nineonefive · October 1, 2020 19:42 · nineonefive · Oct 1, 2020
diff --git a/BannerDisplay.jl b/BannerDisplay.jl
 using Colors
 using Images
 using ProgressMeter
 using Distributed

 # 1.8 base colors, see https://minecraft.gamepedia.com/Map_item_format#Map_colors
 baseColors = [0 0 0;
    127 178 56;
    247 233 163;
    167 167 167;
    255 0 0;
    160 160 255;
    167 167 167;
    0 124 0;
    255 255 255;
    164 168 184;
    183 106 47;
    112 112 112;
    64 64 255;
    104 83 50;
    255 252 245;
    216 127 51;
    178 76 216;
    102 153 216;
    229 229 51;
    127 204 25;
    242 127 165;
    76 76 76;
    153 153 153;
    76 127 153;
    127 63 178;
    51 76 178;
    102 76 51;
    102 127 51;
    153 51 51;
    25 25 25;
    250 238 77;
    92 219 213;
    74 128 255;
    0 217 58;
    21 20 31;
    112 2 0;
    126 84 48]

 # compute the variants
 mapColors = map(eachrow(baseColors)) do color    
    [RGB((floor.(color .* mult) ./ 255)...) for mult in [255 180 220 135] ./ 255]
 end

 # restructure so that each element of the array is an RGB element corresponding to a color variant
 mapColors = hcat(mapColors...)[1, :]

 """
    closestPixel(pixel::RGB; metric=DE_2000())

 Maps the pixel (given in RGB from Colors.jl) to the closest permitted color in Minecraft 1.8 using the Delta-E color metric. Optionally
 takes another color metric to use. Returns a color from `mapColors`.
 """
 function closestPixel(pixel::RGB; m=DE_2000())
    Δ = colordiff.(pixel, mapColors; metric=m)
    Δ₀ = mapColors[argmin(Δ)]
 end

 """
    mcConvert(img; metric=DE_2000())

 Converts an image using as many available processes as possible (can do distributed computing with the Distributed library).

 # Example
 Load an image and convert it with 4 processes
 ```julia-repl
 julia> using Distributed
 julia> addprocs(4) # or start julia with 'julia -p 4'
 julia> @everywhere include("BannerDisplay.jl") # imports this file in all the processes
 julia> img = load("test.png")
 julia> R = mcConvert(img)
 # then can show images with Plots.jl or save them
 ```
 """
 function mcConvert(img; metric=DE_2000())
    img = RGB.(img)

    # Distributed computing, shows progressbar and number of processes.
    println("Starting with $(nworkers()) processes...")
    R = @showprogress pmap(eachcol(img)) do px  # column-wise operations are faster in Julia
        rgb = closestPixel.(px)
    end

    return hcat(R...) # convert our array of arrays to a single matrix of pixels
 end
	using Colors
	using Images
	using ProgressMeter
	using Distributed

	# 1.8 base colors, see https://minecraft.gamepedia.com/Map_item_format#Map_colors
	baseColors = [0 0 0;
	127 178 56;
	247 233 163;
	167 167 167;
	255 0 0;
	160 160 255;
	167 167 167;
	0 124 0;
	255 255 255;
	164 168 184;
	183 106 47;
	112 112 112;
	64 64 255;
	104 83 50;
	255 252 245;
	216 127 51;
	178 76 216;
	102 153 216;
	229 229 51;
	127 204 25;
	242 127 165;
	76 76 76;
	153 153 153;
	76 127 153;
	127 63 178;
	51 76 178;
	102 76 51;
	102 127 51;
	153 51 51;
	25 25 25;
	250 238 77;
	92 219 213;
	74 128 255;
	0 217 58;
	21 20 31;
	112 2 0;
	126 84 48]

	# compute the variants
	mapColors = map(eachrow(baseColors)) do color
	[RGB((floor.(color .* mult) ./ 255)...) for mult in [255 180 220 135] ./ 255]
	end

	# restructure so that each element of the array is an RGB element corresponding to a color variant
	mapColors = hcat(mapColors...)[1, :]

	"""
	closestPixel(pixel::RGB; metric=DE_2000())

	Maps the pixel (given in RGB from Colors.jl) to the closest permitted color in Minecraft 1.8 using the Delta-E color metric. Optionally
	takes another color metric to use. Returns a color from `mapColors`.
	"""
	function closestPixel(pixel::RGB; m=DE_2000())
	Δ = colordiff.(pixel, mapColors; metric=m)
	Δ₀ = mapColors[argmin(Δ)]
	end

	"""
	mcConvert(img; metric=DE_2000())

	Converts an image using as many available processes as possible (can do distributed computing with the Distributed library).

	# Example
	Load an image and convert it with 4 processes
	```julia-repl
	julia> using Distributed
	julia> addprocs(4) # or start julia with 'julia -p 4'
	julia> @everywhere include("BannerDisplay.jl") # imports this file in all the processes
	julia> img = load("test.png")
	julia> R = mcConvert(img)
	# then can show images with Plots.jl or save them
	```
	"""
	function mcConvert(img; metric=DE_2000())
	img = RGB.(img)

	# Distributed computing, shows progressbar and number of processes.
	println("Starting with $(nworkers()) processes...")
	R = @showprogress pmap(eachcol(img)) do px # column-wise operations are faster in Julia
	rgb = closestPixel.(px)
	end

	return hcat(R...) # convert our array of arrays to a single matrix of pixels
	end