低照度图像增强python代码

import numpy as np 
import cv2
from skimage import transform
def Transform(x):
    if x <= 0.05:
        return x * 2.64
    else:
        return 1.099 * np.power(x, 0.9/2.2) - 0.099
if __name__ == '__main__':
    fn = "1.jpg"
    img = cv2.imread(fn)
    #img = transform.resize(img,(600,680))  #将灰度图片大小转换为1024*860
    shape = img.shape
    s = np.zeros((shape[0],shape[1],3),dtype=np.float)
    out_img = np.zeros((shape[0],shape[1],3),dtype=np.uint8)
    rows = shape[0]
    cols = shape[1]
    r = 0; g = 0; b = 0
    lwmax = -1.0; base = 0.75
    for i in range(rows):
        for j in range(cols):
            b = img[i][j][0] / 255.0
            g = img[i][j][1] / 255.0
            r = img[i][j][2] / 255.0
            s[i][j][0] = (0.4124*r + 0.3576*g + 0.1805*b)
            s[i][j][1] = (0.2126*r + 0.7152*g + 0.0722*b)
            s[i][j][2] = (0.0193*r + 0.1192*g + 0.9505*b)
            lwmax = max(lwmax, s[i][j][1])
    for i in range(rows):
        for j in range(cols):
            xx = s[i][j][0] / (s[i][j][0] + s[i][j][1] + s[i][j][2])
            yy = s[i][j][1] / (s[i][j][0] + s[i][j][1] + s[i][j][2])
            tp = s[i][j][1]
            #修改CIE:X,Y,Z
            s[i][j][1] = 1.0 * np.log(s[i][j][1] + 1) / np.log(2 + 8.0*np.power((s[i][j][1] / lwmax), np.log(base) / np.log(0.5))) / np.log10(lwmax + 1)
            x = s[i][j][1] / yy*xx
            y = s[i][j][1]
            z = s[i][j][1] / yy*(1 - xx - yy)
            #转化为用RGB表示
            r = 3.2410*x - 1.5374*y - 0.4986*z
            g = -0.9692*x + 1.8760*y + 0.0416*z
            b = 0.0556*x - 0.2040*y + 1.0570*z
            r = max(0, min(r, 1))
            g = max(0, min(g, 1))
            b = max(0, min(b, 1))
            #修正补偿
            r = Transform(r); g = Transform(g); b = Transform(b)
            out_img[i][j][0] = int(b * 255)
            out_img[i][j][1] = int(g * 255)
            out_img[i][j][2] = int(r * 255)