binary_fill_holes#
- scipy.ndimage.binary_fill_holes(input, structure=None, output=None, origin=0, *, axes=None)[source]#
- Fill the holes in binary objects. - Parameters:
- inputarray_like
- N-D binary array with holes to be filled 
- structurearray_like, optional
- Structuring element used in the computation; large-size elements make computations faster but may miss holes separated from the background by thin regions. The default element (with a square connectivity equal to one) yields the intuitive result where all holes in the input have been filled. 
- outputndarray, optional
- Array of the same shape as input, into which the output is placed. By default, a new array is created. 
- originint, tuple of ints, optional
- Position of the structuring element. 
- 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:
- outndarray
- Transformation of the initial image input where holes have been filled. 
 
 - See also - Notes - The algorithm used in this function consists in invading the complementary of the shapes in input from the outer boundary of the image, using binary dilations. Holes are not connected to the boundary and are therefore not invaded. The result is the complementary subset of the invaded region. - References - Examples - >>> from scipy import ndimage >>> import numpy as np >>> a = np.zeros((5, 5), dtype=int) >>> a[1:4, 1:4] = 1 >>> a[2,2] = 0 >>> a array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> ndimage.binary_fill_holes(a).astype(int) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> # Too big structuring element >>> ndimage.binary_fill_holes(a, structure=np.ones((5,5))).astype(int) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]])