Initial commit

This commit is contained in:
astatin3
2024-12-09 08:01:09 -07:00
commit 9e4ab26005
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# Set default behavior to automatically normalize line endings (LF on check-in).
* text=auto
# Force batch scripts to always use CRLF line endings so that if a repo is accessed
# in Windows via a file share from Linux, the scripts will work.
*.{cmd,[cC][mM][dD]} text eol=crlf
*.{bat,[bB][aA][tT]} text eol=crlf
*.{ics,[iI][cC][sS]} text eol=crlf
# Force bash scripts to always use LF line endings so that if a repo is accessed
# in Unix via a file share from Windows, the scripts will work.
*.sh text eol=lf
# Ensure Spotless does not try to use CRLF line endings on Windows in the local repo.
*.gradle text eol=lf
*.java text eol=lf
*.json text eol=lf
*.md text eol=lf
*.xml text eol=lf
*.h text eol=lf
*.hpp text eol=lf
*.inc text eol=lf
*.inl text eol=lf
*.cpp text eol=lf
# Frontend Files
*.js text eol=lf
*.vue text eol=lf
# Denote all files that are truly binary and should not be modified.
*.png binary
*.jpg binary
*.jpeg binary
*.gif binary
*.so binary
*.dll binary
*.webp binary
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# These owners will be the default owners for everything in the repo.
* @PhotonVision/program-devs
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---
name: Bug report
about: Create a report to help us improve
title: ''
labels: bug
assignees: ''
---
**Describe the bug**
A clear and concise description of what the bug is and what the expected behavior should have been.
**To Reproduce**
Steps to reproduce the behavior:
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error
**Screenshots / Videos**
If applicable, add screenshots to help explain your problem. Additionally, provide journalctl logs and settings zip export. If your issue is regarding the web dashboard, please provide screenshots and the output of the browser console.
**Platform:**
- Hardware Platform (ex. Raspberry Pi 4, Windows x64):
- Network Configuration (Connection between the Radio and any devices in between, such as a Network Switch):
- PhotonVision Version:
- Browser (with Version) (Chrome, Edge, Firefox, etc.):
- Camera(s) Used:
**Additional context**
Add any other context about the problem here.
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---
name: Feature request
about: Suggest an idea for this project
title: ''
labels: enhancement
assignees: ''
---
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.
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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/**'
merge_group:
jobs:
build-client:
name: "PhotonClient Build"
defaults:
run:
working-directory: photon-client
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 18
- name: Install Dependencies
run: npm ci
- name: Build Production Client
run: npm run build
- uses: actions/upload-artifact@v4
with:
name: built-client
path: photon-client/dist/
build-examples:
strategy:
fail-fast: false
matrix:
include:
- os: windows-2022
architecture: x64
- os: macos-14
architecture: aarch64
- os: ubuntu-22.04
name: "Photonlib - Build Examples - ${{ matrix.os }}"
runs-on: ${{ matrix.os }}
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Fetch tags
run: git fetch --tags --force
- name: Install Java 17
uses: actions/setup-java@v4
with:
java-version: 17
distribution: temurin
- name: Install RoboRIO Toolchain
run: ./gradlew installRoboRioToolchain
# Need to publish to maven local first, so that C++ sim can pick it up
- name: Publish photonlib to maven local
run: ./gradlew photon-targeting:publishtomavenlocal photon-lib:publishtomavenlocal -x check
- name: Build Java examples
working-directory: photonlib-java-examples
run: ./gradlew build
- name: Build C++ examples
working-directory: photonlib-cpp-examples
run: ./gradlew build
build-gradle:
name: "Gradle Build"
runs-on: ubuntu-22.04
steps:
# Checkout code.
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Fetch tags
run: git fetch --tags --force
- name: Install Java 17
uses: actions/setup-java@v4
with:
java-version: 17
distribution: temurin
- name: Install mrcal deps
run: sudo apt-get update && sudo apt-get install -y libcholmod3 liblapack3 libsuitesparseconfig5
- name: Gradle Build
run: ./gradlew photon-targeting:build photon-core:build photon-server:build -x check
- name: Gradle Tests
run: ./gradlew testHeadless -i --stacktrace
- name: Gradle Coverage
run: ./gradlew jacocoTestReport
- name: Publish Coverage Report
uses: codecov/codecov-action@v4
with:
file: ./photon-server/build/reports/jacoco/test/jacocoTestReport.xml
- name: Publish Core Coverage Report
uses: codecov/codecov-action@v4
with:
file: ./photon-core/build/reports/jacoco/test/jacocoTestReport.xml
build-offline-docs:
name: "Build Offline Docs"
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install graphviz
run: |
sudo apt-get update
sudo apt-get -y install graphviz
- 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: docs/build/html
build-photonlib-vendorjson:
name: "Build Vendor JSON"
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Install Java 17
uses: actions/setup-java@v4
with:
java-version: 17
distribution: temurin
# grab all tags
- run: git fetch --tags --force
# Generate the JSON and give it the ""standard""" name maven gives it
- run: |
./gradlew photon-lib:generateVendorJson
export VERSION=$(git describe --tags --match=v*)
mv photon-lib/build/generated/vendordeps/photonlib.json photon-lib/build/generated/vendordeps/photonlib-$(git describe --tags --match=v*).json
# Upload it here so it shows up in releases
- uses: actions/upload-artifact@v4
with:
name: photonlib-vendor-json
path: photon-lib/build/generated/vendordeps/photonlib-*.json
build-photonlib-host:
env:
MACOSX_DEPLOYMENT_TARGET: 13
strategy:
fail-fast: false
matrix:
include:
- os: windows-2022
artifact-name: Win64
architecture: x64
- os: macos-14
artifact-name: macOS
architecture: aarch64
- os: ubuntu-22.04
artifact-name: Linux
name: "Photonlib - Build Host - ${{ matrix.artifact-name }}"
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Install Java 17
uses: actions/setup-java@v4
with:
java-version: 17
distribution: temurin
architecture: ${{ matrix.architecture }}
- run: git fetch --tags --force
- run: ./gradlew photon-targeting:build photon-lib:build -i
name: Build with Gradle
- run: ./gradlew photon-lib:publish photon-targeting:publish
name: Publish
env:
ARTIFACTORY_API_KEY: ${{ secrets.ARTIFACTORY_API_KEY }}
if: github.event_name == 'push' && github.repository_owner == 'photonvision'
# Copy artifacts to build/outputs/maven
- run: ./gradlew photon-lib:publish photon-targeting:publish -PcopyOfflineArtifacts
- uses: actions/upload-artifact@v4
with:
name: maven-${{ matrix.artifact-name }}
path: build/outputs
build-photonlib-docker:
strategy:
fail-fast: false
matrix:
include:
- container: wpilib/roborio-cross-ubuntu:2024-22.04
artifact-name: Athena
build-options: "-Ponlylinuxathena"
- container: wpilib/raspbian-cross-ubuntu:bullseye-22.04
artifact-name: Raspbian
build-options: "-Ponlylinuxarm32"
- container: wpilib/aarch64-cross-ubuntu:bullseye-22.04
artifact-name: Aarch64
build-options: "-Ponlylinuxarm64"
runs-on: ubuntu-22.04
container: ${{ matrix.container }}
name: "Photonlib - Build Docker - ${{ matrix.artifact-name }}"
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Config Git
run: |
git config --global --add safe.directory /__w/photonvision/photonvision
- name: Build PhotonLib
# We don't need to run tests, since we specify only non-native platforms
run: ./gradlew photon-targeting:build photon-lib:build ${{ matrix.build-options }} -i -x test
- name: Publish
run: ./gradlew photon-lib:publish photon-targeting:publish ${{ matrix.build-options }}
env:
ARTIFACTORY_API_KEY: ${{ secrets.ARTIFACTORY_API_KEY }}
if: github.event_name == 'push' && github.repository_owner == 'photonvision'
# Copy artifacts to build/outputs/maven
- run: ./gradlew photon-lib:publish photon-targeting:publish -PcopyOfflineArtifacts ${{ matrix.build-options }}
- uses: actions/upload-artifact@v4
with:
name: maven-${{ matrix.artifact-name }}
path: build/outputs
combine:
name: Combine
needs: [build-photonlib-docker, build-photonlib-host]
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- run: git fetch --tags --force
# download all maven-* artifacts to outputs/
- uses: actions/download-artifact@v4
with:
merge-multiple: true
path: output
pattern: maven-*
- run: find .
