skimage2.filters.rank.pop_bilateral

skimage2.filters.rank.pop_bilateral(image, footprint, out=None, mask=None, shift_x=0, shift_y=0, s0=10, s1=10)

Return the local number (population) of pixels.

The number of pixels is defined as the number of pixels which are included in the footprint and the mask. Additionally pixels must have a graylevel inside the interval [g-s0, g+s1] where g is the grayvalue of the center pixel.

Parameters:

image2-D array (uint8, uint16)

Input image.

footprint2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out2-D array, same dtype as input image

If None, a new array is allocated.

maskndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_yint

Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).

s0, s1int

Define the [s0, s1] interval around the grayvalue of the center pixel to be considered for computing the value.

Returns:

out2-D array, same dtype as input image

Output image.

Examples

>>> import numpy as np
>>> from _skimage2.morphology import footprint_rectangle
>>> import _skimage2.filters.rank as rank
>>> img = 255 * np.array([[0, 0, 0, 0, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 1, 1, 1, 0],
...                       [0, 0, 0, 0, 0]], dtype=np.uint16)
>>> rank.pop_bilateral(img, footprint_rectangle((3, 3)), s0=10, s1=10)
array([[3, 4, 3, 4, 3],
       [4, 4, 6, 4, 4],
       [3, 6, 9, 6, 3],
       [4, 4, 6, 4, 4],
       [3, 4, 3, 4, 3]], dtype=uint16)