Book Image

OpenCV 4 with Python Blueprints - Second Edition

By : Dr. Menua Gevorgyan, Arsen Mamikonyan, Michael Beyeler
Book Image

OpenCV 4 with Python Blueprints - Second Edition

By: Dr. Menua Gevorgyan, Arsen Mamikonyan, Michael Beyeler

Overview of this book

OpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks. You’ll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you’ll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you’ll understand how to align images, and detect and track objects using neural networks. By the end of this OpenCV Python book, you’ll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Fun with Filters
Fun with Filters
Getting started
Planning the app
Creating a black-and-white pencil sketch
Generating a warming and cooling filter
Cartoonizing an image
Putting it all together
Summary
Attributions
Free Chapter
2
Hand Gesture Recognition Using a Kinect Depth Sensor
Hand Gesture Recognition Using a Kinect Depth Sensor
Getting started
Planning the app
Setting up the app
Tracking hand gestures in real time
Understanding hand region segmentation
Performing hand shape analysis
Performing hand gesture recognition
Summary
3
Finding Objects via Feature Matching and Perspective Transforms
Finding Objects via Feature Matching and Perspective Transforms
Getting started
Listing the tasks performed by the app
Planning the app
Setting up the app
Understanding the process flow
Learning feature extraction
Understanding feature matching
Learning feature tracking
Seeing the algorithm in action
Summary
Attributions
4
3D Scene Reconstruction Using Structure from Motion
3D Scene Reconstruction Using Structure from Motion
Getting started
Planning the app
Learning about camera calibration
Setting up the app
Estimating the camera motion from a pair of images
Reconstructing the scene
Understanding 3D point cloud visualization
Learning about structure from motion
Summary
5
Using Computational Photography with OpenCV
Using Computational Photography with OpenCV
Getting started
Planning the app
Understanding the 8-bit problem
Understanding high-dynamic-range imaging
Understanding panorama stitching
Summary
Further reading
Attributions
6
Tracking Visually Salient Objects
Tracking Visually Salient Objects
Getting started
Understanding visual saliency
Planning the app
Setting up the app
Mapping visual saliency
Understanding mean-shift tracking
​Learning about the OpenCV Tracking API
Putting it all together
Summary
Dataset attribution
7
Learning to Recognize Traffic Signs
Learning to Recognize Traffic Signs
Getting started
Planning the app
Briefing on supervised learning concepts
Understanding the GTSRB dataset
Learning about dataset feature extraction
Learning about SVMs
Putting it all together
Improving results with neural networks
Summary
Dataset attribution
8
Learning to Recognize Facial Emotions
Learning to Recognize Facial Emotions
Getting started
Planning the app
Learning about face detection
Collecting data
Understanding facial emotion recognition
Putting it all together
Summary
Further reading
Attributions
9
Learning to Classify and Localize Objects
Learning to Classify and Localize Objects
Getting started
Planning the app
Preparing an inference script
Preparing the dataset
Classifying with CNNs
Localizing with CNNs
Seeing inference in action
Summary
Dataset attribution
10
Learning to Detect and Track Objects
Learning to Detect and Track Objects
Getting started
Planning the app
Preparing the main script
Implementing a Sort tracker
Seeing the app in action
Summary
11
Profiling and Accelerating Your Apps
Profiling and Accelerating Your Apps
Accelerating with Numba
12
Setting Up a Docker Container
Setting Up a Docker Container
Defining a Dockerfile
13
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Getting started

As mentioned in all of the chapters of the book, you need an appropriate installation of OpenCV, SciPy, and NumPY.

You can find the code that we present in this chapter at the GitHub repository at https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition/tree/master/chapter10.

When running the app with Docker, the Docker container should have appropriate access to the X11 server. This app cannot run in headless mode. The best environment to run the app with Docker is a Linux desktop environment. On macOS, you can use xQuartz (refer, to https://www.xquartz.org/) in order to create an accessible X11 server.

You can also use one of the available Docker files in the repository in order to run the app.

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