Computer Vision Projects with Python 3 [Video]

More Information
  • Install and run the major computer vision packages within Python
  • Apply powerful support vector machines for simple digit classification
  • Understand deep learning with TensorFlow
  • Work with human faces and perform identification and orientation estimation
  • Build a deep-learning classifier for general images

The Python programming language is an ideal platform for rapidly prototyping and developing production-grade codes for image processing and computer vision with its robust syntax and wealth of powerful libraries.

This video course will start by showing you how to set up Anaconda Python for the major OSes with cutting-edge third-party libraries for computer vision. You’ll learn state-of-the-art techniques to classify images and find and identify humans within videos.

Next, you’ll understand how to set up Anaconda Python 3 for the major OSes (Windows, Mac, and Linux) and augment it with the powerful vision and machine learning tools OpenCV and TensorFlow, as well as Dlib. You’ll be taken through the handwritten digits classifier and then move on to detecting facial features and finally develop a general image classifier.

By the end of this course, you’ll know the basic tools of computer vision and be able to put it into practice.

The code bundle for this video course is available at -

Style and Approach

This video tutorial offers a project-based approach to teach you the skills required to develop computer vision solutions in Python.

  • Build powerful computer vision tools in Python with clear and concise code
  • Discover deep learning methods that can be applied to a wide variety of problems in computer vision
  • Crisp videos that take you directly to a practical approach to solving real-world examples
Course Length 2 hours 19 minutes
ISBN 9781788835565
Date Of Publication 25 Jun 2018


Matthew Rever

Matthew Rever is an image processing and computer vision engineer at a major national laboratory. He has years of experience automating the analysis of complex scientific data as well as controlling sophisticated instruments. He has applied computer vision technology to save a great many hours of valuable human labor. He is also enthusiastic about making the latest developments in computer vision accessible to developers of all backgrounds.