Fix calibration resolution default bug (#2156)

Co-authored-by: Sam Freund <samf.236@proton.me>

.github/workflows/build.yml

@@ -10,7 +10,7 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  IMAGE_VERSION: v2026.0.3
+  IMAGE_VERSION: v2026.0.4
 
 jobs:
 
@@ -445,6 +445,12 @@ jobs:
             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
@@ -538,6 +544,12 @@ jobs:
           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:

build.gradle

@@ -8,7 +8,7 @@ plugins {
     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 "1.0.4" apply false
+    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
 }
@@ -101,7 +101,7 @@ spotless {
 }
 
 wrapper {
-    gradleVersion '8.14.3'
+    gradleVersion = '8.14.3'
 }
 
 ext.getCurrentArch = {

docs/source/conf.py

@@ -21,6 +21,29 @@ 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
@@ -158,4 +181,4 @@ 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"]
+myst_enable_extensions = ["colon_fence", "substitution"]

docs/source/docs/camera-specific-configuration/index.md

@@ -1,4 +1,4 @@
-# Camera-Specifc Configuration
+# Camera-Specific Configuration
 
 ```{toctree}
 :maxdepth: 2

docs/source/docs/contributing/building-photon.md

@@ -275,3 +275,9 @@ Using the [GitHub CLI](https://cli.github.com/), we can download artifacts from
 MacOS builds are not published to releases as MacOS is not an officially
 supported platform. However, MacOS builds are still available from the MacOS
 build action, which can be found [here](https://github.com/PhotonVision/photonvision/actions/workflows/build.yml).
+
+#### Forcing Object Detection in the UI
+
+In order to force the Object Detection interface to be visible, it's necessary to hardcode the platform that `Platform.java` returns. This can be done by changing the function that detects the RK3588S/QCS6490 platform to always return true, and changing the `getCurrentPlatform()` function to always return the RK3588S/QCS6490 architecture.
+Alternatively, it's possible to modify the frontend code by changing all instances of `useSettingsStore().general.supportedBackends.length > 0` to `true`, which will force the card to render.
+Make sure to revert these changes before submitting a Pull Request.

docs/source/docs/objectDetection/opi.md

@@ -14,6 +14,6 @@ PhotonVision currently ONLY supports 640x640 Ultralytics YOLOv5, YOLOv8, and YOL
 Only quantized models are supported, so take care when exporting to select the option for quantization.
 :::
 
-PhotonVision now ships with a [Python Notebook](https://github.com/PhotonVision/photonvision/blob/main/scripts/rknn-convert-tool/rknn_conversion.ipynb) that you can use in [Google Colab](https://colab.research.google.com) or in a local environment. In Google Colab, you can simply paste the PhotonVision GitHub URL into the "GitHub" tab and select the `rknn_conversion.ipynb` notebook without needing to manually download anything.
+PhotonVision now ships with a {{ '[Python Notebook](https://github.com/PhotonVision/photonvision/blob/{}/scripts/rknn_conversion.ipynb)'.format(git_tag_ref) }} that you can use in [Google Colab](https://colab.research.google.com) or in a local environment. In Google Colab, you can simply paste the PhotonVision GitHub URL into the "GitHub" tab and select the `rknn_conversion.ipynb` notebook without needing to manually download anything.
 
 Please ensure that the model you are attempting to convert is among the {ref}`supported models <docs/objectDetection/opi:Supported Models>` and using the PyTorch format.

docs/source/docs/objectDetection/rubik.md

@@ -14,7 +14,7 @@ PhotonVision currently ONLY supports 640x640 Ultralytics YOLOv8 and YOLOv11 mode
 Only quantized models are supported, so take care when exporting to select the option for quantization.
 :::
 
-PhotonVision now ships with a [Python Notebook](https://github.com/PhotonVision/photonvision/blob/main/scripts/rubik_conversion.ipynb) that you can use in [Google Colab](https://colab.research.google.com) or in a local environment. In Google Colab, you can simply paste the PhotonVision GitHub URL into the "GitHub" tab and select the `rubik_conversion.ipynb` notebook without needing to manually download anything.
+PhotonVision now ships with a {{ '[Python Notebook](https://github.com/PhotonVision/photonvision/blob/{}/scripts/rubik_conversion.ipynb)'.format(git_tag_ref) }} that you can use in [Google Colab](https://colab.research.google.com) or in a local environment. In Google Colab, you can simply paste the PhotonVision GitHub URL into the "GitHub" tab and select the `rubik_conversion.ipynb` notebook without needing to manually download anything.
 
 Please ensure that the model you are attempting to convert is among the {ref}`supported models <docs/objectDetection/rubik:Supported Models>` and using the PyTorch format.
 

docs/source/docs/quick-start/quick-install.md

@@ -34,14 +34,17 @@ Balena Etcher can also be used, but historically has had issues such as bootloop
 
 ## Limelight Installation
 
-:::{note}
-In order to mount the Limelight 4 on your computer, it's necessary to use `rpiboot`. To do this, follow the instructions [here](https://docs.limelightvision.io/docs/docs-limelight/getting-started/limelight-4#4-updating-limelightos).
-:::
+In order to flash your Limelight you should follow the instructions on the Limelight documentation for the relevant version. Make sure to replace the Limelight OS image with the relevant PhotonVision image.
 
-Limelights have a different installation processes. Simply connect the limelight to your computer using the proper usb cable. Select the compute module in the [Raspberry Pi Imager](https://www.raspberrypi.com/software/). If it doesn’t show up after 30s try using another USB port, initialization may take a while. If prompted, install the recommended missing drivers. Select the image, and flash.
+| Limelight Version | Limelight Documentation                                                                                 | PhotonVision Image                                                                                                         |     |
+| ----------------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | --- |
+| 2                 | [Updating Limelight 2 OS](https://docs.limelightvision.io/docs/docs-limelight/getting-started/limelight-2#4-updating-limelightos)  | photonvision-{version}-linuxarm64_limelight2.img.xz  |     |
+| 3                 | [Updating Limelight 3 OS](https://docs.limelightvision.io/docs/docs-limelight/getting-started/limelight-3#4-updating-limelightos)  | photonvision-{version}-linuxarm64_limelight3.img.xz  |     |
+| 3G                | [Updating Limelight 3G OS](https://docs.limelightvision.io/docs/docs-limelight/getting-started/limelight-3g#4-updating-limelightos) | photonvision-{version}-linuxarm64_limelight3g.img.xz |     |
+| 4                 | [Updating Limelight 4 OS](https://docs.limelightvision.io/docs/docs-limelight/getting-started/limelight-4#4-updating-limelightos)  | photonvision-{version}-linuxarm64_limelight4.img.xz  |     |
 
 :::{note}
-Limelight 2, 2+, and 3 will need a [custom hardware config file](https://github.com/PhotonVision/photonvision/tree/main/docs/source/docs/advanced-installation/sw_install/files) for lighting to work.
+Limelight models will need a [custom hardware config file](https://github.com/PhotonVision/photonvision/tree/main/docs/source/docs/advanced-installation/sw_install/files) for LEDs or other hardware features to work.
 :::
 
 ## Rubik Pi 3 Installation

photon-client/src/components/app/photon-camera-stream.vue

@@ -105,18 +105,21 @@ onBeforeUnmount(() => {
     />
     <div class="stream-overlay" :style="overlayStyle">
       <pv-icon
+        color="primary"
         icon-name="mdi-camera-image"
         tooltip="Capture and save a frame of this stream"
         class="ma-1 mr-2"
         @click="handleCaptureClick"
       />
       <pv-icon
+        color="primary"
         icon-name="mdi-fullscreen"
         tooltip="Open this stream in fullscreen"
         class="ma-1 mr-2"
         @click="handleFullscreenRequest"
       />
       <pv-icon
+        color="primary"
         icon-name="mdi-open-in-new"
         tooltip="Open this stream in a new window"
         class="ma-1 mr-2"

photon-client/src/components/cameras/CameraCalibrationCard.vue

@@ -1,5 +1,5 @@
 <script setup lang="ts">
-import { computed, ref } from "vue";
+import { computed, ref, watchEffect } from "vue";
 import { useCameraSettingsStore } from "@/stores/settings/CameraSettingsStore";
 import { CalibrationBoardTypes, CalibrationTagFamilies, type VideoFormat } from "@/types/SettingTypes";
 import MonoLogo from "@/assets/images/logoMono.png";
@@ -79,6 +79,18 @@ const calibrationDivisors = computed(() =>
   })
 );
 
+const uniqueVideoResolutionString = ref("");
+
+// Use a watchEffect so the value is populated/reacts when the stores become available or update.
+// This avoids trying to index into an array that may be empty during page reload.
+watchEffect(() => {
+  const currentIndex = useCameraSettingsStore().currentVideoFormat.index ?? 0;
+  useStateStore().calibrationData.videoFormatIndex = currentIndex;
+  const names = useCameraSettingsStore().currentCameraSettings.validVideoFormats.map((f) =>
+    getResolutionString(f.resolution)
+  );
+  uniqueVideoResolutionString.value = names[currentIndex] ?? names[0] ?? "";
+});
 const squareSizeIn = ref(1);
 const markerSizeIn = ref(0.75);
 const patternWidth = ref(8);
@@ -279,13 +291,16 @@ const setSelectedVideoFormat = (format: VideoFormat) => {
                   : 'MrCal failed to load, check journalctl logs for details.'
               "
             />
-            <!-- TODO: the default videoFormatIndex is 0, but the list of unique video mode indexes might not include 0. getUniqueVideoResolutionStrings indexing is also different from the normal video mode indexing -->
             <pv-select
-              v-model="useStateStore().calibrationData.videoFormatIndex"
+              v-model="uniqueVideoResolutionString"
               label="Resolution"
               :select-cols="8"
               :disabled="isCalibrating"
               tooltip="Resolution to calibrate at (you will have to calibrate every resolution you use 3D mode on)"
+              @update:model-value="
+                useStateStore().calibrationData.videoFormatIndex =
+                  getUniqueVideoResolutionStrings().find((v) => v.value === $event)?.value || 0
+              "
               :items="getUniqueVideoResolutionStrings()"
             />
             <pv-select
@@ -527,9 +542,9 @@ const setSelectedVideoFormat = (format: VideoFormat) => {
             <v-card-text>
               Camera has been successfully calibrated for
               {{
-                getUniqueVideoResolutionStrings().find(
-                  (v) => v.value === useStateStore().calibrationData.videoFormatIndex
-                )?.name
+                useCameraSettingsStore().currentCameraSettings.validVideoFormats.map((f) =>
+                  getResolutionString(f.resolution)
+                )[useStateStore().calibrationData.videoFormatIndex]
               }}!
             </v-card-text>
           </template>

photon-client/src/stores/settings/CameraSettingsStore.ts

@@ -142,7 +142,8 @@ export const useCameraSettingsStore = defineStore("cameraSettings", {
           maxWhiteBalanceTemp: d.maxWhiteBalanceTemp,
           matchedCameraInfo: d.matchedCameraInfo,
           isConnected: d.isConnected,
-          hasConnected: d.hasConnected
+          hasConnected: d.hasConnected,
+          mismatch: d.mismatch
         };
         return acc;
       }, {});

photon-client/src/types/SettingTypes.ts

@@ -266,6 +266,7 @@ export interface UiCameraConfiguration {
   matchedCameraInfo: PVCameraInfo;
   isConnected: boolean;
   hasConnected: boolean;
+  mismatch: boolean;
 }
 
 export interface CameraSettingsChangeRequest {
@@ -388,7 +389,8 @@ export const PlaceholderCameraSettings: UiCameraConfiguration = {
     PVUsbCameraInfo: undefined
   },
   isConnected: true,
-  hasConnected: true
+  hasConnected: true,
+  mismatch: false
 };
 
 export enum CalibrationBoardTypes {

photon-client/src/types/WebsocketDataTypes.ts

@@ -69,6 +69,7 @@ export interface WebsocketCameraSettingsUpdate {
   matchedCameraInfo: PVCameraInfo;
   isConnected: boolean;
   hasConnected: boolean;
+  mismatch: boolean;
 }
 export interface WebsocketNTUpdate {
   connected: boolean;

photon-client/src/views/CameraMatchingView.vue

@@ -168,64 +168,7 @@ const deleteThisCamera = (cameraName: string) => {
     });
 };
 
-const camerasMatch = (camera1: PVCameraInfo, camera2: PVCameraInfo) => {
-  if (camera1.PVUsbCameraInfo && camera2.PVUsbCameraInfo)
-    return (
-      camera1.PVUsbCameraInfo.name === camera2.PVUsbCameraInfo.name &&
-      camera1.PVUsbCameraInfo.vendorId === camera2.PVUsbCameraInfo.vendorId &&
-      camera1.PVUsbCameraInfo.productId === camera2.PVUsbCameraInfo.productId &&
-      camera1.PVUsbCameraInfo.uniquePath === camera2.PVUsbCameraInfo.uniquePath
-    );
-  else if (camera1.PVCSICameraInfo && camera2.PVCSICameraInfo)
-    return (
-      camera1.PVCSICameraInfo.uniquePath === camera2.PVCSICameraInfo.uniquePath &&
-      camera1.PVCSICameraInfo.baseName === camera2.PVCSICameraInfo.baseName
-    );
-  else if (camera1.PVFileCameraInfo && camera2.PVFileCameraInfo)
-    return (
-      camera1.PVFileCameraInfo.uniquePath === camera2.PVFileCameraInfo.uniquePath &&
-      camera1.PVFileCameraInfo.name === camera2.PVFileCameraInfo.name
-    );
-  else return false;
-};
-
-const cameraInfoFor = (camera: PVCameraInfo | null): PVUsbCameraInfo | PVCSICameraInfo | PVFileCameraInfo | any => {
-  if (!camera) return null;
-  if (camera.PVUsbCameraInfo) {
-    return camera.PVUsbCameraInfo;
-  }
-  if (camera.PVCSICameraInfo) {
-    return camera.PVCSICameraInfo;
-  }
-  if (camera.PVFileCameraInfo) {
-    return camera.PVFileCameraInfo;
-  }
-  return {};
-};
-
-/**
- * Find the PVCameraInfo currently occupying the same uniquepath as the the given module
- */
-const getMatchedDevice = (info: PVCameraInfo | undefined): PVCameraInfo => {
-  if (!info) {
-    return {
-      PVFileCameraInfo: undefined,
-      PVCSICameraInfo: undefined,
-      PVUsbCameraInfo: undefined
-    };
-  }
-  return (
-    useStateStore().vsmState.allConnectedCameras.find(
-      (it) => cameraInfoFor(it).uniquePath === cameraInfoFor(info).uniquePath
-    ) || {
-      PVFileCameraInfo: undefined,
-      PVCSICameraInfo: undefined,
-      PVUsbCameraInfo: undefined
-    }
-  );
-};
-
-const cameraCononected = (uniquePath: string): boolean => {
+const cameraConnected = (uniquePath: string): boolean => {
   return (
     useStateStore().vsmState.allConnectedCameras.find((it) => cameraInfoFor(it).uniquePath === uniquePath) !== undefined
   );
@@ -252,8 +195,8 @@ const activeVisionModules = computed(() =>
     // Display connected cameras first
     .sort(
       (first, second) =>
-        (cameraCononected(cameraInfoFor(second.matchedCameraInfo).uniquePath) ? 1 : 0) -
-        (cameraCononected(cameraInfoFor(first.matchedCameraInfo).uniquePath) ? 1 : 0)
+        (cameraConnected(cameraInfoFor(second.matchedCameraInfo).uniquePath) ? 1 : 0) -
+        (cameraConnected(cameraInfoFor(first.matchedCameraInfo).uniquePath) ? 1 : 0)
     )
 );
 
@@ -274,6 +217,45 @@ const setCameraDeleting = (camera: UiCameraConfiguration | WebsocketCameraSettin
   cameraToDelete.value = camera;
 };
 const yesDeleteMySettingsText = ref("");
+
+/**
+ * Get the connection-type-specific camera info from the given PVCameraInfo object.
