skimage2.util.montage#

skimage2.util.montage(arr_in, fill='mean', rescale_intensity=False, grid_shape=None, padding_width=0, *, channel_axis=None)[source]#

Create a montage of several single- or multichannel images.

Create a rectangular montage from an input array representing an ensemble of equally shaped single- (gray) or multichannel (color) images.

For example, montage(arr_in) called with the following arr_in

1

2

3

will return

1	2
3

where the ‘*’ patch will be determined by the fill parameter.

Parameters:

arr_inndarray, shape (K, M, N[, C]): An array representing an ensemble of K images of equal shape.
fillfloat or array_like of dtype float or Literal[‘mean’], optional: Value to fill the padding areas and/or the extra tiles in the output array. Has to be float for single channel collections. For multichannel collections has to be an array-like of shape of number of channels. If mean, uses the mean value over all images.
rescale_intensitybool, optional: Whether to rescale the intensity of each image to [0, 1].
grid_shapetuple, optional: The desired grid shape for the montage (ntiles_row, ntiles_column). The default aspect ratio is square.
padding_widthint, optional: The size of the spacing between the tiles and between the tiles and the borders. If non-zero, makes the boundaries of individual images easier to perceive.
channel_axisint or None, optional: If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels.

Returns:

arr_outndarray of shape (W, Q): Output array with input images glued together (including padding p). The shape (W, Q) is calculated as (K*(M+p)+p, Q=K*(N+p)+p[, C]).

Examples

>>> import numpy as np
>>> from _skimage2.util import montage
>>> arr_in = np.arange(3 * 2 * 2).reshape(3, 2, 2)
>>> arr_in
array([[[ 0,  1],
        [ 2,  3]],
       [[ 4,  5],
        [ 6,  7]],
       [[ 8,  9],
        [10, 11]]])
>>> arr_out = montage(arr_in)
>>> arr_out.shape
(4, 4)
>>> arr_out
array([[ 0,  1,  4,  5],
       [ 2,  3,  6,  7],
       [ 8,  9,  5,  5],
       [10, 11,  5,  5]])
>>> arr_in.mean()
5.5
>>> arr_out_nonsquare = montage(arr_in, grid_shape=(1, 3))
>>> arr_out_nonsquare
array([[ 0,  1,  4,  5,  8,  9],
       [ 2,  3,  6,  7, 10, 11]])
>>> arr_out_nonsquare.shape
(2, 6)