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  • Book Overview & Buying iOS Application Development with OpenCV 3
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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
Buy this Book

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
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Setting Up Software and Hardware
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1. Setting Up Software and Hardware
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Setting up Apple's developer tools
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Setting up the OpenCV framework
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Developing a minimal application
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Distributing to testers and customers
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Finding documentation and support
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Understanding the camera and setting up photographic accessories
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Summary
2
2. Capturing, Storing, and Sharing Photos
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2. Capturing, Storing, and Sharing Photos
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Planning a photo sharing application
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Configuring the project
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Defining and laying out the view controller
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Controlling the camera
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Working with various color formats
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Starting and stopping the busy mode
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Saving an image to the Photos library
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Displaying an alert
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Sharing an image via social media
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Running the application
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Summary
3
3. Blending Images
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3. Blending Images
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Thinking about hybrid images
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Planning the blending controls
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Expanding the view controller's interface
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Expanding the view controller's implementation
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Using the application for practical purposes
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Summary
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4. Detecting and Merging Faces of Mammals
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4. Detecting and Merging Faces of Mammals
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Understanding detection with cascade classifiers
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Understanding transformations
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Planning a face merging application
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Configuring the project
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Defining faces and a face detector
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Defining and laying out the view controllers
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Detecting a hierarchy of face elements
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Aligning and blending face elements
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Using the application and acting like a cat
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Learning more about face analysis
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Summary
5
5. Classifying Coins and Commodities
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5. Classifying Coins and Commodities
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Understanding blob detection
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Understanding histogram analysis
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Understanding keypoint matching
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Planning an object classification application
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Configuring the project
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Defining blobs and a blob detector
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Defining blob descriptors and a blob classifier
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Laying out the splash screen
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Defining and laying out the view controllers
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Detecting blobs against a plain background
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Classifying blobs by color and keypoints
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Using the application and testing the tough cases
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Taking your study of OpenCV to the next level
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Summary
6
Index
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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 of problems:

  1. Segmentation: Distinguish between...

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