Process Profile Pictures with magick

By Garrick Aden-Buie in Blog

July 12, 2022

Posted on:
July 12, 2022
Length:
10 minute read, 2074 words
Categories:
Blog
Tags:
R magick Images Interesting Uses of R
Source:
content/blog/2022/process-profile-picture-magick/index.Rmarkdown
See Also:
countdown v0.4.0 – Now on CRAN!
xaringanExtra v0.6.0 — Now on CRAN!
Wordle Guess Helper

rstudio::conf(2022) runs on R #

rstudio::conf(2022) is just around the corner! This year, I’ve been fortunate to be part of the conference program committee, the small group of RStudio people who gather and evaluate talk submissions, make the tough scheduling decisions about the sessions and talks in the conference program, and generally wrangle and herd all the speaker and talk information into the final schedule.

My favorite part of the process — apart from seeing all of the wonderful and creative ways our community approaches data science with R (okay, and Python too) — is finding out how many creative ways we use R to manage the conference. Let’s just say Jenny Bryan is a literal Google Forms/Sheets/Drive wizard.

One of the things I love about R is the cycle of starting a task wondering Can I do this with R? and ending with Wow, I can do this with R! I’ve been using R for a while and I’m still occasionally surprised when I find myself on this virtuous loop.

This post is about an otherwise mundane conference admin task that would have involved a lot of manual labor (in the form of clicks and mouse movements) that I automated with the help of a few R packages. Maybe in future posts I’ll share more cool things that we did with R in the making of rstudio::conf.

Oh and I hope to see you there, either in person, online or on Twitter at #RStudioConf2022! Learn more at rstd.io/conf.

Send me your profile picture, please #

Suppose you’ve asked 100-ish people to send you a profile picture and to your surprise they all followed through and sent you an actual image.

But, of course, you now have a new problem. Each of those 100-ish people has used slightly different sizes for their profile picture. They’re all sorts of different shapes, sizes, and resolutions.

Some people’s profile images feature their faces, centered and tightly cropped. Others are photographed at a distance or off-center.

A profile picture of a man, early 30s and smiling, against a soft gray background.
An example profile picture you received. Image by christian buehner.

In their final placement, you want all of the profile images to be circular images centered on the person’s face as much as possible. If we took the image above and simply centered it inside a circle, we would get something like this:

The example profile image clipped to fit a circular avatar image. The subject appears at the right edge of the circle. About 40% of their face is clipped.

Obviously, we’d rather not clip half of the person out of their profile image, so we’ll need to edit this photo. But there are hundreds of them and most of them will need some adjustment.

Good news! You have access to R, where we can use tools like magick to read and process the images, or face detection with neural networks. So with a few hours of work you can save yourself an hour of mindless clicking. Let’s do this!

Collect the Profile Pictures #

To see how this all works, I’ve downloaded four example profile pictures from unsplash1.

dir.create("profiles")
photo_ids <- c("DItYlc26zVI", "bpxgyD4YYt4", "6anudmpILw4", "3dqSZidOkvs")

for (id in photo_ids) {
  download.file(
    sprintf("https://source.unsplash.com/%s", id),
    sprintf("profiles/%s.jpg", id)
  )
}

I’ve put the photos in a profiles/ directory so that I can list the them all at once:

fs::dir_ls("profiles")
## profiles/3dqSZidOkvs.jpg profiles/6anudmpILw4.jpg profiles/DItYlc26zVI.jpg 
## profiles/bpxgyD4YYt4.jpg

Magick with R #

The first step is to use the magick package to read in our profile pictures.

library(magick)
library(purrr)

profiles <- 
  fs::dir_ls("profiles") |>
  map(image_read)

profiles
## $`profiles/3dqSZidOkvs.jpg`
##   format width height colorspace matte filesize density
## 1   JPEG  1080    810       sRGB FALSE   135368   72x72
## 
## $`profiles/6anudmpILw4.jpg`
##   format width height colorspace matte filesize density
## 1   JPEG  1080    720       sRGB FALSE    46181   72x72
## 
## $`profiles/DItYlc26zVI.jpg`
##   format width height colorspace matte filesize density
## 1   JPEG  1080    720       sRGB FALSE    87827   72x72
## 
## $`profiles/bpxgyD4YYt4.jpg`
##   format width height colorspace matte filesize density
## 1   JPEG  1080    608       sRGB FALSE    80277   72x72

Here are the four profiles. As you can see, they come in a variety of sizes and the person in the frame is rarely centered.

