content: # -*- coding: utf-8 -*-
  """
  ==================================
  Color Quantization using K-Means
  ==================================
  
  Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace
  (China), reducing the number of colors required to show the image from 96,615
  unique colors to 64, while preserving the overall appearance quality.
  
  In this example, pixels are represented in a 3D-space and K-means is used to
  find 64 color clusters. In the image processing literature, the codebook
  obtained from K-means (the cluster centers) is called the color palette. Using
  a single byte, up to 256 colors can be addressed, whereas an RGB encoding
  requires 3 bytes per pixel. The GIF file format, for example, uses such a
  palette.
  
  For comparison, a quantized image using a random codebook (colors picked up
  randomly) is also shown.
  """
  # Authors: Robert Layton <robertlayton@gmail.com>
  #          Olivier Grisel <olivier.grisel@ensta.org>
  #          Mathieu Blondel <mathieu@mblondel.org>
  #
  # License: BSD 3 clause
  
  print(__doc__)
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.cluster import KMeans
  from sklearn.metrics import pairwise_distances_argmin
  from sklearn.datasets import load_sample_image
  from sklearn.utils import shuffle
  from time import time
  
  n_colors = 64
  
  # Load the Summer Palace photo
  china = load_sample_image("china.jpg")
  
  # Convert to floats instead of the default 8 bits integer coding. Dividing by
  # 255 is important so that plt.imshow behaves works well on float data (need to
  # be in the range [0-1])
  china = np.array(china, dtype=np.float64) / 255
  
  # Load Image and transform to a 2D numpy array.
  w, h, d = original_shape = tuple(china.shape)
  assert d == 3
  image_array = np.reshape(china, (w * h, d))
  
  print("Fitting model on a small sub-sample of the data")
  t0 = time()
  image_array_sample = shuffle(image_array, random_state=0)[:1000]
  kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)
  print("done in %0.3fs." % (time() - t0))
  
  # Get labels for all points
  print("Predicting color indices on the full image (k-means)")
  t0 = time()
  labels = kmeans.predict(image_array)
  print("done in %0.3fs." % (time() - t0))
  
  
  codebook_random = shuffle(image_array, random_state=0)[:n_colors + 1]
  print("Predicting color indices on the full image (random)")
  t0 = time()
  labels_random = pairwise_distances_argmin(codebook_random,
                                            image_array,
                                            axis=0)
  print("done in %0.3fs." % (time() - t0))
  
  
  def recreate_image(codebook, labels, w, h):
      """Recreate the (compressed) image from the code book & labels"""
      d = codebook.shape[1]
      image = np.zeros((w, h, d))
      label_idx = 0
      for i in range(w):
          for j in range(h):
              image[i][j] = codebook[labels[label_idx]]
              label_idx += 1
      return image
  
  # Display all results, alongside original image
  plt.figure(1)
  plt.clf()
  ax = plt.axes([0, 0, 1, 1])
  plt.axis('off')
  plt.title('Original image (96,615 colors)')
  plt.imshow(china)
  
  plt.figure(2)
  plt.clf()
  ax = plt.axes([0, 0, 1, 1])
  plt.axis('off')
  plt.title('Quantized image (64 colors, K-Means)')
  plt.imshow(recreate_image(kmeans.cluster_centers_, labels, w, h))
  
  plt.figure(3)
  plt.clf()
  ax = plt.axes([0, 0, 1, 1])
  plt.axis('off')
  plt.title('Quantized image (64 colors, Random)')
  plt.imshow(recreate_image(codebook_random, labels_random, w, h))
  plt.show()
  