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3 Commits

Author SHA1 Message Date
Matt
7271c950e1 Run build 2024-08-02 09:02:28 -07:00
Matt M
2f2b6e9693 Oops 2024-05-29 10:19:40 -04:00
Matt M
60c9ea8892 Backport maven changes 2024-05-29 10:18:07 -04:00
1202 changed files with 26195 additions and 397063 deletions

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@@ -68,26 +68,19 @@ ForEachMacros:
- BOOST_FOREACH
IncludeBlocks: Regroup
IncludeCategories:
# C standard library headers
#
# https://en.cppreference.com/w/cpp/header:
# * C compatibility headers
# * Special C compatibility headers
# * Empty C headers
# * Meaningless C headers
# * Unsupported C headers
- Regex: '^<(assert\.h|ctype\.h|errno\.h|fenv\.h|float\.h|inttypes\.h|limits\.h|locale\.h|math\.h|setjmp\.h|signal\.h|stdarg\.h|stddef\.h|stdint\.h|stdio\.h|stdlib\.h|string\.h|time\.h|uchar\.h|wchar\.h|wctype\.h|stdatomic\.h|ccomplex|complex\.h|ctgmath|tgmath\.h|ciso646|cstdalign|cstdbool|iso646\.h|stdalign\.h|stdbool\.h|stdatomic\.h|stdnoreturn\.h|threads\.h)>'
Priority: 1
# C++ standard library headers (lowercase and underscores with no .h suffix)
- Regex: '^<[a-z_]+>'
Priority: 2
# Other library headers (angle brackets)
- Regex: '^<.*'
Priority: 3
# Project headers (double quotes)
- Regex: '^".*'
Priority: 4
IncludeIsMainRegex: '(Test|_test)?$'
- Regex: '^<ext/.*\.h>'
Priority: 2
SortPriority: 0
- Regex: '^<.*\.h>'
Priority: 1
SortPriority: 0
- Regex: '^<.*'
Priority: 2
SortPriority: 0
- Regex: '.*'
Priority: 3
SortPriority: 0
IncludeIsMainRegex: '([-_](test|unittest))?$'
IncludeIsMainSourceRegex: ''
IndentCaseLabels: true
IndentGotoLabels: true
@@ -143,7 +136,7 @@ RawStringFormats:
CanonicalDelimiter: ''
BasedOnStyle: google
ReflowComments: true
SortIncludes: true
SortIncludes: false
SortUsingDeclarations: true
SpaceAfterCStyleCast: false
SpaceAfterLogicalNot: false

3
.gitattributes vendored
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@@ -35,6 +35,3 @@
*.so binary
*.dll binary
*.webp binary
# autogenerated constrained solve pnp code
photon-targeting/src/main/native/cpp/photon/constrained_solvepnp/generate/**/* linguist-generated

4
.github/CODEOWNERS vendored
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@@ -1,6 +1,2 @@
# These owners will be the default owners for everything in the repo.
* @PhotonVision/program-devs
docs/* @PhotonVision/doc-maintainers
photonlib-java-examples/* @PhotonVision/doc-maintainers
photonlib-cpp-examples/* @PhotonVision/doc-maintainers
photonlib-python-examples/* @PhotonVision/doc-maintainers

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@@ -22,7 +22,6 @@ If applicable, add screenshots to help explain your problem. Additionally, provi
**Platform:**
- Hardware Platform (ex. Raspberry Pi 4, Windows x64):
- How is it powered? (ex. Zinc-V, Pololu Buck Converter, Battery Bank):
- 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.):

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@@ -1,17 +0,0 @@
---
name: Documentation Request
about: Something needs to be documented/updated in the documentation
title: ''
labels: documentation
assignees: ''
---
**Are you requesting documentation for a new feature, or updated documentation for an old feature?**
Put the feature you are requesting documentation for here, along with whether the documentation is stale and needs to be updated, or whether the documentation does not exist, and needs to be created.
**Where is it?**
Put the location of the documentation that needs to be updated here. If you're requesting documenation for a new feature, put where you think it should go.
**Additional context**
Add any other context or screenshots about the feature request here.

15
.github/labeler.yml vendored
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@@ -1,15 +0,0 @@
"backend":
- changed-files:
- any-glob-to-any-file: [photon-core/**, photon-server/**]
"documentation":
- changed-files:
- any-glob-to-any-file: [docs/**, photon-docs/**]
"frontend":
- changed-files:
- any-glob-to-any-file: photon-client/**
"photonlib":
- changed-files:
- any-glob-to-any-file: photon-lib*/**
"website":
- changed-files:
- any-glob-to-any-file: website/**

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@@ -1,19 +0,0 @@
## Description
<!-- What changed? Why? (the code + comments should speak for itself on the "how") -->
<!-- Fun screenshots or a cool video or something are super helpful as well. If this touches platform-specific behavior, this is where test evidence should be collected. -->
<!-- Any issues this pull request closes or pull requests this supersedes should be linked with `Closes #issuenumber`. -->
## Meta
Merge checklist:
- [ ] Pull Request title is [short, imperative summary](https://cbea.ms/git-commit/) of proposed changes
- [ ] The description documents the _what_ and _why_
- [ ] This PR has been [linted](https://docs.photonvision.org/en/latest/docs/contributing/linting.html).
- [ ] If this PR changes behavior or adds a feature, user documentation is updated
- [ ] If this PR touches photon-serde, all messages have been regenerated and hashes have not changed unexpectedly
- [ ] If this PR touches configuration, this is backwards compatible with settings back to v2025.3.2
- [ ] If this PR touches pipeline settings or anything related to data exchange, the frontend typing is updated
- [ ] If this PR addresses a bug, a regression test for it is added

