ensure cache copies when events have ended

Based on issue #1976 - specify explicitly that these fields can include environment variables to avoid interpretation that environment variables could be used anywhere.
I am participating in #hacktoberfest, so I would appreciate if you could add the 'hacktoberfest-accepted' label (or add #hacktoberfest topic to your repo). Thanks!

.devcontainer/devcontainer.json

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

.dockerignore

@@ -4,4 +4,8 @@ docs/
 debug
 config/
 *.pyc
-.git
+.git
+core
+*.mp4
+*.db
+*.ts

.github/FUNDING.yml

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

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,56 +0,0 @@
----
-name: Bug report or Support request
-about: Bug report or Support request
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what your issue is.
-
-**Version of frigate**
-Output from `/version`
-
-**Config file**
-Include your full config file wrapped in triple back ticks.
-```yaml
-config here
-```
-
-**Frigate container logs**
-```
-Include relevant log output here
-```
-
-**Frigate stats**
-```json
-Output from frigate's /stats endpoint
-```
-
-**FFprobe from your camera**
-
-Run the following command and paste output below
-```
-ffprobe <stream_url>
-```
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Computer Hardware**
- - OS: [e.g. Ubuntu, Windows]
- - Install method: [e.g. Addon, Docker Compose, Docker Command]
- - Virtualization: [e.g. Proxmox, Virtualbox]
- - Coral Version: [e.g. USB, PCIe, None]
- - Network Setup: [e.g. Wired, WiFi]
-
-**Camera Info:**
- - Manufacturer: [e.g. Dahua]
- - Model: [e.g. IPC-HDW5231R-ZE]
- - Resolution: [e.g. 720p]
- - FPS: [e.g. 5]
-
-**Additional context**
-Add any other context about the problem here.

.github/ISSUE_TEMPLATE/config.yml

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

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: enhancement
+assignees: ''
+
+---
+
+**Describe what you are trying to accomplish and why in non technical terms**
+I want to be able to ... so that I can ... 
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/support_request.yml

@@ -0,0 +1,107 @@
+name: Support Request
+description: Support for Frigate setup or configuration
+title: "[Support]: "
+labels: ["support", "triage"]
+assignees: []
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Describe the problem you are having
+    validations:
+      required: true
+  - type: input
+    id: version
+    attributes:
+      label: Version
+      description: Visible on the Debug page in the Web UI
+    validations:
+      required: true
+  - type: textarea
+    id: config
+    attributes:
+      label: Frigate config file
+      description: This will be automatically formatted into code, so no need for backticks.
+      render: yaml
+    validations:
+      required: true
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant log output
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      render: shell
+    validations:
+      required: true
+  - type: textarea
+    id: ffprobe
+    attributes:
+      label: FFprobe output from your camera
+      description: Run `ffprobe <camera_url>` and provide output below
+      render: shell
+    validations:
+      required: true
+  - type: textarea
+    id: stats
+    attributes:
+      label: Frigate stats
+      description: Output from frigate's /api/stats endpoint
+      render: json
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - HassOS
+        - Debian
+        - Other Linux
+        - Proxmox
+        - UNRAID
+        - Windows
+        - Other
+    validations:
+      required: true
+  - type: dropdown
+    id: install-method
+    attributes:
+      label: Install method
+      options:
+        - HassOS Addon
+        - Docker Compose
+        - Docker CLI
+    validations:
+      required: true
+  - type: dropdown
+    id: coral
+    attributes:
+      label: Coral version
+      options:
+        - USB
+        - PCIe
+        - M.2
+        - Dev Board
+        - Other
+        - CPU (no coral)
+    validations:
+      required: true
+  - type: dropdown
+    id: network
+    attributes:
+      label: Network connection
+      options:
+        - Wired
+        - Wireless
+        - Mixed
+    validations:
+      required: true
+  - type: input
+    id: camera
+    attributes:
+      label: Camera make and model
+      description: Dahua, hikvision, amcrest, reolink, etc and model number
+    validations:
+      required: true
+  - type: textarea
+    id: other
+    attributes:
+      label: Any other information that may be helpful

.github/stale.yml

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

@@ -1,12 +1,16 @@
 .DS_Store
 *.pyc
+*.swp
 debug
 .vscode
 config/config.yml
 models
 *.mp4
+*.ts
 *.db
+*.csv
 frigate/version.py
 web/build
 web/node_modules
 web/coverage
+core

.pylintrc

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

Makefile

@@ -3,7 +3,7 @@ default_target: amd64_frigate
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
 
 version:
-	echo "VERSION='0.8.3-$(COMMIT_HASH)'" > frigate/version.py
+	echo "VERSION='0.10.0-$(COMMIT_HASH)'" > frigate/version.py
 
 web:
 	docker build --tag frigate-web --file docker/Dockerfile.web web/
@@ -12,11 +12,14 @@ amd64_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:1.0.3-amd64 --file docker/Dockerfile.wheels .
 
 amd64_ffmpeg:
-	docker build --tag blakeblackshear/frigate-ffmpeg:1.1.0-amd64 --file docker/Dockerfile.ffmpeg.amd64 .
+	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-amd64 --file docker/Dockerfile.ffmpeg.amd64 .
+
+nginx_frigate:
+	docker buildx build --push --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --tag blakeblackshear/frigate-nginx:1.0.2 --file docker/Dockerfile.nginx .
 
 amd64_frigate: version web
-	docker build --tag frigate-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.base .
-	docker build --tag frigate --file docker/Dockerfile.amd64 .
+	docker build --no-cache --tag frigate-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
+	docker build --no-cache --tag frigate --file docker/Dockerfile.amd64 .
 
 amd64_all: amd64_wheels amd64_ffmpeg amd64_frigate
 
@@ -24,11 +27,11 @@ amd64nvidia_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:1.0.3-amd64nvidia --file docker/Dockerfile.wheels .
 
 amd64nvidia_ffmpeg:
-	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
+	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
 
 amd64nvidia_frigate: version web
-	docker build --tag frigate-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.base .
-	docker build --tag frigate --file docker/Dockerfile.amd64nvidia .
+	docker build --no-cache --tag frigate-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
+	docker build --no-cache --tag frigate --file docker/Dockerfile.amd64nvidia .
 
 amd64nvidia_all: amd64nvidia_wheels amd64nvidia_ffmpeg amd64nvidia_frigate
 
@@ -36,23 +39,23 @@ aarch64_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:1.0.3-aarch64 --file docker/Dockerfile.wheels .
 
 aarch64_ffmpeg:
-	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
+	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.3.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
 
 aarch64_frigate: version web
-	docker build --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.base .
-	docker build --tag frigate --file docker/Dockerfile.aarch64 .
+	docker build --no-cache --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
+	docker build --no-cache --tag frigate --file docker/Dockerfile.aarch64 .
 
-armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
+aarch64_all: aarch64_wheels aarch64_ffmpeg aarch64_frigate
 
 armv7_wheels:
 	docker build --tag blakeblackshear/frigate-wheels:1.0.3-armv7 --file docker/Dockerfile.wheels .
 
 armv7_ffmpeg:
-	docker build --tag blakeblackshear/frigate-ffmpeg:1.0.0-armv7 --file docker/Dockerfile.ffmpeg.armv7 .
+	docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-armv7 --file docker/Dockerfile.ffmpeg.armv7 .
 
 armv7_frigate: version web
-	docker build --tag frigate-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --file docker/Dockerfile.base .
-	docker build --tag frigate --file docker/Dockerfile.armv7 .
+	docker build --no-cache --tag frigate-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
+	docker build --no-cache --tag frigate --file docker/Dockerfile.armv7 .
 
 armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
 

README.md

@@ -4,35 +4,39 @@
 
 # Frigate - NVR With Realtime Object Detection for IP Cameras
 
-A complete and local NVR designed for HomeAssistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
+A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
 
 Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
 
-- Tight integration with HomeAssistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
+- Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
 - Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
 - Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
 - Uses a very low overhead motion detection to determine where to run object detection
 - Object detection with TensorFlow runs in separate processes for maximum FPS
 - Communicates over MQTT for easy integration into other systems
-- Records video clips of detected objects
+- Records video with retention settings based on detected objects
 - 24/7 recording
 - Re-streaming via RTMP to reduce the number of connections to your camera
 
 ## Documentation
 
-View the documentation at https://blakeblackshear.github.io/frigate
+View the documentation at https://docs.frigate.video
 
 ## Donations
+
 If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
 
 ## Screenshots
-Integration into HomeAssistant
+
+Integration into Home Assistant
+
 <div>
 <a href="docs/static/img/media_browser.png"><img src="docs/static/img/media_browser.png" height=400></a>
 <a href="docs/static/img/notification.png"><img src="docs/static/img/notification.png" height=400></a>
 </div>
 
 Also comes with a builtin UI:
+
 <div>
 <a href="docs/static/img/home-ui.png"><img src="docs/static/img/home-ui.png" height=400></a>
 <a href="docs/static/img/camera-ui.png"><img src="docs/static/img/camera-ui.png" height=400></a>

docker-compose.yml

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

docker/Dockerfile.aarch64

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

docker/Dockerfile.amd64

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

docker/Dockerfile.amd64nvidia

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

docker/Dockerfile.armv7

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

docker/Dockerfile.base

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

docker/Dockerfile.dev

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

docker/Dockerfile.ffmpeg.aarch64

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

docker/Dockerfile.ffmpeg.amd64

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

docker/Dockerfile.ffmpeg.amd64nvidia

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

docker/Dockerfile.ffmpeg.armv7

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

docker/Dockerfile.nginx

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

docker/Dockerfile.wheels

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

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

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

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

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

docker/rootfs/usr/local/nginx/conf/nginx.conf

@@ -1,23 +1,23 @@
+daemon off;
+user root;
 worker_processes  1;
 
-error_log  /var/log/nginx/error.log warn;
+error_log  /usr/local/nginx/logs/error.log warn;
 pid        /var/run/nginx.pid;
 
-load_module "modules/ngx_rtmp_module.so";
-
 events {
     worker_connections  1024;
 }
 
 http {
-    include       /etc/nginx/mime.types;
+    include       mime.types;
     default_type  application/octet-stream;
 
     log_format  main  '$remote_addr - $remote_user [$time_local] "$request" '
                         '$status $body_bytes_sent "$http_referer" '
                         '"$http_user_agent" "$http_x_forwarded_for"';
 
-    access_log  /var/log/nginx/access.log  main;
+    access_log  /usr/local/nginx/logs/access.log  main;
 
     sendfile        on;
 
@@ -34,9 +34,56 @@ http {
       keepalive 1024;
     }
 
+    upstream mqtt_ws {
+      server localhost:5002;
+      keepalive 1024;
+    }
+
+    upstream jsmpeg {
+      server localhost:8082;
+      keepalive 1024;
+    }
+
     server {
         listen 5000;
 
+        # vod settings
+        vod_base_url '';
+        vod_segments_base_url '';
+        vod_mode mapped;
+        vod_max_mapping_response_size 1m;
+        vod_upstream_location /api;
+        vod_align_segments_to_key_frames on;
+        vod_manifest_segment_durations_mode accurate;
+
+        # vod caches
+        vod_metadata_cache metadata_cache 512m;
+        vod_mapping_cache mapping_cache 5m;
+
+        # gzip manifests
+        gzip on;
+        gzip_types application/vnd.apple.mpegurl;
+
+        # file handle caching / aio
+        open_file_cache          max=1000 inactive=5m;
+        open_file_cache_valid    2m;
+        open_file_cache_min_uses 1;
+        open_file_cache_errors   on;
+        aio on;
+
+        location /vod/ {
+            vod hls;
+
+            secure_token $args;
+			secure_token_types application/vnd.apple.mpegurl;
+
+            add_header Access-Control-Allow-Headers '*';
+            add_header Access-Control-Expose-Headers 'Server,range,Content-Length,Content-Range';
+            add_header Access-Control-Allow-Methods 'GET, HEAD, OPTIONS';
+            add_header Access-Control-Allow-Origin '*';
+            expires -1;
+        }
+
         location /stream/ {
             add_header 'Cache-Control' 'no-cache';
             add_header 'Access-Control-Allow-Origin' "$http_origin" always;
@@ -81,6 +128,11 @@ http {
             root /media/frigate;
         }
 
+        location /cache/ {
+                internal; # This tells nginx it's not accessible from the outside
+                alias /tmp/cache/;
+        }
+
         location /recordings/ {
             add_header 'Access-Control-Allow-Origin' "$http_origin" always;
             add_header 'Access-Control-Allow-Credentials' 'true';
@@ -103,7 +155,15 @@ http {
         }
 
         location /ws {
-            proxy_pass http://frigate_api/ws;
+            proxy_pass http://mqtt_ws/;
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "Upgrade";
+            proxy_set_header Host $host;
+        }
+
+        location /live/ {
+            proxy_pass http://jsmpeg/;
             proxy_http_version 1.1;
             proxy_set_header Upgrade $http_upgrade;
             proxy_set_header Connection "Upgrade";
@@ -112,6 +172,7 @@ http {
 
         location /api/ {
             add_header 'Access-Control-Allow-Origin' '*';
+            add_header 'Access-Control-Allow-Methods' 'GET, POST, PUT, DELETE, OPTIONS';
             add_header Cache-Control "no-store";
             proxy_pass http://frigate_api/;
             proxy_pass_request_headers on;

docs/README.md

@@ -2,32 +2,4 @@
 
 This website is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator.
 
-## Installation
-
-```console
-yarn install
-```
-
-## Local Development
-
-```console
-yarn 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.
-
-## Build
-
-```console
-yarn build
-```
-
-This command generates static content into the `build` directory and can be served using any static contents hosting service.
-
-## Deployment
-
-```console
-GIT_USER=<Your GitHub username> USE_SSH=true yarn deploy
-```
-
-If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
+For installation and contributing instructions, please follow the [Contributing Docs](https://blakeblackshear.github.io/frigate/contributing).

docs/docs/configuration/advanced.md

@@ -1,50 +1,11 @@
 ---
 id: advanced
-title: Advanced
-sidebar_label: Advanced
+title: Advanced Options
+sidebar_label: Advanced Options
 ---
 
 ## Advanced configuration
 
-### `motion`
-
-Global motion detection config. These may also be defined at the camera level.
-
-```yaml
-motion:
-  # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
-  # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
-  # The value should be between 1 and 255.
-  threshold: 25
-  # Optional: Minimum size in pixels in the resized motion image that counts as motion
-  # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will make motion detection more sensitive to smaller
-  # moving objects.
-  contour_area: 100
-  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
-  # Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
-  # Too low and a fast moving person wont be detected as motion.
-  delta_alpha: 0.2
-  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
-  # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
-  # Low values will cause things like moving shadows to be detected as motion for longer.
-  # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
-  frame_alpha: 0.2
-  # Optional: Height of the resized motion frame  (default: 1/6th of the original frame height)
-  # This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense of higher CPU usage.
-  # Lower values result in less CPU, but small changes may not register as motion.
-  frame_height: 180
-```
-
-### `detect`
-
-Global object detection settings. These may also be defined at the camera level.
-
-```yaml
-detect:
-  # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
-  max_disappeared: 25
-```
-
 ### `logger`
 
 Change the default log level for troubleshooting purposes.
@@ -72,68 +33,32 @@ Examples of available modules are:
 
 ### `environment_vars`
 
-This section can be used to set environment variables for those unable to modify the environment of the container (ie. within Hass.io)
-
-```yaml
-environment_vars:
-  EXAMPLE_VAR: value
-```
+This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
 
 ### `database`
 
-Event and clip information is managed in a sqlite database at `/media/frigate/clips/frigate.db`. If that database is deleted, clips will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within HomeAssistant.
+Event and recording information is managed in a sqlite database at `/media/frigate/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
 
-If you are storing your clips on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
+If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
 
-This may need to be in a custom location if network storage is used for clips.
-
-```yaml
-database:
-  path: /media/frigate/clips/frigate.db
-```
-
-### `detectors`
-
-```yaml
-detectors:
-  # Required: name of the detector
-  coral:
-    # Required: type of the detector
-    # Valid values are 'edgetpu' (requires device property below) and 'cpu'. type: edgetpu
-    # Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
-    device: usb
-    # Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
-    # This value is only used for CPU types
-    num_threads: 3
-```
+This may need to be in a custom location if network storage is used for the media folder.
 
 ### `model`
 
+If using a custom model, the width and height will need to be specified.
+
+The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
+
 ```yaml
 model:
-  # Required: height of the trained model
-  height: 320
-  # Required: width of the trained model
-  width: 320
+  labelmap:
+    2: vehicle
+    3: vehicle
+    5: vehicle
+    7: vehicle
+    15: animal
+    16: animal
+    17: animal
 ```
 
-## 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.
-
-### Customizing the Labelmap
-
-The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. You must retain the same number of labels, but you can change the names. To change:
-
-- Download the [COCO labelmap](https://dl.google.com/coral/canned_models/coco_labels.txt)
-- Modify the label names as desired. For example, change `7 truck` to `7 car`
-- Mount the new file at `/labelmap.txt` in the container with an additional volume
-  ```
-  -v ./config/labelmap.txt:/labelmap.txt
-  ```
+Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.

docs/docs/configuration/camera_specific.md

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

docs/docs/configuration/cameras.md

@@ -5,18 +5,15 @@ title: Cameras
 
 ## Setting Up Camera Inputs
 
-Up to 4 inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create clips from a higher resolution stream, or vice versa.
+Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
 
 Each role can only be assigned to one input per camera. The options for roles are as follows:
 
-| Role     | Description                                                                          |
-| -------- | ------------------------------------------------------------------------------------ |
-| `detect` | Main feed for object detection                                                       |
-| `clips`  | Clips of events from objects detected in the `detect` feed. [docs](#recording-clips) |
-| `record` | Saves 60 second segments of the video feed. [docs](#247-recordings)                  |
-| `rtmp`   | Broadcast as an RTMP feed for other services to consume. [docs](#rtmp-streams)       |
-
-### Example
+| Role     | Description                                                                                     |
+| -------- | ----------------------------------------------------------------------------------------------- |
+| `detect` | Main feed for object detection                                                                  |
+| `record` | Saves segments of the video feed based on configuration settings. [docs](/configuration/record) |
+| `rtmp`   | Broadcast as an RTMP feed for other services to consume. [docs](/configuration/rtmp)            |
 
 ```yaml
 mqtt:
@@ -31,420 +28,18 @@ cameras:
             - rtmp
         - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/live
           roles:
-            - clips
             - record
-    width: 1280
-    height: 720
-    fps: 5
-```
-
-## Masks & Zones
-
-### Masks
-Masks are used to ignore initial detection in areas of your camera's field of view.
-
-There are two types of masks available:
-- **Motion masks**: Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the video feed with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. Over masking will make it more difficult for objects to be tracked. To see this effect, create a mask, and then watch the video feed with `Motion Boxes` enabled again.
-- **Object filter masks**: Object filter masks are used to filter out false positives for a given object type. These should be used to filter any areas where it is not possible for an object of that type to be. The bottom center of the detected object's bounding box is evaluated against the mask. If it is in a masked area, it is assumed to be a false positive. For example, you may want to mask out rooftops, walls, the sky, treetops for people. For cars, masking locations other than the street or your driveway will tell frigate that anything in your yard is a false positive.
-
-To create a poly mask:
-
-1. Visit the [web UI](/usage/web)
-1. Click the camera you wish to create a mask for
-1. Click "Mask & Zone creator"
-1. Click "Add" on the type of mask or zone you would like to create
-1. Click on the camera's latest image to create a masked area. The yaml representation will be updated in real-time
-1. When you've finished creating your mask, click "Copy" and paste the contents into your `config.yaml` file and restart Frigate
-
-Example of a finished row corresponding to the below example image:
-
-```yaml
-motion:
-  mask: '0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432'
-```
-
-![poly](/img/example-mask-poly.png)
-
-```yaml
-# Optional: camera level motion config
-motion:
-  # Optional: motion mask
-  # NOTE: see docs for more detailed info on creating masks
-  mask: 0,900,1080,900,1080,1920,0,1920
-```
-
-### Zones
-
-Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
-
-During testing, `draw_zones` should be set in the config to draw the zone on the frames so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
-
-To create a zone, follow the same steps above for a "Motion mask", but use the section of the web UI for creating a zone instead.
-
-```yaml
-# Optional: zones for this camera
-zones:
-  # Required: name of the zone
-  # NOTE: This must be different than any camera names, but can match with another zone on another
-  #       camera.
-  front_steps:
-    # Required: List of x,y coordinates to define the polygon of the zone.
-    # NOTE: Coordinates can be generated at https://www.image-map.net/
-    coordinates: 545,1077,747,939,788,805
-    # Optional: Zone level object filters.
-    # NOTE: The global and camera filters are applied upstream.
-    filters:
-      person:
-        min_area: 5000
-        max_area: 100000
-        threshold: 0.7
-```
-
-## Objects
-
-```yaml
-# Optional: Camera level object filters config.
-objects:
-  track:
-    - person
-    - car
-  # Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
-  # Checks based on the bottom center of the bounding box of the object. 
-  # NOTE: This mask is COMBINED with the object type specific mask below
-  mask: 0,0,1000,0,1000,200,0,200
-  filters:
-    person:
-      min_area: 5000
-      max_area: 100000
-      min_score: 0.5
-      threshold: 0.7
-      # Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
-      # Checks based on the bottom center of the bounding box of the object
-      mask: 0,0,1000,0,1000,200,0,200
-```
-
-## Clips
-
-Frigate can save video clips without any CPU overhead for encoding by simply copying the stream directly with FFmpeg. It leverages FFmpeg's segment functionality to maintain a cache of video for each camera. The cache files are written to disk at `/tmp/cache` and do not introduce memory overhead. When an object is being tracked, it will extend the cache to ensure it can assemble a clip when the event ends. Once the event ends, it again uses FFmpeg to assemble a clip by combining the video clips without any encoding by the CPU. Assembled clips are are saved to `/media/frigate/clips`. Clips are retained according to the retention settings defined on the config for each object type.
-
-:::caution
-Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
-:::
-
-```yaml
-clips:
-  # Required: enables clips for the camera (default: shown below)
-  # This value can be set via MQTT and will be updated in startup based on retained value
-  enabled: False
-  # Optional: Number of seconds before the event to include in the clips (default: shown below)
-  pre_capture: 5
-  # Optional: Number of seconds after the event to include in the clips (default: shown below)
-  post_capture: 5
-  # Optional: Objects to save clips for. (default: all tracked objects)
-  objects:
-    - person
-  # Optional: Restrict clips to objects that entered any of the listed zones (default: no required zones)
-  required_zones: []
-  # Optional: Camera override for retention settings (default: global values)
-  retain:
-    # Required: Default retention days (default: shown below)
-    default: 10
-    # Optional: Per object retention days
-    objects:
-      person: 15
-```
-
-## Snapshots
-
-Frigate can save a snapshot image to `/media/frigate/clips` for each event named as `<camera>-<id>.jpg`.
-
-```yaml
-# Optional: Configuration for the jpg snapshots written to the clips directory for each event
-snapshots:
-  # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
-  # This value can be set via MQTT and will be updated in startup based on retained value
-  enabled: False
-  # Optional: print a timestamp on the snapshots (default: shown below)
-  timestamp: False
-  # Optional: draw bounding box on the snapshots (default: shown below)
-  bounding_box: False
-  # Optional: crop the snapshot (default: shown below)
-  crop: False
-  # Optional: height to resize the snapshot to (default: original size)
-  height: 175
-  # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
-  required_zones: []
-  # Optional: Camera override for retention settings (default: global values)
-  retain:
-    # Required: Default retention days (default: shown below)
-    default: 10
-    # Optional: Per object retention days
-    objects:
-      person: 15
-```
-
-## 24/7 Recordings
-
-24/7 recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding and are available in HomeAssistant's media browser. Each camera supports a configurable retention policy in the config.
-
-:::caution
-Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
-:::
-
-```yaml
-# Optional: 24/7 recording configuration
-record:
-  # Optional: Enable recording (default: global setting)
-  enabled: False
-  # Optional: Number of days to retain (default: global setting)
-  retain_days: 30
-```
-
-## RTMP streams
-
-Frigate can re-stream your video feed as a RTMP feed for other applications such as HomeAssistant to utilize it at `rtmp://<frigate_host>/live/<camera_name>`. Port 1935 must be open. This allows you to use a video feed for detection in frigate and HomeAssistant live view at the same time without having to make two separate connections to the camera. The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
-
-Some video feeds are not compatible with RTMP. If you are experiencing issues, check to make sure your camera feed is h264 with AAC audio. If your camera doesn't support a compatible format for RTMP, you can use the ffmpeg args to re-encode it on the fly at the expense of increased CPU utilization.
-
-## Full example
-
-The following is a full example of all of the options together for a camera configuration
-
-```yaml
-cameras:
-  # Required: name of the camera
-  back:
-    # Required: ffmpeg settings for the camera
-    ffmpeg:
-      # Required: A list of input streams for the camera. See documentation for more information.
-      inputs:
-        # Required: the path to the stream
-        # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}
-        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          # Required: list of roles for this stream. valid values are: detect,record,clips,rtmp
-          # NOTICE: In addition to assigning the record, clips, and rtmp roles,
-          # they must also be enabled in the camera config.
-          roles:
-            - detect
-            - rtmp
-          # Optional: stream specific global args (default: inherit)
-          global_args:
-          # Optional: stream specific hwaccel args (default: inherit)
-          hwaccel_args:
-          # Optional: stream specific input args (default: inherit)
-          input_args:
-      # Optional: camera specific global args (default: inherit)
-      global_args:
-      # Optional: camera specific hwaccel args (default: inherit)
-      hwaccel_args:
-      # Optional: camera specific input args (default: inherit)
-      input_args:
-      # Optional: camera specific output args (default: inherit)
-      output_args:
-
-    # Required: width of the frame for the input with the detect role
-    width: 1280
-    # Required: height of the frame for the input with the detect role
-    height: 720
-    # Optional: desired fps for your camera for the input with the detect role
-    # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
-    #       Frigate will attempt to autodetect if not specified.
-    fps: 5
-
-    # Optional: camera level motion config
-    motion:
-      # Optional: motion mask
-      # NOTE: see docs for more detailed info on creating masks
-      mask: 0,900,1080,900,1080,1920,0,1920
-
-    # Optional: timeout for highest scoring image before allowing it
-    # to be replaced by a newer image. (default: shown below)
-    best_image_timeout: 60
-
-    # Optional: zones for this camera
-    zones:
-      # Required: name of the zone
-      # NOTE: This must be different than any camera names, but can match with another zone on another
-      #       camera.
-      front_steps:
-        # Required: List of x,y coordinates to define the polygon of the zone.
-        # NOTE: Coordinates can be generated at https://www.image-map.net/
-        coordinates: 545,1077,747,939,788,805
-        # Optional: Zone level object filters.
-        # NOTE: The global and camera filters are applied upstream.
-        filters:
-          person:
-            min_area: 5000
-            max_area: 100000
-            threshold: 0.7
-
-    # Optional: Camera level detect settings
     detect:
-      # Optional: enables detection for the camera (default: True)
-      # This value can be set via MQTT and will be updated in startup based on retained value
-      enabled: True
-      # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
-      max_disappeared: 25
-
-    # Optional: save clips configuration
-    clips:
-      # Required: enables clips for the camera (default: shown below)
-      # This value can be set via MQTT and will be updated in startup based on retained value
-      enabled: False
-      # Optional: Number of seconds before the event to include in the clips (default: shown below)
-      pre_capture: 5
-      # Optional: Number of seconds after the event to include in the clips (default: shown below)
-      post_capture: 5
-      # Optional: Objects to save clips for. (default: all tracked objects)
-      objects:
-        - person
-      # Optional: Restrict clips to objects that entered any of the listed zones (default: no required zones)
-      required_zones: []
-      # Optional: Camera override for retention settings (default: global values)
-      retain:
-        # Required: Default retention days (default: shown below)
-        default: 10
-        # Optional: Per object retention days
-        objects:
-          person: 15
-
-    # Optional: 24/7 recording configuration
-    record:
-      # Optional: Enable recording (default: global setting)
-      enabled: False
-      # Optional: Number of days to retain (default: global setting)
-      retain_days: 30
-
-    # Optional: RTMP re-stream configuration
-    rtmp:
-      # Required: Enable the live stream (default: True)
-      enabled: True
-
-    # Optional: Configuration for the jpg snapshots written to the clips directory for each event
-    snapshots:
-      # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
-      # This value can be set via MQTT and will be updated in startup based on retained value
-      enabled: False
-      # Optional: print a timestamp on the snapshots (default: shown below)
-      timestamp: False
-      # Optional: draw bounding box on the snapshots (default: shown below)
-      bounding_box: False
-      # Optional: crop the snapshot (default: shown below)
-      crop: False
-      # Optional: height to resize the snapshot to (default: original size)
-      height: 175
-      # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
-      required_zones: []
-      # Optional: Camera override for retention settings (default: global values)
-      retain:
-        # Required: Default retention days (default: shown below)
-        default: 10
-        # Optional: Per object retention days
-        objects:
-          person: 15
-
-    # Optional: Configuration for the jpg snapshots published via MQTT
-    mqtt:
-      # Optional: Enable publishing snapshot via mqtt for camera (default: shown below)
-      # NOTE: Only applies to publishing image data to MQTT via 'frigate/<camera_name>/<object_name>/snapshot'.
-      # All other messages will still be published.
-      enabled: True
-      # Optional: print a timestamp on the snapshots (default: shown below)
-      timestamp: True
-      # Optional: draw bounding box on the snapshots (default: shown below)
-      bounding_box: True
-      # Optional: crop the snapshot (default: shown below)
-      crop: True
-      # Optional: height to resize the snapshot to (default: shown below)
-      height: 270
-      # Optional: Restrict mqtt messages to objects that entered any of the listed zones (default: no required zones)
-      required_zones: []
-
-    # Optional: Camera level object filters config.
-    objects:
-      track:
-        - person
-        - car
-      # Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
-      # Checks based on the bottom center of the bounding box of the object. 
-      # NOTE: This mask is COMBINED with the object type specific mask below
-      mask: 0,0,1000,0,1000,200,0,200
-      filters:
-        person:
-          min_area: 5000
-          max_area: 100000
-          min_score: 0.5
-          threshold: 0.7
-          # Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
-          # Checks based on the bottom center of the bounding box of the object
-          mask: 0,0,1000,0,1000,200,0,200
+      width: 1280
+      height: 720
 ```
 
