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bundles / scipy latest / scipy / ndimage / _morphology / distance_transform_edt

function

scipy.ndimage._morphology:distance_transform_edt

source: /scipy/ndimage/_morphology.py :2435

Signature

def   distance_transform_edt ( input ,  sampling = None ,  return_distances = True ,  return_indices = False ,  distances = None ,  indices = None )

Summary

Exact Euclidean distance transform.

Extended Summary

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

input : array_like

Input data to transform. Can be any type but will be converted into binary: 1 wherever input 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 input 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. Default is True.

return_indices : bool, optional

Whether to calculate the feature transform. Default is False.

distances : float64 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 input.

indices : int32 ndarray, optional

An output array to store the calculated feature transform, instead of returning it. return_indicies must be True. Its shape must be (input.ndim,) + input.shape.

Returns

distances : float64 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 the input array.

indices : int32 ndarray, optional

The calculated feature transform. It has an input-shaped array for each dimension of the input. 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 

              n
y_i = sqrt(sum (x[i]-b[i])**2)
              i

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.

Array API Standard Support

distance_transform_edt has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ⛔                   
JAX                   ⚠️ no JIT             ⛔                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

from scipy import ndimage
import numpy as np
a = np.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]))
✓
ndimage.distance_transform_edt(a)
✗
With a sampling of 2 units along x, 1 along y: 
ndimage.distance_transform_edt(a, sampling=[2,1])
✗
Asking for indices as well: 
edt, inds = ndimage.distance_transform_edt(a, return_indices=True)
✓
inds
✗
With arrays provided for inplace outputs: 
indices = np.zeros(((np.ndim(a),) + a.shape), dtype=np.int32)
✓
ndimage.distance_transform_edt(a, return_indices=True, indices=indices)
indices
✗

Aliases

  • scipy.ndimage.distance_transform_edt

Referenced by

Other packages