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Code Issues Packages Projects Releases Wiki Activity

Move docs in-source (#1357)

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This commit is contained in:
Matt
2024-06-30 16:10:12 -04:00
committed by GitHub
parent 0cdd9a74d0
commit dcc7ddc19b
170 changed files with 42686 additions and 13 deletions
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18
.github/workflows/build.yml vendored
View File

@@ -1,12 +1,21 @@
name: Build
on:
# Run on pushes to master and pushed tags, and on pull requests against master, but ignore the docs folder
push:
branches: [ master ]
tags:
- 'v*'
paths:
- '**'
- '!docs/**'
- '.github/**'
pull_request:
branches: [ master ]
paths:
- '**'
- '!docs/**'
- '.github/**'
jobs:
build-client:
@@ -101,24 +110,23 @@ jobs:
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
with:
repository: 'PhotonVision/photonvision-docs.git'
ref: master
- uses: actions/setup-python@v5
with:
python-version: '3.9'
python-version: '3.11'
- name: Install dependencies
working-directory: docs
run: |
python -m pip install --upgrade pip
pip install sphinx sphinx_rtd_theme sphinx-tabs sphinxext-opengraph doc8
pip install -r requirements.txt
- name: Build the docs
working-directory: docs
run: |
make html
- uses: actions/upload-artifact@v4
with:
name: built-docs
path: build/html
path: docs/build/html
build-photonlib-host:
env:
MACOSX_DEPLOYMENT_TARGET: 13

13
.github/workflows/lint-format.yml vendored
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@@ -1,12 +1,21 @@
name: Lint and Format
on:
# Run on pushes to master and pushed tags, and on pull requests against master, but ignore the docs folder
push:
branches: [ master ]
tags:
- 'v*'
paths:
- '**'
- '!docs/**'
- '.github/**'
pull_request:
branches: [ master ]
paths:
- '**'
- '!docs/**'
- '.github/**'
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.ref }}
@@ -26,9 +35,9 @@ jobs:
- name: Set up Python 3.8
uses: actions/setup-python@v4
with:
python-version: 3.8
python-version: 3.11
- name: Install wpiformat
run: pip3 install wpiformat
run: pip3 install wpiformat==2024.37
- name: Run
run: wpiformat
- name: Check output

20
.github/workflows/documentation.yml → .github/workflows/photon-code-docs.yml vendored
View File

@@ -1,12 +1,21 @@
name: Documentation
name: Photon Code Documentation
on:
# Run on pushes to master and pushed tags, and on pull requests against master, but ignore the docs folder
push:
# For now, run on all commits to master
branches: [ master ]
# and also all tags starting with v
tags:
- 'v*'
paths:
- '**'
- '!docs/**'
- '.github/**'
pull_request:
branches: [ master ]
paths:
- '**'
- '!docs/**'
- '.github/**'
# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
@@ -58,12 +67,12 @@ jobs:
- name: Build javadocs/doxygen
run: |
chmod +x gradlew
./gradlew docs:generateJavaDocs docs:doxygen
./gradlew photon-docs:generateJavaDocs photon-docs:doxygen
- uses: actions/upload-artifact@v4
with:
name: built-docs
path: docs/build/docs
path: photon-docs/build/docs
release:
needs: [build-client, run_docs]
@@ -76,6 +85,7 @@ jobs:
- run: find .
- name: copy file via ssh password
if: github.ref == 'refs/heads/master'
uses: appleboy/scp-action@v0.1.7
with:
host: ${{ secrets.WEBMASTER_SSH_HOST }}

46
.github/workflows/photonvision-docs.yml vendored Normal file
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@@ -0,0 +1,46 @@
name: PhotonVision Sphinx Documentation Checks
on:
push:
branches: [ master ]
paths:
- 'docs/**'
- '.github/**'
pull_request:
branches: [ master ]
paths:
- 'docs/**'
- '.github/**'
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: Install and upgrade pip
run: python -m pip install --upgrade pip
- name: Install Python dependencies
working-directory: docs
run: |
pip install sphinx sphinx_rtd_theme sphinx-tabs sphinxext-opengraph doc8
pip install -r requirements.txt
- name: Check links
working-directory: docs
run: make linkcheck
continue-on-error: true
- name: Check lint
working-directory: docs
run: make lint
- name: Compile HTML
working-directory: docs
run: make html

8
.github/workflows/python.yml vendored
View File

@@ -8,8 +8,16 @@ on:
branches: [ master ]
tags:
- 'v*'
paths:
- '**'
- '!docs/**'
- '.github/**'
pull_request:
branches: [ master ]
paths:
- '**'
- '!docs/**'
- '.github/**'
jobs:
buildAndDeploy:

11
docs/.gitignore vendored Normal file
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@@ -0,0 +1,11 @@
build/*
.DS_Store
.vscode/*
.idea/*
source/_build
source/_build
photon-docs/build
source/docs/_build
venv/*
.venv/*

15
docs/.readthedocs.yml Normal file
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@@ -0,0 +1,15 @@
version: 2
sphinx:
builder: html
configuration: source/conf.py
fail_on_warning: true
build:
os: ubuntu-22.04
tools:
python: "3.11"
python:
install:
- requirements: requirements.txt

16
docs/.styleguide Normal file
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@@ -0,0 +1,16 @@
modifiableFileExclude {
\.jpg$
\.jpeg$
\.png$
\.gif$
\.so$
\.pdf$
\.mp4$
\.dll$
\.webp$
\.ico$
\.rknn$
\.svg$
gradlew
}

395
docs/LICENSE Normal file
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@@ -0,0 +1,395 @@
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24
docs/Makefile Normal file
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@@ -0,0 +1,24 @@
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS = -W --keep-going
SPHINXBUILD = sphinx-build
SOURCEDIR = source
LINTER = doc8
LINTEROPTS = --ignore D001 # D001 is linelength
BUILDDIR = build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
lint:
@$(LINTER) $(LINTEROPTS) $(SOURCEDIR)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

9
docs/README.MD Normal file
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@@ -0,0 +1,9 @@
# PhotonVision ReadTheDocs
[![Documentation Status](https://readthedocs.org/projects/photonvision-docs/badge/?version=latest)](https://docs.photonvision.org/en/latest/?badge=latest)
PhotonVision is a free open-source vision processing software for FRC teams.
This repository is the source code for our ReadTheDocs documentation, which can be found [here](https://docs.photonvision.org).
[Contribution and formatting guidelines for this project](https://docs.photonvision.org/en/latest/docs/contributing/photonvision-docs/index.html)

36
docs/make.bat Normal file
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@@ -0,0 +1,36 @@
@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=source
set BUILDDIR=build
set SPHINXOPTS=-W --keep-going
if "%1" == "" goto help
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
:end
popd

42
docs/requirements.txt Normal file
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@@ -0,0 +1,42 @@
alabaster==0.7.13
Babel==2.13.1
beautifulsoup4==4.12.2
certifi==2023.11.17
charset-normalizer==3.3.2
colorama==0.4.6
doc8==0.11.2
docopt==0.6.2
docutils==0.18.1
furo==2023.9.10
idna==3.4
imagesize==1.4.1
Jinja2==3.0.3
MarkupSafe==2.1.3
packaging==23.2
pbr==6.0.0
pipreqs==0.4.13
Pygments==2.17.1
requests==2.31.0
restructuredtext-lint==1.4.0
six==1.16.0
snowballstemmer==2.2.0
soupsieve==2.5
Sphinx==7.2.6
sphinx-basic-ng==1.0.0b2
sphinx-notfound-page==1.0.0
sphinx-rtd-theme==1.3.0
sphinx-tabs==3.4.4
sphinx_design==0.5.0
sphinxcontrib-applehelp==1.0.7
sphinxcontrib-devhelp==1.0.5
sphinxcontrib-ghcontributors==0.2.3
sphinxcontrib-htmlhelp==2.0.4
sphinxcontrib-jquery==4.1
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.6
sphinxcontrib-serializinghtml==1.1.9
sphinxext-opengraph==0.9.0
sphinxext-remoteliteralinclude==0.4.0
stevedore==5.1.0
urllib3==2.1.0
yarg==0.1.9

6
docs/source/404.rst Normal file
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@@ -0,0 +1,6 @@
:orphan:
Requested Page Not Found
========================
This page you were looking for was not found. If you think this is a mistake, `file an issue on our GitHub. <https://github.com/PhotonVision/photonvision-docs/issues>`__

20
docs/source/Makefile Normal file
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@@ -0,0 +1,20 @@
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = source
BUILDDIR = build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

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/*!
* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
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{# Import the theme's layout. #}
{% extends '!layout.html' %}
{%- block extrahead %}
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</script>
{# Call the parent block #}
{{ super() }}
{% endblock %}
{%- block extrafooter %}
{# Add custom things to the head HTML tag #}
<div class="dark-mode-toggle-container">
<strong class="light-label md-icon">&#xE430</strong>
<div class="dark-mode-toggle">
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{# Call the parent block #}
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# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = "PhotonVision"
copyright = "2024, PhotonVision"
author = "Banks Troutman, Matt Morley"
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx_rtd_theme",
"sphinx.ext.autosectionlabel",
"sphinx.ext.todo",
"sphinx_tabs.tabs",
"notfound.extension",
"sphinxext.remoteliteralinclude",
"sphinxext.opengraph",
"sphinxcontrib.ghcontributors",
"sphinx_design",
]
# Configure OpenGraph support
ogp_site_url = "https://docs.photonvision.org/en/latest/"
ogp_site_name = "PhotonVision Documentation"
ogp_image = "https://raw.githubusercontent.com/PhotonVision/photonvision-docs/master/source/assets/RectLogo.png"
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = []
# Enable hover content on glossary term
hoverxref_roles = ["term"]
# Autosection labels prefix document path and filename
autosectionlabel_prefix_document = True
# -- Options for HTML output -------------------------------------------------
html_title = "PhotonVision Docs"
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = "furo"
html_favicon = "assets/RoundLogo.png"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
def setup(app):
app.add_css_file("css/pv-icons.css")
pygments_style = "sphinx"
html_theme_options = {
"sidebar_hide_name": True,
"light_logo": "assets/PhotonVision-Header-onWhite.png",
"dark_logo": "assets/PhotonVision-Header-noBG.png",
"light_css_variables": {
"font-stack": "-apple-system, BlinkMacSystemFont, avenir next, avenir, segoe ui, helvetica neue, helvetica, Ubuntu, roboto, noto, arial, sans-serif;",
"admonition-font-size": "1rem",
"admonition-title-font-size": "1rem",
"color-background-primary": "#ffffff",
"color-background-secondary": "#f7f7f7",
"color-background-hover": "#efeff400",
"color-background-hover--transparent": "#efeff400",
"color-brand-primary": "#006492",
"color-brand-content": "#006492",
"color-foreground-primary": "#2d2d2d",
"color-foreground-secondary": "#39a4d5",
"color-foreground-muted": "#2d2d2d",
"color-foreground-border": "#ffffff",
"color-background-border": "ffffff",
"color-api-overall": "#101010",
},
"dark_css_variables": {
"color-background-primary": "#242c37",
"color-background-secondary": "#006492",
"color-background-hover": "#efeff400",
"color-background-hover--transparent": "#efeff400",
"color-brand-primary": "#ffd843",
"color-brand-secondary": "#39a4d5",
"color-brand-content": "#ffd843",
"color-foreground-primary": "#ffffff",
"color-foreground-secondary": "#ffffff",
"color-foreground-muted": "#ffffff",
"color-foreground-border": "transparent",
"color-background-border": "transparent",
"color-api-overall": "#101010",
"color-inline-code-background": "#0d0d0d",
},
}
suppress_warnings = ["epub.unknown_project_files"]
sphinx_tabs_valid_builders = ["epub", "linkcheck"]
# Excluded links for linkcheck
# These should be periodically checked by hand to ensure that they are still functional
linkcheck_ignore = ["https://www.raspberrypi.com/software/"]

