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iOS Application Development with OpenCV 3

iOS Application Development with OpenCV 3

By : Joseph Howse
4 (5)
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iOS Application Development with OpenCV 3

iOS Application Development with OpenCV 3

4 (5)
By: Joseph Howse

Overview of this book

iOS Application Development with OpenCV 3 enables you to turn your smartphone camera into an advanced tool for photography and computer vision. Using the highly optimized OpenCV library, you will process high-resolution images in real time. You will locate and classify objects, and create models of their geometry. As you develop photo and augmented reality apps, you will gain a general understanding of iOS frameworks and developer tools, plus a deeper understanding of the camera and image APIs. After completing the book's four projects, you will be a well-rounded iOS developer with valuable experience in OpenCV.
Table of Contents (7 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Setting Up Software and Hardware
1. Setting Up Software and Hardware
Setting up Apple's developer tools
Setting up the OpenCV framework
Developing a minimal application
Distributing to testers and customers
Finding documentation and support
Understanding the camera and setting up photographic accessories
Summary
2
2. Capturing, Storing, and Sharing Photos
2. Capturing, Storing, and Sharing Photos
Planning a photo sharing application
Configuring the project
Defining and laying out the view controller
Controlling the camera
Working with various color formats
Starting and stopping the busy mode
Saving an image to the Photos library
Displaying an alert
Sharing an image via social media
Running the application
Summary
3
3. Blending Images
3. Blending Images
Thinking about hybrid images
Planning the blending controls
Expanding the view controller's interface
Expanding the view controller's implementation
Using the application for practical purposes
Summary
4
4. Detecting and Merging Faces of Mammals
4. Detecting and Merging Faces of Mammals
Understanding detection with cascade classifiers
Understanding transformations
Planning a face merging application
Configuring the project
Defining faces and a face detector
Defining and laying out the view controllers
Detecting a hierarchy of face elements
Aligning and blending face elements
Using the application and acting like a cat
Learning more about face analysis
Summary
5
5. Classifying Coins and Commodities
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5. Classifying Coins and Commodities
Understanding blob detection
Understanding histogram analysis
Understanding keypoint matching
Planning an object classification application
Configuring the project
Defining blobs and a blob detector
Defining blob descriptors and a blob classifier
Laying out the splash screen
Defining and laying out the view controllers
Detecting blobs against a plain background
Classifying blobs by color and keypoints
Using the application and testing the tough cases
Taking your study of OpenCV to the next level
Summary
6
Index
Index

Understanding blob detection

A blob is a region that we can discern based on color. Perhaps the blob itself has a distinctive color, or perhaps the background does. Unlike the term "object", the term "blob" does not necessarily imply something with mass and volume. For example, surface variations such as stains can be blobs, even though they have negligible mass and volume. Optical effects can also be blobs. For example, a lens's aperture can produce bokeh balls or out-of-focus highlights that can make lights or shiny things appear strangely large and strangely similar to the aperture's shape. However, in BeanCounter, we tend to assume that a blob is a classifiable object.

Note

The term "bokeh" comes from a Japanese word for bamboo. Different authors give different stories about the etymology, but perhaps someone thought bokeh balls resemble the bright rim of a chopped piece of bamboo.

Typically, a blob detector needs to solve the following sequence...

End of Section 2
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