K-means and hierarchical clustering with Python by Joel Grus

Author:Joel Grus [Joel Grus]
Language: eng
Format: epub
Publisher: O'Reilly Media, Inc.
Published: 2018-09-26T00:00:00+00:00

top_row = img[0] top_left_pixel = top_row[0] red, green, blue = top_left_pixel

In particular, we can get a flattened list of all the pixels as:

pixels = [pixel for row in img for pixel in row]

and then feed them to our clusterer:

clusterer = KMeans(5) clusterer.train(pixels) # this might take a while

Once it finishes, we just construct a new image with the same format:

def recolor(pixel): cluster = clusterer.classify(pixel) # index of the closest cluster return clusterer.means[cluster] # mean of the closest cluster new_img = [[recolor(pixel) for pixel in row] # recolor this row of pixels for row in img] # for each row in the image

and display it, using plt.imshow():

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