Fix Working on the Wrong Branch

docs/README.MD

@@ -6,4 +6,4 @@ PhotonVision is a free open-source vision processing software for FRC teams.
 
 This repository is the source code for our ReadTheDocs documentation, which can be found [here](https://docs.photonvision.org).
 
-[Contribution and formatting guidelines for this project](https://docs.photonvision.org/en/latest/docs/contributing/photonvision-docs/index.html)
+[Contribution and formatting guidelines for this project](https://docs.photonvision.org/en/latest/docs/contributing/index.html)

docs/source/docs/apriltag-pipelines/about-apriltags.md

@@ -10,5 +10,5 @@ AprilTags are a common type of visual fiducial marker. Visual fiducial markers a
 A more technical explanation can be found in the [WPILib documentation](https://docs.wpilib.org/en/latest/docs/software/vision-processing/apriltag/apriltag-intro.html).
 
 :::{note}
-You can get FIRST's [official PDF of the targets used in 2024 here](https://firstfrc.blob.core.windows.net/frc2024/FieldAssets/Apriltag_Images_and_User_Guide.pdf).
+You can get FIRST's [official PDF of the targets used in 2025 here](https://firstfrc.blob.core.windows.net/frc2025/FieldAssets/Apriltag_Images_and_User_Guide.pdf).
 :::

docs/source/docs/apriltag-pipelines/multitag.md

@@ -51,7 +51,7 @@ The returned field to camera transform is a transform from the fixed field origi
 
 ## Updating the Field Layout
 
-PhotonVision ships by default with the [2024 field layout JSON](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2024-crescendo.json). The layout can be inspected by navigating to the settings tab and scrolling down to the "AprilTag Field Layout" card, as shown below.
+PhotonVision ships by default with the [2025 field layout JSON](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2025-reefscape.json). The layout can be inspected by navigating to the settings tab and scrolling down to the "AprilTag Field Layout" card, as shown below.
 
 ```{image} images/field-layout.png
 :alt: The currently saved field layout in the Photon UI

docs/source/docs/examples/aimingatatarget.md

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

docs/source/docs/objectDetection/about-object-detection.md

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

photon-client/src/components/settings/DeviceControlCard.vue

@@ -202,6 +202,57 @@ const handleSettingsImport = () => {
   importFile.value = null;
 };
 
+const showObjectDetectionImportDialog = ref(false);
+const importRKNNFile = ref<File | null>(null);
+const importLabelsFile = ref<File | null>(null);
+
+const handleObjectDetectionImport = () => {
+  if (importRKNNFile.value === null || importLabelsFile.value === null) return;
+
+  const formData = new FormData();
+  formData.append("rknn", importRKNNFile.value);
+  formData.append("labels", importLabelsFile.value);
+
+  useStateStore().showSnackbarMessage({
+    message: "Importing Object Detection Model...",
+    color: "secondary",
+    timeout: -1
+  });
+
+  axios
+    .post("/utils/importObjectDetectionModel", formData, {
+      headers: { "Content-Type": "multipart/form-data" }
+    })
+    .then((response) => {
+      useStateStore().showSnackbarMessage({
+        message: response.data.text || response.data,
+        color: "success"
+      });
+    })
+    .catch((error) => {
+      if (error.response) {
+        useStateStore().showSnackbarMessage({
+          color: "error",
+          message: error.response.data.text || error.response.data
+        });
+      } else if (error.request) {
+        useStateStore().showSnackbarMessage({
+          color: "error",
+          message: "Error while trying to process the request! The backend didn't respond."
+        });
+      } else {
+        useStateStore().showSnackbarMessage({
+          color: "error",
+          message: "An error occurred while trying to process the request."
+        });
+      }
+    });
+
+  showObjectDetectionImportDialog.value = false;
+  importRKNNFile.value = null;
+  importLabelsFile.value = null;
+};
+
 const showFactoryReset = ref(false);
 const expected = "Delete Everything";
 const yesDeleteMySettingsText = ref("");
@@ -355,6 +406,53 @@ const nukePhotonConfigDirectory = () => {
           </v-btn>
         </v-col>
       </v-row>
+      <v-row>
+        <v-col cols="12">
+          <v-btn color="secondary" @click="() => (showImportDialog = true)">
+            <v-icon left class="open-icon"> mdi-import </v-icon>
+            <span class="open-label">Import Object Detection Model</span>
+          </v-btn>
+          <v-dialog
+            v-model="showImportDialog"
+            width="600"
+            @input="
+              () => {
+                importRKNNFile = null;
+                importLabelsFile = null;
+              }">
+            <v-card color="primary" dark>
+              <v-card-title>Import Object Detection Model</v-card-title>
+              <v-card-text>
+                Upload a new object detection model to this device that can be used in a pipeline.
+                Naming convention is that the labels file ought to have the same name as the RKNN file, with -labels appended to the end.
+                For example, if the RKNN file is named <i>foo.rknn</i>, the labels file should be named <i>foo-labels.txt</i>.
+                <v-row class="mt-6 ml-4 mr-8">
+                  <v-file-input
+                    label="RKNN File"
+                    v-model="importRKNNFile"
+                    accept=".rknn"
+                  />
+                </v-row>
+                <v-row class="mt-6 ml-4 mr-8">
+                  <v-file-input
+                    label="Labels File"
+                    v-model="importLabelsFile"
+                    accept=".txt"
+                  />
+                </v-row>
+                <v-row
+                  class="mt-12 ml-8 mr-8 mb-1"
+                  style="display: flex; align-items: center; justify-content: center"
+                  align="center">
+                  <v-btn color="secondary" :disabled="importRKNNFile === null || importLabelsFile === null" @click="handleObjectDetectionImport">
+                    <v-icon left class="open-icon"> mdi-import </v-icon>
+                    <span class="open-label">Import Object Detection Model</span>
+                  </v-btn>
+                </v-row>
+              </v-card-text>
+            </v-card>
+          </v-dialog>
+      </v-row>
       <v-divider class="mt-3 pb-3" />
       <v-row>
         <v-col cols="12">