added in calibrations folder with necessary scripts

README.md

@@ -22,5 +22,10 @@ A base project for future robots that has CTRE generated swerve drive code and P
 - After that, since this project is a template on github, the actual 2025 robot code repo can be generated from this template:
 ![github templates](https://docs.github.com/assets/cb-76823/mw-1440/images/help/repository/use-this-template-button.webp)
 
+# Calibration Process
+- Run `calibration/calibrator.zsh` in zshell in WSL (make sure all packages are installed).
+- Analyze calibration as according to the mrcal tour.
+- Convert the `.cameramodel` calibration data format into `.json` using `calibration/mrcal_to_photon.py` in WSL.
+
 ## 2025 Latest Beta Known Issues
 - To run sim, do `./gradlew simulateJava` instead of using the WPILib extension (for Epilogue to work).

calibration/calibrator.zsh

@@ -0,0 +1,46 @@
+# NOT DONE HERE YET
+
+# names
+echo -n "Enter camera name: "
+read -r name
+
+# echo -n "Enter input video file name: "
+# read -r input
+
+# echo -n "Enter desired ffmpeg fps: "
+# read -r fps
+
+# # frame dir
+# mkdir frames1
+
+# fill up frame directory with png frames
+# ffmpeg
+
+# generate corners.vnl
+# mrgingham
+
+# ---- CALIBRATE ---- 
+evens='frames/*[02468].png'
+odds='frames/*[13579].png'
+all='frames/*.png'
+
+mrcal_calibrate=(mrcal-calibrate-cameras                                         \
+    --corners-cache corners.vnl                                                   \
+    --lensmodel LENSMODEL_SPLINED_STEREOGRAPHIC_order=3_Nx=16_Ny=9_fov_x_deg=70   \
+    --focal 1015                                                                  \
+    --object-spacing 0.012                                                        \
+    --object-width-n 14                                                           \
+)
+
+# cross diffs
+$mrcal_calibrate $evens
+mv camera-0.cameramodel $name-evens.cameramodel
+
+$mrcal_calibrate $odds
+mv camera-0.cameramodel $name-odds.cameramodel
+
+# main calibration
+$mrcal_calibrate $all
+mv camera-0.cameramodel $name.cameramodel
+
+# ---- DONE! ---- 

