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

Applying concurrency to image processing

We have talked a lot about the basics of image processing and some common image processing techniques. We also know why image processing is a heavy number-crunching task, and that concurrent and parallel programming can be applied to speed up independent processing tasks. In this section, we will be looking at a specific example on how to implement a concurrent image processing application that can handle a large number of input images.

First, head to the current folder for this chapter's code. Inside the input folder, there is a subfolder called large_input, which contains 400 images that we will be using for this example. These pictures are different regions in our original ship image, which have been cropped from it using the array-indexing and -slicing options that NumPy provides to slice OpenCV image objects. If you are curious as to how these images were generated, check out the Chapter15/ file.

Our goal in this section is to...