Compare commits

..

4 Commits

Author SHA1 Message Date
blakeblackshear
a7d68a4998 increase queue size and add ability to take every nth frame 2019-04-19 08:23:07 -05:00
blakeblackshear
03e46efcdd add back queue full message 2019-04-19 06:37:29 -05:00
blakeblackshear
27e39edd65 add location masking for detected objects 2019-04-14 11:58:33 -05:00
blakeblackshear
4f829e818e implement person filtering with min/max by y position 2019-04-14 11:28:50 -05:00
220 changed files with 1499 additions and 45893 deletions

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

View File

@@ -1,10 +1 @@
README.md
docs/
.gitignore
debug
config/
*.pyc
.git
core
*.mp4
*.db
README.md

3
.github/FUNDING.yml vendored
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@@ -1,3 +0,0 @@
github:
- blakeblackshear
- paularmstrong

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@@ -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 `/api/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 /api/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.

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

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

View File

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

14
.gitignore vendored
View File

@@ -1,14 +1,2 @@
.DS_Store
*.pyc
*.swp
*.pyc
debug
.vscode
config/config.yml
models
*.mp4
*.db
frigate/version.py
web/build
web/node_modules
web/coverage
core

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

107
Dockerfile Normal file
View File

@@ -0,0 +1,107 @@
FROM ubuntu:16.04
# Install system packages
RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y python3 \
python3-dev \
python-pil \
python-lxml \
python-tk \
build-essential \
cmake \
git \
libgtk2.0-dev \
pkg-config \
libavcodec-dev \
libavformat-dev \
libswscale-dev \
libtbb2 \
libtbb-dev \
libjpeg-dev \
libpng-dev \
libtiff-dev \
libjasper-dev \
libdc1394-22-dev \
x11-apps \
wget \
vim \
ffmpeg \
unzip \
libusb-1.0-0-dev \
python3-setuptools \
python3-numpy \
zlib1g-dev \
libgoogle-glog-dev \
swig \
libunwind-dev \
libc++-dev \
libc++abi-dev \
build-essential \
&& rm -rf /var/lib/apt/lists/*
# Install core packages
RUN wget -q -O /tmp/get-pip.py --no-check-certificate https://bootstrap.pypa.io/get-pip.py && python3 /tmp/get-pip.py
RUN pip install -U pip \
numpy \
pillow \
matplotlib \
notebook \
Flask \
imutils \
paho-mqtt \
PyYAML
# Install tensorflow models object detection
RUN GIT_SSL_NO_VERIFY=true git clone -q https://github.com/tensorflow/models /usr/local/lib/python3.5/dist-packages/tensorflow/models
RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/google/protobuf/releases/download/v3.5.1/protobuf-python-3.5.1.tar.gz
# Download & build protobuf-python
RUN cd /usr/local/src/ \
&& tar xf protobuf-python-3.5.1.tar.gz \
&& rm protobuf-python-3.5.1.tar.gz \
&& cd /usr/local/src/protobuf-3.5.1/ \
&& ./configure \
&& make \
&& make install \
&& ldconfig \
&& rm -rf /usr/local/src/protobuf-3.5.1/
# Download & build OpenCV
RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/4.0.1.zip
RUN cd /usr/local/src/ \
&& unzip 4.0.1.zip \
&& rm 4.0.1.zip \
&& cd /usr/local/src/opencv-4.0.1/ \
&& mkdir build \
&& cd /usr/local/src/opencv-4.0.1/build \
&& cmake -D CMAKE_INSTALL_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ .. \
&& make -j4 \
&& make install \
&& rm -rf /usr/local/src/opencv-4.0.1
# Download and install EdgeTPU libraries
RUN wget -q -O edgetpu_api.tar.gz --no-check-certificate http://storage.googleapis.com/cloud-iot-edge-pretrained-models/edgetpu_api.tar.gz
RUN tar xzf edgetpu_api.tar.gz \
&& cd python-tflite-source \
&& cp -p libedgetpu/libedgetpu_x86_64.so /lib/x86_64-linux-gnu/libedgetpu.so \
&& cp edgetpu/swig/compiled_so/_edgetpu_cpp_wrapper_x86_64.so edgetpu/swig/_edgetpu_cpp_wrapper.so \
&& cp edgetpu/swig/compiled_so/edgetpu_cpp_wrapper.py edgetpu/swig/ \
&& python3 setup.py develop --user
# Minimize image size
RUN (apt-get autoremove -y; \
apt-get autoclean -y)
# symlink the model and labels
RUN ln -s /python-tflite-source/edgetpu/test_data/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /frozen_inference_graph.pb
RUN ln -s /python-tflite-source/edgetpu/test_data/coco_labels.txt /label_map.pbtext
# Set TF object detection available
ENV PYTHONPATH "$PYTHONPATH:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research/slim"
RUN cd /usr/local/lib/python3.5/dist-packages/tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=.
WORKDIR /opt/frigate/
ADD frigate frigate/
COPY detect_objects.py .
CMD ["python3", "-u", "detect_objects.py"]

674
LICENSE
View File

@@ -1,21 +1,661 @@
The MIT License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.

View File

@@ -1,62 +0,0 @@
default_target: amd64_frigate
COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
version:
echo "VERSION='0.9.0-$(COMMIT_HASH)'" > frigate/version.py
web:
docker build --tag frigate-web --file docker/Dockerfile.web web/
amd64_wheels:
docker build --tag blakeblackshear/frigate-wheels:1.0.3-amd64 --file docker/Dockerfile.wheels .
amd64_ffmpeg:
docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-amd64 --file docker/Dockerfile.ffmpeg.amd64 .
nginx_frigate:
docker buildx build --push --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --tag blakeblackshear/frigate-nginx:1.0.2 --file docker/Dockerfile.nginx .
amd64_frigate: version web
docker build --no-cache --tag frigate-base --build-arg ARCH=amd64 --build-arg FFMPEG_VERSION=1.1.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
docker build --no-cache --tag frigate --file docker/Dockerfile.amd64 .
amd64_all: amd64_wheels amd64_ffmpeg amd64_frigate
amd64nvidia_wheels:
docker build --tag blakeblackshear/frigate-wheels:1.0.3-amd64nvidia --file docker/Dockerfile.wheels .
amd64nvidia_ffmpeg:
docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-amd64nvidia --file docker/Dockerfile.ffmpeg.amd64nvidia .
amd64nvidia_frigate: version web
docker build --no-cache --tag frigate-base --build-arg ARCH=amd64nvidia --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
docker build --no-cache --tag frigate --file docker/Dockerfile.amd64nvidia .
amd64nvidia_all: amd64nvidia_wheels amd64nvidia_ffmpeg amd64nvidia_frigate
aarch64_wheels:
docker build --tag blakeblackshear/frigate-wheels:1.0.3-aarch64 --file docker/Dockerfile.wheels .
aarch64_ffmpeg:
docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-aarch64 --file docker/Dockerfile.ffmpeg.aarch64 .
aarch64_frigate: version web
docker build --no-cache --tag frigate-base --build-arg ARCH=aarch64 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
docker build --no-cache --tag frigate --file docker/Dockerfile.aarch64 .
armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
armv7_wheels:
docker build --tag blakeblackshear/frigate-wheels:1.0.3-armv7 --file docker/Dockerfile.wheels .
armv7_ffmpeg:
docker build --no-cache --pull --tag blakeblackshear/frigate-ffmpeg:1.2.0-armv7 --file docker/Dockerfile.ffmpeg.armv7 .
armv7_frigate: version web
docker build --no-cache --tag frigate-base --build-arg ARCH=armv7 --build-arg FFMPEG_VERSION=1.0.0 --build-arg WHEELS_VERSION=1.0.3 --build-arg NGINX_VERSION=1.0.2 --file docker/Dockerfile.base .
docker build --no-cache --tag frigate --file docker/Dockerfile.armv7 .
armv7_all: armv7_wheels armv7_ffmpeg armv7_frigate
.PHONY: web

115
README.md
View File

@@ -1,45 +1,98 @@
<p align="center">
<img align="center" alt="logo" src="docs/static/img/frigate.png">
</p>
# Frigate - Realtime Object Detection for RTSP Cameras
**Note:** This version requires the use of a [Google Coral USB Accelerator](https://coral.withgoogle.com/products/accelerator/)
# Frigate - NVR With Realtime Object Detection for IP Cameras
Uses OpenCV and Tensorflow to perform realtime object detection locally for RTSP cameras. Designed for integration with HomeAssistant or others via MQTT.
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.
- Leverages multiprocessing and threads heavily with an emphasis on realtime over processing every frame
- Allows you to define specific regions (squares) in the image to look for objects
- No motion detection (for now)
- Object detection with Tensorflow runs in a separate thread
- Object info is published over MQTT for integration into HomeAssistant as a binary sensor
- An endpoint is available to view an MJPEG stream for debugging
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.
![Diagram](diagram.png)
- Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection
- Object detection with TensorFlow runs in separate processes for maximum FPS
- Communicates over MQTT for easy integration into other systems
- Records video with retention settings based on detected objects
- 24/7 recording
- Re-streaming via RTMP to reduce the number of connections to your camera
## Example video (from older version)
You see multiple bounding boxes because it draws bounding boxes from all frames in the past 1 second where a person was detected. Not all of the bounding boxes were from the current frame.
[![](http://img.youtube.com/vi/nqHbCtyo4dY/0.jpg)](http://www.youtube.com/watch?v=nqHbCtyo4dY "Frigate")
## Documentation
## Getting Started
Build the container with
```
docker build -t frigate .
```
View the documentation at https://blakeblackshear.github.io/frigate
The `mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite` model is included and used by default. You can use your own model and labels by mounting files in the container at `/frozen_inference_graph.pb` and `/label_map.pbtext`. Models must be compatible with the Coral according to [this](https://coral.withgoogle.com/models/).
## Donations
Run the container with
```
docker run --rm \
--privileged \
-v /dev/bus/usb:/dev/bus/usb \
-v <path_to_config_dir>:/config:ro \
-p 5000:5000 \
-e RTSP_PASSWORD='password' \
frigate:latest
```
If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
Example docker-compose:
```
frigate:
container_name: frigate
restart: unless-stopped
privileged: true
image: frigate:latest
volumes:
- /dev/bus/usb:/dev/bus/usb
- <path_to_config>:/config
ports:
- "5000:5000"
environment:
RTSP_PASSWORD: "password"
```
## Screenshots
A `config.yml` file must exist in the `config` directory. See example [here](config/config.yml).
Integration into Home Assistant
Access the mjpeg stream at `http://localhost:5000/<camera_name>` and the best person snapshot at `http://localhost:5000/<camera_name>/best_person.jpg`
<div>
<a href="docs/static/img/media_browser.png"><img src="docs/static/img/media_browser.png" height=400></a>
<a href="docs/static/img/notification.png"><img src="docs/static/img/notification.png" height=400></a>
</div>
## Integration with HomeAssistant
```
camera:
- name: Camera Last Person
platform: generic
still_image_url: http://<ip>:5000/<camera_name>/best_person.jpg
Also comes with a builtin UI:
sensor:
- name: Camera Person
platform: mqtt
state_topic: "frigate/<camera_name>/objects"
value_template: '{{ value_json.person }}'
device_class: moving
availability_topic: "frigate/available"
```
<div>
<a href="docs/static/img/home-ui.png"><img src="docs/static/img/home-ui.png" height=400></a>
<a href="docs/static/img/camera-ui.png"><img src="docs/static/img/camera-ui.png" height=400></a>
</div>
## Tips
- Lower the framerate of the RTSP feed on the camera to reduce the CPU usage for capturing the feed
![Events](docs/static/img/events-ui.png)
## Future improvements
- [x] Remove motion detection for now
- [x] Try running object detection in a thread rather than a process
- [x] Implement min person size again
- [x] Switch to a config file
- [x] Handle multiple cameras in the same container
- [ ] Attempt to figure out coral symlinking
- [ ] Add object list to config with min scores for mqtt
- [ ] Move mjpeg encoding to a separate process
- [ ] Simplify motion detection (check entire image against mask, resize instead of gaussian blur)
- [ ] See if motion detection is even worth running
- [ ] Scan for people across entire image rather than specfic regions
- [ ] Dynamically resize detection area and follow people
- [ ] Add ability to turn detection on and off via MQTT
- [ ] Output movie clips of people for notifications, etc.
- [ ] Integrate with homeassistant push camera
- [ ] Merge bounding boxes that span multiple regions
- [ ] Implement mode to save labeled objects for training
- [ ] Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
- [ ] Look into GPU accelerated decoding of RTSP stream
- [ ] Send video over a socket and use JSMPEG
- [x] Look into neural compute stick

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@@ -1,93 +0,0 @@
import os
from statistics import mean
import multiprocessing as mp
import numpy as np
import datetime
from frigate.edgetpu import LocalObjectDetector, EdgeTPUProcess, RemoteObjectDetector, load_labels
my_frame = np.expand_dims(np.full((300,300,3), 1, np.uint8), axis=0)
labels = load_labels('/labelmap.txt')
######
# Minimal same process runner
######
# object_detector = LocalObjectDetector()
# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
# start = datetime.datetime.now().timestamp()
# frame_times = []
# for x in range(0, 1000):
# start_frame = datetime.datetime.now().timestamp()
# tensor_input[:] = my_frame
# detections = object_detector.detect_raw(tensor_input)
# parsed_detections = []
# for d in detections:
# if d[1] < 0.4:
# break
# parsed_detections.append((
# labels[int(d[0])],
# float(d[1]),
# (d[2], d[3], d[4], d[5])
# ))
# frame_times.append(datetime.datetime.now().timestamp()-start_frame)
# duration = datetime.datetime.now().timestamp()-start
# print(f"Processed for {duration:.2f} seconds.")
# print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
def start(id, num_detections, detection_queue, event):
object_detector = RemoteObjectDetector(str(id), '/labelmap.txt', detection_queue, event)
start = datetime.datetime.now().timestamp()
frame_times = []
for x in range(0, num_detections):
start_frame = datetime.datetime.now().timestamp()
detections = object_detector.detect(my_frame)
frame_times.append(datetime.datetime.now().timestamp()-start_frame)
duration = datetime.datetime.now().timestamp()-start
object_detector.cleanup()
print(f"{id} - Processed for {duration:.2f} seconds.")
print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
######
# Separate process runner
######
# event = mp.Event()
# detection_queue = mp.Queue()
# edgetpu_process = EdgeTPUProcess(detection_queue, {'1': event}, 'usb:0')
# start(1, 1000, edgetpu_process.detection_queue, event)
# print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
####
# Multiple camera processes
####
camera_processes = []
events = {}
for x in range(0, 10):
events[str(x)] = mp.Event()
detection_queue = mp.Queue()
edgetpu_process_1 = EdgeTPUProcess(detection_queue, events, 'usb:0')
edgetpu_process_2 = EdgeTPUProcess(detection_queue, events, 'usb:1')
for x in range(0, 10):
camera_process = mp.Process(target=start, args=(x, 300, detection_queue, events[str(x)]))
camera_process.daemon = True
camera_processes.append(camera_process)
start_time = datetime.datetime.now().timestamp()
for p in camera_processes:
p.start()
for p in camera_processes:
p.join()
duration = datetime.datetime.now().timestamp()-start_time
print(f"Total - Processed for {duration:.2f} seconds.")

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web_port: 5000
mqtt:
host: mqtt.server.com
topic_prefix: frigate
cameras:
back:
rtsp:
user: viewer
host: 10.0.10.10
port: 554
# values that begin with a "$" will be replaced with environment variable
password: $RTSP_PASSWORD
path: /cam/realmonitor?channel=1&subtype=2
regions:
- size: 350
x_offset: 0
y_offset: 300
- size: 400
x_offset: 350
y_offset: 250
- size: 400
x_offset: 750
y_offset: 250
mask: back-mask.bmp
known_sizes:
- y: 300
min: 700
max: 1800
- y: 400
min: 3000
max: 7200
- y: 500
min: 8500
max: 20400
- y: 600
min: 10000
max: 50000
- y: 700
min: 10000
max: 125000

90
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@@ -0,0 +1,90 @@
import cv2
import time
import queue
import yaml
import numpy as np
from flask import Flask, Response, make_response
import paho.mqtt.client as mqtt
from frigate.video import Camera
from frigate.object_detection import PreppedQueueProcessor
with open('/config/config.yml') as f:
CONFIG = yaml.safe_load(f)
MQTT_HOST = CONFIG['mqtt']['host']
MQTT_PORT = CONFIG.get('mqtt', {}).get('port', 1883)
MQTT_TOPIC_PREFIX = CONFIG.get('mqtt', {}).get('topic_prefix', 'frigate')
MQTT_USER = CONFIG.get('mqtt', {}).get('user')
MQTT_PASS = CONFIG.get('mqtt', {}).get('password')
WEB_PORT = CONFIG.get('web_port', 5000)
DEBUG = (CONFIG.get('debug', '0') == '1')
def main():
# connect to mqtt and setup last will
def on_connect(client, userdata, flags, rc):
print("On connect called")
# publish a message to signal that the service is running
client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
client = mqtt.Client()
client.on_connect = on_connect
client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
if not MQTT_USER is None:
client.username_pw_set(MQTT_USER, password=MQTT_PASS)
client.connect(MQTT_HOST, MQTT_PORT, 60)
client.loop_start()
# Queue for prepped frames, max size set to (number of cameras * 5)
max_queue_size = len(CONFIG['cameras'].items())*10
prepped_frame_queue = queue.Queue(max_queue_size)
cameras = {}
for name, config in CONFIG['cameras'].items():
cameras[name] = Camera(name, config, prepped_frame_queue, client, MQTT_TOPIC_PREFIX, DEBUG)
prepped_queue_processor = PreppedQueueProcessor(
cameras,
prepped_frame_queue
)
prepped_queue_processor.start()
for name, camera in cameras.items():
camera.start()
print("Capture process for {}: {}".format(name, camera.get_capture_pid()))
# create a flask app that encodes frames a mjpeg on demand
app = Flask(__name__)
@app.route('/<camera_name>/best_person.jpg')
def best_person(camera_name):
best_person_frame = cameras[camera_name].get_best_person()
if best_person_frame is None:
best_person_frame = np.zeros((720,1280,3), np.uint8)
ret, jpg = cv2.imencode('.jpg', best_person_frame)
response = make_response(jpg.tobytes())
response.headers['Content-Type'] = 'image/jpg'
return response
@app.route('/<camera_name>')
def mjpeg_feed(camera_name):
# return a multipart response
return Response(imagestream(camera_name),
mimetype='multipart/x-mixed-replace; boundary=frame')
def imagestream(camera_name):
while True:
# max out at 5 FPS
time.sleep(0.2)
frame = cameras[camera_name].get_current_frame_with_objects()
# encode the image into a jpg
ret, jpg = cv2.imencode('.jpg', frame)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
camera.join()
if __name__ == '__main__':
main()

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

View File

@@ -1,28 +0,0 @@
FROM frigate-base
LABEL maintainer "blakeb@blakeshome.com"
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 \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)
# s6-overlay
ADD https://github.com/just-containers/s6-overlay/releases/download/v2.2.0.3/s6-overlay-aarch64-installer /tmp/
RUN chmod +x /tmp/s6-overlay-aarch64-installer && /tmp/s6-overlay-aarch64-installer /
ENTRYPOINT ["/init"]
CMD ["python3", "-u", "-m", "frigate"]

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@@ -1,28 +0,0 @@
FROM frigate-base
LABEL maintainer "blakeb@blakeshome.com"
# By default, use the i965 driver
ENV LIBVA_DRIVER_NAME=i965
# Install packages for apt repo
RUN wget -qO - https://repositories.intel.com/graphics/intel-graphics.key | apt-key add - \
&& echo 'deb [arch=amd64] https://repositories.intel.com/graphics/ubuntu focal main' > /etc/apt/sources.list.d/intel-graphics.list \
&& apt-key adv --keyserver keyserver.ubuntu.com --recv-keys F63F0F2B90935439 \
&& echo 'deb http://ppa.launchpad.net/kisak/kisak-mesa/ubuntu focal main' > /etc/apt/sources.list.d/kisak-mesa-focal.list
RUN apt-get -qq update \
&& apt-get -qq install --no-install-recommends -y \
# ffmpeg dependencies
libgomp1 \
# VAAPI drivers for Intel hardware accel
libva-drm2 libva2 libmfx1 i965-va-driver vainfo intel-media-va-driver-non-free mesa-vdpau-drivers mesa-va-drivers mesa-vdpau-drivers libdrm-radeon1 \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)
# s6-overlay
ADD https://github.com/just-containers/s6-overlay/releases/download/v2.2.0.3/s6-overlay-amd64-installer /tmp/
RUN chmod +x /tmp/s6-overlay-amd64-installer && /tmp/s6-overlay-amd64-installer /
ENTRYPOINT ["/init"]
CMD ["python3", "-u", "-m", "frigate"]

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@@ -1,51 +0,0 @@
FROM frigate-base
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 \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)
# nvidia layer (see https://gitlab.com/nvidia/container-images/cuda/blob/master/dist/11.1/ubuntu20.04-x86_64/base/Dockerfile)
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility,video
RUN apt-get update && apt-get install -y --no-install-recommends \
gnupg2 curl ca-certificates && \
curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub | apt-key add - && \
echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \
echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list && \
apt-get purge --autoremove -y curl \
&& rm -rf /var/lib/apt/lists/*
ENV CUDA_VERSION 11.1.1
# For libraries in the cuda-compat-* package: https://docs.nvidia.com/cuda/eula/index.html#attachment-a
RUN apt-get update && apt-get install -y --no-install-recommends \
cuda-cudart-11-1=11.1.74-1 \
cuda-compat-11-1 \
&& ln -s cuda-11.1 /usr/local/cuda && \
rm -rf /var/lib/apt/lists/*
# Required for nvidia-docker v1
RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf
ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
# nvidia-container-runtime
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"]

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@@ -1,30 +0,0 @@
FROM frigate-base
LABEL maintainer "blakeb@blakeshome.com"
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 \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)
# s6-overlay
ADD https://github.com/just-containers/s6-overlay/releases/download/v2.2.0.3/s6-overlay-armhf-installer /tmp/
RUN chmod +x /tmp/s6-overlay-armhf-installer && /tmp/s6-overlay-armhf-installer /
ENTRYPOINT ["/init"]
CMD ["python3", "-u", "-m", "frigate"]

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@@ -1,55 +0,0 @@
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
LABEL maintainer "blakeb@blakeshome.com"
COPY --from=ffmpeg /usr/local /usr/local/
COPY --from=wheels /wheels/. /wheels/
ENV FLASK_ENV=development
# ENV FONTCONFIG_PATH=/etc/fonts
ENV DEBIAN_FRONTEND=noninteractive
# Install packages for apt repo
RUN apt-get -qq update \
&& apt-get upgrade -y \
&& apt-get -qq install --no-install-recommends -y gnupg wget unzip tzdata libxml2 \
&& apt-get -qq install --no-install-recommends -y python3-pip \
&& pip3 install -U /wheels/*.whl \
&& APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn apt-key adv --fetch-keys https://packages.cloud.google.com/apt/doc/apt-key.gpg \
&& echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
&& echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections \
&& apt-get -qq update && apt-get -qq install --no-install-recommends -y libedgetpu1-max python3-tflite-runtime python3-pycoral \
&& rm -rf /var/lib/apt/lists/* /wheels \
&& (apt-get autoremove -y; apt-get autoclean -y)
RUN pip3 install \
peewee_migrate \
pydantic \
zeroconf \
ws4py
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/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/
ADD migrations migrations/
COPY --from=web /opt/frigate/build web/
COPY docker/rootfs/ /
EXPOSE 5000
EXPOSE 1935

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

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@@ -1,474 +0,0 @@
# inspired by:
# https://github.com/collelog/ffmpeg/blob/master/4.3.1-alpine-rpi4-arm64v8.Dockerfile
# https://github.com/mmastrac/ffmpeg-omx-rpi-docker/blob/master/Dockerfile
# https://github.com/jrottenberg/ffmpeg/pull/158/files
# https://github.com/jrottenberg/ffmpeg/pull/239
FROM ubuntu:20.04 AS base
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 autoremove -y && \
apt-get clean -y
FROM base as build
ENV FFMPEG_VERSION=4.3.2 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
SRC=/usr/local
ARG FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.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"
ARG OPUS_SHA256SUM="77db45a87b51578fbc49555ef1b10926179861d854eb2613207dc79d9ec0a9a9 opus-1.2.tar.gz"
ARG THEORA_SHA256SUM="40952956c47811928d1e7922cda3bc1f427eb75680c3c37249c91e949054916b libtheora-1.1.1.tar.gz"
ARG VORBIS_SHA256SUM="6efbcecdd3e5dfbf090341b485da9d176eb250d893e3eb378c428a2db38301ce libvorbis-1.3.5.tar.gz"
ARG XVID_SHA256SUM="4e9fd62728885855bc5007fe1be58df42e5e274497591fec37249e1052ae316f xvidcore-1.3.4.tar.gz"
ARG LIBZMQ_SHA256SUM="02ecc88466ae38cf2c8d79f09cfd2675ba299a439680b64ade733e26a349edeb v4.3.2.tar.gz"
ARG LD_LIBRARY_PATH=/opt/ffmpeg/lib
ARG MAKEFLAGS="-j2"
ARG PKG_CONFIG_PATH="/opt/ffmpeg/share/pkgconfig:/opt/ffmpeg/lib/pkgconfig:/opt/ffmpeg/lib64/pkgconfig"
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" && \
apt-get -yqq update && \
apt-get install -yq --no-install-recommends ${buildDeps}
## opencore-amr https://sourceforge.net/projects/opencore-amr/
RUN \
DIR=/tmp/opencore-amr && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://versaweb.dl.sourceforge.net/project/opencore-amr/opencore-amr/opencore-amr-${OPENCOREAMR_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## x264 http://www.videolan.org/developers/x264.html
RUN \
DIR=/tmp/x264 && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://download.videolan.org/pub/videolan/x264/snapshots/x264-snapshot-${X264_VERSION}.tar.bz2 | \
tar -jx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared --enable-pic --disable-cli && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### x265 http://x265.org/
RUN \
DIR=/tmp/x265 && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://download.videolan.org/pub/videolan/x265/x265_${X265_VERSION}.tar.gz | \
tar -zx && \
cd x265_${X265_VERSION}/build/linux && \
sed -i "/-DEXTRA_LIB/ s/$/ -DCMAKE_INSTALL_PREFIX=\${PREFIX}/" multilib.sh && \
sed -i "/^cmake/ s/$/ -DENABLE_CLI=OFF/" multilib.sh && \
export CXXFLAGS="${CXXFLAGS} -fPIC" && \
./multilib.sh && \
make -C 8bit install && \
rm -rf ${DIR}
### libogg https://www.xiph.org/ogg/
RUN \
DIR=/tmp/ogg && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/ogg/libogg-${OGG_VERSION}.tar.gz && \
echo ${OGG_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libogg-${OGG_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libopus https://www.opus-codec.org/
RUN \
DIR=/tmp/opus && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://archive.mozilla.org/pub/opus/opus-${OPUS_VERSION}.tar.gz && \
echo ${OPUS_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f opus-${OPUS_VERSION}.tar.gz && \
autoreconf -fiv && \
./configure --prefix="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libvorbis https://xiph.org/vorbis/
RUN \
DIR=/tmp/vorbis && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/vorbis/libvorbis-${VORBIS_VERSION}.tar.gz && \
echo ${VORBIS_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libvorbis-${VORBIS_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libtheora http://www.theora.org/
RUN \
DIR=/tmp/theora && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/theora/libtheora-${THEORA_VERSION}.tar.gz && \
echo ${THEORA_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libtheora-${THEORA_VERSION}.tar.gz && \
curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess && \
curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub && \
./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libvpx https://www.webmproject.org/code/
RUN \
DIR=/tmp/vpx && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://codeload.github.com/webmproject/libvpx/tar.gz/v${VPX_VERSION} | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-vp8 --enable-vp9 --enable-vp9-highbitdepth --enable-pic --enable-shared \
--disable-debug --disable-examples --disable-docs --disable-install-bins && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libwebp https://developers.google.com/speed/webp/
RUN \
DIR=/tmp/vebp && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-${WEBP_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libmp3lame http://lame.sourceforge.net/
RUN \
DIR=/tmp/lame && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://versaweb.dl.sourceforge.net/project/lame/lame/$(echo ${LAME_VERSION} | sed -e 's/[^0-9]*\([0-9]*\)[.]\([0-9]*\)[.]\([0-9]*\)\([0-9A-Za-z-]*\)/\1.\2/')/lame-${LAME_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" --enable-shared --enable-nasm --disable-frontend && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### xvid https://www.xvid.com/
RUN \
DIR=/tmp/xvid && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xvid.org/downloads/xvidcore-${XVID_VERSION}.tar.gz && \
echo ${XVID_SHA256SUM} | sha256sum --check && \
tar -zx -f xvidcore-${XVID_VERSION}.tar.gz && \
cd xvidcore/build/generic && \
./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### fdk-aac https://github.com/mstorsjo/fdk-aac
RUN \
DIR=/tmp/fdk-aac && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://github.com/mstorsjo/fdk-aac/archive/v${FDKAAC_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
autoreconf -fiv && \
./configure --prefix="${PREFIX}" --enable-shared --datadir="${DIR}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## openjpeg https://github.com/uclouvain/openjpeg
RUN \
DIR=/tmp/openjpeg && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://github.com/uclouvain/openjpeg/archive/v${OPENJPEG_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
export CFLAGS="${CFLAGS} -DPNG_ARM_NEON_OPT=0" && \
cmake -DBUILD_THIRDPARTY:BOOL=ON -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## freetype https://www.freetype.org/
RUN \
DIR=/tmp/freetype && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://download.savannah.gnu.org/releases/freetype/freetype-${FREETYPE_VERSION}.tar.gz && \
echo ${FREETYPE_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f freetype-${FREETYPE_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## libvstab https://github.com/georgmartius/vid.stab
RUN \
DIR=/tmp/vid.stab && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/georgmartius/vid.stab/archive/v${LIBVIDSTAB_VERSION}.tar.gz && \
echo ${LIBVIDSTAB_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f v${LIBVIDSTAB_VERSION}.tar.gz && \
cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## fridibi https://www.fribidi.org/
RUN \
DIR=/tmp/fribidi && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/fribidi/fribidi/archive/${FRIBIDI_VERSION}.tar.gz && \
echo ${FRIBIDI_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f ${FRIBIDI_VERSION}.tar.gz && \
sed -i 's/^SUBDIRS =.*/SUBDIRS=gen.tab charset lib bin/' Makefile.am && \
./bootstrap --no-config --auto && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j1 && \
make -j $(nproc) install && \
rm -rf ${DIR}
## kvazaar https://github.com/ultravideo/kvazaar
RUN \
DIR=/tmp/kvazaar && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/ultravideo/kvazaar/archive/v${KVAZAAR_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f v${KVAZAAR_VERSION}.tar.gz && \
./autogen.sh && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/aom && \
git clone --branch ${AOM_VERSION} --depth 1 https://aomedia.googlesource.com/aom ${DIR} ; \
cd ${DIR} ; \
rm -rf CMakeCache.txt CMakeFiles ; \
mkdir -p ./aom_build ; \
cd ./aom_build ; \
cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" -DBUILD_SHARED_LIBS=1 ..; \
make ; \
make install ; \
rm -rf ${DIR}
## libxcb (and supporting libraries) for screen capture https://xcb.freedesktop.org/
RUN \
DIR=/tmp/xorg-macros && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive//individual/util/util-macros-${XORG_MACROS_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f util-macros-${XORG_MACROS_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/xproto && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive/individual/proto/xproto-${XPROTO_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f xproto-${XPROTO_VERSION}.tar.gz && \
curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess && \
curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libXau && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive/individual/lib/libXau-${XAU_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libXau-${XAU_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libpthread-stubs && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libxcb-proto && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libxcb && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/libxcb-${LIBXCB_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libxcb-${LIBXCB_VERSION}.tar.gz && \
ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## libzmq https://github.com/zeromq/libzmq/
RUN \
DIR=/tmp/libzmq && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/zeromq/libzmq/archive/v${LIBZMQ_VERSION}.tar.gz && \
echo ${LIBZMQ_SHA256SUM} | sha256sum --check && \
tar -xz --strip-components=1 -f v${LIBZMQ_VERSION}.tar.gz && \
./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make -j $(nproc) && \
make check && \
make -j $(nproc) install && \
rm -rf ${DIR}
## ffmpeg https://ffmpeg.org/
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
curl -sLO https://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.bz2 && \
tar -jx --strip-components=1 -f ffmpeg-${FFMPEG_VERSION}.tar.bz2
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
./configure \
--disable-debug \
--disable-doc \
--disable-ffplay \
--enable-shared \
--enable-avresample \
--enable-libopencore-amrnb \
--enable-libopencore-amrwb \
--enable-gpl \
--enable-libfreetype \
--enable-libvidstab \
--enable-libmp3lame \
--enable-libopus \
--enable-libtheora \
--enable-libvorbis \
--enable-libvpx \
--enable-libwebp \
--enable-libxcb \
--enable-libx265 \
--enable-libxvid \
--enable-libx264 \
--enable-nonfree \
--enable-openssl \
--enable-libfdk_aac \
--enable-postproc \
--enable-small \
--enable-version3 \
--enable-libzmq \
--extra-libs=-ldl \
--prefix="${PREFIX}" \
--enable-libopenjpeg \
--enable-libkvazaar \
--enable-libaom \
--extra-libs=-lpthread \
# --enable-omx \
# --enable-omx-rpi \
# --enable-mmal \
--enable-v4l2_m2m \
--enable-neon \
--extra-cflags="-I${PREFIX}/include" \
--extra-ldflags="-L${PREFIX}/lib" && \
make -j $(nproc) && \
make -j $(nproc) install && \
make tools/zmqsend && cp tools/zmqsend ${PREFIX}/bin/ && \
make distclean && \
hash -r && \
cd tools && \
make qt-faststart && cp qt-faststart ${PREFIX}/bin/
## cleanup
RUN \
ldd ${PREFIX}/bin/ffmpeg | grep opt/ffmpeg | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
for lib in /usr/local/lib/*.so.*; do ln -s "${lib##*/}" "${lib%%.so.*}".so; done && \
cp ${PREFIX}/bin/* /usr/local/bin/ && \
cp -r ${PREFIX}/share/ffmpeg /usr/local/share/ && \
LD_LIBRARY_PATH=/usr/local/lib ffmpeg -buildconf && \
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##*/}"; \
done
FROM base AS release
ENV LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64:/usr/lib:/usr/lib64:/lib:/lib64
CMD ["--help"]
ENTRYPOINT ["ffmpeg"]
COPY --from=build /usr/local /usr/local/
# Run ffmpeg with -c:v h264_v4l2m2m to enable HW accell for decoding on raspberry pi4 64-bit

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@@ -1,468 +0,0 @@
# inspired by:
# https://github.com/collelog/ffmpeg/blob/master/4.3.1-alpine-rpi4-arm64v8.Dockerfile
# https://github.com/jrottenberg/ffmpeg/pull/158/files
# https://github.com/jrottenberg/ffmpeg/pull/239
FROM ubuntu:20.04 AS base
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 autoremove -y && \
apt-get clean -y
FROM base as build
ENV FFMPEG_VERSION=4.3.2 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
SRC=/usr/local
ARG FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.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"
ARG OPUS_SHA256SUM="77db45a87b51578fbc49555ef1b10926179861d854eb2613207dc79d9ec0a9a9 opus-1.2.tar.gz"
ARG THEORA_SHA256SUM="40952956c47811928d1e7922cda3bc1f427eb75680c3c37249c91e949054916b libtheora-1.1.1.tar.gz"
ARG VORBIS_SHA256SUM="6efbcecdd3e5dfbf090341b485da9d176eb250d893e3eb378c428a2db38301ce libvorbis-1.3.5.tar.gz"
ARG XVID_SHA256SUM="4e9fd62728885855bc5007fe1be58df42e5e274497591fec37249e1052ae316f xvidcore-1.3.4.tar.gz"
ARG LIBZMQ_SHA256SUM="02ecc88466ae38cf2c8d79f09cfd2675ba299a439680b64ade733e26a349edeb v4.3.2.tar.gz"
ARG LD_LIBRARY_PATH=/opt/ffmpeg/lib
ARG MAKEFLAGS="-j2"
ARG PKG_CONFIG_PATH="/opt/ffmpeg/share/pkgconfig:/opt/ffmpeg/lib/pkgconfig:/opt/ffmpeg/lib64/pkgconfig"
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" && \
apt-get -yqq update && \
apt-get install -yq --no-install-recommends ${buildDeps}
## opencore-amr https://sourceforge.net/projects/opencore-amr/
RUN \
DIR=/tmp/opencore-amr && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://versaweb.dl.sourceforge.net/project/opencore-amr/opencore-amr/opencore-amr-${OPENCOREAMR_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
## x264 http://www.videolan.org/developers/x264.html
RUN \
DIR=/tmp/x264 && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://download.videolan.org/pub/videolan/x264/snapshots/x264-snapshot-${X264_VERSION}.tar.bz2 | \
tar -jx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared --enable-pic --disable-cli && \
make && \
make install && \
rm -rf ${DIR}
### x265 http://x265.org/
RUN \
DIR=/tmp/x265 && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://download.videolan.org/pub/videolan/x265/x265_${X265_VERSION}.tar.gz | \
tar -zx && \
cd x265_${X265_VERSION}/build/linux && \
sed -i "/-DEXTRA_LIB/ s/$/ -DCMAKE_INSTALL_PREFIX=\${PREFIX}/" multilib.sh && \
sed -i "/^cmake/ s/$/ -DENABLE_CLI=OFF/" multilib.sh && \
./multilib.sh && \
make -C 8bit install && \
rm -rf ${DIR}
### libogg https://www.xiph.org/ogg/
RUN \
DIR=/tmp/ogg && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/ogg/libogg-${OGG_VERSION}.tar.gz && \
echo ${OGG_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libogg-${OGG_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libopus https://www.opus-codec.org/
RUN \
DIR=/tmp/opus && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://archive.mozilla.org/pub/opus/opus-${OPUS_VERSION}.tar.gz && \
echo ${OPUS_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f opus-${OPUS_VERSION}.tar.gz && \
autoreconf -fiv && \
./configure --prefix="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libvorbis https://xiph.org/vorbis/
RUN \
DIR=/tmp/vorbis && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/vorbis/libvorbis-${VORBIS_VERSION}.tar.gz && \
echo ${VORBIS_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libvorbis-${VORBIS_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libtheora http://www.theora.org/
RUN \
DIR=/tmp/theora && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/theora/libtheora-${THEORA_VERSION}.tar.gz && \
echo ${THEORA_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libtheora-${THEORA_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libvpx https://www.webmproject.org/code/
RUN \
DIR=/tmp/vpx && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://codeload.github.com/webmproject/libvpx/tar.gz/v${VPX_VERSION} | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-vp8 --enable-vp9 --enable-vp9-highbitdepth --enable-pic --enable-shared \
--disable-debug --disable-examples --disable-docs --disable-install-bins && \
make && \
make install && \
rm -rf ${DIR}
### libwebp https://developers.google.com/speed/webp/
RUN \
DIR=/tmp/vebp && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-${WEBP_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libmp3lame http://lame.sourceforge.net/
RUN \
DIR=/tmp/lame && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://versaweb.dl.sourceforge.net/project/lame/lame/$(echo ${LAME_VERSION} | sed -e 's/[^0-9]*\([0-9]*\)[.]\([0-9]*\)[.]\([0-9]*\)\([0-9A-Za-z-]*\)/\1.\2/')/lame-${LAME_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" --enable-shared --enable-nasm --disable-frontend && \
make && \
make install && \
rm -rf ${DIR}
### xvid https://www.xvid.com/
RUN \
DIR=/tmp/xvid && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xvid.org/downloads/xvidcore-${XVID_VERSION}.tar.gz && \
echo ${XVID_SHA256SUM} | sha256sum --check && \
tar -zx -f xvidcore-${XVID_VERSION}.tar.gz && \
cd xvidcore/build/generic && \
./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" && \
make && \
make install && \
rm -rf ${DIR}
### fdk-aac https://github.com/mstorsjo/fdk-aac
RUN \
DIR=/tmp/fdk-aac && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://github.com/mstorsjo/fdk-aac/archive/v${FDKAAC_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
autoreconf -fiv && \
./configure --prefix="${PREFIX}" --enable-shared --datadir="${DIR}" && \
make && \
make install && \
rm -rf ${DIR}
## openjpeg https://github.com/uclouvain/openjpeg
RUN \
DIR=/tmp/openjpeg && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://github.com/uclouvain/openjpeg/archive/v${OPENJPEG_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
cmake -DBUILD_THIRDPARTY:BOOL=ON -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make && \
make install && \
rm -rf ${DIR}
## freetype https://www.freetype.org/
RUN \
DIR=/tmp/freetype && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://download.savannah.gnu.org/releases/freetype/freetype-${FREETYPE_VERSION}.tar.gz && \
echo ${FREETYPE_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f freetype-${FREETYPE_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
## libvstab https://github.com/georgmartius/vid.stab
RUN \
DIR=/tmp/vid.stab && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/georgmartius/vid.stab/archive/v${LIBVIDSTAB_VERSION}.tar.gz && \
echo ${LIBVIDSTAB_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f v${LIBVIDSTAB_VERSION}.tar.gz && \
cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make && \
make install && \
rm -rf ${DIR}
## fridibi https://www.fribidi.org/
RUN \
DIR=/tmp/fribidi && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/fribidi/fribidi/archive/${FRIBIDI_VERSION}.tar.gz && \
echo ${FRIBIDI_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f ${FRIBIDI_VERSION}.tar.gz && \
sed -i 's/^SUBDIRS =.*/SUBDIRS=gen.tab charset lib bin/' Makefile.am && \
./bootstrap --no-config --auto && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j1 && \
make install && \
rm -rf ${DIR}
## kvazaar https://github.com/ultravideo/kvazaar
RUN \
DIR=/tmp/kvazaar && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/ultravideo/kvazaar/archive/v${KVAZAAR_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f v${KVAZAAR_VERSION}.tar.gz && \
./autogen.sh && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/aom && \
git clone --branch ${AOM_VERSION} --depth 1 https://aomedia.googlesource.com/aom ${DIR} ; \
cd ${DIR} ; \
rm -rf CMakeCache.txt CMakeFiles ; \
mkdir -p ./aom_build ; \
cd ./aom_build ; \
cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" -DBUILD_SHARED_LIBS=1 ..; \
make ; \
make install ; \
rm -rf ${DIR}
## libxcb (and supporting libraries) for screen capture https://xcb.freedesktop.org/
RUN \
DIR=/tmp/xorg-macros && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive//individual/util/util-macros-${XORG_MACROS_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f util-macros-${XORG_MACROS_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/xproto && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive/individual/proto/xproto-${XPROTO_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f xproto-${XPROTO_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libXau && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive/individual/lib/libXau-${XAU_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libXau-${XAU_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libpthread-stubs && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libxcb-proto && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libxcb && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/libxcb-${LIBXCB_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libxcb-${LIBXCB_VERSION}.tar.gz && \
ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
## libzmq https://github.com/zeromq/libzmq/
RUN \
DIR=/tmp/libzmq && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/zeromq/libzmq/archive/v${LIBZMQ_VERSION}.tar.gz && \
echo ${LIBZMQ_SHA256SUM} | sha256sum --check && \
tar -xz --strip-components=1 -f v${LIBZMQ_VERSION}.tar.gz && \
./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make && \
make check && \
make install && \
rm -rf ${DIR}
## ffmpeg https://ffmpeg.org/
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
curl -sLO https://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.bz2 && \
tar -jx --strip-components=1 -f ffmpeg-${FFMPEG_VERSION}.tar.bz2
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
./configure \
--disable-debug \
--disable-doc \
--disable-ffplay \
--enable-shared \
--enable-avresample \
--enable-libopencore-amrnb \
--enable-libopencore-amrwb \
--enable-gpl \
--enable-libfreetype \
--enable-libvidstab \
--enable-libmfx \
--enable-libmp3lame \
--enable-libopus \
--enable-libtheora \
--enable-libvorbis \
--enable-libvpx \
--enable-libwebp \
--enable-libxcb \
--enable-libx265 \
--enable-libxvid \
--enable-libx264 \
--enable-nonfree \
--enable-openssl \
--enable-libfdk_aac \
--enable-postproc \
--enable-small \
--enable-version3 \
--enable-libzmq \
--extra-libs=-ldl \
--prefix="${PREFIX}" \
--enable-libopenjpeg \
--enable-libkvazaar \
--enable-libaom \
--extra-libs=-lpthread \
--enable-vaapi \
--extra-cflags="-I${PREFIX}/include" \
--extra-ldflags="-L${PREFIX}/lib" && \
make && \
make install && \
make tools/zmqsend && cp tools/zmqsend ${PREFIX}/bin/ && \
make distclean && \
hash -r && \
cd tools && \
make qt-faststart && cp qt-faststart ${PREFIX}/bin/
## cleanup
RUN \
ldd ${PREFIX}/bin/ffmpeg | grep opt/ffmpeg | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
for lib in /usr/local/lib/*.so.*; do ln -s "${lib##*/}" "${lib%%.so.*}".so; done && \
cp ${PREFIX}/bin/* /usr/local/bin/ && \
cp -r ${PREFIX}/share/ffmpeg /usr/local/share/ && \
LD_LIBRARY_PATH=/usr/local/lib ffmpeg -buildconf && \
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##*/}"; \
done
FROM base AS release
ENV LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64:/usr/lib:/usr/lib64:/lib:/lib64
CMD ["--help"]
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/*