+ */
+const cameraInfoFor = (camera: PVCameraInfo | null): PVUsbCameraInfo | PVCSICameraInfo | PVFileCameraInfo | any => {
+  if (!camera) return null;
+  if (camera.PVUsbCameraInfo) {
+    return camera.PVUsbCameraInfo;
+  }
+  if (camera.PVCSICameraInfo) {
+    return camera.PVCSICameraInfo;
+  }
+  if (camera.PVFileCameraInfo) {
+    return camera.PVFileCameraInfo;
+  }
+  return {};
+};
+
+/**
+ * Find the PVCameraInfo currently occupying the same uniquePath as the the given module
+ */
+const getMatchedDevice = (info: PVCameraInfo | undefined): PVCameraInfo => {
+  if (!info) {
+    return {
+      PVFileCameraInfo: undefined,
+      PVCSICameraInfo: undefined,
+      PVUsbCameraInfo: undefined
+    };
+  }
+  return (
+    useStateStore().vsmState.allConnectedCameras.find(
+      (it) => cameraInfoFor(it).uniquePath === cameraInfoFor(info).uniquePath
+    ) || {
+      PVFileCameraInfo: undefined,
+      PVCSICameraInfo: undefined,
+      PVUsbCameraInfo: undefined
+    }
+  );
+};
 </script>
 
 <template>
@@ -290,14 +272,11 @@ const yesDeleteMySettingsText = ref("");
       >
         <v-card color="surface" class="rounded-12">
           <v-card-title>{{ cameraInfoFor(module.matchedCameraInfo).name }}</v-card-title>
-          <v-card-subtitle v-if="!cameraCononected(cameraInfoFor(module.matchedCameraInfo).uniquePath)"
+          <v-card-subtitle v-if="!cameraConnected(cameraInfoFor(module.matchedCameraInfo).uniquePath)"
             >Status: <span class="inactive-status">Disconnected</span></v-card-subtitle
           >
           <v-card-subtitle
-            v-else-if="
-              cameraCononected(cameraInfoFor(module.matchedCameraInfo).uniquePath) &&
-              camerasMatch(getMatchedDevice(module.matchedCameraInfo), module.matchedCameraInfo)
-            "
+            v-else-if="cameraConnected(cameraInfoFor(module.matchedCameraInfo).uniquePath) && !module.mismatch"
             >Status: <span class="active-status">Active</span></v-card-subtitle
           >
           <v-card-subtitle v-else>Status: <span class="mismatch-status">Mismatch</span></v-card-subtitle>
@@ -306,7 +285,7 @@ const yesDeleteMySettingsText = ref("");
               <tbody>
                 <tr
                   v-if="
-                    cameraCononected(cameraInfoFor(module.matchedCameraInfo).uniquePath) &&
+                    cameraConnected(cameraInfoFor(module.matchedCameraInfo).uniquePath) &&
                     useStateStore().backendResults[module.uniqueName]
                   "
                 >
@@ -348,7 +327,7 @@ const yesDeleteMySettingsText = ref("");
               </tbody>
             </v-table>
             <div
-              v-if="cameraCononected(cameraInfoFor(module.matchedCameraInfo).uniquePath)"
+              v-if="cameraConnected(cameraInfoFor(module.matchedCameraInfo).uniquePath)"
               :id="`stream-container-${index}`"
               class="d-flex flex-column justify-center align-center mt-3"
               style="height: 250px"
@@ -370,7 +349,7 @@ const yesDeleteMySettingsText = ref("");
                   @click="
                     setCameraView(
                       module.matchedCameraInfo,
-                      cameraCononected(cameraInfoFor(module.matchedCameraInfo).uniquePath)
+                      cameraConnected(cameraInfoFor(module.matchedCameraInfo).uniquePath)
                     )
                   "
                 >
@@ -441,7 +420,7 @@ const yesDeleteMySettingsText = ref("");
                 </tr>
                 <tr>
                   <td>Connected</td>
-                  <td>{{ cameraCononected(cameraInfoFor(module.matchedCameraInfo).uniquePath) }}</td>
+                  <td>{{ cameraConnected(cameraInfoFor(module.matchedCameraInfo).uniquePath) }}</td>
                 </tr>
               </tbody>
             </v-table>
@@ -456,7 +435,7 @@ const yesDeleteMySettingsText = ref("");
                   @click="
                     setCameraView(
                       module.matchedCameraInfo,
-                      cameraCononected(cameraInfoFor(module.matchedCameraInfo).uniquePath)
+                      cameraConnected(cameraInfoFor(module.matchedCameraInfo).uniquePath)
                     )
                   "
                 >
@@ -562,7 +541,13 @@ const yesDeleteMySettingsText = ref("");
         <v-card-text v-if="!viewingCamera[1]">
           <PvCameraInfoCard :camera="viewingCamera[0]" />
         </v-card-text>
-        <v-card-text v-else-if="!camerasMatch(getMatchedDevice(viewingCamera[0]), viewingCamera[0])">
+        <v-card-text
+          v-else-if="
+            activeVisionModules.find(
+              (it) => cameraInfoFor(it.matchedCameraInfo).uniquePath === cameraInfoFor(viewingCamera[0]).uniquePath
+            )?.mismatch
+          "
+        >
           <v-alert
             class="mb-3"
             color="buttonActive"

photon-client/src/views/DashboardView.vue

@@ -10,6 +10,7 @@ import { useSettingsStore } from "@/stores/settings/GeneralSettingsStore";
 import { useTheme } from "vuetify";
 
 const theme = useTheme();
+import { PlaceholderCameraSettings } from "@/types/SettingTypes";
 
 const cameraViewType = computed<number[]>({
   get: (): number[] => {
@@ -54,6 +55,17 @@ const arducamWarningShown = computed<boolean>(() => {
   );
 });
 
+const cameraMismatchWarningShown = computed<boolean>(() => {
+  return (
+    Object.values(useCameraSettingsStore().cameras)
+      // Ignore placeholder camera
+      .filter((camera) => JSON.stringify(camera) !== JSON.stringify(PlaceholderCameraSettings))
+      .some((camera) => {
+        return camera.mismatch;
+      })
+  );
+});
+
 const conflictingHostnameShown = computed<boolean>(() => {
   return useSettingsStore().general.conflictingHostname;
 });
@@ -104,6 +116,21 @@ const showCameraSetupDialog = ref(useCameraSettingsStore().needsCameraConfigurat
         {{ useSettingsStore().general.conflictingCameras }}!
       </span>
     </v-alert>
+    <v-banner
+      v-if="cameraMismatchWarningShown"
+      v-model="cameraMismatchWarningShown"
+      rounded
+      color="error"
+      dark
+      class="mb-3"
+      icon="mdi-alert-circle-outline"
+    >
+      <span
+        >Camera Mismatch Detected! Visit the <a href="#/cameraConfigs">Camera Matching</a> page for more information.
+        Note: Camera matching is done by USB port. Ensure cameras are plugged into the same USB ports as when they were
+        activated.
+      </span>
+    </v-banner>
     <v-row no-gutters>
       <v-col cols="12" class="pb-3 pr-lg-3" lg="8" align-self="stretch">
         <CamerasCard v-model="cameraViewType" />

photon-core/build.gradle

@@ -8,24 +8,26 @@ apply from: "${rootDir}/shared/common.gradle"
 wpilibTools.deps.wpilibVersion = wpi.versions.wpilibVersion.get()
 
 def nativeConfigName = 'wpilibNatives'
-def nativeConfig = configurations.create(nativeConfigName)
-
+configurations {
+    wpilibNatives
+}
 def nativeTasks = wpilibTools.createExtractionTasks {
     configurationName = nativeConfigName
 }
 
 nativeTasks.addToSourceSetResources(sourceSets.main)
 
-nativeConfig.dependencies.add wpilibTools.deps.wpilib("wpimath")
-nativeConfig.dependencies.add wpilibTools.deps.wpilib("wpinet")
-nativeConfig.dependencies.add wpilibTools.deps.wpilib("wpiutil")
-nativeConfig.dependencies.add wpilibTools.deps.wpilib("ntcore")
-nativeConfig.dependencies.add wpilibTools.deps.wpilib("cscore")
-nativeConfig.dependencies.add wpilibTools.deps.wpilib("apriltag")
-nativeConfig.dependencies.add wpilibTools.deps.wpilib("hal")
-nativeConfig.dependencies.add wpilibTools.deps.wpilibOpenCv("frc" + openCVYear, wpi.versions.opencvVersion.get())
-
 dependencies {
+    wpilibNatives project(path: ':photon-targeting', configuration: 'wpilibNatives')
+    wpilibNatives wpilibTools.deps.wpilib("wpimath")
+    wpilibNatives wpilibTools.deps.wpilib("wpinet")
+    wpilibNatives wpilibTools.deps.wpilib("wpiutil")
+    wpilibNatives wpilibTools.deps.wpilib("ntcore")
+    wpilibNatives wpilibTools.deps.wpilib("cscore")
+    wpilibNatives wpilibTools.deps.wpilib("apriltag")
+    wpilibNatives wpilibTools.deps.wpilib("hal")
+    wpilibNatives wpilibTools.deps.wpilibOpenCv("frc" + openCVYear, wpi.versions.opencvVersion.get())
+
     // Zip
     implementation 'org.zeroturnaround:zt-zip:1.14'
 

photon-core/src/main/java/org/photonvision/common/configuration/HardwareConfig.java

@@ -21,57 +21,102 @@ import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
 import java.util.ArrayList;
 
 @JsonIgnoreProperties(ignoreUnknown = true)
-public record HardwareConfig(
-        String deviceName,
-        String deviceLogoPath,
-        String supportURL,
-        // LED control
+public class HardwareConfig {
+    public final String deviceName;
+    public final String deviceLogoPath;
+    public final String supportURL;
 
-        ArrayList<Integer> ledPins,
-        String ledSetCommand,
-        boolean ledsCanDim,
-        ArrayList<Integer> ledBrightnessRange,
-        String ledDimCommand,
-        String ledBlinkCommand,
-        ArrayList<Integer> statusRGBPins,
-        // Metrics
+    // LED control
+    public final ArrayList<Integer> ledPins;
+    public final String ledSetCommand;
+    public final boolean ledsCanDim;
+    public final ArrayList<Integer> ledBrightnessRange;
+    public final String ledDimCommand;
+    public final String ledBlinkCommand;
+    public final ArrayList<Integer> statusRGBPins;
 
-        String cpuTempCommand,
-        String cpuMemoryCommand,
-        String cpuUtilCommand,
-        String cpuThrottleReasonCmd,
-        String cpuUptimeCommand,
-        String gpuMemoryCommand,
-        String ramUtilCommand,
-        String gpuMemUsageCommand,
-        String diskUsageCommand,
-        // Device stuff
-        String restartHardwareCommand,
-        double vendorFOV) { // -1 for unmanaged
+    // Metrics
+    public final String cpuTempCommand;
+    public final String cpuMemoryCommand;
+    public final String cpuUtilCommand;
+    public final String cpuThrottleReasonCmd;
+    public final String cpuUptimeCommand;
+    public final String gpuMemoryCommand;
+    public final String ramUtilCommand;
+    public final String gpuMemUsageCommand;
+    public final String diskUsageCommand;
+
+    // Device stuff
+    public final String restartHardwareCommand;
+    public final double vendorFOV; // -1 for unmanaged
+
+    public HardwareConfig(
+            String deviceName,
+            String deviceLogoPath,
+            String supportURL,
+            ArrayList<Integer> ledPins,
+            String ledSetCommand,
+            boolean ledsCanDim,
+            ArrayList<Integer> ledBrightnessRange,
+            String ledDimCommand,
+            String ledBlinkCommand,
+            ArrayList<Integer> statusRGBPins,
+            String cpuTempCommand,
+            String cpuMemoryCommand,
+            String cpuUtilCommand,
+            String cpuThrottleReasonCmd,
+            String cpuUptimeCommand,
+            String gpuMemoryCommand,
+            String ramUtilCommand,
+            String gpuMemUsageCommand,
+            String diskUsageCommand,
+            String restartHardwareCommand,
+            double vendorFOV) {
+        this.deviceName = deviceName;
+        this.deviceLogoPath = deviceLogoPath;
+        this.supportURL = supportURL;
+        this.ledPins = ledPins;
+        this.ledSetCommand = ledSetCommand;
+        this.ledsCanDim = ledsCanDim;
+        this.ledBrightnessRange = ledBrightnessRange;
+        this.ledDimCommand = ledDimCommand;
+        this.ledBlinkCommand = ledBlinkCommand;
+        this.statusRGBPins = statusRGBPins;
+        this.cpuTempCommand = cpuTempCommand;
+        this.cpuMemoryCommand = cpuMemoryCommand;
+        this.cpuUtilCommand = cpuUtilCommand;
+        this.cpuThrottleReasonCmd = cpuThrottleReasonCmd;
+        this.cpuUptimeCommand = cpuUptimeCommand;
+        this.gpuMemoryCommand = gpuMemoryCommand;
+        this.ramUtilCommand = ramUtilCommand;
+        this.gpuMemUsageCommand = gpuMemUsageCommand;
+        this.diskUsageCommand = diskUsageCommand;
+        this.restartHardwareCommand = restartHardwareCommand;
+        this.vendorFOV = vendorFOV;
+    }
 
     public HardwareConfig() {
-        this(
-                "", // deviceName
-                "", // deviceLogoPath
-                "", // supportURL
-                new ArrayList<>(), // ledPins
-                "", // ledSetCommand
-                false, // ledsCanDim
-                new ArrayList<>(), // ledBrightnessRange
-                "", // ledDimCommand
-                "", // ledBlinkCommand
-                new ArrayList<>(), // statusRGBPins
-                "", // cpuTempCommand
-                "", // cpuMemoryCommand
-                "", // cpuUtilCommand
-                "", // cpuThrottleReasonCmd
-                "", // cpuUptimeCommand
-                "", // gpuMemoryCommand
-                "", // ramUtilCommand
-                "", // gpuMemUsageCommand
-                "", // diskUsageCommand
-                "", // restartHardwareCommand
-                -1); // vendorFOV
+        deviceName = "";
+        deviceLogoPath = "";
+        supportURL = "";
+        ledPins = new ArrayList<>();
+        ledSetCommand = "";
+        ledsCanDim = false;
+        ledBrightnessRange = new ArrayList<>();
+        ledDimCommand = "";
+        ledBlinkCommand = "";
+        statusRGBPins = new ArrayList<>();
+        cpuTempCommand = "";
+        cpuMemoryCommand = "";
+        cpuUtilCommand = "";
+        cpuThrottleReasonCmd = "";
+        cpuUptimeCommand = "";
+        gpuMemoryCommand = "";
+        ramUtilCommand = "";
+        gpuMemUsageCommand = "";
+        diskUsageCommand = "";
+        restartHardwareCommand = "";
+        vendorFOV = -1;
     }
 
     /**
@@ -96,4 +141,51 @@ public record HardwareConfig(
                 || gpuMemUsageCommand != ""
                 || diskUsageCommand != "";
     }
+
+    @Override
+    public String toString() {
+        return "HardwareConfig[deviceName="
+                + deviceName
+                + ", deviceLogoPath="
+                + deviceLogoPath
+                + ", supportURL="
+                + supportURL
+                + ", ledPins="
+                + ledPins
+                + ", ledSetCommand="
+                + ledSetCommand
+                + ", ledsCanDim="
+                + ledsCanDim
+                + ", ledBrightnessRange="
+                + ledBrightnessRange
+                + ", ledDimCommand="
+                + ledDimCommand
+                + ", ledBlinkCommand="
+                + ledBlinkCommand
+                + ", statusRGBPins="
+                + statusRGBPins
+                + ", cpuTempCommand="
+                + cpuTempCommand
+                + ", cpuMemoryCommand="
+                + cpuMemoryCommand
+                + ", cpuUtilCommand="
+                + cpuUtilCommand
+                + ", cpuThrottleReasonCmd="
+                + cpuThrottleReasonCmd
+                + ", cpuUptimeCommand="
+                + cpuUptimeCommand
+                + ", gpuMemoryCommand="
+                + gpuMemoryCommand
+                + ", ramUtilCommand="
+                + ramUtilCommand
+                + ", gpuMemUsageCommand="
+                + gpuMemUsageCommand
+                + ", diskUsageCommand="
+                + diskUsageCommand
+                + ", restartHardwareCommand="
+                + restartHardwareCommand
+                + ", vendorFOV="
+                + vendorFOV
+                + "]";
+    }
 }

photon-core/src/main/java/org/photonvision/common/dataflow/networktables/NetworkTablesManager.java

@@ -70,6 +70,8 @@ public class NetworkTablesManager {
     // Creating the alert up here since it should be persistent
     private final Alert conflictAlert = new Alert("PhotonAlerts", "", AlertType.kWarning);
 
+    private final Alert mismatchAlert = new Alert("PhotonAlerts", "", AlertType.kWarning);
+
     public boolean conflictingHostname = false;
     public String conflictingCameras = "";
     private String currentMacAddress;
@@ -95,6 +97,7 @@ public class NetworkTablesManager {
 
         // This should start as false, since we don't know if there's a conflict yet
         conflictAlert.set(false);
+        mismatchAlert.set(false);
 
         // Get the UI state in sync with the backend. NT should fire a callback when it