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Best Practices For Competition
==============================
Before Competition
------------------
* Ensure you have spares of the relevant electronics if you can afford it (switch, coprocessor, cameras, etc.).
* Download the latest release .jar onto your computer and update your Pi if necessary (only update if the release is labeled "critical" or similar, we do not recommend updating right before an event in case there are unforeseen bugs).
* Test out PhotonVision at your home setup.
* Ensure that you have set up SmartDashboard / Shuffleboard to view your camera streams during matches.
* Follow all the recommendations under the Networking section in installation (network switch and static IP).
* Use high quality ethernet cables that have been rigorously tested.
* Set up port forwarding using the guide in the Networking section in installation.
During the Competition
----------------------
* Make sure you take advantage of the field calibration time given at the start of the event:
* Bring your robot to the field at the allotted time.
* Turn on your robot and pull up the dashboard on your driver station.
* Point your robot at the target(s) and ensure you get a consistent tracking (you hold one target consistently, the ceiling lights aren't detected, etc.).
* If you have problems with your pipeline, go to the pipeline tuning section and retune the pipeline using the guide there. You want to make your exposure as low as possible with a tight hue value to ensure no extra targets are detected.
* Move the robot close, far, angled, and around the field to ensure no extra targets are found anywhere when looking for a target.
* Go to a practice match to ensure everything is working correctly.
* After field calibration, use the "Export Settings" button in the "Settings" page to create a backup.
* Do this for each coprocessor on your robot that runs PhotonVision, and name your exports with meaningful names.
* This will contain camera information/calibration, pipeline information, network settings, etc.
* In the event of software/hardware failures (IE lost SD Card, broken device), you can then use the "Import Settings" button and select "All Settings" to restore your settings.
* This effectively works as a snapshot of your PhotonVision data that can be restored at any point.
* Before every match, check the ethernet connection going into your coprocessor and that it is seated fully.
* Ensure that exposure is as low as possible and that you don't have the dashboard up when you don't need it to reduce bandwidth.
* Stream at as low of a resolution as possible while still detecting targets to stay within bandwidth limits.

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Filesystem Directory
====================
PhotonVision stores and loads settings in the :code:`photonvision_config` directory, in the same folder as the PhotonVision JAR is stored. On the Pi image as well as the Gloworm, this is in the :code:`/opt/photonvision` directory. The contents of this directory can be exported as a zip archive from the settings page of the interface, under "export settings". This export will contain everything detailed below. These settings can later be uploaded using "import settings", to restore configurations from previous backups.
Directory Structure
-------------------
The directory structure is outlined below.
.. image:: images/configDir.png
:width: 600
:alt: Config directory structure
* calibImgs
- Images saved from the last run of the calibration routine
* cameras
- Contains a subfolder for each camera. This folder contains the following files:
+ pipelines folder, which contains a :code:`json` file for each user-created pipeline.
+ config.json, which contains all camera-specific configuration. This includes FOV, pitch, current pipeline index, and calibration data
+ drivermode.json, which contains settings for the driver mode pipeline
* imgSaves
- Contains images saved with the input/output save commands.
* logs
- Contains timestamped logs in the format :code:`photonvision-YYYY-MM-D_HH-MM-SS.log`. Note that on Pi or Gloworm these timestamps will likely be significantly behind the real time.
* hardwareSettings.json
- Contains hardware settings. Currently this includes only the LED brightness.
* networkSettings.json
- Contains network settings, including team number (or remote network tables address), static/dynamic settings, and hostname.
Importing and Exporting Settings
--------------------------------
The entire settings directory can be exported as a ZIP archive from the settings page.
.. raw:: html
<video width="85%" controls>
<source src="../../_static/assets/import-export-settings.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
A variety of files can be imported back into PhotonVision:
- ZIP Archive (:code:`.zip`)
- Useful for restoring a full configuration from a different PhotonVision instance.
- Single Config File
- Currently-supported Files
- :code:`hardwareConfig.json`
- :code:`hardwareSettings.json`
- :code:`networkSettings.json`
- Useful for simple hardware or network configuration tasks without overwriting all settings.

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NetworkTables API
=================
About
^^^^^
.. warning::
PhotonVision interfaces with PhotonLib, our vendor dependency, using NetworkTables. If you are running PhotonVision on a robot (ie. with a RoboRIO), you should **turn the NetworkTables server switch (in the settings tab) off** in order to get PhotonLib to work. Also ensure that you set your team number. The NetworkTables server should only be enabled if you know what you're doing!
API
^^^
.. warning:: NetworkTables is not a supported setup/viable option when using PhotonVision as we only send one target at a time (this is problematic when using AprilTags, which will return data from multiple tags at once). We recommend using PhotonLib.
The tables below contain the the name of the key for each entry that PhotonVision sends over the network and a short description of the key. The entries should be extracted from a subtable with your camera's nickname (visible in the PhotonVision UI) under the main ``photonvision`` table.
Getting Target Information
--------------------------
+-------------------+--------------+--------------------------------------------------------------------------+
| Key | Type | Description |
+===================+==============+==========================================================================+
| ``rawBytes`` | ``byte[]`` | A byte-packed string that contains target info from the same timestamp. |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``latencyMillis`` | ``double`` | The latency of the pipeline in milliseconds. |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``hasTarget`` | ``boolean`` | Whether the pipeline is detecting targets or not. |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``targetPitch`` | ``double`` | The pitch of the target in degrees (positive up). |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``targetYaw`` | ``double`` | The yaw of the target in degrees (positive right). |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``targetArea`` | ``double`` | The area (percent of bounding box in screen) as a percent (0-100). |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``targetSkew`` | ``double`` | The skew of the target in degrees (counter-clockwise positive). |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``targetPose`` | ``double[]`` | The pose of the target relative to the robot (x, y, z, qw, qx, qy, qz) |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``targetPixelsX`` | ``double`` | The target crosshair location horizontally, in pixels (origin top-right) |
+-------------------+--------------+--------------------------------------------------------------------------+
| ``targetPixelsY`` | ``double`` | The target crosshair location vertically, in pixels (origin top-right) |
+-------------------+--------------+--------------------------------------------------------------------------+
Changing Settings
-----------------
+-------------------+-------------+-----------------------------+
| Key | Type | Description |
+===================+=============+=============================+
| ``pipelineIndex`` | ``int`` | Changes the pipeline index. |
+-------------------+-------------+-----------------------------+
| ``driverMode`` | ``boolean`` | Toggles driver mode. |
+-------------------+-------------+-----------------------------+
Saving Images
-----------------
PhotonVision can save images to file on command. The image is saved when PhotonVision detects the command went from ``false`` to ``true``.
PhotonVision will automatically set these back to ``false`` after 500ms.
Be careful saving images rapidly - it will slow vision processing performance and take up disk space very quickly.
Images are returned as part of the .zip package from the "Export" operation in the Settings tab.
+----------------------+-------------+----------------------------------------------------+
| Key | Type | Description |
+======================+=============+====================================================+
| ``inputSaveImgCmd`` | ``boolean`` | Triggers saving the current input image to file. |
+----------------------+-------------+----------------------------------------------------+
| ``outputSaveImgCmd`` | ``boolean`` | Triggers saving the current output image to file. |
+----------------------+-------------+----------------------------------------------------+
.. warning:: If you manage to make calls to these commands faster than 500ms (between calls), additional photos will not be captured.
Global Entries
--------------
These entries are global, meaning that they should be called on the main ``photonvision`` table.
+-------------+---------+----------------------------------------------------------+
| Key | Type | Description |
+=============+=========+==========================================================+
| ``ledMode`` | ``int`` | Sets the LED Mode (-1: default, 0: off, 1: on, 2: blink) |
+-------------+---------+----------------------------------------------------------+
.. warning::
Setting the LED mode to -1 (default) when `multiple` cameras are connected may result in unexpected behavior. :ref:`This is a known limitation of PhotonVision. <docs/troubleshooting/common-errors:LED Control>`
Single camera operation should work without issue.