View File

@@ -1,549 +0,0 @@
# inspired by https://github.com/jrottenberg/ffmpeg/blob/master/docker-images/4.3/ubuntu1804/Dockerfile
# ffmpeg - http://ffmpeg.org/download.html
#
# From https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu
#
# https://hub.docker.com/r/jrottenberg/ffmpeg/
#
#
FROM nvidia/cuda:11.1-devel-ubuntu20.04 AS devel-base
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility,video
ENV DEBIAN_FRONTEND=noninteractive
WORKDIR /tmp/workdir
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 nvidia/cuda:11.1-runtime-ubuntu20.04 AS runtime-base
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility,video
ENV DEBIAN_FRONTEND=noninteractive
WORKDIR /tmp/workdir
RUN apt-get -yqq update && \
apt-get install -yq --no-install-recommends ca-certificates expat libgomp1 libxcb-shape0-dev && \
apt-get autoremove -y && \
apt-get clean -y
FROM devel-base as build
ENV NVIDIA_HEADERS_VERSION=9.1.23.1
ENV FFMPEG_VERSION=4.3.2 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.2 \
LIBSRT_VERSION=1.4.1 \
LIBARIBB24_VERSION=1.0.3 \
LIBPNG_VERSION=1.6.9 \
SRC=/usr/local
ARG FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.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"
ARG OPUS_SHA256SUM="77db45a87b51578fbc49555ef1b10926179861d854eb2613207dc79d9ec0a9a9 opus-1.2.tar.gz"
ARG THEORA_SHA256SUM="40952956c47811928d1e7922cda3bc1f427eb75680c3c37249c91e949054916b libtheora-1.1.1.tar.gz"
ARG VORBIS_SHA256SUM="6efbcecdd3e5dfbf090341b485da9d176eb250d893e3eb378c428a2db38301ce libvorbis-1.3.5.tar.gz"
ARG XVID_SHA256SUM="4e9fd62728885855bc5007fe1be58df42e5e274497591fec37249e1052ae316f xvidcore-1.3.4.tar.gz"
ARG LIBZMQ_SHA256SUM="02ecc88466ae38cf2c8d79f09cfd2675ba299a439680b64ade733e26a349edeb v4.3.2.tar.gz"
ARG LIBARIBB24_SHA256SUM="f61560738926e57f9173510389634d8c06cabedfa857db4b28fb7704707ff128 v1.0.3.tar.gz"
ARG LD_LIBRARY_PATH=/opt/ffmpeg/lib
ARG MAKEFLAGS="-j2"
ARG PKG_CONFIG_PATH="/opt/ffmpeg/share/pkgconfig:/opt/ffmpeg/lib/pkgconfig:/opt/ffmpeg/lib64/pkgconfig"
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" && \
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}" && \
rm -rf ${DIR}
## opencore-amr https://sourceforge.net/projects/opencore-amr/
RUN \
DIR=/tmp/opencore-amr && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://versaweb.dl.sourceforge.net/project/opencore-amr/opencore-amr/opencore-amr-${OPENCOREAMR_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
## x264 http://www.videolan.org/developers/x264.html
RUN \
DIR=/tmp/x264 && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://download.videolan.org/pub/videolan/x264/snapshots/x264-snapshot-${X264_VERSION}.tar.bz2 | \
tar -jx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared --enable-pic --disable-cli && \
make && \
make install && \
rm -rf ${DIR}
### x265 http://x265.org/
RUN \
DIR=/tmp/x265 && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://download.videolan.org/pub/videolan/x265/x265_${X265_VERSION}.tar.gz | \
tar -zx && \
cd x265_${X265_VERSION}/build/linux && \
sed -i "/-DEXTRA_LIB/ s/$/ -DCMAKE_INSTALL_PREFIX=\${PREFIX}/" multilib.sh && \
sed -i "/^cmake/ s/$/ -DENABLE_CLI=OFF/" multilib.sh && \
./multilib.sh && \
make -C 8bit install && \
rm -rf ${DIR}
### libogg https://www.xiph.org/ogg/
RUN \
DIR=/tmp/ogg && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/ogg/libogg-${OGG_VERSION}.tar.gz && \
echo ${OGG_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libogg-${OGG_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libopus https://www.opus-codec.org/
RUN \
DIR=/tmp/opus && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://archive.mozilla.org/pub/opus/opus-${OPUS_VERSION}.tar.gz && \
echo ${OPUS_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f opus-${OPUS_VERSION}.tar.gz && \
autoreconf -fiv && \
./configure --prefix="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libvorbis https://xiph.org/vorbis/
RUN \
DIR=/tmp/vorbis && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/vorbis/libvorbis-${VORBIS_VERSION}.tar.gz && \
echo ${VORBIS_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libvorbis-${VORBIS_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libtheora http://www.theora.org/
RUN \
DIR=/tmp/theora && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/theora/libtheora-${THEORA_VERSION}.tar.gz && \
echo ${THEORA_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libtheora-${THEORA_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libvpx https://www.webmproject.org/code/
RUN \
DIR=/tmp/vpx && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://codeload.github.com/webmproject/libvpx/tar.gz/v${VPX_VERSION} | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-vp8 --enable-vp9 --enable-vp9-highbitdepth --enable-pic --enable-shared \
--disable-debug --disable-examples --disable-docs --disable-install-bins && \
make && \
make install && \
rm -rf ${DIR}
### libwebp https://developers.google.com/speed/webp/
RUN \
DIR=/tmp/vebp && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-${WEBP_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
### libmp3lame http://lame.sourceforge.net/
RUN \
DIR=/tmp/lame && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://versaweb.dl.sourceforge.net/project/lame/lame/$(echo ${LAME_VERSION} | sed -e 's/[^0-9]*\([0-9]*\)[.]\([0-9]*\)[.]\([0-9]*\)\([0-9A-Za-z-]*\)/\1.\2/')/lame-${LAME_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" --enable-shared --enable-nasm --disable-frontend && \
make && \
make install && \
rm -rf ${DIR}
### xvid https://www.xvid.com/
RUN \
DIR=/tmp/xvid && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xvid.org/downloads/xvidcore-${XVID_VERSION}.tar.gz && \
echo ${XVID_SHA256SUM} | sha256sum --check && \
tar -zx -f xvidcore-${XVID_VERSION}.tar.gz && \
cd xvidcore/build/generic && \
./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" && \
make && \
make install && \
rm -rf ${DIR}
### fdk-aac https://github.com/mstorsjo/fdk-aac
RUN \
DIR=/tmp/fdk-aac && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://github.com/mstorsjo/fdk-aac/archive/v${FDKAAC_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
autoreconf -fiv && \
./configure --prefix="${PREFIX}" --enable-shared --datadir="${DIR}" && \
make && \
make install && \
rm -rf ${DIR}
## openjpeg https://github.com/uclouvain/openjpeg
RUN \
DIR=/tmp/openjpeg && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://github.com/uclouvain/openjpeg/archive/v${OPENJPEG_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
cmake -DBUILD_THIRDPARTY:BOOL=ON -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make && \
make install && \
rm -rf ${DIR}
## freetype https://www.freetype.org/
RUN \
DIR=/tmp/freetype && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://download.savannah.gnu.org/releases/freetype/freetype-${FREETYPE_VERSION}.tar.gz && \
echo ${FREETYPE_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f freetype-${FREETYPE_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
## libvstab https://github.com/georgmartius/vid.stab
RUN \
DIR=/tmp/vid.stab && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/georgmartius/vid.stab/archive/v${LIBVIDSTAB_VERSION}.tar.gz && \
echo ${LIBVIDSTAB_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f v${LIBVIDSTAB_VERSION}.tar.gz && \
cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make && \
make install && \
rm -rf ${DIR}
## fridibi https://www.fribidi.org/
RUN \
DIR=/tmp/fribidi && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/fribidi/fribidi/archive/${FRIBIDI_VERSION}.tar.gz && \
echo ${FRIBIDI_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f ${FRIBIDI_VERSION}.tar.gz && \
sed -i 's/^SUBDIRS =.*/SUBDIRS=gen.tab charset lib bin/' Makefile.am && \
./bootstrap --no-config --auto && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j1 && \
make install && \
rm -rf ${DIR}
## kvazaar https://github.com/ultravideo/kvazaar
RUN \
DIR=/tmp/kvazaar && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/ultravideo/kvazaar/archive/v${KVAZAAR_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f v${KVAZAAR_VERSION}.tar.gz && \
./autogen.sh && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/aom && \
git clone --branch ${AOM_VERSION} --depth 1 https://aomedia.googlesource.com/aom ${DIR} ; \
cd ${DIR} ; \
rm -rf CMakeCache.txt CMakeFiles ; \
mkdir -p ./aom_build ; \
cd ./aom_build ; \
cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" -DBUILD_SHARED_LIBS=1 ..; \
make ; \
make install ; \
rm -rf ${DIR}
## libxcb (and supporting libraries) for screen capture https://xcb.freedesktop.org/
RUN \
DIR=/tmp/xorg-macros && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive//individual/util/util-macros-${XORG_MACROS_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f util-macros-${XORG_MACROS_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/xproto && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive/individual/proto/xproto-${XPROTO_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f xproto-${XPROTO_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libXau && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive/individual/lib/libXau-${XAU_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libXau-${XAU_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libpthread-stubs && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libxcb-proto && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libxcb && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/libxcb-${LIBXCB_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libxcb-${LIBXCB_VERSION}.tar.gz && \
ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make && \
make install && \
rm -rf ${DIR}
## libzmq https://github.com/zeromq/libzmq/
RUN \
DIR=/tmp/libzmq && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/zeromq/libzmq/archive/v${LIBZMQ_VERSION}.tar.gz && \
echo ${LIBZMQ_SHA256SUM} | sha256sum --check && \
tar -xz --strip-components=1 -f v${LIBZMQ_VERSION}.tar.gz && \
./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make && \
make check && \
make install && \
rm -rf ${DIR}
## libsrt https://github.com/Haivision/srt
RUN \
DIR=/tmp/srt && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/Haivision/srt/archive/v${LIBSRT_VERSION}.tar.gz && \
tar -xz --strip-components=1 -f v${LIBSRT_VERSION}.tar.gz && \
cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make && \
make install && \
rm -rf ${DIR}
## libpng
RUN \
DIR=/tmp/png && \
mkdir -p ${DIR} && \
cd ${DIR} && \
git clone https://git.code.sf.net/p/libpng/code ${DIR} -b v${LIBPNG_VERSION} --depth 1 && \
./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make check && \
make install && \
rm -rf ${DIR}
## libaribb24
RUN \
DIR=/tmp/b24 && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/nkoriyama/aribb24/archive/v${LIBARIBB24_VERSION}.tar.gz && \
echo ${LIBARIBB24_SHA256SUM} | sha256sum --check && \
tar -xz --strip-components=1 -f v${LIBARIBB24_VERSION}.tar.gz && \
autoreconf -fiv && \
./configure CFLAGS="-I${PREFIX}/include -fPIC" --prefix="${PREFIX}" && \
make && \
make install && \
rm -rf ${DIR}
## ffmpeg https://ffmpeg.org/
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
curl -sLO https://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.bz2 && \
tar -jx --strip-components=1 -f ffmpeg-${FFMPEG_VERSION}.tar.bz2
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
./configure \
--disable-debug \
--disable-doc \
--disable-ffplay \
--enable-shared \
--enable-avresample \
--enable-libopencore-amrnb \
--enable-libopencore-amrwb \
--enable-gpl \
--enable-libfreetype \
--enable-libvidstab \
--enable-libmp3lame \
--enable-libopus \
--enable-libtheora \
--enable-libvorbis \
--enable-libvpx \
--enable-libwebp \
--enable-libxcb \
--enable-libx265 \
--enable-libxvid \
--enable-libx264 \
--enable-nonfree \
--enable-openssl \
--enable-libfdk_aac \
--enable-postproc \
--enable-small \
--enable-version3 \
--enable-libzmq \
--extra-libs=-ldl \
--prefix="${PREFIX}" \
--enable-libopenjpeg \
--enable-libkvazaar \
--enable-libaom \
--extra-libs=-lpthread \
--enable-libsrt \
--enable-libaribb24 \
--enable-nvenc \
--enable-cuda \
--enable-cuvid \
--enable-libnpp \
--extra-cflags="-I${PREFIX}/include -I${PREFIX}/include/ffnvcodec -I/usr/local/cuda/include/" \
--extra-ldflags="-L${PREFIX}/lib -L/usr/local/cuda/lib64 -L/usr/local/cuda/lib32/" && \
make && \
make install && \
make tools/zmqsend && cp tools/zmqsend ${PREFIX}/bin/ && \
make distclean && \
hash -r && \
cd tools && \
make qt-faststart && cp qt-faststart ${PREFIX}/bin/
## cleanup
RUN \
LD_LIBRARY_PATH="${PREFIX}/lib:${PREFIX}/lib64:${LD_LIBRARY_PATH}" ldd ${PREFIX}/bin/ffmpeg | grep opt/ffmpeg | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
for lib in /usr/local/lib/*.so.*; do ln -s "${lib##*/}" "${lib%%.so.*}".so; done && \
cp ${PREFIX}/bin/* /usr/local/bin/ && \
cp -r ${PREFIX}/share/* /usr/local/share/ && \
LD_LIBRARY_PATH=/usr/local/lib ffmpeg -buildconf && \
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##*/}"; \
done
FROM runtime-base AS release
ENV LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64
CMD ["--help"]
ENTRYPOINT ["ffmpeg"]
# 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/
COPY --from=build /usr/local/include /usr/local/include/
# Let's make sure the app built correctly
# Convenient to verify on https://hub.docker.com/r/jrottenberg/ffmpeg/builds/ console output

View File

@@ -1,490 +0,0 @@
# inspired by:
# https://github.com/collelog/ffmpeg/blob/master/4.3.1-alpine-rpi4-arm64v8.Dockerfile
# https://github.com/mmastrac/ffmpeg-omx-rpi-docker/blob/master/Dockerfile
# https://github.com/jrottenberg/ffmpeg/pull/158/files
# https://github.com/jrottenberg/ffmpeg/pull/239
FROM ubuntu:20.04 AS base
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 autoremove -y && \
apt-get clean -y
FROM base as build
ENV FFMPEG_VERSION=4.3.2 \
AOM_VERSION=v1.0.0 \
FDKAAC_VERSION=0.1.5 \
FREETYPE_VERSION=2.5.5 \
FRIBIDI_VERSION=0.19.7 \
KVAZAAR_VERSION=1.2.0 \
LAME_VERSION=3.100 \
LIBPTHREAD_STUBS_VERSION=0.4 \
LIBVIDSTAB_VERSION=1.1.0 \
LIBXCB_VERSION=1.13.1 \
XCBPROTO_VERSION=1.13 \
OGG_VERSION=1.3.2 \
OPENCOREAMR_VERSION=0.1.5 \
OPUS_VERSION=1.2 \
OPENJPEG_VERSION=2.1.2 \
THEORA_VERSION=1.1.1 \
VORBIS_VERSION=1.3.5 \
VPX_VERSION=1.8.0 \
WEBP_VERSION=1.0.2 \
X264_VERSION=20170226-2245-stable \
X265_VERSION=3.1.1 \
XAU_VERSION=1.0.9 \
XORG_MACROS_VERSION=1.19.2 \
XPROTO_VERSION=7.0.31 \
XVID_VERSION=1.3.4 \
LIBZMQ_VERSION=4.3.3 \
SRC=/usr/local
ARG FREETYPE_SHA256SUM="5d03dd76c2171a7601e9ce10551d52d4471cf92cd205948e60289251daddffa8 freetype-2.5.5.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"
ARG OPUS_SHA256SUM="77db45a87b51578fbc49555ef1b10926179861d854eb2613207dc79d9ec0a9a9 opus-1.2.tar.gz"
ARG THEORA_SHA256SUM="40952956c47811928d1e7922cda3bc1f427eb75680c3c37249c91e949054916b libtheora-1.1.1.tar.gz"
ARG VORBIS_SHA256SUM="6efbcecdd3e5dfbf090341b485da9d176eb250d893e3eb378c428a2db38301ce libvorbis-1.3.5.tar.gz"
ARG XVID_SHA256SUM="4e9fd62728885855bc5007fe1be58df42e5e274497591fec37249e1052ae316f xvidcore-1.3.4.tar.gz"
ARG LD_LIBRARY_PATH=/opt/ffmpeg/lib
ARG MAKEFLAGS="-j2"
ARG PKG_CONFIG_PATH="/opt/ffmpeg/share/pkgconfig:/opt/ffmpeg/lib/pkgconfig:/opt/ffmpeg/lib64/pkgconfig:/opt/vc/lib/pkgconfig"
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" && \
apt-get -yqq update && \
apt-get install -yq --no-install-recommends ${buildDeps}
## opencore-amr https://sourceforge.net/projects/opencore-amr/
RUN \
DIR=/tmp/opencore-amr && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://versaweb.dl.sourceforge.net/project/opencore-amr/opencore-amr/opencore-amr-${OPENCOREAMR_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## x264 http://www.videolan.org/developers/x264.html
RUN \
DIR=/tmp/x264 && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://download.videolan.org/pub/videolan/x264/snapshots/x264-snapshot-${X264_VERSION}.tar.bz2 | \
tar -jx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared --enable-pic --disable-cli && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
# ### x265 http://x265.org/
# RUN \
# DIR=/tmp/x265 && \
# mkdir -p ${DIR} && \
# cd ${DIR} && \
# curl -sL https://download.videolan.org/pub/videolan/x265/x265_${X265_VERSION}.tar.gz | \
# tar -zx && \
# cd x265_${X265_VERSION}/build/linux && \
# sed -i "/-DEXTRA_LIB/ s/$/ -DCMAKE_INSTALL_PREFIX=\${PREFIX}/" multilib.sh && \
# sed -i "/^cmake/ s/$/ -DENABLE_CLI=OFF/" multilib.sh && \
# # export CXXFLAGS="${CXXFLAGS} -fPIC" && \
# ./multilib.sh && \
# make -C 8bit install && \
# rm -rf ${DIR}
### libogg https://www.xiph.org/ogg/
RUN \
DIR=/tmp/ogg && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/ogg/libogg-${OGG_VERSION}.tar.gz && \
echo ${OGG_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libogg-${OGG_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libopus https://www.opus-codec.org/
RUN \
DIR=/tmp/opus && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://archive.mozilla.org/pub/opus/opus-${OPUS_VERSION}.tar.gz && \
echo ${OPUS_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f opus-${OPUS_VERSION}.tar.gz && \
autoreconf -fiv && \
./configure --prefix="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libvorbis https://xiph.org/vorbis/
RUN \
DIR=/tmp/vorbis && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/vorbis/libvorbis-${VORBIS_VERSION}.tar.gz && \
echo ${VORBIS_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libvorbis-${VORBIS_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libtheora http://www.theora.org/
RUN \
DIR=/tmp/theora && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xiph.org/releases/theora/libtheora-${THEORA_VERSION}.tar.gz && \
echo ${THEORA_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f libtheora-${THEORA_VERSION}.tar.gz && \
curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess && \
curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub && \
./configure --prefix="${PREFIX}" --with-ogg="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libvpx https://www.webmproject.org/code/
RUN \
DIR=/tmp/vpx && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://codeload.github.com/webmproject/libvpx/tar.gz/v${VPX_VERSION} | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-vp8 --enable-vp9 --enable-vp9-highbitdepth --enable-pic --enable-shared \
--disable-debug --disable-examples --disable-docs --disable-install-bins && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libwebp https://developers.google.com/speed/webp/
RUN \
DIR=/tmp/vebp && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-${WEBP_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### libmp3lame http://lame.sourceforge.net/
RUN \
DIR=/tmp/lame && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://versaweb.dl.sourceforge.net/project/lame/lame/$(echo ${LAME_VERSION} | sed -e 's/[^0-9]*\([0-9]*\)[.]\([0-9]*\)[.]\([0-9]*\)\([0-9A-Za-z-]*\)/\1.\2/')/lame-${LAME_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" --enable-shared --enable-nasm --disable-frontend && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### xvid https://www.xvid.com/
RUN \
DIR=/tmp/xvid && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO http://downloads.xvid.org/downloads/xvidcore-${XVID_VERSION}.tar.gz && \
echo ${XVID_SHA256SUM} | sha256sum --check && \
tar -zx -f xvidcore-${XVID_VERSION}.tar.gz && \
cd xvidcore/build/generic && \
./configure --prefix="${PREFIX}" --bindir="${PREFIX}/bin" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
### fdk-aac https://github.com/mstorsjo/fdk-aac
RUN \
DIR=/tmp/fdk-aac && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://github.com/mstorsjo/fdk-aac/archive/v${FDKAAC_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
autoreconf -fiv && \
./configure --prefix="${PREFIX}" --enable-shared --datadir="${DIR}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## openjpeg https://github.com/uclouvain/openjpeg
RUN \
DIR=/tmp/openjpeg && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sL https://github.com/uclouvain/openjpeg/archive/v${OPENJPEG_VERSION}.tar.gz | \
tar -zx --strip-components=1 && \
export CFLAGS="${CFLAGS} -DPNG_ARM_NEON_OPT=0" && \
cmake -DBUILD_THIRDPARTY:BOOL=ON -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## freetype https://www.freetype.org/
RUN \
DIR=/tmp/freetype && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://download.savannah.gnu.org/releases/freetype/freetype-${FREETYPE_VERSION}.tar.gz && \
echo ${FREETYPE_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f freetype-${FREETYPE_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## libvstab https://github.com/georgmartius/vid.stab
RUN \
DIR=/tmp/vid.stab && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/georgmartius/vid.stab/archive/v${LIBVIDSTAB_VERSION}.tar.gz && \
echo ${LIBVIDSTAB_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f v${LIBVIDSTAB_VERSION}.tar.gz && \
cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" . && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## fridibi https://www.fribidi.org/
RUN \
DIR=/tmp/fribidi && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/fribidi/fribidi/archive/${FRIBIDI_VERSION}.tar.gz && \
echo ${FRIBIDI_SHA256SUM} | sha256sum --check && \
tar -zx --strip-components=1 -f ${FRIBIDI_VERSION}.tar.gz && \
sed -i 's/^SUBDIRS =.*/SUBDIRS=gen.tab charset lib bin/' Makefile.am && \
./bootstrap --no-config --auto && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j1 && \
make -j $(nproc) install && \
rm -rf ${DIR}
## kvazaar https://github.com/ultravideo/kvazaar
RUN \
DIR=/tmp/kvazaar && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/ultravideo/kvazaar/archive/v${KVAZAAR_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f v${KVAZAAR_VERSION}.tar.gz && \
./autogen.sh && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
# RUN \
# DIR=/tmp/aom && \
# git clone --branch ${AOM_VERSION} --depth 1 https://aomedia.googlesource.com/aom ${DIR} ; \
# cd ${DIR} ; \
# rm -rf CMakeCache.txt CMakeFiles ; \
# mkdir -p ./aom_build ; \
# cd ./aom_build ; \
# cmake -DCMAKE_INSTALL_PREFIX="${PREFIX}" -DBUILD_SHARED_LIBS=1 ..; \
# make ; \
# make install ; \
# rm -rf ${DIR}
## libxcb (and supporting libraries) for screen capture https://xcb.freedesktop.org/
RUN \
DIR=/tmp/xorg-macros && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive//individual/util/util-macros-${XORG_MACROS_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f util-macros-${XORG_MACROS_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/xproto && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive/individual/proto/xproto-${XPROTO_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f xproto-${XPROTO_VERSION}.tar.gz && \
curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess && \
curl -sL 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libXau && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://www.x.org/archive/individual/lib/libXau-${XAU_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libXau-${XAU_VERSION}.tar.gz && \
./configure --srcdir=${DIR} --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libpthread-stubs && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libpthread-stubs-${LIBPTHREAD_STUBS_VERSION}.tar.gz && \
./configure --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libxcb-proto && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f xcb-proto-${XCBPROTO_VERSION}.tar.gz && \
ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
RUN \
DIR=/tmp/libxcb && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://xcb.freedesktop.org/dist/libxcb-${LIBXCB_VERSION}.tar.gz && \
tar -zx --strip-components=1 -f libxcb-${LIBXCB_VERSION}.tar.gz && \
ACLOCAL_PATH="${PREFIX}/share/aclocal" ./autogen.sh && \
./configure --prefix="${PREFIX}" --disable-static --enable-shared && \
make -j $(nproc) && \
make -j $(nproc) install && \
rm -rf ${DIR}
## libzmq https://github.com/zeromq/libzmq/
RUN \
DIR=/tmp/libzmq && \
mkdir -p ${DIR} && \
cd ${DIR} && \
curl -sLO https://github.com/zeromq/libzmq/archive/v${LIBZMQ_VERSION}.tar.gz && \
tar -xz --strip-components=1 -f v${LIBZMQ_VERSION}.tar.gz && \
./autogen.sh && \
./configure --prefix="${PREFIX}" && \
make -j $(nproc) && \
# make check && \
make -j $(nproc) install && \
rm -rf ${DIR}
## userland https://github.com/raspberrypi/userland
RUN \
DIR=/tmp/userland && \
mkdir -p ${DIR} && \
cd ${DIR} && \
git clone --depth 1 https://github.com/raspberrypi/userland.git . && \
./buildme && \
rm -rf ${DIR}
## ffmpeg https://ffmpeg.org/
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
curl -sLO https://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.bz2 && \
tar -jx --strip-components=1 -f ffmpeg-${FFMPEG_VERSION}.tar.bz2
RUN \
DIR=/tmp/ffmpeg && mkdir -p ${DIR} && cd ${DIR} && \
./configure \
--disable-debug \
--disable-doc \
--disable-ffplay \
--enable-shared \
--enable-avresample \
--enable-libopencore-amrnb \
--enable-libopencore-amrwb \
--enable-gpl \
--enable-libfreetype \
--enable-libvidstab \
--enable-libmp3lame \
--enable-libopus \
--enable-libtheora \
--enable-libvorbis \
--enable-libvpx \
--enable-libwebp \
--enable-libxcb \
--enable-libx265 \
--enable-libxvid \
--enable-libx264 \
--enable-nonfree \
--enable-openssl \
--enable-libfdk_aac \
--enable-postproc \
--enable-small \
--enable-version3 \
--enable-libzmq \
--extra-libs=-ldl \
--prefix="${PREFIX}" \
--enable-libopenjpeg \
--enable-libkvazaar \
--enable-libaom \
--extra-libs=-lpthread \
--enable-omx \
--enable-omx-rpi \
--enable-mmal \
--enable-v4l2_m2m \
--enable-neon \
--extra-cflags="-I${PREFIX}/include" \
--extra-ldflags="-L${PREFIX}/lib" && \
make -j $(nproc) && \
make -j $(nproc) install && \
make tools/zmqsend && cp tools/zmqsend ${PREFIX}/bin/ && \
make distclean && \
hash -r && \
cd tools && \
make qt-faststart && cp qt-faststart ${PREFIX}/bin/
## cleanup
RUN \
ldd ${PREFIX}/bin/ffmpeg | grep opt/ffmpeg | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
# copy userland lib too
ldd ${PREFIX}/bin/ffmpeg | grep opt/vc | cut -d ' ' -f 3 | xargs -i cp {} /usr/local/lib/ && \
for lib in /usr/local/lib/*.so.*; do ln -s "${lib##*/}" "${lib%%.so.*}".so; done && \
cp ${PREFIX}/bin/* /usr/local/bin/ && \
cp -r ${PREFIX}/share/ffmpeg /usr/local/share/ && \
LD_LIBRARY_PATH=/usr/local/lib ffmpeg -buildconf && \
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##*/}"; \
done
FROM base AS release
ENV LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64:/usr/lib:/usr/lib64:/lib:/lib64
RUN \
apt-get -yqq update && \
apt-get install -yq --no-install-recommends libx265-dev libaom-dev && \
apt-get autoremove -y && \
apt-get clean -y
CMD ["--help"]
ENTRYPOINT ["ffmpeg"]
COPY --from=build /usr/local /usr/local/

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

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@@ -1,9 +0,0 @@
ARG NODE_VERSION=14.0
FROM node:${NODE_VERSION}
WORKDIR /opt/frigate
COPY . .
RUN npm install && npm run build

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@@ -1,41 +0,0 @@
FROM ubuntu:20.04 as build
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get -qq update \
&& apt-get -qq install -y \
python3 \
python3-dev \
wget \
# opencv dependencies
build-essential cmake git pkg-config libgtk-3-dev \
libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
gfortran openexr libatlas-base-dev libssl-dev\
libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
# scipy dependencies
gcc gfortran libopenblas-dev liblapack-dev cython
RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
&& python3 get-pip.py "pip==20.2.4"
RUN pip3 install scikit-build
RUN pip3 wheel --wheel-dir=/wheels \
opencv-python-headless \
numpy \
imutils \
scipy \
psutil \
Flask \
paho-mqtt \
PyYAML \
matplotlib \
click \
setproctitle \
peewee
FROM scratch
COPY --from=build /wheels /wheels

