Adds Histogram function for Single Channel Images

docs/src/function_reference.md

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

src/Images.jl

@@ -202,6 +202,7 @@ export # types
     imfilter_fft,
     imfilter_gaussian,
     imfilter_gaussian!,
+    imhist,
     imfilter_LoG,
     imgaussiannoise,
     imgradients,
@@ -266,6 +267,7 @@ Algorithms:
 
     - Reductions: `maxfinite`, `maxabsfinite`, `minfinite`, `meanfinite`, `sad`, `ssd`
     - Resizing: `restrict`, `imresize` (not yet exported)
+    - Exposure: `imhist`
     - 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 +1029,28 @@ function copytail!(dest, A, coloffset, strd, len)
     dest
 end
 
+"""
+```
+hist=imhist(img, 0:0.2:1)
+```
+
+Calculates a histogram of the given single channel image with the given range as bins. 
+The histogram is calculated on the flattened image. For multi dimensional images, use the function 
+separately on each channel to obtain a histogram for each.  
+"""
+function imhist{T}(img::AbstractArray{T,2}, edg::AbstractVector)
+    img_data=raw(img)
+    hist(img_data[:], edg)
+end
+
+# """
+# ```
+
+# ```
+
+# """
+# function histeq()
+
 # 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
+        A = reshape(0.1:0.1:2.0, 4, 5)
+        imgA = convert(Image, A)
+        _, hist = Images.imhist(imgA, 0:0.5:2)
+        @fact hist --> [5,5,5,5] 
+        A = reshape(1.24:0.04:2, 4, 5)
+        imgA = convert(Image, A)
+        _, hist = Images.imhist(imgA, 0:0.2:2)
+        @fact hist --> [0,0,0,0,0,0,5,5,5,5] 
+    end
+
     context("Array padding") do
         A = [1 2; 3 4]
         @fact Images.padindexes(A, 1, 0, 0, "replicate") --> [1,2]