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@@ -1,155 +1,18 @@
name: Build
on:
# Run on pushes to main and pushed tags, and on pull requests against main, but ignore the docs folder
push:
branches:
- master
tags:
- 'v*'
pull_request:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.ref }}
cancel-in-progress: true
env:
IMAGE_VERSION: v2026.0.4
branches: [ master ]
jobs:
validation:
name: "Validation"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: gradle/actions/wrapper-validation@v4
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 }}
needs: [validation]
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
needs: [validation]
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 pnpm
uses: pnpm/action-setup@v4
with:
version: 10
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 22
- 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 and Coverage
run: ./gradlew test jacocoTestReport --stacktrace
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
needs: [validation]
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
MACOSX_DEPLOYMENT_TARGET: 12
strategy:
fail-fast: false
matrix:
@@ -157,15 +20,14 @@ jobs:
- os: windows-2022
artifact-name: Win64
architecture: x64
- os: macos-14
- os: macos-12
artifact-name: macOS
architecture: aarch64
architecture: x64
- os: ubuntu-22.04
artifact-name: Linux
name: "Photonlib - Build Host - ${{ matrix.artifact-name }}"
runs-on: ${{ matrix.os }}
needs: [validation]
steps:
- uses: actions/checkout@v4
with:
@@ -175,41 +37,30 @@ jobs:
with:
java-version: 17
distribution: temurin
architecture: ${{ matrix.architecture }}
- run: git fetch --tags --force
- run: ./gradlew photon-targeting:build photon-lib:build
name: Build with Gradle
- run: |
chmod +x gradlew
./gradlew photon-lib:build --max-workers 1
- 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
if: github.event_name == 'push'
build-photonlib-docker:
strategy:
fail-fast: false
matrix:
include:
- container: wpilib/roborio-cross-ubuntu:2025-24.04
- 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 }}"
needs: [validation]
steps:
- uses: actions/checkout@v4
with:
@@ -218,387 +69,13 @@ jobs:
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 }} -x test
run: |
chmod +x gradlew
./gradlew photon-lib:build --max-workers 1
- name: Publish
run: ./gradlew photon-lib:publish photon-targeting:publish ${{ matrix.build-options }}
run: |
chmod +x gradlew
./gradlew photon-lib:publish photon-targeting: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 ${{ matrix.build-options }}
- uses: actions/upload-artifact@v4
with:
name: maven-${{ 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-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 pnpm
uses: pnpm/action-setup@v4
with:
version: 10
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 22
cache: pnpm
cache-dependency-path: photon-client/pnpm-lock.yaml
- name: Install Arm64 Toolchain
run: ./gradlew installArm64Toolchain
if: ${{ (matrix.artifact-name) == 'LinuxArm64' }}
- uses: actions/download-artifact@v4
with:
name: built-docs
path: photon-server/src/main/resources/web/docs
- run: ./gradlew photon-targeting:jar photon-server:shadowJar -PArchOverride=${{ 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-24.04' }}
# 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: ${{ (matrix.os) == 'windows-latest' }}
run-smoketest-chroot:
needs: [build-package]
strategy:
fail-fast: false
matrix:
include:
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: RaspberryPi
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/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: ${{ github.event_name != 'pull_request' }}
strategy:
fail-fast: false
matrix:
include:
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: RaspberryPi
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_raspi.img.xz
cpu: cortex-a7
image_additional_mb: 0
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: limelight2
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_limelight.img.xz
cpu: cortex-a7
image_additional_mb: 0
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: limelight3
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_limelight3.img.xz
cpu: cortex-a7
image_additional_mb: 0
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: limelight3G
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_limelight3g.img.xz
cpu: cortex-a7
image_additional_mb: 0
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: limelight4
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_limelight4.img.xz
cpu: cortex-a76
image_additional_mb: 0
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: luma_p1
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_luma_p1.img.xz
cpu: cortex-a76
image_additional_mb: 0
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: orangepi5
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_opi5.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: orangepi5b
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_opi5b.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: orangepi5plus
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_opi5plus.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: orangepi5pro
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_opi5pro.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: orangepi5max
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_opi5max.img.xz
cpu: cortex-a8
image_additional_mb: 1024
- os: ubuntu-24.04
artifact-name: LinuxArm64
image_suffix: rock5c
image_url: https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_rock5c.img.xz
cpu: cortex-a8
image_additional_mb: 1024
runs-on: ${{ matrix.os }}
name: "Build image - ${{ matrix.image_suffix }}"
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: actions/download-artifact@v4
with:
name: jar-${{ 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
build-rubik-image:
needs: [build-package]
if: ${{ github.event_name != 'pull_request' }}
runs-on: ubuntu-24.04
name: "Build image - Rubik Pi 3"
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: actions/download-artifact@v4
with:
name: jar-LinuxArm64
- name: Generate image
run: |
wget https://raw.githubusercontent.com/PhotonVision/photon-image-modifier/refs/tags/$IMAGE_VERSION/mount_rubikpi3.sh
chmod +x mount_rubikpi3.sh
./mount_rubikpi3.sh https://github.com/PhotonVision/photon-image-modifier/releases/download/$IMAGE_VERSION/photonvision_rubikpi3.tar.xz /tmp/build/scripts/armrunner.sh
- name: Compress image
run: |
new_jar=$(realpath $(find . -name photonvision\*-linuxarm64.jar))
new_image_name=$(basename "${new_jar/.jar/_rubikpi3.img}")
mv photonvision_rubikpi3 $new_image_name
tar -I 'xz -T0' -cf ${new_image_name}.tar.xz $new_image_name --checkpoint=10000 --checkpoint-action=echo='%T'
- uses: actions/upload-artifact@v4
name: Upload image
with:
name: image-rubikpi3
path: photonvision*.xz
release:
needs: [build-photonlib-vendorjson, build-package, build-image, build-rubik-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@r6
with:
token: ${{ secrets.GITHUB_TOKEN }}
tag: 'Dev'
rm: true
snapshots: false
files: |
**/*.xz
**/*linux*.jar
**/*win*.jar
**/photonlib*.json
**/photonlib*.zip
if: github.event_name == 'push'
- name: Create Vendor JSON Repo PR
uses: wpilibsuite/vendor-json-repo/.github/actions/add_vendordep@main
with:
repo: PhotonVision/vendor-json-repo
token: ${{ secrets.VENDOR_JSON_REPO_PUSH_TOKEN }}
vendordep_file: ${{ github.workspace }}/photonlib-${{ github.ref_name }}.json
pr_title: Update photonlib to ${{ github.ref_name }}
pr_branch: photonlib-${{ github.ref_name }}
if: github.repository == 'PhotonVision/photonvision' && startsWith(github.ref, 'refs/tags/v')

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@@ -1,14 +0,0 @@
name: "Pull Request Labeler"
on:
- pull_request_target
jobs:
labeler:
permissions:
contents: read
pull-requests: write
runs-on: ubuntu-latest
steps:
- uses: actions/labeler@v5
with:
sync-labels: true

View File

@@ -1,85 +0,0 @@
name: Lint and Format
on:
# Run on pushes to main and pushed tags, and on pull requests against main, but ignore the docs folder
push:
pull_request:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.ref }}
cancel-in-progress: true
jobs:
validation:
name: "Validation"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: gradle/actions/wrapper-validation@v4
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 main origin/main
- name: Set up Python 3.8
uses: actions/setup-python@v4
with:
python-version: 3.11
- name: Install wpiformat
run: pip3 install wpiformat==2025.75
- 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"
needs: [validation]
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: Install pnpm
uses: pnpm/action-setup@v4
with:
version: 10
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 22
cache: pnpm
cache-dependency-path: photon-client/pnpm-lock.yaml
- name: Install Dependencies
run: pnpm i --frozen-lockfile
- name: Check Linting
run: pnpm run lint-ci
- name: Check Formatting
run: pnpm run format-ci

View File

@@ -1,150 +0,0 @@
name: Photon API Documentation
on:
# Run on pushes to main and pushed tags, and on pull requests against main, but ignore the docs folder
push:
pull_request:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.ref }}
cancel-in-progress: true
# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
contents: read
pages: write
id-token: write
jobs:
validation:
name: "Validation"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: gradle/actions/wrapper-validation@v4
build_demo:
name: Build PhotonClient Demo
defaults:
run:
working-directory: photon-client
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v4
- name: Install pnpm
uses: pnpm/action-setup@v4
with:
version: 10
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 22
cache: pnpm
cache-dependency-path: photon-client/pnpm-lock.yaml
- name: Install Dependencies
run: pnpm i --frozen-lockfile
- name: Build Production Client
run: pnpm run build-demo
- uses: actions/upload-artifact@v4
with:
name: demo
path: photon-client/dist/
run_java_cpp_docs:
name: Build Java and C++ API Docs
needs: [validation]
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: docs-java-cpp
path: photon-docs/build/docs
run_py_docs:
name: Build Python API Docs
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.10'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r photon-lib/py/docs/requirements.txt
- name: Build Sphinx site
run: |
sphinx-apidoc -o docs/source photonlibpy
make -C docs html
working-directory: photon-lib/py
- name: Upload built site as artifact
uses: actions/upload-artifact@v4
with:
name: docs-python
path: photon-lib/py/docs/build/html
publish_api_docs:
name: Publish API Docs
needs: [ run_java_cpp_docs, run_py_docs ]
runs-on: ubuntu-22.04
steps:
# Download docs artifact
- uses: actions/download-artifact@v4
with:
pattern: docs-*
- run: find .
- name: Publish Docs To Development
# if: github.ref == 'refs/heads/main'
uses: up9cloud/action-rsync@v1.4
env:
HOST: ${{ secrets.WEBMASTER_SSH_HOST }}
USER: ${{ secrets.WEBMASTER_SSH_USERNAME }}
KEY: ${{secrets.WEBMASTER_SSH_KEY}}
TARGET: /var/www/html/photonvision-docs/development/
- name: Publish Docs To Release
if: startsWith(github.ref, 'refs/tags/v')
uses: up9cloud/action-rsync@v1.4
env:
HOST: ${{ secrets.WEBMASTER_SSH_HOST }}
USER: ${{ secrets.WEBMASTER_SSH_USERNAME }}
KEY: ${{ secrets.WEBMASTER_SSH_KEY }}
TARGET: /var/www/html/photonvision-docs/release/
publish_demo:
name: Publish PhotonClient Demo
needs: [build_demo]
runs-on: ubuntu-22.04
steps:
- uses: actions/download-artifact@v4
with:
name: demo
- run: find .
- name: Publish demo
if: github.ref == 'refs/heads/main'
uses: up9cloud/action-rsync@v1.4
env:
HOST: ${{ secrets.WEBMASTER_SSH_HOST }}
USER: ${{ secrets.WEBMASTER_SSH_USERNAME }}
KEY: ${{ secrets.WEBMASTER_SSH_KEY }}
TARGET: /var/www/html/photonvision-demo

View File

@@ -1,51 +0,0 @@
name: PhotonVision ReadTheDocs Checks
on:
push:
pull_request:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.ref }}
cancel-in-progress: true
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
jobs:
build:
name: Build and Check Docs
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

View File

@@ -1,68 +0,0 @@
name: Build and Distribute PhotonLibPy
permissions:
id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
on:
push:
pull_request:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.ref }}
cancel-in-progress: true
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 mkdocs mkdocs-gen-files
- 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
# Disable due to robotpy issue. See
# https://github.com/PhotonVision/photonvision/issues/1968
# - 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

