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Module « scipy.ndimage »

Fonction black_tophat - module scipy.ndimage

Signature de la fonction black_tophat

def black_tophat(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None) 

Description

help(scipy.ndimage.black_tophat)

Multidimensional black tophat filter.

Parameters
----------
input : array_like
    Input.
size : tuple of ints, optional
    Shape of a flat and full structuring element used for the filter.
    Optional if `footprint` or `structure` is provided.
footprint : array of ints, optional
    Positions of non-infinite elements of a flat structuring element
    used for the black tophat filter.
structure : array of ints, optional
    Structuring element used for the filter. `structure` may be a non-flat
    structuring element. The `structure` array applies offsets to the
    pixels in a neighborhood (the offset is additive during dilation and
    subtractive during erosion)
output : array, optional
    An array used for storing the output of the filter may be provided.
mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
    The `mode` parameter determines how the array borders are
    handled, where `cval` is the value when mode is equal to
    'constant'. Default is 'reflect'
cval : scalar, optional
    Value to fill past edges of input if `mode` is 'constant'. Default
    is 0.0.
origin : scalar, optional
    The `origin` parameter controls the placement of the filter.
    Default 0
axes : tuple of int or None
    The axes over which to apply the filter. If None, `input` is filtered
    along all axes. If an `origin` tuple is provided, its length must match
    the number of axes.

Returns
-------
black_tophat : ndarray
    Result of the filter of `input` with `structure`.

See Also
--------
white_tophat, grey_opening, grey_closing

Examples
--------
Change dark peak to bright peak and subtract background.

>>> from scipy.ndimage import generate_binary_structure, black_tophat
>>> import numpy as np
>>> square = generate_binary_structure(rank=2, connectivity=3)
>>> dark_on_gray = np.array([[7, 6, 6, 6, 7],
...                          [6, 5, 4, 5, 6],
...                          [6, 4, 0, 4, 6],
...                          [6, 5, 4, 5, 6],
...                          [7, 6, 6, 6, 7]])
>>> black_tophat(input=dark_on_gray, structure=square)
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 1, 5, 1, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]])

Vous êtes un professionnel et vous avez besoin d'une formation ? RAG (Retrieval-Augmented Generation)
et Fine Tuning d'un LLM Voir le programme détaillé