OpenCV基础 | 3.numpy在图像处理中的基本使用

# 获取图片高宽通道及图像反转
def access_pixels(image):
    print(image.shape)
    height=image.shape[0]    #高
    width=image.shape[1]     #宽
    channels=image.shape[2]  #通道数
    print("width: %s, height: %s, channels: %s"%(width, height, channels))
    
    #自己写的图像反转
    for row in range(height):
        for col in range(width):
            for c in range(channels):
                pv = image[row, col, c]
                image[row, col, c] = 256 - pv  # 图像反转
    cv.imshow('pixels_demo', image)

(512, 512, 3)
width: 512, height: 512, channels: 3

    t1=cv.getTickCount()   #获取cpu时钟滴答数
    access_pixels(src)
    t2=cv.getTickCount()
    time = (t2 - t1) / cv.getTickFrequency()  # 函数执行前后滴答数之差与滴答频率之比为前后时间差
    print("time: %s ms" % (time * 1000))

time: 2870.7665066666664 ms

#调用opencv的API实现图像反转
def inverse(image):
    dst = cv.bitwise_not(image)  # 按位取反，白变黑，黑变白
    cv.imshow("inverse_demo", dst)

time: 100.06570666666667 ms

def create_image():
    #单通道
    img1=np.ones([400,400,1],np.uint8)
    img1=img1*127
    cv.imshow("singalchannels_image",img1)

    # 三通道，opencv是BGR，即0维为B，1维为G，2维为R
    img2=np.zeros([400,400,3],np.uint8)
    #将第二通道赋值为255，得到的图像为绿色
    img2[:,:,1]=np.ones([400,400])*255
    cv.imshow("threechannels_image",img2)

    m1=np.ones([3,3],np.uint8)  #尽量选择int类型和float类型
    m1.fill(12222.388)
    print(m1)

[[190 190 190]
 [190 190 190]
 [190 190 190]]