An fast image augmentation library in Julia for machine learning.



A fast Julia library for increasing the number of training images by applying various transformations.

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Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic augmentation pipeline using simple building blocks. For our purposes, a stochastic augmentation pipeline can be understood as a sequence of operations for which the parameters can (but need not) be random variables.

julia> pipeline = FlipX(0.5) |> Rotate([-5,-3,0,3,5]) |> CropSize(64,64) |> Zoom(1:0.1:1.2)
# 4-step Augmentor.ImmutablePipeline:
#  1.) Either: (50%) Flip the X axis. (50%) No operation.
#  2.) Rotate by θ ∈ [-5, -3, 0, 3, 5] degree
#  3.) Crop a 64×64 window around the center
#  4.) Zoom by I ∈ {1.0×1.0, 1.1×1.1, 1.2×1.2}

The Julia version of Augmentor is engineered specifically for high performance applications. It makes use of multiple heuristics to generate efficient tailor-made code for the concrete user-specified augmentation pipeline. In particular Augmentor tries to avoid the need for any intermediate images, but instead aims to compute the output image directly from the input in one single pass.

Augmentor.jl is the Julia implementation for Augmentor. The Python version of the same name is available here.


The following code snippet shows how a stochastic augmentation pipeline can be specified using simple building blocks that we call "operations". In order to give the example some meaning, we will use a real medical image from the publicly available ISIC archive as input. The concrete image can be downloaded here using their Web API.

julia> using Augmentor, ISICArchive

julia> img = get(ImageThumbnailRequest(id = "5592ac599fc3c13155a57a85"))
# 169×256 Array{RGB{N0f8},2}:
# [...]

julia> pl = Either(1=>FlipX(), 1=>FlipY(), 2=>NoOp()) |>
            Rotate(0:360) |>
            ShearX(-5:5) * ShearY(-5:5) |>
            CropSize(165, 165) |>
            Zoom(1:0.05:1.2) |>
            Resize(64, 64)
# 6-step Augmentor.ImmutablePipeline:
#  1.) Either: (25%) Flip the X axis. (25%) Flip the Y axis. (50%) No operation.
#  2.) Rotate by θ ∈ 0:360 degree
#  3.) Either: (50%) ShearX by ϕ ∈ -5:5 degree. (50%) ShearY by ψ ∈ -5:5 degree.
#  4.) Crop a 165×165 window around the center
#  5.) Zoom by I ∈ {1.0×1.0, 1.05×1.05, 1.1×1.1, 1.15×1.15, 1.2×1.2}
#  6.) Resize to 64×64

julia> img_new = augment(img, pl)
# 64×64 Array{RGB{N0f8},2}:
# [...]

The function augment will generate a single augmented image from the given input image and pipeline. To visualize the effect we compiled a few resulting output images into a GIF using the plotting library Plots.jl with the PyPlot.jl back-end. The code that generated the two figures below can be found here.

Input (img) Output (img_new)
input output

Performance Aspects

While we just used a small preview image in the above example (note the term "thumbnail" in the code), it is already possible to observe Augmentor's behaviour when comparing the memory footprint of augment to a simple copy of the original.

julia> using BenchmarkTools

julia> @btime augment($img, $pl);
  312.121 μs (150 allocations: 22.47 KiB)

julia> @btime copy($img);
  5.866 μs (2 allocations: 126.83 KiB)

Note how the whole process for producing an augmented version of img allocates less memory than a simple unaltered copy of the original. The reason for this is that the output image img_new is smaller than the input image img. Augmentor tries to compose all operations of the pipeline into one single function, which it then queries for each individual pixel in the output image. In general this means that the memory footprint and the runtime depends on the size of the output image.

To take the output-dependent behaviour to its extreme, consider the full sized version of the above thumbnail, which is about 80 MiB in uncompressed size. We will modify our pipeline slightly and insert a Scale operation as the fourth step. Doing this will cause augment to produce a similar looking output as in our original example.

julia> img_big = get(ImageDownloadRequest(id = "5592ac599fc3c13155a57a85"))
# 4399×6628 Array{RGB{N0f8},2}:
# [...]

julia> pl_big = Either(1=>FlipX(), 1=>FlipY(), 2=>NoOp()) |>
                Rotate(0:360) |>
                ShearX(-5:5) * ShearY(-5:5) |>
                Scale(0.05) |> # NEW
                CropSize(165, 165) |>
                Zoom(1:0.05:1.2) |>
                Resize(64, 64);

julia> img_new = augment(img_big, pl_big)
# 64×64 Array{RGB{N0f8},2}:
# [...]

julia> @btime augment($img_big, $pl_big);
  333.493 μs (152 allocations: 22.64 KiB)

As we can see the allocated memory did not change notably. Furthermore, it is worth pointing out explicitly how we added the Scale operation as the fourth step in the pipeline and not the first. This highlights how the operations aren't just applied naively one after the other, but instead combined intelligently before ever computing a single pixel.

