In [1]:

    

%matplotlib inline


    

In [2]:

    

# goal is to blur an image with clustering


    

In [8]:

    

from scipy import ndimage
import matplotlib.pyplot as plt


    

In [9]:

    

img = ndimage.imread("headshot.jpg")


    

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plt.imshow(img)


    

    
Out[10]:





<matplotlib.image.AxesImage at 0x109ec2950>






    

In [11]:

    

img.shape


    

    
Out[11]:





(420, 420, 3)

In [12]:

    

# to quantize an image we need to convert it to a 2 dimensional
# matrix with length being Height x Width of the image and the
# rows of the matrix being the RGB values


    

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x,y,z = img.shape


    

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long_img = img.reshape(x*y,z)


    

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long_img.shape


    

    
Out[15]:





(176400, 3)

In [16]:

    

# we want 5 distinct colors so set n_clusters to 5
from sklearn import cluster


    

In [22]:

    

kmeans = cluster.KMeans(n_clusters=5)
kmeans.fit(long_img)


    

    
Out[22]:





KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=5, n_init=10,
    n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,
    verbose=0)

In [23]:

    

centers = kmeans.cluster_centers_


    

In [24]:

    

centers


    

    
Out[24]:





array([[  86.24652104,   68.83443147,   54.02602728],
       [ 194.36172037,  172.19594433,  149.65347344],
       [  24.64137471,   20.41987179,   16.16600025],
       [ 142.58522868,  206.12439004,  226.03920271],
       [ 149.27290093,  132.19386559,  115.32293566]])

In [25]:

    

labels = kmeans.labels_
labels[:5]


    

    
Out[25]:





array([0, 0, 0, 0, 0], dtype=int32)

In [26]:

    

plt.imshow(centers[labels].reshape(x,y,z))


    

    
Out[26]:





<matplotlib.image.AxesImage at 0x109d86590>






    

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