional: port (default: shown below)
-  port: 1883
-  # Optional: topic prefix (default: shown below)
-  # WARNING: must be unique if you are running multiple instances
-  topic_prefix: frigate
-  # Optional: client id (default: shown below)
-  # WARNING: must be unique if you are running multiple instances
-  client_id: frigate
-  # Optional: user
-  user: mqtt_user
-  # Optional: password
-  # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}.
-  #       eg. password: '{FRIGATE_MQTT_PASSWORD}'
-  password: password
-  # Optional: tls_ca_certs for enabling TLS using self-signed certs (default: None)
-  tls_ca_certs: /path/to/ca.crt
-  # Optional: tls_client_cert and tls_client key in order to use self-signed client
-  # certificates (default: None)
-  # NOTE: certificate must not be password-protected
-  #       do not set user and password when using a client certificate
-  tls_client_cert: /path/to/client.crt
-  tls_client_key: /path/to/client.key
-  # Optional: tls_insecure (true/false) for enabling TLS verification of
-  # the server hostname in the server certificate (default: None)
-  tls_insecure: false
-  # Optional: interval in seconds for publishing stats (default: shown below)
-  stats_interval: 60
-```
-
-## `cameras`
-
-Each of your cameras must be configured. The following is the minimum required to register a camera in Frigate. Check the [camera configuration page](cameras.md) for a complete list of options.
-
-```yaml
-cameras:
-  # Name of your camera
-  front_door:
-    ffmpeg:
-      inputs:
-        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          roles:
-            - detect
-            - rtmp
-    detect:
-      width: 1280
-      height: 720
-      fps: 5
-```
-
-## Optional
-
-### `database`
-
-```yaml
-database:
-  # The path to store the SQLite DB (default: shown below)
-  path: /media/frigate/frigate.db
-```
-
-### `model`
-
-```yaml
-# Optional: model modifications
-model:
-  # Required: Object detection model input width (default: shown below)
-  width: 320
-  # Required: Object detection model input height (default: shown below)
-  height: 320
-  # Optional: Label name modifications
-  labelmap:
-    2: vehicle # previously "car"
-```
-
-### `detectors`
-
-Check the [detectors configuration page](detectors.md) for a complete list of options.
-
-### `logger`
-
-```yaml
-# Optional: logger verbosity settings
-logger:
-  # Optional: Default log verbosity (default: shown below)
-  default: info
-  # Optional: Component specific logger overrides
-  logs:
-    frigate.event: debug
-```
-
-### `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.
-
-Exported clips are also created off of these recordings. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
-
-These recordings will not be playable in the web UI or in Home Assistant's media browser unless your camera sends video as h264.
-
-:::caution
-Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
-:::
-
-```yaml
-record:
-  # Optional: Enable recording (default: shown below)
-  enabled: False
-  # Optional: Number of days to retain (default: shown below)
-  retain_days: 0
-  # Optional: Event recording settings
-  events:
-    # Optional: Enable event recording retention settings (default: shown below)
-    enabled: False
-    # Optional: Maximum length of time to retain video during long events. (default: shown below)
-    # NOTE: If an object is being tracked for longer than this amount of time, the cache
-    #       will begin to expire and the resulting clip will be the last x seconds of the event unless retain_days under record is > 0.
-    max_seconds: 300
-    # Optional: Number of seconds before the event to include (default: shown below)
-    pre_capture: 5
-    # Optional: Number of seconds after the event to include (default: shown below)
-    post_capture: 5
-    # Optional: Objects to save recordings for. (default: all tracked objects)
-    objects:
-      - person
-    # Optional: Restrict recordings to objects that entered any of the listed zones (default: no required zones)
-    required_zones: []
-    # Optional: Retention settings for events
-    retain:
-      # Required: Default retention days (default: shown below)
-      default: 10
-      # Optional: Per object retention days
-      objects:
-        person: 15
-```
-
-### `ffmpeg`
-
-Can be overridden at the camera level.
-
-```yaml
-ffmpeg:
-  # Optional: global ffmpeg args (default: shown below)
-  global_args: -hide_banner -loglevel warning
-  # Optional: global hwaccel args (default: shown below)
-  # NOTE: See hardware acceleration docs for your specific device
-  hwaccel_args: []
-  # Optional: global input args (default: shown below)
-  input_args: -avoid_negative_ts make_zero -fflags +genpts+discardcorrupt -rtsp_transport tcp -stimeout 5000000 -use_wallclock_as_timestamps 1
-  # Optional: global output args
-  output_args:
-    # Optional: output args for detect streams (default: shown below)
-    detect: -f rawvideo -pix_fmt yuv420p
-    # Optional: output args for record streams (default: shown below)
-    record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
-    # Optional: output args for rtmp streams (default: shown below)
-    rtmp: -c copy -f flv
-```
-
-### `objects`
-
-Can be overridden at the camera level. For a list of available objects, see the [objects documentation](./objects.mdx).
-
-```yaml
-objects:
-  # Optional: list of objects to track from labelmap.txt (default: shown below)
-  track:
-    - person
-  # Optional: filters to reduce false positives for specific object types
-  filters:
-    person:
-      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
-      min_area: 5000
-      # Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
-      max_area: 100000
-      # Optional: minimum score for the object to initiate tracking (default: shown below)
-      min_score: 0.5
-      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
-      threshold: 0.7
-```
-
-### `birdseye`
-
-A dynamic combined camera view of all tracked cameras. This is optimized for minimal bandwidth and server resource utilization. Encoding is only performed when actively viewing the video feed, and only active (defined by the mode) cameras are included in the view.
-
-```yaml
-birdseye:
-  # Optional: Enable birdseye view (default: shown below)
-  enabled: True
-  # Optional: Width of the output resolution (default: shown below)
-  width: 1280
-  # Optional: Height of the output resolution (default: shown below)
-  height: 720
-  # Optional: Encoding quality of the mpeg1 feed (default: shown below)
-  # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
-  quality: 8
-  # Optional: Mode of the view. Available options are: objects, motion, and continuous
-  #   objects - cameras are included if they have had a tracked object within the last 30 seconds
-  #   motion - cameras are included if motion was detected in the last 30 seconds
-  #   continuous - all cameras are included always
-  mode: objects
-```

docs/docs/configuration/objects.mdx

@@ -1,25 +0,0 @@
----
-id: objects
-title: Default available objects
-sidebar_label: Available objects
----
-
-import labels from "../../../labelmap.txt";
-
-By default, Frigate includes the following object models from the Google Coral test data.
-
-<ul>
-  {labels.split("\n").map((label) => (
-    <li>{label.replace(/^\d+\s+/, "")}</li>
-  ))}
-</ul>
-
-## Custom Models
-
-Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
-
-- CPU Model: `/cpu_model.tflite`
-- EdgeTPU Model: `/edgetpu_model.tflite`
-- Labels: `/labelmap.txt`
-
-You also need to update the model width/height in the config if they differ from the defaults.

docs/docs/configuration/optimizing.md

@@ -1,72 +0,0 @@
----
-id: optimizing
-title: Optimizing performance
----
-
-- **Google Coral**: It is strongly recommended to use a Google Coral, Frigate will no longer fall back to CPU in the event one is not found. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU. Frigate should work with any supported Coral device from https://coral.ai
-- **Resolution**: For the `detect` input, choose a camera resolution where the smallest object you want to detect barely fits inside a 300x300px square. The model used by Frigate is trained on 300x300px images, so you will get worse performance and no improvement in accuracy by using a larger resolution since Frigate resizes the area where it is looking for objects to 300x300 anyway.
-- **FPS**: 5 frames per second should be adequate. Higher frame rates will require more CPU usage without improving detections or accuracy. Reducing the frame rate on your camera will have the greatest improvement on system resources.
-- **Hardware Acceleration**: Make sure you configure the `hwaccel_args` for your hardware. They provide a significant reduction in CPU usage if they are available.
-- **Masks**: Masks can be used to ignore motion and reduce your idle CPU load. If you have areas with regular motion such as timestamps or trees blowing in the wind, frigate will constantly try to determine if that motion is from a person or other object you are tracking. Those detections not only increase your average CPU usage, but also clog the pipeline for detecting objects elsewhere. If you are experiencing high values for `detection_fps` when no objects of interest are in the cameras, you should use masks to tell frigate to ignore movement from trees, bushes, timestamps, or any part of the image where detections should not be wasted looking for objects.
-
-### FFmpeg Hardware Acceleration
-
-Frigate works on Raspberry Pi 3b/4 and x86 machines. It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible.
-
-Raspberry Pi 3/4 (32-bit OS)
-**NOTICE**: If you are using the addon, ensure you turn off `Protection mode` for hardware acceleration.
-
-```yaml
-ffmpeg:
-  hwaccel_args:
-    - -c:v
-    - h264_mmal
-```
-
-Raspberry Pi 3/4 (64-bit OS)
-**NOTICE**: If you are using the addon, ensure you turn off `Protection mode` for hardware acceleration.
-
-```yaml
-ffmpeg:
-  hwaccel_args:
-    - -c:v
-    - h264_v4l2m2m
-```
-
-Intel-based CPUs (<10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
-
-```yaml
-ffmpeg:
-  hwaccel_args:
-    - -hwaccel
-    - vaapi
-    - -hwaccel_device
-    - /dev/dri/renderD128
-    - -hwaccel_output_format
-    - yuv420p
-```
-
-Intel-based CPUs (>=10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
-
-```yaml
-ffmpeg:
-  hwaccel_args:
-    - -hwaccel
-    - qsv
-    - -qsv_device
-    - /dev/dri/renderD128
-```
-
-AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)
-**Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
-
-```yaml
-ffmpeg:
-  hwaccel_args:
-    - -hwaccel
-    - vaapi
-    - -hwaccel_device
-    - /dev/dri/renderD128
-```
-
-Nvidia GPU based decoding via NVDEC is supported, but requires special configuration. See the [nvidia NVDEC documentation](/configuration/nvdec) for more details.

docs/docs/contributing.md

@@ -1,184 +0,0 @@
----
-id: contributing
-title: Contributing
----
-
-## Getting the source
-
-### Core, Web, Docker, and Documentation
-
-This repository holds the main Frigate application and all of its dependencies.
-
-Fork [blakeblackshear/frigate](https://github.com/blakeblackshear/frigate.git) to your own GitHub profile, then clone the forked repo to your local machine.
-
-From here, follow the guides for:
-
-- [Core](#core)
-- [Web Interface](#web-interface)
-- [Documentation](#documentation)
-
-### Frigate Home Assistant Addon
-
-This repository holds the Home Assistant Addon, for use with Home Assistant OS and compatible installations. It is the piece that allows you to run Frigate from your Home Assistant Supervisor tab.
-
-Fork [blakeblackshear/frigate-hass-addons](https://github.com/blakeblackshear/frigate-hass-addons) to your own Github profile, then clone the forked repo to your local machine.
-
-### Frigate Home Assistant Integration
-
-This repository holds the custom integration that allows your Home Assistant installation to automatically create entities for your Frigate instance, whether you run that with the [addon](#frigate-home-assistant-addon) or in a separate Docker instance.
-
-Fork [blakeblackshear/frigate-hass-integration](https://github.com/blakeblackshear/frigate-hass-integration) to your own GitHub profile, then clone the forked repo to your local machine.
-
-## Core
-
-### Prerequisites
-
-- [Frigate source code](#frigate-core-web-and-docs)
-- GNU make
-- Docker
-- Extra Coral device (optional, but very helpful to simulate real world performance)
-
-### Setup
-
-#### 1. Build the docker container locally with the appropriate make command
-
-For x86 machines, use `make amd64_frigate`
-
-#### 2. Create a local config file for testing
-
-Place the file at `config/config.yml` in the root of the repo.
-
-Here is an example, but modify for your needs:
-
-```yaml
-mqtt:
-  host: mqtt
-
-cameras:
-  test:
-    ffmpeg:
-      inputs:
-        - path: /media/frigate/car-stopping.mp4
-          input_args: -re -stream_loop -1 -fflags +genpts
-          roles:
-            - detect
-            - rtmp
-    detect:
-      height: 1080
-      width: 1920
-      fps: 5
-```
-
-These input args tell ffmpeg to read the mp4 file in an infinite loop. You can use any valid ffmpeg input here.
-
-#### 3. Gather some mp4 files for testing
-
-Create and place these files in a `debug` folder in the root of the repo. This is also where recordings will be created if you enable them in your test config. Update your config from step 2 above to point at the right file. You can check the `docker-compose.yml` file in the repo to see how the volumes are mapped.
-
-#### 4. Open the repo with Visual Studio Code
-
-Upon opening, you should be prompted to open the project in a remote container. This will build a container on top of the base frigate container with all the development dependencies installed. This ensures everyone uses a consistent development environment without the need to install any dependencies on your host machine.
-
-#### 5. Run frigate from the command line
-
-VSCode will start the docker compose file for you and open a terminal window connected to `frigate-dev`.
-
-- Run `python3 -m frigate` to start the backend.
-- In a separate terminal window inside VS Code, change into the `web` directory and run `npm install && npm start` to start the frontend.
-
-#### 6. Teardown
-
-After closing VSCode, you may still have containers running. To close everything down, just run `docker-compose down -v` to cleanup all containers.
-
-## Web Interface
-
-### Prerequisites
-
-- [Frigate source code](#frigate-core-web-and-docs)
-- All [core](#core) prerequisites _or_ another running Frigate instance locally available
-- Node.js 14
-
-### Making changes
-
-#### 1. Set up a Frigate instance
-
-The Web UI requires an instance of Frigate to interact with for all of its data. You can either run an instance locally (recommended) or attach to a separate instance accessible on your network.
-
-To run the local instance, follow the [core](#core) development instructions.
-
-If you won't be making any changes to the Frigate HTTP API, you can attach the web development server to any Frigate instance on your network. Skip this step and go to [3a](#3a-run-the-development-server-against-a-non-local-instance).
-
-#### 2. Install dependencies
-
-```console
-cd web && npm install
-```
-
-#### 3. Run the development server
-
-```console
-cd web && npm run start
-```
-
-#### 3a. Run the development server against a non-local instance
-
-To run the development server against a non-local instance, you will need to provide an environment variable, `SNOWPACK_PUBLIC_API_HOST` that tells the web application how to connect to the Frigate API:
-
-```console
-cd web && SNOWPACK_PUBLIC_API_HOST=http://<ip-address-to-your-frigate-instance>:5000 npm run start
-```
-
-#### 4. Making changes
-
-The Web UI is built using [Snowpack](https://www.snowpack.dev/), [Preact](https://preactjs.com), and [Tailwind CSS](https://tailwindcss.com).
-
-Light guidelines and advice:
-
-- Avoid adding more dependencies. The web UI intends to be lightweight and fast to load.
-- Do not make large sweeping changes. [Open a discussion on GitHub](https://github.com/blakeblackshear/frigate/discussions/new) for any large or architectural ideas.
-- Ensure `lint` passes. This command will ensure basic conformance to styles, applying as many automatic fixes as possible, including Prettier formatting.
-
-```console
-npm run lint
-```
-
-- Add to unit tests and ensure they pass. As much as possible, you should strive to _increase_ test coverage whenever making changes. This will help ensure features do not accidentally become broken in the future.
-
-```console
-npm run test
-```
-
-- Test in different browsers. Firefox, Chrome, and Safari all have different quirks that make them unique targets to interact with.
-
-## Documentation
-
-### Prerequisites
-
-- [Frigate source code](#frigate-core-web-and-docs)
-- Node.js 14
-
-### Making changes
-
-#### 1. Installation
-
-```console
-npm install
-```
-
-#### 2. Local Development
-
-```console
-npm run start
-```
-
-This command starts a local development server and open up a browser window. Most changes are reflected live without having to restart the server.
-
-The docs are built using [Docusaurus v2](https://v2.docusaurus.io). Please refer to the Docusaurus docs for more information on how to modify Frigate's documentation.
-
-#### 3. Build (optional)
-
-```console
-npm run build
-```
-
-This command generates static content into the `build` directory and can be served using any static contents hosting service.

docs/docs/hardware.md

@@ -1,29 +0,0 @@
----
-id: hardware
-title: Recommended hardware
----
-
-## Cameras
-
-Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
-
-## Computer
-
-| Name                    | Inference Speed | Notes                                                                                                                         |
-| ----------------------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| Atomic Pi               | 16ms            | Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding. |
-| Intel NUC NUC7i3BNK     | 8-10ms          | Great performance. Can handle many cameras at 5fps depending on typical amounts of motion.                                    |
-| BMAX B2 Plus            | 10-12ms         | Good balance of performance and cost. Also capable of running many other services at the same time as frigate.                |
-| Minisforum GK41         | 9-10ms          | Great alternative to a NUC with dual Gigabit NICs. Easily handles several 1080p cameras.                                      |
-| Raspberry Pi 3B (32bit) | 60ms            | Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0.                                       |
-| Raspberry Pi 4 (32bit)  | 15-20ms         | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
-| Raspberry Pi 4 (64bit)  | 10-15ms         | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
-
-## Unraid
-
-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) |

docs/docs/how-it-works.md

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

docs/docs/index.md

@@ -1,25 +0,0 @@
----
-id: index
-title: Frigate
-sidebar_label: Features
-slug: /
----
-
-A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
-
-Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
-
-- Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
-- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
-- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
-- Uses a very low overhead motion detection to determine where to run object detection
-- Object detection with TensorFlow runs in separate processes for maximum FPS
-- Communicates over MQTT for easy integration into other systems
-- 24/7 recording
-- Re-streaming via RTMP to reduce the number of connections to your camera
-
-## Screenshots
-
-![Media Browser](/img/media_browser.png)
-
-![Notification](/img/notification.png)

docs/docs/installation.md

@@ -1,124 +0,0 @@
----
-id: installation
-title: Installation
----
-
-Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). See instructions below for installing the HassOS addon.
-
-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 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.
-
-## HassOS Addon
-
-HassOS users can install via the addon repository. Frigate requires an MQTT server.
-
-1. Navigate to Supervisor > Add-on Store > Repositories
-2. Add https://github.com/blakeblackshear/frigate-hass-addons
-3. Setup your network configuration in the `Configuration` tab if deisred
-4. Create the file `frigate.yml` in your `config` directory with your detailed Frigate configuration
-5. Start the addon container
-6. If you are using hardware acceleration for ffmpeg, you will need to disable "Protection mode"
-
-## 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     |
-
-It is recommended to run with docker-compose:
-
-```yaml
-version: "3.9"
-services:
-  frigate:
-    container_name: frigate
-    privileged: true # this may not be necessary for all setups
-    restart: unless-stopped
-    image: blakeblackshear/frigate:<specify_version_tag>
-    devices:
-      - /dev/bus/usb:/dev/bus/usb
-      - /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
-    volumes:
-      - /etc/localtime:/etc/localtime:ro
-      - <path_to_config_file>:/config/config.yml:ro
-      - <path_to_directory_for_media>:/media/frigate
-      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
-        target: /tmp/cache
-        tmpfs:
-          size: 1000000000
-    ports:
-      - "5000:5000"
-      - "1935:1935" # RTMP feeds
-    environment:
-      FRIGATE_RTSP_PASSWORD: "password"
-```
-
-If you can't use docker compose, you can run the container with something similar to this:
-
-```bash
-docker run -d \
-  --name frigate \
-  --restart=unless-stopped \
-  --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
-  --device /dev/bus/usb:/dev/bus/usb \
-  --device /dev/dri/renderD128 \
-  -v <path_to_directory_for_media>:/media/frigate \
-  -v <path_to_config_file>:/config/config.yml:ro \
-  -v /etc/localtime:/etc/localtime:ro \
-  -e FRIGATE_RTSP_PASSWORD='password' \
-  -p 5000:5000 \
-  -p 1935:1935 \
-  blakeblackshear/frigate:<specify_version_tag>
-```
-
-### Calculating shm-size
-
-The default shm-size of 64m is fine for setups with 3 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it could be because you have too many high resolution cameras and you need to specify a higher shm size.
-
-You can calculate the necessary shm-size for each camera with the following formula:
-
-```
-(width * height * 1.5 * 7 + 270480)/1048576 = <shm size in mb>
-```
-
-The shm size cannot be set per container for Home Assistant Addons. You must set `default-shm-size` in `/etc/docker/daemon.json` to increase the default shm size. This will increase the shm size for all of your docker containers. This may or may not cause issues with your setup. https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file
-
-## Kubernetes
-
-Use the [helm chart](https://github.com/blakeblackshear/blakeshome-charts/tree/master/charts/frigate).
-
-## Virtualization
-
-For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices for ffmpeg decoding. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer typically introduces a sizable amount of overhead for communication with Coral devices.
-
-### Proxmox
-
-Some people have had success running Frigate in LXC directly with the following config:
-
-```
-arch: amd64
-cores: 2
-features: nesting=1
-hostname: FrigateLXC
-memory: 4096
-net0: name=eth0,bridge=vmbr0,firewall=1,hwaddr=2E:76:AE:5A:58:48,ip=dhcp,ip6=auto,type=veth
-ostype: debian
-rootfs: local-lvm:vm-115-disk-0,size=12G
-swap: 512
-lxc.cgroup.devices.allow: c 189:385 rwm
-lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file
-lxc.mount.entry: /dev/bus/usb/004/002 dev/bus/usb/004/002 none bind,optional,create=file
-lxc.apparmor.profile: unconfined
-lxc.cgroup.devices.allow: a
-lxc.cap.drop:
-```
-
-### ESX
-
-For details on running Frigate under ESX, see details [here](https://github.com/blakeblackshear/frigate/issues/305).

