OpenCV---The use of numpy arrays and create pictures

One: access to all pixels of the avatar and the UI image for Pixel inversion (a) for loop inversion

Import Cv2 asCvimport NumPy asnpdef access_pixels (image): #对图像的所有像素进行访问Print (image.size) Height,width,channel=image.shape #每个像素3个通道, channel order b,g,r print ("height:%s\r\nwidth:%s\r\nchannel:%s\r\n"%(Height,width,channel))" "Height608Width:343Channel:3    " "     forRowinchRange (height): forColinchRange (width): forCinchRange (channel): #循环会变慢, after 625632 cycles PV=Image[row,col,c] image[row,col,c] = 255- PV #像素取反cv.imshow ("Pixels_demo", image) src= Cv.imread ("./1.png") #读取图片cv. Namedwindow ("Input Image", CV. Window_autosize) #创建GUI窗口 in the form of adaptive cv.imshow ("Input Image", SRC) #通过名字将图像和窗口联系t1= Cv.gettickcount () #获取时间 for precision timing, the number of milliseconds that the operating system started (elapsed) access_pixels (src) T2=cv.gettickcount ()print ((T2 -t1)/ cv.gettickfrequency ()) #getTickFrequency () is the number of seconds to get the result of a second Cv.waitkey (0#等待用户操作, the inside wait parameter is milliseconds, we fill in 0, stands for forever, waits for user Operation Cv.destroyallwindows () #销毁所有窗口

625632 Height: 608 Width: 343 Channel: 3 15.740029368334588 #经历了15秒, is a very time-consuming cycle, we can use the NumPy array access, more convenient and fast

(ii) using built-in methods to reverse (directly using C code execution, more efficient)

def inverse (image):     = Cv.bitwise_not (image)    cv.imshow ("inverse image", IMG)

T1 = Cv.gettickcount ()  = Cv.gettickcount ()

0.09940230583146789

Two: Create a picture using the NumPy array

(i) Creating pictures using multiple channels

def create_img ():     Np.zeros ([400,400,3],np.uint8)    #创建一个三维数组高400, width 400, signal Channel 3, the initial is 0, each channel accounted for 8 bits of   img[: ,:,0] = Np.ones ([400,400]) *255     #将0号通道下 [400,400] area using ones set to 1, then multiply by 255, set it to 255 , note: 3 channels are b,g,r so this is shown as blue    cv.imshow ("new Image", IMG) Create_img () cv.waitkey (0)   #等待用户操作, inside wait parameter is milliseconds, we fill 0, represent is forever, wait for user operation Cv.destroyallwindows ( )  #销毁所有窗口

(ii) Create an image using a single channel (grayscale image)

def create_img ():     = Np.zeros ([400,400,1], np.uint8) #创建一个只有一个信道的三维数组, initially 0    img[:,:,  0] = Np.ones ([400,400]) *127 #修改这个图像的信道为127, gray     cv.imshow ("New Image  ", IMG)

Orso the initial time to use ones will be more flexible）

def create_img ():     = Np.ones ([1, 1    ) #创建一个只有一个信道的三维数组,],np.uint8127 # initially Can be directly operated     cv.imshow ("new Image", IMG)
    Cv.imwrite (". 3.png", IMG) #可以进行保存

Third: The use of supplementary numpy

(i) Use of two-dimensional arrays (choose the right type) 1.float type

def create_arr ():     = Np.ones ([3,3],np.float32) #float类型, allow fractional presence    Ml.fill (122.388)    Print (ML) Create_arr ()

[[122.388122.388122.388] [122.388122.388  122.388] [122.388122.388122.388]

2.int type

def create_arr ():     = Np.ones ([3,3],np.uint8) #不允许小数的存在 with a maximum of 255    Ml.fill (122.388)    Print (ML) Create_arr ()

[[122122122] [122122122] [ 122 122 122]]

def create_arr ():     = Np.ones ([3,3],np.uint8) #有位数限制, the high is truncated, and the low has left a    Ml.fill (256.388  )    print (ml) Create_arr ()

[[000] [000] [0  00]]

(ii) Dimension conversion reshape

def create_arr ():     = Np.ones ([3,3],np.uint8)    Ml.fill (122.388)    = ml.Reshape ([1,9]) #注意: Convert dimension, array size or consistent, or error    print (m2)

[[122122122122122122122122  122]]

(iii) using array to customize arrays

Def create_arr ():    = Np.array ([[2,2,3],[4,5,6 ],[7,8,9]],np.uint8)    print (m3)

[[223] [456] [7  89]]

OpenCV---The use of numpy arrays and create pictures