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Index
Cover
Table of Contents
Learning OpenCV 3 Computer Vision with Python Second Edition
Learning OpenCV 3 Computer Vision with Python Second Edition
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
1. Setting Up OpenCV
Installing the Contrib modules
Running samples
Finding documentation, help, and updates
Summary
2. Handling Files, Cameras, and GUIs
Project Cameo (face tracking and image manipulation)
Cameo – an object-oriented design
Summary
3. Processing Images with OpenCV 3
The Fourier Transform
Creating modules
Edge detection
Custom kernels – getting convoluted
Modifying the application
Edge detection with Canny
Contour detection
Contours – bounding box, minimum area rectangle, and minimum enclosing circle
Contours – convex contours and the Douglas-Peucker algorithm
Line and circle detection
Detecting shapes
Summary
4. Depth Estimation and Segmentation
Capturing frames from a depth camera
Creating a mask from a disparity map
Masking a copy operation
Depth estimation with a normal camera
Object segmentation using the Watershed and GrabCut algorithms
Summary
5. Detecting and Recognizing Faces
Getting Haar cascade data
Using OpenCV to perform face detection
Summary
6. Retrieving Images and Searching Using Image Descriptors
Summary
7. Detecting and Recognizing Objects
Detecting cars
Summary
8. Tracking Objects
Background subtractors – KNN, MOG2, and GMG
CAMShift
The Kalman filter
Summary
9. Neural Networks with OpenCV – an Introduction
The structure of an ANN
ANNs in OpenCV
Handwritten digit recognition with ANNs
Possible improvements and potential applications
Summary
Index
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