Python Resource Optimization Literacy (Beginner Level)

  • 14m
  • 14 questions
The Python Resource Optimization Literacy (Beginner Level) benchmark assesses your ability to use OpenCV to read and write images, explore color scale and grayscale images, and perform basic image transformations. You will be evaluated on your ability to differentiate batch and stream processing, recall the components in a stream processing architecture, and install stream processing using a Faust application that reads streaming messages from a Kafka topic. A learner who scores high on this benchmark demonstrates that they have the skills to perform basic operations in OpenCV and Faust for image transformation and stream processing.

Topics covered

  • implement a simple streaming application using Faust
  • install and set up Kafka and Faust on a local machine
  • list the components that make up the architecture of a stream processing system
  • load images into an OpenCV array from your local storage and also save an array into a local file
  • perform OpenCV's subtract operation between two images
  • read a color image into your Python source as a grayscale image and view it using an interactive window
  • recall the characteristics of batch data and batch processing
  • recall the important characteristics of Faust stream processing applications
  • recall the important characteristics of the Apache Kafka message delivery service
  • separate a color image into blue, green, and red channels
  • summarize the kinds of transformations that can be performed on streaming data
  • use producers and consumers to send and receive messages
  • use the add and add weighted operations in OpenCV to combine two images
  • use the 'faust' command to run workers and send messages to agents