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Merged
merged 1 commit into from
Sep 6, 2021
Merged

Update to ImageCore 0.9 #72

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9 changes: 3 additions & 6 deletions Project.toml
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Expand Up @@ -4,7 +4,6 @@ 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"
Expand All @@ -20,12 +19,11 @@ 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"
ImageCore = "0.9"
ImageFiltering = "0.6, 0.7"
ImageMorphology = "0.2.6"
LightGraphs = "1.1"
MetaGraphs = "0.6.6"
Expand All @@ -38,9 +36,8 @@ julia = "1"
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[targets]
test = ["Documenter", "FileIO", "ImageMagick", "Images", "SparseArrays", "Test"]
test = ["Documenter", "FileIO", "ImageMagick", "SparseArrays", "Test"]
1 change: 1 addition & 0 deletions src/ImageSegmentation.jl
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Expand Up @@ -4,6 +4,7 @@ import Base: show

using LinearAlgebra, Statistics
using DataStructures, StaticArrays, ImageCore, ImageFiltering, ImageMorphology, LightGraphs, SimpleWeightedGraphs, RegionTrees, Distances, StaticArrays, Clustering, MetaGraphs
using ImageCore.ColorVectorSpace: MathTypes
import Clustering: kmeans, fuzzy_cmeans

const PairOrTuple{K,V} = Union{Pair{K,V},Tuple{K,V}}
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8 changes: 8 additions & 0 deletions src/core.jl
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Expand Up @@ -4,6 +4,14 @@ accum_type(::Type{T}) where {T<:Real} = Float64
accum_type(::Type{T}) where {T<:FixedPoint} = floattype(T)
accum_type(::Type{C}) where {C<:Colorant} = base_colorant_type(C){accum_type(eltype(C))}

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`")

_abs2(c::MathTypes) = c ⋅ c
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Maybe stick to the old usage for now?

Suggested change
_abs2(c::MathTypes) = c ⋅ c
_abs2(c::MathTypes) = mapreducec(v->float(v)^2, +, 0, c)

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That sounds like a good idea.

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That seems to fix things, by and large. The few differences:

  • the horse demo with felzenszwalb(img, 100) merges grass and sky, resulting in a dull gray used for both. That's true here (with your suggested change) and on the released version of the package. Both also give 43 segments, making me think that this probably changed some time ago but the image in the docs hasn't been updated. felzenszwalb(img, 10) produces the result shown in the image.
  • There's now an off-by-one in the number of segments in https://juliaimages.org/stable/pkgs/segmentation/#Fast-Scanning (2539 instead of 2538, and 64 instead of 65). There is no visible change in the quality of the result, so to me that seems acceptable.
  • New Julia releases change the order of random numbers for a given seed, and hence the coloration of images for the demos using get_random_color.

_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)))

"""
`SegmentedImage` type contains the index-label mapping, assigned labels,
segment mean intensity and pixel count of each segment.
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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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4 changes: 0 additions & 4 deletions src/region_growing.jl
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@@ -1,7 +1,3 @@

default_diff_fn(c1::CT1,c2::CT2) where {CT1<:Union{Colorant,Real}, CT2<:Union{Colorant,Real}} = sqrt(_abs2((c1)-accum_type(CT2)(c2)))
_abs2(c) = mapreducec(v->float(v)^2, +, 0, c)/length(c)

"""
seg_img = seeded_region_growing(img, seeds, [kernel_dim], [diff_fn])
seg_img = seeded_region_growing(img, seeds, [neighbourhood], [diff_fn])
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2 changes: 1 addition & 1 deletion test/runtests.jl
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@@ -1,4 +1,4 @@
using ImageSegmentation, Images, Test, SimpleWeightedGraphs, LightGraphs, StaticArrays, DataStructures
using ImageSegmentation, ImageCore, ImageFiltering, Test, SimpleWeightedGraphs, LightGraphs, StaticArrays, DataStructures
using RegionTrees: isleaf, Cell, split!
using MetaGraphs: MetaGraph, clear_props!, get_prop, has_prop, set_prop!, props, vertices

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5 changes: 2 additions & 3 deletions test/watershed.jl
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Expand Up @@ -4,8 +4,7 @@ using ImageFiltering

img = zeros(100, 100)
img[25:75, 25:75] .= 1

img, _ = magnitude_phase(img)
img[26:74, 26:74] .= 0

markers = zeros(Int, size(img))
markers[1, 1] = 1
Expand Down Expand Up @@ -45,7 +44,7 @@ using ImageFiltering
# since this is using the compact algorithm with a high value for
# compactness, the boundary between labels 1 and 2 should occur halfway
# between the two markers
@test sum(labels .== 1) == sum(1:50) - 2
@test sum(labels .== 1) == sum(1:50)
end

@testset "h-minima transform" begin
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