         // first connects to the robot
@@ -115,6 +118,14 @@ public class NetworkTablesManager {
         return INSTANCE;
     }
 
+    public void setMismatchAlert(boolean on, String message) {
+        if (mismatchAlert != null) {
+            mismatchAlert.set(on);
+            mismatchAlert.setText(message);
+            SmartDashboard.updateValues();
+        }
+    }
+
     private void logNtMessage(NetworkTableEvent event) {
         String levelmsg = "DEBUG";
         LogLevel pvlevel = LogLevel.DEBUG;

photon-core/src/main/java/org/photonvision/common/dataflow/networktables/TimeSyncManager.java

@@ -25,7 +25,6 @@ import org.photonvision.common.configuration.NetworkConfig;
 import org.photonvision.common.logging.LogGroup;
 import org.photonvision.common.logging.Logger;
 import org.photonvision.common.util.TimedTaskManager;
-import org.photonvision.jni.PhotonTargetingJniLoader;
 import org.photonvision.jni.TimeSyncClient;
 import org.photonvision.jni.TimeSyncServer;
 
@@ -43,10 +42,6 @@ public class TimeSyncManager {
     IntegerPublisher m_lastPongTimePub;
 
     public TimeSyncManager(NetworkTable kRootTable) {
-        if (!PhotonTargetingJniLoader.isWorking) {
-            logger.error("PhotonTargetingJNI was not loaded! Cannot do time-sync");
-        }
-
         this.ntInstance = kRootTable.getInstance();
 
         // Need this subtable to be unique per coprocessor. TODO: consider using MAC address or
@@ -65,18 +60,10 @@ public class TimeSyncManager {
 
     // Since we're spinning off tasks in a new thread, be careful and start it seperately
     public void start() {
-        if (!PhotonTargetingJniLoader.isWorking) {
-            logger.error("PhotonTargetingJNI was not loaded! Cannot start");
-        }
-
         TimedTaskManager.getInstance().addTask("TimeSyncManager::tick", this::tick, 1000);
     }
 
     public synchronized long getOffset() {
-        if (!PhotonTargetingJniLoader.isWorking) {
-            return 0;
-        }
-
         // if we're a client, return the offset to server time
         if (m_client != null) return m_client.getOffset();
         // if we're a server, our time (nt::Now) is the same as network time
@@ -88,10 +75,6 @@ public class TimeSyncManager {
     }
 
     synchronized void setConfig(NetworkConfig config) {
-        if (!PhotonTargetingJniLoader.isWorking) {
-            return;
-        }
-
         if (m_client == null && m_server == null) {
             throw new RuntimeException("Neither client nor server are null?");
         }

photon-core/src/main/java/org/photonvision/common/dataflow/websocket/UICameraConfiguration.java

@@ -52,6 +52,7 @@ public class UICameraConfiguration {
     public double minWhiteBalanceTemp;
     public double maxWhiteBalanceTemp;
     public PVCameraInfo matchedCameraInfo;
+    public boolean mismatch;
 
     // Status for if the underlying device is present and such
     public boolean isConnected;

photon-core/src/main/java/org/photonvision/common/dataflow/websocket/UIPhotonConfiguration.java

@@ -49,7 +49,7 @@ public class UIPhotonConfiguration {
                                 NetworkManager.getInstance().networkingIsDisabled),
                         new UILightingConfig(
                                 c.getHardwareSettings().ledBrightnessPercentage,
-                                !c.getHardwareConfig().ledPins().isEmpty()),
+                                !c.getHardwareConfig().ledPins.isEmpty()),
                         new UIGeneralSettings(
                                 PhotonVersion.versionString,
                                 // TODO add support for other types of GPU accel
@@ -57,9 +57,9 @@ public class UIPhotonConfiguration {
                                 MrCalJNILoader.getInstance().isLoaded(),
                                 c.neuralNetworkPropertyManager().getModels(),
                                 NeuralNetworkModelManager.getInstance().getSupportedBackends(),
-                                c.getHardwareConfig().deviceName().isEmpty()
+                                c.getHardwareConfig().deviceName.isEmpty()
                                         ? Platform.getHardwareModel()
-                                        : c.getHardwareConfig().deviceName(),
+                                        : c.getHardwareConfig().deviceName,
                                 Platform.getPlatformName(),
                                 NetworkTablesManager.getInstance().conflictingHostname,
                                 NetworkTablesManager.getInstance().conflictingCameras),

photon-core/src/main/java/org/photonvision/common/hardware/GPIO/CustomGPIO.java

@@ -92,8 +92,8 @@ public class CustomGPIO extends GPIOBase {
 
     public static void setConfig(HardwareConfig config) {
         if (Platform.isRaspberryPi()) return;
-        commands.replace("setState", config.ledSetCommand());
-        commands.replace("dim", config.ledDimCommand());
-        commands.replace("blink", config.ledBlinkCommand());
+        commands.replace("setState", config.ledSetCommand);
+        commands.replace("dim", config.ledDimCommand);
+        commands.replace("blink", config.ledBlinkCommand);
     }
 }

photon-core/src/main/java/org/photonvision/common/hardware/HardwareManager.java

@@ -97,22 +97,22 @@ public class HardwareManager {
         }
 
         statusLED =
-                hardwareConfig.statusRGBPins().size() == 3
-                        ? new StatusLED(hardwareConfig.statusRGBPins())
+                hardwareConfig.statusRGBPins.size() == 3
+                        ? new StatusLED(hardwareConfig.statusRGBPins)
                         : null;
 
         if (statusLED != null) {
             TimedTaskManager.getInstance().addTask("StatusLEDUpdate", this::statusLEDUpdate, 150);
         }
 
-        var hasBrightnessRange = hardwareConfig.ledBrightnessRange().size() == 2;
+        var hasBrightnessRange = hardwareConfig.ledBrightnessRange.size() == 2;
         visionLED =
-                hardwareConfig.ledPins().isEmpty()
+                hardwareConfig.ledPins.isEmpty()
                         ? null
                         : new VisionLED(
-                                hardwareConfig.ledPins(),
-                                hasBrightnessRange ? hardwareConfig.ledBrightnessRange().get(0) : 0,
-                                hasBrightnessRange ? hardwareConfig.ledBrightnessRange().get(1) : 100,
+                                hardwareConfig.ledPins,
+                                hasBrightnessRange ? hardwareConfig.ledBrightnessRange.get(0) : 0,
+                                hasBrightnessRange ? hardwareConfig.ledBrightnessRange.get(1) : 100,
                                 pigpioSocket,
                                 ledModeState::set);
 
@@ -161,7 +161,7 @@ public class HardwareManager {
             }
         }
         try {
-            return shellExec.executeBashCommand(hardwareConfig.restartHardwareCommand()) == 0;
+            return shellExec.executeBashCommand(hardwareConfig.restartHardwareCommand) == 0;
         } catch (IOException e) {
             logger.error("Could not restart device!", e);
             return false;

photon-core/src/main/java/org/photonvision/common/hardware/metrics/cmds/FileCmds.java

@@ -22,18 +22,18 @@ import org.photonvision.common.configuration.HardwareConfig;
 public class FileCmds extends CmdBase {
     @Override
     public void initCmds(HardwareConfig config) {
-        cpuTemperatureCommand = config.cpuTempCommand();
-        cpuUtilizationCommand = config.cpuUtilCommand();
-        cpuThrottleReasonCmd = config.cpuThrottleReasonCmd();
+        cpuTemperatureCommand = config.cpuTempCommand;
+        cpuUtilizationCommand = config.cpuUtilCommand;
+        cpuThrottleReasonCmd = config.cpuThrottleReasonCmd;
 
-        ramMemCommand = config.cpuMemoryCommand();
-        ramUtilCommand = config.ramUtilCommand();
+        ramMemCommand = config.cpuMemoryCommand;
+        ramUtilCommand = config.ramUtilCommand;
 
-        gpuMemCommand = config.gpuMemoryCommand();
-        gpuMemUtilCommand = config.gpuMemUsageCommand();
+        gpuMemCommand = config.gpuMemoryCommand;
+        gpuMemUtilCommand = config.gpuMemUsageCommand;
 
-        diskUsageCommand = config.diskUsageCommand();
+        diskUsageCommand = config.diskUsageCommand;
 
-        uptimeCommand = config.cpuUptimeCommand();
+        uptimeCommand = config.cpuUptimeCommand;
     }
 }

photon-core/src/main/java/org/photonvision/common/util/TestUtils.java

@@ -26,14 +26,14 @@ import java.io.IOException;
 import java.nio.file.Path;
 import org.opencv.core.Mat;
 import org.opencv.highgui.HighGui;
-import org.photonvision.jni.WpilibLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.vision.calibration.CameraCalibrationCoefficients;
 import org.photonvision.vision.pipeline.result.CVPipelineResult;
 import org.photonvision.vision.target.TrackedTarget;
 
 public class TestUtils {
     public static boolean loadLibraries() {
-        return WpilibLoader.loadLibraries();
+        return LibraryLoader.loadWpiLibraries() && LibraryLoader.loadTargeting();
     }
 
     @SuppressWarnings("unused")

photon-core/src/main/java/org/photonvision/vision/camera/PVCameraInfo.java

@@ -26,6 +26,7 @@ import com.fasterxml.jackson.annotation.JsonTypeInfo;
 import com.fasterxml.jackson.annotation.JsonTypeName;
 import edu.wpi.first.cscore.UsbCameraInfo;
 import java.util.Arrays;
+import java.util.Objects;
 
 @JsonTypeInfo(use = JsonTypeInfo.Id.NAME, include = JsonTypeInfo.As.WRAPPER_OBJECT)
 @JsonIgnoreProperties(ignoreUnknown = true)
@@ -70,8 +71,15 @@ public sealed interface PVCameraInfo {
 
     CameraType type();
 
+    /**
+     * Default equals implementation that delegates to the implementing class's equals method. This
+     * method checks type compatibility first, then delegates to the actual implementation.
+     */
     default boolean equals(PVCameraInfo other) {
-        return uniquePath().equals(other.uniquePath());
+        if (other == null) return false;
+        if (this.type() != other.type()) return false;
+        // Delegate to the actual equals(Object) implementation of this instance
+        return this.equals((Object) other);
     }
 
     @JsonTypeName("PVUsbCameraInfo")
@@ -125,7 +133,17 @@ public sealed interface PVCameraInfo {
         public boolean equals(Object obj) {
             if (this == obj) return true;
             if (obj == null) return false;
-            return obj instanceof PVCameraInfo info && equals(info);
+            if (!(obj instanceof PVUsbCameraInfo info)) return false;
+
+            return super.name.equals(info.name)
+                    && super.vendorId == info.vendorId
+                    && super.productId == info.productId
+                    && uniquePath().equals(info.uniquePath());
+        }
+
+        @Override
+        public int hashCode() {
+            return Objects.hash(super.name, super.vendorId, super.productId, uniquePath());
         }
 
         @Override
@@ -191,7 +209,14 @@ public sealed interface PVCameraInfo {
         public boolean equals(Object obj) {
             if (this == obj) return true;
             if (obj == null) return false;
-            return obj instanceof PVCameraInfo info && equals(info);
+            if (!(obj instanceof PVCSICameraInfo info)) return false;
+
+            return baseName.equals(info.baseName) && path.equals(info.path);
+        }
+
+        @Override
+        public int hashCode() {
+            return Objects.hash(baseName, path);
         }
 
         @Override
@@ -248,7 +273,14 @@ public sealed interface PVCameraInfo {
         public boolean equals(Object obj) {
             if (this == obj) return true;
             if (obj == null) return false;
-            return obj instanceof PVFileCameraInfo info && equals(info);
+            if (!(obj instanceof PVFileCameraInfo info)) return false;
+
+            return name.equals(info.name) && path.equals(info.path);
+        }
+
+        @Override
+        public int hashCode() {
+            return Objects.hash(name, path);
         }
 
         @Override

photon-core/src/main/java/org/photonvision/vision/camera/csi/LibcameraGpuSource.java

@@ -100,7 +100,7 @@ public class LibcameraGpuSource extends VisionSource {
 
     @Override
     public boolean hasLEDs() {
-        return (ConfigManager.getInstance().getConfig().getHardwareConfig().ledPins().size() > 0);
+        return (ConfigManager.getInstance().getConfig().getHardwareConfig().ledPins.size() > 0);
     }
 
     @Override

photon-core/src/main/java/org/photonvision/vision/processes/VisionModule.java

@@ -93,6 +93,8 @@ public class VisionModule {
     MJPGFrameConsumer inputVideoStreamer;
     MJPGFrameConsumer outputVideoStreamer;
 
+    boolean mismatch;
+
     public VisionModule(PipelineManager pipelineManager, VisionSource visionSource) {
         logger =
                 new Logger(
@@ -100,6 +102,8 @@ public class VisionModule {
                         visionSource.getSettables().getConfiguration().nickname,
                         LogGroup.VisionModule);
 
+        mismatch = false;
+
         cameraQuirks = visionSource.getCameraConfiguration().cameraQuirks;
 
         if (visionSource.getCameraConfiguration().cameraQuirks == null)
@@ -160,7 +164,7 @@ public class VisionModule {
 
         // Set vendor FOV
         if (isVendorCamera()) {
-            var fov = ConfigManager.getInstance().getConfig().getHardwareConfig().vendorFOV();
+            var fov = ConfigManager.getInstance().getConfig().getHardwareConfig().vendorFOV;
             logger.info("Setting FOV of vendor camera to " + fov);
             visionSource.getSettables().setFOV(fov);
         }
@@ -568,6 +572,8 @@ public class VisionModule {
 
         ret.deactivated = config.deactivated;
 
+        ret.mismatch = this.mismatch;
+
         // TODO refactor into helper method
         var temp = new HashMap<Integer, HashMap<String, Object>>();
         var videoModes = visionSource.getSettables().getAllVideoModes();

photon-core/src/main/java/org/photonvision/vision/processes/VisionSourceManager.java

@@ -31,6 +31,7 @@ import org.photonvision.common.configuration.CameraConfiguration;
 import org.photonvision.common.configuration.ConfigManager;
 import org.photonvision.common.dataflow.DataChangeService;
 import org.photonvision.common.dataflow.events.OutgoingUIEvent;
+import org.photonvision.common.dataflow.networktables.NetworkTablesManager;
 import org.photonvision.common.dataflow.websocket.UICameraConfiguration;
 import org.photonvision.common.dataflow.websocket.UIPhotonConfiguration;
 import org.photonvision.common.hardware.Platform;
@@ -311,16 +312,118 @@ public class VisionSourceManager {
                     .forEach(cameraInfos::add);
         }
 
-        // FileVisionSources are a bit quirky. They aren't enumerated by the above, but i still want my
+        // FileVisionSources are a bit quirky. They aren't enumerated by the above, but I still want my
         // UI to look like it ought to work
         vmm.getModules().stream()
                 .map(it -> it.getCameraConfiguration().matchedCameraInfo)
                 .filter(info -> info instanceof PVCameraInfo.PVFileCameraInfo)
                 .forEach(cameraInfos::add);
 
+        checkMismatches(cameraInfos);
+
         return cameraInfos;
     }
 
+    /**
+     * Check for mismatches between connected cameras and saved camera configurations.
+     *
+     * <p>Note that if the information for a camera spontaneously changes without it being
+     * disconnected/unplugged and reconnected/replugged, we may experience unexpected behavior.
+     *
+     * @param cameraInfos List of currently connected camera infos, checked against saved configs
+     */
+    protected void checkMismatches(List<PVCameraInfo> cameraInfos) {
+        // from the listed physical camera infos, match them to the camera configs and check for
+        // mismatches
+        for (VisionModule module : vmm.getModules()) {
+            PVCameraInfo matchedCameraInfo = module.getCameraConfiguration().matchedCameraInfo;
+            // We use unique paths to determine if the module has a camera in the port. If no unique path
+            // is found that matches the module, it's removed from the mismatched set as a disconnected
+            // camera cannot be mismatched.
