remove clearborder (#902)

Project.toml

@@ -1,6 +1,6 @@
 name = "Images"
 uuid = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
-version = "0.22.3"
+version = "0.22.4"
 
 [deps]
 AxisArrays = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
@@ -40,7 +40,7 @@ ImageCore = "0.8.3"
 ImageDistances = "0.2.5"
 ImageFiltering = "0.6.3"
 ImageMetadata = "0.6, 0.7, 0.8, 0.9"
-ImageMorphology = "0.2.6"
+ImageMorphology = "0.2.7"
 ImageQualityIndexes = "0.1.3"
 ImageShow = "0.1, 0.2"
 ImageTransformations = "0.4, 0.5, 0.6, 0.7, 0.8"

src/Images.jl

@@ -150,7 +150,6 @@ export
     imROF,
     otsu_threshold,
     yen_threshold,
-    clearborder,
 
     #Exposure
     complement,

src/algorithms.jl

@@ -858,53 +858,3 @@ function yen_threshold(img::AbstractArray{T, N}, bins::Int = 256) where {T<:Unio
     return T(thres)
 
 end
-
-"""
-```
-cleared_img = clearborder(img)
-cleared_img = clearborder(img, width)
-cleared_img = clearborder(img, width, background)
-```
-
-Returns a copy of the original image after clearing objects connected to the border of the image.
-
-Parameters:
-
- -  img          = Binary/Grayscale input image
- -  width        = Width of the border examined (Default value is 1)
- -  background   = Value to be given to pixels/elements that are cleared (Default value is 0)
-
-"""
-function clearborder(img::AbstractArray, width::Int=1, background::Int=0)
-
-    for i in size(img)
-        if(width > i)
-            throw(ArgumentError("Border width must not be greater than size of the image."))
-        end
-    end
-
-    connectivity = ntuple(i -> 3, ndims(img))
-    labels = label_components(img,trues(connectivity))
-    number = maximum(labels) + 1
-
-    dimensions = size(img)
-    outerrange = CartesianIndices(map(i -> 1:i, dimensions))
-    innerrange = CartesianIndices(map(i -> 1+width:i-width, dimensions))
-
-    border_labels = Set{Int64}()
-    for i in EdgeIterator(outerrange,innerrange)
-        push!(border_labels, labels[i])
-    end
-
-    new_img = similar(img)
-    for itr in eachindex(labels)
-        if labels[itr] in border_labels
-            new_img[itr] = background
-        else
-        	new_img[itr] = img[itr]
-        end
-    end
-
-    return new_img
-
-end

test/algorithms.jl

@@ -624,72 +624,6 @@ using Test
         targetc = colorview(RGB, target, target, target)
         @test all(map((x,y)->isapprox(x, y, atol=0.001), channelview(imROF(imgc, 0.2, 1000)), channelview(targetc)))
     end
-
-    @testset "clearborder" begin
-        #Case when given border width is more than image size
-        img = [1 0 1 1 1 1
-               0 1 1 1 0 0
-               1 1 0 0 0 1
-               0 1 0 1 0 1
-               1 1 0 0 0 1
-               0 0 1 1 0 0]
-        @test_throws ArgumentError clearborder(img,7)
-
-        #Normal Case
-        img = [0 0 0 0 0 0 0 1 0
-               0 0 0 0 1 0 0 0 0
-               1 0 0 1 0 1 0 0 0
-               0 0 1 1 1 1 1 0 0
-               0 1 1 1 1 1 1 1 0
-               0 0 0 0 0 0 0 0 0]
-        cleared_img = clearborder(img)
-        check_img = copy(img)
-        check_img[3,1] = 0
-        check_img[1,8] = 0
-        @test cleared_img == check_img
-
-        cleared_img = clearborder(img,2)
-        @test cleared_img == fill!(similar(img), zero(eltype(img)))
-
-        cleared_img = clearborder(img,2,10)
-        @test cleared_img == 10*fill!(similar(img), one(eltype(img)))
-
-        #Multidimentional Case
-        img = cat([0 0 0 0;
-                     0 0 0 0;
-                     0 0 0 0;
-                     1 0 0 0],
-                    [0 0 0 0;
-                     0 1 1 0;
-                     0 0 1 0;
-                     0 0 0 0],
-                    [0 0 0 0;
-                     0 0 0 0;
-                     0 0 0 0;
-                     0 0 0 0], dims=3)
-        cleared_img = clearborder(img)
-        check_img = copy(img)
-        check_img[4,1,1] = 0
-        @test cleared_img == check_img
-
-        cleared_img = clearborder(img,2)
-        @test cleared_img == fill!(similar(img), zero(eltype(img)))
-
-        cleared_img = clearborder(img,2,10)
-        @test cleared_img == 10*fill!(similar(img), one(eltype(img)))
-
-        #Grayscale input image Case
-        img = [1 2 3 1 2
-               3 3 5 4 2
-               3 4 5 4 2
-               3 3 2 1 2]
-        cleared_img = clearborder(img)
-        check_img = [0 0 0 0 0
-                     0 0 5 4 0
-                     0 4 5 4 0
-                     0 0 0 0 0]
-        @test cleared_img == check_img
-    end
 end
 
 nothing