看了这个文章，里面有专门的c++的实现，我这边简单的使用python进行了实现，实现了两个版本，一个是python遍历像素，一个是使用numpy加速，代码如下：

import time
import numpy as np
import cv2


def lighting(img, light):
    assert -100 <= light <= 100
    max_v = 4
    bright = (light/100.0)/max_v
    mid = 1.0+max_v*bright
    print('bright: ', bright, 'mid: ', mid)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).astype(np.float32)/255.0
    thresh = gray*gray
    t = np.mean(thresh)
    mask = np.where(thresh > t, 255, 0).astype(np.float32)
    brightrate = np.zeros_like(mask).astype(np.float32)
    h, w = img.shape[:2]
    # 遍历每个像素点
    for i in range(h):
        for j in range(w):
            if mask[i, j] == 255.0:
                mask[i, j] = mid
                brightrate[i, j] = bright
            else:
                mask[i, j] = (mid-1.0)/t*thresh[i, j]+1.0
                brightrate[i, j] = (1.0/t*thresh[i, j])*bright
    img = img/255.0
    img = np.power(img, 1.0/mask[:, :, np.newaxis])*(1.0/(1.0-brightrate[:, :, np.newaxis]))
    img = np.clip(img, 0, 1.0)*255.0
    return img.astype(np.uint8)


def lighting_fast(img, light):
    assert -100 <= light <= 100
    max_v = 4
    bright = (light/100.0)/max_v
    mid = 1.0+max_v*bright
    print('bright: ', bright, 'mid: ', mid)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).astype(np.float32)/255.0
    thresh = gray*gray
    t = np.mean(thresh)
    # 使用numpy来计算可以加速，速度远快于上面的遍历
    mask = np.where(thresh > t, 255, 0).astype(np.float32)
    brightrate = np.where(mask == 255.0, bright, (1.0/t*thresh)*bright)
    mask = np.where(mask == 255.0, mid, (mid-1.0)/t*thresh+1.0)
    img = img/255.0
    img = np.power(img, 1.0/mask[:, :, np.newaxis])*(1.0/(1.0-brightrate[:, :, np.newaxis]))
    img = np.clip(img, 0, 1.0)*255.0
    return img.astype(np.uint8)


if __name__ == '__main__':
    input_img = cv2.imread('tmp/302.png')
    light = 50
    start_time = time.time()
    res = lighting(input_img, light)
    print('time: {:.3f} s'.format(time.time() - start_time))
    cv2.imwrite('tmp/302_lighting_{}.jpg'.format(light), res)
    start_time = time.time()
    res = lighting_fast(input_img, light)
    print('fast time: {:.3f} s'.format(time.time() - start_time))
    cv2.imwrite('tmp/302_lighting_fast_{}.jpg'.format(light), res)

运行结果如下：

bright:  0.125 mid:  1.5
time: 6.454 s
bright:  0.125 mid:  1.5
fast time: 0.280 s

可以看到numpy加速很多倍，运行图的结果如下

原图：

 python版结果：

+50

-50：

 可以和c++原文对比是一致的