彩色直方图均衡化（源码实现）

原理：计算每个通道像素级别（0-255）的累加概率，然后用累加概率乘以255代替原来的像素

import cv2
import numpy as np
img = cv2.imread('D:/pythonob/imageinpaint/img/flower.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
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
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)
#计算累计概率
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] = count_b[i] * 255
    map_g[i] = count_g[i] * 255
    map_r[i] = count_r[i] * 255
for i in range(0,height):
    for j in range(0,width):
        (b,g,r) = img[i,j]
        b = map_b[b]
        g = map_b[g]
        r = map_b[r]
        img[i,j] = (b,g,r)
cv2.imshow('aftergImg',img)
cv2.waitKey(0)

效果图：