View File

@@ -1,58 +0,0 @@
name: Website
on:
push:
pull_request:
jobs:
rsync:
name: Build and Sync Files
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Install pnpm
uses: pnpm/action-setup@v4
with:
version: 10
- name: Setup Node
uses: actions/setup-node@v4
with:
node-version: 22
cache: pnpm
cache-dependency-path: website/pnpm-lock.yaml
- name: Install packages
run: pnpm i --frozen-lockfile
working-directory: website
- name: Build project
run: pnpm run build
working-directory: website
- uses: up9cloud/action-rsync@v1.4
if: github.ref == 'refs/heads/main'
env:
HOST: ${{ secrets.WEBMASTER_SSH_HOST }}
USER: ${{ secrets.WEBMASTER_SSH_USERNAME }}
KEY: ${{secrets.WEBMASTER_SSH_KEY}}
SOURCE: website/dist/*
TARGET: /var/www/html/photonvision-website
format-check:
name: Check Formatting
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Install pnpm
uses: pnpm/action-setup@v4
with:
version: 10
- name: Setup Node
uses: actions/setup-node@v4
with:
node-version: 22
cache: pnpm
cache-dependency-path: website/pnpm-lock.yaml
- name: Install Packages
run: pnpm i --frozen-lockfile
working-directory: website
- name: Run Formatting Check
run: pnpm prettier -c .
working-directory: website

52
.gitignore vendored
View File

@@ -1,14 +1,19 @@
Python/__pycache__/WebSiteHandler\.cpython-37\.pyc
\.idea/
*.pyc
__pycache__/
Python/app/__pycache__/
Python/app/handlers/__pycache__/
\.vscode/
/.vs
backend/settings/
.vscode/*
!.vscode/settings.json
# Docs
_build
/.vscode/
# Compiled class file
*.class
@@ -104,6 +109,7 @@ fabric.properties
# Temporary build files
**/.gradle
**/target
**/src/main/java/META-INF
**/.settings
**/.classpath
@@ -119,16 +125,33 @@ compile_commands.json
.clangd/
.cache/
New client/photon-client/*
*.prefs
*.jfr
.DS_Store
# *.iml
photon-server/build
photon-server/photon-vision
photon-server/src/main/resources/web
photon-server/src/main/java/org/photonvision/PhotonVersion.java
photon-server/src/main/generated/native/include/org_photonvision_raspi_PicamJNI.h
*.bin
.gradle
.gradle/*
photonvision_config
bin*/
build*/
build/spotlessJava
build/*
build
photon-lib/src/main/java/org/photonvision/PhotonVersion.java
photon-lib/bin/main/images/*
/photonlib-java-examples/bin/
photon-lib/src/generate/native/include/PhotonVersion.h
.gitattributes
lib/*
photon-server/lib/libapriltag.so
photon-server/bin/main/nativelibraries/apriltag/*
photon-server/src/main/resources/nativelibraries/apriltag/*
photonlib-java-examples/*/vendordeps/*
photonlib-cpp-examples/*/vendordeps/*
@@ -138,17 +161,6 @@ photonlib-cpp-examples/*/vendordeps/*
photonlib-cpp-examples/*/networktables.json.bck
photonlib-java-examples/*/networktables.json.bck
*.sqlite
venv
.venv/*
.venv
networktables.json
# Web stuff
photon-server/src/main/resources/web/*
node_modules
dist
components.d.ts
# Py docs stuff
photon-lib/py/docs/build
photon-server/src/main/resources/web/index.html
venv

View File

@@ -1 +0,0 @@
3.11

View File

@@ -1,30 +0,0 @@
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

34
.styleguide Normal file
View File

@@ -0,0 +1,34 @@
cppHeaderFileInclude {
\.h$
\.hpp$
\.inc$
\.inl$
}
cppSrcFileInclude {
\.cpp$
}
modifiableFileExclude {
\.jpg$
\.jpeg$
\.png$
\.gif$
\.so$
\.dll$
\.webp$
\.ico$
\.rknn$
gradlew
}
includeProject {
^photonLib/
}
includeOtherLibs {
^frc/
^networktables/
^units/
^wpi/
}

View File

@@ -1,5 +0,0 @@
{
"python.testing.unittestEnabled": false,
"python.testing.pytestEnabled": true,
"python.testing.cwd": "photon-lib/py"
}

View File

@@ -1,25 +0,0 @@
cppHeaderFileInclude {
\.h$
}
modifiableFileExclude {
\.dll$
\.gif$
\.ico$
\.jpeg$
\.jpg$
\.mp4$
\.pdf$
\.png$
\.rknn$
\.so$
\.svg$
\.tflite$
\.ttf$
\.webp$
\.woff2$
gradlew
photon-lib/py/photonlibpy/generated/
photon-targeting/src/generated/
photon-targeting/src/main/native/cpp/photon/constrained_solvepnp/generate/
}

105
README.md
View File

@@ -1,10 +1,10 @@
# PhotonVision
# Photon Vision
[![Discord](https://img.shields.io/discord/725836368059826228?color=%23738ADB&label=Join%20our%20Discord&logo=discord&logoColor=white)](https://discord.gg/wYxTwym)
[![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).
A copy of the latest Raspberry Pi image is available [here](https://github.com/PhotonVision/photon-pi-gen/releases). A copy of the latest standalone JAR is available [here](https://github.com/PhotonVision/photonvision/releases). If you are a Gloworm user you can find the latest Gloworm image [here](https://github.com/gloworm-vision/pi-gen/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.
@@ -14,44 +14,54 @@ If you are interested in contributing code or documentation to the project, plea
<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: [http://photonvision.global/](http://photonvision.global/)
- Javadocs: [javadocs.photonvision.org](https://javadocs.photonvision.org)
- C++ Doxygen [cppdocs.photonvision.org](https://cppdocs.photonvision.org)
- Python Documentation [pydocs.photonvision.org](https://pydocs.photonvision.org)
## Building
Gradle is used for all C++ and Java code, and pnpm 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
* `-PArchOverride=foobar`: builds for a target system other than your current architecture. Valid overrides are:
* linuxathena
- `-PtgtIP`: Specifies where `./gradlew deploy` should try to copy the fat JAR to
- `-PtgtUser`: Specifies custom username for `./gradlew deploy` to SSH into
- `-PtgtPw`: Specifies custom password for `./gradlew deploy` to SSH into
* linuxarm32
* linuxarm64
* arm32
* arm64
* x86-64
* x86
- `-PtgtIp`: Specifies where `./gradlew deploy` should try to copy the fat JAR to
- `-Pprofile`: enables JVM profiling
- `-PwithSanitizers`: On Linux, enables `-fsanitize=address,undefined,leak`
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`
## 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/photonvision/build-instructions.html?highlight=npm%20install#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` and `photonlib-cpp-examples` subdirectories, respectively. The projects currently available include:
- photonlib-java-examples:
- aimandrange:simulateJava
- aimattarget:simulateJava
- getinrange:simulateJava
- simaimandrange:simulateJava
- simposeest:simulateJava
- photonlib-cpp-examples:
- aimandrange:simulateNative
- getinrange:simulateNative
To run them, use the commands listed below. Photonlib must first be published to your local maven repository, then the `copyPhotonlib` task will copy the generated vendordep json file into each example. After that, the simulateJava/simulateNative task can be used like a normal robot project. Robot simulation with attached debugger is technically possible by using simulateExternalJava and modifying the launch script it exports, though unsupported.
```
~/photonvision$ ./gradlew publishToMavenLocal
~/photonvision$ cd photonlib-java-examples
~/photonvision/photonlib-java-examples$ ./gradlew copyPhotonlib
~/photonvision/photonlib-java-examples$ ./gradlew <example-name>:simulateJava
~/photonvision$ cd photonlib-cpp-examples
~/photonvision/photonlib-cpp-examples$ ./gradlew copyPhotonlib
~/photonvision/photonlib-cpp-examples$ ./gradlew <example-name>:simulateNative
```
## Out-of-Source Dependencies
PhotonVision uses the following additional out-of-source repositories for building code.
PhotonVision uses the following additonal 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
@@ -59,19 +69,12 @@ PhotonVision uses the following additional out-of-source repositories for buildi
- 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/main/cscore), [CameraServer](https://github.com/wpilibsuite/allwpilib/tree/main/cameraserver), [NTCore](https://github.com/wpilibsuite/allwpilib/tree/main/ntcore), and [OpenCV](https://github.com/wpilibsuite/thirdparty-opencv).
* [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/)
@@ -82,9 +85,25 @@ PhotonVision was forked from [Chameleon Vision](https://github.com/Chameleon-Vis
* [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).
PhotonVision is licensed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html)
## Meeting Notes
Our meeting notes can be found in the wiki section of this repository.
Our [meeting notes](https://github.com/PhotonVision/photonvision/wiki/PhotonVision-Meeting-Notes) can be found in the wiki section of this repository.
* [2020 Meeting Notes](https://github.com/PhotonVision/photonvision/wiki/2020-Meeting-Notes)
* [2021 Meeting Notes](https://github.com/PhotonVision/photonvision/wiki/2021-Meeting-Notes)
## Additional packages
For now, using mrcal requires installing these additional packages on Linux systems:
```
sudo apt install libcholmod3 liblapack3 libsuitesparseconfig5
```
## 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/))

