helper function and test cases added

src/houghtransform.jl

@@ -156,7 +156,7 @@ img::AbstractArray{T,2},
 ρ::Real, θ::Range,
 threshold::Integer, lineLength::Integer, lineGap::Integer, linesMax::Integer) where T<:Union{Bool,Gray{Bool}}
 
- ρ > 0 || error("Discrete step size must be positive")
+ ρ > 0 || throw(ArgumentError("Discrete step size must be positive"))
  indsy, indsx = indices(img)
  ρinv = 1 / ρ
  numangle = length(θ)
@@ -165,48 +165,30 @@ threshold::Integer, lineLength::Integer, lineGap::Integer, linesMax::Integer) wh
  accumulator_matrix = zeros(Int, numangle + 2, numrho + 2)
  h, w = size(img)
  mask = zeros(Bool, h, w)
-
+ x0, y0 = 0, 0
  #Pre-Computed sines and cosines in tables
  sinθ, cosθ = sin.(θ).*ρinv, cos.(θ).*ρinv
  nzloc = Vector{Tuple{Int64,Int64}}(0)
  lines = Vector{Tuple{Int64, Int64, Int64, Int64}}(0)
-
- #collect non-zero image points
- for pix in CartesianRange(size(img))
- pix1 = (pix[1], pix[2])
- if(img[pix])
- push!(nzloc, pix1)
- mask[pix] = true
- else
- mask[pix] = false
+ const shift = 16
+
+ #function to mark and collect all non zero points
+ function collect_points()
+ for pix in CartesianRange(size(img))
+ pix1 = (pix[1], pix[2])
+ if(img[pix])
+ push!(nzloc, pix1)
+ mask[pix] = true
+ else
+ mask[pix] = false
+ end
  end
- end
+ end 
 
- count_ = size(nzloc)[1]+1
-
- # stage 2. process all the points in random order
- while(count_>1)
- count_-=1
- good_line = false
- # choose random point out of the remaining ones
- idx = rand(1:count_)
- max_val = threshold-1
+ #function to update the accumulator matrix for every point selected
+ function update_accumulator(point)
  max_n = 1
- point = nzloc[idx]
- line_end = [[0,0],[0,0]]
- i = point[1]-1 
- j = point[2]-1
- x0, y0, dx0, dy0, xflag = 0, 0, 0, 0, 0
- const shift = 16
-
- # "remove" it by overriding it with the last element
- nzloc[idx] = nzloc[count_]
-
- if(!(mask[point[1], point[2]]))
- continue
- end
-
- # update accumulator, find the most probable line
+ max_val = threshold-1
  for n in 0:numangle-1
  dist = round(Int, point[2]*cosθ[n+1] + point[1]*sinθ[n+1])
  dist += constadd
@@ -218,32 +200,12 @@ threshold::Integer, lineLength::Integer, lineGap::Integer, linesMax::Integer) wh
  max_n = n+1
  end 
  end
+ return max_n, max_val
+ end 
 
- # if it is too "weak" candidate, continue with another point
- if(max_val < threshold)
- continue
- end
-
- # from the current point walk in each direction along the found line
- a = -sinθ[max_n]
- b = cosθ[max_n]
- x0 = j
- y0 = i
- good_line = false
-
- if(abs(a) > abs(b))
- xflag = 1
- dx0 = a > 0 ? 1 : -1
- dy0 = round(b*(1 << shift)/abs(a))
- y0 = (y0 << shift) + (1 << (shift-1))
- else
- xflag = 0
- dy0 = b > 0 ? 1 : -1
- dx0 = round( a*(1 << shift)/abs(b) );
- x0 = (x0 << shift) + (1 << (shift-1)); 
- end 
-
- # pass 1: walk the line, merging lines less than specified gap length
+ #function to detect the line segment after merging lines within lineGap
+ function pass_1(xflag, x0, y0, dx0, dy0)
+ line_end = [[0,0],[0,0]]
  for k = 1:2
  gap = 0
  x = x0
@@ -286,10 +248,11 @@ threshold::Integer, lineLength::Integer, lineGap::Integer, linesMax::Integer) wh
  y = Int64(y+dy)
  end
  end
- # confirm line length is sufficient
-  good_line = abs(line_end[2][1] - line_end[1][1]) >= lineLength || abs(line_end[2][2] - line_end[1][2]) >= lineLength  
+ return line_end
+ end 
 
