### Navigation

- index
- modules |
- modules |
- next |
- previous |
- SciPy v0.12 Reference Guide (DRAFT) »
- Multi-dimensional image processing (`scipy.ndimage`) »

# scipy.ndimage.morphology.distance_transform_bf

`scipy.ndimage.morphology.``distance_transform_bf`(input, metric='euclidean', sampling=None, return_distances=True, return_indices=False, distances=None, indices=None)

  Distance transform function by a brute force algorithm.

  This function calculates the distance transform of the input, by replacing each background element (zero values), with its shortest distance to the foreground (any element non-zero). Three types of distance metric are supported: ‘euclidean’, ‘taxicab’ and ‘chessboard’.

  In addition to the distance transform, the feature transform can be calculated. In this case the index of the closest background element is returned along the first axis of the result.

  The return_distances, and return_indices flags can be used to indicate if the distance transform, the feature transform, or both must be returned.

  Optionally the sampling along each axis can be given by the sampling parameter which should be a sequence of length equal to the input rank, or a single number in which the sampling is assumed to be equal along all axes. This parameter is only used in the case of the euclidean distance transform.

  This function employs a slow brute force algorithm, see also the function distance_transform_cdt for more efficient taxicab and chessboard algorithms.

  the distances and indices arguments can be used to give optional output arrays that must be of the correct size and type (float64 and int32).

### Navigation

- index
- modules |
- modules |
- next |
- previous |
- SciPy v0.12 Reference Guide (DRAFT) »
- Multi-dimensional image processing (`scipy.ndimage`) »