force 1-based indexing for im_to_matlab output

src/ImageCore.jl

@@ -9,6 +9,7 @@ using Reexport
 @reexport using PaddedViews
 using MappedArrays, Graphics
 using OffsetArrays # for show.jl
+using OffsetArrays: no_offset_view
 using .ColorTypes: colorant_string
 using Colors: Fractional
 using MappedArrays: AbstractMultiMappedArray

src/matlab.jl

@@ -211,34 +211,34 @@ See also: [`im_from_matlab`](@ref).
 """
 function im_to_matlab end
 
-im_to_matlab(X::AbstractArray{<:Number}) = X
+im_to_matlab(X::AbstractArray{<:Number}) = no_offset_view(X)
 im_to_matlab(img::AbstractArray{CT}) where {CT<:Colorant} = im_to_matlab(eltype(CT), img)
 
 im_to_matlab(::Type{T}, img::AbstractArray{CT}) where {T,CT<:TransparentColor} =
     im_to_matlab(T, of_eltype(base_color_type(CT), img))
 im_to_matlab(::Type{T}, img::AbstractArray{<:Color}) where {T} =
     im_to_matlab(T, of_eltype(RGB{T}, img))
 im_to_matlab(::Type{T}, img::AbstractArray{<:Gray}) where {T} =
-    of_eltype(T, channelview(img))
+    no_offset_view(of_eltype(T, channelview(img)))
 
 # for RGB, unroll the color channel in the last dimension
 function im_to_matlab(::Type{T}, img::AbstractArray{<:RGB, N}) where {T, N}
-    v = of_eltype(T, channelview(img))
+    v = no_offset_view(of_eltype(T, channelview(img)))
     perm = (ntuple(i->i+1, N)..., 1)
     return PermutedDimsArray(v, perm)
 end
 
 # indexed image
 function im_to_matlab(::Type{T}, img::IndirectArray{CT}) where {T<:Real, CT<:Colorant}
-    return img.index, im_to_matlab(T, img.values)
+    return no_offset_view(img.index), im_to_matlab(T, img.values)
 end
 
 
 if VERSION >= v"1.6.0-DEV.1083"
     # this method allows `data === im_to_matlab(im_from_matlab(data))` for gray image
     im_to_matlab(::Type{T}, img::Base.ReinterpretArray{CT,N,T,<:AbstractArray{T,N},true}) where {CT,N,T} =
-        img.parent
+        no_offset_view(img.parent)
 else
     im_to_matlab(::Type{T}, img::Base.ReinterpretArray{CT,N,T,<:AbstractArray{T,N}}) where {CT,N,T} =
-        img.parent
+        no_offset_view(img.parent)
 end

test/matlab.jl

@@ -278,4 +278,44 @@
     data = rand(UInt8, 4, 5)
     img = im_from_matlab(data)
     @test im_to_matlab(img) == data ./ 255
+
+    @testset "offset array" begin
+        # JuliaImages accepts arbitrary offsets thus there's no need to force 1-based indexing,
+        # MATLAB, on the other hand, generally requires 1-based indexing to properly work.
+
+        # Gray
+        x = rand(4, 5)
+        xo = OffsetArray(x, (-2, -3))
+        img = im_from_matlab(xo)
+        @test axes(img) == (-1:2, -2:2)
+        @test eltype(img) == Gray{Float64}
+
+        m_img = im_to_matlab(img)
+        @test axes(m_img) == (1:4, 1:5)
+        @test m_img == x
+
+        # RGB
+        x = rand(4, 5, 3)
+        xo = OffsetArray(x, (-2, -3, 0))
+        img = im_from_matlab(xo)
+        @test axes(img) == (-1:2, -2:2)
+        @test eltype(img) == RGB{Float64}
+
+        m_img = im_to_matlab(img)
+        @test axes(m_img) == (1:4, 1:5, 1:3)
+        @test m_img == x
+
+        # indexed image
+        index = rand(1:5, 4, 5)
+        index_offset = OffsetArray(index, (-1, -1))
+        values = rand(5, 3)
+        img = im_from_matlab(index_offset, values)
+        @test axes(img) == (0:3, 0:4)
+        @test eltype(img) == RGB{Float64}
+
+        m_index, m_values = im_to_matlab(img)
+        @test axes(m_index) == (1:4, 1:5)
+        @test m_index == index
+        @test m_values == values
+    end
 end