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1,源代码：

import cv2 as cvimport numpy as npdef access_pixels(image):    #定义访问图像像素值的函数    print(image.shape);    height = image.shape[0]       width = image.shape[1]    channels = image.shape[2]    print("width : %s, height : %s channels : %s"%(width, height, channels))    for row in range(height):        for col in range(width):            for c in range(channels):                pv = image[row, col, c]        #访问图像像素点的数值                image[row, col, c] = 255 - pv  #修改图像像素点的数值    cv.imshow("pixels_demo", image)def inverse(image):    dst = cv.bitwise_not(image) #对图像（灰度图像或彩色图像均可）每个像素值进行二进制“非”操作    cv.imshow("inverse demo", dst)def create_image(): #创建新的图像    img1 = np.zeros([400, 400, 3], np.uint8)     #定义多通道图像大小并初始化为0    #img[: , : , 0] = np.ones([400, 400])*255   #给单通道赋值    img1[:, :, 2] = np.ones([400, 400]) * 255    cv.imshow("new image1", img1)     img2 = np.ones([400, 400, 1], np.uint8)      #定义单通道图像大小并初始化为1    img 2= img2 * 255    cv.imshow("new image", img)    cv.imwrite("F:/images/myImage.png", img2)      m1 = np.ones([3, 3], np.int32) #选择合适的类型，避免截断    m1.fill(12222.388)            print(m1)    m2 = m1.reshape([1, 9])  #维度转换    print(m2)    m3 = np.array([[2,3,4], [4,5,6],[7,8,9]], np.int32)    #m3.fill(9)    print(m3)print("--------- Hello Python ---------")src = cv.imread("F:/images/lena.png") # blue, green redcv.namedWindow("input image", cv.WINDOW_AUTOSIZE)cv.imshow("input image", src)t1 = cv.getTickCount()  #获取CPU时钟周期个数#access_pixels(src)create_image()inverse(src)t2 = cv.getTickCount()  time = (t2-t1)/cv.getTickFrequency(); #获取时间段print("time : %s ms"%(time*1000))cv.waitKey(0)cv.destroyAllWindows()

2，运行结果：

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