View File

@@ -1,16 +1,12 @@
import edu.wpi.first.toolchain.*
plugins {
id "cpp"
id "com.diffplug.spotless" version "6.24.0"
id "edu.wpi.first.NativeUtils" version "2024.6.1" apply false
id "edu.wpi.first.wpilib.repositories.WPILibRepositoriesPlugin" version "2020.2"
id "edu.wpi.first.GradleRIO" version "2025.3.2"
id "edu.wpi.first.GradleRIO" version "2024.3.2"
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'
id "org.ysb33r.doxygen" version "2.0.0" apply false
id 'com.gradleup.shadow' version '8.3.4' apply false
id "com.github.node-gradle.node" version "7.0.1" apply false
id 'com.google.protobuf' version '0.9.4' apply false
}
allprojects {
@@ -25,23 +21,19 @@ allprojects {
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 = "2025.3.2"
wpilibVersion = "2024.3.2"
wpimathVersion = wpilibVersion
openCVYear = "2025"
openCVversion = "4.10.0-3"
javalinVersion = "6.7.0"
libcameraDriverVersion = "v2025.0.4"
rknnVersion = "dev-v2025.0.0-5-g666c0c6"
rubikVersion = "dev-v2025.1.0-6-g4a5e508"
frcYear = "2025"
mrcalVersion = "v2025.0.0";
openCVversion = "4.8.0-2"
joglVersion = "2.4.0"
javalinVersion = "5.6.2"
photonGlDriverLibVersion = "dev-v2023.1.0-11-g2b7036f"
rknnVersion = "dev-v2024.0.1-4-g0db16ac"
frcYear = "2024"
mrcalVersion = "dev-v2024.0.0-24-gc1efcf0";
pubVersion = versionString
@@ -69,7 +61,7 @@ spotless {
java {
target fileTree('.') {
include '**/*.java'
exclude '**/build/**', '**/build-*/**', '**/src/generated/**'
exclude '**/build/**', '**/build-*/**', "photon-core\\src\\main\\java\\org\\photonvision\\PhotonVersion.java", "photon-lib\\src\\main\\java\\org\\photonvision\\PhotonVersion.java"
}
toggleOffOn()
googleJavaFormat()
@@ -89,10 +81,20 @@ spotless {
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-*/**', '**/node_modules/**'
exclude '**/build/**', '**/build-*/**'
}
trimTrailingWhitespace()
indentWithSpaces(2)
@@ -101,23 +103,9 @@ spotless {
}
wrapper {
gradleVersion = '8.14.3'
gradleVersion '8.4'
}
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")
}
}

View File

@@ -1,17 +1,19 @@
import argparse
import base64
from dataclasses import dataclass
import json
import os
from dataclasses import dataclass
import mrcal
from typing import Union
import cv2
import numpy as np
import mrcal
from wpimath.geometry import Quaternion as _Quat
@dataclass
class Size:
width: float
height: float
width: int
height: int
@dataclass
@@ -22,6 +24,14 @@ class JsonMatOfDoubles:
data: list[float]
@dataclass
class JsonMat:
rows: int
cols: int
type: int
data: str # Base64-encoded PNG data
@dataclass
class Point2:
x: float
@@ -74,7 +84,8 @@ class Observation:
# If we should use this observation when re-calculating camera calibration
includeObservationInCalibration: bool
snapshotName: str
snapshotDataLocation: str
# The actual image the snapshot is from
snapshotData: JsonMat
@dataclass
@@ -86,7 +97,6 @@ class CameraCalibration:
calobjectWarp: list[float]
calobjectSize: Size
calobjectSpacing: float
lensmodel: str
def __convert_cal_to_mrcal_cameramodel(
@@ -117,13 +127,6 @@ def __convert_cal_to_mrcal_cameramodel(
]
return np.concatenate((r, t))
imagersize = (int(cal.resolution.width), int(cal.resolution.height))
def fill_missing_corners(observations: list[list[float]], width: int, height: int):
num_corners = width * height
observations += [[0, 0, -1] for x in range(num_corners - len(observations))]
return observations
imagersize = (cal.resolution.width, cal.resolution.height)
# Always weight=1 for Photon data
@@ -132,12 +135,8 @@ def __convert_cal_to_mrcal_cameramodel(
[
# note that we expect row-major observations here. I think this holds
np.array(
fill_missing_corners(
list(map(lambda it: [it.x, it.y, WEIGHT], o.locationInImageSpace)),
int(cal.calobjectSize.width),
int(cal.calobjectSize.height),
)
).reshape((int(cal.calobjectSize.width), int(cal.calobjectSize.height), 3))
list(map(lambda it: [it.x, it.y, WEIGHT], o.locationInImageSpace))
).reshape((cal.calobjectSize.width, cal.calobjectSize.height, 3))
for o in cal.observations
]
)
@@ -207,6 +206,14 @@ def convert_photon_to_mrcal(photon_cal_json_path: str, output_folder: str):
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")

View File

@@ -1,395 +0,0 @@
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View File

@@ -1,24 +0,0 @@
# 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)

View File

@@ -1,9 +0,0 @@
# 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/index.html)