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

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

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@@ -1,220 +0,0 @@
daemon off;
worker_processes 1;
error_log /usr/local/nginx/logs/error.log warn;
pid /var/run/nginx.pid;
events {
worker_connections 1024;
}
http {
include mime.types;
default_type application/octet-stream;
log_format main '$remote_addr - $remote_user [$time_local] "$request" '
'$status $body_bytes_sent "$http_referer" '
'"$http_user_agent" "$http_x_forwarded_for"';
access_log /usr/local/nginx/logs/access.log main;
sendfile on;
keepalive_timeout 65;
gzip on;
gzip_comp_level 6;
gzip_types text/plain text/css application/json application/x-javascript application/javascript text/javascript image/svg+xml image/x-icon image/bmp image/png image/gif image/jpeg image/jpg;
gzip_proxied no-cache no-store private expired auth;
gzip_vary on;
upstream frigate_api {
server localhost:5001;
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;
add_header 'Access-Control-Allow-Credentials' 'true';
add_header 'Access-Control-Expose-Headers' 'Content-Length';
if ($request_method = 'OPTIONS') {
add_header 'Access-Control-Allow-Origin' "$http_origin";
add_header 'Access-Control-Max-Age' 1728000;
add_header 'Content-Type' 'text/plain charset=UTF-8';
add_header 'Content-Length' 0;
return 204;
}
types {
application/dash+xml mpd;
application/vnd.apple.mpegurl m3u8;
video/mp2t ts;
image/jpeg jpg;
}
root /tmp;
}
location /clips/ {
add_header 'Access-Control-Allow-Origin' "$http_origin" always;
add_header 'Access-Control-Allow-Credentials' 'true';
add_header 'Access-Control-Expose-Headers' 'Content-Length';
if ($request_method = 'OPTIONS') {
add_header 'Access-Control-Allow-Origin' "$http_origin";
add_header 'Access-Control-Max-Age' 1728000;
add_header 'Content-Type' 'text/plain charset=UTF-8';
add_header 'Content-Length' 0;
return 204;
}
types {
video/mp4 mp4;
image/jpeg jpg;
}
autoindex on;
root /media/frigate;
}
location /cache/ {
internal; # This tells nginx it's not accessible from the outside
alias /tmp/cache/;
}
location /recordings/ {
add_header 'Access-Control-Allow-Origin' "$http_origin" always;
add_header 'Access-Control-Allow-Credentials' 'true';
add_header 'Access-Control-Expose-Headers' 'Content-Length';
if ($request_method = 'OPTIONS') {
add_header 'Access-Control-Allow-Origin' "$http_origin";
add_header 'Access-Control-Max-Age' 1728000;
add_header 'Content-Type' 'text/plain charset=UTF-8';
add_header 'Content-Length' 0;
return 204;
}
types {
video/mp4 mp4;
}
autoindex on;
autoindex_format json;
root /media/frigate;
}
location /ws {
proxy_pass http://mqtt_ws/;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "Upgrade";
proxy_set_header Host $host;
}
location /live/ {
proxy_pass http://jsmpeg/;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "Upgrade";
proxy_set_header Host $host;
}
location /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;
proxy_set_header Host $host;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
}
location / {
add_header Cache-Control "no-cache";
location ~* \.(?:js|css|svg|ico|png)$ {
access_log off;
expires 1y;
add_header Cache-Control "public";
}
sub_filter 'href="/' 'href="$http_x_ingress_path/';
sub_filter 'url(/' 'url($http_x_ingress_path/';
sub_filter '"/dist/' '"$http_x_ingress_path/dist/';
sub_filter '"/js/' '"$http_x_ingress_path/js/';
sub_filter '<body>' '<body><script>window.baseUrl="$http_x_ingress_path";</script>';
sub_filter_types text/css application/javascript;
sub_filter_once off;
root /opt/frigate/web;
try_files $uri $uri/ /index.html;
}
}
}
rtmp {
server {
listen 1935;
chunk_size 4096;
allow publish 127.0.0.1;
deny publish all;
allow play all;
application live {
live on;
record off;
meta copy;
}
}
}

20
docs/.gitignore vendored
View File

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

View File

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

View File

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

View File

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

View File

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

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

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

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@@ -1,235 +0,0 @@
---
id: index
title: Configuration
---
For HassOS installations, the default location for the config file is `/config/frigate.yml`.
For all other installations, the default location for the config file is '/config/config.yml'. This can be overridden with the `CONFIG_FILE` environment variable. Camera specific ffmpeg parameters are documented [here](cameras.md).
It is recommended to start with a minimal configuration and add to it:
```yaml
mqtt:
host: mqtt.server.com
cameras:
back:
ffmpeg:
inputs:
- path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
roles:
- detect
- rtmp
detect:
width: 1280
height: 720
fps: 5
```
## Required
## `mqtt`
```yaml
mqtt:
# Required: host name
host: mqtt.server.com
# Optional: port (default: shown below)
port: 1883
# Optional: topic prefix (default: shown below)
# WARNING: must be unique if you are running multiple instances
topic_prefix: frigate
# Optional: client id (default: shown below)
# WARNING: must be unique if you are running multiple instances
client_id: frigate
# Optional: user
user: mqtt_user
# Optional: password
# NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}.
# eg. password: '{FRIGATE_MQTT_PASSWORD}'
password: password
# Optional: tls_ca_certs for enabling TLS using self-signed certs (default: None)
tls_ca_certs: /path/to/ca.crt
# Optional: tls_client_cert and tls_client key in order to use self-signed client
# certificates (default: None)
# NOTE: certificate must not be password-protected
# do not set user and password when using a client certificate
tls_client_cert: /path/to/client.crt
tls_client_key: /path/to/client.key
# Optional: tls_insecure (true/false) for enabling TLS verification of
# the server hostname in the server certificate (default: None)
tls_insecure: false
# Optional: interval in seconds for publishing stats (default: shown below)
stats_interval: 60
```
## `cameras`
Each of your cameras must be configured. The following is the minimum required to register a camera in Frigate. Check the [camera configuration page](cameras.md) for a complete list of options.
```yaml
cameras:
# Name of your camera
front_door:
ffmpeg:
inputs:
- path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
roles:
- detect
- rtmp
detect:
width: 1280
height: 720
fps: 5
```
## Optional
### `database`
```yaml
database:
# The path to store the SQLite DB (default: shown below)
path: /media/frigate/frigate.db
```
### `model`
```yaml
# Optional: model modifications
model:
# Required: Object detection model input width (default: shown below)
width: 320
# Required: Object detection model input height (default: shown below)
height: 320
# Optional: Label name modifications
labelmap:
2: vehicle # previously "car"
```
### `detectors`
Check the [detectors configuration page](detectors.md) for a complete list of options.
### `logger`
```yaml
# Optional: logger verbosity settings
logger:
# Optional: Default log verbosity (default: shown below)
default: info
# Optional: Component specific logger overrides
logs:
frigate.event: debug
```
### `record`
Can be overridden at the camera level. 24/7 recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4`. These recordings are written directly from your camera stream without re-encoding and are available in Home Assistant's media browser. Each camera supports a configurable retention policy in the config.
Exported clips are also created off of these recordings. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
These recordings will not be playable in the web UI or in Home Assistant's media browser unless your camera sends video as h264.
:::caution
Previous versions of frigate included `-vsync drop` in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.
:::
```yaml
record:
# Optional: Enable recording (default: shown below)
enabled: False
# Optional: Number of days to retain (default: shown below)
retain_days: 0
# Optional: Event recording settings
events:
# Optional: Enable event recording retention settings (default: shown below)
enabled: False
# Optional: Maximum length of time to retain video during long events. (default: shown below)
# NOTE: If an object is being tracked for longer than this amount of time, the cache
# will begin to expire and the resulting clip will be the last x seconds of the event unless retain_days under record is > 0.
max_seconds: 300
# Optional: Number of seconds before the event to include (default: shown below)
pre_capture: 5
# Optional: Number of seconds after the event to include (default: shown below)
post_capture: 5
# Optional: Objects to save recordings for. (default: all tracked objects)
objects:
- person
# Optional: Restrict recordings to objects that entered any of the listed zones (default: no required zones)
required_zones: []
# Optional: Retention settings for events
retain:
# Required: Default retention days (default: shown below)
default: 10
# Optional: Per object retention days
objects:
person: 15
```
### `ffmpeg`
Can be overridden at the camera level.
```yaml
ffmpeg:
# Optional: global ffmpeg args (default: shown below)
global_args: -hide_banner -loglevel warning
# Optional: global hwaccel args (default: shown below)
# NOTE: See hardware acceleration docs for your specific device
hwaccel_args: []
# Optional: global input args (default: shown below)
input_args: -avoid_negative_ts make_zero -fflags +genpts+discardcorrupt -rtsp_transport tcp -stimeout 5000000 -use_wallclock_as_timestamps 1
# Optional: global output args
output_args:
# Optional: output args for detect streams (default: shown below)
detect: -f rawvideo -pix_fmt yuv420p
# Optional: output args for record streams (default: shown below)
record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
# Optional: output args for rtmp streams (default: shown below)
rtmp: -c copy -f flv
```
### `objects`
Can be overridden at the camera level. For a list of available objects, see the [objects documentation](./objects.mdx).
```yaml
objects:
# Optional: list of objects to track from labelmap.txt (default: shown below)
track:
- person
# Optional: filters to reduce false positives for specific object types
filters:
person:
# Optional: minimum width*height of the bounding box for the detected object (default: 0)
min_area: 5000
# Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
max_area: 100000
# Optional: minimum score for the object to initiate tracking (default: shown below)
min_score: 0.5
# Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
threshold: 0.7
```
### `birdseye`
A dynamic combined camera view of all tracked cameras. This is optimized for minimal bandwidth and server resource utilization. Encoding is only performed when actively viewing the video feed, and only active (defined by the mode) cameras are included in the view.
```yaml
birdseye:
# Optional: Enable birdseye view (default: shown below)
enabled: True
# Optional: Width of the output resolution (default: shown below)
width: 1280
# Optional: Height of the output resolution (default: shown below)
height: 720
# Optional: Encoding quality of the mpeg1 feed (default: shown below)
# 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
quality: 8
# Optional: Mode of the view. Available options are: objects, motion, and continuous
# objects - cameras are included if they have had a tracked object within the last 30 seconds
# motion - cameras are included if motion was detected in the last 30 seconds
# continuous - all cameras are included always
mode: objects
```

