TensorFlow笔记day4（图片美化效果）

美化效果

（1）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'
    hist=cv2.calcHist([image],[0],None,[256],[0.0,255.0])
    #计算图片直方图 1 图片，2 通道 ，3 mask蒙版 4 size，5 0-255
    minV,maxV,minL,maxL=cv2.minMaxLoc(hist)
    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(r"C:\Users\Administrator\Desktop\222\233.jpg",1)
channels =cv2.split(img)#完成RGB-R G B
print(channels)
for i in range(0,3):
    ImageHist(channels[i],31+i)
cv2.waitKey(0)

[array([[ 96,  96,  97, ..., 124, 129, 131],
       [ 99,  98,  98, ..., 124, 128, 130],
       [101, 101, 100, ..., 125, 126, 127],
       ...,
       [ 69,  83,  57, ...,  28,  16,  13],
       [ 81,  83, 114, ...,  53,  54,  53],
       [ 83,  91, 129, ...,  55,  62,  67]], dtype=uint8), array([[181, 181, 180, ..., 163, 165, 167],
       [182, 181, 181, ..., 163, 164, 166],
       [182, 182, 181, ..., 164, 165, 166],
       ...,
       [ 90, 106,  81, ...,  44,  32,  29],
       [104, 106, 140, ...,  69,  70,  69],
       [106, 116, 155, ...,  73,  80,  83]], dtype=uint8), array([[196, 196, 195, ..., 178, 181, 183],
       [197, 196, 196, ..., 178, 180, 182],
       [197, 197, 196, ..., 179, 180, 181],
       ...,
       [105, 122,  99, ...,  57,  44,  41],
       [119, 122, 157, ...,  85,  86,  85],
       [122, 132, 172, ...,  90,  97, 100]], dtype=uint8)]

（2）直方图均衡化

#灰度图直方图均衡化
import cv2
import numpy as np
img =cv2.imread(r"C:\Users\Administrator\Desktop\222\233.jpg",1)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst=cv2.equalizeHist(gray)
cv2.imshow('gray',gray)
cv2.imshow('dst',dst)
cv2.waitKey(0)

#彩色图直方图均衡化
import cv2
import numpy as np
img =cv2.imread(r"C:\Users\Administrator\Desktop\222\233.jpg",1)
(b,g,r) =cv2.split(img)#完成RGB-R G B
bH=cv2.equalizeHist(b)
gH=cv2.equalizeHist(g)
rH=cv2.equalizeHist(r)
result = cv2.merge((bH,gH,rH))#通道合成
cv2.imshow('img',img)
cv2.imshow('result',result)
cv2.waitKey(0)

#YUV 直方图均衡化
import cv2
import numpy as np
img =cv2.imread(r"C:\Users\Administrator\Desktop\222\233.jpg",1)
imgYUV = cv2.cvtColor(img,cv2.COLOR_BGR2YCrCb)
channelYUV = cv2.split(imgYUV)#完成RGB-R G B
channelYUV[0] = cv2.equalizeHist(channelYUV[0])
channels = cv2.merge(channelYUV)#通道合成
result=cv2.cvtColor(channels,cv2.COLOR_YCrCb2BGR)
cv2.imshow('img',img)
cv2.imshow('result',result)
cv2.waitKey(0)

（3）图片修补

import cv2  
import numpy as np 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\233.jpg",1) 
for i in range(200,300): 
    img[i,200] = (255,255,255) 
    img[i,200+1] = (255,255,255) 
    img[i,200-1] = (255,255,255) 
for i in range(150,250): 
    img[250,i] = (255,255,255) 
    img[250+1,i] = (255,255,255) 
    img[250-1,i] = (255,255,255) 
cv2.imwrite(r"C:\Users\Administrator\Desktop\222\233_damaged.jpg",img) 
cv2.imshow('image',img) 
cv2.waitKey(0)

#1 坏图 2 array 3 inpaint 
import cv2  
import numpy as np 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\233_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,200+1] = 255 
    paint[i,200-1] = 255 
for i in range(150,250): 
    paint[250,i] = 255 
    paint[250+1,i] = 255 
    paint[250-1,i] = 255 
cv2.imshow('paint',paint) 
#1 src 2 mask 
imgDst = cv2.inpaint(img,paint,3,cv2.INPAINT_TELEA) 
  
cv2.imshow('image',imgDst) 
cv2.waitKey(0)

（4）灰度直方图源码

# 1 0-255 2 概率  
 # 本质：统计每个像素灰度 出现的概率 0-255 p 
%matplotlib inline
import cv2 
import numpy as np 
import matplotlib.pyplot as plt 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\233_mini.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] 
        index = int(pixel) 
        count[index] = count[index]+1 
for i in range(0,255): 
    count[i] = count[i]/(height*width) 
x = np.linspace(0,255,256) 
y = count 
plt.bar(x,y,0.9,alpha=1,color='b') 
plt.show() 
cv2.waitKey(0)

（5）彩色直方图源码

import cv2 
import numpy as np 
import matplotlib.pyplot as plt 
%matplotlib inline
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\233_mini.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) 
        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):  
    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) 
y1 = count_b 
plt.figure() 
plt.bar(x,y1,0.9,alpha=1,color='b') 
y2 = count_g 
plt.figure() 
plt.bar(x,y2,0.9,alpha=1,color='g') 
y3 = count_r 
plt.figure() 
plt.bar(x,y3,0.9,alpha=1,color='r') 
plt.show() 
cv2.waitKey(0)

（6）灰度图片直方均衡化

# 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  
# 100 0.5 255*0.5 = new  
import cv2 
import numpy as np 
import matplotlib.pyplot as plt 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\233.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)

（7）彩色图片直方均衡化

# 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  
# 100 0.5 255*0.5 = new  
# 1 统计每个颜色出现的概率 2 累计概率 1 3 0-255 255*p 
# 4 pixel  
import cv2 
import numpy as np 
import matplotlib.pyplot as plt 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\233.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)

（8）图片增加亮度

# p = p+40 
import cv2 
import numpy as np 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\233.jpg",1) 
imgInfo = img.shape 
height = imgInfo[0] 
width = imgInfo[1] 
# cv2.imshow('src',img) 
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)

（9）磨皮美白

#双边滤波 
import cv2 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\444.jpg",1) 
cv2.imshow('src',img) 
dst = cv2.bilateralFilter(img,15,35,35) 
cv2.imshow('dst',dst) 
cv2.waitKey(0)

（10）高斯均值滤波

import cv2 
import numpy as np 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\444.jpg",1) 
cv2.imshow('src',img) 
dst = cv2.GaussianBlur(img,(5,5),1.5) 
cv2.imshow('dst',dst) 
cv2.waitKey(0)

#均值 6*6 1 。 * 【6*6】/36 = mean -》P 
import cv2 
import numpy as np 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\444.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):#-3 -2 -1 0 1 2 
            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

# 中值滤波 3*3  
import cv2 
import numpy as np 
img = cv2.imread(r"C:\Users\Administrator\Desktop\222\233.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)