An image library for Julia

image-processing, julia



An image processing library for Julia.

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Full documentation is found here:


Install via the package manager:


Image I/O

It's helpful to have one or more image I/O libraries (see installed on your system, as Images relies on them for reading and writing many common image types. These libraries are managed by FileIO, and in interactive usage it should prompt you to install any dependencies.

If you have problems loading or saving Images, please report bugs to the appropriate I/O packages. This package does not perform any I/O on its own.

Package interactions

A few other packages define overlapping functions or types (e.g., Winston defines Image). When using both Images and these packages, you can always specify which version you want with Images.Image(data, properties).

Image viewing

If you're using the IJulia notebook, images will be displayed automatically.

Julia code for the display of images can be found in ImageView. Installation of this package is recommended but not required.


When testing ideas or just following along with the documentation, it can be useful to have some images to work with. The TestImages package bundles several "standard" images for you. To load one of the images from this package, say

using TestImages
img = testimage("mandrill")

The examples below will assume you're loading a particular file from your disk, but you can substitute those commands with testimage.

Getting started

For these examples you'll need to install both Images and ImageView. Depending on your task, it's also very useful to have two other packages loaded, Colors and FixedPointNumbers. Load the code for all of these packages with

using Images, Colors, FixedPointNumbers, ImageView

Loading your first image: how images are represented

You likely have a number of images already at your disposal, and you can use these, TestImages.jl, or run readremote.jl in the test/ directory. (This requires an internet connection.) These will be deposited inside an Images directory inside your temporary directory (e.g., /tmp on Linux systems). The "rose.png" image in this example comes from the latter.

Let's begin by reading an image from a file:

julia> img = load("rose.png")
RGB Image with:
  data: 70x46 Array{RGB{UFixed{Uint8,8}},2}
    IMcs: sRGB
    spatialorder:  x y
    pixelspacing:  1 1

If you're using Images through IJulia, rather than this text output you probably see the image itself. This is nice, but often it's quite helpful to see the structure of these Image objects. This happens automatically at the REPL; within IJulia you can call


to see the output above.

The first line tells you that this is an RGB image. It is stored as a two-dimensional Array of RGB{UFixed{Uint8,8}}. To see what this pixel type is, we can do the following:

julia> img[1,1]

This extracts the first pixel, the one visually at the upper-left of the image. You can see that an RGB (which comes from the Colors package) is a triple of values. The UFixed8 number type (which comes from the FixedPointNumbers package), and whose long name is UFixed{Uint8,8}) represents fractional numbers, those that can encode values that lie between 0 and 1, using just 1 byte (8 bits). If you've previously used other image processing libraries, you may be used to thinking of two basic image types, floating point-valued and integer-valued. In those libraries, "saturated" (the color white for an RGB image) would be represented by 1.0 for floating point-valued images, 255 for a Uint8 image, and 0x0fff for an image collected by a 12-bit camera. Images.jl, via Colors and FixedPointNumbers, unifies these so that 1 always means saturated, no matter whether the element type is Float64, UFixed8, or UFixed12. This makes it easier to write generic algorithms and visualization code, while still allowing one to use efficient (and C-compatible) raw representations.

You can see that this image has properties, of which there are three: "IMcs", "spatialorder" and "pixelspacing". We'll talk more about the latter two in the next section. The "IMcs" is really for internal use by ImageMagick; it says that the colorspace is "sRGB", although (depending on which version of the library you have) you may see it say "RGB". Such differences are due to changes in how ImageMagick handles colorspaces, and the fact that both older and newer versions of the library are still widespread.

You can retrieve the properties using props = properties(img). This returns the dictionary used by img; any modifications you make to props will update the properties of img.

Likewise, given an Image img, you can access the underlying array with

A = data(img)

This is handy for those times when you want to call an algorithm that is implemented only for Arrays. At the end, however, you may want to restore the contextual information available in an Image. While you can use the Image constructor directly, two alternatives can be convenient:

imgc = copyproperties(img, A)
imgs = shareproperties(img, A)

imgc has its own properties dictionary, initialized to be a copy of the one used by img. In contrast, imgs shares a properties dictionary with img; any modification to the properties of img will also modify them for imgs. Use either as appropriate to your circumstance.

The Images package is designed to work with either plain arrays or with Image types---in general, though, you're probably best off leaving things as an Image, particularly if you work with movies, 3d images, or other more complex objects.

Storage order and changing the representation of images

In the example above, the "spatialorder" property has value ["x", "y"]. This indicates that the image data are in "horizontal-major" order, meaning that a pixel at spatial location (x,y) would be addressed as img[x,y] rather than img[y,x]. ["y", "x"] would indicate vertical-major. Consequently, this image is 70 pixels wide and 46 pixels high.