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@@ -1,110 +0,0 @@
---
id: nvdec
title: nVidia hardware decoder
---
Certain nvidia cards include a hardware decoder, which can greatly improve the
performance of video decoding. In order to use NVDEC, a special build of
ffmpeg with NVDEC support is required. The special docker architecture 'amd64nvidia'
includes this support for amd64 platforms. An aarch64 for the Jetson, which
also includes NVDEC may be added in the future.
## Docker setup
### Requirements
[nVidia closed source driver](https://www.nvidia.com/en-us/drivers/unix/) required to access NVDEC.
[nvidia-docker](https://github.com/NVIDIA/nvidia-docker) required to pass NVDEC to docker.
### Setting up docker-compose
In order to pass NVDEC, the docker engine must be set to `nvidia` and the environment variables
`NVIDIA_VISIBLE_DEVICES=all` and `NVIDIA_DRIVER_CAPABILITIES=compute,utility,video` must be set.
In a docker compose file, these lines need to be set:
```
services:
frigate:
...
image: blakeblackshear/frigate:stable-amd64nvidia
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=all
- NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
```
### Setting up the configuration file
In your frigate config.yml, you'll need to set ffmpeg to use the hardware decoder.
The decoder you choose will depend on the input video.
A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
```
V..... h263_cuvid Nvidia CUVID H263 decoder (codec h263)
V..... h264_cuvid Nvidia CUVID H264 decoder (codec h264)
V..... hevc_cuvid Nvidia CUVID HEVC decoder (codec hevc)
V..... mjpeg_cuvid Nvidia CUVID MJPEG decoder (codec mjpeg)
V..... mpeg1_cuvid Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
V..... mpeg2_cuvid Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
V..... mpeg4_cuvid Nvidia CUVID MPEG4 decoder (codec mpeg4)
V..... vc1_cuvid Nvidia CUVID VC1 decoder (codec vc1)
V..... vp8_cuvid Nvidia CUVID VP8 decoder (codec vp8)
V..... vp9_cuvid Nvidia CUVID VP9 decoder (codec vp9)
```
For example, for H265 video (hevc), you'll select `hevc_cuvid`. Add
`-c:v hevc_cuvid` to your ffmpeg input arguments:
```
ffmpeg:
input_args:
...
- -c:v
- hevc_cuvid
```
If everything is working correctly, you should see a significant improvement in performance.
Verify that hardware decoding is working by running `nvidia-smi`, which should show the ffmpeg
processes:
```
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 455.38 Driver Version: 455.38 CUDA Version: 11.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce GTX 166... Off | 00000000:03:00.0 Off | N/A |
| 38% 41C P2 36W / 125W | 2082MiB / 5942MiB | 5% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 12737 C ffmpeg 249MiB |
| 0 N/A N/A 12751 C ffmpeg 249MiB |
| 0 N/A N/A 12772 C ffmpeg 249MiB |
| 0 N/A N/A 12775 C ffmpeg 249MiB |
| 0 N/A N/A 12800 C ffmpeg 249MiB |
| 0 N/A N/A 12811 C ffmpeg 417MiB |
| 0 N/A N/A 12827 C ffmpeg 417MiB |
+-----------------------------------------------------------------------------+
```
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
```
This setting, for example, allows Frigate to consume my 10-15fps camera streams on
my relatively low powered Haswell machine with relatively low cpu usage.

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

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

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

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@@ -1,29 +0,0 @@
---
id: hardware
title: Recommended hardware
---
## Cameras
Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
## Computer
| Name | Inference Speed | Notes |
| ----------------------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| Atomic Pi | 16ms | Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding. |
| Intel NUC NUC7i3BNK | 8-10ms | Great performance. Can handle many cameras at 5fps depending on typical amounts of motion. |
| BMAX B2 Plus | 10-12ms | Good balance of performance and cost. Also capable of running many other services at the same time as frigate. |
| Minisforum GK41 | 9-10ms | Great alternative to a NUC with dual Gigabit NICs. Easily handles several 1080p cameras. |
| Raspberry Pi 3B (32bit) | 60ms | Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0. |
| Raspberry Pi 4 (32bit) | 15-20ms | Can handle a small number of cameras. The 2GB version runs fine. |
| Raspberry Pi 4 (64bit) | 10-15ms | Can handle a small number of cameras. The 2GB version runs fine. |
## Unraid
Many people have powerful enough NAS devices or home servers to also run docker. There is a Unraid Community App.
To install make sure you have the [community app plugin here](https://forums.unraid.net/topic/38582-plug-in-community-applications/). Then search for "Frigate" in the apps section within Unraid - you can see the online store [here](https://unraid.net/community/apps?q=frigate#r)
| Name | Inference Speed | Notes |
| ------------------------------------ | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [M2 Coral Edge TPU](http://coral.ai) | 6.2ms | Install the Coral plugin from Unraid Community App Center [info here](https://forums.unraid.net/topic/98064-support-blakeblackshear-frigate/?do=findComment&comment=949789) |

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@@ -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

View File

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

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@@ -1,124 +0,0 @@
---
id: installation
title: Installation
---
Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). See instructions below for installing the HassOS addon.
For Home Assistant users, there is also a [custom component (aka integration)](https://github.com/blakeblackshear/frigate-hass-integration). This custom component adds tighter integration with Home Assistant by automatically setting up camera entities, sensors, media browser for recordings, and a public API to simplify notifications.
Note that HassOS Addons and custom components are different things. If you are already running Frigate with Docker directly, you do not need the Addon since the Addon would run another instance of Frigate.
## HassOS Addon
HassOS users can install via the addon repository. Frigate requires an MQTT server.
1. Navigate to Supervisor > Add-on Store > Repositories
2. Add https://github.com/blakeblackshear/frigate-hass-addons
3. Setup your network configuration in the `Configuration` tab if deisred
4. Create the file `frigate.yml` in your `config` directory with your detailed Frigate configuration
5. Start the addon container
6. If you are using hardware acceleration for ffmpeg, you will need to disable "Protection mode"
## Docker
Make sure you choose the right image for your architecture:
| Arch | Image Name |
| ----------- | ------------------------------------------ |
| amd64 | blakeblackshear/frigate:stable-amd64 |
| amd64nvidia | blakeblackshear/frigate:stable-amd64nvidia |
| armv7 | blakeblackshear/frigate:stable-armv7 |
| aarch64 | blakeblackshear/frigate:stable-aarch64 |
It is recommended to run with docker-compose:
```yaml
version: "3.9"
services:
frigate:
container_name: frigate
privileged: true # this may not be necessary for all setups
restart: unless-stopped
image: blakeblackshear/frigate:<specify_version_tag>
devices:
- /dev/bus/usb:/dev/bus/usb
- /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
volumes:
- /etc/localtime:/etc/localtime:ro
- <path_to_config_file>:/config/config.yml:ro
- <path_to_directory_for_media>:/media/frigate
- type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
target: /tmp/cache
tmpfs:
size: 1000000000
ports:
- "5000:5000"
- "1935:1935" # RTMP feeds
environment:
FRIGATE_RTSP_PASSWORD: "password"
```
If you can't use docker compose, you can run the container with something similar to this:
```bash
docker run -d \
--name frigate \
--restart=unless-stopped \
--mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
--device /dev/bus/usb:/dev/bus/usb \
--device /dev/dri/renderD128 \
-v <path_to_directory_for_media>:/media/frigate \
-v <path_to_config_file>:/config/config.yml:ro \
-v /etc/localtime:/etc/localtime:ro \
-e FRIGATE_RTSP_PASSWORD='password' \
-p 5000:5000 \
-p 1935:1935 \
blakeblackshear/frigate:<specify_version_tag>
```
### Calculating shm-size
The default shm-size of 64m is fine for setups with 3 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it could be because you have too many high resolution cameras and you need to specify a higher shm size.
You can calculate the necessary shm-size for each camera with the following formula:
```
(width * height * 1.5 * 7 + 270480)/1048576 = <shm size in mb>
```
The shm size cannot be set per container for Home Assistant Addons. You must set `default-shm-size` in `/etc/docker/daemon.json` to increase the default shm size. This will increase the shm size for all of your docker containers. This may or may not cause issues with your setup. https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file
## Kubernetes
Use the [helm chart](https://github.com/blakeblackshear/blakeshome-charts/tree/master/charts/frigate).
## Virtualization
For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices for ffmpeg decoding. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer typically introduces a sizable amount of overhead for communication with Coral devices.
### Proxmox
Some people have had success running Frigate in LXC directly with the following config:
```
arch: amd64
cores: 2
features: nesting=1
hostname: FrigateLXC
memory: 4096
net0: name=eth0,bridge=vmbr0,firewall=1,hwaddr=2E:76:AE:5A:58:48,ip=dhcp,ip6=auto,type=veth
ostype: debian
rootfs: local-lvm:vm-115-disk-0,size=12G
swap: 512
lxc.cgroup.devices.allow: c 189:385 rwm
lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file
lxc.mount.entry: /dev/bus/usb/004/002 dev/bus/usb/004/002 none bind,optional,create=file
lxc.apparmor.profile: unconfined
lxc.cgroup.devices.allow: a
lxc.cap.drop:
```
### ESX
For details on running Frigate under ESX, see details [here](https://github.com/blakeblackshear/frigate/issues/305).

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

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

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@@ -1,215 +0,0 @@
---
id: api
title: HTTP API
---
A web server is available on port 5000 with the following endpoints.
### `GET /api/<camera_name>`
An mjpeg stream for debugging. Keep in mind the mjpeg endpoint is for debugging only and will put additional load on the system when in use.
Accepts the following query string parameters:
| param | Type | Description |
| ----------- | ---- | ------------------------------------------------------------------ |
| `fps` | int | Frame rate |
| `h` | int | Height in pixels |
| `bbox` | int | Show bounding boxes for detected objects (0 or 1) |
| `timestamp` | int | Print the timestamp in the upper left (0 or 1) |
| `zones` | int | Draw the zones on the image (0 or 1) |
| `mask` | int | Overlay the mask on the image (0 or 1) |
| `motion` | int | Draw blue boxes for areas with detected motion (0 or 1) |
| `regions` | int | Draw green boxes for areas where object detection was run (0 or 1) |
You can access a higher resolution mjpeg stream by appending `h=height-in-pixels` to the endpoint. For example `http://localhost:5000/api/back?h=1080`. You can also increase the FPS by appending `fps=frame-rate` to the URL such as `http://localhost:5000/api/back?fps=10` or both with `?fps=10&h=1000`.
### `GET /api/<camera_name>/<object_name>/best.jpg[?h=300&crop=1&quality=70]`
The best snapshot for any object type. It is a full resolution image by default.
Example parameters:
- `h=300`: resizes the image to 300 pixes tall
- `crop=1`: crops the image to the region of the detection rather than returning the entire image
- `quality=70`: sets the jpeg encoding quality (0-100)
### `GET /api/<camera_name>/latest.jpg[?h=300]`
The most recent frame that frigate has finished processing. It is a full resolution image by default.
Accepts the following query string parameters:
| param | Type | Description |
| ----------- | ---- | ------------------------------------------------------------------ |
| `h` | int | Height in pixels |
| `bbox` | int | Show bounding boxes for detected objects (0 or 1) |
| `timestamp` | int | Print the timestamp in the upper left (0 or 1) |
| `zones` | int | Draw the zones on the image (0 or 1) |
| `mask` | int | Overlay the mask on the image (0 or 1) |
| `motion` | int | Draw blue boxes for areas with detected motion (0 or 1) |
| `regions` | int | Draw green boxes for areas where object detection was run (0 or 1) |
| `quality` | int | Jpeg encoding quality (0-100). Defaults to 70. |
Example parameters:
- `h=300`: resizes the image to 300 pixes tall
### `GET /api/stats`
Contains some granular debug info that can be used for sensors in Home Assistant.
Sample response:
```json
{
/* Per Camera Stats */
"back": {
/***************
* Frames per second being consumed from your camera. If this is higher
* than it is supposed to be, you should set -r FPS in your input_args.
* camera_fps = process_fps + skipped_fps
***************/
"camera_fps": 5.0,
/***************
* Number of times detection is run per second. This can be higher than
* your camera FPS because frigate often looks at the same frame multiple times
* or in multiple locations
***************/
"detection_fps": 1.5,
/***************
* PID for the ffmpeg process that consumes this camera
***************/
"capture_pid": 27,
/***************
* PID for the process that runs detection for this camera
***************/
"pid": 34,
/***************
* Frames per second being processed by frigate.
***************/
"process_fps": 5.1,
/***************
* Frames per second skip for processing by frigate.
***************/
"skipped_fps": 0.0
},
/***************
* Sum of detection_fps across all cameras and detectors.
* This should be the sum of all detection_fps values from cameras.
***************/
"detection_fps": 5.0,
/* Detectors Stats */
"detectors": {
"coral": {
/***************
* Timestamp when object detection started. If this value stays non-zero and constant
* for a long time, that means the detection process is stuck.
***************/
"detection_start": 0.0,
/***************
* Time spent running object detection in milliseconds.
***************/
"inference_speed": 10.48,
/***************
* PID for the shared process that runs object detection on the Coral.
***************/
"pid": 25321
}
},
"service": {
/* Uptime in seconds */
"uptime": 10,
"version": "0.8.0-8883709",
/* Storage data in MB for important locations */
"storage": {
"/media/frigate/clips": {
"total": 1000,
"used": 700,
"free": 300,
"mnt_type": "ext4"
},
"/media/frigate/recordings": {
"total": 1000,
"used": 700,
"free": 300,
"mnt_type": "ext4"
},
"/tmp/cache": {
"total": 256,
"used": 100,
"free": 156,
"mnt_type": "tmpfs"
},
"/dev/shm": {
"total": 256,
"used": 100,
"free": 156,
"mnt_type": "tmpfs"
}
}
}
}
```
### `GET /api/config`
A json representation of your configuration
### `GET /api/version`
Version info
### `GET /api/events`
Events from the database. Accepts the following query string parameters:
| param | Type | Description |
| -------------------- | ---- | --------------------------------------------- |
| `before` | int | Epoch time |
| `after` | int | Epoch time |
| `camera` | str | Camera name |
| `label` | str | Label name |
| `zone` | str | Zone name |
| `limit` | int | Limit the number of events returned |
| `has_snapshot` | int | Filter to events that have snapshots (0 or 1) |
| `has_clip` | int | Filter to events that have clips (0 or 1) |
| `include_thumbnails` | int | Include thumbnails in the response (0 or 1) |
### `GET /api/events/summary`
Returns summary data for events in the database. Used by the Home Assistant integration.
### `GET /api/events/<id>`
Returns data for a single event.
### `DELETE /api/events/<id>`
Permanently deletes the event along with any clips/snapshots.
### `GET /api/events/<id>/thumbnail.jpg`
Returns a thumbnail for the event id optimized for notifications. Works while the event is in progress and after completion. Passing `?format=android` will convert the thumbnail to 2:1 aspect ratio.
### `GET /api/events/<id>/snapshot.jpg`
Returns the snapshot image for the event id. Works while the event is in progress and after completion.
Accepts the following query string parameters, but they are only applied when an event is in progress. After the event is completed, the saved snapshot is returned from disk without modification:
| param | Type | Description |
| ----------- | ---- | ------------------------------------------------- |
| `h` | int | Height in pixels |
| `bbox` | int | Show bounding boxes for detected objects (0 or 1) |
| `timestamp` | int | Print the timestamp in the upper left (0 or 1) |
| `crop` | int | Crop the snapshot to the (0 or 1) |
| `quality` | int | Jpeg encoding quality (0-100). Defaults to 70. |
### `/clips/<camera>-<id>.jpg`
JPG snapshot for the given camera and event id.
### `/vod/<year>-<month>/<day>/<hour>/<camera>/master.m3u8`
HTTP Live Streaming Video on Demand URL for the specified hour and camera. Can be viewed in an application like VLC.

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

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

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

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

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

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docs/package-lock.json generated

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

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

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

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

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@@ -1,17 +0,0 @@
import faulthandler
faulthandler.enable()
import sys
import threading
threading.current_thread().name = "frigate"
from frigate.app import FrigateApp
cli = sys.modules["flask.cli"]
cli.show_server_banner = lambda *x: None
if __name__ == "__main__":
frigate_app = FrigateApp()
frigate_app.start()

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@@ -1,382 +0,0 @@
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 yaml
from peewee_migrate import Router
from playhouse.sqlite_ext import SqliteExtDatabase
from playhouse.sqliteq import SqliteQueueDatabase
from frigate.config import DetectorTypeEnum, FrigateConfig
from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
from frigate.edgetpu import EdgeTPUProcess
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, Recordings
from frigate.mqtt import create_mqtt_client, MqttSocketRelay
from frigate.object_processing import TrackedObjectProcessor
from frigate.output import output_frames
from frigate.record import RecordingCleanup, RecordingMaintainer
from frigate.stats import StatsEmitter, stats_init
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:
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] = {}
self.detection_out_events: Dict[str, mp.Event] = {}
self.detection_shms: List[mp.shared_memory.SharedMemory] = []
self.log_queue = mp.Queue()
self.camera_metrics = {}
def set_environment_vars(self):
for key, value in self.config.environment_vars.items():
os.environ[key] = value
def ensure_dirs(self):
for d in [RECORD_DIR, CLIPS_DIR, CACHE_DIR]:
if not os.path.exists(d) and not os.path.islink(d):
logger.info(f"Creating directory: {d}")
os.makedirs(d)
else:
logger.debug(f"Skipping directory: {d}")
def init_logger(self):
self.log_process = mp.Process(
target=log_process, args=(self.log_queue,), name="log_process"
)
self.log_process.daemon = True
self.log_process.start()
root_configurer(self.log_queue)
def init_config(self):
config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
user_config = FrigateConfig.parse_file(config_file)
self.config = user_config.runtime_config
for camera_name in self.config.cameras.keys():
# create camera_metrics
self.camera_metrics[camera_name] = {
"camera_fps": mp.Value("d", 0.0),
"skipped_fps": mp.Value("d", 0.0),
"process_fps": mp.Value("d", 0.0),
"detection_enabled": mp.Value(
"i", self.config.cameras[camera_name].detect.enabled
),
"detection_fps": mp.Value("d", 0.0),
"detection_frame": mp.Value("d", 0.0),
"read_start": mp.Value("d", 0.0),
"ffmpeg_pid": mp.Value("i", 0),
"frame_queue": mp.Queue(maxsize=2),
}
def check_config(self):
for name, camera in self.config.cameras.items():
assigned_roles = list(
set([r for i in camera.ffmpeg.inputs for r in i.roles])
)
if not camera.record.enabled and "record" in assigned_roles:
logger.warning(
f"Camera {name} has record assigned to an input, but record is not enabled."
)
elif camera.record.enabled and not "record" in assigned_roles:
logger.warning(
f"Camera {name} has record enabled, but record is not assigned to an input."
)
if not camera.rtmp.enabled and "rtmp" in assigned_roles:
logger.warning(
f"Camera {name} has rtmp assigned to an input, but rtmp is not enabled."
)
elif camera.rtmp.enabled and not "rtmp" in assigned_roles:
logger.warning(
f"Camera {name} has rtmp enabled, but rtmp is not assigned to an input."
)
def set_log_levels(self):
logging.getLogger().setLevel(self.config.logger.default.value.upper())
for log, level in self.config.logger.logs.items():
logging.getLogger(log).setLevel(level.value.upper())
if not "werkzeug" in self.config.logger.logs:
logging.getLogger("werkzeug").setLevel("ERROR")
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
)
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[:]
router = Router(migrate_db)
router.run()
migrate_db.close()
self.db = SqliteQueueDatabase(self.config.database.path)
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,
)
def init_mqtt(self):
self.mqtt_client = create_mqtt_client(self.config, self.camera_metrics)
def start_mqtt_relay(self):
self.mqtt_relay = MqttSocketRelay(
self.mqtt_client, self.config.mqtt.topic_prefix
)
self.mqtt_relay.start()
def start_detectors(self):
model_shape = (self.config.model.height, self.config.model.width)
for name in self.config.cameras.keys():
self.detection_out_events[name] = mp.Event()
try:
shm_in = mp.shared_memory.SharedMemory(
name=name,
create=True,
size=self.config.model.height * self.config.model.width * 3,
)
except FileExistsError:
shm_in = mp.shared_memory.SharedMemory(name=name)
try:
shm_out = mp.shared_memory.SharedMemory(
name=f"out-{name}", create=True, size=20 * 6 * 4
)
except FileExistsError:
shm_out = mp.shared_memory.SharedMemory(name=f"out-{name}")
self.detection_shms.append(shm_in)
self.detection_shms.append(shm_out)
for name, detector in self.config.detectors.items():
if detector.type == DetectorTypeEnum.cpu:
self.detectors[name] = EdgeTPUProcess(
name,
self.detection_queue,
self.detection_out_events,
model_shape,
"cpu",
detector.num_threads,
)
if detector.type == DetectorTypeEnum.edgetpu:
self.detectors[name] = EdgeTPUProcess(
name,
self.detection_queue,
self.detection_out_events,
model_shape,
detector.device,
detector.num_threads,
)
def start_detected_frames_processor(self):
self.detected_frames_processor = TrackedObjectProcessor(
self.config,
self.mqtt_client,
self.config.mqtt.topic_prefix,
self.detected_frames_queue,
self.event_queue,
self.event_processed_queue,
self.video_output_queue,
self.stop_event,
)
self.detected_frames_processor.start()
def start_video_output_processor(self):
output_processor = mp.Process(
target=output_frames,
name=f"output_processor",
args=(
self.config,
self.video_output_queue,
),
)
output_processor.daemon = True
self.output_processor = output_processor
output_processor.start()
logger.info(f"Output process started: {output_processor.pid}")
def start_camera_processors(self):
model_shape = (self.config.model.height, self.config.model.width)
for name, config in self.config.cameras.items():
camera_process = mp.Process(
target=track_camera,
name=f"camera_processor:{name}",
args=(
name,
config,
model_shape,
self.config.model.merged_labelmap,
self.detection_queue,
self.detection_out_events[name],
self.detected_frames_queue,
self.camera_metrics[name],
),
)
camera_process.daemon = True
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.daemon = True
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.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.start()
def start_watchdog(self):
self.frigate_watchdog = FrigateWatchdog(self.detectors, self.stop_event)
self.frigate_watchdog.start()
def start(self):
self.init_logger()
try:
try:
self.init_config()
except Exception as e:
print(f"Error parsing config: {e}")
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()
self.init_mqtt()
except Exception as e:
print(e)
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)
def receiveSignal(signalNumber, frame):
self.stop()
sys.exit()
signal.signal(signal.SIGTERM, receiveSignal)
try:
self.flask_app.run(host="127.0.0.1", port=5001, debug=False)
except KeyboardInterrupt:
pass
self.stop()
def stop(self):
logger.info(f"Stopping...")
self.stop_event.set()
self.mqtt_relay.stop()
self.detected_frames_processor.join()
self.event_processor.join()
self.event_cleanup.join()
self.recording_maintainer.join()
self.recording_cleanup.join()
self.stats_emitter.join()
self.frigate_watchdog.join()
self.db.stop()
for detector in self.detectors.values():
detector.stop()
while len(self.detection_shms) > 0:
shm = self.detection_shms.pop()
shm.close()
shm.unlink()

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@@ -1,778 +0,0 @@
from __future__ import annotations
import json
import logging
import os
from enum import Enum
from typing import Dict, List, Optional, Tuple, Union
import matplotlib.pyplot as plt
import numpy as np
import yaml
from pydantic import BaseModel, Field, validator
from pydantic.fields import PrivateAttr
from frigate.const import BASE_DIR, CACHE_DIR, RECORD_DIR
from frigate.edgetpu import load_labels
from frigate.util import create_mask, deep_merge
logger = logging.getLogger(__name__)
# TODO: Identify what the default format to display timestamps is
DEFAULT_TIME_FORMAT = "%m/%d/%Y %H:%M:%S"
# German Style:
# DEFAULT_TIME_FORMAT = "%d.%m.%Y %H:%M:%S"
FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
DEFAULT_TRACKED_OBJECTS = ["person"]
DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
class DetectorTypeEnum(str, Enum):
edgetpu = "edgetpu"
cpu = "cpu"
class DetectorConfig(BaseModel):
type: DetectorTypeEnum = Field(default=DetectorTypeEnum.cpu, title="Detector Type")
device: str = Field(default="usb", title="Device Type")
num_threads: int = Field(default=3, title="Number of detection threads")
class MqttConfig(BaseModel):
host: str = Field(title="MQTT Host")
port: int = Field(default=1883, title="MQTT Port")
topic_prefix: str = Field(default="frigate", title="MQTT Topic Prefix")
client_id: str = Field(default="frigate", title="MQTT Client ID")
stats_interval: int = Field(default=60, title="MQTT Camera Stats Interval")
user: Optional[str] = Field(title="MQTT Username")
password: Optional[str] = Field(title="MQTT Password")
tls_ca_certs: Optional[str] = Field(title="MQTT TLS CA Certificates")
tls_client_cert: Optional[str] = Field(title="MQTT TLS Client Certificate")
tls_client_key: Optional[str] = Field(title="MQTT TLS Client Key")
tls_insecure: Optional[bool] = Field(title="MQTT TLS Insecure")
@validator("password", pre=True, always=True)
def validate_password(cls, v, values):
if (v is None) != (values["user"] is None):
raise ValueError("Password must be provided with username.")
return v
class RetainConfig(BaseModel):
default: int = Field(default=10, title="Default retention period.")
objects: Dict[str, int] = Field(
default_factory=dict, title="Object retention period."
)
# DEPRECATED: Will eventually be removed
class ClipsConfig(BaseModel):
enabled: bool = Field(default=False, title="Save clips.")
max_seconds: int = Field(default=300, title="Maximum clip duration.")
pre_capture: int = Field(default=5, title="Seconds to capture before event starts.")
post_capture: int = Field(default=5, title="Seconds to capture after event ends.")
required_zones: List[str] = Field(
default_factory=list,
title="List of required zones to be entered in order to save the clip.",
)
objects: Optional[List[str]] = Field(
title="List of objects to be detected in order to save the clip.",
)
retain: RetainConfig = Field(
default_factory=RetainConfig, title="Clip retention settings."
)
class RecordConfig(BaseModel):
enabled: bool = Field(default=False, title="Enable record on all cameras.")
retain_days: int = Field(default=0, title="Recording retention period in days.")
events: ClipsConfig = Field(
default_factory=ClipsConfig, title="Event specific settings."
)
class MotionConfig(BaseModel):
threshold: int = Field(
default=25,
title="Motion detection threshold (1-255).",
ge=1,
le=255,
)
contour_area: Optional[int] = Field(title="Contour Area")
delta_alpha: float = Field(default=0.2, title="Delta Alpha")
frame_alpha: float = Field(default=0.2, title="Frame Alpha")
frame_height: Optional[int] = Field(title="Frame Height")
mask: Union[str, List[str]] = Field(
default="", title="Coordinates polygon for the motion mask."
)
class RuntimeMotionConfig(MotionConfig):
raw_mask: Union[str, List[str]] = ""
mask: np.ndarray = None
def __init__(self, **config):
frame_shape = config.get("frame_shape", (1, 1))
if "frame_height" not in config:
config["frame_height"] = max(frame_shape[0] // 6, 180)
if "contour_area" not in config:
frame_width = frame_shape[1] * config["frame_height"] / frame_shape[0]
config["contour_area"] = (
config["frame_height"] * frame_width * 0.00173611111
)
mask = config.get("mask", "")
config["raw_mask"] = mask
if mask:
config["mask"] = create_mask(frame_shape, mask)
else:
empty_mask = np.zeros(frame_shape, np.uint8)
empty_mask[:] = 255
config["mask"] = empty_mask
super().__init__(**config)
def dict(self, **kwargs):
ret = super().dict(**kwargs)
if "mask" in ret:
ret["mask"] = ret["raw_mask"]
ret.pop("raw_mask")
return ret
class Config:
arbitrary_types_allowed = True
class DetectConfig(BaseModel):
height: int = Field(default=720, title="Height of the stream for the detect role.")
width: int = Field(default=1280, title="Width of the stream for the detect role.")
fps: int = Field(
default=5, title="Number of frames per second to process through detection."
)
enabled: bool = Field(default=True, title="Detection Enabled.")
max_disappeared: Optional[int] = Field(
title="Maximum number of frames the object can dissapear before detection ends."
)
class FilterConfig(BaseModel):
min_area: int = Field(
default=0, title="Minimum area of bounding box for object to be counted."
)
max_area: int = Field(
