## Description
#2255 introduced a new, cross-platform method for monitoring hardware
and removed the custom shell commands that had been used previously.
This PR updates the documentation to reflect the removal of those
commands from hardwareConfig.json.
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---------
Co-authored-by: Gold856 <117957790+Gold856@users.noreply.github.com>
## Description
FRC Team 6413 ran into problems trying to write the 2026.1.1 image using
the Raspberry Pi Imager tool. After some investigation we discovered a
bug was introduced in v2.0.2 of the imager tool which prevents it from
writing the PhotonVision images to any SD card on any Windows 10 or
Windows 11 computer.
Attempting to write a custom image (.img and .img.xz) results in the
following error dialog appearing as soon as the write action is
confirmed:
<img width="684" height="483" alt="photonvisionBurnFailure202"
src="https://github.com/user-attachments/assets/5c802ef0-75f9-4056-ac1f-760dcd3605bf"
/>
Versions 2.0.0 and earlier will successfully write the image files to
the selected SD card.
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## Description
Clarifies when estimate methods return an empty optional. This also
fixes some parity issues with Constrained SolvePnP not checking the
optional returned by the heading buffer.
Resolves#2322.
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## Description
Added specific details to the wiring section of the photonvision docs
for two wiring methods for connecting a power regulator to a
coprocessor. This aims to help prevent any possible misunderstandings of
how to wire a regulator to a coprocessor.
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Co-authored-by: Matt Morley <matthew.morley.ca@gmail.com>
## Description
As per the linked issue, raw NT APIs are old and mostly obsolete, this
PR adds user-facing notifications of that.
Closes#1828
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## 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`. -->
Bump to wpilib 2026 beta. This does not bump our pythonlib, as robotpy
hasn't come out yet.
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---------
Co-authored-by: Gold856 <117957790+Gold856@users.noreply.github.com>
The old GPIO abstraction was replaced with
[`diozero`](https://www.diozero.com), which supports most hardware
running Linux due to its use of GPIO character devices provided by the
Linux kernel. `diozero` also supports [alternate
providers](https://www.diozero.com/concepts/providers.html#providers) if
for some reason the character device API is insufficient. Certain
capabilities outside of the character device API is also implemented for
common hardware.
Custom GPIO commands are implemented via a custom `diozero` provider.
The configuration for custom GPIO will need manually updated according
to the Hardware Config documentation page.
The status LED class was also reworked to support additional statuses or
LED indication types, although none have been added yet.
This was tested on a RPi 5 with LL3 illumination LEDs and an RGB status
led attached. All capabilities worked as expected. All 8 status LED
colors were tested and functional via modifying the code. Basic
functionality of custom GPIO was tested with dummy commands.
## Description
<img width="1918" height="1030" alt="image"
src="https://github.com/user-attachments/assets/5af1a5ee-012d-461d-9162-2d4de6ad0c62"
/>
A wireshark dissector can be handy for quickly visualizing time sync
messages. See the docs for how to use this!
Full disclosure -- this dissector was generated by Claude 4.5, and I
spot-checked all the numbers for correctness. This seems like idiomatic
Lua to me, but I don't know Lua at all. I don't see a nice QOL thing
nobody else will use as being a tech debt concern.
## Meta
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## Description
Camera focus tool pipeline using a Laplacian and finding the variance.
Similar to Limelight.
closes#1597
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## Description
Looks like this is the convention from a quick Google search.
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## Description
Revision of #2164. Instead of only running headless tests during a
build, now only the images are disabled. To reenable showing images, the
`enableTestUi` project property needs to be passed to gradle.
```bash
./gradlew test -PenableTestUi
```
## Meta
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## Description
This adds documentation for how to lint PhotonVision, as it is not
available elsewhere and people have historically needed to figure it out
for themselves. It also adds an alias for linting PV.
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## Description
Simple typo fix:
s/Specifc/Specific
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## Description
There was a recent occurance of journalctl logs saying something like
`[66B blob data]`. We don't log anything binary, so journalctl might be
hiding some lines, thinking they're actually binary data when they're
actually plain text. Use `-a` to always log everything. If it's binary
data anyways, we'll want to know what it is anyways. Use `--output cat`
because we output our own timestamps and we don't want journalctl's own
timestamping. Filter the output through `sed` to remove ANSI color codes
to make reading logs easier.
## Meta
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## Description
See #2026 for the previous iteration of this PR.
This adds the RKNN model trained on the COCO dataset as one of the
models shipped with PV. This model is fairly general, and has been
trained to identify a number of objects, including people, animals,
cars, and more. This model is meant for teams to test object detection,
particularly for teams who might not have access to the game elements
that our other models are trained on.
It additionally acknowledges Ultralytics for the model, and includes the
AGPL copyleft license.
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This reverts commit 753123844b.
## Description
The COCO dataset contains images that use the NC and/or ND variants of
the CC license, and distributing a model based on that dataset is most
likely a violation of licenses. Additionally, the model is licensed under AGPL,
which might be a concern for PhotonVision, and at a minimum, there's no
license file bundled with the model right now.
## Meta
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## Description
This adds the RKNN model trained on the COCO dataset as one of the
models shipped with PV. This model is fairly general, and has been
trained to identify a number of objects, including people, animals,
cars, and more. This model is meant for teams to test object detection,
particularly for teams who might not have access to the game elements
that our other models are trained on.
## Meta
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