opencv 實作灰階圖和彩色圖的均衡化

1，均衡化的了解：

我們拍攝或掃描的照片往往會由于光線太強或太弱，使圖像對比度減弱，細節分辨不清。這樣的圖像直方圖灰階往往都集中在某一色階範圍之内，我們需要将這些灰階拉伸到整個灰階級上，并使它們在直方圖中均勻的分布，以達到增強圖像的目的，這樣我們就引入了圖檔的均衡化，以達到亮度的均衡化。

2，代碼實作灰階圖的均衡化：

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)
#計算累計機率
sum1 = float(0)
for i in range(0,256):
    sum1 = sum1+count[i]
    count[i] = sum1
#print(count)
# 計算映射表
map1 = np.zeros(256,np.uint16)
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]
        gray[i,j] = map1[pixel]
cv2.imshow('dst',gray)
cv2.waitKey(0)
           

實作結果和原圖對比如下：

3,彩色圖的均衡化代碼實作：

import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('image0.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
for i in range(0,255):
    count_b[i] = count_b[i]/(height*width)
    count_g[i] = count_g[i]/(height*width)
    count_r[i] = count_r[i]/(height*width)
#計算累計機率
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
#print(count)
# 計算映射表
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)
# 映射
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)
           

實作結果如下：