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johnnychen94johnnychen94
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add im_to_matlab
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‎src/ImageCore.jl

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@@ -94,7 +94,8 @@ export
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width,
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widthheight,
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# matlab compatibility
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im_from_matlab
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im_from_matlab,
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im_to_matlab
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include("colorchannels.jl")

‎src/matlab.jl

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@@ -94,4 +94,71 @@ function _im_from_matlab(::Type{CT}, X::AbstractArray{T}) where {CT<:Colorant, T
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# FIXME(johnnychen94): not type inferrable here
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return StructArray{_CT}(X; dims=3)
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end
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_im_from_matlab(::Type{CT}, X::AbstractArray{T}) where {CT<:Gray, T<:Real} = of_eltype(CT, X)
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_im_from_matlab(::Type{CT}, X::AbstractArray{T}) where {CT<:Gray, T<:Real} = colorview(CT, X)
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"""
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im_to_matlab([T], X::AbstractArray) -> AbstractArray{T}
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Convert colorant array `X` to numerical array, using MATLAB's image layout convention.
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```julia
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img = rand(Gray{N0f8}, 4, 4)
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im_to_matlab(img) # 4×4 array with element type N0f8
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im_to_matlab(Float64, img) # 4×4 array with element type Float64
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img = rand(RGB{N0f8}, 4, 4)
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im_to_matlab(img) # 4×4×3 array with element type N0f8
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im_to_matlab(Float64, img) # 4×4×3 array with element type Float64
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```
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For color image `X`, it will be converted to RGB colorspace first. The alpha channel, if
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presented, will be removed.
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```jldoctest; setup = :(using ImageCore, Random; Random.seed!(1234))
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julia> img = Lab.(rand(RGB, 4, 4));
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julia> im_to_matlab(img) ≈ im_to_matlab(RGB.(img))
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true
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julia> img = rand(AGray{N0f8}, 4, 4);
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julia> im_to_matlab(img) ≈ im_to_matlab(gray.(img))
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true
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```
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!!! tip "lazy conversion"
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To save memory allocation, the conversion is done in lazy mode. In some cases, this
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could introduce performance overhead due to the repeat computation. This can be easily
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solved by converting eagerly via, e.g., `collect(im_to_matlab(...))`.
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!!! info "value range"
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The output value is always in range \$[0, 1]\$. Thus the equality
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`data ≈ im_to_matlab(im_from_matlab(data))` only holds when `data` is in also range
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\$[0, 1]\$. For example, if `eltype(data) == UInt8`, this equality will not hold.
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See also: [`im_from_matlab`](@ref).
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"""
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function im_to_matlab end
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im_to_matlab(X::AbstractArray{<:Number}) = X
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im_to_matlab(img::AbstractArray{CT}) where CT<:Colorant = im_to_matlab(eltype(CT), img)
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im_to_matlab(::Type{T}, img::AbstractArray{CT}) where {T,CT<:TransparentColor} =
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im_to_matlab(T, of_eltype(base_color_type(CT), img))
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im_to_matlab(::Type{T}, img::AbstractArray{<:Color}) where T =
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im_to_matlab(T, of_eltype(RGB{T}, img))
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im_to_matlab(::Type{T}, img::AbstractArray{<:Gray}) where T =
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of_eltype(T, channelview(img))
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# for RGB, only 1d and 2d cases are supported as other cases are not well-defined in MATLAB.
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im_to_matlab(::Type{T}, img::AbstractVector{<:RGB}) where T =
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im_to_matlab(T, reshape(img, (length(img), 1)))
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im_to_matlab(::Type{T}, img::AbstractMatrix{<:RGB}) where T =
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PermutedDimsArray(of_eltype(T, channelview(img)), (2, 3, 1))
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im_to_matlab(::Type{T}, img::AbstractArray{<:RGB}) where T =
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throw(ArgumentError("For $(ndims(img)) dimensional color image, manual conversion to MATLAB layout is required."))
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# this method allows `data === im_to_matlab(im_from_matlab(data))` for gray image
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im_to_matlab(::Type{T}, img::Base.ReinterpretArray{CT,N,T,<:AbstractArray{T,N}, true}) where {CT,N,T} =
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img.parent

‎test/matlab.jl

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msg = "Unrecognized MATLAB image layout."
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@test_throws ArgumentError(msg) im_from_matlab(data)
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end
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@testset "im_to_matlab" begin
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@testset "Gray" begin
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img = rand(Gray{N0f8}, 4, 5)
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data = @inferred im_to_matlab(img)
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@test eltype(data) == N0f8
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@test size(data) == (4, 5)
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@test img == data
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data = @inferred im_to_matlab(Float64, img)
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@test eltype(data) == Float64
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@test img == data
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img = rand(Gray{Float64}, 4, 5)
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data = @inferred im_to_matlab(img)
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@test eltype(data) == Float64
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@test size(data) == (4, 5)
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@test img == data
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img = rand(UInt8, 4, 5)
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@test img === @inferred im_to_matlab(img)
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img = rand(Gray{Float64}, 4)
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data = @inferred im_to_matlab(img)
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@test eltype(data) == Float64
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@test size(data) == (4, )
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end
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@testset "RGB" begin
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img = rand(RGB{N0f8}, 4, 5)
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data = @inferred im_to_matlab(img)
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@test eltype(data) == N0f8
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@test size(data) == (4, 5, 3)
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@test permutedims(channelview(img), (2, 3, 1)) == data
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data = @inferred im_to_matlab(Float64, img)
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@test eltype(data) == Float64
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@test size(data) == (4, 5, 3)
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@test permutedims(channelview(img), (2, 3, 1)) == data
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img = rand(RGB{Float64}, 4, 5)
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data = @inferred im_to_matlab(img)
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@test eltype(data) == Float64
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@test size(data) == (4, 5, 3)
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@test permutedims(channelview(img), (2, 3, 1)) == data
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img = rand(UInt8, 4, 5, 3)
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@test img === @inferred im_to_matlab(img)
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img = rand(RGB{Float64}, 4)
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data = @inferred im_to_matlab(img)
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@test eltype(data) == Float64
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@test size(data) == (4, 1, 3) # oh yes, we add one extra dimension for RGB but not for Gray
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img = rand(RGB{Float64}, 2, 3, 4)
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msg = "For 3 dimensional color image, manual conversion to MATLAB layout is required."
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@test_throws ArgumentError(msg) im_to_matlab(img)
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end
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@testset "Color3" begin
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img = Lab.(rand(RGB, 4, 5))
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@test @inferred(im_to_matlab(img)) ≈ @inferred(im_to_matlab(RGB.(img)))
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end
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@testset "transparent" begin
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img = rand(AGray, 4, 5)
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@test @inferred(im_to_matlab(img)) == @inferred(im_to_matlab(Gray.(img)))
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img = rand(RGBA, 4, 5)
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@test @inferred(im_to_matlab(img)) == @inferred(im_to_matlab(RGB.(img)))
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end
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end
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# test `im_from_matlab` and `im_to_matlab` are inverses of each other.
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data = rand(4, 5)
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@test data === im_to_matlab(im_from_matlab(data))
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# For RGB, ideally we would want to ensure this === equality, but it's not possible at the moment.
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data = rand(4, 5, 3)
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@test data == im_to_matlab(im_from_matlab(data))
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# the output range are always in [0, 1]; in this case they're not inverse of each other.
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data = rand(UInt8, 4, 5)
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img = im_from_matlab(data)
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@test im_to_matlab(img) == data ./ 255
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end

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