OpenCV with Python Blueprints

Design and develop advanced computer vision projects using OpenCV with Python
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OpenCV with Python Blueprints

Michael Beyeler

1 customer reviews
Design and develop advanced computer vision projects using OpenCV with Python
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Book Details

ISBN 139781785282690
Paperback230 pages

Book Description

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. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions

This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.

By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.

Table of Contents

Chapter 1: Fun with Filters
Planning the app
Creating a black-and-white pencil sketch
Generating a warming/cooling filter
Cartoonizing an image
Putting it all together
Summary
Chapter 2: Hand Gesture Recognition Using a Kinect Depth Sensor
Planning the app
Setting up the app
Tracking hand gestures in real time
Hand region segmentation
Hand shape analysis
Hand gesture recognition
Summary
Chapter 3: Finding Objects via Feature Matching and Perspective Transforms
Tasks performed by the app
Planning the app
Setting up the app
The process flow
Feature extraction
Feature matching
Feature tracking
Seeing the algorithm in action
Summary
Chapter 4: 3D Scene Reconstruction Using Structure from Motion
Planning the app
Camera calibration
Setting up the app
Estimating the camera motion from a pair of images
Reconstructing the scene
3D point cloud visualization
Summary
Chapter 5: Tracking Visually Salient Objects
Planning the app
Setting up the app
Visual saliency
Mean-shift tracking
Putting it all together
Summary
Chapter 6: Learning to Recognize Traffic Signs
Planning the app
Supervised learning
The GTSRB dataset
Feature extraction
Support Vector Machine
Putting it all together
Summary
Chapter 7: Learning to Recognize Emotions on Faces
Planning the app
Face detection
Facial expression recognition
Putting it all together
Summary

What You Will Learn

  • Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning
  • Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
  • Learn feature extraction and feature matching for tracking arbitrary objects of interest
  • Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
  • Track visually salient objects by searching for and focusing on important regions of an image
  • Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)
  • Recognize street signs using a multi-class adaptation of support vector machines (SVMs)
  • Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features

Authors

Table of Contents

Chapter 1: Fun with Filters
Planning the app
Creating a black-and-white pencil sketch
Generating a warming/cooling filter
Cartoonizing an image
Putting it all together
Summary
Chapter 2: Hand Gesture Recognition Using a Kinect Depth Sensor
Planning the app
Setting up the app
Tracking hand gestures in real time
Hand region segmentation
Hand shape analysis
Hand gesture recognition
Summary
Chapter 3: Finding Objects via Feature Matching and Perspective Transforms
Tasks performed by the app
Planning the app
Setting up the app
The process flow
Feature extraction
Feature matching
Feature tracking
Seeing the algorithm in action
Summary
Chapter 4: 3D Scene Reconstruction Using Structure from Motion
Planning the app
Camera calibration
Setting up the app
Estimating the camera motion from a pair of images
Reconstructing the scene
3D point cloud visualization
Summary
Chapter 5: Tracking Visually Salient Objects
Planning the app
Setting up the app
Visual saliency
Mean-shift tracking
Putting it all together
Summary
Chapter 6: Learning to Recognize Traffic Signs
Planning the app
Supervised learning
The GTSRB dataset
Feature extraction
Support Vector Machine
Putting it all together
Summary
Chapter 7: Learning to Recognize Emotions on Faces
Planning the app
Face detection
Facial expression recognition
Putting it all together
Summary

Book Details

ISBN 139781785282690
Paperback230 pages
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