-## Camera specific configuration
-
-### RTMP Cameras
-
-The input parameters need to be adjusted for RTMP cameras
+Additional cameras are simply added to the config under the `cameras` entry.
 
 ```yaml
-ffmpeg:
-  input_args:
-    - -avoid_negative_ts
-    - make_zero
-    - -fflags
-    - nobuffer
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -use_wallclock_as_timestamps
-    - '1'
-```
-
-### Reolink 410/520 (possibly others)
-Several users have reported success with the rtmp video from Reolink cameras.
-
-```yaml
-ffmpeg:
-  input_args:
-    - -avoid_negative_ts
-    - make_zero
-    - -fflags
-    - nobuffer
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -rw_timeout
-    - '5000000'
-    - -use_wallclock_as_timestamps
-    - '1'
-```
-
-
-### Blue Iris RTSP Cameras
-
-You will need to remove `nobuffer` flag for Blue Iris RTSP cameras
-
-```yaml
-ffmpeg:
-  input_args:
-    - -avoid_negative_ts
-    - make_zero
-    - -flags
-    - low_delay
-    - -strict
-    - experimental
-    - -fflags
-    - +genpts+discardcorrupt
-    - -rtsp_transport
-    - tcp
-    - -stimeout
-    - '5000000'
-    - -use_wallclock_as_timestamps
-    - '1'
+mqtt: ...
+cameras:
+  back: ...
+  front: ...
+  side: ...
 ```

docs/docs/configuration/detectors.md

@@ -3,13 +3,13 @@ id: detectors
 title: Detectors
 ---
 
-The default config will look for a USB Coral device. If you do not have a Coral, you will need to configure a CPU detector. If you have PCI or multiple Coral devices, you need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
+By default, Frigate will use a single CPU detector. If you have a Coral, you will need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.
 
 Frigate supports `edgetpu` and `cpu` as detector types. The device value should be specified according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api).
 
 **Note**: There is no support for Nvidia GPUs to perform object detection with tensorflow. It can be used for ffmpeg decoding, but not object detection.
 
-Single USB Coral:
+### Single USB Coral
 
 ```yaml
 detectors:
@@ -18,7 +18,7 @@ detectors:
     device: usb
 ```
 
-Multiple USB Corals:
+### Multiple USB Corals
 
 ```yaml
 detectors:
@@ -30,7 +30,29 @@ detectors:
     device: usb:1
 ```
 
-Mixing Corals:
+### Native Coral (Dev Board)
+_warning: may have [compatibility issues](https://github.com/blakeblackshear/frigate/issues/1706) after `v0.9.x`_
+
+```yaml
+detectors:
+  coral:
+    type: edgetpu
+    device: ""
+```
+
+### Multiple PCIE/M.2 Corals
+
+```yaml
+detectors:
+  coral1:
+    type: edgetpu
+    device: pci:0
+  coral2:
+    type: edgetpu
+    device: pci:1
+```
+
+### Mixing Corals
 
 ```yaml
 detectors:
@@ -42,12 +64,16 @@ detectors:
     device: pci
 ```
 
-CPU Detectors (not recommended):
+### CPU Detectors (not recommended)
 
 ```yaml
 detectors:
   cpu1:
     type: cpu
+    num_threads: 3
   cpu2:
     type: cpu
+    num_threads: 3
 ```
+
+When using CPU detectors, you can add a CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.

docs/docs/configuration/hardware_acceleration.md

@@ -0,0 +1,70 @@
+---
+id: hardware_acceleration
+title: Hardware Acceleration
+---
+
+It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
+
+### Raspberry Pi 3/4 (32-bit OS)
+
+Ensure you increase the allocated RAM for your GPU to at least 128 (raspi-config > Performance Options > GPU Memory).
+**NOTICE**: If you are using the addon, you may need to turn off `Protection mode` for hardware acceleration.
+
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -c:v
+    - h264_mmal
+```
+
+### Raspberry Pi 3/4 (64-bit OS)
+
+**NOTICE**: If you are using the addon, you may need to turn off `Protection mode` for hardware acceleration.
+
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -c:v
+    - h264_v4l2m2m
+```
+
+### Intel-based CPUs (<10th Generation) via Quicksync
+
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -hwaccel
+    - vaapi
+    - -hwaccel_device
+    - /dev/dri/renderD128
+    - -hwaccel_output_format
+    - yuv420p
+```
+
+### Intel-based CPUs (>=10th Generation) via Quicksync
+
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -hwaccel
+    - qsv
+    - -qsv_device
+    - /dev/dri/renderD128
+```
+
+### AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
+
+**Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
+
+```yaml
+ffmpeg:
+  hwaccel_args:
+    - -hwaccel
+    - vaapi
+    - -hwaccel_device
+    - /dev/dri/renderD128
+```
+
+### NVIDIA GPU
+
+NVIDIA GPU based decoding via NVDEC is supported, but requires special configuration. See the [NVIDIA NVDEC documentation](/configuration/nvdec) for more details.

docs/docs/configuration/index.md

@@ -1,11 +1,13 @@
 ---
 id: index
-title: Configuration
+title: Configuration File
 ---
 
-HassOS users can manage their configuration directly in the addon Configuration tab. For other installations, the default location for the config file is `/config/config.yml`. This can be overridden with the `CONFIG_FILE` environment variable. Camera specific ffmpeg parameters are documented [here](cameras.md).
+For Home Assistant Addon installations, the config file needs to be in the root of your Home Assistant config directory (same location as `configuration.yaml`) and named `frigate.yml`.
 
-It is recommended to start with a minimal configuration and add to it:
+For all other installation types, the config file should be mapped to `/config/config.yml` inside the container.
+
+It is recommended to start with a minimal configuration and add to it as described in [this guide](/guides/getting_started):
 
 ```yaml
 mqtt:
@@ -18,14 +20,18 @@ cameras:
           roles:
             - detect
             - rtmp
-    width: 1280
-    height: 720
-    fps: 5
+    detect:
+      width: 1280
+      height: 720
 ```
 
-## Required
+### Full configuration reference:
 
-## `mqtt`
+:::caution
+
+It is not recommended to copy this full configuration file. Only specify values that are different from the defaults. Configuration options and default values may change in future versions.
+
+:::
 
 ```yaml
 mqtt:
@@ -34,67 +40,93 @@ mqtt:
   # Optional: port (default: shown below)
   port: 1883
   # Optional: topic prefix (default: shown below)
-  # WARNING: must be unique if you are running multiple instances
+  # NOTE: must be unique if you are running multiple instances
   topic_prefix: frigate
   # Optional: client id (default: shown below)
-  # WARNING: must be unique if you are running multiple instances
+  # NOTE: must be unique if you are running multiple instances
   client_id: frigate
   # Optional: user
   user: mqtt_user
   # Optional: password
-  # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}.
-  #       eg. password: '{FRIGATE_MQTT_PASSWORD}'
+  # NOTE: MQTT password can be specified with an environment variables that must begin with 'FRIGATE_'.
+  #       e.g. password: '{FRIGATE_MQTT_PASSWORD}'
   password: password
+  # Optional: tls_ca_certs for enabling TLS using self-signed certs (default: None)
+  tls_ca_certs: /path/to/ca.crt
+  # Optional: tls_client_cert and tls_client key in order to use self-signed client
+  # certificates (default: None)
+  # NOTE: certificate must not be password-protected
+  #       do not set user and password when using a client certificate
+  tls_client_cert: /path/to/client.crt
+  tls_client_key: /path/to/client.key
+  # Optional: tls_insecure (true/false) for enabling TLS verification of
+  # the server hostname in the server certificate (default: None)
+  tls_insecure: false
   # Optional: interval in seconds for publishing stats (default: shown below)
   stats_interval: 60
-```
 
-## `cameras`
+# Optional: Detectors configuration. Defaults to a single CPU detector
+detectors:
+  # Required: name of the detector
+  coral:
+    # Required: type of the detector
+    # Valid values are 'edgetpu' (requires device property below) and 'cpu'.
+    type: edgetpu
+    # Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
+    device: usb
+    # Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
+    # This value is only used for CPU types
+    num_threads: 3
 
-Each of your cameras must be configured. The following is the minimum required to register a camera in Frigate. Check the [camera configuration page](cameras.md) for a complete list of options.
+# Optional: Database configuration
+database:
+  # The path to store the SQLite DB (default: shown below)
+  path: /media/frigate/frigate.db
 
-```yaml
-cameras:
-  # Name of your camera
-  front_door:
-    ffmpeg:
-      inputs:
-        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          roles:
-            - detect
-            - rtmp
-    width: 1280
-    height: 720
-    fps: 5
-```
+# Optional: model modifications
+model:
+  # Optional: path to the model (default: automatic based on detector)
+  path: /edgetpu_model.tflite
+  # Optional: path to the labelmap (default: shown below)
+  labelmap_path: /labelmap.txt
+  # Required: Object detection model input width (default: shown below)
+  width: 320
+  # Required: Object detection model input height (default: shown below)
+  height: 320
+  # Optional: Label name modifications. These are merged into the standard labelmap.
+  labelmap:
+    2: vehicle
 
-## Optional
+# Optional: logger verbosity settings
+logger:
+  # Optional: Default log verbosity (default: shown below)
+  default: info
+  # Optional: Component specific logger overrides
+  logs:
+    frigate.event: debug
 
-### `clips`
+# Optional: set environment variables
+environment_vars:
+  EXAMPLE_VAR: value
 
-```yaml
-clips:
-  # Optional: Maximum length of time to retain video during long events. (default: shown below)
-  # NOTE: If an object is being tracked for longer than this amount of time, the cache
-  #       will begin to expire and the resulting clip will be the last x seconds of the event.
-  max_seconds: 300
-  # Optional: size of tmpfs mount to create for cache files (default: not set)
-  # mount -t tmpfs -o size={tmpfs_cache_size} tmpfs /tmp/cache
-  # NOTICE: Addon users must have Protection mode disabled for the addon when using this setting.
-  # Also, if you have mounted a tmpfs volume through docker, this value should not be set in your config.
-  tmpfs_cache_size: 256m
-  # Optional: Retention settings for clips (default: shown below)
-  retain:
-    # Required: Default retention days (default: shown below)
-    default: 10
-    # Optional: Per object retention days
-    objects:
-      person: 15
-```
+# Optional: birdseye configuration
+birdseye:
+  # Optional: Enable birdseye view (default: shown below)
+  enabled: True
+  # Optional: Width of the output resolution (default: shown below)
+  width: 1280
+  # Optional: Height of the output resolution (default: shown below)
+  height: 720
+  # Optional: Encoding quality of the mpeg1 feed (default: shown below)
+  # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
+  quality: 8
+  # Optional: Mode of the view. Available options are: objects, motion, and continuous
+  #   objects - cameras are included if they have had a tracked object within the last 30 seconds
+  #   motion - cameras are included if motion was detected in the last 30 seconds
+  #   continuous - all cameras are included always
+  mode: objects
 
-### `ffmpeg`
-
-```yaml
+# Optional: ffmpeg configuration
 ffmpeg:
   # Optional: global ffmpeg args (default: shown below)
   global_args: -hide_banner -loglevel warning
@@ -108,22 +140,38 @@ ffmpeg:
     # Optional: output args for detect streams (default: shown below)
     detect: -f rawvideo -pix_fmt yuv420p
     # Optional: output args for record streams (default: shown below)
-    record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
-    # Optional: output args for clips streams (default: shown below)
-    clips: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
+    record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
     # Optional: output args for rtmp streams (default: shown below)
     rtmp: -c copy -f flv
-```
 
-### `objects`
+# Optional: Detect configuration
+# NOTE: Can be overridden at the camera level
+detect:
+  # Optional: width of the frame for the input with the detect role (default: shown below)
+  width: 1280
+  # Optional: height of the frame for the input with the detect role (default: shown below)
+  height: 720
+  # Optional: desired fps for your camera for the input with the detect role (default: shown below)
+  # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
+  fps: 5
+  # Optional: enables detection for the camera (default: True)
+  # This value can be set via MQTT and will be updated in startup based on retained value
+  enabled: True
+  # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
+  max_disappeared: 25
+  # Optional: Frequency for running detection on stationary objects (default: 10x the frame rate)
+  stationary_interval: 50
 
-Can be overridden at the camera level
-
-```yaml
+# Optional: Object configuration
+# NOTE: Can be overridden at the camera level
 objects:
   # Optional: list of objects to track from labelmap.txt (default: shown below)
   track:
     - person
+  # Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
+  # Checks based on the bottom center of the bounding box of the object.
+  # NOTE: This mask is COMBINED with the object type specific mask below
+  mask: 0,0,1000,0,1000,200,0,200
   # Optional: filters to reduce false positives for specific object types
   filters:
     person:
@@ -135,4 +183,211 @@ objects:
       min_score: 0.5
       # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
       threshold: 0.7
+      # Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
+      # Checks based on the bottom center of the bounding box of the object
+      mask: 0,0,1000,0,1000,200,0,200
+
+# Optional: Motion configuration
+# NOTE: Can be overridden at the camera level
+motion:
+  # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
+  # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
+  # The value should be between 1 and 255.
+  threshold: 25
+  # Optional: Minimum size in pixels in the resized motion image that counts as motion (default: 30)
+  # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
+  # make motion detection more sensitive to smaller moving objects.
+  # As a rule of thumb:
+  #  - 15 - high sensitivity
+  #  - 30 - medium sensitivity
+  #  - 50 - low sensitivity
+  contour_area: 30
+  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
+  # Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
+  # Too low and a fast moving person wont be detected as motion.
+  delta_alpha: 0.2
+  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
+  # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
+  # Low values will cause things like moving shadows to be detected as motion for longer.
+  # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
+  frame_alpha: 0.2
+  # Optional: Height of the resized motion frame  (default: 50)
+  # This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense
+  # of higher CPU usage. Lower values result in less CPU, but small changes may not register as motion.
+  frame_height: 50
+  # Optional: motion mask
+  # NOTE: see docs for more detailed info on creating masks
+  mask: 0,900,1080,900,1080,1920,0,1920
+
+# Optional: Record configuration
+# NOTE: Can be overridden at the camera level
+record:
+  # Optional: Enable recording (default: shown below)
+  enabled: False
+  # Optional: Number of days to retain recordings regardless of events (default: shown below)
+  # NOTE: This should be set to 0 and retention should be defined in events section below
+  #       if you only want to retain recordings of events.
+  retain_days: 0
+  # Optional: Event recording settings
+  events:
+    # Optional: Maximum length of time to retain video during long events. (default: shown below)
+    # NOTE: If an object is being tracked for longer than this amount of time, the retained recordings
+    #       will be the last x seconds of the event unless retain_days under record is > 0.
+    max_seconds: 300
+    # Optional: Number of seconds before the event to include (default: shown below)
+    pre_capture: 5
+    # Optional: Number of seconds after the event to include (default: shown below)
+    post_capture: 5
+    # Optional: Objects to save recordings for. (default: all tracked objects)
+    objects:
+      - person
+    # Optional: Restrict recordings to objects that entered any of the listed zones (default: no required zones)
+    required_zones: []
+    # Optional: Retention settings for recordings of events
+    retain:
+      # Required: Default retention days (default: shown below)
+      default: 10
+      # Optional: Per object retention days
+      objects:
+        person: 15
+
+# Optional: Configuration for the jpg snapshots written to the clips directory for each event
+# NOTE: Can be overridden at the camera level
+snapshots:
+  # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
+  # This value can be set via MQTT and will be updated in startup based on retained value
+  enabled: False
+  # Optional: print a timestamp on the snapshots (default: shown below)
+  timestamp: False
+  # Optional: draw bounding box on the snapshots (default: shown below)
+  bounding_box: False
+  # Optional: crop the snapshot (default: shown below)
+  crop: False
+  # Optional: height to resize the snapshot to (default: original size)
+  height: 175
+  # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
+  required_zones: []
+  # Optional: Camera override for retention settings (default: global values)
+  retain:
+    # Required: Default retention days (default: shown below)
+    default: 10
+    # Optional: Per object retention days
+    objects:
+      person: 15
+
+# Optional: RTMP configuration
+# NOTE: Can be overridden at the camera level
+rtmp:
+  # Optional: Enable the RTMP stream (default: True)
+  enabled: True
+
+# Optional: Live stream configuration for WebUI
+# NOTE: Can be overridden at the camera level
+live:
+  # Optional: Set the height of the live stream. (default: 720)
+  # This must be less than or equal to the height of the detect stream. Lower resolutions
+  # reduce bandwidth required for viewing the live stream. Width is computed to match known aspect ratio.
+  height: 720
+  # Optional: Set the encode quality of the live stream (default: shown below)
+  # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
+  quality: 8
+
+# Optional: in-feed timestamp style configuration
+# NOTE: Can be overridden at the camera level
+timestamp_style:
+  # Optional: Position of the timestamp (default: shown below)
+  #           "tl" (top left), "tr" (top right), "bl" (bottom left), "br" (bottom right)
+  position: "tl"
+  # Optional: Format specifier conform to the Python package "datetime" (default: shown below)
+  #           Additional Examples:
+  #             german: "%d.%m.%Y %H:%M:%S"
+  format: "%m/%d/%Y %H:%M:%S"
+  # Optional: Color of font
+  color:
+    # All Required when color is specified (default: shown below)
+    red: 255
+    green: 255
+    blue: 255
+  # Optional: Line thickness of font (default: shown below)
+  thickness: 2
+  # Optional: Effect of lettering (default: shown below)
+  #           None (No effect),
+  #           "solid" (solid background in inverse color of font)
+  #           "shadow" (shadow for font)
+  effect: None
+
+# Required
+cameras:
+  # Required: name of the camera
+  back:
+    # Required: ffmpeg settings for the camera
+    ffmpeg:
+      # Required: A list of input streams for the camera. See documentation for more information.
+      inputs:
+        # Required: the path to the stream
+        # NOTE: path may include environment variables, which must begin with 'FRIGATE_' and be referenced in {}
+        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          # Required: list of roles for this stream. valid values are: detect,record,rtmp
+          # NOTICE: In addition to assigning the record, and rtmp roles,
+          # they must also be enabled in the camera config.
+          roles:
+            - detect
+            - rtmp
+          # Optional: stream specific global args (default: inherit)
+          # global_args:
+          # Optional: stream specific hwaccel args (default: inherit)
+          # hwaccel_args:
+          # Optional: stream specific input args (default: inherit)
+          # input_args:
+      # Optional: camera specific global args (default: inherit)
+      # global_args:
+      # Optional: camera specific hwaccel args (default: inherit)
+      # hwaccel_args:
+      # Optional: camera specific input args (default: inherit)
+      # input_args:
+      # Optional: camera specific output args (default: inherit)
+      # output_args:
+
+    # Optional: timeout for highest scoring image before allowing it
+    # to be replaced by a newer image. (default: shown below)
+    best_image_timeout: 60
+
+    # Optional: zones for this camera
+    zones:
+      # Required: name of the zone
+      # NOTE: This must be different than any camera names, but can match with another zone on another
+      #       camera.
+      front_steps:
+        # Required: List of x,y coordinates to define the polygon of the zone.
+        # NOTE: Coordinates can be generated at https://www.image-map.net/
+        coordinates: 545,1077,747,939,788,805
+        # Optional: List of objects that can trigger this zone (default: all tracked objects)
+        objects:
+          - person
+        # Optional: Zone level object filters.
+        # NOTE: The global and camera filters are applied upstream.
+        filters:
+          person:
+            min_area: 5000
+            max_area: 100000
+            threshold: 0.7
+
+    # Optional: Configuration for the jpg snapshots published via MQTT
+    mqtt:
+      # Optional: Enable publishing snapshot via mqtt for camera (default: shown below)
+      # NOTE: Only applies to publishing image data to MQTT via 'frigate/<camera_name>/<object_name>/snapshot'.
+      # All other messages will still be published.
+      enabled: True
+      # Optional: print a timestamp on the snapshots (default: shown below)
+      timestamp: True
+      # Optional: draw bounding box on the snapshots (default: shown below)
+      bounding_box: True
+      # Optional: crop the snapshot (default: shown below)
+      crop: True
+      # Optional: height to resize the snapshot to (default: shown below)
+      height: 270
+      # Optional: jpeg encode quality (default: shown below)
+      quality: 70
+      # Optional: Restrict mqtt messages to objects that entered any of the listed zones (default: no required zones)
+      required_zones: []
 ```

docs/docs/configuration/masks.md

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

docs/docs/configuration/nvdec.md

@@ -1,6 +1,6 @@
 ---
 id: nvdec
-title: nVidia hardware decoder
+title: NVIDIA hardware decoder
 ---
 
 Certain nvidia cards include a hardware decoder, which can greatly improve the
@@ -9,6 +9,8 @@ ffmpeg with NVDEC support is required. The special docker architecture 'amd64nvi
 includes this support for amd64 platforms. An aarch64 for the Jetson, which
 also includes NVDEC may be added in the future.
 
