dask_image.ndmorph package¶
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dask_image.ndmorph.binary_closing(input, structure=None, iterations=1, origin=0)[source]¶ Wrapped copy of “scipy.ndimage.morphology.binary_closing”
Excludes the output parameter as it would not work with Dask arrays.
Original docstring:
Multi-dimensional binary closing with the given structuring element.
The closing of an input image by a structuring element is the erosion of the dilation of the image by the structuring element.
Parameters: - input (array_like) – Binary array_like to be closed. Non-zero (True) elements form the subset to be closed.
- structure (array_like, optional) – Structuring element used for the closing. Non-zero 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, diagonally-connected elements are not considered neighbors).
- iterations ({int, float}, optional) – The dilation step of the closing, then the erosion step are each repeated iterations times (one, by default). If iterations is less than 1, each operations is repeated until the result does not change anymore.
- origin (int or tuple of ints, optional) – Placement of the filter, by default 0.
- 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.
New in version 1.1.0.
- border_value (int (cast to 0 or 1), optional) –
Value at the border in the output array.
New in version 1.1.0.
- 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 in the current iteration; if true al 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_closing – Closing of the input by the structuring element.
Return type: ndarray of bools
See also
grey_closing(),binary_opening(),binary_dilation(),binary_erosion(),generate_binary_structure()Notes
Closing [1]_ is a mathematical morphology operation [2]_ that consists in the succession of a dilation and an erosion of the input with the same structuring element. Closing therefore fills holes smaller than the structuring element.
Together with opening (binary_opening), closing can be used for noise removal.
References
[1] http://en.wikipedia.org/wiki/Closing_%28morphology%29 [2] http://en.wikipedia.org/wiki/Mathematical_morphology Examples
>>> from scipy import ndimage >>> a = np.zeros((5,5), dtype=int) >>> a[1:-1, 1:-1] = 1; a[2,2] = 0 >>> a array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> # Closing removes small holes >>> ndimage.binary_closing(a).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]]) >>> # Closing is the erosion of the dilation of the input >>> ndimage.binary_dilation(a).astype(int) array([[0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0]]) >>> ndimage.binary_erosion(ndimage.binary_dilation(a)).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]])
>>> a = np.zeros((7,7), dtype=int) >>> a[1:6, 2:5] = 1; a[1:3,3] = 0 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 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]]) >>> # In addition to removing holes, closing can also >>> # coarsen boundaries with fine hollows. >>> ndimage.binary_closing(a).astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 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_closing(a, structure=np.ones((2,2))).astype(int) 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]])
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dask_image.ndmorph.binary_dilation(input, structure=None, iterations=1, mask=None, border_value=0, origin=0, brute_force=False)[source]¶ Wrapped copy of “scipy.ndimage.morphology.binary_dilation”
Excludes the output parameter as it would not work with Dask arrays.
Original docstring:
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.
- 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 (dilated) in the current iteration; if True all pixels are considered as candidates for dilation, regardless of what happened in the previous iteration. False by default.
Returns: binary_dilation – Dilation of the input by the structuring element.
Return type: ndarray of bools
See also
grey_dilation(),binary_erosion(),binary_closing(),binary_opening(),generate_binary_structure()Notes
Dilation [1]_ is a mathematical morphology operation [2]_ 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
[1] http://en.wikipedia.org/wiki/Dilation_%28morphology%29 [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.]])
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dask_image.ndmorph.binary_erosion(input, structure=None, iterations=1, mask=None, border_value=0, origin=0, brute_force=False)[source]¶ Wrapped copy of “scipy.ndimage.morphology.binary_erosion”
Excludes the output parameter as it would not work with Dask arrays.
Original docstring:
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.
- 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 – Erosion of the input by the structuring element.
Return type: ndarray of bools
See also
grey_erosion(),binary_dilation(),binary_closing(),binary_opening(),generate_binary_structure()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 non-zero elements of the image.
References
[1] http://en.wikipedia.org/wiki/Erosion_%28morphology%29 [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]])
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dask_image.ndmorph.binary_opening(input, structure=None, iterations=1, origin=0)[source]¶ Wrapped copy of “scipy.ndimage.morphology.binary_opening”
Excludes the output parameter as it would not work with Dask arrays.
Original docstring:
Multi-dimensional 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: - input (array_like) – Binary array_like to be opened. Non-zero (True) elements form the subset to be opened.
- structure (array_like, optional) – Structuring element used for the opening. Non-zero 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, diagonally-connected 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.
- origin (int or tuple of ints, optional) – Placement of the filter, by default 0.
- 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.
New in version 1.1.0.
- border_value (int (cast to 0 or 1), optional) –
Value at the border in the output array.
New in version 1.1.0.
- 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 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_opening – Opening of the input by the structuring element.
Return type: ndarray of bools
See also
grey_opening(),binary_closing(),binary_erosion(),binary_dilation(),generate_binary_structure()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
[1] http://en.wikipedia.org/wiki/Opening_%28morphology%29 [2] http://en.wikipedia.org/wiki/Mathematical_morphology 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]])