Computer Vision with Python 3

Saurabh Kapur

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Key Features

  • Learn how to build a full-fledged image processing application using free tools and libraries
  • Perform basic to advanced image and video stream processing with OpenCV’s Python APIs
  • Understand and optimize various features of OpenCV with the help of easy-to-grasp examples

Book Description

This book is a thorough guide for developers who want to get started with building computer vision applications using Python 3. The book is divided into five sections: The Fundamentals of Image Processing, Applied Computer Vision, Making Applications Smarter,Extending your Capabilities using OpenCV, and Getting Hands on. Throughout this book, three image processing libraries Pillow, Scikit-Image, and OpenCV will be used to implement different computer vision algorithms.

The book aims to equip readers to build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that we will look at in the book are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet.

What you will learn

  • Working with open source libraries such Pillow, Scikit-image, and OpenCV
  • Writing programs such as edge detection, color processing, image feature extraction, and more
  • Implementing feature detection algorithms like LBP and ORB
  • Tracking objects using an external camera or a video file
  • Optical Character Recognition using Machine Learning.
  • Understanding Convolutional Neural Networks to learn patterns in images
  • Leveraging Cloud Infrastructure to provide Computer Vision as a Service

About the Author

Saurabh Kapur is a computer science student at Indraprastha Institute of Information Technology, Delhi.

His interests are in computer vision, numerical analysis, and algorithm design. He often spends time solving competitive programming questions. Saurabh also enjoys working on IoT applications and tinkering with hardware.

He likes to spend his free time playing or watching cricket. He can be reached at

Table of Contents

  1. Introduction to Image Processing
  2. Filters and Features
  3. Drilling Deeper into features- detecting objects
  4. Segmentation – Understanding Images Better
  5. Integrating Machine Learning with Computer Vision
  6. Image Classification using Neural Networks
  7. Introduction to Computer Vision using OpenCV
  8. Object Detection using OpenCV
  9. Video Processing using open CV
  10. Computer Vision as a Service