default=24000000, title="Maximum area of bounding box for object to be counted."
)
threshold: float = Field(
default=0.7,
title="Average detection confidence threshold for object to be counted.",
)
min_score: float = Field(
default=0.5, title="Minimum detection confidence for object to be counted."
)
mask: Optional[Union[str, List[str]]] = Field(
title="Detection area polygon mask for this filter configuration.",
)
class RuntimeFilterConfig(FilterConfig):
mask: Optional[np.ndarray]
raw_mask: Optional[Union[str, List[str]]]
def __init__(self, **config):
mask = config.get("mask")
config["raw_mask"] = mask
if mask is not None:
config["mask"] = create_mask(config.get("frame_shape", (1, 1)), mask)
super().__init__(**config)
def dict(self, **kwargs):
ret = super().dict(**kwargs)
if "mask" in ret:
ret["mask"] = ret["raw_mask"]
ret.pop("raw_mask")
return ret
class Config:
arbitrary_types_allowed = True
class ZoneConfig(BaseModel):
filters: Dict[str, FilterConfig] = Field(
default_factory=dict, title="Zone filters."
)
coordinates: Union[str, List[str]] = Field(
title="Coordinates polygon for the defined zone."
)
objects: List[str] = Field(
default_factory=list,
title="List of objects that can trigger the zone.",
)
_color: Optional[Tuple[int, int, int]] = PrivateAttr()
_contour: np.ndarray = PrivateAttr()
@property
def color(self) -> Tuple[int, int, int]:
return self._color
@property
def contour(self) -> np.ndarray:
return self._contour
def __init__(self, **config):
super().__init__(**config)
self._color = config.get("color", (0, 0, 0))
coordinates = config["coordinates"]
if isinstance(coordinates, list):
self._contour = np.array(
[[int(p.split(",")[0]), int(p.split(",")[1])] for p in coordinates]
)
elif isinstance(coordinates, str):
points = coordinates.split(",")
self._contour = np.array(
[[int(points[i]), int(points[i + 1])] for i in range(0, len(points), 2)]
)
else:
self._contour = np.array([])
class ObjectConfig(BaseModel):
track: List[str] = Field(default=DEFAULT_TRACKED_OBJECTS, title="Objects to track.")
filters: Optional[Dict[str, FilterConfig]] = Field(title="Object filters.")
mask: Union[str, List[str]] = Field(default="", title="Object mask.")
class BirdseyeModeEnum(str, Enum):
objects = "objects"
motion = "motion"
continuous = "continuous"
class BirdseyeConfig(BaseModel):
enabled: bool = Field(default=True, title="Enable birdseye view.")
width: int = Field(default=1280, title="Birdseye width.")
height: int = Field(default=720, title="Birdseye height.")
quality: int = Field(
default=8,
title="Encoding quality.",
ge=1,
le=31,
)
mode: BirdseyeModeEnum = Field(
default=BirdseyeModeEnum.objects, title="Tracking mode."
)
FFMPEG_GLOBAL_ARGS_DEFAULT = ["-hide_banner", "-loglevel", "warning"]
FFMPEG_INPUT_ARGS_DEFAULT = [
"-avoid_negative_ts",
"make_zero",
"-fflags",
"+genpts+discardcorrupt",
"-rtsp_transport",
"tcp",
"-stimeout",
"5000000",
"-use_wallclock_as_timestamps",
"1",
]
DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-f", "rawvideo", "-pix_fmt", "yuv420p"]
RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-c", "copy", "-f", "flv"]
RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT = [
"-f",
"segment",
"-segment_time",
"10",
"-segment_format",
"mp4",
"-reset_timestamps",
"1",
"-strftime",
"1",
"-c",
"copy",
"-an",
]
class FfmpegOutputArgsConfig(BaseModel):
detect: Union[str, List[str]] = Field(
default=DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT,
title="Detect role FFmpeg output arguments.",
)
record: Union[str, List[str]] = Field(
default=RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT,
title="Record role FFmpeg output arguments.",
)
rtmp: Union[str, List[str]] = Field(
default=RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT,
title="RTMP role FFmpeg output arguments.",
)
class FfmpegConfig(BaseModel):
global_args: Union[str, List[str]] = Field(
default=FFMPEG_GLOBAL_ARGS_DEFAULT, title="Global FFmpeg arguments."
)
hwaccel_args: Union[str, List[str]] = Field(
default_factory=list, title="FFmpeg hardware acceleration arguments."
)
input_args: Union[str, List[str]] = Field(
default=FFMPEG_INPUT_ARGS_DEFAULT, title="FFmpeg input arguments."
)
output_args: FfmpegOutputArgsConfig = Field(
default_factory=FfmpegOutputArgsConfig,
title="FFmpeg output arguments per role.",
)
class CameraRoleEnum(str, Enum):
record = "record"
rtmp = "rtmp"
detect = "detect"
class CameraInput(BaseModel):
path: str = Field(title="Camera input path.")
roles: List[CameraRoleEnum] = Field(title="Roles assigned to this input.")
global_args: Union[str, List[str]] = Field(
default_factory=list, title="FFmpeg global arguments."
)
hwaccel_args: Union[str, List[str]] = Field(
default_factory=list, title="FFmpeg hardware acceleration arguments."
)
input_args: Union[str, List[str]] = Field(
default_factory=list, title="FFmpeg input arguments."
)
class CameraFfmpegConfig(FfmpegConfig):
inputs: List[CameraInput] = Field(title="Camera inputs.")
@validator("inputs")
def validate_roles(cls, v):
roles = [role for i in v for role in i.roles]
roles_set = set(roles)
if len(roles) > len(roles_set):
raise ValueError("Each input role may only be used once.")
if not "detect" in roles:
raise ValueError("The detect role is required.")
return v
class SnapshotsConfig(BaseModel):
enabled: bool = Field(default=False, title="Snapshots enabled.")
clean_copy: bool = Field(
default=True, title="Create a clean copy of the snapshot image."
)
timestamp: bool = Field(
default=False, title="Add a timestamp overlay on the snapshot."
)
bounding_box: bool = Field(
default=True, title="Add a bounding box overlay on the snapshot."
)
crop: bool = Field(default=False, title="Crop the snapshot to the detected object.")
required_zones: List[str] = Field(
default_factory=list,
title="List of required zones to be entered in order to save a snapshot.",
)
height: Optional[int] = Field(title="Snapshot image height.")
retain: RetainConfig = Field(
default_factory=RetainConfig, title="Snapshot retention."
)
quality: int = Field(
default=70,
title="Quality of the encoded jpeg (0-100).",
ge=0,
le=100,
)
class ColorConfig(BaseModel):
red: int = Field(default=255, le=0, ge=255, title="Red")
green: int = Field(default=255, le=0, ge=255, title="Green")
blue: int = Field(default=255, le=0, ge=255, title="Blue")
class TimestampStyleConfig(BaseModel):
position: str = Field(default="tl", title="Timestamp position.")
format: str = Field(default=DEFAULT_TIME_FORMAT, title="Timestamp format.")
color: ColorConfig = Field(default_factory=ColorConfig, title="Timestamp color.")
scale: float = Field(default=1.0, title="Timestamp scale.")
thickness: int = Field(default=2, title="Timestamp thickness.")
effect: Optional[str] = Field(title="Timestamp effect.")
class CameraMqttConfig(BaseModel):
enabled: bool = Field(default=True, title="Send image over MQTT.")
timestamp: bool = Field(default=True, title="Add timestamp to MQTT image.")
bounding_box: bool = Field(default=True, title="Add bounding box to MQTT image.")
crop: bool = Field(default=True, title="Crop MQTT image to detected object.")
height: int = Field(default=270, title="MQTT image height.")
required_zones: List[str] = Field(
default_factory=list,
title="List of required zones to be entered in order to send the image.",
)
quality: int = Field(
default=70,
title="Quality of the encoded jpeg (0-100).",
ge=0,
le=100,
)
class CameraRtmpConfig(BaseModel):
enabled: bool = Field(default=True, title="RTMP restreaming enabled.")
class CameraLiveConfig(BaseModel):
height: int = Field(default=720, title="Live camera view height")
quality: int = Field(default=8, ge=1, le=31, title="Live camera view quality")
class CameraConfig(BaseModel):
name: Optional[str] = Field(title="Camera name.")
ffmpeg: CameraFfmpegConfig = Field(title="FFmpeg configuration for the camera.")
best_image_timeout: int = Field(
default=60,
title="How long to wait for the image with the highest confidence score.",
)
zones: Dict[str, ZoneConfig] = Field(
default_factory=dict, title="Zone configuration."
)
record: RecordConfig = Field(
default_factory=RecordConfig, title="Record configuration."
)
rtmp: CameraRtmpConfig = Field(
default_factory=CameraRtmpConfig, title="RTMP restreaming configuration."
)
live: CameraLiveConfig = Field(
default_factory=CameraLiveConfig, title="Live playback settings."
)
snapshots: SnapshotsConfig = Field(
default_factory=SnapshotsConfig, title="Snapshot configuration."
)
mqtt: CameraMqttConfig = Field(
default_factory=CameraMqttConfig, title="MQTT configuration."
)
objects: ObjectConfig = Field(
default_factory=ObjectConfig, title="Object configuration."
)
motion: Optional[MotionConfig] = Field(title="Motion detection configuration.")
detect: DetectConfig = Field(
default_factory=DetectConfig, title="Object detection configuration."
)
timestamp_style: TimestampStyleConfig = Field(
default_factory=TimestampStyleConfig, title="Timestamp style configuration."
)
def __init__(self, **config):
# Set zone colors
if "zones" in config:
colors = plt.cm.get_cmap("tab10", len(config["zones"]))
config["zones"] = {
name: {**z, "color": tuple(round(255 * c) for c in colors(idx)[:3])}
for idx, (name, z) in enumerate(config["zones"].items())
}
super().__init__(**config)
@property
def frame_shape(self) -> Tuple[int, int]:
return self.detect.height, self.detect.width
@property
def frame_shape_yuv(self) -> Tuple[int, int]:
return self.detect.height * 3 // 2, self.detect.width
@property
def ffmpeg_cmds(self) -> List[Dict[str, List[str]]]:
ffmpeg_cmds = []
for ffmpeg_input in self.ffmpeg.inputs:
ffmpeg_cmd = self._get_ffmpeg_cmd(ffmpeg_input)
if ffmpeg_cmd is None:
continue
ffmpeg_cmds.append({"roles": ffmpeg_input.roles, "cmd": ffmpeg_cmd})
return ffmpeg_cmds
def _get_ffmpeg_cmd(self, ffmpeg_input: CameraInput):
ffmpeg_output_args = []
if "detect" in ffmpeg_input.roles:
detect_args = (
self.ffmpeg.output_args.detect
if isinstance(self.ffmpeg.output_args.detect, list)
else self.ffmpeg.output_args.detect.split(" ")
)
ffmpeg_output_args = (
[
"-r",
str(self.detect.fps),
"-s",
f"{self.detect.width}x{self.detect.height}",
]
+ detect_args
+ ffmpeg_output_args
+ ["pipe:"]
)
if "rtmp" in ffmpeg_input.roles and self.rtmp.enabled:
rtmp_args = (
self.ffmpeg.output_args.rtmp
if isinstance(self.ffmpeg.output_args.rtmp, list)
else self.ffmpeg.output_args.rtmp.split(" ")
)
ffmpeg_output_args = (
rtmp_args + [f"rtmp://127.0.0.1/live/{self.name}"] + ffmpeg_output_args
)
if "record" in ffmpeg_input.roles and self.record.enabled:
record_args = (
self.ffmpeg.output_args.record
if isinstance(self.ffmpeg.output_args.record, list)
else self.ffmpeg.output_args.record.split(" ")
)
ffmpeg_output_args = (
record_args
+ [f"{os.path.join(CACHE_DIR, self.name)}-%Y%m%d%H%M%S.mp4"]
+ ffmpeg_output_args
)
# if there arent any outputs enabled for this input
if len(ffmpeg_output_args) == 0:
return None
global_args = ffmpeg_input.global_args or self.ffmpeg.global_args
hwaccel_args = ffmpeg_input.hwaccel_args or self.ffmpeg.hwaccel_args
input_args = ffmpeg_input.input_args or self.ffmpeg.input_args
global_args = (
global_args if isinstance(global_args, list) else global_args.split(" ")
)
hwaccel_args = (
hwaccel_args if isinstance(hwaccel_args, list) else hwaccel_args.split(" ")
)
input_args = (
input_args if isinstance(input_args, list) else input_args.split(" ")
)
cmd = (
["ffmpeg"]
+ global_args
+ hwaccel_args
+ input_args
+ ["-i", ffmpeg_input.path]
+ ffmpeg_output_args
)
return [part for part in cmd if part != ""]
class DatabaseConfig(BaseModel):
path: str = Field(
default=os.path.join(BASE_DIR, "frigate.db"), title="Database path."
)
class ModelConfig(BaseModel):
width: int = Field(default=320, title="Object detection model input width.")
height: int = Field(default=320, title="Object detection model input height.")
labelmap: Dict[int, str] = Field(
default_factory=dict, title="Labelmap customization."
)
_merged_labelmap: Optional[Dict[int, str]] = PrivateAttr()
_colormap: Dict[int, Tuple[int, int, int]] = PrivateAttr()
@property
def merged_labelmap(self) -> Dict[int, str]:
return self._merged_labelmap
@property
def colormap(self) -> Dict[int, tuple[int, int, int]]:
return self._colormap
def __init__(self, **config):
super().__init__(**config)
self._merged_labelmap = {
**load_labels("/labelmap.txt"),
**config.get("labelmap", {}),
}
cmap = plt.cm.get_cmap("tab10", len(self._merged_labelmap.keys()))
self._colormap = {}
for key, val in self._merged_labelmap.items():
self._colormap[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
class LogLevelEnum(str, Enum):
debug = "debug"
info = "info"
warning = "warning"
error = "error"
critical = "critical"
class LoggerConfig(BaseModel):
default: LogLevelEnum = Field(
default=LogLevelEnum.info, title="Default logging level."
)
logs: Dict[str, LogLevelEnum] = Field(
default_factory=dict, title="Log level for specified processes."
)
class FrigateConfig(BaseModel):
mqtt: MqttConfig = Field(title="MQTT Configuration.")
database: DatabaseConfig = Field(
default_factory=DatabaseConfig, title="Database configuration."
)
environment_vars: Dict[str, str] = Field(
default_factory=dict, title="Frigate environment variables."
)
model: ModelConfig = Field(
default_factory=ModelConfig, title="Detection model configuration."
)
detectors: Dict[str, DetectorConfig] = Field(
default={name: DetectorConfig(**d) for name, d in DEFAULT_DETECTORS.items()},
title="Detector hardware configuration.",
)
logger: LoggerConfig = Field(
default_factory=LoggerConfig, title="Logging configuration."
)
record: RecordConfig = Field(
default_factory=RecordConfig, title="Global record configuration."
)
snapshots: SnapshotsConfig = Field(
default_factory=SnapshotsConfig, title="Global snapshots configuration."
)
birdseye: BirdseyeConfig = Field(
default_factory=BirdseyeConfig, title="Birdseye configuration."
)
ffmpeg: FfmpegConfig = Field(
default_factory=FfmpegConfig, title="Global FFmpeg configuration."
)
objects: ObjectConfig = Field(
default_factory=ObjectConfig, title="Global object configuration."
)
motion: Optional[MotionConfig] = Field(
title="Global motion detection configuration."
)
detect: DetectConfig = Field(
default_factory=DetectConfig, title="Global object tracking configuration."
)
cameras: Dict[str, CameraConfig] = Field(title="Camera configuration.")
@property
def runtime_config(self) -> FrigateConfig:
"""Merge camera config with globals."""
config = self.copy(deep=True)
# MQTT password substitution
if config.mqtt.password:
config.mqtt.password = config.mqtt.password.format(**FRIGATE_ENV_VARS)
# Global config to propegate down to camera level
global_config = config.dict(
include={
"record": ...,
"snapshots": ...,
"objects": ...,
"motion": ...,
"detect": ...,
"ffmpeg": ...,
},
exclude_unset=True,
)
for name, camera in config.cameras.items():
merged_config = deep_merge(camera.dict(exclude_unset=True), global_config)
camera_config: CameraConfig = CameraConfig.parse_obj(
{"name": name, **merged_config}
)
# Default max_disappeared configuration
max_disappeared = camera_config.detect.fps * 5
if camera_config.detect.max_disappeared is None:
camera_config.detect.max_disappeared = max_disappeared
# FFMPEG input substitution
for input in camera_config.ffmpeg.inputs:
input.path = input.path.format(**FRIGATE_ENV_VARS)
# Add default filters
object_keys = camera_config.objects.track
if camera_config.objects.filters is None:
camera_config.objects.filters = {}
object_keys = object_keys - camera_config.objects.filters.keys()
for key in object_keys:
camera_config.objects.filters[key] = FilterConfig()
# Apply global object masks and convert masks to numpy array
for object, filter in camera_config.objects.filters.items():
if camera_config.objects.mask:
filter_mask = []
if filter.mask is not None:
filter_mask = (
filter.mask
if isinstance(filter.mask, list)
else [filter.mask]
)
object_mask = (
camera_config.objects.mask
if isinstance(camera_config.objects.mask, list)
else [camera_config.objects.mask]
)
filter.mask = filter_mask + object_mask
# Set runtime filter to create masks
camera_config.objects.filters[object] = RuntimeFilterConfig(
frame_shape=camera_config.frame_shape,
**filter.dict(exclude_unset=True),
)
# Convert motion configuration
if camera_config.motion is None:
camera_config.motion = RuntimeMotionConfig(
frame_shape=camera_config.frame_shape
)
else:
camera_config.motion = RuntimeMotionConfig(
frame_shape=camera_config.frame_shape,
raw_mask=camera_config.motion.mask,
**camera_config.motion.dict(exclude_unset=True),
)
config.cameras[name] = camera_config
return config
@validator("cameras")
def ensure_zones_and_cameras_have_different_names(cls, v: Dict[str, CameraConfig]):
zones = [zone for camera in v.values() for zone in camera.zones.keys()]
for zone in zones:
if zone in v.keys():
raise ValueError("Zones cannot share names with cameras")
return v
@classmethod
def parse_file(cls, config_file):
with open(config_file) as f:
raw_config = f.read()
if config_file.endswith(".yml"):
config = yaml.safe_load(raw_config)
elif config_file.endswith(".json"):
config = json.loads(raw_config)
return cls.parse_obj(config)

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@@ -1,4 +0,0 @@
BASE_DIR = "/media/frigate"
CLIPS_DIR = f"{BASE_DIR}/clips"
RECORD_DIR = f"{BASE_DIR}/recordings"
CACHE_DIR = "/tmp/cache"

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@@ -1,278 +0,0 @@
import datetime
import logging
import multiprocessing as mp
import os
import queue
import signal
import threading
from abc import ABC, abstractmethod
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)}
class ObjectDetector(ABC):
@abstractmethod
def detect(self, tensor_input, threshold=0.4):
pass
class LocalObjectDetector(ObjectDetector):
def __init__(self, tf_device=None, num_threads=3, labels=None):
self.fps = EventsPerSecond()
if labels is None:
self.labels = {}
else:
self.labels = load_labels(labels)
device_config = {"device": "usb"}
if not tf_device is None:
device_config = {"device": tf_device}
edge_tpu_delegate = None
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)
logger.info("TPU found")
self.interpreter = tflite.Interpreter(
model_path="/edgetpu_model.tflite",
experimental_delegates=[edge_tpu_delegate],
)
except ValueError:
logger.error(
"No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
)
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
)
self.interpreter.allocate_tensors()
self.tensor_input_details = self.interpreter.get_input_details()
self.tensor_output_details = self.interpreter.get_output_details()
def detect(self, tensor_input, threshold=0.4):
detections = []
raw_detections = self.detect_raw(tensor_input)
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]))
)
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.invoke()
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,
):
threading.current_thread().name = f"detector:{name}"
logger = logging.getLogger(f"detector.{name}")
logger.info(f"Starting detection process: {os.getpid()}")
setproctitle(f"frigate.detector.{name}")
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)
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 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)
)
if input_frame is None:
continue
# 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[:]
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,
):
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.detect_process = None
self.model_shape = model_shape
self.tf_device = tf_device
self.num_threads = num_threads
self.start_or_restart()
def stop(self):
self.detect_process.terminate()
logging.info("Waiting for detection process to exit gracefully...")
self.detect_process.join(timeout=30)
if self.detect_process.exitcode is None:
logging.info("Detection process didnt exit. Force killing...")
self.detect_process.kill()
self.detect_process.join()
def start_or_restart(self):
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.daemon = True
self.detect_process.start()
class RemoteObjectDetector:
def __init__(self, name, labels, detection_queue, event, model_shape):
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=0.4):
detections = []
# copy input to shared memory
self.np_shm[:] = tensor_input[:]
self.event.clear()
self.detection_queue.put(self.name)
result = self.event.wait(timeout=10.0)
# if it timed out
if result is None:
return detections
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]))
)
self.fps.update()
return detections
def cleanup(self):
self.shm.unlink()
self.out_shm.unlink()

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@@ -1,254 +0,0 @@
import datetime
import logging
import os
import queue
import threading
import time
from pathlib import Path
from frigate.config import FrigateConfig, RecordConfig
from frigate.const import CLIPS_DIR
from frigate.models import Event, Recordings
from peewee import fn
logger = logging.getLogger(__name__)
class EventProcessor(threading.Thread):
def __init__(
self, config, camera_processes, event_queue, event_processed_queue, stop_event
):
threading.Thread.__init__(self)
self.name = "event_processor"
self.config = config
self.camera_processes = camera_processes
self.cached_clips = {}
self.event_queue = event_queue
self.event_processed_queue = event_processed_queue
self.events_in_process = {}
self.stop_event = stop_event
def should_create_clip(self, camera, event_data):
if event_data["false_positive"]:
return False
record_config: RecordConfig = self.config.cameras[camera].record
# Recording clips is disabled
if not record_config.enabled or (
record_config.retain_days == 0 and not record_config.events.enabled
):
return False
# If there are required zones and there is no overlap
required_zones = record_config.events.required_zones
if len(required_zones) > 0 and not set(event_data["entered_zones"]) & set(
required_zones
):
logger.debug(
f"Not creating clip for {event_data['id']} because it did not enter required zones"
)
return False
# If the required objects are not present
if (
record_config.events.objects is not None
and event_data["label"] not in record_config.events.objects
):
logger.debug(
f"Not creating clip for {event_data['id']} because it did not contain required objects"
)
return False
return True
def run(self):
while not self.stop_event.is_set():
try:
event_type, camera, event_data = self.event_queue.get(timeout=10)
except queue.Empty:
continue
logger.debug(f"Event received: {event_type} {camera} {event_data['id']}")
if event_type == "start":
self.events_in_process[event_data["id"]] = event_data
if event_type == "end":
record_config: RecordConfig = self.config.cameras[camera].record
has_clip = self.should_create_clip(camera, event_data)
if has_clip or event_data["has_snapshot"]:
Event.create(
id=event_data["id"],
label=event_data["label"],
camera=camera,
start_time=event_data["start_time"],
end_time=event_data["end_time"],
top_score=event_data["top_score"],
false_positive=event_data["false_positive"],
zones=list(event_data["entered_zones"]),
thumbnail=event_data["thumbnail"],
has_clip=has_clip,
has_snapshot=event_data["has_snapshot"],
)
del self.events_in_process[event_data["id"]]
self.event_processed_queue.put((event_data["id"], camera, has_clip))
logger.info(f"Exiting event processor...")
class EventCleanup(threading.Thread):
def __init__(self, config: FrigateConfig, stop_event):
threading.Thread.__init__(self)
self.name = "event_cleanup"
self.config = config
self.stop_event = stop_event
self.camera_keys = list(self.config.cameras.keys())
def expire(self, media_type):
## Expire events from unlisted cameras based on the global config
if media_type == "clips":
retain_config = self.config.record.events.retain
file_extension = "mp4"
update_params = {"has_clip": False}
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()
)
# 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()
# 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,
)
# delete the media from disk
for event in expired_events:
media_name = f"{event.camera}-{event.id}"
media_path = Path(
f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
)
media_path.unlink(missing_ok=True)
if file_extension == "jpg":
media_path = Path(
f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"
)
media_path.unlink(missing_ok=True)
# 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.execute()
## Expire events from cameras based on the camera config
for name, camera in self.config.cameras.items():
if media_type == "clips":
retain_config = camera.record.events.retain
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()
)
# 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()
# grab all events after specific time
expired_events = Event.select().where(
Event.camera == name,
Event.start_time < expire_after,
Event.label == l.label,
)
# delete the grabbed clips from disk
for event in expired_events:
media_name = f"{event.camera}-{event.id}"
media_path = Path(
f"{os.path.join(CLIPS_DIR, media_name)}.{file_extension}"
)
media_path.unlink(missing_ok=True)
if file_extension == "jpg":
media_path = Path(
f"{os.path.join(CLIPS_DIR, media_name)}-clean.png"
)
media_path.unlink(missing_ok=True)
# 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.execute()
def purge_duplicates(self):
duplicate_query = """with grouped_events as (
select id,
label,
camera,
has_snapshot,
has_clip,
row_number() over (
partition by label, camera, round(start_time/5,0)*5
order by end_time-start_time desc
) as copy_number
from event
)
select distinct id, camera, has_snapshot, has_clip from grouped_events
where copy_number > 1;"""
duplicate_events = Event.raw(duplicate_query)
for event in duplicate_events:
logger.debug(f"Removing duplicate: {event.id}")
media_name = f"{event.camera}-{event.id}"
if event.has_snapshot:
media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
media_path.unlink(missing_ok=True)
if event.has_clip:
media_path = Path(f"{os.path.join(CLIPS_DIR, media_name)}.mp4")
media_path.unlink(missing_ok=True)
(
Event.delete()
.where(Event.id << [event.id for event in duplicate_events])
.execute()
)
def run(self):
# only expire events every 5 minutes
while not self.stop_event.wait(300):
self.expire("clips")
self.expire("snapshots")
self.purge_duplicates()
# 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.execute()
logger.info(f"Exiting event cleanup...")

View File

@@ -1,734 +0,0 @@
import base64
from collections import OrderedDict
from datetime import datetime, timedelta
import json
import glob
import logging
import os
import re
import subprocess as sp
import time
from functools import reduce
from pathlib import Path
import cv2
from flask.helpers import send_file
import numpy as np
from flask import (
Blueprint,
Flask,
Response,
current_app,
jsonify,
make_response,
request,
)
from peewee import SqliteDatabase, operator, fn, DoesNotExist, Value
from playhouse.shortcuts import model_to_dict
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__)
def create_app(
frigate_config,
database: SqliteDatabase,
stats_tracking,
detected_frames_processor,
):
app = Flask(__name__)
@app.before_request
def _db_connect():
if database.is_closed():
database.connect()
@app.teardown_request
def _db_close(exc):
if not database.is_closed():
database.close()
app.frigate_config = frigate_config
app.stats_tracking = stats_tracking
app.detected_frames_processor = detected_frames_processor
app.register_blueprint(bp)
return app
@bp.route("/")
def is_healthy():
return "Frigate is running. Alive and healthy!"
@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)
clauses = []
if not has_clip is None:
clauses.append((Event.has_clip == has_clip))
if not has_snapshot is None:
clauses.append((Event.has_snapshot == has_snapshot))
if len(clauses) == 0:
clauses.append((True))
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,
)
)
return jsonify([e for e in groups.dicts()])
@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>", 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")
thumbnail_bytes = None
try:
event = Event.get(Event.id == id)
thumbnail_bytes = base64.b64decode(event.thumbnail)
except DoesNotExist:
# see if the object is currently being tracked
try:
camera_states = current_app.detected_frames_processor.camera_states.values()
for camera_state in camera_states:
if id in camera_state.tracked_objects:
tracked_obj = camera_state.tracked_objects.get(id)
if not tracked_obj is None:
thumbnail_bytes = tracked_obj.get_thumbnail()
except:
return "Event not found", 404
if thumbnail_bytes is None:
return "Event not found", 404
# android notifications prefer a 2:1 ratio
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_bytes = jpg.tobytes()
response = make_response(thumbnail_bytes)
response.headers["Content-Type"] = "image/jpg"
return response
@bp.route("/events/<id>/snapshot.jpg")
def event_snapshot(id):
download = request.args.get("download", type=bool)
jpg_bytes = None
try:
event = Event.get(Event.id == id)
if not event.has_snapshot:
return "Snapshot not available", 404
# read snapshot from disk
with open(
os.path.join(CLIPS_DIR, f"{event.camera}-{id}.jpg"), "rb"
) as image_file:
jpg_bytes = image_file.read()
except DoesNotExist:
# see if the object is currently being tracked
try:
camera_states = current_app.detected_frames_processor.camera_states.values()
for camera_state in camera_states:
if id in camera_state.tracked_objects:
tracked_obj = camera_state.tracked_objects.get(id)
if not tracked_obj is None:
jpg_bytes = tracked_obj.get_jpg_bytes(
timestamp=request.args.get("timestamp", type=int),
bounding_box=request.args.get("bbox", type=int),
crop=request.args.get("crop", type=int),
height=request.args.get("h", type=int),
quality=request.args.get("quality", default=70, type=int),
)
except:
return "Event not found", 404
except:
return "Event not found", 404
if jpg_bytes is None:
return "Event not found", 404
response = make_response(jpg_bytes)
response.headers["Content-Type"] = "image/jpg"
if download:
response.headers[
"Content-Disposition"
] = f"attachment; filename=snapshot-{id}.jpg"
return response
@bp.route("/events/<id>/clip.mp4")
def event_clip(id):
download = request.args.get("download", type=bool)
try:
event: Event = Event.get(Event.id == id)
except DoesNotExist:
return "Event not found.", 404
if not event.has_clip:
return "Clip not available", 404
event_config = current_app.frigate_config.cameras[event.camera].record.events
start_ts = event.start_time - event_config.pre_capture
end_ts = event.end_time + event_config.post_capture
file_name = f"{event.camera}-{id}.mp4"
clip_path = os.path.join(CLIPS_DIR, file_name)
if not os.path.isfile(clip_path):
return recording_clip(event.camera, start_ts, end_ts)
response = make_response()
response.headers["Content-Description"] = "File Transfer"
response.headers["Cache-Control"] = "no-cache"
response.headers["Content-Type"] = "video/mp4"
