Bitwise Operations on Binary Image
Bitwise operations are used in image manipulation and used for extracting essential parts in the image. Bitwise operations used are :
- AND
- OR
- XOR
- NOT
Bitwise AND operation
Bit-wise conjunction of input array elements.
Input Image 1:
Input Image 2:
Python3
# Python program to illustrate # arithmetic operation of # bitwise AND of two images # organizing imports import cv2 import numpy as np # path to input images are specified and # images are loaded with imread command img1 = cv2.imread('input1.png') img2 = cv2.imread('input2.png') # cv2.bitwise_and is applied over the # image inputs with applied parameters dest_and = cv2.bitwise_and(img2, img1, mask = None) # the window showing output image # with the Bitwise AND operation # on the input images cv2.imshow('Bitwise And', dest_and) # De-allocate any associated memory usage if cv2.waitKey(0) & 0xff == 27: cv2.destroyAllWindows() |
Output:
Bitwise OR operation
Bit-wise disjunction of input array elements.
Python3
# Python program to illustrate # arithmetic operation of # bitwise OR of two images # organizing imports import cv2 import numpy as np # path to input images are specified and # images are loaded with imread command img1 = cv2.imread('input1.png') img2 = cv2.imread('input2.png') # cv2.bitwise_or is applied over the # image inputs with applied parameters dest_or = cv2.bitwise_or(img2, img1, mask = None) # the window showing output image # with the Bitwise OR operation # on the input images cv2.imshow('Bitwise OR', dest_or) # De-allocate any associated memory usage if cv2.waitKey(0) & 0xff == 27: cv2.destroyAllWindows() |
Output:
Bitwise XOR operation
Bit-wise exclusive-OR operation on input array elements.
Python3
# Python program to illustrate # arithmetic operation of # bitwise XOR of two images # organizing imports import cv2 import numpy as np # path to input images are specified and # images are loaded with imread command img1 = cv2.imread('input1.png') img2 = cv2.imread('input2.png') # cv2.bitwise_xor is applied over the # image inputs with applied parameters dest_xor = cv2.bitwise_xor(img1, img2, mask = None) # the window showing output image # with the Bitwise XOR operation # on the input images cv2.imshow('Bitwise XOR', dest_xor) # De-allocate any associated memory usage if cv2.waitKey(0) & 0xff == 27: cv2.destroyAllWindows() |
Output:
Bitwise NOT operation
Inversion of input array elements.
Python3
# Python program to illustrate # arithmetic operation of # bitwise NOT on input image # organizing imports import cv2 import numpy as np # path to input images are specified and # images are loaded with imread command img1 = cv2.imread('input1.png') img2 = cv2.imread('input2.png') # cv2.bitwise_not is applied over the # image input with applied parameters dest_not1 = cv2.bitwise_not(img1, mask = None) dest_not2 = cv2.bitwise_not(img2, mask = None) # the windows showing output image # with the Bitwise NOT operation # on the 1st and 2nd input image cv2.imshow('Bitwise NOT on image 1', dest_not1) cv2.imshow('Bitwise NOT on image 2', dest_not2) # De-allocate any associated memory usage if cv2.waitKey(0) & 0xff == 27: cv2.destroyAllWindows() |
Output:
Bitwise NOT on Image 1
Bitwise NOT on Image 2
Getting Started with Python OpenCV
Computer Vision is one of the techniques from which we can understand images and videos and can extract information from them. It is a subset of artificial intelligence that collects information from digital images or videos.
Python OpenCV is the most popular computer vision library. By using it, one can process images and videos to identify objects, faces, or even handwriting of a human. When it is integrated with various libraries, such as NumPy, python is capable of processing the OpenCV array structure for analysis.
In this article, we will discuss Python OpenCV in detail along with some common operations like resizing, cropping, reading, saving images, etc with the help of good examples.