Visual image comparison#

Image comparison is particularly useful when performing image processing tasks such as exposure manipulations, filtering, and restoration.

This example shows how to easily compare two images with various approaches.

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

from skimage import data, transform, exposure
from skimage.util import compare_images


img1 = data.coins()
img1_equalized = exposure.equalize_hist(img1)
img2 = transform.rotate(img1, 2)


comp_equalized = compare_images(img1, img1_equalized, method='checkerboard')
diff_rotated = compare_images(img1, img2, method='diff')
blend_rotated = compare_images(img1, img2, method='blend')

Checkerboard#

The checkerboard method alternates tiles from the first and the second images.

fig = plt.figure(figsize=(8, 9))

gs = GridSpec(3, 2)
ax0 = fig.add_subplot(gs[0, 0])
ax1 = fig.add_subplot(gs[0, 1])
ax2 = fig.add_subplot(gs[1:, :])

ax0.imshow(img1, cmap='gray')
ax0.set_title('Original')
ax1.imshow(img1_equalized, cmap='gray')
ax1.set_title('Equalized')
ax2.imshow(comp_equalized, cmap='gray')
ax2.set_title('Checkerboard comparison')
for a in (ax0, ax1, ax2):
    a.axis('off')
plt.tight_layout()
plt.plot()
Original, Equalized, Checkerboard comparison
[]

Diff#

The diff method computes the absolute difference between the two images.

fig = plt.figure(figsize=(8, 9))

gs = GridSpec(3, 2)
ax0 = fig.add_subplot(gs[0, 0])
ax1 = fig.add_subplot(gs[0, 1])
ax2 = fig.add_subplot(gs[1:, :])

ax0.imshow(img1, cmap='gray')
ax0.set_title('Original')
ax1.imshow(img2, cmap='gray')
ax1.set_title('Rotated')
ax2.imshow(diff_rotated, cmap='gray')
ax2.set_title('Diff comparison')
for a in (ax0, ax1, ax2):
    a.axis('off')
plt.tight_layout()
plt.plot()
Original, Rotated, Diff comparison
[]

Blend#

blend is the result of the average of the two images.

fig = plt.figure(figsize=(8, 9))

gs = GridSpec(3, 2)
ax0 = fig.add_subplot(gs[0, 0])
ax1 = fig.add_subplot(gs[0, 1])
ax2 = fig.add_subplot(gs[1:, :])

ax0.imshow(img1, cmap='gray')
ax0.set_title('Original')
ax1.imshow(img2, cmap='gray')
ax1.set_title('Rotated')
ax2.imshow(blend_rotated, cmap='gray')
ax2.set_title('Blend comparison')
for a in (ax0, ax1, ax2):
    a.axis('off')
plt.tight_layout()
plt.plot()
Original, Rotated, Blend comparison
[]

Total running time of the script: (0 minutes 3.010 seconds)

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