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# scipy.ndimage.binary_dilation

`scipy.ndimage.``binary_dilation`(input, structure=None, iterations=1, mask=None, output=None, border_value=0, origin=0, brute_force=False)

  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.

  See also

  `grey_dilation`, `binary_erosion`, `binary_closing`, `binary_opening`, `generate_binary_structure`

  Notes

  Dilation [R135] is a mathematical morphology operation [R136] 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

  [R135] | (1, 2) http://en.wikipedia.org/wiki/Dilation_%28morphology%29

  [R136] | (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.]])