It is used to wait for the key. When the user presses the keyboard, the statement will be executed and its return value will be obtained. When delay > 0, the program will wait for the user's key to trigger within a given delay time or wait for a delay time, and the program will continue to execute. If delay = 0, it means that the user must click the keyboard to trigger the program to continue.

cv2.waitKey(5000) # 5000ms, 5s program continues to execute. Of course, you can click the keyboard to execute immediately

4.cv2.destroyWindow()

Used to release or destroy the specified window. winname is the name of the window

None = cv2.destroyWindow(winname)

5.cv2.destroyAllWindows()

Used to release or destroy all windows

None = cv2.destroyAllWindows()

6.cv2.imwwrite()

Used to save images. retval is the return value. If the save is successful, the logical value True (True) will be returned; If the save is unsuccessful, the logical value False is returned

filename is the pathname of the target file to save, including the file extension. img is the name of the saved image.

params is a save type parameter and is optional.

 retval = cv2.imwwrite(filename,img[, params])

Operational case

import cv2 as cv
import numpy as np
img = np.ones([400, 400, 1], np.uint8)
img = img*0
# img[:, :, 0] = np.ones([400, 400])*127
cv.imshow("new image", img)
cv.waitKey(5000)  # Click 5000S and the program can be executed immediately
cv.imwrite("D:/myImage.png", img)

7. Image classification

1. Binary image

Binary image (black and white only)

White pixels are 1 and black pixels are 0

2. Gray image

Grayscale image (the grayscale processing is 256 grayscale levels (exactly one byte), which is represented by the numerical interval [0255],

Among them. [0] represents pure black and [255] represents pure white

3. Color image

Three primary colors of color image (red, green and blue)

In RGB color space, there are R red channel, G green channel and B blue channel

Common 256256256 = 16777216 colors can be prepared after different combinations

The channel order is R - > G - > b, for example (205, 89, 68)

8. Pixel processing

#Generate an 8 * 8 two-dimensional array, all values are 0,,, and the data type is NP Uint8, (in fact, it can be regarded as a black image.)
#img[0, 3] = 255 is to set the pixels in row 0 and column 3 to 255
#img = np.zeros((8, 8), dtype=np.uint8)

import cv2
import numpy as np
img = np.zeros((8, 8), dtype=np.uint8)
print("img = \n", img)
cv2.imshow("one",img)
print("Read pixels img[0, 3]=", img[0, 3])
img[0, 3] = 255
print("After modification img = \n", img)
print("Read the modified pixels img [0, 3] = ",img[0, 3])
cv2.imshow("two", img)
cv2.waitKey()
cv2.destroyAllWindows()

9. Editing an image

(that is to change a whole piece into a certain color)

import cv2
img = cv2.imread("C:/Users/Lenovo/PycharmProjects/pythonProject3/IMG_0228.JPG", 0)
cv2.imshow("before", img)
for i in range(10, 100):
    for j in range(80, 100):
        img[i, j] = 255
cv2.imshow("after", img)
cv2.waitKey()
cv2.destroyAllWindows()

10. Create three monochrome pictures

Among them! The first two: represent the number of all rows and columns of the selected picture, and 1 represents the second channel (0 represents the first channel).

If there is a picture img, img[20:30, 60:70, 1] indicates the 20th to 30th rows, 60th to 70th columns and the second channel of the selected picture.

#[300 (number of rows of picture), 300 (number of columns of picture, 3 (number of channels of picture)]

import numpy as np
import cv2
# ---------------blue---------------
blue = np.zeros((300, 300, 3), dtype=np.uint8)
blue[:, :, 0] = 255
print("blue = \n", blue)
cv2.imshow("blue", blue)
# -------------green---------------
green = np.zeros((300, 300, 3), dtype=np.uint8)
green[:, :, 1] = 255
print("green = \n", green)
cv2.imshow("green", green)
# -------------red---------------
red = np.zeros((300, 300, 3), dtype=np.uint8)
red[:, :, 2] = 255
print("red = \n", red)
cv2.imshow("red", red)
# -------------Release window---------------
cv2.waitKey()
cv2.destroyAllWindows()