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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

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Product type Course
Published in Feb 2019
Publisher Packt
ISBN-13 9781838551216
Length 672 pages
Edition 1st Edition
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Authors (3):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
Sakis Kasampalis Sakis Kasampalis
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Sakis Kasampalis
Dr. Gabriele Lanaro Dr. Gabriele Lanaro
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Dr. Gabriele Lanaro
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Table of Contents (41) Chapters Close

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 Index

Chapter 15


What is an image processing task?

Image processing is the task of analyzing and manipulating digital image files to create new versions of the images, or to extract important data from them.

What is the smallest unit of digital imaging? How is it represented in computers?

The smallest unit of digital imaging is a pixel, which typically contains an RGB value: a tuple of integers between 0 and 255.

What is grayscaling? What purpose does the technique serve?

Grayscaling is the process of converting an image to gray colors by considering only the intensity information of each pixel, represented by the amount of light available. It reduces the dimensionality of the image pixel matrix by mapping traditional three-dimensional color data to one-dimensional gray data.

What is thresholding? What purpose does the technique serve?

Thresholding replaces each pixel in an image with a white pixel if the pixel's intensity is greater than a previously specified threshold, and with a black pixel if the pixel's intensity is less than that threshold. After performing thresholding on an image, each pixel of that image can only hold two possible values, significantly reducing the complexity of image data.

Why should image processing be made concurrent?

Heavy computational number-crunching processes are typically involved when it comes to image processing, as each image is a matrix of integer tuples. However, these processes can be executed independently, which suggests that the whole task should be made concurrent.

What are some good practices for concurrent image processing?

Some good practices for concurrent image processing are as follows:

  • Choosing the correct method (out of many)
  • Spawning an appropriate amount of processes
  • Processing input/output concurrently
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