2018.12.6—2018.12.7计算机视觉加强之图像美化

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本文深入探讨了图像处理中的关键技巧，包括直方图均衡化、亮度调整、磨皮美白等，通过具体代码实例展示了如何利用OpenCV进行图像美化，如直方图统计与均衡化、图像修补、亮度增强及各种滤波技术。

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12.6 01.彩色图片直方图

import cv2
import numpy as np
def ImageHist(image,type):
    color = (255,255,255)
    windowName = 'Gray'
    if type == 31:
        color = (255,0,0)
        windowName = 'B Hist'
    elif type == 32:
        color = (0,255,0)
        windowName = 'G Hist'
    elif type ==33:
        color = (0,0,255)
        windowName = 'R Hist'
    # 直方图统计 1:image 图片的数据 2:用于计算的直方图的通道(这里因为是灰度所以取[0]) 3:mask蒙版 是一个蒙版 4:256直方图的size 5:0-255遍历的像素
    hist = cv2.calcHist([image],[0],None,[256],[0.0,255.0])
    minV,maxV,minL,maxL = cv2.minMaxLoc(hist)#为了把坐标归一化 必须要求出hist中 所有像素 所有数据 他们的 最大值 最小值 以及他们的下标  并且进行归一化处理
    histImg = np.zeros([256,256,3],np.uint8)
    for h in range(256):
        intenNormal = int(hist[h]*256/maxV)
        cv2.line(histImg,(h,256),(h,256-intenNormal),color)
    cv2.imshow(windowName,histImg)
    return histImg

img = cv2.imread('image0.jpg',1)
channels= cv2.split(img)#RGB - R G B 拆分通道
for i in range(0,3):
    ImageHist(channels[i],31+i)

#灰度图像直方图
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ImageHist(gray,30)
cv2.waitKey(0)

02.直方图均衡化

'''
# 灰度 直方图均衡化
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('src',gray)
dst = cv2.equalizeHist(gray)
cv2.imshow('dst',dst)
cv2.waitKey(0)
'''

'''
# 彩色 直方图均衡化
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
cv2.imshow('src',img)
(b,g,r) = cv2.split(img)#RGB - R G B 通道的分解
bH = cv2.equalizeHist(b)
gH = cv2.equalizeHist(g)
rH = cv2.equalizeHist(r)
result = cv2.merge((bH,gH,rH))#通道的合成
cv2.imshow('dst',result)
cv2.waitKey(0)
'''

# YUV 直方图均衡化
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
# imgYUV = cv2.cvtColor(img,cv2.COLOR_BGR2YUV)
imgYUV = cv2.cvtColor(img,cv2.COLOR_BGR2YCrCb)
cv2.imshow('src',img)
channelYUV = cv2.split(imgYUV)
channelYUV[0] = cv2.equalizeHist(channelYUV[0])
channels = cv2.merge(channelYUV)
result = cv2.cvtColor(channels,cv2.COLOR_YCrCb2BGR)
cv2.imshow('dst',result)
cv2.waitKey(0)

03 图片的修补

'''
# 生成污染的图片
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
for i in range(200,300):
    img[i,200] = (255,255,255)
    img[i+1,200] = (255,255,255)
    img[i-1,200] = (255,255,255)
for j in range(150,250):
    img[250,j] = (255,255,255)
    img[250,j+1] = (255,255,255)    
    img[250,j-1] = (255,255,255)    
cv2.imwrite('damaged.jpg',img)
cv2.imshow("image",img)
cv2.waitKey(0)
'''

# 图片修补
import cv2
import numpy as np
img = cv2.imread('damaged.jpg',1)
cv2.imshow('src',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
paint = np.zeros((height,width,1),np.uint8)
for i in range(200,300):
    paint[i,200] = 255
    paint[i+1,200] = 255
    paint[i-1,200] = 255
for j in range(150,250):
    paint[250,j] = 255
    paint[250,j+1] = 255  
    paint[250,j-1] = 255   
cv2.imshow('paint',paint)
# cv2.rectangle(paint,(150,200),(250,300),(255,255,255),-1)
# cv2.imshow('paint',paint)