- run: zip -r photonlib-$(git describe --tags --match=v*).zip .
name: ZIP stuff up
working-directory: output
- run: ls output
- uses: actions/upload-artifact@v4
with:
name: photonlib-offline
path: output/*.zip
build-package:
needs: [build-client, build-gradle, build-offline-docs]
strategy:
fail-fast: false
matrix:
include:
- os: windows-latest
artifact-name: Win64
architecture: x64
arch-override: winx64
- os: macos-latest
artifact-name: macOS
architecture: x64
arch-override: macx64
- os: macos-latest
artifact-name: macOSArm
architecture: x64
arch-override: macarm64
- os: ubuntu-22.04
artifact-name: Linux
architecture: x64
arch-override: linuxx64
- os: ubuntu-22.04
artifact-name: LinuxArm64
architecture: x64
arch-override: linuxarm64
runs-on: ${{ matrix.os }}
name: "Build fat JAR - ${{ matrix.artifact-name }}"
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Install Java 17
uses: actions/setup-java@v4
with:
java-version: 17
distribution: temurin
architecture: ${{ matrix.architecture }}
- name: Install Arm64 Toolchain
run: ./gradlew installArm64Toolchain
if: ${{ (matrix.artifact-name) == 'LinuxArm64' }}
- run: |
rm -rf photon-server/src/main/resources/web/*
mkdir -p photon-server/src/main/resources/web/docs
if: ${{ (matrix.os) != 'windows-latest' }}
- run: |
del photon-server\src\main\resources\web\*.*
mkdir photon-server\src\main\resources\web\docs
if: ${{ (matrix.os) == 'windows-latest' }}
- uses: actions/download-artifact@v4
with:
name: built-client
path: photon-server/src/main/resources/web/
- uses: actions/download-artifact@v4
with:
name: built-docs
path: photon-server/src/main/resources/web/docs
- run: ./gradlew photon-targeting:jar photon-server:shadowJar -PArchOverride=${{ matrix.arch-override }}
if: ${{ (matrix.arch-override != 'none') }}
- run: ./gradlew photon-server:shadowJar
if: ${{ (matrix.arch-override == 'none') }}
- uses: actions/upload-artifact@v4
with:
name: jar-${{ matrix.artifact-name }}
path: photon-server/build/libs
- uses: actions/upload-artifact@v4
with:
name: photon-targeting_jar-${{ matrix.artifact-name }}
path: photon-targeting/build/libs
run-smoketest-native:
needs: [build-package]
strategy:
fail-fast: false
matrix:
include:
- os: ubuntu-22.04
artifact-name: jar-Linux
extraOpts: -Djdk.lang.Process.launchMechanism=vfork
- os: windows-latest
artifact-name: jar-Win64
extraOpts: ""
- os: macos-latest
artifact-name: jar-macOS
architecture: x64
runs-on: ${{ matrix.os }}
steps:
- name: Install Java 17
uses: actions/setup-java@v4
with:
java-version: 17
distribution: temurin
- uses: actions/download-artifact@v4
with:
name: ${{ matrix.artifact-name }}
# On linux, install mrcal packages
- run: |
sudo apt-get update
sudo apt-get install --yes libcholmod3 liblapack3 libsuitesparseconfig5
if: ${{ (matrix.os) == 'ubuntu-22.04' }}
# and actually run the jar
- run: java -jar ${{ matrix.extraOpts }} *.jar --smoketest
if: ${{ (matrix.os) != 'windows-latest' }}
- run: ls *.jar | %{ Write-Host "Running $($_.Name)"; Start-Process "java" -ArgumentList "-jar `"$($_.FullName)`" --smoketest" -NoNewWindow -Wait; break }
if: ${{ (matrix.os) == 'windows-latest' }}
run-smoketest-chroot:
needs: [build-package]
strategy:
fail-fast: false
matrix:
include:
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: RaspberryPi
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-4/photonvision_raspi.img.xz
cpu: cortex-a7
image_additional_mb: 0
extraOpts: -Djdk.lang.Process.launchMechanism=vfork
runs-on: ${{ matrix.os }}
name: smoketest-${{ matrix.image_suffix }}
steps:
- uses: actions/download-artifact@v4
with:
name: jar-${{ matrix.artifact-name }}
- uses: pguyot/arm-runner-action@v2
name: Run photon smoketest
id: generate_image
with:
base_image: ${{ matrix.image_url }}
image_additional_mb: ${{ matrix.image_additional_mb }}
optimize_image: yes
cpu: ${{ matrix.cpu }}
# We do _not_ wanna copy photon into the image. Bind mount instead
bind_mount_repository: true
# our image better have java installed already
commands: |
java -jar ${{ matrix.extraOpts }} *.jar --smoketest
build-image:
needs: [build-package]
if: ${{ github.event_name != 'pull_request' }}
strategy:
fail-fast: false
matrix:
include:
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: RaspberryPi
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-6/photonvision_raspi.img.xz
cpu: cortex-a7
image_additional_mb: 0
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: limelight2
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-6/photonvision_limelight.img.xz
cpu: cortex-a7
image_additional_mb: 0
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: limelight3
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-6/photonvision_limelight3.img.xz
cpu: cortex-a7
image_additional_mb: 0
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: orangepi5
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-6/photonvision_opi5.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: orangepi5b
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-6/photonvision_opi5b.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: orangepi5plus
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-6/photonvision_opi5plus.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: orangepi5pro
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-6/photonvision_opi5pro.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-22.04
artifact-name: LinuxArm64
image_suffix: orangepi5max
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/v2025.0.0-beta-6/photonvision_opi5max.img.xz
cpu: cortex-a8
image_additional_mb: 1024
runs-on: ${{ matrix.os }}
name: "Build image - ${{ matrix.image_url }}"
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: actions/download-artifact@v4
with:
name: jar-${{ matrix.artifact-name }}
- uses: pguyot/arm-runner-action@HEAD
name: Generate image
id: generate_image
with:
base_image: ${{ matrix.image_url }}
image_additional_mb: ${{ matrix.image_additional_mb }}
optimize_image: yes
cpu: ${{ matrix.cpu }}
# We do _not_ wanna copy photon into the image. Bind mount instead
bind_mount_repository: true
commands: |
chmod +x scripts/armrunner.sh
./scripts/armrunner.sh
- name: Compress image
run: |
new_jar=$(realpath $(find . -name photonvision\*-linuxarm64.jar))
new_image_name=$(basename "${new_jar/.jar/_${{ matrix.image_suffix }}.img}")
mv ${{ steps.generate_image.outputs.image }} $new_image_name
sudo xz -T 0 -v $new_image_name
- uses: actions/upload-artifact@v4
name: Upload image
with:
name: image-${{ matrix.image_suffix }}
path: photonvision*.xz
release:
needs: [build-package, build-image, combine]
runs-on: ubuntu-22.04
steps:
# Download all fat JARs
- uses: actions/download-artifact@v4
with:
merge-multiple: true
pattern: jar-*
# Download offline photonlib
- uses: actions/download-artifact@v4
with:
merge-multiple: true
pattern: photonlib-offline
# Download vendor json
- uses: actions/download-artifact@v4
with:
merge-multiple: true
pattern: photonlib-vendor-json
# Download all images
- uses: actions/download-artifact@v4
with:
merge-multiple: true
pattern: image-*
- run: find
# Push to dev release
- uses: pyTooling/Actions/releaser@r0
with:
token: ${{ secrets.GITHUB_TOKEN }}
tag: 'Dev'
rm: true
files: |
**/*.xz
**/*.jar
**/photonlib*.json
**/photonlib*.zip
if: github.event_name == 'push'
# Upload all jars and xz archives
# Split into two uploads to work around max size limits in action-gh-releases
# https://github.com/softprops/action-gh-release/issues/353
- uses: softprops/action-gh-release@v2.0.9
with:
files: |
**/*orangepi5*.xz
if: startsWith(github.ref, 'refs/tags/v')
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- uses: softprops/action-gh-release@v2.0.9
with:
files: |
**/!(*orangepi5*).xz
**/*.jar
**/photonlib*.json
**/photonlib*.zip
if: startsWith(github.ref, 'refs/tags/v')
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
dispatch:
name: dispatch
needs: [build-photonlib-vendorjson, release]
runs-on: ubuntu-22.04
steps:
- uses: peter-evans/repository-dispatch@v3
if: |
github.repository == 'PhotonVision/photonvision' &&
startsWith(github.ref, 'refs/tags/v')
with:
token: ${{ secrets.VENDOR_JSON_REPO_PUSH_TOKEN }}
repository: PhotonVision/vendor-json-repo
event-type: tag
client-payload: '{"run_id": "${{ github.run_id }}", "package_version": "${{ github.ref_name }}"}'
+97
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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/**'
merge_group:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.ref }}
cancel-in-progress: true
jobs:
wpiformat:
name: "wpiformat"
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- name: Fetch all history and metadata
run: |
git fetch --prune --unshallow
git checkout -b pr
git branch -f master origin/master
- name: Set up Python 3.8
uses: actions/setup-python@v4
with:
python-version: 3.11
- name: Install wpiformat
run: pip3 install wpiformat==2024.45
- name: Run
run: wpiformat
- name: Check output
run: git --no-pager diff --exit-code HEAD
- name: Generate diff
run: git diff HEAD > wpiformat-fixes.patch
if: ${{ failure() }}
- uses: actions/upload-artifact@v4
with:
name: wpiformat fixes
path: wpiformat-fixes.patch
if: ${{ failure() }}
javaformat:
name: "Java Formatting"
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: actions/setup-java@v4
with:
java-version: 17
distribution: temurin
- run: ./gradlew spotlessCheck
name: Run spotless
client-lint-format:
name: "PhotonClient Lint and Formatting"
defaults:
run:
working-directory: photon-client
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 18
- name: Install Dependencies
run: npm ci
- name: Check Linting
run: npm run lint-ci
- name: Check Formatting
run: npm run format-ci
server-index:
name: "Check server index.html not changed"
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- name: Fetch all history and metadata
run: |
git fetch --prune --unshallow
git checkout -b pr
git branch -f master origin/master
- name: Check index.html not changed
run: git --no-pager diff --exit-code origin/master photon-server/src/main/resources/web/index.html
+97
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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:
branches: [ master ]
tags:
- 'v*'
paths:
- '**'
- '!docs/**'
- '.github/**'
pull_request:
branches: [ master ]
paths:
- '**'
- '!docs/**'
- '.github/**'
merge_group:
# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
contents: read
pages: write
id-token: write
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
build-client:
name: "PhotonClient Build"
defaults:
run:
working-directory: photon-client
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 18
- name: Install Dependencies
run: npm ci
- name: Build Production Client
run: npm run build-demo
- uses: actions/upload-artifact@v4
with:
name: built-client
path: photon-client/dist/
run_docs:
runs-on: "ubuntu-22.04"
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Fetch tags
run: git fetch --tags --force
- name: Install Java 17
uses: actions/setup-java@v4
with:
java-version: 17
distribution: temurin
- name: Build javadocs/doxygen
run: |
chmod +x gradlew
./gradlew photon-docs:generateJavaDocs photon-docs:doxygen
- uses: actions/upload-artifact@v4
with:
name: built-docs
path: photon-docs/build/docs
release:
needs: [build-client, run_docs]
runs-on: ubuntu-22.04
steps:
