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Adds support for specifying kernels using StaticArrays #254

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2 changes: 1 addition & 1 deletion Project.toml
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Expand Up @@ -30,7 +30,7 @@ ImageCore = "0.9"
OffsetArrays = "1.9"
Reexport = "1.1"
StaticArrays = "0.10, 0.11, 0.12, 1.0"
TiledIteration = "0.2, 0.3, 0.4"
TiledIteration = "0.2, 0.3"
julia = "1"

[extras]
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19 changes: 19 additions & 0 deletions src/border.jl
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Expand Up @@ -997,6 +997,25 @@ expand(inds::Indices, kernel) = expand(inds, calculate_padding(kernel))
expand(inds::Indices, pad::Indices) = firsttype(map_copytail(expand, inds, pad))
expand(ind::AbstractUnitRange, pad::AbstractUnitRange) = typeof(ind)(first(ind)+first(pad):last(ind)+last(pad))
expand(ind::Base.OneTo, pad::AbstractUnitRange) = expand(UnitRange(ind), pad)
expand(ind::SOneTo{T₁}, pad::AbstractUnitRange) where {T₁} = represent_unit_range_with_SOneTo(ind, pad)
expand(ind::OffsetArrays.IdOffsetRange{T₁, SOneTo{T₂}}, pad::AbstractUnitRange) where {T₁, T₂} = determine_offset_with_SOneTo(ind, pad)

function represent_unit_range_with_SOneTo(ind::SOneTo{T₁}, pad::AbstractUnitRange) where {T₁}
lo = first(ind)+first(pad)
hi = last(ind)+last(pad)
interval = 1:length(lo:hi)
T = typeof(T₁)
I = typeof(SOneTo(length(interval)))
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This is unfortunately a type-unstable patch: SOneTo{n} where n = length(interval) is runtime information.

IIUC, unless the image itself is also a SMatrix, we can't get a static pad, and thus we can't make it type stable.

The solution to support static array inputs might be converting it back to normal array and unit ranges.

return OffsetArrays.IdOffsetRange{T, I}(interval, lo - 1)
end

function determine_offset_with_SOneTo(ind::OffsetArrays.IdOffsetRange{T₁, SOneTo{T₂}}, pad::AbstractUnitRange) where {T₁, T₂}
lo = first(ind)+first(pad)
hi = last(ind)+last(pad)
interval = 1:length(lo:hi)
I = typeof(SOneTo(length(interval)))
return OffsetArrays.IdOffsetRange{T₁, I}(interval, lo - 1)
end

"""
shrink(inds::Indices, kernel)
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4 changes: 2 additions & 2 deletions src/imfilter.jl
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Expand Up @@ -502,8 +502,8 @@ See also: [`Pad`](@ref), [`padarray`](@ref), [`Inner`](@ref), [`NA`](@ref) and


## Choices for `alg`
The `alg` parameter allows you to choose the particular algorithm: `Algorithm.FIR()`
(finite impulse response, aka traditional digital filtering) or `Algorithm.FFT()`
The `alg` parameter allows you to choose the particular algorithm: `FIR()`
(finite impulse response, aka traditional digital filtering) or `FFT()`
(Fourier-based filtering). If no choice is specified, one will be chosen based
on the size of the image and kernel in a way that strives to deliver good
performance. Alternatively you can use a custom filter type, like
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17 changes: 17 additions & 0 deletions test/2d.jl
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Expand Up @@ -341,3 +341,20 @@ end
@test_throws DimensionMismatch imfilter(CPU1(), A, kern, Fill(0, (0,1)))
@test_throws DimensionMismatch imfilter(CPU1(), A, kern, Fill(0, (0,0)))
end

@testset "Static Arrays (issue #252)" begin
img = rand(RGB{Float32}, 9, 9)
k₁ = [0.2f0 -0.5f0 0.3f0]
k₂ = SMatrix{1, 3, Float32}(k₁)
border_styles = ["replicate", "circular", "reflect", "symmetric"]
for border_style in border_styles
A = imfilter(img, (k₁', k₁), border_style, ImageFiltering.Algorithm.FIR())
B = imfilter(img, (k₂', k₂), border_style, ImageFiltering.Algorithm.FIR())
@test A ≈ B
k₁ = centered(k₁)
k₂ = centered(k₂)
A = imfilter(img, (k₁', k₁), border_style, ImageFiltering.Algorithm.FIR())
B = imfilter(img, (k₂', k₂), border_style, ImageFiltering.Algorithm.FIR())
@test A ≈ B
end
end
131 changes: 72 additions & 59 deletions test/nd.jl
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Expand Up @@ -13,38 +13,42 @@ Base.zero(::Type{WrappedFloat}) = WrappedFloat(0.0)

