# cupyx.scipy.ndimage.distance_transform_edt#

cupyx.scipy.ndimage.distance_transform_edt(image, sampling=None, return_distances=True, return_indices=False, distances=None, indices=None, *, block_params=None, float64_distances=True)[source]#

Exact Euclidean distance transform.

This function calculates the distance transform of the input, by replacing each foreground (non-zero) element, with its shortest distance to the background (any zero-valued element).

In addition to the distance transform, the feature transform can be calculated. In this case the index of the closest background element to each foreground element is returned in a separate array.

Parameters
• image (array_like) – Input data to transform. Can be any type but will be converted into binary: 1 wherever image equates to True, 0 elsewhere.

• sampling (float, or sequence of float, optional) – Spacing of elements along each dimension. If a sequence, must be of length equal to the image rank; if a single number, this is used for all axes. If not specified, a grid spacing of unity is implied.

• return_distances (bool, optional) – Whether to calculate the distance transform.

• return_indices (bool, optional) – Whether to calculate the feature transform.

• distances (cupy.ndarray, optional) – An output array to store the calculated distance transform, instead of returning it. return_distances must be True. It must be the same shape as image. Should have dtype cp.float32 if float64_distances is False, otherwise it should be cp.float64.

• indices (cupy.ndarray, optional) – An output array to store the calculated feature transform, instead of returning it. return_indicies must be True. Its shape must be (image.ndim,) + image.shape. Its dtype must be a signed or unsigned integer type of at least 16-bits in 2D or 32-bits in 3D.

• block_params (3-tuple of int) – The m1, m2, m3 algorithm parameters as described in 2. If None, suitable defaults will be chosen. Note: This parameter is specific to cuCIM and does not exist in SciPy.

• float64_distances (bool, optional) – If True, use double precision in the distance computation (to match SciPy behavior). Otherwise, single precision will be used for efficiency. Note: This parameter is specific to cuCIM and does not exist in SciPy.

Returns

• distances (cupy.ndarray, optional) – The calculated distance transform. Returned only when return_distances is True and distances is not supplied. It will have the same shape as image. Will have dtype cp.float64 if float64_distances is True, otherwise it will have dtype cp.float32.

• indices (ndarray, optional) – The calculated feature transform. It has an image-shaped array for each dimension of the image. See example below. Returned only when return_indices is True and indices is not supplied.

Notes

The Euclidean distance transform gives values of the Euclidean distance.

$y_i = \sqrt{\sum_{i}^{n} (x[i] - b[i])^2}$

where $$b[i]$$ is the background point (value 0) with the smallest Euclidean distance to input points $$x[i]$$, and $$n$$ is the number of dimensions.

Note that the indices output may differ from the one given by scipy.ndimage.distance_transform_edt() in the case of input pixels that are equidistant from multiple background points.

The parallel banding algorithm implemented here was originally described in 1. The kernels used here correspond to the revised PBA+ implementation that is described on the author’s website 2. The source code of the author’s PBA+ implementation is available at 3.

References

1

Thanh-Tung Cao, Ke Tang, Anis Mohamed, and Tiow-Seng Tan. 2010. Parallel Banding Algorithm to compute exact distance transform with the GPU. In Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games (I3D ’10). Association for Computing Machinery, New York, NY, USA, 83–90. DOI:https://doi.org/10.1145/1730804.1730818

2(1,2)

https://www.comp.nus.edu.sg/~tants/pba.html

3

orzzzjq/Parallel-Banding-Algorithm-plus

Examples

>>> import cupy as cp
>>> from cucim.core.operations import morphology
>>> a = cp.array(([0,1,1,1,1],
...               [0,0,1,1,1],
...               [0,1,1,1,1],
...               [0,1,1,1,0],
...               [0,1,1,0,0]))
>>> morphology.distance_transform_edt(a)
array([[ 0.    ,  1.    ,  1.4142,  2.2361,  3.    ],
[ 0.    ,  0.    ,  1.    ,  2.    ,  2.    ],
[ 0.    ,  1.    ,  1.4142,  1.4142,  1.    ],
[ 0.    ,  1.    ,  1.4142,  1.    ,  0.    ],
[ 0.    ,  1.    ,  1.    ,  0.    ,  0.    ]])


With a sampling of 2 units along x, 1 along y:

>>> morphology.distance_transform_edt(a, sampling=[2,1])
array([[ 0.    ,  1.    ,  2.    ,  2.8284,  3.6056],
[ 0.    ,  0.    ,  1.    ,  2.    ,  3.    ],
[ 0.    ,  1.    ,  2.    ,  2.2361,  2.    ],
[ 0.    ,  1.    ,  2.    ,  1.    ,  0.    ],
[ 0.    ,  1.    ,  1.    ,  0.    ,  0.    ]])


>>> edt, inds = morphology.distance_transform_edt(a, return_indices=True)
>>> inds
array([[[0, 0, 1, 1, 3],
[1, 1, 1, 1, 3],
[2, 2, 1, 3, 3],
[3, 3, 4, 4, 3],
[4, 4, 4, 4, 4]],
[[0, 0, 1, 1, 4],
[0, 1, 1, 1, 4],
[0, 0, 1, 4, 4],
[0, 0, 3, 3, 4],
[0, 0, 3, 3, 4]]])