docs/docs/mdx.md

@@ -1,17 +0,0 @@
----
-id: mdx
-title: Powered by MDX
----
-
-You can write JSX and use React components within your Markdown thanks to [MDX](https://mdxjs.com/).
-
-export const Highlight = ({children, color}) => ( <span style={{
-      backgroundColor: color,
-      borderRadius: '2px',
-      color: '#fff',
-      padding: '0.2rem',
-    }}>{children}</span> );
-
-<Highlight color="#25c2a0">Docusaurus green</Highlight> and <Highlight color="#1877F2">Facebook blue</Highlight> are my favorite colors.
-
-I can write **Markdown** alongside my _JSX_!

docs/docs/troubleshooting.md

@@ -1,30 +0,0 @@
----
-id: troubleshooting
-title: Troubleshooting and FAQ
----
-
-### I am seeing a solid green image for my camera.
-
-A solid green image means that frigate has not received any frames from ffmpeg. Check the logs to see why ffmpeg is exiting and adjust your ffmpeg args accordingly.
-
-### How can I get sound or audio in my recordings?
-
-By default, Frigate removes audio from recordings to reduce the likelihood of failing for invalid data. If you would like to include audio, you need to override the output args to remove `-an` for where you want to include audio. The recommended audio codec is `aac`. Not all audio codecs are supported by RTMP, so you may need to re-encode your audio with `-c:a aac`. The default ffmpeg args are shown [here](/frigate/configuration/index#ffmpeg).
-
-### My mjpeg stream or snapshots look green and crazy
-
-This almost always means that the width/height defined for your camera are not correct. Double check the resolution with vlc or another player. Also make sure you don't have the width and height values backwards.
-
-![mismatched-resolution](/img/mismatched-resolution.jpg)
-
-### I can't view events or recordings in the Web UI.
-
-Ensure your cameras send h264 encoded video
-
-### "[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5639eeb6e140] moov atom not found"
-
-These messages in the logs are expected in certain situations. Frigate checks the integrity of the recordings before storing. Occasionally these cached files will be invalid and cleaned up automatically.
-
-### "On connect called"
-
-If you see repeated "On connect called" messages in your config, check for another instance of frigate. This happens when multiple frigate containers are trying to connect to mqtt with the same client_id.

docs/docs/usage/api.md

@@ -1,215 +0,0 @@
----
-id: api
-title: HTTP API
----
-
-A web server is available on port 5000 with the following endpoints.
-
-### `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.
-
-Accepts the following query string parameters:
-
-| param       | Type | Description                                                        |
-| ----------- | ---- | ------------------------------------------------------------------ |
-| `fps`       | int  | Frame rate                                                         |
-| `h`         | int  | Height in pixels                                                   |
-| `bbox`      | int  | Show bounding boxes for detected objects (0 or 1)                  |
-| `timestamp` | int  | Print the timestamp in the upper left (0 or 1)                     |
-| `zones`     | int  | Draw the zones on the image (0 or 1)                               |
-| `mask`      | int  | Overlay the mask on the image (0 or 1)                             |
-| `motion`    | int  | Draw blue boxes for areas with detected motion (0 or 1)            |
-| `regions`   | int  | Draw green boxes for areas where object detection was run (0 or 1) |
-
-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`.
-
-### `GET /api/<camera_name>/<object_name>/best.jpg[?h=300&crop=1&quality=70]`
-
-The best snapshot for any object type. It is a full resolution image by default.
-
-Example parameters:
-
-- `h=300`: resizes the image to 300 pixes tall
-- `crop=1`: crops the image to the region of the detection rather than returning the entire image
-- `quality=70`: sets the jpeg encoding quality (0-100)
-
-### `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.
-
-Accepts the following query string parameters:
-
-| param       | Type | Description                                                        |
-| ----------- | ---- | ------------------------------------------------------------------ |
-| `h`         | int  | Height in pixels                                                   |
-| `bbox`      | int  | Show bounding boxes for detected objects (0 or 1)                  |
-| `timestamp` | int  | Print the timestamp in the upper left (0 or 1)                     |
-| `zones`     | int  | Draw the zones on the image (0 or 1)                               |
-| `mask`      | int  | Overlay the mask on the image (0 or 1)                             |
-| `motion`    | int  | Draw blue boxes for areas with detected motion (0 or 1)            |
-| `regions`   | int  | Draw green boxes for areas where object detection was run (0 or 1) |
-| `quality`   | int  | Jpeg encoding quality (0-100). Defaults to 70.                     |
-
-Example parameters:
-
-- `h=300`: resizes the image to 300 pixes tall
-
-### `GET /api/stats`
-
-Contains some granular debug info that can be used for sensors in Home Assistant.
-
-Sample response:
-
-```json
-{
-  /* Per Camera Stats */
-  "back": {
-    /***************
-     * Frames per second being consumed from your camera. If this is higher
-     * than it is supposed to be, you should set -r FPS in your input_args.
-     * camera_fps = process_fps + skipped_fps
-     ***************/
-    "camera_fps": 5.0,
-    /***************
-     * Number of times detection is run per second. This can be higher than
-     * your camera FPS because frigate often looks at the same frame multiple times
-     * or in multiple locations
-     ***************/
-    "detection_fps": 1.5,
-    /***************
-     * PID for the ffmpeg process that consumes this camera
-     ***************/
-    "capture_pid": 27,
-    /***************
-     * PID for the process that runs detection for this camera
-     ***************/
-    "pid": 34,
-    /***************
-     * Frames per second being processed by frigate.
-     ***************/
-    "process_fps": 5.1,
-    /***************
-     * Frames per second skip for processing by frigate.
-     ***************/
-    "skipped_fps": 0.0
-  },
-  /***************
-   * Sum of detection_fps across all cameras and detectors.
-   * This should be the sum of all detection_fps values from cameras.
-   ***************/
-  "detection_fps": 5.0,
-  /* Detectors Stats */
-  "detectors": {
-    "coral": {
-      /***************
-       * Timestamp when object detection started. If this value stays non-zero and constant
-       * for a long time, that means the detection process is stuck.
-       ***************/
-      "detection_start": 0.0,
-      /***************
-       * Time spent running object detection in milliseconds.
-       ***************/
-      "inference_speed": 10.48,
-      /***************
-       * PID for the shared process that runs object detection on the Coral.
-       ***************/
-      "pid": 25321
-    }
-  },
-  "service": {
-    /* Uptime in seconds */
-    "uptime": 10,
-    "version": "0.8.0-8883709",
-    /* Storage data in MB for important locations */
-    "storage": {
-      "/media/frigate/clips": {
-        "total": 1000,
-        "used": 700,
-        "free": 300,
-        "mnt_type": "ext4"
-      },
-      "/media/frigate/recordings": {
-        "total": 1000,
-        "used": 700,
-        "free": 300,
-        "mnt_type": "ext4"
-      },
-      "/tmp/cache": {
-        "total": 256,
-        "used": 100,
-        "free": 156,
-        "mnt_type": "tmpfs"
-      },
-      "/dev/shm": {
-        "total": 256,
-        "used": 100,
-        "free": 156,
-        "mnt_type": "tmpfs"
-      }
-    }
-  }
-}
-```
-
-### `GET /api/config`
-
-A json representation of your configuration
-
-### `GET /api/version`
-
-Version info
-
-### `GET /api/events`
-
-Events from the database. Accepts the following query string parameters:
-
-| param                | Type | Description                                   |
-| -------------------- | ---- | --------------------------------------------- |
-| `before`             | int  | Epoch time                                    |
-| `after`              | int  | Epoch time                                    |
-| `camera`             | str  | Camera name                                   |
-| `label`              | str  | Label name                                    |
-| `zone`               | str  | Zone name                                     |
-| `limit`              | int  | Limit the number of events returned           |
-| `has_snapshot`       | int  | Filter to events that have snapshots (0 or 1) |
-| `has_clip`           | int  | Filter to events that have clips (0 or 1)     |
-| `include_thumbnails` | int  | Include thumbnails in the response (0 or 1)   |
-
-### `GET /api/events/summary`
-
-Returns summary data for events in the database. Used by the Home Assistant integration.
-
-### `GET /api/events/<id>`
-
-Returns data for a single event.
-
-### `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.
-
-### `GET /api/events/<id>/snapshot.jpg`
-
-Returns the snapshot image for the event id. Works while the event is in progress and after completion.
-
-Accepts the following query string parameters, but they are only applied when an event is in progress. After the event is completed, the saved snapshot is returned from disk without modification:
-
-| param       | Type | Description                                       |
-| ----------- | ---- | ------------------------------------------------- |
-| `h`         | int  | Height in pixels                                  |
-| `bbox`      | int  | Show bounding boxes for detected objects (0 or 1) |
-| `timestamp` | int  | Print the timestamp in the upper left (0 or 1)    |
-| `crop`      | int  | Crop the snapshot to the (0 or 1)                 |
-| `quality`   | int  | Jpeg encoding quality (0-100). Defaults to 70.    |
-
-### `/clips/<camera>-<id>.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.

docs/docs/usage/home-assistant.md

@@ -1,281 +0,0 @@
----
-id: home-assistant
-title: Integration with Home Assistant
-sidebar_label: Home Assistant
----
-
-The best way to integrate with Home Assistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration).
-
-## Installation
-
-Available via HACS as a [custom repository](https://hacs.xyz/docs/faq/custom_repositories). To install:
-
-- Add the custom repository:
-
-```
-Home Assistant > HACS > Integrations > [...] > Custom Repositories
-```
-
-| Key            | Value                                                       |
-| -------------- | ----------------------------------------------------------- |
-| Repository URL | https://github.com/blakeblackshear/frigate-hass-integration |
-| Category       | Integration                                                 |
-
-- Use [HACS](https://hacs.xyz/) to install the integration:
-
-```
-Home Assistant > HACS > Integrations > "Explore & Add Integrations" > Frigate
-```
-
-- Restart Home Assistant.
-- Then add/configure the integration:
-
-```
-Home Assistant > Configuration > Integrations > Add Integration > Frigate
-```
-
-Note: You will also need
-[media_source](https://www.home-assistant.io/integrations/media_source/) enabled
-in your Home Assistant configuration for the Media Browser to appear.
-
-## Configuration
-
-When configuring the integration, you will be asked for the following parameters:
-
-| Variable | Description                                                                                                                                                                                                                                                                                                                                                                      |
-| -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| URL      | The `URL` of your frigate instance, the URL you use to access Frigate in the browser. This may look like `http://<host>:5000/`. If you are using HassOS with the addon, the URL should be `http://ccab4aaf-frigate:5000` (or `http://ccab4aaf-frigate-beta:5000` if your are using the beta version of the addon). Live streams required port 1935, see [RTMP streams](#streams) |
-
-<a name="options"></a>
-
-## Options
-
-```
-Home Assistant > Configuration > Integrations > Frigate > Options
-```
-
-| Option            | Description                                                                                                                                                                                                                                                                                                                          |
-| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| RTMP URL Template | A [jinja2](https://jinja.palletsprojects.com/) template that is used to override the standard RTMP stream URL (e.g. for use with reverse proxies). This option is only shown to users who have [advanced mode](https://www.home-assistant.io/blog/2019/07/17/release-96/#advanced-mode) enabled. See [RTMP streams](#streams) below. |
-
-## Entities Provided
-
-| Platform        | Description                                                                       |
-| --------------- | --------------------------------------------------------------------------------- |
-| `camera`        | Live camera stream (requires RTMP), camera for image of the last detected object. |
-| `sensor`        | States to monitor Frigate performance, object counts for all zones and cameras.   |
-| `switch`        | Switch entities to toggle detection, recordings and snapshots.                    |
-| `binary_sensor` | A "motion" binary sensor entity per camera/zone/object.                           |
-
-## Media Browser Support
-
-The integration provides:
-
-- Rich UI with thumbnails for browsing event recordings
-- Rich UI for browsing 24/7 recordings by month, day, camera, time
-
-This is accessible via "Media Browser" on the left menu panel in Home Assistant.
-
-<a name="api"></a>
-
-## API
-
-- Notification API with public facing endpoints for images in notifications
-
-### Notifications
-
-Frigate publishes event information in the form of a change feed via MQTT. This
-allows lots of customization for notifications to meet your needs. Event changes
-are published with `before` and `after` information as shown
-[here](#frigateevents). Note that some people may not want to expose frigate to
-the web, so you can leverage the HA API that frigate custom_integration ties
-into (which is exposed to the web, and thus can be used for mobile notifications
-etc):
-
-To load an image taken by frigate from Home Assistants API see below:
-
-```
-https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
-```
-
-To load a video clip taken by frigate from Home Assistants API :
-
-```
-https://HA_URL/api/frigate/notifications/<event-id>/<camera>/clip.mp4
-```
-
-Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as frigate finds a "better" image.
-
-```yaml
-automation:
-  - alias: Notify of events
-    trigger:
-      platform: mqtt
-      topic: frigate/events
-    action:
-      - service: notify.mobile_app_pixel_3
-        data_template:
-          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
-          data:
-            image: 'https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg?format=android'
-            tag: '{{trigger.payload_json["after"]["id"]}}'
-            when: '{{trigger.payload_json["after"]["start_time"]|int}}'
-```
-
-```yaml
-automation:
-  - alias: When a person enters a zone named yard
-    trigger:
-      platform: mqtt
-      topic: frigate/events
-    condition:
-      - "{{ trigger.payload_json['after']['label'] == 'person' }}"
-      - "{{ 'yard' in trigger.payload_json['after']['entered_zones'] }}"
-    action:
-      - service: notify.mobile_app_pixel_3
-        data_template:
-          message: "A {{trigger.payload_json['after']['label']}} has entered the yard."
-          data:
-            image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
-            tag: "{{trigger.payload_json['after']['id']}}"
-            when: '{{trigger.payload_json["after"]["start_time"]|int}}'
-```
-
-```yaml
-- alias: When a person leaves a zone named yard
-  trigger:
-    platform: mqtt
-    topic: frigate/events
-  condition:
-    - "{{ trigger.payload_json['after']['label'] == 'person' }}"
-    - "{{ 'yard' in trigger.payload_json['before']['current_zones'] }}"
-    - "{{ not 'yard' in trigger.payload_json['after']['current_zones'] }}"
-  action:
-    - service: notify.mobile_app_pixel_3
-      data_template:
-        message: "A {{trigger.payload_json['after']['label']}} has left the yard."
-        data:
-          image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
-          tag: "{{trigger.payload_json['after']['id']}}"
-          when: '{{trigger.payload_json["after"]["start_time"]|int}}'
-```
-
-```yaml
-- alias: Notify for dogs in the front with a high top score
-  trigger:
-    platform: mqtt
-    topic: frigate/events
-  condition:
-    - "{{ trigger.payload_json['after']['label'] == 'dog' }}"
-    - "{{ trigger.payload_json['after']['camera'] == 'front' }}"
-    - "{{ trigger.payload_json['after']['top_score'] > 0.98 }}"
-  action:
-    - service: notify.mobile_app_pixel_3
-      data_template:
-        message: "High confidence dog detection."
-        data:
-          image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
-          tag: "{{trigger.payload_json['after']['id']}}"
-          when: '{{trigger.payload_json["after"]["start_time"]|int}}'
-```
-
-If you are using telegram, you can fetch the image directly from Frigate:
-
-```yaml
-automation:
-  - alias: Notify of events
-    trigger:
-      platform: mqtt
-      topic: frigate/events
-    action:
-      - service: notify.telegram_full
-        data_template:
-          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
-          data:
-            photo:
-              # this url should work for addon users
-              - url: 'http://ccab4aaf-frigate:5000/api/events/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg'
-                caption: 'A {{trigger.payload_json["after"]["label"]}} was detected on {{ trigger.payload_json["after"]["camera"] }} camera'
-```
-
-<a name="streams"></a>
-
-## RTMP stream
-
-In order for the live streams to function they need to be accessible on the RTMP
-port (default: `1935`) at `<frigatehost>:1935`. Home Assistant will directly
-connect to that streaming port when the live camera is viewed.
-
-#### RTMP URL Template
-
-For advanced usecases, this behavior can be changed with the [RTMP URL
-template](#options) option. When set, this string will override the default stream
-address that is derived from the default behavior described above. This option supports
-[jinja2 templates](https://jinja.palletsprojects.com/) and has the `camera` dict
-variables from [Frigate API](https://blakeblackshear.github.io/frigate/usage/api#apiconfig)
-available for the template. Note that no Home Assistant state is available to the
-template, only the camera dict from Frigate.
-
-This is potentially useful when Frigate is behind a reverse proxy, and/or when
-the default stream port is otherwise not accessible to Home Assistant (e.g.
-firewall rules).
-
-###### RTMP URL Template Examples
-
-Use a different port number:
-
-```
-rtmp://<frigate_host>:2000/live/front_door
-```
-
-Use the camera name in the stream URL:
-
-```
-rtmp://<frigate_host>:2000/live/{{ name }}
-```
-
-Use the camera name in the stream URL, converting it to lowercase first:
-
-```
-rtmp://<frigate_host>:2000/live/{{ name|lower }}
-```
-
-## Multiple Instance Support
-
-The Frigate integration seamlessly supports the use of multiple Frigate servers.
-
-### Requirements for Multiple Instances
-
-In order for multiple Frigate instances to function correctly, the
-`topic_prefix` and `client_id` parameters must be set differently per server.
-See [MQTT
-configuration](https://blakeblackshear.github.io/frigate/configuration/index#mqtt)
-for how to set these.
-
-#### API URLs
-
-When multiple Frigate instances are configured, [API](#api) URLs should include an
-identifier to tell Home Assistant which Frigate instance to refer to. The
-identifier used is the MQTT `client_id` paremeter included in the configuration,
-and is used like so:
-
-```
-https://HA_URL/api/frigate/<client-id>/notifications/<event-id>/thumbnail.jpg
-```
-
-```
-https://HA_URL/api/frigate/<client-id>/clips/front_door-1624599978.427826-976jaa.mp4
-```
-
-#### Default Treatment
-
-When a single Frigate instance is configured, the `client-id` parameter need not
-be specified in URLs/identifiers -- that single instance is assumed. When
-multiple Frigate instances are configured, the user **must** explicitly specify
-which server they are referring to.
-
-## FAQ
-
-### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
-
-The [HomeKit integration](https://www.home-assistant.io/integrations/homekit/) randomly links one of the binary sensors (motion sensor entities) grouped with the camera device in Home Assistant. You can specify a `linked_motion_sensor` in the Home Assistant [HomeKit configuration](https://www.home-assistant.io/integrations/homekit/#linked_motion_sensor) for each camera.

docs/docs/usage/howtos.md

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

docs/docs/usage/mqtt.md

@@ -1,105 +0,0 @@
----
-id: mqtt
-title: MQTT
----
-
-These are the MQTT messages generated by Frigate. The default topic_prefix is `frigate`, but can be changed in the config file.
-
-### `frigate/available`
-
-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/restart`
-
-Causes frigate to exit. Docker should be configured to automatically restart the container on exit.
-
-### `frigate/<camera_name>/<object_name>`
-
-Publishes the count of objects for the camera for use as a sensor in Home Assistant.
-
-### `frigate/<zone_name>/<object_name>`
-
-Publishes the count of objects for the zone for use as a sensor in Home Assistant.
-
-### `frigate/<camera_name>/<object_name>/snapshot`
-
-Publishes a jpeg encoded frame of the detected object type. When the object is no longer detected, the highest confidence image is published or the original image
-is published again.
-
-The height and crop of snapshots can be configured in the config.
-
-### `frigate/events`
-
-Message published for each changed event. The first message is published when the tracked object is no longer marked as a false_positive. When frigate finds a better snapshot of the tracked object or when a zone change occurs, it will publish a message with the same id. When the event ends, a final message is published with `end_time` set.
-
-```json
-{
-  "type": "update", // new, update, end or clip_ready
-  "before": {
-    "id": "1607123955.475377-mxklsc",
-    "camera": "front_door",
-    "frame_time": 1607123961.837752,
-    "snapshot_time": 1607123961.837752,
-    "label": "person",
-    "top_score": 0.958984375,
-    "false_positive": false,
-    "start_time": 1607123955.475377,
-    "end_time": null,
-    "score": 0.7890625,
-    "box": [424, 500, 536, 712],
-    "area": 23744,
-    "region": [264, 450, 667, 853],
-    "current_zones": ["driveway"],
-    "entered_zones": ["yard", "driveway"],
-    "thumbnail": null
-  },
-  "after": {
-    "id": "1607123955.475377-mxklsc",
-    "camera": "front_door",
-    "frame_time": 1607123962.082975,
-    "snapshot_time": 1607123961.837752,
-    "label": "person",
-    "top_score": 0.958984375,
-    "false_positive": false,
-    "start_time": 1607123955.475377,
-    "end_time": null,
-    "score": 0.87890625,
-    "box": [432, 496, 544, 854],
-    "area": 40096,
-    "region": [218, 440, 693, 915],
-    "current_zones": ["yard", "driveway"],
-    "entered_zones": ["yard", "driveway"],
-    "thumbnail": null
-  }
-}
-```
-
-### `frigate/stats`
-
-Same data available at `/api/stats` published at a configurable interval.
-
-### `frigate/<camera_name>/detect/set`
-
-Topic to turn detection for a camera on and off. Expected values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/detect/state`
-
-Topic with current state of detection for a camera. Published values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/recordings/set`
-
-Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/recordings/state`
-
-Topic with current state of recordings for a camera. Published values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/snapshots/set`
-
-Topic to turn snapshots for a camera on and off. Expected values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/snapshots/state`
-
-Topic with current state of snapshots for a camera. Published values are `ON` and `OFF`.

