以下内容需要直方图均衡化、规定化知识

直方图均衡化应用：

图像直方图均衡化能拉伸灰度图，让像素值均匀分布在0,255之间，使图像看起来不会太亮或太暗，常用于图像增强；

直方图规定化应用：

举个例子，当我们需要对多张图像进行拼接时，我们希望这些图片的亮度、饱和度保持一致，事实上就是让它们的直方图分布一致，这时就需要直方图规定化。

直方图规定化与均衡化的思想一致，事实上就是找到各个灰度级别的映射关系。具体实现的过程中一般会选一个参考图像记为A，找到A的直方图与目标图像的直方图的映射关系，从而找到目标图像的像素以A为“参考”时的映射关系。

具体实现可参考文中链接(看完茅塞顿开)

基于python利用直方图规定化统一图像风格

参考图像

原始图像(第一行)/处理后的图像(第二行)

源码：

import os

import cv2

import numpy as np

def get_map(Hist):

# 计算概率分布Pr

sum_Hist = sum(Hist)

Pr = Hist/sum_Hist

# 计算累计概率Sk

Sk = []

temp_sum = 0

for n in Pr:

temp_sum = temp_sum + n

Sk.append(temp_sum)

Sk = np.array(Sk)

# 计算映射关系img_map

img_map = []

for m in range(256):

temp_map = int(255*Sk[m] + 0.5)

img_map.append(temp_map)

img_map = np.array(img_map)

return img_map

def get_off_map(map_): # 计算反向映射，寻找最小期望

map_2 = list(map_)

off_map = []

temp_pre = 0 # 如果循环开始就找不到映射时，默认映射为0

for n in range(256):

try:

temp1 = map_2.index(n)

temp_pre = temp1

except BaseException:

temp1 = temp_pre # 找不到映射关系时，近似取向前最近的有效映射值

off_map.append(temp1)

off_map = np.array(off_map)

return off_map

def get_infer_map(infer_img):

infer_Hist_b = cv2.calcHist([infer_img], [0], None, [256], [0,255])

infer_Hist_g = cv2.calcHist([infer_img], [1], None, [256], [0,255])

infer_Hist_r = cv2.calcHist([infer_img], [2], None, [256], [0,255])

infer_b_map = get_map(infer_Hist_b)

infer_g_map = get_map(infer_Hist_g)

infer_r_map = get_map(infer_Hist_r)

infer_b_off_map = get_off_map(infer_b_map)

infer_g_off_map = get_off_map(infer_g_map)

infer_r_off_map = get_off_map(infer_r_map)

return [infer_b_off_map, infer_g_off_map, infer_r_off_map]

def get_finalmap(org_map, infer_off_map): # 计算原始图像到最终输出图像的映射关系

org_map = list(org_map)

infer_off_map = list(infer_off_map)

final_map = []

for n in range(256):

temp1 = org_map[n]

temp2 = infer_off_map[temp1]

final_map.append(temp2)

final_map = np.array(final_map)

return final_map

def get_newimg(img_org, org2infer_maps):

w, h, _ = img_org.shape

b, g ,r =cv2.split(img_org)

for i in range(w):

for j in range(h):

temp1 = b[i,j]

b[i,j] = org2infer_maps[0][temp1]

for i in range(w):

for j in range(h):

temp1 = g[i,j]

g[i,j] = org2infer_maps[1][temp1]

for i in range(w):

for j in range(h):

temp1 = r[i,j]

r[i,j] = org2infer_maps[2][temp1]

newimg = cv2.merge([b,g,r])

return newimg

def get_new_img(img_org, infer_map):

org_Hist_b = cv2.calcHist([img_org], [0], None, [256], [0,255])

org_Hist_g = cv2.calcHist([img_org], [1], None, [256], [0,255])

org_Hist_r = cv2.calcHist([img_org], [2], None, [256], [0,255])

org_b_map = get_map(org_Hist_b)

org_g_map = get_map(org_Hist_g)

org_r_map = get_map(org_Hist_r)

org2infer_map_b = get_finalmap(org_b_map, infer_map[0])

org2infer_map_g = get_finalmap(org_g_map, infer_map[1])

org2infer_map_r = get_finalmap(org_r_map, infer_map[2])

return get_newimg(img_org, [org2infer_map_b, org2infer_map_g, org2infer_map_r])

if __name__ == "__main__":

dstroot = './imgs'

infer_img_path = './abc.png'

infer_img = cv2.imread(infer_img_path)

outroot = './out1'

infer_map = get_infer_map(infer_img) # 计算参考映射关系

dstlist = os.listdir(dstroot)

for n in dstlist:

img_path = os.path.join(dstroot, n)

print(img_path)

img_org = cv2.imread(img_path)

new_img = get_new_img(img_org, infer_map) # 根据映射关系获得新的图像

new_path = os.path.join(outroot, n)

cv2.imwrite(new_path, new_img)