windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None)

Parameters

image : 2-D array (integer or float)

Input image.

selem : 2-D array (integer or float)

The neighborhood expressed as a 2-D array of 1's and 0's.

out : 2-D array (integer or float), optional

If None, a new array is allocated.

mask : ndarray (integer 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 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 (float)

Array 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.

Normalized sliding window histogram

Examples

This example is valid syntax, but we were not able to check execution

>>> 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))

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.filters.rank.generic.windowed_histogram

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