pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
The number of pixels is defined as the number of pixels which are included in the structuring element and the mask.
Input image.
The neighborhood expressed as a 2-D array of 1's and 0's.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Return the local number (population) of pixels.
>>> from skimage.morphology import squareSee :
... 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.uint8)
... rank.pop(img, square(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)
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skimage.filters.rank.generic.pop
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