docs/docs/usage/web.md

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

docs/docusaurus.config.js

@@ -1,79 +0,0 @@
-const path = require('path');
-
-module.exports = {
-  title: 'Frigate',
-  tagline: 'NVR With Realtime Object Detection for IP Cameras',
-  url: 'https://blakeblackshear.github.io',
-  baseUrl: '/frigate/',
-  onBrokenLinks: 'throw',
-  onBrokenMarkdownLinks: 'warn',
-  favicon: 'img/favicon.ico',
-  organizationName: 'blakeblackshear',
-  projectName: 'frigate',
-  themeConfig: {
-    algolia: {
-      apiKey: '81ec882db78f7fed05c51daf973f0362',
-      indexName: 'frigate',
-    },
-    navbar: {
-      title: 'Frigate',
-      logo: {
-        alt: 'Frigate',
-        src: 'img/logo.svg',
-        srcDark: 'img/logo-dark.svg',
-      },
-      items: [
-        {
-          to: '/',
-          activeBasePath: 'docs',
-          label: 'Docs',
-          position: 'left',
-        },
-        {
-          href: 'https://github.com/blakeblackshear/frigate',
-          label: 'GitHub',
-          position: 'right',
-        },
-      ],
-    },
-    sidebarCollapsible: false,
-    hideableSidebar: true,
-    footer: {
-      style: 'dark',
-      links: [
-        {
-          title: 'Community',
-          items: [
-            {
-              label: 'GitHub',
-              href: 'https://github.com/blakeblackshear/frigate',
-            },
-            {
-              label: 'Discussions',
-              href: 'https://github.com/blakeblackshear/frigate/discussions',
-            },
-          ],
-        },
-      ],
-      copyright: `Copyright © ${new Date().getFullYear()} Blake Blackshear`,
-    },
-  },
-  plugins: [path.resolve(__dirname, 'plugins', 'raw-loader')],
-  presets: [
-    [
-      '@docusaurus/preset-classic',
-      {
-        docs: {
-          routeBasePath: '/',
-          sidebarPath: require.resolve('./sidebars.js'),
-          // Please change this to your repo.
-          editUrl: 'https://github.com/blakeblackshear/frigate/edit/master/docs/',
-        },
-
-        theme: {
-          customCss: require.resolve('./src/css/custom.css'),
-        },
-      },
-    ],
-  ],
-};

docs/how-frigate-works.md

@@ -0,0 +1,10 @@
+# How Frigate Works
+Frigate is designed to minimize resource and maximize performance by only looking for objects when and where it is necessary
+
+![Diagram](diagram.png)
+
+## 1. Look for Motion
+
+## 2. Calculate Detection Regions
+
+## 3. Run Object Detection

docs/notification-examples.md

@@ -0,0 +1,71 @@
+# Notification examples
+
+Here are some examples of notifications for the HomeAssistant android companion app:
+```yaml
+automation:
+
+  - alias: When a person enters a zone named yard
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ trigger.payload_json['after']['label'] == 'person' }}"
+      - "{{ 'yard' in trigger.payload_json['after']['entered_zones'] }}"
+    action:
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: "A {{trigger.payload_json['after']['label']}} has entered the yard."
+          data:
+            image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
+            tag: "{{trigger.payload_json['after']['id']}}"
+
+  - alias: When a person leaves a zone named yard
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ trigger.payload_json['after']['label'] == 'person' }}"
+      - "{{ 'yard' in trigger.payload_json['before']['current_zones'] }}"
+      - "{{ not 'yard' in trigger.payload_json['after']['current_zones'] }}"
+    action:
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: "A {{trigger.payload_json['after']['label']}} has left the yard."
+          data:
+            image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
+            tag: "{{trigger.payload_json['after']['id']}}"
+
+  - alias: Notify for dogs in the front with a high top score
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    conditions: 
+      - "{{ trigger.payload_json['after']['label'] == 'dog' }}"
+      - "{{ trigger.payload_json['after']['camera'] == 'front' }}"
+      - "{{ trigger.payload_json['after']['top_score'] > 0.98 }}"
+    action:
+      - service: notify.mobile_app_pixel_3
+        data_template:
+          message: 'High confidence dog detection.'
+          data:
+            image: "https://url.com/api/frigate/notifications/{{trigger.payload_json['after']['id']}}/thumbnail.jpg"
+            tag: "{{trigger.payload_json['after']['id']}}"
+```
+
+If you are using telegram, you can fetch the image directly from Frigate:
+```yaml
+automation:
+  - alias: Notify of events
+    trigger:
+      platform: mqtt
+      topic: frigate/events
+    action:
+      - service: notify.telegram_full
+        data_template:
+          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
+          data:
+            photo:
+              # this url should work for addon users
+              - url: 'http://ccab4aaf-frigate:5000/api/events/{{trigger.payload_json["after"]["id"]}}/thumbnail.jpg'
+                caption : 'A {{trigger.payload_json["after"]["label"]}} was detected on {{ trigger.payload_json["after"]["camera"] }} camera'
+```

docs/nvdec.md

@@ -1,18 +1,14 @@
----
-id: nvdec
-title: nVidia hardware decoder
----
+# nVidia hardware decoder (NVDEC)
 
 Certain nvidia cards include a hardware decoder, which can greatly improve the
-performance of video decoding. In order to use NVDEC, a special build of
+performance of video decoding. In order to use NVDEC, a special build of 
 ffmpeg with NVDEC support is required. The special docker architecture 'amd64nvidia'
-includes this support for amd64 platforms. An aarch64 for the Jetson, which
+includes this support for amd64 platforms. An aarch64 for the Jetson, which 
 also includes NVDEC may be added in the future.
 
 ## Docker setup
 
 ### Requirements
-
 [nVidia closed source driver](https://www.nvidia.com/en-us/drivers/unix/) required to access NVDEC.
 [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) required to pass NVDEC to docker.
 
@@ -22,7 +18,6 @@ In order to pass NVDEC, the docker engine must be set to `nvidia` and the enviro
 `NVIDIA_VISIBLE_DEVICES=all` and `NVIDIA_DRIVER_CAPABILITIES=compute,utility,video` must be set.
 
 In a docker compose file, these lines need to be set:
-
 ```
 services:
   frigate:
@@ -31,16 +26,15 @@ services:
     runtime: nvidia
     environment:
       - NVIDIA_VISIBLE_DEVICES=all
-      - NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
+      - NVIDIA_DRIVER_CAPABILITIES=compute,utility,video 
 ```
 
 ### Setting up the configuration file
 
-In your frigate config.yml, you'll need to set ffmpeg to use the hardware decoder.
-The decoder you choose will depend on the input video.
+In your frigate config.yml, you'll need to set ffmpeg to use the hardware decoder. 
+The decoder you choose will depend on the input video. 
 
 A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
-
 ```
  V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
  V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
@@ -52,16 +46,16 @@ A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the c
  V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)
  V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)
  V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)
-```
+ ```
 
 For example, for H265 video (hevc), you'll select `hevc_cuvid`. Add
-`-c:v hevc_cuvid` to your ffmpeg input arguments:
+`-c:v hevc_covid` to your ffmpeg input arguments:
 
 ```
 ffmpeg:
   input_args:
     ...
-    - -c:v
+    - -c:v 
     - hevc_cuvid
 ```
 
@@ -81,7 +75,7 @@ processes:
 | 38%   41C    P2    36W / 125W |   2082MiB /  5942MiB |      5%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
-
+                                                                               
 +-----------------------------------------------------------------------------+
 | Processes:                                                                  |
 |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
@@ -102,9 +96,10 @@ using the fps filter:
 
 ```
   output_args:
-    - -filter:v
+    - -filter:v 
     - fps=fps=5
 ```
 
 This setting, for example, allows Frigate to consume my 10-15fps camera streams on
 my relatively low powered Haswell machine with relatively low cpu usage.
+

docs/package.json

@@ -1,35 +0,0 @@
-{
-  "name": "docs",
-  "version": "0.0.0",
-  "private": true,
-  "scripts": {
-    "docusaurus": "docusaurus",
-    "start": "docusaurus start",
-    "build": "docusaurus build",
-    "swizzle": "docusaurus swizzle",
-    "deploy": "docusaurus deploy",
-    "serve": "docusaurus serve",
-    "clear": "docusaurus clear"
-  },
-  "dependencies": {
-    "@docusaurus/core": "2.0.0-alpha.70",
-    "@docusaurus/preset-classic": "2.0.0-alpha.70",
-    "@mdx-js/react": "^1.6.21",
-    "clsx": "^1.1.1",
-    "raw-loader": "^4.0.2",
-    "react": "^16.8.4",
-    "react-dom": "^16.8.4"
-  },
-  "browserslist": {
-    "production": [
-      ">0.5%",
-      "not dead",
-      "not op_mini all"
-    ],
-    "development": [
-      "last 1 chrome version",
-      "last 1 firefox version",
-      "last 1 safari version"
-    ]
-  }
-}

docs/plugins/raw-loader.js

@@ -1,12 +0,0 @@
-module.exports = function (context, options) {
-  return {
-    name: 'labelmap',
-    configureWebpack(config, isServer, utils) {
-      return {
-        module: {
-          rules: [{ test: /\.txt$/, use: 'raw-loader' }],
-        },
-      };
-    },
-  };
-};

docs/sidebars.js

@@ -1,16 +0,0 @@
-module.exports = {
-  docs: {
-    Frigate: ['index', 'how-it-works', 'hardware', 'installation', 'troubleshooting'],
-    Configuration: [
-      'configuration/index',
-      'configuration/cameras',
-      'configuration/optimizing',
-      'configuration/detectors',
-      'configuration/false_positives',
-      'configuration/objects',
-      'configuration/advanced',
-    ],
-    Usage: ['usage/home-assistant', 'usage/web', 'usage/api', 'usage/mqtt'],
-    Development: ['contributing'],
-  },
-};

docs/src/css/custom.css

@@ -1,25 +0,0 @@
-/* stylelint-disable docusaurus/copyright-header */
-/**
- * Any CSS included here will be global. The classic template
- * bundles Infima by default. Infima is a CSS framework designed to
- * work well for content-centric websites.
- */
-
-/* You can override the default Infima variables here. */
-:root {
-  --ifm-color-primary: #3b82f7;
-  --ifm-color-primary-dark: #1d4ed8;
-  --ifm-color-primary-darker: #1e40af;
-  --ifm-color-primary-darkest: #1e3a8a;
-  --ifm-color-primary-light: #60a5fa;
-  --ifm-color-primary-lighter: #93c5fd;
-  --ifm-color-primary-lightest: #dbeafe;
-  --ifm-code-font-size: 95%;
-}
-
-.docusaurus-highlight-code-line {
-  background-color: rgb(72, 77, 91);
-  display: block;
-  margin: 0 calc(-1 * var(--ifm-pre-padding));
-  padding: 0 var(--ifm-pre-padding);
-}

docs/static/img/logo-dark.svg

@@ -1,3 +0,0 @@
-<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M130 446.5C131.6 459.3 145 468 137 470C129 472 94 406.5 86 378.5C78 350.5 73.5 319 75.4999 301C77.4999 283 181 255 181 247.5C181 240 147.5 247 146 241C144.5 235 171.3 238.6 178.5 229C189.75 214 204 216.5 213 208.5C222 200.5 233 170 235 157C237 144 215 129 209 119C203 109 222 102 268 83C314 64 460 22 462 27C464 32 414 53 379 66C344 79 287 104 287 111C287 118 290 123.5 288 139.5C286 155.5 285.76 162.971 282 173.5C279.5 180.5 277 197 282 212C286 224 299 233 305 235C310 235.333 323.8 235.8 339 235C358 234 385 236 385 241C385 246 344 243 344 250C344 257 386 249 385 256C384 263 350 260 332 260C317.6 260 296.333 259.333 287 256L285 263C281.667 263 274.7 265 267.5 265C258.5 265 258 268 241.5 268C225 268 230 267 215 266C200 265 144 308 134 322C124 336 130 370 130 385.5C130 399.428 128 430.5 130 446.5Z" fill="white"/>
-</svg>

docs/static/img/logo.svg

@@ -1,3 +0,0 @@
-<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M130 446.5C131.6 459.3 145 468 137 470C129 472 94 406.5 86 378.5C78 350.5 73.5 319 75.5 301C77.4999 283 181 255 181 247.5C181 240 147.5 247 146 241C144.5 235 171.3 238.6 178.5 229C189.75 214 204 216.5 213 208.5C222 200.5 233 170 235 157C237 144 215 129 209 119C203 109 222 102 268 83C314 64 460 22 462 27C464 32 414 53 379 66C344 79 287 104 287 111C287 118 290 123.5 288 139.5C286 155.5 285.76 162.971 282 173.5C279.5 180.5 277 197 282 212C286 224 299 233 305 235C310 235.333 323.8 235.8 339 235C358 234 385 236 385 241C385 246 344 243 344 250C344 257 386 249 385 256C384 263 350 260 332 260C317.6 260 296.333 259.333 287 256L285 263C281.667 263 274.7 265 267.5 265C258.5 265 258 268 241.5 268C225 268 230 267 215 266C200 265 144 308 134 322C124 336 130 370 130 385.5C130 399.428 128 430.5 130 446.5Z" fill="black"/>
-</svg>

frigate/__main__.py

@@ -1,6 +1,4 @@
-import faulthandler
-
-faulthandler.enable()
+import faulthandler; faulthandler.enable()
 import sys
 import threading
 
@@ -8,10 +6,10 @@ 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()

frigate/app.py

@@ -2,28 +2,25 @@ import json
 import logging
 import multiprocessing as mp
 import os
-import signal
-import sys
-import threading
 from logging.handlers import QueueHandler
 from typing import Dict, List
+import sys
+import signal
 
 import yaml
 from peewee_migrate import Router
 from playhouse.sqlite_ext import SqliteExtDatabase
-from playhouse.sqliteq import SqliteQueueDatabase
 
-from frigate.config import DetectorTypeEnum, FrigateConfig
-from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
+from frigate.config import FrigateConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
 from frigate.edgetpu import EdgeTPUProcess
-from frigate.events import EventCleanup, EventProcessor
+from frigate.events import EventProcessor, EventCleanup
 from frigate.http import create_app
 from frigate.log import log_process, root_configurer
-from frigate.models import Event, Recordings
-from frigate.mqtt import create_mqtt_client, MqttSocketRelay
+from frigate.models import Event
+from frigate.mqtt import create_mqtt_client
 from frigate.object_processing import TrackedObjectProcessor
-from frigate.output import output_frames
-from frigate.record import RecordingCleanup, RecordingMaintainer
+from frigate.record import RecordingMaintainer
 from frigate.stats import StatsEmitter, stats_init
 from frigate.video import capture_camera, track_camera
 from frigate.watchdog import FrigateWatchdog
@@ -31,11 +28,9 @@ from frigate.zeroconf import broadcast_zeroconf
 
 logger = logging.getLogger(__name__)
 
-
-class FrigateApp:
+class FrigateApp():
     def __init__(self):
         self.stop_event = mp.Event()
-        self.base_config: FrigateConfig = None
         self.config: FrigateConfig = None
         self.detection_queue = mp.Queue()
         self.detectors: Dict[str, EdgeTPUProcess] = {}
@@ -56,254 +51,145 @@ 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")
-        user_config = FrigateConfig.parse_file(config_file)
-        self.config = user_config.runtime_config
+        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.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."
-                )
+            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.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.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.")
+    
     def set_log_levels(self):
-        logging.getLogger().setLevel(self.config.logger.default.value.upper())
+        logging.getLogger().setLevel(self.config.logger.default)
         for log, level in self.config.logger.logs.items():
-            logging.getLogger(log).setLevel(level.value.upper())
-
-        if not "werkzeug" in self.config.logger.logs:
-            logging.getLogger("werkzeug").setLevel("ERROR")
+            logging.getLogger(log).setLevel(level)
+        
+        if not 'werkzeug' in self.config.logger.logs:
+            logging.getLogger('werkzeug').setLevel('ERROR')
 
     def init_queues(self):
         # Queues for clip processing
         self.event_queue = mp.Queue()
         self.event_processed_queue = mp.Queue()
-        self.video_output_queue = mp.Queue(maxsize=len(self.config.cameras.keys()) * 2)
 
         # Queue for cameras to push tracked objects to
-        self.detected_frames_queue = mp.Queue(
-            maxsize=len(self.config.cameras.keys()) * 2
-        )
+        self.detected_frames_queue = mp.Queue(maxsize=len(self.config.cameras.keys())*2)
 
     def init_database(self):
-        # Migrate DB location
-        old_db_path = os.path.join(CLIPS_DIR, "frigate.db")
-        if not os.path.isfile(self.config.database.path) and os.path.isfile(
-            old_db_path
-        ):
-            os.rename(old_db_path, self.config.database.path)
-
-        # Migrate DB schema
-        migrate_db = SqliteExtDatabase(self.config.database.path)
+        self.db = SqliteExtDatabase(self.config.database.path)
 
         # Run migrations
-        del logging.getLogger("peewee_migrate").handlers[:]
-        router = Router(migrate_db)
+        del(logging.getLogger('peewee_migrate').handlers[:])
+        router = Router(self.db)
         router.run()
 
-        migrate_db.close()
-
-        self.db = SqliteQueueDatabase(self.config.database.path)
-        models = [Event, Recordings]
+        models = [Event]
         self.db.bind(models)
 
     def init_stats(self):
         self.stats_tracking = stats_init(self.camera_metrics, self.detectors)
 
     def init_web_server(self):
-        self.flask_app = create_app(
-            self.config,
-            self.db,
-            self.stats_tracking,
-            self.detected_frames_processor,
-        )
+        self.flask_app = create_app(self.config, self.db, self.stats_tracking, self.detected_frames_processor)
 
     def init_mqtt(self):
         self.mqtt_client = create_mqtt_client(self.config, self.camera_metrics)
 
-    def start_mqtt_relay(self):
-        self.mqtt_relay = MqttSocketRelay(
-            self.mqtt_client, self.config.mqtt.topic_prefix
-        )
-        self.mqtt_relay.start()
-
     def start_detectors(self):
         model_shape = (self.config.model.height, self.config.model.width)
         for name in self.config.cameras.keys():
             self.detection_out_events[name] = mp.Event()
-
-            try:
-                shm_in = mp.shared_memory.SharedMemory(
-                    name=name,
-                    create=True,
-                    size=self.config.model.height * self.config.model.width * 3,
-                )
-            except FileExistsError:
-                shm_in = mp.shared_memory.SharedMemory(name=name)
-
-            try:
-                shm_out = mp.shared_memory.SharedMemory(
-                    name=f"out-{name}", create=True, size=20 * 6 * 4
-                )
-            except FileExistsError:
-                shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}")
-
+            shm_in = mp.shared_memory.SharedMemory(name=name, create=True, size=self.config.model.height*self.config.model.width*3)
+            shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}", create=True, size=20*6*4)
             self.detection_shms.append(shm_in)
             self.detection_shms.append(shm_out)
 
         for name, detector in self.config.detectors.items():
-            if detector.type == DetectorTypeEnum.cpu:
-                self.detectors[name] = EdgeTPUProcess(
-                    name,
-                    self.detection_queue,
-                    self.detection_out_events,
-                    model_shape,
-                    "cpu",
-                    detector.num_threads,
-                )
-            if detector.type == DetectorTypeEnum.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.video_output_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_video_output_processor(self):
-        output_processor = mp.Process(
-            target=output_frames,
-            name=f"output_processor",
-            args=(
-                self.config,
-                self.video_output_queue,
-            ),
-        )
-        output_processor.daemon = True
-        self.output_processor = output_processor
-        output_processor.start()
-        logger.info(f"Output process started: {output_processor.pid}")
-
     def start_camera_processors(self):
         model_shape = (self.config.model.height, self.config.model.width)
         for name, config in self.config.cameras.items():
-            camera_process = mp.Process(
-                target=track_camera,
-                name=f"camera_processor:{name}",
-                args=(
-                    name,
-                    config,
-                    model_shape,
-                    self.config.model.merged_labelmap,
-                    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):
         self.event_cleanup = EventCleanup(self.config, self.stop_event)
         self.event_cleanup.start()
-
+    
     def start_recording_maintainer(self):
         self.recording_maintainer = RecordingMaintainer(self.config, self.stop_event)
         self.recording_maintainer.start()
 