+            if (!cameraInfos.stream()
+                    .map(PVCameraInfo::uniquePath)
+                    .toList()
+                    .contains(matchedCameraInfo.uniquePath())) {
+                module.mismatch = false;
+                continue;
+            }
+
+            for (PVCameraInfo info : cameraInfos) {
+                // if the unique path doesn't match, skip cause it's not in the same port
+                if (!matchedCameraInfo.uniquePath().equals(info.uniquePath())) {
+                    continue;
+                }
+
+                // If the camera info doesn't match, log an error
+                if (!matchedCameraInfo.equals(info) && !module.mismatch) {
+                    logger.error("Camera mismatch error!");
+                    logger.error("Camera config mismatch for " + matchedCameraInfo.name());
+                    logCameraInfoDiff(matchedCameraInfo, info);
+                    module.mismatch = true;
+                }
+            }
+        }
+
+        // Set the NetworkTables mismatch alert
+        if (vmm.getModules().stream().anyMatch(m -> m.mismatch)) {
+            NetworkTablesManager.getInstance()
+                    .setMismatchAlert(
+                            true,
+                            "Camera mismatch error! See logs for details. ("
+                                    + vmm.getModules().stream()
+                                            .filter(m -> m.mismatch)
+                                            .map(m -> m.getCameraConfiguration().nickname)
+                                            .toList()
+                                            .toString()
+                                            .replaceAll("[\\[\\]()]", "")
+                                    + " affected)");
+        } else {
+            NetworkTablesManager.getInstance().setMismatchAlert(false, "");
+        }
+    }
+
+    /** Log the differences between two PVCameraInfo objects. */
+    private static void logCameraInfoDiff(PVCameraInfo saved, PVCameraInfo current) {
+        String expected = "Expected: Name: " + saved.name();
+        String actual = "Actual: Name: " + current.name();
+        if (saved instanceof PVCameraInfo.PVCSICameraInfo savedCsi
+                && current instanceof PVCameraInfo.PVCSICameraInfo currentCsi) {
+            expected += " Base Name: " + savedCsi.baseName;
+            actual += " Base Name: " + currentCsi.baseName;
+        }
+
+        expected += " Type: " + saved.type().toString();
+        actual += " Type: " + current.type().toString();
+
+        if (saved instanceof PVCameraInfo.PVUsbCameraInfo savedUsb
+                && current instanceof PVCameraInfo.PVUsbCameraInfo currentUsb) {
+            expected +=
+                    " Device Number: "
+                            + savedUsb.dev
+                            + " Vendor ID: "
+                            + savedUsb.vendorId
+                            + " Product ID: "
+                            + savedUsb.productId;
+            actual +=
+                    " Device Number: "
+                            + currentUsb.dev
+                            + " Vendor ID: "
+                            + currentUsb.vendorId
+                            + " Product ID: "
+                            + currentUsb.productId;
+        }
+
+        expected += " Path: " + saved.path();
+        actual += " Path: " + current.path();
+        expected += " Unique Path: " + saved.uniquePath();
+        actual += " Unique Path: " + current.uniquePath();
+        expected += " Other Paths: " + Arrays.toString(saved.otherPaths());
+        actual += " Other Paths: " + Arrays.toString(current.otherPaths());
+
+        logger.error(expected);
+        logger.error(actual);
+    }
+
     private static List<PVCameraInfo> filterAllowedDevices(List<PVCameraInfo> allDevices) {
         Platform platform = Platform.getCurrentPlatform();
         ArrayList<PVCameraInfo> filteredDevices = new ArrayList<>();

photon-core/src/test/java/org/photonvision/hardware/HardwareConfigTest.java

@@ -34,10 +34,13 @@ public class HardwareConfigTest {
             System.out.println("Loading Hardware configs...");
             var config =
                     new ObjectMapper().readValue(TestUtils.getHardwareConfigJson(), HardwareConfig.class);
-            assertEquals(config.deviceName(), "PhotonVision");
-            assertEquals(config.deviceLogoPath(), "photonvision.png");
-            assertEquals(config.supportURL(), "https://support.photonvision.com");
-            assertArrayEquals(config.ledPins().stream().mapToInt(i -> i).toArray(), new int[] {2, 13});
+            assertEquals(config.deviceName, "PhotonVision");
+            assertEquals(config.deviceLogoPath, "photonvision.png");
+            assertEquals(config.supportURL, "https://support.photonvision.com");
+            // Ensure defaults are not null
+            assertEquals(config.cpuThrottleReasonCmd, "");
+            assertEquals(config.diskUsageCommand, "");
+            assertArrayEquals(config.ledPins.stream().mapToInt(i -> i).toArray(), new int[] {2, 13});
             CustomGPIO.setConfig(config);
 
         } catch (IOException e) {

photon-core/src/test/java/org/photonvision/hardware/HardwareTest.java

@@ -20,7 +20,6 @@ package org.photonvision.hardware;
 import static org.junit.jupiter.api.Assertions.assertFalse;
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
-import java.io.IOException;
 import org.junit.jupiter.api.Test;
 import org.photonvision.common.hardware.GPIO.CustomGPIO;
 import org.photonvision.common.hardware.GPIO.GPIOBase;
@@ -28,17 +27,11 @@ import org.photonvision.common.hardware.GPIO.pi.PigpioPin;
 import org.photonvision.common.hardware.Platform;
 import org.photonvision.common.hardware.metrics.MetricsManager;
 import org.photonvision.common.util.TestUtils;
-import org.photonvision.jni.PhotonTargetingJniLoader;
 
 public class HardwareTest {
     @Test
     public void testHardware() {
-        try {
-            TestUtils.loadLibraries();
-            PhotonTargetingJniLoader.load();
-        } catch (UnsatisfiedLinkError | IOException e) {
-            e.printStackTrace();
-        }
+        TestUtils.loadLibraries();
         MetricsManager mm = new MetricsManager();
 
         if (!Platform.isRaspberryPi()) return;

photon-core/src/test/java/org/photonvision/vision/frame/consumer/FileSaveFrameConsumerTest.java

@@ -41,24 +41,20 @@ import org.junitpioneer.jupiter.cartesian.CartesianTest.Values;
 import org.photonvision.common.configuration.ConfigManager;
 import org.photonvision.common.dataflow.networktables.NetworkTablesManager;
 import org.photonvision.common.util.TestUtils;
-import org.photonvision.jni.PhotonTargetingJniLoader;
-import org.photonvision.jni.WpilibLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.vision.frame.provider.FileFrameProvider;
 
 public class FileSaveFrameConsumerTest {
     NetworkTableInstance inst = null;
 
     @BeforeAll
-    public static void init() throws UnsatisfiedLinkError, IOException {
-        if (!WpilibLoader.loadLibraries()) {
+    public static void init() throws IOException {
+        if (!LibraryLoader.loadWpiLibraries()) {
             fail();
         }
 
-        try {
-            if (!PhotonTargetingJniLoader.load()) fail();
-        } catch (UnsatisfiedLinkError | IOException e) {
-            e.printStackTrace();
-            fail(e);
+        if (!LibraryLoader.loadTargeting()) {
+            fail();
         }
     }
 

photon-core/src/test/java/org/photonvision/vision/processes/VisionModuleManagerTest.java

@@ -22,7 +22,6 @@ import static org.junit.jupiter.api.Assertions.assertTrue;
 import static org.junit.jupiter.api.Assertions.fail;
 
 import edu.wpi.first.cscore.VideoMode;
-import java.io.IOException;
 import java.util.Arrays;
 import java.util.HashMap;
 import java.util.List;
@@ -32,7 +31,7 @@ import org.photonvision.common.configuration.CameraConfiguration;
 import org.photonvision.common.configuration.ConfigManager;
 import org.photonvision.common.dataflow.CVPipelineResultConsumer;
 import org.photonvision.common.util.TestUtils;
-import org.photonvision.jni.PhotonTargetingJniLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.vision.camera.PVCameraInfo;
 import org.photonvision.vision.camera.QuirkyCamera;
 import org.photonvision.vision.camera.USBCameras.USBCameraSource;
@@ -48,12 +47,7 @@ public class VisionModuleManagerTest {
         System.out.print(classpathStr);
 
         TestUtils.loadLibraries();
-        try {
-            if (!PhotonTargetingJniLoader.load()) fail();
-        } catch (UnsatisfiedLinkError | IOException e) {
-            e.printStackTrace();
-            fail(e);
-        }
+        if (!LibraryLoader.loadTargeting()) fail();
     }
 
     private static class TestSource extends VisionSource {

photon-core/src/test/java/org/photonvision/vision/processes/VisionSourceManagerTest.java

@@ -17,8 +17,8 @@
 
 package org.photonvision.vision.processes;
 
-import static org.junit.jupiter.api.Assertions.assertDoesNotThrow;
 import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
 import edu.wpi.first.cscore.UsbCameraInfo;
@@ -33,7 +33,6 @@ import org.photonvision.common.configuration.CameraConfiguration;
 import org.photonvision.common.configuration.ConfigManager;
 import org.photonvision.common.util.TestUtils;
 import org.photonvision.common.util.file.JacksonUtils;
-import org.photonvision.jni.PhotonTargetingJniLoader;
 import org.photonvision.vision.camera.PVCameraInfo;
 
 public class VisionSourceManagerTest {
@@ -59,9 +58,7 @@ public class VisionSourceManagerTest {
 
     @BeforeAll
     public static void loadLibraries() {
-        TestUtils.loadLibraries();
-        assertDoesNotThrow(PhotonTargetingJniLoader::load);
-        assertTrue(PhotonTargetingJniLoader.isWorking);
+        assertTrue(TestUtils.loadLibraries());
 
         // Broadcast all still calls into configmanager (ew) so set that up here
         ConfigManager.getInstance().load();
@@ -277,4 +274,55 @@ public class VisionSourceManagerTest {
         assertEquals(2, vsm.getVsmState().disabledConfigs.size());
         assertEquals(1, vsm.vmm.getModules().size());
     }
+
+    @Test
+    public void testMismatch() throws InterruptedException {
+        var vsm = new TestVsm();
+
+        // Create a saved camera configuration that expects a device at /dev/video0 with a name
+        PVCameraInfo savedInfo =
+                PVCameraInfo.fromUsbCameraInfo(
+                        new UsbCameraInfo(
+                                0, "/dev/video0", "CamA", new String[] {"/dev/v4l/by-path/1"}, 111, 222));
+        CameraConfiguration savedConf = new CameraConfiguration(savedInfo);
+        savedConf.deactivated = false;
+        savedConf.nickname = "SavedCam";
+
+        // Register the saved config so VSM creates a VisionModule
+        vsm.registerLoadedConfigs(List.of(savedConf));
+
+        // Now simulate a connected camera at same uniquePath but with a different name (mismatch)
+        List<PVCameraInfo> currentInfo =
+                List.of(
+                        PVCameraInfo.fromUsbCameraInfo(
+                                new UsbCameraInfo(
+                                        0,
+                                        "/dev/video0",
+                                        "CamDifferent",
+                                        new String[] {"/dev/v4l/by-path/1"},
+                                        111,
+                                        222)));
+
+        // Trigger state evaluation
+        vsm.checkMismatches(currentInfo);
+
+        // The module should have detected a mismatch
+        assertTrue(vsm.getVisionModules().stream().anyMatch(m -> m.mismatch));
+
+        // Now simulate the device being disconnected
+        currentInfo = List.of();
+        vsm.checkMismatches(currentInfo);
+
+        // Mismatch should be cleared when device is disconnected
+        assertFalse(vsm.getVisionModules().stream().anyMatch(m -> m.mismatch));
+
+        // Test with a matching camera info
+        currentInfo = List.of(savedInfo);
+        vsm.checkMismatches(currentInfo);
+
+        // The mismatch should be cleared
+        assertFalse(vsm.getVisionModules().stream().anyMatch(m -> m.mismatch));
+
+        vsm.teardown();
+    }
 }

photon-docs/build.gradle

@@ -39,13 +39,10 @@ doxygen {
     }
 }
 
-doxygen {
-    option 'generate_html', true
-    option 'html_extra_stylesheet', 'theme.css'
-
+doxygen.sourceSets.main {
     cppProjectZips.each {
-        dependsOn it
-        source it.source
+        doxygenDox.dependsOn it
+        sources it.source
         it.ext.includeDirs.each {
             cppIncludeRoots.add(it.absolutePath)
         }
@@ -100,7 +97,7 @@ tasks.register("zipCppDocs", Zip) {
 // Java
 configurations {
     javaSource {
-        transitive false
+        transitive = false
     }
 }
 
@@ -158,22 +155,22 @@ publishing {
             artifact zipJavaDocs
 
             artifactId = "${baseArtifactIdJava}"
-            groupId artifactGroupIdJava
-            version pubVersion
+            groupId = artifactGroupIdJava
+            version = pubVersion
         }
         cpp(MavenPublication) {
             artifact zipCppDocs
 
             artifactId = "${baseArtifactIdCpp}"
-            groupId artifactGroupIdCpp
-            version pubVersion
+            groupId = artifactGroupIdCpp
+            version = pubVersion
         }
     }
 
     repositories {
         maven {
             // Just throw everything into build/maven
-            url(localMavenURL)
+            url = localMavenURL
         }
     }
 }

photon-lib/build.gradle

@@ -282,7 +282,7 @@ if (!project.hasProperty('copyOfflineArtifacts')) {
 
                 artifactId = "${nativeName}-json"
                 groupId = "org.photonvision"
-                version "1.0"
+                version = "1.0"
             }
         }
     }
@@ -344,8 +344,8 @@ publishing {
                 artifact combinedHeadersZip
 
                 artifactId = "${nativeName}-combinedcpp"
-                groupId artifactGroupId
-                version pubVersion
+                groupId = artifactGroupId
+                version = pubVersion
             }
         }
     }
@@ -363,6 +363,9 @@ def nativeTasks = wpilibTools.createExtractionTasks {
 
 nativeTasks.addToSourceSetResources(sourceSets.test)
 
+dependencies {
+    wpilibNatives project(":photon-targeting")
+}
 nativeConfig.dependencies.add wpilibTools.deps.wpilib("wpimath")
 nativeConfig.dependencies.add wpilibTools.deps.wpilib("wpinet")
 nativeConfig.dependencies.add wpilibTools.deps.wpilib("wpiutil")

photon-lib/src/main/java/org/photonvision/timesync/TimeSyncSingleton.java

@@ -24,8 +24,8 @@
 
 package org.photonvision.timesync;
 
+import edu.wpi.first.util.RuntimeLoader;
 import java.io.IOException;
-import org.photonvision.jni.PhotonTargetingJniLoader;
 import org.photonvision.jni.TimeSyncServer;
 
 /** Helper to hold a single TimeSyncServer instance with some default config */
@@ -35,12 +35,11 @@ public class TimeSyncSingleton {
     public static boolean load() {
         if (INSTANCE == null) {
             try {
-                if (!PhotonTargetingJniLoader.load()) {
-                    return false;
-                }
-            } catch (UnsatisfiedLinkError | IOException e) {
+                RuntimeLoader.loadLibrary("photontargetingJNI");
+            } catch (IOException e) {
+                // Don't want to return early. We want to create the TimeSyncServer so the program crashes
+                // because we need it in order to function.
