Book Image

Computer Vision with Python 3

By : Saurabh Kapur
Book Image

Computer Vision with Python 3

By: Saurabh Kapur

Overview of this book

<p>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.</p> <p>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.</p>

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Introduction to Image Processing
Introduction to Image Processing
Image processing - its applications
Image processing libraries
Summary
2
Filters and Features
Filters and Features
Image derivatives
Convolution
Understanding image filters
Edge detection
Summary
3
Drilling Deeper into Features - Object Detection
Drilling Deeper into Features - Object Detection
Revisiting image features
Harris corner detection
Local Binary Patterns
Oriented FAST and Rotated BRIEF (ORB)
Image stitching
Summary
4
Segmentation - Understanding Images Better
Segmentation - Understanding Images Better
Introduction to segmentation
Contour detection
The Watershed algorithm
Superpixels
Normalized graph cut
Summary
5
Integrating Machine Learning with Computer Vision
Integrating Machine Learning with Computer Vision
Introduction to machine learning
Applications of machine learning for computer vision
Logistic regression
Support vector machines
K-means clustering
Summary
6
Image Classification Using Neural Networks
Image Classification Using Neural Networks
Introduction to neural networks
Convolutional neural networks
Challenges in machine learning
Summary
7
Introduction to Computer Vision using OpenCV
Introduction to Computer Vision using OpenCV
Installation
OpenCV APIs
Summary
8
Object Detection Using OpenCV
Object Detection Using OpenCV
Haar Cascades
Scale Invariant Feature Transformation (SIFT)
Speeded up robust features
Summary
9
Video Processing Using OpenCV
Video Processing Using OpenCV
Reading/writing videos
Basic operations on videos
Color tracking
Object tracking
Summary
10
Computer Vision as a Service
Computer Vision as a Service
Computer vision as a service – architecture overview
Environment setup
Developing a server-client model
Computer vision engine
Putting it all together
Summary

Installation

In this section, we will cover the steps to install OpenCV in various operating systems.

macOS

The following steps will install OpenCV on macOS:

  1. The first step is to install Xcode on your system. You can install Xcode from the App Store for free. Install the open Terminal and execute this command to accept the license:
        sudo xcodebuild –license
  1. Now, type agree at the end of the document. Then, you need to install command-line tools using Xcode by executing this command:
        sudo xcode-select –install
  1. The next step is to install OpenCV using the homebrew command. To install using homebrew, execute these commands:
        brew tap homebrew/science
        brew install opencv3 --with-contrib --with-python3 --without-python
  1. Now, the final step is to link OpenCV and Python. For doing this, first you need to change the filename in /usr/local/opt/opencv3/lib/python3.5/site-packages/ from cv2.cpython-35m-darwin.so to cv2.so by executing these commands:
        cd/usr/local/opt/opencv3...
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