skimage2.filters.rank.median#
- skimage2.filters.rank.median(image, footprint=None, out=None, mask=None, shift_x=0, shift_y=0, shift_z=0)[source]#
Return local median of an image.
- 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. If None, a full square of size 3 is used.
- 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:
- out([P,] M, N) ndarray, same dtype as
image Output image.
- out([P,] M, N) ndarray, same dtype as
See also
skimage.filters.medianImplementation of a median filtering which handles images with floating precision.
Examples
>>> from _skimage2 import data >>> from _skimage2.morphology import disk, ball >>> from _skimage2.filters.rank import median >>> import numpy as np >>> img = data.camera() >>> rng = np.random.default_rng() >>> volume = rng.integers(0, 255, size=(10,10,10), dtype=np.uint8) >>> med = median(img, disk(5)) >>> med_vol = median(volume, ball(5))