Final Exam: Resource Optimization with Python

WHAT YOU WILL LEARN

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  Recognize the results of bitwise and, or, not and xor operations on images
  

  use web views to handle get, put, post, delete, http requests
  

  use trained classifiers to detect faces and people in images
  

  draw a polygon and an arrow in an opencv image and introduce a text element
  

  perform gaussian and median blur operations in order to smooth an image
  

  load images from your file system into an opencv array and then perform the reverse operation by saving an array into a local file
  

  identify the results of bitwise and, or, not and xor operations on images
  

  read a color image into your python source as a grayscale image
  

  implement the cv2.resize method to reduce the size of a color image
  

  create a workspace for the demos and install opencv from a jupyter notebook
  

  implement trained classifiers to detect eyes, faces and people in images
  

  read a color image into your python source
  

  use trained classifiers to detect eyes, faces and people in images
  

  read a color image into your python source as a grayscale image and view it using an interactive window
  

  identify attributes of tumbling windows
  

  add noise to an image
  

  add noise to an image and apply a blur which obscures minute details in an image
  

  using trained classifiers to detect faces, eyes and people in images
  

  implement gaussian and median blur operations in order to smooth an image
  

  implement the subtract method in opencv to perform a subtract operation between two images
  

  apply cv2.resize to scale up an image along individual dimensions
  

  implement processing time tumbling windows
  

  create models with multiple fields and different data types
  

  implement the "faust" command to run workers and send messages to agents
  

  use models to represent stream elements
  

  plot a circle, line, rectangle and ellipse in an image
  

  perform a variety of translations and rotations in increments of 90 degrees in order to orient an image according to your specifications
  

  perform grouping operations and understand table sharding
  

  identify the components that make up the architecture of a stream processing system
  

  forward messages to destination topics
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  list the components that make up the architecture of a stream processing system
  

  recall the important characteristics of the faust stream processing applications
  

  use the subtract method in opencv to perform a subtract operation between two images
  

  identify attributes of hopping tumbling
  

  invoke the cast() method to await processing results from an agent
  

  handle get, put, post, delete, http requests with web views
  

  use the "faust" command to run workers and send messages to agents
  

  implement event time hopping windows
  

  contrast tumbling windows and hopping windows
  

  use the pykafka library to publish messages to a kafka topic
  

  implement the key index to iterate over keys, values, and items in windowed tables
  

  use the add and addweighted methods in opencv to combine two images
  

  publish messages to a kafka topic using the pykafka library
  

  send and receive messages using channels
  

  use channels to send and receive messages
  

  use the cv2.resize method to reduce the size of a color image
  

  separate a color image into blue, green and red channels
  

  save table state to an embedded rocksdb database
  

  recognize the use of the bgr and rgb color spaces used by opencv and the pillow libraries
  

  perform group-by operations on streams
  

  recall the different kinds of sinks that can be used with a faust agent
  

  compute aggregations on streaming data
  

  add noise to an image and apply a blur
  

  aggregate data on a per-key, per-window basis
  

  identify the different kinds of sinks that can be used with a faust agent
  

  apply the laplacian operator to detect the edges in an image
  

  apply the laplacian, sobel and canny operators to detect the edges in an image
  

  use the key index to iterate over keys, values, and items in windowed tables
  

  recall the differences between event time, ingestion time, and processing time
  

  identify the differences between event time, ingestion time, and processing time