skimage2.filters.rank.pop#

skimage2.filters.rank.pop(image, footprint, out=None, mask=None, shift_x=0, shift_y=0, shift_z=0)[source]#

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.

Parameters:

imagendarray of shape ([P,] M, N) and dtype (uint8 or uint16): Input image.
footprintndarray: The neighborhood expressed as an ndarray of 1’s and 0’s.
outndarray of shape ([P,] M, N), same dtype as input image: If None, a new array is allocated.
maskndarray of dtype (int or float), optional: 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, shift_zint: Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).

Returns:

outndarray of shape ([P,] M, N), same dtype as input image: Output image.

Examples

>>> from _skimage2.morphology import footprint_rectangle # Need to add 3D example
>>> 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.uint8)
>>> rank.pop(img, footprint_rectangle((3, 3)))
array([[4, 6, 6, 6, 4],
       [6, 9, 9, 9, 6],
       [6, 9, 9, 9, 6],
       [6, 9, 9, 9, 6],
       [4, 6, 6, 6, 4]], dtype=uint8)