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[compat] ImageCore 0.10 #97

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Jul 20, 2023
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[compat] ImageCore 0.10 #97

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4 changes: 2 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "ImageSegmentation"
uuid = "80713f31-8817-5129-9cf8-209ff8fb23e1"
version = "1.8.1"
version = "1.8.2"

[deps]
Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
Expand All @@ -23,7 +23,7 @@ DataStructures = "0.17.11, 0.18"
Distances = "0.8, 0.9.2, 0.10"
Documenter = "0.24, 0.25"
Graphs = "1.4.1"
ImageCore = "0.9"
ImageCore = "0.10"
ImageFiltering = "0.6, 0.7"
ImageMorphology = "0.2.6, 0.3, 0.4"
MetaGraphs = "0.7"
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7 changes: 1 addition & 6 deletions src/core.jl
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Expand Up @@ -7,12 +7,7 @@ accum_type(::Type{C}) where {C<:Colorant} = base_colorant_type(C){accum_type(e
accum_type(val) = isa(val, Type) ? throw_accum_type(val) : convert(accum_type(typeof(val)), val)
throw_accum_type(T) = error("type $T not supported in `accum_type`")

# TODO:
# _abs2(c::MathTypes) = c ⋅ c
_abs2(c::MathTypes) = mapreducec(v->float(v)^2, +, 0, c)
_abs2(x) = abs2(x)

default_diff_fn(c1::CT1,c2::CT2) where {CT1<:Union{Colorant,Real}, CT2<:Union{Colorant,Real}} = sqrt(_abs2(c1-accum_type(c2)))
default_diff_fn(c1::CT1,c2::CT2) where {CT1<:Union{Colorant,Real}, CT2<:Union{Colorant,Real}} = sqrt(abs2(c1-accum_type(c2)))

"""
`SegmentedImage` type contains the index-label mapping, assigned labels,
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2 changes: 1 addition & 1 deletion src/felzenszwalb.jl
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Expand Up @@ -102,7 +102,7 @@ function felzenszwalb(img::AbstractArray{T}, k::Real, min_size::Int = 0) where T
if I >= J
continue
end
edges[num+=1] = ImageEdge(L[I], L[J], sqrt(_abs2(imgI-meantype(T)(img[J]))))
edges[num+=1] = ImageEdge(L[I], L[J], sqrt(abs2(imgI-meantype(T)(img[J]))))
end
end
deleteat!(edges, num+1:num_edges) # compensate for the ones we were missing at the image edges
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2 changes: 1 addition & 1 deletion test/flood_fill.jl
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Expand Up @@ -56,7 +56,7 @@ using Test
# Colors
path = download("https://github.com/JuliaImages/juliaimages.github.io/raw/source/docs/src/pkgs/segmentation/assets/flower.jpg")
img = load(path)
seg = flood(img, CartesianIndex(87,280); thresh=0.3*sqrt(3)) # TODO: eliminate the sqrt(3) when we transition to `abs2(c) = c ⋅ c`
seg = flood(img, CartesianIndex(87,280); thresh=0.3)
@test 0.2*length(seg) <= sum(seg) <= 0.25*length(seg)
c = mean(img[seg])
# N0f8 makes for easier approximate testing
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