-    def start_recording_cleanup(self):
-        self.recording_cleanup = RecordingCleanup(self.config, self.stop_event)
-        self.recording_cleanup.start()
-
     def start_stats_emitter(self):
-        self.stats_emitter = StatsEmitter(
-            self.config,
-            self.stats_tracking,
-            self.mqtt_client,
-            self.config.mqtt.topic_prefix,
-            self.stop_event,
-        )
+        self.stats_emitter = StatsEmitter(self.config, self.stats_tracking, self.mqtt_client, self.config.mqtt.topic_prefix, self.stop_event)
         self.stats_emitter.start()
 
     def start_watchdog(self):
@@ -331,17 +217,14 @@ class FrigateApp:
             self.log_process.terminate()
             sys.exit(1)
         self.start_detectors()
-        self.start_video_output_processor()
         self.start_detected_frames_processor()
         self.start_camera_processors()
         self.start_camera_capture_processes()
         self.init_stats()
         self.init_web_server()
-        self.start_mqtt_relay()
         self.start_event_processor()
         self.start_event_cleanup()
         self.start_recording_maintainer()
-        self.start_recording_cleanup()
         self.start_stats_emitter()
         self.start_watchdog()
         # self.zeroconf = broadcast_zeroconf(self.config.mqtt.client_id)
@@ -349,29 +232,22 @@ class FrigateApp:
         def receiveSignal(signalNumber, frame):
             self.stop()
             sys.exit()
-
+        
         signal.signal(signal.SIGTERM, receiveSignal)
 
-        try:
-            self.flask_app.run(host="127.0.0.1", port=5001, debug=False)
-        except KeyboardInterrupt:
-            pass
-
+        self.flask_app.run(host='127.0.0.1', port=5001, debug=False)
         self.stop()
-
+    
     def stop(self):
         logger.info(f"Stopping...")
         self.stop_event.set()
 
-        self.mqtt_relay.stop()
         self.detected_frames_processor.join()
         self.event_processor.join()
         self.event_cleanup.join()
         self.recording_maintainer.join()
-        self.recording_cleanup.join()
         self.stats_emitter.join()
         self.frigate_watchdog.join()
-        self.db.stop()
 
         for detector in self.detectors.values():
             detector.stop()

frigate/const.py

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

frigate/edgetpu.py

@@ -1,49 +1,48 @@
 import datetime
+import hashlib
 import logging
 import multiprocessing as mp
 import os
 import queue
-import signal
 import threading
+import signal
 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 {}
 
-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)}
-
+    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=0.4):
+    def detect(self, tensor_input, threshold = .4):
         pass
 
-
 class LocalObjectDetector(ObjectDetector):
     def __init__(self, tf_device=None, num_threads=3, labels=None):
         self.fps = EventsPerSecond()
@@ -58,34 +57,27 @@ 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.error(
-                    "No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
-                )
+                logger.info("No EdgeTPU detected.")
                 raise
         else:
-            logger.warning(
-                "CPU detectors are not recommended and should only be used for testing or for trial purposes."
-            )
             self.interpreter = tflite.Interpreter(
-                model_path="/cpu_model.tflite", num_threads=num_threads
-            )
-
+                model_path='/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=0.4):
+    
+    def detect(self, tensor_input, threshold=.4):
         detections = []
 
         raw_detections = self.detect_raw(tensor_input)
@@ -93,50 +85,28 @@ 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 = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
-        class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
-        scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
-        count = int(
-            self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
-        )
-
-        detections = np.zeros((20, 6), np.float32)
-
-        for i in range(count):
-            if scores[i] < 0.4 or i == 20:
-                break
-            detections[i] = [
-                class_ids[i],
-                float(scores[i]),
-                boxes[i][0],
-                boxes[i][1],
-                boxes[i][2],
-                boxes[i][3],
-            ]
-
+        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]]
+        
         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()}")
@@ -144,10 +114,9 @@ def run_detector(
     listen()
 
     stop_event = mp.Event()
-
     def receiveSignal(signalNumber, frame):
         stop_event.set()
-
+    
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
@@ -157,17 +126,21 @@ def run_detector(
     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}
+        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
 
-    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
@@ -175,35 +148,26 @@ def run_detector(
         # detect and send the output
         start.value = datetime.datetime.now().timestamp()
         detections = object_detector.detect_raw(input_frame)
-        duration = datetime.datetime.now().timestamp() - start.value
-        outputs[connection_id]["np"][:] = detections[:]
+        duration = datetime.datetime.now().timestamp()-start.value
+        outputs[connection_id]['np'][:] = detections[:]
         out_events[connection_id].set()
         start.value = 0.0
 
-        avg_speed.value = (avg_speed.value * 9 + duration) / 10
-
-
-class EdgeTPUProcess:
-    def __init__(
-        self,
-        name,
-        detection_queue,
-        out_events,
-        model_shape,
-        tf_device=None,
-        num_threads=3,
-    ):
+        avg_speed.value = (avg_speed.value*9 + duration)/10
+        
+class EdgeTPUProcess():
+    def __init__(self, name, detection_queue, out_events, model_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
         self.num_threads = num_threads
         self.start_or_restart()
-
+    
     def stop(self):
         self.detect_process.terminate()
         logging.info("Waiting for detection process to exit gracefully...")
@@ -217,41 +181,23 @@ 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 = labels
+        self.labels = load_labels(labels)
         self.name = name
         self.fps = EventsPerSecond()
         self.detection_queue = detection_queue
         self.event = event
         self.shm = mp.shared_memory.SharedMemory(name=self.name, create=False)
-        self.np_shm = np.ndarray(
-            (1, model_shape[0], model_shape[1], 3), dtype=np.uint8, buffer=self.shm.buf
-        )
-        self.out_shm = mp.shared_memory.SharedMemory(
-            name=f"out-{self.name}", create=False
-        )
-        self.out_np_shm = np.ndarray((20, 6), dtype=np.float32, buffer=self.out_shm.buf)
-
-    def detect(self, tensor_input, threshold=0.4):
+        self.np_shm = np.ndarray((1,model_shape[0],model_shape[1],3), dtype=np.uint8, buffer=self.shm.buf)
+        self.out_shm = mp.shared_memory.SharedMemory(name=f"out-{self.name}", create=False)
+        self.out_np_shm = np.ndarray((20,6), dtype=np.float32, buffer=self.out_shm.buf)
+    
+    def detect(self, tensor_input, threshold=.4):
         detections = []
 
         # copy input to shared memory
@@ -267,12 +213,14 @@ class RemoteObjectDetector:
         for d in self.out_np_shm:
             if d[1] < threshold:
                 break
-            detections.append(
-                (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
-            )
+            detections.append((
+                self.labels[int(d[0])],
+                float(d[1]),
+                (d[2], d[3], d[4], d[5])
+            ))
         self.fps.update()
         return detections
-
+    
     def cleanup(self):
         self.shm.unlink()
         self.out_shm.unlink()

frigate/events.py

@@ -1,26 +1,28 @@
 import datetime
+import json
 import logging
 import os
 import queue
+import subprocess as sp
 import threading
 import time
+from collections import defaultdict
 from pathlib import Path
 
-from frigate.config import FrigateConfig, RecordConfig
-from frigate.const import CLIPS_DIR
-from frigate.models import Event, Recordings
+import psutil
+
+from frigate.config import FrigateConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.models import Event
 
 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 = {}
@@ -28,227 +30,277 @@ class EventProcessor(threading.Thread):
         self.event_processed_queue = event_processed_queue
         self.events_in_process = {}
         self.stop_event = stop_event
+    
+    def refresh_cache(self):
+        cached_files = os.listdir(CACHE_DIR)
 
-    def should_create_clip(self, camera, event_data):
-        if event_data["false_positive"]:
+        files_in_use = []
+        for process in psutil.process_iter():
+            try:
+                if process.name() != 'ffmpeg':
+                    continue
+
+                flist = process.open_files()
+                if flist:
+                    for nt in flist:
+                        if nt.path.startswith(CACHE_DIR):
+                            files_in_use.append(nt.path.split('/')[-1])
+            except:
+                continue
+
+        for f in cached_files:
+            if f in files_in_use or f in self.cached_clips:
+                continue
+
+            camera = '-'.join(f.split('-')[:-1])
+            start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
+        
+            ffprobe_cmd = " ".join([
+                'ffprobe',
+                '-v',
+                'error',
+                '-show_entries',
+                'format=duration',
+                '-of',
+                'default=noprint_wrappers=1:nokey=1',
+                f"{os.path.join(CACHE_DIR,f)}"
+            ])
+            p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
+            (output, err) = p.communicate()
+            p_status = p.wait()
+            if p_status == 0:
+                duration = float(output.decode('utf-8').strip())
+            else:
+                logger.info(f"bad file: {f}")
+                os.remove(os.path.join(CACHE_DIR,f))
+                continue
+
+            self.cached_clips[f] = {
+                'path': f,
+                'camera': camera,
+                'start_time': start_time.timestamp(),
+                'duration': duration
+            }
+
+        if len(self.events_in_process) > 0:
+            earliest_event = min(self.events_in_process.values(), key=lambda x:x['start_time'])['start_time']
+        else:
+            earliest_event = datetime.datetime.now().timestamp()
+
+        # if the earliest event exceeds the max seconds, cap it
+        max_seconds = self.config.clips.max_seconds
+        if datetime.datetime.now().timestamp()-earliest_event > max_seconds:
+            earliest_event = datetime.datetime.now().timestamp()-max_seconds
+        
+        for f, data in list(self.cached_clips.items()):
+            if earliest_event-90 > data['start_time']+data['duration']:
+                del self.cached_clips[f]
+                os.remove(os.path.join(CACHE_DIR,f))
+
+    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'])
+
+        while len(sorted_clips) == 0 or sorted_clips[-1]['start_time'] + sorted_clips[-1]['duration'] < event_data['end_time']+post_capture:
+            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'])
+        
+        playlist_start = event_data['start_time']-pre_capture
+        playlist_end = event_data['end_time']+post_capture
+        playlist_lines = []
+        for clip in sorted_clips:
+            # clip ends before playlist start time, skip
+            if clip['start_time']+clip['duration'] < playlist_start:
+                continue
+            # clip starts after playlist ends, finish
+            if clip['start_time'] > playlist_end:
+                break
+            playlist_lines.append(f"file '{os.path.join(CACHE_DIR,clip['path'])}'")
+            # if this is the starting clip, add an inpoint
+            if clip['start_time'] < playlist_start:
+                playlist_lines.append(f"inpoint {int(playlist_start-clip['start_time'])}")
+            # if this is the ending clip, add an outpoint
+            if clip['start_time']+clip['duration'] > playlist_end:
+                playlist_lines.append(f"outpoint {int(playlist_end-clip['start_time'])}")
+
+        clip_name = f"{camera}-{event_data['id']}"
+        ffmpeg_cmd = [
+            'ffmpeg',
+            '-y',
+            '-protocol_whitelist',
+            'pipe,file',
+            '-f',
+            'concat',
+            '-safe',
+            '0',
+            '-i',
+            '-',
+            '-c',
+            'copy',
+            '-movflags',
+            '+faststart',
+            f"{os.path.join(CLIPS_DIR, clip_name)}.mp4"
+        ]
+
+        p = sp.run(ffmpeg_cmd, input="\n".join(playlist_lines), encoding='ascii', capture_output=True)
+        if p.returncode != 0:
+            logger.error(p.stderr)
             return False
-
-        record_config: RecordConfig = self.config.cameras[camera].record
-
-        # Recording clips is disabled
-        if not record_config.enabled or (
-            record_config.retain_days == 0 and not record_config.events.enabled
-        ):
-            return False
-
-        # If there are required zones and there is no overlap
-        required_zones = record_config.events.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
-
-        # If the required objects are not present
-        if (
-            record_config.events.objects is not None
-            and event_data["label"] not in record_config.events.objects
-        ):
-            logger.debug(
-                f"Not creating clip for {event_data['id']} because it did not contain required objects"
-            )
-            return False
-
         return True
 
     def run(self):
-        while not self.stop_event.is_set():
+        while True:
+            if self.stop_event.is_set():
+                logger.info(f"Exiting event processor...")
+                break
+
             try:
                 event_type, camera, event_data = self.event_queue.get(timeout=10)
             except queue.Empty:
+                if not self.stop_event.is_set():
+                    self.refresh_cache()
                 continue
 
             logger.debug(f"Event received: {event_type} {camera} {event_data['id']}")
+            self.refresh_cache()
 
-            if event_type == "start":
-                self.events_in_process[event_data["id"]] = event_data
+            if event_type == 'start':
+                self.events_in_process[event_data['id']] = event_data
 
-            if event_type == "end":
-                record_config: RecordConfig = self.config.cameras[camera].record
+            if event_type == 'end':
+                clips_config = self.config.cameras[camera].clips
 
-                has_clip = self.should_create_clip(camera, event_data)
-
-                if has_clip or event_data["has_snapshot"]:
+                if not event_data['false_positive']:
+                    clip_created = False
+                    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)
+                    
                     Event.create(
-                        id=event_data["id"],
-                        label=event_data["label"],
+                        id=event_data['id'],
+                        label=event_data['label'],
                         camera=camera,
-                        start_time=event_data["start_time"],
-                        end_time=event_data["end_time"],
-                        top_score=event_data["top_score"],
-                        false_positive=event_data["false_positive"],
-                        zones=list(event_data["entered_zones"]),
-                        thumbnail=event_data["thumbnail"],
-                        has_clip=has_clip,
-                        has_snapshot=event_data["has_snapshot"],
+                        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'],
                     )
-
-                del self.events_in_process[event_data["id"]]
-                self.event_processed_queue.put((event_data["id"], camera, has_clip))
-
-        logger.info(f"Exiting event processor...")
-
+                del self.events_in_process[event_data['id']]
+                self.event_processed_queue.put((event_data['id'], camera))
 
 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_type):
+    def expire(self, media):
         ## Expire events from unlisted cameras based on the global config
-        if media_type == "clips":
-            retain_config = self.config.record.events.retain
-            file_extension = "mp4"
-            update_params = {"has_clip": False}
+        if media == 'clips':
+            retain_config = self.config.clips.retain
+            file_extension = 'mp4'
+            update_params = {'has_clip': False}
         else:
             retain_config = self.config.snapshots.retain
-            file_extension = "jpg"
-            update_params = {"has_snapshot": False}
-
-        distinct_labels = (
-            Event.select(Event.label)
-            .where(Event.camera.not_in(self.camera_keys))
-            .distinct()
-        )
-
+            file_extension = 'jpg'
+            update_params = {'has_snapshot': False}
+        
+        distinct_labels = (Event.select(Event.label)
+                    .where(Event.camera.not_in(self.camera_keys))
+                    .distinct())
+        
         # loop over object types in db
         for l in distinct_labels:
             # get expiration time for this label
             expire_days = retain_config.objects.get(l.label, retain_config.default)
-            expire_after = (
-                datetime.datetime.now() - datetime.timedelta(days=expire_days)
-            ).timestamp()
+            expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
             # grab all events after specific time
-            expired_events = Event.select().where(
-                Event.camera.not_in(self.camera_keys),
-                Event.start_time < expire_after,
-                Event.label == l.label,
+            expired_events = (
+                Event.select()
+                    .where(Event.camera.not_in(self.camera_keys), 
+                        Event.start_time < expire_after, 
+                        Event.label == l.label)
             )
             # delete the media from disk
             for event in expired_events:
                 media_name = f"{event.camera}-{event.id}"
-                media_path = Path(
-                    f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
-                )
-                media_path.unlink(missing_ok=True)
-                if file_extension == "jpg":
-                    media_path = Path(
-                        f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"
-                    )
-                    media_path.unlink(missing_ok=True)
-
+                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_type == "clips":
-                retain_config = camera.record.events.retain
+            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 = Path(
-                        f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
-                    )
-                    media_path.unlink(missing_ok=True)
-                    if file_extension == "jpg":
-                        media_path = Path(
-                            f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"
-                        )
-                        media_path.unlink(missing_ok=True)
+                    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()
-
-    def purge_duplicates(self):
-        duplicate_query = """with grouped_events as (
-          select id,
-            label,
-            camera,
-          	has_snapshot,
-          	has_clip,
-          	row_number() over (
-              partition by label, camera, round(start_time/5,0)*5
-              order by end_time-start_time desc
-            ) as copy_number
-          from event
-        )
-
-        select distinct id, camera, has_snapshot, has_clip from grouped_events
-        where copy_number > 1;"""
-
-        duplicate_events = Event.raw(duplicate_query)
-        for event in duplicate_events:
-            logger.debug(f"Removing duplicate: {event.id}")
-            media_name = f"{event.camera}-{event.id}"
-            if event.has_snapshot:
-                media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
-                media_path.unlink(missing_ok=True)
-            if event.has_clip:
-                media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
-                media_path.unlink(missing_ok=True)
-
-        (
-            Event.delete()
-            .where(Event.id << [event.id for event in duplicate_events])
-            .execute()
-        )
-
+    
     def run(self):
-        # only expire events every 5 minutes
-        while not self.stop_event.wait(300):
-            self.expire("clips")
-            self.expire("snapshots")
-            self.purge_duplicates()
+        counter = 0
+        while(True):
+            if self.stop_event.is_set():
+                logger.info(f"Exiting event cleanup...")
+                break
+
+            # only expire events every 10 minutes, but check for stop events every 10 seconds
+            time.sleep(10)
+            counter = counter + 1
+            if counter < 60:
+                continue
+            counter = 0
+
+            self.expire('clips')
+            self.expire('snapshots')
 
             # 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...")

frigate/http.py

@@ -1,56 +1,32 @@
 import base64
-from collections import OrderedDict
-from datetime import datetime, timedelta
-import json
-import glob
+import datetime
 import logging
 import os
-import re
-import subprocess as sp
 import time
 from functools import reduce
-from pathlib import Path
 
 import cv2
-from flask.helpers import send_file
-
 import numpy as np
-from flask import (
-    Blueprint,
-    Flask,
-    Response,
-    current_app,
-    jsonify,
-    make_response,
-    request,
-)
-
-from peewee import SqliteDatabase, operator, fn, DoesNotExist, Value
+from flask import (Blueprint, Flask, Response, current_app, jsonify,
+                   make_response, request)
+from peewee import SqliteDatabase, operator, fn, DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
-from frigate.const import CLIPS_DIR, RECORD_DIR
-from frigate.models import Event, Recordings
+from frigate.models import Event
 from frigate.stats import stats_snapshot
 from frigate.util import calculate_region
 from frigate.version import VERSION
 
 logger = logging.getLogger(__name__)
 
-bp = Blueprint("frigate", __name__)
+bp = Blueprint('frigate', __name__)
 
-
-def create_app(
-    frigate_config,
-    database: SqliteDatabase,
-    stats_tracking,
-    detected_frames_processor,
-):
+def create_app(frigate_config, database: SqliteDatabase, stats_tracking, detected_frames_processor):
     app = Flask(__name__)
 
     @app.before_request
     def _db_connect():
-        if database.is_closed():
-            database.connect()
+        database.connect()
 
     @app.teardown_request
     def _db_close(exc):
@@ -65,86 +41,56 @@ def create_app(
 
     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 = []
 
     if not has_clip is None:
         clauses.append((Event.has_clip == has_clip))
-
+    
     if not has_snapshot is None:
         clauses.append((Event.has_snapshot == has_snapshot))
 
     if len(clauses) == 0:
-        clauses.append((True))
+        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"),
+        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
+            )
         )
-        .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>", methods=("GET",))
+@bp.route('/events/<id>')
 def event(id):
     try:
         return model_to_dict(Event.get(Event.id == id))
     except DoesNotExist:
         return "Event not found", 404
 
-
-@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")
+@bp.route('/events/<id>/thumbnail.jpg')
+def event_snapshot(id):
+    format = request.args.get('format', 'ios')
     thumbnail_bytes = None
     try:
         event = Event.get(Event.id == id)
@@ -152,12 +98,11 @@ def event_thumbnail(id):
     except DoesNotExist:
         # see if the object is currently being tracked
         try:
-            camera_states = current_app.detected_frames_processor.camera_states.values()
-            for camera_state in camera_states:
+            for camera_state in current_app.detected_frames_processor.camera_states.values():
                 if id in camera_state.tracked_objects:
                     tracked_obj = camera_state.tracked_objects.get(id)
                     if not tracked_obj is None:
-                        thumbnail_bytes = tracked_obj.get_thumbnail()
+                        thumbnail_bytes = tracked_obj.get_jpg_bytes()
         except:
             return "Event not found", 404
 
