Remove old deprecations, add new ones (#1038)

Project.toml

@@ -38,7 +38,7 @@ Graphics = "0.4, 1.0"
 ImageAxes = "0.6.9"
 ImageBase = "0.1.5"
 ImageBinarization = "0.2, 0.3"
-ImageContrastAdjustment = "0.3.3"
+ImageContrastAdjustment = "0.3.12"
 ImageCore = "0.9.3, 0.10"
 ImageCorners = "0.1.2"
 ImageDistances = "0.2.5"
@@ -61,6 +61,7 @@ TiledIteration = "0.2, 0.3, 0.4, 0.5"
 julia = "1.6"
 
 [extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -69,4 +70,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
 
 [targets]
-test = ["FFTW", "LinearAlgebra", "Random", "Suppressor", "Test", "TestImages"]
+test = ["Aqua", "FFTW", "LinearAlgebra", "Random", "Suppressor", "Test", "TestImages"]

src/Images.jl

@@ -48,33 +48,17 @@ import FileIO: metadata
 import Graphics: Point
 
 include("compat.jl")
-include("labeledarrays.jl")
 include("algorithms.jl")
 include("deprecations.jl")
 include("edge.jl")
 
 export
- # types
- ColorizedArray,
-
  # macros
  @test_approx_eq_sigma_eps,
 
  # algorithms
  imedge, # TODO: deprecate?
  imgaussiannoise,
- otsu_threshold,
- yen_threshold,
-
- #Exposure
- imhist,
- histeq,
- adjust_gamma,
- histmatch,
- clahe,
- imadjustintensity,
- imstretch,
- cliphist,
 
  magnitude,
  magnitude_phase,
@@ -85,10 +69,8 @@ export
  thin_edges_nonmaxsup,
  thin_edges_nonmaxsup_subpix,
  canny,
- gaussian_pyramid,
+ gaussian_pyramid
 
- # phantoms
- shepp_logan
 
 """
 Images.jl is an "umbrella package" that exports a set of packages which are useful for

src/algorithms.jl

@@ -120,85 +120,3 @@ function pyramid_scale(img::OffsetArray, downsample)
  off = (.-ceil.(Int,(.-iterate.(map(x->UnitRange(x).-1,axes(img)))[1])./downsample))
  OffsetArray(imresize(img, sz_next), off)
 end
-
-"""
-```
-thres = otsu_threshold(img)
-thres = otsu_threshold(img, bins)
-```
-
-Computes threshold for grayscale image using Otsu's method.
-
-Parameters:
-- img = Grayscale input image
-- bins = Number of bins used to compute the histogram. Needed for floating-point images.
-
-"""
-function otsu_threshold(img::AbstractArray{T, N}, bins::Int = 256) where {T<:Union{Gray,Real}, N}
-
- min, max = extrema(img)
- edges, counts = imhist(img, range(gray(min), stop=gray(max), length=bins))
- histogram = counts./sum(counts)
-
- ω0 = 0
- μ0 = 0
- μt = 0
- μT = sum((1:(bins+1)).*histogram)
- max_σb=0.0
- thres=1
-
- for t in 1:bins
- ω0 += histogram[t]
- ω1 = 1 - ω0
- μt += t*histogram[t]
-
- σb = (μT*ω0-μt)^2/(ω0*ω1)
-
- if(σb > max_σb)
- max_σb = σb
- thres = t
- end
- end
-
- return T((edges[thres-1]+edges[thres])/2)
-end
-
-"""
-```
-thres = yen_threshold(img)
-thres = yen_threshold(img, bins)
-```
-
-Computes threshold for grayscale image using Yen's maximum correlation criterion for bilevel thresholding
-
-Parameters:
-- img = Grayscale input image
-- bins = Number of bins used to compute the histogram. Needed for floating-point images.
-
-
-#Citation
-Yen J.C., Chang F.J., and Chang S. (1995) “A New Criterion for Automatic Multilevel Thresholding” IEEE Trans. on Image Processing, 4(3): 370-378. DOI:10.1109/83.366472
-"""
-function yen_threshold(img::AbstractArray{T, N}, bins::Int = 256) where {T<:Union{Gray, Real}, N}
-
- min, max = extrema(img)
- if(min == max)
- return T(min)
- end
-
- edges, counts = imhist(img, range(gray(min), stop=gray(max), length=bins))
-
- prob_mass_function = counts./sum(counts)
- clamp!(prob_mass_function,eps(),Inf)
- prob_mass_function_sq = prob_mass_function.^2
- cum_pmf = cumsum(prob_mass_function)
- cum_pmf_sq_1 = cumsum(prob_mass_function_sq)
- cum_pmf_sq_2 = reverse!(cumsum(reverse!(prob_mass_function_sq)))
-
- #Equation (4) cited in the paper.
- criterion = log.(((cum_pmf[1:end-1].*(1.0 .- cum_pmf[1:end-1])).^2) ./ (cum_pmf_sq_1[1:end-1].*cum_pmf_sq_2[2:end]))
-
- thres = edges[findmax(criterion)[2]]
- return T(thres)
-
-end