Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
About Packt

Good concurrent image processing practices

Up until this point, you have most likely realized that image processing is quite an involved process, and implementing concurrent and parallel programming in an image processing application can add more complexity to our work. There are, however, good practices that will guide us in the right direction while developing our image processing applications. The following section discusses some of the most common practices that we should keep in mind.

Choosing the correct way (out of many)

We have hinted at this practice briefly when we learned about thresholding. How an image processing application handles and processes its image data heavily depends on the problems it is supposed to solve, and what kind of data will be fed to it. Therefore, there is significant variability when it comes to choosing specific parameters when processing your image.

For example, as we have seen earlier, there are various ways to threshold an image, and each will result in...