I have a set of images and X-ray data generated from coupled scanning electron microscopy and energy dispersive spectroscopy. Here's my problem:

I imaged transects of a rock surface like this (purple box outlines transect region):

I wanted really high resolution, so I did this using 7 images at 3000X magnification and stitched them together with the photomerge script in Photoshop. Here's an example of an individual image:

And its position in the photomerged image transect:

At each of these 7 locations, I also collected X-ray data that generates an element map for each element detected and writes this to a TIFF. I want to also stitch together each element map TIFF so I can overlay it on the merged transect image of the rock. This is the result I want:

The problem is that the element maps don't have enough features in them to be able to stitch them together with photomerge. It's basically binary - if an element is detected, the pixel is some color (like red for iron or yellow for sulfur in my example images), or black if the element is not detected. You can see there are large portions of the element maps that are mostly black.

I now have ~20 transects x 7 images each x ~10 elements which results in ~1400 images that need to be put together, hence the need for automation.

My idea was to stitch together the rock images with photomerge. The output of photomerge is a smart object where each image is a layer. Then, I would use a script to get the top left corner coordinates, the width, and height for each of the 7 images in the photomerged image object. I would then place and assign these properties to each of the corresponding element maps for the 7 images to generate the "merged" element maps to overlay on the image. I tried to work on this myself but I'm not proficient in javascript and couldn't wrap my head around the Photoshop API.

I uploaded an example dataset on Github here. The 7 transect positions are from left to right: -2, -1, 0, 1, 2, 3, 4. There are images of the rock and subdirectories with element data for each position.

I have a set of images and X-ray data generated from coupled scanning electron microscopy and energy dispersive spectroscopy. Here's my problem:

I imaged transects of a rock surface like this (purple box outlines transect region):

I wanted really high resolution, so I did this using 7 images at 3000X magnification and stitched them together with the photomerge script in Photoshop. Here's an example of an individual image:

And its position in the photomerged image transect:

At each of these 7 locations, I also collected X-ray data that generates an element map for each element detected and writes this to a TIFF. I want to also stitch together each element map TIFF so I can overlay it on the merged transect image of the rock. This is the result I want:

The problem is that the element maps don't have enough features in them to be able to stitch them together with photomerge. It's basically binary - if an element is detected, the pixel is some color (like red for iron or yellow for sulfur in my example images), or black if the element is not detected. You can see there are large portions of the element maps that are mostly black.

I now have ~20 transects x 7 images each x ~10 elements which results in ~1400 images that need to be put together, hence the need for automation.

My idea was to stitch together the rock images with photomerge. The output of photomerge is a smart object where each image is a layer. Then, I would use a script to get the top left corner coordinates, the width, and height for each of the 7 images in the photomerged image object. I would then place and assign these properties to each of the corresponding element maps for the 7 images to generate the "merged" element maps to overlay on the image. I tried to work on this myself but I'm not proficient in javascript and couldn't wrap my head around the Photoshop API.

I uploaded an example dataset on Github here. The 7 transect positions are from left to right: -2, -1, 0, 1, 2, 3, 4. There are images of the rock and subdirectories with element data for each position.

• javascript
• r
• raster
• photoshop
• extendscript

• 2 Hi @Caitlin, I don't quite understand what exactly you need as the result? A Photoshop file with all layers? Or a set of exported images (with each image positioned at the correct location)? – mdomino Commented Mar 13, 2020 at 19:07
• 2 @Caitlin LGTM!! – Nosniw Commented Mar 13, 2020 at 19:10
• 5 Ok, just wanted to ask, because someone might know of a way to achieve this without Photoshop. But if you need all the files in Photoshop at the end, it has to be done in Photoshop of course. Having written a lot of ExtendScript scripts in the past, I have to say what you are asking is probably to big of a task to be solved here on Stack Overflow. You are basically asking for an entire script that usually would require to hire someone for you. As you will need to load files by name, arrange them on the correct layers, position them by coordinates and so on. This is quite involved. – mdomino Commented Mar 13, 2020 at 19:58
• 1 @mdomino ah I was hoping for a photoshop file output for convenience but exported images would definitely work as well! – Caitlin Commented Mar 13, 2020 at 20:01
• 2 Here's another blog post about StackOverflow that, sadly, pares it to the Stanford prison experiment. @Caitlin it sounds like you're looking specifically and only for Photoshop script solutions to this problem? – Codebling Commented Mar 17, 2020 at 2:05

