Open CV系列学习笔记(四)像素运算
1.算术运算
注意:两幅图像的像素大小要一致
进行图像像素之间的算术运算,首先要导入图像,读取其中信息:
像素的相加
def add_demo(m1,m2):#像素相加
dst = cv.add(m1,m2)
cv.imshow("add_demo",dst)
输出结果:
像素相减
def subtract_demo(m1, m2):#像素相减
dst = cv.subtract(m1, m2)
cv.imshow("subtract_demo", dst)
运行结果:
像素相乘
def multiply_demo(m1, m2):#像素相乘
dst = cv.multiply(m1, m2)
cv.imshow("multiply_demo", dst)
运行结果:
像素相除
def divide_demo(m1, m2):#像素相除
dst = cv.divide(m1, m2)
cv.imshow("divide_demo", dst)
运行结果:
均值与方差
def others_demo(m1, m2):#像素的均值 与方差
M1,dev1 = cv.meanStdDev(m1)
M2,dev2 = cv.meanStdDev(m2)
h,w = m1.shape[:2]
print(M1)
print(M2)
print(dev1)
print(dev2)
img = np.zeros([h,w],np.uint8)
m,dev = cv.meanStdDev(img)
print(m)
print(dev)
运行结果:
----- Hellow Python -----
(300, 400, 3)
(300, 400, 3)
[[83.81364167]
[91.66739167]
[75.61489167]]
[[ 83.54694167]
[ 99.4413 ]
[107.59351667]]
[[57.0020891 ]
[51.46196197]
[50.04020184]]
[[50.8311838]
[56.0005978]
[57.1263416]]
[[0.]]
[[0.]]
2.逻辑运算
与运算
def logic_demo(m1,m2):
dst = cv.bitwise_and(m1,m2)
cv.imshow("logic_demo",dst)
运算结果:
或运算
def logic_demo(m1,m2):
dst = cv.bitwise_or(m1,m2)
cv.imshow("logic_demo",dst)
运行结果:
非运算
def logic_demo(m1,m2):
image = cv.imread(r"E:/picture/10.bmp")
dst = cv.bitwise_not(image)
cv.imshow("logic_demo",dst)
运行结果:
3.调节对比度和亮度
def contrast_brightness_dome(image,c,b):
h,w,ch = image.shape
blank = np.zeros([h,w,ch],image.dtype)
dst = cv.addWeighted(image,c,blank,1-c,b)
cv.imshow("con-bri-demo",dst)
运行结果:
所有完整代码:`
import cv2 as cv
import numpy as np
def add_demo(m1,m2):#像素相加
dst = cv.add(m1,m2)
cv.imshow("add_demo",dst)
def subtract_demo(m1, m2):#像素相减
dst = cv.subtract(m1, m2)
cv.imshow("subtract_demo", dst)
def multiply_demo(m1, m2):#像素相乘
dst = cv.multiply(m1, m2)
cv.imshow("multiply_demo", dst)
def divide_demo(m1, m2):#像素相除
dst = cv.divide(m1, m2)
cv.imshow("divide_demo", dst)
def logic_demo(m1,m2):
#dst = cv.bitwise_and(m1,m2)
#dst = cv.bitwise_or(m1,m2)
image = cv.imread(r"E:/picture/10.bmp")
#cv.imshow("image",image)
dst = cv.bitwise_not(image)
cv.imshow("logic_demo",dst)
def contrast_brightness_dome(image,c,b):
h,w,ch = image.shape
blank = np.zeros([h,w,ch],image.dtype)
dst = cv.addWeighted(image,c,blank,1-c,b)
cv.imshow("con-bri-demo",dst)
def others_demo(m1, m2):#像素的均值 与方差
M1,dev1 = cv.meanStdDev(m1)
M2,dev2 = cv.meanStdDev(m2)
h,w = m1.shape[:2]
print(M1)
print(M2)
print(dev1)
print(dev2)
img = np.zeros([h,w],np.uint8)
m,dev = cv.meanStdDev(img)
print(m)
print(dev)
print("----- Hellow Python -----")
src1 = cv.imread(r"E:\python\opencv study\101.jpg")
src2 = cv.imread(r"E:\python\opencv study\102.jpg")
print(src1.shape)
print(src2.shape)
cv.namedWindow("image1",cv.WINDOW_AUTOSIZE)
cv.imshow("image1",src1)
cv.imshow("image2",src2)
src = cv.imread(r"E:/picture/10.bmp")
cv.imshow("image",src)
contrast_brightness_dome(src,1.2,10)#调整亮度对比度
#add_demo(src1,src2)
#subtract_demo(src1,src2)
#multiply_demo(src1,src2)
#divide_demo(src1,src2)
#others_demo(src1,src2)
logic_demo(src1,src2)
cv.waitKey(0)
cv.destroyAllWindows()