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Changed x/y to r/c notation. Documentation correction.

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dan
2015-06-15 22:04:09 +02:00
parent fe85552596
commit c3e23b0604
4 changed files with 106 additions and 102 deletions
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doc/examples/plot_multiblock_local_binary_pattern.py
+10 -11
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@@ -28,17 +28,15 @@ import numpy as np
from numpy.testing import assert_equal
from skimage.transform import integral_image
# Create test matrix where first and fifth
# rectangles starting from top left clockwise
# have greater value than the central one.
# Create test matrix where first and fifth rectangles starting
# from top left clockwise have greater value than the central one.
test_img = np.zeros((9, 9), dtype='uint8')
test_img[3:6, 3:6] = 1
test_img[:3, :3] = 50
test_img[6:, 6:] = 50
# First and fifth bits should be filled.
# This correct value will be compared to
# the computed one.
# First and fifth bits should be filled. This correct value will
# be compared to the computed one.
correct_answer = 0b10001000
int_img = integral_image(test_img)
@@ -48,7 +46,8 @@ 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
@@ -69,8 +68,8 @@ plt.imshow(img, interpolation='nearest')
"""
.. 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.
"""