R/firstJulia.R

In Gdlv/trackJR: Using Julia language and R for tracking tiny insects

#' firstJulia
#'
#'@description First step to check and start using Julia function for tracking tiny insect
#'
#' @return It return the linked function of Julia
#' @export
#'
#' @examples
#'
#'  #Just type the function
#' firstJulia()
#'#

firstJulia<-function() {
  JuliaCall::julia_setup()
  cat("This could take few minutes, let's make a 'Mate' until Julia install some packages to work if they are not installed!")
  cat("-")

  JuliaCall::julia_command("
  tryusing(pkgsym) = try
    @eval using $pkgsym
    return true
  catch e
    return e
  end
")
  JuliaCall::julia_command('
  if tryusing("Images") !== true
         import Pkg; Pkg.add("Images")
       end
')
  JuliaCall::julia_command('
  if tryusing("Images") !== true
         import Pkg; Pkg.add("ImageMagick")
       end
')
  JuliaCall::julia_command('
  if tryusing("Images") !== true
         import Pkg; Pkg.add("Statistics")
       end
')
  JuliaCall::julia_command('
  if tryusing("Images") !== true
         import Pkg; Pkg.add("LinearAlgebra")
       end
')
  JuliaCall::julia_command('
  if tryusing("Images") !== true
         import Pkg; Pkg.add("ImageFiltering")
       end
')
  JuliaCall::julia_command('
  if tryusing("Images") !== true
         import Pkg; Pkg.add("DataFrames")
       end
')

  JuliaCall::julia_command("
begin
	using Images
	using ImageMagick
	using Statistics
	using LinearAlgebra
	using ImageFiltering
	using DataFrames
end
")

  JuliaCall::julia_command("
brightness(c) = 0.3 * c.r + 0.59 * c.g + 0.11 * c.b
")


  JuliaCall::julia_command("
function convolve(M, kernel)
    height, width = size(kernel)

    half_height = fld(height,2)
    half_width = fld(width,2)

    new_image = similar(M)

    # (i, j) loop over the original image
	m, n = size(M)
    @inbounds for i in 1:m
        for j in 1:n
            # (k, l) loop over the neighbouring pixels
			accumulator = 0 * M[1, 1]
			for k in -half_height:-half_height + height - 1
				for l in -half_width:-half_width + width - 1
					Mi = i - k
					Mj = j - l
					# First index into M
					if Mi < 1
						Mi = 1
					elseif Mi > m
						Mi = m
					end
					# Second index into M
					if Mj < 1
						Mj = 1
					elseif Mj > n
						Mj = n
					end

					accumulator += kernel[k, l] * M[Mi, Mj]
				end
			end
			new_image[i, j] = accumulator
        end
    end

    return new_image
end")

  JuliaCall::julia_command("
function edgeness(img)
	Sy, Sx = Kernel.sobel()
	b = brightness.(img)

	Gy = convolve(b, Sy)
	Gx = convolve(b, Sx)

	sqrt.(Gx.^2 + Gy.^2)
end
")

  JuliaCall::julia_command("
function trackingR(path)
	vidimg = readdir(path)
	coords= Dict()
	Threads.@threads for i = 1:length(vidimg)
	    imgg= load(string(path,vidimg[i]))
			edi=edgeness(imgg)
			coords[i]=findmax(edi)
		end

			data1= DataFrame()
			data1.fr = [vidimg[j] for j=1:length(coords)]
			data1.x = [coords[j][2][2] for j=1:length(coords)]
			data1.y = [coords[j][2][1] for j=1:length(coords)]
		return data1
end
")


}