Four profile pictures of irregular sizes in a 2 by 2 grid. None of the
subjects are perfectly centered in the images. Clockwise: a late 20s
black woman against a green, natural background; a mid-50s white male on a
gray background; a mid-20s woman in a blue shirt against a tan background;
an early-30s male with curly hair and a floral print shirt on a light gray
background.

Finding Faces #

Now for the most fun of this entire post. After a quick search on r-pkg.org, I found a few packages that provide methods for facial detection; I tried image.libfacedetection first and it worked out so well that I didn’t have to look any further on the list.

As it says on the CRAN page, image.libfacedetection is

An open source library for face detection in images. Provides a pretrained convolutional neural network based on https://github.com/ShiqiYu/libfacedetection which can be used to detect faces which have size greater than 10x10 pixels.

The best feature — apart from reliably detecting faces — is that it works really well with magick. The core functionality is all wrapped up in a single function, image.libfacedetection::image_detect_faces(), and the example in the README tells you just about everything you need to know.

In short, after reading the image into R with magick::image_read(), you can call image_detect_faces() to find faces in the image. image_detect_faces() returns data about the detected faces, and you can use its plot() method to overlay boxes over the found faces in the image.

library(image.libfacedetection)
faces <- all_profiles |> image_detect_faces()
plot(faces, all_profiles, only_box = TRUE)

The four profile images from the previous example. A red square outline
marking the detected face regions has been overlaid over each face in the
examples profile images.

image_detect_faces() returns some interesting data about the detected faces:

The data frame detections indicates the locations of these. This data.frame has columns x, y, width and height as well as a column called confidence. The values of x and y are the top left of the start of the box.

faces
## $nr
## [1] 4
## 
## $detections
##     x   y width height confidence landmark1_x landmark1_y landmark2_x
## 1 153  48    43     57         99         158          70         175
## 2 140 239    43     58         99         150         260         171
## 3 365  64    33     44         99         373          79         387
##   landmark2_y landmark3_x landmark3_y landmark4_x landmark4_y landmark5_x
## 1          68         162          80         162          92         177
## 2         260         160         272         151         282         168
## 3          80         377          86         372          94         385
##   landmark5_y
## 1          89
## 2         282
## 3          95
##  [ reached 'max' / getOption("max.print") -- omitted 1 rows ]
## 
## attr(,"class")
## [1] "libfacedetection"

Since we asked for a profile picture, we can reasonably expect that there’s only one person in the image. So we’ll take the detection with the highest confidence (in case something else registers as a face), and find the center of the detected region.

find_face_center <- function(image) {
  detections <- image.libfacedetection::image_detect_faces(image)$detections
  best_face <- which(detections$confidence == max(detections$confidence))
  dims <- as.list(detections[best_face[[1]], ])
  list(
    x = dims$x + dims$width / 2,
    y = dims$y + dims$height / 2
  )
}

So when applied to our example profile image, we find that our subject’s face is centered at (697.5, 290).

face_center <- find_face_center(profiles[[3]])
str(face_center)
## List of 2
##  $ x: num 698
##  $ y: num 290

The primary example profile image, with a medium yellow dot placed on the
man's nose marking the center of his face as detected by the algorithm.

In the next steps, we’ll resize and crop the photo so that it’s centered, as much as possible, on this point.

Resize #

Our goal is to resize and crop the photo into an 600px square image. If we start with an image smaller than 600px in either dimension, then we won’t scale up. We also take another shortcut: since most people will provide a profile image that prominently features their face, we can start by shrinking the smaller side of the image down to match the desired image size.

This shortcut keeps us from perfectly framing the person’s face. Sometimes their face is too close to the edge of the picture, and in other cases there may be negative space around their head that will end up in the cropped profile image. I’d argue that this is okay. It keeps our cropping from being too perfect and the final images still retain some of the character of the original photo.

