deprecate Kernel.gabor

src/kernel.jl

@@ -427,66 +427,6 @@ function laplacian2d(alpha::Number=0)
     return centered([lc lb lc; lb lm lb; lc lb lc])
 end
 
-"""
-    gabor(size_x,size_y,σ,θ,λ,γ,ψ) -> (k_real,k_complex)
-
-Returns a 2 Dimensional Complex Gabor kernel contained in a tuple where
-
-  - `size_x`, `size_y` denote the size of the kernel
-  - `σ` denotes the standard deviation of the Gaussian envelope
-  - `θ` represents the orientation of the normal to the parallel stripes of a Gabor function
-  - `λ` represents the wavelength of the sinusoidal factor
-  - `γ` is the spatial aspect ratio, and specifies the ellipticity of the support of the Gabor function
-  - `ψ` is the phase offset
-
-#Citation
-N. Petkov and P. Kruizinga, “Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells,” Biological Cybernetics, vol. 76, no. 2, pp. 83–96, Feb. 1997. doi.org/10.1007/s004220050323
-"""
-function gabor(size_x::Integer, size_y::Integer, σ::Real, θ::Real, λ::Real, γ::Real, ψ::Real)
-
-    σx = σ
-    σy = σ/γ
-    nstds = 3
-    c = cos(θ)
-    s = sin(θ)
-
-    validate_gabor(σ,λ,γ)
-
-    if(size_x > 0)
-        xmax = floor(Int64,size_x/2)
-    else
-        @warn "The input parameter size_x should be positive. Using size_x = 6 * σx + 1 (Default value)"
-        xmax = round(Int64,max(abs(nstds*σx*c),abs(nstds*σy*s),1))
-    end
-
-    if(size_y > 0)
-        ymax = floor(Int64,size_y/2)
-    else
-        @warn "The input parameter size_y should be positive. Using size_y = 6 * σy + 1 (Default value)"
-        ymax = round(Int64,max(abs(nstds*σx*s),abs(nstds*σy*c),1))
-    end
-
-    xmin = -xmax
-    ymin = -ymax
-
-    x = [j for i in xmin:xmax,j in ymin:ymax]
-    y = [i for i in xmin:xmax,j in ymin:ymax]
-    xr = x*c + y*s
-    yr = -x*s + y*c
-
-    kernel_real = (exp.(-0.5*(((xr.*xr)/σx^2) + ((yr.*yr)/σy^2))).*cos.(2*(π/λ)*xr .+ ψ))
-    kernel_imag = (exp.(-0.5*(((xr.*xr)/σx^2) + ((yr.*yr)/σy^2))).*sin.(2*(π/λ)*xr .+ ψ))
-
-    kernel = (kernel_real,kernel_imag)
-    return kernel
-end
-
-function validate_gabor(σ::Real,λ::Real,γ::Real)
-    if !(σ>0 && λ>0 && γ>0)
-        throw(ArgumentError("The parameters σ, λ and γ must be positive numbers."))
-    end
-end
-
 """
     moffat(α, β, ls) -> k
 
@@ -540,4 +480,6 @@ if Base.VERSION >= v"1.4.2" && ccall(:jl_generating_output, Cint, ()) == 1
     end
 end
 
+include("kernel_deprecated.jl")
+
 end

src/kernel_deprecated.jl

@@ -0,0 +1,62 @@
+# Deprecated
+
+"""
+    gabor(size_x,size_y,σ,θ,λ,γ,ψ) -> (k_real,k_complex)
+
+Returns a 2 Dimensional Complex Gabor kernel contained in a tuple where
+
+  - `size_x`, `size_y` denote the size of the kernel
+  - `σ` denotes the standard deviation of the Gaussian envelope
+  - `θ` represents the orientation of the normal to the parallel stripes of a Gabor function
+  - `λ` represents the wavelength of the sinusoidal factor
+  - `γ` is the spatial aspect ratio, and specifies the ellipticity of the support of the Gabor function
+  - `ψ` is the phase offset
+
+# Citation
+
+N. Petkov and P. Kruizinga, “Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells,” Biological Cybernetics, vol. 76, no. 2, pp. 83–96, Feb. 1997. doi.org/10.1007/s004220050323
+"""
+function gabor(size_x::Integer, size_y::Integer, σ::Real, θ::Real, λ::Real, γ::Real, ψ::Real)
+    Base.depwarn("use `Kernel.Gabor` instead.", :gabor)
+    σx = σ
+    σy = σ/γ
+    nstds = 3
+    c = cos(θ)
+    s = sin(θ)
+
+    validate_gabor(σ,λ,γ)
+
+    if(size_x > 0)
+        xmax = floor(Int64,size_x/2)
+    else
+        @warn "The input parameter size_x should be positive. Using size_x = 6 * σx + 1 (Default value)"
+        xmax = round(Int64,max(abs(nstds*σx*c),abs(nstds*σy*s),1))
+    end
+
+    if(size_y > 0)
+        ymax = floor(Int64,size_y/2)
+    else
+        @warn "The input parameter size_y should be positive. Using size_y = 6 * σy + 1 (Default value)"
+        ymax = round(Int64,max(abs(nstds*σx*s),abs(nstds*σy*c),1))
+    end
+
+    xmin = -xmax
+    ymin = -ymax
+
+    x = [j for i in xmin:xmax,j in ymin:ymax]
+    y = [i for i in xmin:xmax,j in ymin:ymax]
+    xr = x*c + y*s
+    yr = -x*s + y*c
+
+    kernel_real = (exp.(-0.5*(((xr.*xr)/σx^2) + ((yr.*yr)/σy^2))).*cos.(2*(π/λ)*xr .+ ψ))
+    kernel_imag = (exp.(-0.5*(((xr.*xr)/σx^2) + ((yr.*yr)/σy^2))).*sin.(2*(π/λ)*xr .+ ψ))
+
+    kernel = (kernel_real,kernel_imag)
+    return kernel
+end
+
+function validate_gabor(σ::Real,λ::Real,γ::Real)
+    if !(σ>0 && λ>0 && γ>0)
+        throw(ArgumentError("The parameters σ, λ and γ must be positive numbers."))
+    end
+end

test/gabor.jl → test/deprecated.jl

@@ -1,13 +1,10 @@
-using ImageFiltering, Test, Statistics
-
 @testset "gabor" begin
     σx = 8
     σy = 12
     size_x = 6*σx+1
     size_y = 6*σy+1
     γ = σx/σy
     # zero size forces default kernel width, with warnings
-    @info "Four warnings are expected"
     kernel = Kernel.gabor(0,0,σx,0,5,γ,0)
     @test isequal(size(kernel[1]),(size_x,size_y))
     kernel = Kernel.gabor(0,0,σx,π,5,γ,0)

test/runtests.jl

@@ -6,6 +6,7 @@ using ImageQualityIndexes
 import StaticArrays
 using Random
 using FFTW
+using Statistics
 
 @testset "Project meta quality checks" begin
     # Ambiguity test
@@ -44,7 +45,6 @@ include("gradient.jl")
 include("mapwindow.jl")
 include("extrema.jl")
 include("basic.jl")
-# include("gabor.jl")
 include("gaborkernels.jl")
 include("models.jl")
 
@@ -72,4 +72,7 @@ if CUDA_FUNCTIONAL
 else
     @warn "CUDA test: disabled"
 end
+
+@info "Beginning deprecation tests, warnings are expected"
+include("deprecated.jl")
 nothing