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2D AprilTag Tuning / Tracking
=============================
Tracking Apriltags
------------------
Before you get started tracking AprilTags, ensure that you have followed the previous sections on installation, wiring and networking. Next, open the Web UI, go to the top right card, and switch to the "AprilTag" or "Aruco" type. You should see a screen similar to the one below.
.. image:: images/apriltag.png
:align: center
|
You are now able to detect and track AprilTags in 2D (yaw, pitch, roll, etc.). In order to get 3D data from your AprilTags, please see :ref:`here. <docs/apriltag-pipelines/3D-tracking:3D Tracking>`
Tuning AprilTags
----------------
AprilTag pipelines come with reasonable defaults to get you up and running with tracking. However, in order to optimize your performance and accuracy, you must tune your AprilTag pipeline using the settings below. Note that the settings below are different between the AprilTag and Aruco detectors but the concepts are the same.
.. image:: images/apriltag-tune.png
:scale: 45 %
:align: center
|
Target Family
^^^^^^^^^^^^^
Target families are defined by two numbers (before and after the h). The first number is the number of bits the tag is able to encode (which means more tags are available in the respective family) and the second is the hamming distance. Hamming distance describes the ability for error correction while identifying tag ids. A high hamming distance generally means that it will be easier for a tag to be identified even if there are errors. However, as hamming distance increases, the number of available tags decreases. The 2024 FRC game will be using 36h11 tags, which can be found `here <https://github.com/AprilRobotics/apriltag-imgs/tree/master/tag36h11>`_.
Decimate
^^^^^^^^
Decimation (also known as down-sampling) is the process of reducing the sampling frequency of a signal (in our case, the image). Increasing decimate will lead to an increased detection rate while decreasing detection distance. We recommend keeping this at the default value.
Blur
^^^^
This controls the sigma of Gaussian blur for tag detection. In clearer terms, increasing blur will make the image blurrier, decreasing it will make it closer to the original image. We strongly recommend that you keep blur to a minimum (0) due to it's high performance intensity unless you have an extremely noisy image.
Threads
^^^^^^^
Threads refers to the threads within your coprocessor's CPU. The theoretical maximum is device dependent, but we recommend that users to stick to one less than the amount of CPU threads that your coprocessor has. Increasing threads will increase performance at the cost of increased CPU load, temperature increase, etc. It may take some experimentation to find the most optimal value for your system.
Refine Edges
^^^^^^^^^^^^
The edges of the each polygon are adjusted to "snap to" high color differences surrounding it. It is recommended to use this in tandem with decimate as it can increase the quality of the initial estimate.
Pose Iterations
^^^^^^^^^^^^^^^
Pose iterations represents the amount of iterations done in order for the AprilTag algorithm to converge on its pose solution(s). A smaller number between 0-100 is recommended. A smaller amount of iterations cause a more noisy set of poses when looking at the tag straight on, while higher values much more consistently stick to a (potentially wrong) pair of poses. WPILib contains many useful filter classes in order to account for a noisy tag reading.
Max Error Bits
^^^^^^^^^^^^^^
Max error bits, also known as hamming distance, is the number of positions at which corresponding pieces of data / tag are different. Put more generally, this is the number of bits (think of these as squares in the tag) that need to be changed / corrected in the tag to correctly detect it. A higher value means that more tags will be detected while a lower value cuts out tags that could be "questionable" in terms of detection.
We recommend a value of 0 for the 16h5 and 7+ for the 36h11 family.
Decision Margin Cutoff
^^^^^^^^^^^^^^^^^^^^^^
The decision margin cutoff is how much “margin” the detector has left before it rejects a tag; increasing this rejects poorer tags. We recommend you keep this value around a 30.

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3D Tracking
===========
3D AprilTag tracking will allow you to track the real-world position and rotation of a tag relative to the camera's image sensor. This is useful for robot pose estimation and other applications like autonomous scoring. In order to use 3D tracking, you must first :ref:`calibrate your camera <docs/calibration/calibration:Calibrating Your Camera>`. Once you have, you need to enable 3D mode in the UI and you will now be able to get 3D pose information from the tag! For information on getting and using this information in your code, see :ref:`the programming reference. <docs/programming/index:Programming Reference>`.
Ambiguity
---------
Translating from 2D to 3D using data from the calibration and the four tag corners can lead to "pose ambiguity", where it appears that the AprilTag pose is flipping between two different poses. You can read more about this issue `here. <https://docs.wpilib.org/en/stable/docs/software/vision-processing/apriltag/apriltag-intro.html#d-to-3d-ambiguity>` Ambiguity is calculated as the ratio of reprojection errors between two pose solutions (if they exist), where reprojection error is the error corresponding to the image distance between where the apriltag's corners are detected vs where we expect to see them based on the tag's estimated camera relative pose.
There are a few steps you can take to resolve/mitigate this issue:
1. Mount cameras at oblique angles so it is less likely that the tag will be seen straight on.
2. Use the :ref:`MultiTag system <docs/apriltag-pipelines/multitag:MultiTag Localization>` in order to combine the corners from multiple tags to get a more accurate and unambiguous pose.
3. Reject all tag poses where the ambiguity ratio (available via PhotonLib) is greater than 0.2.

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About Apriltags
===============
.. image:: images/pv-apriltag.png
:align: center
:scale: 20 %
AprilTags are a common type of visual fiducial marker. Visual fiducial markers are artificial landmarks added to a scene to allow "localization" (finding your current position) via images. In simpler terms, tags mark known points of reference that you can use to find your current location. They are similar to QR codes in which they encode information, however, they hold only a single number. By placing AprilTags in known locations around the field and detecting them using PhotonVision, you can easily get full field localization / pose estimation. Alternatively, you can use AprilTags the same way you used retroreflective tape, simply using them to turn to goal without any pose estimation.
A more technical explanation can be found in the `WPILib documentation <https://docs.wpilib.org/en/latest/docs/software/vision-processing/apriltag/apriltag-intro.html>`_.
.. note:: You can get FIRST's `official PDF of the targets used in 2024 here <https://firstfrc.blob.core.windows.net/frc2024/FieldAssets/Apriltag_Images_and_User_Guide.pdf>`_.

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Coordinate Systems
==================
Field and Robot Coordinate Frame
--------------------------------
PhotonVision follows the WPILib conventions for the robot and field coordinate systems, as defined `here <https://docs.wpilib.org/en/stable/docs/software/advanced-controls/geometry/coordinate-systems.html>`_.
You define the camera to robot transform in the robot coordinate frame.
Camera Coordinate Frame
-----------------------
OpenCV by default uses x-left/y-down/z-out for camera transforms. PhotonVision applies a base rotation to this transformation to make robot to tag transforms more in line with the WPILib coordinate system. The x, y, and z axes are also shown in red, green, and blue in the 3D mini-map and targeting overlay in the UI.
* The origin is the focal point of the camera lens
* The x-axis points out of the camera
* The y-axis points to the left
* The z-axis points upwards
.. image:: images/camera-coord.png
:scale: 45 %
:align: center
|
.. image:: images/multiple-tags.png
:scale: 45 %
:align: center
|
AprilTag Coordinate Frame
-------------------------
The AprilTag coordinate system is defined as follows, relative to the center of the AprilTag itself, and when viewing the tag as a robot would. Again, PhotonVision changes this coordinate system to be more in line with WPILib. This means that a robot facing a tag head-on would see a robot-to-tag transform with a translation only in x, and a rotation of 180 degrees about z. The tag coordinate system is also shown with x/y/z in red/green/blue in the UI target overlay and mini-map.
* The origin is the center of the tag
* The x-axis is normal to the plane the tag is printed on, pointing outward from the visible side of the tag.
* The y-axis points to the right
* The z-axis points upwards
.. image:: images/apriltag-coords.png
:scale: 45 %
:align: center
|

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AprilTag Pipeline Types
=======================
PhotonVision offers two different AprilTag pipeline types based on different implementations of the underlying algorithm. Each one has its advantages / disadvantages, which are detailed below.
.. note:: Note that both of these pipeline types detect AprilTag markers and are just two different algorithms for doing so.
AprilTag
--------
The AprilTag pipeline type is based on the `AprilTag <https://april.eecs.umich.edu/software/apriltag.html>`_ library from the University of Michigan and we recommend it for most use cases. It is (to our understanding) most accurate pipeline type, but is also ~2x slower than AruCo. This was the pipeline type used by teams in the 2023 season and is well tested.
AruCo
-----
The AruCo pipeline is based on the `AruCo <https://docs.opencv.org/4.8.0/d9/d6a/group__aruco.html>`_ library implementation from OpenCV. It is ~2x higher fps and ~2x lower latency than the AprilTag pipeline type, but is less accurate. We recommend this pipeline type for teams that need to run at a higher framerate or have a lower powered device. This pipeline type is new for the 2024 season and is not as well tested as AprilTag.

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AprilTag Detection
==================
.. toctree::
about-apriltags
detector-types
2D-tracking-tuning
3D-tracking
multitag
coordinate-systems

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MultiTag Localization
=====================
PhotonVision can combine AprilTag detections from multiple simultaneously observed AprilTags from a particular camera with information about where tags are expected to be located on the field to produce a better estimate of where the camera (and therefore robot) is located on the field. PhotonVision can calculate this multi-target result on your coprocessor, reducing CPU usage on your RoboRio. This result is sent over NetworkTables along with other detected targets as part of the ``PhotonPipelineResult`` provided by PhotonLib.
.. warning:: MultiTag requires an accurate field layout JSON to be uploaded! Differences between this layout and the tags' physical location will drive error in the estimated pose output.
Enabling MultiTag
^^^^^^^^^^^^^^^^^
Ensure that your camera is calibrated and 3D mode is enabled. Navigate to the Output tab and enable "Do Multi-Target Estimation". This enables MultiTag to use the uploaded field layout JSON to calculate your camera's pose in the field. This 3D transform will be shown as an additional table in the "targets" tab, along with the IDs of AprilTags used to compute this transform.
.. image:: images/multitag-ui.png
:width: 600
:alt: Multitarget enabled and running in the PhotonVision UI
.. note:: By default, enabling multi-target will disable calculating camera-to-target transforms for each observed AprilTag target to increase performance; the X/Y/angle numbers shown in the target table of the UI are instead calculated using the tag's expected location (per the field layout JSON) and the field-to-camera transform calculated using MultiTag. If you additionally want the individual camera-to-target transform calculated using SolvePNP for each target, enable "Always Do Single-Target Estimation".
This multi-target pose estimate can be accessed using PhotonLib. We suggest using :ref:`the PhotonPoseEstimator class <docs/programming/photonlib/robot-pose-estimator:AprilTags and PhotonPoseEstimator>` with the ``MULTI_TAG_PNP_ON_COPROCESSOR`` strategy to simplify code, but the transform can be directly accessed using ``getMultiTagResult``/``MultiTagResult()`` (Java/C++).
.. tab-set-code::
.. code-block:: java
var result = camera.getLatestResult();
if (result.getMultiTagResult().estimatedPose.isPresent) {
Transform3d fieldToCamera = result.getMultiTagResult().estimatedPose.best;
}
.. code-block:: C++
auto result = camera.GetLatestResult();
if (result.MultiTagResult().result.isPresent) {
frc::Transform3d fieldToCamera = result.MultiTagResult().result.best;
}
.. note:: The returned field to camera transform is a transform from the fixed field origin to the camera's coordinate system. This does not change based on alliance color, and by convention is on the BLUE ALLIANCE wall.
Updating the Field Layout
^^^^^^^^^^^^^^^^^^^^^^^^^
PhotonVision ships by default with the `2024 field layout JSON <https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2024-crescendo.json>`_. The layout can be inspected by navigating to the settings tab and scrolling down to the "AprilTag Field Layout" card, as shown below.
.. image:: images/field-layout.png
:width: 600
:alt: The currently saved field layout in the Photon UI
An updated field layout can be uploaded by navigating to the "Device Control" card of the Settings tab and clicking "Import Settings". In the pop-up dialog, select the "AprilTag Layout" type and choose an updated layout JSON (in the same format as the WPILib field layout JSON linked above) using the paperclip icon, and select "Import Settings". The AprilTag layout in the "AprilTag Field Layout" card below should be updated to reflect the new layout.
.. note:: Currently, there is no way to update this layout using PhotonLib, although this feature is under consideration.