2 Answers 2

I do not know Photoshop or R, so did it JavaScript:

const names = { // map from directory names to patterns (where "#" stands for position index) of names of images therein
 "SEM_images" : "pos# image.tif",
 "Al" : "Al Kα1 pos# map data.tif",
 "Ba" : "Ba Lα1 pos# map data.tif",
 "C"  : "C Kα1_2 pos# map data.tif",
 "Ca" : "Ca Kα1 pos# map data.tif",
 "Fe" : "Fe Kα1 pos# map data.tif",
 "Hg" : "Hg Lα1 pos# map data.tif",
 "Ir" : "Ir Lα1 pos# map data.tif",
 "K"  : "K Kα1 pos# map data.tif",
 "Mg" : "Mg Kα1_2 pos# map data.tif",
 "Mn" : "Mn Kα1 pos# map data.tif",
 "Na" : "Na Kα1_2 pos# map data.tif",
 "O"  : "O Kα1 pos# map data.tif",
 "Os" : "Os Lα1 pos# map data.tif",
 "P"  : "P Kα1 pos# map data.tif",
 "S"  : "S Kα1 pos# map data.tif",
 "Si" : "Si Kα1 pos# map data.tif",
 "Ti" : "Ti Kα1 pos# map data.tif"
}

const SCALE = 1/10 // scale of output images

const OVERLAP = 1.0 // minimum *tested* (horizontal) overlap of images relative to their width
const H_BOX = 0.1 // height of parison box relative to height of images
const W_BOX = 0.1 // width  of parison box relative to width  of images
const ADJUSTMENT = 0 // (vertical) adjustment of parison box [pixels]

/* Merge images given:
 * dataset - dataset address as String
 * directory - directory name for images as String
 * pattern - pattern (where "#" stands for position index) of names of images in directory
 * pos_min - minimum position index of images as Number
 * pos_max - maximum position index of images as Number
 */
function Merge(dataset, directory, pos_min, pos_max) {
  if (dataset[dataset.length - 1] != "/") dataset += "/"
  const images = []
  for (let pos = pos_min; pos <= pos_max; ++pos) (images[pos - pos_min] = new Image).src = dataset + directory + "/" + names[directory].replace("#", pos)
  merge(images, dataset, pos_min, pos_max)
}

function Laplacian(imagedata) { // 5-point stencil approximation
  const data = imagedata.data
  const L = data.length/4
  const grayscale = new Float32Array(L)
  for (let i = 0; i < L; ++i) {
    const I = 4*i
    grayscale[i] = (data[I    ] + data[I + 1] + data[I + 2])/3
  }
  const Laplacian = new Float32Array(L)
  //const H = imagedata.height
  const Hm1 = imagedata.height - 1
  const W = imagedata.width
  const Wm1 = W - 1
  for (let r = 1; r < Hm1; ++r) {
    const R = r*W
    for (let c = 1; c < Wm1; ++c) {
      const i = R + c
      Laplacian[i] = grayscale[i - W] + grayscale[i + W] + grayscale[i - 1] + grayscale[i + 1] - 4*grayscale[i]
    }
  }
  for (let c = 1; c < Wm1; ++c) {
    //const i = c
    Laplacian[c] = grayscale[c + W] + grayscale[c - 1] + grayscale[c + 1] - 4*grayscale[c]
  }
  for (let c = 1; c < Wm1; ++c) {
    const i = Hm1*W + c
    Laplacian[i] = grayscale[i - W] + grayscale[i - 1] + grayscale[i + 1] - 4*grayscale[i]
  }
  for (let r = 1; r < Hm1; ++r) {
    const i = r*W
    Laplacian[i] = grayscale[i - W] + grayscale[i + W] + grayscale[i + 1] - 4*grayscale[i]
  }
  for (let r = 1; r < Hm1; ++r) {
    const i = r*W + Wm1
    Laplacian[i] = grayscale[i - W] + grayscale[i + W] + grayscale[i - 1] - 4*grayscale[i]
  }
  {
    const Lm1 = L - 1
    const LmW = L - W
    Laplacian[0  ] = grayscale[W      ] + grayscale[1      ] - 4*grayscale[0  ]
    Laplacian[W  ] = grayscale[2*W    ] + grayscale[Wm1    ] - 4*grayscale[W  ]
    Laplacian[LmW] = grayscale[LmW - W] + grayscale[LmW + 1] - 4*grayscale[LmW]
    Laplacian[Lm1] = grayscale[Lm1 - W] + grayscale[Lm1 - 1] - 4*grayscale[Lm1]
  }
  return Laplacian
}

