Merge pull request #75 from itan1/make-flip-and-zoom-nd-compatible

Make Flips ND compatible and fix ND ScaleKeepAspect, ScaleFixed and PinOrigin

docs/src/projective/gallery.md

@@ -135,14 +135,14 @@ tfms = [
 showgrid([apply(tfm, (image, bbox)) for tfm in tfms]; ncol=6, npad=8)
 ```
 
-## [`FlipX`](@ref), [`FlipY`](@ref), [`Reflect`](@ref)
+## [`FlipX`](@ref), [`FlipY`](@ref), [`FlipZ`](@ref), [`Reflect`](@ref)
 
 Flip the data on the horizontally and vertically, respectively. More generally, reflect around an angle from the x-axis.
 
 ```@example deps
 tfms = [
-    FlipX(),
-    FlipY(),
+    FlipX{2}(),
+    FlipY{2}(),
     Reflect(30),
 ]
 showgrid([apply(tfm, (image, bbox)) for tfm in tfms]; ncol=6, npad=8)

docs/src/projective/intro.md

@@ -7,7 +7,7 @@ We can break down most augmentation used in practive into a single (possibly sto
 As an example, consider an image augmentation pipeline: A random horizontal flip, followed by a random resized crop. The latter resizes and crops (irregularly sized) images to a common size without distorting the aspect ratio.
 
 ```julia
-Maybe(FlipX()) |> RandomResizeCrop((h, w))
+Maybe(FlipX{2}()) |> RandomResizeCrop((h, w))
 ```
 
 Let's pull apart the steps involved. 

docs/src/ref.md

@@ -6,8 +6,10 @@ BoundingBox
 CenterCrop
 CenterResizeCrop
 Crop
+FlipDim
 FlipX
 FlipY
+FlipZ
 Image
 Keypoints
 MaskBinary

docs/src/transformations.md

@@ -6,7 +6,7 @@ using DataAugmentation
 Using  transformations is easy. Simply `compose` them:
 
 ```@example tsm
-tfm = Rotate(10) |> ScaleRatio((0.7,0.1,1.2)) |> FlipX() |> Crop((128, 128))
+tfm = Rotate(10) |> ScaleRatio((0.7,0.1,1.2)) |> FlipX{2}() |> Crop((128, 128))
 ```
 
