In [84]:

    

%matplotlib inline
"""
Lossy compression to replace a range of colors with a single color.
"""


    

    
Out[84]:





'\nLossy compression to replace a range of colors with a single color.\n'

In [85]:

    

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.utils import shuffle
import mahotas as mh


    

In [86]:

    

original_img = np.array(mh.imread('./data/image-quant/me-n-kelly.jpg'), dtype=np.float64) / 255


    

In [87]:

    

original_dimensions = tuple(original_img.shape)


    

In [88]:

    

width, height, depth = tuple(original_img.shape)


    

In [89]:

    

image_flattened = np.reshape(original_img, (width * height, depth))


    

In [90]:

    

image_array_sample = shuffle(image_flattened, random_state=0)[:1000]


    

In [91]:

    

estimator = KMeans(n_clusters=64, random_state=0)


    

In [92]:

    

estimator.fit(image_array_sample)


    

    
Out[92]:





KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=64, n_init=10,
    n_jobs=1, precompute_distances=True, random_state=0, tol=0.0001,
    verbose=0)

In [93]:

    

cluster_assignments = estimator.predict(image_flattened)


    

In [94]:

    

compressed_palette = estimator.cluster_centers_


    

In [95]:

    

compressed_img = np.zeros((width, height, compressed_palette.shape[1]))
print compressed_img.shape
print original_img.shape


    

    




(540, 960, 3)
(540, 960, 3)

In [96]:

    

label_idx = 0
for i in range(width):
    for j in range(height):
        compressed_img[i][j] = compressed_palette[cluster_assignments[label_idx]]
        label_idx += 1


    

In [113]:

    

plt.subplots(figsize=(20,80))

plt.subplot(122)
plt.title('Original Image')
plt.imshow(original_img)
plt.axis('off')
plt.subplot(121)
plt.title('Compressed Image')
plt.imshow(compressed_img)
plt.axis('off')
plt.show()


    

In [ ]: