Remove Images.jl dep (#70)

Author: ashwanirathee

Project.toml

@@ -4,10 +4,12 @@ version = "1.5.1"
 
 [deps]
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
+ColorVectorSpace = "c3611d14-8923-5661-9e6a-0046d554d3a4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534"
 ImageFiltering = "6a3955dd-da59-5b1f-98d4-e7296123deb5"
-Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+ImageMorphology = "787d08f9-d448-5407-9aad-5290dd7ab264"
 LightGraphs = "093fc24a-ae57-5d10-9952-331d41423f4d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5"
@@ -18,11 +20,13 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 Clustering = "0.10, 0.11, 0.12, 0.13, 0.14"
+ColorVectorSpace = "0.7, 0.8"
 DataStructures = "0.17.11, 0.18"
 Distances = "0.8, 0.9.2, 0.10"
 Documenter = "0.24, 0.25"
+ImageCore = "0.8.6"
 ImageFiltering = "0.6"
-Images = "0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24"
+ImageMorphology = "0.2.6"
 LightGraphs = "1.1"
 MetaGraphs = "0.6.6"
 RegionTrees = "0.2, 0.3"
@@ -32,7 +36,8 @@ julia = "1"
 
 [extras]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Documenter", "Test"]
+test = ["Documenter", "Images", "Test"]

src/ImageSegmentation.jl

@@ -3,7 +3,7 @@ module ImageSegmentation
 import Base: show
 
 using LinearAlgebra, Statistics
-using Images, DataStructures, StaticArrays, ImageFiltering, LightGraphs, SimpleWeightedGraphs, RegionTrees, Distances, StaticArrays, Clustering, MetaGraphs
+using DataStructures, StaticArrays, ImageCore, ImageFiltering, ImageMorphology, LightGraphs, SimpleWeightedGraphs, RegionTrees, Distances, StaticArrays, Clustering, MetaGraphs
 import Clustering: kmeans, fuzzy_cmeans
 
 include("compat.jl")

src/clustering.jl

@@ -1,5 +1,5 @@
 function img_to_data(img::AbstractArray{T,N}) where T<:Colorant where N
-    AT = Images.accum(T)
+    AT = accum_type(T)
     aimg = AT.(img)
     pimg = parent(aimg)
     ch = channelview(pimg)

src/core.jl

@@ -1,3 +1,8 @@
+accum_type(::Type{T}) where {T<:Integer} = Int
+accum_type(::Type{Float32})    = Float32
+accum_type(::Type{T}) where {T<:Real} = Float64
+accum_type(::Type{C}) where {C<:Colorant} = base_colorant_type(C){accum_type(eltype(C))}
+
 """
 `SegmentedImage` type contains the index-label mapping, assigned labels,
 segment mean intensity and pixel count of each segment.

src/fast_scanning.jl

@@ -1,6 +1,6 @@
-sharpness(img::AbstractArray{CT,N}) where {CT<:Images.NumberLike,N} = var(imfilter(img, Kernel.Laplacian(ntuple(i->true, Val(N)))))
+sharpness(img::AbstractArray{CT,N}) where {CT<:ImageCore.NumberLike,N} = var(imfilter(img, Kernel.Laplacian(ntuple(i->true, Val(N)))))
 
-function adaptive_thres(img::AbstractArray{CT,N}, block::NTuple{N,Int}) where {CT<:Images.NumberLike,N}
+function adaptive_thres(img::AbstractArray{CT,N}, block::NTuple{N,Int}) where {CT<:ImageCore.NumberLike,N}
     threshold = zeros(Float64, block)
     block_length = CartesianIndex(ntuple(i->ceil(Int,length(axes(img,i))/block[i]),Val(N)))
     net_s = sharpness(img)
@@ -20,7 +20,7 @@ getscalar(A::AbstractArray{T,N}, i::CartesianIndex{N}, block_length::CartesianIn
 
 getscalar(a::Real, i...) = a
 
-fast_scanning(img::AbstractArray{CT,N}, block::NTuple{N,Int} = ntuple(i->4,Val(N))) where {CT<:Images.NumberLike,N} =
+fast_scanning(img::AbstractArray{CT,N}, block::NTuple{N,Int} = ntuple(i->4,Val(N))) where {CT<:ImageCore.NumberLike,N} =
     fast_scanning(img, adaptive_thres(img, block))
 
 """
@@ -40,7 +40,7 @@ Segments the N-D image using a fast scanning algorithm and returns a
 