if download:
response.headers["Content-Disposition"] = "attachment; filename=%s" % file_name
response.headers["Content-Length"] = os.path.getsize(clip_path)
response.headers[
"X-Accel-Redirect"
] = f"/clips/{file_name}" # nginx: http://wiki.nginx.org/NginxXSendfile
return response
@bp.route("/events")
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)
clauses = []
excluded_fields = []
if camera:
clauses.append((Event.camera == camera))
if label:
clauses.append((Event.label == label))
if zone:
clauses.append((Event.zones.cast("text") % f'*"{zone}"*'))
if after:
clauses.append((Event.start_time >= after))
if before:
clauses.append((Event.start_time <= before))
if not has_clip is None:
clauses.append((Event.has_clip == has_clip))
if not has_snapshot is None:
clauses.append((Event.has_snapshot == has_snapshot))
if not include_thumbnails:
excluded_fields.append(Event.thumbnail)
if len(clauses) == 0:
clauses.append((True))
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():
config = current_app.frigate_config.dict()
# add in the ffmpeg_cmds
for camera_name, camera in current_app.frigate_config.cameras.items():
camera_dict = config["cameras"][camera_name]
camera_dict["ffmpeg_cmds"] = camera.ffmpeg_cmds
for cmd in camera_dict["ffmpeg_cmds"]:
cmd["cmd"] = " ".join(cmd["cmd"])
return jsonify(config)
@bp.route("/config/schema")
def config_schema():
return current_app.response_class(
current_app.frigate_config.schema_json(), mimetype="application/json"
)
@bp.route("/version")
def version():
return VERSION
@bp.route("/stats")
def stats():
stats = stats_snapshot(current_app.stats_tracking)
return jsonify(stats)
@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")
if best_frame is None:
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))
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]]
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), resize_quality]
)
response = make_response(jpg.tobytes())
response.headers["Content-Type"] = "image/jpg"
return response
else:
return "Camera named {} not found".format(camera_name), 404
@bp.route("/<camera_name>")
def mjpeg_feed(camera_name):
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),
}
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",
)
else:
return "Camera named {} not found".format(camera_name), 404
@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),
}
resize_quality = request.args.get("quality", default=70, type=int)
if camera_name in current_app.frigate_config.cameras:
frame = current_app.detected_frames_processor.get_current_frame(
camera_name, draw_options
)
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), resize_quality]
)
response = make_response(jpg.tobytes())
response.headers["Content-Type"] = "image/jpg"
return response
else:
return "Camera named {} not found".format(camera_name), 404
@bp.route("/<camera_name>/recordings")
def recordings(camera_name):
dates = OrderedDict()
# Retrieve all recordings for this camera
recordings = (
Recordings.select()
.where(Recordings.camera == camera_name)
.order_by(Recordings.start_time.asc())
)
last_end = 0
recording: Recordings
for recording in recordings:
date = datetime.fromtimestamp(recording.start_time)
key = date.strftime("%Y-%m-%d")
hour = date.strftime("%H")
# Create Day Record
if key not in dates:
dates[key] = OrderedDict()
# Create Hour Record
if hour not in dates[key]:
dates[key][hour] = {"delay": {}, "events": []}
# Check for delay
the_hour = datetime.strptime(f"{key} {hour}", "%Y-%m-%d %H").timestamp()
# diff current recording start time and the greater of the previous end time or top of the hour
diff = recording.start_time - max(last_end, the_hour)
# Determine seconds into recording
seconds = 0
if datetime.fromtimestamp(last_end).strftime("%H") == hour:
seconds = int(last_end - the_hour)
# Determine the delay
delay = min(int(diff), 3600 - seconds)
if delay > 1:
# Add an offset for any delay greater than a second
dates[key][hour]["delay"][seconds] = delay
last_end = recording.end_time
# Packing intervals to return all events with same label and overlapping times as one row.
# See: https://blogs.solidq.com/en/sqlserver/packing-intervals/
events = Event.raw(
"""WITH C1 AS
(
SELECT id, label, camera, top_score, start_time AS ts, +1 AS type, 1 AS sub
FROM event
WHERE camera = ?
UNION ALL
SELECT id, label, camera, top_score, end_time + 15 AS ts, -1 AS type, 0 AS sub
FROM event
WHERE camera = ?
),
C2 AS
(
SELECT C1.*,
SUM(type) OVER(PARTITION BY label ORDER BY ts, type DESC
ROWS BETWEEN UNBOUNDED PRECEDING
AND CURRENT ROW) - sub AS cnt
FROM C1
),
C3 AS
(
SELECT id, label, camera, top_score, ts,
(ROW_NUMBER() OVER(PARTITION BY label ORDER BY ts) - 1) / 2 + 1
AS grpnum
FROM C2
WHERE cnt = 0
)
SELECT MIN(id) as id, label, camera, MAX(top_score) as top_score, MIN(ts) AS start_time, max(ts) AS end_time
FROM C3
GROUP BY label, grpnum
ORDER BY start_time;""",
camera_name,
camera_name,
)
event: Event
for event in events:
date = datetime.fromtimestamp(event.start_time)
key = date.strftime("%Y-%m-%d")
hour = date.strftime("%H")
if key in dates and hour in dates[key]:
dates[key][hour]["events"].append(
model_to_dict(
event,
exclude=[
Event.false_positive,
Event.zones,
Event.thumbnail,
Event.has_clip,
Event.has_snapshot,
],
)
)
return jsonify(
[
{
"date": date,
"events": sum([len(value["events"]) for value in hours.values()]),
"recordings": [
{"hour": hour, "delay": value["delay"], "events": value["events"]}
for hour, value in hours.items()
],
}
for date, hours in dates.items()
]
)
@bp.route("/<camera>/start/<int:start_ts>/end/<int:end_ts>/clip.mp4")
@bp.route("/<camera>/start/<float:start_ts>/end/<float:end_ts>/clip.mp4")
def recording_clip(camera, start_ts, end_ts):
download = request.args.get("download", type=bool)
recordings = (
Recordings.select()
.where(
(Recordings.start_time.between(start_ts, end_ts))
| (Recordings.end_time.between(start_ts, end_ts))
| ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
)
.where(Recordings.camera == camera)
.order_by(Recordings.start_time.asc())
)
playlist_lines = []
clip: Recordings
for clip in recordings:
playlist_lines.append(f"file '{clip.path}'")
# if this is the starting clip, add an inpoint
if clip.start_time < start_ts:
playlist_lines.append(f"inpoint {int(start_ts - clip.start_time)}")
# if this is the ending clip, add an outpoint
if clip.end_time > end_ts:
playlist_lines.append(f"outpoint {int(end_ts - clip.start_time)}")
file_name = f"clip_{camera}_{start_ts}-{end_ts}.mp4"
path = f"/tmp/cache/{file_name}"
ffmpeg_cmd = [
"ffmpeg",
"-y",
"-protocol_whitelist",
"pipe,file",
"-f",
"concat",
"-safe",
"0",
"-i",
"-",
"-c",
"copy",
"-movflags",
"+faststart",
path,
]
p = sp.run(
ffmpeg_cmd,
input="\n".join(playlist_lines),
encoding="ascii",
capture_output=True,
)
if p.returncode != 0:
logger.error(p.stderr)
return f"Could not create clip from recordings for {camera}.", 500
response = make_response()
response.headers["Content-Description"] = "File Transfer"
response.headers["Cache-Control"] = "no-cache"
response.headers["Content-Type"] = "video/mp4"
if download:
response.headers["Content-Disposition"] = "attachment; filename=%s" % file_name
response.headers["Content-Length"] = os.path.getsize(path)
response.headers[
"X-Accel-Redirect"
] = f"/cache/{file_name}" # nginx: http://wiki.nginx.org/NginxXSendfile
return response
@bp.route("/vod/<camera>/start/<int:start_ts>/end/<int:end_ts>")
@bp.route("/vod/<camera>/start/<float:start_ts>/end/<float:end_ts>")
def vod_ts(camera, start_ts, end_ts):
recordings = (
Recordings.select()
.where(
Recordings.start_time.between(start_ts, end_ts)
| Recordings.end_time.between(start_ts, end_ts)
| ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
)
.where(Recordings.camera == camera)
.order_by(Recordings.start_time.asc())
)
clips = []
durations = []
recording: Recordings
for recording in recordings:
clip = {"type": "source", "path": recording.path}
duration = int(recording.duration * 1000)
# Determine if offset is needed for first clip
if recording.start_time < start_ts:
offset = int((start_ts - recording.start_time) * 1000)
clip["clipFrom"] = offset
duration -= offset
# Determine if we need to end the last clip early
if recording.end_time > end_ts:
duration -= int((recording.end_time - end_ts) * 1000)
clips.append(clip)
durations.append(duration)
if not clips:
return "No recordings found.", 404
hour_ago = datetime.now() - timedelta(hours=1)
return jsonify(
{
"cache": hour_ago.timestamp() > start_ts,
"discontinuity": False,
"durations": durations,
"sequences": [{"clips": clips}],
}
)
@bp.route("/vod/<year_month>/<day>/<hour>/<camera>")
def vod_hour(year_month, day, hour, camera):
start_date = datetime.strptime(f"{year_month}-{day} {hour}", "%Y-%m-%d %H")
end_date = start_date + timedelta(hours=1) - timedelta(milliseconds=1)
start_ts = start_date.timestamp()
end_ts = end_date.timestamp()
return vod_ts(camera, start_ts, end_ts)
@bp.route("/vod/event/<id>")
def vod_event(id):
try:
event: Event = Event.get(Event.id == id)
except DoesNotExist:
return "Event not found.", 404
if not event.has_clip:
return "Clip not available", 404
event_config = current_app.frigate_config.cameras[event.camera].record.events
start_ts = event.start_time - event_config.pre_capture
end_ts = event.end_time + event_config.post_capture
clip_path = os.path.join(CLIPS_DIR, f"{event.camera}-{id}.mp4")
if not os.path.isfile(clip_path):
return vod_ts(event.camera, start_ts, end_ts)
duration = int((end_ts - start_ts) * 1000)
return jsonify(
{
"cache": True,
"discontinuity": False,
"durations": [duration],
"sequences": [{"clips": [{"type": "source", "path": clip_path}]}],
}
)
def imagestream(detected_frames_processor, camera_name, fps, height, draw_options):
while True:
# max out at specified FPS
time.sleep(1 / fps)
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)
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"
)

View File

@@ -1,76 +0,0 @@
# adapted from https://medium.com/@jonathonbao/python3-logging-with-multiprocessing-f51f460b8778
import logging
import threading
import os
import signal
import queue
import multiprocessing as mp
from logging import handlers
from setproctitle import setproctitle
from collections import deque
def listener_configurer():
root = logging.getLogger()
console_handler = logging.StreamHandler()
formatter = logging.Formatter(
"[%(asctime)s] %(name)-30s %(levelname)-8s: %(message)s", "%Y-%m-%d %H:%M:%S"
)
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):
threading.current_thread().name = f"logger"
setproctitle("frigate.logger")
listener_configurer()
while True:
try:
record = log_queue.get(timeout=5)
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):
"""Setup the object with a logger and a loglevel
and start the thread
"""
threading.Thread.__init__(self)
self.daemon = False
self.logger = logging.getLogger(log_name)
self.level = level
self.deque = deque(maxlen=100)
self.fdRead, self.fdWrite = os.pipe()
self.pipeReader = os.fdopen(self.fdRead)
self.start()
def fileno(self):
"""Return the write file descriptor of the pipe"""
return self.fdWrite
def run(self):
"""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."""
os.close(self.fdWrite)

View File

@@ -1,26 +0,0 @@
from numpy import unique
from peewee import *
from playhouse.sqlite_ext import *
class Event(Model):
id = CharField(null=False, primary_key=True, max_length=30)
label = CharField(index=True, max_length=20)
camera = CharField(index=True, max_length=20)
start_time = DateTimeField()
end_time = DateTimeField()
top_score = FloatField()
false_positive = BooleanField()
zones = JSONField()
thumbnail = TextField()
has_clip = BooleanField(default=True)
has_snapshot = BooleanField(default=True)
class Recordings(Model):
id = CharField(null=False, primary_key=True, max_length=30)
camera = CharField(index=True, max_length=20)
path = CharField(unique=True)
start_time = DateTimeField()
end_time = DateTimeField()
duration = FloatField()

View File

@@ -1,111 +0,0 @@
import cv2
import imutils
import numpy as np
from frigate.config import MotionConfig
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.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])
def detect(self, frame):
motion_boxes = []
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,
)
# TODO: can I improve the contrast of the grayscale image here?
# convert to grayscale
# resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
# mask frame
resized_frame[self.mask] = [255]
# it takes ~30 frames to establish a baseline
# dont bother looking for motion
if self.frame_counter < 30:
self.frame_counter += 1
else:
# compare to average
frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
# compute the average delta over the past few frames
# 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
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]
# black out everything in the avg_delta where there isnt motion in the current frame
avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
avg_delta_image = cv2.bitwise_and(avg_delta_image, current_thresh)
# 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]
# 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)
cnts = imutils.grab_contours(cnts)
# loop over the contours
for c in cnts:
# if the contour is big enough, count it as motion
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),
)
)
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
)
else:
# when no motion, just keep averaging the frames together
cv2.accumulateWeighted(
resized_frame, self.avg_frame, self.config.frame_alpha
)
self.motion_frame_count = 0
return motion_boxes

View File

@@ -1,253 +1,33 @@
import json
import logging
import threading
from wsgiref.simple_server import make_server
import paho.mqtt.client as mqtt
from ws4py.server.wsgirefserver import (
WebSocketWSGIHandler,
WebSocketWSGIRequestHandler,
WSGIServer,
)
from ws4py.server.wsgiutils import WebSocketWSGIApplication
from ws4py.websocket import WebSocket
from frigate.config import FrigateConfig
from frigate.util import restart_frigate
logger = logging.getLogger(__name__)
def create_mqtt_client(config: FrigateConfig, camera_metrics):
mqtt_config = config.mqtt
def on_recordings_command(client, userdata, message):
payload = message.payload.decode()
logger.debug(f"on_recordings_toggle: {message.topic} {payload}")
camera_name = message.topic.split("/")[-3]
record_settings = config.cameras[camera_name].record
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}")
state_topic = f"{message.topic[:-4]}/state"
client.publish(state_topic, payload, retain=True)
def on_snapshots_command(client, userdata, message):
payload = message.payload.decode()
logger.debug(f"on_snapshots_toggle: {message.topic} {payload}")
camera_name = message.topic.split("/")[-3]
snapshots_settings = config.cameras[camera_name].snapshots
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":
if snapshots_settings.enabled:
logger.info(f"Turning off snapshots for {camera_name} via mqtt")
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]
detect_settings = config.cameras[camera_name].detect
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":
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
else:
logger.warning(f"Received unsupported value at {message.topic}: {payload}")
state_topic = f"{message.topic[:-4]}/state"
client.publish(state_topic, payload, retain=True)
def on_restart_command(client, userdata, message):
restart_frigate()
def on_connect(client, userdata, flags, rc):
threading.current_thread().name = "mqtt"
if rc != 0:
if rc == 3:
logger.error("Unable to connect to MQTT server: MQTT Server unavailable")
elif rc == 4:
logger.error("Unable to connect to MQTT server: MQTT Bad username or password")
elif rc == 5:
logger.error("Unable to connect to MQTT server: MQTT Not authorized")
else:
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 = 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
)
# register callbacks
for name in config.cameras.keys():
client.message_callback_add(
f"{mqtt_config.topic_prefix}/{name}/recordings/set", on_recordings_command
)
client.message_callback_add(
f"{mqtt_config.topic_prefix}/{name}/snapshots/set", on_snapshots_command
)
client.message_callback_add(
f"{mqtt_config.topic_prefix}/{name}/detect/set", on_detect_command
)
client.message_callback_add(
f"{mqtt_config.topic_prefix}/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:
client.connect(mqtt_config.host, mqtt_config.port, 60)
except Exception as e:
logger.error(f"Unable to connect to MQTT server: {e}")
raise
client.loop_start()
for name in config.cameras.keys():
client.publish(
f"{mqtt_config.topic_prefix}/{name}/recordings/state",
"ON" if config.cameras[name].record.enabled else "OFF",
retain=True,
)
client.publish(
f"{mqtt_config.topic_prefix}/{name}/snapshots/state",
"ON" if config.cameras[name].snapshots.enabled else "OFF",
retain=True,
)
client.publish(
f"{mqtt_config.topic_prefix}/{name}/detect/state",
"ON" if config.cameras[name].detect.enabled else "OFF",
retain=True,
)
return client
class MqttSocketRelay:
def __init__(self, mqtt_client, topic_prefix):
self.mqtt_client = mqtt_client
class MqttObjectPublisher(threading.Thread):
def __init__(self, client, topic_prefix, objects_parsed, detected_objects):
threading.Thread.__init__(self)
self.client = client
self.topic_prefix = topic_prefix
self.objects_parsed = objects_parsed
self._detected_objects = detected_objects
def start(self):
class MqttWebSocket(WebSocket):
topic_prefix = self.topic_prefix
mqtt_client = self.mqtt_client
def run(self):
last_sent_payload = ""
while True:
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
# initialize the payload
payload = {}
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"],
)
# wait until objects have been parsed
with self.objects_parsed:
self.objects_parsed.wait()
# 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
)
# add all the person scores in detected objects
detected_objects = self._detected_objects.copy()
person_score = sum([obj['score'] for obj in detected_objects if obj['name'] == 'person'])
# if the person score is more than 100, set person to ON
payload['person'] = 'ON' if int(person_score*100) > 100 else 'OFF'
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()
# send message for objects if different
new_payload = json.dumps(payload, sort_keys=True)
if new_payload != last_sent_payload:
last_sent_payload = new_payload
self.client.publish(self.topic_prefix+'/objects', new_payload, retain=False)

110
frigate/object_detection.py Normal file
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import datetime
import time
import cv2
import threading
import numpy as np
from edgetpu.detection.engine import DetectionEngine
from . util import tonumpyarray
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = '/label_map.pbtext'
# Function to read labels from text files.
def ReadLabelFile(file_path):
with open(file_path, 'r') as f:
lines = f.readlines()
ret = {}
for line in lines:
pair = line.strip().split(maxsplit=1)
ret[int(pair[0])] = pair[1].strip()
return ret
class PreppedQueueProcessor(threading.Thread):
def __init__(self, cameras, prepped_frame_queue):
threading.Thread.__init__(self)
self.cameras = cameras
self.prepped_frame_queue = prepped_frame_queue
# Load the edgetpu engine and labels
self.engine = DetectionEngine(PATH_TO_CKPT)
self.labels = ReadLabelFile(PATH_TO_LABELS)
def run(self):
# process queue...
while True:
frame = self.prepped_frame_queue.get()
# Actual detection.
objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=0.5, top_k=3)
# parse and pass detected objects back to the camera
parsed_objects = []
for obj in objects:
box = obj.bounding_box.flatten().tolist()
parsed_objects.append({
'frame_time': frame['frame_time'],
'name': str(self.labels[obj.label_id]),
'score': float(obj.score),
'xmin': int((box[0] * frame['region_size']) + frame['region_x_offset']),
'ymin': int((box[1] * frame['region_size']) + frame['region_y_offset']),
'xmax': int((box[2] * frame['region_size']) + frame['region_x_offset']),
'ymax': int((box[3] * frame['region_size']) + frame['region_y_offset'])
})
self.cameras[frame['camera_name']].add_objects(parsed_objects)
# should this be a region class?
class FramePrepper(threading.Thread):
def __init__(self, camera_name, shared_frame, frame_time, frame_ready,
frame_lock,
region_size, region_x_offset, region_y_offset,
prepped_frame_queue):
threading.Thread.__init__(self)
self.camera_name = camera_name
self.shared_frame = shared_frame
self.frame_time = frame_time
self.frame_ready = frame_ready
self.frame_lock = frame_lock
self.region_size = region_size
self.region_x_offset = region_x_offset
self.region_y_offset = region_y_offset
self.prepped_frame_queue = prepped_frame_queue
def run(self):
frame_time = 0.0
while True:
now = datetime.datetime.now().timestamp()
with self.frame_ready:
# if there isnt a frame ready for processing or it is old, wait for a new frame
if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
self.frame_ready.wait()
# make a copy of the cropped frame
with self.frame_lock:
cropped_frame = self.shared_frame[self.region_y_offset:self.region_y_offset+self.region_size, self.region_x_offset:self.region_x_offset+self.region_size].copy()
frame_time = self.frame_time.value
# convert to RGB
cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
# Resize to 300x300 if needed
if cropped_frame_rgb.shape != (300, 300, 3):
cropped_frame_rgb = cv2.resize(cropped_frame_rgb, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
# Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
frame_expanded = np.expand_dims(cropped_frame_rgb, axis=0)
# add the frame to the queue
if not self.prepped_frame_queue.full():
self.prepped_frame_queue.put({
'camera_name': self.camera_name,
'frame_time': frame_time,
'frame': frame_expanded.flatten().copy(),
'region_size': self.region_size,
'region_x_offset': self.region_x_offset,
'region_y_offset': self.region_y_offset
})
else:
print("queue full. moving on")

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@@ -1,833 +0,0 @@
import base64
import copy
import datetime
import hashlib
import itertools
import json
import logging
import os
import queue
import threading
import time
from collections import Counter, defaultdict
from statistics import mean, median
from typing import Callable, Dict
import cv2
import numpy as np
from frigate.config import CameraConfig, FrigateConfig
from frigate.const import CACHE_DIR, CLIPS_DIR, RECORD_DIR
from frigate.edgetpu import load_labels
from frigate.util import (
SharedMemoryFrameManager,
calculate_region,
draw_box_with_label,
draw_timestamp,
)
logger = logging.getLogger(__name__)
def on_edge(box, frame_shape):
if (
box[0] == 0
or box[1] == 0
or box[2] == frame_shape[1] - 1
or box[3] == frame_shape[0] - 1
):
return True
def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool:
# larger is better
# cutoff images are less ideal, but they should also be smaller?
# better scores are obviously better too
# if the new_thumb is on an edge, and the current thumb is not
if on_edge(new_obj["box"], frame_shape) and not on_edge(
current_thumb["box"], frame_shape
):
return False
# if the score is better by more than 5%
if new_obj["score"] > current_thumb["score"] + 0.05:
return True
# if the area is 10% larger
if new_obj["area"] > current_thumb["area"] * 1.1:
return True
return False
class TrackedObject:
def __init__(
self, camera, colormap, camera_config: CameraConfig, frame_cache, obj_data
):
self.obj_data = obj_data
self.camera = camera
self.colormap = colormap
self.camera_config = camera_config
self.frame_cache = frame_cache
self.current_zones = []
self.entered_zones = set()
self.false_positive = True
self.top_score = self.computed_score = 0.0
self.thumbnail_data = None
self.last_updated = 0
self.last_published = 0
self.frame = None
self.previous = self.to_dict()
# start the score history
self.score_history = [self.obj_data["score"]]
def _is_false_positive(self):
# once a true positive, always a true positive
if not self.false_positive:
return False
threshold = self.camera_config.objects.filters[self.obj_data["label"]].threshold
return self.computed_score < threshold
def compute_score(self):
scores = self.score_history[:]
# pad with zeros if you dont have at least 3 scores
if len(scores) < 3:
scores += [0.0] * (3 - len(scores))
return median(scores)
def update(self, current_frame_time, obj_data):
significant_update = False
zone_change = False
self.obj_data.update(obj_data)
# if the object is not in the current frame, add a 0.0 to the score history
if self.obj_data["frame_time"] != current_frame_time:
self.score_history.append(0.0)
else:
self.score_history.append(self.obj_data["score"])
# only keep the last 10 scores
if len(self.score_history) > 10:
self.score_history = self.score_history[-10:]
# calculate if this is a false positive
self.computed_score = self.compute_score()
if self.computed_score > self.top_score:
self.top_score = self.computed_score
self.false_positive = self._is_false_positive()
if not self.false_positive:
# determine if this frame is a better thumbnail
if self.thumbnail_data is None or is_better_thumbnail(
self.thumbnail_data, self.obj_data, self.camera_config.frame_shape
):
self.thumbnail_data = {
"frame_time": self.obj_data["frame_time"],
"box": self.obj_data["box"],
"area": self.obj_data["area"],
"region": self.obj_data["region"],
"score": self.obj_data["score"],
}
significant_update = True
# check zones
current_zones = []
bottom_center = (self.obj_data["centroid"][0], self.obj_data["box"][3])
# check each zone
for name, zone in self.camera_config.zones.items():
# if the zone is not for this object type, skip
if len(zone.objects) > 0 and not self.obj_data["label"] in zone.objects:
continue
contour = zone.contour
# check if the object is in the zone
if cv2.pointPolygonTest(contour, bottom_center, False) >= 0:
# if the object passed the filters once, dont apply again
if name in self.current_zones or not zone_filtered(self, zone.filters):
current_zones.append(name)
self.entered_zones.add(name)
# if the zones changed, signal an update
if not self.false_positive and set(self.current_zones) != set(current_zones):
zone_change = True
self.current_zones = current_zones
return (significant_update, zone_change)
def to_dict(self, include_thumbnail: bool = False):
snapshot_time = (
self.thumbnail_data["frame_time"]
if not self.thumbnail_data is None
else 0.0
)
event = {
"id": self.obj_data["id"],
"camera": self.camera,
"frame_time": self.obj_data["frame_time"],
"snapshot_time": snapshot_time,
"label": self.obj_data["label"],
"top_score": self.top_score,
"false_positive": self.false_positive,
"start_time": self.obj_data["start_time"],
"end_time": self.obj_data.get("end_time", None),
"score": self.obj_data["score"],
"box": self.obj_data["box"],
"area": self.obj_data["area"],
"region": self.obj_data["region"],
"current_zones": self.current_zones.copy(),
"entered_zones": list(self.entered_zones).copy(),
}
if include_thumbnail:
event["thumbnail"] = base64.b64encode(self.get_thumbnail()).decode("utf-8")
return event
def get_thumbnail(self):
if (
self.thumbnail_data is None
or self.thumbnail_data["frame_time"] not in self.frame_cache
):
ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
jpg_bytes = self.get_jpg_bytes(
timestamp=False, bounding_box=False, crop=True, height=175
)
if jpg_bytes:
return jpg_bytes
else:
ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
return jpg.tobytes()
def get_clean_png(self):
if self.thumbnail_data is None:
return None
try:
best_frame = cv2.cvtColor(
self.frame_cache[self.thumbnail_data["frame_time"]],
cv2.COLOR_YUV2BGR_I420,
)
except KeyError:
logger.warning(
f"Unable to create clean png because frame {self.thumbnail_data['frame_time']} is not in the cache"
)
return None
ret, png = cv2.imencode(".png", best_frame)
if ret:
return png.tobytes()
else:
return None
def get_jpg_bytes(
self, timestamp=False, bounding_box=False, crop=False, height=None, quality=70
):
if self.thumbnail_data is None:
return None
try:
best_frame = cv2.cvtColor(
self.frame_cache[self.thumbnail_data["frame_time"]],
cv2.COLOR_YUV2BGR_I420,
)
except KeyError:
logger.warning(
f"Unable to create jpg because frame {self.thumbnail_data['frame_time']} is not in the cache"
)
return None
if bounding_box:
thickness = 2
color = self.colormap[self.obj_data["label"]]
# draw the bounding boxes on the frame
box = self.thumbnail_data["box"]
draw_box_with_label(
best_frame,
box[0],
box[1],
box[2],
box[3],
self.obj_data["label"],
f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}",
thickness=thickness,
color=color,
)
if crop:
box = self.thumbnail_data["box"]
region = calculate_region(
best_frame.shape, box[0], box[1], box[2], box[3], 1.1
)
best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
if height:
width = int(height * best_frame.shape[1] / best_frame.shape[0])
best_frame = cv2.resize(
best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA
)
if timestamp:
color = self.camera_config.timestamp_style.color
draw_timestamp(
best_frame,
self.thumbnail_data["frame_time"],
self.camera_config.timestamp_style.format,
font_effect=self.camera_config.timestamp_style.effect,
font_scale=self.camera_config.timestamp_style.scale,
font_thickness=self.camera_config.timestamp_style.thickness,
font_color=(color.red, color.green, color.blue),
position=self.camera_config.timestamp_style.position,
)
ret, jpg = cv2.imencode(
".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), quality]
)
if ret:
return jpg.tobytes()
else:
return None
def zone_filtered(obj: TrackedObject, object_config):
object_name = obj.obj_data["label"]
if object_name in object_config:
obj_settings = object_config[object_name]
# if the min area is larger than the
# detected object, don't add it to detected objects
if obj_settings.min_area > obj.obj_data["area"]:
return True
# if the detected object is larger than the
# max area, don't add it to detected objects
if obj_settings.max_area < obj.obj_data["area"]:
return True
# if the score is lower than the threshold, skip
if obj_settings.threshold > obj.computed_score:
return True
return False
# Maintains the state of a camera
class CameraState:
def __init__(
self, name, config: FrigateConfig, frame_manager: SharedMemoryFrameManager
):
self.name = name
self.config = config
self.camera_config = config.cameras[name]
self.frame_manager = frame_manager
self.best_objects: Dict[str, TrackedObject] = {}
self.object_counts = defaultdict(int)
self.tracked_objects: Dict[str, TrackedObject] = {}
self.frame_cache = {}
self.zone_objects = defaultdict(list)
self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
self.current_frame_lock = threading.Lock()
self.current_frame_time = 0.0
self.motion_boxes = []
self.regions = []
self.previous_frame_id = None
