.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_cluster_plot_coin_segmentation.py:
================================================
Segmenting the picture of greek coins in regions
================================================
This example uses :ref:`spectral_clustering` on a graph created from
voxel-to-voxel difference on an image to break this image into multiple
partly-homogeneous regions.
This procedure (spectral clustering on an image) is an efficient
approximate solution for finding normalized graph cuts.
There are two options to assign labels:
* with 'kmeans' spectral clustering will cluster samples in the embedding space
using a kmeans algorithm
* whereas 'discrete' will iteratively search for the closest partition
space to the embedding space.
.. code-block:: default
print(__doc__)
# Author: Gael Varoquaux , Brian Cheung
# License: BSD 3 clause
import time
import numpy as np
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
import skimage
from skimage.data import coins
from skimage.transform import rescale
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
from sklearn.utils.fixes import parse_version
# these were introduced in skimage-0.14
if parse_version(skimage.__version__) >= parse_version('0.14'):
rescale_params = {'anti_aliasing': False, 'multichannel': False}
else:
rescale_params = {}
# load the coins as a numpy array
orig_coins = coins()
# Resize it to 20% of the original size to speed up the processing
# Applying a Gaussian filter for smoothing prior to down-scaling
# reduces aliasing artifacts.
smoothened_coins = gaussian_filter(orig_coins, sigma=2)
rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect",
**rescale_params)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(rescaled_coins)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 10
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 25
Visualize the resulting regions
.. code-block:: default
for assign_labels in ('kmeans', 'discretize'):
t0 = time.time()
labels = spectral_clustering(graph, n_clusters=N_REGIONS,
assign_labels=assign_labels, random_state=42)
t1 = time.time()
labels = labels.reshape(rescaled_coins.shape)
plt.figure(figsize=(5, 5))
plt.imshow(rescaled_coins, cmap=plt.cm.gray)
for l in range(N_REGIONS):
plt.contour(labels == l,
colors=[plt.cm.nipy_spectral(l / float(N_REGIONS))])
plt.xticks(())
plt.yticks(())
title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))
print(title)
plt.title(title)
plt.show()
.. rst-class:: sphx-glr-horizontal
*
.. image:: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_001.png
:alt: Spectral clustering: kmeans, 2.14s
:class: sphx-glr-multi-img
*
.. image:: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_002.png
:alt: Spectral clustering: discretize, 1.93s
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Spectral clustering: kmeans, 2.14s
Spectral clustering: discretize, 1.93s
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 4.566 seconds)
.. _sphx_glr_download_auto_examples_cluster_plot_coin_segmentation.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_coin_segmentation.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_coin_segmentation.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_coin_segmentation.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_