Color Quantization With The K-Means Algorithm
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In this post we will see how to use the K-Means algorithm to perform color clustering and how to apply the quantization. Let's see the code:
from pylab import imread,imshow,figure,show,subplot from numpy import reshape,uint8,flipud from scipy.cluster.vq import kmeans,vq img = imread('clearsky.jpg') # reshaping the pixels matrix pixel = reshape(img,(img.shape*img.shape,3)) # performing the clustering centroids,_ = kmeans(pixel,6) # six colors will be found # quantization qnt,_ = vq(pixel,centroids) # reshaping the result of the quantization centers_idx = reshape(qnt,(img.shape,img.shape)) clustered = centroids[centers_idx] figure(1) subplot(211) imshow(flipud(img)) subplot(212) imshow(flipud(clustered)) show()The result shoud be as follows:
We have the original image on the top and the quantized version on the bottom. We can see that the image on the bottom has only six colors. Now, we can plot the colors found with the clustering in the RGB space with the following code:
# visualizing the centroids into the RGB space from mpl_toolkits.mplot3d import Axes3D fig = figure(2) ax = fig.gca(projection='3d') ax.scatter(centroids[:,0],centroids[:,1],centroids[:,2],c=centroids/255.,s=100) show()And this is the result:
This is the result of the same script on another:
In this case I used four colors. Here's the plot of the color in the RGB space:
Published at DZone with permission of Giuseppe Vettigli, DZone MVB. See the original article here.
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