The aim of color clustering is to produce a small set of representative colors which captures the color properties of an image. Using the small set of color found by the clustering, a quantization process can be applied to the image to find a new version of the image that has been "simplified," both in colors and shapes.
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 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[0]*img.shape[1],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[0],img.shape[1]))
clustered = centroids[centers_idx]
figure(1)
subplot(211)
imshow(flipud(img))
subplot(212)
imshow(flipud(clustered))
show()
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:
CODE:
from pylab import imread,imshow,figure,show,subplotfrom 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[0]*img.shape[1],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[0],img.shape[1]))
clustered = centroids[centers_idx]
figure(1)
subplot(211)
imshow(flipud(img))
subplot(212)
imshow(flipud(clustered))
show()
来自 “ ITPUB博客 ” ，链接：https://blog.itpub.net/301743/viewspace-734950/ ，如需转载，请注明出处，否则将追究法律责任。
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