- # pass 2: walk the line again and reset accumulator and mask
+ #function to reset the mask and accumulator_matrix 
+ function pass_2(xflag, x0, y0, dx0, dy0, good_line, line_end)
  for k = 1:2
  x = x0
  y = y0
@@ -331,8 +294,70 @@ threshold::Integer, lineLength::Integer, lineGap::Integer, linesMax::Integer) wh
  x = Int64(x+dx)
  y = Int64(y+dy) 
  end
+ end
+ end 
+
+ #collect non-zero image points
+ collect_points()
+
+ count_ = size(nzloc)[1]+1
+
+ # stage 2. process all the points in random order
+ while(count_>1)
+ count_-=1
+ good_line = false
+ # choose random point out of the remaining ones
+ idx = rand(1:count_)
+ max_n = 1
+ point = nzloc[idx]
+ i = point[1]-1 
+ j = point[2]-1
+ x0, y0, dx0, dy0, xflag = 0, 0, 0, 0, 0
+ max_n = 1
+
+ # "remove" it by overriding it with the last element
+ nzloc[idx] = nzloc[count_]
+
+ if(!(mask[point[1], point[2]]))
+ continue
+ end
+
+ # update accumulator, find the most probable line
+ max_n, max_val = update_accumulator(point)
+
+ # if it is too "weak" candidate, continue with another point
+ if(max_val < threshold)
+ continue
+ end
+
+ # from the current point walk in each direction along the found line
+ a = -sinθ[max_n]
+ b = cosθ[max_n]
+ x0 = j
+ y0 = i
+ good_line = false
+
+ if(abs(a) > abs(b))
+ xflag = 1
+ dx0 = a > 0 ? 1 : -1
+ dy0 = round(b*(1 << shift)/abs(a))
+ y0 = (y0 << shift) + (1 << (shift-1))
+ else
+ xflag = 0
+ dy0 = b > 0 ? 1 : -1
+ dx0 = round( a*(1 << shift)/abs(b) );
+ x0 = (x0 << shift) + (1 << (shift-1)); 
+ end 
+
+ # pass 1: walk the line, merging lines less than specified gap length
+ line_end = pass_1(xflag, x0, y0, dx0, dy0)
+
+ # confirm line length is sufficient
+ good_line = abs(line_end[2][1] - line_end[1][1]) >= lineLength || abs(line_end[2][2] - line_end[1][2]) >= lineLength 
+
+ # pass 2: walk the line again and reset accumulator and mask
+ pass_2(xflag, x0, y0, dx0, dy0, good_line, line_end) 
 
- end 
  # add line to the result
  if(good_line)
  push!(lines, (line_end[1][1], line_end[1][2], line_end[2][1], line_end[2][2]))

test/houghtransform.jl

@@ -37,6 +37,47 @@ using ImageFeatures
  @test er <= 0.1
  end
 
+ @testset "Hough Line Probabilistic" begin
+ #one horizontal line segment with few scattered points 
+ img = zeros(Bool,20,20)
+ for j in 5:15
+ img[15,j] = true
+ end
+
+ srand(1234)
+ for k in 1:5
+ img[rand(1:13),rand(6:14)] = true
+ end
+ lines = hough_line_probabilistic(img, 1, linspace(0,π,180),7,5,10,4)
+ @test length(lines) == 1
+ @test lines[1] == (15,5,15,15)
+
+ #for a square image
+ img = zeros(Bool, 20, 20)
+
+ for i in (3,14)
+ for j in 3:14
+ img[i,j] = true
+ end
+ end 
+
+ for i in (3,14)
+ for j in 3:14
+ img[j,i] = true
+ end
+ end
+ srand(1234)
+ for k in 1:4
+ img[rand(6:10),rand(6:10)] = true
+ end
+ # srand(1234)
+ lines = hough_line_probabilistic(img, 1, linspace(0,π,180),9,7,15,4)
+ lines_ = [line for line in lines]
+ @test length(lines) == 4
+ @test all(lines_ == [(3, 3, 3, 14), (14, 3, 14, 14), (13, 14, 4, 14), (13, 3, 4, 3)])
+
+ end
+
  @testset "Hough Circle Gradient" begin
 
  dist(a, b) = sqrt(sum(abs2, (a-b).I))