# Download literally every single artifact.
- uses: actions/download-artifact@v4
- 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 }}
username: ${{ secrets.WEBMASTER_SSH_USERNAME }}
password: ${{ secrets.WEBMASTER_SSH_KEY }}
port: ${{ secrets.WEBMASTER_SSH_PORT }}
source: "*"
target: /var/www/html/photonvision-docs/
+55
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@@ -0,0 +1,55 @@
name: PhotonVision Sphinx Documentation Checks
on:
push:
branches: [ master ]
paths:
- 'docs/**'
- '.github/**'
pull_request:
branches: [ master ]
paths:
- 'docs/**'
- '.github/**'
merge_group:
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
jobs:
build:
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: Install and upgrade pip
run: python -m pip install --upgrade pip
- name: Install graphviz
run: |
sudo apt-get update
sudo apt-get -y install graphviz
- 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
+76
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name: Build and Distribute PhotonLibPy
permissions:
id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
on:
push:
branches: [ master ]
tags:
- 'v*'
paths:
- '**'
- '!docs/**'
- '.github/**'
pull_request:
branches: [ master ]
paths:
- '**'
- '!docs/**'
- '.github/**'
merge_group:
jobs:
buildAndDeploy:
runs-on: ubuntu-22.04
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: 3.11
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install setuptools wheel pytest mypy
- name: Build wheel
working-directory: ./photon-lib/py
run: |
python setup.py sdist bdist_wheel
- name: Run Unit Tests
working-directory: ./photon-lib/py
run: |
pip install --no-cache-dir dist/*.whl
pytest
- name: Run mypy type checking
uses: liskin/gh-problem-matcher-wrap@v3
with:
linters: mypy
run: |
mypy --show-column-numbers --config-file photon-lib/py/pyproject.toml photon-lib
- name: Upload artifacts
uses: actions/upload-artifact@master
with:
name: dist
path: ./photon-lib/py/dist/
- name: Publish package distributions to TestPyPI
# Only upload on tags
if: startsWith(github.ref, 'refs/tags/v')
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages_dir: ./photon-lib/py/dist/
permissions:
id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
+152
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Python/__pycache__/WebSiteHandler\.cpython-37\.pyc
\.idea/
*.pyc
Python/app/__pycache__/
Python/app/handlers/__pycache__/
\.vscode/
/.vs
backend/settings/
/.vscode/
# Compiled class file
*.class
# Log file
*.log
# BlueJ files
*.ctxt
# Mobile Tools for Java (J2ME)
.mtj.tmp/
# Package Files #
*.jar
*.war
*.nar
*.ear
*.zip
*.xz
*.tar.gz
*.rar
# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
hs_err_pid*
# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
# User-specific stuff
.idea/**/workspace.xml
.idea/**/tasks.xml
.idea/**/usage.statistics.xml
.idea/**/dictionaries
.idea/**/shelf
# Generated files
.idea/**/contentModel.xml
# Sensitive or high-churn files
.idea/**/dataSources/
.idea/**/dataSources.ids
.idea/**/dataSources.local.xml
.idea/**/sqlDataSources.xml
.idea/**/dynamic.xml
.idea/**/uiDesigner.xml
.idea/**/dbnavigator.xml
# Gradle
.idea/**/gradle.xml
.idea/**/libraries
# Gradle and Maven with auto-import
# When using Gradle or Maven with auto-import, you should exclude module files,
# since they will be recreated, and may cause churn. Uncomment if using
# auto-import.
# .idea/modules.xml
# .idea/*.iml
# .idea/modules
# *.iml
# *.ipr
# CMake
cmake-build-*/
# Mongo Explorer plugin
.idea/**/mongoSettings.xml
# File-based project format
*.iws
# IntelliJ
out/
# mpeltonen/sbt-idea plugin
.idea_modules/
# JIRA plugin
atlassian-ide-plugin.xml
# Cursive Clojure plugin
.idea/replstate.xml
# Crashlytics plugin (for Android Studio and IntelliJ)
com_crashlytics_export_strings.xml
crashlytics.properties
crashlytics-build.properties
fabric.properties
# Editor-based Rest Client
.idea/httpRequests
# Android studio 3.1+ serialized cache file
.idea/caches/build_file_checksums.ser
# Temporary build files
**/.gradle
**/target
**/src/main/java/META-INF
**/.settings
**/.classpath
**/.project
**/dependency-reduced-pom.xml
# photon-server/photon-vision.iml
# compile_commands
compile_commands.json
# clang configuration and clangd cache
.clang
.clangd/
.cache/
New client/photon-client/*
*.prefs
*.jfr
.DS_Store
# *.iml
*.bin
.gradle
.gradle/*
photonvision_config
bin*/
build*/
photonlib-java-examples/*/vendordeps/*
photonlib-cpp-examples/*/vendordeps/*
*/networktables.json
*/networktables.json.bck
photonlib-cpp-examples/*/networktables.json.bck
photonlib-java-examples/*/networktables.json.bck
*.sqlite
photon-server/src/main/resources/web/*
venv
.venv/*
.venv
+30
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@@ -0,0 +1,30 @@
version: 2
sphinx:
builder: html
configuration: docs/source/conf.py
fail_on_warning: true
build:
os: ubuntu-22.04
tools:
python: "3.11"
apt_packages:
- graphviz
jobs:
post_checkout:
# Cancel building pull requests when there aren't changed in the docs directory or YAML file.
# You can add any other files or directories that you'd like here as well,
# like your docs requirements file, or other files that will change your docs build.
#
# If there are no changes (git diff exits with 0) we force the command to return with 183.
# This is a special exit code on Read the Docs that will cancel the build immediately.
- |
if [ "$READTHEDOCS_VERSION_TYPE" = "external" ] && git diff --quiet origin/master -- docs/ .readthedocs.yaml;
then
exit 183;
fi
python:
install:
- requirements: docs/requirements.txt
+36
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@@ -0,0 +1,36 @@
cppHeaderFileInclude {
\.h$
\.hpp$
\.inc$
\.inl$
}
cppSrcFileInclude {
\.cpp$
}
modifiableFileExclude {
\.jpg$
\.jpeg$
\.png$
\.gif$
\.so$
\.dll$
\.webp$
\.ico$
\.rknn$
gradlew
photon-lib/py/photonlibpy/generated/
photon-targeting/src/generated/
}
includeProject {
^photonLib/
}
includeOtherLibs {
^frc/
^networktables/
^units/
^wpi/
}
+16
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@@ -0,0 +1,16 @@
/*
* Copyright (C) Photon Vision.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <https://www.gnu.org/licenses/>.
*/
+674
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@@ -0,0 +1,674 @@
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
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Some devices are designed to deny users access to install or run
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Finally, every program is threatened constantly by software patents.