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Calibrating Your Camera
=======================
.. important:: In order to detect AprilTags and use 3D mode, your camera must be calibrated at the desired resolution! Inaccurate calibration will lead to poor performance.
To calibrate a camera, images of a chessboard (or grid of dots, or other target) are taken. by comparing where the grid corners (or dots) should be in object space (for example, a dot once every inch in an 8x6 grid) with where they appear in the camera image, we can find a least-squares estimate for intrinsic camera properties like focal lengths, center point, and distortion coefficients. For more on camera calibration, please review the `OpenCV documentation <https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html>`_.
.. warning:: While any resolution can be calibrated, resolutions lower than 960x720 are often too low to provide accurate results. Additionally, high resolutions may be too performance intensive for a coprocessor like a Raspberry Pi to handle (solutions to this are being looked into). Thus, we recommend 960x720 when using 3D mode.
.. note::The calibration data collected during calibration is specific to each physical camera, as well as each individual resolution.
Calibration Tips
----------------
Accurate camera calibration is required in order to get accurate pose measurements when using AprilTags and 3D mode. The tips below should help ensure success:
1. Practice calibration using your laptop webcam and https://www.calibdb.net/. The target can be found on the website and should be printed out if possible. Once you print it out, try to line up your target with the overlay on the screen as best as possible. The point of this practice is to notice how you are prompted to place targets in certain positions on the screen that make sure you account for all regions of the sensor. The chessboard should (in general) not be facing parallel to the camera (straight on), nor should it be aligned with any of the camera axes (ie, rotated only about an axis going left/right, up/down, or out-of-the-camera).
2. Ensure your the images you take have the target in different positions and angles, with as big of a difference between angles as possible. It is important to make sure the target overlay still lines up with the board while doing this. Tilt no more than 45 degrees.
3. Use as big of a calibration target as your printer can print.
4. Ensure that your printed pattern has enough white border around it.
5. Ensure your camera stays in one position during the duration of the calibration.
6. Make sure you get all 12 images from varying distances and angles.
7. Take at least one image that covers the total image area, and generally ensure that you get even coverage of the lens with your image set.
8. Have good lighting, having a diffusely lit target would be best (light specifically shining on the target without shadows).
9. Ensure the calibration target is completely flat and does not bend or fold in any way. It should be mounted/taped down to something flat and then used for calibration, do not just hold it up.
10. Avoid having targets that are parallel to the lens of the camera / straight on towards the camera as much as possible. You want angles and variations within your calibration images.
Following the ideas above should help in getting an accurate calibration.
Calibration Steps
-----------------
Your camera can be calibrated using either the utility built into PhotonVision, which performs all the calculations on your coprocessor, or using a website such as `calibdb <https://calibdb.net/>`_, which uses a USB webcam connected to your laptop. The integrated calibration utility is currently the only one that works with ribbon-cable CSI cameras or Limelights, but for USB webcams, calibdb is the preferred option.
Calibrating using calibdb
-------------------------
Calibdb uses a modified chessboard/aruco marker combination target called `ChArUco targets. <https://docs.opencv.org/4.8.0/df/d4a/tutorial_charuco_detection.html>`_ The website currently only supports Chrome browser.
Download and print out (or display on a monitor) the calibration by clicking Show Pattern. Click "Calibrate" and align your camera with the ghost overlay of the calibration board. The website automatically calculates the next position and displays it for you. When complete, download the calibration (do **not** use the OpenCV format). Reconnect your camera to your coprocessor and navigate to the PhotonVision web interface's camera tab. Ensure the correct camera is selected, and click the "Import from CalibDB" button. Your calibration data will be automatically saved and applied!
Calibrating using PhotonVision
------------------------------
1. Navigate to the calibration section in the UI.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The Cameras tab of the UI houses PhotonVision's camera calibration tooling. It assists users with calibrating their cameras, as well as allows them to view previously calibrated resolutions. We support both dot and chessboard calibrations.
2. Print out the calibration target.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In the Camera Calibration tab, we'll print out the calibration target using the "Download" button. This should be printed on 8.5x11 printer paper. This page shows using an 8x8 chessboard.
.. warning:: Ensure that there is no scaling applied during printing (it should be at 100%) and that the PDF is printed as is on regular printer paper. Check the square size with calipers or an accurate measuring device after printing to ensure squares are sized properly, and enter the true size of the square in the UI text box. For optimal results, various resources are available online to calibrate your specific printer if needed.
3. Select calibration resolution and fill in appropriate target data.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We'll next select a resolution to calibrate and populate our pattern spacing and board size. The provided chessboard is 8 squares in width and height, and each square should be about 1in across. Mine measured with a caliper was 0.96in, but this will vary per printer. Finally, once our entered data is correct, we'll click "start calibration."
4. Take at calibration images from various angles.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Now, we'll capture images of our chessboard from various angles. The most important part of this step is to make sure that the chessboard overlay matches the chessboard in your image. The further the overdrawn points are from the true position of the chessboard corners, the less accurate the final calibration will be. We'll want to capture at least 12 images, trying to take one in each region of the camera sensor. Once we've got our images, we'll click "Finish calibration" and wait for the calibration process to complete. If all goes well, the mean error and standard deviation will be shown in the table on the right.
.. raw:: html
<video width="85%" controls>
<source src="../../_static/assets/calibration_small.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
Accessing Calibration Images
----------------------------
Details about a particular calibration can be viewed by clicking on that resolution in the calibrations tab. This tab allows you to download raw calibration data, upload a previous calibration, and inspect details about calculated camera intrinsics.
.. image:: images/cal-details.png
:width: 600
:alt: Captured calibration images
.. note:: More info on what these parameters mean can be found in `OpenCV's docs <https://docs.opencv.org/4.8.0/d4/d94/tutorial_camera_calibration.html>`_
- Fx/Fy: Estimated camera focal length, in mm
- Fx/Cy: Estimated camera optical center, in pixels. This should be at about the center of the image
- Distortion: OpenCV camera model distortion coefficients
- FOV: calculated using estimated focal length and image size. Useful for gut-checking calibration results
- Mean Err: Mean reprojection error, or distance between expected and observed chessboard cameras for the full calibration dataset
Below these outputs are the snapshots collected for calibration, along with a per-snapshot mean reprojection error. A snapshot with a larger reprojection error might indicate a bad snapshot, due to effects such as motion blur or misidentified chessboard corners.
Calibration images can also be extracted from the downloaded JSON file using `this Python script <https://raw.githubusercontent.com/PhotonVision/photonvision/master/devTools/calibrationUtils.py>`_. This script will unpack calibration images, and also generate a VNL file for use `with mrcal <https://mrcal.secretsauce.net/>`_.
::
python3 /path/to/calibrationUtils.py path/to/photon_calibration.json /path/to/output/folder
.. image:: images/unpacked-json.png
:width: 600
:alt: Captured calibration images
Investigating Calibration Data with mrcal
-----------------------------------------
`mrcal <https://mrcal.secretsauce.net/tour.html>`_ is a command-line tool for camera calibration and visualization. PhotonVision has the option to use the mrcal backend during camera calibration to estimate intrinsics. mrcal can also be used post-calibration to inspect snapshots and provide feedback. These steps will closely follow the `mrcal tour <https://mrcal.secretsauce.net/tour-initial-calibration.html>`_ -- I'm aggregating commands and notes here, but the mrcal documentation is much more thorough.
Start by `Installing mrcal <https://mrcal.secretsauce.net/install.html>`_. Note that while mrcal *calibration* using PhotonVision is supported on all platforms, but investigation right now only works on Linux. Some users have also reported luck using `WSL 2 on Windows <https://learn.microsoft.com/en-us/windows/wsl/tutorials/gui-apps>`ap_ as well. You may also need to install ``feedgnuplot``. On Ubuntu systems, these commands should be run from a standalone terminal and *not* the one `built into vscode <https://github.com/ros2/ros2/issues/1406>`_.
Let's run ``calibrationUtils.py`` as described above, and then cd into the output folder. From here, you can follow the mrcal tour, just replacing the VNL filename and camera imager size as necessary. My camera calibration was at 1280x720, so I've set the XY limits to that below.
::
$ cd /path/to/output/folder
$ ls
matt@photonvision:~/Documents/Downloads/2024-01-02_lifecam_1280$ ls
corners.vnl img0.png img10.png img11.png img12.png img13.png img1.png
img2.png img3.png img4.png img5.png img6.png img7.png img8.png
img9.png cameramodel_0.cameramodel
$ < corners.vnl \
vnl-filter -p x,y | \
feedgnuplot --domain --square --set 'xrange [0:1280] noextend' --set 'yrange [720:0] noextend'
.. image:: images/mrcal-coverage.svg
:alt: A diagram showing the locations of all detected chessboard corners.
As you can see, we didn't do a fantastic job of covering our whole camera sensor -- there's a big gap across the whole right side, for example. We also only have 14 calibration images. We've also got our "cameramodel" file, which can be used by mrcal to display additional debug info.
Let's inspect our reprojection error residuals. We expect their magnitudes and directions to be random -- if there's patterns in the colors shown, then our calibration probably doesn't fully explain our physical camera sensor.
::
$ mrcal-show-residuals --magnitudes --set 'cbrange [0:1.5]' ./camera-0.cameramodel
$ mrcal-show-residuals --directions --unset key ./camera-0.cameramodel
.. image:: images/residual-magnitudes.svg
:alt: A diagram showing residual magnitudes
.. image:: images/residual-directions.svg
:alt: A diagram showing residual directions
Clearly we don't have anywhere near enough data to draw any meaningful conclusions (yet). But for fun, let's dig into `camera uncertainty estimation <https://mrcal.secretsauce.net/tour-uncertainty.html>`_. This diagram shows how expected projection error changes due to noise in calibration inputs. Lower projection error across a larger area of the sensor imply a better calibration that more fully covers the whole sensor. For my calibration data, you can tell the projection error isolines (lines of constant expected projection error) are skewed to the left, following my dataset (which was also skewed left).
::
$ mrcal-show-projection-uncertainty --unset key ./cameramodel_0.cameramodel
.. image:: images/camera-uncertainty.svg
:alt: A diagram showing camera uncertainty

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Contributing to PhotonVision Projects
=====================================
.. toctree::
photonvision/index
photonvision-docs/index

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Building the PhotonVision Documentation
=======================================
To build the PhotonVision documentation, you will require `Git <https://git-scm.com>`_ and `Python 3.6 or greater <https://www.python.org>`_.
Cloning the Documentation Repository
------------------------------------
If you are planning on contributing, it is recommended to create a fork of the `main docs repository <https://github.com/PhotonVision/photonvision-docs>`_. To clone this fork, run the following command in a terminal window:
``git clone https://github.com/[your username]/photonvision-docs``
Installing Python Dependencies
------------------------------
You must install a set of Python dependencies in order to build the documentation. To do so, you can run the following command in the root project directory:
``python -m pip install -r requirements.txt``
Building the Documentation
--------------------------
In order to build the documentation, you can run the following command in the root project directory:
``make html``
.. note:: You may have to run ``./make html`` on Windows.
Opening the Documentation
-------------------------
The built documentation is located at ``build/html/index.html``.
Docs Builds on Pull Requests
----------------------------
Pre-merge builds of docs can be found at: ``https://photonvision-docs--PRNUMBER.org.readthedocs.build/en/PRNUMBER/index.html``. These docs are republished on every commit to a pull request made to PhotonVision/photonvision-docs. For example, PR 325 would have pre-merge documentation published to ``https://photonvision-docs--325.org.readthedocs.build/en/325/index.html``

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Contributing to PhotonVision Documentation
==========================================
.. toctree::
building-docs
style-guide
top-contributors

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Style Guide
===========
PhotonVision follows the frc-docs style guide which can be found `here <https://docs.wpilib.org/en/stable/docs/contributing/style-guide.html>`_. In order to run the linter locally (which builds on doc8 and checks for compliance with the style guide), follow the instructions `on GitHub <https://github.com/wpilibsuite/ohnoyoudidnt>`_.