Our example profile image is 1080px wide and 720px tall, so we’ll resize the image proportionally down to an image with height 600px.

resize_fit <- function(image, size = 600) {
  info <- image_info(image)
  size <- min(size, info$width, info$height)
  image_resize(
    image,
    geometry_size_pixels(
      height = if (info$width >= info$height) size,
      width = if (info$height > info$width) size
    )
  )
}

When applied to our example profile image, we end up with a 900px × 600px image.

resized_profile <-
  profiles[[3]] |> 
  resize_fit()

resized_profile |> image_info()
##   format width height colorspace matte filesize density
## 1   JPEG   900    600       sRGB FALSE        0   72x72

In the next step, we’ll figure out which 600px horizontal region best covers the person’s face.

Find Resized Faces #

Wait. I showed the face-center discovery step above because it’s the coolest part of this pipeline, but we don’t actually perform the facial detection first. We need to know where the person’s face is located after we scale down their profile image.

resized_profile |> 
  find_face_center()
## $x
## [1] 579
## 
## $y
## [1] 240.5

Cropping #

Now that we know where the center point of the person’s face is located in the image, and also because we’ve already resized the image so we don’t have to worry about its height, we only need to crop the image in one direction. The problem now is that we need to pick a 600px width region within the full 900px range.

           point
|------[=====*=====]---|
       ^~~ width ~~^
^......................^ range

This isn’t too complicated. There are three cases:

  1. The point is so close to the start of the range that it we can’t center the point in our width and instead have to start at 0.
  2. Similarly, the point might be so close to the end of the range that our crop width lines up with the end. Or, in other words, the crop width starts at range - width.
  3. Or finally, we can center the point in our crop width, so it should start at point - width/2.
  4. Oh, and there’s an edge case: if the width is greater than or equal to the full range, then the offset is 0, too.

This logic gives us the following crop_offset() function:

crop_offset <- function(point, range, width) {
  # 4. Catch the edge case first
  if (width >= range) return(0)
  
  if ((point - width / 2) < 0) {
    # 1. must start at left edge
    return(0)
  }
  if ((point + width / 2) > range) {
    # 2. must start at right edge
    return(range - width)
  }
  # 3. enough space on both sides to center width in range
  point - width / 2
}

Which in our example case tells us that we could crop our resized profile image to a 600px square, offset by the following amount in the x direction:

offset <- crop_offset(
  point = 579,
  range = 900,
  width = 600
)
offset
## [1] 279

We can use magick::image_crop() with the magick::geometry_area() helper function:

The example profile image, cropped to a square
and centered on the man's face.

When this image is used as a profile or avatar picture, it ends up looking much better than the uncropped and uncentered version!

The example profile image cropped and centered to fit a circular avatar image. The subject appears directly in the middle of the circle.

Put it all together #

The last step is to put everything we’ve sketched out above into a single function that takes a magick image and returns a new cropped and centered version. And here’s that function.

resize_crop_to_face <- function(image, size = 600) {
  image <- resize_fit(image, size)
  info <- image_info(image)
  
  # size may have changed after refit
  size <- min(info$height, info$width)

  is_image_square <- info$width == info$height
  if (is_image_square) {
    return(image)
  }

  face <- find_face_center(image)

  image_crop(
    image,
    geometry = geometry_area(
      width = size,
      height = size,
      x_off = crop_offset(face$x, info$width, size),
      y_off = crop_offset(face$y, info$height, size)
    )
  )
}

Starting over from the beginning, we can read all of the profile images and resize and crop them around the subject’s face in just a few lines

profiles <- 
  fs::dir_ls("profiles") |>
  map(image_read) |> 
  map(resize_crop_to_face)

and then we can write them back into the profiles directory.

fs::dir_create("profiles_cropped")

profiles |>
  iwalk(function(image, path) {
    new_path <- fs::path("profiles_cropped", fs::path_file(path))
    image_write(image, new_path)
  })

The end result is four perfect profile pictures!

A young black woman, centered in the image, against a green background.
An older white man on a gray background, centered in the image.
The example profile: a young white male centered in the image.
A young woman against a tan background, centered in the image.

  1. Images by christian buehner, Eunice Lituañas, Foto Sushi, and Eye for Ebony↩︎