.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/applications/plot_coins_segmentation.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_applications_plot_coins_segmentation.py>`
to download the full example code or to run this example in your browser via JupyterLite or Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_applications_plot_coins_segmentation.py:
==================================================
Comparing edge-based and region-based segmentation
==================================================
In this example, we will see how to segment objects from a background. We use
the ``coins`` image from ``skimage.data``, which shows several coins outlined
against a darker background.
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.. code-block:: default
import numpy as np
import matplotlib.pyplot as plt
from skimage import data
from skimage.exposure import histogram
coins = data.coins()
hist, hist_centers = histogram(coins)
fig, axes = plt.subplots(1, 2, figsize=(8, 3))
axes[0].imshow(coins, cmap=plt.cm.gray)
axes[0].axis('off')
axes[1].plot(hist_centers, hist, lw=2)
axes[1].set_title('histogram of gray values')
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Thresholding
============
A simple way to segment the coins is to choose a threshold based on the
histogram of gray values. Unfortunately, thresholding this image gives a
binary image that either misses significant parts of the coins or merges
parts of the background with the coins:
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.. code-block:: default
fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)
axes[0].imshow(coins > 100, cmap=plt.cm.gray)
axes[0].set_title('coins > 100')
axes[1].imshow(coins > 150, cmap=plt.cm.gray)
axes[1].set_title('coins > 150')
for a in axes:
a.axis('off')
plt.tight_layout()
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Edge-based segmentation
=======================
Next, we try to delineate the contours of the coins using edge-based
segmentation. To do this, we first get the edges of features using the
Canny edge-detector.
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.. code-block:: default
from skimage.feature import canny
edges = canny(coins)
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(edges, cmap=plt.cm.gray)
ax.set_title('Canny detector')
ax.axis('off')
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These contours are then filled using mathematical morphology.
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.. code-block:: default
from scipy import ndimage as ndi
fill_coins = ndi.binary_fill_holes(edges)
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(fill_coins, cmap=plt.cm.gray)
ax.set_title('filling the holes')
ax.axis('off')
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Small spurious objects are easily removed by setting a minimum size for
valid objects.
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.. code-block:: default
from skimage import morphology
coins_cleaned = morphology.remove_small_objects(fill_coins, 21)
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(coins_cleaned, cmap=plt.cm.gray)
ax.set_title('removing small objects')
ax.axis('off')
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However, this method is not very robust, since contours that are not
perfectly closed are not filled correctly, as is the case for one unfilled
coin above.
Region-based segmentation
=========================
We therefore try a region-based method using the watershed transform.
First, we find an elevation map using the Sobel gradient of the image.
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.. code-block:: default
from skimage.filters import sobel
elevation_map = sobel(coins)
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(elevation_map, cmap=plt.cm.gray)
ax.set_title('elevation map')
ax.axis('off')
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Next we find markers of the background and the coins based on the extreme
parts of the histogram of gray values.
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.. code-block:: default
markers = np.zeros_like(coins)
markers[coins < 30] = 1
markers[coins > 150] = 2
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(markers, cmap=plt.cm.nipy_spectral)
ax.set_title('markers')
ax.axis('off')
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Finally, we use the watershed transform to fill regions of the elevation
map starting from the markers determined above:
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.. code-block:: default
from skimage import segmentation
segmentation_coins = segmentation.watershed(elevation_map, markers)
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(segmentation_coins, cmap=plt.cm.gray)
ax.set_title('segmentation')
ax.axis('off')
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This last method works even better, and the coins can be segmented and
labeled individually.
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.. code-block:: default
from skimage.color import label2rgb
segmentation_coins = ndi.binary_fill_holes(segmentation_coins - 1)
labeled_coins, _ = ndi.label(segmentation_coins)
image_label_overlay = label2rgb(labeled_coins, image=coins, bg_label=0)
fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)
axes[0].imshow(coins, cmap=plt.cm.gray)
axes[0].contour(segmentation_coins, [0.5], linewidths=1.2, colors='y')
axes[1].imshow(image_label_overlay)
for a in axes:
a.axis('off')
plt.tight_layout()
plt.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.000 seconds)
.. _sphx_glr_download_auto_examples_applications_plot_coins_segmentation.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-image/scikit-image/main?filepath=notebooks/auto_examples/applications/plot_coins_segmentation.ipynb
:alt: Launch binder
:width: 150 px
.. container:: lite-badge
.. image:: images/jupyterlite_badge_logo.svg
:target: ../../lite/lab/?path=auto_examples/applications/plot_coins_segmentation.ipynb
:alt: Launch JupyterLite
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_coins_segmentation.py <plot_coins_segmentation.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_coins_segmentation.ipynb <plot_coins_segmentation.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_