Aside from the memory requirement we can also measure how the execution time remains approximately constant, even though the image is significantly larger and we added an additional operation to the pipeline.

julia> @btime augment($img, $pl); # small image
  312.610 μs (150 allocations: 22.47 KiB)

julia> @btime augment($img_big, $pl_big); # big image
  335.518 μs (152 allocations: 22.64 KiB)

julia> @btime copy($img_big); # simple memory copy
  18.304 ms (2 allocations: 83.42 MiB)

To be fair, the way we aggressively downscaled the large image in this example was rather untypical, because doing it this way would cause aliasing effects that may not be tolerable (although for this particular image these weren't that bad). The point of this example was to convey an intuition of how Augmentor works.

shell> date
Thu Jun 22 11:31:24 CEST 2017

shell> git show --oneline -s
6086196 drop 0.5 support

julia> versioninfo()
Julia Version 0.6.1-pre.0
Commit dcf39a1 (2017-06-19 13:06 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
  LAPACK: libopenblas64_
  LIBM: libopenlibm
  LLVM: libLLVM-3.9.1 (ORCJIT, haswell)

Package Overview

Images are a special class of data that have some interesting properties in respect to their structure. For example do the dimensions of an image (i.e. the pixel) exhibit a spatial relationship to each other. As such, a lot of commonly used augmentation strategies for image data revolve around affine transformations, such as translations or rotations.

Augmentor ships with a number of predefined operations that should be sufficient to describe some of the most commonly used augmentation strategies. Each operation is a represented as its own unique type (see table below for a concise overview). For a more detailed description of all the predefined operations take a look at the corresponding section of the documentation.

Category Operation Description
Mirroring: FlipX Reverse the order of each pixel row.
FlipY Reverse the order of each pixel column.
Rotating: Rotate90 Rotate upwards 90 degree.
Rotate270 Rotate downwards 90 degree.
Rotate180 Rotate 180 degree.
Rotate Rotate for any arbitrary angle(s).
Shearing: ShearX Shear horizontally for the given degree(s).
ShearY Shear vertically for the given degree(s).
Resizing: Scale Scale X and Y axis by some (random) factor(s).
Zoom Scale X and Y axis while preserving image size.
Resize Resize image to the specified pixel dimensions.
Distorting: ElasticDistortion Displace image with a smoothed random vector field.
Cropping: Crop Crop specific region of the image.
CropNative Crop specific region of the image in relative space.
CropSize Crop area around the center with specified size.
CropRatio Crop to specified aspect ratio.
RCropRatio Crop random window of specified aspect ratio.
Conversion: ConvertEltype Convert the array elements to the given type.
Layout: SplitChannels Separate the color channels into a dedicated array dimension.
CombineChannels Collapse the first dimension into a specific colorant.
PermuteDims Reorganize the array dimensions into a specific order.
Reshape Change or reinterpret the shape of the array.
Utilities: NoOp Identity function. Pass image along unchanged.
CacheImage Buffer the current image into (preallocated) memory.
Either Apply one of the given operations at random.

The purpose of an operation is to simply serve as a "dumb placeholder" to specify the intent and parameters of the desired transformation. What that means is that a pipeline of operations can be thought of as a list of instructions (a cookbook of sorts), that Augmentor uses internally to construct the required code that implements the desired behaviour in the most efficient way it can.

The way an operation is implemented depends on the rest of the specified pipeline. For example, Augmentor knows three different ways to implement the behaviour of the operation Rotate90 and will choose the one that best coincides with the other operations of the pipeline and their concrete order.

  1. Call the function rotl90 of Julia's base library, which makes use of the fact that a 90 degree rotation can be implemented very efficiently. While by itself this is the fastest way to compute the result, this function is "eager" and will allocate a new array. If Rotate90 is followed by another operation this may not be the best choice, since it will cause a temporary image that is later discarded.

  2. Create a SubArray of a PermutedDimsArray. This is more or less a lazy version of rotl90 that makes use of the fact that a 90 degree rotation can be described 1-to-1 using just the original pixels. By itself this strategy is slower than rotl90, but if it is followed by an operation such as Crop or CropSize it can be significantly faster. The reason for this is that it avoids the computation of unused pixels and also any allocation of temporary memory. The computation overhead per output pixel, while small, grows linearly with the number of chained operations.

  3. Create an AffineMap using a rotation matrix that describes a 90 degree rotation around the center of the image. This will result in a lazy transformation of the original image that is further compose-able with other AffineMap. This is the slowest available strategy, unless multiple affine operations are chained together. If that is the case, then chaining the operations can be reduced to composing the tiny affine maps instead. This effectively fuses multiple operations into a single operation for which the computation overhead per output pixel remains approximately constant in respect to the number of chained operations.


For a more detailed treatment check out the latest documentation.

Additionally, you can make use of Julia's native docsystem. The following example shows how to get additional information on augment within Julia's REPL:



To install Augmentor.jl, start up Julia and type the following code snipped into the REPL. It makes use of the native Julia package manager. Once installed the Augmentor package can be imported just as any other Julia package.

using Augmentor

Additionally, for example if you encounter any sudden issues, you can manually choose to be on the latest (untagged) development version.



This code is free to use under the terms of the MIT license.


This package makes heavy use of the following packages in order to provide it's main functionality. To see at full list of utilized packages, please take a look at the REQUIRE file.

Note that this version Augmentor.jl is a complete rewrite of an initial implementation that had the same name. The old implementation is now located at AugmentorDeprecated.jl.

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