function merge(images, dataset, pos_min, pos_max) {
  for (const image of images) if (!image.plete) {
    setTimeout(merge, 1000, images, dataset, pos_min, pos_max) // wait 1000ms = 1s
    return
  }
  let Row, Col
  const Coords = [[Row = 0, Col = 0]]
  let index = 0
  let image = images[index]
  const H = image.naturalHeight
  const W = image.naturalWidth
  if (W*H == 0) return []
  const canvas = document.createElement("canvas")
  canvas.height = H
  canvas.width  = W
  const context = canvas.getContext('2d')
  context.drawImage(image, 0, 0)
  let prev = Laplacian(context.getImageData(0, 0, W, H))
  const length = images.length
  const h = Math.round(H_BOX*H)
  const Hmh = H - h
  const w = Math.round(W_BOX*W)
  const o = Math.max(Math.round((1 - OVERLAP)*W), w)
  const Wmw = W - w
  const row_offset = Math.round(Hmh/2) + ADJUSTMENT
  const offset = row_offset*W
  for (++index; index < length; ++index) {
    image = images[index]
    if (image.naturalHeight != H || image.naturalWidth != W) alert("Dimension mismatch: " + image.src)
    context.drawImage(image, 0, 0)
    const curr = Laplacian(context.getImageData(0, 0, W, H))
    let max = -1
    let row, col
    for (let r = 0; r < Hmh; ++r) {
      const R = r*W
      for (let c = o; c < Wmw; ++c) {
        let m = 0
        for (let i = 0; i < h; ++i) {
          const I = i*W
          const K = R + I + c
          const k = offset + I
          for (let j = 0; j < w; ++j) if (prev[K + j]*curr[k + j] > 0) ++m
        }
        if (m > max) {
          max = m
          row = r
          col = c
        }
      }
    }
    Coords[index] = [(Row += row - row_offset)/H, (Col += col)/W]
    prev = curr
  }
  Stitch(dataset, pos_min, pos_max, Coords)
}

function Stitch(dataset, pos_min, pos_max, Coords) {
  if (dataset[dataset.length - 1] != "/") dataset += "/"
  document.body.appendChild(document.createElement("h1")).innerText = `${dataset} :[${pos_min},${pos_max}] @${JSON.stringify(Coords)}`
  const tasks = []
  for (const directory in names) {
    document.body.appendChild(document.createElement("h2")).innerText = directory
    const images = []
    for (let pos = pos_min; pos <= pos_max; ++pos) (images[pos - pos_min] = new Image).src = dataset + directory + "/" + names[directory].replace("#", pos)
    const target = document.body.appendChild(document.createElement("img"))
    target.height = 0
    target.width  = 0
    tasks.push([images, target])
  }
  process(tasks, Coords)
}