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Top Contributors
================
.. ghcontributors:: PhotonVision/photonvision-docs
:limit: 10

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Build Instructions
==================
This section contains the build instructions from the source code available at `our GitHub page <https://github.com/PhotonVision/photonvision>`_.
Development Setup
-----------------
Prerequisites
~~~~~~~~~~~~~
| **Java Development Kit:** This project requires Java Development Kit (JDK) 17 to be compiled. This is the same Java version that comes with WPILib for 2025+. If you don't have this JDK with WPILib, you can follow the instructions to install JDK 17 for your platform `here <https://bell-sw.com/pages/downloads/#jdk-17-lts>`_.
| **Node JS:** The UI is written in Node JS. To compile the UI, Node 14.18.0 to Node 16.0.0 is required. To install Node JS follow the instructions for your platform `on the official Node JS website <https://nodejs.org/en/download/>`_. However, modify this line
.. code-block:: bash
nvm install 20
so that it instead reads
.. code-block:: javascript
nvm install 14.18.0
Compiling Instructions
----------------------
Getting the Source Code
~~~~~~~~~~~~~~~~~~~~~~~
Get the source code from git:
.. code-block:: bash
git clone https://github.com/PhotonVision/photonvision
or alternatively download the source code from github and extract the zip:
.. image:: assets/git-download.png
:width: 600
:alt: Download source code from git
Install Necessary Node JS Dependencies
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In the photon-client directory:
.. code-block:: bash
npm install
Build and Copy UI to Java Source
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In the root directory:
.. tab-set::
.. tab-item:: Linux
``./gradlew buildAndCopyUI``
.. tab-item:: macOS
``./gradlew buildAndCopyUI``
.. tab-item:: Windows (cmd)
``gradlew buildAndCopyUI``
Build and Run PhotonVision
~~~~~~~~~~~~~~~~~~~~~~~~~~
To compile and run the project, issue the following command in the root directory:
.. tab-set::
.. tab-item:: Linux
``./gradlew run``
.. tab-item:: macOS
``./gradlew run``
.. tab-item:: Windows (cmd)
``gradlew run``
Running the following command under the root directory will build the jar under ``photon-server/build/libs``:
.. tab-set::
.. tab-item:: Linux
``./gradlew shadowJar``
.. tab-item:: macOS
``./gradlew shadowJar``
.. tab-item:: Windows (cmd)
``gradlew shadowJar``
Build and Run PhotonVision on a Raspberry Pi Coprocessor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
As a convenience, the build has a built-in `deploy` command which builds, deploys, and starts the current source code on a coprocessor.
An architecture override is required to specify the deploy target's architecture.
.. tab-set::
.. tab-item:: Linux
``./gradlew clean``
``./gradlew deploy -PArchOverride=linuxarm64``
.. tab-item:: macOS
``./gradlew clean``
``./gradlew deploy -PArchOverride=linuxarm64``
.. tab-item:: Windows (cmd)
``gradlew clean``
``gradlew deploy -PArchOverride=linuxarm64``
The ``deploy`` command is tested against Raspberry Pi coprocessors. Other similar coprocessors may work too.
Using PhotonLib Builds
~~~~~~~~~~~~~~~~~~~~~~
The build process includes the following task:
.. tab-set::
.. tab-item:: Linux
``./gradlew generateVendorJson``
.. tab-item:: macOS
``./gradlew generateVendorJson``
.. tab-item:: Windows (cmd)
``gradlew generateVendorJson``
This generates a vendordep JSON of your local build at ``photon-lib/build/generated/vendordeps/photonlib.json``.
The photonlib source can be published to your local maven repository after building:
.. tab-set::
.. tab-item:: Linux
``./gradlew publishToMavenLocal``
.. tab-item:: macOS
``./gradlew publishToMavenLocal``
.. tab-item:: Windows (cmd)
``gradlew publishToMavenLocal``
After adding the generated vendordep to your project, add the following to your project's ``build.gradle`` under the ``plugins {}`` block.
.. code-block:: Java
repositories {
mavenLocal()
}
Debugging PhotonVision Running Locally
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
One way is by running the program using gradle with the :code:`--debug-jvm` flag. Run the program with :code:`./gradlew run --debug-jvm`, and attach to it with VSCode by adding the following to :code:`launch.json`. Note args can be passed with :code:`--args="foobar"`.
.. code-block::
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"type": "java",
"name": "Attach to Remote Program",
"request": "attach",
"hostName": "localhost",
"port": "5005",
"projectName": "photon-core",
}
]
}
PhotonVision can also be run using the gradle tasks plugin with :code:`"args": "--debug-jvm"` added to launch.json.
Debugging PhotonVision Running on a CoProcessor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Set up a VSCode configuration in :code:`launch.json`
.. code-block::
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"type": "java",
"name": "Attach to CoProcessor",
"request": "attach",
"hostName": "photonvision.local",
"port": "5801",
"projectName": "photon-core"
},
]
}
Stop any existing instance of PhotonVision.
Launch the program with the following additional argument to the JVM: :code:`java -jar -agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=*:5801 photonvision.jar`
Once the program says it is listening on port 5801, launch the debug configuration in VSCode.
The program will wait for the VSCode debugger to attach before proceeding.
Running examples
~~~~~~~~~~~~~~~~
You can run one of the many built in examples straight from the command line, too! They contain a fully featured robot project, and some include simulation support. The projects can be found inside the photonlib-java-examples and photonlib-cpp-examples subdirectories, respectively. The projects currently available include:
- photonlib-java-examples:
- aimandrange:simulateJava
- aimattarget:simulateJava
- getinrange:simulateJava
- simaimandrange:simulateJava
- simposeest:simulateJava
- photonlib-cpp-examples:
- aimandrange:simulateNative
- getinrange:simulateNative
To run them, use the commands listed below. Photonlib must first be published to your local maven repository, then the copyPhotonlib task will copy the generated vendordep json file into each example. After that, the simulateJava/simulateNative task can be used like a normal robot project. Robot simulation with attached debugger is technically possible by using simulateExternalJava and modifying the launch script it exports, though unsupported.
.. code-block::
~/photonvision$ ./gradlew publishToMavenLocal
~/photonvision$ cd photonlib-java-examples
~/photonvision/photonlib-java-examples$ ./gradlew copyPhotonlib
~/photonvision/photonlib-java-examples$ ./gradlew <example-name>:simulateJava
~/photonvision$ cd photonlib-cpp-examples
~/photonvision/photonlib-cpp-examples$ ./gradlew copyPhotonlib
~/photonvision/photonlib-cpp-examples$ ./gradlew <example-name>:simulateNative

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Contributing to PhotonVision
============================
.. toctree::
build-instructions
top-contributors

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Top Contributors
================
.. ghcontributors:: PhotonVision/photonvision
:limit: 10

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About PhotonVision
==================
Description
^^^^^^^^^^^
PhotonVision is a free, fast, and easy-to-use vision processing solution for the *FIRST*\ Robotics Competition. PhotonVision is designed to get vision working on your robot *quickly*, without the significant cost of other similar solutions.
Using PhotonVision, teams can go from setting up a camera and coprocessor to detecting and tracking targets by simply tuning sliders. With an easy to use interface, comprehensive documentation, and a feature rich vendor dependency, no experience is necessary to use PhotonVision. No matter your resources, using PhotonVision is easy compared to its alternatives.
Advantages
^^^^^^^^^^
PhotonVision has a myriad of advantages over similar solutions, including:
Affordable
----------
Compared to alternatives, PhotonVision is much cheaper to use (at the cost of your coprocessor and camera) compared to alternatives that cost $400. This allows your team to save money while still being competitive.
Easy to Use User Interface
--------------------------
The PhotonVision user interface is simple and modular, making things easier for the user. With a simpler interface, you can focus on what matters most, tracking targets, rather than how to use our UI. A major unique quality is that the PhotonVision UI includes an offline copy of our documentation for your ease of access at competitions.
PhotonLib Vendor Dependency
---------------------------
The PhotonLib vendor dependency allows you to easily get necessary target data (without having to work directly with NetworkTables) while also providing utility methods to get distance and position on the field. This helps your team focus less on getting data and more on using it to do cool things. This also has the benefit of having a structure that ensures all data is from the same timestamp, which is helpful for latency compensation.
User Calibration
----------------
Using PhotonVision allows the user to calibrate for their specific camera, which will get you the best tracking results. This is extremely important as every camera (even if it is the same model) will have it's own quirks and user calibration allows for those to be accounted for.
High FPS Processing
-------------------
Compared to alternative solutions, PhotonVision boasts higher frames per second which allows for a smoother video stream and detection of targets to ensure you aren't losing out on any performance.
Low Latency
-----------
PhotonVision provides low latency processing to make sure you get vision measurements as fast as possible, which makes complex vision tasks easier. We guarantee that all measurements are sent from the same timestamp, making life easier for your programmers.
Fully Open Source and Active Developer Community
------------------------------------------------
You can find all of our code on `GitHub <https://github.com/PhotonVision>`_, including code for our main program, documentation, vendor dependency (PhotonLib), and more. This helps you see everything working behind the scenes and increases transparency. This also allows users to make pull requests for features that they want to add in to PhotonVision that will be reviewed by the development team. PhotonVision is licensed under the GNU General Public License (GPLv3) which you can learn more about `here <https://www.gnu.org/licenses/quick-guide-gplv3.html>`_.
Multi-Camera Support
--------------------
You can use multiple cameras within PhotonVision, allowing you to see multiple angles without the need to buy multiple coprocessors. This makes vision processing more affordable and simpler for your team.
Comprehensive Documentation
---------------------------
Using our comprehensive documentation, you will be able to easily start vision processing by following a series of simple steps.

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Combining Aiming and Getting in Range
=====================================
The following example is from the PhotonLib example repository (`Java <https://github.com/PhotonVision/photonvision/tree/master/photonlib-java-examples/aimandrange>`_/`C++ <https://github.com/PhotonVision/photonvision/tree/master/photonlib-cpp-examples/aimandrange>`_).
Knowledge and Equipment Needed
-----------------------------------------------
- Everything required in :ref:`Aiming at a Target <docs/examples/aimingatatarget:Knowledge and Equipment Needed>` and :ref:`Getting in Range of the Target <docs/examples/gettinginrangeofthetarget:Knowledge and Equipment Needed>`.