@testset "1d" begin
img = 1:8
# Exercise all the different ways to call imfilter
kern = centered([1/3,1/3,1/3])
imgf = imfilter(img, kern)
r = CPU1(Algorithm.FIR())
@test imfilter(Float64, img, kern) == imgf
@test imfilter(Float64, img, (kern,)) == imgf
@test imfilter(Float64, img, kern, "replicate") == imgf
@test imfilter(Float64, img, (kern,), "replicate") == imgf
@test_throws ArgumentError imfilter(Float64, img, (kern,), "inner")
@test_throws ArgumentError imfilter(Float64, img, (kern,), "nonsense")
@test imfilter(Float64, img, kern, "replicate", Algorithm.FIR()) == imgf
@test imfilter(Float64, img, (kern,), "replicate", Algorithm.FIR()) == imgf
@test imfilter(r, img, kern) == imgf
@test imfilter(r, img, kern) == imgf
@test imfilter(r, Float64, img, kern) == imgf
@test imfilter(r, Float64, img, (kern,)) == imgf
@test imfilter(r, img, kern, "replicate") == imgf
@test imfilter(r, Float64, img, kern, "replicate") == imgf
@test imfilter(r, img, (kern,), "replicate") == imgf
@test imfilter(r, Float64, img, (kern,), "replicate") == imgf
@test_throws MethodError imfilter(r, img, (kern,), "replicate", Algorithm.FIR())
@test_throws MethodError imfilter(r, Float64, img, (kern,), "replicate", Algorithm.FIR())
out = similar(imgf)
@test imfilter!(out, img, kern) == imgf
@test imfilter!(out, img, (kern,)) == imgf
@test imfilter!(out, img, (kern,), "replicate") == imgf
@test imfilter!(out, img, (kern,), "replicate", Algorithm.FIR()) == imgf
@test imfilter!(r, out, img, kern) == imgf
@test imfilter!(r, out, img, (kern,)) == imgf
@test imfilter!(r, out, img, kern, "replicate") == imgf
@test imfilter!(r, out, img, (kern,), "replicate") == imgf
@test_throws MethodError imfilter!(r, out, img, (kern,), "replicate", Algorithm.FIR())
array_types = [Vector, SVector{3}]
values = [1/3, 1/3, 1/3]
for array_type in array_types
# Exercise all the different ways to call imfilter
kern = centered(array_type(values))
imgf = imfilter(img, kern)
r = CPU1(Algorithm.FIR())
@test imfilter(Float64, img, kern) == imgf
@test imfilter(Float64, img, (kern,)) == imgf
@test imfilter(Float64, img, kern, "replicate") == imgf
@test imfilter(Float64, img, (kern,), "replicate") == imgf
@test_throws ArgumentError imfilter(Float64, img, (kern,), "inner")
@test_throws ArgumentError imfilter(Float64, img, (kern,), "nonsense")
@test imfilter(Float64, img, kern, "replicate", Algorithm.FIR()) == imgf
@test imfilter(Float64, img, (kern,), "replicate", Algorithm.FIR()) == imgf
@test imfilter(r, img, kern) == imgf
@test imfilter(r, img, kern) == imgf
@test imfilter(r, Float64, img, kern) == imgf
@test imfilter(r, Float64, img, (kern,)) == imgf
@test imfilter(r, img, kern, "replicate") == imgf
@test imfilter(r, Float64, img, kern, "replicate") == imgf
@test imfilter(r, img, (kern,), "replicate") == imgf
@test imfilter(r, Float64, img, (kern,), "replicate") == imgf
@test_throws MethodError imfilter(r, img, (kern,), "replicate", Algorithm.FIR())
@test_throws MethodError imfilter(r, Float64, img, (kern,), "replicate", Algorithm.FIR())
out = similar(imgf)
@test imfilter!(out, img, kern) == imgf
@test imfilter!(out, img, (kern,)) == imgf
@test imfilter!(out, img, (kern,), "replicate") == imgf
@test imfilter!(out, img, (kern,), "replicate", Algorithm.FIR()) == imgf
@test imfilter!(r, out, img, kern) == imgf
@test imfilter!(r, out, img, (kern,)) == imgf
@test imfilter!(r, out, img, kern, "replicate") == imgf
@test imfilter!(r, out, img, (kern,), "replicate") == imgf
@test_throws MethodError imfilter!(r, out, img, (kern,), "replicate", Algorithm.FIR())
end

