Adds Histogram Equalisation Function

docs/src/function_reference.md

@@ -268,6 +268,12 @@ include the following functions:
     padarray
 }
 
+# Exposure
+
+@{
+    histeq
+}
+
 # Filtering kernels
 
 @{

src/Images.jl

@@ -202,6 +202,7 @@ export # types
     imfilter_fft,
     imfilter_gaussian,
     imfilter_gaussian!,
+    histeq,
     imfilter_LoG,
     imgaussiannoise,
     imgradients,
@@ -266,6 +267,7 @@ Algorithms:
 
     - Reductions: `maxfinite`, `maxabsfinite`, `minfinite`, `meanfinite`, `sad`, `ssd`
     - Resizing: `restrict`, `imresize` (not yet exported)
+    - Exposure: `histeq`
     - Filtering: `imfilter`, `imfilter_fft`, `imfilter_gaussian`, `imfilter_LoG`, `imROF`, `ncc`, `padarray`
     - Filtering kernels: `ando[345]`, `guassian2d`, `imaverage`, `imdog`, `imlaplacian`, `prewitt`, `sobel`
     - Gradients: `backdiffx`, `backdiffy`, `forwarddiffx`, `forwarddiffy`, `imgradients`

src/algorithms.jl

@@ -1029,6 +1029,42 @@ function copytail!(dest, A, coloffset, strd, len)
     dest
 end
 
+
+# Histogram Equalisation Function
+"""
+```
+hist_equalised_img = histeq(img, nbins)
+
+Returns a grayscale histogram equalised image with a granularity of `nbins` number of bins.
+```
+
+"""
+function histeq{T,N}(img::AbstractArray{T,N}, nbins::Int)
+    imgv = img[:]
+    imgmax=maxfinite(imgv)
+    imgmin=minfinite(imgv)
+    newmaxintensity=1
+    if T<:Colorant
+        imgv = [(ColorTypes.cconvert(Gray, imgv[i])) for i=1:length(imgv)]
+    elseif T<:Number
+        imgv = [(imgv[i]-imgmin)/(imgmax-imgmin) for i=1:length(imgv)]
+    end
+    _,histogram = hist(imgv, 0:1/nbins:1)
+    cdfmin = 0
+    cdf = zeros(Int64, nbins)
+    for i = 1:nbins
+        cdf[i:nbins] += histogram[i]
+        if cdf[i] == 0
+            cdfmin += 1
+        end
+    end
+    retimg = [(imgv[i] == 0)? 1:Int(ceil(nbins*imgv[i])) for i = 1:length(imgv)]
+    retimg = [newmaxintensity*((cdf[retimg[i]]-cdfmin)/(length(imgv)-cdfmin)) for i = 1:length(retimg)]
+    retimg = reshape(retimg, size(img))
+    retimg = convert(Image{ColorTypes.Gray{U8}}, retimg')
+    retimg
+end
+
 # Laplacian of Gaussian filter
 # Separable implementation from Huertas and Medioni,
 # IEEE Trans. Pat. Anal. Mach. Int., PAMI-8, 651, (1986)

test/algorithms.jl

@@ -130,6 +130,17 @@ facts("Algorithms") do
         @fact img2 --> roughly(reinterpret(Float32, img))
     end
 
+    context("Exposure") do
+        im1 = reshape(1:1:100, 10, 10)
+        ret1 = histeq(im1, 10)
+        @fact hist(im1[:], 0:10:100)[2]-->hist(ret1[:], 0:0.1:1)[2]
+
+        im2 = reshape(1:1:100, 10, 10)
+        im2 = grayim(im2)
+        ret2 = histeq(im2, 10)
+        @fact hist(im2[:], 0:10:100)[2]-->hist(ret2[:], 0:0.1:1)[2]
+    end
+
     context("Array padding") do
         A = [1 2; 3 4]
         @fact Images.padindexes(A, 1, 0, 0, "replicate") --> [1,2]