opencv 实现灰度图和彩色图的均衡化

Dear 丶 2021-11-11 12:08 407阅读 0赞

1，均衡化的理解：

我们拍摄或扫描的照片往往会由于光线太强或太弱，使图像对比度减弱，细节分辨不清。这样的图像直方图灰度往往都集中在某一色阶范围之内，我们需要将这些灰度拉伸到整个灰度级上，并使它们在直方图中均匀的分布，以达到增强图像的目的，这样我们就引入了图片的均衡化，以达到亮度的均衡化。

2，代码实现灰度图的均衡化：

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):
        sum1 = sum1+count[i]
        count[i] = sum1
    #print(count)
    # 计算映射表
    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)

实现结果和原图对比如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70][]

3,彩色图的均衡化代码实现：

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)
    #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
    #print(count)
    # 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] = 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)

实现结果如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70 1][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70]: /images/20211109/63ede49b66c2468f98521b38c7db86e5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwMDA4MzI1_size_16_color_FFFFFF_t_70 1]: /images/20211109/7abebc684e6f4d23b416f13295792d33.png