

In [1]:

    
%reload_ext autoreload
%autoreload 2
%matplotlib inline

import matplotlib.pyplot as plt
import seaborn as sns

import numpy as np
import pandas as pd

import sys
sys.path.append('..')

from helper import kmeans as km

load image as `ndarray`

http://scikit-image.org/



In [2]:

    
from skimage import io

# cast to float, you need to do this otherwise the color would be weird after clustring
pic = io.imread('data/bird_small.png') / 255.
io.imshow(pic)









    Out[2]:





<matplotlib.image.AxesImage at 0x112c14550>



In [3]:

    
pic.shape









    Out[3]:





(128, 128, 3)



In [12]:

    
# serialize data
data = pic.reshape(128*128, 3)

let's do k-mean

my version will take more than 10 mins... ok. I know why I shouldn't implement my own ML library.
In the future I will only implement ML algorithm for the sake of learning it XD



In [5]:

    
# C, centroids, cost = km.k_means(pd.DataFrame(data), 16, epoch = 10, n_init=3)

sklearn KMeans



In [6]:

    
from sklearn.cluster import KMeans

model = KMeans(n_clusters=16, n_init=100, n_jobs=-1)



In [7]:

    
model.fit(data)









    Out[7]:





KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=16, n_init=100,
    n_jobs=-1, precompute_distances='auto', random_state=None, tol=0.0001,
    verbose=0)



In [8]:

    
centroids = model.cluster_centers_
print(centroids.shape)

C = model.predict(data)
print(C.shape)









    



(16, 3)
(16384,)



In [9]:

    
centroids[C].shape









    Out[9]:





(16384, 3)



In [10]:

    
compressed_pic = centroids[C].reshape((128,128,3))



In [11]:

    
fig, ax = plt.subplots(1, 2)
ax[0].imshow(pic)
ax[1].imshow(compressed_pic)









    Out[11]:





<matplotlib.image.AxesImage at 0x106b640b8>

load image as ndarray

let's do k-mean

sklearn KMeans

load image as `ndarray`