You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: camera_calibration.m
    path of file: ./repos/lidar_camera_calibration/matlab
    
    the code of the file until where you have to start completion: function camera_calibration(imagePath, resultPath, squareSize)
    % Define images to process
    imageFiles  = dir(fullfile(imagePath, '*.jpg'));
    imageFileNames = fullfile(imagePath, {imageFiles.name});


    % Detect checkerboards in images
    [imagePoints, boardSize, imagesUsed] = detectCheckerboardPoints(imageFileNames);
    imageFileNames = imageFileNames(imagesUsed);
    disp('Detect checkerboard points finish!');

    % Read the first image to obtain image size
    originalImage = imread(imageFileNames{1});
    [mrows, ncols, ~] = size(originalImage);

    % Generate world coordinates of the corners of the squares
    worldPoints = generateCheckerboardPoints(boardSize, squareSize);
    disp('Generate checkerboard points finish!');

    % Calibrate the camera
    [cameraParams, imagesUsed, estimationErrors] = estimateCameraParameters(imagePoints, worldPoints, ...
        'EstimateSkew', false, 'EstimateTangentialDistortion', true, ...
        'NumRadialDistortionCoefficients', 2, 'WorldUnits', 'millimeters', ...
        'InitialIntrinsicMatrix', [], 'InitialRadialDistortion', [], ...
        'ImageSize', [mrows, ncols]);
    imageFileNames = imageFileNames(imagesUsed);
    disp('Estimate camera parameters finish!');

    % View reprojection errors
    % h1=figure; showReprojectionErrors(cameraParams);

    % Visualize pattern locations
    % h2=figure; showExtrinsics(cameraParams, 'CameraCentric');

    % Display parameter estimation errors
    % displayErrors(estimationErrors, cameraParams);

    % For example, you can use the calibration data to remove effects of lens distortion.
    % undistortedImage = undistortImage(originalImage, cameraParams);


    
function camera_calibration(imagePath, resultPath, squareSize)
    % Define images to process
    imageFiles  = dir(fullfile(imagePath, '*.jpg'));
    imageFileNames = fullfile(imagePath, {imageFiles.name});


    % Detect checkerboards in images
    [imagePoints, boardSize, imagesUsed] = detectCheckerboardPoints(imageFileNames);
    imageFileNames = imageFileNames(imagesUsed);
    disp('Detect checkerboard points finish!');

    % Read the first image to obtain image size
    originalImage = imread(imageFileNames{1});
    [mrows, ncols, ~] = size(originalImage);

    % Generate world coordinates of the corners of the squares
    worldPoints = generateCheckerboardPoints(boardSize, squareSize);
    disp('Generate checkerboard points finish!');

    % Calibrate the camera
    [cameraParams, imagesUsed, estimationErrors] = estimateCameraParameters(imagePoints, worldPoints, ...
        'EstimateSkew', false, 'EstimateTangentialDistortion', true, ...
        'NumRadialDistortionCoefficients', 2, 'WorldUnits', 'millimeters', ...
        'InitialIntrinsicMatrix', [], 'InitialRadialDistortion', [], ...
        'ImageSize', [mrows, ncols]);
    imageFileNames = imageFileNames(imagesUsed);
    disp('Estimate camera parameters finish!');

    % View reprojection errors
    % h1=figure; showReprojectionErrors(cameraParams);

    % Visualize pattern locations
    % h2=figure; showExtrinsics(cameraParams, 'CameraCentric');

    % Display parameter estimation errors
    % displayErrors(estimationErrors, cameraParams);

    % For example, you can use the calibration data to remove effects of lens distortion.
    % undistortedImage = undistortImage(originalImage, cameraParams);

    % Get file indices
    fileIndices = cell(size(imageFileNames, 2), 1);
    for i = 1 : size(imageFileNames, 2)
        [~, index, ~] = fileparts(string(imageFileNames(i)));
        fileIndices{i} = index;
    end