Book Image

Mastering Concurrency in Python

By : Quan Nguyen
Book Image

Mastering Concurrency in Python

By: Quan Nguyen

Overview of this book

Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming. Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples. By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language
Table of Contents (22 chapters)

Image processing fundamentals

Digital/computational image processing (which we will refer to simply as image processing from this point forward) has become so popular in the modern era that it exists in numerous aspects in our everyday life. Image processing and manipulation is involved when you take a picture with your camera or phone using different filters, or when advanced image editing software such as Adobe Photoshop is used, or even when you simply edit images using Microsoft Paint.

Many of the techniques and algorithms used in image processing were developed in the early 1960s for various purposes such as medical imaging, satellite image analysis, character recognition, and so on. However, these image processing techniques required significant computing power, and the fact that the available computer equipment at the time was unable to accommodate the need for fast number...