 # Projective transformations
@@ -26,8 +26,10 @@ Projective transformations include:
 Affine transformations are a subgroup of projective transformations that can be composed very efficiently: composing two affine transformations results in another affine transformation. Affine transformations can represent translation, scaling, reflection and rotation. Available `Transform`s are:
 
 ```@docs; canonical=false
+FlipDim
 FlipX
 FlipY
+FlipZ
 Reflect
 Rotate
 RotateX
@@ -73,7 +75,7 @@ Let's say we have an image classification dataset. For most datasets, horizontal
 ```@example
 using DataAugmentation, TestImages
 item = Image(testimage("lighthouse"))
-tfm = Maybe(FlipX())
+tfm = Maybe(FlipX{2}())
 titems = [apply(tfm, item) for _ in 1:8]
 showgrid(titems; ncol = 4, npad = 16)
 ```

src/DataAugmentation.jl

@@ -84,8 +84,10 @@ export Item,
     apply,
     Reflect,
     WarpAffine,
+    FlipDim,
     FlipX,
     FlipY,
+    FlipZ,
     PinOrigin,
     AdjustBrightness,
     AdjustContrast,

src/projective/affine.jl

@@ -43,15 +43,15 @@ struct ScaleKeepAspect{N} <: ProjectiveTransform
 end
 
 
-function getprojection(scale::ScaleKeepAspect{N}, bounds; randstate = nothing) where N
+function getprojection(scale::ScaleKeepAspect{N}, bounds::Bounds{N}; randstate = nothing) where N
     # If no scaling needs to be done, return a noop transform
     scale.minlengths == length.(bounds.rs) && return IdentityTransformation()
 
     # Offset `minlengths` by 1 to avoid black border on one side
     ratio = maximum((scale.minlengths .+ 1) ./ length.(bounds.rs))
     upperleft = SVector{N, Float32}(minimum.(bounds.rs)) .- 0.5
     P = scaleprojection(Tuple(ratio for _ in 1:N))
-    if upperleft != SVector(0, 0)
+    if any(upperleft .!= 0)
         P = P ∘ Translation((Float32.(P(upperleft)) .+ 0.5f0))
     end
     return P
@@ -79,11 +79,11 @@ struct ScaleFixed{N} <: ProjectiveTransform
 end
 
 
-function getprojection(scale::ScaleFixed, bounds; randstate = nothing)
+function getprojection(scale::ScaleFixed, bounds::Bounds{N}; randstate = nothing) where N
     ratios = (scale.sizes .+ 1) ./ length.(bounds.rs)
-    upperleft = SVector{2, Float32}(minimum.(bounds.rs)) .- 1
+    upperleft = SVector{N, Float32}(minimum.(bounds.rs)) .- 1
     P = scaleprojection(ratios)
-    if upperleft != SVector(0, 0)
+    if any(upperleft .!= 0)
         P = P  ∘ Translation(-upperleft)
     end
     return P
@@ -92,7 +92,7 @@ end
 
 function projectionbounds(tfm::ScaleFixed{N}, P, bounds::Bounds{N}; randstate = nothing) where N
     bounds_ = transformbounds(bounds, P)
-    return offsetcropbounds(tfm.sizes, bounds_, (1., 1.))
+    return offsetcropbounds(tfm.sizes, bounds_, ntuple(_ -> 1., N))
 end
 
 """
@@ -230,7 +230,7 @@ struct Reflect <: ProjectiveTransform
 end
 
 
-function getprojection(tfm::Reflect, bounds; randstate = getrandstate(tfm))
+function getprojection(tfm::Reflect, bounds::Bounds{2}; randstate = getrandstate(tfm))
     r = tfm.γ / 360 * 2pi
     return centered(LinearMap(reflectionmatrix(r)), bounds)
 end
@@ -241,26 +241,73 @@ end
 Transform `P` so that is applied around the center of `bounds`
 instead of the origin
 """
-function centered(P, bounds::Bounds{2})
+function centered(P, bounds::Bounds{N}) where N
     upperleft = minimum.(bounds.rs)
     bottomright = maximum.(bounds.rs)
 
-    midpoint = SVector{2, Float32}((bottomright .- upperleft) ./ 2) .+ SVector{2, Float32}(.5, .5)
+    midpoint = SVector{N, Float32}((bottomright .- upperleft) ./ 2) .+ .5f0
     return recenter(P, midpoint)
 end
 
+
+function reflectionmatrix(r)
+    A = SMatrix{2, 2, Float32}(cos(2r), sin(2r), sin(2r), -cos(2r))
+    return round.(A; digits = 12)
+end
+
+
+"""
+    FlipDim{N}(dim)
+
+Reflect `N` dimensional data along the axis of dimension `dim`. Must satisfy 1 <= `dim` <= `N`.
+
+## Examples
+
+```julia
+tfm = FlipDim{2}(1)
+```
+"""
+struct FlipDim{N} <: ProjectiveTransform
+    dim::Int
+    FlipDim{N}(dim) where N = 1 <= dim <= N ? new{N}(dim) : error("invalid dimension")
+end
+
 """
-Reflect(180)
+    FlipX{N}()
+
+Flip `N` dimensional data along the x-axis. 2D images use (r, c) = (y, x)
+convention such that x-axis flips occur along the second dimension. For N >= 3,
+x-axis flips occur along the first dimension.
 """
-FlipX() = Reflect(180)
+struct FlipX{N}
+    FlipX{N}() where N = FlipDim{N}(N==2 ? 2 : 1)
+end
+
 """
-Reflect(90)
+    FlipY{N}()
+
+Flip `N` dimensional data along the y-axis. 2D images use (r, c) = (y, x)
+convention such that y-axis flips occur along the first dimension. For N >= 3,
+y-axis flips occur along the second dimension.
 """
-FlipY() = Reflect(90)
+struct FlipY{N}
+    FlipY{N}() where N = FlipDim{N}(N==2 ? 1 : 2)
