one->oneunit for CartesianIndex (#51)

`one->oneunit` for `CartesianIndex` (for Julia <= 1.0.5)

Fixes #49

Co-authored-by: Johnny Chen <[email protected]>

Author: timholy

src/ImageSegmentation.jl

@@ -6,6 +6,7 @@ using LinearAlgebra, Statistics
 using Images, DataStructures, StaticArrays, ImageFiltering, LightGraphs, SimpleWeightedGraphs, RegionTrees, Distances, StaticArrays, Clustering
 import Clustering: kmeans, fuzzy_cmeans
 
+include("compat.jl")
 include("core.jl")
 include("region_growing.jl")
 include("felzenszwalb.jl")

src/compat.jl

@@ -0,0 +1,7 @@
+if VERSION <= v"1.0.5"
+    # https://github.com/JuliaLang/julia/pull/29442
+    _oneunit(::CartesianIndex{N}) where {N} = _oneunit(CartesianIndex{N})
+    _oneunit(::Type{CartesianIndex{N}}) where {N} = CartesianIndex(ntuple(x -> 1, Val(N)))
+else
+    _oneunit = Base.oneunit
+end

src/core.jl

@@ -82,7 +82,7 @@ function region_adjacency_graph(s::SegmentedImage, weight_fn::Function)
 
     function neighbor_regions!(n::Set{Int}, visited::AbstractArray, s::SegmentedImage, I::CartesianIndex)
         R = CartesianIndices(axes(s.image_indexmap))
-        I1 = one(CartesianIndex{ndims(visited)})
+        I1 = _oneunit(CartesianIndex{ndims(visited)})
         Ibegin, Iend = first(R), last(R)
         t = Vector{CartesianIndex{ndims(visited)}}()
         push!(t, I)

src/fast_scanning.jl

@@ -8,7 +8,7 @@ function adaptive_thres(img::AbstractArray{CT,N}, block::NTuple{N,Int}) where {C
     net_end = last(CartesianIndices(axes(img)))
     for i in CartesianIndices(block)
         si = CartesianIndex(ntuple(j->(i[j]-1)*block_length[j]+1,Val(N)))
-        ei = min(si + block_length - one(si), net_end)
+        ei = min(si + block_length - _oneunit(si), net_end)
         wi = view(img, map((i,j)->i:j, si.I, ei.I)...)
         threshold[i] = 0.02 + min(sharpness(wi)/net_s*0.04,0.1) + min(var(wi)/net_var*0.04,0.1)
     end

src/meanshift.jl

@@ -41,7 +41,7 @@ function meanshift(img::Array{CT, 2}, spatial_radius::Real, range_radius::Real;
         I1, Iend = first(R), last(R)
         I = CartesianIndex(rowbin(pt[1]), colbin(pt[2]), colorbin(pt[3]))
 
-        for J in CartesianIndices(_colon(max(I1, I-one(I)), min(Iend, I+one(I))))
+        for J in CartesianIndices(_colon(max(I1, I-_oneunit(I)), min(Iend, I+_oneunit(I))))
             for point in buckets[J]
                 if dist2(pt, SVector(point[1], point[2], img[point])) <= 1
                     num += SVector(point[1], point[2], img[point])

src/watershed.jl

@@ -64,7 +64,7 @@ function watershed(img::AbstractArray{T, N},
     Istart, Iend = first(R), last(R)
     for i in R
         if markers[i] > 0
-            for j in CartesianIndices(_colon(max(Istart,i-one(i)), min(i+one(i),Iend)))
+            for j in CartesianIndices(_colon(max(Istart,i-_oneunit(i)), min(i+_oneunit(i),Iend)))
                 if segments[j] == 0
                     segments[j] = markers[i]
                     enqueue!(pq, j, PK(img[i], time_step, j))
@@ -90,7 +90,7 @@ function watershed(img::AbstractArray{T, N},
         end
 
         img_current = img[curr_idx]
-        for j in CartesianIndices(_colon(max(Istart,curr_idx-one(curr_idx)), min(curr_idx+one(curr_idx),Iend)))
+        for j in CartesianIndices(_colon(max(Istart,curr_idx-_oneunit(curr_idx)), min(curr_idx+_oneunit(curr_idx),Iend)))
 
             # if this location is false in the mask, we skip it
             (!mask[j]) && continue