const ROW = 0
const COL = 1
function stitch(images, Coords) {
  let image
  let index
  for (index in images) {
    image = images[index]
    if (image.naturalHeight != 0 && image.naturalWidth != 0) break
  }
  const H = image.naturalHeight
  const W = image.naturalWidth
  const canvas = document.createElement("canvas")
  let r_min = 0
  let r_max = 0
  let c_min = 0
  let c_max = 0
  for (coords of Coords) {
    const r = coords[ROW]
    const c = coords[COL]
    if (r < r_min) r_min = r
    if (r > r_max) r_max = r
    if (c < c_min) c_min = c
    if (c > c_max) c_max = c
  }
  canvas.height = (r_max - r_min + 1)*H
  canvas.width  = (c_max - c_min + 1)*W
  const context = canvas.getContext('2d')
  if (context == null) {
    let list = ""
    for (const image of images) list += "\n- " + image.src
    alert("Too large: stitching area required for:" + list)
    return
  }
  let coords = Coords[index]
  let row = (coords[ROW] - r_min)*H
  let col = (coords[COL] - c_min)*W
  context.drawImage(image, col, row)
  const length = images.length
  for (++index; index < length; ++index) {
    image = images[index]
    if (image.naturalHeight == 0 || image.naturalWidth == 0) continue
    if (image.naturalHeight != H || image.naturalWidth != W) alert("Dimension mismatch: " + image.src)
    coords = Coords[index]
    row = coords[ROW]*H
    col = coords[COL]*W
    context.drawImage(image, col, row)
  }
  return canvas.toDataURL()
}

function process(tasks, Coords) {
  const task = tasks.shift()
  const images = task[0]
  for (const image of images) if (!image.plete) {
    tasks.push(task)
    setTimeout(process, 1000, tasks, Coords) // wait 1000ms = 1s
    return
  }
  const target = task[1]
  target.src = stitch(images, Coords)
  target.onload = function () {
    this.height = SCALE*this.naturalHeight
    this.width  = SCALE*this.naturalWidth
    this.style = "border: solid black 1px"
  }
  if (tasks.length > 0) process(tasks, Coords)
}

To run, do something like:

Merge("https://raw.githubusercontent./CaitlinCasar/dataStitcher/master/example_dataset/", "SEM_images", -2, 4)

Example SEM_images with Fe overlay:

It would have been nice to do this entirely in a Photoshop script or entirely in R, but I have a working solution that uses both. I manually pulled XY coords from the bottom left corner of each layer in the photomerge output from Photoshop and assigned them to rasterized images in R:

#!/usr/bin/env Rscript

#Caitlin Casar
#github./caitlincasar
#19 March 2020
#DataStitchR stitches panoramic images of SEM images coupled to x-ray energy dispersive spectroscopy.

# usage: ./dataStitchR. -f "/Users/Caitlin/Desktop/dataStitcher/example_dataset" -b "/Users/Caitlin/Desktop/dataStitcher/example_dataset/SEM_images" -c "/Users/Caitlin/Desktop/dataStitcher/coordinates.txt" -z ".tif" -m ".tif" -a "Unknown|Os|SEM" -d "overview" -y "overview" -p TRUE -o "example" -n "example"
# usage Rscript dataStitchR.R -f "/Users/Caitlin/Desktop/dataStitcher/example_dataset" -b "/Users/Caitlin/Desktop/dataStitcher/example_dataset/SEM_images" -c "/Users/Caitlin/Desktop/dataStitcher/coordinates.txt" -z ".tif" -m ".tif" -a "Unknown|Os|SEM" -d "overview" -y "overview" -p TRUE -o "example" -n "example"

message("
                                        #####                                    ######  
        #####     ##    #####    ##    #     #  #####  #  #####   ####   #    #  #     # 
        #    #   #  #     #     #  #   #          #    #    #    #    #  #    #  #     # 
        #    #  #    #    #    #    #   #####     #    #    #    #       ######  ######  
        #    #  ######    #    ######        #    #    #    #    #       #    #  #   #   
        #    #  #    #    #    #    #  #     #    #    #    #    #    #  #    #  #    #  
        #####   #    #    #    #    #   #####     #    #    #     ####   #    #  #     # 
        

        ")

message("DataStitchR was created by Caitlin Casar and is maintained at github./CaitlinCasar/dataStitchR.
        ")
message("DataStitchR stitches panoramic images of SEM images coupled to x-ray energy dispersive spectroscopy.
        ")
message("For help, run 'dataStitchR.R --help'.
        