+Some more detailed setup instructions are also available in [this issue](https://github.com/blakeblackshear/frigate/issues/1847#issuecomment-932076731).
+
 ## Docker setup
 
 ### Requirements
@@ -23,7 +25,7 @@ In order to pass NVDEC, the docker engine must be set to `nvidia` and the enviro
 
 In a docker compose file, these lines need to be set:
 
-```
+```yaml
 services:
   frigate:
     ...
@@ -41,7 +43,7 @@ The decoder you choose will depend on the input video.
 
 A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
 
-```
+```shell
  V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
  V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
  V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
@@ -55,12 +57,11 @@ A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the c
 ```
 
 For example, for H265 video (hevc), you'll select `hevc_cuvid`. Add
-`-c:v hevc_covid` to your ffmpeg input arguments:
+`-c:v hevc_cuvid` to your ffmpeg input arguments:
 
-```
+```yaml
 ffmpeg:
-  input_args:
-    ...
+  input_args: ...
     - -c:v
     - hevc_cuvid
 ```
@@ -100,10 +101,10 @@ processes:
 To further improve performance, you can set ffmpeg to skip frames in the output,
 using the fps filter:
 
-```
-  output_args:
-    - -filter:v
-    - fps=fps=5
+```yaml
+output_args:
+  - -filter:v
+  - fps=fps=5
 ```
 
 This setting, for example, allows Frigate to consume my 10-15fps camera streams on

docs/docs/configuration/objects.mdx

@@ -0,0 +1,24 @@
+---
+id: objects
+title: Objects
+---
+
+import labels from "../../../labelmap.txt";
+
+By default, Frigate includes the following object models from the Google Coral test data. Note that `car` is listed twice because `truck` has been renamed to `car` by default. These object types are frequently confused.
+
+<ul>
+  {labels.split("\n").map((label) => (
+    <li>{label.replace(/^\d+\s+/, "")}</li>
+  ))}
+</ul>
+
+## Custom Models
+
+Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
+
+- CPU Model: `/cpu_model.tflite`
+- EdgeTPU Model: `/edgetpu_model.tflite`
+- Labels: `/labelmap.txt`
+
+You also need to update the [model config](/configuration/advanced#model) 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, but Frigate will fall back to CPU in the event one is not found. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU. Frigate should work with any supported Coral device from https://coral.ai
-- **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/configuration/record.md

@@ -0,0 +1,25 @@
+---
+id: record
+title: Recording
+---
+
+Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy in the config. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
+
+H265 recordings can be viewed in Edge and Safari only. All other browsers require recordings to be encoded with H264.
+
+## What if I don't want 24/7 recordings?
+
+If you only used clips in previous versions with recordings disabled, you can use the following config to get the same behavior. This is also the default behavior when recordings are enabled.
+
+```yaml
+record:
+  enabled: True
+  retain_days: 0
+  events:
+    retain:
+      default: 10
+```
+
+This configuration will retain recording segments that overlap with events for 10 days. Because multiple events can reference the same recording segments, this avoids storing duplicate footage for overlapping events and reduces overall storage needs.
+
+When `retain_days` is set to `0`, segments will be deleted from the cache if no events are in progress

docs/docs/configuration/rtmp.md

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

docs/docs/configuration/snapshots.md

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

docs/docs/configuration/zones.md

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

docs/docs/contributing.md

@@ -0,0 +1,184 @@
+---
+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/faqs.md

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

docs/docs/guides/camera_setup.md

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

docs/docs/guides/getting_started.md

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

docs/docs/guides/ha_notifications.md

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

docs/docs/guides/stationary_objects.md

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

docs/docs/hardware.md

@@ -5,25 +5,64 @@ title: Recommended hardware
 
 ## Cameras
 
-Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and HomeAssistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, clips, and recordings without re-encoding.
+Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
 
-## Computer
+I recommend Dahua, Hikvision, and Amcrest in that order. Dahua edges out Hikvision because they are easier to find and order, not because they are better cameras. I personally use Dahua cameras because they are easier to purchase directly. In my experience Dahua and Hikvision both have multiple streams with configurable resolutions and frame rates and rock solid streams. They also both have models with large sensors well known for excellent image quality at night. Not all the models are equal. Larger sensors are better than higher resolutions; especially at night. Amcrest is the fallback recommendation because they are rebranded Dahuas. They are rebranding the lower end models with smaller sensors or less configuration options.
 
-| Name                    | Inference Speed | Notes                                                                                                                         |
-| ----------------------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| Atomic Pi               | 16ms            | Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding. |
-| Intel NUC NUC7i3BNK     | 8-10ms          | Great performance. Can handle many cameras at 5fps depending on typical amounts of motion.                                    |
-| BMAX B2 Plus            | 10-12ms         | Good balance of performance and cost. Also capable of running many other services at the same time as frigate.                |
-| Minisforum GK41         | 9-10ms          | Great alternative to a NUC with dual Gigabit NICs. Easily handles several 1080p cameras.                                      |
-| Raspberry Pi 3B (32bit) | 60ms            | Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0.                                       |
-| Raspberry Pi 4 (32bit)  | 15-20ms         | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
-| Raspberry Pi 4 (64bit)  | 10-15ms         | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
+Many users have reported various issues with Reolink cameras, so I do not recommend them. If you are using Reolink, I suggest the [Reolink specific configuration](configuration/camera_specific#reolink-410520-possibly-others). Wifi cameras are also not recommended. Their streams are less reliable and cause connection loss and/or lost video data.
 
-## Unraid
+Here are some of the camera's I recommend:
 
-Many people have powerful enough NAS devices or home servers to also run docker. There is a Unraid Community App.
-To install make sure you have the [community app plugin here](https://forums.unraid.net/topic/38582-plug-in-community-applications/). Then search for "Frigate" in the apps section within Unraid - you can see the online store [here](https://unraid.net/community/apps?q=frigate#r)
+- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
+- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
+- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
 
-| Name                    | Inference Speed | Notes                                                                                                                         |
-| ----------------------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| [M2 Coral Edge TPU](http://coral.ai)       | 6.2ms            | Little complicated to get installed, as needs drivers on the host OS, [info here](https://forums.unraid.net/topic/98064-support-blakeblackshear-frigate/?do=findComment&comment=945776)                         |
+I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
+
+## Server
+
+My current favorite is the Odyssey X86 Blue J4125 because the Coral M.2 compatibility and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
+
+| Name                                                                                                                             | Inference Speed | Coral Compatibility | Notes                                                                                                                         |
+| -------------------------------------------------------------------------------------------------------------------------------- | --------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| <a href="https://amzn.to/3oH4BKi" target="_blank" rel="nofollow noopener sponsored">Odyssey X86 Blue J4125</a> (affiliate link)  | 9-10ms          | M.2 B+M             | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                     |
+| <a href="https://amzn.to/3oxEC8m" target="_blank" rel="nofollow noopener sponsored">Minisforum GK41</a> (affiliate link)         | 9-10ms          | USB                 | Great alternative to a NUC. Easily handles several 1080p cameras.                                                             |
+| <a href="https://amzn.to/3ixJFlb" target="_blank" rel="nofollow noopener sponsored">Minisforum GK50</a> (affiliate link)         | 9-10ms          | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                     |
+| <a href="https://amzn.to/3l7vCEI" target="_blank" rel="nofollow noopener sponsored">Intel NUC</a> (affiliate link)               | 8-10ms          | USB                 | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion.             |
+| <a href="https://amzn.to/3a6TBh8" target="_blank" rel="nofollow noopener sponsored">BMAX B2 Plus</a> (affiliate link)            | 10-12ms         | USB                 | Good balance of performance and cost. Also capable of running many other services at the same time as frigate.                |
+| <a href="https://amzn.to/2YjpY9m" target="_blank" rel="nofollow noopener sponsored">Atomic Pi</a> (affiliate link)               | 16ms            | USB                 | Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding. |
+| <a href="https://amzn.to/2WIpwRU" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 3B (32bit)</a> (affiliate link) | 60ms            | USB                 | Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0.                                       |
+| <a href="https://amzn.to/2YhSGHH" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 4 (32bit)</a> (affiliate link)  | 15-20ms         | USB                 | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
+| <a href="https://amzn.to/2YhSGHH" target="_blank" rel="nofollow noopener sponsored">Raspberry Pi 4 (64bit)</a> (affiliate link)  | 10-15ms         | USB                 | Can handle a small number of cameras. The 2GB version runs fine.                                                              |
+
+## Google Coral TPU
+
+It is strongly recommended to use a Google Coral. Frigate is designed around the expectation that a Coral is used to achieve very low inference speeds. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU. Frigate should work with any supported Coral device from https://coral.ai
+
+The USB version is compatible with the widest variety of hardware and does not require a driver on the host machine. However, it does lack the automatic throttling features of the other versions.
+
+The PCIe and M.2 versions require installation of a driver on the host. Follow the instructions for your version from https://coral.ai
+
+A single Coral can handle many cameras and will be sufficient for the majority of users. You can calculate the maximum performance of your Coral based on the inference speed reported by Frigate. With an inference speed of 10, your Coral will top out at `1000/10=100`, or 100 frames per second. If your detection fps is regularly getting close to that, you should first consider tuning motion masks. If those are already properly configured, a second Coral may be needed.
+
+### What does Frigate use the CPU for and what does it use the Coral for? (ELI5 Version)
+
+This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
+
+CPU Usage: I am a CPU, Mendel is a Google Coral
+
+My buddy Mendel and I have been tasked with keeping the neighbor's red footed booby off my parent's yard. Now I'm really bad at identifying birds. It takes me forever, but my buddy Mendel is incredible at it.
+
+Mendel however, struggles at pretty much anything else. So we make an agreement. I wait till I see something that moves, and snap a picture of it for Mendel. I then show him the picture and he tells me what it is. Most of the time it isn't anything. But eventually I see some movement and Mendel tells me it is the Booby. Score!
+
+_What happens when I increase the resolution of my camera?_
+
+However we realize that there is a problem. There is still booby poop all over the yard. How could we miss that! I've been watching all day! My parents check the window and realize its dirty and a bit small to see the entire yard so they clean it and put a bigger one in there. Now there is so much more to see! However I now have a much bigger area to scan for movement and have to work a lot harder! Even my buddy Mendel has to work harder, as now the pictures have a lot more detail in them that he has to look at to see if it is our sneaky booby.
+
+Basically - When you increase the resolution and/or the frame rate of the stream there is now significantly more data for the CPU to parse. That takes additional computing power. The Google Coral is really good at doing object detection, but it doesn't have time to look everywhere all the time (especially when there are many windows to check). To balance it, Frigate uses the CPU to look for movement, then sends those frames to the Coral to do object detection. This allows the Coral to be available to a large number of cameras and not overload it.
+
+### Do hwaccel args help if I am using a Coral?
+
+YES! The Coral does not help with decoding video streams.
+
+Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://blog.video.ibm.com/streaming-video-tips/keyframes-interframe-video-compression/). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.

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

docs/docs/installation.md

@@ -3,59 +3,135 @@ id: installation
 title: Installation
 ---
 
-Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). See instructions below for installing the HassOS addon.
+Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). Note that a Home Assistant Addon is **not** the same thing as the integration. The [integration](integrations/home-assistant) is required to integrate Frigate into Home Assistant.
 
-For HomeAssistant users, there is also a [custom component (aka integration)](https://github.com/blakeblackshear/frigate-hass-integration). This custom component adds tighter integration with HomeAssistant by automatically setting up camera entities, sensors, media browser for clips and recordings, and a public API to simplify notifications.
+## Dependencies
 
-Note that HassOS Addons and custom components are different things. If you are already running Frigate with Docker directly, you do not need the Addon since the Addon would run another instance of Frigate.
+**MQTT broker** - Frigate requires an MQTT broker. If using Home Assistant, Frigate and Home Assistant must be connected to the same MQTT broker.
 
-## HassOS Addon
+## Preparing your hardware
 
-HassOS users can install via the addon repository. Frigate requires an MQTT server.
+### Operating System
 
-1. Navigate to Supervisor > Add-on Store > Repositories
-1. Add https://github.com/blakeblackshear/frigate-hass-addons
-1. Setup your configuration in the `Configuration` tab
-1. Start the addon container
-1. If you are using hardware acceleration for ffmpeg, you will need to disable "Protection mode"
+Frigate runs best with docker installed on bare metal debian-based distributions. For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer often introduces a sizable amount of overhead for communication with Coral devices, but [not in all circumstances](https://github.com/blakeblackshear/frigate/discussions/1837).
+
+Windows is not officially supported, but some users have had success getting it to run under WSL or Virtualbox. Getting the GPU and/or Coral devices properly passed to Frigate may be difficult or impossible. Search previous discussions or issues for help.
+
+### Storage
+
+Frigate uses the following locations for read/write operations in the container. Docker volume mappings can be used to map these to any location on your host machine.
+
+- `/media/frigate/clips`: Used for snapshot storage. In the future, it will likely be renamed from `clips` to `snapshots`. The file structure here cannot be modified and isn't intended to be browsed or managed manually.
+- `/media/frigate/recordings`: Internal system storage for recording segments. The file structure here cannot be modified and isn't intended to be browsed or managed manually.
+- `/media/frigate/frigate.db`: Default location for the sqlite database. You will also see several files alongside this file while frigate is running. If moving the database location (often needed when using a network drive at `/media/frigate`), it is recommended to mount a volume with docker at `/db` and change the storage location of the database to `/db/frigate.db` in the config file.
+- `/tmp/cache`: Cache location for recording segments. Initial recordings are written here before being checked and converted to mp4 and moved to the recordings folder.
+- `/dev/shm`: It is not recommended to modify this directory or map it with docker. This is the location for raw decoded frames in shared memory and it's size is impacted by the `shm-size` calculations below.
+- `/config/config.yml`: Default location of the config file.
+
+#### Common docker compose storage configurations
+
+Writing to a local disk or external USB drive:
+
+```yaml
+version: "3.9"
+services:
+  frigate:
+    ...
+    volumes:
+      - /path/to/your/config.yml:/config/config.yml:ro
+      - /path/to/your/storage:/media/frigate
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+        target: /tmp/cache
+        tmpfs:
+          size: 1000000000
+    ...
+```
+
+Writing to a network drive with database on a local drive:
+
+```yaml
+version: "3.9"
+services:
+  frigate:
+    ...
+    volumes:
+      - /path/to/your/config.yml:/config/config.yml:ro
+      - /path/to/network/storage:/media/frigate
+      - /path/to/local/disk:/db
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+        target: /tmp/cache
+        tmpfs:
+          size: 1000000000
+    ...
+```
+
+frigate.yml
+
+```yaml
+database:
+  path: /db/frigate.db
+```
+
+### Calculating required shm-size
+
+Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is 64m.
+
+The default shm-size of 64m is fine for setups with 2 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size.
+
+You can calculate the necessary shm-size for each camera with the following formula using the resolution specified for detect:
+
+```
+(width * height * 1.5 * 9 + 270480)/1048576 = <shm size in mb>
+```
+
+The shm size cannot be set per container for Home Assistant Addons. You must set `default-shm-size` in `/etc/docker/daemon.json` to increase the default shm size. This will increase the shm size for all of your docker containers. This may or may not cause issues with your setup. https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file
+
+### Raspberry Pi 3/4
+
+By default, the Raspberry Pi limits the amount of memory available to the GPU. In order to use ffmpeg hardware acceleration, you must increase the available memory by setting `gpu_mem` to the maximum recommended value in `config.txt` as described in the [official docs](https://www.raspberrypi.org/documentation/computers/config_txt.html#memory-options).
+
+Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with it's own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
 
 ## Docker
 
+Running in Docker directly is the recommended install method.
+
 Make sure you choose the right image for your architecture:
 
-|Arch|Image Name|
-|-|-|
-|amd64|blakeblackshear/frigate:stable-amd64|
-|amd64nvidia|blakeblackshear/frigate:stable-amd64nvidia|
-|armv7|blakeblackshear/frigate:stable-armv7|
-|aarch64|blakeblackshear/frigate:stable-aarch64|
+| Arch        | Image Name                                 |
+| ----------- | ------------------------------------------ |
+| amd64       | blakeblackshear/frigate:stable-amd64       |
+| amd64nvidia | blakeblackshear/frigate:stable-amd64nvidia |
+| armv7       | blakeblackshear/frigate:stable-armv7       |
+| aarch64     | blakeblackshear/frigate:stable-aarch64     |
 
 It is recommended to run with docker-compose:
 
 ```yaml
-version: '3.9'
+version: "3.9"
 services:
   frigate:
     container_name: frigate
     privileged: true # this may not be necessary for all setups
     restart: unless-stopped
     image: blakeblackshear/frigate:<specify_version_tag>
+    shm_size: "64mb" # update for your cameras based on calculation above
     devices:
-      - /dev/bus/usb:/dev/bus/usb
+      - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
       - /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
     volumes:
       - /etc/localtime:/etc/localtime:ro
-      - <path_to_config_file>:/config/config.yml:ro
-      - <path_to_directory_for_media>:/media/frigate
+      - /path/to/your/config.yml:/config/config.yml:ro
+      - /path/to/your/storage:/media/frigate
       - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
         target: /tmp/cache
         tmpfs:
           size: 1000000000
     ports:
-      - '5000:5000'
-      - '1935:1935' # RTMP feeds
+      - "5000:5000"
+      - "1935:1935" # RTMP feeds
     environment:
-      FRIGATE_RTSP_PASSWORD: 'password'
+      FRIGATE_RTSP_PASSWORD: "password"
 ```
 
 If you can't use docker compose, you can run the container with something similar to this:
@@ -66,9 +142,10 @@ docker run -d \
   --restart=unless-stopped \
   --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
   --device /dev/bus/usb:/dev/bus/usb \
-  --device /dev/dri/renderD128
-  -v <path_to_directory_for_media>:/media/frigate \
-  -v <path_to_config_file>:/config/config.yml:ro \
+  --device /dev/dri/renderD128 \
+  --shm-size=64m \
+  -v /path/to/your/storage:/media/frigate \
+  -v /path/to/your/config.yml:/config/config.yml:ro \
   -v /etc/localtime:/etc/localtime:ro \
   -e FRIGATE_RTSP_PASSWORD='password' \
   -p 5000:5000 \
@@ -76,48 +153,62 @@ docker run -d \
   blakeblackshear/frigate:<specify_version_tag>
 ```
 
-### Calculating shm-size
+## Home Assistant Operating System (HassOS)
 
-The default shm-size of 64m is fine for setups with 3 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it could be because you have too many high resolution cameras and you need to specify a higher shm size.
+:::caution
 
-You can calculate the necessary shm-size for each camera with the following formula:
+Due to limitations in Home Assistant Operating System, utilizing external storage for recordings or snapshots requires [modifying udev rules manually](https://community.home-assistant.io/t/solved-mount-usb-drive-in-hassio-to-be-used-on-the-media-folder-with-udev-customization/258406/46).
 
-```
-(width * height * 1.5 * 7 + 270480)/1048576 = <shm size in mb>
+:::
+
+:::tip
+
+If possible, it is recommended to run Frigate standalone in Docker and use [Frigate's Proxy Addon](https://github.com/blakeblackshear/frigate-hass-addons/blob/main/frigate_proxy/README.md).
+
+:::
+
+HassOS users can install via the addon repository.
+
+1. Navigate to Supervisor > Add-on Store > Repositories
+2. Add https://github.com/blakeblackshear/frigate-hass-addons
+3. Install your desired Frigate NVR Addon and navigate to it's page
+4. Setup your network configuration in the `Configuration` tab
+5. (not for proxy addon) Create the file `frigate.yml` in your `config` directory with your detailed Frigate configuration
+6. Start the addon container
+7. (not for proxy addon) If you are using hardware acceleration for ffmpeg, you may need to disable "Protection mode"
+
+## Home Assistant Supervised
+
+:::tip
+
+If possible, it is recommended to run Frigate standalone in Docker and use [Frigate's Proxy Addon](https://github.com/blakeblackshear/frigate-hass-addons/blob/main/frigate_proxy/README.md).
+
+:::
+
+When running Home Assistant with the [Supervised install method](https://github.com/home-assistant/supervised-installer), you can get the benefit of running the Addon along with the ability to customize the storage used by Frigate.
+
+In order to customize the storage location for Frigate, simply use `fstab` to mount the drive you want at `/usr/share/hassio/media`. Here is an example fstab entry:
+
+```shell
+UUID=1a65fec6-c25f-404a-b3d2-1f2fcf6095c8 /media/data ext4 defaults 0 0
+/media/data/homeassistant/media /usr/share/hassio/media none bind 0 0
 ```
 
-The shm size cannot be set per container for HomeAssistant Addons. You must set `default-shm-size` in `/etc/docker/daemon.json` to increase the default shm size. This will increase the shm size for all of your docker containers. This may or may not cause issues with your setup. https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file
+Then follow the instructions listed for [Home Assistant Operating System](#home-assistant-operating-system-hassos).
 
 ## Kubernetes
 
 Use the [helm chart](https://github.com/blakeblackshear/blakeshome-charts/tree/master/charts/frigate).
 
-## Virtualization
+## Unraid
 
-For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices for ffmpeg decoding. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer typically introduces a sizable amount of overhead for communication with Coral devices.
+Many people have powerful enough NAS devices or home servers to also run docker. There is a Unraid Community App.
+To install make sure you have the [community app plugin here](https://forums.unraid.net/topic/38582-plug-in-community-applications/). Then search for "Frigate" in the apps section within Unraid - you can see the online store [here](https://unraid.net/community/apps?q=frigate#r)
 
-### Proxmox
+## Proxmox
 
-Some people have had success running Frigate in LXC directly with the following config:
+It is recommended to run Frigate in LXC for maximum performance. See [this discussion](https://github.com/blakeblackshear/frigate/discussions/1111) for more information.
 
-```
-arch: amd64
-cores: 2
-features: nesting=1
-hostname: FrigateLXC
-memory: 4096
-net0: name=eth0,bridge=vmbr0,firewall=1,hwaddr=2E:76:AE:5A:58:48,ip=dhcp,ip6=auto,type=veth
-ostype: debian
-rootfs: local-lvm:vm-115-disk-0,size=12G
-swap: 512
-lxc.cgroup.devices.allow: c 189:385 rwm
-lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file
-lxc.mount.entry: /dev/bus/usb/004/002 dev/bus/usb/004/002 none bind,optional,create=file
-lxc.apparmor.profile: unconfined
-lxc.cgroup.devices.allow: a
-lxc.cap.drop:
-```
+## ESX
 
-### ESX
 For details on running Frigate under ESX, see details [here](https://github.com/blakeblackshear/frigate/issues/305).
-

docs/docs/integrations/api.md

@@ -5,7 +5,7 @@ title: HTTP API
 
 A web server is available on port 5000 with the following endpoints.
 
-### `/api/<camera_name>`
+### `GET /api/<camera_name>`
 
 An mjpeg stream for debugging. Keep in mind the mjpeg endpoint is for debugging only and will put additional load on the system when in use.
 
@@ -22,9 +22,9 @@ Accepts the following query string parameters:
 | `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/back?h=1080`. You can also increase the FPS by appending `fps=frame-rate` to the URL such as `http://localhost:5000/back?fps=10` or both with `?fps=10&h=1000`.
+You can access a higher resolution mjpeg stream by appending `h=height-in-pixels` to the endpoint. For example `http://localhost:5000/api/back?h=1080`. You can also increase the FPS by appending `fps=frame-rate` to the URL such as `http://localhost:5000/api/back?fps=10` or both with `?fps=10&h=1000`.
 
-### `/api/<camera_name>/<object_name>/best.jpg[?h=300&crop=1]`
+### `GET /api/<camera_name>/<object_name>/best.jpg[?h=300&crop=1&quality=70]`
 
 The best snapshot for any object type. It is a full resolution image by default.
 
@@ -32,8 +32,9 @@ Example parameters:
 
 - `h=300`: resizes the image to 300 pixes tall
 - `crop=1`: crops the image to the region of the detection rather than returning the entire image
+- `quality=70`: sets the jpeg encoding quality (0-100)
 
-### `/api/<camera_name>/latest.jpg[?h=300]`
+### `GET /api/<camera_name>/latest.jpg[?h=300]`
 
 The most recent frame that frigate has finished processing. It is a full resolution image by default.
 
@@ -48,14 +49,15 @@ Accepts the following query string parameters:
 | `mask`      | int  | Overlay the mask on the image (0 or 1)                             |
 | `motion`    | int  | Draw blue boxes for areas with detected motion (0 or 1)            |
 | `regions`   | int  | Draw green boxes for areas where object detection was run (0 or 1) |
+| `quality`   | int  | Jpeg encoding quality (0-100). Defaults to 70.                     |
 
 Example parameters:
 
 - `h=300`: resizes the image to 300 pixes tall
 
-### `/api/stats`
+### `GET /api/stats`
 
-Contains some granular debug info that can be used for sensors in HomeAssistant.
+Contains some granular debug info that can be used for sensors in Home Assistant.
 
 Sample response:
 
@@ -125,40 +127,40 @@ Sample response:
         "total": 1000,
         "used": 700,
         "free": 300,
-        "mnt_type": "ext4",
+        "mnt_type": "ext4"
       },
       "/media/frigate/recordings": {
         "total": 1000,
         "used": 700,
         "free": 300,
-        "mnt_type": "ext4",
+        "mnt_type": "ext4"
       },
       "/tmp/cache": {
         "total": 256,
         "used": 100,
         "free": 156,
-        "mnt_type": "tmpfs",
+        "mnt_type": "tmpfs"
       },
       "/dev/shm": {
         "total": 256,
         "used": 100,
         "free": 156,
-        "mnt_type": "tmpfs",
-      },
+        "mnt_type": "tmpfs"
+      }
     }
   }
 }
 ```
 
-### `/api/config`
+### `GET /api/config`
 
 A json representation of your configuration
 
-### `/api/version`
+### `GET /api/version`
 
 Version info
 
-### `/api/events`
+### `GET /api/events`
 
 Events from the database. Accepts the following query string parameters:
 
@@ -174,19 +176,27 @@ Events from the database. Accepts the following query string parameters:
 | `has_clip`           | int  | Filter to events that have clips (0 or 1)     |
 | `include_thumbnails` | int  | Include thumbnails in the response (0 or 1)   |
 
-### `/api/events/summary`
+### `GET /api/events/summary`
 
-Returns summary data for events in the database. Used by the HomeAssistant integration.
+Returns summary data for events in the database. Used by the Home Assistant integration.
 