287
docs/build.gradle Normal file
View File

@@ -0,0 +1,287 @@
// From allwpilib/docs. Licensed under the WPILib BSD License
plugins {
id 'java'
id "org.ysb33r.doxygen" version "0.7.0"
}
evaluationDependsOn ':photon-targeting'
evaluationDependsOn ':photon-core'
evaluationDependsOn ':photon-server'
evaluationDependsOn ':photon-lib'
def baseArtifactIdCpp = 'documentation'
def artifactGroupIdCpp = 'org.photonvision.wpilibc'
def zipBaseNameCpp = '_GROUP_org.photonvision_cpp_ID_documentation_CLS'
def baseArtifactIdJava = 'documentation'
def artifactGroupIdJava = 'org.photonvision.wpilibj'
def zipBaseNameJava = '_GROUP_org.photonvision_java_ID_documentation_CLS'
def outputsFolder = file("$project.buildDir/outputs")
def cppProjectZips = []
def cppIncludeRoots = []
cppProjectZips.add(project(':photon-lib').cppHeadersZip)
cppProjectZips.add(project(':photon-targeting').cppHeadersZip)
doxygen {
// Doxygen binaries are only provided for x86_64 platforms
// Other platforms will need to provide doxygen via their system
// See below maven and https://doxygen.nl/download.html for provided binaries
String arch = System.getProperty("os.arch");
if (arch.equals("x86_64") || arch.equals("amd64")) {
executables {
doxygen version : '1.9.4',
baseURI : 'https://frcmaven.wpi.edu/artifactory/generic-release-mirror/doxygen'
}
}
}
doxygen {
generate_html true
html_extra_stylesheet 'theme.css'
cppProjectZips.each {
dependsOn it
source it.source
it.ext.includeDirs.each {
cppIncludeRoots.add(it.absolutePath)
}
}
cppIncludeRoots << '../ntcore/build/generated/main/native/include/'
if (project.hasProperty('docWarningsAsErrors')) {
// Eigen
exclude 'Eigen/**'
exclude 'unsupported/**'
// LLVM
exclude 'wpi/AlignOf.h'
exclude 'wpi/Casting.h'
exclude 'wpi/Chrono.h'
exclude 'wpi/Compiler.h'
exclude 'wpi/ConvertUTF.h'
exclude 'wpi/DenseMap.h'
exclude 'wpi/DenseMapInfo.h'
exclude 'wpi/Endian.h'
exclude 'wpi/EpochTracker.h'
exclude 'wpi/Errc.h'
exclude 'wpi/Errno.h'
exclude 'wpi/ErrorHandling.h'
exclude 'wpi/bit.h'
exclude 'wpi/fs.h'
exclude 'wpi/FunctionExtras.h'
exclude 'wpi/function_ref.h'
exclude 'wpi/Hashing.h'
exclude 'wpi/iterator.h'
exclude 'wpi/iterator_range.h'
exclude 'wpi/ManagedStatic.h'
exclude 'wpi/MapVector.h'
exclude 'wpi/MathExtras.h'
exclude 'wpi/MemAlloc.h'
exclude 'wpi/PointerIntPair.h'
exclude 'wpi/PointerLikeTypeTraits.h'
exclude 'wpi/PointerUnion.h'
exclude 'wpi/raw_os_ostream.h'
exclude 'wpi/raw_ostream.h'
exclude 'wpi/SmallPtrSet.h'
exclude 'wpi/SmallSet.h'
exclude 'wpi/SmallString.h'
exclude 'wpi/SmallVector.h'
exclude 'wpi/StringExtras.h'
exclude 'wpi/StringMap.h'
exclude 'wpi/SwapByteOrder.h'
exclude 'wpi/type_traits.h'
exclude 'wpi/VersionTuple.h'
exclude 'wpi/WindowsError.h'
// fmtlib
exclude 'fmt/**'
// libuv
exclude 'uv.h'
exclude 'uv/**'
exclude 'wpinet/uv/**'
// json
exclude 'wpi/adl_serializer.h'
exclude 'wpi/byte_container_with_subtype.h'
exclude 'wpi/detail/**'
exclude 'wpi/json.h'
exclude 'wpi/json_fwd.h'
exclude 'wpi/ordered_map.h'
exclude 'wpi/thirdparty/**'
// memory
exclude 'wpi/memory/**'
// mpack
exclude 'wpi/mpack.h'
// units
exclude 'units/**'
}
//TODO: building memory docs causes search to break
exclude 'wpi/memory/**'
exclude '*.pb.h'
// Save space by excluding protobuf and eigen
exclude 'Eigen/**'
exclude 'google/protobuf/**'
aliases 'effects=\\par <i>Effects:</i>^^',
'notes=\\par <i>Notes:</i>^^',
'requires=\\par <i>Requires:</i>^^',
'requiredbe=\\par <i>Required Behavior:</i>^^',
'concept{2}=<a href=\"md_doc_concepts.html#\1\">\2</a>',
'defaultbe=\\par <i>Default Behavior:</i>^^'
case_sense_names false
extension_mapping 'inc=C++', 'no_extension=C++'
extract_all true
extract_static true
file_patterns '*'
full_path_names true
generate_html true
generate_latex false
generate_treeview true
html_extra_stylesheet 'theme.css'
html_timestamp true
javadoc_autobrief true
project_name 'PhotonVision C++'
project_logo '../wpiutil/src/main/native/resources/wpilib-128.png'
project_number pubVersion
quiet true
recursive true
strip_code_comments false
strip_from_inc_path cppIncludeRoots as String[]
strip_from_path cppIncludeRoots as String[]
use_mathjax true
warnings false
warn_if_incomplete_doc true
warn_if_undocumented false
warn_no_paramdoc true
//enable doxygen preprocessor expansion of WPI_DEPRECATED to fix MotorController docs
enable_preprocessing true
macro_expansion true
expand_only_predef true
predefined "WPI_DEPRECATED(x)=[[deprecated(x)]]\"\\\n" +
"\"__cplusplus\"\\\n" +
"\"HAL_ENUM(name)=enum name : int32_t"
if (project.hasProperty('docWarningsAsErrors')) {
warn_as_error 'FAIL_ON_WARNINGS'
}
}
tasks.register("zipCppDocs", Zip) {
archiveBaseName = zipBaseNameCpp
destinationDirectory = outputsFolder
dependsOn doxygen
from ("$buildDir/docs/doxygen/html")
into '/'
}
// Java
configurations {
javaSource {
transitive false
}
}
ext {
sharedCvConfigs = [:]
staticCvConfigs = [:]
useJava = true
useCpp = false
skipDev = true
useDocumentation = true
}
task generateJavaDocs(type: Javadoc) {
def exportedProjects = [
':photon-core',
':photon-server',
':photon-targeting',
':photon-lib'
]
source exportedProjects.collect { project(it).sourceSets.main.allJava }
classpath = files(exportedProjects.collect { project(it).sourceSets.main.compileClasspath })
dependsOn project(':photon-core').writeCurrentVersion
options.links("https://docs.oracle.com/en/java/javase/17/docs/api/")
options.addStringOption("tag", "pre:a:Pre-Condition")
options.addBooleanOption("Xdoclint:html,missing,reference,syntax", true)
options.addBooleanOption('html5', true)
failOnError = true
title = "PhotonVision $pubVersion"
ext.entryPoint = "$destinationDir/index.html"
if (JavaVersion.current().isJava8Compatible() && project.hasProperty('docWarningsAsErrors')) {
// Treat javadoc warnings as errors.
//
// The second argument '-quiet' is a hack. The one parameter
// addStringOption() doesn't work, so we add '-quiet', which is added
// anyway by gradle. See https://github.com/gradle/gradle/issues/2354.
//
// See JDK-8200363 (https://bugs.openjdk.java.net/browse/JDK-8200363)
// for information about the nonstandard -Xwerror option. JDK 15+ has
// -Werror.
options.addStringOption('Xwerror', '-quiet')
}
if (JavaVersion.current().isJava11Compatible()) {
if (!JavaVersion.current().isJava12Compatible()) {
options.addBooleanOption('-no-module-directories', true)
}
doLast {
// This is a work-around for https://bugs.openjdk.java.net/browse/JDK-8211194. Can be removed once that issue is fixed on JDK's side
// Since JDK 11, package-list is missing from javadoc output files and superseded by element-list file, but a lot of external tools still need it
// Here we generate this file manually
new File(destinationDir, 'package-list').text = new File(destinationDir, 'element-list').text
}
}
}
tasks.register("zipJavaDocs", Zip) {
archiveBaseName = zipBaseNameJava
destinationDirectory = outputsFolder
dependsOn generateJavaDocs
from ("$buildDir/docs/javadoc")
into '/'
}
tasks.register("zipDocs") {
dependsOn zipCppDocs
dependsOn zipJavaDocs
}
apply plugin: 'maven-publish'
publishing {
publications {
java(MavenPublication) {
artifact zipJavaDocs
artifactId = "${baseArtifactIdJava}"
groupId artifactGroupIdJava
version pubVersion
}
cpp(MavenPublication) {
artifact zipCppDocs
artifactId = "${baseArtifactIdCpp}"
groupId artifactGroupIdCpp
version pubVersion
}
}
}

View File

@@ -1,36 +0,0 @@
@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

View File

@@ -1,59 +0,0 @@
alabaster==0.7.16
anyio==4.9.0
babel==2.17.0
beautifulsoup4==4.13.4
certifi==2025.4.26
charset-normalizer==3.4.2
click==8.1.8
colorama==0.4.6
doc8==1.1.2
docopt==0.6.2
docutils==0.20.1
furo==2024.8.6
h11==0.16.0
idna==3.10
imagesize==1.4.1
Jinja2==3.1.6
markdown-it-py==3.0.0
MarkupSafe==3.0.2
mdit-py-plugins==0.4.2
mdurl==0.1.2
myst-parser==4.0.1
packaging==25.0
pbr==6.1.1
pipreqs==0.5.0
Pygments==2.19.1
PyYAML==6.0.2
requests==2.32.4
restructuredtext-lint==1.4.0
roman-numerals-py==3.1.0
setuptools==80.3.1
six==1.17.0
sniffio==1.3.1
snowballstemmer==3.0.0.1
soupsieve==2.7
Sphinx==8.1.3
sphinx-autobuild==2024.10.3
sphinx-basic-ng==1.0.0b2
sphinx-notfound-page==1.1.0
sphinx-rtd-theme==3.0.2
sphinx-tabs==3.4.7
sphinx_design==0.6.1
sphinxcontrib-applehelp==2.0.0
sphinxcontrib-devhelp==2.0.0
sphinxcontrib-ghcontributors==0.2.3
sphinxcontrib-htmlhelp==2.1.0
sphinxcontrib-jquery==4.1
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==2.0.0
sphinxcontrib-serializinghtml==2.0.0
sphinxext-opengraph==0.10.0
sphinxext-remoteliteralinclude==0.5.0
starlette==0.47.2
stevedore==5.4.1
typing_extensions==4.13.2
urllib3==2.5.0
uvicorn==0.34.2
watchfiles==1.0.5
websockets==15.0.1
yarg==0.1.9

View File

@@ -1,7 +0,0 @@
---
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)

