`skimage.util._montage:montage`

source: /dev/scikit-image/src/skimage/util/_montage.py :9

Signature

   def     montage (    arr_in    ,    fill    =  mean   ,    rescale_intensity    =  False   ,    grid_shape    =  None   ,    padding_width    =  0   ,  * ,     channel_axis    =  None     )

Summary

Create a montage of several single- or multichannel images.

Extended Summary

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_in : ndarray, shape (K, M, N[, C]): An array representing an ensemble of K images of equal shape.
fill : float 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_intensity : bool, optional: Whether to rescale the intensity of each image to [0, 1].
grid_shape : tuple, optional: The desired grid shape for the montage (ntiles_row, ntiles_column). The default aspect ratio is square.
padding_width : int, 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_axis : int 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_out : ndarray 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 skimage.util import montage
arr_in = np.arange(3 * 2 * 2).reshape(3, 2, 2)
arr_in  # doctest: +NORMALIZE_WHITESPACE
arr_out = montage(arr_in)
arr_out.shape
arr_out

✓

arr_in.mean()

✗

arr_out_nonsquare = montage(arr_in, grid_shape=(1, 3))
arr_out_nonsquare
arr_out_nonsquare.shape

✓

Aliases

skimage.util.montage