@@ -165,120 +110,29 @@ 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)
         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")
-def event_snapshot(id):
-    download = request.args.get("download", type=bool)
-    jpg_bytes = None
-    try:
-        event = Event.get(Event.id == id)
-        if not event.has_snapshot:
-            return "Snapshot not available", 404
-        # read snapshot from disk
-        with open(
-            os.path.join(CLIPS_DIR, f"{event.camera}-{id}.jpg"), "rb"
-        ) as image_file:
-            jpg_bytes = image_file.read()
-    except DoesNotExist:
-        # see if the object is currently being tracked
-        try:
-            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),
-                            quality=request.args.get("quality", default=70, type=int),
-                        )
-        except:
-            return "Event not found", 404
-    except:
-        return "Event not found", 404
-
-    if jpg_bytes is None:
-        return "Event not found", 404
-
-    response = make_response(jpg_bytes)
-    response.headers["Content-Type"] = "image/jpg"
-    if download:
-        response.headers[
-            "Content-Disposition"
-        ] = f"attachment; filename=snapshot-{id}.jpg"
-    return response
-
-
-@bp.route("/events/<id>/clip.mp4")
-def event_clip(id):
-    download = request.args.get("download", type=bool)
-
-    try:
-        event: Event = Event.get(Event.id == id)
-    except DoesNotExist:
-        return "Event not found.", 404
-
-    if not event.has_clip:
-        return "Clip not available", 404
-
-    event_config = current_app.frigate_config.cameras[event.camera].record.events
-    start_ts = event.start_time - event_config.pre_capture
-    end_ts = event.end_time + event_config.post_capture
-    file_name = f"{event.camera}-{id}.mp4"
-    clip_path = os.path.join(CLIPS_DIR, file_name)
-
-    if not os.path.isfile(clip_path):
-        return recording_clip(event.camera, start_ts, end_ts)
-
-    response = make_response()
-    response.headers["Content-Description"] = "File Transfer"
-    response.headers["Cache-Control"] = "no-cache"
-    response.headers["Content-Type"] = "video/mp4"
-    if download:
-        response.headers["Content-Disposition"] = "attachment; filename=%s" % file_name
-    response.headers["Content-Length"] = os.path.getsize(clip_path)
-    response.headers[
-        "X-Accel-Redirect"
-    ] = f"/clips/{file_name}"  # nginx: http://wiki.nginx.org/NginxXSendfile
-
-    return response
-
-
-@bp.route("/events")
+@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=int)
+    before = request.args.get('before', type=int)
+    has_clip = request.args.get('has_clip', type=int)
+    has_snapshot = request.args.get('has_snapshot', type=int)
 
     clauses = []
-    excluded_fields = []
 
     if camera:
         clauses.append((Event.camera == camera))
@@ -287,7 +141,7 @@ 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))
@@ -297,438 +151,118 @@ def events():
 
     if not has_clip is None:
         clauses.append((Event.has_clip == has_clip))
-
+    
     if not has_snapshot is None:
         clauses.append((Event.has_snapshot == has_snapshot))
 
-    if not include_thumbnails:
-        excluded_fields.append(Event.thumbnail)
-
     if len(clauses) == 0:
-        clauses.append((True))
+        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])
+    return jsonify([model_to_dict(e) for e in events])
 
-
-@bp.route("/config")
+@bp.route('/config')
 def config():
-    config = current_app.frigate_config.dict()
+    return jsonify(current_app.frigate_config.to_dict())
 
-    # add in the ffmpeg_cmds
-    for camera_name, camera in current_app.frigate_config.cameras.items():
-        camera_dict = config["cameras"][camera_name]
-        camera_dict["ffmpeg_cmds"] = camera.ffmpeg_cmds
-        for cmd in camera_dict["ffmpeg_cmds"]:
-            cmd["cmd"] = " ".join(cmd["cmd"])
-
-    return jsonify(config)
-
-
-@bp.route("/config/schema")
-def config_schema():
-    return current_app.response_class(
-        current_app.frigate_config.schema_json(), mimetype="application/json"
-    )
-
-
-@bp.route("/version")
+@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])
-        resize_quality = request.args.get("quality", default=70, type=int)
+        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), resize_quality]
-        )
+        best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
+        ret, jpg = cv2.imencode('.jpg', best_frame)
         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),
     }
-    resize_quality = request.args.get("quality", default=70, type=int)
-
     if camera_name in current_app.frigate_config.cameras:
-        frame = current_app.detected_frames_processor.get_current_frame(
-            camera_name, draw_options
-        )
+        # max out at specified FPS
+        frame = current_app.detected_frames_processor.get_current_frame(camera_name, draw_options)
         if frame is None:
-            frame = np.zeros((720, 1280, 3), np.uint8)
+            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), resize_quality]
-        )
+        ret, jpg = cv2.imencode('.jpg', frame)
         response = make_response(jpg.tobytes())
-        response.headers["Content-Type"] = "image/jpg"
+        response.headers['Content-Type'] = 'image/jpg'
         return response
     else:
         return "Camera named {} not found".format(camera_name), 404
 
-
-@bp.route("/<camera_name>/recordings")
-def recordings(camera_name):
-    dates = OrderedDict()
-
-    # Retrieve all recordings for this camera
-    recordings = (
-        Recordings.select()
-        .where(Recordings.camera == camera_name)
-        .order_by(Recordings.start_time.asc())
-    )
-
-    last_end = 0
-    recording: Recordings
-    for recording in recordings:
-        date = datetime.fromtimestamp(recording.start_time)
-        key = date.strftime("%Y-%m-%d")
-        hour = date.strftime("%H")
-
-        # Create Day Record
-        if key not in dates:
-            dates[key] = OrderedDict()
-
-        # Create Hour Record
-        if hour not in dates[key]:
-            dates[key][hour] = {"delay": {}, "events": []}
-
-        # Check for delay
-        the_hour = datetime.strptime(f"{key} {hour}", "%Y-%m-%d %H").timestamp()
-        # diff current recording start time and the greater of the previous end time or top of the hour
-        diff = recording.start_time - max(last_end, the_hour)
-        # Determine seconds into recording
-        seconds = 0
-        if datetime.fromtimestamp(last_end).strftime("%H") == hour:
-            seconds = int(last_end - the_hour)
-        # Determine the delay
-        delay = min(int(diff), 3600 - seconds)
-        if delay > 1:
-            # Add an offset for any delay greater than a second
-            dates[key][hour]["delay"][seconds] = delay
-
-        last_end = recording.end_time
-
-    # Packing intervals to return all events with same label and overlapping times as one row.
-    # See: https://blogs.solidq.com/en/sqlserver/packing-intervals/
-    events = Event.raw(
-        """WITH C1 AS
-        (
-        SELECT id, label, camera, top_score, start_time AS ts, +1 AS type, 1 AS sub
-        FROM event
-        WHERE camera = ?
-        UNION ALL
-        SELECT id, label, camera, top_score, end_time + 15 AS ts, -1 AS type, 0 AS sub
-        FROM event
-        WHERE camera = ?
-        ),
-        C2 AS
-        (
-        SELECT C1.*,
-        SUM(type) OVER(PARTITION BY label ORDER BY ts, type DESC
-        ROWS BETWEEN UNBOUNDED PRECEDING
-        AND CURRENT ROW) - sub AS cnt
-        FROM C1
-        ),
-        C3 AS
-        (
-        SELECT id, label, camera, top_score, ts,
-        (ROW_NUMBER() OVER(PARTITION BY label ORDER BY ts) - 1) / 2 + 1
-        AS grpnum
-        FROM C2
-        WHERE cnt = 0
-        )
-        SELECT MIN(id) as id, label, camera, MAX(top_score) as top_score, MIN(ts) AS start_time, max(ts) AS end_time
-        FROM C3
-        GROUP BY label, grpnum
-        ORDER BY start_time;""",
-        camera_name,
-        camera_name,
-    )
-
-    event: Event
-    for event in events:
-        date = datetime.fromtimestamp(event.start_time)
-        key = date.strftime("%Y-%m-%d")
-        hour = date.strftime("%H")
-        if key in dates and hour in dates[key]:
-            dates[key][hour]["events"].append(
-                model_to_dict(
-                    event,
-                    exclude=[
-                        Event.false_positive,
-                        Event.zones,
-                        Event.thumbnail,
-                        Event.has_clip,
-                        Event.has_snapshot,
-                    ],
-                )
-            )
-
-    return jsonify(
-        [
-            {
-                "date": date,
-                "events": sum([len(value["events"]) for value in hours.values()]),
-                "recordings": [
-                    {"hour": hour, "delay": value["delay"], "events": value["events"]}
-                    for hour, value in hours.items()
-                ],
-            }
-            for date, hours in dates.items()
-        ]
-    )
-
-
-@bp.route("/<camera>/start/<int:start_ts>/end/<int:end_ts>/clip.mp4")
-@bp.route("/<camera>/start/<float:start_ts>/end/<float:end_ts>/clip.mp4")
-def recording_clip(camera, start_ts, end_ts):
-    download = request.args.get("download", type=bool)
-
-    recordings = (
-        Recordings.select()
-        .where(
-            (Recordings.start_time.between(start_ts, end_ts))
-            | (Recordings.end_time.between(start_ts, end_ts))
-            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
-        )
-        .where(Recordings.camera == camera)
-        .order_by(Recordings.start_time.asc())
-    )
-
-    playlist_lines = []
-    clip: Recordings
-    for clip in recordings:
-        playlist_lines.append(f"file '{clip.path}'")
-        # if this is the starting clip, add an inpoint
-        if clip.start_time < start_ts:
-            playlist_lines.append(f"inpoint {int(start_ts - clip.start_time)}")
-        # if this is the ending clip, add an outpoint
-        if clip.end_time > end_ts:
-            playlist_lines.append(f"outpoint {int(end_ts - clip.start_time)}")
-
-    file_name = f"clip_{camera}_{start_ts}-{end_ts}.mp4"
-    path = f"/tmp/cache/{file_name}"
-
-    ffmpeg_cmd = [
-        "ffmpeg",
-        "-y",
-        "-protocol_whitelist",
-        "pipe,file",
-        "-f",
-        "concat",
-        "-safe",
-        "0",
-        "-i",
-        "-",
-        "-c",
-        "copy",
-        "-movflags",
-        "+faststart",
-        path,
-    ]
-
-    p = sp.run(
-        ffmpeg_cmd,
-        input="\n".join(playlist_lines),
-        encoding="ascii",
-        capture_output=True,
-    )
-    if p.returncode != 0:
-        logger.error(p.stderr)
-        return f"Could not create clip from recordings for {camera}.", 500
-
-    response = make_response()
-    response.headers["Content-Description"] = "File Transfer"
-    response.headers["Cache-Control"] = "no-cache"
-    response.headers["Content-Type"] = "video/mp4"
-    if download:
-        response.headers["Content-Disposition"] = "attachment; filename=%s" % file_name
-    response.headers["Content-Length"] = os.path.getsize(path)
-    response.headers[
-        "X-Accel-Redirect"
-    ] = f"/cache/{file_name}"  # nginx: http://wiki.nginx.org/NginxXSendfile
-
-    return response
-
-
-@bp.route("/vod/<camera>/start/<int:start_ts>/end/<int:end_ts>")
-@bp.route("/vod/<camera>/start/<float:start_ts>/end/<float:end_ts>")
-def vod_ts(camera, start_ts, end_ts):
-    recordings = (
-        Recordings.select()
-        .where(
-            Recordings.start_time.between(start_ts, end_ts)
-            | Recordings.end_time.between(start_ts, end_ts)
-            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
-        )
-        .where(Recordings.camera == camera)
-        .order_by(Recordings.start_time.asc())
-    )
-
-    clips = []
-    durations = []
-
-    recording: Recordings
-    for recording in recordings:
-        clip = {"type": "source", "path": recording.path}
-        duration = int(recording.duration * 1000)
-        # Determine if offset is needed for first clip
-        if recording.start_time < start_ts:
-            offset = int((start_ts - recording.start_time) * 1000)
-            clip["clipFrom"] = offset
-            duration -= offset
-        # Determine if we need to end the last clip early
-        if recording.end_time > end_ts:
-            duration -= int((recording.end_time - end_ts) * 1000)
-        clips.append(clip)
-        durations.append(duration)
-
-    if not clips:
-        return "No recordings found.", 404
-
-    hour_ago = datetime.now() - timedelta(hours=1)
-    return jsonify(
-        {
-            "cache": hour_ago.timestamp() > start_ts,
-            "discontinuity": False,
-            "durations": durations,
-            "sequences": [{"clips": clips}],
-        }
-    )
-
-
-@bp.route("/vod/<year_month>/<day>/<hour>/<camera>")
-def vod_hour(year_month, day, hour, camera):
-    start_date = datetime.strptime(f"{year_month}-{day} {hour}", "%Y-%m-%d %H")
-    end_date = start_date + timedelta(hours=1) - timedelta(milliseconds=1)
-    start_ts = start_date.timestamp()
-    end_ts = end_date.timestamp()
-
-    return vod_ts(camera, start_ts, end_ts)
-
-
-@bp.route("/vod/event/<id>")
-def vod_event(id):
-    try:
-        event: Event = Event.get(Event.id == id)
-    except DoesNotExist:
-        return "Event not found.", 404
-
-    if not event.has_clip:
-        return "Clip not available", 404
-
-    event_config = current_app.frigate_config.cameras[event.camera].record.events
-    start_ts = event.start_time - event_config.pre_capture
-    end_ts = event.end_time + event_config.post_capture
-    clip_path = os.path.join(CLIPS_DIR, f"{event.camera}-{id}.mp4")
-
-    if not os.path.isfile(clip_path):
-        return vod_ts(event.camera, start_ts, end_ts)
-
-    duration = int((end_ts - start_ts) * 1000)
-    return jsonify(
-        {
-            "cache": True,
-            "discontinuity": False,
-            "durations": [duration],
-            "sequences": [{"clips": [{"type": "source", "path": clip_path}]}],
-        }
-    )
-
-
 def imagestream(detected_frames_processor, camera_name, fps, height, draw_options):
     while True:
         # max out at specified FPS
-        time.sleep(1 / fps)
+        time.sleep(1/fps)
         frame = detected_frames_processor.get_current_frame(camera_name, draw_options)
         if frame is None:
-            frame = np.zeros((height, int(height * 16 / 9), 3), np.uint8)
+            frame = np.zeros((height,int(height*16/9),3), np.uint8)
 
-        width = int(height * frame.shape[1] / frame.shape[0])
+        width = int(height*frame.shape[1]/frame.shape[0])
         frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_LINEAR)
 
-        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
-        yield (
-            b"--frame\r\n"
-            b"Content-Type: image/jpeg\r\n\r\n" + jpg.tobytes() + b"\r\n\r\n"
-        )
+        ret, jpg = cv2.imencode('.jpg', frame)
+        yield (b'--frame\r\n'
+            b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')

frigate/log.py

@@ -7,40 +7,43 @@ import queue
 import multiprocessing as mp
 from logging import handlers
 from setproctitle import setproctitle
-from collections import deque
 
 
 def listener_configurer():
     root = logging.getLogger()
     console_handler = logging.StreamHandler()
-    formatter = logging.Formatter(
-        "[%(asctime)s] %(name)-30s %(levelname)-8s: %(message)s", "%Y-%m-%d %H:%M:%S"
-    )
+    formatter = logging.Formatter('%(name)-30s %(levelname)-8s: %(message)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, KeyboardInterrupt):
+        except queue.Empty:
             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):
@@ -51,26 +54,24 @@ class LogPipe(threading.Thread):
         self.daemon = False
         self.logger = logging.getLogger(log_name)
         self.level = level
-        self.deque = deque(maxlen=100)
         self.fdRead, self.fdWrite = os.pipe()
         self.pipeReader = os.fdopen(self.fdRead)
         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.logger.log(self.level, line.strip('\n'))
 
         self.pipeReader.close()
 
-    def dump(self):
-        while len(self.deque) > 0:
-            self.logger.log(self.level, self.deque.popleft())
-
     def close(self):
-        """Close the write end of the pipe."""
+        """Close the write end of the pipe.
+        """
         os.close(self.fdWrite)

frigate/models.py

@@ -1,4 +1,3 @@
-from numpy import unique
 from peewee import *
 from playhouse.sqlite_ext import *
 
@@ -15,12 +14,3 @@ class Event(Model):
     thumbnail = TextField()
     has_clip = BooleanField(default=True)
     has_snapshot = BooleanField(default=True)
-
-
-class Recordings(Model):
-    id = CharField(null=False, primary_key=True, max_length=30)
-    camera = CharField(index=True, max_length=20)
-    path = CharField(unique=True)
-    start_time = DateTimeField()
-    end_time = DateTimeField()
-    duration = FloatField()

frigate/motion.py

@@ -4,37 +4,26 @@ 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?
 
@@ -59,9 +48,7 @@ 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)
@@ -69,9 +56,7 @@ 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
@@ -85,27 +70,16 @@ 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

frigate/mqtt.py

@@ -1,42 +1,31 @@
-import json
 import logging
 import threading
-from wsgiref.simple_server import make_server
 
 import paho.mqtt.client as mqtt
-from ws4py.server.wsgirefserver import (
-    WebSocketWSGIHandler,
-    WebSocketWSGIRequestHandler,
-    WSGIServer,
-)
-from ws4py.server.wsgiutils import WebSocketWSGIApplication
-from ws4py.websocket import WebSocket
 
 from frigate.config import FrigateConfig
-from frigate.util import restart_frigate
 
 logger = logging.getLogger(__name__)
 
-
 def create_mqtt_client(config: FrigateConfig, camera_metrics):
     mqtt_config = config.mqtt
 
-    def on_recordings_command(client, userdata, message):
+    def on_clips_command(client, userdata, message):
         payload = message.payload.decode()
-        logger.debug(f"on_recordings_toggle: {message.topic} {payload}")
+        logger.debug(f"on_clips_toggle: {message.topic} {payload}")
 
-        camera_name = message.topic.split("/")[-3]
+        camera_name = message.topic.split('/')[-3]
 
-        record_settings = config.cameras[camera_name].record
+        clips_settings = config.cameras[camera_name].clips
 
-        if payload == "ON":
-            if not record_settings.enabled:
-                logger.info(f"Turning on recordings for {camera_name} via mqtt")
-                record_settings.enabled = True
-        elif payload == "OFF":
-            if record_settings.enabled:
-                logger.info(f"Turning off recordings for {camera_name} via mqtt")
-                record_settings.enabled = False
+        if payload == 'ON':
+            if not clips_settings.enabled:
+                logger.info(f"Turning on clips for {camera_name} via mqtt")
+                clips_settings._enabled = True
+        elif payload == 'OFF':
+            if clips_settings.enabled:
+                logger.info(f"Turning off clips for {camera_name} via mqtt")
+                clips_settings._enabled = False
         else:
             logger.warning(f"Received unsupported value at {message.topic}: {payload}")
 
@@ -47,106 +36,73 @@ def create_mqtt_client(config: FrigateConfig, camera_metrics):
         payload = message.payload.decode()
         logger.debug(f"on_snapshots_toggle: {message.topic} {payload}")
 
-        camera_name = message.topic.split("/")[-3]
+        camera_name = message.topic.split('/')[-3]
 
         snapshots_settings = config.cameras[camera_name].snapshots
 
-        if payload == "ON":
+        if payload == 'ON':
             if not snapshots_settings.enabled:
                 logger.info(f"Turning on snapshots for {camera_name} via mqtt")
-                snapshots_settings.enabled = True
-        elif payload == "OFF":
+                snapshots_settings._enabled = True
+        elif payload == 'OFF':
             if snapshots_settings.enabled:
                 logger.info(f"Turning off snapshots for {camera_name} via mqtt")
-                snapshots_settings.enabled = False
+                snapshots_settings._enabled = False
         else:
             logger.warning(f"Received unsupported value at {message.topic}: {payload}")
 
         state_topic = f"{message.topic[:-4]}/state"
         client.publish(state_topic, payload, retain=True)
-
+    
     def on_detect_command(client, userdata, message):
         payload = message.payload.decode()
         logger.debug(f"on_detect_toggle: {message.topic} {payload}")
 
-        camera_name = message.topic.split("/")[-3]
+        camera_name = message.topic.split('/')[-3]
 
         detect_settings = config.cameras[camera_name].detect
 
-        if payload == "ON":
+        if payload == 'ON':
             if not camera_metrics[camera_name]["detection_enabled"].value:
                 logger.info(f"Turning on detection for {camera_name} via mqtt")
                 camera_metrics[camera_name]["detection_enabled"].value = True
-                detect_settings.enabled = True
-        elif payload == "OFF":
+                detect_settings._enabled = True
+        elif payload == 'OFF':
             if camera_metrics[camera_name]["detection_enabled"].value:
                 logger.info(f"Turning off detection for {camera_name} via mqtt")
                 camera_metrics[camera_name]["detection_enabled"].value = False
-                detect_settings.enabled = False
+                detect_settings._enabled = False
         else:
             logger.warning(f"Received unsupported value at {message.topic}: {payload}")
 
         state_topic = f"{message.topic[:-4]}/state"
         client.publish(state_topic, payload, retain=True)
 
