Adopt ImageBase for minfinite/maxfinite

Project.toml

@@ -1,9 +1,10 @@
 name = "ImageContrastAdjustment"
 uuid = "f332f351-ec65-5f6a-b3d1-319c6670881a"
 authors = ["Dr. Zygmunt L. Szpak <[email protected]>"]
-version = "0.3.11"
+version = "0.3.12"
 
 [deps]
+ImageBase = "c817782e-172a-44cc-b673-b171935fbb9e"
 ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534"
 ImageTransformations = "02fcd773-0e25-5acc-982a-7f6622650795"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"

src/HistogramAdjustmentAPI/HistogramAdjustmentAPI.jl

@@ -5,7 +5,7 @@ module HistogramAdjustmentAPI
 using ImageCore # ColorTypes is sufficient
 
 # TODO Relax this to all image color types
-const GenericGrayImage = AbstractArray{<:Union{Number, AbstractGray}}
+using ..ImageContrastAdjustment: GenericGrayImage
 
 """
     AbstractImageAlgorithm

src/ImageContrastAdjustment.jl

@@ -1,20 +1,21 @@
 module ImageContrastAdjustment
 
 using ImageCore
+using ImageBase
 using ImageTransformations: imresize
 # Where possible we avoid a direct dependency to reduce the number of [compat] bounds
 using ImageCore.MappedArrays
 using Parameters: @with_kw # Same as Base.@kwdef but works on Julia 1.0
 
+# TODO Relax this to all image color types
+const GenericGrayImage = AbstractArray{<:Union{Number, AbstractGray}}
+
 # TODO: port HistogramAdjustmentAPI to ImagesAPI
 include("HistogramAdjustmentAPI/HistogramAdjustmentAPI.jl")
 import .HistogramAdjustmentAPI: AbstractHistogramAdjustmentAlgorithm,
                                 adjust_histogram, adjust_histogram!
 
-# TODO Relax this to all image color types
-const GenericGrayImage = AbstractArray{<:Union{Number, AbstractGray}}
 
-include("core.jl")
 include("build_histogram.jl")
 include("algorithms/common.jl")
 include("algorithms/adaptive_equalization.jl")

src/algorithms/common.jl

@@ -52,7 +52,7 @@ function construct_pdfs(img::GenericGrayImage, targetimg::AbstractArray, nbins::
     if eltype(img) <: AbstractGray
         imin, imax = 0, 1
     else
-        imin, imax = min(minfinite(img), minfinite(targetimg)), max(maxfinite(img), maxfinite(targetimg))
+        imin, imax = min(minimum_finite(img), minimum_finite(targetimg)), max(maximum_finite(img), maximum_finite(targetimg))
     end
     edges, histogram = build_histogram(img, nbins, minval = imin, maxval = imax)
     _, target_histogram = build_histogram(targetimg, edges)

src/algorithms/linear_stretching.jl

@@ -146,7 +146,7 @@ end
 function (f::LinearStretching)(out::GenericGrayImage, img::GenericGrayImage)
     T = eltype(out)
     FT = eltype(floattype(T))
-    img_min, img_max = minfinite(img), maxfinite(img)
+    img_min, img_max = minimum_finite(img), maximum_finite(img)
     # explicit annotation is needed because the ?: line mixes three value types:
     # Nothing, T, and typeof(f.src_minval)
     src_minval::FT = isnothing(f.src_minval) ? img_min : f.src_minval

src/build_histogram.jl

@@ -147,8 +147,8 @@ edges, counts  = build_histogram(r, 256, minval = 0, maxval = 1)
 [1] E. Herrholz, "Parsimonious Histograms," Ph.D. dissertation, Inst. of Math. and Comp. Sci., University of Greifswald, Greifswald, Germany, 2011.
 """
 function build_histogram(img::GenericGrayImage, nbins::Integer = 256;
-                         minval::Union{Real,AbstractGray}=minfinite(img),
-                         maxval::Union{Real,AbstractGray}=maxfinite(img))
+                         minval::Union{Real,AbstractGray}=minimum_finite(img),
+                         maxval::Union{Real,AbstractGray}=maximum_finite(img))
     edges = partition_interval(nbins, minval, maxval)
     build_histogram(img, edges)
 end
@@ -172,8 +172,8 @@ build_histogram(img::AbstractArray{C}) where C<:Color{N0f8} =
     build_histogram(mappedarray(Gray{N0f8}, img))
 
 function build_histogram(img::AbstractArray{T}, nbins::Integer;
-                         minval::Union{Real,AbstractGray}=minfinite(img),
-                         maxval::Union{Real,AbstractGray}=maxfinite(img))  where T<:Union{N0f8, AbstractGray{N0f8}}
+                         minval::Union{Real,AbstractGray}=minimum_finite(img),
+                         maxval::Union{Real,AbstractGray}=maximum_finite(img))  where T<:Union{N0f8, AbstractGray{N0f8}}
     edgesraw, countsraw = build_histogram(img)
     return rebin(edgesraw, countsraw, nbins, minval, maxval)
 end

test/core.jl

@@ -1,20 +1,20 @@
 @testset "Core" begin
         A = [NaN 1 2 3;
              NaN 6 5 4]
-        @test ImageContrastAdjustment.minfinite(A) == 1
-        @test ImageContrastAdjustment.maxfinite(A) == 6
+        @test ImageContrastAdjustment.minimum_finite(A) == 1
+        @test ImageContrastAdjustment.maximum_finite(A) == 6
         A = rand(10:20, 5, 5)
-        @test ImageContrastAdjustment.minfinite(A) == minimum(A)
-        @test ImageContrastAdjustment.maxfinite(A) == maximum(A)
+        @test ImageContrastAdjustment.minimum_finite(A) == minimum(A)
+        @test ImageContrastAdjustment.maximum_finite(A) == maximum(A)
         A = reinterpret(N0f8, rand(0x00:0xff, 5, 5))
-        @test ImageContrastAdjustment.minfinite(A) == minimum(A)
-        @test ImageContrastAdjustment.maxfinite(A) == maximum(A)
+        @test ImageContrastAdjustment.minimum_finite(A) == minimum(A)
+        @test ImageContrastAdjustment.maximum_finite(A) == maximum(A)
         A = rand(Float32,3,5,5)
         img = colorview(RGB, A)
-        dc = ImageContrastAdjustment.minfinite(img)-RGB{Float32}(minimum(A, dims=(2,3))...)
+        dc = ImageContrastAdjustment.minimum_finite(img)-RGB{Float32}(minimum(A, dims=(2,3))...)
         @test norm(dc) < 1e-6
-        dc = ImageContrastAdjustment.maxfinite(img)-RGB{Float32}(maximum(A, dims=(2,3))...)
+        dc = ImageContrastAdjustment.maximum_finite(img)-RGB{Float32}(maximum(A, dims=(2,3))...)
         @test norm(dc) < 1e-6
-        @test ImageContrastAdjustment.minfinite(x->x^2,[NaN,10,2]) == 4
-        @test ImageContrastAdjustment.maxfinite(x->x^2,[NaN,10,2]) == 100
+        @test ImageContrastAdjustment.minimum_finite(x->x^2,[NaN,10,2]) == 4
+        @test ImageContrastAdjustment.maximum_finite(x->x^2,[NaN,10,2]) == 100
 end