python⽤直⽅图规定化实现图像风格转换
以下内容需要直⽅图均衡化、规定化知识
均衡化：
规定化：
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):租约
# 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别处
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
小组面试for n in range(256):
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temp1 = map_2.index(n)
temp_pre = temp1
怎么回复邮件except BaException:
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)燃烧吧剑
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):怎么样炒股
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 = ([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)青春个性签名
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)
