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Build gallery with sphinx-gallery

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Modified comments in some gallery examples for compatibility with
sphinx-gallery parsing. Also modified some links in the narrative doc
since image file names have changed.
This commit is contained in:
emmanuelle
2016-05-10 08:00:50 +02:00
parent ef9bcfb778
commit 844858f01b
23 changed files with 688 additions and 945 deletions
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doc/examples/features_detection/plot_local_binary_pattern.py
+19 -29
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@@ -69,21 +69,18 @@ for ax, values, name in zip(axes, binary_patterns, titles):
plot_lbp_model(ax, values)
ax.set_title(name)
"""
.. image:: PLOT2RST.current_figure
The figure above shows example results with black (or white) representing
pixels that are less (or more) intense than the central pixel. When surrounding
pixels are all black or all white, then that image region is flat (i.e.
featureless). Groups of continuous black or white pixels are considered
"uniform" patterns that can be interpreted as corners or edges. If pixels
switch back-and-forth between black and white pixels, the pattern is considered
"non-uniform".
When using LBP to detect texture, you measure a collection of LBPs over an
image patch and look at the distribution of these LBPs. Lets apply LBP to
a brick texture.
"""
######################################################################
# The figure above shows example results with black (or white) representing
# pixels that are less (or more) intense than the central pixel. When
# surrounding pixels are all black or all white, then that image region is
# flat (i.e. featureless). Groups of continuous black or white pixels are
# considered "uniform" patterns that can be interpreted as corners or edges.
# If pixels switch back-and-forth between black and white pixels, the pattern
# is considered "non-uniform".
#
# When using LBP to detect texture, you measure a collection of LBPs over an
# image patch and look at the distribution of these LBPs. Lets apply LBP to a
# brick texture.
from skimage.transform import rotate
from skimage.feature import local_binary_pattern
@@ -145,16 +142,13 @@ for ax in ax_img:
ax.axis('off')
"""
.. image:: PLOT2RST.current_figure
The above plot highlights flat, edge-like, and corner-like regions of the
image.
The histogram of the LBP result is a good measure to classify textures. Here,
we test the histogram distributions against each other using the
Kullback-Leibler-Divergence.
"""
######################################################################
# The above plot highlights flat, edge-like, and corner-like regions of the
# image.
#
# The histogram of the LBP result is a good measure to classify textures.
# Here, we test the histogram distributions against each other using the
# Kullback-Leibler-Divergence.
# settings for LBP
radius = 2
@@ -222,8 +216,4 @@ ax3.imshow(wall)
ax3.axis('off')
hist(ax6, refs['wall'])
"""
.. image:: PLOT2RST.current_figure
"""
plt.show()