scipy.ndimage.binary_opening¶

scipy.ndimage.
binary_opening
(input, structure=None, iterations=1, output=None, origin=0, mask=None, border_value=0, brute_force=False)[source]¶ Multidimensional binary opening with the given structuring element.
The opening of an input image by a structuring element is the dilation of the erosion of the image by the structuring element.
 Parameters
 inputarray_like
Binary array_like to be opened. Nonzero (True) elements form the subset to be opened.
 structurearray_like, optional
Structuring element used for the opening. Nonzero elements are considered True. If no structuring element is provided an element is generated with a square connectivity equal to one (i.e., only nearest neighbors are connected to the center, diagonallyconnected elements are not considered neighbors).
 iterations{int, float}, optional
The erosion step of the opening, then the dilation step are each repeated iterations times (one, by default). If iterations is less than 1, each operation is repeated until the result does not change anymore.
 outputndarray, optional
Array of the same shape as input, into which the output is placed. By default, a new array is created.
 originint or tuple of ints, optional
Placement of the filter, by default 0.
 maskarray_like, optional
If a mask is given, only those elements with a True value at the corresponding mask element are modified at each iteration.
New in version 1.1.0.
 border_valueint (cast to 0 or 1), optional
Value at the border in the output array.
New in version 1.1.0.
 brute_forceboolean, optional
Memory condition: if False, only the pixels whose value was changed in the last iteration are tracked as candidates to be updated in the current iteration; if true all pixels are considered as candidates for update, regardless of what happened in the previous iteration. False by default.
New in version 1.1.0.
 Returns
 binary_openingndarray of bools
Opening of the input by the structuring element.
Notes
Opening [1] is a mathematical morphology operation [2] that consists in the succession of an erosion and a dilation of the input with the same structuring element. Opening therefore removes objects smaller than the structuring element.
Together with closing (
binary_closing
), opening can be used for noise removal.References
Examples
>>> from scipy import ndimage >>> a = np.zeros((5,5), dtype=int) >>> a[1:4, 1:4] = 1; a[4, 4] = 1 >>> a 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, 1]]) >>> # Opening removes small objects >>> ndimage.binary_opening(a, structure=np.ones((3,3))).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]]) >>> # Opening can also smooth corners >>> ndimage.binary_opening(a).astype(int) 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]]) >>> # Opening is the dilation of the erosion of the input >>> ndimage.binary_erosion(a).astype(int) 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(ndimage.binary_erosion(a)).astype(int) 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]])