I am using the local_binary_pattern function in the scikit-image package. I would like to compute the rotation invariant uniform LBP of 8 neighbors within radius 1. Here is my Python code:
import numpy as np
from skimage.feature import local_binary_pattern
image = np.array([[150, 137, 137, 146, 146, 148],
[145, 144, 144, 144, 142, 144],
[149, 144, 144, 143, 153, 147],
[145, 144, 147, 150, 145, 150],
[146, 146, 139, 148, 144, 148],
[129, 139, 142, 150, 146, 140]]).astype(np.uint8)
lbp = local_binary_pattern(image, 8, 1, "uniform")
print("image =")
print(image)
print("lbp =")
print(lbp)
And here is the output
image =
[[150 137 137 146 146 148]
[145 144 144 144 142 144]
[149 144 144 143 153 147]
[145 144 147 150 145 150]
[146 146 139 148 144 148]
[129 139 142 150 146 140]]
lbp =
[[ 0. 5. 5. 1. 9. 0.]
[ 9. 6. 9. 9. 8. 9.]
[ 0. 8. 6. 8. 0. 3.]
[ 9. 7. 1. 0. 7. 0.]
[ 1. 1. 8. 9. 7. 1.]
[ 3. 4. 9. 0. 2. 3.]]
What confuses me is that some same values in lbp do not correspond to the same uniform pattern. E.g., lbp[1, 1] and lbp[2, 2] are both 6, but the LBP of image[1, 1] is:
1 0 0
1 x 1
1 1 1
The LBP of image[2, 2] is:
1 1 1
1 x 0
1 1 1
where based on the values in lbp, I assume the local_binary_pattern function uses 'greater or equal to' to compare with neighbors.
The LBPs of image[1, 1] and image[2, 2] are both uniform. But how could image[1, 1] and image[2, 2] have the same LBP value?
The rotation-invariant LBP does not use the pixel values of neighbors directly, but rather values interpolated on a circle (for the rotation invariance). See https://github.com/scikit-image/scikit-image/blob/master/skimage/feature/_texture.pyx#L156
Also see the original LBP paper http://vision.stanford.edu/teaching/cs231b_spring1415/papers/lbp.pdf, which mentions "The gray values of neighbors which do not fall exactly in the center of pixels are estimated by interpolation."