update doctest reference for Rotation and Clustering (#149)

Author: johnnychen94

Project.toml

@@ -24,6 +24,7 @@ MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
 MosaicViews = "e94cdb99-869f-56ef-bcf0-1ae2bcbe0389"
 OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 PaddedViews = "5432bcbf-9aad-5242-b902-cca2824c8663"
+Rotations = "6038ab10-8711-5258-84ad-4b1120ba62dc"
 SimpleTraits = "699a6c99-e7fa-54fc-8d76-47d257e15c1d"
 TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
@@ -54,6 +55,7 @@ MappedArrays = "0"
 MosaicViews = "0"
 OffsetArrays = "1.0"
 PaddedViews = "0"
+Rotations = "1"
 SimpleTraits = "0"
 TestImages = "0, 1.0"
 Unitful = "0, 1.0"

docs/src/pkgs/features/index.md

@@ -29,7 +29,7 @@ this example.
 First, let us create the two images we will match using BRISK.
 
 ```@example 3
-using ImageFeatures, TestImages, Images, ImageDraw, CoordinateTransformations
+using ImageFeatures, TestImages, Images, ImageDraw, CoordinateTransformations, Rotations
 
 img = testimage("lighthouse")
 img1 = Gray.(img)

docs/src/pkgs/segmentation/index.md

@@ -62,9 +62,9 @@ julia> length(segment_labels(segments))
 
 julia> segment_mean(segments)
 Dict{Int64,RGB{Float64}} with 3 entries:
-  2 => RGB{Float64}(0.793679,0.839473,0.932494)
-  3 => RGB{Float64}(0.329867,0.357842,0.237361)
-  1 => RGB{Float64}(0.0650002,0.0586348,0.074091)
+  2 => RGB{Float64}(0.793598,0.839543,0.932374)
+  3 => RGB{Float64}(0.329863,0.35779,0.237457)
+  1 => RGB{Float64}(0.0646509,0.0587034,0.0743471)
 ```
 
 We can visualize each segment using its mean color:
@@ -106,7 +106,7 @@ Segmented Image with:
 julia> segments = felzenszwalb(img, 10)  #smaller segments but noisy segmentation
 Segmented Image with:
   labels map: 240×360 Array{Int64,2}
-  number of labels: 275
+  number of labels: 312
 ```
 
 | k = 100 | k = 10 |
@@ -242,7 +242,7 @@ julia> img = load("src/pkgs/segmentation/assets/tree.jpg");
 julia> seg = unseeded_region_growing(img, 0.05) # here 0.05 is the threshold
 Segmented Image with:
   labels map: 320×480 Array{Int64,2}
-  number of labels: 774
+  number of labels: 698
 ```
 
 | Threshold | Output | Compression percentage|
@@ -297,7 +297,7 @@ julia> img = imresize(img, (128, 128));
 julia> segments = meanshift(img, 16, 8/255) # parameters are smoothing radii: spatial=16, intensity-wise=8/255
 Segmented Image with:
   labels map: 128×128 Array{Int64,2}
-  number of labels: 42
+  number of labels: 44
 ```
 ![img1](assets/small_house.jpg) ![img2](assets/meanshift.jpg)
 
@@ -329,7 +329,7 @@ julia> img = testimage("camera");
 julia> seg = fast_scanning(img, 0.1)  # threshold = 0.1
 Segmented Image with:
   labels map: 512×512 Array{Int64,2}
-  number of labels: 2536
+  number of labels: 2538
 
 julia> seg = prune_segments(seg, i->(segment_pixel_count(seg,i)<50), (i,j)->(-segment_pixel_count(seg,j)))
 Segmented Image with:

docs/src/tutorials/arrays_colors.md

@@ -309,14 +309,9 @@ However, you should be aware that for *integer* inputs, the default is
 to use the `N0f8` element type, and this type cannot represent values
 outside the range from 0 to 1:
 
-```jldoctest; setup = :(using ColorTypes), filter = r"at \(.*\)"
-julia> RGB(8,2,0)
-ERROR: ArgumentError: (8, 2, 0) are integers in the range 0-255, but integer inputs are encoded with the N0f8
-  type, an 8-bit type representing 256 discrete values between 0 and 1.
-  Consider dividing your input values by 255, for example: RGB{N0f8}(8/255,2/255,0/255)
-  See the READMEs for FixedPointNumbers and ColorTypes for more information.
-Stacktrace:
-[...]
+```@repl
+using ColorTypes # hide
+RGB(8,2,0)
 ```
 
 The error message here reminds you how to resolve a common mistake,

docs/src/tutorials/conversions_views.md

@@ -501,7 +501,7 @@ which may be somewhat easier to read than the type:
 
 ```jldoctest; setup = :(using Images; A = rand(UInt8, 5, 6, 3, 10); mov = colorview(RGB, normedview(PermutedDimsArray(A, (3,1,2,4))))), filter=r"Symbol\(.*\)"
 julia> typeof(mov)
-Base.ReshapedArray{RGB{Normed{UInt8,8}},3,Base.ReinterpretArray{RGB{Normed{UInt8,8}},4,Normed{UInt8,8},MappedArrays.MappedArray{Normed{UInt8,8},4,PermutedDimsArray{UInt8,4,(3, 1, 2, 4),(2, 3, 1, 4),Array{UInt8,4}},getfield(ImageCore, Symbol("##36#37")){Normed{UInt8,8}},typeof(reinterpret)}},Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}
+Base.ReshapedArray{RGB{Normed{UInt8,8}},3,Base.ReinterpretArray{RGB{Normed{UInt8,8}},4,Normed{UInt8,8},MappedArrays.MappedArray{Normed{UInt8,8},4,PermutedDimsArray{UInt8,4,(3, 1, 2, 4),(2, 3, 1, 4),Array{UInt8,4}},ImageCore.var"#37#38"{Normed{UInt8,8}},typeof(reinterpret)}},Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64},Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}
 ```
 
 While there is little or no performance cost to making use of

docs/src/tutorials/indexing.md

@@ -23,10 +23,10 @@ overlay the two images
 doing.
 
 ```jldoctest; setup = :(using Images; img = load(joinpath(@__DIR__, "src/tutorials/assets/indexing/cmrot.png")))
-julia> using Images, CoordinateTransformations
+julia> using Images, CoordinateTransformations, Rotations
 
 julia> tfm = recenter(RotMatrix(pi/8), center(img))
-AffineMap([0.92388 -0.382683; 0.382683 0.92388], [88.7786, -59.3199])
+AffineMap([0.9238795325112867 -0.3826834323650898; 0.3826834323650898 0.9238795325112867], [88.7785546217109, -59.31993370357884])
 
 julia> imgrot = warp(img, tfm);