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patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
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How to Apply These Terms to Your New Programs
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Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
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This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
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The hypothetical commands `show w' and `show c' should show the appropriate
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For more information on this, and how to apply and follow the GNU GPL, see
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The GNU General Public License does not permit incorporating your program
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may consider it more useful to permit linking proprietary applications with
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# PhotonVision
[![CI](https://github.com/PhotonVision/photonvision/workflows/CI/badge.svg)](https://github.com/PhotonVision/photonvision/actions?query=workflow%3ACI) [![codecov](https://codecov.io/gh/PhotonVision/photonvision/branch/master/graph/badge.svg)](https://codecov.io/gh/PhotonVision/photonvision) [![Discord](https://img.shields.io/discord/725836368059826228?color=%23738ADB&label=Join%20our%20Discord&logo=discord&logoColor=white)](https://discord.gg/wYxTwym)
PhotonVision is the free, fast, and easy-to-use computer vision solution for the *FIRST* Robotics Competition. You can read an overview of our features [on our website](https://photonvision.org). You can find our comprehensive documentation [here](https://docs.photonvision.org).
The latest release of platform-specific jars and images is found [here](https://github.com/PhotonVision/photonvision/releases).
If you are interested in contributing code or documentation to the project, please [read our getting started page for contributors](https://docs.photonvision.org/en/latest/docs/contributing/index.html) and **[join the Discord](https://discord.gg/wYxTwym) to introduce yourself!** We hope to provide a welcoming community to anyone who is interested in helping.
## Authors
<a href="https://github.com/PhotonVision/photonvision/graphs/contributors">
<img src="https://contrib.rocks/image?repo=PhotonVision/photonvision" />
</a>
## Documentation
- Our main documentation page: [docs.photonvision.org](https://docs.photonvision.org)
- Photon UI demo: [demo.photonvision.org](https://demo.photonvision.org) (or [manual link](https://photonvision.github.io/photonvision/built-client/))
- Javadocs: [javadocs.photonvision.org](https://javadocs.photonvision.org) (or [manual link](https://photonvision.github.io/photonvision/built-docs/javadoc/))
- C++ Doxygen [cppdocs.photonvision.org](https://cppdocs.photonvision.org) (or [manual link](https://photonvision.github.io/photonvision/built-docs/doxygen/html/))
## Building
Gradle is used for all C++ and Java code, and NPM is used for the web UI. Instructions to compile PhotonVision yourself can be found [in our docs](https://docs.photonvision.org/en/latest/docs/contributing/building-photon.html#compiling-instructions).
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`](photonlib-java-examples) and [`photonlib-cpp-examples`](photonlib-cpp-examples) subdirectories, respectively. Instructions for running these examples directly from the repo are found [in the docs](https://docs.photonvision.org/en/latest/docs/contributing/building-photon.html#running-examples).
## Gradle Arguments
Note that these are case sensitive!
* `-PArchOverride=foobar`: builds for a target system other than your current architecture. [Valid overrides](https://github.com/wpilibsuite/wpilib-tool-plugin/blob/main/src/main/java/edu/wpi/first/tools/NativePlatforms.java) are:
* winx32
* winx64
* winarm64
* macx64
* macarm64
* linuxx64
* linuxarm64
* linuxathena
- `-PtgtIP`: Specifies where `./gradlew deploy` should try to copy the fat JAR to
- `-Pprofile`: enables JVM profiling
If you're cross-compiling, you'll need the wpilib toolchain installed. This can be done via Gradle: for example `./gradlew installArm64Toolchain` or `./gradlew installRoboRioToolchain`
## Out-of-Source Dependencies
PhotonVision uses the following additional out-of-source repositories for building code.
- Base system images for Raspberry Pi & Orange Pi: https://github.com/PhotonVision/photon-image-modifier
- C++ driver for Raspberry Pi CSI cameras: https://github.com/PhotonVision/photon-libcamera-gl-driver
- JNI code for [mrcal](https://mrcal.secretsauce.net/): https://github.com/PhotonVision/mrcal-java
- Custom build of OpenCV with GStreamer/Protobuf/other custom flags: https://github.com/PhotonVision/thirdparty-opencv
- JNI code for aruco-nano: https://github.com/PhotonVision/aruconano-jni
## Additional packages
For now, using mrcal requires installing these additional packages on Linux systems:
```
sudo apt install libcholmod3 liblapack3 libsuitesparseconfig5
```
## Acknowledgments
PhotonVision was forked from [Chameleon Vision](https://github.com/Chameleon-Vision/chameleon-vision/). Thank you to everyone who worked on the original project.
* [WPILib](https://github.com/wpilibsuite) - Specifically [cscore](https://github.com/wpilibsuite/allwpilib/tree/master/cscore), [CameraServer](https://github.com/wpilibsuite/allwpilib/tree/master/cameraserver), [NTCore](https://github.com/wpilibsuite/allwpilib/tree/master/ntcore), and [OpenCV](https://github.com/wpilibsuite/thirdparty-opencv).
* [Apache Commons](https://commons.apache.org/) - Specifically [Commons Math](https://commons.apache.org/proper/commons-math/), and [Commons Lang](https://commons.apache.org/proper/commons-lang/)
* [Javalin](https://javalin.io/)
* [JSON](https://json.org)
* [FasterXML](https://github.com/FasterXML) - Specifically [jackson](https://github.com/FasterXML/jackson)
## License
PhotonVision is licensed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html).
## Meeting Notes
Our [meeting notes](https://github.com/PhotonVision/photonvision/wiki/PhotonVision-Meeting-Notes) can be found in the wiki section of this repository.
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import edu.wpi.first.toolchain.*
plugins {
id "java"
id "cpp"
id "com.diffplug.spotless" version "6.24.0"
id "edu.wpi.first.wpilib.repositories.WPILibRepositoriesPlugin" version "2020.2"
id "edu.wpi.first.GradleRIO" version "2025.1.1-beta-2"
id 'edu.wpi.first.WpilibTools' version '1.3.0'
id 'com.google.protobuf' version '0.9.3' apply false
id 'edu.wpi.first.GradleJni' version '1.1.0'
}
allprojects {
repositories {
mavenCentral()
mavenLocal()
maven { url = "https://maven.photonvision.org/releases" }
maven { url = "https://maven.photonvision.org/snapshots" }
maven { url = "https://jogamp.org/deployment/maven/" }
}
wpilibRepositories.addAllReleaseRepositories(it)
wpilibRepositories.addAllDevelopmentRepositories(it)
}
ext.localMavenURL = file("$project.buildDir/outputs/maven")
ext.allOutputsFolder = file("$project.buildDir/outputs")
// Configure the version number.
