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Updated on 2025-03-04

Python OpenCV image bit operation

introduction

Bit operation is a basic and efficient operation in computer science, which directly processes binary bits. In image processing, bit operations are also an important technology, especially in scenarios such as image synthesis, separation, and mask processing, where in-place operations are often used. Python's OpenCV library provides a variety of bit operations, making image processing easier and more efficient.

This article will introduce common bit operation operations in image processing, and display how to perform bit operation of images in actual applications through the interface provided by OpenCV.

1. Introduction to bit operations

Bit operation is a direct operation of the binary representation of a number. Common bit operators include:

  • bitwise and (&): The result is only 1 when the two operands are the same bit as 1.
  • bitwise or (|): As long as there is an operand corresponding bit of 1, the result is 1.
  • bitwise xor (^): When the corresponding bits of the two operands are different, the result is 1, otherwise it is 0.
  • Bitwise invert (~): Inverse each bit of the operand (0 changes to 1, 1 changes to 0).
  • bitwise left shift (<<): Move the binary bit of the operand to the left by the specified number of bits.
  • Move right by bit (>>): Move the binary bit of the operand to the right by the specified number of bits.

In image processing, bit operations are usually used in the following scenarios:

  • Mask operation of the image (mask)
  • Synthesize images
  • Extract specific image areas
  • Image comparison

2. Bit operation in OpenCV

OpenCV provides a variety of functions to implement bit operations of images. Commonly used bit operation functions arecv2.bitwise_and(), cv2.bitwise_or(), cv2.bitwise_xor(), cv2.bitwise_not(). These functions can operate bitwise per pixel of the image.

2.1 Bitwise and operation: cv2.bitwise_and()

Bitwise and operation is the "AMP" operation of two pixels corresponding to the image. The result is only 1 when the corresponding bits of the two pixel values ​​are 1. Usually used for mask operation of images.

import cv2
import numpy as np

# Read the imageimg1 = ('')
img2 = ('')

# Perform bitwise and operationresult = cv2.bitwise_and(img1, img2)

# Show results('Bitwise AND', result)
(0)
()

In this example,cv2.bitwise_and()The function willimg1andimg2The corresponding pixels of the image are bitwise and calculated to generate a new imageresult. Only when the pixel values ​​of the two images are 1 at the same time, the corresponding pixel of the result is 1.

2.2 Bitwise or operation: cv2.bitwise_or()

Bitwise or operation is to perform an "OR" operation on the corresponding pixels of two images. As long as the corresponding bit of one pixel value is 1, the resulting pixel is 1.

# Perform bitwise or operationresult_or = cv2.bitwise_or(img1, img2)

# Show results('Bitwise OR', result_or)
(0)
()

cv2.bitwise_or()The function performs bitwise or operation on each pixel of the two images. In the final generated new image, any place where the pixel value is 1 will remain as 1 in the result image.

2.3 Bitwise XOR operation: cv2.bitwise_xor()

Bitwise exclusive OR operation performs the "exclusive OR" operation of the pixels of two images. When the values ​​of the corresponding pixels are different (one is 1 and the other is 0), the result is 1.

# Perform bitwise XOR operationresult_xor = cv2.bitwise_xor(img1, img2)

# Show results('Bitwise XOR', result_xor)
(0)
()

cv2.bitwise_xor() sets the positions with different pixel values ​​in the two images to 1 and the same one sets to 0. This operation is very useful in image comparison and difference analysis.

2.4 Bitwise inversion operation: cv2.bitwise_not()

The bitwise inversion operation is to invert the binary bits of all pixels in the image, and 0 becomes 1 and 1 becomes 0. This operation can be used for the inversion processing of the image.

# Perform bitwise inversion operationresult_not = cv2.bitwise_not(img1)

# Show results('Bitwise NOT', result_not)
(0)
()

In this code,cv2.bitwise_not()rightimg1All pixels of the image are inverted to generate a new imageresult_not。

3. Application of bit operations in image processing

3.1 Image Mask and Segmentation

Bit operation is often used for mask processing of images. In many image processing tasks, we only care about one part of the image, while others need to be ignored. You can then use bitwise (bitwise_and) Operation to extract the area of ​​interest. The mask is usually a binary image, and only the required area can be retained by bitwise operation with the original image.

# Create a simple mask imagemask = np.zeros_like(img1)
mask[100:400, 100:400] = 255  # Set the area of ​​interest to white
# Apply maskresult_masked = cv2.bitwise_and(img1, img1, mask=mask)

('Masked Image', result_masked)
(0)
()

Here, the mask imagemaskThe area of ​​interest is defined. Through bitwise and operation, the parts of the original image that are not in the area of ​​interest can be removed.

3.2 Image synthesis

Bit operation can also be used for image synthesis. For example, it can be done by bitwise or (bitwise_or) Operations combine the two images together to create a mixed image.

# Perform image synthesisresult_composite = cv2.bitwise_or(img1, img2)

('Composite Image', result_composite)
(0)
()

This method can be used to synthesize two images, or to superimpose images with transparent backgrounds onto other images.

4. Summary

Bit operation is a very basic and efficient type of operation in image processing. Through the functions provided by OpenCV, such as cv2.bitwise_and(), cv2.bitwise_or(), cv2.bitwise_xor(), cv2.bitwise_not(), etc., users can conveniently perform various bit operations on images. Bit operation is widely used in many image processing tasks such as image masks, synthesis, and extraction areas. Mastering bit operations is crucial to improving image processing efficiency and implementing complex image analysis tasks.

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