scipy.ndimage.binary_dilation¶
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scipy.ndimage.
binary_dilation
(input, structure=None, iterations=1, mask=None, output=None, border_value=0, origin=0, brute_force=False)[source]¶ Multi-dimensional binary dilation with the given structuring element.
Parameters: input : array_like
Binary array_like to be dilated. Non-zero (True) elements form the subset to be dilated.
structure : array_like, optional
Structuring element used for the dilation. 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 dilation is repeated iterations times (one, by default). If iterations is less than 1, the dilation 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_dilation : ndarray of bools
Dilation of the input by the structuring element.
Notes
Dilation [R165] is a mathematical morphology operation [R166] that uses a structuring element for expanding the shapes in an image. The binary dilation of an image by a structuring element is the locus of the points covered by the structuring element, when its center lies within the non-zero points of the image.
References
[R165] (1, 2) http://en.wikipedia.org/wiki/Dilation_%28morphology%29 [R166] (1, 2) http://en.wikipedia.org/wiki/Mathematical_morphology Examples
>>> from scipy import ndimage >>> a = np.zeros((5, 5)) >>> a[2, 2] = 1 >>> a array([[ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(a) array([[False, False, False, False, False], [False, False, True, False, False], [False, True, True, True, False], [False, False, True, False, False], [False, False, False, False, False]], dtype=bool) >>> ndimage.binary_dilation(a).astype(a.dtype) array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> # 3x3 structuring element with connectivity 1, used by default >>> struct1 = ndimage.generate_binary_structure(2, 1) >>> struct1 array([[False, True, False], [ True, True, True], [False, True, False]], dtype=bool) >>> # 3x3 structuring element with connectivity 2 >>> struct2 = ndimage.generate_binary_structure(2, 2) >>> struct2 array([[ True, True, True], [ True, True, True], [ True, True, True]], dtype=bool) >>> ndimage.binary_dilation(a, structure=struct1).astype(a.dtype) array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(a, structure=struct2).astype(a.dtype) 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.]]) >>> ndimage.binary_dilation(a, structure=struct1,\ ... iterations=2).astype(a.dtype) array([[ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 1., 1., 1., 1., 1.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.]])