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Advanced Python Programming

You're reading from Advanced Python Programming Build high performance, concurrent, and multi-threaded apps with Python using proven design patterns

Product type Course

Published in Feb 2019

Publisher Packt

ISBN-13 9781838551216

Length 672 pages

Edition 1st Edition

Languages

Python

Tools

Dask

Concepts

Design Patterns

Authors (3):

Quan Nguyen

Sakis Kasampalis

Dr. Gabriele Lanaro

View More author details

Table of Contents (41) Chapters

Title Page

Copyright

About Packt

Contributors

Preface

Benchmarking and Profiling FREE CHAPTER

Pure Python Optimizations

Fast Array Operations with NumPy and Pandas

C Performance with Cython

Exploring Compilers

Implementing Concurrency

Parallel Processing

Advanced Introduction to Concurrent and Parallel Programming

Amdahl's Law

Working with Threads in Python

Using the with Statement in Threads

Concurrent Web Requests

Working with Processes in Python

Reduction Operators in Processes

Concurrent Image Processing

Introduction to Asynchronous Programming

Implementing Asynchronous Programming in Python

Building Communication Channels with asyncio

Deadlocks

Starvation

Race Conditions

The Global Interpreter Lock

The Factory Pattern

The Builder Pattern

Other Creational Patterns

The Adapter Pattern

The Decorator Pattern

The Bridge Pattern

The Facade Pattern

Other Structural Patterns

The Chain of Responsibility Pattern

The Command Pattern

The Observer Pattern

1. Appendix

2. Other Books You May Enjoy

Leave a Review - Let Other Readers Know What You Think

Index

Chapter 15. Concurrent Image Processing

This chapter analyzes the process of processing and manipulating images through concurrent programming, especially multiprocessing. Since images are processed independently of one another, concurrent programming can provide image processing with a significant speedup. This chapter discusses the basics behind image processing techniques, illustrates the improvements that concurrent programming provides, and finally, goes over some of the best practices used in image processing applications.

The following topics will be covered in this chapter:

The idea behind image processing and a number of basic techniques in image processing
How to apply concurrency to image processing, and how to analyze the improvements it provides
Best practices in concurrent image processing

The rest of the chapter is locked

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Authors (3)

Dr. Gabriele Lanaro

Dr. Gabriele Lanaro

Dr. Gabriele Lanaro is passionate about good software and is the author of the chemlab and chemview open source packages. His interests span machine learning, numerical computing visualization, and web technologies. In 2013, he authored the first edition of the book High Performance Python Programming. He has been conducting research to study the formation and growth of crystals using medium and large-scale computer simulations. In 2017, he obtained his PhD in theoretical chemistry.

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Sakis Kasampalis

Sakis Kasampalis

Sakis Kasampalis is a software architect living in the Netherlands. He is not dogmatic about particular programming languages and tools; his principle is that the right tool should be used for the right job. One of his favorite tools is Python because he finds it very productive. Sakis was also the technical reviewer of Mastering Object-oriented Python and Learning Python Design Patterns, published by Packt Publishing.

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Quan Nguyen

Quan Nguyen

Quan Nguyen, the author of the first edition of this book, is a Python programmer with a strong passion for machine learning. He holds a dual degree in mathematics and computer science, with a minor in philosophy, earned from DePauw University. Quan is deeply involved in the Python community and has authored multiple Python books, contributing to the Python Software Foundation and regularly sharing insights on DataScience portal. He is currently pursuing a Ph.D. in computer science at Washington University in St. Louis.

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