# 1:src 2:mask蒙版（黑白图，白色表示坏了的区域）3:修补方法
imgDst = cv2.inpaint(img,paint,3,cv2.INPAINT_TELEA)
cv2.imshow('dst',imgDst)
cv2.waitKey(0)

12.7 04 灰度直方图源码

# 本质:统计每个像素灰度  出现的概率
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)
count = np.zeros(256,np.float)
for i in range(0,height):
    for j in range(0,width):
        pixel = gray[i,j]  #像素
        index = int(pixel)
        count[index] = count[index]+1  #统计个数
for i in range(0,255):
    count[i] = count[i]/(height*width)
x= np.linspace(0,255,256)#创建一个一维数组
y= count
# 绘制柱状图 1:x轴坐标  2:y轴坐标   3:间宽  4:透明度  5:颜色
plt.bar(x,y,0.9,alpha=1,color='b')
plt.show()
cv2.waitKey(0)

05彩色直方图源码

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]

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)        
        #统计每一个颜色值出现的次数
        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(0,256):#遍历每一个灰度等级
    #对每一个灰度等级出现的次数进行归一化处理
    #（宽度*高度）整个图片所有的像素之和  也就是总共出现的个数
    count_b[i] = count_b[i]/(height*width)
    count_g[i] = count_g[i]/(height*width)
    count_r[i] = count_r[i]/(height*width)
x = np.linspace(0,255,256) #创建一个一维数组

y_b = count_b    
plt.figure
plt.bar(x,y_b,0.9,alpha=1,color='b')

y_g = count_g
plt.figure
plt.bar(x,y_g,0.9,alpha=1,color='g')

y_r = count_r    
plt.figure
plt.bar(x,y_r,0.9,alpha=1,color='r')

plt.show()
cv2.waitKey(0)

06 灰度直方图均衡化1

# 本质:统计每个像素灰度  出现频率 0-255 p
# 累计概率
#比如：  概率         累计概率   
# 1      0.2      0.2
# 2      0.3      0.2+0.3=0.5
# 3      0.1      0.5+0.1=0.6
# 总共256个灰度等级    每一个灰度等级都会有一个概率  同时也都会有一个累计概率
# 比如：100累计概率为0.5  ，255*0.5=new  ,把100与new做一个映射  以后每一个100这样的像素都用new来替代 ，替代完成后这就叫做直方图的均衡化
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]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow("gray",gray)
count = np.zeros(256,np.float)
for i in range(0,height):
    for j in range(0,width):
        pixel = gray[i,j]  #像素
        index = int(pixel)
        count[index] = count[index]+1  #统计个数
for i in range(0,255):
    count[i] = count[i]/(height*width)   #占用的百分比   也就是概率
# 计算累积概率
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)

07彩色图像直方图均衡化

# 本质:统计每个像素灰度  出现频率 0-255 p
# 累计概率
#比如：  概率         累计概率   
# 1      0.2      0.2
# 2      0.3      0.2+0.3=0.5
# 3      0.1      0.5+0.1=0.6
# 总共256个灰度等级    每一个灰度等级都会有一个概率  同时也都会有一个累计概率
# 比如：100累计概率为0.5  ，255*0.5=new  ,把100与new做一个映射  以后每一个100这样的像素都用new来替代 ，替代完成后这就叫做直方图的均衡化
# 
# 使用这种方式颜色会发生变化
# 若不想改变图片颜色  请使用YUV的方式 （只对Y通道进行分解）
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]  #像素
        index_b = int(b)
        index_g = int(g)
        index_r = int(r)
        count_b[index_b] = count_b[index_b]+1  #统计个数
        count_g[index_g] = count_g[index_g]+1  #统计个数
        count_r[index_r] = count_r[index_r]+1  #统计个数
for i in range(0,255):
    count_b[i] = count_b[i]/(height*width)   #占用的百分比   也就是概率
    count_g[i] = count_g[i]/(height*width)   #占用的百分比   也就是概率
    count_r[i] = count_r[i]/(height*width)   #占用的百分比   也就是概率