                 e.printStackTrace();
-                return false;
             }
 
             INSTANCE = new TimeSyncServer(5810);

photon-lib/src/test/java/org/photonvision/PhotonCameraTest.java

@@ -35,6 +35,7 @@ import edu.wpi.first.hal.HAL;
 import edu.wpi.first.math.geometry.Rotation2d;
 import edu.wpi.first.networktables.NetworkTableInstance;
 import edu.wpi.first.networktables.NetworkTablesJNI;
+import edu.wpi.first.util.RuntimeLoader;
 import edu.wpi.first.wpilibj.DataLogManager;
 import edu.wpi.first.wpilibj.Timer;
 import edu.wpi.first.wpilibj.simulation.SimHooks;
@@ -55,9 +56,8 @@ import org.junit.jupiter.params.ParameterizedTest;
 import org.junit.jupiter.params.provider.Arguments;
 import org.junit.jupiter.params.provider.MethodSource;
 import org.photonvision.common.dataflow.structures.Packet;
-import org.photonvision.jni.PhotonTargetingJniLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.jni.TimeSyncClient;
-import org.photonvision.jni.WpilibLoader;
 import org.photonvision.simulation.PhotonCameraSim;
 import org.photonvision.targeting.PhotonPipelineMetadata;
 import org.photonvision.targeting.PhotonPipelineResult;
@@ -68,8 +68,9 @@ class PhotonCameraTest {
     NetworkTableInstance inst = null;
 
     @BeforeAll
-    public static void load_wpilib() {
-        WpilibLoader.loadLibraries();
+    public static void load() throws IOException {
+        LibraryLoader.loadWpiLibraries();
+        RuntimeLoader.loadLibrary("photontargetingJNI");
     }
 
     @BeforeEach
@@ -111,9 +112,6 @@ class PhotonCameraTest {
     @Test
     @Order(3)
     public void testTimeSyncServerWithPhotonCamera() throws InterruptedException, IOException {
-        load_wpilib();
-        PhotonTargetingJniLoader.load();
-
         inst.stopClient();
         inst.startServer();
 

photon-lib/src/test/java/org/photonvision/PhotonPoseEstimatorTest.java

@@ -48,6 +48,7 @@ import edu.wpi.first.math.geometry.Transform3d;
 import edu.wpi.first.math.geometry.Translation2d;
 import edu.wpi.first.math.geometry.Translation3d;
 import edu.wpi.first.math.util.Units;
+import edu.wpi.first.util.RuntimeLoader;
 import java.io.IOException;
 import java.util.ArrayList;
 import java.util.List;
@@ -59,8 +60,7 @@ import org.junit.jupiter.api.Test;
 import org.photonvision.PhotonPoseEstimator.ConstrainedSolvepnpParams;
 import org.photonvision.PhotonPoseEstimator.PoseStrategy;
 import org.photonvision.estimation.TargetModel;
-import org.photonvision.jni.PhotonTargetingJniLoader;
-import org.photonvision.jni.WpilibLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.simulation.PhotonCameraSim;
 import org.photonvision.simulation.SimCameraProperties;
 import org.photonvision.simulation.VisionTargetSim;
@@ -76,13 +76,11 @@ class PhotonPoseEstimatorTest {
     @AutoClose final PhotonCameraInjector cameraOne = new PhotonCameraInjector();
 
     @BeforeAll
-    public static void init() throws UnsatisfiedLinkError, IOException {
-        if (!WpilibLoader.loadLibraries()) {
-            fail();
-        }
-        if (!PhotonTargetingJniLoader.load()) {
+    public static void init() throws IOException {
+        if (!LibraryLoader.loadWpiLibraries()) {
             fail();
         }
+        RuntimeLoader.loadLibrary("photontargetingJNI");
 
         HAL.initialize(1000, 0);
 

photon-lib/src/test/java/org/photonvision/VisionSystemSimTest.java

@@ -28,7 +28,6 @@ import static org.junit.jupiter.api.Assertions.assertDoesNotThrow;
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertFalse;
 import static org.junit.jupiter.api.Assertions.assertTrue;
-import static org.junit.jupiter.api.Assertions.fail;
 import static org.photonvision.UnitTestUtils.waitForSequenceNumber;
 
 import edu.wpi.first.apriltag.AprilTag;
@@ -44,6 +43,7 @@ import edu.wpi.first.math.geometry.Translation2d;
 import edu.wpi.first.math.geometry.Translation3d;
 import edu.wpi.first.math.util.Units;
 import edu.wpi.first.networktables.NetworkTableInstance;
+import edu.wpi.first.util.RuntimeLoader;
 import edu.wpi.first.wpilibj.smartdashboard.SmartDashboard;
 import java.io.IOException;
 import java.util.ArrayList;
@@ -59,8 +59,7 @@ import org.junit.jupiter.params.provider.MethodSource;
 import org.junit.jupiter.params.provider.ValueSource;
 import org.photonvision.estimation.TargetModel;
 import org.photonvision.estimation.VisionEstimation;
-import org.photonvision.jni.PhotonTargetingJniLoader;
-import org.photonvision.jni.WpilibLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.simulation.PhotonCameraSim;
 import org.photonvision.simulation.VisionSystemSim;
 import org.photonvision.simulation.VisionTargetSim;
@@ -72,15 +71,9 @@ class VisionSystemSimTest {
     NetworkTableInstance inst;
 
     @BeforeAll
-    public static void setUp() {
-        assertTrue(WpilibLoader.loadLibraries());
-
-        try {
-            assertTrue(PhotonTargetingJniLoader.load());
-        } catch (UnsatisfiedLinkError | IOException e) {
-            e.printStackTrace();
-            fail(e);
-        }
+    public static void setUp() throws IOException {
+        assertTrue(LibraryLoader.loadWpiLibraries());
+        RuntimeLoader.loadLibrary("photontargetingJNI");
 
         OpenCvLoader.forceStaticLoad();
     }

photon-server/build.gradle

@@ -14,8 +14,8 @@ dependencies {
     implementation "org.slf4j:slf4j-simple:2.0.7"
 }
 
-group 'org.photonvision'
-version versionString + (project.hasProperty('pionly') ? "-raspi" : "")
+group = 'org.photonvision'
+version  = versionString + (project.hasProperty('pionly') ? "-raspi" : "")
 
 application {
     mainClass = 'org.photonvision.Main'

photon-server/src/main/java/org/photonvision/Main.java

@@ -38,7 +38,7 @@ import org.photonvision.common.logging.Logger;
 import org.photonvision.common.logging.PvCSCoreLogger;
 import org.photonvision.common.networking.NetworkManager;
 import org.photonvision.common.util.TestUtils;
-import org.photonvision.jni.PhotonTargetingJniLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.jni.RknnDetectorJNI;
 import org.photonvision.jni.RubikDetectorJNI;
 import org.photonvision.mrcal.MrCalJNILoader;
@@ -205,7 +205,7 @@ public class Main {
         logger.info("WPI JNI libraries loaded.");
 
         try {
-            boolean success = PhotonTargetingJniLoader.load();
+            boolean success = LibraryLoader.loadTargeting();
 
             if (!success) {
                 logger.error("Failed to load native libraries! Giving up :(");

photon-targeting/build.gradle

@@ -23,20 +23,6 @@ nativeUtils {
 
 sourceSets.main.java.srcDir "${projectDir}/src/generated/main/java"
 
-// Folder whose contents will be included in the final jar
-def outputsFolder = file("$buildDir/extra_resources")
-
-// Sync task: like the copy task, but all files that exist in the destination directory will be deleted before copying files
-task syncOutputsFolder(type: Sync) {
-    into outputsFolder
-}
-
-// And package our outputs folder into the final jar
-jar {
-    from outputsFolder
-    dependsOn syncOutputsFolder
-}
-
 model {
     components {
         "${nativeName}"(NativeLibrarySpec) {
@@ -115,21 +101,6 @@ model {
                     platName = "osxuniversal";
                     realWpilibName = "osxuniversal";
                 }
-
-                if (binary.targetPlatform.name == platName) {
-                    // only include release binaries (hard coded for now)
-                    def isDebug = binary.buildType.name.contains('debug')
-                    if (!isDebug) {
-                        syncOutputsFolder {
-                            // Just shove the shared library into the root of the jar output by photon-targeting:jar
-                            from(binary.sharedLibraryFile) {
-                                into "nativelibraries/${realWpilibName}/"
-                            }
-                            // And (not sure if this is a hack) make the jar task depend on the build task
-                            dependsOn binary.identifier.projectScopedName
-                        }
-                    }
-                }
             }
         }
     }
@@ -202,27 +173,6 @@ model {
 
 apply from: "${rootDir}/shared/javacpp/publish.gradle"
 
-// quickly hack our raw jar into publishing
-def rawjavaJar = tasks.register("rawjavaJar", Jar) {
-    dependsOn classes
-    includeEmptyDirs = false
-    from sourceSets.main.output
-    archiveClassifier = 'raw'
-}
-
-publishing {
-    publications {
-        rawjava(MavenPublication) {
-            artifact (rawjavaJar) {
-                classifier = null
-            }
-            artifactId = "${nativeName}-rawjava"
-            groupId artifactGroupId
-            version pubVersion
-        }
-    }
-}
-
 // Add photon serde headers to our published sources
 cppHeadersZip {
     from('src/generated/main/native/include') {
@@ -230,12 +180,6 @@ cppHeadersZip {
     }
 }
 
-// make sure native libraries can be loaded in tests
-test {
-    classpath += files(outputsFolder)
-    dependsOn syncOutputsFolder
-}
-
 // setup wpilib bundled native libs
 wpilibTools.deps.wpilibVersion = wpi.versions.wpilibVersion.get()
 

photon-targeting/src/main/java/org/photonvision/jni/LibraryLoader.java

@@ -29,11 +29,12 @@ import edu.wpi.first.util.WPIUtilJNI;
 import java.io.IOException;
 import org.opencv.core.Core;
 
-public class WpilibLoader {
-    private static boolean has_loaded = false;
+public class LibraryLoader {
+    private static boolean hasWpiLoaded = false;
+    private static boolean hasTargetingLoaded = false;
 
-    public static boolean loadLibraries() {
-        if (has_loaded) return true;
+    public static boolean loadWpiLibraries() {
+        if (hasWpiLoaded) return true;
 
         NetworkTablesJNI.Helper.setExtractOnStaticLoad(false);
         WPIUtilJNI.Helper.setExtractOnStaticLoad(false);
@@ -45,10 +46,10 @@ public class WpilibLoader {
         AprilTagJNI.Helper.setExtractOnStaticLoad(false);
         try {
             // Need to load wpiutil first before checking if the MSVC runtime is valid
-            CombinedRuntimeLoader.loadLibraries(WpilibLoader.class, "wpiutiljni");
+            CombinedRuntimeLoader.loadLibraries(LibraryLoader.class, "wpiutiljni");
             WPIUtilJNI.checkMsvcRuntime();
             CombinedRuntimeLoader.loadLibraries(
-                    WpilibLoader.class,
+                    LibraryLoader.class,
                     "wpimathjni",
                     "ntcorejni",
                     "wpinetjni",
@@ -56,13 +57,25 @@ public class WpilibLoader {
                     "cscorejni",
                     "apriltagjni");
 
-            CombinedRuntimeLoader.loadLibraries(WpilibLoader.class, Core.NATIVE_LIBRARY_NAME);
-            has_loaded = true;
+            CombinedRuntimeLoader.loadLibraries(LibraryLoader.class, Core.NATIVE_LIBRARY_NAME);
+            hasWpiLoaded = true;
         } catch (IOException e) {
             e.printStackTrace();
-            has_loaded = false;
+            hasWpiLoaded = false;
         }
 
-        return has_loaded;
+        return hasWpiLoaded;
+    }
+
+    public static boolean loadTargeting() {
+        if (hasTargetingLoaded) return true;
+        try {
+            CombinedRuntimeLoader.loadLibraries(LibraryLoader.class, "photontargetingJNI");
+            hasTargetingLoaded = true;
+        } catch (IOException e) {
+            e.printStackTrace();
+            hasTargetingLoaded = false;
+        }
+        return hasTargetingLoaded;
     }
 }

photon-targeting/src/main/java/org/photonvision/jni/PhotonTargetingJniLoader.java

@@ -1,88 +0,0 @@
-/*
- * Copyright (C) Photon Vision.
- *
- * This program is free software: you can redistribute it and/or modify
- * it under the terms of the GNU General Public License as published by
- * the Free Software Foundation, either version 3 of the License, or
- * (at your option) any later version.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- * GNU General Public License for more details.
- *
- * You should have received a copy of the GNU General Public License
- * along with this program.  If not, see <https://www.gnu.org/licenses/>.
- */
-
-package org.photonvision.jni;
-
-import edu.wpi.first.util.RuntimeLoader;
-import java.io.File;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.nio.file.Files;
-import java.util.List;
-import org.photonvision.common.hardware.Platform;
-
-public class PhotonTargetingJniLoader {
-    public static boolean isWorking = false;
-
-    public static boolean load() throws IOException, UnsatisfiedLinkError {
-        if (isWorking) return true;
-        isWorking = load_();
-        return isWorking;
-    }
-
-    public static boolean load_() throws IOException, UnsatisfiedLinkError {
-        // We always extract the shared object (we could hash each so, but that's a lot
-        // of work)
-        String arch_name = Platform.getNativeLibraryFolderName();
-        var clazz = PhotonTargetingJniLoader.class;
-
-        for (var libraryName : List.of("photontargeting", "photontargetingJNI")) {
-            try {
-                RuntimeLoader.loadLibrary(libraryName);
-                continue;
-            } catch (Exception e) {
-                System.out.println("Direct library load failed; falling back to extraction");
-            }
-
-            var nativeLibName = System.mapLibraryName(libraryName);
-            var path = "/nativelibraries/" + arch_name + "/" + nativeLibName;
-            var in = clazz.getResourceAsStream(path);
-
-            if (in == null) {
-                System.err.println("Could not get resource at path " + path);
-                return false;
-            }
-
-            // It's important that we don't mangle the names of these files on Windows at
-            // least
-            var tempfolder = Files.createTempDirectory("nativeextract");
-            File temp = new File(tempfolder.toAbsolutePath().toString(), nativeLibName);
-            System.out.println(temp.getAbsolutePath().toString());
-            FileOutputStream fos = new FileOutputStream(temp);
-
-            int read = -1;
-            byte[] buffer = new byte[1024];
-            while ((read = in.read(buffer)) != -1) {
-                fos.write(buffer, 0, read);
-            }
-            fos.close();
-            in.close();
-
-            try {
-                System.load(temp.getAbsolutePath());
-            } catch (Throwable t) {
-                System.err.println("Unable to System.load " + temp.getName() + " : " + t.getMessage());
-                t.printStackTrace();
-                return false;
-            }
-
-            System.out.println("Successfully loaded shared object " + temp.getName());
-        }
-
-        return true;
-    }
-}

photon-targeting/src/test/java/ConstrainedSolvepnpTest.java

@@ -21,24 +21,22 @@ import static org.junit.jupiter.api.Assertions.fail;
 import edu.wpi.first.hal.HAL;
 import edu.wpi.first.math.MatBuilder;
 import edu.wpi.first.math.Nat;
+import edu.wpi.first.util.RuntimeLoader;
 import java.io.IOException;
 import java.util.Arrays;
 import org.junit.jupiter.api.AfterAll;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
 import org.photonvision.jni.ConstrainedSolvepnpJni;
-import org.photonvision.jni.PhotonTargetingJniLoader;
-import org.photonvision.jni.WpilibLoader;
+import org.photonvision.jni.LibraryLoader;
 
 public class ConstrainedSolvepnpTest {
     @BeforeAll
-    public static void load_wpilib() throws UnsatisfiedLinkError, IOException {
-        if (!WpilibLoader.loadLibraries()) {
-            fail();
-        }
-        if (!PhotonTargetingJniLoader.load()) {
+    public static void load() throws IOException {
+        if (!LibraryLoader.loadWpiLibraries()) {
             fail();
         }
+        RuntimeLoader.loadLibrary("photontargetingJNI");
 
         HAL.initialize(1000, 0);
     }

photon-targeting/src/test/java/jni/CscoreExtrasTest.java

@@ -28,23 +28,21 @@ import edu.wpi.first.cscore.UsbCamera;
 import edu.wpi.first.hal.HAL;
 import edu.wpi.first.util.PixelFormat;
 import edu.wpi.first.util.RawFrame;
+import edu.wpi.first.util.RuntimeLoader;
 import java.io.IOException;
 import org.junit.jupiter.api.AfterAll;
 import org.junit.jupiter.api.BeforeAll;
 import org.opencv.core.Mat;
 import org.photonvision.jni.CscoreExtras;
-import org.photonvision.jni.PhotonTargetingJniLoader;
-import org.photonvision.jni.WpilibLoader;
+import org.photonvision.jni.LibraryLoader;
 
 public class CscoreExtrasTest {
     @BeforeAll
-    public static void load_wpilib() throws UnsatisfiedLinkError, IOException {
-        if (!WpilibLoader.loadLibraries()) {
-            fail();
-        }
-        if (!PhotonTargetingJniLoader.load()) {
+    public static void load() throws IOException {
+        if (!LibraryLoader.loadWpiLibraries()) {
             fail();
         }
+        RuntimeLoader.loadLibrary("photontargetingJNI");
 
         HAL.initialize(1000, 0);
     }

photon-targeting/src/test/java/jni/FileLoggerTest.java

@@ -21,24 +21,22 @@ import static org.junit.jupiter.api.Assertions.fail;
 import static org.junit.jupiter.api.Assumptions.assumeTrue;
 
 import edu.wpi.first.hal.HAL;
+import edu.wpi.first.util.RuntimeLoader;
 import java.io.IOException;
 import org.junit.jupiter.api.AfterAll;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
 import org.photonvision.common.hardware.Platform;
-import org.photonvision.jni.PhotonTargetingJniLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.jni.QueuedFileLogger;
-import org.photonvision.jni.WpilibLoader;
 
 public class FileLoggerTest {
     @BeforeAll
-    public static void load_wpilib() throws UnsatisfiedLinkError, IOException {
-        if (!WpilibLoader.loadLibraries()) {
-            fail();
-        }
-        if (!PhotonTargetingJniLoader.load()) {
+    public static void load() throws IOException {
+        if (!LibraryLoader.loadWpiLibraries()) {
             fail();
         }
+        RuntimeLoader.loadLibrary("photontargetingJNI");
 
         HAL.initialize(1000, 0);
     }

photon-targeting/src/test/java/net/TimeSyncTest.java

@@ -17,25 +17,21 @@
 
 package net;
 
-import static org.junit.jupiter.api.Assertions.fail;
-
 import edu.wpi.first.hal.HAL;
+import edu.wpi.first.util.RuntimeLoader;
 import java.io.IOException;
 import org.junit.jupiter.api.AfterAll;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
-import org.photonvision.jni.PhotonTargetingJniLoader;
+import org.photonvision.jni.LibraryLoader;
 import org.photonvision.jni.TimeSyncClient;
 import org.photonvision.jni.TimeSyncServer;
-import org.photonvision.jni.WpilibLoader;
 
 public class TimeSyncTest {
     @BeforeAll
-    public static void load_wpilib() throws UnsatisfiedLinkError, IOException {
-        WpilibLoader.loadLibraries();
-        if (!PhotonTargetingJniLoader.load()) {
-            fail();
-        }
+    public static void load() throws IOException {
+        LibraryLoader.loadWpiLibraries();
+        RuntimeLoader.loadLibrary("photontargetingJNI");
 
         HAL.initialize(1000, 0);
     }

scripts/rknn-convert-tool/MAINTAINERS.md

@@ -1,7 +0,0 @@
-# Notebook
-
-In the first cell of the RKNN conversion notebook, the installation script uses a structured list of dictionaries to define the download URLs and filenames for required scripts. Each dictionary includes a `url` (a permalink to a specific commit) and the corresponding `filename`.
-
-Please ensure that all URLs in this array use permalinks—that is, links pointing to a specific commit hash rather than a branch name (e.g., main). This guarantees that the correct version of each script is always fetched, and prevents unexpected changes if the repository is updated in the future.
-
-You typically won’t need to update these permalinks unless one of the referenced scripts is modified. In that case, update the commit hash in the URLs accordingly.