-### `/api/events/<id>`
+### `GET /api/events/<id>`
 
 Returns data for a single event.
 
-### `/api/events/<id>/thumbnail.jpg`
+### `DELETE /api/events/<id>`
+
+Permanently deletes the event along with any clips/snapshots.
+
+### `GET /api/events/<id>/thumbnail.jpg`
 
 Returns a thumbnail for the event id optimized for notifications. Works while the event is in progress and after completion. Passing `?format=android` will convert the thumbnail to 2:1 aspect ratio.
 
-### `/api/events/<id>/snapshot.jpg`
+### `GET /api/events/<id>/clip.mp4`
+
+Returns the clip for the event id. Works after the event has ended.
+
+### `GET /api/events/<id>/snapshot.jpg`
 
 Returns the snapshot image for the event id. Works while the event is in progress and after completion.
 
@@ -198,11 +208,24 @@ Accepts the following query string parameters, but they are only applied when an
 | `bbox`      | int  | Show bounding boxes for detected objects (0 or 1) |
 | `timestamp` | int  | Print the timestamp in the upper left (0 or 1)    |
 | `crop`      | int  | Crop the snapshot to the (0 or 1)                 |
+| `quality`   | int  | Jpeg encoding quality (0-100). Defaults to 70.    |
 
-### `/clips/<camera>-<id>.mp4`
-
-Video clip for the given camera and event id.
-
-### `/clips/<camera>-<id>.jpg`
+### `GET /clips/<camera>-<id>.jpg`
 
 JPG snapshot for the given camera and event id.
+
+### `GET /vod/<year>-<month>/<day>/<hour>/<camera>/master.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the specified hour and camera. Can be viewed in an application like VLC.
+
+### `GET /vod/event/<event-id>/index.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed in an application like VLC.
+
+### `GET /vod/event/<event-id>/index.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed in an application like VLC.
+
+### `GET /vod/<camera>/start/<start-timestamp>/end/<end-timestamp>/index.m3u8`
+
+HTTP Live Streaming Video on Demand URL for the camera with the specified time range. Can be viewed in an application like VLC.

docs/docs/integrations/home-assistant.md

@@ -0,0 +1,189 @@
+---
+id: home-assistant
+title: Home Assistant Integration
+---
+
+The best way to integrate with Home Assistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration).
+
+## Installation
+
+### Preparation
+
+The Frigate integration requires the `mqtt` integration to be installed and
+manually configured first.
+
+See the [MQTT integration
+documentation](https://www.home-assistant.io/integrations/mqtt/) for more
+details.
+
+### Integration installation
+
+Available via HACS as a default repository. To install:
+
+- 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.
+
+### (Optional) Lovelace Card Installation
+
+To install the optional companion Lovelace card, please see the [separate
+installation instructions](https://github.com/dermotduffy/frigate-hass-card) for
+that card.
+
+## 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:
+
+- Browsing event recordings with thumbnails
+- Browsing snapshots
+- Browsing recordings by month, day, camera, time
+
+This is accessible via "Media Browser" on the left menu panel in Home Assistant.
+
+<a name="api"></a>
+
+## Notification API
+
+Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.
+
+To load a thumbnail for an event:
+
+```
+https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
+```
+
+To load a snapshot for an event:
+
+```
+https://HA_URL/api/frigate/notifications/<event-id>/snapshot.jpg
+```
+
+To load a video clip of an event:
+
+```
+https://HA_URL/api/frigate/notifications/<event-id>/clip.mp4
+```
+
+<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/integrations/mqtt.md

@@ -7,17 +7,21 @@ These are the MQTT messages generated by Frigate. The default topic_prefix is `f
 
 ### `frigate/available`
 
-Designed to be used as an availability topic with HomeAssistant. Possible message are:
+Designed to be used as an availability topic with Home Assistant. Possible message are:
 "online": published when frigate is running (on startup)
 "offline": published right before frigate stops
 
+### `frigate/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 HomeAssistant.
+Publishes the count of objects for the camera for use as a sensor in Home Assistant.
 
 ### `frigate/<zone_name>/<object_name>`
 
-Publishes the count of objects for the zone for use as a sensor in HomeAssistant.
+Publishes the count of objects for the zone for use as a sensor in Home Assistant.
 
 ### `frigate/<camera_name>/<object_name>/snapshot`
 
@@ -32,11 +36,12 @@ Message published for each changed event. The first message is published when th
 
 ```json
 {
-  "type": "update", // new, update, or end
+  "type": "update", // new, update, end
   "before": {
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
     "frame_time": 1607123961.837752,
+    "snapshot_time": 1607123961.837752,
     "label": "person",
     "top_score": 0.958984375,
     "false_positive": false,
@@ -48,12 +53,15 @@ Message published for each changed event. The first message is published when th
     "region": [264, 450, 667, 853],
     "current_zones": ["driveway"],
     "entered_zones": ["yard", "driveway"],
-    "thumbnail": null
+    "thumbnail": null,
+    "has_snapshot": false,
+    "has_clip": false
   },
   "after": {
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
     "frame_time": 1607123962.082975,
+    "snapshot_time": 1607123961.837752,
     "label": "person",
     "top_score": 0.958984375,
     "false_positive": false,
@@ -65,7 +73,9 @@ Message published for each changed event. The first message is published when th
     "region": [218, 440, 693, 915],
     "current_zones": ["yard", "driveway"],
     "entered_zones": ["yard", "driveway"],
-    "thumbnail": null
+    "thumbnail": null,
+    "has_snapshot": false,
+    "has_clip": false
   }
 }
 ```
@@ -82,13 +92,13 @@ Topic to turn detection for a camera on and off. Expected values are `ON` and `O
 
 Topic with current state of detection for a camera. Published values are `ON` and `OFF`.
 
-### `frigate/<camera_name>/clips/set`
+### `frigate/<camera_name>/recordings/set`
 
-Topic to turn clips for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`.
 
-### `frigate/<camera_name>/clips/state`
+### `frigate/<camera_name>/recordings/state`
 
-Topic with current state of clips for a camera. Published values are `ON` and `OFF`.
+Topic with current state of recordings for a camera. Published values are `ON` and `OFF`.
 
 ### `frigate/<camera_name>/snapshots/set`
 

docs/docs/troubleshooting.md

@@ -1,20 +0,0 @@
----
-id: troubleshooting
-title: Troubleshooting and FAQ
----
-
-### How can I get sound or audio in my clips and recordings?
-By default, Frigate removes audio from clips and recordings to reduce the likelihood of failing for invalid data. If you would like to include audio, you need to override the output args to remove `-an` for where you want to include audio. The recommended audio codec is `aac`. Not all audio codecs are supported by RTMP, so you may need to re-encode your audio with `-c:a aac`. The default ffmpeg args are shown [here](/frigate/configuration/index#ffmpeg).
-
-### My mjpeg stream or snapshots look green and crazy
-This almost always means that the width/height defined for your camera are not correct. Double check the resolution with vlc or another player. Also make sure you don't have the width and height values backwards.
-
-![mismatched-resolution](/img/mismatched-resolution.jpg)
-
-## "[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5639eeb6e140] moov atom not found"
-
-These messages in the logs are expected in certain situations. Frigate checks the integrity of the video cache before assembling clips. Occasionally these cached files will be invalid and cleaned up automatically.
-
-## "On connect called"
-
-If you see repeated "On connect called" messages in your config, check for another instance of frigate. This happens when multiple frigate containers are trying to connect to mqtt with the same client_id.

docs/docs/usage/home-assistant.md

@@ -1,132 +0,0 @@
----
-id: home-assistant
-title: Integration with Home Assistant
-sidebar_label: Home Assistant
----
-
-The best way to integrate with HomeAssistant is to use the [official integration](https://github.com/blakeblackshear/frigate-hass-integration). When configuring the integration, you will be asked for the `Host` of your frigate instance. This value should be the url you use to access Frigate in the browser and will look like `http://<host>:5000/`. If you are using HassOS with the addon, the host should be `http://ccab4aaf-frigate:5000` (or `http://ccab4aaf-frigate-beta:5000` if your are using the beta version of the addon). HomeAssistant needs access to port 5000 (api) and 1935 (rtmp) for all features. The integration will setup the following entities within HomeAssistant:
-
-## Sensors:
-
-- Stats to monitor frigate performance
-- Object counts for all zones and cameras
-
-## Cameras:
-
-- Cameras for image of the last detected object for each camera
-- Camera entities with stream support (requires RTMP)
-
-## Media Browser:
-
-- Rich UI with thumbnails for browsing event clips
-- Rich UI for browsing 24/7 recordings by month, day, camera, time
-
-## 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 HomeAssistants API see below:
-``` 
-https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
-```
-
-To load a video clip taken by frigate from HomeAssistants 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"]}}'
-```
-
-
-```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']}}"
-```
-
-```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']}}"
-```
-
-```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']}}"
-```
-
-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/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

@@ -1,8 +1,10 @@
+const path = require('path');
+
 module.exports = {
   title: 'Frigate',
   tagline: 'NVR With Realtime Object Detection for IP Cameras',
-  url: 'https://blakeblackshear.github.io',
-  baseUrl: '/frigate/',
+  url: 'https://docs.frigate.video',
+  baseUrl: '/',
   onBrokenLinks: 'throw',
   onBrokenMarkdownLinks: 'warn',
   favicon: 'img/favicon.ico',
@@ -11,7 +13,7 @@ module.exports = {
   themeConfig: {
     algolia: {
       apiKey: '81ec882db78f7fed05c51daf973f0362',
-      indexName: 'frigate'
+      indexName: 'frigate',
     },
     navbar: {
       title: 'Frigate',
@@ -27,6 +29,16 @@ module.exports = {
           label: 'Docs',
           position: 'left',
         },
+        {
+          href: 'https://frigate.video',
+          label: 'Website',
+          position: 'right',
+        },
+        {
+          href: 'https://demo.frigate.video',
+          label: 'Demo',
+          position: 'right',
+        },
         {
           href: 'https://github.com/blakeblackshear/frigate',
           label: 'GitHub',
@@ -56,6 +68,7 @@ module.exports = {
       copyright: `Copyright © ${new Date().getFullYear()} Blake Blackshear`,
     },
   },
+  plugins: [path.resolve(__dirname, 'plugins', 'raw-loader')],
   presets: [
     [
       '@docusaurus/preset-classic',

docs/package.json

@@ -12,10 +12,11 @@
     "clear": "docusaurus clear"
   },
   "dependencies": {
-    "@docusaurus/core": "2.0.0-alpha.70",
-    "@docusaurus/preset-classic": "2.0.0-alpha.70",
+    "@docusaurus/core": "^2.0.0-beta.6",
+    "@docusaurus/preset-classic": "^2.0.0-beta.6",
     "@mdx-js/react": "^1.6.21",
     "clsx": "^1.1.1",
+    "raw-loader": "^4.0.2",
     "react": "^16.8.4",
     "react-dom": "^16.8.4"
   },

docs/plugins/raw-loader.js

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

docs/sidebars.js

@@ -1,14 +1,34 @@
 module.exports = {
   docs: {
-    Frigate: ['index', 'how-it-works', 'hardware', 'installation', 'troubleshooting'],
+    Frigate: [
+      'index',
+      'hardware',
+      'installation',
+    ],
+    Guides: [
+      'guides/camera_setup',
+      'guides/getting_started',
+      'guides/false_positives',
+      'guides/ha_notifications',
+      'guides/stationary_objects',
+    ],
     Configuration: [
       'configuration/index',
-      'configuration/cameras',
-      'configuration/optimizing',
       'configuration/detectors',
-      'configuration/false_positives',
+      'configuration/cameras',
+      'configuration/masks',
+      'configuration/record',
+      'configuration/snapshots',
+      'configuration/objects',
+      'configuration/rtmp',
+      'configuration/zones',
       'configuration/advanced',
+      'configuration/hardware_acceleration',
+      'configuration/nvdec',
+      'configuration/camera_specific',
     ],
-    Usage: ['usage/home-assistant', 'usage/web', 'usage/api', 'usage/mqtt'],
+    Integrations: ['integrations/home-assistant', 'integrations/api', 'integrations/mqtt'],
+    Troubleshooting: ['faqs'],
+    Development: ['contributing'],
   },
 };

frigate/__main__.py

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

frigate/app.py

@@ -2,38 +2,41 @@ import json
 import logging
 import multiprocessing as mp
 import os
+import signal
+import sys
+import threading
 from logging.handlers import QueueHandler
 from typing import Dict, List
-import sys
-import signal
 
 import yaml
-from gevent import pywsgi
-from geventwebsocket.handler import WebSocketHandler
 from peewee_migrate import Router
 from playhouse.sqlite_ext import SqliteExtDatabase
 from playhouse.sqliteq import SqliteQueueDatabase
+from pydantic import ValidationError
 
-from frigate.config import FrigateConfig
-from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.config import DetectorTypeEnum, FrigateConfig
+from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
 from frigate.edgetpu import EdgeTPUProcess
-from frigate.events import EventProcessor, EventCleanup
+from frigate.events import EventCleanup, EventProcessor
 from frigate.http import create_app
 from frigate.log import log_process, root_configurer
-from frigate.models import Event
-from frigate.mqtt import create_mqtt_client
+from frigate.models import Event, Recordings
+from frigate.mqtt import MqttSocketRelay, create_mqtt_client
 from frigate.object_processing import TrackedObjectProcessor
-from frigate.record import RecordingMaintainer
+from frigate.output import output_frames
+from frigate.record import RecordingCleanup, RecordingMaintainer
 from frigate.stats import StatsEmitter, stats_init
+from frigate.version import VERSION
 from frigate.video import capture_camera, track_camera
 from frigate.watchdog import FrigateWatchdog
-from frigate.zeroconf import broadcast_zeroconf
 
 logger = logging.getLogger(__name__)
 
-class FrigateApp():
+
+class FrigateApp:
     def __init__(self):
         self.stop_event = mp.Event()
+        self.base_config: FrigateConfig = None
         self.config: FrigateConfig = None
         self.detection_queue = mp.Queue()
         self.detectors: Dict[str, EdgeTPUProcess] = {}
@@ -54,148 +57,237 @@ class FrigateApp():
             else:
                 logger.debug(f"Skipping directory: {d}")
 
-        tmpfs_size = self.config.clips.tmpfs_cache_size
-        if tmpfs_size:
-             logger.info(f"Creating tmpfs of size {tmpfs_size}")
-             rc = os.system(f"mount -t tmpfs -o size={tmpfs_size} tmpfs {CACHE_DIR}")
-             if rc != 0:
-                 logger.error(f"Failed to create tmpfs, error code: {rc}")
-    
     def init_logger(self):
-        self.log_process = mp.Process(target=log_process, args=(self.log_queue,), name='log_process')
+        self.log_process = mp.Process(
+            target=log_process, args=(self.log_queue,), name="log_process"
+        )
         self.log_process.daemon = True
         self.log_process.start()
         root_configurer(self.log_queue)
-    
+
     def init_config(self):
-        config_file = os.environ.get('CONFIG_FILE', '/config/config.yml')
-        self.config = FrigateConfig(config_file=config_file)
+        config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
+        user_config = FrigateConfig.parse_file(config_file)
+        self.config = user_config.runtime_config
 
         for camera_name in self.config.cameras.keys():
+            # generage the ffmpeg commands
+            self.config.cameras[camera_name].create_ffmpeg_cmds()
+
             # create camera_metrics
             self.camera_metrics[camera_name] = {
-                'camera_fps': mp.Value('d', 0.0),
-                'skipped_fps': mp.Value('d', 0.0),
-                'process_fps': mp.Value('d', 0.0),
-                'detection_enabled': mp.Value('i', self.config.cameras[camera_name].detect.enabled),
-                'detection_fps': mp.Value('d', 0.0),
-                'detection_frame': mp.Value('d', 0.0),
-                'read_start': mp.Value('d', 0.0),
-                'ffmpeg_pid': mp.Value('i', 0),
-                'frame_queue': mp.Queue(maxsize=2),
+                "camera_fps": mp.Value("d", 0.0),
+                "skipped_fps": mp.Value("d", 0.0),
+                "process_fps": mp.Value("d", 0.0),
+                "detection_enabled": mp.Value(
+                    "i", self.config.cameras[camera_name].detect.enabled
+                ),
+                "detection_fps": mp.Value("d", 0.0),
+                "detection_frame": mp.Value("d", 0.0),
+                "read_start": mp.Value("d", 0.0),
+                "ffmpeg_pid": mp.Value("i", 0),
+                "frame_queue": mp.Queue(maxsize=2),
             }
-        
-    def check_config(self):
-        for name, camera in self.config.cameras.items():
-            assigned_roles = list(set([r for i in camera.ffmpeg.inputs for r in i.roles]))
-            if not camera.clips.enabled and 'clips' in assigned_roles:
-                logger.warning(f"Camera {name} has clips assigned to an input, but clips is not enabled.")
-            elif camera.clips.enabled and not 'clips' in assigned_roles:
-                logger.warning(f"Camera {name} has clips enabled, but clips is not assigned to an input.")
 
-            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)
+        logging.getLogger().setLevel(self.config.logger.default.value.upper())
         for log, level in self.config.logger.logs.items():
-            logging.getLogger(log).setLevel(level)
-        
-        if not 'geventwebsocket.handler' in self.config.logger.logs:
-            logging.getLogger('geventwebsocket.handler').setLevel('ERROR')
+            logging.getLogger(log).setLevel(level.value.upper())
+
+        if not "werkzeug" in self.config.logger.logs:
+            logging.getLogger("werkzeug").setLevel("ERROR")
 
     def init_queues(self):
         # Queues for clip processing
         self.event_queue = mp.Queue()
         self.event_processed_queue = mp.Queue()
+        self.video_output_queue = mp.Queue(maxsize=len(self.config.cameras.keys()) * 2)
 
         # Queue for cameras to push tracked objects to
-        self.detected_frames_queue = mp.Queue(maxsize=len(self.config.cameras.keys())*2)
+        self.detected_frames_queue = mp.Queue(
+            maxsize=len(self.config.cameras.keys()) * 2
+        )
 
     def init_database(self):
+        # Migrate DB location
+        old_db_path = os.path.join(CLIPS_DIR, "frigate.db")
+        if not os.path.isfile(self.config.database.path) and os.path.isfile(
+            old_db_path
+        ):
+            os.rename(old_db_path, self.config.database.path)
+
+        # Migrate DB schema
         migrate_db = SqliteExtDatabase(self.config.database.path)
 
         # Run migrations
-        del(logging.getLogger('peewee_migrate').handlers[:])
+        del logging.getLogger("peewee_migrate").handlers[:]
         router = Router(migrate_db)
         router.run()
 
         migrate_db.close()
 
         self.db = SqliteQueueDatabase(self.config.database.path)
-        models = [Event]
+        models = [Event, Recordings]
         self.db.bind(models)
 
     def init_stats(self):
         self.stats_tracking = stats_init(self.camera_metrics, self.detectors)
 
     def init_web_server(self):
-        self.flask_app = create_app(self.config, self.db, self.stats_tracking, self.detected_frames_processor, self.mqtt_client)
+        self.flask_app = create_app(
+            self.config,
+            self.db,
+            self.stats_tracking,
+            self.detected_frames_processor,
+        )
 
     def init_mqtt(self):
         self.mqtt_client = create_mqtt_client(self.config, self.camera_metrics)
 
+    def start_mqtt_relay(self):
+        self.mqtt_relay = MqttSocketRelay(
+            self.mqtt_client, self.config.mqtt.topic_prefix
+        )
+        self.mqtt_relay.start()
+
     def start_detectors(self):
+        model_path = self.config.model.path
         model_shape = (self.config.model.height, self.config.model.width)
         for name in self.config.cameras.keys():
             self.detection_out_events[name] = mp.Event()
-            shm_in = mp.shared_memory.SharedMemory(name=name, create=True, size=self.config.model.height*self.config.model.width*3)
-            shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}", create=True, size=20*6*4)
+
+            try:
+                shm_in = mp.shared_memory.SharedMemory(
+                    name=name,
+                    create=True,
+                    size=self.config.model.height * self.config.model.width * 3,
+                )
+            except FileExistsError:
+                shm_in = mp.shared_memory.SharedMemory(name=name)
+
+            try:
+                shm_out = mp.shared_memory.SharedMemory(
+                    name=f"out-{name}", create=True, size=20 * 6 * 4
+                )
+            except FileExistsError:
+                shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}")
+
             self.detection_shms.append(shm_in)
             self.detection_shms.append(shm_out)
 
         for name, detector in self.config.detectors.items():
-            if detector.type == 'cpu':
-                self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, 'cpu', detector.num_threads)
-            if detector.type == 'edgetpu':
-                self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, detector.device, detector.num_threads)
+            if detector.type == DetectorTypeEnum.cpu:
+                self.detectors[name] = EdgeTPUProcess(
+                    name,
+                    self.detection_queue,
+                    self.detection_out_events,
+                    model_path,
+                    model_shape,
+                    "cpu",
+                    detector.num_threads,
+                )
+            if detector.type == DetectorTypeEnum.edgetpu:
+                self.detectors[name] = EdgeTPUProcess(
+                    name,
+                    self.detection_queue,
+                    self.detection_out_events,
+                    model_path,
+                    model_shape,
+                    detector.device,
+                    detector.num_threads,
+                )
 
     def start_detected_frames_processor(self):
-        self.detected_frames_processor = TrackedObjectProcessor(self.config, self.mqtt_client, self.config.mqtt.topic_prefix, 
-            self.detected_frames_queue, self.event_queue, self.event_processed_queue, self.stop_event)
+        self.detected_frames_processor = TrackedObjectProcessor(
+            self.config,
+            self.mqtt_client,
+            self.config.mqtt.topic_prefix,
+            self.detected_frames_queue,
+            self.event_queue,
+            self.event_processed_queue,
+            self.video_output_queue,
+            self.stop_event,
+        )
         self.detected_frames_processor.start()
 
+    def start_video_output_processor(self):
+        output_processor = mp.Process(
+            target=output_frames,
+            name=f"output_processor",
+            args=(
+                self.config,
+                self.video_output_queue,
+            ),
+        )
+        output_processor.daemon = True
+        self.output_processor = output_processor
+        output_processor.start()
+        logger.info(f"Output process started: {output_processor.pid}")
+
     def start_camera_processors(self):
         model_shape = (self.config.model.height, self.config.model.width)
         for name, config in self.config.cameras.items():
-            camera_process = mp.Process(target=track_camera, name=f"camera_processor:{name}", args=(name, config, model_shape,
-                self.detection_queue, self.detection_out_events[name], self.detected_frames_queue, 
-                self.camera_metrics[name]))
+            camera_process = mp.Process(
+                target=track_camera,
+                name=f"camera_processor:{name}",
+                args=(
+                    name,
+                    config,
+                    model_shape,
+                    self.config.model.merged_labelmap,
+                    self.detection_queue,
+                    self.detection_out_events[name],
+                    self.detected_frames_queue,
+                    self.camera_metrics[name],
+                ),
+            )
             camera_process.daemon = True
-            self.camera_metrics[name]['process'] = camera_process
+            self.camera_metrics[name]["process"] = camera_process
             camera_process.start()
             logger.info(f"Camera processor started for {name}: {camera_process.pid}")
 
     def start_camera_capture_processes(self):
         for name, config in self.config.cameras.items():
-            capture_process = mp.Process(target=capture_camera, name=f"camera_capture:{name}", args=(name, config,
-                self.camera_metrics[name]))
+            capture_process = mp.Process(
+                target=capture_camera,
+                name=f"camera_capture:{name}",
+                args=(name, config, self.camera_metrics[name]),
+            )
             capture_process.daemon = True
-            self.camera_metrics[name]['capture_process'] = capture_process
+            self.camera_metrics[name]["capture_process"] = capture_process
             capture_process.start()
             logger.info(f"Capture process started for {name}: {capture_process.pid}")
-    
+
     def start_event_processor(self):
-        self.event_processor = EventProcessor(self.config, self.camera_metrics, self.event_queue, self.event_processed_queue, self.stop_event)
+        self.event_processor = EventProcessor(
+            self.config,
+            self.camera_metrics,
+            self.event_queue,
+            self.event_processed_queue,
+            self.stop_event,
+        )
         self.event_processor.start()
-    
+
     def start_event_cleanup(self):
         self.event_cleanup = EventCleanup(self.config, self.stop_event)
         self.event_cleanup.start()
-    
+
     def start_recording_maintainer(self):
         self.recording_maintainer = RecordingMaintainer(self.config, self.stop_event)
         self.recording_maintainer.start()
 
