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Update references in docs to 2025 (#1685)

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Fixes #1651
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Sam Freund
2025-01-07 16:38:20 -06:00
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parent 5993e79e00
commit 36e3a1f72e
5 changed files with 6 additions and 14 deletions
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docs/README.MD
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@@ -6,4 +6,4 @@ PhotonVision is a free open-source vision processing software for FRC teams.
This repository is the source code for our ReadTheDocs documentation, which can be found [here](https://docs.photonvision.org).
[Contribution and formatting guidelines for this project](https://docs.photonvision.org/en/latest/docs/contributing/photonvision-docs/index.html)
[Contribution and formatting guidelines for this project](https://docs.photonvision.org/en/latest/docs/contributing/index.html)

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docs/source/docs/apriltag-pipelines/about-apriltags.md
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@@ -10,5 +10,5 @@ AprilTags are a common type of visual fiducial marker. Visual fiducial markers a
A more technical explanation can be found in the [WPILib documentation](https://docs.wpilib.org/en/latest/docs/software/vision-processing/apriltag/apriltag-intro.html).
:::{note}
You can get FIRST's [official PDF of the targets used in 2024 here](https://firstfrc.blob.core.windows.net/frc2024/FieldAssets/Apriltag_Images_and_User_Guide.pdf).
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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docs/source/docs/apriltag-pipelines/multitag.md
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@@ -51,7 +51,7 @@ The returned field to camera transform is a transform from the fixed field origi
## Updating the Field Layout
PhotonVision ships by default with the [2024 field layout JSON](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2024-crescendo.json). The layout can be inspected by navigating to the settings tab and scrolling down to the "AprilTag Field Layout" card, as shown below.
PhotonVision ships by default with the [2025 field layout JSON](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2025-reefscape.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

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docs/source/docs/examples/aimingatatarget.md
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@@ -7,7 +7,7 @@ The following example is from the PhotonLib example repository ([Java](https://g
- A Robot
- A camera mounted rigidly to the robot's frame, cenetered and pointed forward.
- A coprocessor running PhotonVision with an AprilTag or Aurco 2D Pipeline.
- [A printout of AprilTag 7](https://firstfrc.blob.core.windows.net/frc2024/FieldAssets/Apriltag_Images_and_User_Guide.pdf), mounted on a rigid and flat surface.
- [A printout of AprilTag 7](https://firstfrc.blob.core.windows.net/frc2025/FieldAssets/Apriltag_Images_and_User_Guide.pdf), mounted on a rigid and flat surface.
## Code

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docs/source/docs/objectDetection/about-object-detection.md
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@@ -4,13 +4,7 @@
PhotonVision supports object detection using neural network accelerator hardware built into Orange Pi 5/5+ coprocessors. The Neural Processing Unit, or NPU, is [used by PhotonVision](https://github.com/PhotonVision/rknn_jni/tree/main) to massively accelerate certain math operations like those needed for running ML-based object detection.
For the 2024 season, PhotonVision shipped with a **pre-trained NOTE detector** (shown above), as well as a mechanism for swapping in custom models. Future development will focus on enabling lower friction management of multiple custom models.
```{image} images/notes-ui.png
```
For the 2025 season, we intend to release a new trained model once gamepiece data is released.
For the 2025 season, PhotonVision does not currently ship with a pre-trained detector. If teams are interested in using object detection, they can follow the custom process outlined {ref}`below <docs/objectDetection/about-object-detection:Uploading Custom Models>`.
## Tracking Objects
@@ -49,6 +43,4 @@ Coming soon!
PhotonVision currently ONLY supports YOLOv5 models trained and converted to `.rknn` format for RK3588 CPUs! Other models require different post-processing code and will NOT work. The model conversion process is also highly particular. Proceed with care.
:::
Our [pre-trained NOTE model](https://github.com/PhotonVision/photonvision/blob/main/photon-server/src/main/resources/models/note-640-640-yolov5s.rknn) is automatically extracted from the JAR when PhotonVision starts, only if a file named “note-640-640-yolov5s.rknn” and "labels.txt" does not exist in the folder `photonvision_config/models/`. This technically allows power users to replace the model and label files with new ones without rebuilding Photon from source and uploading a new JAR.
Use a program like WinSCP or FileZilla to access your coprocessor's filesystem, and copy the new `.rknn` model file into /home/pi. Next, SSH into the coprocessor and `sudo mv /path/to/new/model.rknn /opt/photonvision/photonvision_config/models/note-640-640-yolov5s.rknn`. Repeat this process with the labels file, which should contain one line per label the model outputs with no training newline. Next, restart PhotonVision via the web UI.
Use a program like WinSCP or FileZilla to access your coprocessor's filesystem, and copy the new `.rknn` model file into /home/pi. Next, SSH into the coprocessor and `sudo mv /path/to/new/model.rknn /opt/photonvision/photonvision_config/models/NEW-MODEL-NAME.rknn`. Repeat this process with the labels file, which should contain one line per label the model outputs with no training newline. Next, restart PhotonVision via the web UI.