View File

@@ -1,20 +0,0 @@
# 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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.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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@@ -1,6 +0,0 @@
/*!
* Font Awesome Free 6.7.2 by @fontawesome - https://fontawesome.com
* License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
* Copyright 2024 Fonticons, Inc.
*/
@font-face{font-family:"FontAwesome";font-display:block;src:url(../webfonts/fa-solid-900.woff2) format("woff2"),url(../webfonts/fa-solid-900.ttf) format("truetype")}@font-face{font-family:"FontAwesome";font-display:block;src:url(../webfonts/fa-brands-400.woff2) format("woff2"),url(../webfonts/fa-brands-400.ttf) format("truetype")}@font-face{font-family:"FontAwesome";font-display:block;src:url(../webfonts/fa-regular-400.woff2) format("woff2"),url(../webfonts/fa-regular-400.ttf) format("truetype");unicode-range:u+f003,u+f006,u+f014,u+f016-f017,u+f01a-f01b,u+f01d,u+f022,u+f03e,u+f044,u+f046,u+f05c-f05d,u+f06e,u+f070,u+f087-f088,u+f08a,u+f094,u+f096-f097,u+f09d,u+f0a0,u+f0a2,u+f0a4-f0a7,u+f0c5,u+f0c7,u+f0e5-f0e6,u+f0eb,u+f0f6-f0f8,u+f10c,u+f114-f115,u+f118-f11a,u+f11c-f11d,u+f133,u+f147,u+f14e,u+f150-f152,u+f185-f186,u+f18e,u+f190-f192,u+f196,u+f1c1-f1c9,u+f1d9,u+f1db,u+f1e3,u+f1ea,u+f1f7,u+f1f9,u+f20a,u+f247-f248,u+f24a,u+f24d,u+f255-f25b,u+f25d,u+f271-f274,u+f278,u+f27b,u+f28c,u+f28e,u+f29c,u+f2b5,u+f2b7,u+f2ba,u+f2bc,u+f2be,u+f2c0-f2c1,u+f2c3,u+f2d0,u+f2d2,u+f2d4,u+f2dc}@font-face{font-family:"FontAwesome";font-display:block;src:url(../webfonts/fa-v4compatibility.woff2) format("woff2"),url(../webfonts/fa-v4compatibility.ttf) format("truetype");unicode-range:u+f041,u+f047,u+f065-f066,u+f07d-f07e,u+f080,u+f08b,u+f08e,u+f090,u+f09a,u+f0ac,u+f0ae,u+f0b2,u+f0d0,u+f0d6,u+f0e4,u+f0ec,u+f10a-f10b,u+f123,u+f13e,u+f148-f149,u+f14c,u+f156,u+f15e,u+f160-f161,u+f163,u+f175-f178,u+f195,u+f1f8,u+f219,u+f27a}

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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"
# -- Git configuration -----------------------------------------------------
import subprocess
try:
# Use closest tag
git_tag_ref = (
subprocess.check_output(
[
"git",
"describe",
"--tags",
],
stderr=subprocess.DEVNULL,
)
.strip()
.decode()
)
except subprocess.CalledProcessError:
# Couldn't find closest tag, fallback to main
git_tag_ref = "main"
myst_substitutions = {"git_tag_ref": git_tag_ref}
# -- 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",
"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/main/source/assets/RectLogo.png"
# 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"
# Specify canonical root
# This tells search engines that this domain is preferred
html_baseurl = "https://docs.photonvision.org/en/latest/"
# 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/v4-font-face.min.css")
app.add_css_file("css/pv-icons.css")
pygments_style = "sphinx"
html_theme_options = {
"sidebar_hide_name": True,
"top_of_page_buttons": ["view", "edit"],
"source_edit_link": "https://github.com/PhotonVision/photonvision/edit/main/docs/source/{filename}",
"source_view_link": "https://github.com/PhotonVision/photonvision/blob/main/docs/source/{filename}",
"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\..+",
R"https://www.gnu.org/",
]
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", "substitution"]

View File

@@ -1,37 +0,0 @@
# Best Practices For Competition
## Before Competition
- Ensure you have spares of the relevant electronics if you can afford it (switch, coprocessor, cameras, etc.).
- Stay on the latest version of PhotonVision until you have tested your full robot system to be functional.
- Some time before the competition, lock down the version you are using and do not upgrade unless you encounter a critical bug.
- Have a copy of the installation image for the version you are using on your programming laptop, in case re-imaging (without internet) is needed.
- Extensively test at your home setup. Practice tuning from scratch under different lighting conditions.
- Confirm you have followed all the recommendations under the {ref}`Networking<docs/quick-start/networking:Networking>` documentation (network switch and static IP).
- Only use high quality ethernet cables that have been rigorously tested.
## Camera Streaming
- All camera streams are published under the NetworkTables table `CameraPublisher`.
- The only subtable under `CameraPublisher` that will work for viewing a driver mode camera stream is the one that contains `Output` in the name.
- To view a camera stream in a dashboard, drag the correct subtable from the NetworkTables tree into your dashboard.
- Use the latest driver dashboard recommended by [WPILib](https://docs.wpilib.org/en/stable/docs/software/dashboards/dashboard-intro.html) on your driver station laptop.
## During the Competition
- Use the field calibration time given at the start of the event:
- Bring your robot to the field at the allotted time.
- Make sure the field has match-accurate lighting conditions active.
- Turn on your robot and pull up the dashboard on your driver station.
- Point your robot at the targets and ensure you get a consistent tracking (you hold one targets consistently, the ceiling lights aren't detected, etc.).
- If you have problems with your pipeline, retune the pipeline following the {ref}`camera tuning <docs/pipelines/input:Camera Tuning / Input>` documentation.
- Move the robot close, far, angled, and around the field to ensure no extra targets are found.
- Monitor camera feeds during 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 and USB connectors are seated fully.
- Close streaming dashboards when you don't need them to reduce bandwidth.
- Stream at as low of a resolution as possible while still detecting AprilTags to stay within field bandwidth limits.

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@@ -1,53 +0,0 @@
# 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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@@ -1,70 +0,0 @@
# 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
```

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# 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 `main` 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 main. To test a particular commit to main, navigate to the [PhotonVision commit list](https://github.com/PhotonVision/photonvision/commits/main/) 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
```

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@@ -1,56 +0,0 @@
# 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`.
:::

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@@ -1,9 +0,0 @@
{
"deviceName" : "Limelight 2+",
"supportURL" : "https://limelightvision.io",
"ledPins" : [ 13, 18 ],
"ledsCanDim" : true,
"ledPWMRange" : [ 0, 100 ],
"ledPWMFrequency" : 30000,
"vendorFOV" : 75.76079874010732
}

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{
"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
```

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# 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.

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# 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 M Series 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.
:::

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# 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
```

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# 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 install 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 v2023.4.2
```
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 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.
:::

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@@ -1,45 +0,0 @@
# 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/v2025.3.2) 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.

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# 2D AprilTag Tuning / Tracking
## Tracking AprilTags
Before you get started tracking AprilTags, ensure that you have followed the previous sections on installation, wiring and networking. Next, open the Web UI, go to the top right card, and switch to the "AprilTag" or "ArUco" type. You should see a screen similar to the one below.
```{image} images/apriltag.png
:align: center
```
You are now able to detect and track AprilTags in 2D (yaw, pitch, roll, etc.). In order to get 3D data from your AprilTags, please see {ref}`here. <docs/apriltag-pipelines/3D-tracking:3D Tracking>`
## Tuning AprilTags
AprilTag pipelines come with reasonable defaults to get you up and running with tracking. However, in order to optimize your performance and accuracy, you must tune your AprilTag pipeline using the settings below. Note that the settings below are different between the AprilTag and ArUco detectors but the concepts are the same.
```{image} images/apriltag-tune.png
: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 2025 FRC game will be using 36h11 tags, which can be found [here](https://github.com/AprilRobotics/apriltag-imgs/tree/main/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.