-    def on_restart_command(client, userdata, message):
-        restart_frigate()
-
     def on_connect(client, userdata, flags, rc):
         threading.current_thread().name = "mqtt"
         if rc != 0:
             if rc == 3:
-                logger.error("Unable to connect to MQTT server: MQTT Server unavailable")
+                logger.error("MQTT Server unavailable")
             elif rc == 4:
-                logger.error("Unable to connect to MQTT server: MQTT Bad username or password")
+                logger.error("MQTT Bad username or password")
             elif rc == 5:
-                logger.error("Unable to connect to MQTT server: MQTT Not authorized")
+                logger.error("MQTT Not authorized")
             else:
-                logger.error(
-                    "Unable to connect to MQTT server: 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 = 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}/recordings/set", on_recordings_command
-        )
-        client.message_callback_add(
-            f"{mqtt_config.topic_prefix}/{name}/snapshots/set", on_snapshots_command
-        )
-        client.message_callback_add(
-            f"{mqtt_config.topic_prefix}/{name}/detect/set", on_detect_command
-        )
+        client.message_callback_add(f"{mqtt_config.topic_prefix}/{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}/restart", on_restart_command
-    )
-
-    if not mqtt_config.tls_ca_certs is None:
-        if (
-            not mqtt_config.tls_client_cert is None
-            and not mqtt_config.tls_client_key is None
-        ):
-            client.tls_set(
-                mqtt_config.tls_ca_certs,
-                mqtt_config.tls_client_cert,
-                mqtt_config.tls_client_key,
-            )
-        else:
-            client.tls_set(mqtt_config.tls_ca_certs)
-    if not mqtt_config.tls_insecure is None:
-        client.tls_insecure_set(mqtt_config.tls_insecure)
     if not mqtt_config.user is None:
         client.username_pw_set(mqtt_config.user, password=mqtt_config.password)
     try:
@@ -158,96 +114,12 @@ 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}/recordings/state",
-            "ON" if config.cameras[name].record.enabled else "OFF",
-            retain=True,
-        )
-        client.publish(
-            f"{mqtt_config.topic_prefix}/{name}/snapshots/state",
-            "ON" if config.cameras[name].snapshots.enabled else "OFF",
-            retain=True,
-        )
-        client.publish(
-            f"{mqtt_config.topic_prefix}/{name}/detect/state",
-            "ON" if config.cameras[name].detect.enabled else "OFF",
-            retain=True,
-        )
+        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].clips.enabled else 'OFF', retain=True)
+        client.publish(f"{mqtt_config.topic_prefix}/{name}/detect/state", 'ON' if config.cameras[name].clips.enabled else 'OFF', retain=True)
+
+    client.subscribe(f"{mqtt_config.topic_prefix}/+/clips/set")
+    client.subscribe(f"{mqtt_config.topic_prefix}/+/snapshots/set")
+    client.subscribe(f"{mqtt_config.topic_prefix}/+/detect/set")
 
     return client
-
-
-class MqttSocketRelay:
-    def __init__(self, mqtt_client, topic_prefix):
-        self.mqtt_client = mqtt_client
-        self.topic_prefix = topic_prefix
-
-    def start(self):
-        class MqttWebSocket(WebSocket):
-            topic_prefix = self.topic_prefix
-            mqtt_client = self.mqtt_client
-
-            def received_message(self, message):
-                try:
-                    json_message = json.loads(message.data.decode("utf-8"))
-                    json_message = {
-                        "topic": f"{self.topic_prefix}/{json_message['topic']}",
-                        "payload": json_message.get("payload"),
-                        "retain": json_message.get("retain", False),
-                    }
-                except Exception as e:
-                    logger.warning("Unable to parse websocket message as valid json.")
-                    return
-
-                logger.debug(
-                    f"Publishing mqtt message from websockets at {json_message['topic']}."
-                )
-                self.mqtt_client.publish(
-                    json_message["topic"],
-                    json_message["payload"],
-                    retain=json_message["retain"],
-                )
-
-        # start a websocket server on 5002
-        WebSocketWSGIHandler.http_version = "1.1"
-        self.websocket_server = make_server(
-            "127.0.0.1",
-            5002,
-            server_class=WSGIServer,
-            handler_class=WebSocketWSGIRequestHandler,
-            app=WebSocketWSGIApplication(handler_cls=MqttWebSocket),
-        )
-        self.websocket_server.initialize_websockets_manager()
-        self.websocket_thread = threading.Thread(
-            target=self.websocket_server.serve_forever
-        )
-
-        def send(client, userdata, message):
-            """Sends mqtt messages to clients."""
-            try:
-                logger.debug(f"Received mqtt message on {message.topic}.")
-                ws_message = json.dumps(
-                    {
-                        "topic": message.topic.replace(f"{self.topic_prefix}/", ""),
-                        "payload": message.payload.decode(),
-                    }
-                )
-            except Exception as e:
-                # if the payload can't be decoded don't relay to clients
-                logger.debug(
-                    f"MQTT payload for {message.topic} wasn't text. Skipping..."
-                )
-                return
-
-            self.websocket_server.manager.broadcast(ws_message)
-
-        self.mqtt_client.message_callback_add(f"{self.topic_prefix}/#", send)
-
-        self.websocket_thread.start()
-
-    def stop(self):
-        self.websocket_server.manager.close_all()
-        self.websocket_server.manager.stop()
-        self.websocket_server.manager.join()
-        self.websocket_server.shutdown()
-        self.websocket_thread.join()

frigate/object_processing.py

@@ -1,5 +1,5 @@
-import base64
 import copy
+import base64
 import datetime
 import hashlib
 import itertools
@@ -14,60 +14,57 @@ from statistics import mean, median
 from typing import Callable, Dict
 
 import cv2
+import matplotlib.pyplot as plt
 import numpy as np
 
-from frigate.config import CameraConfig, FrigateConfig
-from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
+from frigate.config import FrigateConfig, CameraConfig
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
 from frigate.edgetpu import load_labels
-from frigate.util import (
-    SharedMemoryFrameManager,
-    calculate_region,
-    draw_box_with_label,
-    draw_timestamp,
-)
+from frigate.util import SharedMemoryFrameManager, draw_box_with_label, calculate_region
 
 logger = logging.getLogger(__name__)
 
+PATH_TO_LABELS = '/labelmap.txt'
+
+LABELS = load_labels(PATH_TO_LABELS)
+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"] + 0.05:
+    if new_obj['score'] > current_thumb['score']+.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:
-    def __init__(
-        self, camera, colormap, camera_config: CameraConfig, frame_cache, obj_data
-    ):
+class TrackedObject():
+    def __init__(self, camera, camera_config: CameraConfig, frame_cache, obj_data):
         self.obj_data = obj_data
         self.camera = camera
-        self.colormap = colormap
         self.camera_config = camera_config
         self.frame_cache = frame_cache
         self.current_zones = []
@@ -75,38 +72,37 @@ class TrackedObject:
         self.false_positive = True
         self.top_score = self.computed_score = 0.0
         self.thumbnail_data = None
-        self.last_updated = 0
-        self.last_published = 0
         self.frame = None
         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
-        return self.computed_score < threshold
+        threshold = self.camera_config.objects.filters[self.obj_data['label']].threshold
+        if self.computed_score < threshold:
+            return True
+        return False
 
     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
-        zone_change = 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:]
@@ -119,29 +115,27 @@ 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():
-            # if the zone is not for this object type, skip
-            if len(zone.objects) > 0 and not self.obj_data["label"] in zone.objects:
-                continue
             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)
@@ -149,161 +143,94 @@ class TrackedObject:
 
         # if the zones changed, signal an update
         if not self.false_positive and set(self.current_zones) != set(current_zones):
-            zone_change = True
+            significant_update = True
 
         self.current_zones = current_zones
-        return (significant_update, zone_change)
+        return significant_update
 
     def to_dict(self, include_thumbnail: bool = False):
-        snapshot_time = (
-            self.thumbnail_data["frame_time"]
-            if not self.thumbnail_data is None
-            else 0.0
-        )
-        event = {
-            "id": self.obj_data["id"],
-            "camera": self.camera,
-            "frame_time": self.obj_data["frame_time"],
-            "snapshot_time": snapshot_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(),
+        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
         }
 
-        if include_thumbnail:
-            event["thumbnail"] = base64.b64encode(self.get_thumbnail()).decode("utf-8")
-
-        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))
+        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))
 
-        jpg_bytes = self.get_jpg_bytes(
-            timestamp=False, bounding_box=False, crop=True, height=175
-        )
+        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_clean_png(self):
+    
+    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,
-            )
-        except KeyError:
-            logger.warning(
-                f"Unable to create clean png because frame {self.thumbnail_data['frame_time']} is not in the cache"
-            )
-            return None
-
-        ret, png = cv2.imencode(".png", best_frame)
-        if ret:
-            return png.tobytes()
-        else:
-            return None
-
-    def get_jpg_bytes(
-        self, timestamp=False, bounding_box=False, crop=False, height=None, quality=70
-    ):
-        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,
-            )
-        except KeyError:
-            logger.warning(
-                f"Unable to create jpg because frame {self.thumbnail_data['frame_time']} is not in the cache"
-            )
-            return None
-
+            
+        best_frame = cv2.cvtColor(self.frame_cache[self.thumbnail_data['frame_time']], cv2.COLOR_YUV2BGR_I420)
+ 
         if bounding_box:
             thickness = 2
-            color = self.colormap[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
-            )
-        if timestamp:
-            color = self.camera_config.timestamp_style.color
-            draw_timestamp(
-                best_frame,
-                self.thumbnail_data["frame_time"],
-                self.camera_config.timestamp_style.format,
-                font_effect=self.camera_config.timestamp_style.effect,
-                font_scale=self.camera_config.timestamp_style.scale,
-                font_thickness=self.camera_config.timestamp_style.thickness,
-                font_color=(color.red, color.green, color.blue),
-                position=self.camera_config.timestamp_style.position,
-            )
+            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), quality]
-        )
+        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)
+            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)
+
+        ret, jpg = cv2.imencode('.jpg', best_frame)
         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
@@ -312,110 +239,70 @@ def zone_filtered(obj: TrackedObject, object_config):
 
     return False
 
-
 # Maintains the state of a camera
-class CameraState:
-    def __init__(
-        self, name, config: FrigateConfig, frame_manager: SharedMemoryFrameManager
-    ):
+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(int)
+        self.object_counts = defaultdict(lambda: 0)
         self.tracked_objects: Dict[str, TrackedObject] = {}
         self.frame_cache = {}
-        self.zone_objects = defaultdict(list)
+        self.zone_objects = defaultdict(lambda: [])
         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(list)
+        self.callbacks = defaultdict(lambda: [])
 
     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():
-                if obj["frame_time"] == frame_time:
-                    thickness = 2
-                    color = self.config.model.colormap[obj["label"]]
-                else:
+                thickness = 2
+                color = COLOR_MAP[obj['label']]
+
+                if obj['frame_time'] != frame_time:
                     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"{obj['score']:.0%} {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"{int(obj['score']*100)}% {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"):
-            color = self.camera_config.timestamp_style.color
-            draw_timestamp(
-                frame_copy,
-                frame_time,
-                self.camera_config.timestamp_style.format,
-                font_effect=self.camera_config.timestamp_style.effect,
-                font_scale=self.camera_config.timestamp_style.scale,
-                font_thickness=self.camera_config.timestamp_style.thickness,
-                font_color=(color.red, color.green, color.blue),
-                position=self.camera_config.timestamp_style.position,
-            )
+        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)
 
         return frame_copy
 
@@ -426,164 +313,106 @@ 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)
 
-        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)
+        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))
 
         for id in new_ids:
-            new_obj = tracked_objects[id] = TrackedObject(
-                self.name,
-                self.config.model.colormap,
-                self.camera_config,
-                self.frame_cache,
-                current_detections[id],
-            )
+            new_obj = self.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 = tracked_objects[id]
-            significant_update, zone_change = updated_obj.update(
-                frame_time, current_detections[id]
-            )
+            updated_obj = self.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 or
-            # the object has changed zones
-            if (
-                frame_time - updated_obj.last_published > 5
-                and updated_obj.last_updated > updated_obj.last_published
-            ) or zone_change:
                 # 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 = 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 = 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']:
                     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 tracked_objects.values():
-            object_type = obj.obj_data["label"]
+        for obj in self.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"] != frame_time:
+            if obj.false_positive or obj.thumbnail_data['frame_time'] != self.current_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(
-            obj.obj_data["label"]
-            for obj in tracked_objects.values()
-            if not obj.false_positive
-        )
+        obj_counter = Counter()
+        for obj in self.tracked_objects.values():
+            if not obj.false_positive:
+                obj_counter[obj.obj_data['label']] += 1
 
         # 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 obj_name not in obj_counter
-        ]
+        expired_objects = [obj_name for obj_name, count in self.object_counts.items() if count > 0 and not obj_name 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 = {
-            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
-        ]
+        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]
         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 self.previous_frame_id is not None:
-                self.frame_manager.close(self.previous_frame_id)
+            if not self.previous_frame_id is 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,
-        video_output_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
@@ -592,114 +421,60 @@ class TrackedObjectProcessor(threading.Thread):
         self.tracked_objects_queue = tracked_objects_queue
         self.event_queue = event_queue
         self.event_processed_queue = event_processed_queue
-        self.video_output_queue = video_output_queue
         self.stop_event = stop_event
         self.camera_states: Dict[str, CameraState] = {}
         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):
+                if snapshot_config.enabled:
                     jpg_bytes = obj.get_jpg_bytes(
                         timestamp=snapshot_config.timestamp,
                         bounding_box=snapshot_config.bounding_box,
                         crop=snapshot_config.crop,
-                        height=snapshot_config.height,
-                        quality=snapshot_config.quality,
+                        height=snapshot_config.height
                     )
-                    if jpg_bytes is None:
-                        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:
-                            j.write(jpg_bytes)
-                        event_data["has_snapshot"] = True
-
-                    # write clean snapshot if enabled
-                    if snapshot_config.clean_copy:
-                        png_bytes = obj.get_clean_png()
-                        if png_bytes is None:
-                            logger.warning(
-                                f"Unable to save clean snapshot for {obj.obj_data['id']}."
-                            )
-                        else:
-                            with open(
-                                os.path.join(
-                                    CLIPS_DIR,
-                                    f"{camera}-{obj.obj_data['id']}-clean.png",
-                                ),
-                                "wb",
-                            ) as p:
-                                p.write(png_bytes)
-            self.event_queue.put(("end", camera, event_data))
-
+                    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))
+        
         def snapshot(camera, obj: TrackedObject, current_frame_time):
             mqtt_config = self.config.cameras[camera].mqtt
-            if mqtt_config.enabled and self.should_mqtt_snapshot(camera, obj):
+            if mqtt_config.enabled:
                 jpg_bytes = obj.get_jpg_bytes(
                     timestamp=mqtt_config.timestamp,
                     bounding_box=mqtt_config.bounding_box,
                     crop=mqtt_config.crop,
-                    height=mqtt_config.height,
-                    quality=mqtt_config.quality,
+                    height=mqtt_config.height
                 )
-
-                if jpg_bytes is None:
-                    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
 
         # {
@@ -710,29 +485,7 @@ class TrackedObjectProcessor(threading.Thread):
         #       }
         #   }
         # }
-        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"
-            )
-            return False
-
-        return True
-
-    def should_mqtt_snapshot(self, camera, obj: TrackedObject):
-        # 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"
-            )
-            return False
-
-        return True
+        self.zone_data = defaultdict(lambda: defaultdict(lambda: {}))
 
     def get_best(self, camera, label):
         # TODO: need a lock here
@@ -740,9 +493,7 @@ 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 {}
@@ -751,83 +502,46 @@ class TrackedObjectProcessor(threading.Thread):
         return self.camera_states[camera].get_current_frame(draw_options)
 
     def run(self):
-        while not self.stop_event.is_set():
+        while True:
+            if self.stop_event.is_set():
+                logger.info(f"Exiting object processor...")
+                break
+
             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
-            )
-
-            self.video_output_queue.put(
-                (
-                    camera,
-                    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(
-                    obj.obj_data["label"]
-                    for obj in camera_state.tracked_objects.values()
-                    if zone in obj.current_zones and not obj.false_positive
-                )
+                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
 
                 # update counts and publish status
-                for label in set(self.zone_data[zone].keys()) | set(obj_counter.keys()):
+                for label in set(list(self.zone_data[zone].keys()) + list(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, clip_created = self.event_processed_queue.get()
-                if clip_created:
-                    obj = self.camera_states[camera].tracked_objects[event_id]
-                    message = {
-                        "before": obj.previous,
-                        "after": obj.to_dict(),
-                        "type": "clip_ready",
-                    }
-                    self.client.publish(
-                        f"{self.topic_prefix}/events", json.dumps(message), retain=False
-                    )
+                event_id, camera = self.event_processed_queue.get()
                 self.camera_states[camera].finished(event_id)
-
-        logger.info(f"Exiting object processor...")

frigate/objects.py

@@ -16,24 +16,24 @@ from frigate.config import DetectConfig
 from frigate.util import draw_box_with_label
 
 
-class ObjectTracker:
+class ObjectTracker():
     def __init__(self, config: DetectConfig):
         self.tracked_objects = {}
         self.disappeared = {}
         self.max_disappeared = config.max_disappeared
 
     def register(self, index, obj):
-        rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
+        rand_id = ''.join(random.choices(string.ascii_lowercase + string.digits, k=6))
         id = f"{obj['frame_time']}-{rand_id}"
-        obj["id"] = id
-        obj["start_time"] = obj["frame_time"]
+        obj['id'] = id
+        obj['start_time'] = obj['frame_time']
         self.tracked_objects[id] = obj
         self.disappeared[id] = 0
 
     def deregister(self, id):
         del self.tracked_objects[id]
         del self.disappeared[id]
-
+    
     def update(self, id, new_obj):
         self.disappeared[id] = 0
         self.tracked_objects[id].update(new_obj)
@@ -42,90 +42,97 @@ 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)
-                continue
-
-            new_centroids = np.array([o["centroid"] for o in group])
+                return
+            
+            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 current centroid to a new
+            # goal will be to match each new centroid to an existing
             # 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 (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
+            # 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
             rows = D.min(axis=1).argsort()
 
-            # next, we determine which new object each existing object matched
-            # against, and apply the same sorting as was applied previously
+            # 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
             cols = D.argmin(axis=1)[rows]
 
-            # 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]
+            # 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()
 
-            # 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
+            # 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
                 objectID = current_ids[row]
                 self.update(objectID, group[col])
 
-            # 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)
+                # 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)
 
             # 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]

frigate/output.py

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

frigate/process_clip.py

@@ -14,41 +14,39 @@ import numpy as np
 from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
 from frigate.edgetpu import LocalObjectDetector
 from frigate.motion import MotionDetector
-from frigate.object_processing import CameraState
+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 = [
-        "ffprobe",
-        "-v",
-        "panic",
-        "-show_error",
-        "-show_streams",
-        "-of",
-        "json",
-        source,
-    ]
-    p = sp.run(ffprobe_cmd, capture_output=True)
-    info = json.loads(p.stdout)
+    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)
 
-    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)
+    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)
+    
     # fallback to using opencv if ffprobe didnt succeed
     video = cv2.VideoCapture(source)
     ret, frame = video.read()
@@ -56,17 +54,14 @@ 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()
@@ -75,66 +70,37 @@ 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
@@ -142,85 +108,48 @@ 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)
 
-        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()
+        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.camera_state.update(
-                frame_time, current_tracked_objects, motion_boxes, regions
-            )
+                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.frame_manager.delete(self.camera_state.previous_frame_id)
-
-        return {"object_detected": obj_detected, "top_score": top_computed_score}
-
+        
+        return {
+            'object_detected': obj_detected,
+            'top_score': top_computed_score
+        }
+    
     def save_debug_frame(self, debug_path, frame_time, tracked_objects):
-        current_frame = cv2.cvtColor(
-            self.frame_manager.get(
-                f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv
-            ),
-            cv2.COLOR_YUV2BGR_I420,
-        )
+        current_frame = cv2.cvtColor(self.frame_manager.get(f"{self.camera_name}{frame_time}", self.camera_config.frame_shape_yuv), cv2.COLOR_YUV2BGR_I420)
         # draw the bounding boxes on the frame
         for obj in tracked_objects:
             thickness = 2
-            color = (0, 0, 175)
+            color = (0,0,175)
 
-            if obj["frame_time"] != frame_time:
+            if obj['frame_time'] != frame_time:
                 thickness = 1
-                color = (255, 0, 0)
+                color = (255,0,0)
             else:
-                color = (255, 255, 0)
+                color = (255,255,0)
 
             # draw the bounding boxes on the frame
-            box = obj["box"]
-            draw_box_with_label(
-                current_frame,
-                box[0],
-                box[1],
-                box[2],
-                box[3],
-                obj["id"],
-                f"{int(obj['score']*100)}% {int(obj['area'])}",
-                thickness=thickness,
-                color=color,
-            )
+            box = obj['box']
+            draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['id'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
             # draw the regions on the frame
-            region = obj["region"]
-            draw_box_with_label(
-                current_frame,
-                region[0],
-                region[1],
-                region[2],
-                region[3],
-                "region",
-                "",
-                thickness=1,
-                color=(0, 255, 0),
-            )
-
-        cv2.imwrite(
-            f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg",
-            current_frame,
-        )
-
+            region = obj['region']
+            draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0))
+        
+        cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", current_frame)
 
 @click.command()
 @click.option("-p", "--path", required=True, help="Path to clip or directory to test.")
-@click.option("-l", "--label", default="person", help="Label name to detect.")
+@click.option("-l", "--label", default='person', help="Label name to detect.")
 @click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.")
 @click.option("-s", "--scores", default=None, help="File to save csv of top scores")
 @click.option("--debug-path", default=None, help="Path to output frames for debugging.")
@@ -230,37 +159,34 @@ def process(path, label, threshold, scores, debug_path):
         files = os.listdir(path)
         files.sort()
         clips = [os.path.join(path, file) for file in files]
-    elif os.path.isfile(path):
+    elif os.path.isfile(path):  
         clips.append(path)
 
     json_config = {
-        "mqtt": {"host": "mqtt"},
-        "cameras": {
-            "camera": {
-                "ffmpeg": {
-                    "inputs": [
-                        {
-                            "path": "path.mp4",
-                            "global_args": "",
-                            "input_args": "",
-                            "roles": ["detect"],
-                        }
+        'mqtt': {
+            'host': 'mqtt'
+        },
+        'cameras': {
+            'camera': {
+                'ffmpeg': {
+                    'inputs': [
+                        { 'path': 'path.mp4', 'global_args': '', 'input_args': '', 'roles': ['detect'] }
                     ]
                 },
-                "height": 1920,
-                "width": 1080,
+                'height': 1920,
+                'width': 1080
             }
-        },
+        }
     }
 
     results = []
     for c in clips:
         logger.info(c)
         frame_shape = get_frame_shape(c)
-
-        json_config["cameras"]["camera"]["height"] = frame_shape[0]
-        json_config["cameras"]["camera"]["width"] = frame_shape[1]
-        json_config["cameras"]["camera"]["ffmpeg"]["inputs"][0]["path"] = c
+        
+        json_config['cameras']['camera']['height'] = frame_shape[0]
+        json_config['cameras']['camera']['width'] = frame_shape[1]
+        json_config['cameras']['camera']['ffmpeg']['inputs'][0]['path'] = c
 
         config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config))
 