apply from: "versioningHelper.gradle"
ext {
wpilibVersion = "2025.1.1-beta-2"
wpimathVersion = wpilibVersion
openCVYear = "2024"
openCVversion = "4.8.0-4"
joglVersion = "2.4.0"
javalinVersion = "5.6.2"
libcameraDriverVersion = "dev-v2023.1.0-15-gc8988b3"
rknnVersion = "dev-v2024.0.1-4-g0db16ac"
frcYear = "2025"
mrcalVersion = "dev-v2024.0.0-27-g41d7868";
pubVersion = versionString
isDev = pubVersion.startsWith("dev")
// A list, for legacy reasons, with only the current platform contained
wpilibNativeName = wpilibTools.platformMapper.currentPlatform.platformName;
def nativeName = wpilibNativeName
if (wpilibNativeName == "linuxx64") nativeName = "linuxx86-64";
if (wpilibNativeName == "winx64") nativeName = "windowsx86-64";
if (wpilibNativeName == "macx64") nativeName = "osxx86-64";
if (wpilibNativeName == "macarm64") nativeName = "osxarm64";
jniPlatform = nativeName
println("Building for platform " + jniPlatform + " wpilib: " + wpilibNativeName)
println("Using Wpilib: " + wpilibVersion)
println("Using OpenCV: " + openCVversion)
photonMavenURL = 'https://maven.photonvision.org/' + (isDev ? 'snapshots' : 'releases');
println("Publishing Photonlib to " + photonMavenURL)
}
spotless {
java {
target fileTree('.') {
include '**/*.java'
exclude '**/build/**', '**/build-*/**', '**/src/generated/**'
}
toggleOffOn()
googleJavaFormat()
indentWithTabs(2)
indentWithSpaces(4)
removeUnusedImports()
trimTrailingWhitespace()
endWithNewline()
}
groovyGradle {
target fileTree('.') {
include '**/*.gradle'
exclude '**/build/**', '**/build-*/**'
}
greclipse()
indentWithSpaces(4)
trimTrailingWhitespace()
endWithNewline()
}
format 'xml', {
target fileTree('.') {
include '**/*.xml'
exclude '**/build/**', '**/build-*/**', "**/.idea/**"
}
eclipseWtp('xml')
trimTrailingWhitespace()
indentWithSpaces(2)
endWithNewline()
}
format 'misc', {
target fileTree('.') {
include '**/*.md', '**/.gitignore'
exclude '**/build/**', '**/build-*/**'
}
trimTrailingWhitespace()
indentWithSpaces(2)
endWithNewline()
}
}
wrapper {
gradleVersion '8.11'
}
ext.getCurrentArch = {
return NativePlatforms.desktop
}
subprojects {
tasks.withType(JavaCompile) {
options.compilerArgs.add '-XDstringConcat=inline'
options.encoding = 'UTF-8'
}
// Enables UTF-8 support in Javadoc
tasks.withType(Javadoc) {
options.addStringOption("charset", "utf-8")
options.addStringOption("docencoding", "utf-8")
options.addStringOption("encoding", "utf-8")
}
}
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coverage:
# Turning off commit status to prevent failed checks if coverage decreases
status:
project: no
patch: no
+255
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import argparse
import base64
import json
import os
from dataclasses import dataclass
import cv2
import mrcal
import numpy as np
from wpimath.geometry import Quaternion as _Quat
@dataclass
class Size:
width: int
height: int
@dataclass
class JsonMatOfDoubles:
rows: int
cols: int
type: int
data: list[float]
@dataclass
class JsonMat:
rows: int
cols: int
type: int
data: str # Base64-encoded PNG data
@dataclass
class Point2:
x: float
y: float
@dataclass
class Translation3d:
x: float
y: float
z: float
@dataclass
class Quaternion:
X: float
Y: float
Z: float
W: float
@dataclass
class Rotation3d:
quaternion: Quaternion
@dataclass
class Pose3d:
translation: Translation3d
rotation: Rotation3d
@dataclass
class Point3:
x: float
y: float
z: float
@dataclass
class Observation:
# Expected feature 3d location in the camera frame
locationInObjectSpace: list[Point3]
# Observed location in pixel space
locationInImageSpace: list[Point2]
# (measured location in pixels) - (expected from FK)
reprojectionErrors: list[Point2]
# Solver optimized board poses
optimisedCameraToObject: Pose3d
# If we should use this observation when re-calculating camera calibration
includeObservationInCalibration: bool
snapshotName: str
# The actual image the snapshot is from
snapshotData: JsonMat
@dataclass
class CameraCalibration:
resolution: Size
cameraIntrinsics: JsonMatOfDoubles
distCoeffs: JsonMatOfDoubles
observations: list[Observation]
calobjectWarp: list[float]
calobjectSize: Size
calobjectSpacing: float
def __convert_cal_to_mrcal_cameramodel(
cal: CameraCalibration,
) -> mrcal.cameramodel | None:
if len(cal.distCoeffs.data) == 5:
model = "LENSMODEL_OPENCV5"
elif len(cal.distCoeffs.data) == 8:
model = "LENSMODEL_OPENCV8"
else:
print("Unknown camera model? giving up")
return None
def opencv_to_mrcal_intrinsics(ocv):
return [ocv[0], ocv[4], ocv[2], ocv[5]]
def pose_to_rt(pose: Pose3d):
r = _Quat(
w=pose.rotation.quaternion.W,
x=pose.rotation.quaternion.X,
y=pose.rotation.quaternion.Y,
z=pose.rotation.quaternion.Z,
).toRotationVector()
t = [
pose.translation.x,
pose.translation.y,
pose.translation.z,
]
return np.concatenate((r, t))
imagersize = (cal.resolution.width, cal.resolution.height)
# Always weight=1 for Photon data
WEIGHT = 1
observations_board = np.array(
[
# note that we expect row-major observations here. I think this holds
np.array(
list(map(lambda it: [it.x, it.y, WEIGHT], o.locationInImageSpace))
).reshape((cal.calobjectSize.width, cal.calobjectSize.height, 3))
for o in cal.observations
]
)
optimization_inputs = {
"intrinsics": np.array(
[
opencv_to_mrcal_intrinsics(cal.cameraIntrinsics.data)
+ cal.distCoeffs.data
],
dtype=np.float64,
),
"extrinsics_rt_fromref": np.zeros((0, 6), dtype=np.float64),
"frames_rt_toref": np.array(
[pose_to_rt(o.optimisedCameraToObject) for o in cal.observations]
),
"points": None,
"observations_board": observations_board,
"indices_frame_camintrinsics_camextrinsics": np.array(
[[i, 0, -1] for i in range(len(cal.observations))], dtype=np.int32
),
"observations_point": None,
"indices_point_camintrinsics_camextrinsics": None,
"lensmodel": model,
"imagersizes": np.array([imagersize], dtype=np.int32),
"calobject_warp": (
np.array(cal.calobjectWarp) if len(cal.calobjectWarp) > 0 else None
),
# We always do all the things
"do_optimize_intrinsics_core": True,
"do_optimize_intrinsics_distortions": True,
"do_optimize_extrinsics": True,
"do_optimize_frames": True,
"do_optimize_calobject_warp": len(cal.calobjectWarp) > 0,
"do_apply_outlier_rejection": True,
"do_apply_regularization": True,
"verbose": False,
"calibration_object_spacing": cal.calobjectSpacing,
"imagepaths": np.array([it.snapshotName for it in cal.observations]),
}
return mrcal.cameramodel(
optimization_inputs=optimization_inputs,
icam_intrinsics=0,
)
def convert_photon_to_mrcal(photon_cal_json_path: str, output_folder: str):
"""
Unpack a Photon calibration JSON (eg, photon_calibration_Microsoft_LifeCam_HD-3000_800x600.json) into
the output_folder directory with images and corners.vnl file for use with mrcal.
"""
with open(photon_cal_json_path, "r") as cal_json:
# Convert to nested objects instead of nameddicts on json-loads
class Generic:
@classmethod
def from_dict(cls, dict):
obj = cls()
obj.__dict__.update(dict)
return obj
camera_cal_data: CameraCalibration = json.loads(
cal_json.read(), object_hook=Generic.from_dict
)
# Create output_folder if not exists
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Decode each image and save it as a png
for obs in camera_cal_data.observations:
image = obs.snapshotData.data
decoded_data = base64.b64decode(image)
np_data = np.frombuffer(decoded_data, np.uint8)
img = cv2.imdecode(np_data, cv2.IMREAD_UNCHANGED)
cv2.imwrite(f"{output_folder}/{obs.snapshotName}", img)
# And create a VNL file for use with mrcal
with open(f"{output_folder}/corners.vnl", "w+") as vnl_file:
vnl_file.write("# filename x y level\n")
for obs in camera_cal_data.observations:
for corner in obs.locationInImageSpace:
# Always level zero
vnl_file.write(f"{obs.snapshotName} {corner.x} {corner.y} 0\n")
vnl_file.flush()
mrcal_model = __convert_cal_to_mrcal_cameramodel(camera_cal_data)
with open(f"{output_folder}/camera-0.cameramodel", "w+") as mrcal_file:
mrcal_model.write(
mrcal_file,
note="Generated from PhotonVision calibration file: "
+ photon_cal_json_path
+ "\nCalobject_warp (m): "
+ str(camera_cal_data.calobjectWarp),
)
def main():
parser = argparse.ArgumentParser(
description="Convert Photon calibration JSON for use with mrcal"
)
parser.add_argument("input", type=str, help="Path to Photon calibration JSON file")
parser.add_argument(
"output_folder", type=str, help="Output folder for mrcal VNL file + images"
)
args = parser.parse_args()
convert_photon_to_mrcal(args.input, args.output_folder)
if __name__ == "__main__":
main()
+9
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@@ -0,0 +1,9 @@
build/*
.DS_Store
.vscode/*
.idea/*
source/_build
source/docs/_build
venv/*
.venv/*
+16
View File
@@ -0,0 +1,16 @@
modifiableFileExclude {
\.jpg$
\.jpeg$
\.png$
\.gif$
\.so$
\.pdf$
\.mp4$
\.dll$
\.webp$
\.ico$
\.rknn$
\.svg$
gradlew
}
+395
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@@ -0,0 +1,395 @@
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+24
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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
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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
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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
+44
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@@ -0,0 +1,44 @@
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
sphinx-autobuild==2024.4.16
myst_parser==3.0.1
+7
View File
@@ -0,0 +1,7 @@
---
orphan: true
---
# 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
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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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+17
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@@ -0,0 +1,17 @@
/*!
* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
* License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
*/
@font-face {
font-family: FontAwesome;
src: url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713);
src: url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix&v=4.7.0) format("embedded-opentype"), url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"), url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"), url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"), url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#fontawesomeregular) format("svg");
font-weight: 400;
font-style:normal
}
.code-block-caption>.headerlink, dl dt>.headerlink, h1>.headerlink, h2>.headerlink, h3>.headerlink, h4>.headerlink, h5>.headerlink, h6>.headerlink, p.caption>.headerlink, table>caption>.headerlink {
font-family: FontAwesome;
font-size: 0.75em;
}
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{# Import the theme's layout. #}
{% extends '!layout.html' %}
{%- block extrahead %}
<script>
if (localStorage.getItem("colorTheme") === "dark") {
document.documentElement.setAttribute('data-theme', 'dark');
} else if (localStorage.getItem("colorTheme") === "light") {
document.documentElement.setAttribute('data-theme', 'light');
} else {
var userPrefersDark = window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches;
if (userPrefersDark) {
document.documentElement.setAttribute('data-theme', 'dark');
} else {
document.documentElement.setAttribute('data-theme', 'light');
}
}
</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">
<input type="checkbox" id="switch" name="theme"/><label class="toggle" for="switch">Toggle</label>
</div>
<strong class="dark-label md-icon">&#xE42D</strong>
</div>
<script>
var checkbox = document.querySelector('input[name=theme]');
var element = document.documentElement.getAttribute('data-theme');
if (element == 'dark') {
// Auto check the checkbox if the set theme is "dark".
checkbox.checked = true;
}
checkbox.addEventListener('change', function() {
if (this.checked) {
document.documentElement.setAttribute('data-theme', 'dark');
localStorage.setItem("colorTheme", "dark");
} else {
document.documentElement.setAttribute('data-theme', 'light');
localStorage.setItem("colorTheme", "light");
}
});
window.matchMedia('(prefers-color-scheme: dark)')
.addEventListener('change', event => {
if (event.matches) {
document.documentElement.setAttribute('data-theme', 'dark');
localStorage.setItem("colorTheme", "dark");
checkbox.checked = true;
} else {
document.documentElement.setAttribute('data-theme', 'light');
localStorage.setItem("colorTheme", "light");
checkbox.checked = false;
}
});
</script>
{# Call the parent block #}
{{ super() }}
{%- endblock %}
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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",
"myst_parser",
"sphinx.ext.mathjax",
"sphinx.ext.graphviz",
]
# 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"]
source_suffix = [".rst", ".md"]
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",
},
"footer_icons": [
{
"name": "GitHub",
"url": "https://github.com/photonvision/photonvision",
"html": """
<svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
<path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
</svg>
""",
"class": "",
},
],
}
suppress_warnings = ["epub.unknown_project_files"]
sphinx_tabs_valid_builders = ["epub", "linkcheck"]
# -- Options for linkcheck -------------------------------------------------
# Excluded links for linkcheck
# These should be periodically checked by hand to ensure that they are still functional
linkcheck_ignore = [R"https://www.raspberrypi.com/software/", R"http://10\..+"]
token = os.environ.get("GITHUB_TOKEN", None)
if token:
linkcheck_auth = [(R"https://github.com/.+", token)]
# MyST configuration (https://myst-parser.readthedocs.io/en/latest/configuration.html)
myst_enable_extensions = ["colon_fence"]
@@ -0,0 +1,29 @@
# 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 AprilTags(s) and ensure you get a consistent tracking (you hold one AprilTag 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.
- Move the robot close, far, angled, and around the field to ensure no extra AprilTags are found.
- 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 AprilTags to stay within field bandwidth limits.
@@ -0,0 +1,50 @@
# Filesystem Directory
PhotonVision stores and loads settings in the {code}`photonvision_config` directory, in the same folder as the PhotonVision JAR is stored. On supported hardware, 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
:alt: Config directory structure
:width: 600
```
- 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`. These timestamps will likely be significantly behind the real time. Coprocessors on the robot have no way to get current 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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# Advanced Installation
This page will help you install PhotonVision on non-supported coprocessor.
## 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
prerelease-software
```
@@ -0,0 +1,23 @@
# Installing Pre-Release Versions
Pre-release/development version of PhotonVision can be tested by installing/downloading artifacts from Github Actions (see below), which are built automatically on commits to open pull requests and to PhotonVision's `master` branch, or by {ref}`compiling PhotonVision locally <docs/contributing/building-photon:Build Instructions>`.
:::{warning}
If testing a pre-release version of PhotonVision with a robot, PhotonLib must be updated to match the version downloaded! If not, packet schema definitions may not match and unexpected things will occur. To update PhotonLib, refer to {ref}`installing specific version of PhotonLib<docs/programming/photonlib/adding-vendordep:Install Specific Version - Java/C++>`.
:::
GitHub Actions builds pre-release version of PhotonVision automatically on PRs and on each commit merged to master. To test a particular commit to master, navigate to the [PhotonVision commit list](https://github.com/PhotonVision/photonvision/commits/master/) and click on the check mark (below). Scroll to "Build / Build fat JAR - PLATFORM", click details, and then summary. From here, JAR and image files can be downloaded to be flashed or uploaded using "Offline Update".
```{image} images/gh_actions_1.png
:alt: Github Actions Badge
```
```{image} images/gh_actions_2.png
:alt: Github Actions artifact list
```
Built JAR files (but not image files) can also be downloaded from PRs before they are merged. Navigate to the PR in GitHub, and select Checks at the top. Click on "Build" to display the same artifact list as above.
```{image} images/gh_actions_3.png
:alt: Github Actions artifacts from PR
```
@@ -0,0 +1,56 @@
# 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:
```bash
java -jar /path/to/photonvision/photonvision.jar
```
## Updating a JAR File
When you need to update your JAR file, run the following:
```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:
```
[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`.
:::
@@ -0,0 +1,9 @@
{
"deviceName" : "Limelight 2+",
"supportURL" : "https://limelightvision.io",
"ledPins" : [ 13, 18 ],
"ledsCanDim" : true,
"ledPWMRange" : [ 0, 100 ],
"ledPWMFrequency" : 30000,
"vendorFOV" : 75.76079874010732
}
@@ -0,0 +1,7 @@
{
"deviceName" : "Limelight 2",
"supportURL" : "https://limelightvision.io",
"ledPins" : [ 17, 18 ],
"ledsCanDim" : false,
"vendorFOV" : 75.76079874010732
}
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# Software Installation
## Desktop Environments
```{toctree}
:maxdepth: 1
windows-pc
linux-pc
mac-os
```
## Other
```{toctree}
:maxdepth: 1
other-coprocessors
advanced-cmd
romi
```
@@ -0,0 +1,47 @@
# Linux PC Installation
PhotonVision may be run on a Debian-based Linux Desktop PC for basic testing and evaluation.
:::{note}
You do not need to install PhotonVision on a Windows PC in order to access the webdashboard (assuming you are using an external coprocessor like a Raspberry Pi).
:::
## Installing Java
PhotonVision requires a JDK installed and on the system path. JDK 17 is needed (different versions will not work). If you don't have JDK 17 already, run the following to install it:
```
$ sudo apt-get install openjdk-17-jdk
```
:::{warning}
Using a JDK other than JDK17 will cause issues when running PhotonVision and is not supported.
:::
## Downloading the Latest Stable Release of PhotonVision
Go to the [GitHub releases page](https://github.com/PhotonVision/photonvision/releases) and download the relevant .jar file for your coprocessor.
:::{note}
If your coprocessor has a 64 bit ARM based CPU architecture (OrangePi, Raspberry Pi, etc.), download the LinuxArm64.jar file.
If your coprocessor has an 64 bit x86 based CPU architecture (Mini PC, laptop, etc.), download the Linuxx64.jar file.
:::
:::{warning}
Be careful to pick the latest stable release. "Draft" or "Pre-Release" versions are not stable and often have bugs.
:::
## Running PhotonVision
To run PhotonVision, open a terminal window of your choice and run the following command:
```
$ java -jar /path/to/photonvision/photonvision-xxx.jar
```
If your computer has a compatible webcam connected, PhotonVision should startup without any error messages. If there are error messages, your webcam isn't supported or another issue has occurred. If it is the latter, please open an issue on the [PhotonVision issues page](https://github.com/PhotonVision/photonvision/issues).
## Accessing the PhotonVision Interface
Once the Java backend is up and running, you can access the main vision interface by navigating to `localhost:5800` inside your browser.
@@ -0,0 +1,53 @@
# Mac OS Installation
:::{warning}
Due to current [cscore](https://github.com/wpilibsuite/allwpilib/tree/main/cscore) restrictions, the PhotonVision server backend may have issues running macOS.
:::
:::{note}
You do not need to install PhotonVision on a Mac in order to access the webdashboard (assuming you are using an external coprocessor like a Raspberry Pi).
:::
VERY Limited macOS support is available.
## Installing Java
PhotonVision requires a JDK installed and on the system path. JDK 17 is needed (different versions will not work). You may already have this if you have installed WPILib 2025+. If not, [download and install it from here](https://adoptium.net/temurin/releases?version=17).
:::{warning}
Using a JDK other than JDK17 will cause issues when running PhotonVision and is not supported.
:::
## Downloading the Latest Stable Release of PhotonVision
Go to the [GitHub releases page](https://github.com/PhotonVision/photonvision/releases) and download the relevant .jar file for your coprocessor.
:::{note}
If you have an M1/M2 Mac, download the macarm64.jar file.
If you have an Intel based Mac, download the macx64.jar file.
:::
:::{warning}
Be careful to pick the latest stable release. "Draft" or "Pre-Release" versions are not stable and often have bugs.
:::
## Running PhotonVision
To run PhotonVision, open a terminal window of your choice and run the following command:
```
$ java -jar /path/to/photonvision/photonvision-xxx.jar
```
:::{warning}
Due to current [cscore](https://github.com/wpilibsuite/allwpilib/tree/main/cscore) restrictions, the PhotonVision using test mode is all that is known to work currently.