Code
-------
Now that you know how to both aim and get in range of the target, it is time to combine them both at the same time. This example will take the previous two code examples and make them into one function using the same tools as before. With this example, you now have all the knowledge you need to use PhotonVision on your robot in any game.
.. tab-set::
.. tab-item:: Java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-java-examples/aimandrange/src/main/java/frc/robot/Robot.java
:language: java
:lines: 42-111
:linenos:
:lineno-start: 42
.. tab-item:: C++ (Header)
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-cpp-examples/aimandrange/src/main/include/Robot.h
:language: cpp
:lines: 27-71
:linenos:
:lineno-start: 27
.. tab-item:: C++ (Source)
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-cpp-examples/aimandrange/src/main/cpp/Robot.cpp
:language: cpp
:lines: 25-67
:linenos:
:lineno-start: 25

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Aiming at a Target
==================
The following example is from the PhotonLib example repository (`Java <https://github.com/PhotonVision/photonvision/tree/master/photonlib-java-examples/aimattarget>`_/`C++ <https://github.com/PhotonVision/photonvision/tree/master/photonlib-cpp-examples/aimattarget>`_).
Knowledge and Equipment Needed
------------------------------
- Robot with a vision system running PhotonVision
- Target
- Ability to track a target by properly tuning a pipeline
Code
-------
Now that you have properly set up your vision system and have tuned a pipeline, you can now aim your robot/turret at the target using the data from PhotonVision. This data is reported over NetworkTables and includes: latency, whether there is a target detected or not, pitch, yaw, area, skew, and target pose relative to the robot. This data will be used/manipulated by our vendor dependency, PhotonLib. The documentation for the Network Tables API can be found :ref:`here <docs/additional-resources/nt-api:Getting Target Information>` and the documentation for PhotonLib :ref:`here <docs/programming/photonlib/adding-vendordep:What is PhotonLib?>`.
For this simple example, only yaw is needed.
In this example, while the operator holds a button down, the robot will turn towards the goal using the P term of a PID loop. To learn more about how PID loops work, how WPILib implements them, and more, visit `Advanced Controls (PID) <https://docs.wpilib.org/en/stable/docs/software/advanced-control/introduction/index.html>`_ and `PID Control in WPILib <https://docs.wpilib.org/en/stable/docs/software/advanced-controls/controllers/pidcontroller.html#pid-control-in-wpilib>`_.
.. tab-set::
.. tab-item:: Java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-java-examples/aimattarget/src/main/java/frc/robot/Robot.java
:language: java
:lines: 41-98
:linenos:
:lineno-start: 41
.. tab-item:: C++ (Header)
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-cpp-examples/aimattarget/src/main/include/Robot.h
:language: c++
:lines: 27-53
:linenos:
:lineno-start: 27
.. tab-item:: C++ (Source)
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-cpp-examples/aimattarget/src/main/cpp/Robot.cpp
:language: c++
:lines: 25-52
:linenos:
:lineno-start: 25

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Getting in Range of the Target
==============================
The following example is from the PhotonLib example repository (`Java <https://github.com/PhotonVision/photonvision/tree/master/photonlib-java-examples/getinrange>`_/`C++ <https://github.com/PhotonVision/photonvision/tree/master/photonlib-cpp-examples/getinrange>`_).
Knowledge and Equipment Needed
-----------------------------------------------
- Everything required in :ref:`Aiming at a Target <docs/examples/aimingatatarget:Knowledge and Equipment Needed>`.
- Large space where your robot can move around freely
Code
-------
In FRC, a mechanism usually has to be a certain distance away from its target in order to be effective and score. In the previous example, we showed how to aim your robot at the target. Now we will show how to move to a certain distance from the target.
For proper functionality of just this example, ensure that your robot is pointed towards the target.
While the operator holds down a button, the robot will drive towards the target and get in range.
This example uses P term of the PID loop and PhotonLib and the distance function of PhotonUtils.
.. warning:: The PhotonLib utility to calculate distance depends on the camera being at a different vertical height than the target. If this is not the case, a different method for estimating distance, such as target width or area, should be used. In general, this method becomes more accurate as range decreases and as the height difference increases.
.. note:: There is no strict minimum delta-height necessary for this method to be applicable, just a requirement that a delta exists.
.. tab-set::
.. tab-item:: Java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-java-examples/getinrange/src/main/java/frc/robot/Robot.java
:language: java
:lines: 42-107
:linenos:
:lineno-start: 42
.. tab-item:: C++ (Header)
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-cpp-examples/getinrange/src/main/include/Robot.h
:language: c++
:lines: 27-67
:linenos:
:lineno-start: 27
.. tab-item:: C++ (Source)
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-cpp-examples/getinrange/src/main/cpp/Robot.cpp
:language: c++
:lines: 25-58
:linenos:
:lineno-start: 25
.. hint:: The accuracy of the measurement of the camera's pitch (:code:`CAMERA_PITCH_RADIANS` in the above example), as well as the camera's FOV, will determine the overall accuracy of this method.

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Code Examples
=============
.. toctree::
:maxdepth: 1
aimingatatarget
gettinginrangeofthetarget
aimandrange
simaimandrange
simposeest

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Simulating Aiming and Getting in Range
======================================
The following example comes from the PhotonLib example repository (`Java <https://github.com/PhotonVision/photonvision/tree/661f8b2c0495474015f6ea9a89d65f9788436a05/photonlib-java-examples/src/main/java/org/photonlib/examples/simaimandrange>`_/`C++ <https://github.com/PhotonVision/photonvision/tree/661f8b2c0495474015f6ea9a89d65f9788436a05/photonlib-cpp-examples/src/main/cpp/examples/simaimandrange>`_). Full code is available at those links.
Knowledge and Equipment Needed
-----------------------------------------------
- Everything required in :ref:`Combining Aiming and Getting in Range <docs/examples/aimandrange:Knowledge and Equipment Needed>`.
Background
----------
The previous examples show how to run PhotonVision on a real robot, with a physical robot drivetrain moving around and interacting with the software.
This example builds upon that, adding support for simulating robot motion and incorporating that motion into a :code:`SimVisionSystem`. This allows you to test control algorithms on your development computer, without requiring access to a real robot.
.. raw:: html
<video width="85%" controls>
<source src="../../_static/assets/simaimandrange.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
Walkthrough
-----------
First, in the main :code:`Robot` source file, we add support to periodically update a new simulation-specific object. This logic only gets used while running in simulation:
.. tab-set-code::
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-java-examples/simaimandrange/src/main/java/frc/robot/Robot.java
:language: java
:lines: 118-128
:linenos:
:lineno-start: 118
Then, we add in the implementation of our new `DrivetrainSim` class. Please reference the `WPILib documentation on physics simulation <https://docs.wpilib.org/en/stable/docs/software/wpilib-tools/robot-simulation/physics-sim.html>`_.
Simulated Vision support is added with the following steps:
Creating the Simulated Vision System
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
First, we create a new :code:`SimVisionSystem` to represent our camera and coprocessor running PhotonVision.
.. tab-set-code::
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-java-examples/simaimandrange/src/main/java/frc/robot/sim/DrivetrainSim.java
:language: java
:lines: 73-93
:linenos:
:lineno-start: 72
Next, we create objects to represent the physical location and size of the vision targets we are calibrated to detect. This example models the down-field high goal vision target from the 2020 and 2021 games.
.. tab-set-code::
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-java-examples/simaimandrange/src/main/java/frc/robot/sim/DrivetrainSim.java
:language: java
:lines: 95-111
:linenos:
:lineno-start: 95
Finally, we add our target to the simulated vision system.
.. tab-set-code::
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-java-examples/simaimandrange/src/main/java/frc/robot/sim/DrivetrainSim.java
:language: java
:lines: 116-117
:linenos:
:lineno-start: 113
If you have additional targets you want to detect, you can add them in the same way as the first one.
Updating the Simulated Vision System
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Once we have all the properties of our simulated vision system defined, the work to do at runtime becomes very minimal. Simply pass in the robot's pose periodically to the simulated vision system.
.. tab-set-code::
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/ebef19af3d926cf87292177c9a16d01b71219306/photonlib-java-examples/simaimandrange/src/main/java/frc/robot/sim/DrivetrainSim.java
:language: java
:lines: 124-142
:linenos:
:lineno-start: 122
The rest is done behind the scenes.

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Using WPILib Pose Estimation, Simulation, and PhotonVision Together
===================================================================
The following example comes from the PhotonLib example repository (`Java <https://github.com/PhotonVision/photonvision/tree/master/photonlib-java-examples/>`_). Full code is available at that links.
Knowledge and Equipment Needed
-----------------------------------------------
- Everything required in :ref:`Combining Aiming and Getting in Range <docs/examples/aimandrange:Knowledge and Equipment Needed>`, plus some familiarity with WPILib pose estimation functionality.
Background
----------
This example builds upon WPILib's `Differential Drive Pose Estimator <https://github.com/wpilibsuite/allwpilib/tree/main/wpilibjExamples/src/main/java/edu/wpi/first/wpilibj/examples/differentialdriveposeestimator>`_. It adds a :code:`PhotonCamera` to gather estimates of the robot's position on the field. This in turn can be used for aligning with vision targets, and increasing accuracy of autonomous routines.
To support simulation, a :code:`SimVisionSystem` is used to drive data into the :code:`PhotonCamera`. The far high goal target from 2020 is modeled.
Walkthrough
-----------
WPILib's :code:`Pose2d` class is used to represent robot positions on the field.
Three different :code:`Pose2d` positions are relevant for this example:
1) Desired Pose: The location some autonomous routine wants the robot to be in.
2) Estimated Pose: The location the software `believes` the robot to be in, based on physics models and sensor feedback.
3) Actual Pose: The locations the robot is actually at. The physics simulation generates this in simulation, but it cannot be directly measured on the real robot.
Estimating Pose
^^^^^^^^^^^^^^^
The :code:`DrivetrainPoseEstimator` class is responsible for generating an estimated robot pose using sensor readings (including PhotonVision).
Please reference the `WPILib documentation <https://docs.wpilib.org/en/stable/docs/software/advanced-controls/state-space/state-space-pose_state-estimators.html>`_ on using the :code:`DifferentialDrivePoseEstimator` class.