+    def start_recording_cleanup(self):
+        self.recording_cleanup = RecordingCleanup(self.config, self.stop_event)
+        self.recording_cleanup.start()
+
     def start_stats_emitter(self):
-        self.stats_emitter = StatsEmitter(self.config, self.stats_tracking, self.mqtt_client, self.config.mqtt.topic_prefix, self.stop_event)
+        self.stats_emitter = StatsEmitter(
+            self.config,
+            self.stats_tracking,
+            self.mqtt_client,
+            self.config.mqtt.topic_prefix,
+            self.stop_event,
+        )
         self.stats_emitter.start()
 
     def start_watchdog(self):
@@ -204,16 +296,28 @@ class FrigateApp():
 
     def start(self):
         self.init_logger()
+        logger.info(f"Starting Frigate ({VERSION})")
         try:
             try:
                 self.init_config()
             except Exception as e:
-                print(f"Error parsing config: {e}")
+                print("*************************************************************")
+                print("*************************************************************")
+                print("***    Your config file is not valid!                     ***")
+                print("***    Please check the docs at                           ***")
+                print("***    https://docs.frigate.video/configuration/index     ***")
+                print("*************************************************************")
+                print("*************************************************************")
+                print("***    Config Validation Errors                           ***")
+                print("*************************************************************")
+                print(e)
+                print("*************************************************************")
+                print("***    End Config Validation Errors                       ***")
+                print("*************************************************************")
                 self.log_process.terminate()
                 sys.exit(1)
             self.set_environment_vars()
             self.ensure_dirs()
-            self.check_config()
             self.set_log_levels()
             self.init_queues()
             self.init_database()
@@ -223,14 +327,17 @@ class FrigateApp():
             self.log_process.terminate()
             sys.exit(1)
         self.start_detectors()
+        self.start_video_output_processor()
         self.start_detected_frames_processor()
         self.start_camera_processors()
         self.start_camera_capture_processes()
         self.init_stats()
         self.init_web_server()
+        self.start_mqtt_relay()
         self.start_event_processor()
         self.start_event_cleanup()
         self.start_recording_maintainer()
+        self.start_recording_cleanup()
         self.start_stats_emitter()
         self.start_watchdog()
         # self.zeroconf = broadcast_zeroconf(self.config.mqtt.client_id)
@@ -238,22 +345,26 @@ class FrigateApp():
         def receiveSignal(signalNumber, frame):
             self.stop()
             sys.exit()
-        
+
         signal.signal(signal.SIGTERM, receiveSignal)
 
-        server = pywsgi.WSGIServer(('127.0.0.1', 5001), self.flask_app, handler_class=WebSocketHandler)
-        server.serve_forever()
+        try:
+            self.flask_app.run(host="127.0.0.1", port=5001, debug=False)
+        except KeyboardInterrupt:
+            pass
 
         self.stop()
-    
+
     def stop(self):
         logger.info(f"Stopping...")
         self.stop_event.set()
 
+        self.mqtt_relay.stop()
         self.detected_frames_processor.join()
         self.event_processor.join()
         self.event_cleanup.join()
         self.recording_maintainer.join()
+        self.recording_cleanup.join()
         self.stats_emitter.join()
         self.frigate_watchdog.join()
         self.db.stop()

frigate/const.py

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

frigate/edgetpu.py

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

frigate/events.py

@@ -1,29 +1,26 @@
 import datetime
-import json
 import logging
 import os
 import queue
-import subprocess as sp
 import threading
 import time
-from collections import defaultdict
 from pathlib import Path
 
-import psutil
-import shutil
-
-from frigate.config import FrigateConfig
-from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
-from frigate.models import Event
-
 from peewee import fn
 
+from frigate.config import EventsConfig, FrigateConfig, RecordConfig
+from frigate.const import CLIPS_DIR
+from frigate.models import Event
+
 logger = logging.getLogger(__name__)
 
+
 class EventProcessor(threading.Thread):
-    def __init__(self, config, camera_processes, event_queue, event_processed_queue, stop_event):
+    def __init__(
+        self, config, camera_processes, event_queue, event_processed_queue, stop_event
+    ):
         threading.Thread.__init__(self)
-        self.name = 'event_processor'
+        self.name = "event_processor"
         self.config = config
         self.camera_processes = camera_processes
         self.cached_clips = {}
@@ -32,292 +29,183 @@ class EventProcessor(threading.Thread):
         self.events_in_process = {}
         self.stop_event = stop_event
 
-    def should_create_clip(self, camera, event_data):
-        if event_data['false_positive']:
-            return False
-        
-        # if there are required zones and there is no overlap
-        required_zones = self.config.cameras[camera].clips.required_zones
-        if len(required_zones) > 0 and not set(event_data['entered_zones']) & set(required_zones):
-            logger.debug(f"Not creating clip for {event_data['id']} because it did not enter required zones")
-            return False
-
-        return True
-    
-    def refresh_cache(self):
-        cached_files = os.listdir(CACHE_DIR)
-
-        files_in_use = []
-        for process in psutil.process_iter():
-            try:
-                if process.name() != 'ffmpeg':
-                    continue
-
-                flist = process.open_files()
-                if flist:
-                    for nt in flist:
-                        if nt.path.startswith(CACHE_DIR):
-                            files_in_use.append(nt.path.split('/')[-1])
-            except:
-                continue
-
-        for f in cached_files:
-            if f in files_in_use or f in self.cached_clips:
-                continue
-
-            camera = '-'.join(f.split('-')[:-1])
-            start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
-        
-            ffprobe_cmd = " ".join([
-                'ffprobe',
-                '-v',
-                'error',
-                '-show_entries',
-                'format=duration',
-                '-of',
-                'default=noprint_wrappers=1:nokey=1',
-                f"{os.path.join(CACHE_DIR,f)}"
-            ])
-            p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
-            (output, err) = p.communicate()
-            p_status = p.wait()
-            if p_status == 0:
-                duration = float(output.decode('utf-8').strip())
-            else:
-                logger.info(f"bad file: {f}")
-                os.remove(os.path.join(CACHE_DIR,f))
-                continue
-
-            self.cached_clips[f] = {
-                'path': f,
-                'camera': camera,
-                'start_time': start_time.timestamp(),
-                'duration': duration
-            }
-
-        if len(self.events_in_process) > 0:
-            earliest_event = min(self.events_in_process.values(), key=lambda x:x['start_time'])['start_time']
-        else:
-            earliest_event = datetime.datetime.now().timestamp()
-
-        # if the earliest event exceeds the max seconds, cap it
-        max_seconds = self.config.clips.max_seconds
-        if datetime.datetime.now().timestamp()-earliest_event > max_seconds:
-            earliest_event = datetime.datetime.now().timestamp()-max_seconds
-        
-        for f, data in list(self.cached_clips.items()):
-            if earliest_event-90 > data['start_time']+data['duration']:
-                del self.cached_clips[f]
-                logger.debug(f"Cleaning up cached file {f}")
-                os.remove(os.path.join(CACHE_DIR,f))
-        
-        # if we are still using more than 90% of the cache, proactively cleanup
-        cache_usage = shutil.disk_usage("/tmp/cache")
-        if cache_usage.used/cache_usage.total > .9 and cache_usage.free < 200000000 and len(self.cached_clips) > 0:
-            logger.warning("More than 90% of the cache is used.")
-            logger.warning("Consider increasing space available at /tmp/cache or reducing max_seconds in your clips config.")
-            logger.warning("Proactively cleaning up the cache...")
-            while cache_usage.used/cache_usage.total > .9:
-                oldest_clip = min(self.cached_clips.values(), key=lambda x:x['start_time'])
-                del self.cached_clips[oldest_clip['path']]
-                os.remove(os.path.join(CACHE_DIR,oldest_clip['path']))
-                cache_usage = shutil.disk_usage("/tmp/cache")
-
-    def create_clip(self, camera, event_data, pre_capture, post_capture):
-        # get all clips from the camera with the event sorted
-        sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
-
-        # if there are no clips in the cache or we are still waiting on a needed file check every 5 seconds
-        wait_count = 0
-        while len(sorted_clips) == 0 or sorted_clips[-1]['start_time'] + sorted_clips[-1]['duration'] < event_data['end_time']+post_capture:
-            if wait_count > 4:
-                logger.warning(f"Unable to create clip for {camera} and event {event_data['id']}. There were no cache files for this event.")
-                return False
-            logger.debug(f"No cache clips for {camera}. Waiting...")
-            time.sleep(5)
-            self.refresh_cache()
-            # get all clips from the camera with the event sorted
-            sorted_clips = sorted([c for c in self.cached_clips.values() if c['camera'] == camera], key = lambda i: i['start_time'])
-            wait_count += 1
-        
-        playlist_start = event_data['start_time']-pre_capture
-        playlist_end = event_data['end_time']+post_capture
-        playlist_lines = []
-        for clip in sorted_clips:
-            # clip ends before playlist start time, skip
-            if clip['start_time']+clip['duration'] < playlist_start:
-                continue
-            # clip starts after playlist ends, finish
-            if clip['start_time'] > playlist_end:
-                break
-            playlist_lines.append(f"file '{os.path.join(CACHE_DIR,clip['path'])}'")
-            # if this is the starting clip, add an inpoint
-            if clip['start_time'] < playlist_start:
-                playlist_lines.append(f"inpoint {int(playlist_start-clip['start_time'])}")
-            # if this is the ending clip, add an outpoint
-            if clip['start_time']+clip['duration'] > playlist_end:
-                playlist_lines.append(f"outpoint {int(playlist_end-clip['start_time'])}")
-
-        clip_name = f"{camera}-{event_data['id']}"
-        ffmpeg_cmd = [
-            'ffmpeg',
-            '-y',
-            '-protocol_whitelist',
-            'pipe,file',
-            '-f',
-            'concat',
-            '-safe',
-            '0',
-            '-i',
-            '-',
-            '-c',
-            'copy',
-            '-movflags',
-            '+faststart',
-            f"{os.path.join(CLIPS_DIR, clip_name)}.mp4"
-        ]
-
-        p = sp.run(ffmpeg_cmd, input="\n".join(playlist_lines), encoding='ascii', capture_output=True)
-        if p.returncode != 0:
-            logger.error(p.stderr)
-            return False
-        return True
-
     def run(self):
-        while True:
-            if self.stop_event.is_set():
-                logger.info(f"Exiting event processor...")
-                break
+        # set an end_time on events without an end_time on startup
+        Event.update(end_time=Event.start_time + 30).where(
+            Event.end_time == None
+        ).execute()
 
+        while not self.stop_event.is_set():
             try:
                 event_type, camera, event_data = self.event_queue.get(timeout=10)
             except queue.Empty:
-                if not self.stop_event.is_set():
-                    self.refresh_cache()
                 continue
 
             logger.debug(f"Event received: {event_type} {camera} {event_data['id']}")
-            self.refresh_cache()
 
-            if event_type == 'start':
-                self.events_in_process[event_data['id']] = event_data
+            event_config: EventsConfig = self.config.cameras[camera].record.events
 
-            if event_type == 'end':
-                clips_config = self.config.cameras[camera].clips
+            if event_type == "start":
+                self.events_in_process[event_data["id"]] = event_data
 
-                clip_created = False
-                if self.should_create_clip(camera, event_data):
-                    if clips_config.enabled and (clips_config.objects is None or event_data['label'] in clips_config.objects):
-                        clip_created = self.create_clip(camera, event_data, clips_config.pre_capture, clips_config.post_capture)
-                
-                if clip_created or event_data['has_snapshot']:
-                    Event.create(
-                        id=event_data['id'],
-                        label=event_data['label'],
+            elif event_type == "update":
+                self.events_in_process[event_data["id"]] = event_data
+                # TODO: this will generate a lot of db activity possibly
+                if event_data["has_clip"] or event_data["has_snapshot"]:
+                    Event.replace(
+                        id=event_data["id"],
+                        label=event_data["label"],
                         camera=camera,
-                        start_time=event_data['start_time'],
-                        end_time=event_data['end_time'],
-                        top_score=event_data['top_score'],
-                        false_positive=event_data['false_positive'],
-                        zones=list(event_data['entered_zones']),
-                        thumbnail=event_data['thumbnail'],
-                        has_clip=clip_created,
-                        has_snapshot=event_data['has_snapshot'],
-                    )
-                del self.events_in_process[event_data['id']]
-                self.event_processed_queue.put((event_data['id'], camera))
+                        start_time=event_data["start_time"] - event_config.pre_capture,
+                        end_time=None,
+                        top_score=event_data["top_score"],
+                        false_positive=event_data["false_positive"],
+                        zones=list(event_data["entered_zones"]),
+                        thumbnail=event_data["thumbnail"],
+                        region=event_data["region"],
+                        box=event_data["box"],
+                        area=event_data["area"],
+                        has_clip=event_data["has_clip"],
+                        has_snapshot=event_data["has_snapshot"],
+                    ).execute()
+
+            elif event_type == "end":
+                if event_data["has_clip"] or event_data["has_snapshot"]:
+                    Event.replace(
+                        id=event_data["id"],
+                        label=event_data["label"],
+                        camera=camera,
+                        start_time=event_data["start_time"] - event_config.pre_capture,
+                        end_time=event_data["end_time"] + event_config.post_capture,
+                        top_score=event_data["top_score"],
+                        false_positive=event_data["false_positive"],
+                        zones=list(event_data["entered_zones"]),
+                        thumbnail=event_data["thumbnail"],
+                        region=event_data["region"],
+                        box=event_data["box"],
+                        area=event_data["area"],
+                        has_clip=event_data["has_clip"],
+                        has_snapshot=event_data["has_snapshot"],
+                    ).execute()
+
+                del self.events_in_process[event_data["id"]]
+                self.event_processed_queue.put((event_data["id"], camera))
+
+        # set an end_time on events without an end_time before exiting
+        Event.update(end_time=datetime.datetime.now().timestamp()).where(
+            Event.end_time == None
+        ).execute()
+        logger.info(f"Exiting event processor...")
+
 
 class EventCleanup(threading.Thread):
     def __init__(self, config: FrigateConfig, stop_event):
         threading.Thread.__init__(self)
-        self.name = 'event_cleanup'
+        self.name = "event_cleanup"
         self.config = config
         self.stop_event = stop_event
         self.camera_keys = list(self.config.cameras.keys())
 
-    def expire(self, media):
+    def expire(self, media_type):
         ## Expire events from unlisted cameras based on the global config
-        if media == 'clips':
-            retain_config = self.config.clips.retain
-            file_extension = 'mp4'
-            update_params = {'has_clip': False}
+        if media_type == "clips":
+            retain_config = self.config.record.events.retain
+            file_extension = "mp4"
+            update_params = {"has_clip": False}
         else:
             retain_config = self.config.snapshots.retain
-            file_extension = 'jpg'
-            update_params = {'has_snapshot': False}
-        
-        distinct_labels = (Event.select(Event.label)
-                    .where(Event.camera.not_in(self.camera_keys))
-                    .distinct())
-        
+            file_extension = "jpg"
+            update_params = {"has_snapshot": False}
+
+        distinct_labels = (
+            Event.select(Event.label)
+            .where(Event.camera.not_in(self.camera_keys))
+            .distinct()
+        )
+
         # loop over object types in db
         for l in distinct_labels:
             # get expiration time for this label
             expire_days = retain_config.objects.get(l.label, retain_config.default)
-            expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
+            expire_after = (
+                datetime.datetime.now() - datetime.timedelta(days=expire_days)
+            ).timestamp()
             # grab all events after specific time
-            expired_events = (
-                Event.select()
-                    .where(Event.camera.not_in(self.camera_keys), 
-                        Event.start_time < expire_after, 
-                        Event.label == l.label)
+            expired_events = Event.select().where(
+                Event.camera.not_in(self.camera_keys),
+                Event.start_time < expire_after,
+                Event.label == l.label,
             )
             # delete the media from disk
             for event in expired_events:
                 media_name = f"{event.camera}-{event.id}"
-                media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}")
-                media.unlink(missing_ok=True)
+                media_path = Path(
+                    f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
+                )
+                media_path.unlink(missing_ok=True)
+                if file_extension == "jpg":
+                    media_path = Path(
+                        f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"
+                    )
+                    media_path.unlink(missing_ok=True)
+
             # update the clips attribute for the db entry
-            update_query = (
-                Event.update(update_params)
-                    .where(Event.camera.not_in(self.camera_keys), 
-                        Event.start_time < expire_after, 
-                        Event.label == l.label)
+            update_query = Event.update(update_params).where(
+                Event.camera.not_in(self.camera_keys),
+                Event.start_time < expire_after,
+                Event.label == l.label,
             )
             update_query.execute()
 
         ## Expire events from cameras based on the camera config
         for name, camera in self.config.cameras.items():
-            if media == 'clips':
-                retain_config = camera.clips.retain
+            if media_type == "clips":
+                retain_config = camera.record.events.retain
             else:
                 retain_config = camera.snapshots.retain
             # get distinct objects in database for this camera
-            distinct_labels = (Event.select(Event.label)
-                    .where(Event.camera == name)
-                    .distinct())
+            distinct_labels = (
+                Event.select(Event.label).where(Event.camera == name).distinct()
+            )
 
             # loop over object types in db
             for l in distinct_labels:
                 # get expiration time for this label
                 expire_days = retain_config.objects.get(l.label, retain_config.default)
-                expire_after = (datetime.datetime.now() - datetime.timedelta(days=expire_days)).timestamp()
+                expire_after = (
+                    datetime.datetime.now() - datetime.timedelta(days=expire_days)
+                ).timestamp()
                 # grab all events after specific time
-                expired_events = (
-                    Event.select()
-                        .where(Event.camera == name, 
-                            Event.start_time < expire_after, 
-                            Event.label == l.label)
+                expired_events = Event.select().where(
+                    Event.camera == name,
+                    Event.start_time < expire_after,
+                    Event.label == l.label,
                 )
                 # delete the grabbed clips from disk
                 for event in expired_events:
                     media_name = f"{event.camera}-{event.id}"
-                    media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}")
-                    media.unlink(missing_ok=True)
+                    media_path = Path(
+                        f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
+                    )
+                    media_path.unlink(missing_ok=True)
+                    if file_extension == "jpg":
+                        media_path = Path(
+                            f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"
+                        )
+                        media_path.unlink(missing_ok=True)
                 # update the clips attribute for the db entry
-                update_query = (
-                    Event.update(update_params)
-                        .where( Event.camera == name, 
-                            Event.start_time < expire_after, 
-                            Event.label == l.label)
+                update_query = Event.update(update_params).where(
+                    Event.camera == name,
+                    Event.start_time < expire_after,
+                    Event.label == l.label,
                 )
                 update_query.execute()
 
     def purge_duplicates(self):
         duplicate_query = """with grouped_events as (
           select id,
-            label, 
-            camera, 
+            label,
+            camera,
           	has_snapshot,
           	has_clip,
           	row_number() over (
@@ -327,46 +215,37 @@ class EventCleanup(threading.Thread):
           from event
         )
 
-        select distinct id, camera, has_snapshot, has_clip from grouped_events 
+        select distinct id, camera, has_snapshot, has_clip from grouped_events
         where copy_number > 1;"""
 
         duplicate_events = Event.raw(duplicate_query)
         for event in duplicate_events:
             logger.debug(f"Removing duplicate: {event.id}")
             media_name = f"{event.camera}-{event.id}"
-            if event.has_snapshot:
-                media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
-                media.unlink(missing_ok=True)
-            if event.has_clip:
-                media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
-                media.unlink(missing_ok=True)
+            media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
+            media_path.unlink(missing_ok=True)
+            media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}-clean.png")
+            media_path.unlink(missing_ok=True)
+            media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
+            media_path.unlink(missing_ok=True)
+
+        (
+            Event.delete()
+            .where(Event.id << [event.id for event in duplicate_events])
+            .execute()
+        )
 
-        (Event.delete()
-            .where( Event.id << [event.id for event in duplicate_events] )
-            .execute())
-    
     def run(self):
-        counter = 0
-        while(True):
-            if self.stop_event.is_set():
-                logger.info(f"Exiting event cleanup...")
-                break
-
-            # only expire events every 5 minutes, but check for stop events every 10 seconds
-            time.sleep(10)
-            counter = counter + 1
-            if counter < 30:
-                continue
-            counter = 0
-
-            self.expire('clips')
-            self.expire('snapshots')
+        # only expire events every 5 minutes
+        while not self.stop_event.wait(300):
+            self.expire("clips")
+            self.expire("snapshots")
             self.purge_duplicates()
 
             # drop events from db where has_clip and has_snapshot are false
-            delete_query = (
-                Event.delete()
-                    .where( Event.has_clip == False, 
-                        Event.has_snapshot == False)
+            delete_query = Event.delete().where(
+                Event.has_clip == False, Event.has_snapshot == False
             )
             delete_query.execute()
+
+        logger.info(f"Exiting event cleanup...")

frigate/http.py

@@ -1,92 +1,57 @@
 import base64
-import datetime
+from collections import OrderedDict
+from datetime import datetime, timedelta
+import copy
 import json
+import glob
 import logging
 import os
+import re
+import subprocess as sp
 import time
 from functools import reduce
+from pathlib import Path
 
 import cv2
-import gevent
+from flask.helpers import send_file
+
 import numpy as np
-from flask import (Blueprint, Flask, Response, current_app, jsonify,
-                   make_response, request)
-from flask_sockets import Sockets
-from peewee import SqliteDatabase, operator, fn, DoesNotExist
+from flask import (
+    Blueprint,
+    Flask,
+    Response,
+    current_app,
+    jsonify,
+    make_response,
+    request,
+)
+
+from peewee import SqliteDatabase, operator, fn, DoesNotExist, Value
 from playhouse.shortcuts import model_to_dict
 
-from frigate.const import CLIPS_DIR
-from frigate.models import Event
+from frigate.const import CLIPS_DIR, RECORD_DIR
+from frigate.models import Event, Recordings
 from frigate.stats import stats_snapshot
 from frigate.util import calculate_region
 from frigate.version import VERSION
 
 logger = logging.getLogger(__name__)
 
-bp = Blueprint('frigate', __name__)
-ws = Blueprint('ws', __name__)
+bp = Blueprint("frigate", __name__)
 
-class MqttBackend():
-    """Interface for registering and updating WebSocket clients."""
 
-    def __init__(self, mqtt_client, topic_prefix):
-        self.clients = list()
-        self.mqtt_client = mqtt_client
-        self.topic_prefix = topic_prefix
-
-    def register(self, client):
-        """Register a WebSocket connection for Mqtt updates."""
-        self.clients.append(client)
-
-    def publish(self, message):
-        try:
-            json_message = json.loads(message)
-            json_message = {
-                'topic': f"{self.topic_prefix}/{json_message['topic']}",
-                'payload': json_message['payload'],
-                'retain': json_message.get('retain', False)
-            }
-        except:
-            logger.warning("Unable to parse websocket message as valid json.")
-            return
-
-        logger.debug(f"Publishing mqtt message from websockets at {json_message['topic']}.")
-        self.mqtt_client.publish(json_message['topic'], json_message['payload'], retain=json_message['retain'])
-
-    def run(self):
-        def send(client, userdata, message):
-            """Sends mqtt messages to clients."""
-            try:
-                logger.debug(f"Received mqtt message on {message.topic}.")
-                ws_message = json.dumps({
-                    'topic': message.topic.replace(f"{self.topic_prefix}/",""),
-                    'payload': message.payload.decode()
-                })
-            except:
-                # if the payload can't be decoded don't relay to clients
-                logger.debug(f"MQTT payload for {message.topic} wasn't text. Skipping...")
-                return
-
-            for client in self.clients:
-                try:
-                    client.send(ws_message)
-                except:
-                    logger.debug("Removing websocket client due to a closed connection.")
-                    self.clients.remove(client)
-
-        self.mqtt_client.message_callback_add(f"{self.topic_prefix}/#", send)
-
-    def start(self):
-        """Maintains mqtt subscription in the background."""
-        gevent.spawn(self.run)
-
-def create_app(frigate_config, database: SqliteDatabase, stats_tracking, detected_frames_processor, mqtt_client):
+def create_app(
+    frigate_config,
+    database: SqliteDatabase,
+    stats_tracking,
+    detected_frames_processor,
+):
     app = Flask(__name__)
-    sockets = Sockets(app)
 
     @app.before_request
     def _db_connect():
-        database.connect()
+        if database.is_closed():
+            database.connect()
 
     @app.teardown_request
     def _db_close(exc):
@@ -98,21 +63,19 @@ def create_app(frigate_config, database: SqliteDatabase, stats_tracking, detecte
     app.detected_frames_processor = detected_frames_processor
 
     app.register_blueprint(bp)
-    sockets.register_blueprint(ws)
-
-    app.mqtt_backend = MqttBackend(mqtt_client, frigate_config.mqtt.topic_prefix)
-    app.mqtt_backend.start()
 
     return app
 
-@bp.route('/')
+
+@bp.route("/")
 def is_healthy():
     return "Frigate is running. Alive and healthy!"
 
