本文用 Python 实现 PS 图像调整中的饱和度调整算法，具体的算法原理和效果可以参考之前的博客：

http://blog.csdn.net/matrix_space/article/details/22992961

import matplotlib.pyplot as plt

from skimage import io

file_name = 'D:/Visual Effects/PS Algorithm/4.jpg'

img=io.imread(file_name)

img = img * 1.0

img_out = img * 1.0

# -1 ~ 1

Increment = 0.5

img_min = img.min(axis=2)

img_max = img.max(axis=2)

Delta = (img_max - img_min) / 255.0

value = (img_max + img_min) / 255.0

L = value/2.0

mask_1 = L < 0.5

s1 = Delta/(value + 0.001)

s2 = Delta/(2 - value + 0.001)

s = s1 * mask_1 + s2 * (1 - mask_1)

if Increment >= 0 :

    temp = Increment + s

    mask_2 = temp >  1

    alpha_1 = s

    alpha_2 = s * 0 + 1 - Increment

    alpha = alpha_1 * mask_2 + alpha_2 * (1 - mask_2)

    alpha = 1/(alpha + 0.001) -1

    img_out[:, :, 0] = img[:, :, 0] + (img[:, :, 0] - L * 255.0) * alpha

    img_out[:, :, 1] = img[:, :, 1] + (img[:, :, 1] - L * 255.0) * alpha

    img_out[:, :, 2] = img[:, :, 2] + (img[:, :, 2] - L * 255.0) * alpha

else:

    alpha = Increment

    img_out[:, :, 0] = L * 255.0 + (img[:, :, 0] - L * 255.0) * (1 + alpha)

    img_out[:, :, 1] = L * 255.0 + (img[:, :, 1] - L * 255.0) * (1 + alpha)

    img_out[:, :, 2] = L * 255.0 + (img[:, :, 2] - L * 255.0) * (1 + alpha) 

img_out = img_out/255.0

# 饱和处理

mask_1 = img_out  < 0

mask_2 = img_out  > 1

img_out = img_out * (1-mask_1)

img_out = img_out * (1-mask_2) + mask_2

plt.figure()

plt.imshow(img/255.0)

plt.axis('off')

plt.figure(2)

plt.imshow(img_out)

plt.axis('off')

plt.show()