视觉图像比较

在执行图像处理任务（例如曝光处理、滤波和恢复）时，图像比较特别有用。

此示例展示了如何使用各种方法轻松比较两个图像。

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')

棋盘格

checkerboard 方法交替使用来自第一张和第二张图像的瓦片。

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.set_axis_off()

fig.tight_layout()

差异

diff 方法计算两个图像之间的绝对差值。

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.set_axis_off()

fig.tight_layout()

混合

blend 是两个图像平均值的结果。

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.set_axis_off()

fig.tight_layout()

plt.show()