-@bp.route('/events/summary')
+
+@bp.route("/events/summary")
 def events_summary():
-    has_clip = request.args.get('has_clip', type=int)
-    has_snapshot = request.args.get('has_snapshot', type=int)
+    has_clip = request.args.get("has_clip", type=int)
+    has_snapshot = request.args.get("has_snapshot", type=int)
 
     clauses = []
 
@@ -123,38 +86,66 @@ def events_summary():
         clauses.append((Event.has_snapshot == has_snapshot))
 
     if len(clauses) == 0:
-        clauses.append((1 == 1))
+        clauses.append((True))
 
     groups = (
-        Event
-            .select(
-                Event.camera,
-                Event.label,
-                fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')).alias('day'),
-                Event.zones,
-                fn.COUNT(Event.id).alias('count')
-            )
-            .where(reduce(operator.and_, clauses))
-            .group_by(
-                Event.camera,
-                Event.label,
-                fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')),
-                Event.zones
-            )
+        Event.select(
+            Event.camera,
+            Event.label,
+            fn.strftime(
+                "%Y-%m-%d", fn.datetime(Event.start_time, "unixepoch", "localtime")
+            ).alias("day"),
+            Event.zones,
+            fn.COUNT(Event.id).alias("count"),
         )
+        .where(reduce(operator.and_, clauses))
+        .group_by(
+            Event.camera,
+            Event.label,
+            fn.strftime(
+                "%Y-%m-%d", fn.datetime(Event.start_time, "unixepoch", "localtime")
+            ),
+            Event.zones,
+        )
+    )
 
     return jsonify([e for e in groups.dicts()])
 
-@bp.route('/events/<id>')
+
+@bp.route("/events/<id>", methods=("GET",))
 def event(id):
     try:
         return model_to_dict(Event.get(Event.id == id))
     except DoesNotExist:
         return "Event not found", 404
 
-@bp.route('/events/<id>/thumbnail.jpg')
+
+@bp.route("/events/<id>", methods=("DELETE",))
+def delete_event(id):
+    try:
+        event = Event.get(Event.id == id)
+    except DoesNotExist:
+        return make_response(
+            jsonify({"success": False, "message": "Event" + id + " not found"}), 404
+        )
+
+    media_name = f"{event.camera}-{event.id}"
+    if event.has_snapshot:
+        media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
+        media.unlink(missing_ok=True)
+    if event.has_clip:
+        media = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
+        media.unlink(missing_ok=True)
+
+    event.delete_instance()
+    return make_response(
+        jsonify({"success": True, "message": "Event" + id + " deleted"}), 200
+    )
+
+
+@bp.route("/events/<id>/thumbnail.jpg")
 def event_thumbnail(id):
-    format = request.args.get('format', 'ios')
+    format = request.args.get("format", "ios")
     thumbnail_bytes = None
     try:
         event = Event.get(Event.id == id)
@@ -162,7 +153,8 @@ def event_thumbnail(id):
     except DoesNotExist:
         # see if the object is currently being tracked
         try:
-            for camera_state in current_app.detected_frames_processor.camera_states.values():
+            camera_states = current_app.detected_frames_processor.camera_states.values()
+            for camera_state in camera_states:
                 if id in camera_state.tracked_objects:
                     tracked_obj = camera_state.tracked_objects.get(id)
                     if not tracked_obj is None:
@@ -174,60 +166,114 @@ def event_thumbnail(id):
         return "Event not found", 404
 
     # android notifications prefer a 2:1 ratio
-    if format == 'android':
+    if format == "android":
         jpg_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
         img = cv2.imdecode(jpg_as_np, flags=1)
-        thumbnail = cv2.copyMakeBorder(img, 0, 0, int(img.shape[1]*0.5), int(img.shape[1]*0.5), cv2.BORDER_CONSTANT, (0,0,0))
-        ret, jpg = cv2.imencode('.jpg', thumbnail, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+        thumbnail = cv2.copyMakeBorder(
+            img,
+            0,
+            0,
+            int(img.shape[1] * 0.5),
+            int(img.shape[1] * 0.5),
+            cv2.BORDER_CONSTANT,
+            (0, 0, 0),
+        )
+        ret, jpg = cv2.imencode(".jpg", thumbnail, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
         thumbnail_bytes = jpg.tobytes()
 
     response = make_response(thumbnail_bytes)
-    response.headers['Content-Type'] = 'image/jpg'
+    response.headers["Content-Type"] = "image/jpg"
     return response
 
-@bp.route('/events/<id>/snapshot.jpg')
+
+@bp.route("/events/<id>/snapshot.jpg")
 def event_snapshot(id):
+    download = request.args.get("download", type=bool)
     jpg_bytes = None
     try:
-        event = Event.get(Event.id == id)
+        event = Event.get(Event.id == id, Event.end_time != None)
         if not event.has_snapshot:
             return "Snapshot not available", 404
         # read snapshot from disk
-        with open(os.path.join(CLIPS_DIR, f"{event.camera}-{id}.jpg"), 'rb') as image_file:
+        with open(
+            os.path.join(CLIPS_DIR, f"{event.camera}-{id}.jpg"), "rb"
+        ) as image_file:
             jpg_bytes = image_file.read()
     except DoesNotExist:
         # see if the object is currently being tracked
         try:
-            for camera_state in current_app.detected_frames_processor.camera_states.values():
+            camera_states = current_app.detected_frames_processor.camera_states.values()
+            for camera_state in camera_states:
                 if id in camera_state.tracked_objects:
                     tracked_obj = camera_state.tracked_objects.get(id)
                     if not tracked_obj is None:
                         jpg_bytes = tracked_obj.get_jpg_bytes(
-                            timestamp=request.args.get('timestamp', type=int),
-                            bounding_box=request.args.get('bbox', type=int),
-                            crop=request.args.get('crop', type=int),
-                            height=request.args.get('h', type=int)
+                            timestamp=request.args.get("timestamp", type=int),
+                            bounding_box=request.args.get("bbox", type=int),
+                            crop=request.args.get("crop", type=int),
+                            height=request.args.get("h", type=int),
+                            quality=request.args.get("quality", default=70, type=int),
                         )
         except:
             return "Event not found", 404
     except:
         return "Event not found", 404
 
+    if jpg_bytes is None:
+        return "Event not found", 404
+
     response = make_response(jpg_bytes)
-    response.headers['Content-Type'] = 'image/jpg'
+    response.headers["Content-Type"] = "image/jpg"
+    if download:
+        response.headers[
+            "Content-Disposition"
+        ] = f"attachment; filename=snapshot-{id}.jpg"
     return response
 
-@bp.route('/events')
+
+@bp.route("/events/<id>/clip.mp4")
+def event_clip(id):
+    download = request.args.get("download", type=bool)
+
+    try:
+        event: Event = Event.get(Event.id == id)
+    except DoesNotExist:
+        return "Event not found.", 404
+
+    if not event.has_clip:
+        return "Clip not available", 404
+
+    file_name = f"{event.camera}-{id}.mp4"
+    clip_path = os.path.join(CLIPS_DIR, file_name)
+
+    if not os.path.isfile(clip_path):
+        return recording_clip(event.camera, event.start_time, event.end_time)
+
+    response = make_response()
+    response.headers["Content-Description"] = "File Transfer"
+    response.headers["Cache-Control"] = "no-cache"
+    response.headers["Content-Type"] = "video/mp4"
+    if download:
+        response.headers["Content-Disposition"] = "attachment; filename=%s" % file_name
+    response.headers["Content-Length"] = os.path.getsize(clip_path)
+    response.headers[
+        "X-Accel-Redirect"
+    ] = f"/clips/{file_name}"  # nginx: http://wiki.nginx.org/NginxXSendfile
+
+    return response
+
+
+@bp.route("/events")
 def events():
-    limit = request.args.get('limit', 100)
-    camera = request.args.get('camera')
-    label = request.args.get('label')
-    zone = request.args.get('zone')
-    after = request.args.get('after', type=float)
-    before = request.args.get('before', type=float)
-    has_clip = request.args.get('has_clip', type=int)
-    has_snapshot = request.args.get('has_snapshot', type=int)
-    include_thumbnails = request.args.get('include_thumbnails', default=1, type=int)
+    limit = request.args.get("limit", 100)
+    camera = request.args.get("camera")
+    label = request.args.get("label")
+    zone = request.args.get("zone")
+    after = request.args.get("after", type=float)
+    before = request.args.get("before", type=float)
+    has_clip = request.args.get("has_clip", type=int)
+    has_snapshot = request.args.get("has_snapshot", type=int)
+    include_thumbnails = request.args.get("include_thumbnails", default=1, type=int)
 
     clauses = []
     excluded_fields = []
@@ -239,7 +285,7 @@ def events():
         clauses.append((Event.label == label))
 
     if zone:
-        clauses.append((Event.zones.cast('text') % f"*\"{zone}\"*"))
+        clauses.append((Event.zones.cast("text") % f'*"{zone}"*'))
 
     if after:
         clauses.append((Event.start_time >= after))
@@ -257,125 +303,437 @@ def events():
         excluded_fields.append(Event.thumbnail)
 
     if len(clauses) == 0:
-        clauses.append((1 == 1))
+        clauses.append((True))
 
-    events =    (Event.select()
-                .where(reduce(operator.and_, clauses))
-                .order_by(Event.start_time.desc())
-                .limit(limit))
+    events = (
+        Event.select()
+        .where(reduce(operator.and_, clauses))
+        .order_by(Event.start_time.desc())
+        .limit(limit)
+    )
 
     return jsonify([model_to_dict(e, exclude=excluded_fields) for e in events])
 
-@bp.route('/config')
-def config():
-    return jsonify(current_app.frigate_config.to_dict())
 
-@bp.route('/version')
+@bp.route("/config")
+def config():
+    config = current_app.frigate_config.dict()
+
+    # add in the ffmpeg_cmds
+    for camera_name, camera in current_app.frigate_config.cameras.items():
+        camera_dict = config["cameras"][camera_name]
+        camera_dict["ffmpeg_cmds"] = copy.deepcopy(camera.ffmpeg_cmds)
+        for cmd in camera_dict["ffmpeg_cmds"]:
+            cmd["cmd"] = " ".join(cmd["cmd"])
+
+    return jsonify(config)
+
+
+@bp.route("/config/schema")
+def config_schema():
+    return current_app.response_class(
+        current_app.frigate_config.schema_json(), mimetype="application/json"
+    )
+
+
+@bp.route("/version")
 def version():
     return VERSION
 
-@bp.route('/stats')
+
+@bp.route("/stats")
 def stats():
     stats = stats_snapshot(current_app.stats_tracking)
     return jsonify(stats)
 
-@bp.route('/<camera_name>/<label>/best.jpg')
+
+@bp.route("/<camera_name>/<label>/best.jpg")
 def best(camera_name, label):
     if camera_name in current_app.frigate_config.cameras:
         best_object = current_app.detected_frames_processor.get_best(camera_name, label)
-        best_frame = best_object.get('frame')
+        best_frame = best_object.get("frame")
         if best_frame is None:
-            best_frame = np.zeros((720,1280,3), np.uint8)
+            best_frame = np.zeros((720, 1280, 3), np.uint8)
         else:
             best_frame = cv2.cvtColor(best_frame, cv2.COLOR_YUV2BGR_I420)
 
-        crop = bool(request.args.get('crop', 0, type=int))
+        crop = bool(request.args.get("crop", 0, type=int))
         if crop:
-            box = best_object.get('box', (0,0,300,300))
-            region = calculate_region(best_frame.shape, box[0], box[1], box[2], box[3], 1.1)
-            best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
+            box = best_object.get("box", (0, 0, 300, 300))
+            region = calculate_region(
+                best_frame.shape, box[0], box[1], box[2], box[3], 1.1
+            )
+            best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
 
-        height = int(request.args.get('h', str(best_frame.shape[0])))
-        width = int(height*best_frame.shape[1]/best_frame.shape[0])
+        height = int(request.args.get("h", str(best_frame.shape[0])))
+        width = int(height * best_frame.shape[1] / best_frame.shape[0])
+        resize_quality = request.args.get("quality", default=70, type=int)
 
-        best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
-        ret, jpg = cv2.imencode('.jpg', best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+        best_frame = cv2.resize(
+            best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA
+        )
+        ret, jpg = cv2.imencode(
+            ".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), resize_quality]
+        )
         response = make_response(jpg.tobytes())
-        response.headers['Content-Type'] = 'image/jpg'
+        response.headers["Content-Type"] = "image/jpg"
         return response
     else:
         return "Camera named {} not found".format(camera_name), 404
 
-@bp.route('/<camera_name>')
+
+@bp.route("/<camera_name>")
 def mjpeg_feed(camera_name):
-    fps = int(request.args.get('fps', '3'))
-    height = int(request.args.get('h', '360'))
+    fps = int(request.args.get("fps", "3"))
+    height = int(request.args.get("h", "360"))
     draw_options = {
-        'bounding_boxes': request.args.get('bbox', type=int),
-        'timestamp': request.args.get('timestamp', type=int),
-        'zones': request.args.get('zones', type=int),
-        'mask': request.args.get('mask', type=int),
-        'motion_boxes': request.args.get('motion', type=int),
-        'regions': request.args.get('regions', type=int),
+        "bounding_boxes": request.args.get("bbox", type=int),
+        "timestamp": request.args.get("timestamp", type=int),
+        "zones": request.args.get("zones", type=int),
+        "mask": request.args.get("mask", type=int),
+        "motion_boxes": request.args.get("motion", type=int),
+        "regions": request.args.get("regions", type=int),
     }
     if camera_name in current_app.frigate_config.cameras:
         # return a multipart response
-        return Response(imagestream(current_app.detected_frames_processor, camera_name, fps, height, draw_options),
-                        mimetype='multipart/x-mixed-replace; boundary=frame')
+        return Response(
+            imagestream(
+                current_app.detected_frames_processor,
+                camera_name,
+                fps,
+                height,
+                draw_options,
+            ),
+            mimetype="multipart/x-mixed-replace; boundary=frame",
+        )
     else:
         return "Camera named {} not found".format(camera_name), 404
 
-@bp.route('/<camera_name>/latest.jpg')
+
+@bp.route("/<camera_name>/latest.jpg")
 def latest_frame(camera_name):
     draw_options = {
-        'bounding_boxes': request.args.get('bbox', type=int),
-        'timestamp': request.args.get('timestamp', type=int),
-        'zones': request.args.get('zones', type=int),
-        'mask': request.args.get('mask', type=int),
-        'motion_boxes': request.args.get('motion', type=int),
-        'regions': request.args.get('regions', type=int),
+        "bounding_boxes": request.args.get("bbox", type=int),
+        "timestamp": request.args.get("timestamp", type=int),
+        "zones": request.args.get("zones", type=int),
+        "mask": request.args.get("mask", type=int),
+        "motion_boxes": request.args.get("motion", type=int),
+        "regions": request.args.get("regions", type=int),
     }
-    if camera_name in current_app.frigate_config.cameras:
-        # max out at specified FPS
-        frame = current_app.detected_frames_processor.get_current_frame(camera_name, draw_options)
-        if frame is None:
-            frame = np.zeros((720,1280,3), np.uint8)
+    resize_quality = request.args.get("quality", default=70, type=int)
 
-        height = int(request.args.get('h', str(frame.shape[0])))
-        width = int(height*frame.shape[1]/frame.shape[0])
+    if camera_name in current_app.frigate_config.cameras:
+        frame = current_app.detected_frames_processor.get_current_frame(
+            camera_name, draw_options
+        )
+        if frame is None:
+            frame = np.zeros((720, 1280, 3), np.uint8)
+
+        height = int(request.args.get("h", str(frame.shape[0])))
+        width = int(height * frame.shape[1] / frame.shape[0])
 
         frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
 
-        ret, jpg = cv2.imencode('.jpg', frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+        ret, jpg = cv2.imencode(
+            ".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), resize_quality]
+        )
         response = make_response(jpg.tobytes())
-        response.headers['Content-Type'] = 'image/jpg'
+        response.headers["Content-Type"] = "image/jpg"
         return response
     else:
         return "Camera named {} not found".format(camera_name), 404
 
+
+@bp.route("/<camera_name>/recordings")
+def recordings(camera_name):
+    dates = OrderedDict()
+
+    # Retrieve all recordings for this camera
+    recordings = (
+        Recordings.select()
+        .where(Recordings.camera == camera_name)
+        .order_by(Recordings.start_time.asc())
+    )
+
+    last_end = 0
+    recording: Recordings
+    for recording in recordings:
+        date = datetime.fromtimestamp(recording.start_time)
+        key = date.strftime("%Y-%m-%d")
+        hour = date.strftime("%H")
+
+        # Create Day Record
+        if key not in dates:
+            dates[key] = OrderedDict()
+
+        # Create Hour Record
+        if hour not in dates[key]:
+            dates[key][hour] = {"delay": {}, "events": []}
+
+        # Check for delay
+        the_hour = datetime.strptime(f"{key} {hour}", "%Y-%m-%d %H").timestamp()
+        # diff current recording start time and the greater of the previous end time or top of the hour
+        diff = recording.start_time - max(last_end, the_hour)
+        # Determine seconds into recording
+        seconds = 0
+        if datetime.fromtimestamp(last_end).strftime("%H") == hour:
+            seconds = int(last_end - the_hour)
+        # Determine the delay
+        delay = min(int(diff), 3600 - seconds)
+        if delay > 1:
+            # Add an offset for any delay greater than a second
+            dates[key][hour]["delay"][seconds] = delay
+
+        last_end = recording.end_time
+
+    # Packing intervals to return all events with same label and overlapping times as one row.
+    # See: https://blogs.solidq.com/en/sqlserver/packing-intervals/
+    events = Event.raw(
+        """WITH C1 AS
+        (
+        SELECT id, label, camera, top_score, start_time AS ts, +1 AS type, 1 AS sub
+        FROM event
+        WHERE camera = ?
+        UNION ALL
+        SELECT id, label, camera, top_score, end_time + 15 AS ts, -1 AS type, 0 AS sub
+        FROM event
+        WHERE camera = ?
+        ),
+        C2 AS
+        (
+        SELECT C1.*,
+        SUM(type) OVER(PARTITION BY label ORDER BY ts, type DESC
+        ROWS BETWEEN UNBOUNDED PRECEDING
+        AND CURRENT ROW) - sub AS cnt
+        FROM C1
+        ),
+        C3 AS
+        (
+        SELECT id, label, camera, top_score, ts,
+        (ROW_NUMBER() OVER(PARTITION BY label ORDER BY ts) - 1) / 2 + 1
+        AS grpnum
+        FROM C2
+        WHERE cnt = 0
+        )
+        SELECT MIN(id) as id, label, camera, MAX(top_score) as top_score, MIN(ts) AS start_time, max(ts) AS end_time
+        FROM C3
+        GROUP BY label, grpnum
+        ORDER BY start_time;""",
+        camera_name,
+        camera_name,
+    )
+
+    event: Event
+    for event in events:
+        date = datetime.fromtimestamp(event.start_time)
+        key = date.strftime("%Y-%m-%d")
+        hour = date.strftime("%H")
+        if key in dates and hour in dates[key]:
+            dates[key][hour]["events"].append(
+                model_to_dict(
+                    event,
+                    exclude=[
+                        Event.false_positive,
+                        Event.zones,
+                        Event.thumbnail,
+                        Event.has_clip,
+                        Event.has_snapshot,
+                    ],
+                )
+            )
+
+    return jsonify(
+        [
+            {
+                "date": date,
+                "events": sum([len(value["events"]) for value in hours.values()]),
+                "recordings": [
+                    {"hour": hour, "delay": value["delay"], "events": value["events"]}
+                    for hour, value in hours.items()
+                ],
+            }
+            for date, hours in dates.items()
+        ]
+    )
+
+
+@bp.route("/<camera>/start/<int:start_ts>/end/<int:end_ts>/clip.mp4")
+@bp.route("/<camera>/start/<float:start_ts>/end/<float:end_ts>/clip.mp4")
+def recording_clip(camera, start_ts, end_ts):
+    download = request.args.get("download", type=bool)
+
+    recordings = (
+        Recordings.select()
+        .where(
+            (Recordings.start_time.between(start_ts, end_ts))
+            | (Recordings.end_time.between(start_ts, end_ts))
+            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
+        )
+        .where(Recordings.camera == camera)
+        .order_by(Recordings.start_time.asc())
+    )
+
+    playlist_lines = []
+    clip: Recordings
+    for clip in recordings:
+        playlist_lines.append(f"file '{clip.path}'")
+        # if this is the starting clip, add an inpoint
+        if clip.start_time < start_ts:
+            playlist_lines.append(f"inpoint {int(start_ts - clip.start_time)}")
+        # if this is the ending clip, add an outpoint
+        if clip.end_time > end_ts:
+            playlist_lines.append(f"outpoint {int(end_ts - clip.start_time)}")
+
+    file_name = f"clip_{camera}_{start_ts}-{end_ts}.mp4"
+    path = f"/tmp/cache/{file_name}"
+
+    ffmpeg_cmd = [
+        "ffmpeg",
+        "-y",
+        "-protocol_whitelist",
+        "pipe,file",
+        "-f",
+        "concat",
+        "-safe",
+        "0",
+        "-i",
+        "-",
+        "-c",
+        "copy",
+        "-movflags",
+        "+faststart",
+        path,
+    ]
+
+    p = sp.run(
+        ffmpeg_cmd,
+        input="\n".join(playlist_lines),
+        encoding="ascii",
+        capture_output=True,
+    )
+    if p.returncode != 0:
+        logger.error(p.stderr)
+        return f"Could not create clip from recordings for {camera}.", 500
+
+    response = make_response()
+    response.headers["Content-Description"] = "File Transfer"
+    response.headers["Cache-Control"] = "no-cache"
+    response.headers["Content-Type"] = "video/mp4"
+    if download:
+        response.headers["Content-Disposition"] = "attachment; filename=%s" % file_name
+    response.headers["Content-Length"] = os.path.getsize(path)
+    response.headers[
+        "X-Accel-Redirect"
+    ] = f"/cache/{file_name}"  # nginx: http://wiki.nginx.org/NginxXSendfile
+
+    return response
+
+
+@bp.route("/vod/<camera>/start/<int:start_ts>/end/<int:end_ts>")
+@bp.route("/vod/<camera>/start/<float:start_ts>/end/<float:end_ts>")
+def vod_ts(camera, start_ts, end_ts):
+    recordings = (
+        Recordings.select()
+        .where(
+            Recordings.start_time.between(start_ts, end_ts)
+            | Recordings.end_time.between(start_ts, end_ts)
+            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
+        )
+        .where(Recordings.camera == camera)
+        .order_by(Recordings.start_time.asc())
+    )
+
+    clips = []
+    durations = []
+
+    recording: Recordings
+    for recording in recordings:
+        clip = {"type": "source", "path": recording.path}
+        duration = int(recording.duration * 1000)
+        # Determine if offset is needed for first clip
+        if recording.start_time < start_ts:
+            offset = int((start_ts - recording.start_time) * 1000)
+            clip["clipFrom"] = offset
+            duration -= offset
+        # Determine if we need to end the last clip early
+        if recording.end_time > end_ts:
+            duration -= int((recording.end_time - end_ts) * 1000)
+
+        if duration > 0:
+            clips.append(clip)
+            durations.append(duration)
+        else:
+            logger.warning(f"Recording clip is missing or empty: {recording.path}")
+
+    if not clips:
+        logger.error("No recordings found for the requested time range")
+        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:
+        logger.error(f"Event not found: {id}")
+        return "Event not found.", 404
+
+    if not event.has_clip:
+        logger.error(f"Event does not have recordings: {id}")
+        return "Recordings not available", 404
+
+    clip_path = os.path.join(CLIPS_DIR, f"{event.camera}-{id}.mp4")
+
+    if not os.path.isfile(clip_path):
+        end_ts = (
+            datetime.now().timestamp() if event.end_time is None else event.end_time
+        )
+        return vod_ts(event.camera, event.start_time, end_ts)
+
+    duration = int((event.end_time - event.start_time) * 1000)
+    return jsonify(
+        {
+            "cache": True,
+            "discontinuity": False,
+            "durations": [duration],
+            "sequences": [{"clips": [{"type": "source", "path": clip_path}]}],
+        }
+    )
+
+
 def imagestream(detected_frames_processor, camera_name, fps, height, draw_options):
     while True:
         # max out at specified FPS
-        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')
-
-@ws.route('/ws')
-def echo_socket(socket):
-    current_app.mqtt_backend.register(socket)
-
-    while not socket.closed:
-        # Sleep to prevent *constant* context-switches.
-        gevent.sleep(0.1)
-
-        message = socket.receive()
-        if message:
-            current_app.mqtt_backend.publish(message)
+        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
+        yield (
+            b"--frame\r\n"
+            b"Content-Type: image/jpeg\r\n\r\n" + jpg.tobytes() + b"\r\n\r\n"
+        )

frigate/log.py

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

frigate/models.py

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

frigate/motion.py

@@ -4,31 +4,48 @@ 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])
+        self.save_images = False
 
     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?
-
-        # convert to grayscale
-        # resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
+        # Improve contrast
+        minval = np.percentile(resized_frame, 4)
+        maxval = np.percentile(resized_frame, 96)
+        # don't adjust if the image is a single color
+        if minval < maxval:
+            resized_frame = np.clip(resized_frame, minval, maxval)
+            resized_frame = (
+                ((resized_frame - minval) / (maxval - minval)) * 255
+            ).astype(np.uint8)
 
         # mask frame
         resized_frame[self.mask] = [255]
@@ -38,6 +55,8 @@ class MotionDetector():
         if self.frame_counter < 30:
             self.frame_counter += 1
         else:
+            if self.save_images:
+                self.frame_counter += 1
             # compare to average
             frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
 
