# scipy.ndimage.distance_transform_cdt

scipy.ndimage.distance_transform_cdt(input, metric='chessboard', return_distances=True, return_indices=False, distances=None, indices=None)

  Distance transform for chamfer type of transforms.

  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. Values of 0 are treated as background.

    metric{‘chessboard’, ‘taxicab’} or array_like, optional

      The metric determines the type of chamfering that is done. If the metric is equal to ‘taxicab’ a structure is generated using `generate_binary_structure` with a squared distance equal to 1. If the metric is equal to ‘chessboard’, a metric is generated using `generate_binary_structure` with a squared distance equal to the dimensionality of the array. These choices correspond to the common interpretations of the ‘taxicab’ and the ‘chessboard’ distance metrics in two dimensions. A custom metric may be provided, in the form of a matrix where each dimension has a length of three. ‘cityblock’ and ‘manhattan’ are also valid, and map to ‘taxicab’. The default is ‘chessboard’.

    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 int32 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 int32 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 distance_transform_edt documentation for an example. Returned only when return_indices is True, and indices is not supplied.

  See also

  `distance_transform_edt`

    Fast distance transform for euclidean metric

  `distance_transform_bf`

    Distance transform for different metrics using a slower brute force algorithm

  Examples

  Import the necessary modules.

      >>> import numpy as np
      >>> from scipy.ndimage import distance_transform_cdt
      >>> import matplotlib.pyplot as plt
      >>> from mpl_toolkits.axes_grid1 import ImageGrid

  First, we create a toy binary image.

      >>> def add_circle(center_x, center_y, radius, image, fillvalue=1):
      ...     # fill circular area with 1
      ...     xx, yy = np.mgrid[:image.shape[0], :image.shape[1]]
      ...     circle = (xx - center_x) ** 2 + (yy - center_y) ** 2
      ...     circle_shape = np.sqrt(circle) < radius
      ...     image[circle_shape] = fillvalue
      ...     return image
      >>> image = np.zeros((100, 100), dtype=np.uint8)
      >>> image[35:65, 20:80] = 1
      >>> image = add_circle(28, 65, 10, image)
      >>> image = add_circle(37, 30, 10, image)
      >>> image = add_circle(70, 45, 20, image)
      >>> image = add_circle(45, 80, 10, image)

  Next, we set up the figure.

      >>> fig = plt.figure(figsize=(5, 15))
      >>> grid = ImageGrid(fig, 111, nrows_ncols=(3, 1), axes_pad=(0.5, 0.3),
      ...                  label_mode="1", share_all=True,
      ...                  cbar_location="right", cbar_mode="each",
      ...                  cbar_size="7%", cbar_pad="2%")
      >>> for ax in grid:
      ...     ax.axis('off')
      >>> top, middle, bottom = grid
      >>> colorbar_ticks = [0, 10, 20]

  The top image contains the original binary image.

      >>> binary_image = top.imshow(image, cmap='gray')
      >>> cbar_binary_image = top.cax.colorbar(binary_image)
      >>> cbar_binary_image.set_ticks([0, 1])
      >>> top.set_title("Binary image: foreground in white")

  The middle image contains the distance transform using the `taxicab` metric.

      >>> distance_taxicab = distance_transform_cdt(image, metric="taxicab")
      >>> taxicab_transform = middle.imshow(distance_taxicab, cmap='gray')
      >>> cbar_taxicab = middle.cax.colorbar(taxicab_transform)
      >>> cbar_taxicab.set_ticks(colorbar_ticks)
      >>> middle.set_title("Taxicab metric")

  The bottom image contains the distance transform using the `chessboard` metric.

      >>> distance_chessboard = distance_transform_cdt(image,
      ...                                              metric="chessboard")
      >>> chessboard_transform = bottom.imshow(distance_chessboard, cmap='gray')
      >>> cbar_chessboard = bottom.cax.colorbar(chessboard_transform)
      >>> cbar_chessboard.set_ticks(colorbar_ticks)
      >>> bottom.set_title("Chessboard metric")
      >>> plt.tight_layout()
      >>> plt.show()