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@@ -1,13 +0,0 @@
# 3D Tracking
3D AprilTag tracking will allow you to track the real-world position and rotation of a tag relative to the camera's image sensor. This is useful for robot pose estimation and other applications like autonomous scoring. In order to use 3D tracking, you must first {ref}`calibrate your camera <docs/calibration/calibration:Calibrating Your Camera>`. Once you have, you need to enable 3D mode in the UI and you will now be able to get 3D pose information from the tag! For information on getting and using this information in your code, see {ref}`the programming reference <docs/programming/index:Programming Reference>`.
## Ambiguity
Translating from 2D to 3D using data from the calibration and the four tag corners can lead to "pose ambiguity", where it appears that the AprilTag pose is flipping between two different poses. You can read more about this issue [here](https://docs.wpilib.org/en/stable/docs/software/vision-processing/apriltag/apriltag-intro.html#d-to-3d-ambiguity). Ambiguity is calculated as the ratio of reprojection errors between two pose solutions (if they exist), where reprojection error is the error corresponding to the image distance between where the apriltag's corners are detected vs where we expect to see them based on the tag's estimated camera relative pose.
There are a few steps you can take to resolve/mitigate this issue:
1. Mount cameras at oblique angles so it is less likely that the tag will be seen straight on.
2. Use the {ref}`MultiTag system <docs/apriltag-pipelines/multitag:MultiTag Localization>` in order to combine the corners from multiple tags to get a more accurate and unambiguous pose.
3. Reject all tag poses where the ambiguity ratio (available via PhotonLib) is greater than 0.2.

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@@ -1,14 +0,0 @@
# 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 2025 here](https://firstfrc.blob.core.windows.net/frc2025/FieldAssets/Apriltag_Images_and_User_Guide.pdf).
:::

