Opencv基础：图像美化_opencv美化图片

#彩色图片直方图
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'
    #计算图片直方图 para1.图片数据 2.计算直方图通道3，mask模板4，0-255表明水平轴统计多少点
    #5.0-255.0
    hist =cv2.calcHist([image],[0],None,[256],[0.0,255.0])
    minV,maxV,minL,maxL = cv2.minMaxLoc(hist) #直接获取最大最小值坐标和值
    histImg = np.zeros([256,256,3],np.uint8)
    for h in range(256):
        intenNormal = int(hist[h]*256/maxV) #值比较大，归一化到0-256之间
        cv2.line(histImg,(h,256),(h,256-intenNormal),color)#图片是反向绘制的
    cv2.imshow(windowName,histImg)
    return histImg
img = cv2.imread('image0.jpg',1)
channels = cv2.split(img) #BGR->B G R
for i in range(0,3):
    ImageHist(channels[i],31+i)
cv2.waitKey(0)

import cv2
import numpy as np
###灰度图 直方图均衡化
img = cv2.imread('image0.jpg',1)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('gray1',gray)
dst = cv2.equalizeHist(gray)
cv2.imshow('grayreslult',dst)
###彩色图 直方图均衡化
img = cv2.imread('image0.jpg',1)
(b,g,r) = cv2.split(img)# 通道分解
bH = cv2.equalizeHist(b)
gH = cv2.equalizeHist(g)
rH = cv2.equalizeHist(r)
result =cv2.merge((bH,gH,rH)) #通道合成
cv2.imshow('color',result)
#YUV通道均衡化
img = cv2.imread('image0.jpg',1)
imgYUV = cv2.cvtColor(img,cv2.COLOR_BGR2YCrCb)
channelYUV = cv2.split(imgYUV)
channelYUV[0] =cv2.equalizeHist(channelYUV[0])
channels =cv2.merge(channelYUV)
result = cv2.cvtColor(channels,cv2.COLOR_YCrCb2BGR)
cv2.imshow('YUVresult',result)
cv2.waitKey(0)

import cv2
import numpy as np
####图片修补###
#1.损坏的图片 2.找到一个数组描述损坏图片的损坏部分
#3.inpoint
img = cv2.imread('image0.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('damaged.jpg',img)
img = cv2.imread('damaged.jpg',1)
cv2.imshow('image',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)
##para1.损坏的数组2.mask模板
imgDst = cv2.inpaint(img,paint,3,cv2.INPAINT_TELEA)
cv2.imshow('imgDst',imgDst)
cv2.waitKey(0)

import cv2
import numpy as np
import matplotlib.pyplot as plt
###灰度图直方图源码##
##中心思想：统计图像中每个像素出现的概率
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]
        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)

import cv2
import numpy as np
import matplotlib.pyplot as plt
##灰度直方图均值化源码实现
#累计概率
#1 原始概率为0.2，如果为累计概率则为 0.2
#2 0.3  0.5
#3 0.1  0.6
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)
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
#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('result',gray)
cv2.waitKey(0)

import cv2
import numpy as np
import matplotlib.pyplot as plt
##彩色图直方图均值化源码实现
#操作方法与灰度图相同，将bgr分别处理
#累计概率
#1 原始概率为0.2，如果为累计概率则为 0.2
#2 0.3  0.5
#3 0.1  0.6
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
#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)