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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 21:49:15 +02:00
parent ef9bcfb778
commit 844858f01b
23 changed files with 688 additions and 945 deletions
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doc/examples/features_detection/plot_holes_and_peaks.py
+19 -30
View File
@@ -29,16 +29,13 @@ ax[0].imshow(image)
ax[0].set_title('Original image')
ax[0].axis('off')
"""
.. image:: PLOT2RST.current_figure
Now we need to create the seed image, where the minima represent the starting
points for erosion. To fill holes, we initialize the seed image to the maximum
value of the original image. Along the borders, however, we use the original
values of the image. These border pixels will be the starting points for the
erosion process. We then limit the erosion by setting the mask to the values
of the original image.
"""
######################################################################
# Now we need to create the seed image, where the minima represent the
# starting points for erosion. To fill holes, we initialize the seed image
# to the maximum value of the original image. Along the borders, however, we
# use the original values of the image. These border pixels will be the
# starting points for the erosion process. We then limit the erosion by
# setting the mask to the values of the original image.
import numpy as np
from skimage.morphology import reconstruction
@@ -52,28 +49,24 @@ filled = reconstruction(seed, mask, method='erosion')
ax[1].imshow(filled)
ax[1].set_title('after filling holes')
ax[1].axis('off')
"""
.. image:: PLOT2RST.current_figure
As shown above, eroding inward from the edges removes holes, since (by
definition) holes are surrounded by pixels of brighter value. Finally, we can
isolate the dark regions by subtracting the reconstructed image from the
original image.
"""
######################################################################
# As shown above, eroding inward from the edges removes holes, since (by
# definition) holes are surrounded by pixels of brighter value. Finally, we
# can isolate the dark regions by subtracting the reconstructed image from
# the original image.
ax[2].imshow(image-filled)
ax[2].set_title('holes')
ax[2].axis('off')
"""
.. image:: PLOT2RST.current_figure
Alternatively, we can find bright spots in an image using morphological
reconstruction by dilation. Dilation is the inverse of erosion and expands the
*maximal* values of the seed image until it encounters a mask image. Since this
is an inverse operation, we initialize the seed image to the minimum image
intensity instead of the maximum. The remainder of the process is the same.
"""
######################################################################
# Alternatively, we can find bright spots in an image using morphological
# reconstruction by dilation. Dilation is the inverse of erosion and expands
# the *maximal* values of the seed image until it encounters a mask image.
# Since this is an inverse operation, we initialize the seed image to the
# minimum image intensity instead of the maximum. The remainder of the
# process is the same.
seed = np.copy(image)
seed[1:-1, 1:-1] = image.min()
@@ -83,7 +76,3 @@ ax[3].imshow(image-rec)
ax[3].set_title('peaks')
ax[3].axis('off')
plt.show()
"""
.. image:: PLOT2RST.current_figure
"""
doc/examples/features_detection/plot_local_binary_pattern.py
+19 -29
View File
@@ -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()
doc/examples/features_detection/plot_multiblock_local_binary_pattern.py
+10 -12
View File
@@ -43,10 +43,10 @@ lbp_code = multiblock_lbp(int_img, 0, 0, 3, 3)
assert_equal(correct_answer, lbp_code)
"""
Now let's apply the operator to a real image and see how the
visualization works.
"""
######################################################################
# Now let's apply the operator to a real image and see how the visualization
# works.
from skimage import data
from matplotlib import pyplot as plt
from skimage.feature import draw_multiblock_lbp
@@ -65,11 +65,9 @@ plt.imshow(img, interpolation='nearest')
plt.show()
"""
.. image:: PLOT2RST.current_figure
On the above plot we see the result of computing a MB-LBP and visualization of
the computed feature. The rectangles that have less intensities' sum than the
central rectangle are marked in cyan. The ones that have higher intensity
values are marked in white. The central rectangle is left untouched.
"""
######################################################################
# On the above plot we see the result of computing a MB-LBP and visualization
# of the computed feature. The rectangles that have less intensities' sum
# than the central rectangle are marked in cyan. The ones that have higher
# intensity values are marked in white. The central rectangle is left
# untouched.