calibration/mrcal_to_photon.py

@@ -0,0 +1,111 @@
+import argparse
+import base64
+import json
+import os
+import cv2
+import numpy as np
+import mrcal # type: ignore (this exists when running in wsl)
+from dataclasses import dataclass, asdict
+
+# Assuming similar dataclass structures for simplicity and compatibility
+@dataclass
+class Size:
+    width: int
+    height: int
+
+@dataclass
+class JsonMatOfDoubles:
+    rows: int
+    cols: int
+    type: int  # Assuming type follows OpenCV conventions, 6 for CV_64F
+    data: list[float]
+
+@dataclass
+class JsonMat:
+    rows: int
+    cols: int
+    type: int  # Assuming type follows OpenCV conventions
+    data: str  # Base64-encoded image data
+
+@dataclass
+class CameraCalibration:
+    resolution: Size
+    cameraIntrinsics: JsonMatOfDoubles
+    distCoeffs: JsonMatOfDoubles
+    observations: list  # This would need to be fleshed out based on actual use
+    calobjectWarp: list[float]
+    calobjectSize: Size
+    calobjectSpacing: float
+
+def __convert_mrcal_to_photon_cameramodel(cameramodel_path: str) -> CameraCalibration:
+    model = mrcal.cameramodel(cameramodel_path)
+
+    # Extracting necessary data from mrcal model
+    intrinsics = model.intrinsics()
+    lensmodel, intrinsics_data = intrinsics
+    dist_coeffs = intrinsics_data[4:]  # Assuming the first four are the core intrinsics
+    resolution = model.imagersize()
+
+    # Placeholder for generating/synthesizing observation data
+    observations = []
+
+    # Constructing the CameraCalibration object
+    camera_calibration = CameraCalibration(
+        resolution=Size(width=resolution[0], height=resolution[1]),
+        cameraIntrinsics=JsonMatOfDoubles(rows=1, cols=4, type=6, data=intrinsics_data[:4]),
+        distCoeffs=JsonMatOfDoubles(rows=1, cols=len(dist_coeffs), type=6, data=dist_coeffs),
+        observations=observations,
+        calobjectWarp=[],  # Placeholder, as warp is not directly available from mrcal
+        calobjectSize=Size(width=0, height=0),  # Placeholder
+        calobjectSpacing=0  # Placeholder
+    )
+
+    return camera_calibration
+
+def __convert_mrcal_to_photon_cameramodel(cameramodel_path: str) -> CameraCalibration:
+    model = mrcal.cameramodel(cameramodel_path)
+
+    # Extracting necessary data from mrcal model
+    intrinsics = model.intrinsics()
+    lensmodel, intrinsics_data = intrinsics
+    dist_coeffs = intrinsics_data[4:].tolist()  # Convert to list for JSON serialization
+    resolution = model.imagersize()
+
+    # Placeholder for generating/synthesizing observation data
+    observations = []
+
+    intrinsics_list = intrinsics_data[:4].tolist()
+    camera_intrinsics = [intrinsics_list[0], 0, intrinsics_list[2], 0, intrinsics_list[1], intrinsics_list[3], 0, 0, 1]
+
+    # Constructing the CameraCalibration object, ensuring all data is in a serializable form
+    camera_calibration = CameraCalibration(
+        resolution=Size(width=int(resolution[0]), height=int(resolution[1])),  # Convert to int
+        cameraIntrinsics=JsonMatOfDoubles(rows=3, cols=3, type=6, data=camera_intrinsics),
+        distCoeffs=JsonMatOfDoubles(rows=1, cols=len(dist_coeffs), type=6, data=dist_coeffs),
+        observations=observations,
+        calobjectWarp=[],  # Placeholder, as warp is not directly available from mrcal
+        calobjectSize=Size(width=0, height=0),  # Placeholder
+        calobjectSpacing=0  # Placeholder
+    )
+
+    return camera_calibration
+
+
+def convert_mrcal_to_photon(mrcal_model_path: str, output_json_path: str):
+    camera_cal_data = __convert_mrcal_to_photon_cameramodel(mrcal_model_path)
+
+    # Writing the calibration data to a JSON file
+    with open(output_json_path, "w") as outfile:
+        json.dump(asdict(camera_cal_data), outfile, indent=4)
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert mrcal cameramodel to PhotonVision calibration JSON")
+    parser.add_argument("input", type=str, help="Path to mrcal cameramodel file")
+    parser.add_argument("output", type=str, help="Output path for the PhotonVision calibration JSON")
+
+    args = parser.parse_args()
+
+    convert_mrcal_to_photon(args.input, args.output)
+
+if __name__ == "__main__":
+    main()

src/main/java/frc/robot/Robot.java

@@ -68,7 +68,7 @@ public Robot(NetworkTableInstance ntInst) {
     _ntInst = ntInst;
 
     // set up loggers
-    DogLog.getOptions().withCaptureDs(true);
+    DogLog.setOptions(DogLog.getOptions().withCaptureDs(true));
 
     setFileOnly(false); // file-only once connected to fms
 
@@ -94,7 +94,7 @@ public Robot(NetworkTableInstance ntInst) {
 
   // set logging to be file only or not
   private void setFileOnly(boolean fileOnly) {
-    DogLog.getOptions().withNtPublish(!fileOnly);
+    DogLog.setOptions(DogLog.getOptions().withNtPublish(!fileOnly));
 
     if (fileOnly) {
       Epilogue.getConfig().backend = new FileBackend(DataLogManager.getLog());