windowed_histogram
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skimage.filters.rank.windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None)
[source] -
Normalized sliding window histogram
Parameters: image : ndarray
Image array (uint8 array).
selem : 2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
out : ndarray
If None, a new array will be 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).
n_bins : int or None
The number of histogram bins. Will default to
image.max() + 1
if None is passed.Returns: out : 3-D array with float dtype of dimensions (H,W,N), where (H,W) are
the dimensions of the input image and N is n_bins or
image.max() + 1
if no value is provided as a parameter. Effectively, each pixel is a N-D feature vector that is the histogram. The sum of the elements in the feature vector will be 1, unless no pixels in the window were covered by both selem and mask, in which case all elements will be 0.Examples
>>> from skimage import data >>> from skimage.filters.rank import windowed_histogram >>> from skimage.morphology import disk >>> img = data.camera() >>> hist_img = windowed_histogram(img, disk(5))
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