CODEWITHSUNDEEP
pythonimage
Mark K
Mark K

Reputation: 9348

Python to compare colored areas of images

Assuming there are only 2 colors in an image. What's the simplest way in Python to tell an image has more (the colored areas) of these 2 colors than the other (group of similar images)?

Definition of "more": the area of total colored blocks of one picture, is bigger than the other. (Note the shape of colors might be irregular).

enter image description here

Upvotes: 0

Views: 1804

Answers (1)

chang_trenton
chang_trenton

Reputation: 915

Okay, after some experimentation, I have a possible solution. You can use Pillow, a common image-loading/handling library, to convert the images to an ndarray, and then use the count_nonzero() method to get your desired results. As a fun side-effect, this works with an arbitrary amount of colors. Here's full working code that I just tried:

from PIL import Image # because for some reason, that's how you import something from Pillow
import numpy as np

im = Image.open("/path/to/image.png")
arr = np.array(im.getdata())
unique_colors, counts = np.unique(arr.reshape(-1, arr.shape[1]), axis=0, return_counts=True)

Now the unique_colors variable holds the unique colors that appear in your image, and counts holds the corresponding counts for each color in the image; that is to say, counts[i] is the number of times unique_colors[i] appears in the image for any i.

How does the unique + reshaping line work? This is borrowed from this particular answer. Basically, you flatten out your image array such that it has shape (num_pixels, num_channels), which could be 1, 3, or 4 depending on your image format (single-channel, RGB, RGBA, etc.). Now that I have a giant 2D "table" of pixels, I simply find which row values (hence axis=0) are unique, and then use the return_counts keyword to return, well, the counts.

At this point, you have extracted the unique colors and counts of those colors for a single image. To compare multiple images, you would repeat this process on multiple images, find the colors they have in common, and then you can simply compare integers to find out which image has more of a particular color.

For my particular image, the format of the channels happened to be RGBA; in any case, I would recommend printing out arr.shape prior to the reshape step to verify that you have the correct index. If you/anyone else knows of a more general method to find the channel index of an image obtained in this fashion — I'm all ears. Thus, you may have to change the index of arr.shape to something else depending on your image. For the record, I tried this on a .png image, like you specified. Hope this helps!

Upvotes: 1

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