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MBLBP optimized by taking out constants definition out of function. Visualization function of MBLBP now supports colors and opacity. Examples are updated.

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dan
2015-06-09 14:18:58 +02:00
parent 83c3bd10c8
commit d39434c30e
4 changed files with 60 additions and 70 deletions
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doc/examples/plot_multiblock_local_binary_pattern.py
+7 -9
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@@ -7,18 +7,18 @@ In this example, we will see how to compute the multi-block
local binary pattern at a specified image and how to visualize it.
The features are calculated in a way similar to local binary
patterns, except that block summed up pixel values
patterns, except that summed up pixel values
rather than pixel values are used.
`MB-LBP` is an extension of LBP that can be computed on any
scales in a constant time using integral image. It consists of
scale in a constant time using integral image. It consists of
`9` equal-sized rectangles. They are used to compute a feature.
Sum of pixels' intensity values in each of them are compared
to the central rectangle and depending on comparison result,
the feature descriptor is computed.
We will start with a simple image that we will generate by our
own to show how the `MB-LBP` works. We will create a `(9, 9)`
We will start with a simple image that we will generate
to show how the `MB-LBP` works. We will create a `(9, 9)`
rectangle with and divide it into `9` blocks. After this
we will apply `MB-LBP` on it.
@@ -55,10 +55,8 @@ print(lbp_code == correct_answer)
"""
Now let's apply the operator to a real image and see how the visualization works.
"""
from __future__ import print_function
from skimage.feature import (multiblock_local_binary_pattern,
visualize_multiblock_lbp)
draw_multiblock_lbp)
from skimage.util import img_as_float
from skimage.transform import integral_image
from skimage import data
@@ -71,8 +69,8 @@ int_img = integral_image(test_img)
lbp_code = multiblock_local_binary_pattern(int_img, 0, 0, 90, 90)
img = visualize_multiblock_lbp(test_img, 0, 0, 90, 90,
lbp_code=lbp_code)
img = draw_multiblock_lbp(test_img, 0, 0, 90, 90,
lbp_code=lbp_code, alpha=0.5)
plt.imshow(img, interpolation='nearest')