06灰度直方图均衡化1

# 本质：统计每个像素灰度 出现的概率 0-255 p
# 累计概率 
# 1 0.2  0.2
# 2 0.3  0.5
# 3 0.1  0.6
# 256 
# 假设颜色数值100 出现的概率为 0.5 则 255*0.5 = new ，以后100这个像素值都用new这个新值来替代，这个过程就是直方图的均衡化

import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('src',gray)

count = np.zeros(256,np.float)
for i in range(0,height):
    for j in range(0,width):
        pixel = gray[i,j]
        index = int(pixel)
        count[index] = count[index]+1

for i in range(0,255):
    count[i] = count[i]/(height*width)

#计算累计概率
sum1 = float(0)
for i in range(0,256):   #0到255共有256个数据
    sum1 = sum1+count[i]
    count[i] = sum1
#print(count)

# 计算映射表
map1 = np.zeros(256,np.uint16)   #映射表
for i in range(0,256):
    map1[i] = np.uint16(count[i]*255) 

# 映射
for i in range(0,height):
    for j in range(0,width):
        pixel = gray[i,j]    #获取当前像素值
        gray[i,j] = map1[pixel]

cv2.imshow('dst',gray)
cv2.waitKey(0)

image.png image.png