Module « scipy.ndimage »

Fonction white_tophat - module scipy.ndimage

Signature de la fonction white_tophat

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

Description

white_tophat.__doc__

    Multidimensional white tophat filter.

    Parameters
    ----------
    input : array_like
        Input.
    size : tuple of ints
        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 elements of a flat structuring element
        used for the white tophat filter.
    structure : array of ints, optional
        Structuring element used for the filter. `structure`
        may be a non-flat structuring element.
    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 is 0.

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

    Examples
    --------
    Subtract gray background from a bright peak.

    >>> from scipy.ndimage import generate_binary_structure, white_tophat
    >>> square = generate_binary_structure(rank=2, connectivity=3)
    >>> bright_on_gray = np.array([[2, 3, 3, 3, 2],
    ...                            [3, 4, 5, 4, 3],
    ...                            [3, 5, 9, 5, 3],
    ...                            [3, 4, 5, 4, 3],
    ...                            [2, 3, 3, 3, 2]])
    >>> white_tophat(input=bright_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]])

    See also
    --------
    black_tophat