WIP: replace Images with specific packages

Project.toml

@@ -4,20 +4,26 @@ version = "0.5.0"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+ImageBase = "c817782e-172a-44cc-b673-b171935fbb9e"
+ImageContrastAdjustment = "f332f351-ec65-5f6a-b3d1-319c6670881a"
+ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534"
+ImageCorners = "89d5987c-236e-4e32-acd0-25bd6bd87b70"
+ImageFiltering = "6a3955dd-da59-5b1f-98d4-e7296123deb5"
+ImageTransformations = "02fcd773-0e25-5acc-982a-7f6622650795"
+IntegralArrays = "1d092043-8f09-5a30-832f-7509e371ab51"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
 Distributions = "0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25"
-Images = "0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25"
+ImageCore = "0.10"
 julia = "1.6"
 
 [extras]
-ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
+ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
 
 [targets]
-test = ["ImageMagick", "LinearAlgebra", "Test", "TestImages"]
+test = ["ImageIO", "LinearAlgebra", "Test", "TestImages"]

docs/Project.toml

@@ -3,8 +3,6 @@ CoordinateTransformations = "150eb455-5306-5404-9cee-2592286d6298"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 ImageDraw = "4381153b-2b60-58ae-a1ba-fd683676385f"
 ImageFeatures = "92ff4b2b-8094-53d3-b29d-97f740f06cef"
-ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
-Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
 Rotations = "6038ab10-8711-5258-84ad-4b1120ba62dc"
 TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
 

docs/make.jl

@@ -1,4 +1,4 @@
-using Documenter, ImageFeatures, Images
+using Documenter, ImageFeatures, ImageCore
 
 makedocs(sitename = "ImageFeatures",
          format = Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),

docs/src/index.md

@@ -23,4 +23,3 @@ Installing the package is extremely easy with julia's package manager -
 Pkg.add("ImageFeatures")
 ```
 
-ImageFeatures.jl requires [Images.jl](https://github.com/JuliaImages/Images.jl).

docs/src/tutorials/brief.md

@@ -16,13 +16,13 @@ BRIEF is a very simple feature descriptor and does not provide scale or rotation
 
 ## Example
 
-Let us take a look at a simple example where the BRIEF descriptor is used to match two images where one has been translated by `(100, 200)` pixels. We will use the `lena_gray` image from the [TestImages](https://github.com/timholy/TestImages.jl) package for this example.
+Let us take a look at a simple example where the BRIEF descriptor is used to match two images where one has been translated by `(100, 200)` pixels. We will use the `lena_gray` image from the [TestImages](https://github.com/JuliaImages/TestImages.jl) package for this example.
 
 
 Now, let us create the two images we will match using BRIEF.
 
 ```@example 1
-using ImageFeatures, TestImages, Images, ImageDraw, CoordinateTransformations
+using ImageFeatures, TestImages, ImageCore, ImageDraw, CoordinateTransformations
 
 img = testimage("lighthouse")
 img1 = Gray.(img)

docs/src/tutorials/brisk.md

@@ -13,12 +13,12 @@ The descriptor is built using intensity comparisons. For each short pair if the
 
 ## Example
 
-Let us take a look at a simple example where the BRISK descriptor is used to match two images where one has been translated by `(50, 40)` pixels and then rotated by an angle of 75 degrees. We will use the `lighthouse` image from the [TestImages](https://github.com/timholy/TestImages.jl) package for this example.
+Let us take a look at a simple example where the BRISK descriptor is used to match two images where one has been translated by `(50, 40)` pixels and then rotated by an angle of 75 degrees. We will use the `lighthouse` image from the [TestImages](https://github.com/JuliaImages/TestImages.jl) package for this example.
 
 First, let us create the two images we will match using BRISK.
 
 ```@example 4
-using ImageFeatures, TestImages, Images, ImageDraw, CoordinateTransformations, Rotations
+using ImageFeatures, TestImages, ImageCore, ImageDraw, CoordinateTransformations, Rotations
 
 img = testimage("lighthouse")
 img1 = Gray.(img)

docs/src/tutorials/freak.md

@@ -9,12 +9,12 @@ The descriptor is built using intensity comparisons of a predetermined set of 51
 
 ## Example
 
-Let us take a look at a simple example where the FREAK descriptor is used to match two images where one has been translated by `(50, 40)` pixels and then rotated by an angle of 75 degrees. We will use the `lighthouse` image from the [TestImages](https://github.com/timholy/TestImages.jl) package for this example.
+Let us take a look at a simple example where the FREAK descriptor is used to match two images where one has been translated by `(50, 40)` pixels and then rotated by an angle of 75 degrees. We will use the `lighthouse` image from the [TestImages](https://github.com/JuliaImages/TestImages.jl) package for this example.
 
 First, let us create the two images we will match using FREAK.
 