@@ -47,8 +66,9 @@ class MotionDetector():
             cv2.accumulateWeighted(frameDelta, self.avg_delta, self.config.delta_alpha)
 
             # compute the threshold image for the current frame
-            # TODO: threshold
-            current_thresh = cv2.threshold(frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
+            current_thresh = cv2.threshold(
+                frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY
+            )[1]
 
             # black out everything in the avg_delta where there isnt motion in the current frame
             avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
@@ -56,12 +76,16 @@ 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
-            thresh = cv2.dilate(thresh, None, iterations=2)
-            cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            thresh_dilated = cv2.dilate(thresh, None, iterations=2)
+            cnts = cv2.findContours(
+                thresh_dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+            )
             cnts = imutils.grab_contours(cnts)
 
             # loop over the contours
@@ -70,16 +94,56 @@ 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 self.save_images:
+                thresh_dilated = cv2.cvtColor(thresh_dilated, cv2.COLOR_GRAY2BGR)
+                # print("--------")
+                # print(self.frame_counter)
+                for c in cnts:
+                    contour_area = cv2.contourArea(c)
+                    # print(contour_area)
+                    if contour_area > self.config.contour_area:
+                        x, y, w, h = cv2.boundingRect(c)
+                        cv2.rectangle(
+                            thresh_dilated,
+                            (x, y),
+                            (x + w, y + h),
+                            (0, 0, 255),
+                            2,
+                        )
+                # print("--------")
+                image_row_1 = cv2.hconcat(
+                    [
+                        cv2.cvtColor(frameDelta, cv2.COLOR_GRAY2BGR),
+                        cv2.cvtColor(avg_delta_image, cv2.COLOR_GRAY2BGR),
+                    ]
+                )
+                image_row_2 = cv2.hconcat(
+                    [cv2.cvtColor(thresh, cv2.COLOR_GRAY2BGR), thresh_dilated]
+                )
+                combined_image = cv2.vconcat([image_row_1, image_row_2])
+                cv2.imwrite(f"motion/motion-{self.frame_counter}.jpg", combined_image)
+
         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

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

frigate/objects.py

@@ -13,126 +13,127 @@ import numpy as np
 from scipy.spatial import distance as dist
 
 from frigate.config import DetectConfig
-from frigate.util import draw_box_with_label
+from frigate.util import intersection_over_union
 
 
-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"]
+        obj["motionless_count"] = 0
         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
+        if (
+            intersection_over_union(self.tracked_objects[id]["box"], new_obj["box"])
+            > 0.9
+        ):
+            self.tracked_objects[id]["motionless_count"] += 1
+        else:
+            self.tracked_objects[id]["motionless_count"] = 0
         self.tracked_objects[id].update(new_obj)
 
     def match_and_update(self, frame_time, new_objects):
         # group by name
         new_object_groups = defaultdict(lambda: [])
         for obj in new_objects:
-            new_object_groups[obj[0]].append({
-                'label': obj[0],
-                'score': obj[1],
-                'box': obj[2],
-                'area': obj[3],
-                'region': obj[4],
-                'frame_time': frame_time
-            })
-        
+            new_object_groups[obj[0]].append(
+                {
+                    "label": obj[0],
+                    "score": obj[1],
+                    "box": obj[2],
+                    "area": obj[3],
+                    "region": obj[4],
+                    "frame_time": frame_time,
+                }
+            )
+
         # update any tracked objects with labels that are not
         # seen in the current objects and deregister if needed
         for obj in list(self.tracked_objects.values()):
-            if not obj['label'] in new_object_groups:
-                if self.disappeared[obj['id']] >= self.max_disappeared:
-                    self.deregister(obj['id'])
+            if not obj["label"] in new_object_groups:
+                if self.disappeared[obj["id"]] >= self.max_disappeared:
+                    self.deregister(obj["id"])
                 else:
-                    self.disappeared[obj['id']] += 1
-        
+                    self.disappeared[obj["id"]] += 1
+
         if len(new_objects) == 0:
             return
-        
+
         # track objects for each label type
         for label, group in new_object_groups.items():
-            current_objects = [o for o in self.tracked_objects.values() if o['label'] == label]
-            current_ids = [o['id'] for o in current_objects]
-            current_centroids = np.array([o['centroid'] for o in current_objects])
+            current_objects = [
+                o for o in self.tracked_objects.values() if o["label"] == label
+            ]
+            current_ids = [o["id"] for o in current_objects]
+            current_centroids = np.array([o["centroid"] for o in current_objects])
 
             # compute centroids of new objects
             for obj in group:
-                centroid_x = int((obj['box'][0]+obj['box'][2]) / 2.0)
-                centroid_y = int((obj['box'][1]+obj['box'][3]) / 2.0)
-                obj['centroid'] = (centroid_x, centroid_y)
+                centroid_x = int((obj["box"][0] + obj["box"][2]) / 2.0)
+                centroid_y = int((obj["box"][1] + obj["box"][3]) / 2.0)
+                obj["centroid"] = (centroid_x, centroid_y)
 
             if len(current_objects) == 0:
                 for index, obj in enumerate(group):
                     self.register(index, obj)
-                return
-            
-            new_centroids = np.array([o['centroid'] for o in group])
+                continue
+
+            new_centroids = np.array([o["centroid"] for o in group])
 
             # compute the distance between each pair of tracked
             # centroids and new centroids, respectively -- our
-            # goal will be to match each new centroid to an existing
+            # goal will be to match each current centroid to a new
             # object centroid
             D = dist.cdist(current_centroids, new_centroids)
 
-            # in order to perform this matching we must (1) find the
-            # smallest value in each row and then (2) sort the row
-            # indexes based on their minimum values so that the row
-            # with the smallest value is at the *front* of the index
-            # list
+            # in order to perform this matching we must (1) find the smallest
+            # value in each row (i.e. the distance from each current object to
+            # the closest new object) and then (2) sort the row indexes based
+            # on their minimum values so that the row with the smallest
+            # distance (the best match) is at the *front* of the index list
             rows = D.min(axis=1).argsort()
 
-            # next, we perform a similar process on the columns by
-            # finding the smallest value in each column and then
-            # sorting using the previously computed row index list
+            # next, we determine which new object each existing object matched
+            # against, and apply the same sorting as was applied previously
             cols = D.argmin(axis=1)[rows]
 
-            # in order to determine if we need to update, register,
-            # or deregister an object we need to keep track of which
-            # of the rows and column indexes we have already examined
-            usedRows = set()
-            usedCols = set()
+            # many current objects may register with each new object, so only
+            # match the closest ones.  unique returns the indices of the first
+            # occurrences of each value, and because the rows are sorted by
+            # distance, this will be index of the closest match
+            _, index = np.unique(cols, return_index=True)
+            rows = rows[index]
+            cols = cols[index]
 
-            # loop over the combination of the (row, column) index
-            # tuples
-            for (row, col) in zip(rows, cols):
-                # if we have already examined either the row or
-                # column value before, ignore it
-                if row in usedRows or col in usedCols:
-                    continue
-
-                # otherwise, grab the object ID for the current row,
-                # set its new centroid, and reset the disappeared
-                # counter
+            # loop over the combination of the (row, column) index tuples
+            for row, col in zip(rows, cols):
+                # grab the object ID for the current row, set its new centroid,
+                # and reset the disappeared counter
                 objectID = current_ids[row]
                 self.update(objectID, group[col])
 
-                # indicate that we have examined each of the row and
-                # column indexes, respectively
-                usedRows.add(row)
-                usedCols.add(col)
-
-            # compute the column index we have NOT yet examined
-            unusedRows = set(range(0, D.shape[0])).difference(usedRows)
-            unusedCols = set(range(0, D.shape[1])).difference(usedCols)
+            # compute the row and column indices we have NOT yet examined
+            unusedRows = set(range(D.shape[0])).difference(rows)
+            unusedCols = set(range(D.shape[1])).difference(cols)
 
             # in the event that the number of object centroids is
-			# equal or greater than the number of input centroids
-			# we need to check and see if some of these objects have
-			# potentially disappeared
+            # equal or greater than the number of input centroids
+            # we need to check and see if some of these objects have
+            # potentially disappeared
             if D.shape[0] >= D.shape[1]:
                 for row in unusedRows:
                     id = current_ids[row]

frigate/output.py

@@ -0,0 +1,455 @@
+import datetime
+import glob
+import logging
+import math
+import multiprocessing as mp
+import queue
+import signal
+import subprocess as sp
+import threading
+from multiprocessing import shared_memory
+from wsgiref.simple_server import make_server
+
+import cv2
+import numpy as np
+from setproctitle import setproctitle
+from ws4py.server.wsgirefserver import (
+    WebSocketWSGIHandler,
+    WebSocketWSGIRequestHandler,
+    WSGIServer,
+)
+from ws4py.server.wsgiutils import WebSocketWSGIApplication
+from ws4py.websocket import WebSocket
+
+from frigate.config import BirdseyeModeEnum, FrigateConfig
+from frigate.util import SharedMemoryFrameManager, copy_yuv_to_position, get_yuv_crop
+
+logger = logging.getLogger(__name__)
+
+
+class FFMpegConverter:
+    def __init__(self, in_width, in_height, out_width, out_height, quality):
+        ffmpeg_cmd = f"ffmpeg -f rawvideo -pix_fmt yuv420p -video_size {in_width}x{in_height} -i pipe: -f mpegts -s {out_width}x{out_height} -codec:v mpeg1video -q {quality} -bf 0 pipe:".split(
+            " "
+        )
+        self.process = sp.Popen(
+            ffmpeg_cmd,
+            stdout=sp.PIPE,
+            stderr=sp.DEVNULL,
+            stdin=sp.PIPE,
+            start_new_session=True,
+        )
+
+    def write(self, b):
+        self.process.stdin.write(b)
+
+    def read(self, length):
+        try:
+            return self.process.stdout.read1(length)
+        except ValueError:
+            return False
+
+    def exit(self):
+        self.process.terminate()
+        try:
+            self.process.communicate(timeout=30)
+        except sp.TimeoutExpired:
+            self.process.kill()
+            self.process.communicate()
+
+
+class BroadcastThread(threading.Thread):
+    def __init__(self, camera, converter, websocket_server):
+        super(BroadcastThread, self).__init__()
+        self.camera = camera
+        self.converter = converter
+        self.websocket_server = websocket_server
+
+    def run(self):
+        while True:
+            buf = self.converter.read(65536)
+            if buf:
+                manager = self.websocket_server.manager
+                with manager.lock:
+                    websockets = manager.websockets.copy()
+                    ws_iter = iter(websockets.values())
+
+                for ws in ws_iter:
+                    if (
+                        not ws.terminated
+                        and ws.environ["PATH_INFO"] == f"/{self.camera}"
+                    ):
+                        try:
+                            ws.send(buf, binary=True)
+                        except:
+                            pass
+            elif self.converter.process.poll() is not None:
+                break
+
+
+class BirdsEyeFrameManager:
+    def __init__(self, config, frame_manager: SharedMemoryFrameManager):
+        self.config = config
+        self.mode = config.birdseye.mode
+        self.frame_manager = frame_manager
+        width = config.birdseye.width
+        height = config.birdseye.height
+        self.frame_shape = (height, width)
+        self.yuv_shape = (height * 3 // 2, width)
+        self.frame = np.ndarray(self.yuv_shape, dtype=np.uint8)
+
+        # initialize the frame as black and with the frigate logo
+        self.blank_frame = np.zeros(self.yuv_shape, np.uint8)
+        self.blank_frame[:] = 128
+        self.blank_frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]] = 16
+
+        # find and copy the logo on the blank frame
+        logo_files = glob.glob("/opt/frigate/frigate/birdseye.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

@@ -1,208 +0,0 @@
-import datetime
-import json
-import logging
-import multiprocessing as mp
-import os
-import subprocess as sp
-import sys
-from unittest import TestCase, main
-
-import click
-import cv2
-import numpy as np
-
-from frigate.config import FRIGATE_CONFIG_SCHEMA, FrigateConfig
-from frigate.edgetpu import LocalObjectDetector
-from frigate.motion import MotionDetector
-from frigate.object_processing import COLOR_MAP, CameraState
-from frigate.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)
-
-logging.basicConfig()
-logging.root.setLevel(logging.DEBUG)
-
-logger = logging.getLogger(__name__)
-
-def get_frame_shape(source):
-    ffprobe_cmd = " ".join([
-        'ffprobe',
-        '-v',
-        'panic',
-        '-show_error',
-        '-show_streams',
-        '-of',
-        'json',
-        '"'+source+'"'
-    ])
-    p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
-    (output, err) = p.communicate()
-    p_status = p.wait()
-    info = json.loads(output)
-
-    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()
-    frame_shape = frame.shape
-    video.release()
-    return frame_shape
-
-class ProcessClip():
-    def __init__(self, clip_path, frame_shape, config: FrigateConfig):
-        self.clip_path = clip_path
-        self.camera_name = 'camera'
-        self.config = config
-        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.frame_manager = SharedMemoryFrameManager()
-        self.frame_queue = mp.Queue()
-        self.detected_objects_queue = mp.Queue()
-        self.camera_state = CameraState(self.camera_name, config, self.frame_manager)
-
-    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)
-        ffmpeg_process.wait()
-        ffmpeg_process.communicate()
-    
-    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)
-
-        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)
-        }
-        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)
-    
-    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
-            if obj.computed_score > top_computed_score:
-                top_computed_score = obj.computed_score
-            if not obj.false_positive:
-                obj_detected = True
-        self.camera_state.on('new', handle_event)
-        self.camera_state.on('update', handle_event)
-
-        while(not self.detected_objects_queue.empty()):
-            camera_name, frame_time, current_tracked_objects, motion_boxes, regions = self.detected_objects_queue.get()
-            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.frame_manager.delete(self.camera_state.previous_frame_id)
-        
-        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)
-        # draw the bounding boxes on the frame
-        for obj in tracked_objects:
-            thickness = 2
-            color = (0,0,175)
-
-            if obj['frame_time'] != frame_time:
-                thickness = 1
-                color = (255,0,0)
-            else:
-                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)
-            # 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)
-
-@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("-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.")
-def process(path, label, threshold, scores, debug_path):
-    clips = []
-    if os.path.isdir(path):
-        files = os.listdir(path)
-        files.sort()
-        clips = [os.path.join(path, file) for file in files]
-    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'] }
-                    ]
-                },
-                '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
-
-        config = FrigateConfig(config=FRIGATE_CONFIG_SCHEMA(json_config))
-
-        process_clip = ProcessClip(c, frame_shape, config)
-        process_clip.load_frames()
-        process_clip.process_frames(objects_to_track=[label])
-
-        results.append((c, process_clip.top_object(debug_path)))
-
-    if not scores is None:
-        with open(scores, 'w') as writer:
-            for result in results:
-                writer.write(f"{result[0]},{result[1]['top_score']}\n")
-    
-    positive_count = sum(1 for result in results if result[1]['object_detected'])
-    print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).")
-
-if __name__ == '__main__':
-    process()

frigate/record.py

@@ -1,125 +1,458 @@
 import datetime
-import json
+import time
+import itertools
 import logging
 import os
-import queue
+import random
+import shutil
+import string
 import subprocess as sp
 import threading
-import time
 from collections import defaultdict
 from pathlib import Path
 
 import psutil
+from peewee import JOIN, DoesNotExist
 
 from frigate.config import FrigateConfig
-from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
+from frigate.const import CACHE_DIR, RECORD_DIR
+from frigate.models import Event, Recordings
 
 logger = logging.getLogger(__name__)
 
 SECONDS_IN_DAY = 60 * 60 * 24
 
+
 def remove_empty_directories(directory):
-  # list all directories recursively and sort them by path,
-  # longest first
-  paths = sorted(
-      [x[0] for x in os.walk(RECORD_DIR)],
-      key=lambda p: len(str(p)),
-      reverse=True,
-  )
-  for path in paths:
-      # don't delete the parent
-      if path == RECORD_DIR:
-          continue
-      if len(os.listdir(path)) == 0:
-          os.rmdir(path)
+    # list all directories recursively and sort them by path,
+    # longest first
+    paths = sorted(
+        [x[0] for x in os.walk(RECORD_DIR)],
+        key=lambda p: len(str(p)),
+        reverse=True,
+    )
+    for path in paths:
+        # don't delete the parent
+        if path == RECORD_DIR:
+            continue
+        if len(os.listdir(path)) == 0:
+            os.rmdir(path)
+
 
 class RecordingMaintainer(threading.Thread):
     def __init__(self, config: FrigateConfig, stop_event):
         threading.Thread.__init__(self)
-        self.name = 'recording_maint'
+        self.name = "recording_maint"
         self.config = config
         self.stop_event = stop_event
+        self.first_pass = True
+        self.end_time_cache = {}
 
     def move_files(self):
-        recordings = [d for d in os.listdir(RECORD_DIR) if os.path.isfile(os.path.join(RECORD_DIR, d)) and d.endswith(".mp4")]
+        cache_files = [
+            d
+            for d in os.listdir(CACHE_DIR)
+            if os.path.isfile(os.path.join(CACHE_DIR, d))
+            and d.endswith(".mp4")
+            and not d.startswith("clip_")
+        ]
 
         files_in_use = []
         for process in psutil.process_iter():
             try:
-                if process.name() != 'ffmpeg':
+                if process.name() != "ffmpeg":
                     continue
                 flist = process.open_files()
                 if flist:
                     for nt in flist:
-                        if nt.path.startswith(RECORD_DIR):
-                            files_in_use.append(nt.path.split('/')[-1])
+                        if nt.path.startswith(CACHE_DIR):
+                            files_in_use.append(nt.path.split("/")[-1])
             except:
                 continue
 
-        for f in recordings:
+        # group recordings by camera
+        grouped_recordings = defaultdict(list)
+        for f in cache_files:
+            # Skip files currently in use
             if f in files_in_use:
                 continue
 
-            camera = '-'.join(f.split('-')[:-1])
-            start_time = datetime.datetime.strptime(f.split('-')[-1].split('.')[0], '%Y%m%d%H%M%S')
-        
-            ffprobe_cmd = " ".join([
-                'ffprobe',
-                '-v',
-                'error',
-                '-show_entries',
-                'format=duration',
-                '-of',
-                'default=noprint_wrappers=1:nokey=1',
-                f"{os.path.join(RECORD_DIR,f)}"
-            ])
-            p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
-            (output, err) = p.communicate()
-            p_status = p.wait()
-            if p_status == 0:
-                duration = float(output.decode('utf-8').strip())
-            else:
-                logger.info(f"bad file: {f}")
-                os.remove(os.path.join(RECORD_DIR,f))
-                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")
 
-            directory = os.path.join(RECORD_DIR, start_time.strftime('%Y-%m/%d/%H'), camera)
+            grouped_recordings[camera].append(
+                {
+                    "cache_path": cache_path,
+                    "start_time": start_time,
+                }
+            )
 
-            if not os.path.exists(directory):
-                os.makedirs(directory)
+        # delete all cached files past the most recent 5
+        keep_count = 5
+        for camera in grouped_recordings.keys():
+            if len(grouped_recordings[camera]) > keep_count:
+                sorted_recordings = sorted(
+                    grouped_recordings[camera], key=lambda i: i["start_time"]
+                )
+                to_remove = sorted_recordings[:-keep_count]
+                for f in to_remove:
+                    Path(f["cache_path"]).unlink(missing_ok=True)
+                    self.end_time_cache.pop(f["cache_path"], None)
+                grouped_recordings[camera] = sorted_recordings[-keep_count:]
 
-            file_name = f"{start_time.strftime('%M.%S.mp4')}"
+        for camera, recordings in grouped_recordings.items():
+            # get all events with the end time after the start of the oldest cache file
+            # or with end_time None
+            events: Event = (
+                Event.select()
+                .where(
+                    Event.camera == camera,
+                    (Event.end_time == None)
+                    | (Event.end_time >= recordings[0]["start_time"].timestamp()),
+                    Event.has_clip,
+                )
+                .order_by(Event.start_time)
+            )
+            for r in recordings:
+                cache_path = r["cache_path"]
+                start_time = r["start_time"]
 
-            os.rename(os.path.join(RECORD_DIR,f), os.path.join(directory,file_name))
+                # 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)
+                    self.end_time_cache.pop(cache_path, None)
+                    continue
 
-    def expire_files(self):
-        delete_before = {}
-        for name, camera in self.config.cameras.items():
-            delete_before[name] = datetime.datetime.now().timestamp() - SECONDS_IN_DAY*camera.record.retain_days
+                if cache_path in self.end_time_cache:
+                    end_time, duration = self.end_time_cache[cache_path]
+                else:
+                    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)
+                        self.end_time_cache[cache_path] = (end_time, duration)
+                    else:
+                        logger.warning(f"Discarding a corrupt recording segment: {f}")
+                        Path(cache_path).unlink(missing_ok=True)
+                        continue
 
-        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)
+                # if cached file's start_time is earlier than the retain_days for the camera
+                if start_time <= (
+                    (
+                        datetime.datetime.now()
+                        - datetime.timedelta(
+                            days=self.config.cameras[camera].record.retain_days
+                        )
+                    )
+                ):
+                    # if the cached segment overlaps with the events:
+                    overlaps = False
+                    for event in events:
+                        # if the event starts in the future, stop checking events
+                        # and remove this segment
+                        if event.start_time > end_time.timestamp():
+                            overlaps = False
+                            break
+
+                        # if the event is in progress or ends after the recording starts, keep it
+                        # and stop looking at events
+                        if (
+                            event.end_time is None
+                            or event.end_time >= start_time.timestamp()
+                        ):
+                            overlaps = True
+                            break
+
+                    if overlaps:
+                        # move from cache to recordings immediately
+                        self.store_segment(
+                            camera,
+                            start_time,
+                            end_time,
+                            duration,
+                            cache_path,
+                        )
+                # else retain_days includes this segment
+                else:
+                    self.store_segment(
+                        camera, start_time, end_time, duration, cache_path
+                    )
+
+    def store_segment(self, camera, start_time, end_time, duration, cache_path):
+        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)
+
+        try:
+            start_frame = datetime.datetime.now().timestamp()
+            # copy then delete is required when recordings are stored on some network drives
+            shutil.copyfile(cache_path, file_path)
+            logger.debug(
+                f"Copied {file_path} in {datetime.datetime.now().timestamp()-start_frame} seconds."
+            )
+            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,
+            )
+        except Exception as e:
+            logger.error(f"Unable to store recording segment {cache_path}")
+            Path(cache_path).unlink(missing_ok=True)
+            logger.error(e)
+
+        # clear end_time cache
+        self.end_time_cache.pop(cache_path, None)
 
