简单图像处理（opencv-python入门）

本文主要内容：
负片、调节图片亮度与对比度、图像裁剪、图像尺寸变换、图像旋转、图像添加噪声、图像模糊与滤波

所有代码已经在Spyder上编译通过的，python版本是3.7，希望能有帮助

参考书目：《Python人脸识别——从入门到工程实践》王天庆著

1.负片（相机底片）

import cv2
import numpy as np

img=cv2.imread("1.png")
cv2.imshow("lala",img)
cv2.waitKey(1000)
print(img)
height=img.shape[0]
width=img.shape[1]

negative_file=np.zeros((height,width,3))
b,g,r=cv2.split(img)
r=255-r
b=255-b
g=255-g

negative_file[:,:,0]=b
negative_file[:,:,1]=g
negative_file[:,:,2]=r

cv2.imwrite("negative.jpg",negative_file)
img2=cv2.imread("negative.jpg")
cv2.imshow("fupian",img2)
cv2.waitKey(2000)
cv2.destroyAllWindows();

2.调节图片亮度与对比度

import cv2
import numpy as np

def convert_img1(img,alpha,beta):
    blank=np.zeros(img.shape,img.dtype)
    return cv2.addWeighted(img,alpha,blank,0,beta)

def convert_img2(img,alpha,beta):
    rows,cols,channel=img.shape
    new_img=np.zeros(img.shape,img.dtype)
    for i in range(0,rows):
        for j in range(0,cols):
            for k in range(0,channel):
                new_img[i,j,k]=np.clip(alpha *img[i,j,k]+beta,0,255)
    return new_img

img=cv2.imread('1.png')
cv2.imshow('old',img)
cv2.waitKey(1000)
cv2.imwrite('convert3.jpg',convert_img1(img,1.5,50))
img1=cv2.imread('convert3.jpg')
cv2.imshow('convert',img1)
cv2.waitKey(2000)
cv2.destroyAllWindows()

3.图像裁剪

import cv2
#import numpy as np
image=cv2.imread('1.png')
cv2.imshow('lena',image)
cv2.waitKey(1000)
cv2.imshow('lena2',image[50:400,50:400])
cv2.waitKey(5000)
cv2.destroyAllWindows()

4.图像尺寸变换（3种）

import cv2
#import numpy as np

image=cv2.imread('1.png')
cv2.imshow('lena',image)
cv2.waitKey(1000)

new_img1=cv2.resize(image,(200,200),interpolation=cv2.INTER_AREA)
cv2.imshow('lena1',new_img1)
cv2.waitKey(1000)

new_img2=cv2.resize(image,(200,200),interpolation=cv2.INTER_CUBIC)
cv2.imshow('lena2',new_img2)
cv2.waitKey(1000)

new_img3=cv2.resize(image,(200,200),interpolation=cv2.INTER_LINEAR)
cv2.imshow('lena3',new_img3)
cv2.waitKey(10000)
cv2.destroyAllWindows()

5.图像旋转

import cv2
#import numpy as np

image=cv2.imread('1.png')
cv2.imshow('lena',image)
cv2.waitKey(1000)

rotate_img=cv2.getRotationMatrix2D((image.shape[1]/2,image.shape[0]/2),45,0.5)
rmg = cv2.warpAffine(image, rotate_img, (image.shape[0], image.shape[1]))
cv2.imwrite('rotate_img.jpg',rmg)
image2=cv2.imread('rotate_img.jpg')
cv2.imshow('lena1',image2)
cv2.waitKey(5000)

6.图像添加噪声（椒盐噪声、高斯噪声）

import cv2
import numpy as np
import random

def salt_and_pepper_noise(img,percentage):
    rows,cols=img.shape
    num=int(percentage*rows*cols)
    for i in range(num):
        x=random.randint(0,rows-1)
        y=random.randint(0,cols-1)
        if random.randint(0,1)==1:
            img[x,y]=0
        else:
            img[x,y]=255
    return img

def gaussian_noise(img,mu,sigma,k):
    rows,cols=img.shape
    for i in range(rows):
        for j in range(cols):
            value=int(img[i,j]+k *random.gauss(mu=mu,sigma=sigma))
            value=np.clip(a_max=255,a_min=0,a=value)
            img[i,j]=value
    return img

image=cv2.imread('1.png')
gray_img=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
cv2.imshow('gray_lena',gray_img)
cv2.waitKey(1000)
gray_img2=gray_img.copy()

gaus_img=gaussian_noise(gray_img,0,1,32)
cv2.imshow('gaus_lena',gaus_img)
cv2.waitKey(1000)

salt_img=salt_and_pepper_noise(gray_img2,0.3)
cv2.imshow('salt_lena',salt_img)
cv2.waitKey(1000)

7.图像模糊与滤波

import cv2
import numpy as np
import random

def salt_and_pepper_noise(img,percentage):
    rows,cols=img.shape
    num=int(percentage*rows*cols)
    for i in range(num):
        x=random.randint(0,rows-1)
        y=random.randint(0,cols-1)
        if random.randint(0,1)==1:
            img[x,y]=0
        else:
            img[x,y]=255
    return img

def gaussian_noise(img,mu,sigma,k):
    rows,cols=img.shape
    for i in range(rows):
        for j in range(cols):
            value=int(img[i,j]+k *random.gauss(mu=mu,sigma=sigma))
            value=np.clip(a_max=255,a_min=0,a=value)
            img[i,j]=value
    return img

image=cv2.imread('1.png')
gray_img=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
cv2.imshow('gray_lena',gray_img)
cv2.waitKey(1000)
gray_img2=gray_img.copy()

gaus_img=gaussian_noise(gray_img,0,1,32)
cv2.imshow('gaus_lena',gaus_img)
cv2.waitKey(1000)

salt_img=salt_and_pepper_noise(gray_img2,0.3)
cv2.imshow('salt_lena',salt_img)
cv2.waitKey(1000)

kernel=np.ones((5,5),np.float32)/25
conv_2d_img=cv2.filter2D(salt_img,-1,kernel)
cv2.imshow('conv_2d',conv_2d_img)
cv2.waitKey(1000)

median_img=cv2.medianBlur(salt_img,5)
cv2.imshow('median_blur',median_img)
cv2.waitKey(1000)

gaussian_img=cv2.GaussianBlur(gaus_img,(5,5),0)
cv2.imshow('guass_blur',gaussian_img)
cv2.waitKey(1000)

bilateral_img=cv2.bilateralFilter(gaus_img,9,75,75)
cv2.imshow('bilateral_filter',bilateral_img)
cv2.waitKey(8000)

cv2.destroyAllWindows()