Course: Pythonista to Python Master - Part 5 Resource Optimalisatie with Python

A cross-platform library, OpenCV facilitates image processing and analysis. In this course, you'll discover fundamental concepts related to computer vision and the basic operations which can be performed on images using OpenCV. You'll begin by outlining how to read images from your file system into your Python source in the form of arrays and then save an image array into a local file. Next, you'll explore color images represented as a combination of blue, green, and red channels, how to convert color images to grayscale, and how grayscale images are defined. Finally, you'll perform basic operations on images by investigating how to combine two images using an add operation and make one of the added images more prominent than the other using a weighted addition. Conversely, you'll also perform a subtract operation using two images.

Images often require to be manipulated to extract meaningful portions of an image or prepare them for a machine learning pipeline. OpenCV can help with this. In this course, you'll investigate a variety of image manipulation operations using OpenCV. You'll begin by recognizing how to filter certain portions of an image using bitwise operations. Next, you'll explore the concept of masks and how to use them while extracting parts of an image. You'll then outline how to apply geometrical operations by resizing an image to specific dimensions and discover challenges that such operations present. You'll finish the course by examining image transformations such as rotations and translations to help orient an image to your requirements. Finally, you'll discover how to flip and warp images to present them from a different perspective.

Many image processing operations involve complex math, but when using OpenCV, much of that is abstracted from the developer. In this course, you'll gain a high-level understanding of advanced image operations in OpenCV. You'll begin by recognizing how to apply different blur operations to an image. These range from simple blurs to Gaussian and median blurs. While doing so, you'll examine their specific advantages and disadvantages and how to distinguish between them. Moving on, you'll outline how to highlight objects in an image using edge detection and augment images by adding shapes and objects to them. Finally, you'll discover how to work with pre-trained classifiers to detect people in an image and perform morphological transformations to emphasize or suppress specific parts of an image.

Faust is a stream processing library that allows you to write code to process data available as streams. In this course, you'll explore the basics of stream processing and how it fundamentally differs from batch processing. You'll start by examining the components of a stream processing system architecture, specifically the role of stream transport. You'll then investigate the Faust stream processing library, which uses native Python code for transformations on streaming data. Moving on, you'll explore what's meant by producers, consumers, and topics in Apache Kafka. You'll install Faust on your local machine as well as the Apache Kafka messaging service. You'll then use these to write a stream processing application. When you're finished with this course, you'll be able to clearly articulate the characteristics of stream processing and work with Apache Kafka and Faust to perform simple operations on input streams.

Stream processing in Faust uses native Python code, meaning you don't have to learn a new domain-specific language to perform data transformations. All you need to know is how existing concepts, such as models, work within a Faust context. Faust models allow you to specify fields and their data types and use this well-defined data structure to access streaming data. In this course, you'll learn how to represent the individual elements of a stream using Faust models. You'll work with agents, which are at the heart of every Faust stream processing application. You'll perform operations using agents and invoke agents synchronously and asynchronously from within your application. You'll then work with channels in Faust. At the end of this course, you'll have the confidence to use models, agents, and channels in the right way to build a successful application.

Faust streams support a wide range of operations. In this course, you'll learn how to perform several of these. You'll also work with Faust tables - which store state in the form of key-value pairs and allow for the recovery of failed processing, making Faust fault-tolerant. You'll start off by using the group by operation to designate a key used to repartition an input stream and create a new topic in Kafka. You'll then use the items() operation to access the key and message value and take() operation to buffer multiple elements in a stream. Next, you'll work with tables to conduct stateful stream processing, illustrating how table data is stored in an embedded RocksDB database. When you've finished this course, you'll be able to apply a wide range of operations on input streams and perform stateful stream processing using tables.

When working with data, windows are a handy tool to accumulate data subsets from input streams and perform aggregation operations on this specific data. In this course, you'll learn how to perform stream processing through windowing operations in Faust. You'll start by examining the different windowing operations possible on input streams, including tumbling, sliding, count, session, and global windows. Next, you'll distinguish the three notions of time associated with streaming events: event, ingestion, and processing time. You'll then use Faust window features to perform windowing operations on input streams and emit aggregation results for every window. Finally, you'll use the REST API server, which all Faust applications have, to make streaming code metrics and table data accessible to the user. Once you're done with this course, you'll be able to use windowing operations via Faust and expose metrics using web views.

In this practice lab, learners will be presented with a series of exercises to practice developing in Python. Exercises include tasks such as using OpenCV to read and write images, perform bitwise operations, detect and blur noisy images, and detect people. Learners will also practice with Faust to peform batch processing, create a stream model, and perform operations. Learners can also use the environment as an open sandbox. No installation or configuration is required, so you can gain immediate hands-on experience. Create new files or upload your own from a storage location of your choice, such as GitHub, and you can practice coding right away! You can even download a copy of your work when you're done. Whether you're looking to dive into the code presented within our courses or you want to work on your own coding projects, this lab environment will provide you with everything you need. So, go ahead and start coding today! See below for a complete list of available software. - Python 3.7 - Python 2.7 - Tkinter GUI - Python 3 libraries: - Django - Flask - Flask-Migrate - Flask-SQLAlchemy - Flask-WTF - beautifulsoup4 - selenium - Python 2/3 libraries: - ipython - pytest - testbook - Pip 2 and 3 - Node.js - npm - Git - Vim editor - Standard Python libraries This lab is part of the Resource Optimization with Python track of the Skillsoft Aspire Pythonista to Python Master Journey.

Final Exam: Resource Optimization with Python will test your knowledge and application of the topics presented throughout the Resource Optimization with Python track of the Skillsoft Aspire Pythonista to Python Master Journey.