scripts/rknn-convert-tool/create_onnx.py

@@ -1,179 +0,0 @@
-import argparse
-import os.path
-import subprocess
-import sys
-
-# This will work for all models that don't use anchors (e.g. all YOLO models except YOLOv5/v7)
-# This includes YOLOv5u
-yolo_non_anchor_repo = "https://github.com/airockchip/ultralytics_yolo11"
-
-# For original YOLOv5 models
-yolov5_repo = "https://github.com/airockchip/yolov5"
-
-valid_yolo_versions = ["yolov5", "yolov8", "yolov11"]
-comma_sep_yolo_versions = ", ".join(valid_yolo_versions)
-
-ultralytics_folder_name_yolov5 = "airockchip_yolo_pkg_yolov5"
-ultralytics_default_folder_name = "airockchip_yolo_pkg"
-
-bad_model_msg = """
-This is usually due to passing in the wrong model version.
-Please make sure you have the right model version and try again.
-"""
-
-
-def print_bad_model_msg(cause):
-    print(f"{cause}{bad_model_msg}")
-
-
-def run_and_exit_with_error(cmd, error_msg, enable_error_output=True):
-    try:
-        if enable_error_output:
-            subprocess.run(
-                cmd,
-                stderr=subprocess.STDOUT,
-                stdout=subprocess.PIPE,
-                universal_newlines=True,
-            ).check_returncode()
-        else:
-            subprocess.run(cmd).check_returncode()
-    except subprocess.CalledProcessError as e:
-        print(error_msg)
-
-        if enable_error_output:
-            print(e.stdout)
-
-        sys.exit(1)
-
-
-def check_git_installed():
-    run_and_exit_with_error(
-        ["git", "--version"],
-        """Git is not installed or not found in your PATH.
-Please install Git from https://git-scm.com/downloads and try again.""",
-    )
-
-
-def check_or_clone_rockchip_repo(repo_url, repo_name=ultralytics_default_folder_name):
-    if os.path.exists(repo_name):
-        print(
-            f'Existing Rockchip repo "{repo_name}" detected, skipping installation...'
-        )
-    else:
-        print(f'Cloning Rockchip repo to "{repo_name}"')
-        run_and_exit_with_error(
-            ["git", "clone", repo_url, repo_name],
-            "Failed to clone Rockchip repo, please see error output",
-        )
-
-
-def run_pip_install_or_else_exit(args):
-    print("Running pip install...")
-    run_and_exit_with_error(
-        ["pip", "install"] + args,
-        "Pip install rockchip repo failed, please see error output",
-    )
-
-
-def run_onnx_conversion_yolov5(model_path):
-    check_or_clone_rockchip_repo(yolov5_repo, ultralytics_folder_name_yolov5)
-    run_pip_install_or_else_exit(
-        [
-            "-r",
-            os.path.join(ultralytics_folder_name_yolov5, "requirements.txt"),
-            "torch<2.6.0",
-            "onnx",
-        ]
-    )
-
-    model_abspath = os.path.abspath(model_path)
-
-    try:
-        subprocess.run(
-            [
-                "python",
-                f"{ultralytics_folder_name_yolov5}/export.py",
-                "--weights",
-                model_abspath,
-                "--rknpu",
-                "--include",
-                "onnx",
-            ],
-            stderr=subprocess.STDOUT,
-            stdout=subprocess.PIPE,
-            universal_newlines=True,
-        ).check_returncode()
-    except subprocess.CalledProcessError as e:
-        print("Failed to run YOLOv5 export, please see error output")
-
-        if "ModuleNotFoundError" in e.stdout and "ultralytics" in e.stdout:
-            print_bad_model_msg(
-                "It seems the YOLOv5 repo could not find an ultralytics installation."
-            )
-        elif "AttributeError" in e.stdout and "_register_detect_seperate" in e.stdout:
-            print_bad_model_msg("It seems that you received a model attribute error.")
-        else:
-            print("Unknown Error when converting:")
-            print(e.stdout)
-
-        sys.exit(1)
-
-
-def run_onnx_conversion_no_anchor(model_path):
-    check_or_clone_rockchip_repo(yolo_non_anchor_repo)
-    run_pip_install_or_else_exit(["-e", ultralytics_default_folder_name, "onnx"])
-
-    sys.path.insert(0, os.path.abspath(ultralytics_default_folder_name))
-    model_abs_path = os.path.abspath(model_path)
-
-    from ultralytics import YOLO
-
-    try:
-        model = YOLO(model_abs_path)
-        model.export(format="rknn")
-    except TypeError as e:
-        if "originally trained" in str(e):
-            print_bad_model_msg(
-                "Ultralytics has detected that this model is a YOLOv5 model."
-            )
-        else:
-            raise e
-
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Generate valid ONNX file for yolo model"
-    )
-
-    parser.add_argument(
-        "-m",
-        "--model_path",
-        required=True,
-        help=(f"Path to YOLO model"),
-    )
-
-    parser.add_argument(
-        "-v",
-        "--version",
-        required=True,
-        choices=valid_yolo_versions,
-        help=(f"Model version, must be one of: {comma_sep_yolo_versions}"),
-    )
-
-    args = parser.parse_args()
-
-    check_git_installed()
-
-    try:
-        if args.version.lower() == "yolov5":
-            run_onnx_conversion_yolov5(args.model_path)
-        else:
-            run_onnx_conversion_no_anchor(args.model_path)
-
-        print(
-            "Model export finished. Please use the generated ONNX file to convert to RKNN."
-        )
-    except SystemExit:
-        print("Model export failed. Please see output above.")

scripts/rknn-convert-tool/create_rknn.py

@@ -1,221 +0,0 @@
-import argparse
-import os
-import random
-import sys
-
-from rknn.api import RKNN
-
-image_extensions = (".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff", ".webp")
-DEFAULT_PLATFORM = "rk3588"
-
-
-def list_img_dir(img_dir):
-    return [
-        os.path.abspath(os.path.join(img_dir, f))
-        for f in os.listdir(img_dir)
-        if f.lower().endswith(image_extensions)
-    ]
-
-
-def sample_imgs(num, img_list):
-    if len(img_list) < num:
-        return img_list
-    else:
-        return random.sample(img_list, num)
-
-
-def get_image_list_from_dataset(num_imgs, yaml_dir):
-    print(f"Dataset detected with {yaml_dir} file")
-    img_raw_paths = []
-
-    with open(yaml_dir, "r") as yaml_file:
-        for line in yaml_file:
-            line = line.strip()
-            if (
-                line.startswith("train:")
-                or line.startswith("val:")
-                or line.startswith("test:")
-            ):
-                img_raw_paths.append(line.split(":", 1)[1].strip())
-
-    no_yaml_dir = yaml_dir.replace(
-        "data.yaml", "dummy_dir"
-    )  # data.yaml sets dirs one level up
-    img_set_paths = []
-
-    for img_raw_path in img_raw_paths:
-        p = (
-            img_raw_path
-            if os.path.isabs(img_raw_path)
-            else os.path.realpath(os.path.join(no_yaml_dir, img_raw_path))
-        )
-
-        if os.path.exists(p):
-            img_set_paths.append(p)
-
-    if len(img_set_paths) < 1:
-        return None
-
-    all_imgs = [list_img_dir(path) for path in img_set_paths]
-
-    for imgs in all_imgs:
-        print(len(imgs))
-
-    total_imgs = sum(len(group) for group in all_imgs)
-
-    sampled_imgs = [
-        sample_imgs(round((len(group) / total_imgs) * num_imgs), group)
-        for group in all_imgs
-    ]
-
-    return [img for group in sampled_imgs for img in group]
-
-
-def get_image_list_from_img_dir(num_imgs, img_dir):
-    return sample_imgs(num_imgs, list_img_dir(img_dir))
-
-
-def get_image_list(num_imgs, image_dir):
-    yaml_path = os.path.join(image_dir, "data.yaml")
-
-    if os.path.exists(yaml_path):
-        return get_image_list_from_dataset(num_imgs, yaml_path)
-    else:
-        return get_image_list_from_img_dir(num_imgs, image_dir)
-
-
-def run_rknn_conversion(
-    img_list_txt, disable_quant, model_path, rknn_output, verbose_logging
-):
-    rknn = RKNN(
-        verbose=verbose_logging,
-        verbose_file=("rknn_convert.log" if verbose_logging else None),
-    )
-
-    rknn.config(
-        mean_values=[[0, 0, 0]],
-        std_values=[[255, 255, 255]],
-        target_platform=DEFAULT_PLATFORM,
-    )
-
-    print("Attempted RKNN load")
-    ret = rknn.load_onnx(model=model_path)
-    if ret != 0:
-        print("Loading model failed!")
-        exit(ret)
-
-    print("Attempting RKNN build")
-    ret = rknn.build(do_quantization=(not disable_quant), dataset=img_list_txt)
-    if ret != 0:
-        print("Building model failed!")
-        exit(ret)
-
-    print("Build succeeded! Starting export...")
-    ret = rknn.export_rknn(rknn_output)
-    if ret != 0:
-        print("Exporting model failed!")
-        exit(ret)
-    print("Finished export!")
-
-    # Release
-    rknn.release()
-
-    print(f'Your model is in "{rknn_output}" and ready to use!')
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Generate valid ONNX file for yolo model"
-    )
-
-    parser.add_argument(
-        "-ni",
-        "--num_imgs",
-        type=int,
-        default=300,
-        help="Number of images to use for calibration (default: 300)",
-    )
-
-    parser.add_argument(
-        "-d",
-        "--img_dir",
-        help="Directory where your dataset is located (must have data.yaml), or images are located",
-    )
-
-    parser.add_argument(
-        "-m",
-        "--model_path",
-        required=True,
-        help=(f"Path to generated ONNX model"),
-    )
-
-    parser.add_argument(
-        "-dq",
-        "--disable_quantize",
-        action="store_true",
-        help="Whether to skip quantization (default: False)",
-    )
-
-    parser.add_argument(
-        "-o",
-        "--rknn_output",
-        default="out.rknn",
-        help="Where the rknn model should be outputted (default: ./out.rknn)",
-    )
-
-    parser.add_argument(
-        "-ds",
-        "--img_dataset_txt",
-        default="imgs.txt",
-        help="Where the list of images used for quantization should be outputted (default: ./imgs.txt)",
-    )
-
-    parser.add_argument(
-        "-vb",
-        "--verbose",
-        action="store_true",
-        help="Whether to enable verbose logging",
-    )
-
-    args = parser.parse_args()
-
-    if not args.rknn_output.endswith(".rknn"):
-        print("RKNN output path must end in .rknn!")
-        sys.exit(1)
-
-    if not args.disable_quantize:
-        if args.img_dir == None or len(args.img_dir) < 1:
-            print(f"Must specify list of images to use with --img_dir")
-            sys.exit(1)
-
-        img_dir_abs = os.path.abspath(args.img_dir)
-
-        img_list = get_image_list(args.num_imgs, img_dir_abs)
-        img_list_len = 0 if img_list is None else len(img_list)
-
-        if img_list_len == 0:
-            print(f"No images found in {img_dir_abs}")
-            sys.exit(1)
-        elif img_list_len < args.num_imgs:
-            print(
-                f"Not enough images in your dataset/directory, you have {img_list_len} images, but need {args.num_imgs}"
-            )
-            sys.exit(1)
-
-        if not args.img_dataset_txt.endswith(".txt"):
-            print(f"Image dataset text file path must end in .txt")
-            sys.exit(1)
-
-        with open(args.img_dataset_txt, "w") as set_file:
-            set_file.writelines(f"{img}\n" for img in img_list)
-
-    try:
-        run_rknn_conversion(
-            args.img_dataset_txt,
-            args.disable_quantize,
-            args.model_path,
-            args.rknn_output,
-            args.verbose,
-        )
-    except SystemExit:
-        print("RKNN Conversion failed, see output above")

scripts/rknn-convert-tool/rknn_conversion.ipynb

@@ -1,310 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bb5367ce",
-   "metadata": {},
-   "source": [
-    "# RKNN Conversion Guide\n",
-    "\n",
-    "----------------------------\n",
-    "\n",
-    "### Before you start\n",
-    "\n",
-    "If you are not using Google Colab, it is recommended to create a separate [Python virtual environment](https://docs.python.org/3/library/venv.html) before you run the scripts or the Python notebook from this project. This ensures that packages installed for the conversion process do not conflict with other packages you may already have setup."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f42d0a144caceb6",
-   "metadata": {},
-   "source": [
-    "### Preinstallation\n",
-    "\n",
-    "This notebook requires the use of external Python scripts. Please run the installation script below to import these external scripts if you do not have them already."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7903189e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import subprocess\n",
-    "\n",
-    "# Define scripts with URLs and inferred filenames\n",
-    "# DO NOT modify the filenames\n",
-    "scripts = [\n",
-    "    {\n",
-    "        \"url\": \"https://raw.githubusercontent.com/PhotonVision/photonvision/2bf166bc3f377fa8af9d9d38ee46e0db978a4036/scripts/rknn-convert-tool/create_onnx.py\",\n",
-    "        \"filename\": \"create_onnx.py\"  # CREATE_ONNX_SCRIPT\n",
-    "    },\n",
-    "    {\n",
-    "        \"url\": \"https://raw.githubusercontent.com/PhotonVision/photonvision/2bf166bc3f377fa8af9d9d38ee46e0db978a4036/scripts/rknn-convert-tool/create_rknn.py\",\n",
-    "        \"filename\": \"create_rknn.py\"  # CREATE_RKNN_SCRIPT\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "# Download each script\n",
-    "for script in scripts:\n",
-    "    try:\n",
-    "        subprocess.run([\"wget\", script[\"url\"], \"-O\", script[\"filename\"]]).check_returncode()\n",
-    "        print(f\"Successfully downloaded: {script['filename']}\")\n",
-    "    except subprocess.CalledProcessError as e:\n",
-    "        print(f\"Failed to download script from URL: {script['url']}\")\n",
-    "        print(e)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d68be4aba4d3022b",
-   "metadata": {},
-   "source": [
-    "#### *Numpy Fix* - Important for Google Colab Users\n",
-    "\n",
-    "Google Colab comes with an incompatible version of Numpy installed. To fix this, please run the following cells below and **restart your session** when prompted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de0310a3e4401233",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip uninstall numpy -y\n",
-    "%pip install \"numpy>=1.23.0,<2.0.0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d498ed79",
-   "metadata": {},
-   "source": [
-    "### Step 1: Convert to ONNX\n",
-    "\n",
-    "To convert to ONNX, simply run the `create_onnx.py` script, providing the path to your model weights and specifying the model version, as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0659e15f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# where version is either yolov5, yolov8, or yolov11, and model_path is the path to your weights file (.pt)\n",
-    "%run create_onnx.py --version yolov8 --model_path myyolov8model.pt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "86ff07e6",
-   "metadata": {},
-   "source": [
-    "### Step 2: Download RKNN API\n",
-    "\n",
-    "You can either use `pip` below to automatically detect and install the correct RKNN API Python library for you, or install it manually.\n",
-    "\n",
-    "#### Automatic installation\n",
-    "\n",
-    "Please run `pip` below. If it does not work, refer to the instructions for manual installation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7ec11f96",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install rknn-toolkit2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b57fe4d",
-   "metadata": {},
-   "source": [
-    "#### Manual Installation (If Automatic Installation Fails)\n",
-    "Visit the [RKNN Toolkit 2](https://github.com/airockchip/rknn-toolkit2) Github repository, then click on rknn-toolkit2, followed by packages.\n",
-    "If you are running an x86_64 CPU (e.g., most Intel and AMD processors), select that option; otherwise, choose arm64 for ARM-based computers (e.g., M-series Macs or Snapdragon processors). If you're unsure which CPU you're using, check your system settings for processor architecture information.\n",
-    "\n",
-    "Once you've selected the correct CPU architecture, you'll see multiple packages. The file names will look something like:\n",
-    "`rknn_toolkit2-2.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl`.\n",
-    "The numbers after cp correspond to your Python version. For example, if you're using Python 3.10, look for a package with cp310 in the name. For Python 3.8, look for cp38; for Python 3.7, cp37, and so on.\n",
-    "\n",
-    "Once you've found the correct package, click the \"Raw\" button to download the .whl file. Then, run the following command in your terminal, replacing rknn_toolkit2.whl with the actual path to the file you downloaded:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7414b120",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install rknn_toolkit2.whl"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1db5ef0",
-   "metadata": {},
-   "source": [
-    "### Step 3: Convert to RKNN\n",
-    "\n",
-    "Please review the notes about quantization before running the RKNN conversion."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e56b2f64bf6e85f",
-   "metadata": {},
-   "source": [
-    "### Quantization\n",
-    "\n",
-    "When performing quantization, it is critical to provide representative images of the objects or scenes you are trying to detect. These images are used to calibrate the model’s internal activations and greatly influence the final performance.\n",
-    "\n",
-    "It is recommended to use 300–500 representative images that reflect the real-world input your model will encounter. As the old saying goes, it’s quality over quantity — having a diverse, relevant set matters more than simply having many images.\n",
-    "\n",
-    "Quantization will cause some loss in model accuracy. However, if your calibration images are chosen wisely, this accuracy drop should be minimal and acceptable. If the sampled images are too uniform or unrelated, your quantized model's performance may worsen significantly.\n",
-    "\n",
-    "The script will automatically sample representative images randomly from the provided dataset. While this usually works well, please verify that the dataset contains diverse and relevant examples of your target objects. As a reminder, the images used to quantize the model are stored in the text file specified by `--img_dataset_txt`.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93e0d0622df170e",
-   "metadata": {},
-   "source": [
-    "### Optional: Download a dataset from Roboflow for quantization\n",
-    "\n",
-    "If you do not already have a dataset or set of images containing the objects you want to detect, follow the steps below to download one from Roboflow Universe.\n",
-    "\n",
-    "#### **Step 1: Search for a Dataset**\n",
-    "\n",
-    "Go to [Roboflow Universe](https://universe.roboflow.com) and use the search bar to locate a dataset relevant to what you want to detect.\n",
-    "**Note:** The dataset must include the classes or object types you intend to detect.\n",
-    "\n",
-    "#### **Step 2: Access the Dataset Tab**\n",
-    "\n",
-    "After selecting a suitable project, navigate to the **Dataset** tab. Click the **\"Download Dataset\"** button. A prompt will appear with several options, including:\n",
-    "\n",
-    "- Train a model with this dataset\n",
-    "- Train from a portion of this dataset\n",
-    "- Download dataset\n",
-    "\n",
-    "Select **Download dataset**.\n",
-    "\n",
-    "#### **Step 3: Choose Format and View Download Code**\n",
-    "\n",
-    "- Under **Image and Annotation Format**, choose the version of YOLO you are using:\n",
-    "  - For **YOLOv5**, choose `YOLOv5 PyTorch`\n",
