scipy.ndimage.binary_erosion¶

scipy.ndimage.
binary_erosion
(input, structure=None, iterations=1, mask=None, output=None, border_value=0, origin=0, brute_force=False)[source]¶ Multidimensional binary erosion with a given structuring element.
Binary erosion is a mathematical morphology operation used for image processing.
Parameters:  input : array_like
Binary image to be eroded. Nonzero (True) elements form the subset to be eroded.
 structure : array_like, optional
Structuring element used for the erosion. Nonzero elements are considered True. If no structuring element is provided, an element is generated with a square connectivity equal to one.
 iterations : {int, float}, optional
The erosion is repeated iterations times (one, by default). If iterations is less than 1, the erosion is repeated until the result does not change anymore.
 mask : array_like, optional
If a mask is given, only those elements with a True value at the corresponding mask element are modified at each iteration.
 output : ndarray, optional
Array of the same shape as input, into which the output is placed. By default, a new array is created.
 border_value : int (cast to 0 or 1), optional
Value at the border in the output array.
 origin : int or tuple of ints, optional
Placement of the filter, by default 0.
 brute_force : boolean, optional
Memory condition: if False, only the pixels whose value was changed in the last iteration are tracked as candidates to be updated (eroded) in the current iteration; if True all pixels are considered as candidates for erosion, regardless of what happened in the previous iteration. False by default.
Returns:  binary_erosion : ndarray of bools
Erosion of the input by the structuring element.
Notes
Erosion [1] is a mathematical morphology operation [2] that uses a structuring element for shrinking the shapes in an image. The binary erosion of an image by a structuring element is the locus of the points where a superimposition of the structuring element centered on the point is entirely contained in the set of nonzero elements of the image.
References
[1] (1, 2) https://en.wikipedia.org/wiki/Erosion_%28morphology%29 [2] (1, 2) https://en.wikipedia.org/wiki/Mathematical_morphology Examples
>>> from scipy import ndimage >>> a = np.zeros((7,7), dtype=int) >>> a[1:6, 2:5] = 1 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.binary_erosion(a).astype(a.dtype) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> #Erosion removes objects smaller than the structure >>> ndimage.binary_erosion(a, structure=np.ones((5,5))).astype(a.dtype) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]])