Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Applying concurrency to image processing

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 of different regions in our original ship image, and they have been cropped from it using the array-indexing and -slicing options that NumPy provides for slicing OpenCV image objects. If you are curious as to how these images were generated, check out the generate_input.py file.

Our goal in this section is to implement a program that can concurrently process these images using thresholding. To do this, let's look at the example5.py file:

from multiprocessing import Pool
import cv2
    
import sys
from timeit import default_timer as timer
    
    
THRESH_METHOD = cv2.ADAPTIVE_THRESH_GAUSSIAN_C
INPUT_PATH = 'input/large_input/'
OUTPUT_PATH = 'output...