# 计算累积概率
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)

08亮度增强

# p = p+40  把每个像素的b，g，r通道分别加上一个固定的值
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
cv2.imshow("src",img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
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]
        bb = int(b)+40
        gg = int(g)+40
        rr = int(r)+40
        
        if bb>255:
            bb=255
        if gg>255:
            gg = 255
        if rr>255:
            rr = 255
        dst[i,j] = (bb,gg,rr)
cv2.imshow('dst',dst)
cv2.waitKey(0)

09磨皮美白

'''
# 只调亮两个通道  实现 提亮偏色
# p = p*1.2+40  把每个像素的g，b通道分别  乘以一个比例  再加上一个固定的值
import cv2
import numpy as np
img = cv2.imread('image0.jpg',1)
cv2.imshow("src",img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
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]
        bb = int(b*1.3)+10
        gg = int(g*1.2)+15
        if bb>255:
            bb=255
        if gg>255:
            gg = 255
        dst[i,j] = (bb,gg,rr)
cv2.imshow('dst',dst)
cv2.waitKey(0)
'''

# 磨皮美白
# 双边滤波
import cv2
img = cv2.imread('1.png',1)
cv2.imshow("src",img)
# 双边滤波器
# 高斯核 与 r的距离核 加卷   可以算出一个共同的核p 
# 用p 乘以 像素（与像素进行卷积） 就可以得到一个新的  像素new  就是经过双边滤波之后的
dst = cv2.bilateralFilter(img,15,35,35)
cv2.imshow('dst',dst)
cv2.waitKey(0)

10高斯均值滤波

'''
#高斯滤波
import cv2
import numpy as np
img = cv2.imread('image11.jpg',1)
cv2.imshow('src',img)
dst = cv2.GaussianBlur(img,(5,5),1.5) #高斯滤波
cv2.imshow('dst',dst)
cv2.waitKey(0)
'''

# 均值滤波 6*6  1。 * [6*6]/36 =mean -> p
# 首先定义一个模板  比如为 6x6  数据全为1  ，
# 然后用  这个全为1的数据 乘以 6*6矩阵中所有的图像数组
# 然后 除以  36  就得到一个均值  
# 把这个均值替换到中心的一个元素    就实现了  中值滤波
import cv2
import numpy as np
img = cv2.imread('image11.jpg',1)
cv2.imshow('src',img)
imgInfo  = img.shape
height  = imgInfo[0]
width = imgInfo[1]
dst = np.zeros((height,width,3),np.uint8)
for i in range(3,height-3):
    for j in range(3,width-3):
        sum_b = int(0)
        sum_g = int(0)
        sum_r = int(0)
        for m in range(-3,3):
            for n in range(-3,3):
                (b,g,r)=img[i+m,j+n]
                sum_b = sum_b+int(b)
                sum_g = sum_g+int(g)
                sum_r = sum_r+int(r)
        
        b =np.uint8(sum_b/36)
        g =np.uint8(sum_g/36)
        r =np.uint8(sum_r/36)
        dst[i,j] = (b,g,r)
cv2.imshow('dst',dst)
cv2.waitKey(0)

11 中值滤波

# 比如定义3*3的模板 总共是9个像素  然后对这些像素进行排序  取中间的值  替换掉原来的像素值
import cv2
import numpy as np
img = cv2.imread('image11.jpg',1)
imgInfo  = img.shape
height  = imgInfo[0]
width = imgInfo[1]
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('src',img)
dst = np.zeros((height,width,3),np.uint8)
collect = np.zeros(9,np.uint8)
for i in range(1,height-1):
    for j in range(1,width-1):
        k = 0
        for m in range(-1,2):
            for n in range(-1,2):
                gray = img[i+m,j+n]
                collect[k] = gray
                k = k+1
        # 0 1 2 3 4 5 6 7 8
        #   1
        for k in range(0,9):
            p1 = collect[k]
            for t in range(k+1,9):
                if p1<collect[t]:
                    mid = collect[t]
                    collect[t] = p1
                    p1 = mid
        dst[i,j]=collect[4]
cv2.imshow('dst',dst)
cv2.waitKey(0)