Tuesday, August 23, 2016 From rOpenSci (https://ropensci.org/blog/2016/08/23/z-magick-release/). Except where otherwise noted, content on this site is licensed under the CC-BY license.
The new magick package is an ambitious effort to modernize and simplify high-quality image processing in R. It wraps the ImageMagick STL which is perhaps the most comprehensive open-source image processing library available today.
The ImageMagick library has an overwhelming amount of functionality. The current version of Magick exposes a decent chunk of it, but being a first release, documentation is still sparse. This post briefly introduces the most important concepts to get started. There will also be an rOpenSci community call on Wednesday in which we demonstrate basic functionality.
On Windows or OS-X the package is most easily installed via CRAN.
On Linux you need to install the ImageMagick++ library: on Debian/Ubuntu this is called libmagick++-dev:
sudo apt-get install libmagick++-dev
On Fedora or CentOS/RHEL we need ImageMagick-c++-devel:
sudo yum install ImageMagick-c++-devel
To install from source on OS-X you need
imagemagick from homebrew.
brew install imagemagick --with-fontconfig --with-librsvg --with-fftw
The default imagemagick configuration on homebrew disables a bunch of features. I recommend you install
--with-librsvg to get high quality font / svg rendering (the CRAN OSX binary package enables these as well).
magick_config to see which features and formats are supported by your version of ImageMagick.
Images can be read directly from a file path, URL, or raw vector with image data. Similarly we can write images back to disk, or in memory by setting
# Render svg to png tiger <- image_read('https://upload.wikimedia.org/wikipedia/commons/f/fd/Ghostscript_Tiger.svg') image_write(tiger, path = "tiger.png", format = "png")
IDE’s with a built-in web browser (such as RStudio) automatically display magick images in the viewer. This results in a neat interactive image editing environment.
Alternatively, on Linux you can use
image_display to preview the image in an X11 window. Finally
image_browse opens the image in your system’s default application for a given type.
# X11 only image_display(tiger) # System dependent image_browse(tiger)
There is some functionality to convert images to R raster graphics and plot it on R’s graphics display, but this doesn’t always work too well yet.
frink <- image_read("https://jeroen.github.io/images/frink.png") raster <- as.raster(frink) plot(raster)
Also the R graphics device is relatively slow for displaying bitmap images.
The best way to get a sense of available transformations is walk through the examples in the
?transformations help page in RStudio. Below a few examples to get a sense of what is possible.
# Example image frink <- image_read("https://jeroen.github.io/images/frink.png") # Trim margins image_trim(frink) # Passport pica image_crop(frink, "100x150+50") # Resize image_scale(frink, "200x") # width: 200px image_scale(frink, "x200") # height: 200px # Rotate or mirror image_rotate(frink, 45) image_flip(frink) image_flop(frink) # Set a background color image_background(frink, "pink", flatten = TRUE) # World-cup outfit (Flood fill) image_fill(frink, "orange", "+100+200", 30000)
ImageMagick also has a bunch of standard effects that are worth checking out.
# Add randomness image_blur(frink, 10, 5) image_noise(frink) # Silly filters image_charcoal(frink) image_oilpaint(frink) image_emboss(frink) image_edge(frink) image_negate(frink)
Finally it can be useful to print some text on top of images:
# Add some text image_annotate(frink, "I like R!", size = 50) # Customize text image_annotate(frink, "CONFIDENTIAL", size = 30, color = "red", boxcolor = "pink", degrees = 60, location = "+50+100") # Only works if ImageMagick has fontconfig image_annotate(frink, "The quick brown fox", font = 'times-new-roman', size = 30)
Maybe this is enough to get started.
The examples above concern single images. However all functions in magick have been vectorized to support working with layers, compositions or animation.
The standard base vector methods
length() are used to manipulate sets of images which can then be treated as layers or frames. This system is actually so extensive that we will do a separate blog post about it later.
For now here is an example on how to generate the instant classic dancing banana on R logo (which is probably why you are here):
# Background image logo <- image_read("https://www.r-project.org/logo/Rlogo.png") background <- image_scale(logo, "400") # Foreground image banana <- image_read(system.file("banana.gif", package = "magick")) front <- image_scale(banana, "300") # Combine and flatten frames frames <- lapply(as.list(front), function(x) image_flatten(c(background, x))) # Turn frames into animation animation <- image_animate(image_join(frames)) print(animation) # Save as GIF image_write(animation, "Rlogo-banana.gif")
If time permits we will demonstrate more examples during our community call on Wednesday!