        
        ")

suppressPackageStartupMessages(require(optparse))

option_list = list(
  make_option(c("-f", "--file"), action="store", default=getwd(), type='character',
              help="Input parent directory with subdirectories of element xray data to be stitched. The element names should be abbreviated, i.e. 'Ca' for calcium."),
  make_option(c("-o", "--out"), action="store", default=NA, type='character',
              help="Output file directory. This is where your x-ray raster brick and output figures will be saved."),
  make_option(c("-n", "--name"), action="store", default=NA, type='character',
              help="Optional name for output files."),
  make_option(c("-b", "--base-images"), action="store", default=NA, type='character',
              help="SEM image file directory."),
  make_option(c("-c", "--coords"), action="store", default=NA, type='character',
              help="Tab-delimited file of xy coordinates for each image. A third column should denote stitching positions that correspond to the file names for each image."),
  make_option(c("-u", "--use-positions"), action="store", default="-?(?<![Kα1||Kα1_2])\\d+", type='character',
              help="Optional regex pattern to extract position IDs from each file name that corresponds to positions in the xy file. The default searches for numbers that appear after 'Kα1' or 'Kα2'. Numbers can include signs, i.e. -1 is acceptable."),
  make_option(c("-z", "--z-format"), action="store", default="*", type='character',
              help="Optional regex pattern of x-ray image formats to select for stitching, i.e. '.tif'."),
  make_option(c("-m", "--make"), action="store", default="*", type='character',
              help="Optional regex pattern of SEM image formats to select for stitching, i.e. '.tif'. You do not need to specify this unless you are generating a PDF output."),
  make_option(c("-a", "--all-exclude"), action="store", default=NA, type='character',
              help="Optional regex pattern of x-ray file directories to exclude from stitiching, i.e. the element your sample was coated with."),
  make_option(c("-d", "--drop"), action="store", default=NA, type='character',
              help="Optional regex pattern of files to exclude from x-ray data stitching."),
  make_option(c("-y", "--y-exclude"), action="store", default=NA, type='character',
              help="Optional regex pattern of files to exclude from SEM image stitiching. You do not need to specify this unless you are generating a PDF output."),
  make_option(c("-v", "--verbose"), action="store_true", default=TRUE,
              help="Print updates to console [default %default]."),
  make_option(c("-q", "--quiet"), action="store_false", dest="verbose",
              help="Do not print anything to the console."),
  make_option(c("-p", "--pdf"), action="store", default=FALSE,
              help="Generate PDF of x-ray brick colored by element superimposed on the SEM image, default is TRUE [default %default].")  
)
opt = parse_args(OptionParser(option_list=option_list))


#load dependencies 
suppressPackageStartupMessages(require(pacman))
pacman::p_load(raster, magick, tidyverse, rasterVis, ggnewscale, Hmisc, cowplot)

#create list all sub-directories within main directory
directories <- list.dirs(opt$f, full.names = T , recursive =F)
if(!is.na(opt$a)){
  directories <- directories[!str_detect(directories, opt$a)]
}


#set image coordinates
xy <- read_delim(opt$c, delim = "\t", col_types = cols())
positions <- xy %>%
  select(-x, -y)


