Visual image comparison

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

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

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

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, 5)


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')

Traceback (most recent call last):
  File "/build/skimage-Lp2Zl4/skimage-0.16.2/doc/examples/applications/plot_image_comparison.py", line 1
    =======================
    ^
SyntaxError: invalid syntax

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()

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(img1_equalized, 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()

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(img1_equalized, 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()

Total running time of the script: ( 0 minutes 0.000 seconds)