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Merge pull request #1247 from emmanuelle/gallery_edges

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Add a second example to the edge filtering gallery example
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Stefan van der Walt
2014-12-03 19:28:04 +02:00
parent 3423b43394 d78c9aab08
commit b9a71663af
2 changed files with 115 additions and 4 deletions
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doc/examples/plot_edge_filter.py
+53 -1
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@@ -8,10 +8,11 @@ They are discrete differentiation operators, computing an approximation of the
gradient of the image intensity function.
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage.data import camera
from skimage.filters import roberts, sobel
from skimage.filters import roberts, sobel, scharr
image = camera()
@@ -28,4 +29,55 @@ ax1.imshow(edge_sobel, cmap=plt.cm.gray)
ax1.set_title('Sobel Edge Detection')
ax1.axis('off')
plt.tight_layout()
"""
.. image:: PLOT2RST.current_figure
Different operators compute different finite-difference approximations of the
gradient. For example, the Scharr filter results in a better rotational
variance than other filters such as the Sobel filter [1]_ [2]_. The difference
between the two filters is illustrated below on an image that is the
discretization of a rotation-invariant continuous function. The discrepancy
between the two filters is stronger for regions of the image where the
direction of the gradient is close to diagonal, and for regions with high
spatial frequencies.
.. [1] http://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators
.. [2] B. Jaehne, H. Scharr, and S. Koerkel. Principles of filter design. In
Handbook of Computer Vision and Applications. Academic Press, 1999.
"""
x, y = np.ogrid[:100, :100]
# Rotation-invariant image with different spatial frequencies
img = np.exp(1j * np.hypot(x, y)**1.3 / 20.).real
edge_sobel = sobel(img)
edge_scharr = scharr(img)
fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2)
ax0.imshow(edge_sobel, cmap=plt.cm.gray)
ax0.set_title('Sobel Edge Detection')
ax0.axis('off')
ax1.imshow(edge_scharr, cmap=plt.cm.gray)
ax1.set_title('Scharr Edge Detection')
ax1.axis('off')
ax2.imshow(img, cmap=plt.cm.gray)
ax2.set_title('Original image')
ax2.axis('off')
ax3.imshow(edge_scharr - edge_sobel, cmap=plt.cm.jet)
ax3.set_title('difference (Scharr - Sobel)')
ax3.axis('off')
plt.tight_layout()
plt.show()
"""
.. image:: PLOT2RST.current_figure
"""