OpenCV__Python图像的加减乘除运算_教程8

1、加减运算的例子

图像的加减乘除运算运算在很多地方用到，比如说，在一幅杂论的图中减去背景，比如说运动检测。比如说多个检测的合成。

#引入opencv模块
import cv2 as cv
#引入numpy模块
import numpy as np
#引入sys模块
import sys


#图像相加
def img_add(img1,img2):
    img_result = cv.add(img1,img2)
    return img_result


#图像相减
def img_sub(img1,img2):
    img_result = cv.subtract(img1,img2)
    return img_result
  

#图像相乘
def img_mul(img1,img2):
    img_result = cv.multiply(img1,img2)
    return img_result 


#图像相除
def img_div(img1,img2):
    img_result = cv.divide(img1,img2)
    return img_result 


#获取图像均值和方差
def img_mean_dev(img):
    mean , dev = cv.meanStdDev(img)
    print("mean: %s , dev: %s"%(mean,dev))



#取与运算
def img_logic_and(img1,img2):
    img_result = cv.bitwise_and(img1,img2)
    return img_result 

#取或运算
def img_logic_or(img1,img2):
    img_result = cv.bitwise_or(img1,img2)
    return img_result 

#取反操作，也就是反色
def img_logic_not(img):
    img_result = cv.bitwise_not(img)
    return img_result 


def img_test():
    img1 = cv.imread('E:/chenopencvblogimg/lena150W200H.jpg')
    img2 = cv.imread('E:/chenopencvblogimg/chen150W200H.bmp')     
    #判断是否读取成功
    if img1 is None or img2 is None:
        print("Could not read the image,may be path error")
        return
    #图像原图
    cv.namedWindow("origin img1",cv.WINDOW_NORMAL)
    cv.imshow("origin img1",img1)
    cv.namedWindow("origin img2",cv.WINDOW_NORMAL)
    cv.imshow("origin img2",img2)  
    #图像相加
    img = img_add(img1,img2)
    cv.namedWindow("img_add",cv.WINDOW_NORMAL)
    cv.imshow("img_add",img)   
    #图像相减
    img = img_sub(img1,img2)
    cv.namedWindow("img_sub",cv.WINDOW_NORMAL)
    cv.imshow("img_sub",img) 
    #图像相乘
    img = img_mul(img1,img2)
    cv.namedWindow("img_mul",cv.WINDOW_NORMAL)
    cv.imshow("img_mul",img) 
    #图像相除
    img = img_div(img1,img2)
    cv.namedWindow("img_div",cv.WINDOW_NORMAL)
    cv.imshow("img_div",img) 
    #取与运算
    img = img_logic_and(img1,img2)
    cv.namedWindow("img_logic_and",cv.WINDOW_NORMAL)
    cv.imshow("img_logic_and",img) 
    #取或运算
    img = img_logic_or(img1,img2)
    cv.namedWindow("img_logic_or",cv.WINDOW_NORMAL)
    cv.imshow("img_logic_or",img) 
    #取反操作，也就是反色
    img = img_logic_not(img1) 
    cv.namedWindow("img_logic_not1",cv.WINDOW_NORMAL)
    cv.imshow("img_logic_not1",img) 
    img = img_logic_not(img2) 
    cv.namedWindow("img_logic_not2",cv.WINDOW_NORMAL)
    cv.imshow("img_logic_not2",img) 

    #获取图像均值和方差
    img_mean_dev(img1)
    img_mean_dev(img2)   


    #让显示等待键盘输入维持在那里，否则程序跑完就闪退啦！
    cv.waitKey(0)
    #销毁窗口
    cv.destroyAllWindows()

if __name__ == '__main__':
    sys.exit(img_test() or 0)

2、基于位预算获取魔方蓝色的图

在第7个教程的基础上修改一句语句即可

用到了函数bitwise_and,定义如下

#引入opencv模块
import cv2 as cv
#引入numpy模块
import numpy as np
#引入sys模块
import sys


#颜色空间测试
def color_space_detect(img):
    #转换为hsv
    hsv = cv.cvtColor(img,cv.COLOR_BGR2HSV)
    #黄色检测
    yellow_lower_hsv = np.array([26,43,46])
    yellow_uper_hsv = np.array([34,255,255])

    yellowmask = cv.inRange(hsv,lowerb=yellow_lower_hsv,upperb=yellow_uper_hsv)
    cv.namedWindow("yellow detected",cv.WINDOW_NORMAL)
    cv.imshow("yellow detected",yellowmask)

    #蓝色检测
    blue_lower_hsv = np.array([100,43,46])
    blue_uper_hsv =  np.array([124,255,255])
    bluewmask = cv.inRange(hsv,lowerb=blue_lower_hsv,upperb=blue_uper_hsv)
    #教程8添加的语句
    showblueimg = cv.bitwise_and(img,img,mask=bluewmask)
    cv.namedWindow("blue detected",cv.WINDOW_NORMAL)
    cv.imshow("blue detected",bluewmask)
    #显示蓝色的区域
    cv.namedWindow("showblueimg",cv.WINDOW_NORMAL)
    cv.imshow("showblueimg",showblueimg)
  

#颜色空间测试
def color_space_test(img):
    #转换为gray
    gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    cv.namedWindow("gray",cv.WINDOW_NORMAL)   
    cv.imshow("gray",gray)
    #转换为hsv
    hsv = cv.cvtColor(img,cv.COLOR_BGR2HSV)
    cv.namedWindow("hsv",cv.WINDOW_NORMAL)   
    cv.imshow("hsv",hsv)
    #转换为yuv
    yuv = cv.cvtColor(img,cv.COLOR_BGR2YUV)
    cv.namedWindow("yuv",cv.WINDOW_NORMAL)
    cv.imshow("yuv",yuv)
    #转换为ycrcb
    ycrcb = cv.cvtColor(img,cv.COLOR_BGR2YCrCb)
    cv.namedWindow("ycrcb",cv.WINDOW_NORMAL)
    cv.imshow("ycrcb",ycrcb)


def img_test():
    img = cv.imread('E:/chenopencvblogimg/mofan.jpg')
    #判断是否读取成功
    if img is None:
        print("Could not read the image,may be path error")
        return
    cv.namedWindow("origin Pic",cv.WINDOW_NORMAL)
    cv.imshow("origin Pic",img)
    #检测颜色
    color_space_detect(img)
    #让显示等待键盘输入维持在那里，否则程序跑完就闪退啦！
    cv.waitKey(0)
    #销毁窗口
    cv.destroyAllWindows()

if __name__ == '__main__':
    sys.exit(img_test() or 0)

3、大家还可尝试通过图像加法运算获取魔方的红色的区间