@@ -271,15 +197,12 @@ 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()

frigate/record.py

@@ -1,301 +1,125 @@
 import datetime
-import itertools
+import json
 import logging
 import os
-import random
-import shutil
-import string
+import queue
 import subprocess as sp
 import threading
+import time
+from collections import defaultdict
 from pathlib import Path
 
 import psutil
 
-from peewee import JOIN
-
 from frigate.config import FrigateConfig
-from frigate.const import CACHE_DIR, RECORD_DIR
-from frigate.models import Event, Recordings
+from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
 
 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(CACHE_DIR)
-            if os.path.isfile(os.path.join(CACHE_DIR, d))
-            and d.endswith(".mp4")
-            and not d.startswith("clip_")
-        ]
+        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(CACHE_DIR):
-                            files_in_use.append(nt.path.split("/")[-1])
+                        if nt.path.startswith(RECORD_DIR):
+                            files_in_use.append(nt.path.split('/')[-1])
             except:
                 continue
 
         for f in recordings:
-            # Skip files currently in use
             if f in files_in_use:
                 continue
 
-            cache_path = os.path.join(CACHE_DIR, f)
-            basename = os.path.splitext(f)[0]
-            camera, date = basename.rsplit("-", maxsplit=1)
-            start_time = datetime.datetime.strptime(date, "%Y%m%d%H%M%S")
-
-            # Just delete files if recordings are turned off
-            if (
-                not camera in self.config.cameras
-                or not self.config.cameras[camera].record.enabled
-            ):
-                Path(cache_path).unlink(missing_ok=True)
-                continue
-
-            ffprobe_cmd = [
-                "ffprobe",
-                "-v",
-                "error",
-                "-show_entries",
-                "format=duration",
-                "-of",
-                "default=noprint_wrappers=1:nokey=1",
-                f"{cache_path}",
-            ]
-            p = sp.run(ffprobe_cmd, capture_output=True)
-            if p.returncode == 0:
-                duration = float(p.stdout.decode().strip())
-                end_time = start_time + datetime.timedelta(seconds=duration)
+            camera = '-'.join(f.split('-')[:-1])
+            start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
+        
+            ffprobe_cmd = " ".join([
+                'ffprobe',
+                '-v',
+                'error',
+                '-show_entries',
+                'format=duration',
+                '-of',
+                'default=noprint_wrappers=1:nokey=1',
+                f"{os.path.join(RECORD_DIR,f)}"
+            ])
+            p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
+            (output, err) = p.communicate()
+            p_status = p.wait()
+            if p_status == 0:
+                duration = float(output.decode('utf-8').strip())
             else:
                 logger.info(f"bad file: {f}")
-                Path(cache_path).unlink(missing_ok=True)
+                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')}"
-            file_path = os.path.join(directory, file_name)
 
-            # copy then delete is required when recordings are stored on some network drives
-            shutil.copyfile(cache_path, file_path)
-            os.remove(cache_path)
-
-            rand_id = "".join(
-                random.choices(string.ascii_lowercase + string.digits, k=6)
-            )
-            Recordings.create(
-                id=f"{start_time.timestamp()}-{rand_id}",
-                camera=camera,
-                path=file_path,
-                start_time=start_time.timestamp(),
-                end_time=end_time.timestamp(),
-                duration=duration,
-            )
-
-    def run(self):
-        # Check for new files every 5 seconds
-        while not self.stop_event.wait(5):
-            self.move_files()
-
-        logger.info(f"Exiting recording maintenance...")
-
-
-class RecordingCleanup(threading.Thread):
-    def __init__(self, config: FrigateConfig, stop_event):
-        threading.Thread.__init__(self)
-        self.name = "recording_cleanup"
-        self.config = config
-        self.stop_event = stop_event
-
-    def clean_tmp_clips(self):
-        # delete any clips more than 5 minutes old
-        for p in Path("/tmp/cache").rglob("clip_*.mp4"):
-            logger.debug(f"Checking tmp clip {p}.")
-            if p.stat().st_mtime < (datetime.datetime.now().timestamp() - 60 * 1):
-                logger.debug("Deleting tmp clip.")
-                p.unlink(missing_ok=True)
-
-    def expire_recordings(self):
-        logger.debug("Start expire recordings (new).")
-
-        logger.debug("Start deleted cameras.")
-        # Handle deleted cameras
-        no_camera_recordings: Recordings = Recordings.select().where(
-            Recordings.camera.not_in(list(self.config.cameras.keys())),
-        )
-
-        for recording in no_camera_recordings:
-            expire_days = self.config.record.retain_days
-            expire_before = (
-                datetime.datetime.now() - datetime.timedelta(days=expire_days)
-            ).timestamp()
-            if recording.end_time < expire_before:
-                Path(recording.path).unlink(missing_ok=True)
-                Recordings.delete_by_id(recording.id)
-        logger.debug("End deleted cameras.")
-
-        logger.debug("Start all cameras.")
-        for camera, config in self.config.cameras.items():
-            logger.debug(f"Start camera: {camera}.")
-            # When deleting recordings without events, we have to keep at LEAST the configured max clip duration
-            min_end = (
-                datetime.datetime.now()
-                - datetime.timedelta(seconds=config.record.events.max_seconds)
-            ).timestamp()
-            expire_days = config.record.retain_days
-            expire_before = (
-                datetime.datetime.now() - datetime.timedelta(days=expire_days)
-            ).timestamp()
-            expire_date = min(min_end, expire_before)
-
-            # Get recordings to remove
-            recordings: Recordings = Recordings.select().where(
-                Recordings.camera == camera,
-                Recordings.end_time < expire_date,
-            )
-
-            for recording in recordings:
-                # See if there are any associated events
-                events: Event = Event.select().where(
-                    Event.camera == recording.camera,
-                    (
-                        Event.start_time.between(
-                            recording.start_time, recording.end_time
-                        )
-                        | Event.end_time.between(
-                            recording.start_time, recording.end_time
-                        )
-                        | (
-                            (recording.start_time > Event.start_time)
-                            & (recording.end_time < Event.end_time)
-                        )
-                    ),
-                )
-                keep = False
-                event_ids = set()
-
-                event: Event
-                for event in events:
-                    event_ids.add(event.id)
-                    # Check event/label retention and keep the recording if within window
-                    expire_days_event = (
-                        0
-                        if not config.record.events.enabled
-                        else config.record.events.retain.objects.get(
-                            event.label, config.record.events.retain.default
-                        )
-                    )
-                    expire_before_event = (
-                        datetime.datetime.now()
-                        - datetime.timedelta(days=expire_days_event)
-                    ).timestamp()
-                    if recording.end_time >= expire_before_event:
-                        keep = True
-
-                # Delete recordings outside of the retention window
-                if not keep:
-                    Path(recording.path).unlink(missing_ok=True)
-                    Recordings.delete_by_id(recording.id)
-                    if event_ids:
-                        # Update associated events
-                        Event.update(has_clip=False).where(
-                            Event.id.in_(list(event_ids))
-                        ).execute()
-
-            logger.debug(f"End camera: {camera}.")
-
-        logger.debug("End all cameras.")
-        logger.debug("End expire recordings (new).")
+            os.rename(os.path.join(RECORD_DIR,f), os.path.join(directory,file_name))
 
     def expire_files(self):
-        logger.debug("Start expire files (legacy).")
-
-        default_expire = (
-            datetime.datetime.now().timestamp()
-            - SECONDS_IN_DAY * self.config.record.retain_days
-        )
         delete_before = {}
-
         for name, camera in self.config.cameras.items():
-            delete_before[name] = (
-                datetime.datetime.now().timestamp()
-                - SECONDS_IN_DAY * camera.record.retain_days
-            )
+            delete_before[name] = datetime.datetime.now().timestamp() - SECONDS_IN_DAY*camera.record.retain_days
 
-        # find all the recordings older than the oldest recording in the db
-        oldest_recording = (
-            Recordings.select().order_by(Recordings.start_time.desc()).get()
-        )
-
-        oldest_timestamp = (
-            oldest_recording.start_time
-            if oldest_recording
-            else datetime.datetime.now().timestamp()
-        )
-
-        logger.debug(f"Oldest recording in the db: {oldest_timestamp}")
-        process = sp.run(
-            ["find", RECORD_DIR, "-type", "f", "-newermt", f"@{oldest_timestamp}"],
-            capture_output=True,
-            text=True,
-        )
-        files_to_check = process.stdout.splitlines()
-
-        for f in files_to_check:
-            p = Path(f)
-            if p.stat().st_mtime < delete_before.get(p.parent.name, default_expire):
+        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)
 
-        logger.debug("End expire files (legacy).")
-
     def run(self):
-        # Expire recordings every minute, clean directories every hour.
-        for counter in itertools.cycle(range(60)):
-            if self.stop_event.wait(60):
-                logger.info(f"Exiting recording cleanup...")
+        counter = 0
+        self.expire_files()
+        while(True):
+            if self.stop_event.is_set():
+                logger.info(f"Exiting recording maintenance...")
                 break
 
-            self.expire_recordings()
-            self.clean_tmp_clips()
-
-            if counter == 0:
+            # only expire events every 10 minutes, but check for new files every 10 seconds
+            time.sleep(10)
+            counter = counter + 1
+            if counter > 60:
                 self.expire_files()
                 remove_empty_directories(RECORD_DIR)
+                counter = 0
+
+            self.move_files()
+
+
+   

frigate/stats.py

@@ -2,90 +2,57 @@ import json
 import logging
 import threading
 import time
-import psutil
-import shutil
 
 from frigate.config import FrigateConfig
-from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
 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 = ""
-    for part in psutil.disk_partitions(all=True):
-        if path.startswith(part.mountpoint) and len(bestMatch) < len(part.mountpoint):
-            fsType = part.fstype
-            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
     }
 
-    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),
-        }
-
     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
@@ -94,9 +61,10 @@ class StatsEmitter(threading.Thread):
 
     def run(self):
         time.sleep(10)
-        while not self.stop_event.wait(self.config.mqtt.stats_interval):
+        while True:
+            if self.stop_event.is_set():
+                logger.info(f"Exiting watchdog...")
+                break
             stats = stats_snapshot(self.stats_tracking)
-            self.mqtt_client.publish(
-                f"{self.topic_prefix}/stats", json.dumps(stats), retain=False
-            )
-        logger.info(f"Exiting watchdog...")
+            self.mqtt_client.publish(f"{self.topic_prefix}/stats", json.dumps(stats), retain=False)
+            time.sleep(self.config.mqtt.stats_interval)

frigate/test/test_copy_yuv_to_position.py

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

frigate/test/test_yuv_region_2_rgb.py

@@ -3,39 +3,37 @@ 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)

frigate/util.py

@@ -1,10 +1,8 @@
 import collections
-import copy
 import datetime
 import hashlib
 import json
 import logging
-import math
 import signal
 import subprocess as sp
 import threading
@@ -17,124 +15,13 @@ from typing import AnyStr
 import cv2
 import matplotlib.pyplot as plt
 import numpy as np
-import os
 
 logger = logging.getLogger(__name__)
 
 
-def deep_merge(dct1: dict, dct2: dict, override=False, merge_lists=False) -> dict:
-    """
-    :param dct1: First dict to merge
-    :param dct2: Second dict to merge
-    :param override: if same key exists in both dictionaries, should override? otherwise ignore. (default=True)
-    :return: The merge dictionary
-    """
-    merged = copy.deepcopy(dct1)
-    for k, v2 in dct2.items():
-        if k in merged:
-            v1 = merged[k]
-            if isinstance(v1, dict) and isinstance(v2, collections.Mapping):
-                merged[k] = deep_merge(v1, v2, override)
-            elif isinstance(v1, list) and isinstance(v2, list):
-                if merge_lists:
-                    merged[k] = v1 + v2
-            else:
-                if override:
-                    merged[k] = copy.deepcopy(v2)
-        else:
-            merged[k] = copy.deepcopy(v2)
-    return merged
-
-
-def draw_timestamp(
-    frame,
-    timestamp,
-    timestamp_format,
-    font_effect=None,
-    font_scale=1.0,
-    font_thickness=2,
-    font_color=(255, 255, 255),
-    position="tl",
-):
-    time_to_show = datetime.datetime.fromtimestamp(timestamp).strftime(timestamp_format)
-    size = cv2.getTextSize(
-        time_to_show,
-        cv2.FONT_HERSHEY_SIMPLEX,
-        fontScale=font_scale,
-        thickness=font_thickness,
-    )
-    image_width = frame.shape[1]
-    image_height = frame.shape[0]
-    text_width = size[0][0]
-    text_height = size[0][1]
-    line_height = text_height + size[1]
-
-    if position == "tl":
-        text_offset_x = 0
-        text_offset_y = 0 if 0 < line_height else 0 - (line_height + 8)
-    elif position == "tr":
-        text_offset_x = image_width - text_width
-        text_offset_y = 0 if 0 < line_height else 0 - (line_height + 8)
-    elif position == "bl":
-        text_offset_x = 0
-        text_offset_y = image_height - (line_height + 8)
-    elif position == "br":
-        text_offset_x = image_width - text_width
-        text_offset_y = image_height - (line_height + 8)
-
-    if font_effect == "solid":
-        # make the coords of the box with a small padding of two pixels
-        timestamp_box_coords = np.array(
-            [
-                [text_offset_x, text_offset_y],
-                [text_offset_x + text_width, text_offset_y],
-                [text_offset_x + text_width, text_offset_y + line_height + 8],
-                [text_offset_x, text_offset_y + line_height + 8],
-            ]
-        )
-
-        cv2.fillPoly(
-            frame,
-            [timestamp_box_coords],
-            # inverse color of text for background for max. contrast
-            (255 - font_color[0], 255 - font_color[1], 255 - font_color[2]),
-        )
-    elif font_effect == "shadow":
-        cv2.putText(
-            frame,
-            time_to_show,
-            (text_offset_x + 3, text_offset_y + line_height),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            fontScale=font_scale,
-            color=(255 - font_color[0], 255 - font_color[1], 255 - font_color[2]),
-            thickness=font_thickness,
-        )
-
-    cv2.putText(
-        frame,
-        time_to_show,
-        (text_offset_x, text_offset_y + line_height - 3),
-        cv2.FONT_HERSHEY_SIMPLEX,
-        fontScale=font_scale,
-        color=font_color,
-        thickness=font_thickness,
-    )
-
-
-def draw_box_with_label(
-    frame,
-    x_min,
-    y_min,
-    x_max,
-    y_max,
-    label,
-    info,
-    thickness=2,
-    color=None,
-    position="ul",
-):
+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
@@ -145,350 +32,208 @@ def draw_box_with_label(
     text_height = size[0][1]
     line_height = text_height + size[1]
     # set the text start position
-    if position == "ul":
+    if position == 'ul':
         text_offset_x = x_min
-        text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
-    elif position == "ur":
-        text_offset_x = x_max - (text_width + 8)
-        text_offset_y = 0 if y_min < line_height else y_min - (line_height + 8)
-    elif position == "bl":
+        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
+    elif position == 'ur':
+        text_offset_x = x_max - (text_width+8)
+        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
+    elif position == 'bl':
         text_offset_x = x_min
         text_offset_y = y_max
-    elif position == "br":
-        text_offset_x = x_max - (text_width + 8)
+    elif position == 'br':
+        text_offset_x = x_max - (text_width+8)
         text_offset_y = y_max
     # make the coords of the box with a small padding of two pixels
-    textbox_coords = (
-        (text_offset_x, text_offset_y),
-        (text_offset_x + text_width + 2, text_offset_y + line_height),
-    )
+    textbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y + line_height))
     cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
-    cv2.putText(
-        frame,
-        display_text,
-        (text_offset_x, text_offset_y + line_height - 3),
-        font,
-        fontScale=font_scale,
-        color=(0, 0, 0),
-        thickness=2,
-    )
+    cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2)
 
-
-def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):
+def calculate_region(frame_shape, xmin, ymin, xmax, ymax, multiplier=2):    
     # size is the longest edge and divisible by 4
-    size = int(max(xmax - xmin, ymax - ymin) // 4 * 4 * multiplier)
+    size = int(max(xmax-xmin, ymax-ymin)//4*4*multiplier)
     # dont go any smaller than 300
     if size < 300:
         size = 300
 
     # x_offset is midpoint of bounding box minus half the size
-    x_offset = int((xmax - xmin) / 2.0 + xmin - size / 2.0)
+    x_offset = int((xmax-xmin)/2.0+xmin-size/2.0)
     # if outside the image
     if x_offset < 0:
         x_offset = 0
-    elif x_offset > (frame_shape[1] - size):
-        x_offset = max(0, (frame_shape[1] - size))
+    elif x_offset > (frame_shape[1]-size):
+        x_offset = max(0, (frame_shape[1]-size))
 
     # y_offset is midpoint of bounding box minus half the size
-    y_offset = int((ymax - ymin) / 2.0 + ymin - size / 2.0)
+    y_offset = int((ymax-ymin)/2.0+ymin-size/2.0)
     # # if outside the image
     if y_offset < 0:
         y_offset = 0
-    elif y_offset > (frame_shape[0] - size):
-        y_offset = max(0, (frame_shape[0] - size))
-
-    return (x_offset, y_offset, x_offset + size, y_offset + 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)
 
 def get_yuv_crop(frame_shape, crop):
     # crop should be (x1,y1,x2,y2)
-    frame_height = frame_shape[0] // 3 * 2
+    frame_height = frame_shape[0]//3*2
     frame_width = frame_shape[1]
 
     # compute the width/height of the uv channels
-    uv_width = frame_width // 2  # width of the uv channels
-    uv_height = frame_height // 4  # height of the uv channels
+    uv_width = frame_width//2 # width of the uv channels
+    uv_height = frame_height//4 # height of the uv channels
 
     # compute the offset for upper left corner of the uv channels
-    uv_x_offset = crop[0] // 2  # x offset of the uv channels
-    uv_y_offset = crop[1] // 4  # y offset of the uv channels
+    uv_x_offset = crop[0]//2 # x offset of the uv channels
+    uv_y_offset = crop[1]//4 # y offset of the uv channels
 
     # compute the width/height of the uv crops
-    uv_crop_width = (crop[2] - crop[0]) // 2  # width of the cropped uv channels
-    uv_crop_height = (crop[3] - crop[1]) // 4  # height of the cropped uv channels
+    uv_crop_width  = (crop[2] - crop[0])//2 # width of the cropped uv channels
+    uv_crop_height = (crop[3] - crop[1])//4 # height of the cropped uv channels
 