self.callbacks = defaultdict(list)
def get_current_frame(self, draw_options={}):
with self.current_frame_lock:
frame_copy = np.copy(self._current_frame)
frame_time = self.current_frame_time
tracked_objects = {k: v.to_dict() for k, v in self.tracked_objects.items()}
motion_boxes = self.motion_boxes.copy()
regions = self.regions.copy()
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
# draw on the frame
if draw_options.get("bounding_boxes"):
# draw the bounding boxes on the frame
for obj in tracked_objects.values():
if obj["frame_time"] == frame_time:
thickness = 2
color = self.config.model.colormap[obj["label"]]
else:
thickness = 1
color = (255, 0, 0)
# draw the bounding boxes on the frame
box = obj["box"]
draw_box_with_label(
frame_copy,
box[0],
box[1],
box[2],
box[3],
obj["label"],
f"{obj['score']:.0%} {int(obj['area'])}",
thickness=thickness,
color=color,
)
if draw_options.get("regions"):
for region in regions:
cv2.rectangle(
frame_copy,
(region[0], region[1]),
(region[2], region[3]),
(0, 255, 0),
2,
)
if draw_options.get("zones"):
for name, zone in self.camera_config.zones.items():
thickness = (
8
if any(
name in obj["current_zones"] for obj in tracked_objects.values()
)
else 2
)
cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
if draw_options.get("mask"):
mask_overlay = np.where(self.camera_config.motion.mask == [0])
frame_copy[mask_overlay] = [0, 0, 0]
if draw_options.get("motion_boxes"):
for m_box in motion_boxes:
cv2.rectangle(
frame_copy,
(m_box[0], m_box[1]),
(m_box[2], m_box[3]),
(0, 0, 255),
2,
)
if draw_options.get("timestamp"):
color = self.camera_config.timestamp_style.color
draw_timestamp(
frame_copy,
frame_time,
self.camera_config.timestamp_style.format,
font_effect=self.camera_config.timestamp_style.effect,
font_scale=self.camera_config.timestamp_style.scale,
font_thickness=self.camera_config.timestamp_style.thickness,
font_color=(color.red, color.green, color.blue),
position=self.camera_config.timestamp_style.position,
)
return frame_copy
def finished(self, obj_id):
del self.tracked_objects[obj_id]
def on(self, event_type: str, callback: Callable[[Dict], None]):
self.callbacks[event_type].append(callback)
def update(self, frame_time, current_detections, motion_boxes, regions):
# get the new frame
frame_id = f"{self.name}{frame_time}"
current_frame = self.frame_manager.get(
frame_id, self.camera_config.frame_shape_yuv
)
tracked_objects = self.tracked_objects.copy()
current_ids = set(current_detections.keys())
previous_ids = set(tracked_objects.keys())
removed_ids = previous_ids.difference(current_ids)
new_ids = current_ids.difference(previous_ids)
updated_ids = current_ids.intersection(previous_ids)
for id in new_ids:
new_obj = tracked_objects[id] = TrackedObject(
self.name,
self.config.model.colormap,
self.camera_config,
self.frame_cache,
current_detections[id],
)
# call event handlers
for c in self.callbacks["start"]:
c(self.name, new_obj, frame_time)
for id in updated_ids:
updated_obj = tracked_objects[id]
significant_update, zone_change = updated_obj.update(
frame_time, current_detections[id]
)
if significant_update:
# ensure this frame is stored in the cache
if (
updated_obj.thumbnail_data["frame_time"] == frame_time
and frame_time not in self.frame_cache
):
self.frame_cache[frame_time] = np.copy(current_frame)
updated_obj.last_updated = frame_time
# if it has been more than 5 seconds since the last publish
# and the last update is greater than the last publish or
# the object has changed zones
if (
frame_time - updated_obj.last_published > 5
and updated_obj.last_updated > updated_obj.last_published
) or zone_change:
# call event handlers
for c in self.callbacks["update"]:
c(self.name, updated_obj, frame_time)
updated_obj.last_published = frame_time
for id in removed_ids:
# publish events to mqtt
removed_obj = tracked_objects[id]
if not "end_time" in removed_obj.obj_data:
removed_obj.obj_data["end_time"] = frame_time
for c in self.callbacks["end"]:
c(self.name, removed_obj, frame_time)
# TODO: can i switch to looking this up and only changing when an event ends?
# maintain best objects
for obj in tracked_objects.values():
object_type = obj.obj_data["label"]
# if the object's thumbnail is not from the current frame
if obj.false_positive or obj.thumbnail_data["frame_time"] != frame_time:
continue
if object_type in self.best_objects:
current_best = self.best_objects[object_type]
now = datetime.datetime.now().timestamp()
# if the object is a higher score than the current best score
# or the current object is older than desired, use the new object
if (
is_better_thumbnail(
current_best.thumbnail_data,
obj.thumbnail_data,
self.camera_config.frame_shape,
)
or (now - current_best.thumbnail_data["frame_time"])
> self.camera_config.best_image_timeout
):
self.best_objects[object_type] = obj
for c in self.callbacks["snapshot"]:
c(self.name, self.best_objects[object_type], frame_time)
else:
self.best_objects[object_type] = obj
for c in self.callbacks["snapshot"]:
c(self.name, self.best_objects[object_type], frame_time)
# update overall camera state for each object type
obj_counter = Counter(
obj.obj_data["label"]
for obj in tracked_objects.values()
if not obj.false_positive
)
# report on detected objects
for obj_name, count in obj_counter.items():
if count != self.object_counts[obj_name]:
self.object_counts[obj_name] = count
for c in self.callbacks["object_status"]:
c(self.name, obj_name, count)
# expire any objects that are >0 and no longer detected
expired_objects = [
obj_name
for obj_name, count in self.object_counts.items()
if count > 0 and obj_name not in obj_counter
]
for obj_name in expired_objects:
self.object_counts[obj_name] = 0
for c in self.callbacks["object_status"]:
c(self.name, obj_name, 0)
for c in self.callbacks["snapshot"]:
c(self.name, self.best_objects[obj_name], frame_time)
# cleanup thumbnail frame cache
current_thumb_frames = {
obj.thumbnail_data["frame_time"]
for obj in tracked_objects.values()
if not obj.false_positive
}
current_best_frames = {
obj.thumbnail_data["frame_time"] for obj in self.best_objects.values()
}
thumb_frames_to_delete = [
t
for t in self.frame_cache.keys()
if t not in current_thumb_frames and t not in current_best_frames
]
for t in thumb_frames_to_delete:
del self.frame_cache[t]
with self.current_frame_lock:
self.tracked_objects = tracked_objects
self.current_frame_time = frame_time
self.motion_boxes = motion_boxes
self.regions = regions
self._current_frame = current_frame
if self.previous_frame_id is not None:
self.frame_manager.close(self.previous_frame_id)
self.previous_frame_id = frame_id
class TrackedObjectProcessor(threading.Thread):
def __init__(
self,
config: FrigateConfig,
client,
topic_prefix,
tracked_objects_queue,
event_queue,
event_processed_queue,
video_output_queue,
stop_event,
):
threading.Thread.__init__(self)
self.name = "detected_frames_processor"
self.config = config
self.client = client
self.topic_prefix = topic_prefix
self.tracked_objects_queue = tracked_objects_queue
self.event_queue = event_queue
self.event_processed_queue = event_processed_queue
self.video_output_queue = video_output_queue
self.stop_event = stop_event
self.camera_states: Dict[str, CameraState] = {}
self.frame_manager = SharedMemoryFrameManager()
def start(camera, obj: TrackedObject, current_frame_time):
self.event_queue.put(("start", camera, obj.to_dict()))
def update(camera, obj: TrackedObject, current_frame_time):
after = obj.to_dict()
message = {
"before": obj.previous,
"after": after,
"type": "new" if obj.previous["false_positive"] else "update",
}
self.client.publish(
f"{self.topic_prefix}/events", json.dumps(message), retain=False
)
obj.previous = after
def end(camera, obj: TrackedObject, current_frame_time):
snapshot_config = self.config.cameras[camera].snapshots
event_data = obj.to_dict(include_thumbnail=True)
event_data["has_snapshot"] = False
if not obj.false_positive:
message = {
"before": obj.previous,
"after": obj.to_dict(),
"type": "end",
}
self.client.publish(
f"{self.topic_prefix}/events", json.dumps(message), retain=False
)
# write snapshot to disk if enabled
if snapshot_config.enabled and self.should_save_snapshot(camera, obj):
jpg_bytes = obj.get_jpg_bytes(
timestamp=snapshot_config.timestamp,
bounding_box=snapshot_config.bounding_box,
crop=snapshot_config.crop,
height=snapshot_config.height,
quality=snapshot_config.quality,
)
if jpg_bytes is None:
logger.warning(
f"Unable to save snapshot for {obj.obj_data['id']}."
)
else:
with open(
os.path.join(
CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"
),
"wb",
) as j:
j.write(jpg_bytes)
event_data["has_snapshot"] = True
# write clean snapshot if enabled
if snapshot_config.clean_copy:
png_bytes = obj.get_clean_png()
if png_bytes is None:
logger.warning(
f"Unable to save clean snapshot for {obj.obj_data['id']}."
)
else:
with open(
os.path.join(
CLIPS_DIR,
f"{camera}-{obj.obj_data['id']}-clean.png",
),
"wb",
) as p:
p.write(png_bytes)
self.event_queue.put(("end", camera, event_data))
def snapshot(camera, obj: TrackedObject, current_frame_time):
mqtt_config = self.config.cameras[camera].mqtt
if mqtt_config.enabled and self.should_mqtt_snapshot(camera, obj):
jpg_bytes = obj.get_jpg_bytes(
timestamp=mqtt_config.timestamp,
bounding_box=mqtt_config.bounding_box,
crop=mqtt_config.crop,
height=mqtt_config.height,
quality=mqtt_config.quality,
)
if jpg_bytes is None:
logger.warning(
f"Unable to send mqtt snapshot for {obj.obj_data['id']}."
)
else:
self.client.publish(
f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot",
jpg_bytes,
retain=True,
)
def object_status(camera, object_name, status):
self.client.publish(
f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False
)
for camera in self.config.cameras.keys():
camera_state = CameraState(camera, self.config, self.frame_manager)
camera_state.on("start", start)
camera_state.on("update", update)
camera_state.on("end", end)
camera_state.on("snapshot", snapshot)
camera_state.on("object_status", object_status)
self.camera_states[camera] = camera_state
# {
# 'zone_name': {
# 'person': {
# 'camera_1': 2,
# 'camera_2': 1
# }
# }
# }
self.zone_data = defaultdict(lambda: defaultdict(dict))
def should_save_snapshot(self, camera, obj: TrackedObject):
# if there are required zones and there is no overlap
required_zones = self.config.cameras[camera].snapshots.required_zones
if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
logger.debug(
f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones"
)
return False
return True
def should_mqtt_snapshot(self, camera, obj: TrackedObject):
# if there are required zones and there is no overlap
required_zones = self.config.cameras[camera].mqtt.required_zones
if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
logger.debug(
f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones"
)
return False
return True
def get_best(self, camera, label):
# TODO: need a lock here
camera_state = self.camera_states[camera]
if label in camera_state.best_objects:
best_obj = camera_state.best_objects[label]
best = best_obj.thumbnail_data.copy()
best["frame"] = camera_state.frame_cache.get(
best_obj.thumbnail_data["frame_time"]
)
return best
else:
return {}
def get_current_frame(self, camera, draw_options={}):
return self.camera_states[camera].get_current_frame(draw_options)
def run(self):
while not self.stop_event.is_set():
try:
(
camera,
frame_time,
current_tracked_objects,
motion_boxes,
regions,
) = self.tracked_objects_queue.get(True, 10)
except queue.Empty:
continue
camera_state = self.camera_states[camera]
camera_state.update(
frame_time, current_tracked_objects, motion_boxes, regions
)
self.video_output_queue.put(
(
camera,
frame_time,
current_tracked_objects,
motion_boxes,
regions,
)
)
# update zone counts for each label
# for each zone in the current camera
for zone in self.config.cameras[camera].zones.keys():
# count labels for the camera in the zone
obj_counter = Counter(
obj.obj_data["label"]
for obj in camera_state.tracked_objects.values()
if zone in obj.current_zones and not obj.false_positive
)
# update counts and publish status
for label in set(self.zone_data[zone].keys()) | set(obj_counter.keys()):
# if we have previously published a count for this zone/label
zone_label = self.zone_data[zone][label]
if camera in zone_label:
current_count = sum(zone_label.values())
zone_label[camera] = (
obj_counter[label] if label in obj_counter else 0
)
new_count = sum(zone_label.values())
if new_count != current_count:
self.client.publish(
f"{self.topic_prefix}/{zone}/{label}",
new_count,
retain=False,
)
# if this is a new zone/label combo for this camera
else:
if label in obj_counter:
zone_label[camera] = obj_counter[label]
self.client.publish(
f"{self.topic_prefix}/{zone}/{label}",
obj_counter[label],
retain=False,
)
# cleanup event finished queue
while not self.event_processed_queue.empty():
event_id, camera, clip_created = self.event_processed_queue.get()
if clip_created:
obj = self.camera_states[camera].tracked_objects[event_id]
message = {
"before": obj.previous,
"after": obj.to_dict(),
"type": "clip_ready",
}
self.client.publish(
f"{self.topic_prefix}/events", json.dumps(message), retain=False
)
self.camera_states[camera].finished(event_id)
logger.info(f"Exiting object processor...")

View File

@@ -1,142 +1,96 @@
import copy
import datetime
import itertools
import multiprocessing as mp
import random
import string
import threading
import time
from collections import defaultdict
import datetime
import threading
import cv2
import numpy as np
from scipy.spatial import distance as dist
from object_detection.utils import visualization_utils as vis_util
from frigate.config import DetectConfig
from frigate.util import draw_box_with_label
class ObjectCleaner(threading.Thread):
def __init__(self, objects_parsed, detected_objects):
threading.Thread.__init__(self)
self._objects_parsed = objects_parsed
self._detected_objects = detected_objects
def run(self):
while True:
# wait a bit before checking for expired frames
time.sleep(0.2)
# expire the objects that are more than 1 second old
now = datetime.datetime.now().timestamp()
# look for the first object found within the last second
# (newest objects are appended to the end)
detected_objects = self._detected_objects.copy()
num_to_delete = 0
for obj in detected_objects:
if now-obj['frame_time']<2:
break
num_to_delete += 1
if num_to_delete > 0:
del self._detected_objects[:num_to_delete]
# notify that parsed objects were changed
with self._objects_parsed:
self._objects_parsed.notify_all()
class ObjectTracker:
def __init__(self, config: DetectConfig):
self.tracked_objects = {}
self.disappeared = {}
self.max_disappeared = config.max_disappeared
# Maintains the frame and person with the highest score from the most recent
# motion event
class BestPersonFrame(threading.Thread):
def __init__(self, objects_parsed, recent_frames, detected_objects):
threading.Thread.__init__(self)
self.objects_parsed = objects_parsed
self.recent_frames = recent_frames
self.detected_objects = detected_objects
self.best_person = None
self.best_frame = None
def register(self, index, obj):
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"]
self.tracked_objects[id] = obj
self.disappeared[id] = 0
def run(self):
while True:
def deregister(self, id):
del self.tracked_objects[id]
del self.disappeared[id]
# wait until objects have been parsed
with self.objects_parsed:
self.objects_parsed.wait()
def update(self, id, new_obj):
self.disappeared[id] = 0
self.tracked_objects[id].update(new_obj)
# make a copy of detected objects
detected_objects = self.detected_objects.copy()
detected_people = [obj for obj in detected_objects if obj['name'] == 'person']
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,
}
)
# get the highest scoring person
new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person)
# 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"])
else:
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])
# 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)
if len(current_objects) == 0:
for index, obj in enumerate(group):
self.register(index, obj)
# if there isnt a person, continue
if new_best_person is None:
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 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 (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 determine which new object each existing object matched
# against, and apply the same sorting as was applied previously
cols = D.argmin(axis=1)[rows]
# many current objects may register with each new object, so only
# match the closest ones. unique returns the indices of the first
# occurrences of each value, and because the rows are sorted by
# distance, this will be index of the closest match
_, index = np.unique(cols, return_index=True)
rows = rows[index]
cols = cols[index]
# 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])
# 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
if D.shape[0] >= D.shape[1]:
for row in unusedRows:
id = current_ids[row]
if self.disappeared[id] >= self.max_disappeared:
self.deregister(id)
else:
self.disappeared[id] += 1
# if the number of input centroids is greater
# than the number of existing object centroids we need to
# register each new input centroid as a trackable object
# if there is no current best_person
if self.best_person is None:
self.best_person = new_best_person
# if there is already a best_person
else:
for col in unusedCols:
self.register(col, group[col])
now = datetime.datetime.now().timestamp()
# if the new best person is a higher score than the current best person
# or the current person is more than 1 minute old, use the new best person
if new_best_person['score'] > self.best_person['score'] or (now - self.best_person['frame_time']) > 60:
self.best_person = new_best_person
# make a copy of the recent frames
recent_frames = self.recent_frames.copy()
if not self.best_person is None and self.best_person['frame_time'] in recent_frames:
best_frame = recent_frames[self.best_person['frame_time']]
best_frame = cv2.cvtColor(best_frame, cv2.COLOR_BGR2RGB)
# draw the bounding box on the frame
vis_util.draw_bounding_box_on_image_array(best_frame,
self.best_person['ymin'],
self.best_person['xmin'],
self.best_person['ymax'],
self.best_person['xmax'],
color='red',
thickness=2,
display_str_list=["{}: {}%".format(self.best_person['name'],int(self.best_person['score']*100))],
use_normalized_coordinates=False)
# convert back to BGR
self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)

View File

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

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@@ -1,285 +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 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 = [
"ffprobe",
"-v",
"panic",
"-show_error",
"-show_streams",
"-of",
"json",
source,
]
p = sp.run(ffprobe_cmd, capture_output=True)
info = json.loads(p.stdout)
video_info = [s for s in info["streams"] if s["codec_type"] == "video"][0]
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()

View File

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

View File

@@ -1,102 +0,0 @@
import json
import logging
import threading
import time
import psutil
import shutil
from frigate.config import FrigateConfig
from frigate.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
from frigate.version import VERSION
logger = logging.getLogger(__name__)
def stats_init(camera_metrics, detectors):
stats_tracking = {
"camera_metrics": camera_metrics,
"detectors": detectors,
"started": int(time.time()),
}
return stats_tracking
def get_fs_type(path):
bestMatch = ""
fsType = ""
for part in psutil.disk_partitions(all=True):
if path.startswith(part.mountpoint) and len(bestMatch) < len(part.mountpoint):
fsType = part.fstype
bestMatch = part.mountpoint
return fsType
def stats_snapshot(stats_tracking):
camera_metrics = stats_tracking["camera_metrics"]
stats = {}
total_detection_fps = 0
for name, camera_stats in camera_metrics.items():
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,
}
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["detection_fps"] = round(total_detection_fps, 2)
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),
}
return stats
class StatsEmitter(threading.Thread):
def __init__(
self,
config: FrigateConfig,
stats_tracking,
mqtt_client,
topic_prefix,
stop_event,
):
threading.Thread.__init__(self)
self.name = "frigate_stats_emitter"
self.config = config
self.stats_tracking = stats_tracking
self.mqtt_client = mqtt_client
self.topic_prefix = topic_prefix
self.stop_event = stop_event
def run(self):
time.sleep(10)
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
)
logger.info(f"Exiting watchdog...")

View File

@@ -1,889 +0,0 @@
import unittest
import numpy as np
from pydantic import ValidationError
from frigate.config import (
FrigateConfig,
DetectorTypeEnum,
)
class TestConfig(unittest.TestCase):
def setUp(self):
self.minimal = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
def test_config_class(self):
frigate_config = FrigateConfig(**self.minimal)
assert self.minimal == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "cpu" in runtime_config.detectors.keys()
assert runtime_config.detectors["cpu"].type == DetectorTypeEnum.cpu
def test_invalid_mqtt_config(self):
config = {
"mqtt": {"host": "mqtt", "user": "test"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
def test_inherit_tracked_objects(self):
config = {
"mqtt": {"host": "mqtt"},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "dog" in runtime_config.cameras["back"].objects.track
def test_override_tracked_objects(self):
config = {
"mqtt": {"host": "mqtt"},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"objects": {"track": ["cat"]},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "cat" in runtime_config.cameras["back"].objects.track
def test_default_object_filters(self):
config = {
"mqtt": {"host": "mqtt"},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "dog" in runtime_config.cameras["back"].objects.filters
def test_inherit_object_filters(self):
config = {
"mqtt": {"host": "mqtt"},
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "dog" in runtime_config.cameras["back"].objects.filters
assert runtime_config.cameras["back"].objects.filters["dog"].threshold == 0.7
def test_override_object_filters(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "dog" in runtime_config.cameras["back"].objects.filters
assert runtime_config.cameras["back"].objects.filters["dog"].threshold == 0.7
def test_global_object_mask(self):
config = {
"mqtt": {"host": "mqtt"},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"objects": {
"mask": "0,0,1,1,0,1",
"filters": {"dog": {"mask": "1,1,1,1,1,1"}},
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
back_camera = runtime_config.cameras["back"]
assert "dog" in back_camera.objects.filters
assert len(back_camera.objects.filters["dog"].raw_mask) == 2
assert len(back_camera.objects.filters["person"].raw_mask) == 1
def test_default_input_args(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "-rtsp_transport" in runtime_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
def test_ffmpeg_params_global(self):
config = {
"ffmpeg": {"input_args": "-re"},
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "-re" in runtime_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
def test_ffmpeg_params_camera(self):
config = {
"mqtt": {"host": "mqtt"},
"ffmpeg": {"input_args": ["test"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
],
"input_args": ["-re"],
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "-re" in runtime_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
assert "test" not in runtime_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
def test_ffmpeg_params_input(self):
config = {
"mqtt": {"host": "mqtt"},
"ffmpeg": {"input_args": ["test2"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
"input_args": "-re test",
}
],
"input_args": "test3",
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"objects": {
"track": ["person", "dog"],
"filters": {"dog": {"threshold": 0.7}},
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert "-re" in runtime_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
assert "test" in runtime_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
assert "test2" not in runtime_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
assert "test3" not in runtime_config.cameras["back"].ffmpeg_cmds[0]["cmd"]
def test_inherit_clips_retention(self):
config = {
"mqtt": {"host": "mqtt"},
"record": {
"events": {"retain": {"default": 20, "objects": {"person": 30}}}
},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert (
runtime_config.cameras["back"].record.events.retain.objects["person"] == 30
)
def test_roles_listed_twice_throws_error(self):
config = {
"mqtt": {"host": "mqtt"},
"record": {
"events": {"retain": {"default": 20, "objects": {"person": 30}}}
},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]},
{"path": "rtsp://10.0.0.1:554/video2", "roles": ["detect"]},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
def test_zone_matching_camera_name_throws_error(self):
config = {
"mqtt": {"host": "mqtt"},
"record": {
"events": {"retain": {"default": 20, "objects": {"person": 30}}}
},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"zones": {"back": {"coordinates": "1,1,1,1,1,1"}},
}
},
}
self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
def test_zone_assigns_color_and_contour(self):
config = {
"mqtt": {"host": "mqtt"},
"record": {
"events": {"retain": {"default": 20, "objects": {"person": 30}}}
},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"zones": {"test": {"coordinates": "1,1,1,1,1,1"}},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert isinstance(
runtime_config.cameras["back"].zones["test"].contour, np.ndarray
)
assert runtime_config.cameras["back"].zones["test"].color != (0, 0, 0)
def test_clips_should_default_to_global_objects(self):
config = {
"mqtt": {"host": "mqtt"},
"record": {
"events": {"retain": {"default": 20, "objects": {"person": 30}}}
},
"objects": {"track": ["person", "dog"]},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
"record": {"events": {"enabled": True}},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
back_camera = runtime_config.cameras["back"]
assert back_camera.record.events.objects is None
assert back_camera.record.events.retain.objects["person"] == 30
def test_role_assigned_but_not_enabled(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect", "rtmp"],
},
{"path": "rtsp://10.0.0.1:554/record", "roles": ["record"]},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
ffmpeg_cmds = runtime_config.cameras["back"].ffmpeg_cmds
assert len(ffmpeg_cmds) == 1
assert not "clips" in ffmpeg_cmds[0]["roles"]
def test_max_disappeared_default(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"enabled": True,
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.cameras["back"].detect.max_disappeared == 5 * 5
def test_motion_frame_height_wont_go_below_120(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.cameras["back"].motion.frame_height >= 120
def test_motion_contour_area_dynamic(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert round(runtime_config.cameras["back"].motion.contour_area) == 99
def test_merge_labelmap(self):
config = {
"mqtt": {"host": "mqtt"},
"model": {"labelmap": {7: "truck"}},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.model.merged_labelmap[7] == "truck"
def test_default_labelmap_empty(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.model.merged_labelmap[0] == "person"
def test_default_labelmap(self):
config = {
"mqtt": {"host": "mqtt"},
"model": {"width": 320, "height": 320},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.model.merged_labelmap[0] == "person"
def test_fails_on_invalid_role(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect", "clips"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
def test_global_detect(self):
config = {
"mqtt": {"host": "mqtt"},
"detect": {"max_disappeared": 1},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.cameras["back"].detect.max_disappeared == 1
assert runtime_config.cameras["back"].detect.height == 1080
def test_default_detect(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
}
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.cameras["back"].detect.max_disappeared == 25
assert runtime_config.cameras["back"].detect.height == 720
def test_global_detect_merge(self):
config = {
"mqtt": {"host": "mqtt"},
"detect": {"max_disappeared": 1, "height": 720},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"detect": {
"height": 1080,
"width": 1920,
"fps": 5,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.cameras["back"].detect.max_disappeared == 1
assert runtime_config.cameras["back"].detect.height == 1080
assert runtime_config.cameras["back"].detect.width == 1920
def test_global_snapshots(self):
config = {
"mqtt": {"host": "mqtt"},
"snapshots": {"enabled": True},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"snapshots": {
"height": 100,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.cameras["back"].snapshots.enabled
assert runtime_config.cameras["back"].snapshots.height == 100
def test_default_snapshots(self):
config = {
"mqtt": {"host": "mqtt"},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
}
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.cameras["back"].snapshots.bounding_box
assert runtime_config.cameras["back"].snapshots.quality == 70
def test_global_snapshots_merge(self):
config = {
"mqtt": {"host": "mqtt"},
"snapshots": {"bounding_box": False, "height": 300},
"cameras": {
"back": {
"ffmpeg": {
"inputs": [
{
"path": "rtsp://10.0.0.1:554/video",
"roles": ["detect"],
},
]
},
"snapshots": {
"height": 150,
"enabled": True,
},
}
},
}
frigate_config = FrigateConfig(**config)
assert config == frigate_config.dict(exclude_unset=True)
runtime_config = frigate_config.runtime_config
assert runtime_config.cameras["back"].snapshots.bounding_box == False
assert runtime_config.cameras["back"].snapshots.height == 150
assert runtime_config.cameras["back"].snapshots.enabled
if __name__ == "__main__":
unittest.main(verbosity=2)

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

View File

@@ -1,41 +0,0 @@
import cv2
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)
# 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))
# ensure the upper left pixel is blue
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))
# 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])
# ensure the bottom right is black
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)
# 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__":
main(verbosity=2)

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