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@@ -1,40 +0,0 @@
# 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 %
```

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# AprilTag Pipeline Types
PhotonVision offers two different AprilTag pipeline types based on different implementations of the underlying algorithm. Each one has its advantages / disadvantages, which are detailed below.
:::{note}
Note that both of these pipeline types detect AprilTag markers and are just two different algorithms for doing so.
:::
## AprilTag
The AprilTag pipeline type is based on the [AprilTag](https://april.eecs.umich.edu/software/apriltag.html) library from the University of Michigan and we recommend it for most use cases. It is (to our understanding) most accurate pipeline type, but is also ~2x slower than ArUco. This was the pipeline type used by teams in the 2023 season and is well tested.
## ArUco
The ArUco pipeline is based on the [ArUco](https://docs.opencv.org/4.8.0/d9/d6a/group__aruco.html) library implementation from OpenCV. It is ~2x higher fps and ~2x lower latency than the AprilTag pipeline type, but is less accurate. We recommend this pipeline type for teams that need to run at a higher framerate or have a lower powered device. This pipeline type was new for the 2024 season.

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# AprilTag Detection
```{toctree}
about-apriltags
detector-types
2D-tracking-tuning
3D-tracking
multitag
coordinate-systems
```

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# MultiTag Localization
PhotonVision can combine AprilTag detections from multiple simultaneously observed AprilTags from a particular camera with information about where tags are expected to be located on the field to produce a better estimate of where the camera (and therefore robot) is located on the field. PhotonVision can calculate this multi-target result on your coprocessor, reducing CPU usage on your RoboRio. This result is sent over NetworkTables along with other detected targets as part of the `PhotonPipelineResult` provided by PhotonLib.
:::{warning}
MultiTag requires an accurate field layout JSON to be uploaded! Differences between this layout and the tags' physical location will drive error in the estimated pose output.
:::
:::{warning}
For the 2025 Reefscape Season, there are two different field layouts. The first is the [welded field layout](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2025-reefscape-welded.json), which photonvision ships with. The second is the [Andymark field layout](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2025-reefscape-andymark.json). It is very important to ensure that you use the correct field layout, both in the [PhotonPoseEstimator](https://docs.photonvision.org/en/latest/docs/programming/photonlib/robot-pose-estimator.html#apriltags-and-photonposeestimator) and on the [coprocessor](https://docs.photonvision.org/en/latest/docs/apriltag-pipelines/multitag.html#updating-the-field-layout).
:::
## 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()`/`multitagResult` (Java/C++/Python).
```{eval-rst}
.. tab-set-code::
.. code-block:: java
var results = camera.getAllUnreadResults();
for (var result : results) {
var multiTagResult = result.getMultiTagResult();
if (multiTagResult.isPresent()) {
var fieldToCamera = multiTagResult.get().estimatedPose.best;
}
}
.. code-block:: c++
auto results = camera.GetAllUnreadResults();
for (auto &result : results)
{
auto multiTagResult = result.MultiTagResult();
if (multiTagResult.has_value()) {
frc::Transform3d fieldToCamera = multiTagResult->estimatedPose.best;
}
}
.. code-block:: python
results = camera.getAllUnreadResults()
for result in results:
multitagResult = result.multitagResult
if multitagResult is not None:
fieldToCamera = multitagResult.estimatedPose.best
```
:::{note}
The returned field to camera transform is a transform from the fixed field origin to the camera's coordinate system. This does not change based on alliance color, and by convention is on the BLUE ALLIANCE wall.
:::
## Updating the Field Layout
PhotonVision ships by default with the [2025 welded field layout JSON](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2025-reefscape-welded.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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@@ -1,8 +0,0 @@
# Performance Benchmarks
```{toctree}
:maxdepth: 0
:titlesonly: true
rknn-model-benchmarks
```

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@@ -1,125 +0,0 @@
# RKNN Benchmarks
## Description
This benchmark compares the performance of four object detection models: YOLOv5, YOLOv5u, YOLOv8, and YOLOv11 on the [COCO 2017 Validation Set](http://images.cocodataset.org/zips/val2017.zip). The main purpose is to assess and compare the inference speed and detection accuracy of these models when deployed on the Orange Pi devices using the RKNN framework and int8 quantization.
## Methodology
- **Dataset**: [COCO 2017 Validation Set](http://images.cocodataset.org/zips/val2017.zip) (5,000 images)
- **Platform**: Orange Pi 5 with RK3588
- **Quantization**: int8 using 20 randomly selected images from the validation set
- **Framework**: RKNN Toolkit 2
## Operator-Level Benchmark Results
The following tables break down the average CPU time, NPU time, and total execution time (in microseconds) for each operator used by the models. Each value represents the mean ± standard deviation across 5,000 inferences.
### YOLOv5
| OpType | CPU Time (μs) | NPU Time (μs) | Total Time (μs) | Time Ratio (%) | Number of Times Called |
|-----------------|---------------------|----------------------|-----------------------|---------------------|-----------------------|
| ConvExSwish | 0.00 ± 0.00 | 10968.81 ± 1126.00 | 10968.81 ± 1126.00 | 73.06 ± 0.94 | 57 |
| ConvSigmoid | 0.00 ± 0.00 | 1243.49 ± 67.66 | 1243.49 ± 67.66 | 8.33 ± 0.57 | 3 |
| Concat | 0.00 ± 0.00 | 1080.68 ± 259.40 | 1080.68 ± 259.40 | 7.09 ± 0.87 | 13 |
| Conv | 0.00 ± 0.00 | 732.15 ± 29.42 | 732.15 ± 29.42 | 4.92 ± 0.42 | 1 |
| Add | 0.00 ± 0.00 | 473.71 ± 131.48 | 473.71 ± 131.48 | 3.10 ± 0.50 | 7 |
| MaxPool | 0.00 ± 0.00 | 272.40 ± 110.52 | 272.40 ± 110.52 | 1.76 ± 0.51 | 6 |
| Resize | 0.00 ± 0.00 | 147.61 ± 38.89 | 147.61 ± 38.89 | 0.97 ± 0.15 | 2 |
| OutputOperator | 106.60 ± 15.00 | 0.00 ± 0.00 | 106.60 ± 15.00 | 0.72 ± 0.13 | 3 |
| InputOperator | 8.64 ± 1.79 | 0.00 ± 0.00 | 8.64 ± 1.79 | 0.06 ± 0.02 | 1 |
| **Total** | **115.24 ± 16.16** | **14918.85 ± 1735.45**| **15034.09 ± 1734.28**| | **93** |
### YOLOv5u
| OpType | CPU Time (μs) | NPU Time (μs) | Total Time (μs) | Time Ratio (%) | Number of Times Called |
|-----------------|---------------------|----------------------|-----------------------|---------------------|-----------------------|
| ConvExSwish | 0.00 ± 0.00 | 16828.24 ± 1332.73 | 16828.24 ± 1332.73 | 83.04 ± 1.61 | 69 |
| Concat | 0.00 ± 0.00 | 1265.94 ± 250.24 | 1265.94 ± 250.24 | 6.17 ± 0.69 | 13 |
| ConvSigmoid | 0.00 ± 0.00 | 613.88 ± 62.97 | 613.88 ± 62.97 | 3.03 ± 0.15 | 3 |
| Add | 0.00 ± 0.00 | 553.75 ± 131.17 | 553.75 ± 131.17 | 2.69 ± 0.44 | 7 |
| Conv | 0.00 ± 0.00 | 298.61 ± 72.72 | 298.61 ± 72.72 | 1.45 ± 0.25 | 3 |
| ConvClip | 0.00 ± 0.00 | 256.02 ± 64.48 | 256.02 ± 64.48 | 1.24 ± 0.23 | 3 |
| MaxPool | 0.00 ± 0.00 | 178.68 ± 58.72 | 178.68 ± 58.72 | 0.86 ± 0.23 | 3 |
| Resize | 0.00 ± 0.00 | 170.87 ± 40.14 | 170.87 ± 40.14 | 0.83 ± 0.13 | 2 |
| OutputOperator | 126.89 ± 16.53 | 0.00 ± 0.00 | 126.89 ± 16.53 | 0.63 ± 0.10 | 9 |
| InputOperator | 8.69 ± 1.45 | 0.00 ± 0.00 | 8.69 ± 1.45 | 0.04 ± 0.01 | 1 |
| **Total** | **135.57 ± 17.51** | **20165.99 ± 1963.70**| **20301.56 ± 1965.88**| | **113** |
### YOLOv8
| OpType | CPU Time (μs) | NPU Time (μs) | Total Time (μs) | Time Ratio (%) | Number of Times Called |
|-----------------|---------------------|----------------------|-----------------------|---------------------|-----------------------|
| ConvExSwish | 0.00 ± 0.00 | 13017.04 ± 1165.76 | 13017.04 ± 1165.76 | 75.66 ± 1.96 | 57 |
| Concat | 0.00 ± 0.00 | 1489.94 ± 257.22 | 1489.94 ± 257.22 | 8.58 ± 0.53 | 13 |
| Split | 0.00 ± 0.00 | 681.47 ± 166.62 | 681.47 ± 166.62 | 3.89 ± 0.53 | 8 |
| ConvSigmoid | 0.00 ± 0.00 | 596.08 ± 75.01 | 596.08 ± 75.01 | 3.45 ± 0.18 | 3 |
| Add | 0.00 ± 0.00 | 443.60 ± 118.05 | 443.60 ± 118.05 | 2.53 ± 0.41 | 6 |
| Conv | 0.00 ± 0.00 | 269.61 ± 78.65 | 269.61 ± 78.65 | 1.54 ± 0.30 | 3 |
| Resize | 0.00 ± 0.00 | 236.79 ± 37.74 | 236.79 ± 37.74 | 1.37 ± 0.08 | 2 |
| ConvClip | 0.00 ± 0.00 | 231.82 ± 68.44 | 231.82 ± 68.44 | 1.32 ± 0.27 | 3 |
| MaxPool | 0.00 ± 0.00 | 156.85 ± 56.94 | 156.85 ± 56.94 | 0.89 ± 0.23 | 3 |
| OutputOperator | 124.86 ± 20.74 | 0.00 ± 0.00 | 124.86 ± 20.74 | 0.73 ± 0.15 | 9 |
| InputOperator | 8.47 ± 1.66 | 0.00 ± 0.00 | 8.47 ± 1.66 | 0.05 ± 0.01 | 1 |
| **Total** | **133.33 ± 21.95** | **17123.19 ± 1985.72**| **17256.52 ± 1986.77** | | **108** |
---
### YOLOv11
| OpType | CPU Time (μs) | NPU Time (μs) | Total Time (μs) | Time Ratio (%) | Number of Times Called |
|-----------------|---------------------|----------------------|-----------------------|---------------------|-----------------------|
| ConvExSwish | 0.00 ± 0.00 | 16034.00 ± 1331.95 | 16034.00 ± 1331.95 | 69.90 ± 1.55 | 77 |
| Concat | 0.00 ± 0.00 | 1888.89 ± 293.99 | 1888.89 ± 293.99 | 8.17 ± 0.51 | 17 |
| exSDPAttention | 0.00 ± 0.00 | 1210.88 ± 17.73 | 1210.88 ± 17.73 | 5.32 ± 0.52 | 1 |
| Split | 0.00 ± 0.00 | 908.30 ± 183.92 | 908.30 ± 183.92 | 3.91 ± 0.45 | 10 |
| Add | 0.00 ± 0.00 | 871.64 ± 212.79 | 871.64 ± 212.79 | 3.73 ± 0.60 | 12 |
| ConvSigmoid | 0.00 ± 0.00 | 617.61 ± 59.61 | 617.61 ± 59.61 | 2.69 ± 0.16 | 3 |
| Conv | 0.00 ± 0.00 | 419.72 ± 89.88 | 419.72 ± 89.88 | 1.80 ± 0.24 | 5 |
| Resize | 0.00 ± 0.00 | 272.09 ± 49.91 | 272.09 ± 49.91 | 1.18 ± 0.12 | 2 |
| ConvClip | 0.00 ± 0.00 | 260.08 ± 59.12 | 260.08 ± 59.12 | 1.12 ± 0.18 | 3 |
| MaxPool | 0.00 ± 0.00 | 181.93 ± 53.32 | 181.93 ± 53.32 | 0.78 ± 0.18 | 3 |
| OutputOperator | 131.48 ± 22.93 | 0.00 ± 0.00 | 131.48 ± 22.93 | 0.58 ± 0.12 | 9 |
| ConvAdd | 0.00 ± 0.00 | 126.79 ± 35.28 | 126.79 ± 35.28 | 0.54 ± 0.11 | 2 |
| Reshape | 0.00 ± 0.00 | 56.61 ± 18.03 | 56.61 ± 18.03 | 0.24 ± 0.06 | 3 |
| InputOperator | 8.66 ± 1.59 | 0.00 ± 0.00 | 8.66 ± 1.59 | 0.04 ± 0.01 | 1 |
| **Total** | **140.14 ± 24.26** | **22848.54 ± 2351.95**| **22988.68 ± 2355.97**| | **148** |
## Model Summary and Accuracy Metrics
The table below summarizes the mean average precision (mAP) and total inference time for each model. These metrics provide a high-level view of how each model performs in terms of both detection accuracy and runtime efficiency.
### Mean Average Precision (mAP) by Model
| Metric | YOLOv5 | YOLOv5u | YOLOv8 | YOLOv11 |
|--------|------------|------------|------------|------------|
| **mAP** | 0.2243 | 0.2745 | 0.3051 | 0.3251 |
| **mAP50** | 0.3538 | 0.3834 | 0.4145 | 0.4406 |
| **mAP75** | 0.2432 | 0.2997 | 0.3349 | 0.3568 |
| **mAP85** | 0.3054 | 0.3472 | 0.3867 | 0.4068 |
| **mAP95** | 0.3708 | 0.4822 | 0.5483 | 0.5858 |
### Model Execution Time and Call Frequency
| Model | Total Time (μs) | Number of Processing Calls |
|---------|------------------------|----------------------------|
| **YOLOv5** | 15034.09 ± 1734.28 | 93 |
| **YOLOv5u** | 20301.56 ± 1965.88 | 113 |
| **YOLOv8** | 17256.52 ± 1986.77 | 108 |
| **YOLOv11** | 22988.68 ± 2355.97 | 148 |
## Conclusion
The benchmark reveals a clear performance trade-off between inference time and detection accuracy:
- **YOLOv5** is the fastest model with the lowest total inference time, making it well-suited for situations where speed is more important than high detection precision.
- **YOLOv11** achieves the highest accuracy (mAP) across all IoU thresholds but comes with the longest inference time, which may limit its use in real-time applications.
- **YOLOv8** offers a strong balance between speed and accuracy, making it a practical choice when both factors matter.
- **YOLOv5u** improves accuracy compared to YOLOv5 but falls behind YOLOv8 in both speed and detection quality.
When choosing a model for edge devices like the Orange Pi 5, its important to weigh how much latency your system can tolerate versus how much accuracy you need. A faster model may give quicker results, while a more accurate one may offer better detection reliability, but at the cost of speed.

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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
:::{warning}
The usage of chessboards can result in bad calibration results if multiple similar images are taken. We strongly recommend that teams use ChArUco boards instead!
:::
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 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).
:::
:::{note}
If you have a [calib.io](https://calib.io/) ChArUco Target you will have to enter the paramaters of your target. For example if your target says "9x12 | Checker Size: 30 mm | Marker Size: 22 mm | Dictionary: ArUco DICT 5x5", you would have to set the board type to Dict_5x5_1000, the pattern spacing to 1.1811 in (30 mm converted to inches), the marker size 0.866142 in (22 mm converted to inches), the board width to 12 and the board height to 9. If you chose the wrong tag family the board wont be detected during calibration. If you swap the width and height your calibration will have a very high error.
:::
### 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/main/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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