# Element-type widening (issue #17)
v = fill(0xff, 10)
Expand All @@ -56,40 +60,49 @@ Base.zero(::Type{WrappedFloat}) = WrappedFloat(0.0)
@test all(x->x==0x0002fa03, vout)

# Cascades don't result in out-of-bounds values
k1 = centered([0.25, 0.5, 0.25])
k2 = OffsetArray([0.5, 0.5], 1:2)
casc = imfilter(img, (k1, k2, k1))
A0 = padarray(img, Pad(:replicate, (2,), (4,)))
A1 = imfilter(A0, k1, Inner())
@test A1 ≈ OffsetArray([1.0,1.25,2.0,3.0,4.0,5.0,6.0,7.0,7.75,8.0,8.0,8.0], 0:11)
A2 = imfilter(A1, k2, Inner())
@test A2 ≈ OffsetArray([1.625,2.5,3.5,4.5,5.5,6.5,7.375,7.875,8.0,8.0], 0:9)
A3 = imfilter(A2, k1, Inner())
@test casc ≈ A3
@test size(casc) == size(img)
# copy! kernels, presence/order doesn't matter
kc = centered([1])
@test ImageFiltering.iscopy(kc)
@test imfilter(img, (k1,)) ≈ [1.25,2.0,3.0,4.0,5.0,6.0,7.0,7.75]
@test imfilter(img, (kc, k1)) ≈ [1.25,2.0,3.0,4.0,5.0,6.0,7.0,7.75]
@test imfilter(img, (k1, kc)) ≈ [1.25,2.0,3.0,4.0,5.0,6.0,7.0,7.75]

array_types₁ = [Vector, SVector{3}]
array_types₂ = [Vector, SVector{2}]
for (array_type₁, array_type₂) in zip(array_types₁, array_types₂)
values1 = [0.25, 0.5, 0.25]
values2 = [0.5, 0.5]
k1 = centered(array_type₁(values1))
k2 = OffsetArray(array_type₂(values2), 1:2)
casc = imfilter(img, (k1, k2, k1))
A0 = padarray(img, Pad(:replicate, (2,), (4,)))
A1 = imfilter(A0, k1, Inner())
@test A1 ≈ OffsetArray([1.0,1.25,2.0,3.0,4.0,5.0,6.0,7.0,7.75,8.0,8.0,8.0], 0:11)
A2 = imfilter(A1, k2, Inner())
@test A2 ≈ OffsetArray([1.625,2.5,3.5,4.5,5.5,6.5,7.375,7.875,8.0,8.0], 0:9)
A3 = imfilter(A2, k1, Inner())
@test casc ≈ A3
@test size(casc) == size(img)
# copy! kernels, presence/order doesn't matter
kc = centered([1])
@test ImageFiltering.iscopy(kc)
@test imfilter(img, (k1,)) ≈ [1.25,2.0,3.0,4.0,5.0,6.0,7.0,7.75]
@test imfilter(img, (kc, k1)) ≈ [1.25,2.0,3.0,4.0,5.0,6.0,7.0,7.75]
@test imfilter(img, (k1, kc)) ≈ [1.25,2.0,3.0,4.0,5.0,6.0,7.0,7.75]
end
# FFT without padding
img = collect(1:8)
img[1] = img[8] = 0
out = imfilter!(CPU1(Algorithm.FFT()), similar(img, Float64), img, kern, NoPad())
@test out ≈ imfilter(img, kern)

# Inputs that have non-1 axes
img = OffsetArray(zeros(11), -5:5)
img[0] = 1
for border in ("replicate", "circular", "symmetric", "reflect",
Fill(zero(eltype(img))), Inner(1), NA())
imgf = imfilter(img, centered([0.25, 0.5, 0.25]), border)
@test imgf[-1] == imgf[1] == 0.25
@test imgf[0] == 0.5
inds = axes(imgf,1)
@test all(x->x==0, imgf[first(inds):-2]) && all(x->x==0, imgf[2:last(inds)])
array_types = [Vector, MVector{11}]
values = zeros(11)
for array_type in array_types
img = OffsetArray(array_type(values), -5:5)
img[0] = 1
for border in ("replicate", "circular", "symmetric", "reflect",
Fill(zero(eltype(img))), Inner(1), NA())
imgf = imfilter(img, centered([0.25, 0.5, 0.25]), border)
@test imgf[-1] == imgf[1] == 0.25
@test imgf[0] == 0.5
inds = axes(imgf,1)
@test all(x->x==0, imgf[first(inds):-2]) && all(x->x==0, imgf[2:last(inds)])
end
end

# Non-finite input
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