Python converting an image to use less colors

问题:

I want to take an image and (somehow) read it as an array of pixels. Meaning each element of the 2d array would be either a hex code or RGB 3-tuple that represent the color of that pixel.

I have looked into image processing and found things like Pillow or SciPy but I only found overly simple things such as adding a filter or changing the general color properties of the image(making it generally greener by multiplying the read and blue values of every pixel by something like 0.3 - 0.5 while multiplying the green value by 1, effectively keeping it the same). What I need to do is to be able to examine every pixel individually based on its color.

After that I need to convert the image into one that uses less colors, (something like 4 or 8). I think the best way to do this would be to define some "thresholds" for these desired colors and when the color of a pixel is within the range of a certain color from those defined, then the pixel gets that respective color.

I have also not found too much about this, both in the way of actual information or people trying to do the same.

I would like to ask for any information or resources regarding to this kind of problem: - Library(libraries) to use? - Methods?(are there any widely used algorithms for this kind of problem?) - Am I using the wrong programming language?(is there one that offers this kind of functionality but easier to use?)

Any kind of help or information would be greatly appreciated, thank you in advance!

EDIT: I have found this question that refers to getting the values of a pixel, but I still need to split them into less colors.

回答1:

Library (libraries) to use?

scikit-image or OpenCV would be my preferred choices.

Methods? (Are there any widely used algorithms for this kind of problem?)

K-means clustering is a popular approach to color quantization.

Am I using the wrong programming language? (Is there one that offers this kind of functionality but easier to use?)

Python is arguably the "easiest" language for this task.

DEMO

Consider this image:

The following code reduces the number of colors from +500K to only 6:

import numpy as np
from skimage import io
from sklearn.cluster import KMeans

original = io.imread('https://i.stack.imgur.com/QCl8D.jpg')
n_colors = 6

arr = original.reshape((-1, 3))
kmeans = KMeans(n_clusters=n_colors, random_state=42).fit(arr)
labels = kmeans.labels_
centers = kmeans.cluster_centers_
less_colors = centers[labels].reshape(original.shape).astype('uint8')

io.imshow(less_colors)

And this is how the color quantized image looks:

回答2:

What you are after is called color quantization. Several algorihtms have been designed, such as the Median Cut or the Popularity algorithm. They allow you to build an "optimal" color table with the desired number of entries.

These methods were popular in the times of expensive graphics cards, but have lost fame since the generalization of true color.

Complementarily to color quantization, one often uses color dithering, a method that restores some of the smoothness of color gradients and avoids banding effects.

https://nl.mathworks.com/help/images/reduce-the-number-of-colors-in-an-image.html?requestedDomain=true