 ```@example 3
-using ImageFeatures, TestImages, Images, ImageDraw, CoordinateTransformations, Rotations
+using ImageFeatures, TestImages, ImageCore, ImageDraw, CoordinateTransformations, Rotations
 
 img = testimage("lighthouse")
 img1 = Gray.(img)

docs/src/tutorials/object_detection.md

@@ -11,16 +11,16 @@ representation which is invariant to local geometric and photometric changes (i.
 Download the script to get the training data [here](https://drive.google.com/file/d/11G_9zh9N-0veQ2EL5WDGsnxRpihsqLX5/view?usp=sharing). Download tutorial.zip, decompress it and run get_data.bash. (Change the variable `path_to_tutorial` in preprocess.jl and path to julia executable in get_data.bash). This script will download the required datasets. We will start by loading the data and computing HOG features of all the images.
 
 ```julia
-using Images, ImageFeatures
+using ImageCore, ImageFeatures
 
 path_to_tutorial = ""    # specify this path
 pos_examples = "$path_to_tutorial/tutorial/humans/"
 neg_examples = "$path_to_tutorial/tutorial/not_humans/"
 
 n_pos = length(readdir(pos_examples))   # number of positive training examples
 n_neg = length(readdir(neg_examples))   # number of negative training examples
-n = n_pos + n_neg                       # number of training examples 
-data = Array{Float64}(undef, 3780, n)   # Array to store HOG descriptor of each image. Each image in our training data has size 128x64 and so has a 3780 length 
+n = n_pos + n_neg                       # number of training examples
+data = Array{Float64}(undef, 3780, n)   # Array to store HOG descriptor of each image. Each image in our training data has size 128x64 and so has a 3780 length
 labels = Vector{Int}(undef, n)          # Vector to store label (1=human, 0=not human) of each image.
 
 for (i, file) in enumerate([readdir(pos_examples); readdir(neg_examples)])
@@ -31,7 +31,7 @@ for (i, file) in enumerate([readdir(pos_examples); readdir(neg_examples)])
 end
 ```
 
-Basically we now have an encoded version of images in our training data. This encoding captures useful information but discards extraneous information 
+Basically we now have an encoded version of images in our training data. This encoding captures useful information but discards extraneous information
 (illumination changes, pose variations etc). We will train a linear SVM on this data.
 
 ```julia
@@ -94,7 +94,7 @@ end
 
 ![Original](../img/scores.png)
 
-You can see that classifier gave low score to not-human class (i.e. high score to human class) at positions corresponding to humans in the original image. 
+You can see that classifier gave low score to not-human class (i.e. high score to human class) at positions corresponding to humans in the original image.
 Below we threshold the image and supress non-minimal values to get the human locations. We then plot the bounding boxes using `ImageDraw`.
 
