`skimage.filters.rank.bilateral:pop_bilateral`

source: /dev/scikit-image/src/skimage/filters/rank/bilateral.py :128

Signature

   def     pop_bilateral (    image    ,    footprint    ,    out    =  None   ,    mask    =  None   ,    shift_x    =  0   ,    shift_y    =  0   ,    s0    =  10   ,    s1    =  10     )

Summary

Return the local number (population) of pixels.

Extended Summary

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

image : 2-D array (uint8, uint16): Input image.
footprint : 2-D array: The neighborhood expressed as a 2-D array of 1's and 0's.
out : 2-D array, same dtype as input `image`: If None, a new array is allocated.
mask : ndarray: 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_y : int: Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).
s0, s1 : int: Define the [s0, s1] interval around the grayvalue of the center pixel to be considered for computing the value.

Returns

out : 2-D array, same dtype as input `image`: Output image.

Examples

import numpy as np
from skimage.morphology import footprint_rectangle
import skimage.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)

✓

Aliases

skimage.filters.rank.pop_bilateral