-    "  - For **YOLOv8**, choose `YOLOv8`\n",
-    "  - For **YOLOv11**, choose `YOLOv11`\n",
-    "- If multiple annotation formats are listed for your model, always select the one ending in **\"PyTorch\"**.\n",
-    "\n",
-    "Then, under **Download Options**, click **\"Show Download Code\"** and continue.\n",
-    "\n",
-    "In the resulting screen, you will see three tabs:\n",
-    "- **Jupyter**\n",
-    "- **Terminal**\n",
-    "- **Raw URL**\n",
-    "\n",
-    "Select the **Terminal** tab and copy the provided command.\n",
-    "\n",
-    "#### **Step 4: Paste and Run**\n",
-    "\n",
-    "Paste the copied command into the notebook cell below and run it. This will download and extract the dataset into your environment, making it ready for use in the quantization process.\n",
-    "\n",
-    "Make sure to prefix the command with \"`!`\" so it executes properly in this Jupyter Notebook environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bf75c9dcb328c84",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!curl -L \"https://universe.roboflow.com/ds/FaF3HbDmF7?key=iMoJR25O9H\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72bad9cac670f1ab",
-   "metadata": {},
-   "source": [
-    "### RKNN Conversion Script\n",
-    "To get started, run the `create_rknn.py` script below, replacing the arguments with your own values. Refer to the table below for detailed information on each argument’s purpose and usage. The `--model_path` argument should point to your exported ONNX model from Step 1, and `--img_dir` must reference a valid directory containing either a dataset or a set of images to be used for quantization.\n",
-    "\n",
-    "#### Overview of the `create_rknn.py` script\n",
-    "\n",
-    "This script converts a YOLO ONNX model to RKNN format using a set of calibration images. It's designed to work with either:\n",
-    "\n",
-    "- A flat directory of images (e.g. `train/images`), **or**\n",
-    "- A dataset directory containing a `data.yaml` file that defines `train`, `val`, and/or `test` folders.\n",
-    "\n",
-    "##### Arguments\n",
-    "\n",
-    "| Argument | Type | Description                                                                                                     |\n",
-    "|----------|------|-----------------------------------------------------------------------------------------------------------------|\n",
-    "| `--img_dir` (`-d`) | `str` (required) | Path to your image directory. This can either be a folder of images **or** a dataset folder with a `data.yaml`. |\n",
-    "| `--model_path` (`-m`) | `str` (required) | Path to your YOLO ONNX model, created in Step 1.                                                                |\n",
-    "| `--num_imgs` (`-ni`) | `int` (default: `300`) | Number of images to use for quantization calibration.                                                           |\n",
-    "| `--disable_quantize` (`-dq`) | `bool` (default: `False`) | Set to `True` to skip quantization entirely. Not recommended for performance, and should not be used for deployment on PhotonVision, which requires quantization.                                  |\n",
-    "| `--rknn_output` (`-o`) | `str` (default: `out.rknn`) | File path where the final RKNN model should be saved.                                                           |\n",
-    "| `--img_dataset_txt` (`-ds`) | `str` (default: `imgs.txt`) | File path to store the list of images used during quantization.                                                 |\n",
-    "| `--verbose` (`-vb`) | `bool` (default: `False`) | Enable detailed logging from the RKNN API during conversion.                                                    |\n",
-    "\n",
-    "\n",
-    "##### *Notes*\n",
-    "\n",
-    "1. This script is designed for use with [PhotonVision](https://photonvision.org), and by default sets the target platform for RKNN conversion to `RK3588`, a chipset commonly found in many variants of the Orange Pi 5 series (e.g., Orange Pi 5, 5 Pro, 5 Plus, 5 Max, etc.). You may modify the `DEFAULT_PLATFORM` value in the `create_rknn.py` script to match your specific hardware or deployment requirements if necessary.\n",
-    "\n",
-    "2. If you followed the Roboflow dataset download instructions from the previous section, the dataset will have been extracted to your **current working directory**. In that case, you can simply set `--img_dir` to \"`.`\" to reference the current directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b09656dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%run create_rknn.py --img_dir ./datasets --model_path weights.onnx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b3a6806",
-   "metadata": {},
-   "source": [
-    "And that’s it! Your RKNN model file is now ready for deployment on an Orange Pi."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

scripts/rknn_conversion.ipynb

@@ -0,0 +1,617 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# RKNN Conversion Guide\n",
+    "\n",
+    "----------------------------\n",
+    "\n",
+    "### Before you start\n",
+    "\n",
+    "If you are not using Google Colab, it is recommended to create a separate [Python virtual environment](https://docs.python.org/3/library/venv.html) before you run this project. This ensures that packages installed for the conversion process do not conflict with other packages you may already have set up."
+   ],
+   "id": "65e9f457d12dcc6b"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Setup\n",
+    "\n",
+    "This notebook requires the use of custom Python code. Please run the installation script below to import these external scripts if you do not have them already.\n",
+    "\n",
+    "**You may need to run the `Create ONNX/RKNN` cell multiple times when you restart your session. If you see a `create_onnx`\n",
+    "or `create_rknn` not found error, rerun the cell below and then retry.**\n",
+    "\n",
+    "**Do not modify the cells in this setup section unless you know what you’re doing or have a specific reason to.**"
+   ],
+   "id": "500d656b7cc0ebd7"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "#### Create ONNX/RKNN\n",
+    "\n",
+    "Please run the cell below to be able to use the `create_onnx` and `create_rknn` functions."
+   ],
+   "id": "798298b1dbe33d2d"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "import os.path\n",
+    "import subprocess\n",
+    "import sys\n",
+    "import random\n",
+    "\n",
+    "# This will work for all models that don't use anchors (e.g. all YOLO models except YOLOv5/v7)\n",
+    "# This includes YOLOv5u\n",
+    "yolo_non_anchor_repo = \"https://github.com/airockchip/ultralytics_yolo11\"\n",
+    "\n",
+    "# For original YOLOv5 models\n",
+    "yolov5_repo = \"https://github.com/airockchip/yolov5\"\n",
+    "\n",
+    "valid_yolo_versions = [\"yolov5\", \"yolov8\", \"yolov11\"]\n",
+    "\n",
+    "ultralytics_folder_name_yolov5 = \"airockchip_yolo_pkg_yolov5\"\n",
+    "ultralytics_default_folder_name = \"airockchip_yolo_pkg\"\n",
+    "\n",
+    "bad_model_msg = \"\"\"\n",
+    "This is usually due to passing in the wrong model version.\n",
+    "Please make sure you have the right model version and try again.\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def print_bad_model_msg(cause):\n",
+    "    print(f\"{cause}{bad_model_msg}\")\n",
+    "\n",
+    "def run_and_exit_with_error(cmd, error_msg, enable_error_output=True):\n",
+    "    try:\n",
+    "        if enable_error_output:\n",
+    "            subprocess.run(\n",
+    "                cmd,\n",
+    "                stderr=subprocess.STDOUT,\n",
+    "                stdout=subprocess.PIPE,\n",
+    "                universal_newlines=True,\n",
+    "            ).check_returncode()\n",
+    "        else:\n",
+    "            subprocess.run(cmd).check_returncode()\n",
+    "    except subprocess.CalledProcessError as e:\n",
+    "        print(error_msg)\n",
+    "\n",
+    "        if enable_error_output:\n",
+    "            print(e.stdout)\n",
+    "\n",
+    "        sys.exit(1)\n",
+    "\n",
+    "\n",
+    "def check_git_installed():\n",
+    "    run_and_exit_with_error(\n",
+    "        [\"git\", \"--version\"],\n",
+    "        \"\"\"Git is not installed or not found in your PATH.\n",
+    "Please install Git from https://git-scm.com/downloads and try again.\"\"\",\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def check_or_clone_rockchip_repo(repo_url, repo_name=ultralytics_default_folder_name):\n",
+    "    if os.path.exists(repo_name):\n",
+    "        print(\n",
+    "            f'Existing Rockchip repo \"{repo_name}\" detected, skipping installation...'\n",
+    "        )\n",
+    "    else:\n",
+    "        print(f'Cloning Rockchip repo to \"{repo_name}\"')\n",
+    "        run_and_exit_with_error(\n",
+    "            [\"git\", \"clone\", repo_url, repo_name],\n",
+    "            \"Failed to clone Rockchip repo, please see error output\",\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "def run_pip_install_or_else_exit(args):\n",
+    "    print(\"Running pip install...\")\n",
+    "    run_and_exit_with_error(\n",
+    "        [\"pip\", \"install\"] + args,\n",
+    "        \"Pip install rockchip repo failed, please see error output\",\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def run_onnx_conversion_yolov5(model_path):\n",
+    "    check_or_clone_rockchip_repo(yolov5_repo, ultralytics_folder_name_yolov5)\n",
+    "    run_pip_install_or_else_exit(\n",
+    "        [\n",
+    "            \"-r\",\n",
+    "            os.path.join(ultralytics_folder_name_yolov5, \"requirements.txt\"),\n",
+    "            \"torch<2.6.0\",\n",
+    "            \"onnx==1.18.0\",\n",
+    "            \"onnxscript\",\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    model_abspath = os.path.abspath(model_path)\n",
+    "\n",
+    "    try:\n",
+    "        subprocess.run(\n",
+    "            [\n",
+    "                \"python\",\n",
+    "                f\"{ultralytics_folder_name_yolov5}/export.py\",\n",
+    "                \"--weights\",\n",
+    "                model_abspath,\n",
+    "                \"--rknpu\",\n",
+    "                \"--include\",\n",
+    "                \"onnx\",\n",
+    "            ],\n",
+    "            stderr=subprocess.STDOUT,\n",
+    "            stdout=subprocess.PIPE,\n",
+    "            universal_newlines=True,\n",
+    "        ).check_returncode()\n",
+    "    except subprocess.CalledProcessError as e:\n",
+    "        print(\"Failed to run YOLOv5 export, please see error output\")\n",
+    "\n",
+    "        if \"ModuleNotFoundError\" in e.stdout and \"ultralytics\" in e.stdout:\n",
+    "            print_bad_model_msg(\n",
+    "                \"It seems the YOLOv5 repo could not find an ultralytics installation.\"\n",
+    "            )\n",
+    "        elif \"AttributeError\" in e.stdout and \"_register_detect_seperate\" in e.stdout:\n",
+    "            print_bad_model_msg(\"It seems that you received a model attribute error.\")\n",
+    "        else:\n",
+    "            print(\"Unknown Error when converting:\")\n",
+    "            print(e.stdout)\n",
+    "\n",
+    "        sys.exit(1)\n",
+    "\n",
+    "\n",
+    "def run_onnx_conversion_no_anchor(model_path):\n",
+    "    check_or_clone_rockchip_repo(yolo_non_anchor_repo)\n",
+    "    run_pip_install_or_else_exit(\n",
+    "        [\"-e\", ultralytics_default_folder_name, \"onnx==1.18.0\", \"onnxscript\"]\n",
+    "    )\n",
+    "\n",
+    "    sys.path.insert(0, os.path.abspath(ultralytics_default_folder_name))\n",
+    "    model_abs_path = os.path.abspath(model_path)\n",
+    "\n",
+    "    from ultralytics import YOLO\n",
+    "\n",
+    "    try:\n",
+    "        model = YOLO(model_abs_path)\n",
+    "        model.export(format=\"rknn\")\n",
+    "    except TypeError as e:\n",
+    "        if \"originally trained\" in str(e):\n",
+    "            print_bad_model_msg(\n",
+    "                \"Ultralytics has detected that this model is a YOLOv5 model.\"\n",
+    "            )\n",
+    "        else:\n",
+    "            raise e\n",
+    "\n",
+    "        sys.exit(1)\n",
+    "\n",
+    "\n",
+    "def create_onnx(model_path: str, version: str):\n",
+    "    check_git_installed()\n",
+    "\n",
+    "    if not version in valid_yolo_versions:\n",
+    "        print(f\"YOLO version \\\"{version}\\\" is not a valid version! Valid versions are: {\", \".join(valid_yolo_versions)}\")\n",
+    "\n",
+    "    try:\n",
+    "        if version.lower() == \"yolov5\":\n",
+    "            run_onnx_conversion_yolov5(model_path)\n",
+    "        else:\n",
+    "            run_onnx_conversion_no_anchor(model_path)\n",
+    "\n",
+    "        print(\n",
+    "            \"Model export finished. Please use the generated ONNX file to convert to RKNN.\"\n",
+    "        )\n",
+    "    except SystemExit:\n",
+    "        raise RuntimeError(\"Model export failed. Please see output above.\")\n",
+    "\n",
+    "# RKNN Conversion code\n",
+    "\n",
+    "image_extensions = (\".jpg\", \".jpeg\", \".png\", \".bmp\", \".gif\", \".tiff\", \".webp\")\n",
+    "DEFAULT_PLATFORM = \"rk3588\"\n",
+    "\n",
+    "\n",
+    "def list_img_dir(img_dir):\n",
+    "    return [\n",
+    "        os.path.abspath(os.path.join(img_dir, f))\n",
+    "        for f in os.listdir(img_dir)\n",
+    "        if f.lower().endswith(image_extensions)\n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "def sample_imgs(num, img_list):\n",
+    "    if len(img_list) < num:\n",
+    "        return img_list\n",
+    "    else:\n",
+    "        return random.sample(img_list, num)\n",
+    "\n",
+    "\n",
+    "def get_image_list_from_dataset(num_imgs, yaml_dir):\n",
+    "    print(f\"Dataset detected with {yaml_dir} file\")\n",
+    "    img_raw_paths = []\n",
+    "\n",
+    "    with open(yaml_dir, \"r\") as yaml_file:\n",
+    "        for line in yaml_file:\n",
+    "            line = line.strip()\n",
+    "            if (\n",
+    "                    line.startswith(\"train:\")\n",
+    "                    or line.startswith(\"val:\")\n",
+    "                    or line.startswith(\"test:\")\n",
+    "            ):\n",
+    "                img_raw_paths.append(line.split(\":\", 1)[1].strip())\n",
+    "\n",
+    "    no_yaml_dir = yaml_dir.replace(\n",
+    "        \"data.yaml\", \"dummy_dir\"\n",
+    "    )  # data.yaml sets dirs one level up\n",
+    "    img_set_paths = []\n",
+    "\n",
+    "    for img_raw_path in img_raw_paths:\n",
+    "        p = (\n",
+    "            img_raw_path\n",
+    "            if os.path.isabs(img_raw_path)\n",
+    "            else os.path.realpath(os.path.join(no_yaml_dir, img_raw_path))\n",
+    "        )\n",
+    "\n",
+    "        if os.path.exists(p):\n",
+    "            img_set_paths.append(p)\n",
+    "\n",
+    "    if len(img_set_paths) < 1:\n",
+    "        return None\n",
+    "\n",
+    "    all_imgs = [list_img_dir(path) for path in img_set_paths]\n",
+    "\n",
+    "    for imgs in all_imgs:\n",
+    "        print(len(imgs))\n",
+    "\n",
+    "    total_imgs = sum(len(group) for group in all_imgs)\n",
+    "\n",
+    "    sampled_imgs = [\n",
+    "        sample_imgs(round((len(group) / total_imgs) * num_imgs), group)\n",
+    "        for group in all_imgs\n",
+    "    ]\n",
+    "\n",
+    "    return [img for group in sampled_imgs for img in group]\n",
+    "\n",
+    "\n",
+    "def get_image_list_from_img_dir(num_imgs, img_dir):\n",
+    "    return sample_imgs(num_imgs, list_img_dir(img_dir))\n",
+    "\n",
+    "\n",
+    "def get_image_list(num_imgs, image_dir):\n",
+    "    yaml_path = os.path.join(image_dir, \"data.yaml\")\n",
+    "\n",
+    "    if os.path.exists(yaml_path):\n",
+    "        return get_image_list_from_dataset(num_imgs, yaml_path)\n",
+    "    else:\n",
+    "        return get_image_list_from_img_dir(num_imgs, image_dir)\n",
+    "\n",
+    "\n",
+    "def run_rknn_conversion(\n",
+    "        img_list_txt, disable_quant, model_path, rknn_output, verbose_logging\n",
+    "):\n",
+    "    from rknn.api import RKNN\n",
+    "\n",
+    "    rknn = RKNN(\n",
+    "        verbose=verbose_logging,\n",
+    "        verbose_file=(\"rknn_convert.log\" if verbose_logging else None),\n",
+    "    )\n",
+    "\n",
+    "    rknn.config(\n",
+    "        mean_values=[[0, 0, 0]],\n",
+    "        std_values=[[255, 255, 255]],\n",
+    "        target_platform=DEFAULT_PLATFORM,\n",
+    "    )\n",
+    "\n",
+    "    print(\"Attempted RKNN load\")\n",
+    "    ret = rknn.load_onnx(model=model_path)\n",
+    "    if ret != 0:\n",
+    "        print(\"Loading model failed!\")\n",
+    "        exit(ret)\n",
+    "\n",
+    "    print(\"Attempting RKNN build\")\n",
+    "    ret = rknn.build(do_quantization=(not disable_quant), dataset=img_list_txt)\n",
+    "    if ret != 0:\n",
+    "        print(\"Building model failed!\")\n",
+    "        exit(ret)\n",
+    "\n",
+    "    print(\"Build succeeded! Starting export...\")\n",
+    "    ret = rknn.export_rknn(rknn_output)\n",
+    "    if ret != 0:\n",
+    "        print(\"Exporting model failed!\")\n",
+    "        exit(ret)\n",
+    "    print(\"Finished export!\")\n",
+    "\n",
+    "    # Release\n",
+    "    rknn.release()\n",
+    "\n",
+    "    print(f'Your model is in \"{rknn_output}\" and ready to use!')\n",
+    "\n",
+    "\n",
+    "def create_rknn(\n",
+    "        model_path: str,\n",
+    "        rknn_output: str = \"out.rknn\",\n",
+    "        num_imgs: int = 300,\n",
+    "        img_dir: str = None,\n",
+    "        img_dataset_txt: str = \"imgs.txt\",\n",
+    "        disable_quantize: bool = False,\n",
+    "        verbose: bool = False,\n",
+    "):\n",
+    "    if not rknn_output.endswith(\".rknn\"):\n",
+    "        print(\"RKNN output path must end in .rknn!\")\n",
+    "        return\n",
+    "\n",
+    "    if not disable_quantize:\n",
+    "        if img_dir is None or len(img_dir) < 1:\n",
+    "            print(f\"Must specify list of images to use with --img_dir\")\n",
+    "            return\n",
+    "\n",
+    "        img_dir_abs = os.path.abspath(img_dir)\n",
+    "\n",
+    "        img_list = get_image_list(num_imgs, img_dir_abs)\n",
+    "        img_list_len = 0 if img_list is None else len(img_list)\n",
+    "\n",
+    "        if img_list_len == 0:\n",
+    "            print(f\"No images found in {img_dir_abs}\")\n",
+    "            return\n",
+    "        elif img_list_len < num_imgs:\n",
+    "            print(\n",
+    "                f\"Not enough images in your dataset/directory, you have {img_list_len} images, but need {num_imgs}\"\n",
+    "            )\n",
+    "            return\n",
+    "\n",
+    "        if not img_dataset_txt.endswith(\".txt\"):\n",
+    "            print(f\"Image dataset text file path must end in .txt\")\n",
+    "            return\n",
+    "\n",
+    "        with open(img_dataset_txt, \"w\") as set_file:\n",
+    "            set_file.writelines(f\"{img}\\n\" for img in img_list)\n",
+    "\n",
+    "    try:\n",
+    "        run_rknn_conversion(\n",
+    "            img_dataset_txt,\n",
+    "            disable_quantize,\n",
+    "            model_path,\n",
+    "            rknn_output,\n",
+    "            verbose,\n",
+    "        )\n",
+    "    except SystemExit:\n",
+    "        print(\"RKNN Conversion failed, see output above\")\n"
+   ],
+   "id": "ea6869140a61126d"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "#### *Numpy Fix* - Important for Google Colab Users\n",
+    "\n",
+    "Google Colab comes with an incompatible version of Numpy installed. To fix this, please run the following cells below and **restart your session** when prompted."