     # ensure crop dimensions are multiples of 2 and 4
-    y = (crop[0], crop[1], crop[0] + uv_crop_width * 2, crop[1] + uv_crop_height * 4)
+    y = (
+        crop[0],
+        crop[1],
+        crop[0]      + uv_crop_width*2,
+        crop[1]      + uv_crop_height*4
+    )
 
     u1 = (
-        0 + uv_x_offset,
+        0            + uv_x_offset,
         frame_height + uv_y_offset,
-        0 + uv_x_offset + uv_crop_width,
-        frame_height + uv_y_offset + uv_crop_height,
+        0            + uv_x_offset  +  uv_crop_width,
+        frame_height + uv_y_offset  +  uv_crop_height
     )
 
     u2 = (
-        uv_width + uv_x_offset,
+        uv_width     + uv_x_offset,
         frame_height + uv_y_offset,
-        uv_width + uv_x_offset + uv_crop_width,
-        frame_height + uv_y_offset + uv_crop_height,
+        uv_width     + uv_x_offset  +  uv_crop_width,
+        frame_height + uv_y_offset  +  uv_crop_height
     )
 
     v1 = (
-        0 + uv_x_offset,
-        frame_height + uv_height + uv_y_offset,
-        0 + uv_x_offset + uv_crop_width,
-        frame_height + uv_height + uv_y_offset + uv_crop_height,
+        0            + uv_x_offset,
+        frame_height + uv_height    +  uv_y_offset,
+        0            + uv_x_offset  +  uv_crop_width,
+        frame_height + uv_height    +  uv_y_offset  +  uv_crop_height
     )
 
     v2 = (
-        uv_width + uv_x_offset,
-        frame_height + uv_height + uv_y_offset,
-        uv_width + uv_x_offset + uv_crop_width,
-        frame_height + uv_height + uv_y_offset + uv_crop_height,
+        uv_width     + uv_x_offset,
+        frame_height + uv_height    +  uv_y_offset,
+        uv_width     + uv_x_offset  +  uv_crop_width,
+        frame_height + uv_height    +  uv_y_offset + uv_crop_height
     )
 
     return y, u1, u2, v1, v2
 
-
-def yuv_crop_and_resize(frame, region, height=None):
-    # Crops and resizes a YUV frame while maintaining aspect ratio
-    # https://stackoverflow.com/a/57022634
-    height = frame.shape[0] // 3 * 2
-    width = frame.shape[1]
-
-    # get the crop box if the region extends beyond the frame
-    crop_x1 = max(0, region[0])
-    crop_y1 = max(0, region[1])
-    # ensure these are a multiple of 4
-    crop_x2 = min(width, region[2])
-    crop_y2 = min(height, region[3])
-    crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
-
-    y, u1, u2, v1, v2 = get_yuv_crop(frame.shape, crop_box)
-
-    # if the region starts outside the frame, indent the start point in the cropped frame
-    y_channel_x_offset = abs(min(0, region[0]))
-    y_channel_y_offset = abs(min(0, region[1]))
-
-    uv_channel_x_offset = y_channel_x_offset // 2
-    uv_channel_y_offset = y_channel_y_offset // 4
-
-    # create the yuv region frame
-    # make sure the size is a multiple of 4
-    # TODO: this should be based on the size after resize now
-    size = (region[3] - region[1]) // 4 * 4
-    yuv_cropped_frame = np.zeros((size + size // 2, size), np.uint8)
-    # fill in black
-    yuv_cropped_frame[:] = 128
-    yuv_cropped_frame[0:size, 0:size] = 16
-
-    # copy the y channel
-    yuv_cropped_frame[
-        y_channel_y_offset : y_channel_y_offset + y[3] - y[1],
-        y_channel_x_offset : y_channel_x_offset + y[2] - y[0],
-    ] = frame[y[1] : y[3], y[0] : y[2]]
-
-    uv_crop_width = u1[2] - u1[0]
-    uv_crop_height = u1[3] - u1[1]
-
-    # copy u1
-    yuv_cropped_frame[
-        size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
-        0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
-    ] = frame[u1[1] : u1[3], u1[0] : u1[2]]
-
-    # copy u2
-    yuv_cropped_frame[
-        size + uv_channel_y_offset : size + uv_channel_y_offset + uv_crop_height,
-        size // 2
-        + uv_channel_x_offset : size // 2
-        + uv_channel_x_offset
-        + uv_crop_width,
-    ] = frame[u2[1] : u2[3], u2[0] : u2[2]]
-
-    # copy v1
-    yuv_cropped_frame[
-        size
-        + size // 4
-        + uv_channel_y_offset : size
-        + size // 4
-        + uv_channel_y_offset
-        + uv_crop_height,
-        0 + uv_channel_x_offset : 0 + uv_channel_x_offset + uv_crop_width,
-    ] = frame[v1[1] : v1[3], v1[0] : v1[2]]
-
-    # copy v2
-    yuv_cropped_frame[
-        size
-        + size // 4
-        + uv_channel_y_offset : size
-        + size // 4
-        + uv_channel_y_offset
-        + uv_crop_height,
-        size // 2
-        + uv_channel_x_offset : size // 2
-        + uv_channel_x_offset
-        + uv_crop_width,
-    ] = frame[v2[1] : v2[3], v2[0] : v2[2]]
-
-    return yuv_cropped_frame
-
-
-def copy_yuv_to_position(
-    destination_frame,
-    destination_offset,
-    destination_shape,
-    source_frame=None,
-    source_channel_dim=None,
-):
-    # get the coordinates of the channels for this position in the layout
-    y, u1, u2, v1, v2 = get_yuv_crop(
-        destination_frame.shape,
-        (
-            destination_offset[1],
-            destination_offset[0],
-            destination_offset[1] + destination_shape[1],
-            destination_offset[0] + destination_shape[0],
-        ),
-    )
-
-    # clear y
-    destination_frame[
-        y[1] : y[3],
-        y[0] : y[2],
-    ] = 16
-
-    # clear u1
-    destination_frame[u1[1] : u1[3], u1[0] : u1[2]] = 128
-    # clear u2
-    destination_frame[u2[1] : u2[3], u2[0] : u2[2]] = 128
-    # clear v1
-    destination_frame[v1[1] : v1[3], v1[0] : v1[2]] = 128
-    # clear v2
-    destination_frame[v2[1] : v2[3], v2[0] : v2[2]] = 128
-
-    if not source_frame is None:
-        # calculate the resized frame, maintaining the aspect ratio
-        source_aspect_ratio = source_frame.shape[1] / (source_frame.shape[0] // 3 * 2)
-        dest_aspect_ratio = destination_shape[1] / destination_shape[0]
-
-        if source_aspect_ratio <= dest_aspect_ratio:
-            y_resize_height = int(destination_shape[0] // 4 * 4)
-            y_resize_width = int((y_resize_height * source_aspect_ratio) // 4 * 4)
-        else:
-            y_resize_width = int(destination_shape[1] // 4 * 4)
-            y_resize_height = int((y_resize_width / source_aspect_ratio) // 4 * 4)
-
-        uv_resize_width = int(y_resize_width // 2)
-        uv_resize_height = int(y_resize_height // 4)
-
-        y_y_offset = int((destination_shape[0] - y_resize_height) / 4 // 4 * 4)
-        y_x_offset = int((destination_shape[1] - y_resize_width) / 2 // 4 * 4)
-
-        uv_y_offset = y_y_offset // 4
-        uv_x_offset = y_x_offset // 2
-
-        interpolation = cv2.INTER_LINEAR
-        # resize/copy y channel
-        destination_frame[
-            y[1] + y_y_offset : y[1] + y_y_offset + y_resize_height,
-            y[0] + y_x_offset : y[0] + y_x_offset + y_resize_width,
-        ] = cv2.resize(
-            source_frame[
-                source_channel_dim["y"][1] : source_channel_dim["y"][3],
-                source_channel_dim["y"][0] : source_channel_dim["y"][2],
-            ],
-            dsize=(y_resize_width, y_resize_height),
-            interpolation=interpolation,
-        )
-
-        # resize/copy u1
-        destination_frame[
-            u1[1] + uv_y_offset : u1[1] + uv_y_offset + uv_resize_height,
-            u1[0] + uv_x_offset : u1[0] + uv_x_offset + uv_resize_width,
-        ] = cv2.resize(
-            source_frame[
-                source_channel_dim["u1"][1] : source_channel_dim["u1"][3],
-                source_channel_dim["u1"][0] : source_channel_dim["u1"][2],
-            ],
-            dsize=(uv_resize_width, uv_resize_height),
-            interpolation=interpolation,
-        )
-        # resize/copy u2
-        destination_frame[
-            u2[1] + uv_y_offset : u2[1] + uv_y_offset + uv_resize_height,
-            u2[0] + uv_x_offset : u2[0] + uv_x_offset + uv_resize_width,
-        ] = cv2.resize(
-            source_frame[
-                source_channel_dim["u2"][1] : source_channel_dim["u2"][3],
-                source_channel_dim["u2"][0] : source_channel_dim["u2"][2],
-            ],
-            dsize=(uv_resize_width, uv_resize_height),
-            interpolation=interpolation,
-        )
-        # resize/copy v1
-        destination_frame[
-            v1[1] + uv_y_offset : v1[1] + uv_y_offset + uv_resize_height,
-            v1[0] + uv_x_offset : v1[0] + uv_x_offset + uv_resize_width,
-        ] = cv2.resize(
-            source_frame[
-                source_channel_dim["v1"][1] : source_channel_dim["v1"][3],
-                source_channel_dim["v1"][0] : source_channel_dim["v1"][2],
-            ],
-            dsize=(uv_resize_width, uv_resize_height),
-            interpolation=interpolation,
-        )
-        # resize/copy v2
-        destination_frame[
-            v2[1] + uv_y_offset : v2[1] + uv_y_offset + uv_resize_height,
-            v2[0] + uv_x_offset : v2[0] + uv_x_offset + uv_resize_width,
-        ] = cv2.resize(
-            source_frame[
-                source_channel_dim["v2"][1] : source_channel_dim["v2"][3],
-                source_channel_dim["v2"][0] : source_channel_dim["v2"][2],
-            ],
-            dsize=(uv_resize_width, uv_resize_height),
-            interpolation=interpolation,
-        )
-
-
 def yuv_region_2_rgb(frame, region):
     try:
-        # TODO: does this copy the numpy array?
-        yuv_cropped_frame = yuv_crop_and_resize(frame, region)
+        height = frame.shape[0]//3*2
+        width = frame.shape[1]
+
+        # get the crop box if the region extends beyond the frame
+        crop_x1 = max(0, region[0])
+        crop_y1 = max(0, region[1])
+        # ensure these are a multiple of 4
+        crop_x2 = min(width,  region[2])
+        crop_y2 = min(height, region[3])
+        crop_box = (crop_x1, crop_y1, crop_x2, crop_y2)
+
+        y, u1, u2, v1, v2 = get_yuv_crop(frame.shape, crop_box)
+
+        # if the region starts outside the frame, indent the start point in the cropped frame
+        y_channel_x_offset = abs(min(0, region[0]))
+        y_channel_y_offset = abs(min(0, region[1]))
+
+        uv_channel_x_offset = y_channel_x_offset//2
+        uv_channel_y_offset = y_channel_y_offset//4
+
+        # create the yuv region frame
+        # make sure the size is a multiple of 4
+        size = (region[3] - region[1])//4*4
+        yuv_cropped_frame = np.zeros((size+size//2, size), np.uint8)
+        # fill in black
+        yuv_cropped_frame[:] = 128
+        yuv_cropped_frame[0:size,0:size] = 16
+
+        # copy the y channel
+        yuv_cropped_frame[
+                y_channel_y_offset:y_channel_y_offset + y[3] - y[1],
+                y_channel_x_offset:y_channel_x_offset + y[2] - y[0]
+            ] = frame[
+                y[1]:y[3], 
+                y[0]:y[2]
+            ]
+
+        uv_crop_width = u1[2] - u1[0]
+        uv_crop_height = u1[3] - u1[1]
+
+        # copy u1
+        yuv_cropped_frame[
+                size + uv_channel_y_offset:size + uv_channel_y_offset + uv_crop_height,
+                0    + uv_channel_x_offset:0    + uv_channel_x_offset + uv_crop_width
+            ] = frame[
+                u1[1]:u1[3], 
+                u1[0]:u1[2]
+            ]
+
+        # copy u2
+        yuv_cropped_frame[
+                size    + uv_channel_y_offset:size    + uv_channel_y_offset + uv_crop_height,
+                size//2 + uv_channel_x_offset:size//2 + uv_channel_x_offset + uv_crop_width
+            ] = frame[
+                u2[1]:u2[3], 
+                u2[0]:u2[2]
+            ]
+
+        # copy v1
+        yuv_cropped_frame[
+                size+size//4 + uv_channel_y_offset:size+size//4 + uv_channel_y_offset + uv_crop_height,
+                0            + uv_channel_x_offset:0            + uv_channel_x_offset + uv_crop_width
+            ] = frame[
+                v1[1]:v1[3], 
+                v1[0]:v1[2]
+            ]
+
+        # copy v2
+        yuv_cropped_frame[
+                size+size//4 + uv_channel_y_offset:size+size//4 + uv_channel_y_offset + uv_crop_height,
+                size//2      + uv_channel_x_offset:size//2      + uv_channel_x_offset + uv_crop_width
+            ] = frame[
+                v2[1]:v2[3], 
+                v2[0]:v2[2]
+            ]
+
         return cv2.cvtColor(yuv_cropped_frame, cv2.COLOR_YUV2RGB_I420)
     except:
         print(f"frame.shape: {frame.shape}")
         print(f"region: {region}")
         raise
 
-
 def intersection(box_a, box_b):
     return (
         max(box_a[0], box_b[0]),
         max(box_a[1], box_b[1]),
         min(box_a[2], box_b[2]),
-        min(box_a[3], box_b[3]),
+        min(box_a[3], box_b[3])
     )
 
-
 def area(box):
-    return (box[2] - box[0] + 1) * (box[3] - box[1] + 1)
-
-
+    return (box[2]-box[0] + 1)*(box[3]-box[1] + 1)
+    
 def intersection_over_union(box_a, box_b):
     # determine the (x, y)-coordinates of the intersection rectangle
     intersect = intersection(box_a, box_b)
 
     # compute the area of intersection rectangle
-    inter_area = max(0, intersect[2] - intersect[0] + 1) * max(
-        0, intersect[3] - intersect[1] + 1
-    )
+    inter_area = max(0, intersect[2] - intersect[0] + 1) * max(0, intersect[3] - intersect[1] + 1)
 
     if inter_area == 0:
         return 0.0
-
+    
     # compute the area of both the prediction and ground-truth
     # rectangles
     box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1)
@@ -502,33 +247,25 @@ def intersection_over_union(box_a, box_b):
     # return the intersection over union value
     return iou
 
-
 def clipped(obj, frame_shape):
     # if the object is within 5 pixels of the region border, and the region is not on the edge
     # consider the object to be clipped
     box = obj[2]
     region = obj[4]
-    if (
-        (region[0] > 5 and box[0] - region[0] <= 5)
-        or (region[1] > 5 and box[1] - region[1] <= 5)
-        or (frame_shape[1] - region[2] > 5 and region[2] - box[2] <= 5)
-        or (frame_shape[0] - region[3] > 5 and region[3] - box[3] <= 5)
-    ):
+    if ((region[0] > 5 and box[0]-region[0] <= 5) or 
+        (region[1] > 5 and box[1]-region[1] <= 5) or
+        (frame_shape[1]-region[2] > 5 and region[2]-box[2] <= 5) or
+        (frame_shape[0]-region[3] > 5 and region[3]-box[3] <= 5)):
         return True
     else:
         return False
 
-
-def restart_frigate():
-    os.kill(os.getpid(), signal.SIGTERM)
-
-
 class EventsPerSecond:
     def __init__(self, max_events=1000):
         self._start = None
         self._max_events = max_events
         self._timestamps = []
-
+    
     def start(self):
         self._start = datetime.datetime.now().timestamp()
 
@@ -537,28 +274,23 @@ 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
@@ -572,15 +304,11 @@ 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:
@@ -598,31 +326,29 @@ class FrameManager(ABC):
     def delete(self, name):
         pass
 
-
 class DictFrameManager(FrameManager):
     def __init__(self):
         self.frames = {}
-
+    
     def create(self, name, size) -> AnyStr:
         mem = bytearray(size)
         self.frames[name] = mem
         return mem
-
+    
     def get(self, name, shape):
         mem = self.frames[name]
         return np.ndarray(shape, dtype=np.uint8, buffer=mem)
-
+    
     def close(self, name):
         pass
-
+    
     def delete(self, name):
         del self.frames[name]
 
-
 class SharedMemoryFrameManager(FrameManager):
     def __init__(self):
         self.shm_store = {}
-
+    
     def create(self, name, size) -> AnyStr:
         shm = shared_memory.SharedMemory(name=name, create=True, size=size)
         self.shm_store[name] = shm

frigate/video.py

@@ -1,7 +1,12 @@
+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
@@ -11,7 +16,7 @@ from collections import defaultdict
 from setproctitle import setproctitle
 from typing import Dict, List
 
-from cv2 import cv2
+import cv2
 import numpy as np
 
 from frigate.config import CameraConfig
@@ -19,25 +24,19 @@ 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,
-    calculate_region,
-    clipped,
-    listen,
-    yuv_region_2_rgb,
-)
+from frigate.util import (EventsPerSecond, FrameManager,
+                          SharedMemoryFrameManager, area, calculate_region,
+                          clipped, draw_box_with_label, intersection,
+                          intersection_over_union, listen, yuv_region_2_rgb)
 
 logger = logging.getLogger(__name__)
 
-
 def filtered(obj, objects_to_track, object_filters):
     object_name = obj[0]
 
     if not object_name in objects_to_track:
         return True
-
+    
     if object_name in object_filters:
         obj_settings = object_filters[object_name]
 
@@ -45,7 +44,7 @@ def filtered(obj, objects_to_track, object_filters):
         # detected object, don't add it to detected objects
         if obj_settings.min_area > obj[3]:
             return True
-
+        
         # if the detected object is larger than the
         # max area, don't add it to detected objects
         if obj_settings.max_area < obj[3]:
@@ -54,36 +53,29 @@ def filtered(obj, objects_to_track, object_filters):
         # if the score is lower than the min_score, skip
         if obj_settings.min_score > obj[1]:
             return True
-
+    
         if not obj_settings.mask is None:
             # compute the coordinates of the object and make sure
             # the location isnt outside the bounds of the image (can happen from rounding)
-            y_location = min(int(obj[2][3]), len(obj_settings.mask) - 1)
-            x_location = min(
-                int((obj[2][2] - obj[2][0]) / 2.0) + obj[2][0],
-                len(obj_settings.mask[0]) - 1,
-            )
+            y_location = min(int(obj[2][3]), len(obj_settings.mask)-1)
+            x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(obj_settings.mask[0])-1)
 
             # if the object is in a masked location, don't add it to detected objects
             if obj_settings.mask[y_location][x_location] == 0:
                 return True
-
+        
     return False
 
-
 def create_tensor_input(frame, model_shape, region):
     cropped_frame = yuv_region_2_rgb(frame, region)
 
     # Resize to 300x300 if needed
     if cropped_frame.shape != (model_shape[0], model_shape[1], 3):
-        cropped_frame = cv2.resize(
-            cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR
-        )
-
+        cropped_frame = cv2.resize(cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR)
+    
     # Expand dimensions since the model expects images to have shape: [1, height, width, 3]
     return np.expand_dims(cropped_frame, axis=0)
 
-
 def stop_ffmpeg(ffmpeg_process, logger):
     logger.info("Terminating the existing ffmpeg process...")
     ffmpeg_process.terminate()
@@ -96,43 +88,18 @@ 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 ffmpeg_process is not None:
+def start_or_restart_ffmpeg(ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None):
+    if not ffmpeg_process is 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()
@@ -152,9 +119,7 @@ def capture_frames(
             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
@@ -173,11 +138,8 @@ def capture_frames(
         # 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
@@ -197,38 +159,31 @@ 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 not self.stop_event.wait(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
+
             now = datetime.datetime.now().timestamp()
 
             if not self.capture_thread.is_alive():
-                self.logger.error(
-                    f"FFMPEG process crashed unexpectedly for {self.camera_name}."
-                )
-                self.logger.error(
-                    "The following ffmpeg logs include the last 100 lines prior to exit."
-                )
-                self.logger.error("You may have invalid args defined for this camera.")
-                self.logpipe.dump()
                 self.start_ffmpeg_detect()
             elif now - self.capture_thread.current_frame.value > 20:
-                self.logger.info(
-                    f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg..."
-                )
+                self.logger.info(f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg...")
                 self.ffmpeg_detect_process.terminate()
                 try:
                     self.logger.info("Waiting for ffmpeg to exit gracefully...")
@@ -237,40 +192,24 @@ class CameraWatchdog(threading.Thread):
                     self.logger.info("FFmpeg didnt exit. Force killing...")
                     self.ffmpeg_detect_process.kill()
                     self.ffmpeg_detect_process.communicate()
-
+            
             for p in self.ffmpeg_other_processes:
-                poll = p["process"].poll()
-                if poll is None:
+                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"]
-                )
-
-        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()
-
+                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)
+    
     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)
@@ -282,60 +221,32 @@ 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,
-    labelmap,
-    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()
-
+    
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
@@ -343,118 +254,72 @@ def track_camera(
     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, 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]
-        ):
-            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 not stop_event.is_set():
+    while True:
+        if stop_event.is_set():
+            break
+
         if exit_on_empty and frame_queue.empty():
             logger.info(f"Exiting track_objects...")
             break
@@ -466,9 +331,7 @@ def process_frames(
 
         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.")
@@ -477,9 +340,7 @@ def process_frames(
         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
@@ -487,45 +348,27 @@ def process_frames(
         # look for motion
         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()]
 
         # 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
         #########
@@ -543,10 +386,8 @@ def process_frames(
             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)
 
@@ -555,26 +396,17 @@ def process_frames(
                     if clipped(obj, frame_shape):
                         box = obj[2]
                         # calculate a new region that will hopefully get the entire object
-                        region = calculate_region(
-                            frame_shape, box[0], box[1], box[2], box[3]
-                        )
+                        region = calculate_region(frame_shape, 
+                            box[0], box[1],
+                            box[2], box[3])
 
                         regions.append(region)
-
-                        selected_objects.extend(
-                            detect(
-                                object_detector,
-                                frame,
-                                model_shape,
-                                region,
-                                objects_to_track,
-                                object_filters,
-                            )
-                        )
+                        
+                        selected_objects.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
 
                         refining = True
                     else:
-                        selected_objects.append(obj)
+                        selected_objects.append(obj)            
             # set the detections list to only include top, complete objects
             # and new detections
             detections = selected_objects
@@ -582,30 +414,16 @@ def process_frames(
             if refining:
                 refine_count += 1
 
-        # 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)
-        ]
-
         # now that we have refined our detections, we need to track objects
-        object_tracker.match_and_update(frame_time, detections_with_motion)
+        object_tracker.match_and_update(frame_time, detections)
 
         # 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}")

frigate/watchdog.py

@@ -2,34 +2,35 @@ import datetime
 import logging
 import threading
 import time
-import os
-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 not self.stop_event.wait(10):
+        while True:
+            # wait a bit before checking
+            time.sleep(10)
+
+            if self.stop_event.is_set():
+                logger.info(f"Exiting watchdog...")
+                break
+
             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...")
+                    logger.info("Detection appears to have stopped. Restarting detection process")
+                    detector.start_or_restart()

frigate/zeroconf.py

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