:::
## Accessing the PhotonVision Interface
Once the Java backend is up and running, you can access the main vision interface by navigating to `localhost:5800` inside your browser.
:::{warning}
Due to current [cscore](https://github.com/wpilibsuite/allwpilib/tree/main/cscore) restrictions, it is unlikely any streams will open from real webcams.
:::
@@ -0,0 +1,39 @@
# Other Debian-Based Co-Processor Installation
:::{warning}
Working with unsupported coprocessors requires some level of "know how" of your system. The install script has only been tested on Debian/Raspberry Pi OS Buster and Ubuntu Bionic. If any issues arise with your specific OS, please open an issue on our [issues page](https://github.com/PhotonVision/photonvision/issues).
:::
:::{note}
We'd love to have your input! If you get PhotonVision working on another coprocessor, consider documenting your steps and submitting a [docs issue](https://github.com/PhotonVision/photonvision-docs/issues)., [pull request](https://github.com/PhotonVision/photonvision-docs/pulls) , or [ping us on Discord](https://discord.com/invite/wYxTwym). For example, Limelight and Romi install instructions came about because someone spent the time to figure it out, and did a writeup.
:::
## Installing PhotonVision
We provide an [install script](https://git.io/JJrEP) for other Debian-based systems (with `apt`) that will automatically install PhotonVision and make sure that it runs on startup.
```bash
$ wget https://git.io/JJrEP -O install.sh
$ sudo chmod +x install.sh
$ sudo ./install.sh
$ sudo reboot now
```
:::{note}
Your co-processor will require an Internet connection for this process to work correctly.
:::
For installation on any other co-processors, we recommend reading the {ref}`advanced command line documentation <docs/advanced-installation/sw_install/advanced-cmd:Advanced Command Line Usage>`.
## Updating PhotonVision
PhotonVision can be updated by downloading the latest jar file, copying it onto the processor, and restarting the service.
For example, from another computer, run the following commands. Substitute the correct username for "\[user\]" ( Provided images use username "pi")
```bash
$ scp [jar name].jar [user]@photonvision.local:~/
$ ssh [user]@photonvision.local
$ sudo mv [jar name].jar /opt/photonvision/photonvision.jar
$ sudo systemctl restart photonvision.service
```
@@ -0,0 +1,43 @@
# Romi Installation
The [Romi](https://docs.wpilib.org/en/latest/docs/romi-robot/index.html) is a small robot that can be controlled with the WPILib software. The main controller is a Raspberry Pi that must be imaged with [WPILibPi](https://docs.wpilib.org/en/latest/docs/romi-robot/imaging-romi.html) .
## Installation
The WPILibPi image includes FRCVision, which reserves USB cameras; to use PhotonVision, we need to edit the `/home/pi/runCamera` script to disable it. First we will need to make the file system writeable; the easiest way to do this is to go to `10.0.0.2` and choose "Writable" at the top.
SSH into the Raspberry Pi (using Windows command line, or a tool like [Putty](https://www.chiark.greenend.org.uk/~sgtatham/putty/) ) at the Romi's default address `10.0.0.2`. The default user is `pi`, and the password is `raspberry`.
:::.. The following paragraph can be restored when WPILibPi becomes compatible with the current version of PhotonVision.
:::.. Follow the process for installing PhotonVision on {ref}`"Other Debian-Based Co-Processor Installation" <docs/advanced-installation/sw_install/other-coprocessors:Other Debian-Based Co-Processor Installation>`. As it mentions, this will require an internet connection so connecting the Raspberry Pi to an internet-connected router via an Ethernet cable will be the easiest solution. The pi must remain writable while you are following these steps!
:::..Temporary instructions explaining how to install the older version of PhotonVision on a Romi. Remove when no longer needed.
:::{attention}
The version of WPILibPi for the Romi is 2023.2.1, which is not compatible with the current version of PhotonVision. **If you are using WPILibPi 2023.2.1 on your Romi, you must install PhotonVision v2023.4.2 or earlier!**
To install a compatible version of PhotonVision, enter these commands in the SSH terminal connected to the Raspberry Pi. This will download and run the install script, which will intall PhotonVision on your Raspberry Pi and configure it to run at startup.
```bash
$ wget https://git.io/JJrEP -O install.sh
$ sudo chmod +x install.sh
$ sudo ./install.sh -v 2023.4.2
```
The install script requires an internet connection, so connecting the Raspberry Pi to an internet-connected router via an Ethernet cable will be the easiest solution. The pi must remain writable while you are following these steps!
:::
:::..End of temporary instructions.
Next, from the SSH terminal, run `sudo nano /home/pi/runCamera` then arrow down to the start of the exec line and press "Enter" to add a new line. Then add `#` before the exec command to comment it out. Then, arrow up to the new line and type `sleep 10000`. Hit "Ctrl + O" and then "Enter" to save the file. Finally press "Ctrl + X" to exit nano. Now, reboot the Romi by typing `sudo reboot now`.
```{image} images/nano.png
```
After the Romi reboots, you should be able to open the PhotonVision UI at: [`http://10.0.0.2:5800/`](http://10.0.0.2:5800/). From here, you can adjust {ref}`Settings <docs/settings:Settings>` and configure {ref}`Pipelines <docs/pipelines/index:Pipelines>`.
:::{warning}
In order for settings, logs, etc. to be saved / take effect, ensure that PhotonVision is in writable mode.
:::
:::{attention}
When using an older version of PhotonVision, the user interface and features may be different than what appears in the online documentation. The [Documentation](http://10.0.0.2:5800/#/docs) link in the User Interface will open a bundled version of the documentation that matches the PhotonVision version running on your coprocessor.
:::
@@ -0,0 +1,45 @@
# Windows PC Installation
PhotonVision may be run on a Windows Desktop PC for basic testing and evaluation.
:::{note}
You do not need to install PhotonVision on a Windows PC in order to access the webdashboard (assuming you are using an external coprocessor like a Raspberry Pi).
:::
## Install Bonjour
Bonjour provides more stable networking when using Windows PCs. Install [Bonjour here](https://support.apple.com/downloads/DL999/en_US/BonjourPSSetup.exe) before continuing to ensure a stable experience while using PhotonVision.
## Installing Java
PhotonVision requires a JDK installed and on the system path. **JDK 17 is needed. Windows Users must use the JDK that ships with WPILib.** [Download and install it from here.](https://github.com/wpilibsuite/allwpilib/releases/tag/v2025.1.1-beta-2) Either ensure the only Java on your PATH is the WPILIB Java or specify it to gradle with `-Dorg.gradle.java.home=C:\Users\Public\wpilib\2025\jdk`:
```
> ./gradlew run "-Dorg.gradle.java.home=C:\Users\Public\wpilib\2025\jdk"
```
:::{warning}
Using a JDK other than WPILIB's JDK17 will cause issues when running PhotonVision and is not supported.
:::
## Downloading the Latest Stable Release of PhotonVision
Go to the [GitHub releases page](https://github.com/PhotonVision/photonvision/releases) and download the winx64.jar file.
## Running PhotonVision
To run PhotonVision, open a terminal window of your choice and run the following command:
```
> java -jar C:\path\to\photonvision\NAME OF JAR FILE GOES HERE.jar
```
If your computer has a compatible webcam connected, PhotonVision should startup without any error messages. If there are error messages, your webcam isn't supported or another issue has occurred. If it is the latter, please open an issue on the [PhotonVision issues page](https://github.com/PhotonVision/photonvision/issues).
:::{warning}
Using an integrated laptop camera may cause issues when trying to run PhotonVision. If you are unable to run PhotonVision on a laptop with an integrated camera, try disabling the camera's driver in Windows Device Manager.
:::
## Accessing the PhotonVision Interface
Once the Java backend is up and running, you can access the main vision interface by navigating to `localhost:5800` inside your browser.
@@ -0,0 +1,54 @@
# 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
:align: center
:scale: 45 %
```
### 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 at most 3 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.
@@ -0,0 +1,13 @@
# 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.
@@ -0,0 +1,14 @@
# 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).
:::
@@ -0,0 +1,40 @@
# 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
:align: center
:scale: 45 %
```
```{image} images/multiple-tags.png
:align: center
:scale: 45 %
```
## 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
:align: center
:scale: 45 %
```
@@ -0,0 +1,15 @@
# 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
```
@@ -0,0 +1,65 @@
# 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
:alt: Multitarget enabled and running in the PhotonVision UI
:width: 600
```
:::{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++).
```{eval-rst}
.. 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;
}
.. code-block:: Python
# Coming Soon!
```
:::{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
:alt: The currently saved field layout in the Photon UI
:width: 600
```
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 Charuco board (or chessboard) are taken. By comparing where the grid corners should be in object space (for example, a corner 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, higher resolutions may be too performance-intensive for some coprocessors to handle. Therefore, we recommend experimenting to see what works best for your coprocessor.
