Practical Python and OpenCV

Book cover: Practical Python and OpenCV

Interested in computer vision, but don't know where to start? Practical Python and OpenCV is your quick start guide to learning the basics of computer vision and image processing...in a single weekend.

Practical Python and OpenCV covers the very basics of computer vision, starting from answering the question “what's a pixel?” all the way up to more challenging tasks such as edge detection, thresholding, and finding objects in images.

This book is very example driven, with lots of visual examples and tons of code.

So, if you're a student, programmer, or developer looking to get started in computer vision, look no further than Practical Python and OpenCV!

Here's a breakdown of what this book covers:
  • Introduction to Computer Vision
  • Python and Required Packages
  • Loading, Displaying, and Saving
  • Image Basics
  • Drawing
  • Image Processing

    • Image Transformations
    • Image Arithmetic
    • Bitwise Operations
    • Masking
    • Splitting and Merging Channels
    • Converting Color Spaces
  • Histograms
  • Smoothing and Blurring
  • Gradients and Edge Detection
  • Contours

The book (and accompanying source code) can be purchased from the PyImageSearch website.

Case Studies

Book cover: Practical Python and OpenCV (with stamp 'case studies' on it)

From face detection, to handwriting recognition, to matching keypoints and SIFT descriptors, Case Studies: Solving real-world problems with computer vision has one goal: to make you awesome at solving computer vision problems.

This book is intended for developers and programmers who understand the basics of computer vision and are ready to apply their skills to solve actual, real-world problems.

Here's a breakdown of what types of real-world computer vision problems you'll learn to solve:

  • Face and Eye Detection in Photos and Video
  • Color Object Tracking in Video
  • Handwriting Recognition using Histogram of Oriented Gradients (HOG)
  • Plant Classification with Machine Learning
  • Matching Keypoints and SIFT descriptors to build a Book Cover Identifier

All problems are covered in detail, with lots of visual examples and code. After reading Case Studies, you'll be able to apply these techniques to solve computer vision problems of your own.

Case Studies (and accompanying source code) can be purchased from the PyImageSearch website.

The author, Adrian Rosebrock holds a Ph.D in computer science from the University of Maryland, Baltimore County.

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