mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 05:35:03 +08:00
Nicolas Pinto
99 lines
2.6 KiB
Python
99 lines
2.6 KiB
Python
__all__ = ['montage2d']
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import numpy as np
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from .. import exposure
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EPSILON = 1e-6
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def montage2d(arr_in, fill='mean', rescale_intensity=False):
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"""Create a 2-dimensional 'montage' from a 3-dimensional input array
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representing an ensemble of equally shaped 2-dimensional images.
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For example, montage2d(arr_in, fill) with the following `arr_in`
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+---+---+---+
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| 1 | 2 | 3 |
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+---+---+---+
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will return:
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+---+---+
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| 1 | 2 |
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+---+---+
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| 3 | * |
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+---+---+
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Where the '*' patch will be determined by the `fill` parameter.
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Parameters
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----------
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arr_in: ndarray, shape=[n_images, height, width]
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3-dimensional input array representing an ensemble of n_images
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of equal shape (i.e. [height, width]).
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fill: float or 'mean', optional
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How to fill the 2-dimensional output array when sqrt(n_images)
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is not an integer. If 'mean' is chosen, then fill = arr_in.mean().
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rescale_intensity: bool, optional
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Whether to rescale the intensity of each image to [0, 1].
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Returns
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-------
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arr_out: ndarray, shape=[alpha * height, alpha * width]
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Output array where 'alpha' has been determined automatically to
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fit (at least) the `n_images` in `arr_in`.
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Examples
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--------
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>>> import numpy as np
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>>> from skimage.util.montage import montage2d
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>>> arr_in = np.arange(3 * 2 * 2).reshape(3, 2, 2)
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>>> print arr_in # doctest: +NORMALIZE_WHITESPACE
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[[[ 0 1]
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[ 2 3]]
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[[ 4 5]
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[ 6 7]]
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[[ 8 9]
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[10 11]]]
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>>> arr_out = montage2d(arr_in)
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>>> print arr_out.shape
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(4, 4)
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>>> print arr_out
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[[ 0. 1. 4. 5. ]
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[ 2. 3. 6. 7. ]
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[ 8. 9. 5.5 5.5]
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[ 10. 11. 5.5 5.5]]
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>>> print arr_in.mean()
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5.5
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"""
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assert arr_in.ndim == 3
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n_images, height, width = arr_in.shape
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arr_in = arr_in.copy()
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# -- rescale intensity if necessary
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if rescale_intensity:
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for i in range(n_images):
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arr_in[i] = exposure.rescale_intensity(arr_in[i])
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# -- determine alpha
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alpha = int(np.ceil(np.sqrt(n_images)))
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# -- fill missing patches
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if fill == 'mean':
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fill = arr_in.mean()
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n_missing = int((alpha**2.) - n_images)
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missing = np.ones((n_missing, height, width), dtype=arr_in.dtype) * fill
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arr_out = np.vstack((arr_in, missing))
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# -- reshape to 2d montage, step by step
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arr_out = arr_out.reshape(alpha, alpha, height, width)
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arr_out = arr_out.swapaxes(1, 2)
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arr_out = arr_out.reshape(alpha * height, alpha * width)
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return arr_out
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