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# scipy.ndimage.white_tophat

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

  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.

  See also

  `black_tophat`

  Examples

  Subtract gray background from a bright peak.

      >>> from scipy.ndimage import generate_binary_structure, white_tophat
      >>> import numpy as np
      >>> 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]])