Restore inadvertently deleted docstring, add NEWS.md

NEWS.md

@@ -0,0 +1,10 @@
+# NEWS
+
+Release 1.6:
+
+- New `flood` and `flood_fill!` algorithms allow segmenting just the portion of the image connected to a seed point.
+- `seeded_region_growing` now allows seeds to be supplied with pair syntax, e.g.,
+  `[CartesianIndex(300,97) => 1, CartesianIndex(145,218) => 2]`.
+- Kernel/window dimensions supplied in vector format are deprecated. Instead of supplying the neighborhood size as `[3,3]`, use `(3, 3)` (`seeded_region_growing` and `unseeded_region_growing`).
+- `felzenswalb` now supports multidimensional images.
+- Output types use `floattype` in more places. In some cases this has resulted in `RGB{Float32}` rather than `RGB{Float64}` outputs.

Project.toml

@@ -1,6 +1,6 @@
 name = "ImageSegmentation"
 uuid = "80713f31-8817-5129-9cf8-209ff8fb23e1"
-version = "1.5.1"
+version = "1.6.0"
 
 [deps]
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"

src/core.jl

@@ -7,7 +7,9 @@ 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`")
 
-_abs2(c::MathTypes) = c ⋅ c
+# 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)))

src/fast_scanning.jl

@@ -20,6 +20,44 @@ getscalar(A::AbstractArray{T,N}, i::CartesianIndex{N}, block_length::CartesianIn
 
 getscalar(a::Real, i...) = a
 
+"""
+    seg_img = fast_scanning(img, threshold, [diff_fn])
+
+Segments the N-D image using a fast scanning algorithm and returns a
+[`SegmentedImage`](@ref) containing information about the segments.
+
+# Arguments:
+* `img`         : N-D image to be segmented (arbitrary axes are allowed)
+* `threshold`   : Upper bound of the difference measure (δ) for considering
+                  pixel into same segment; an `AbstractArray` can be passed
+                  having same number of dimensions as that of `img` for adaptive
+                  thresholding
+* `diff_fn`     : (Optional) Function that returns a difference measure (δ)
+                  between the mean color of a region and color of a point
+
+# Examples:
+
+```jldoctest; setup = :(using ImageCore, ImageMorphology, ImageSegmentation)
+julia> img = zeros(Float64, (3,3));
+
+julia> img[2,:] .= 0.5;
+
+julia> img[:,2] .= 0.6;
+
+julia> seg = fast_scanning(img, 0.2);
+
+julia> labels_map(seg)
+3×3 $(Matrix{Int}):
+ 1  4  5
+ 4  4  4
+ 3  4  6
+```
+
+# Citation:
+
+Jian-Jiun Ding, Cheng-Jin Kuo, Wen-Chih Hong,
+"An efficient image segmentation technique by fast scanning and adaptive merging"
+"""
 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))
 

test/flood_fill.jl

@@ -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)
+    seg = flood(img, CartesianIndex(87,280); thresh=0.3*sqrt(3))   # TODO: eliminate the sqrt(3) when we transition to `abs2(c) = c ⋅ c`
     @test 0.2*length(seg) <= sum(seg) <= 0.25*length(seg)
     c = mean(img[seg])
     # N0f8 makes for easier approximate testing