#' @title Segment Lungs from CT scans

#' @description Segment Lungs from a non-contrast CT scan

#'

#' @param img Filename or \code{antsImage}

#' @param mask Should the image be masked

#' @param lthresh lower threshold for the image

#' @param verbose Print diagnostic messages

#' @param remove_background should background be removed first?

#'

#' @return List of image, lung, lung mask

#' @export

#' @importFrom extrantsr check_ants filler

#' @importFrom neurobase zero_pad maskEmptyImageDimensions mask_img

#' @importFrom oro.nifti voxdim

#' @importFrom stats median

#' @importFrom ANTsRCore resampleImage iMath smoothImage as.antsImage

#' @importFrom ANTsRCore antsImageClone

segment_lung = function(

  img,

  remove_background = FALSE,

  mask = TRUE,

  lthresh = -300,

  verbose = TRUE

) {

  if (verbose) {

    message("# Checking Inputs")

  }

  reg_img = check_ants(img)

  if (remove_background) {

    background = iMath(img = reg_img <= -1024,

                       operation = "GetLargestComponent")

    reg_img[background == 1] = -Inf

  }

  img = antsImageClone(reg_img)

  vres = voxdim(reg_img)

  if (verbose) {

    message("# Resampling Image to 1x1x1")

  }

  reg_img = resampleImage(reg_img, c(1,1,1))

  adder = 1025

  res = segment_human(

    img = reg_img,

    adder = adder,

    lthresh = -300,

    verbose = TRUE

  )

  ebody = coarse_body(res$body)

  ss = res$smoothed

  ss = mask_img(ss, ebody)

  # inds = getEmptyImageDimensions(body)

  # ss = maskEmptyImageDimensions(

  #   img = ss,

  #   inds = inds,

  #   mask.value = 0)

  # if (verbose) {

  #   message("# smoothing image")

  # }

  # ss = smoothImage(inimg = ss, sigma = 10,

  #                  max_kernel_width = 200)

  # # smoothed image is greater than some thresh

  # if (mask) {

  #   vals = as.numeric(ss)

  # } else {

  #   vals = as.numeric(ss)

  #   vals = vals[ as.numeric(body) > 0]

  # }

  # med = median(vals)

  # body = ss > med

  #

  # # body = ss > (-100 + adder)

  # if (verbose) {

  #   message("# Getting Humans")

  # }

  #

  # # zero padding in case for any connectedness

  # zp = as.array(body)

  # kdim = c(1,1,1)

  # zp = zero_pad(zp, kdim = kdim)

  # zp = as.antsImage(zp, reference = body)

  # # getting connected component

  # cc = iMath(img = zp, operation = "GetLargestComponent")

  # rm(list = "zp"); gc(); gc()

  # cc = as.array(cc)

  # cc = zero_pad(cc, kdim = kdim, invert = TRUE)

  # cc = as.antsImage(cc, reference = body)

  #

  # if (verbose) {

  #   message("# Filling Holes")

  # }

  # # cc = iMath(img = cc, operation = "FillHoles")

  # cc = filler(cc, fill_size = 60)

  # # cc = filler(cc, fill_size = 40)

  # cc = filler(cc, fill_size = 5, dilate = FALSE)

  #

  # # Dropping non-human stuff

  # inds = getEmptyImageDimensions(cc)

  #

  # if (verbose) {

  #   message("# Making New image")

  # }

  #

  # # Don't want to drop the indices,

  # # just blank them out

  # newimg = maskEmptyImageDimensions(img = reg_img, inds = inds)

  if (verbose) {

    message("# Getting Lungs: Thresholding")

  }

  newimg = ss

  cc = ebody

  lung = newimg < (lthresh + adder) & newimg > 0 & cc == 1

  if (verbose) {

    message("# Getting Lungs: Largest Component")

  }

  lung = iMath(img = lung, operation = "GetLargestComponent")

  # lung = iMath(img = lung, operation = "FillHoles")

  if (verbose) {

    message("# Getting Lungs: Filling Holes")

  }

  lung = filler(lung, fill_size = 2)

  if (verbose) {

    message("# Resampling Back to Original Image Size")

  }

  lung_mask = resampleImage(lung,

                            resampleParams = dim(img),

                            useVoxels = TRUE,

                            interpType = 1)

  # lung_mask = resampleImageToTarget(

  #   lung, target = img,

  #   interpType = "nearestNeighbor",

  #   verbose = verbose)

  lung = mask_img(img, lung_mask)

  # reg_img = reg_img - adder

  L = list(img = img,

           lung_mask = lung_mask,

           lung = lung

  )

  return(L)

}