# stitch xray images ------------------------------------------------------

#stitch xray images into panoramas and store in raster brick 
xray_brick_list <- list()
xray_data <- list()
for(j in 1:length(directories)){
  path = directories[j]
  files <- list.files(path, full.names = T, pattern = opt$z)
  if(!is.na(opt$d)){
    files <- files[!str_detect(files, opt$d)]
  }
  if(length(files) >0){
    xray_data[[j]] <- str_extract(path, "([^/]+$)")
    message(paste0("Stitching ", str_extract(xray_data[[j]], "([^/]+$)"), " data (element ", j, " of ", length(directories), ")..."))
    xy_id <- which(positions[[1]] %in% str_extract(files, opt$u))
    panorama <- list()
    for(i in 1:length(files)){
      message(paste0("Processing image ", i, " of ", length(files), "..."))
      image <- files[i] %>% image_read() %>% 
        image_quantize(colorspace = 'gray') %>% 
        image_equalize() 
      temp_file <- tempfile()
      image_write(image, path = temp_file, format = 'tiff')
      image <- raster(temp_file) %>%
        cut(breaks = c(-Inf, 150, Inf)) - 1
      image <- aggregate(image, fact=4)
      image_extent <- extent(matrix(c(xy$x[xy_id[i]], xy$x[xy_id[i]] + 1024, xy$y[xy_id[i]], xy$y[xy_id[i]]+704), nrow = 2, ncol = 2, byrow = T))
      image_raster <- setExtent(raster(nrows = 704, ncols = 1024), image_extent, keepres = F)
      values(image_raster) <- values(image)
      panorama[[xy_id[i]]] <- image_raster
    }
      empty_xy_id <- which(!positions[[1]] %in% str_extract(files, opt$u))
      if(length(empty_xy_id) > 0){
        for(k in 1:length(empty_xy_id)){
          empty_raster_extent <- extent(matrix(c(xy$x[empty_xy_id[k]], xy$x[empty_xy_id[k]] + 1024, xy$y[empty_xy_id[k]], xy$y[empty_xy_id[k]]+704), nrow = 2, ncol = 2, byrow = T))
          empty_raster <- setExtent(raster(nrows = 704, ncols = 1024), empty_raster_extent, keepres = F)
          values(empty_raster) <- 0
          panorama[[empty_xy_id[k]]] <- empty_raster
        }
      }
      panorama_merged <- do.call(merge, panorama)
      xray_brick_list[[j]] <- panorama_merged
  }
}

message("Stitching plete. Creating x-ray brick...")
xray_brick <- do.call(brick, xray_brick_list)
names(xray_brick) <- unlist(xray_data)
message("...plete.")

#write the brick 
message("Writing brick...")


if(!is.na(opt$o)){
  dir.create(opt$o)
  if(!is.na(opt$n)){
    out_brick <- writeRaster(xray_brick, paste0(opt$o, "/", opt$n,"_brick.grd"), overwrite=TRUE, format="raster")
    x <- writeRaster(xray_brick, paste0(opt$o, "/", opt$n,"_brick.tif"), overwrite=TRUE, format="GTiff",options=c("INTERLEAVE=BAND","COMPRESS=LZW"))
  }else{
    out_brick <- writeRaster(xray_brick, path = opt$o, "brick.grd", overwrite=TRUE, format="raster")
    x <- writeRaster(xray_brick, path = opt$o, "brick.tif", overwrite=TRUE, format="GTiff",options=c("INTERLEAVE=BAND","COMPRESS=LZW"))
  }
}else{
  if(!is.na(opt$n)){
    out_brick <- writeRaster(xray_brick, paste0(opt$n,"_brick.grd"), overwrite=TRUE, format="raster")
    x <- writeRaster(xray_brick, paste0(opt$n,"_brick.tif"), overwrite=TRUE, format="GTiff",options=c("INTERLEAVE=BAND","COMPRESS=LZW"))
  }else{
    out_brick <- writeRaster(xray_brick, "brick.grd", overwrite=TRUE, format="raster")
    x <- writeRaster(xray_brick, "brick.tif", overwrite=TRUE, format="GTiff",options=c("INTERLEAVE=BAND","COMPRESS=LZW"))
  }
}


message("...plete.")

