import cv2
import numpy as np

img = cv2.imread('../../Assets/Images/lena.jpg', 1)
cv2.imshow('src', img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]

count_b = np.zeros(256, np.float)
count_g = np.zeros(256, np.float)
count_r = np.zeros(256, np.float)

for i in range(0, height):
    for j in range(0, width):
        (b, g, r) = img[i, j]
        index_b = int(b)
        index_g = int(g)
        index_r = int(r)
        count_b[index_b] = count_b[index_b]+1
        count_g[index_g] = count_g[index_g]+1
        count_r[index_r] = count_r[index_r]+1

#计算每个灰度值出现
for i in range(0, 256):
    count_b[i] = count_b[i]/(height*width)
    count_g[i] = count_g[i]/(height*width)
    count_r[i] = count_r[i]/(height*width)

#计算累积概率
sum_b = float(0)
sum_g = float(0)
sum_r = float(0)

for i in range(0, 256):
    sum_b = sum_b + count_b[i]
    sum_g = sum_g + count_g[i]
    sum_r = sum_r + count_r[i]
    count_b[i] = sum_b
    count_g[i] = sum_g
    count_r[i] = sum_r

#计算映射表
map_b = np.zeros(256, np.uint16)
map_g = np.zeros(256, np.uint16)
map_r = np.zeros(256, np.uint16)

for i in range(0, 256):
    map_b[i] = np.uint16(count_b[i]*255)
    map_g[i] = np.uint16(count_g[i]*255)
    map_r[i] = np.uint16(count_r[i]*255)

dst = np.zeros((height, width, 3), np.uint8)

for i in range(0, height):
    for j in range(0, width):
        (b, g, r) = img[i,j]
        b = map_b[b]
        g = map_g[g]
        r = map_r[r]
        dst[i, j] = (b, g, r)
cv2.imshow('dst', dst)
cv2.waitKey(0)
