feat: histeq with nbins

src/histogram_equalization.jl

@@ -280,46 +280,12 @@ function rgb2gray(img::Matrix{RGB{Float64}})
     return round.(Int, Gray.(img) * 255)
 end
 
-function _imhist(img, rng)
-    d = Dict(k => 0 for k in rng)
-    for i in img
-        d[i] = d[i] + 1
-    end
-    k, heights = collect.([keys(d), values(d)])
-    order = sortperm(k)
-    k, heights = k[order], heights[order]
-    return k, heights
-end
-
-function imhist(img, imgtype::AbstractString="uint8")
-
-    # TODO: add validation for arr: either uint8 0:255 or grayscale 0:1
-    rng = imgtype == "uint8" ? range(0, 255) : range(0; stop=1, length=256)
-    # use range(0, stop=1, length=256) for grayscale images
-
-    return _imhist(img, rng)
-end
-
-"""
-    imhistp(img)
-Parsimonious version of `imhist` that uses the maximum value of the image to determine the number of bins.
-"""
-function imhistp(img)
-    nbins = nextpow(2, maximum(img) + 1)
-    return _imhist(img, 0:(nbins-1))
-end
-
-
 """
     histeq(img)
-Histogram equalization of `img` according to [1].
-[1] R. C. Gonzalez and R. E. Woods. Digital Image Processing (3rd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, 2006.
+    histeq(img; nbins=64)
+
+Histogram equalization of `img` using `nbins` bins.
 """
-function histeq(img::S)::S where {S<:AbstractArray{<:Integer}}
-    k, heights = imhistp(img)
-    cdf = last(k) * cumsum(heights) / sum(heights)
-    s = round.(Int, cdf, RoundNearestTiesAway)
-    mapping = Dict(zip(k, s))
-    T(r) = mapping[r]
-    return T.(img)
+function histeq(img::S; nbins=64)::S where {S<:AbstractArray{<:Integer}}
+    return to_uint8(sk_exposure.equalize_hist(img, nbins=nbins) * 255)
 end