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Learning OpenCV 3 Computer Vision with Python (Update)

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Learning OpenCV 3 Computer Vision with Python (Update)

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Learning OpenCV 3 Computer Vision with Python (Update)
Sep, 2015 | 266 pages
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Table of Contents (16 chapters)
Learning OpenCV 3 Computer Vision with Python Second Edition
Learning OpenCV 3 Computer Vision with Python Second Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Setting Up OpenCV
Setting Up OpenCV
Choosing and using the right setup tools
Installing the Contrib modules
Running samples
Finding documentation, help, and updates
Summary
2
Handling Files, Cameras, and GUIs
Handling Files, Cameras, and GUIs
Basic I/O scripts
Project Cameo (face tracking and image manipulation)
Cameo – an object-oriented design
Summary
3
Processing Images with OpenCV 3
Processing Images with OpenCV 3
Converting between different color spaces
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
Depth Estimation and Segmentation
Creating modules
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
Detecting and Recognizing Faces
Conceptualizing Haar cascades
Getting Haar cascade data
Using OpenCV to perform face detection
Summary
6
Retrieving Images and Searching Using Image Descriptors
Retrieving Images and Searching Using Image Descriptors
Feature detection algorithms
Summary
7
Detecting and Recognizing Objects
Detecting and Recognizing Objects
Object detection and recognition techniques
Detecting cars
Summary
8
Tracking Objects
Tracking Objects
Detecting moving objects
Background subtractors – KNN, MOG2, and GMG
CAMShift
The Kalman filter
Summary
9
Neural Networks with OpenCV – an Introduction
Neural Networks with OpenCV – an Introduction
Artificial neural networks
The structure of an ANN
ANNs in OpenCV
Handwritten digit recognition with ANNs
Possible improvements and potential applications
Summary
Index
Index

Summary

In this chapter, we learned about gathering three-dimensional information from bi-dimensional input (a video frame or an image). Firstly, we examined depth cameras, and then epipolar geometry and stereo images, so we are now able to calculate disparity maps. Finally, we looked at image segmentation with two of the most popular methods: GrabCut and Watershed.

This chapter has introduced us to the world of interpreting information provided by images and we are now ready to explore another important feature of OpenCV: feature descriptors and keypoint detection.

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