pop

pop

skimage.filters.rank.pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source]

Return the local number (population) of pixels.

The number of pixels is defined as the number of pixels which are included in the structuring element and the mask.

Parameters:

image : 2-D array (uint8, uint16) Input image. selem : 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) 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 structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns:

out : 2-D array (same dtype as input image) Output image.

Examples

1 2 3 4 5 6 7 8 9 10 11 12 13

>>> from skimage.morphology import square >>> 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)