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