`skimage.filters.rank.generic:windowed_histogram`

source: /dev/scikit-image/src/skimage/filters/rank/generic.py :1623

Signature

   def     windowed_histogram (    image    ,    footprint    ,    out    =  None   ,    mask    =  None   ,    shift_x    =  0   ,    shift_y    =  0   ,    n_bins    =  None     )

Summary

Compute normalized sliding window histogram.

Parameters

image : ndarray of shape (H, W) and dtype (int or float): Input image.
footprint : ndarray of dtype (int or float): The neighborhood expressed as a 2-D array of 1's and 0's.
out : ndarray of shape (H, W, N) and dtype (int or float), optional: If None, a new array is allocated.
mask : ndarray 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 : int, optional: Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).
n_bins : int or None: The number of histogram bins. Defaults to image.max() + 1 if None is passed.

Returns

out : ndarray of shape (H, W, N) and dtype float: N is n_bins or image.max() + 1 if no value is passed to n_bins. Effectively, each pixel is a N-D feature vector that is the histogram. The sum of the elements in the feature vector is 1, unless no pixels in the window were covered by both footprint and mask, in which case all elements are 0.

Examples

from skimage import data
from skimage.filters.rank import windowed_histogram
from skimage.morphology import disk, ball
import numpy as np
img = data.camera()
rng = np.random.default_rng()
volume = rng.integers(0, 255, size=(10,10,10), dtype=np.uint8)
hist_img = windowed_histogram(img, disk(5))

✓

Aliases

skimage.filters.rank.windowed_histogram