fix: docs updates for the different apriltag field layouts (#1787)

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

@@ -6,6 +6,10 @@ PhotonVision can combine AprilTag detections from multiple simultaneously observ
 MultiTag requires an accurate field layout JSON to be uploaded! Differences between this layout and the tags' physical location will drive error in the estimated pose output.
 :::
 
+:::{warning}
+For the 2025 Reefscape Season, there are two different field layouts. The first is the [welded field layout](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2025-reefscape-welded.json), which photonvision ships with. The second is the [Andymark field layout](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2025-reefscape-andymark.json). It is very important to ensure that you use the correct field layout, both in the [PhotonPoseEstimator](https://docs.photonvision.org/en/latest/docs/programming/photonlib/robot-pose-estimator.html#apriltags-and-photonposeestimator) and on the [coprocessor](https://docs.photonvision.org/en/latest/docs/apriltag-pipelines/multitag.html#updating-the-field-layout).
+:::
+
 ## Enabling MultiTag
 
 Ensure that your camera is calibrated and 3D mode is enabled. Navigate to the Output tab and enable "Do Multi-Target Estimation". This enables MultiTag to use the uploaded field layout JSON to calculate your camera's pose in the field. This 3D transform will be shown as an additional table in the "targets" tab, along with the IDs of AprilTags used to compute this transform.
@@ -51,7 +55,7 @@ The returned field to camera transform is a transform from the fixed field origi
 
 ## Updating the Field Layout
 
-PhotonVision ships by default with the [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.
+PhotonVision ships by default with the [2025 welded field layout JSON](https://github.com/wpilibsuite/allwpilib/blob/main/apriltag/src/main/native/resources/edu/wpi/first/apriltag/2025-reefscape-welded.json). The layout can be inspected by navigating to the settings tab and scrolling down to the "AprilTag Field Layout" card, as shown below.
 
 ```{image} images/field-layout.png
 :alt: The currently saved field layout in the Photon UI

docs/source/docs/calibration/calibration.md

@@ -51,6 +51,10 @@ We'll next select a resolution to calibrate and populate our pattern spacing, ma
 :::{warning} Old OpenCV Pattern selector. This should be used in the case that the calibration image is generated from a version of OpenCV before version 4.6.0. This would include targets created by calib.io. If this selector is not set correctly the calibration will be completely invalid. For more info view [this GitHub issue](https://github.com/opencv/opencv_contrib/issues/3291).
 :::
 
+:::{note}
+If you have a [calib.io](https://calib.io/) CharuCo Target you will have to enter the paramaters of your target. For example if your taget says "9x12 | Chceker Size: 30 mm | Marker Size: 22 mm | Dictionary: AruCo DICT 5x5", you would have to set the board type to Dict_5x5_1000, the pattern spacing to 1.1811 in (30 mm converted to inches), the marker size 0.866142 in (22 mm converted to inches), the board width to 12 and the board height to 9. If you chose the wrong tag family the baord wont be detected duting calibration. If you swap the width and height your calibration will have a very high error.
+:::
+
 ### 4. Take at calibration images from various angles.
 
 Now, we'll capture images of our board from various angles. It's important to check that the board overlay matches the board in your image. The further the overdrawn points are from the true position of the chessboard corners, the less accurate the final calibration will be. We'll want to capture enough images to cover the whole camera's FOV (with a minimum of 12). Once we've got our images, we'll click "Finish calibration" and wait for the calibration process to complete. If all goes well, the mean error and FOVs will be shown in the table on the right. The FOV should be close to the camera's specified FOV (usually found in a datasheet) usually within + or - 10 degrees. The mean error should also be low, usually less than 1 pixel.