:::
:::{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:
01. 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.
02. Use as big of a calibration target as your printer can print.
03. Ensure that your printed pattern has enough white border around it.
04. Ensure your camera stays in one position during the duration of the calibration.
05. Make sure you get all 12 images from varying distances and angles.
06. 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.
07. Have good lighting, having a diffusely lit target would be best (light specifically shining on the target without shadows).
08. 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.
09. 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.
## 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 charuco 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 charuco board (or chessboard depending on the selected calibration type).
:::{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, marker size, and board size. The provided chessboard and charuco board are an 8x8 grid of 1 inch square. The provided charuco board uses the 4x4 dictionary with a marker size of 0.75 inches (this board does not need the old OpenCV pattern selector selected). Printers are not perfect, and you need to measure your calibration target and enter the correct marker size (size of the aruco marker) and pattern spacing (aka size of the black square) using calipers or similar. Finally, once our entered data is correct, we'll click "start calibration."
:::{warning} Old OpenCV Pattern selector. This should be used in the case that the calibration image is generated from a version of OpenCV before version 4.6.0. This would include targets created by calib.io. If this selector is not set correctly the calibration will be completely invalid. For more info view [this GitHub issue](https://github.com/opencv/opencv_contrib/issues/3291).
:::
### 4. Take at calibration images from various angles.
Now, we'll capture images of our board from various angles. It's important to check that the board overlay matches the board 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 enough images to cover the whole camera's FOV (with a minimum of 12). 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 FOVs will be shown in the table on the right. The FOV should be close to the camera's specified FOV (usually found in a datasheet) usually within + or - 10 degrees. The mean error should also be low, usually less than 1 pixel.
```{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 intrinsic.
```{image} images/cal-details.png
:alt: Captured calibration images
:width: 600
```
:::{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
:alt: Captured calibration images
:width: 600
```
## 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) 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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# 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
Documentation lives within the main PhotonVision repository within the `docs` sub-folder. If you are planning on contributing, it is recommended to create a fork of the [PhotonVision repository](https://github.com/PhotonVision/photonvision). To clone this fork, run the following command in a terminal window:
`git clone https://github.com/[your username]/photonvision`
## 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 docs sub-folder:
`~/photonvision/docs$ python -m pip install -r requirements.txt`
## Building the Documentation
In order to build the documentation, you can run the following command in the docs sub-folder. This will automatically build docs every time a file changes, and serves them locally at `localhost:8000` by default.
`~/photonvision/docs$ sphinx-autobuild --open-browser source source/_build/html`
## Opening the Documentation
The built documentation is located at `docs/build/html/index.html` relative to the root project directory, or can be accessed via the local web server if using sphinx-autobuild.
## 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`. Additionally, the pull request will have a link directly to the pre-release build of the docs. This build only runs when there is a change to files in the docs sub-folder.
## 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).
@@ -0,0 +1,274 @@
# 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+. **Windows Users must use the JDK that ships with WPILib.** For other platforms, 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 18.20.4 to Node 20.0.0 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
```bash
nvm install 20
```
so that it instead reads
```javascript
nvm install 18.20.4
```
## Compiling Instructions
### Getting the Source Code
Get the source code from git:
```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
:alt: Download source code from git
:width: 600
```
### Install Necessary Node JS Dependencies
In the photon-client directory:
```bash
npm install
```
### Build and Copy UI to Java Source
In the root directory:
```{eval-rst}
.. 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:
```{eval-rst}
.. 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`:
```{eval-rst}
.. 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.
```{eval-rst}
.. 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 automatically 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:
```{eval-rst}
.. 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.
```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"`.
```
{
// 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`
```
{
// 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-*-examples subdirectories for each language.
#### Running C++/Java
PhotonLib must first be published to your local maven repository. This will also 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 not yet supported.
```
~/photonvision$ ./gradlew publishToMavenLocal
~/photonvision$ cd photonlib-java-examples
~/photonvision/photonlib-java-examples$ ./gradlew <example-name>:simulateJava
~/photonvision$ cd photonlib-cpp-examples
~/photonvision/photonlib-cpp-examples$ ./gradlew <example-name>:simulateNative
```
#### Running Python
PhotonLibPy must first be built into a wheel.
```
> cd photon-lib/py
> buildAndTest.bat
```
Then, you must enable using the development wheels. robotpy will use pip behind the scenes, and this bat file tells pip about your development artifacts.
Note: This is best done in a virtual environment.
```
> enableUsingDevBuilds.bat
```
Then, run the examples:
```
> cd photonlib-python-examples
> run.bat <example name>
```
#### Downloading Pipeline Artifacts
Using the [GitHub CLI](https://cli.github.com/), we can download artifacts from pipelines by run ID and name:
```
~/photonvision$ gh run download 11759699679 -n jar-Linux
```
@@ -0,0 +1,61 @@
# Calibration and Image Rotation
## Rotating Points
To stay consistent with the OpenCV camera coordinate frame, we put the origin in the top left, with X right, Y down, and Z out (as required by the right-hand rule). Intuitively though, if I ask you to rotate an image 90 degrees clockwise though, you'd probably rotate it about -Z in this coordinate system. Just be aware of this inconsistency.
![](images/image_corner_frames.png)
If we have any one point in any of those coordinate systems, we can transform it into any of the other ones using standard geometry libraries by performing relative transformations (like in this pseudocode):
```
Translation2d tag_corner1 = new Translation2d();
Translation2d rotated = tag_corner1.relativeTo(ORIGIN_ROTATED_90_CCW);
```
## Image Distortion
The distortion coefficients for OPENCV8 is given in order `[k1 k2 p1 p2 k3 k4 k5 k6]`. Mrcal names these coefficients `[k_0 k_1, k_2, k_3, k_4, k_5, k_6, k_7]`.
```{math}
\begin{align*}
\vec P &\equiv \frac{\vec p_{xy}}{p_z} \\
r &\equiv \left|\vec P\right| \\
\vec P_\mathrm{radial} &\equiv \frac{ 1 + k_0 r^2 + k_1 r^4 + k_4 r^6}{ 1 + k_5 r^2 + k_6 r^4 + k_7 r^6} \vec P \\
\vec P_\mathrm{tangential} &\equiv
\left[ \begin{aligned}
2 k_2 P_0 P_1 &+ k_3 \left(r^2 + 2 P_0^2 \right) \\
2 k_3 P_0 P_1 &+ k_2 \left(r^2 + 2 P_1^2 \right)
\end{aligned}\right] \\
\vec q &= \vec f_{xy} \left( \vec P_\mathrm{radial} + \vec P_\mathrm{tangential} \right) + \vec c_{xy}
\end{align*}
```
From this, we observe at `k_0, k_1, k_4, k_5, k_6, k_7` depend only on the norm of {math}`\vec P`, and will be constant given a rotated image. However, `k_2` and `k_3` go with {math}`P_0 \cdot P_1`, `k_3` with {math}`P_0^2`, and `k_2` with {math}`P_1^2`.
Let's try a concrete example. With a 90 degree CCW rotation, we have {math}`P0=-P_{1\mathrm{rotated}}` and {math}`P1=P_{0\mathrm{rotated}}`. Let's substitute in
```{math}
\begin{align*}
\left[ \begin{aligned}
2 k_2 P_0 P_1 &+ k_3 \left(r^2 + 2 P_0^2 \right) \\
2 k_3 P_0 P_1 &+ k_2 \left(r^2 + 2 P_1^2 \right)
\end{aligned}\right] &=
\left[ \begin{aligned}
2 k_{2\mathrm{rotated}} (-P_{1\mathrm{rotated}}) P_{0\mathrm{rotated}} &+ k_{3\mathrm{rotated}} \left(r^2 + 2 (-P_{1\mathrm{rotated}})^2 \right) \\
2 k_{3\mathrm{rotated}} (-P_{1\mathrm{rotated}}) P_{0\mathrm{rotated}} &+ k_{2\mathrm{rotated}} \left(r^2 + 2 P_{0\mathrm{rotated}}^2 \right)
\end{aligned}\right] \\
&=
\left[ \begin{aligned}
-2 k_{2\mathrm{rotated}} P_{1\mathrm{rotated}} P_{0\mathrm{rotated}} &+ k_{3\mathrm{rotated}} \left(r^2 + 2 P_{1\mathrm{rotated}}^2 \right) \\
-2 k_{3\mathrm{rotated}} P_{1\mathrm{rotated}} P_{0\mathrm{rotated}} &+ k_{2\mathrm{rotated}} \left(r^2 + 2 P_{0\mathrm{rotated}}^2 \right)
\end{aligned}\right]
\end{align*}
```
By inspection, this results in just applying another 90 degree rotation to the k2/k3 parameters. Proof is left as an exercise for the reader. Note that we can repeat this rotation to yield equations for tangential distortion for 180 and 270 degrees.
```{math}
k_2'=-k_3
k_3'=k_2
```

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