像素运算

import cv2 as cv
import numpy as np

# 数值运算：加减乘除
def shu_image(srcA,srcB):
    src = cv.add(srcA,srcB) # 加
    cv.imshow('add',src)
    src = cv.subtract(srcA,srcB) # 减
    cv.imshow('subtract',src)
    src = cv.multiply(srcA,srcB) # 乘
    cv.imshow('multiply',src)
    src = cv.divide(srcA,srcB) # 除
    cv.imshow('divide',src)


# 其他运算 求均值、方差

def others(m1,m2):
    M1,dev1 = cv.meanStdDev(m1) # M1是均值，dev1是方差
    M2,dev2 = cv.meanStdDev(m2)
    h,w = m1.shape[:2]

    print(M1)
    print(M2)
    print('----上面是均值；下面是方差')
    print(dev1)
    print(dev2)

# 逻辑运算：与或非的操作
def luo_image(srcA,srcB):
    src = cv.bitwise_and(srcA,srcB) # 与 两张图片同一位置的色素两个值均不为零的才会有输出
    cv.imshow('and',src)
    src = cv.bitwise_or(srcA,srcB) # 或 两张图片同一位置的色素两个值不全为零的才会有输出
    cv.imshow('or',src)

# 粗略的调节对比度和亮度 a是对比度 g是亮度
def contrast_brightness_image(src1,a,g):
    h,w,ch = src1.shape #  获取shape的数值，height和width、通道

    # 新建全零图片数组src2,将height和width，类型设置为原图片的通道类型(色素全为零，输出为全黑图片)
    src2 = np.zeros([h,w,ch],src1.dtype)
    dst = cv.addWeighted(src1,a,src2,1-a,g) # addWeighted函数说明如下
    cv.imshow("con-bri-demo", dst)


if __name__ == '__mian__':
    """
    数值运算shu_image()部分
    """
    src1 = cv.imread('pic/bizhi.jpg')
    cv.imshow('original1',src1)
    # cv.waitKey(0)
    # cv.destroyAllWindows()


    """
    其他运算others()部分
    """
    src2 = cv.imread('pic/bizhi.jpg')
    src3 = cv.imread('pic/bizhi.jpg')
    others(src2,src3)


    '''
    逻辑运算luo_image()部分
    '''
    luo_image(src2,src3)


    '''
    调节亮度和对比度contrast_brightness_image()
    '''
    contrast_brightness_image(src1,1,10)
    cv.waitKey()
    cv.destroyAllWindows()