white_tophat#
- scipy.ndimage.white_tophat(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None)[source]#
- Multidimensional white tophat filter. - Parameters:
- inputarray_like
- Input. 
- sizetuple of ints
- Shape of a flat and full structuring element used for the filter. Optional if footprint or structure is provided. 
- footprintarray of ints, optional
- Positions of elements of a flat structuring element used for the white tophat filter. 
- structurearray 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) 
- outputarray, 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’ 
- cvalscalar, optional
- Value to fill past edges of input if mode is ‘constant’. Default is 0.0. 
- originscalar, optional
- The origin parameter controls the placement of the filter. Default is 0. 
- axestuple 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:
- outputndarray
- Result of the filter of input with structure. 
 
 - See also - 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]])