(src1, alpha, src2, beta, gamma)
图像混合,alpha, beta为这两张图片的权重
gamma, 图1与图2作和后添加的数值. 不要太大, 不然图片一片白.
dst = cv.addWeighted(image, c, blank, 1-c, b)
对像素值进行逻辑运算
cv.bitwise_xx()
运用上述的与运算特性可以对之前章节的特定颜色进行提取
import cv2 as cv
import numpy as np
# 对图像像素进行加减乘除
def add_demo(m1, m2):
dst = cv.add(m1, m2)
cv.imshow("add_demo", dst)
def subtract_demo(m1, m2):
dst = cv.subtract(m1, m2)
cv.imshow("subtract_demo", dst)
def divide_demo(m1, m2):
dst = cv.divide(m1, m2)
cv.imshow("divide_demo", dst)
def multiply_demo(m1, m2):
dst = cv.multiply(m1, m2)
cv.imshow("multiply_demo", dst)
# 图像的逻辑运算: and, or, not, xor
def logic_demo(m1, m2):
dst1 = cv.bitwise_and(m1, m2)
dst2 = cv.bitwise_or(m1, m2)
dst3 = cv.bitwise_not(m1)
dst4 = cv.bitwise_xor(m1, m2)
cv.imshow("dst1", dst1)
cv.imshow("dst2", dst2)
cv.imshow("dst3", dst3)
cv.imshow("dst4", dst4)
# 函数是将两张相同大小,相同类型的图片融合
def contrast_brightness_demo(image, c, b):
h, w, ch = image.shape
blank = np.zeros([h, w, ch], image.dtype)
# (src1, alpha, src2, beta, gamma)
# 图像混合,alpha, beta为这两张图片的权重
# gamma, 图1与图2作和后添加的数值. 不要太大, 不然图片一片白.
dst = cv.addWeighted(image, c, blank, 1-c, b)
cv.imshow("contrast_bright", dst)
def others(m1, m2):
M1 = cv.mean(m1)
M2 = cv.mean(m2)
print(M1)
print(M2)
mean1, dev1 = cv.meanStdDev(m1)
print("均值:", mean1, "方差:", dev1)
if __name__ == "__main__":
src1 = cv.imread("./images/01.jpg")
src2 = cv.imread("./images/02.jpg")
# cv.namedWindow("image", cv.WINDOW_AUTOSIZE)
# cv.imshow("image1", src1)
# cv.imshow("image2", src2)
# add_demo(src1, src2)
# subtract_demo(src1, src2)
# multiply_demo(src1, src2)
# divide_demo(src2, src1)
# logic_demo(src1, src2)
src = cv.imread("./images/CrystalLiu2.jpg")
cv.imshow("src", src)
contrast_brightness_demo(src, 0.7, 50)
cv.waitKey(0)
cv.destroyAllWindows()
add_demo
subtract_demo
multiply_demo
divide_demo
logic_demo
contrast_brightness_demo