catalogue
(1) Randomly generated gray image
(4) Image pixel addition constant
(2) Restore image with grayscale image added
7. Significance of image logic operation
1. Image addition
Image addition operation is to add an image to another image or operate a pixel value of the image, and add a constant to change its brightness or color
(1) Randomly generated gray image
reference material: Pixel addition and subtraction of image
import numpy as np
import cv2
#Np.random.random (a, b) generates random integers greater than or equal to a and less than B
img_gray = np.random.randint(0, 256, size=[406, 600], dtype=np.uint8)#Generate a 406 * 600 random gray image
cv2.namedWindow("gray")
cv2.imshow("gray", img_gray)
cv2.waitKey(delay = 0)
(2) Read original image
First, get the downloaded image from the computer:
import numpy as np
import cv2
img1 = cv2.imread('E:\From Zhihu\For the desk\de.jpg') #Read image
cv2.namedWindow("awindow") #create a window
img1_1 = cv2.resize(img1, dsize= None, fx = 0.5, fy = 0.5)#Adjust image size
cv2.imshow("awindow", img1_1) #Display image
cv2.waitKey(delay = 0) #wait for
print(img1_1.shape) #Read image information
And get the image information:
(406, 600, 3) The resolution and channel of the image. The previous gray image is generated based on the size of this image
(3) Add two images
Use the cv2.addWeighted() function
The function includes parameters: image 1, image 1 weight, image 2, image 2 weight, which is the number added to the pixel value of the synthesized image
When using this function, the resolution of the two images must be the same, and the number of image channels must be the same
In this example, the above gray image and the read image are added
The main implementation methods are as follows. I have added comments
"""
Author:Huijun
date:2021/10/15
"""
import numpy as np
import cv2
img1 = cv2.imread('E:\From Zhihu\For the desk\de.jpg') #Read image
cv2.namedWindow("awindow") #create a window
img1_1 = cv2.resize(img1, dsize= None, fx = 0.5, fy = 0.5)#Resize
img_gray0 = cv2.cvtColor(img1_1, cv2.COLOR_BGR2GRAY) #Gray processing of image
cv2.imshow("awindow", img_gray0) #Display image
cv2.waitKey(delay = 0) #wait for
img_gray = np.random.randint(0, 256, size=[406, 600], dtype=np.uint8)#Generate random grayscale image
cv2.namedWindow("gray")
cv2.imshow("gray", img_gray)
print(img_gray.shape) #Read grayscale attributes
cv2.waitKey(delay = 0)
final_img = cv2.addWeighted(img_gray0, 0.8, img_gray, 0.2, 8) #Add two graphs
cv2.namedWindow("windowfinal")
cv2.imshow("windowfinal", final_img)
cv2.waitKey(delay = 0)Get the final image:
It can be seen that the two images are added, but the beauty can still be distinguished. This is because we adjust the weight of the beauty to 0.8 when adding, so the beauty is mainly displayed in the whole picture
If you modify the weight:
final_img = cv2.addWeighted(img_gray0, 0.5, img_gray, 0.5, 8)
Let the two be fifty-five to get the following image:
(4) Image pixel addition constant
As mentioned in the previous article, the pixel value of an image represents its brightness
Here we try to add 50 to each pixel and compare it with the original image
First, we have to create a matrix of the same size as the original drawing
Mix = np.ones(img1_1.shape, dtype=np.uint8)*50 #Generate an identity matrix and multiply by 50, identity matrix dimension and img1_1 same
Addimg = cv2.add(Mix, img1_1) #Add 50 to each pixel
cv2.namedWindow("Addimg")
cv2.imshow("Addimg", Addimg)
cv2.waitKey(delay = 0)The resulting images are as follows:
2. Image subtraction
(1) Reduce brightness
cv2.subtract() function can realize subtraction between images
Let's restore the above image:
img1_2 = cv2.subtract(Addimg, Mix) #Subtract the matrix from the resulting image
cv2.namedWindow("subimg")
cv2.imshow("subimg", img1_2)
cv2.waitKey(delay = 0)The results are as follows:
It can be seen that the top image restores the image in the middle
(2) Restore image with grayscale image added
In front, we add the gray image and the original image to get a fuzzy image. Here we try to restore it
img1_3 = cv2.subtract(final_img, img_gray)
cv2.namedWindow("subimg1")
cv2.imshow("subimg1", img1_3)
cv2.waitKey(delay = 0)The final image obtained is as follows (subimg1 window):
It can be seen that the image obtained by subtracting the gray image does not restore the original image. This is because we added the two images according to the weight, so directly subtracting the gray image will reduce the brightness of the whole image
3. Image and operation
Reference materials in image operation: openCV -- Python(6) -- image arithmetic and logical operation
img1_4 = cv2.bitwise_and(img_gray0, img_gray) #And operation of two images
cv2.namedWindow("And")
cv2.imshow("And", img1_4)
cv2.waitKey(delay = 0)The resulting images are as follows:
Since the generated gray image is a single channel image, it is necessary to convert the original image into a gray image before it can be matched, and then read in a three channel image for and operation:
The code is roughly as follows:
"""
Author:Huijun
date:2021/10/15
"""
import numpy as np
import cv2
img1 = cv2.imread('E:\From Zhihu\For the desk\de.jpg')
cv2.namedWindow("beauty0")
img1_1 = cv2.resize(img1, dsize= None, fx = 0.5, fy = 0.5)
cv2.imshow("beauty0", img1_1)
print(img1_1.shape)
cv2.waitKey(delay = 0)
img1_5 = cv2.imread('E:\From Zhihu\For the desk\de1.jpg')
img1_50 = cv2.resize(img1_5, dsize= None, fx = 0.834, fy = 0.903) #Resize to match previous image
cv2.namedWindow("beauty1")
cv2.imshow("beauty1", img1_50)
cv2.waitKey(delay = 0)
print(img1_50.shape)
img1_51 = cv2.bitwise_and(img1_1, img1_50)
cv2.namedWindow("Add1")
cv2.imshow("Add1", img1_51)
cv2.waitKey(delay = 0)Image obtained:
. . . . . It's a big spectrum! beauty0 and beauty1 are gone
4. Image or operation
The operation mode is the same as the image operation, but the name of the function has changed
img1_51 = cv2.bitwise_or(img1_1, img1_50) #Or operation on two images
Final image obtained:
5. Image XOR operation
img1_51 = cv2.bitwise_xor(img1_1, img1_50) #XOR two images
The resulting images are as follows:
6. Image non operation
Non operation on an image
img1_51 = cv2.bitwise_not(img1_1) #Non operation on the read image
cv2.namedWindow("Not")
cv2.imshow("Not", img1_51)
cv2.waitKey(delay = 0)
7. Significance of image logic operation
reference material: Know how to answer
Image processing - logic operation
First of all, simple arithmetic operation is to process the pixel value for an image, add or subtract some elements for an image to observe the change or difference of the image
In the logical operation, the intersecting part of the two images can be obtained by the and operation, that is, if the corresponding pixels are true at the same time, the result is true;
Non operation mainly changes black into white and white into black, which is very effective for gray image processing;
Or operation can calculate the combination of two images. When one pixel is true, the result is true;
XOR operation when two pixels are the same, the result is true and the result is false at different times, the disjoint parts of the two images can be obtained, and the images can be deducted or other operations.
Conclusion
This article mainly summarizes some arithmetic operations and logical operations in the process of image processing, and briefly introduces their significance, mainly involving some basic operations, so as to lay a foundation for later learning; For some reference materials, I have also added them in the article. If you want to have an in-depth understanding, you can click in to learn.