For both simulation and on-robot code, we create objects to represent the physical location and size of the vision targets we are calibrated to detect. This example models the down-field high goal vision target from the 2020 and 2021 games.
.. tab-set::
.. tab-item:: Java
:sync: java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/80e16ece87c735e30755dea271a56a2ce217b588/photonlib-java-examples/simposeest/src/main/java/frc/robot/Constants.java
:language: java
:lines: 83-106
:linenos:
:lineno-start: 83
To incorporate PhotonVision, we need to create a :code:`PhotonCamera`:
.. tab-set::
.. tab-item:: Java
:sync: java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/80e16ece87c735e30755dea271a56a2ce217b588/photonlib-java-examples/simposeest/src/main/java/frc/robot/DrivetrainPoseEstimator.java
:language: java
:lines: 46
:linenos:
:lineno-start: 46
During periodic execution, we read back camera results. If we see a target in the image, we pass the camera-measured pose of the robot to the :code:`DifferentialDrivePoseEstimator`.
.. tab-set::
.. tab-item:: Java
:sync: java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/80e16ece87c735e30755dea271a56a2ce217b588/photonlib-java-examples/simposeest/src/main/java/frc/robot/DrivetrainPoseEstimator.java
:language: java
:lines: 81-92
:linenos:
:lineno-start: 81
That's it!
Simulating the Camera
^^^^^^^^^^^^^^^^^^^^^
First, we create a new :code:`SimVisionSystem` to represent our camera and coprocessor running PhotonVision.
.. tab-set::
.. tab-item:: Java
:sync: java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/80e16ece87c735e30755dea271a56a2ce217b588/photonlib-java-examples/simposeest/src/main/java/frc/robot/DrivetrainSim.java
:language: java
:lines: 76-95
:linenos:
:lineno-start: 76
Then, we add our target to the simulated vision system.
.. tab-set::
.. tab-item:: Java
:sync: java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/80e16ece87c735e30755dea271a56a2ce217b588/photonlib-java-examples/simposeest/src/main/java/frc/robot/DrivetrainSim.java
:lines: 97-99
:linenos:
:lineno-start: 97
If you have additional targets you want to detect, you can add them in the same way as the first one.
Updating the Simulated Vision System
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Once we have all the properties of our simulated vision system defined, the remaining work is minimal. Periodically, pass in the robot's pose to the simulated vision system.
.. tab-set::
.. tab-item:: Java
:sync: java
.. rli:: https://raw.githubusercontent.com/PhotonVision/photonvision/80e16ece87c735e30755dea271a56a2ce217b588/photonlib-java-examples/simposeest/src/main/java/frc/robot/DrivetrainSim.java
:language: java
:lines: 138-139
:linenos:
:lineno-start: 138
The rest is done behind the scenes.

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Deploying on Custom Hardware
============================
Configuration
-------------
By default, PhotonVision attempts to make minimal assumptions of the hardware it runs on. However, it may be configured to enable custom LED control, branding, and other functionality.
``hardwareConfig.json`` is the location for this configuration. It is included when settings are exported, and can be uploaded as part of a .zip, or on its own.
LED Support
-----------
For Raspberry-Pi based hardware, PhotonVision can use `PiGPIO <https://abyz.me.uk/rpi/pigpio/>`_ to control IO pins. The mapping of which pins control which LED's is part of the hardware config. The pins are active-high: set high when LED's are commanded on, and set low when commanded off.
.. tab-set-code::
.. code-block:: json
{
"ledPins" : [ 13 ],
"ledSetCommand" : "",
"ledsCanDim" : true,
"ledPWMRange" : [ 0, 100 ],
"ledPWMSetRange" : "",
"ledPWMFrequency" : 0,
"ledDimCommand" : "",
"ledBlinkCommand" : "",
"statusRGBPins" : [ ],
}
.. note:: No hardware boards with status RGB LED pins or non-dimming LED's have been tested yet. Please reach out to the development team if these features are desired, they can assist with configuration and testing.
Hardware Interaction Commands
-----------------------------
For Non-Raspberry-Pi hardware, users must provide valid hardware-specific commands for some parts of the UI interaction (including performance metrics, and executing system restarts).
Leaving a command blank will disable the associated functionality.
.. tab-set-code::
.. code-block:: json
{
"cpuTempCommand" : "",
"cpuMemoryCommand" : "",
"cpuUtilCommand" : "",
"gpuMemoryCommand" : "",
"gpuTempCommand" : "",
"ramUtilCommand" : "",
"restartHardwareCommand" : "",
}
.. note:: These settings have no effect if PhotonVision detects it is running on a Raspberry Pi. See `the MetricsBase class <https://github.com/PhotonVision/photonvision/blob/dbd631da61b7c86b70fa6574c2565ad57d80a91a/photon-core/src/main/java/org/photonvision/common/hardware/metrics/MetricsBase.java>`_ for the commands utilized.
Known Camera FOV
----------------
If your hardware contains a camera with a known field of vision, it can be entered into the hardware configuration. This will prevent users from editing it in the GUI.
.. tab-set-code::
.. code-block:: json
{
"vendorFOV" : 98.9
}
Cosmetic & Branding
-------------------
To help differentiate your hardware from other solutions, some customization is allowed.
.. tab-set-code::
.. code-block:: json
{
"deviceName" : "Super Cool Custom Hardware",
"deviceLogoPath" : "",
"supportURL" : "https://cat-bounce.com/",
}
.. note:: Not all configuration is currently presented in the User Interface. Additional file uploads may be needed to support custom images.
Example
-------
Here is a complete example ``hardwareConfig.json``:
.. tab-set-code::
.. code-block:: json
{
"deviceName" : "Blinky McBlinkface",
"deviceLogoPath" : "",
"supportURL" : "https://www.youtube.com/watch?v=b-CvLWbfZhU",
"ledPins" : [2, 13],
"ledSetCommand" : "",
"ledsCanDim" : true,
"ledPWMRange" : [ 0, 100 ],
"ledPWMSetRange" : "",
"ledPWMFrequency" : 0,
"ledDimCommand" : "",
"ledBlinkCommand" : "",
"statusRGBPins" : [ ],
"cpuTempCommand" : "",
"cpuMemoryCommand" : "",
"cpuUtilCommand" : "",
"gpuMemoryCommand" : "",
"gpuTempCommand" : "",
"ramUtilCommand" : "",
"restartHardwareCommand" : "",
"vendorFOV" : 72.5
}

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Hardware Selection
==================
.. toctree::
:maxdepth: 2
selecting-hardware
picamconfig
customhardware

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Pi Camera Configuration
=======================
Background
----------
The Raspberry Pi CSI Camera port is routed through and processed by the GPU. Since the GPU boots before the CPU, it must be configured properly for the attached camera. Additionally, this configuration cannot be changed without rebooting.
The GPU is not always capable of detecting other cameras automatically. The file ``/boot/config.txt`` is parsed by the GPU at boot time to determine what camera, if any, is expected to be attached. This file must be updated for some cameras.
.. warning:: Incorrect camera configuration will cause the camera to not be detected. It looks exactly the same as if the camera was unplugged.
Updating ``config.txt``
-----------------------
After flashing the pi image onto an SD card, open the ``boot`` segment in a file browser.
.. note:: Windows may report "There is a problem with this drive". This should be ignored.
Locate ``config.txt`` in the folder, and open it with your favorite text editor.
.. image:: images/bootConfigTxt.png
Within the file, find this block of text:
.. code-block::
##############################################################
### PHOTONVISION CAM CONFIG
### Comment/Uncomment to change which camera is supported
### Picam V1, V2 or HQ: uncomment (remove leading # ) from camera_auto_detect=1,
### and comment out all following lines
### IMX290/327/OV9281/Any other cameras that require additional overlays:
### Comment out (add a # ) to camera_auto_detect=1, and uncomment the line for
### the sensor you're trying to user
cameraAutoDetect=1
# dtoverlay=imx290,clock-frequency=74250000
# dtoverlay=imx290,clock-frequency=37125000
# dtoverlay=imx378
# dtoverlay=ov9281
##############################################################
Remove the leading ``#`` character to uncomment the line associated with your camera. Add a ``#`` in front of other cameras.
.. warning:: Leave lines outside the PhotonVision Camera Config block untouched. They are necessary for proper raspberry pi functionality.
Save the file, close the editor, and eject the drive. The boot configuration should now be ready for your selected camera.
Additional Information
----------------------
See `the libcamera documentation <https://github.com/raspberrypi/documentation/blob/679fab721855a3e8f17aa51819e5c2a7c447e98d/documentation/asciidoc/computers/camera/rpicam_configuration.adoc>`_ for more details on configuring cameras.

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Selecting Hardware
==================
In order to use PhotonVision, you need a coprocessor and a camera. This page will help you select the right hardware for your team depending on your budget, needs, and experience.
Choosing a Coprocessor
----------------------
Minimum System Requirements
^^^^^^^^^^^^^^^^^^^^^^^^^^^
* Ubuntu 22.04 LTS or Windows 10/11
* We don't recommend using Windows for anything except testing out the system on a local machine.
* CPU: ARM Cortex-A53 (the CPU on Raspberry Pi 3) or better
* At least 8GB of storage
* 2GB of RAM
* PhotonVision isn't very RAM intensive, but you'll need at least 2GB to run the OS and PhotonVision.
* The following IO:
* At least 1 USB or MIPI-CSI port for the camera
* Note that we only support using the Raspberry Pi's MIPI-CSI port, other MIPI-CSI ports from other coprocessors may not work.
* Ethernet port for networking
Coprocessor Recommendations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
When selecting a coprocessor, it is important to consider various factors, particularly when it comes to AprilTag detection. Opting for a coprocessor with a more powerful CPU can generally result in higher FPS AprilTag detection, leading to more accurate pose estimation. However, it is important to note that there is a point of diminishing returns, where the benefits of a more powerful CPU may not outweigh the additional cost. Below is a list of supported hardware, along with some notes on each.
* Orange Pi 5 ($99)
* This is the recommended coprocessor for most teams. It has a powerful CPU that can handle AprilTag detection at high FPS, and is relatively cheap compared to processors of a similar power.
* Raspberry Pi 4/5 ($55-$80)
* This is the recommended coprocessor for teams on a budget. It has a less powerful CPU than the Orange Pi 5, but is still capable of running PhotonVision at a reasonable FPS.
* Mini PCs (such as Beelink N5095)
* This coprocessor will likely have similar performance to the Orange Pi 5 but has a higher performance ceiling (when using more powerful CPUs). Do note that this would require extra effort to wire to the robot / get set up. More information can be found in the set up guide `here. <https://docs.google.com/document/d/1lOSzG8iNE43cK-PgJDDzbwtf6ASyf4vbW8lQuFswxzw/edit?usp=drivesdk>`_
* Other coprocessors can be used but may require some extra work / command line usage in order to get it working properly.
Choosing a Camera
-----------------
PhotonVision works with Pi Cameras and most USB Cameras, the recommendations below are known to be working and have been tested. Other cameras such as webcams, virtual cameras, etc. are not officially supported and may not work. It is important to note that fisheye cameras should only be used as a driver camera and not for detecting targets.
