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]¶
Multi-dimensional 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. Non-zero (True) elements form the subset to be eroded.
structure : array_like, optional
Structuring element used for the erosion. Non-zero 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.
origin : int or tuple of ints, optional
Placement of the filter, by default 0.
border_value : int (cast to 0 or 1), optional
Value at the border in the output array.
Returns: binary_erosion : ndarray of bools
Erosion of the input by the structuring element.
Notes
Erosion [R123] is a mathematical morphology operation [R124] 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 non-zero elements of the image.
References
[R123] (1, 2) http://en.wikipedia.org/wiki/Erosion_%28morphology%29 [R124] (1, 2) http://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]])