+   ],
+   "id": "b3a9e1a334bce144"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "%pip uninstall numpy -y\n",
+    "%pip install \"numpy>=1.23.0,<2.0.0\""
+   ],
+   "id": "7156e69495f48f49"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Step 1: Convert to ONNX\n",
+    "\n",
+    "To convert to ONNX, simply run the `create_onnx` function, providing the path to your model weights and specifying the model version, as shown below."
+   ],
+   "id": "332942d8582c9bae"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "create_onnx(model_path=\"weights.pt\", version=\"yolov8\") # Valid versions are yolov5, yolov8, and yolov11",
+   "id": "408440b32de224ed"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Step 2: Download RKNN API\n",
+    "\n",
+    "You can either use `pip` below to automatically detect and install the correct RKNN API Python library for you, or install it manually.\n",
+    "\n",
+    "#### Automatic installation\n",
+    "\n",
+    "Please run `pip` below. If it does not work, refer to the instructions for manual installation. You may need to restart your session after running the command below.\n"
+   ],
+   "id": "ebd148faaa2b1933"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "%pip install rknn-toolkit2",
+   "id": "7c7ef3010c663fc2"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "#### Manual Installation (If Automatic Installation Fails)\n",
+    "Visit the [RKNN Toolkit 2](https://github.com/airockchip/rknn-toolkit2) Github repository, then click on rknn-toolkit2, followed by packages.\n",
+    "If you are running an x86_64 CPU (e.g., most Intel and AMD processors), select that option; otherwise, choose arm64 for ARM-based computers (e.g., M-series Macs or Snapdragon processors). If you're unsure which CPU you're using, check your system settings for processor architecture information.\n",
+    "\n",
+    "Once you've selected the correct CPU architecture, you'll see multiple packages. The file names will look something like:\n",
+    "`rknn_toolkit2-2.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl`.\n",
+    "The numbers after cp correspond to your Python version. For example, if you're using Python 3.10, look for a package with cp310 in the name. For Python 3.8, look for cp38; for Python 3.7, cp37, and so on.\n",
+    "\n",
+    "Once you've found the correct package, click the \"Raw\" button to download the .whl file. Then, run the following command in your terminal, replacing rknn_toolkit2.whl with the actual path to the file you downloaded:"
+   ],
+   "id": "f44b71c5a820ab2"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "%pip install rknn_toolkit2.whl",
+   "id": "c9b9d3da532eb916"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Step 3: Convert to RKNN\n",
+    "\n",
+    "Please review the notes about quantization before running the RKNN conversion."
+   ],
+   "id": "fc74ebc99b596f76"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Quantization\n",
+    "\n",
+    "When performing quantization, it is critical to provide representative images of the objects or scenes you are trying to detect. These images are used to calibrate the model’s internal activations and greatly influence the final performance.\n",
+    "\n",
+    "It is recommended to use 300–500 representative images that reflect the real-world input your model will encounter. As the old saying goes, it’s quality over quantity — having a diverse, relevant set matters more than simply having many images.\n",
+    "\n",
+    "Quantization will cause some loss in model accuracy. However, if your calibration images are chosen wisely, this accuracy drop should be minimal and acceptable. If the sampled images are too uniform or unrelated, your quantized model's performance may worsen significantly.\n",
+    "\n",
+    "The script will automatically sample representative images randomly from the provided dataset. While this usually works well, please verify that the dataset contains diverse and relevant examples of your target objects. As a reminder, the images used to quantize the model are stored in the text file specified by `--img_dataset_txt`.\n"
+   ],
+   "id": "80cf63a7f16f9af"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Optional: Download a dataset from Roboflow for quantization\n",
+    "\n",
+    "If you do not already have a dataset or set of images containing the objects you want to detect, follow the steps below to download one from Roboflow Universe.\n",
+    "\n",
+    "#### **Step 1: Search for a Dataset**\n",
+    "\n",
+    "Go to [Roboflow Universe](https://universe.roboflow.com) and use the search bar to locate a dataset relevant to what you want to detect.\n",
+    "**Note:** The dataset must include the classes or object types you intend to detect.\n",
+    "\n",
+    "#### **Step 2: Access the Dataset Tab**\n",
+    "\n",
+    "After selecting a suitable project, navigate to the **Dataset** tab. Click the **\"Download Dataset\"** button. A prompt will appear with several options, including:\n",
+    "\n",
+    "- Train a model with this dataset\n",
+    "- Train from a portion of this dataset\n",
+    "- Download dataset\n",
+    "\n",
+    "Select **Download dataset**.\n",
+    "\n",
+    "#### **Step 3: Choose Format and View Download Code**\n",
+    "\n",
+    "- Under **Image and Annotation Format**, choose the version of YOLO you are using:\n",
+    "  - For **YOLOv5**, choose `YOLOv5 PyTorch`\n",
+    "  - For **YOLOv8**, choose `YOLOv8`\n",
+    "  - For **YOLOv11**, choose `YOLOv11`\n",
+    "- If multiple annotation formats are listed for your model, always select the one ending in **\"PyTorch\"**.\n",
+    "\n",
+    "Then, under **Download Options**, click **\"Show Download Code\"** and continue.\n",
+    "\n",
+    "In the resulting screen, you will see three tabs:\n",
+    "- **Jupyter**\n",
+    "- **Terminal**\n",
+    "- **Raw URL**\n",
+    "\n",
+    "Select the **Terminal** tab and copy the provided command.\n",
+    "\n",
+    "#### **Step 4: Paste and Run**\n",
+    "\n",
+    "Paste the copied command into the notebook cell below and run it. This will download and extract the dataset into your environment, making it ready for use in the quantization process.\n",
+    "\n",
+    "Make sure to prefix the command with \"`!`\" so it executes properly in this Jupyter Notebook environment."
+   ],
+   "id": "ae229bef78e3bc0c"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "!curl -L \"https://universe.roboflow.com/ds/FaF3HbDmF7?key=iMoJR25O9H\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip",
+   "id": "e16fb4f928fd956b"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### RKNN Conversion Script\n",
+    "To get started, run the `create_rknn` script below, replacing the arguments with your own values. Refer to the table below for detailed information on each argument’s purpose and usage. The `model_path` argument should point to your exported ONNX model from Step 1, and `img_dir` must reference a valid directory containing either a dataset or a set of images to be used for quantization.\n",
+    "\n",
+    "#### Overview of the `create_rknn` function\n",
+    "\n",
+    "This script converts a YOLO ONNX model to RKNN format using a set of calibration images. It's designed to work with either:\n",
+    "\n",
+    "- A flat directory of images (e.g. `train/images`), **or**\n",
+    "- A dataset directory containing a `data.yaml` file that defines `train`, `val`, and/or `test` folders.\n",
+    "\n",
+    "##### Arguments\n",
+    "\n",
+    "| Argument | Type | Description                                                                                                     |\n",
+    "|----------|------|-----------------------------------------------------------------------------------------------------------------|\n",
+    "| `img_dir` | `str` (required) | Path to your image directory. This can either be a folder of images **or** a dataset folder with a `data.yaml`. |\n",
+    "| `model_path` | `str` (required) | Path to your YOLO ONNX model, created in Step 1.                                                                |\n",
+    "| `num_imgs` | `int` (default: `300`) | Number of images to use for quantization calibration.                                                           |\n",
+    "| `disable_quantize` | `bool` (default: `False`) | Set to `True` to skip quantization entirely. Not recommended for performance, and should not be used for deployment on PhotonVision, which requires quantization.                                  |\n",
+    "| `rknn_output` | `str` (default: `out.rknn`) | File path where the final RKNN model should be saved.                                                           |\n",
+    "| `img_dataset_txt` | `str` (default: `imgs.txt`) | File path to store the list of images used during quantization.                                                 |\n",
+    "| `verbose` | `bool` (default: `False`) | Enable detailed logging from the RKNN API during conversion.                                                    |\n",
+    "\n",
+    "\n",
+    "##### *Notes*\n",
+    "\n",
+    "1. This script is designed for use with [PhotonVision](https://photonvision.org), and by default sets the target platform for RKNN conversion to `RK3588`, a chipset commonly found in many variants of the Orange Pi 5 series (e.g., Orange Pi 5, 5 Pro, 5 Plus, 5 Max, etc.). You may modify the `DEFAULT_PLATFORM` value in the setup cell to match your specific hardware or deployment requirements if necessary.\n",
+    "\n",
+    "2. If you followed the Roboflow dataset download instructions from the previous section, the dataset will have been extracted to your **current working directory**. In that case, you can simply set `img_dir` to \"`.`\" to reference the current directory."
+   ],
+   "id": "f8f48b3139509618"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "create_rknn(img_dir=\"./datasets\", model_path=\"weights.onnx\")",
+   "id": "2c48b133f5c93c7a"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

shared/common.gradle

@@ -67,7 +67,7 @@ tasks.register('testHeadless', Test) {
     useJUnitPlatform()
     testLogging {
         events "passed", "skipped", "failed", "standardOut", "standardError"
-        exceptionFormat "full"
+        exceptionFormat = "full"
         showStandardStreams = true
     }
     exclude '**/*BenchmarkTest*'

shared/config.gradle

@@ -45,7 +45,7 @@ model {
 
 task copyAllOutputs(type: Copy) {
     def outputsFolder = file("$project.buildDir/outputs")
-    destinationDir outputsFolder
+    destinationDir = outputsFolder
 }
 
 ext.addTaskToCopyAllOutputs = { task ->
@@ -127,8 +127,14 @@ ext.createComponentZipTasks = { components, names, base, type, project, func ->
 
         project.build.dependsOn task
 
-        project.artifacts {
-            task
+        // If the zip artifact matches the platform we're building for (either host or whatever the ArchOverride is), and it's a shared library built in release mode, add it to the list of artifacts the project exposes for use
+        if (key.contains(wpilibTools.getPlatformMapper().getWpilibClassifier()) && !key.contains("debug") && !key.contains("static")) {
+            // For more information, see https://docs.gradle.org/current/userguide/variant_model.html and the outgoingVariants task
+            project.artifacts.add("wpilibNatives", task)
+        } else {
+            project.artifacts {
+                task
+            }
         }
         addTaskToCopyAllOutputs(task)
     }

shared/javacommon.gradle

@@ -85,8 +85,8 @@ publishing {
             artifact javadocJar
 
             artifactId = "${baseArtifactId}-java"
-            groupId artifactGroupId
-            version pubVersion
+            groupId = artifactGroupId
+            version = pubVersion
         }
     }
 
@@ -97,10 +97,10 @@ publishing {
             if (project.hasProperty('copyOfflineArtifacts')) {
                 url(localMavenURL)
             } else {
-                url(photonMavenURL)
+                url = photonMavenURL
                 credentials {
-                    username 'ghactions'
-                    password System.getenv("ARTIFACTORY_API_KEY")
+                    username = 'ghactions'
+                    password =  System.getenv("ARTIFACTORY_API_KEY")
                 }
             }
         }
@@ -112,7 +112,7 @@ test {
     systemProperty 'junit.jupiter.extensions.autodetection.enabled', 'true'
     testLogging {
         events "failed"
-        exceptionFormat "full"
+        exceptionFormat = "full"
         showStandardStreams = true
     }
     forkEvery = 1

versioningHelper.gradle

@@ -8,7 +8,7 @@ gradle.allprojects {
         def stdout = new ByteArrayOutputStream()
         String tagIsh
         try {
-            exec {
+            project.exec {
                 commandLine 'git', 'describe', '--tags', "--match=v*"
                 standardOutput = stdout
             }
@@ -19,7 +19,7 @@ gradle.allprojects {
 
         // Dev tags: v2021.1.6-3-gf922466d
         // We're specifically looking to capture the middle -3-
-        boolean isDev = tagIsh.matches(".*-[0-9]*-g[0-9a-f]*")
+        boolean isDev = tagIsh.matches(".*-[0-9a-z]*-g[0-9a-f]*")
         if (isDev && !tagIsh.startsWith("dev-")) tagIsh = "dev-" + tagIsh
         println("Picked up version: " + tagIsh)
         return tagIsh