PhotonVision relies on `CSCore <https://github.com/wpilibsuite/allwpilib/tree/main/cscore>`_ to detect and process cameras, so camera support is determined based off compatibility with CScore along with native support for the camera within your OS (ex. `V4L compatibility <https://en.wikipedia.org/wiki/Video4Linux>`_ if using a Linux machine like a Raspberry Pi).
.. note::
Logitech Cameras and integrated laptop cameras will not work with PhotonVision due to oddities with their drivers. We recommend using a different camera.
.. note::
We do not currently support the usage of two of the same camera on the same coprocessor. You can only use two or more cameras if they are of different models or they are from Arducam, which has a `tool that allows for cameras to be renamed <https://docs.arducam.com/UVC-Camera/Serial-Number-Tool-Guide/>`_.
Recommended Cameras
^^^^^^^^^^^^^^^^^^^
For colored shape detection, any non-fisheye camera supported by PhotonVision will work. We recommend the Pi Camera V1 or a high fps USB camera.
For driver camera, we recommend a USB camera with a fisheye lens, so your driver can see more of the field.
For AprilTag detection, we recommend you use a global shutter camera that has ~100 degree diagonal FOV. This will allow you to see more AprilTags in frame, and will allow for more accurate pose estimation. You also want a camera that supports high FPS, as this will allow you to update your pose estimator at a higher frequency.
* Recommendations For AprilTag Detection
* Arducam USB OV9281
* This is the recommended camera for AprilTag detection as it is a high FPS, global shutter camera USB camera that has a ~70 degree FOV.
* Innomaker OV9281
* Spinel AR0144
* Pi Camera Module V1
* The V1 is strongly preferred over the V2 due to the V2 having undesirable FOV choices
AprilTags and Motion Blur
^^^^^^^^^^^^^^^^^^^^^^^^^
When detecting AprilTags, you want to reduce the "motion blur" as much as possible. Motion blur is the visual streaking/smearing on the camera stream as a result of movement of the camera or object of focus. You want to mitigate this as much as possible because your robot is constantly moving and you want to be able to read as many tags as you possibly can. The possible solutions to this include:
1. Cranking your exposure as low as it goes and increasing your gain/brightness. This will decrease the effects of motion blur and increase FPS.
2. Using a global shutter (as opposed to rolling shutter) camera. This should eliminate most, if not all motion blur.
3. Only rely on tags when not moving.
.. image:: images/motionblur.gif
:align: center
Using Multiple Cameras
^^^^^^^^^^^^^^^^^^^^^^
Using multiple cameras on your robot will help you detect more AprilTags at once and improve your pose estimation as a result. In order to use multiple cameras, you will need to create multiple PhotonPoseEstimators and add all of their measurements to a single drivetrain pose estimator. Please note that the accuracy of your robot to camera transform is especially important when using multiple cameras as any error in the transform will cause your pose estimations to "fight" each other. For more information, see :ref:`the programming reference. <docs/programming/index:programming reference>`.
Performance Matrix
------------------
.. raw:: html
<embed>
<iframe src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTojOew2d2NQY4PRA98vjkS1ECZ2YNvods-aOdk2x-Q4aF_7r4mcwlyTe8GjUKmUxEiVgGNnJNhEdyd/pubhtml?gid=1779881081&amp;single=true&amp;widget=true&amp;headers=false" width="760" height="500" frameborder="0" marginheight="0" marginwidth="0">Loading…</iframe>
</embed>
Please submit performance data to be added to the matrix here:
.. raw:: html
<embed>
<iframe src="https://docs.google.com/forms/d/e/1FAIpQLSf5iK3pX0Tn8bxpRYgcTAy4scUu14rUvJqkTyfzoKc-GiV7Vg/viewform?embedded=true" width="760" height="500" frameborder="0" marginheight="0" marginwidth="0">Loading…</iframe>
</embed>

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Installation & Setup
====================
This page will help you install PhotonVision on your coprocessor, wire it, and properly setup the networking in order to start tracking targets.
Step 1: Software Install
------------------------
This section will walk you through how to install PhotonVision on your coprocessor. Your coprocessor is the device that has the camera and you are using to detect targets (ex. if you are using a Limelight / Raspberry Pi, that is your coprocessor and you should follow those instructions).
.. warning:: You only need to install PhotonVision on the coprocessor/device that is being used to detect targets, you do NOT need to install it on the device you use to view the webdashboard. All you need to view the webdashboard is for a device to be on the same network as your vision coprocessor and an internet browser.
.. toctree::
:maxdepth: 3
sw_install/index
updating
Step 2: Wiring
--------------
This section will walk you through how to wire your coprocessor to get power.
.. toctree::
:maxdepth: 1
wiring
Step 3: Networking
------------------
This section will walk you though how to connect your coprocessor to a network. This section is very important (and easy to get wrong), so we recommend you read it thoroughly.
.. toctree::
:maxdepth: 1
networking

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Networking
==========
Physical Networking
-------------------
.. note:: When using PhotonVision off robot, you *MUST* plug the coprocessor into a physical router/radio. You can then connect your laptop/device used to view the webdashboard to the same network. Any other networking setup will not work and will not be supported in any capacity.
After imaging your coprocessor, run an ethernet cable from your coprocessor to a router/radio and power on your coprocessor by plugging it into the wall. Then connect whatever device you're using to view the webdashboard to the same network and navigate to photonvision.local:5800.
PhotonVision *STRONGLY* recommends the usage of a network switch on your robot. This is because the second radio port on the current FRC radios is known to be buggy and cause frequent connection issues that are detrimental during competition. An in-depth guide on how to install a network switch can be found `on FRC 900's website <https://team900.org/blog/ZebraSwitch/>`_.
.. image:: images/networking-diagram.png
:alt: Correctly set static IP
Digital Networking
------------------
PhotonVision *STRONGLY* recommends the usage of Static IPs as it increases reliability on the field and when using PhotonVision in general. To properly set up your static IP, follow the steps below:
.. warning:: Only use a static IP when connected to the **robot radio**, and never when testing at home, unless you are well versed in networking or have the relevant "know how".
1. Ensure your robot is on and you are connected to the robot network.
2. Navigate to ``photonvision.local:5800`` (this may be different if you are using a Gloworm / Limelight) in your browser.
3. Open the settings tab on the left pane.
4. Under the Networking section, set your team number.
5. Change your IP to Static.
6. Set your coprocessor's IP address to “10.TE.AM.11”. More information on IP format can be found `here <https://docs.wpilib.org/en/stable/docs/networking/networking-introduction/ip-configurations.html#on-the-field-static-configuration>`_.
7. Click the “Save” button.
8. Set your roboRIO to the following static IP address: “10.TE.AM.2”. This can be done via the `roboRIO web dashboard <https://docs.wpilib.org/en/stable/docs/software/roborio-info/roborio-web-dashboard.html#roborio-web-dashboard>`_.
Power-cycle your robot and then you will now be access the PhotonVision dashboard at ``10.TE.AM.11:5800``.
.. image:: images/static.png
:alt: Correctly set static IP
Port Forwarding
---------------
If you would like to access your Ethernet-connected vision device from a computer when tethered to the USB port on the roboRIO, you can use `WPILib's <https://docs.wpilib.org/en/stable/docs/networking/networking-utilities/portforwarding.html>`_ ``PortForwarder``.
.. tab-set-code::
.. code-block:: java
PortForwarder.add(5800, "photonvision.local", 5800);
.. code-block:: C++
wpi::PortForwarder::GetInstance().Add(5800, "photonvision.local", 5800);
.. note:: The address in the code above (``photonvision.local``) is the hostname of the coprocessor. This can be different depending on your hardware, and can be checked in the settings tab under "hostname".
Camera Stream Ports
-------------------
The camera streams start at they begin at 1181 with two ports for each camera (ex. 1181 and 1182 for camera one, 1183 and 1184 for camera two, etc.). The easiest way to identify the port of the camera that you want is by double clicking on the stream, which opens it in a separate page. The port will be listed below the stream.

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Advanced Command Line Usage
===========================
PhotonVision exposes some command line options which may be useful for customizing execution on Debian-based installations.
Running a JAR File
------------------
Assuming ``java`` has been installed, and the appropriate environment variables have been set upon installation (a package manager like ``apt`` should automatically set these), you can use ``java -jar`` to run a JAR file. If you downloaded the latest stable JAR of PhotonVision from the `GitHub releases page <https://github.com/PhotonVision/photonvision/releases>`_, you can run the following to start the program:
.. code-block:: bash
java -jar /path/to/photonvision/photonvision.jar
Updating a JAR File
-------------------
When you need to update your JAR file, run the following:
.. code-block:: bash
wget https://git.io/JqkQ9 -O update.sh
sudo chmod +x update.sh
sudo ./update.sh
sudo reboot now
Creating a ``systemd`` Service
------------------------------
You can also create a systemd service that will automatically run on startup. To do so, first navigate to ``/lib/systemd/system``. Create a file called ``photonvision.service`` (or name it whatever you want) using ``touch photonvision.service``. Then open this file in the editor of your choice and paste the following text:
.. code-block::
[Unit]
Description=Service that runs PhotonVision
[Service]
WorkingDirectory=/path/to/photonvision
# Optional: run photonvision at "nice" -10, which is higher priority than standard
# Nice=-10
ExecStart=/usr/bin/java -jar /path/to/photonvision/photonvision.jar
[Install]
WantedBy=multi-user.target
Then copy the ``.service`` file to ``/etc/systemd/system/`` using ``cp photonvision.service /etc/systemd/system/photonvision.service``. Then modify the file to have ``644`` permissions using ``chmod 644 /etc/systemd/system/photonvision.service``.
.. note::
Many ARM processors have a big.LITTLE architecture where some of the CPU cores are more powerful than others. On this type of architecture, you may get more consistent performance by limiting which cores PhotonVision can use. To do this, add the parameter ``AllowedCPUs`` to the systemd service file in the ``[Service]`` section.
For instance, for an Orange Pi 5, cores 4 through 7 are the fast ones, and you can target those cores with the line ``AllowedCPUs=4-7``.
Installing the ``systemd`` Service
----------------------------------
To install the service, simply run ``systemctl enable photonvision.service``.
.. note:: It is recommended to reload configurations by running ``systemctl daemon-reload``.

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{
"deviceName" : "Limelight 2+",
"supportURL" : "https://limelightvision.io",
"ledPins" : [ 13, 18 ],
"ledsCanDim" : true,
"ledPWMRange" : [ 0, 100 ],
"ledPWMFrequency" : 30000,
"vendorFOV" : 75.76079874010732
}

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