Images returns this image in horizontal-major order because this is how it was stored on disk. Because the Images package is designed to scale to terabyte-sized images, a general philosophy is to work with whatever format users provide without forcing changes to the raw array representation. Consequently, when you load an image, its representation will match that used in the file.

Of course, if you prefer to work with plain arrays, you can convert it:

julia> imA = convert(Array, img);

julia> summary(imA)
"46x70 Array{RGB{UFixed{Uint8,8}},2}"

You can see that this permuted the dimensions into vertical-major order, consistent with the column-major order with which Julia stores Arrays. Note that this preserved the element type, returning an Array{RGB}. If you prefer to extract into an array of plain numbers in color-last order (typical of Matlab), you can use

julia> imsep = separate(img)
RGB Image with:
  data: 46x70x3 Array{UFixed{Uint8,8},3}
    IMcs: sRGB
    colorspace: RGB
    colordim: 3
    spatialorder:  y x
    pixelspacing:  1 1

You can see that "spatialorder" was changed to reflect the new layout, and that two new properties were added: "colordim", which specifies which dimension of the array is used to encode color, and "colorspace" so you know how to interpret these colors.

Compare this to

julia> imr = reinterpret(UFixed8, img)
RGB Image with:
  data: 3x70x46 Array{UFixed{Uint8,8},3}
    IMcs: sRGB
    colorspace: RGB
    colordim: 1
    spatialorder:  x y
    pixelspacing:  1 1

reinterpret gives you a new view of the same underlying memory as img, whereas convert(Array, img) and separate(img) create new arrays if the memory-layout needs alteration.

You can go back to using Colors to encode your image this way:

julia> imcomb = convert(Image{RGB}, imsep)
RGB Image with:
  data: 46x70 Array{RGB{UFixed{Uint8,8}},2}
    IMcs: sRGB
    spatialorder:  y x
    pixelspacing:  1 1

or even change to a new colorspace like this:

julia> imhsv = convert(Image{HSV}, float32(img))
HSV Image with:
  data: 70x46 Array{HSV{Float32},2}
    IMcs: sRGB
    spatialorder:  x y
    pixelspacing:  1 1

Many of the colorspaces supported by Colors (or rather, its base package ColorTypes) need a wider range of values than [0,1], so it's necessary to convert to floating point.

If you say view(imhsv), you may be surprised to see something that looks like the original RGB image. Since the colorspace is known, it converts to RGB before rendering it. If, for example, you wanted to see what a "pure-V" image looks like, you can do this:

imv = shareproperties(imhsv, [HSV(0, 0, imhsv[i,j].v) for i = 1:size(imhsv,1),j = 1:size(imhsv,2)])

and a pure-H image like this:

imh = shareproperties(imhsv, [HSV(imhsv[i,j].h, 0.5, 0.5) for i = 1:size(imhsv,1),j = 1:size(imhsv,2)])

(Hue without saturation or value generates gray or black, so we used a constant different from zero for these parameters.)


Of course, you can combine these commands, for example

A = reinterpret(Uint8, data(img))

will, for a RGB{UFixed8} image, return a raw 3d array. This can be useful if you want to interact with external code (a C-library, for example). Assuming you don't want to lose orientation information, you can wrap a returned array B as shareproperties(img, B).

Other properties, and usage of Units

The "pixelspacing" property informs ImageView that this image has an aspect ratio 1. In scientific or medical imaging, you can use actual units to encode this property, for example through the SIUnits package. For example, if you're doing microscopy you might specify

using SIUnits
img["pixelspacing"] = [0.32Micro*Meter,0.32Micro*Meter]

If you're performing three-dimensional imaging, you might set different values for the different axes:

using SIUnits.ShortUnits
mriscan["pixelspacing"] = [0.2mm, 0.2mm, 2mm]

ImageView includes facilities for scale bars, and by supplying your pixel spacing you can ensure that the scale bars are accurate.

A brief demonstration of image processing

Now let's work through a more sophisticated example:

using Images, TestImages, ImageView
img = testimage("mandrill")
# Let's do some blurring
kern = ones(Float32,7,7)/49
imgf = imfilter(img, kern)
# Let's make an oversaturated image
imgs = 2imgf



Elements of this package descend from "image.jl" that once lived in Julia's extras/ directory. That file had several authors, of which the primary were Jeff Bezanson, Stefan Kroboth, Tim Holy, Mike Nolta, and Stefan Karpinski. This repository has been quite heavily reworked; the current package maintainer is Tim Holy, and important contributions have been made by Ron Rock, Kevin Squire, Lucas Beyer, Tony Kelman, Isaiah Norton, Elliot Saba, Cody Greer, Daniel Perry, Waldir Pimenta, Tobias Knopp, Jason Merrill, Sean Garborg, romainFr, Dahua Lin, and several others.