#flush everything we don't need from memory
remove(list = c("x", "xray_brick_list", "xray_data", "empty_raster", "empty_raster_extent",
                "i", "j", "k", "path", "positions", "xy_id", 
                "panorama_merged", "panorama", "empty_xy_id", "files", "directories"))


# create base SEM image ---------------------------------------------------
if(opt$p){
  
if(!is.na(opt$b)){
  SEM_images <- list.files(opt$b, full.names = T, pattern = opt$m)
}else{
  message("Please provide a file path for your SEM images to stitch. For help, see 'dataStitchR.R --help.'")
  break
}

if(!is.na(opt$y)){
  SEM_images  <- SEM_images[!str_detect(SEM_images , opt$y)]
}
SEM_panorama <- list()

message("Stitching SEM images into panorama...")
for(i in 1:length(SEM_images)){
  image <- SEM_images[i] %>% image_read() %>%
    image_quantize(colorspace = 'gray') %>%
    image_equalize()
  temp_file <- tempfile()
  image_write(image, path = temp_file, format = 'tiff')
  image <- raster(temp_file)
  image_extent <- extent(matrix(c(xy$x[i], xy$x[i] + 1024, xy$y[i], xy$y[i]+704), nrow = 2, ncol = 2, byrow = T))
  image_raster <- setExtent(raster(nrows = 704, ncols = 1024), image_extent, keepres = F)
  values(image_raster) <- values(image)
  SEM_panorama[[i]] <- image_raster
}
SEM_panorama_merged <- do.call(merge, SEM_panorama)
message("...plete.")

#flush everything we don't need from memory
remove(list = c("SEM_panorama", "SEM_images", "image", "image_extent", "image_raster"))

#optinal plot to check if SEM image merge looks correct 
#plot(SEM_panorama_merged, col = gray.colors(10, start = 0.3, end = 0.9, gamma = 2.2, alpha = NULL))


# plot the data -----------------------------------------------------------

message("Generating plot...")
# Set color palette

#palette source: https://sciencenotes/molecule-atom-colors-cpk-colors/

element_colors <- c("#FFFFFF", "#D9FFFF", "#CC80FF", "#C2FF00", "#FFB5B5", "#909090", "#3050F8",
                    "#FF0D0D", "#90E050", "#B3E3F5", "#AB5CF2", "#8AFF00", "#BFA6A6", "#F0C8A0",
                    "#FF8000", "#FFFF30", "#1FF01F", "#80D1E3", "#8F40D4", "#3DFF00", "#E6E6E6",
                    "#BFC2C7", "#A6A6AB", "#8A99C7", "#9C7AC7", "#E06633", "#F090A0", "#50D050",
                    "#C88033", "#7D80B0", "#C28F8F", "#668F8F", "#BD80E3", "#FFA100", "#A62929",
                    "#5CB8D1", "#702EB0", "#00FF00", "#94FFFF", "#94E0E0", "#73C2C9", "#54B5B5",
                    "#3B9E9E", "#248F8F", "#0A7D8C", "#006985", "#C0C0C0", "#FFD98F", "#A67573",
                    "#668080", "#9E63B5", "#D47A00", "#940094", "#429EB0", "#57178F", "#00C900",
                    "#70D4FF", "#FFFFC7", "#D9FFC7", "#C7FFC7", "#A3FFC7", "#8FFFC7", "#61FFC7",
                    "#45FFC7", "#30FFC7", "#1FFFC7", "#00FF9C", "#00E675", "#00D452", "#00BF38",
                    "#00AB24", "#4DC2FF", "#4DA6FF", "#2194D6", "#267DAB", "#266696", "#175487",
                    "#D0D0E0", "#FFD123", "#B8B8D0", "#A6544D", "#575961", "#9E4FB5", "#AB5C00",
                    "#754F45", "#428296", "#420066", "#007D00", "#70ABFA", "#00BAFF", "#00A1FF",
                    "#008FFF", "#0080FF", "#006BFF", "#545CF2", "#785CE3", "#8A4FE3", "#A136D4",
                    "#B31FD4", "#B31FBA", "#B30DA6", "#BD0D87", "#C70066", "#CC0059", "#D1004F",
                    "#D90045", "#E00038", "#E6002E", "#EB0026")
names(element_colors) <- c("H",  "He", "Li", "Be", "B",  "C",  "N",  "O",  "F",  "Ne", "Na", "Mg", "Al", "Si",
                           "P",  "S",  "Cl", "Ar", "K",  "Ca", "Sc", "Ti", "V",  "Cr", "Mn", "Fe", "Co", "Ni",
                           "Cu", "Zn", "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y",  "Zr", "Nb", "Mo",
                           "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", "Sb", "Te", "I",  "Xe", "Cs", "Ba",
                           "La", "Ce", "Pr", "Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb",
                           "Lu", "Hf", "Ta", "W",  "Re", "Os", "Ir", "Pt", "Au", "Hg", "Tl", "Pb", "Bi", "Po",
                           "At", "Rn", "Fr", "Ra", "Ac", "Th", "Pa", "U",  "Np", "Pu", "Am", "Cm", "Bk", "Cf",
                           "Es", "Fm", "Md", "No", "Lr", "Rf", "Db", "Sg", "Bh", "Hs", "Mt")