+end
 
-function reflectionmatrix(r)
-    A = SMatrix{2, 2, Float32}(cos(2r), sin(2r), sin(2r), -cos(2r))
-    return round.(A; digits = 12)
+"""
+    FlipZ{N}()
+
+Flip `N` dimensional data along the z-axis.
+"""
+struct FlipZ{N}
+    FlipZ{N}() where N = FlipDim{N}(3)
+end
+
+function getprojection(tfm::FlipDim{N}, bounds::Bounds{N}; randstate = nothing) where N
+    arr = 1I(N)
+    arr[tfm.dim, tfm.dim] = -1
+    M = SMatrix{N, N, Float32}(arr)
+    return DataAugmentation.centered(LinearMap(M), bounds)
 end
 
 
@@ -281,8 +328,8 @@ at one.
 """
 struct PinOrigin <: ProjectiveTransform end
 
-function getprojection(::PinOrigin, bounds; randstate = nothing)
-    p = (-SVector{2, Float32}(minimum.(bounds.rs))) .+ 1
+function getprojection(::PinOrigin, bounds::Bounds{N}; randstate = nothing) where N
+    p = (-SVector{N, Float32}(minimum.(bounds.rs))) .+ 1
     P = Translation(p)
     return P
 end

src/projective/compose.jl

@@ -26,6 +26,9 @@ compose(composed::ComposedProjectiveTransform, tfm::ProjectiveTransform) =
 compose(tfm::ProjectiveTransform, composed::ComposedProjectiveTransform) =
     ComposedProjectiveTransform(tfm, composed.tfms...)
 
+compose(composed1::ComposedProjectiveTransform, composed2::ComposedProjectiveTransform) =
+    ComposedProjectiveTransform(composed1.tfms..., composed2.tfms...)
+
 
 # The random state is collected from the transformations that make up the
 # `ComposedProjectiveTransform`:

test/projective/affine.jl

@@ -192,16 +192,60 @@ include("../imports.jl")
         @test_nowarn apply!(buffer, tfm, image2)
     end
 
-    @testset ExtendedTestSet "`RandomCrop` correct indices" begin
-        # Flipping and cropping should be the same as reverse-indexing
-        # the flipped dimension
-        tfm = FlipX() |> RandomCrop((64, 64)) |> PinOrigin()
-        img = rand(RGB, 64, 64)
+
+    @testset ExtendedTestSet "FlipX 2D correct indices" begin
+        tfm = FlipX{2}() |> RandomCrop((10,10)) |> PinOrigin()
+        img = rand(RGB, 10, 10)
+        item = Image(img)
+        @test_nowarn titem = apply(tfm, item)
+        titem = apply(tfm, item)
+        @test itemdata(titem) == img[:, end:-1:1]
+    end
+
+    @testset ExtendedTestSet "FlipY 2D correct indices" begin
+        tfm = FlipY{2}() |> RandomCrop((10,10)) |> PinOrigin()
+        img = rand(RGB, 10, 10)
+        item = Image(img)
+        @test_nowarn titem = apply(tfm, item)
+        titem = apply(tfm, item)
+        @test itemdata(titem) == img[end:-1:1, :]
+    end
+
+
+    @testset ExtendedTestSet "FlipX 3D correct indices" begin
+        tfm = FlipX{3}() |> RandomCrop((10,10,10)) |> PinOrigin()
+        img = rand(RGB, 10, 10, 10)
         item = Image(img)
+        @test_nowarn titem = apply(tfm, item)
         titem = apply(tfm, item)
-        timg = itemdata(titem)
-        rimg = img[:, end:-1:1]
-        @test titem.data == rimg
+        @test itemdata(titem) == img[end:-1:1, :, :]
+    end
+
+    @testset ExtendedTestSet "FlipY 3D correct indices" begin
+        tfm = FlipY{3}() |> RandomCrop((10,10,10)) |> PinOrigin()
+        img = rand(RGB, 10, 10, 10)
+        item = Image(img)
+        @test_nowarn titem = apply(tfm, item)
+        titem = apply(tfm, item)
+        @test itemdata(titem) == img[:, end:-1:1, :]
+    end
+
+    @testset ExtendedTestSet "FlipZ 3D correct indices" begin
+        tfm = FlipZ{3}() |> RandomCrop((10,10,10)) |> PinOrigin()
+        img = rand(RGB, 10, 10, 10)
+        item = Image(img)
+        @test_nowarn titem = apply(tfm, item)
+        titem = apply(tfm, item)
+        @test itemdata(titem) == img[:, :, end:-1:1]
+    end
+
+    @testset ExtendedTestSet "Double flip is identity" begin
+        tfm = FlipZ{3}() |> FlipZ{3}() |> RandomCrop((10,10,10)) |> PinOrigin()
+        img = rand(RGB, 10, 10, 10)
+        item = Image(img)
+        @test_nowarn titem = apply(tfm, item)
+        titem = apply(tfm, item)
+        @test itemdata(titem) == img
     end
 end
 
@@ -210,8 +254,8 @@ end
     @testset ExtendedTestSet "2D" begin
         tfms = compose(
             Rotate(10),
-            FlipX(),
-            FlipY(),
+            FlipX{2}(),
+            FlipY{2}(),
             ScaleRatio((.8, .8)),
             WarpAffine(0.1),
             Zoom((1., 1.2)),
@@ -230,9 +274,14 @@ end
         )
 
         tfms = compose(
+            FlipX{3}(),
+            FlipY{3}(),
+            FlipZ{3}(),
+            ScaleFixed((30, 40, 50)),
             Rotate(10, 20, 30),
             ScaleRatio((.8, .8, .8)),
             ScaleKeepAspect((12, 10, 10)),
+            Zoom((1., 1.2)),
             RandomCrop((10, 10, 10))
         )
         testprojective(tfms, items)