Programmer/Data Scientist/Instructor・Mostly write Python & R・Big fan of OpenCV & p5js
Published Mar 02, 2018
This post is about finding an image’s dominant color. To illustrate this concept we’ll be working with app icons from the Apple App Store. Why do we care about an images dominant color? A couple of uses we’ll go over are sorting images in a collage and automatically making appropriate color selections in plots.
Before we can play around with app icons we need to have some images of the icons. To do this I wrote a little scraping script using BeautifulSoup. I won’t go into detail on the scraping, but if you’re interested you can check out the code here.
A note on color before we start: Images are typically stored in the RGB colorspace, but the HSV colorspace relates more to how we perceive color. Because of this feature of HSV we’ll be working with it throughout this post.
So how do you find the dominant color in an image? A good first guess of how to do it might be to take the average color of all the pixels in the image. However, unless our image is all one color, an average will end up with a result that doesn’t resemble our image at all. We can see this illustrated in the example with the Stack Jump icon below (the average color of the icon is displayed immediately to the right of the original icon).
So if the average doesn’t work well then what does? It turns out a good strategy of finding dominant color involves k-means. If we choose the right value of k then the centroid of the largest cluster will be a pretty good representation of the image’s dominant color.
Let’s revisist our Stack Jump example. We can see that the icon is really only made up of 4 colors: green, pink, white, and black. So choosing a k of 4 makes a lot of sense for this case. Below we see that this strategy performs way better than the using the average color.
Let’s jump into some python code to perform this k-means dominant color extraction. I won’t be adding too much commentary since the docstring takes care of most of what I’d say about the function.
One thing to note is that the function doesn’t convert the image to the HSV colorspace. So if you want the output to be HSV then the input image will need to already be converted before calling the function.
from sklearn.cluster import KMeans from collections import Counter import cv2 #for resizing image def get_dominant_color(image, k=4, image_processing_size = None): """ takes an image as input returns the dominant color of the image as a list dominant color is found by running k means on the pixels & returning the centroid of the largest cluster processing time is sped up by working with a smaller image; this resizing can be done with the image_processing_size param which takes a tuple of image dims as input >>> get_dominant_color(my_image, k=4, image_processing_size = (25, 25)) [56.2423442, 34.0834233, 70.1234123] """ #resize image if new dims provided if image_processing_size is not None: image = cv2.resize(image, image_processing_size, interpolation = cv2.INTER_AREA) #reshape the image to be a list of pixels image = image.reshape((image.shape * image.shape, 3)) #cluster and assign labels to the pixels clt = KMeans(n_clusters = k) labels = clt.fit_predict(image) #count labels to find most popular label_counts = Counter(labels) #subset out most popular centroid dominant_color = clt.cluster_centers_[label_counts.most_common(1)] return list(dominant_color)
I’ve also written a script to test out this function. The script can be found here. With the script we can use the command line to test out what effect k has on the dominant color of our image of interest. Below is an example of how to call the script in the context of the github repo, and example output of using the script on the Florence app icon.
python -i icons/paid-apps_florence.jpg -k 3
*k* = 1 (average color)
*k* = 3
One possible application of dominant color is for use in sorting images.
An example of doing this with the app icon data can be seen at the top
of this post. To do this I used the
and then sorted the images by the hue component of HSV. The full script
used to create the output can be found
Another possible application of this dominant color extraction is in data viz. Let’s say we want to generate line plots for our sample of apps. We could use a default color palette, but it might add to our users’ experience if our line colors matched the colors of the app icons they’re familiar with. Using dominant color extraction we can assign appropriate colors for use in our plot automatically.
Note the plot data is a random walk, it doesn’t actually relate to any app metric (on purpose).
You might have noticed the plot avoids the issue of apps having similiar/identical dominant colors. A strategy of using secondary colors and/or adjusting colors to be disimilar could be possible strategies to deal with this issue. At this point I haven’t put in the time to implement these ideas.