Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/skimage/util/compare.py

60 lines
2 KiB
Python

import numpy as np
from ..util import img_as_float
from itertools import product
def compare_images(image1, image2, method='diff', *, n_tiles=(8, 8)):
"""
Return an image showing the differences between two images.
.. versionadded:: 0.16
Parameters
----------
image1, image2 : 2-D array
Images to process, must be of the same shape.
method : string, optional
Method used for the comparison.
Valid values are {'diff', 'blend', 'checkerboard'}.
Details are provided in the note section.
n_tiles : tuple, optional
Used only for the `checkerboard` method. Specifies the number
of tiles (row, column) to divide the image.
Returns
-------
comparison : 2-D array
Image showing the differences.
Notes
-----
``'diff'`` computes the absolute difference between the two images.
``'blend'`` computes the mean value.
``'checkerboard'`` makes tiles of dimension `n_tiles` that display
alternatively the first and the second image.
"""
if image1.shape != image2.shape:
raise ValueError('Images must have the same shape.')
img1 = img_as_float(image1)
img2 = img_as_float(image2)
if method == 'diff':
comparison = np.abs(img2 - img1)
elif method == 'blend':
comparison = 0.5 * (img2 + img1)
elif method == 'checkerboard':
shapex, shapey = img1.shape
mask = np.full((shapex, shapey), False)
stepx = int(shapex / n_tiles[0])
stepy = int(shapey / n_tiles[1])
for i, j in product(range(n_tiles[0]), range(n_tiles[1])):
if (i + j) % 2 == 0:
mask[i * stepx:(i + 1)*stepx, j * stepy:(j + 1) * stepy] = True
comparison = np.zeros_like(img1)
comparison[mask] = img1[mask]
comparison[~mask] = img2[~mask]
else:
raise ValueError('Wrong value for `method`. '
'Must be either "diff", "blend" or "checkerboard".')
return comparison