 # Examples:
 
-```jldoctest; setup = :(using Images, ImageSegmentation)
+```jldoctest; setup = :(using ImageCore, ImageMorphology, ImageSegmentation)
 julia> img = zeros(Float64, (3,3));
 
 julia> img[2,:] .= 0.5;

src/felzenszwalb.jl

@@ -80,7 +80,7 @@ function felzenszwalb(edges::Array{ImageEdge}, num_vertices::Int, k::Real, min_s
     return index_map, num_sets
 end
 
-meantype(::Type{T}) where T = typeof(zero(Images.accum(T))/2)
+meantype(::Type{T}) where T = typeof(zero(accum_type(T))/2)
 
 function felzenszwalb(img::AbstractArray{T, 2}, k::Real, min_size::Int = 0) where T<:Union{Real,Color}
 

src/region_growing.jl

@@ -1,5 +1,5 @@
 
-default_diff_fn(c1::CT1,c2::CT2) where {CT1<:Union{Colorant,Real}, CT2<:Union{Colorant,Real}} = sqrt(sum(abs2,(c1)-Images.accum(CT2)(c2)))
+default_diff_fn(c1::CT1,c2::CT2) where {CT1<:Union{Colorant,Real}, CT2<:Union{Colorant,Real}} = sqrt(sum(abs2,(c1)-accum_type(CT2)(c2)))
 
 """
     seg_img = seeded_region_growing(img, seeds, [kernel_dim], [diff_fn])
@@ -30,7 +30,7 @@ and returns a [`SegmentedImage`](@ref) containing information about the segments
 
 # Examples
 
-```jldoctest; setup = :(using Images, ImageSegmentation)
+```jldoctest; setup = :(using ImageCore, ImageMorphology,ImageSegmentation)
 julia> img = zeros(Gray{N0f8},4,4);
 
 julia> img[2:4,2:4] .= 1;
@@ -213,7 +213,7 @@ and returns a [`SegmentedImage`](@ref) containing information about the segments
 
 # Examples
 
-```jldoctest; setup = :(using Images, ImageSegmentation)
+```jldoctest; setup = :(using ImageCore, ImageMorphology, ImageSegmentation)
 julia> img = zeros(Gray{N0f8},4,4);
 
 julia> img[2:4,2:4] .= 1;

src/region_merging.jl

@@ -43,7 +43,7 @@ Returns true if `img` is homogeneous.
 
 # Examples
 
-```jldoctest; setup = :(using Images, ImageSegmentation)
+```jldoctest; setup = :(using ImageCore, ImageMorphology, ImageSegmentation)
 julia> img = 0.1*rand(6, 6);
 
 julia> img[4:end, 4:end] .+= 10;
@@ -101,7 +101,7 @@ Returns true if `img` is homogeneous.
 
 # Examples
 
-```jldoctest; setup = :(using Images, ImageSegmentation)
+```jldoctest; setup = :(using ImageCore, ImageMorphology, ImageSegmentation)
 julia> img = 0.1*rand(6, 6);
 
 julia> img[4:end, 4:end] .+= 10;

src/watershed.jl

@@ -31,7 +31,7 @@ Parameters:
 
 # Example
 
-```jldoctest; setup = :(using Images, ImageSegmentation)
+```jldoctest; setup = :(using ImageCore, ImageMorphology, ImageSegmentation)
 julia> seeds = falses(100, 100); seeds[50, 25] = true; seeds[50, 75] = true;
 
 julia> dists = distance_transform(feature_transform(seeds)); # calculate distances from seeds
@@ -46,7 +46,7 @@ julia> labels_map(results); # labels of segmented image
 function watershed(img::AbstractArray{T, N},
                    markers::AbstractArray{S,N};
                    mask::AbstractArray{Bool, N}=fill(true, axes(img)),
-                   compactness::Real = 0.0) where {T<:Images.NumberLike, S<:Integer, N}
+                   compactness::Real = 0.0) where {T<:ImageCore.NumberLike, S<:Integer, N}
 
     if axes(img) != axes(markers)
         error("image size doesn't match marker image size")
@@ -149,7 +149,7 @@ Suppresses all minima in grayscale image whose depth is less than h.
 
 H-minima transform is defined as the reconstruction by erosion of (img + h) by img. See Morphological image analysis by Soille pg 170-172.
 """
-function hmin_transform(img::Array{T, N}, h::Real) where {T<:Images.NumberLike, N}
+function hmin_transform(img::Array{T, N}, h::Real) where {T<:ImageCore.NumberLike, N}
     out = img.+h
     while true
         temp = max.(img, erode(out))