Added Hough Line Transform (#28)

Author: annimesh2809

src/ImageFeatures.jl

@@ -12,6 +12,7 @@ include("brief.jl")
 include("orb.jl")
 include("freak.jl")
 include("brisk.jl")
+include("houghtransform.jl")
 
 export Keypoint, Keypoints, Feature, Features, Params, BRIEF, ORB, FREAK, BRISK
 
@@ -57,5 +58,8 @@ export
 	random_coarse,
 	gaussian,
 	gaussian_local,
-	center_sample
+	center_sample,
+
+	#Lines and Ellipses
+	hough_transform_standard
 end

src/houghtransform.jl

@@ -0,0 +1,88 @@
+
+"""
+```
+lines = hough_transform_standard(image, ρ, θ, threshold, linesMax)
+```
+
+Returns an vector of tuples corresponding to the tuples of (r,t) where r and t are parameters for normal form of line:
+    x*cos(t) + y*sin(t) = r
+
+r = length of perpendicular from (1,1) to the line
+t = angle between perpendicular from (1,1) to the line and x-axis
+
+The lines are generated by applying hough transform on the image.
+
+Parameters:
+    image       = Image to be transformed (eltype should be `Bool`)
+    ρ           = Discrete step size for perpendicular length of line
+    θ           = List of angles for which the transform is computed
+    threshold   = No of points to pass through line for considering it valid
+    linesMax    = Maximum no of lines to return
+
+"""
+
+function hough_transform_standard{T<:Union{Bool,Gray{Bool}}}(
+            img::AbstractArray{T,2},
+            ρ::Number, θ::Range,
+            threshold::Integer, linesMax::Integer)
+   
+
+    #function to compute local maximum lines with values > threshold and return a vector containing them
+    function findlocalmaxima(accumulator_matrix::Array{Integer,2},threshold::Integer)
+        validLines = Vector{CartesianIndex}(0)
+        for val in CartesianRange(size(accumulator_matrix))
+            if  accumulator_matrix[val] > threshold &&
+                accumulator_matrix[val] > accumulator_matrix[val[1],val[2] - 1] &&
+                accumulator_matrix[val] >= accumulator_matrix[val[1],val[2] + 1] &&
+                accumulator_matrix[val] > accumulator_matrix[val[1] - 1,val[2]] &&
+                accumulator_matrix[val] >= accumulator_matrix[val[1] + 1,val[2]]
+                push!(validLines,val)
+            end
+        end
+        validLines
+    end
+
+    ρ > 0 || error("Discrete step size must be positive")
+    
+    height, width = size(img)
+    ρinv = 1 / ρ
+    numangle = length(θ)
+    numrho = round(Integer,(2(width + height) + 1)*ρinv)
+
+    accumulator_matrix = zeros(Integer, numangle + 2, numrho + 2)
+
+    #Pre-Computed sines and cosines in tables
+    sinθ, cosθ = sin.(θ).*ρinv, cos.(θ).*ρinv
+
+    #Hough Transform implementation
+    constadd = round(Integer,(numrho -1)/2)
+    for pix in CartesianRange(size(img))
+        if img[pix]
+            for i in 1:numangle
+                dist = round(Integer, pix[1] * sinθ[i] + pix[2] * cosθ[i])
+                dist += constadd
+                accumulator_matrix[i + 1, dist + 1] += 1
+            end
+        end
+    end
+
+    #Finding local maximum lines
+    validLines = findlocalmaxima(accumulator_matrix, threshold)
+    
+    #Sorting by value in accumulator_matrix
+    sort!(validLines, by = (x)->accumulator_matrix[x], rev = true)
+
+    linesMax = min(linesMax, length(validLines))
+
+    lines = Vector{Tuple{Number,Number}}(0)
+
+    #Getting lines with Maximum value in accumulator_matrix && size(lines) < linesMax
+    for l in 1:linesMax
+        lrho = ((validLines[l][2]-1) - (numrho - 1)*0.5)*ρ
+        langle = θ[validLines[l][1]-1]
+        push!(lines,(lrho,langle))
+    end
+
+    lines
+
+end

test/houghtransform.jl

@@ -0,0 +1,39 @@
+using ImageFeatures
+
+@testset "Hough_Transform" begin
+    @testset "Hough Line Transform" begin
+
+    #For images containing a straight line parallel to axes
+        img = zeros(Bool,10,10)
+        for i in 1:size(img)[1]
+            for j in 1:size(img)[2]
+                img[i,j] = true
+            end
+            h = hough_transform_standard(img,1,linspace(0,π/2,100),9,2)
+            @test length(h) == 1
+            @test h[1][1] == i
+            for j in 1:size(img)[2]
+                img[i,j] = false
+            end
+        end
+
+    #For images with diagonal line
+        img = diagm([true, true ,true])
+        h = hough_transform_standard(img,1,linspace(0,π,100),2,3)
+        @test length(h) == 1
+        @test h[1][1] == 0
+
+    #For a square image
+        img = zeros(Bool,10,10)
+        for i in 1:10
+            img[2,i] = img[i,2] = img[7,i] = img[i,9] = true
+        end
+        h = hough_transform_standard(img,1,linspace(0,π/2,100),9,10)
+        @test length(h) == 4
+        r = [h[i][1] for i in CartesianRange(size(h))]
+        @test all(r .== [2,2,7,9])
+        theta = [h[i][2] for i in CartesianRange(size(h))]
+        er = sum(map((t1,t2) -> abs(t1-t2), theta, [0, π/2, π/2, 0]))
+        @test er <= 0.1
+    end
+end

test/runtests.jl

@@ -53,6 +53,7 @@ include("corner.jl")
 include("orb.jl")
 include("freak.jl")
 include("brisk.jl")
+include("houghtransform.jl")
 
 isinteractive() || FactCheck.exitstatus()