Update compat for DataStructures 0.19, bump julia compat to 1.10 (#110)

Also:
* Require ImageMorphology 0.4.7
* Update CI workflow to use 'lts' and 'pre' version
* Fix doctest that checked anonymous function name

Fixes #108

Author: Ickaser

.github/workflows/CI.yml

@@ -13,9 +13,9 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6'
+          - 'lts'
           - '1'
-#          - 'nightly'
+          - 'pre'
         os:
           - ubuntu-latest
           - macOS-latest

Project.toml

@@ -1,6 +1,6 @@
 name = "ImageSegmentation"
 uuid = "80713f31-8817-5129-9cf8-209ff8fb23e1"
-version = "1.9.0"
+version = "1.10.0"
 
 [deps]
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
@@ -19,18 +19,18 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 Clustering = "0.15"
-DataStructures = "0.17.11, 0.18"
+DataStructures = "0.19"
 Distances = "0.8, 0.9.2, 0.10"
 Documenter = "1"
 Graphs = "1.4.1"
 ImageCore = "0.10"
 ImageFiltering = "0.6, 0.7"
-ImageMorphology = "0.2.6, 0.3, 0.4"
+ImageMorphology = "0.4.7"
 MetaGraphs = "0.7, 0.8"
 RegionTrees = "0.2, 0.3"
 SimpleWeightedGraphs = "1.2"
 StaticArrays = "0.9, 0.10, 0.11, 0.12, 1.0"
-julia = "1.6"
+julia = "1.10"
 
 [extras]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"

src/core.jl

@@ -260,7 +260,7 @@ defined for objects of the type of `d`.
 function prune_segments(s::SegmentedImage, is_rem::Function, diff_fn::Function)
 
     G, vert_map = region_adjacency_graph(s, (i,j)->1)
-    u = IntDisjointSets(nv(G))
+    u = IntDisjointSet(nv(G))
     for v in vertices(G)
         if is_rem(s.segment_labels[v])
             neigh = neighbors(G, v)
@@ -334,8 +334,7 @@ Return a function that constructs a diamond-shaped iterable region.
 
 # Examples
 ```jldoctest; setup=:(using ImageSegmentation), filter=r"#\\d+"
-julia> fiter = ImageSegmentation.diamond_iterator((3, 3))
-#18 (generic function with 1 method)
+julia> fiter = ImageSegmentation.diamond_iterator((3, 3));
 
 julia> center = CartesianIndex(17, 24)
 CartesianIndex(17, 24)

src/fast_scanning.jl

@@ -78,7 +78,7 @@ function fast_scanning!(result, img::AbstractArray{CT,N}, threshold::Union{Abstr
     TM = meantype(CT)
     region_means        =   Dict{Int, TM}()                                 # A map conatining (label, mean) pairs
     region_pix_count    =   Dict{Int, Int}()                                # A map conatining (label, count) pairs
-    temp_labels         =   IntDisjointSets(0)                              # Disjoint set to map labels to their equivalence class
+    temp_labels         =   IntDisjointSet(0)                              # Disjoint set to map labels to their equivalence class
     v_neigh             =   MVector{N,Int}(undef)                           # MVector to store valid neighbours
 
     block_length = CartesianIndex(ntuple(i->ceil(Int,length(axes(img,i))/size(threshold,i)), Val(N)))

src/felzenszwalb.jl

@@ -14,7 +14,7 @@ Parameters:
 function felzenszwalb(edges::Array{ImageEdge}, num_vertices::Int, k::Float64, min_size::Int = 0)
 
     num_edges = length(edges)
-    G = IntDisjointSets(num_vertices)
+    G = IntDisjointSet(num_vertices)
     set_size = ones(num_vertices)
     threshold = fill(convert(Float64,k), num_vertices)
 

src/region_growing.jl

@@ -127,10 +127,10 @@ function seeded_region_growing(img::AbstractArray{CT,N}, seeds::AbstractVector{<
 
         # Get the pixels with minimum δ from `pq` and add them to `holdingq` and their labels to `labelsq`
         if !isempty(pq)
-            δ_min = peek(pq)[2]
+            δ_min = first(pq)[2]
         end
-        while (!isempty(pq) && isapprox(peek(pq)[2], δ_min)) #, atol=1e-8))
-            p = cil[dequeue!(pq)]
+        while (!isempty(pq) && isapprox(first(pq)[2], δ_min)) #, atol=1e-8))
+            p = cil[popfirst!(pq)[1]]
             @assert result[p] <= 0
             @inbounds imgp = img[p]
             mindifflabel = -1
@@ -262,12 +262,12 @@ function unseeded_region_growing(img::AbstractArray{CT,N}, threshold::Real, neig
     # Enqueue neighouring points of `start_point`
     for p in neighbourhood(start_point)
         if p != start_point && checkbounds(Bool, img, p) && result[p] == -1
-            enqueue!(neighbours, lic[p], diff_fn(region_means[result[start_point]], img[p]))
+            push!(neighbours, lic[p] => diff_fn(region_means[result[start_point]], img[p]))
         end
     end
 
     while !isempty(neighbours)
-        point = cil[dequeue!(neighbours)]
+        point = cil[popfirst!(neighbours)[1]]
         δ = Inf
         minlabel = -1
         pixelval = img[point]
@@ -322,7 +322,7 @@ function unseeded_region_growing(img::AbstractArray{CT,N}, threshold::Real, neig
                         δ = min(δ, diff_fn(region_means[result[tp]], img[p]))
                     end
                 end
-                enqueue!(neighbours, lic[p], δ)
+                push!(neighbours, lic[p] => δ)
             end
         end
 

src/watershed.jl

@@ -67,15 +67,15 @@ function watershed(img::AbstractArray{T, N},
             for j in _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))
+                    push!(pq, j => PK(img[i], time_step, j))
                     time_step += 1
                 end
             end
         end
     end
 
     while !isempty(pq)
-        curr_idx, curr_elem = dequeue_pair!(pq)
+        curr_idx, curr_elem = popfirst!(pq)
         segments_current = segments[curr_idx]
 
         # If we're using the compact algorithm, we need assign grouping for a given location