sum

sum

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

Return the local sum of pixels.

Note that the sum may overflow depending on the data type of the input array.

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 = 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.sum(img, square(3)) array([[1, 2, 3, 2, 1],        [2, 4, 6, 4, 2],        [3, 6, 9, 6, 3],        [2, 4, 6, 4, 2],        [1, 2, 3, 2, 1]], dtype=uint8)