 ```julia

docs/src/tutorials/orb.md

@@ -20,7 +20,7 @@ Let us take a look at a simple example where the ORB descriptor is used to match
 First, let us create the two images we will match using ORB.
 
 ```@example 2
-using ImageFeatures, TestImages, Images, ImageDraw, CoordinateTransformations, Rotations
+using ImageFeatures, TestImages, ImageCore, ImageDraw, CoordinateTransformations, Rotations
 
 img = testimage("lighthouse")
 img1 = Gray.(img)

src/ImageFeatures.jl

@@ -1,10 +1,18 @@
 module ImageFeatures
 
 # package code goes here
-using Images, Distributions
+using ImageCore
+using ImageCore: NumberLike
+using ImageBase
+using ImageTransformations  # imresize
+using ImageCorners
+using ImageFiltering
+using ImageContrastAdjustment # build_histogram
+using IntegralArrays
+using Distributions
 using SparseArrays
 import Random.seed!
-using Images.ImageTransformations.Interpolations
+using ImageTransformations.Interpolations
 
 include("core.jl")
 include("const.jl")

src/hog.jl

@@ -24,7 +24,7 @@ function HOG(; orientations::Int = 9, cell_size::Int = 8, block_size::Int = 2, b
     HOG(orientations, cell_size, block_size, block_stride, norm_method)
 end
 
-function create_descriptor(img::AbstractArray{CT, 2}, params::HOG) where CT<:Images.NumberLike
+function create_descriptor(img::AbstractArray{CT, 2}, params::HOG) where CT<:NumberLike
     #compute gradient
     gx = imfilter(img, centered([-1 0 1]))
     gy = imfilter(img, centered([-1 0 1]'))
@@ -34,7 +34,7 @@ function create_descriptor(img::AbstractArray{CT, 2}, params::HOG) where CT<:Ima
     create_hog_descriptor(mag, phase, params)
 end
 
-function create_descriptor(img::AbstractArray{CT, 2}, params::HOG) where CT<:Images.Color{T, N} where T where N
+function create_descriptor(img::AbstractArray{CT, 2}, params::HOG) where CT<:Color{T, N} where T where N
     #for color images, compute seperate gradient for each color channel and take one with largest norm as pixel's gradient vector
     rows, cols = size(img)
     gx = channelview(imfilter(img, centered([-1 0 1])))
@@ -56,7 +56,7 @@ function create_descriptor(img::AbstractArray{CT, 2}, params::HOG) where CT<:Ima
     create_hog_descriptor(max_mag, max_phase, params)
 end
 
-function create_hog_descriptor(mag::AbstractArray{T, 2}, phase::AbstractArray{T, 2}, params::HOG) where T<:Images.NumberLike
+function create_hog_descriptor(mag::AbstractArray{T, 2}, phase::AbstractArray{T, 2}, params::HOG) where T<:NumberLike
     orientations = params.orientations
     cell_size = params.cell_size
     block_size = params.block_size

src/houghtransform.jl

@@ -1,5 +1,3 @@
-using Images
-
 """
 ```
 lines = hough_transform_standard(
@@ -133,7 +131,7 @@ Parameters:
 # Example
 
 ```julia
-julia> using Images, ImageFeatures, FileIO, ImageView
+julia> using ImageCore, ImageFeatures, FileIO, ImageView
 
 julia> img = load(download("http://docs.opencv.org/3.1.0/water_coins.jpg"));
 

src/lbp.jl

@@ -144,6 +144,6 @@ function create_descriptor(img::AbstractArray{Gray{T}, 2}, yblocks::Integer = 4,
     y_padded = ceil(Int, h / (yblocks)) * yblocks
     x_padded = ceil(Int, w / (xblocks)) * xblocks
 
-    img_padded = Images.imresize(img, (y_padded, x_padded))
+    img_padded = imresize(img, (y_padded, x_padded))
     _create_descriptor(img_padded, yblocks, xblocks, lbp_type, args...)
 end

test/brief.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images, TestImages, Distributions
+using Test, ImageFeatures, ImageCore, TestImages, Distributions
 
 @testset "Generating brief params" begin
     brief_params = BRIEF(size = 8, window = 3, seed = 123)

test/brisk.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images, TestImages, Distributions
+using Test, ImageFeatures, ImageCore, TestImages, Distributions
 
 @testset "Testing brisk params" begin
     brisk_params = BRISK(pattern_scale = 2.0)

test/core.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images
+using Test, ImageFeatures, ImageCore
 
 @testset "Types" begin
     img = zeros(10, 10)

test/corner.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images
+using Test, ImageFeatures, ImageCore
 
 @testset "Orientations" begin
     img = zeros(20, 20)

test/freak.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images, TestImages, Distributions
+using Test, ImageFeatures, ImageCore, TestImages, Distributions
 
 @testset "Test freak params" begin
     freak_params = FREAK(pattern_scale = 20.0)

test/glcm.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images
+using Test, ImageFeatures, ImageCore, ImageBase
 
 @testset "GLCM" begin
     img = [ 0 0 1 1

test/hog.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images
+using Test, ImageFeatures, ImageCore
 import ImageFeatures.trilinear_interpolate!
 
 @testset "HOG Feature" begin

test/lbp.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images
+using Test, ImageFeatures, ImageCore
 
 @testset "circular_offsets" begin
     img = [  0x4c  0x19  0xac  0x2e  0x8c  0xcc  0x96  0x4c  0xdb  0x4f
@@ -13,7 +13,7 @@ using Test, ImageFeatures, Images
              0x1a  0xe0  0x52  0x5a  0x6a  0x03  0xe8  0xcb  0x95  0xfc
           ]
 
-    global img_gray = map(i -> Images.Gray(reinterpret(N0f8, i)), img)
+    global img_gray = map(i -> Gray(reinterpret(N0f8, i)), img)
 end
 
 @test ImageFeatures.circular_offsets(8, 1) == [ (-0.0,1.0), (-0.70711,0.70711), (-1.0,0.0), (-0.70711,-0.70711), (-0.0,-1.0), (0.70711,-0.70711), (1.0,-0.0), (0.70711,0.70711)]

test/orb.jl

@@ -1,4 +1,4 @@
-using Test, ImageFeatures, Images, TestImages, Distributions
+using Test, ImageFeatures, ImageCore, TestImages, Distributions
 
 @testset "Testing ORB params" begin
     orb_params = ORB(num_keypoints = 1000, threshold = 0.2)

test/runtests.jl

@@ -1,10 +1,9 @@
 module ImageFeatureTests
 
-using ImageFeatures, Images, TestImages, Distributions
+using ImageFeatures, ImageCore, ImageTransformations, ImageCorners, ImageFiltering, TestImages, Distributions
 using Test
 using LinearAlgebra
 import Random.seed!
-using Images.ImageTransformations: imrotate
 
 function check_samples(sample_one, sample_two, size::Int, window::Int)
     check_bool = true