xray_frame <- as.data.frame(xray_brick, xy=TRUE) 
xray_frame <- gather(xray_frame, element, value, colnames(xray_frame)[3]:colnames(xray_frame)[ncol(xray_frame)])



element_plotter<-function(coord_frame, brick, SEM_image, colors){
  p <-rasterVis::gplot(SEM_image) +
    geom_tile(aes(fill = value)) +
    scale_fill_gradient(low = 'black', high = 'white') +
    ggnewscale::new_scale_fill()
  for(i in names(brick)){ 
    message(paste0("Adding ", names(brick[[i]]), " to plot..."))
    element_coords <- coord_frame %>%
      filter(element == names(brick[[i]]) & value!=0)
    p <- p+geom_raster(element_coords, mapping = aes(x, y, fill = element, alpha = value)) +
      scale_fill_manual(values = colors) +
      ggnewscale::new_scale_fill()
  }
  message("Writing plot...")
  suppressWarnings(print(p + 
                           coord_fixed() +
                           theme(axis.title = element_blank(),
                                 axis.text = element_blank(),
                                 legend.position = "none")))
}

element_plot <- element_plotter(xray_frame, xray_brick, SEM_panorama_merged, element_colors)

element_plot_legend <- data.frame(element = unique(xray_frame$element)) %>%
  rownames_to_column() %>% 
  ggplot(aes(element, rowname, fill=element)) + 
  geom_bar(stat= "identity") + 
  scale_fill_manual(values = element_colors) +
  guides(fill=guide_legend(nrow=2)) +
  theme(legend.title = ggplot2::element_blank(),
        legend.text = ggplot2::element_text(size = 8))

element_plot_with_legend <- plot_grid(
  element_plot, 
  plot_grid(get_legend(element_plot_legend), 
            ncol = 1), 
  nrow = 2, 
  rel_heights = c(8,2)
)


# generate PDF ------------------------------------------------------------

if(!is.na(opt$n)){
  if(!is.na(opt$o)){
    pdf(paste0(opt$o, "/", opt$n, "_element_plot.pdf"),
        width = 13.33, 
        height = 7.5)
  }else{
    pdf(paste0(opt$n, "_element_plot.pdf"),
        width = 13.33, 
        height = 7.5)
  }
}else{
  if(!is.na(opt$o)){
    pdf(paste0(opt$o, "/", "element_plot.pdf"),
        width = 13.33, 
        height = 7.5)
  }else{
    pdf("element_plot.pdf",
        width = 13.33, 
        height = 7.5) 
  }
}


print(element_plot_with_legend)

dev.off()

message("...plete.")

remove(list = ls())

}