     def run(self):
-        counter = 0
-        self.expire_files()
-        while(True):
-            if self.stop_event.is_set():
-                logger.info(f"Exiting recording maintenance...")
+        # Check for new files every 5 seconds
+        wait_time = 5
+        while not self.stop_event.wait(wait_time):
+            run_start = datetime.datetime.now().timestamp()
+            try:
+                self.move_files()
+            except Exception as e:
+                logger.error(
+                    "Error occurred when attempting to maintain recording cache"
+                )
+                logger.error(e)
+            duration = datetime.datetime.now().timestamp() - run_start
+            wait_time = max(0, 5 - duration)
+            if wait_time == 0 and not self.first_pass:
+                logger.warning(
+                    "Cache is taking longer than 5 seconds to clear. Your recordings disk may be too slow."
+                )
+            if self.first_pass:
+                self.first_pass = False
+
+        logger.info(f"Exiting recording maintenance...")
+
+
+class RecordingCleanup(threading.Thread):
+    def __init__(self, config: FrigateConfig, stop_event):
+        threading.Thread.__init__(self)
+        self.name = "recording_cleanup"
+        self.config = config
+        self.stop_event = stop_event
+
+    def clean_tmp_clips(self):
+        # delete any clips more than 5 minutes old
+        for p in Path("/tmp/cache").rglob("clip_*.mp4"):
+            logger.debug(f"Checking tmp clip {p}.")
+            if p.stat().st_mtime < (datetime.datetime.now().timestamp() - 60 * 1):
+                logger.debug("Deleting tmp clip.")
+                p.unlink(missing_ok=True)
+
+    def expire_recordings(self):
+        logger.debug("Start expire recordings (new).")
+
+        logger.debug("Start deleted cameras.")
+        # Handle deleted cameras
+        expire_days = self.config.record.retain_days
+        expire_before = (
+            datetime.datetime.now() - datetime.timedelta(days=expire_days)
+        ).timestamp()
+        no_camera_recordings: Recordings = Recordings.select().where(
+            Recordings.camera.not_in(list(self.config.cameras.keys())),
+            Recordings.end_time < expire_before,
+        )
+
+        deleted_recordings = set()
+        for recording in no_camera_recordings:
+            Path(recording.path).unlink(missing_ok=True)
+            deleted_recordings.add(recording.id)
+
+        logger.debug(f"Expiring {len(deleted_recordings)} recordings")
+        Recordings.delete().where(Recordings.id << deleted_recordings).execute()
+        logger.debug("End deleted cameras.")
+
+        logger.debug("Start all cameras.")
+        for camera, config in self.config.cameras.items():
+            logger.debug(f"Start camera: {camera}.")
+            # When deleting recordings without events, we have to keep at LEAST the configured max clip duration
+            min_end = (
+                datetime.datetime.now()
+                - datetime.timedelta(seconds=config.record.events.max_seconds)
+            ).timestamp()
+            expire_days = config.record.retain_days
+            expire_before = (
+                datetime.datetime.now() - datetime.timedelta(days=expire_days)
+            ).timestamp()
+            expire_date = min(min_end, expire_before)
+
+            # Get recordings to check for expiration
+            recordings: Recordings = (
+                Recordings.select()
+                .where(
+                    Recordings.camera == camera,
+                    Recordings.end_time < expire_date,
+                )
+                .order_by(Recordings.start_time)
+            )
+
+            # Get all the events to check against
+            events: Event = (
+                Event.select()
+                .where(
+                    Event.camera == camera,
+                    # need to ensure segments for all events starting
+                    # before the expire date are included
+                    Event.start_time < expire_date,
+                    Event.has_clip,
+                )
+                .order_by(Event.start_time)
+                .objects()
+            )
+
+            # loop over recordings and see if they overlap with any non-expired events
+            event_start = 0
+            deleted_recordings = set()
+            for recording in recordings.objects().iterator():
+                keep = False
+                # Now look for a reason to keep this recording segment
+                for idx in range(event_start, len(events)):
+                    event = events[idx]
+
+                    # if the event starts in the future, stop checking events
+                    # and let this recording segment expire
+                    if event.start_time > recording.end_time:
+                        keep = False
+                        break
+
+                    # if the event is in progress or ends after the recording starts, keep it
+                    # and stop looking at events
+                    if event.end_time is None or event.end_time >= recording.start_time:
+                        keep = True
+                        break
+
+                    # if the event ends before this recording segment starts, skip
+                    # this event and check the next event for an overlap.
+                    # since the events and recordings are sorted, we can skip events
+                    # that end before the previous recording segment started on future segments
+                    if event.end_time < recording.start_time:
+                        event_start = idx
+
+                # Delete recordings outside of the retention window
+                if not keep:
+                    Path(recording.path).unlink(missing_ok=True)
+                    deleted_recordings.add(recording.id)
+
+            logger.debug(f"Expiring {len(deleted_recordings)} recordings")
+            Recordings.delete().where(Recordings.id << deleted_recordings).execute()
+
+            logger.debug(f"End camera: {camera}.")
+
+        logger.debug("End all cameras.")
+        logger.debug("End expire recordings (new).")
+
+    def expire_files(self):
+        logger.debug("Start expire files (legacy).")
+
+        default_expire = (
+            datetime.datetime.now().timestamp()
+            - SECONDS_IN_DAY * self.config.record.retain_days
+        )
+        delete_before = {}
+
+        for name, camera in self.config.cameras.items():
+            delete_before[name] = (
+                datetime.datetime.now().timestamp()
+                - SECONDS_IN_DAY * camera.record.retain_days
+            )
+
+        # find all the recordings older than the oldest recording in the db
+        try:
+            oldest_recording = Recordings.select().order_by(Recordings.start_time).get()
+
+            p = Path(oldest_recording.path)
+            oldest_timestamp = p.stat().st_mtime - 1
+        except DoesNotExist:
+            oldest_timestamp = datetime.datetime.now().timestamp()
+        except FileNotFoundError:
+            logger.warning(f"Unable to find file from recordings database: {p}")
+            oldest_timestamp = datetime.datetime.now().timestamp()
+
+        logger.debug(f"Oldest recording in the db: {oldest_timestamp}")
+        process = sp.run(
+            ["find", RECORD_DIR, "-type", "f", "!", "-newermt", f"@{oldest_timestamp}"],
+            capture_output=True,
+            text=True,
+        )
+        files_to_check = process.stdout.splitlines()
+
+        for f in files_to_check:
+            p = Path(f)
+            try:
+                if p.stat().st_mtime < delete_before.get(p.parent.name, default_expire):
+                    p.unlink(missing_ok=True)
+            except FileNotFoundError:
+                logger.warning(f"Attempted to expire missing file: {f}")
+
+        logger.debug("End expire files (legacy).")
+
+    def sync_recordings(self):
+        logger.debug("Start sync recordings.")
+
+        # get all recordings in the db
+        recordings: Recordings = Recordings.select()
+
+        # get all recordings files on disk
+        process = sp.run(
+            ["find", RECORD_DIR, "-type", "f"],
+            capture_output=True,
+            text=True,
+        )
+        files_on_disk = process.stdout.splitlines()
+
+        recordings_to_delete = []
+        for recording in recordings.objects().iterator():
+            if not recording.path in files_on_disk:
+                recordings_to_delete.append(recording.id)
+
+        logger.debug(
+            f"Deleting {len(recordings_to_delete)} recordings with missing files"
+        )
+        Recordings.delete().where(Recordings.id << recordings_to_delete).execute()
+
+        logger.debug("End sync recordings.")
+
+    def run(self):
+        # Expire recordings every minute, clean directories every hour.
+        for counter in itertools.cycle(range(60)):
+            if self.stop_event.wait(60):
+                logger.info(f"Exiting recording cleanup...")
                 break
 
-            # 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_recordings()
+            self.clean_tmp_clips()
+
+            if counter == 0:
                 self.expire_files()
                 remove_empty_directories(RECORD_DIR)
-                counter = 0
-
-            self.move_files()
-
-
-   
+                self.sync_recordings()

frigate/stats.py

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

frigate/test/test_copy_yuv_to_position.py

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

frigate/test/test_reduce_boxes.py

@@ -0,0 +1,27 @@
+import cv2
+import numpy as np
+from unittest import TestCase, main
+from frigate.video import box_overlaps, reduce_boxes
+
+
+class TestBoxOverlaps(TestCase):
+    def test_overlap(self):
+        assert box_overlaps((100, 100, 200, 200), (50, 50, 150, 150))
+
+    def test_overlap_2(self):
+        assert box_overlaps((50, 50, 150, 150), (100, 100, 200, 200))
+
+    def test_no_overlap(self):
+        assert not box_overlaps((100, 100, 200, 200), (250, 250, 350, 350))
+
+
+class TestReduceBoxes(TestCase):
+    def test_cluster(self):
+        clusters = reduce_boxes(
+            [(144, 290, 221, 459), (225, 178, 426, 341), (343, 105, 584, 250)]
+        )
+        assert len(clusters) == 2
+
+
+if __name__ == "__main__":
+    main(verbosity=2)

frigate/test/test_yuv_region_2_rgb.py

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

frigate/util.py

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

frigate/video.py

@@ -1,42 +1,46 @@
-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
+import subprocess as sp
 import threading
 import time
 from collections import defaultdict
-from setproctitle import setproctitle
 from typing import Dict, List
 
-import cv2
 import numpy as np
+from cv2 import cv2, reduce
+from setproctitle import setproctitle
 
-from frigate.config import CameraConfig
+from frigate.config import CameraConfig, DetectConfig
 from frigate.edgetpu import RemoteObjectDetector
 from frigate.log import LogPipe
 from frigate.motion import MotionDetector
 from frigate.objects import ObjectTracker
-from frigate.util import (EventsPerSecond, FrameManager,
-                          SharedMemoryFrameManager, area, calculate_region,
-                          clipped, draw_box_with_label, intersection,
-                          intersection_over_union, listen, yuv_region_2_rgb)
+from frigate.util import (
+    EventsPerSecond,
+    FrameManager,
+    SharedMemoryFrameManager,
+    area,
+    calculate_region,
+    clipped,
+    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]
 
@@ -44,7 +48,7 @@ def filtered(obj, objects_to_track, object_filters):
         # detected object, don't add it to detected objects
         if obj_settings.min_area > obj[3]:
             return True
-        
+
         # if the detected object is larger than the
         # max area, don't add it to detected objects
         if obj_settings.max_area < obj[3]:
@@ -53,29 +57,36 @@ def filtered(obj, objects_to_track, object_filters):
         # if the score is lower than the min_score, skip
         if obj_settings.min_score > obj[1]:
             return True
-    
+
         if not obj_settings.mask is None:
             # compute the coordinates of the object and make sure
             # the location isnt outside the bounds of the image (can happen from rounding)
-            y_location = min(int(obj[2][3]), len(obj_settings.mask)-1)
-            x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(obj_settings.mask[0])-1)
+            y_location = min(int(obj[2][3]), len(obj_settings.mask) - 1)
+            x_location = min(
+                int((obj[2][2] - obj[2][0]) / 2.0) + obj[2][0],
+                len(obj_settings.mask[0]) - 1,
+            )
 
             # if the object is in a masked location, don't add it to detected objects
             if obj_settings.mask[y_location][x_location] == 0:
                 return True
-        
+
     return False
 
+
 def create_tensor_input(frame, model_shape, region):
     cropped_frame = yuv_region_2_rgb(frame, region)
 
     # Resize to 300x300 if needed
     if cropped_frame.shape != (model_shape[0], model_shape[1], 3):
-        cropped_frame = cv2.resize(cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR)
-    
+        cropped_frame = cv2.resize(
+            cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR
+        )
+
     # Expand dimensions since the model expects images to have shape: [1, height, width, 3]
     return np.expand_dims(cropped_frame, axis=0)
 
+
 def stop_ffmpeg(ffmpeg_process, logger):
     logger.info("Terminating the existing ffmpeg process...")
     ffmpeg_process.terminate()
@@ -88,18 +99,43 @@ def stop_ffmpeg(ffmpeg_process, logger):
         ffmpeg_process.communicate()
     ffmpeg_process = None
 
-def start_or_restart_ffmpeg(ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None):
-    if not ffmpeg_process is None:
+
+def start_or_restart_ffmpeg(
+    ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None
+):
+    if ffmpeg_process is not None:
         stop_ffmpeg(ffmpeg_process, logger)
 
     if frame_size is None:
-        process = sp.Popen(ffmpeg_cmd, stdout = sp.DEVNULL, stderr=logpipe, stdin = sp.DEVNULL, start_new_session=True)
+        process = sp.Popen(
+            ffmpeg_cmd,
+            stdout=sp.DEVNULL,
+            stderr=logpipe,
+            stdin=sp.DEVNULL,
+            start_new_session=True,
+        )
     else:
-        process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stderr=logpipe, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True)
+        process = sp.Popen(
+            ffmpeg_cmd,
+            stdout=sp.PIPE,
+            stderr=logpipe,
+            stdin=sp.DEVNULL,
+            bufsize=frame_size * 10,
+            start_new_session=True,
+        )
     return process
 
-def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager, 
-    frame_queue, fps:mp.Value, skipped_fps: mp.Value, current_frame: mp.Value):
+
+def capture_frames(
+    ffmpeg_process,
+    camera_name,
+    frame_shape,
+    frame_manager: FrameManager,
+    frame_queue,
+    fps: mp.Value,
+    skipped_fps: mp.Value,
+    current_frame: mp.Value,
+):
 
     frame_size = frame_shape[0] * frame_shape[1]
     frame_rate = EventsPerSecond()
@@ -119,7 +155,9 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
             logger.info(f"{camera_name}: ffmpeg sent a broken frame. {e}")
 
             if ffmpeg_process.poll() != None:
-                logger.info(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
+                logger.info(
+                    f"{camera_name}: ffmpeg process is not running. exiting capture thread..."
+                )
                 frame_manager.delete(frame_name)
                 break
             continue
@@ -138,8 +176,11 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
         # add to the queue
         frame_queue.put(current_frame.value)
 
+
 class CameraWatchdog(threading.Thread):
-    def __init__(self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event):
+    def __init__(
+        self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event
+    ):
         threading.Thread.__init__(self)
         self.logger = logging.getLogger(f"watchdog.{camera_name}")
         self.camera_name = camera_name
@@ -159,32 +200,38 @@ class CameraWatchdog(threading.Thread):
         self.start_ffmpeg_detect()
 
         for c in self.config.ffmpeg_cmds:
-            if 'detect' in c['roles']:
+            if "detect" in c["roles"]:
                 continue
-            logpipe = LogPipe(f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}", logging.ERROR)
-            self.ffmpeg_other_processes.append({
-                'cmd': c['cmd'],
-                'logpipe': logpipe,
-                'process': start_or_restart_ffmpeg(c['cmd'], self.logger, logpipe)
-            })
-        
-        time.sleep(10)
-        while True:
-            if self.stop_event.is_set():
-                stop_ffmpeg(self.ffmpeg_detect_process, self.logger)
-                for p in self.ffmpeg_other_processes:
-                    stop_ffmpeg(p['process'], self.logger)
-                    p['logpipe'].close()
-                self.logpipe.close()
-                break
+            logpipe = LogPipe(
+                f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}",
+                logging.ERROR,
+            )
+            self.ffmpeg_other_processes.append(
+                {
+                    "cmd": c["cmd"],
+                    "logpipe": logpipe,
+                    "process": start_or_restart_ffmpeg(c["cmd"], self.logger, logpipe),
+                }
+            )
 
+        time.sleep(10)
+        while not self.stop_event.wait(10):
             now = datetime.datetime.now().timestamp()
 
             if not self.capture_thread.is_alive():
+                self.logger.error(
+                    f"FFMPEG process crashed unexpectedly for {self.camera_name}."
+                )
+                self.logger.error(
+                    "The following ffmpeg logs include the last 100 lines prior to exit."
+                )
+                self.logger.error("You may have invalid args defined for this camera.")
                 self.logpipe.dump()
                 self.start_ffmpeg_detect()
             elif now - self.capture_thread.current_frame.value > 20:
-                self.logger.info(f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg...")
+                self.logger.info(
+                    f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg..."
+                )
                 self.ffmpeg_detect_process.terminate()
                 try:
                     self.logger.info("Waiting for ffmpeg to exit gracefully...")
@@ -193,25 +240,40 @@ class CameraWatchdog(threading.Thread):
                     self.logger.info("FFmpeg didnt exit. Force killing...")
                     self.ffmpeg_detect_process.kill()
                     self.ffmpeg_detect_process.communicate()
-            
+
             for p in self.ffmpeg_other_processes:
-                poll = p['process'].poll()
-                if poll == None:
+                poll = p["process"].poll()
+                if poll is None:
                     continue
-                p['logpipe'].dump()
-                p['process'] = start_or_restart_ffmpeg(p['cmd'], self.logger, p['logpipe'], ffmpeg_process=p['process'])
-            
-            # wait a bit before checking again
-            time.sleep(10)
-    
+                p["logpipe"].dump()
+                p["process"] = start_or_restart_ffmpeg(
+                    p["cmd"], self.logger, p["logpipe"], ffmpeg_process=p["process"]
+                )
+
+        stop_ffmpeg(self.ffmpeg_detect_process, self.logger)
+        for p in self.ffmpeg_other_processes:
+            stop_ffmpeg(p["process"], self.logger)
+            p["logpipe"].close()
+        self.logpipe.close()
+
     def start_ffmpeg_detect(self):
-        ffmpeg_cmd = [c['cmd'] for c in self.config.ffmpeg_cmds if 'detect' in c['roles']][0]
-        self.ffmpeg_detect_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.logger, self.logpipe, self.frame_size)
+        ffmpeg_cmd = [
+            c["cmd"] for c in self.config.ffmpeg_cmds if "detect" in c["roles"]
+        ][0]
+        self.ffmpeg_detect_process = start_or_restart_ffmpeg(
+            ffmpeg_cmd, self.logger, self.logpipe, self.frame_size
+        )
         self.ffmpeg_pid.value = self.ffmpeg_detect_process.pid
-        self.capture_thread = CameraCapture(self.camera_name, self.ffmpeg_detect_process, self.frame_shape, self.frame_queue, 
-            self.camera_fps)
+        self.capture_thread = CameraCapture(
+            self.camera_name,
+            self.ffmpeg_detect_process,
+            self.frame_shape,
+            self.frame_queue,
+            self.camera_fps,
+        )
         self.capture_thread.start()
 
+
 class CameraCapture(threading.Thread):
     def __init__(self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps):
         threading.Thread.__init__(self)
@@ -223,32 +285,60 @@ class CameraCapture(threading.Thread):
         self.skipped_fps = EventsPerSecond()
         self.frame_manager = SharedMemoryFrameManager()
         self.ffmpeg_process = ffmpeg_process
-        self.current_frame = mp.Value('d', 0.0)
+        self.current_frame = mp.Value("d", 0.0)
         self.last_frame = 0
 
     def run(self):
         self.skipped_fps.start()
-        capture_frames(self.ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue,
-            self.fps, self.skipped_fps, self.current_frame)
+        capture_frames(
+            self.ffmpeg_process,
+            self.camera_name,
+            self.frame_shape,
+            self.frame_manager,
+            self.frame_queue,
+            self.fps,
+            self.skipped_fps,
+            self.current_frame,
+        )
+
 
 def capture_camera(name, config: CameraConfig, process_info):
     stop_event = mp.Event()
+
     def receiveSignal(signalNumber, frame):
         stop_event.set()
-    
+
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
-    frame_queue = process_info['frame_queue']
-    camera_watchdog = CameraWatchdog(name, config, frame_queue, process_info['camera_fps'], process_info['ffmpeg_pid'], stop_event)
+    frame_queue = process_info["frame_queue"]
+    camera_watchdog = CameraWatchdog(
+        name,
+        config,
+        frame_queue,
+        process_info["camera_fps"],
+        process_info["ffmpeg_pid"],
+        stop_event,
+    )
     camera_watchdog.start()
     camera_watchdog.join()
 
-def track_camera(name, config: CameraConfig, model_shape, detection_queue, result_connection, detected_objects_queue, process_info):
+
+def track_camera(
+    name,
+    config: CameraConfig,
+    model_shape,
+    labelmap,
+    detection_queue,
+    result_connection,
+    detected_objects_queue,
+    process_info,
+):
     stop_event = mp.Event()
+
     def receiveSignal(signalNumber, frame):
         stop_event.set()
-    
+
     signal.signal(signal.SIGTERM, receiveSignal)
     signal.signal(signal.SIGINT, receiveSignal)
 
@@ -256,79 +346,130 @@ def track_camera(name, config: CameraConfig, model_shape, detection_queue, resul
     setproctitle(f"frigate.process:{name}")
     listen()
 
-    frame_queue = process_info['frame_queue']
-    detection_enabled = process_info['detection_enabled']
+    frame_queue = process_info["frame_queue"]
+    detection_enabled = process_info["detection_enabled"]
 
     frame_shape = config.frame_shape
     objects_to_track = config.objects.track
     object_filters = config.objects.filters
 
     motion_detector = MotionDetector(frame_shape, config.motion)
-    object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection, model_shape)
+    object_detector = RemoteObjectDetector(
+        name, labelmap, detection_queue, result_connection, model_shape
+    )
 
     object_tracker = ObjectTracker(config.detect)
 
     frame_manager = SharedMemoryFrameManager()
 
-    process_frames(name, frame_queue, frame_shape, model_shape, frame_manager, motion_detector, object_detector,
-        object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, detection_enabled, stop_event)
+    process_frames(
+        name,
+        frame_queue,
+        frame_shape,
+        model_shape,
+        config.detect,
+        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]
-    return [tuple(b) for b in reduced_boxes]
 
-# modified from https://stackoverflow.com/a/40795835
+def box_overlaps(b1, b2):
+    if b1[2] < b2[0] or b1[0] > b2[2] or b1[1] > b2[3] or b1[3] < b2[1]:
+        return False
+    return True
+
+
+def reduce_boxes(boxes, iou_threshold=0.0):
+    clusters = []
+
+    for box in boxes:
+        matched = 0
+        for cluster in clusters:
+            if intersection_over_union(box, cluster) > iou_threshold:
+                matched = 1
+                cluster[0] = min(cluster[0], box[0])
+                cluster[1] = min(cluster[1], box[1])
+                cluster[2] = max(cluster[2], box[2])
+                cluster[3] = max(cluster[3], box[3])
+
+        if not matched:
+            clusters.append(list(box))
+
+    return [tuple(c) for c in clusters]
+
+
 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
+        if box_overlaps(box_a, box):
+            return True
+    return False
 
-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,
+    detect_config: DetectConfig,
+    frame_manager: FrameManager,
+    motion_detector: MotionDetector,
+    object_detector: RemoteObjectDetector,
+    object_tracker: ObjectTracker,
+    detected_objects_queue: mp.Queue,
+    process_info: Dict,
+    objects_to_track: List[str],
+    object_filters,
+    detection_enabled: mp.Value,
+    stop_event,
+    exit_on_empty: bool = False,
+):
+
+    fps = process_info["process_fps"]
+    detection_fps = process_info["detection_fps"]
+    current_frame_time = process_info["detection_frame"]
 
     fps_tracker = EventsPerSecond()
     fps_tracker.start()
 
-    while True:
-        if stop_event.is_set():
-            break
-
+    while not stop_event.is_set():
         if exit_on_empty and frame_queue.empty():
             logger.info(f"Exiting track_objects...")
             break
@@ -340,7 +481,9 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
 
         current_frame_time.value = frame_time
 
-        frame = frame_manager.get(f"{camera_name}{frame_time}", (frame_shape[0]*3//2, frame_shape[1]))
+        frame = frame_manager.get(
+            f"{camera_name}{frame_time}", (frame_shape[0] * 3 // 2, frame_shape[1])
+        )
 
         if frame is None:
             logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
@@ -349,7 +492,9 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
         if not detection_enabled.value:
             fps.value = fps_tracker.eps()
             object_tracker.match_and_update(frame_time, [])
-            detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects, [], []))
+            detected_objects_queue.put(
+                (camera_name, frame_time, object_tracker.tracked_objects, [], [])
+            )
             detection_fps.value = object_detector.fps.eps()
             frame_manager.close(f"{camera_name}{frame_time}")
             continue
@@ -357,28 +502,70 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
         # 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)]
+        # get stationary object ids
+        # check every Nth frame for stationary objects
+        # disappeared objects are not stationary
+        # also check for overlapping motion boxes
+        stationary_object_ids = [
+            obj["id"]
+            for obj in object_tracker.tracked_objects.values()
+            # if there hasn't been motion for 10 frames
+            if obj["motionless_count"] >= 10
+            # and it isn't due for a periodic check
+            and obj["motionless_count"] % detect_config.stationary_interval != 0
+            # and it hasn't disappeared
+            and object_tracker.disappeared[obj["id"]] == 0
+            # and it doesn't overlap with any current motion boxes
+            and not intersects_any(obj["box"], motion_boxes)
+        ]
+
+        # get tracked object boxes that aren't stationary
+        tracked_object_boxes = [
+            obj["box"]
+            for obj in object_tracker.tracked_objects.values()
+            if not obj["id"] in stationary_object_ids
+        ]
 
         # 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]
+        # consolidate regions with heavy overlap
+        regions = [
+            calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
+            for a in reduce_boxes(regions, 0.4)
+        ]
 
         # resize regions and detect
-        detections = []
+        # seed with stationary objects
+        detections = [
+            (
+                obj["label"],
+                obj["score"],
+                obj["box"],
+                obj["area"],
+                obj["region"],
+            )
+            for obj in object_tracker.tracked_objects.values()
+            if obj["id"] in stationary_object_ids
+        ]
         for region in regions:
-            detections.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
-        
+            detections.extend(
+                detect(
+                    object_detector,
+                    frame,
+                    model_shape,
+                    region,
+                    objects_to_track,
+                    object_filters,
+                )
+            )
+
         #########
         # merge objects, check for clipped objects and look again up to 4 times
         #########
@@ -396,8 +583,10 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
             for group in detected_object_groups.values():
 
                 # apply non-maxima suppression to suppress weak, overlapping bounding boxes
-                boxes = [(o[2][0], o[2][1], o[2][2]-o[2][0], o[2][3]-o[2][1])
-                    for o in group]
+                boxes = [
+                    (o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
+                    for o in group
+                ]
                 confidences = [o[1] for o in group]
                 idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
 
@@ -406,17 +595,26 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
                     if clipped(obj, frame_shape):
                         box = obj[2]
                         # calculate a new region that will hopefully get the entire object
-                        region = calculate_region(frame_shape, 
-                            box[0], box[1],
-                            box[2], box[3])
+                        region = calculate_region(
+                            frame_shape, box[0], box[1], box[2], box[3]
+                        )
 
                         regions.append(region)
-                        
-                        selected_objects.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
+
+                        selected_objects.extend(
+                            detect(
+                                object_detector,
+                                frame,
+                                model_shape,
+                                region,
+                                objects_to_track,
+                                object_filters,
+                            )
+                        )
 
                         refining = True
                     else:
-                        selected_objects.append(obj)            
+                        selected_objects.append(obj)
             # set the detections list to only include top, complete objects
             # and new detections
             detections = selected_objects
@@ -424,20 +622,62 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
             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)]
+        ## drop detections that overlap too much
+        consolidated_detections = []
+        # group by name
+        detected_object_groups = defaultdict(lambda: [])
+        for detection in detections:
+            detected_object_groups[detection[0]].append(detection)
+
+        # loop over detections grouped by label
+        for group in detected_object_groups.values():
+            # if the group only has 1 item, skip
+            if len(group) == 1:
+                consolidated_detections.append(group[0])
+                continue
+
+            # sort smallest to largest by area
+            sorted_by_area = sorted(group, key=lambda g: g[3])
+
+            for current_detection_idx in range(0, len(sorted_by_area)):
+                current_detection = sorted_by_area[current_detection_idx][2]
+                overlap = 0
+                for to_check_idx in range(
+                    min(current_detection_idx + 1, len(sorted_by_area)),
+                    len(sorted_by_area),
+                ):
+                    to_check = sorted_by_area[to_check_idx][2]
+                    # if 90% of smaller detection is inside of another detection, consolidate
+                    if (
+                        area(intersection(current_detection, to_check))
+                        / area(current_detection)
+                        > 0.9
+                    ):
+                        overlap = 1
+                        break
+                if overlap == 0:
+                    consolidated_detections.append(
+                        sorted_by_area[current_detection_idx]
+                    )
 
         # 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, consolidated_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

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